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"mc-q-bio-QM-005", "mc-q-bio-QM-006", "mc-q-bio-QM-007", "mc-q-bio-QM-008", "mc-q-bio-QM-009", "mc-q-bio-QM-010", "mc-q-bio-QM-011", "mc-q-bio-QM-012", "mc-q-bio-QM-013", "mc-q-bio-QM-014", "mc-q-bio-QM-015", "mc-q-bio-QM-016", "mc-q-bio-QM-017", "mc-q-bio-QM-018", "mc-q-bio-QM-019", "mc-q-bio-QM-020", "mc-q-bio-QM-021", "mc-q-bio-QM-022", "mc-q-bio-QM-023", "mc-q-fin-ST-001", "mc-q-fin-ST-002", "mc-q-fin-ST-003", "mc-q-fin-ST-004", "mc-q-fin-ST-005", "mc-q-fin-ST-006", "mc-q-fin-ST-007", "mc-q-fin-ST-008", "mc-q-fin-ST-009", "mc-q-fin-ST-010", "mc-q-fin-ST-011", "mc-q-fin-ST-012", "mc-q-fin-ST-013", "mc-q-fin-ST-014", "mc-q-fin-ST-015", "mc-q-fin-ST-016", "mc-q-fin-ST-017", "mc-q-fin-ST-018", "mc-q-fin-ST-019", "mc-q-fin-ST-020", "mc-q-fin-ST-021", "mc-q-fin-ST-022", "mc-q-fin-ST-023", "mc-q-fin-ST-024", "mc-q-fin-ST-025", "mc-stat-ME-001", "mc-stat-ME-002", "mc-stat-ME-003", "mc-stat-ME-004", "mc-stat-ME-005", "mc-stat-ME-006", "mc-stat-ME-007", "mc-stat-ME-008", "mc-stat-ME-009", "mc-stat-ME-010", "mc-stat-ME-011", "mc-stat-ME-012", "mc-stat-ME-013", "mc-stat-ME-014", "mc-stat-ME-015", "mc-stat-ME-016", "mc-stat-ME-017", "mc-stat-ME-018", "mc-stat-ME-019", "mc-stat-ME-020", "mc-stat-ME-021", "mc-stat-ME-022", "mc-stat-ME-023", "mc-stat-ME-024", "mc-stat-ME-025", "mcmc-cosmo-001", "mcmc-crypto-002", "mcmc-nlp-002", "mcmc-phylo-001", "mcmc-statphys-001", "ot-cosmology-001", "ot-ml-001", "pa-cities-noname-001", "pa-economics-001", "pa-linguistics-001", "pa-networks-001", "pa-scientometrics-002", "pa-web-002", "pca-causal-001", "pca-genetics-001", "pca-polmeth-001", "pca-popgen-002", "pca-psychometrics-001", "pca-recsys-001", "pca-recsys-distractor-001", "pca-spectroscopy-002", "replv-ecology-001", "replv-economics-001", "replv-evolution-001", "replv-finance-002", "replv-linguistics-002", "replv-mwu-noname-001", "smoke-01", "sparse-finance-001", "sparse-finance-distractor-001", "sparse-genomics-001", "sparse-genomics-002", "sparse-mri-001", "sparse-radioastro-002", "sparse-signal-001"], "text": ["A patient\u2019s electronic medical record contains a large number of medical reports and imaging studies. Identifying the relevant information in order to make a diagnosis can be a time consuming process that can easily overwhelm the physician. Summarizing key clinical information for physicians evaluating brain tumor patients is an ongoing research project at our institution. Notably, identifying documents associated with brain tumor is an important step in collecting the data relevant for summarization. Current electronic medical record systems lack meta-information which is useful in structuring heterogeneous medical information. Thus, identifying reports relevant to a particular task cannot be easily retrieved from a structured database. This necessitates content analysis methods for identifying relevant reports. This paper reports a system designed to identify brain-tumor related reports from an assorted collection of clinical reports. A large collection of clinical reports was obtained from our university hospital database. A domain expert manually annotated the documents classifying them into `related\u2019 and \u00f9nrelated\u2019 categories. A multinomial na\u00efve Bayes classifier was trained to use word level and UMLS concept level features from the reports to identify brain tumor related reports from the assorted collection. The system was trained on 90% and tested on 10% of the manually annotated corpus. A ten-fold cross validation is reported. Performance of the system was best (f-score 94.7) when the system was trained using both word level and UMLS concept level features. Using UMLS concepts improved classifier accuracy. **Keywords:** Document Classification, Information Filtering, Content Analysis, Na\u00efve Bayes, Medical Reports", "# Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection Performance Comparison and Implementation of Bayesian Variants for Network Intrusion Detection 1st Tosin Ige Department of Computer Science University of Texas at El Paso Texas, USA 2nd Christopher Kiekintveld Department of Computer Science University of Texas at El Paso Texas, USA Abstract\u2014Bayesian classifiers perform well when each of the features is completely independent of the other which is not always valid in real world application. The aim of this study is to implement and compare the performances of each variant of Bayesian classifier (Multinomial, Bernoulli, and Gaussian) on anomaly detection in network intrusion, and to investigate whether there is any association between each variant\u2019s assumption and their performance. Our investigation showed that each variant of Bayesian algorithm blindly follows its assumption regardless of feature property, and that the assumption is the single most important factor that influences their accuracy. Experimental results show that Bernoulli has accuracy of 69.9% test (71% train), Multinomial has accuracy of 31.2% test (31.2% train), while G", "# More than Word Frequencies: Authorship Attribution via Natural Frequency Zoned Word Distribution Analysis More than Word Frequencies: Authorship Attribution via Natural Frequency Zoned Word Distribution Analysis Zhili Chen, Liusheng Huang, Wei Yang, Peng Meng, and Haibo Miao Abstract - With such increasing popularity and availability of digital text data, authorships of digital texts can not be taken for granted due to the ease of copying and parsing. This paper presents a new text style analysis called natural frequency zoned word distribution analysis (NFZ-WDA), and then a basic authorship attribution scheme and an open authorship attribution scheme for digital texts based on the analysis. NFZ-WDA is based on the observation that all authors leave distinct intrinsic word usage traces on texts written by them and these intrinsic styles can be identified and employed to analyze the authorship. The intrinsic word usage styles can be estimated through the analysis of word distribution within a text, which is more than normal word frequency analysis and can be expressed as: which groups of words are used in the text; how frequently does each group of words occur; how are the occurre", "Viral sequence classification is an important task in pathogen detection, epidemiological surveys and evolutionary studies. Statistical learning methods are widely used to classify and identify viral sequences in samples from environments. These methods face several challenges associated with the nature and properties of viral genomes such as recombination, mutation rate and diversity. Also, new generations of sequencing technologies rise other difficulties by generating massive amounts of fragmented sequences. While linear classifiers are often used to classify viruses, there is a lack of exploration of the accuracy space of existing models in the context of alignment free approaches. In this study, we present an exhaustive assessment procedure exploring the power of linear classifiers in genotyping and subtyping partial and complete genomes. It is applied to the Hepatitis C viruses (HCV). Several variables are considered in this investigation such as classifier types (generative and discriminative) and their hyper-parameters (smoothing value and regularization penalty function), the classification task (genotyping and subtyping), the length of the tested sequences (partial and complete) and the length of k -mer words. Overall, several classifiers perform well given a set of precise combination of the experimental variables mentioned above. Finally, we provide the procedure and benchmark data to allow for more robust assessment of classification from virus genomes.", "Linear discriminant analysis (LDA) is one of the most popular classification algorithms for brain-computer interfaces (BCI). LDA assumes Gaussian distribution of the data, with equal covariance matrices for the concerned classes, however, the assumption is not usually held in actual BCI applications, where the heteroscedastic class distributions are usually observed. This paper proposes an enhanced version of LDA, namely z-score linear discriminant analysis (Z-LDA), which introduces a new decision boundary definition strategy to handle with the heteroscedastic class distributions. Z-LDA defines decision boundary through z-score utilizing both mean and standard deviation information of the projected data, which can adaptively adjust the decision boundary to fit for heteroscedastic distribution situation. Results derived from both simulation dataset and two actual BCI datasets consistently show that Z-LDA achieves significantly higher average classification accuracies than conventional LDA, indicating the superiority of the new proposed decision boundary definition strategy.", "We consider the problem of classifying documents not by topic, but by overall sentiment, e.g., determining whether a review is positive or negative. Using movie reviews as data, we find that standard machine learning techniques definitively outperform human-produced baselines. However, the three machine learning methods we employed (Naive Bayes, maximum entropy classification, and support vector machines) do not perform as well on sentiment classification as on traditional topic-based categorization. We conclude by examining factors that make the sentiment classification problem more challenging.", "# SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing ________________________________________ *Corresponding author: Email: adebolak.ojo@gmail.com; J. Adv. Math. Com. Sci., vol. 38, no. 10, pp. 144-156, 2023 Journal of Advances in Mathematics and Computer Science Volume 38, Issue 10, Page 144-156, 2023; Article no.JAMCS.108409 ISSN: 2456-9968 (Past name: British Journal of Mathematics & Computer Science, Past ISSN: 2231-0851) _______________________________________________________________________________________________________________________________________ SMS Spam Detection and Classification to Combat Abuse in Telephone Networks Using Natural Language Processing Dare Azeez Oyeyemi a and Adebola K. Ojo a* a Computer Science Department, University of Ibadan, Nigeria. Authors\u2019 contributions This work was carried out in collaboration between both authors. Both authors read and approved the final manuscript. Article Information DOI: 10.9734/JAMCS/2023/v38i101832 Open Peer Review History: This journal follows the Advanced Open Peer Review policy. Identity of the Reviewers, Editor(s) and additional Reviewers, peer review comm", "In this paper, we investigate the spreading properties of solutions of farmer and hunter-gatherer model which is a three-component reaction-diffusion system. Ecologically, the model describes the geographical spreading of an initially localized population of farmers into a region occupied by hunter-gatherers. This model was proposed by Aoki, Shida and Shigesada in 1996. By numerical simulations and some formal linearization arguments, they concluded that there are four different types of spreading behaviors depending on the parameter values. Despite such intriguing observations, no mathematically rigorous studies have been made to justify their claims. The main difficulty comes from the fact that the comparison principle does not hold for the entire system. In this paper, we give theoretical justification to all of the four types of spreading behaviors observed by Aoki et al. . Furthermore, we show that a logarithmic phase drift of the front position occurs as in the scalar KPP equation. We also investigate the case where the motility of the hunter-gatherers is larger than that of the farmers, which is not discussed in the paper of Aoki et al. . Key words: Farmer and hunter-gatherer model; long time behavior; spreading speed; logarithmic correction", "# Extending the Black-Scholes Option Pricing Theory to Account for an Option Market Maker's Funding Costs Lou Page 1 11/29/2014 Extending the Black-Scholes Option Pricing Theory to Account for an Option Market Maker\u2019s Funding Costs Lou Wujiang1 HSBC, March 8, 2014, updated Aug 22, 2014 Abstract An option market maker incurs funding costs when carrying and hedging inventory. To hedge a net long delta inventory, for example, she pays a fee to borrow stock from the securities lending market. Because of haircuts, she posts additional cash margin to the lender which needs to be financed at her unsecured debt rate. This paper incorporates funding asymmetry (borrowed cash and invested cash earning different interest rates) and realistic stock financing cost into the classic option pricing theory. It is shown that an option position can be dynamically replicated and self financed in the presence of these funding costs. Noting that the funding amounts and costs are different for long and short positions, we extend Black-Scholes partial differential equations (PDE) per position side. The PDE\u2019s nonlinear funding cost terms create a free funding boundary and would result in the bid price for a", "In this paper, we introduce a relaxation filtering closure approach to account for subgrid scale effects in explicitly filtered large eddy simulations using the concept of anisotropic diffusion. We utilize the Perona-Malik diffusion model and demonstrate its shock capturing ability and spectral performance for solving the Burgers turbulence problem, which is a simplified prototype for more realistic turbulent flows showing the same quadratic nonlinearity. Our numerical assessments present the behavior of various diffusivity functions in conjunction with a detailed sensitivity analysis with respect to the free modeling parameters. In comparison to direct numerical simulation (DNS) and under-resolved DNS results, we find that the proposed closure model is efficient in the prevention of energy accumulation at grid cut-off and is also adept at preventing any possible spurious numerical oscillations due to shock formation under the optimal parameter choices. In contrast to other relaxation filtering approaches, it is also shown that a larger inertial range can be obtained by the proposed anisotropic diffusion model using a compact stencil scheme in an efficient way.", "# A time-fractional Fisher-KPP equation for tumor growth: Analysis and numerical simulation ## 1. Introduction The Fisher\u2013KPP equation is a fundamental model in the theory of reaction-diffusion equations, originally introduced to describe the spread of advantageous genes in a population . Beyond population dynamics, it has found applications in fields as diverse as ecology and tumor growth , providing a paradigmatic example of nonlinear spatiotemporal dynamics in complex biological systems. In recent years, there has been growing interest in nonlocal extensions of reaction\u2013diffusion equations , motivated by the need to describe anomalous transport phenomena, memory effects, and long-range interactions that classical local diffusion models fail to capture. Among these approaches, time-fractional derivatives have emerged as a powerful framework for modeling subdiffusive dynamics, where the mean squared displacement grows sublinearly in time due to mechanisms such as trapping, crowding, or viscoelastic damping . Such effects are widely observed in biological tissues, porous and heterogeneous media, and complex fluids, where transport processes deviate markedly from Fickian behavior .", "Pattern formation in reaction-diffusion systems is of great importance in surface micro-patterning [Grzybowski et al. Soft Matter. 1 , 114 (2005)], self-organization of cellular micro-organisms [Schulz et al. Annu. Rev. Microbiol. 55 , 105 (2001)] and in developmental biology [Barkai et al. FEBS Journal 276 , 1196 (2009)]. In this work, we apply the Lattice Boltzmann method (LBM) to study pattern formation in reaction-diffusion systems. As a first methodological step, we consider the case of a single species undergoing transformation reaction and diffusion. In this case, we perform a third-order Chapman-Enskog multiscale expansion and study the dependence of the Lattice Boltzmann truncation error on the diffusion coefficient and the reaction rate. These findings are in good agreement with numerical simulations. Furthermore, taking the Gray-Scott model as a prominent example, we provide evidence for the maturity of the LBM in studying pattern formation in non-linear reaction-diffusion systems. For this purpose, we perform linear stability analysis of the Gray-Scott model and determine the relevant parameter range for pattern formation. Lattice Boltzmann simulations allow not only to test the validity of the linear stability phase diagram including Turing and Hopf instabilities, but also permit going beyond the linear stability regime, where large perturbations give rise to interesting dynamical behavior such as the so called self replicating spots. We also show that the length scale of the patterns may be tuned by rescaling all relevant diffusion coefficients in the system with the same factor while letting all the reaction constants unchanged.", "The development of novel materials for vacuum electron sources in particle accelerators is an active field of research that can greatly benefit from the results of ab initio calculations for the characterization of the electronic structure of target systems. As state-of-the-art many-body perturbation theory calculations are too expensive for large-scale material screening, density functional theory offers the best compromise between accuracy and computational feasibility. The quality of the obtained results, however, crucially depends on the choice of the exchange-correlation potential, $v_{xc}$ . To address this essential point, we systematically analyze the performance of three popular approximations of $v_{xc}$ (PBE, SCAN, and HSE06) on the structural and electronic properties of bulk \\ce Cs3Sb and \\ce Cs2Te as representative materials of Cs-based semiconductors employed in photocathode applications. Among the adopted approximations, PBE shows expectedly the largest discrepancies from the target: the unit cell volume is overestimated compared to the experimental value, while the band gap is severely underestimated. On the other hand, both SCAN and HSE06 perform remarkably well in reproducing both structural and electronic properties. Spin-orbit coupling, which mainly impacts the valence region of both materials inducing a band splitting and, consequently, a band-gap reduction of the order of 0.2 eV, is equally captured by all functionals. Our results indicate SCAN as the best trade-off between accuracy and computational costs, outperforming the considerably more expensive HSE06.", "# AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs AnySeq/GPU: A Novel Approach for Faster Sequence Alignment on GPUs Andr\u00e9 M\u00fcller Bertil Schmidt {muelan,bertil.schmidt}@uni-mainz.de Johannes Gutenberg University Mainz, Germany Richard Membarth\u2217 richard.membarth@thi.de Technische Hochschule Ingolstadt (THI) Research Institute AImotion Bavaria Ingolstadt, Germany Roland Lei\u00dfa leissa@uni-mannheim.de University of Mannheim Mannheim, Germany Sebastian Hack hack@cs.uni-saarland.de Saarland University, Saarland Informatics Campus Saarbr\u00fccken, Germany ABSTRACT In recent years, the rapidly increasing number of reads produced by next-generation sequencing (NGS) technologies has driven the demand for efficient implementations of sequence alignments in bioinformatics. However, current state-of-the-art approaches are not able to leverage the massively parallel processing capabilities of modern GPUs with close-to-peak performance. We present AnySeq/GPU\u2014a sequence alignment library that augments the AnySeq 1 library with a novel approach for accelerating dynamic programming (DP) alignment on GPUs by minimizing memory accesses using warp shuffles and half-precision arithmetic. O", "In this paper, we show that the soft-decision input to the main decoder in an SST Viterbi decoder is regarded as the innovation as well from the viewpoint of mutual information and mean-square error. It is assumed that a code sequence is transmitted symbol by symbol over an AWGN channel using BPSK modulation. Then we can consider the signal model, where the signal is composed of the signal-to-noise ratio (SNR) and the equiprobable binary input. By assuming that the soft-decision input to the main decoder is the innovation, we show that the minimum mean-square error (MMSE) in estimating the binary input is expressed in terms of the distribution of the encoded block for the main decoder. It is shown that the obtained MMSE satisfies indirectly the known relation between the mutual information and the MMSE in Gaussian channels. Thus the derived MMSE is justified, which in turn implies that the soft-decision input to the main decoder can be regarded as the innovation. Moreover, we see that the input-output mutual information is connected with the distribution of the encoded block for the main decoder.", "We introduce a new method to price American options based on Chebyshev interpolation. In each step of a dynamic programming time-stepping we approximate the value function with Chebyshev polynomials. The key advantage of this approach is that it allows to shift the model-dependent computations into an offline phase prior to the time-stepping. In the offline part a family of generalised (conditional) moments is computed by an appropriate numerical technique such as a Monte Carlo, PDE or Fourier transform based method. Thanks to this methodological flexibility the approach applies to a large variety of models. Online, the backward induction is solved on a discrete Chebyshev grid, and no (conditional) expectations need to be computed. For each time step the method delivers a closed form approximation of the price function along with the options\u2019 delta and gamma. Moreover, the same family of (conditional) moments yield multiple outputs including the option prices for different strikes, maturities and different payoff profiles. We provide a theoretical error analysis and find conditions that imply explicit error bounds for a variety of stock price models. Numerical experiments confirm the fast convergence of prices and sensitivities. An empirical investigation of accuracy and runtime also shows an efficiency gain compared with the least-square Monte-Carlo method introduced by Longstaff and Schwartz (2001).", "We present here a regress later based Monte Carlo approach that uses neural networks for pricing high-dimensional contingent claims. The choice of specific architecture of the neural networks used in the proposed algorithm provides for interpretability of the model, a feature that is often desirable in the financial context. Specifically, the interpretation leads us to demonstrate that any contingent claim \u2014possibly high dimensional and path-dependent\u2014 under Markovian and no-arbitrage assumptions, can be semi-statically hedged using a portfolio of short maturity options. We show how the method can be used to obtain an upper and lower bound to the true price, where the lower bound is obtained by following a sub-optimal policy, while the upper bound by exploiting the dual formulation. Unlike other duality based upper bounds where one typically has to resort to nested simulation for constructing super-martingales, the martingales in the current approach come at no extra cost, without the need for any sub-simulations. We demonstrate through numerical examples the simplicity and efficiency of the method for both pricing and semi-static hedging of path-dependent options.", "We present a method to calculate the similarity between words based on their phonetic transcription (their pronunciation) using the Needleman\u2013Wunsch algorithm. We implement this algorithm in Rust and parallelize it on both CPU and GPU to handle large datasets efficiently. The GPU implementation leverages CUDA and the cudarc Rust library to achieve significant performance improvements. We validate our approach by constructing a fully-connected graph where nodes represent words and edges have weights according to the similarity between the words. This graph is then analyzed using clustering algorithms to identify groups of phonetically similar words. Our results demonstrate the feasibility and effectiveness of the proposed method in analyzing the phonetic structure of languages. It might be easily expanded to other languages.", "# Value Iteration with Guessing for Markov Chains and Markov Decision Processes arXiv:2505.06769v1 [cs.AI] 10 May 2025 Value Iteration with Guessing for Markov Chains and Markov Decision Processes Krishnendu Chatterjee1[0000\u22120002\u22124561\u2212241X], Mahdi JafariRaviz2[0009\u22120002\u22120495\u22123805], Raimundo Saona\u22171[0000\u22120001\u22125103\u2212038X], and Jakub Svoboda1[0000\u22120002\u22121419\u22123267] 1 Institute of Science and Technology Austria (ISTA) https://ista.ac.at/ raimundo.saona@gmail.com {jakub.svoboda, krishnendu.chatterjee}@ist.ac.at 2 University of Maryland College Park https://umd.edu mahdij@umd.edu Abstract. Two standard models for probabilistic systems are Markov chains (MCs) and Markov decision processes (MDPs). Classic objectives for such probabilistic models for control and planning problems are reachability and stochastic shortest path. The widely studied algorithmic approach for these problems is the Value Iteration (VI) algorithm which iteratively applies local updates called Bellman updates. There are many practical approaches for VI in the literature but they all require exponentially many Bellman updates for MCs in the worst case. A preprocessing step is an algorithm that is discrete, graph-theoreti", "Background: Hidden Markov models (HMM) are powerful machine learning tools successfully applied to problems of computational Molecular Biology. In a predictive task, the HMM is endowed with a decoding algorithm in order to assign the most probable state path, and in turn the class labeling, to an unknown sequence. The Viterbi and the posterior decoding algorithms are the most common. The former is very efficient when one path dominates, while the latter, even though does not guarantee to preserve the automaton grammar, is more effective when several concurring paths have similar probabilities. A third good alternative is 1-best, which was shown to perform equal or better than Viterbi. Results: In this paper we introduce the posterior-Viterbi (PV) a new decoding which combines the posterior and Viterbi algorithms. PV is a two step process: first the posterior probability of each state is computed and then the best posterior allowed path through the model is evaluated by a Viterbi algorithm. Conclusions: We show that PV decoding performs better than other algorithms first on toy models and then on the computational biological problem of the prediction of the topology of beta-barrel membrane proteins. Contacts: piero.fariselli@unibo.it", "The recently synthesized M\u00f6bius carbon nanobelts (CNBs) have gained attention owing to their unique $\\pi$ -conjugation topology, which results in distinctive electronic properties with both fundamental and practical implications. Although M\u00f6bius conjugation with phase inversion in atomic orbital (AO) basis is well-established for monocyclic systems, the extension of this understanding to double-stranded M\u00f6bius CNBs remains uncertain. This study thoroughly examines the simple H\u00fcckel molecular orbital (SHMO) theory for describing the $\\pi$ electronic structures of M\u00f6bius CNBs. We demonstrate that the adjacency matrix for M\u00f6bius CNB can preserve its eigenvalues and eigenvectors (with possibly flipped directions) under different placements of the sign inversion, ensuring identical SHMO results regardless of AO phase inversion location. Representative examples of M\u00f6bius CNBs, including the experimentally synthesized one, show that the H\u00fcckel molecular orbitals (MOs) strikingly resemble the DFT-computed $\\pi$ MOs, which were obtained using a herein proposed technique based on the localization and re-delocalization of DFT canonical MOs. Interestingly, the lower-lying $\\pi$ MOs exhibit an odd number of nodal planes and are doubly quasidegenerate as a consequence of the phase inversion in M\u00f6bius macrocycles, contrasting with macrocyclic H\u00fcckel systems. Coulson bond orders derived from SHMO theory correlate well with DFT-calculated Wiberg bond indices for all C\u2013C bonds in tested M\u00f6bius CNBs. Additionally, a remarkable correlation is observed between HOMO\u2013LUMO gaps obtained from the SHMO and GFN2-xTB calculations for a large number of topoisomers of M\u00f6bius CNBs. Thus, the SHMO model not only captures the essence of $\\pi$ electronic structure of M\u00f6bius CNBs, but also provides reliable quantitative predictions comparable to DFT results.", "Ecologists and conservation biologists need to identify the relative importance of species to make sound management decisions and effectively allocate scarce resources. We introduce a new method, termed environ centrality, to determine the relative importance of a species in an ecosystem network with respect to ecosystem energy\u2013matter exchange. We demonstrate the uniqueness of environ centrality by comparing it to other common centrality metrics and then show its ecological significance. Specifically, we tested two hypotheses on a set of 50 empirically-based ecosystem network models. The first concerned the distribution of centrality in the community. We hypothesized that the functional importance of species would tend to be concentrated into a few dominant species followed by a group of species with lower, more even importance as is often seen in dominance\u2013diversity curves. Second, we tested the systems ecology hypothesis that indirect relationships homogenize the functional importance of species in ecosystems. Our results support both hypotheses and highlight the importance of detritus and nutrient recyclers such as fungi and bacteria in generating the energy\u2013matter flow in ecosystems. Our homogenization results suggest that indirect effects are important in part because they tend to even the importance of species in ecosystems. A core contribution of this work is that it creates a formal, mathematical method to quantify the importance species play in generating ecosystem activity by integrating direct, indirect, and boundary effects in ecological systems.", "Functional magnetic resonance data acquired in a task-absent condition (\u201cresting state\u201d) require new data analysis techniques that do not depend on an activation model. In this work, we introduce an alternative assumption- and parameter-free method based on a particular form of node centrality called eigenvector centrality. Eigenvector centrality attributes a value to each voxel in the brain such that a voxel receives a large value if it is strongly correlated with many other nodes that are themselves central within the network. Google's PageRank algorithm is a variant of eigenvector centrality. Thus far, other centrality measures - in particular \u201cbetweenness centrality\u201d - have been applied to fMRI data using a pre-selected set of nodes consisting of several hundred elements. Eigenvector centrality is computationally much more efficient than betweenness centrality and does not require thresholding of similarity values so that it can be applied to thousands of voxels in a region of interest covering the entire cerebrum which would have been infeasible using betweenness centrality. Eigenvector centrality can be used on a variety of different similarity metrics. Here, we present applications based on linear correlations and on spectral coherences between fMRI times series. This latter approach allows us to draw conclusions of connectivity patterns in different spectral bands. We apply this method to fMRI data in task-absent conditions where subjects were in states of hunger or satiety. We show that eigenvector centrality is modulated by the state that the subjects were in. Our analyses demonstrate that eigenvector centrality is a computationally efficient tool for capturing intrinsic neural architecture on a voxel-wise level.", "Determining the principal energy pathways for allosteric communication in biomolecules, that occur as a result of thermal motion, remains challenging due to the intrinsic complexity of the systems involved. Graph theory provides an approach for making sense of such complexity, where allosteric proteins can be represented as networks of amino acids. In this work, we establish the eigenvector centrality metric in terms of the mutual information, as a mean of elucidating the allosteric mechanism that regulates the enzymatic activity of proteins. Moreover, we propose a strategy to characterize the range of the physical interactions that underlie the allosteric process. In particular, the well known enzyme, imidazol glycerol phosphate synthase (IGPS), is utilized to test the proposed methodology. The eigenvector centrality measurement successfully describes the allosteric pathways of IGPS, and allows to pinpoint key amino acids in terms of their relevance in the momentum transfer process. The resulting insight can be utilized for refining the control of IGPS activity, widening the scope for its engineering. Furthermore, we propose a new centrality metric quantifying the relevance of the surroundings of each residue. In addition, the proposed technique is validated against experimental solution NMR measurements yielding fully consistent results. Overall, the methodologies proposed in the present work constitute a powerful and cost effective strategy to gain insight on the allosteric mechanism of proteins.", "# Eigenfactor 4.7 Eigenfactor Grischa Fraumann, Jennifer D\u2019Souza, and Kim Holmberg Abstract: The EigenfactorTMis a journal metric, which was developed by Bergstrom and his colleagues at the University of Washington. They invented the Eigenfactor as a response to the criticism against the use of simple citation counts. The Eigenfactor makes use of the network structure of citations, i.e. citations between journals, and establishes the importance, influence or impact of a journal based on its location in a network of journals. The importance is defined based on the number of citations between journals. As such, the Eigenfactor algorithm is based on Eigenvector centrality. While journal based metrics have been criticized, the Eigenfactor has also been suggested as an alternative in the widely used San Francisco Declaration on Research Assessment (DORA). Keywords: Eigenfactor, alternative metrics, metrics, journals, bibliometrics, Journal Impact Factor, citations, rating. Introduction This chapter provides an overview on the EigenfactorTM, a journal metric, which was developed by Bergstrom (2007) and his colleagues at the University of Washington. They invented the Eigenfactor as a res", "# Ranking journals: Could Google Scholar Metrics be an alternative to Journal Citation Reports and Scimago Journal Rank? Delgado-L\u00f3pez-C\u00f3zar, Emilio and Cabezas-Clavijo, \u00c1lvaro (2013). Ranking journals: could Google Scholar Metrics be an alternative to Journal Citation Reports and Scimago Journal Rank? Learned Publishing, v. 26(2), 101-114. http://dx.doi.org/10.1087/20130206 1 Ranking journals: Could Google Scholar Metrics be an alternative to Journal Citation Reports and Scimago Journal Rank? Emilio Delgado-L\u00f3pez-C\u00f3zar & \u00c1lvaro Cabezas-Clavijo edelgado@ugr.es; acabezasclavijo@gmail.com EC3: Evaluaci\u00f3n de la Ciencia y de la Comunicaci\u00f3n Cient\u00edfica, Departamento de Informaci\u00f3n y Comunicaci\u00f3n, Universidad de Granada, Campus de Cartuja s/n E-18071 Granada (Spain) This is the authors\u2019 final version of a paper published in Learned Publishing. Please cite as: Delgado-L\u00f3pez-C\u00f3zar, Emilio and Cabezas-Clavijo, \u00c1lvaro (2013). Ranking journals: could Google Scholar Metrics be an alternative to Journal Citation Reports and Scimago Journal Rank? Learned Publishing, v. 26(2), 101-114. http://dx.doi.org/10.1087/20130206 ABSTRACT: The launch of Google Scholar Metrics as a tool for assessing scient", "# Personalized PageRank with Node-dependent Restart ## 1 Introduction and definitions PageRank has become a standard algorithm to classify the importance of nodes in a network. Let us start by introducing some notation. Let $G=(V,E)$ be a finite graph, where $V$ is the node set and $E\\subseteq V\\times V$ the collection of (directed) edges. Then, PageRank can be interpreted as the stationary distribution of a random walk on $G$ that restarts from a uniform location in $V$ at each time with probability $\\alpha\\in(0,1)$ . Thus, in the Standard PageRank centrality measure , the random walk restarts after a geometrically distributed number of steps, and the restart takes place from a uniform location in the graph, and otherwise jumps to any one of the neighbours in the graph with equal probability. Personalized PageRank is a modification of the Standard PageRank where the restart distribution is not uniform. Both the Standard and Personalized PageRank have many applications in data mining and machine learning (see e.g., ). In the (standard) Personalized PageRank, the random walker restarts with a given fixed probability $1-\\alpha$ at each visited node. We suggest a generalization where", "Context. Measurements of the galaxy density and weak-lensing profiles of galaxy clusters typically rely on an assumed cluster center, which is taken to be the brightest cluster galaxy or other proxies for the true halo center. Departure of the assumed cluster center from the true halo center bias the resultant profile measurements, an effect known as miscentering bias. Aims. Currently, miscentering is typically modeled in stacked profiles of clusters with a two parameter model. We use an alternate approach in which the profiles of individual clusters are used with the corresponding likelihood computed using a Gaussian mixture model. Methods. We test the approach using halos and the corresponding subhalo profiles from the IllustrisTNG hydrodynamic simulations. Results. We obtain significantly improved estimates of the miscentering parameters for both 3D and projected 2D profiles relevant for imaging surveys. We discuss applications to upcoming cosmological surveys. \u2020 \u2020 thanks: Our Python package for implementing the Gaussian mixture model is publicly avaliable at https://github.com/KyleMiller1/Halo-Miscentering-Mixture-Model", "We introduce the Uchuu suite of large high-resolution cosmological $N$ -body simulations. The largest simulation, named Uchuu, consists of 2.1 trillion ( $12800^{3}$ ) dark matter particles in a box of side-length 2.0 $\\,h^{-1}\\rm Gpc$ , with particle mass $3.27\\times 10^{8}$ $\\,h^{-1}\\rm M_{\\odot}$ . The highest resolution simulation, Shin-Uchuu, consists of 262 billion ( $6400^{3}$ ) particles in a box of side-length 140 $\\,h^{-1}\\rm Mpc$ , with particle mass $8.97\\times 10^{5}$ $\\,h^{-1}\\rm M_{\\odot}$ . Combining these simulations we can follow the evolution of dark matter halos and subhalos spanning those hosting dwarf galaxies to massive galaxy clusters across an unprecedented volume. In this first paper, we present basic statistics, dark matter power spectra, and the halo and subhalo mass functions, which demonstrate the wide dynamic range and superb statistics of the Uchuu suite. From an analysis of the evolution of the power spectra we conclude that our simulations remain accurate from the Baryon Acoustic Oscillation scale down to the very small. We also provide parameters of a mass-concentration model, which describes the evolution of halo concentration and reproduces our simulation data to within 5 per cent for halos with masses spanning nearly eight orders of magnitude at redshift $0\\leq z\\leq 14$ . There is an upturn in the mass-concentration relation for the population of all halos and of relaxed halos at $z\\gtrsim 0.5$ , whereas no upturn is detected at $z<0.5$ . We make publicly available various $N$ -body products as part of Uchuu Data Release 1 on the Skies & Universes site . Future releases will include gravitational lensing maps and mock galaxy, X-ray cluster, and active galactic nuclei catalogues.", "Machine learning, algorithms to extract empirical knowledge from data, can be used to classify data, which is one of the most common tasks in observational astronomy. In this paper, we focus on Bayesian data classification algorithms using the Gaussian mixture model and show two applications in pulsar astronomy. After reviewing the Gaussian mixture model and the related Expectation-Maximization algorithm, we present a data classification method using the Neyman-Pearson test. To demonstrate the method, we apply the algorithm to two classification problems. Firstly, it is applied to the well known period-period derivative diagram, where we find that the pulsar distribution can be modeled with six Gaussian clusters, with two clusters for millisecond pulsars (recycled pulsars) and the rest for normal pulsars. From this distribution, we derive an empirical definition for millisecond pulsars as $\\frac{\\dot{P}}{10^{-17}}\\leq 3.23\\left(\\frac{P}{100\\,\\textrm{ ms}}\\right)^{-2.34}$ . The two millisecond pulsar clusters may have different evolutionary origins, since the companion stars to these pulsars in the two clusters show different chemical composition. Four clusters are found for normal pulsars. Possible implications for these clusters are also discussed. Our second example is to calculate the likelihood of unidentified Fermi point sources being pulsars and rank them accordingly. In the ranked point source list, the top 5% sources contain 50% known pulsars, the top 50% contain 99% known pulsars, and no known active galaxy (the other major population) appears in the top 6%. Such a ranked list can be used to help the future follow-up observations for finding pulsars in unidentified Fermi point sources.", "This paper represents a preliminary (pre-reviewing) version of a sublinear variational algorithm for isotropic Gaussian mixture models (GMMs). Further developments of the algorithm for GMMs with diagonal covariance matrices (instead of isotropic clusters) and their corresponding benchmarking results have been published by TPAMI ( doi:10.1109/TPAMI.2021.3133763 ) in the paper \u201cA Variational EM Acceleration for Efficient Clustering at Very Large Scales\u201d. We kindly refer the reader to the TPAMI paper instead of this much earlier arXiv version (the TPAMI paper is also open access). Publicly available source code accompanies the paper (see https://github.com/variational-sublinear-clustering ). Please note that the TPAMI paper does not contain the benchmark on the 80 Million Tiny Images dataset anymore because we followed the call of the dataset creators to discontinue the use of that dataset. The aim of the project (which resulted in this arXiv version and the later TPAMI paper) is the exploration of the current efficiency and large-scale limits in fitting a parametric model for clustering to data distributions. To reduce computational complexity, we used a clustering objective based on truncated variational EM (which reduces complexity for many clusters) in combination with coreset objectives (which reduce complexity for many data points). We used efficient coreset construction and efficient seeding to translate the theoretical sublinear complexity gains into an efficient algorithm. In applications to standard large-scale benchmarks for clustering, we then observed substantial wall-clock speedups compared to already highly efficient clustering approaches. To demonstrate that the observed efficiency enables applications previously considered unfeasible, we clustered the entire and unscaled 80 Million Tiny Images dataset into up to 32,000 clusters.", "Population pharmacokinetic (PK) modeling has become a cornerstone of drug development and optimal patient dosing. This approach offers great benefits for datasets with sparse sampling, such as in pediatric patients, and can describe between-patient variability. While most current algorithms assume normal or log-normal distributions for PK parameters, we present a mathematically consistent nonparametric maximum likelihood (NPML) method for estimating multivariate mixing distributions without any assumption about the shape of the distribution. This approach can handle distributions with any shape for all PK parameters. It is shown in convexity theory that the NPML estimator is discrete, meaning that it has finite number of points with nonzero probability. In fact, there are at most *N* points where *N* is the number of observed subjects. The original infinite NPML problem then becomes the finite dimensional problem of finding the location and probability of the support points. In the simplest case, each point essentially represents the set of PK parameters for one patient. The probability of the points is found by a primal-dual interior-point method; the location of the support points is found by an adaptive grid method. Our method is able to handle high-dimensional and complex multivariate mixture models. An important application is discussed for the problem of population pharmacokinetics and a nontrivial example is treated. Our algorithm has been successfully applied in hundreds of published pharmacometric studies. In addition to population pharmacokinetics, this research also applies to empirical Bayes estimation and many other areas of applied mathematics. Thereby, this approach presents an important addition to the pharmacometric toolbox for drug development and optimal patient dosing. **Keywords:** mixture distribution, mixture model, high dimensional statistics, nonparametric maximum likelihood, primal-dual interior-point method, adaptive grid, population model", "# Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian Tatarinova et al, Journal of Pharmacokinetics and Pharmacodynamics, 2013, vol. 40 no 1 DOI 10.1007/s10928-013-9302-8 1 Two General Methods for Population Pharmacokinetic Modeling: Non-Parametric Adaptive Grid and Non-Parametric Bayesian Tatiana Tatarinova*1, Michael Neely*1, Jay Bartroff1,2, Michael van Guilder1, Walter Yamada1,4, David Bayard1,3, Roger Jelliffe1, Robert Leary1,5, Alyona Chubatiuk2 and Alan Schumitzky1,2 1 Laboratory of Applied Pharmacokinetics, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA 2 Department of Mathematics, Dornsife College of Letters and Science University of Southern California, Los Angeles, CA, USA 3 Jet Propulsion Laboratory, California Institute of Technology, Mail Stop 198326, 4800 Oak Grove Drive, Pasadena, CA, USA 4 Department of Psychology, Azusa Pacific University, Azusa, CA, USA 5 Pharsight Corporation, Cary, NC, USA * Joint first authors Corresponding author email: tatiana.tatarinova@lapk.org ABSTRACT: Population pharmacokinetic (PK) modeling methods can be statistically classified as eith", "Positron Emission Particle Tracking (PEPT) is an imaging method that tracks individual radioactive particles. PEPT relies on the detection of back-to-back photon pairs emitted by positron annihilation. It requires an algorithm to locate the radioactive particles based on the set of lines defined by successive photon-pair detections. We propose and test a new algorithm for this task. The algorithm relies on the maximization of a likelihood arising from a simple Gaussian-mixture model defined in the space of lines. The model includes a component that accounts for spurious lines caused by scattering and random coincidence, and treats the relative activity of particles as well as their positions as parameters to be inferred. Values of these parameters that approximately maximize the likelihood are computed by application of an expectation-maximization algorithm. A generalization of the model that includes the particle velocities and accelerations as additional parameters takes advantage of the information contained in the exact timing of positron annihilations to reconstruct pieces of trajectories rather than fixed positions, with clear benefits. We test the algorithm on both simulated and experimental data. The results show the algorithm to be highly effective for the simultaneous tracking of many particles (up to 80 in one test). It provides estimates of particle positions that are easily mapped to entire trajectories and handles a variable number of particles in the field of view. The ability to track a large number of particles robustly offers the possibility of a dramatic expansion of the scope of PEPT", "Detection of a signal hidden by noise within a time series is an important problem in many astronomical searches, i.e. for light curves containing the contributions of periodic/semi-periodic components due to rotating objects and all other astrophysical time-dependent phenomena. One of the most popular tools for use in such studies is the periodogram , whose use in an astronomical context is often not trivial. The optimal statistical properties of the periodogram are lost in the case of irregular sampling of signals, which is a common situation in astronomical experiments. Parts of these properties are recovered by the Lomb-Scargle (LS) technique, but at the price of theoretical difficulties, that can make its use unclear, and of algorithms that require the development of dedicated software if a fast implementation is necessary. Such problems would be irrelevant if the LS periodogram could be used to significantly improve the results obtained by approximated but simpler techniques. In this work we show that in many astronomical applications simpler techniques provide results similar to those obtainable with the LS periodogram. The meaning of the Nyquist frequency is also discussed in the case of irregular sampling.", "Context. New photometric space missions to detect and characterise transiting exoplanets are focusing on bright stars to obtain high cadence, high signal-to-noise light curves. Since these missions will be sensitive to stellar oscillations and granulation even for dwarf stars, they will be limited by stellar variability. Therefore, it is crucial and timely to develop robust methods to account for and correct for stellar variability. Aims. We tested the performance of Gaussian process (GP) regression on the characterisation of transiting planets, and in particular to determine how many components of variability are necessary to describe high cadence, high signal-to-noise light curves expected from CHEOPS and PLATO. To achieve this, we selected a sample of bright stars observed in the asteroseismology field of CoRoT at high cadence (32 sec) and high signal-to-noise ratio. Methods. We used GPs to model stellar variability including different combinations of stellar oscillations, granulation, and rotational modulation models. We preformed model comparison to find the best activity model fit to our data. We compared the best multi-component model with the usual one-component model used for transit retrieval and with a non-GP model. Results. We found that the best GP stellar variability model contains four to five variability components: one stellar oscillation component, two to four granulation components, and/or one rotational modulation component, which is consistent with results from asteroseismology. However, this high number of components is in contrast with the one-component GP model (1GP) commonly used in the literature for transit characterisation. Therefore, we compared the performance of the best multi-component GP model with the 1GP model in the derivation of transit parameters of simulated transits. We found that for Jupiter- and Neptune-size planets the best multi-component GP model is slightly better than the 1GP model, and much better than the non-GP model that gives biased results. For Earth-size planets, the 1GP model fails to retrieve the transit because it is a poor description of stellar activity. The non-GP model gives some biased results and the best multi-component GP is capable of retrieving the correct transit model parameters. Conclusions. We conclude that when characterising transiting exoplanets with high signal-to-noise ratios and high cadence light curves, we need models that couple the description of stellar variability with the transits analysis, like GPs. Moreover, for Earth-like exoplanets a better description of stellar variability (achieved using multi-component models) improves the planetary characterisation. Our results are particularly important for the analysis of TESS, CHEOPS, and PLATO light curves.", "# A Spectral Analysis of Business Cycle Patterns in UK Sectoral Output 1 A Spectral Analysis of Business Cycle Patterns in UK Sectoral Output Peijie Wang* Business School University of Hull Hull HU6 7RX Email : p.wang@hull.ac.uk and Trefor Jones Manchester Business School University of Manchester Booth Street West Manchester M15 6PB Abstract - This paper studies business cycle patterns in UK sectoral output. It analyzes the distinction between white noise processes and their non-white noise counterparts in the frequency domain and further examines the associated features and patterns for the process where white noise conditions are violated. The characteristics of these sectors, arising from their institutional features that may influence business cycles behavior and patterns, are discussed. The study then investigates the output of UK GDP sectors empirically, revealing their similarities and differences in their business cycle patterns. JEL No: E32, C32 Key words: business cycle patterns, frequency domain * corresponding author 2 1. Introduction This paper examines business cycle patterns in UK GDP sectors in the frequency domain. It analyses the spectra of sectoral output and foc", "Power spectral density (PSD) estimation is a critical step in gravitational wave (GW) detectors data analysis. The Welch method is a typical non-parametric spectral estimation approach that estimates the PSD of stationary noise by averaging periodograms of several time segments, or by taking the median of periodograms to adapt to non-stationary noise. In this work, we propose a wavelet-based approach for fast PSD estimation of both stationary and non-stationary noise. For stationary noise, we apply wavelet smoothing to the periodogram, avoiding the segmentation step in the Welch method, and enabling PSD estimates with high frequency resolution and low variance. The wavelet smoothing PSD outperforms Welch PSD in matched filtering and parameter estimation. For non-stationary noise, we estimate the PSD by taking the median of wavelet packet coefficients in each frequency bin, which offers greater robustness than the traditional median periodogram method. This work introduces a new PSD estimation approach for GW data analysis and expands the application of wavelet methods in this field.", "Alzheimer disease (AD) is the most common cause of dementia in geriatric population. At present, no effective treatments exist to reverse the progress of AD, however, early diagnosis and intervention might delay its progression. The search for biomarkers with good safety, repeatable detection, reliable sensitivity and community application is necessary for AD screening and early diagnosis and timely intervention. Electroencephalogram (EEG) examination is a non-invasive, quantitative, reproducible, and cost-effective technique which is suitable for screening large population for possible AD. The power spectrum, complexity and synchronization characteristics of EEG waveforms in AD patients have distinct deviation from normal elderly, indicating these EEG features can be a promising candidate biomarker of AD. However, current reported deviation results are inconsistent, possibly due to multiple factors such as diagnostic criteria, sample sizes and the use of different computational measures. In this study, we collected two neurological tests scores (MMSE and MoCA) and the resting-state EEG of 30 normal control elderly subjects (NC group) and 30 probable AD patients confirmed by Pittsburgh compound B positron emission tomography (PiB-PET) inspection (AD group). We calculated the power spectrum, spectral entropy and phase synchronization index features of these two groups\u2019 EEG at left/right frontal, temporal, central and occipital brain regions in 4 frequency bands: \u03b4 oscillation (1\u20134 Hz), \u03b8 oscillation (4\u20138 Hz), \u03b1 oscillation (8\u201313 Hz), and \u03b2 oscillation (13\u201330 Hz). In most brain areas, we found that the AD group had significant differences compared to NC group: (1) decreased \u03b1 oscillation power and increased \u03b8 oscillation power; (2) decreased spectral entropy in \u03b1 oscillation and elevated spectral entropy in \u03b2 oscillation; and (3) decrease phase synchronization index in \u03b4, \u03b8, and \u03b2 oscillation. We also found that \u03b1 oscillation spectral power and \u03b2 oscillation phase synchronization index correlated well with the MMSE/MoCA test scores in AD groups. Our study suggests that these two EEG features might be useful metrics for population screening of probable AD patients. **Keywords:** Alzheimer disease, electroencephalogram (EEG), power spectrum, spectral entropy (SE), phase synchronization index", "Objective: A characteristic of neurological signal processing is high levels of noise from sub-cellular ion channels up to whole-brain processes. In this paper, we propose a new model of electroencephalogram (EEG) background periodograms, based on a family of functions which we call generalized van der Ziel\u2013McWhorter (GVZM) power spectral densities (PSDs). To the best of our knowledge, the GVZM PSD function is the only EEG noise model which has relatively few parameters, matches recorded EEG PSD\u2019s with high accuracy from 0 Hz to over 30 Hz, and has approximately $1/f^{\\theta}$ behavior in the mid-frequencies without infinities. Methods: We validate this model using three approaches. First, we show how GVZM PSDs can arise in population of ion channels in maximum entropy equilibrium. Second, we present a class of mixed autoregressive models, which simulate brain background noise and whose periodograms are asymptotic to the GVZM PSD. Third, we present two real-time estimation algorithms for steady-state visual evoked potential (SSVEP) frequencies, and analyze their performance statistically. Results: In pairwise comparisons, the GVZM-based algorithms showed statistically significant accuracy improvement over two well-known and widely-used SSVEP estimators. Conclusion: The GVZM noise model can be a useful and reliable technique for EEG signal processing. Significance: Understanding EEG noise is essential for EEG-based neurology and applications such as real-time brain-computer interfaces (BCIs), which must make accurate control decisions from very short data epochs. The GVZM approach represents a successful new paradigm for understanding and managing this neurological noise.", "We present a new Bayesian nonparametric approach to estimating the spectral density of a stationary time series. A nonparametric prior based on a mixture of B-spline distributions is specified and can be regarded as a generalization of the Bernstein polynomial prior of and . Whittle\u2019s likelihood approximation is used to obtain the pseudo-posterior distribution. This method allows for a data-driven choice of the number of mixture components and the location of knots. Posterior samples are obtained using a Metropolis-within-Gibbs Markov chain Monte Carlo algorithm, and mixing is improved using parallel tempering. We conduct a simulation study to demonstrate that for complicated spectral densities, the B-spline prior provides more accurate Monte Carlo estimates in terms of $L_{1}$ -error and uniform coverage probabilities than the Bernstein polynomial prior. We apply the algorithm to annual mean sunspot data to estimate the solar cycle. Finally, we demonstrate the algorithm\u2019s ability to estimate a spectral density with sharp features, using real gravitational wave detector data from LIGO\u2019s sixth science run, recoloured to match the Advanced LIGO target sensitivity.", "In recent years, Gaussian Process (GP) regression has become widely used to analyse stellar and exoplanet time-series data sets. For spotted stars, the most popular GP covariance function is the quasi-periodic (QP) kernel, whose the hyperparameters of the GP have a plausible interpretation in terms of physical properties of the star and spots. In this paper, we test the reliability of this interpretation by modelling data simulated using a spot model using a QP GP, and the recently proposed quasi-periodic plus cosine (QPC) GP, comparing the posterior distributions of the GP hyperparameters to the input parameters of the spot model. We find excellent agreement between the input stellar rotation period and the QP and QPC GP period, and very good agreement between the spot decay timescale and the length scale of the squared exponential term. We also compare the hyperparameters derived from light and radial velocity (RV) curves for a given star, finding that the period and evolution timescales are in good agreement. However, the harmonic complexity of the GP, while displaying no clear correlation with the spot properties in our simulations, is systematically higher for the RV than for the light curve data. Finally, for the QP kernel, we investigate the impact of noise and time-sampling on the hyperparameters in the case of RVs. Our results indicate that good coverage of rotation period and spot evolution time-scales is more important than the total number of points, and noise characteristics govern the harmonic complexity.", "The Gaussian random field (GRF) and the Gaussian Markov random field (GMRF) have been widely used to accommodate spatial dependence under the generalized linear mixed model framework. These models have limitations rooted in the symmetry and thin tail of the Gaussian distribution. We introduce a new class of random fields, termed transformed GRF (TGRF), and a new class of Markov random fields, termed transformed GMRF (TGMRF). They are constructed by transforming the margins of GRFs and GMRFs, respectively, to desired marginal distributions to accommodate asymmetry and heavy tail as needed in practice. The Gaussian copula that characterizes the dependence structure facilitates inferences and applications in modeling spatial dependence. This construction leads to new models such as gamma or beta Markov fields with Gaussian copulas, which can be used to model Poisson intensity or Bernoulli rate in a spatial generalized linear mixed model. The method is naturally implemented in a Bayesian framework. We illustrate the utility of the methodology in an ecological application with spatial count data and spatial presence/absence data of some snail species, where the new models are shown to outperform the traditional spatial models. The validity of Bayesian inferences and model selection are assessed through simulation studies for both spatial Poisson regression and spatial Bernoulli regression.", "Trace metals present in high amounts in aquatic systems are a perpetual concern. This study applied geostatistical and machine learning models namely Ordinary Kriging (OK), Ordinary Cokriging (OCK) and Artificial Neural Network (ANN) to assess the spatial variability of trace metals and pollution indices in surface sediments along the Lom River in an abandoned gold mining site at Bekao (Adamawa Cameroon). For this purpose, thirty-one (31) surface sediment samples are collected in order to determine the total concentrations of As, Cr, Cu, Fe, Mn, Ni, Pb, Sn and Zn. These trace metals are used to compute pollution indices as the sediment pollution index (SPI), the Nemerow index (NI), the modified contamination degree (mCD), and the potential ecological risk assessment (RI). OK, OCK and ANN models are compared to determine the best model performance. The best models are selected based on the values of the root mean square error (RMSE), the coefficient of determination (R 2 ), the scatter index (SI) and the BIAS. Results showed that the sequence of trace metal mean concentrations in the sediments is Fe > Mn > Cu > Ni > Sn > Cr > Zn > Pb > As. The mean concentrations of Ni, Cu, Zn and Sn are above the average shale values (ASV) and the pollution status is globally moderate to significant with a low potential ecological risk. The spatial dependency obtained with semivariogram models are moderate to weak for Mn, Fe, Ni, Pb, SPI, NI, mCD, RI As, Cr, and Sn and strong for Cu and Zn. According to cross-validation parameters, ANN model is the best method for the prediction on trace metal concentrations and pollution indices in surface sediments along the Lom River in the abandoned gold mining site of Bekao.", "The accelerating pace and expanding scope of materials discovery demand optimization frameworks that efficiently navigate vast, nonlinear design spaces while judiciously allocating limited evaluation resources. We present a cost-aware, batch Bayesian optimization scheme powered by deep Gaussian process (DGP) surrogates and a heterotopic querying strategy. Our DGP surrogate, formed by stacking GP layers, models complex hierarchical relationships among high-dimensional compositional features and captures correlations across multiple target properties, propagating uncertainty through successive layers. We integrate evaluation cost into an upper-confidence-bound acquisition extension, which, together with heterotopic querying, proposes small batches of candidates in parallel, balancing exploration of under-characterized regions with exploitation of high-mean, low-variance predictions across correlated properties. Applied to refractory high-entropy alloys for high-temperature applications, our framework converges to optimal formulations in fewer iterations with cost-aware queries than conventional GP-based BO, highlighting the value of deep, uncertainty-aware, cost-sensitive strategies in materials campaigns.", "State-space models have been successfully used for more than fifty years in different areas of science and engineering. We present a procedure for efficient variational Bayesian learning of nonlinear state-space models based on sparse Gaussian processes. The result of learning is a tractable posterior over nonlinear dynamical systems. In comparison to conventional parametric models, we offer the possibility to straightforwardly trade off model capacity and computational cost whilst avoiding overfitting. Our main algorithm uses a hybrid inference approach combining variational Bayes and sequential Monte Carlo. We also present stochastic variational inference and online learning approaches for fast learning with long time series.", "# Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix Strengthening the Training of Convolutional Neural Networks By Using Walsh Matrix. Tamer \u00d6lmez and Z\u00fcmray Dokur Istanbul Technical University, Department of Electronics and Communication Engineering, Istanbul, Turkey corresponding author: olmezt@itu.edu.tr ABSTRACT: DNN structures are continuously developing and achieving high performances in classification problems. Also, it is observed that success rates obtained with DNNs are higher than those obtained with traditional neural networks. In addition, one of the advantages of DNNs is that there is no need to spend an extra effort to determine the features; the CNN automatically extracts the features from the dataset during the training. Besides their benefits, the DNNs have the following three major drawbacks among the others: (i) Researchers have struggled with over-fitting and under-fitting issues in the training of DNNs, (ii) determination of even a coarse structure for the DNN may take days, and (iii) most of the time, the proposed network structure is too large to be too bulky to be used in real time applications. We have modified the training", "The increasing penetration of renewable energy resources in power systems, represented as random processes, converts the traditional deterministic economic dispatch problem into a stochastic one. To solve this stochastic economic dispatch, the conventional Monte Carlo method is prohibitively time consuming for medium- and large-scale power systems. To overcome this problem, we propose in this paper a novel Gaussian-process-emulator-based approach to quantify the uncertainty in the stochastic economic dispatch considering wind power penetration. Based on the dimension-reduction results obtained by the Karhunen-Lo\u00e8ve expansion, a Gaussian-process emulator is constructed. This surrogate allows us to evaluate the economic dispatch solver at sampled values with a negligible computational cost while maintaining a desirable accuracy. Simulation results conducted on the IEEE 118-bus system reveal that the proposed method has an excellent performance as compared to the traditional Monte Carlo method.", "Highly clustered event sequences are observed in certain types of crime data, such as burglary and gang violence, due to crime-specific patterns of criminal behavior. Similar clustering patterns are observed by seismologists, as earthquakes are well known to increase the risk of subsequent earthquakes, or aftershocks, near the location of an initial event. Space\u2013time clustering is modeled in seismology by self-exciting point processes and the focus of this article is to show that these methods are well suited for criminological applications. We first review self-exciting point processes in the context of seismology. Next, using residential burglary data provided by the Los Angeles Police Department, we illustrate the implementation of self-exciting point process models in the context of urban crime. For this purpose we use a fully nonparametric estimation methodology to gain insight into the form of the space\u2013time triggering function and temporal trends in the background rate of burglary.", "The ETAS model is widely employed to model the spatio-temporal distribution of earthquakes, generally using spatially invariant parameters. We propose an efficient method for the estimation of spatially varying parameters, using the Expectation-Maximization algorithm and spatial Voronoi tessellation ensembles. We use the Bayesian Information Criterion to rank inverted models given their likelihood and complexity and select the best models to compute an ensemble model at any location. Using a synthetic catalog, we check that the proposed method correctly inverts the known parameters. We apply the method to earthquakes in the ANSS catalog within a polygon around California for 1981-2015. The results indicate significant spatial variation of the parameters; the efficiency of earthquakes to trigger future ones positively correlates with surface heat flow, and triggering is mostly dominated by small earthquakes.", "1. Electronic telemetry is frequently used to document animal movement through time. Methods that can identify underlying behaviors driving specific movement patterns can help us understand how and why animals use available space, thereby aiding conservation and management efforts. For aquatic animal tracking data with significant measurement error, a Bayesian state-space model called the first-Difference Correlated Random Walk with Switching (DCRWS) has often been used for this purpose. However, for aquatic animals, highly accurate tracking data of animal movement are now becoming more common. 2. We developed a new Hidden Markov Model (HMM) for identifying behavioral states from animal tracks with negligible error, which we called the Hidden Markov Movement Model (HMMM). We implemented as the basis for the HMMM the process equation of the DCRWS, but we used the method of maximum likelihood and the R package TMB for rapid model fitting. 3. We compared the HMMM to a modified version of the DCRWS for highly accurate tracks, the DCRWS NOME , and to a common HMM for animal tracks fitted with the R package moveHMM . We show that the HMMM is both accurate and suitable for multiple species by fitting it to real tracks from a grey seal, lake trout, and blue shark, as well as to simulated data. 4. The HMMM is a fast and reliable tool for making meaningful inference from animal movement data that is ideally suited for ecologists who want to use the popular DCRWS implementation for highly accurate tracking data. It additionally provides a groundwork for development of more complex modelling of animal movement with TMB . To facilitate its uptake, we make it available through the R package swim .", "Markov switching models are a popular family of models that introduces time-variation in the parameters in the form of their state- or regime-specific values. Importantly, this time-variation is governed by a discrete-valued latent stochastic process with limited memory. More specifically, the current value of the state indicator is determined only by the value of the state indicator from the previous period, thus the Markov property, and the transition matrix. The latter characterizes the properties of the Markov process by determining with what probability each of the states can be visited next period, given the state in the current period. This setup decides on the two main advantages of the Markov switching models. Namely, the estimation of the probability of state occurrences in each of the sample periods by using filtering and smoothing methods and the estimation of the state-specific parameters. These two features open the possibility for improved interpretations of the parameters associated with specific regimes combined with the corresponding regime probabilities, as well as for improved forecasting performance based on persistent regimes and parameters characterizing them. The most commonly applied models from this family are the Markov switching ones that presume a finite number of regimes and exogeneity of the Markov process which is defined as its independence of the model unpredictable innovations. In many such applications, desired properties of the Markov switching model have been obtained either by imposing appropriate restrictions on transition probabilities or by introducing the time-dependence of these probabilities determined by explanatory variables or functions of the state indicator. One of the recent extensions of this basic specification includes Infinite Hidden Markov models that grant great flexibility and improved forecasting performance by allowing the number of states to go to infinity. Another one, namely, endogenous Markov switching model, explicitly relates the state indicator to model\u2019s innovations making it more interpretable and offering promising avenues for future developments.", "The article presents an application of Hidden Markov Models (HMMs) for pattern recognition on genome sequences. We apply HMM for identifying genes encoding the Variant Surface Glycoprotein (VSG) in the genomes of Trypanosoma brucei (T. brucei) and other African trypanosomes. These are parasitic protozoa causative agents of sleeping sickness and several diseases in domestic and wild animals. These parasites have a peculiar strategy to evade the host\u2019s immune system that consists in periodically changing their predominant cellular surface protein (VSG). The motivation for using patterns recognition methods to identify these genes, instead of traditional homology based ones, is that the levels of sequence identity (amino acid and DNA sequence) amongst these genes is often below of what is considered reliable in these methods. Among pattern recognition approaches, HMM are particularly suitable to tackle this problem because they can handle more naturally the determination of gene edges. We evaluate the performance of the model using different number of states in the Markov model, as well as several performance metrics. The model is applied using public genomic data. Our empirical results show that the VSG genes on T. brucei can be safely identified (high sensitivity and low rate of false positives) using HMM.", "DNA sequence alignment, an important genomic task, involves assigning short DNA reads to the most probable locations on an extensive reference genome. Conventional methods tackle this challenge in two steps: genome indexing followed by efficient search to locate likely positions for given reads. Building on the success of Large Language Models (LLM) in encoding text into embeddings, where the distance metric captures semantic similarity, recent efforts have encoded DNA sequences into vectors using Transformers and have shown promising results in tasks involving classification of short DNA sequences. Performance at sequence classification tasks does not, however, guarantee sequence alignment , where it is necessary to conduct a genome-wide search to align every read successfully, a significantly longer-range task by comparison . We bridge this gap by developing a \u201c E mbed- S earch- A lign\u201d (ESA) framework, where a novel Reference-Free DNA Embedding ( RDE ) Transformer model generates vector embeddings of reads and fragments of the reference in a shared vector space; read-fragment distance metric is then used as a surrogate for sequence similarity. ESA introduces: (1) Contrastive loss for self-supervised training of DNA sequence representations, facilitating rich reference-free, sequence-level embeddings, and (2) a DNA vector store to enable search across fragments on a global scale. RDE is 99% accurate when aligning 250-length reads onto a human reference genome of 3 gigabases (single-haploid), rivaling conventional algorithmic sequence alignment methods such as Bowtie and BWA-Mem . RDE far exceeds the performance of $6$ recent DNA-Transformer model baselines such as Nucleotide Transformer, Hyena-DNA , and shows task transfer across chromosomes and species.", "Instead of conformation states of single residues, refined conformation states of quintuplets are proposed to reflect conformation correlation. Simple hidden Markov models combining with sliding window scores are used for predicting secondary structure of a protein from its amino acid sequence. Since the length of protein conformation segments varies in a narrow range, we ignore the duration effect of the length distribution. The window scores for residues are a window version of the Chou-Fasman propensities estimated under an approximation of conditional independency. Different window widths are examined, and the optimal width is found to be 17. A high accuracy about 70% is achieved.", "# A Comprehensive Survey on Bengali Phoneme Recognition ICERIE_ MIE_XYZ * Corresponding author: sadiatasnimswarna@gmail.com Proceedings of the International Conference on Engineering Research, Innovation and Education 2017 ICERIE 2017, 13 15 January, SUST, Sylhet, Bangladesh A Comprehensive Survey on Bengali Phoneme Recognition Sadia Tasnim Swarna 1*, Shamim Ehsan 1, Md. Saiful Islam1 and Marium E Jannat 1 1 Department of Computer Science and Engineering, Shahjalal University of Science & Technology, Sylhet, Bangladesh. Email: sadiatasnimswarna@gmail.com, ehsanhrid@gmail.com, saiful-cse@sust.edu, jannatcse@yahoo.com. Keywords: \uf0b7 multilayer neural network, hidden Markov model, phonetic feature \uf0b7 SUST, ICERIE Abstract: Hidden Markov model based various phoneme recognition methods for Bengali language is reviewed. Automatic phoneme recognition for Bengali language using multilayer neural network is reviewed. Usefulness of multilayer neural network over single layer neural network is discussed. Bangla phonetic feature table construction and enhancement for Bengali speech recognition is also discussed. Comparison among these methods is discussed. 1. INTRODUCTION A phoneme is one of the", ". A common problem faced in statistical inference is drawing conclusions from paired comparisons, in which two objects compete and one is declared the victor. A probabilistic approach to such a problem is the Bradley-Terry model , first studied by Zermelo in 1929 and rediscovered by Bradley and Terry in 1952. One obvious area of application for such a model is sporting events, and in particular Major League Baseball. With this in mind, we describe a hierarchical Bayesian version of Bradley-Terry suitable for use in ranking and prediction problems, and compare results from these application domains to standard maximum likelihood approaches. Our Bayesian methods outperform the MLE-based analogues, while being simple to construct, implement, and interpret.", "Occupancy models are frequently used by ecologists to quantify spatial variation in species distributions while accounting for observational biases in the collection of detection-nondetection data. However, the common assumption that a single set of regression coefficients can adequately explain species-environment relationships is often unrealistic, especially across large spatial domains. Here we develop single-species (i.e., univariate) and multi-species (i.e., multivariate) spatially-varying coefficient (SVC) occupancy models to account for spatially-varying species-environment relationships. We employ Nearest Neighbor Gaussian Processes and P\u00f3lya-Gamma data augmentation in a hierarchical Bayesian framework to yield computationally efficient Gibbs samplers, which we implement in the spOccupancy R package. For multi-species models, we use spatial factor dimension reduction to efficiently model datasets with large numbers of species (e.g., $>10$ ). The hierarchical Bayesian framework readily enables generation of posterior predictive maps of the SVCs, with fully propagated uncertainty. We apply our SVC models to quantify spatial variability in the relationships between maximum breeding season temperature and occurrence probability of 21 grassland bird species across the U.S. Jointly modeling species generally outperformed single-species models, which all revealed substantial spatial variability in species occurrence relationships with maximum temperatures. Our models are particularly relevant for quantifying species-environment relationships using detection-nondetection data from large-scale monitoring programs, which are becoming increasingly prevalent for answering macroscale ecological questions regarding wildlife responses to global change. Keywords: Bayesian, species distribution model, wildlife, monitoring, nonstationarity", "Evaluating the abilities of learners is a fundamental objective in the field of education. In particular, there is an increasing need to assess higher-order abilities such as expressive skills and logical thinking. Constructed-response tests such as short-answer and essay-based questions have become widely used as a method to meet this demand. Although these tests are effective, they require substantial manual grading, making them both labor-intensive and costly. Item response theory (IRT) provides a promising solution by enabling the estimation of ability from incomplete score data, where human raters grade only a subset of answers provided by learners across multiple test items. However, the accuracy of ability estimation declines as the proportion of missing scores increases. Although data augmentation techniques for imputing missing scores have been explored in order to address this limitation, they often struggle with inaccuracy for sparse or heterogeneous data. To overcome these challenges, this study proposes a novel method for imputing missing scores by leveraging automated scoring technologies for accurate IRT-based ability estimation. The proposed method achieves high accuracy in ability estimation while markedly reducing manual grading workload.", "Item Response Theory (IRT) models aim to assess latent abilities of $n$ examinees along with latent difficulty characteristics of $m$ test items from categorical data that indicates the quality of their corresponding answers. Classical psychometric assessments are based on a relatively small number of examinees and items, say a class of $200$ students solving an exam comprising $10$ problems. More recent global large scale assessments such as PISA, or internet studies, may lead to significantly increased numbers of participants. Additionally, in the context of Machine Learning where algorithms take the role of examinees and data analysis problems take the role of items, both $n$ and $m$ may become very large, challenging the efficiency and scalability of computations. To learn the latent variables in IRT models from large data, we leverage the similarity of these models to logistic regression, which can be approximated accurately using small weighted subsets called coresets . We develop coresets for their use in alternating IRT training algorithms, facilitating scalable learning from large data.", "The Rasch model, a classical model in the item response theory, is widely used in psychometrics to model the relationship between individuals\u2019 latent traits and their binary responses to assessments or questionnaires. In this paper, we introduce a new likelihood-based estimator\u2014random pairing maximum likelihood estimator ( $\\mathrm{RP\\text{-}MLE}$ ) and its bootstrapped variant multiple random pairing MLE ( $\\mathrm{MRP\\text{-}MLE}$ ) which faithfully estimate the item parameters in the Rasch model. The new estimators have several appealing features compared to existing ones. First, both work for sparse observations, an increasingly important scenario in the big data era. Second, both estimators are provably minimax optimal in terms of finite sample $\\ell_{\\infty}$ estimation error. Lastly, both admit precise distributional characterization that allows uncertainty quantification on the item parameters, e.g., construction of confidence intervals for the item parameters. The main idea underlying $\\mathrm{RP\\text{-}MLE}$ and $\\mathrm{MRP\\text{-}MLE}$ is to randomly pair user-item responses to form item-item comparisons. This is carefully designed to reduce the problem size while retaining statistical independence. We also provide empirical evidence of the efficacy of the two new estimators using both simulated and real data.", ". We describe the application of the Bradley-Terry model to NCAA Division I Men\u2019s Ice Hockey. A Bayesian construction gives a joint posterior probability distribution for the log-strength parameters, given a set of game results and a choice of prior distribution. For several suitable choices of prior, it is straightforward to find the maximum a posteriori point (MAP) and a Hessian matrix, allowing a Gaussian approximation to be constructed. Posterior predictive probabilities can be estimated by 1) setting the log-strengths to their MAP values, 2) using the Gaussian approximation for analytical or Monte Carlo integration, or 3) applying importance sampling to re-weight the results of a Monte Carlo simulation. We define a method to evaluate any models which generate predicted probabilities for future outcomes, using the Bayes factor given the actual outcomes, and apply it to NCAA tournament results. Finally, we describe an on-line tool which currently estimates probabilities of future results using MAP evaluation and describe how it can be refined using the Gaussian approximation or importance sampling.", "The Football World Cup as world\u2019s favorite sporting event is a source of both entertainment and overwhelming amount of data about the games played. In this paper we analyse the available data on football world championships since 1930 until today. Our goal is to rank the national teams based on all matches during the championships. For this purpose, we apply the PageRank with restarts algorithm to a graph built from the games played during the tournaments. Several statistics such as matches won and goals scored are combined in different metrics that assign weights to the links in the graph. Finally, our results indicate that the Random walk approach with the use of right metrics can indeed produce relevant rankings comparable to the FIFA official all-time ranking board.", "Neural populations encode information about their stimulus in a collective fashion, by joint activity patterns of spiking and silence. A full account of this mapping from stimulus to neural activity is given by the conditional probability distribution over neural codewords given the sensory input. To infer a model for this distribution from large-scale neural recordings, we introduce a stimulus-dependent maximum entropy (SDME) model, a minimal extension of the canonical linear-nonlinear model of a single neuron to a pairwise-coupled neural population. The model captures single-cell response properties as well as correlations in neural spiking due to shared stimulus and effective neuron-to-neuron connections. In a population of 100 retinal ganglion cells responding to temporal white-noise stimuli, dependencies between cells play an important encoding role, and the SDME model gives a more accurate account of single-cell responses and outperforms uncoupled models in reproducing the distributions of codewords emitted in response to a stimulus.", "Spatially proximate amino acids in a protein tend to coevolve. A protein's three-dimensional structure hence leaves an echo of correlations in the evolutionary record. Reverse engineering 3D structures from such correlations is an open problem in structural biology. Within this task lies a statistical inference problem: correlation between two sites can arise from firsthand interaction but can also be network-propagated via intermediate sites. To separate direct from indirect interactions is an instance of the general problem of inverse statistical mechanics, where the task is to learn model parameters (fields, couplings) from observables (magnetizations, correlations, samples) in large systems; in protein sequences the approach is called direct-coupling analysis. Here we show that the pseudolikelihood method, applied to 21-state Potts models describing families of evolutionarily related proteins, significantly outperforms existing direct-coupling-analysis approaches based on mean-field techniques, also relying on a modified coupling-strength score. The results are verified using known crystal structures.", "Accurate estimation of power system dynamics is very important for the enhancement of power system reliability, resilience, security, and stability of power system. With the increasing integration of inverter-based distributed energy resources, the knowledge of power system dynamics has become more necessary and critical than ever before for proper control and operation of the power system. Although recent advancement of measurement devices and the transmission technologies have reduced the measurement and transmission error significantly, these measurements are still not completely free from the measurement noises. Therefore, the noisy measurements need to be filtered to obtain the accurate power system operating dynamics. In this work, the power system dynamic states are estimated using extended Kalman filter (EKF) and unscented Kalman filter (UKF). We have performed case studies on Western Electricity Coordinating Council (WECC)\u2019s $3$ -machine $9$ -bus system and New England $10$ -machine $39$ -bus. The results show that the UKF and EKF can accurately estimate the power system dynamics. The comparative performance of EKF and UKF for the tested case is also provided. Other Kalman filtering techniques along with the machine learning based estimator will be updated in this report soon. All the sources code including Newton Raphson power flow, admittance matrix calculation, EKF calculation, and UKF calculation are publicly available in Github on Power System Dynamic State Estimation.", "Influenza recurs seasonally in temperate regions of the world; however, our ability to predict the timing, duration, and magnitude of local seasonal outbreaks of influenza remains limited. Here we develop a framework for initializing real-time forecasts of seasonal influenza outbreaks, using a data assimilation technique commonly applied in numerical weather prediction. The availability of real-time, web-based estimates of local influenza infection rates makes this type of quantitative forecasting possible. Retrospective ensemble forecasts are generated on a weekly basis following assimilation of these web-based estimates for the 2003-2008 influenza seasons in New York City. The findings indicate that real-time skillful predictions of peak timing can be made more than 7 wk in advance of the actual peak. In addition, confidence in those predictions can be inferred from the spread of the forecast ensemble. This work represents an initial step in the development of a statistically rigorous system for real-time forecast of seasonal influenza.", "Stochastic volatility models have existed in Option pricing theory ever since the crash of 1987 which violated the Black-Scholes model assumption of constant volatility. Heston model is one such stochastic volatility model that is widely used for volatility estimation and option pricing. In this paper, we design a novel method to estimate parameters of Heston model under state-space representation using Bayesian filtering theory and Posterior Cramer-Rao Lower Bound (PCRLB), integrating it with Normal Maximum Likelihood Estimation (NMLE) proposed in [1]. Several Bayesian filters like Extended Kalman Filter (EKF), Unscented Kalman Filter (UKF), Particle Filter (PF) are used for latent state and parameter estimation. We employ a switching strategy proposed in [2] for adaptive state estimation of the non-linear, discrete-time state-space model (SSM) like Heston model. We use a particle filter approximated PCRLB [3] based performance measure to judge the best filter at each time step. We test our proposed framework on pricing data from S&P 500 and NSE Index, estimating the underlying volatility and parameters from the index. Our proposed method is compared with the VIX measure and historical volatility for both the indexes. The results indicate an effective framework for estimating volatility adaptively with changing market dynamics. Keywords- Stochastic volatility; Heston Model; Normal MLE; Bayesian Filtering; Posterior Cramer-Rao Lower Bound (PCRLB)", "In this work, we apply machine learning techniques to historical stock prices to forecast future prices. To achieve this, we use recursive approaches that are appropriate for handling time series data. In particular, we apply a linear Kalman filter and different varieties of long short-term memory (LSTM) architectures to historical stock prices over a 10-year range (1/1/2011 \u2013 1/1/2021). We quantify the results of these models by computing the error of the predicted values versus the historical (i.e., \u201ctrue\u201d) values of each stock. We find that of the algorithms we investigated, a simple linear Kalman filter can predict the next-day value of stocks with low-volatility (e.g., Microsoft, \u2019MSFT\u2019) surprisingly well. However, in the case of high-volatility stocks (e.g., Tesla, \u2019TSLA\u2019) the more complex LSTM algorithms significantly outperform the Kalman filter. Our results show that we can classify different types of stocks (i.e., clustering) and then train an LSTM for each stock type. This method could be used to automate portfolio generation for a target return rate.", "ICESEE (ICE Sheet statE and parameter Estimator) is a Python-based, open-source data assimilation framework designed for seamless integration with ice-sheet and other Earth system models. ICESEE implements a parallel Ensemble Kalman Filter (EnKF) architecture with full Message Passing Interface (MPI) support, enabling scalable assimilation in both model state and parameter spaces. Its core algorithm employs a nonlinear, transformation-based update scheme originally proposed by , which avoids explicit construction of the forecast error covariance matrix. This matrix-free formulation eliminates the need for localization while retaining robustness in high-dimensional, nonlinear systems. In addition to the core EnKF, ICESEE provides serial implementations of four alternative EnKF variants, including a localized formulation, offering flexibility for methodological testing and comparative studies. Beyond state estimation, ICESEE supports the indirect inference of unobserved or weakly constrained model parameters through a hybrid assimilation\u2013inversion strategy. In this approach, ensemble-based data assimilation corrects the model state using available observations, while physics-based inverse methods are subsequently applied to infer parameters such as basal friction. The framework features modular coupling interfaces, adaptive state indexing, and efficient parallel input/output, making it readily extensible to a wide range of numerical modeling environments. ICESEE has been successfully coupled with several existing models, including the MATLAB/C++-based Ice Sheet and Sea-level System Model (ISSM), the Firedrake-based Python model Icepack, a one-dimensional flowline model, and the reduced-order Lorenz\u201396 system. In this study, we focus on applications with ISSM and Icepack to demonstrate ICESEE\u2019s interoperability, numerical performance, scalability, and its ability to jointly improve state estimates and infer uncertain model parameters. Performance benchmarks show strong and weak scaling on high-performance computing platforms, underscoring ICESEE\u2019s potential to enable long-term, observation-constrained reanalyses of ice-sheet evolution at continental scales.", "The Kalman filter is an algorithm for the estimation of hidden variables in dynamical systems under linear Gauss-Markov assumptions with widespread applications across different fields. Recently, its Bayesian interpretation has received a growing amount of attention especially in neuroscience, robotics and machine learning. In neuroscience, in particular, models of perception and control under the banners of predictive coding, optimal feedback control, active inference and more generally the so-called Bayesian brain hypothesis, have all heavily relied on ideas behind the Kalman filter. Active inference, an algorithmic theory based on the free energy principle, specifically builds on approximate Bayesian inference methods proposing a variational account of neural computation and behaviour in terms of gradients of variational free energy. Using this ambitious framework, several works have discussed different possible relations between free energy minimisation and standard Kalman filters. With a few exceptions, however, such relations point at a mere qualitative resemblance or are built on a set of very diverse comparisons based on purported differences between free energy minimisation and Kalman filtering. In this work, we present a straightforward derivation of Kalman filters consistent with active inference via a variational treatment of free energy minimisation in terms of gradient descent. The approach considered here offers a more direct link between models of neural dynamics as gradient descent and standard accounts of perception and decision making based on probabilistic inference, further bridging the gap between hypotheses about neural implementation and computational principles in brain and behavioural sciences.", "Mathematical modeling and real-time dynamics identification of the mean arterial blood pressure (MAP) response of a patient to vasoactive drug infusion can provide a reliable tool for automated drug administration and therefore, reduce the emergency costs and significantly benefit the patient\u2019s MAP regulation in an intensive care unit. To this end, a dynamic first-order linear parameter-varying (LPV) model with varying parameters and varying input delay is considered to capture the MAP response dynamics. Such a model effectively addresses the complexity and the intra- and inter-patient variability of the physiological response. We discretize the model and augment the state vector with model parameters as unknown states of the system and a Bayesian-based multiple-model square root cubature Kalman filtering (MMSRCKF) approach is utilized to estimate the model time-varying parameters. Since, unlike the other model parameters, the input delay cannot be captured by a random-walk process, a multiple-model module with a posterior probability estimation is implemented to provide the delay identification. Validation results confirm the effectiveness of the proposed identification algorithm both in simulation scenarios and also using animal experiment data.", "Pharmacokinetic (PK) parameter estimation is a critical and complex step in the model\u2010informed precision dosing (MIPD) approach. The mapbayr package was developed to perform maximum *a posteriori* Bayesian estimation (MAP\u2010BE) in R from any population PK model coded in mrgsolve. The performances of mapbayr were assessed using two approaches. First, \u201ctest\u201d models with different features were coded, for example, first\u2010order and zero\u2010order absorption, lag time, time\u2010varying covariates, Michaelis\u2013Menten elimination, combined and exponential residual error, parent drug and metabolite, and small or large inter\u2010individual variability (IIV). A total of 4000 PK profiles (combining single/multiple dosing and rich/sparse sampling) were simulated from each test model, and MAP\u2010BE of parameters was performed in both mapbayr and NONMEM. Second, a similar procedure was conducted with seven \u201creal\u201d previously published models to compare mapbayr and NONMEM on a PK outcome used in MIPD. For the test models, 98% of mapbayr estimations were identical to those given by NONMEM. Some discordances could be observed when dose\u2010related parameters were estimated or when models with large IIV were used. The exploration of objective function values suggested that mapbayr might outdo NONMEM in specific cases. For the real models, a concordance close to 100% on PK outcomes was observed. The mapbayr package provides a reliable solution to perform MAP\u2010BE of PK parameters in R. It also includes functions dedicated to data formatting and reporting and enables the creation of standalone Shiny web applications dedicated to MIPD, whatever the model or the clinical protocol and without additional software other than R. ---", "# SE(3) based Extended Kalman Filter for Spacecraft Attitude Estimation ( ) 3 \uf053\uf045 based Extended Kalman Filter for Spacecraft Attitude Estimation Lubin Chang* Naval University of Engineering, Wuhan, 430033, China Abstract: In this paper, the spacecraft attitude estimation problem has been investigated making use of the concept of matrix Lie group. Through formulation of the attitude and gyroscope bias as elements of ( ) 3 \uf053\uf045 , the corresponding extended Kalman filter, termed as ( ) 3 \uf053\uf045 -EKF, has been derived. It is shown that the resulting ( ) 3 \uf053\uf045 -EKF is just the newly-derived geometric extended Kalman filter (GEKF) for spacecraft attitude estimation. This provides a new perspective on the GEKF besides the common frame errors definition. Moreover, the ( ) 3 \uf053\uf045 -EKF with reference frame attitude error is also derived and the resulting algorithm bears much resemblance to the right invariant EKF. Keywords: Attitude estimation; extended Kalman filter; matrix Lie group; special Euclidean group. 1. Introduction Spacecraft attitude estimation using vector observations has drawn much attention since the 1960s. For this problem, two facts have been widely approved, that is, gyroscopes are", "This paper proposes a novel particle filter for tracking time-varying states of multiple targets jointly from superpositional data, which depend on the sum of contributions of all targets. Many conventional tracking methods rely on preprocessing for detection ( e.g. , thresholding), which severely limits tracking performance at a low signal-to-noise ratio (SNR). In contrast, the proposed method can operate directly on raw sensor signals without requiring such preprocessing. Though there also exist methods applicable to raw sensor signals called track-before-detect , the proposed method has significant advantages over them. First, it is general without any restrictions on observation/process noise statistics ( e.g. , Gaussian) or the functional form of each target\u2019s contribution to the sensors ( e.g. , linear, separable, binary). Especially, it includes Salmond et al. \u2019s track-before-detect particle filter for a single target as a particular example up to some implementation details. Second, it can track an unknown, time-varying number of targets without knowing their initial states owing to a target birth/death model. We present a simulation example of radio-frequency tomography, where it significantly outperformed Nannuru et al. \u2019s state-of-the-art method based on random finite sets in terms of the optimal subpattern assignment (OSPA) metric.", "Retrieval-Augmented Generation (RAG) grounds Large Language Models (LLMs) to mitigate factual hallucinations. Recent paradigms shift from static pipelines to Modular and Agentic RAG frameworks, granting models autonomy for multi-hop reasoning or self-correction. However, current reflective RAG heavily relies on massive LLMs as universal evaluators. In high-throughput systems, executing complete forward passes for billion-parameter models merely for binary routing introduces severe computational redundancy. Furthermore, in autonomous agent scenarios, inaccurate retrieval causes models to expend excessive tokens on spurious reasoning and redundant tool calls, inflating Time-to-First-Token (TTFT) and costs. We propose Tiny-Critic RAG, decoupling evaluation by deploying a parameter-efficient Small Language Model (SLM) via Low-Rank Adaptation (LoRA). Acting as a deterministic gatekeeper, Tiny-Critic employs constrained decoding and non-thinking inference modes for ultra-low latency binary routing. Evaluations on noise-injected datasets demonstrate Tiny-Critic RAG achieves routing accuracy comparable to GPT-4o-mini while reducing latency by an order of magnitude, establishing a highly cost-effective paradigm for agent deployment.", "Recommendation Systems are effective in managing the ever-increasing amount of multimodal data available today and help users discover interesting new items. These systems can handle various media types such as images, text, audio, and video data, and this has made it possible to handle content-based recommendation utilizing features extracted from items while also incorporating user preferences. Graph Neural Network (GNN)-based recommendation systems are a special class of recommendation systems that can handle relationships between items and users, making them particularly attractive for content-based recommendations. Their popularity also stems from the fact that they use advanced machine learning techniques, such as deep learning on graph-structured data, to exploit user-to-item interactions. The nodes in the graph can access higher-order neighbor information along with state-of-the-art vision-language models for processing multimodal content, and there are well-designed algorithms for embedding, message passing, and propagation. In this work, we present the design of a GNN-based recommendation system on a novel data set collected from field research. Designed for an endangered performing art form, the recommendation system uses multimodal content (text and image data) to suggest similar paintings for viewing and purchase. To the best of our knowledge, there is no recommendation system designed for narrative scroll paintings \u2013 our work therefore serves several purposes, including art conservation, a data storage system for endangered art objects, and a state-of-the-art recommendation system that leverages both the novel characteristics of the data and preferences of the user population interested in narrative scroll paintings.", "Counterfactual learning has become promising for understanding and modeling causality in complex and dynamic systems. This paper presents a novel method for counterfactual learning in the context of multivariate time series analysis and forecast. The primary objective is to uncover hidden causal relationships and identify potential interventions to achieve desired outcomes. The proposed methodology integrates genetic algorithms and rigorous causality tests to infer and validate counterfactual dependencies within temporal sequences. More specifically, we employ Granger causality to enhance the reliability of identified causal relationships, rigorously assessing their statistical significance. Then, genetic algorithms, in conjunction with quantile regression, are used to exploit these intricate causal relationships to project future scenarios. The synergy between genetic algorithms and causality tests ensures a thorough exploration of the temporal dynamics present in the data, revealing hidden dependencies and enabling the projection of outcomes under hypothetical interventions. We evaluate the performance of our algorithm on real-world data, showcasing its ability to handle complex causal relationships, revealing meaningful counterfactual insights, and allowing for the prediction of outcomes under hypothetical interventions.", "Predicting the physicochemical properties of small molecules across a diverse chemical space is an important step for chemical engineering, drug discovery and materials science. Existing molecular foundation models pretrain on millions of molecules to learn general-purpose representations, but their standard MLP output layers impose no physical constraints: vapour pressure predictions may violate monotonic temperature dependence, and viscosity curves may lack the functional form required by process simulators. Domain-informed approaches that do guarantee thermodynamic consistency have, by contrast, been limited to single properties and small datasets, and the existing multimodal molecular foundation models are typically aimed at biological-activity prediction rather than thermophysical properties. This work introduces MultiPUFFIN, a domain-informed multimodal foundation model that fills this gap. MultiPUFFIN fuses SMILES sequences, 2D molecular graphs and three-dimensional conformer geometries through bidirectional cross-modal attention and gated fusion, supplemented by auxiliary encoders for experimental conditions and molecular descriptors. The backbone is pretrained on 500,000 unlabelled PubChem molecules with three complementary self-supervised objectives, and a condition-aware refinement stack of five conditioners (T, pH, P, polymorph, method) routes per property to a four-head tournament that selects the best-performing thermodynamically-informed head per property. Two cross-property training-loss couplings (flash-point\u2013vapor-pressure consistency and a normal-boiling-point anchor on the vapor-pressure head) softly enforce inter-property thermodynamic consistency. On a scaffold-split test set of $8877$ molecules drawn from a multi-source dataset of $37\\,968$ unique molecules across nine thermophysical properties, MultiPUFFIN reaches an in-scope mean test $R^{2}=0.784$ and outperforms fine tuned ChemBERTa-2 on all nine properties despite training on roughly 2000 $\\times$ fewer labelled molecules; the largest margins are on the temperature-dependent properties (vapor pressure, viscosity) where ChemBERTa-2 has no mechanism to distinguish measurements at different temperatures. Per-property G+ applicability-domain filters explicitly bound the deployment scope. Systematic ablations quantify the contribution of each architectural component, of the choice of domain-informed equation per property, and of each coupling term.", "Discrete diffusion and flow matching models capture complex, non-additive and non-autoregressive structure in high-dimensional objective landscapes through parallel, iterative refinement. However, their implicit generative nature precludes direct integration with principled variational frameworks for online black-box optimisation, such as variational search distributions (VSD) and conditioning by adaptive sampling (CbAS). We introduce Active Flow Matching (AFM) , which reformulates variational objectives to operate on conditional endpoint distributions along the flow, enabling gradient-based steering of flow models toward high-fitness regions while preserving the rigour of VSD and CbAS. We derive forward and reverse Kullback-Leibler (KL) variants using self-normalised importance sampling. Across a suite of online protein and small molecule design tasks, forward-KL AFM consistently performs competitively compared to state-of-the-art baselines, demonstrating effective exploration-exploitation under tight experimental budgets.", "LLMs increasingly excel on AI benchmarks, but doing so does not guarantee validity for downstream tasks. This study evaluates the performance of leading foundation models (FMs, i.e., generative pre-trained base LLMs) with out-of-distribution (OOD) tasks of the teaching and learning of schoolchildren. Across all FMs, inter-model behaviors on disparate tasks correlate higher than they do with expert human behaviors on target tasks. These biases shared across LLMs are poorly aligned with downstream measures of teaching quality and often negatively aligned with learning outcomes . Further, we find multi-model ensembles, both unanimous model voting and expert-weighting by benchmark performance, further exacerbate misalignment with learning. We measure that 50% of the variation in misalignment error is shared across foundation models, suggesting that common pretraining accounts for much of the misalignment in these tasks. We demonstrate methods for robustly measuring alignment of complex tasks and provide unique insights into both educational applications of foundation models and to understanding limitations of models.", "# Probabilistic Learning and Generation in Deep Sequence Models ### 1.1 Thesis Outline and Contributions The remaining chapters in this thesis are organized as follows. * \u2022 Chapter 2 introduces the technical background for the thesis. We review key concepts and ideas in deep sequence models, probabilistic modeling, and Bayesian deep learning, which form the basis of the methods proposed in this thesis. The chapter is concluded with two core applications of deep probabilistic modeling, namely uncertainty quantification and deep generative models. * \u2022 Chapter 3 aims at calibrating the uncertainty of Transformer models by identifying connection between dot-product attention and the posterior mean of sparse Gaussian process (SGP; ). Based on this key connection, we propose Sparse Gaussian Process attention (SGPA), which replaces the scaled dot-product operation in Transformer with a valid symmetric kernel and uses sparse Gaussian processes techniques to approximate the posterior processes in the output space of multi-head attention blocks (MHAs) directly. Empirically, on a suite of prediction tasks on text, images and graphs, SGPA-based Transformers achieve competitive predictive accur", "Inspired by the human learning and memory system, particularly the interplay between the hippocampus and cerebral cortex, this study proposes a dual-learner framework comprising a fast learner and a meta learner to address continual Reinforcement Learning (RL) problems. These two learners are coupled to perform distinct yet complementary roles: the fast learner focuses on knowledge transfer, while the meta learner ensures knowledge integration. In contrast to traditional multi-task RL approaches that share knowledge through average return maximization, our meta learner incrementally integrates new experiences by explicitly minimizing catastrophic forgetting, thereby supporting efficient cumulative knowledge transfer for the fast learner. To facilitate rapid adaptation in new environments, we introduce an adaptive meta warm-up mechanism that selectively harnesses past knowledge. We conduct experiments in various pixel-based and continuous control benchmarks, revealing the superior performance of continual learning for our proposed dual-learner approach relative to baseline methods. The code is released in https://github.com/datake/FAME .", "Layer-wise capacity in large language models is highly non-uniform: some layers contribute disproportionately to loss reduction while others are near-redundant. Existing methods for exploiting this non-uniformity, such as influence-function-based layer scoring, produce sensitivity estimates but offer no principled mechanism for translating them into allocation or pruning decisions under hardware constraints. We address this gap with a unified, curvature-aware framework grounded in the Minimum Description Length (MDL) principle. Our central quantity is the curvature-adjusted layer gain $\\zeta_{k}^{2}=g_{k}^{\\top}\\widetilde{H}_{kk}^{-1}g_{k}$ , which we show equals twice the maximal second-order reduction in empirical risk achievable by updating layer $k$ alone, and which strictly dominates gradient-norm-based scores by incorporating local curvature. Normalizing these gains into layer quality scores $q_{k}$ , we formulate two convex MDL programs: a capacity allocation program that distributes expert slots or LoRA rank preferentially to high-curvature layers under diminishing returns, and a pruning program that concentrates sparsity on low-gain layers while protecting high-gain layers from degradation. Both programs admit unique closed-form solutions parameterized by a single dual variable, computable in $O(K\\log 1/\\varepsilon)$ via bisection. We prove an $O(\\delta^{2})$ transfer regret bound showing that source-domain allocations remain near-optimal on target tasks when curvature scores drift by $\\delta$ , with explicit constants tied to the condition number of the target program. Together, these results elevate layer-wise capacity optimization from an empirical heuristic to a theoretically grounded, computationally efficient framework with provable optimality and generalization guarantees.", "Latent-space Bayesian optimization (LSBO) extends Bayesian optimization to structured domains, such as molecular design, by searching in the continuous latent space of a generative model. However, most LSBO methods assume a fixed objective, whereas real design campaigns often face temporal drift (e.g., evolving preferences or shifting targets). Bringing time-varying BO into LSBO is non-trivial: drift can affect not only the surrogate, but also the latent search space geometry induced by the representation. We propose Time-Aware Latent-space Bayesian Optimization ( TALBO ), which incorporates time in both the surrogate and the learned generative representation via a GP-prior variational autoencoder, yielding a latent space aligned as objectives evolve. To evaluate time-varying LSBO systematically, we adapt widely used molecular design tasks to drifting multi-property objectives and introduce metrics tailored to changing targets. Across these benchmarks, TALBO consistently outperforms strong LSBO baselines and remains robust across drift speeds and design choices, while remaining competitive under actually time-invariant objectives.", "We study non-rectangular robust Markov decision processes under the average-reward criterion, where the ambiguity set couples transition probabilities across states and the adversary commits to a stationary kernel for the entire horizon. We show that any history-dependent policy achieving sublinear expected regret uniformly over the ambiguity set is robust-optimal, and that the robust value admits a minimax representation as the infimum over the ambiguity set of the classical optimal gains, without requiring any form of rectangularity or robust dynamic programming principle. Under the weak communication assumption, we establish the existence of such policies by converting high-probability regret bounds from the average-reward reinforcement learning literature into the expected-regret criterion. We then introduce a transient-value framework to evaluate finite-time performance of robust optimal policies, proving that average-reward optimality alone can mask arbitrarily poor transients and deriving regret-based lower bounds on transient values. Finally, we construct an epoch-based policy that combines an optimal stationary policy for the worst-case model with an anytime-valid sequential test and an online learning fallback, achieving a constant-order transient value.", "Prioritizing disease-associated genes is central to understanding the molecular mechanisms of complex disorders such as Alzheimer\u2019s disease (AD). Traditional network-based approaches rely on static centrality measures and often fail to capture cross-modal biological heterogeneity. We propose NETRA (Node Evaluation through Transformer-based Representation and Attention), a multimodal graph transformer framework that replaces heuristic centrality metrics with attention-driven relevance scoring. Using AD as a case study, gene regulatory networks are independently constructed from microarray, single-cell RNA-seq, and single-nucleus RNA-seq data. Random-walk sequences derived from these networks are used to train a BERT-based model for learning global gene embeddings, while modality-specific gene expression profiles are compressed using variational autoencoders. These representations are integrated with auxiliary biological networks, including protein-protein interactions, Gene Ontology semantic similarity, and diffusion-based gene similarity, into a unified multimodal graph. A graph transformer assigns NETRA scores that quantify gene relevance in a disease-specific and context-aware manner. Gene set enrichment analysis shows that NETRA achieves a normalized enrichment score of about 3.9 for the Alzheimer\u2019s disease pathway, substantially outperforming classical centrality measures and diffusion models. Top-ranked genes enrich multiple neurodegenerative pathways, recover a known late-onset AD susceptibility locus at chr12q13, and reveal conserved cross-disease gene modules. The framework preserves biologically realistic heavy-tailed network topology and is readily extensible to other complex disorders.", "Contrastive Forward-Forward (CFF) learning trains Vision Transformers layer by layer against supervised contrastive objectives. CFF training can be sensitive to random seed, but the sources of this instability are poorly understood. We focus on one implementation detail: the positive-pair margin in the contrastive loss is applied through saturating similarity clamping, $\\min(s+m,\\,1)$ . We prove that an alternative formulation, subtracting the margin after the log-probability, is gradient-neutral under the mean-over-positives reduction. On CIFAR-10 ( $2\\times 2$ factorial, $n{=}7$ seeds per cell), clamping produces $5.90\\times$ higher pooled test-accuracy variance ( $p{=}0.003$ ) with no difference in mean accuracy. Analyses of clamp activation rates, layerwise gradient norms, and a reduced-margin probe point to saturation-driven gradient truncation at early layers. The effect does not transfer cleanly to other datasets: on CIFAR-100, SVHN, and Fashion-MNIST, clamping produces equal or lower variance. Two factors account for the discrepancy. First, positive-pair density per batch controls how often saturation occurs. Second, task difficulty compresses seed-to-seed spread when accuracy is high. An SVHN difficulty sweep confirms the interaction on a single dataset, with the variance ratio moving from $0.25\\times$ at high accuracy to $16.73\\times$ under aggressive augmentation. In moderate-accuracy regimes with many same-class pairs per batch, switching to the gradient-neutral subtraction reference removes this variance inflation at no cost to mean accuracy. Measuring the layer-0 clamp activation rate serves as a simple check for whether the problem applies.", "While reinforcement learning (RL) has been central to the recent success of large language models (LLMs), RL optimization is notoriously unstable, especially when compared to supervised fine-tuning (SFT). In this work, we investigate the stability gap between SFT and RL from a gradient-based perspective, and show that the convexity of the SFT loss with respect to model logits plays a key role in enabling stable training. Our theoretical analysis demonstrates that this property induces favorable gradient directionality during optimization. In contrast, Proximal Policy Optimization (PPO), a widely adopted policy gradient algorithm utilizing a clipped surrogate objective, lacks this stabilizing property. Motivated by this observation, we propose Logits Convex Optimization (LCO) , a simple yet effective policy optimization framework that aligns the learned policy with an optimal target derived from the original RL objective, thereby emulating the stabilizing effects of logits-level convexity. Extensive experiments across multiple model families show that our LCO framework consistently improves training stability and outperforms conventional RL methods on a broad range of benchmarks.", "In this work, we investigate the generalization properties of random feature methods. Our analysis extends prior results for Tikhonov regularization to a broad class of spectral regularization techniques and further generalizes the setting to operator-valued kernels. This unified framework enables a rigorous theoretical analysis of neural operators and neural networks through the lens of the Neural Tangent Kernel (NTK). In particular, it allows us to establish optimal learning rates and provides a good understanding of how many neurons are required to achieve a given accuracy. Furthermore, we establish minimax rates in the well-specified case and also in the misspecified case, where the target is not contained in the reproducing kernel Hilbert space. These results sharpen and complete earlier findings for specific kernel algorithms.", "# Forgetting is Competition: Rethinking Unlearning as Representation Interference in Diffusion Models Forgetting is Competition: Rethinking Unlearning as Representation Interference in Diffusion Models Ashutosh Ranjan TCS Research, India ashutosh.ranjan2@tcs.com Vivek Srivastava TCS Research, India srivastava.vivek2@tcs.com Shirish Karande TCS Research, India shirish.karande@tcs.com Murari Mandal Kalinga Institute of Industrial Technology murari.mandalfcs@kiit.ac.in Abstract Deployed text-to-image diffusion models increasingly require post-hoc concept unlearning for copyright claims, artist opt-outs, safety updates, and protectedcontent mitigation without full retraining. A central challenge is erase\u2013retain imbalance: aggressive updates suppress targets but damage shared capabilities, while conservative or anchor-based updates preserve quality yet leave concepts recoverable through related, compositional, paraphrased, or adversarial prompts. Inspired by retroactive interference, we propose SurgUn, which treats forgetting as controlled competition rather than direct deletion or one-to-one reassignment. SurgUn instantiates retroactive concept interference via distractor-conditioned g", "Large language model (LLM) agents have recently demonstrated strong capabilities in interactive decision-making, yet they remain fundamentally limited in long-horizon tasks that require structured planning and reliable execution. Existing approaches predominantly rely on flat autoregressive policies, where high-level reasoning and low-level actions are generated within a single token sequence, leading to inefficient exploration and severe error propagation over extended trajectories. In this work, we propose HiMAC , a hierarchical agentic RL framework that explicitly decomposes long-horizon decision-making into macro-level planning and micro-level execution. HiMAC models reasoning as a structured blueprint generation process followed by goal-conditioned action execution, enabling robust long-horizon planning within LLM-based agents. To train this hierarchy efficiently, we introduce a critic-free hierarchical policy optimization paradigm that extends group-based reinforcement learning to bi-level structures through hierarchical relative advantage estimation. Furthermore, we propose an iterative co-evolution training strategy that alternates between planner exploration and executor adaptation, mitigating the non-stationarity inherent in hierarchical learning. Extensive experiments on ALFWorld, WebShop, and Sokoban demonstrate that HiMAC consistently outperforms strong prompting and reinforcement learning baselines, achieving state-of-the-art performance and substantially improved sample efficiency across both text-based and visually grounded environments. Our results show that introducing structured hierarchy, rather than increasing model scale alone, is a key factor for enabling robust long-horizon agentic intelligence.", ". High-Level Synthesis (HLS) is a pivotal electronic design automation (EDA) technology that enables the generation of hardware circuits from high-level language descriptions. A critical step in HLS is Design Space Exploration (DSE), which seeks to identify high-quality hardware architectures under given constraints. However, the enormous size of the design space makes DSE computationally prohibitive. Although numerous algorithms have been proposed to accelerate DSE, our extensive experimental studies reveal that no single algorithm consistently achieves Pareto dominance across all problem instances. Consequently, the inability of any single algorithm to dominate all benchmarks necessitates an automated selection mechanism to identify the best-performing DSE algorithm for each specific case. To address this challenge, we propose the SoberDSE framework, which recommends suitable algorithm based on benchmark characteristics. Experimental results demonstrate that our SoberDSE framework significantly outperforms state-of-the-art heuristic-based DSE algorithms by up to 5.7 $\\times$ and state-of-the-art learning-based DSE methods by up to 4.2 $\\times$ . Furthermore, compared to conventional classification models, SoberDSE delivers superior accuracy in small-sample learning scenarios, with an average enhancement of 35.57%. Code and models are available at https://anonymous.4open.science/r/Sober-4377 .", "The powerful generative capabilities of diffusion models have raised growing privacy and safety concerns regarding generating sensitive or undesired content. In response, machine unlearning (MU) \u2013 commonly referred to as concept erasure (CE) in diffusion models \u2013 has been introduced to remove specific knowledge from model parameters meanwhile preserving innocent knowledge. Despite recent advancements, existing unlearning methods often suffer from excessive and indiscriminate removal, which leads to substantial degradation in the quality of innocent generations. To preserve model utility, prior works rely on compensation , i.e. , re-assimilating a subset of the remaining data or explicitly constraining the divergence from the pre-trained model on remaining concepts. However, we reveal that generations beyond the compensation scope still suffer, suggesting such post-remedial compensations are inherently insufficient for preserving the general utility of large-scale generative models. Therefore, in this paper, we advocate for developing compensation-free concept erasure operations, which precisely identify and eliminate the undesired knowledge such that the impact on other generations is minimal. In technique, we propose to MiM-MU , which is to unlearn a concept by minimizing the mutual information with a delicate design for computational effectiveness and for maintaining sampling distribution for other concepts. Extensive evaluations demonstrate that our proposed method achieves effective concept removal meanwhile maintaining high-quality generations for other concepts, and remarkably, without relying on any post-remedial compensation for the first time.", "Accurate traffic prediction is a key task for intelligent transportation systems. The core difficulty lies in accurately modeling the complex spatial-temporal dependencies in traffic data. In recent years, improvements in network architecture have failed to bring significant performance enhancements, while embedding technology has shown great potential. However, existing embedding methods often ignore graph structure information or rely solely on static graph structures, making it difficult to effectively capture the dynamic associations between nodes that evolve over time. To address this issue, this letter proposes a novel dynamic weighted graph structure (DWGS) embedding method, which relies on a graph structure that can truly reflect the changes in the strength of dynamic associations between nodes over time. By first combining the DWGS embedding with the spatial-temporal adaptive embedding, as well as the temporal embedding and feature embedding, and then integrating attention and frequency-domain multi-layer perceptrons (MLPs), we design a novel traffic prediction model, termed the DWGS embedding integrated with attention and frequency-domain MLPs (DWAFM). Experiments on five real-world traffic datasets show that the DWAFM achieves better prediction performance than some state-of-the-arts.", "REPresentation Alignment (REPA) improves the training of generative flow models by aligning intermediate hidden states with pretrained teacher features, but its effectiveness in token-conditioned audio Flow Matching critically depends on the choice of supervised layers, which is typically made heuristically based on the depth. In this work, we introduce A ttribution- G uided REP resentation A lignment (AG-REPA) , a novel causal layer selection strategy for representation alignment in audio Flow Matching. Firstly, we find that layers that best store semantic/acoustic information (high teacher-space similarity) are not necessarily the layers that contribute most to the velocity field that drives generation, and we call it S tore- C ontribute D issociation (SCD) . To turn this insight into an actionable training guidance, we propose a forward-only gate ablation (FoG-A) that quantifies each layer\u2019s causal contribution via the induced change in the predicted velocity field, enabling sparse layer selection and adaptive weighting for alignment. Across unified speech and general-audio training (LibriSpeech + AudioSet) under different token-conditioning topologies, AG-REPA consistently outperforms REPA baselines. Overall, our results show that alignment is most effective when applied to the causally dominant layers that drive the velocity field, rather than to layers that are representationally rich but functionally passive.", "In the social sciences, it is often necessary to debias studies and surveys before valid conclusions can be drawn. Debiasing algorithms enable the computational removal of bias using sample weights. However, an issue arises when only a subset of features is highly biased, while the rest is already representative. Algorithms need to strongly alter the sample distribution to manage a few highly biased features, which can in turn introduce bias into already representative variables. To address this issue, we developed a method that uses feature weights to minimize the impact of highly biased features on the computation of sample weights. Our algorithm is based on Maximum Representative Subsampling (MRS), which debiases datasets by aligning a non-representative sample with a representative one through iterative removal of elements to create a representative subsample. The new algorithm, named feature-weighted MRS (FW-MRS), decreases the emphasis on highly biased features, allowing it to retain more instances for downstream tasks. The feature weights are derived from the feature importance of a domain classifier trained to differentiate between the representative and non-representative datasets. We validated FW-MRS using eight tabular datasets, each of which we artificially biased. Biased features can be important for downstream tasks, and focusing less on them could lead to a decline in generalization. For this reason, we assessed the generalization performance of FW-MRS on downstream tasks and found no statistically significant differences. Additionally, FW-MRS was applied to a real-world dataset from the social sciences. The source code is available at https://github.com/kramerlab/FeatureWeightDebiasing .", "Self-supervised diffusion models learn high-quality visual representations via latent space denoising. However, their representation layer poses a distinct threat: unlike traditional attacks targeting generative outputs, its unconstrained latent semantic space allows for stealthy backdoors, permitting malicious control upon triggering. In this paper, we propose BadRSSD, the first backdoor attack targeting the representation layer of self-supervised diffusion models. Specifically, it hijacks the semantic representations of poisoned samples with triggers in Principal Component Analysis (PCA) space toward those of a target image, then controls the denoising trajectory during diffusion by applying coordinated constraints across latent, pixel, and feature distribution spaces to steer the model toward generating the specified target. Additionally, we integrate representation dispersion regularization into the constraint framework to maintain feature space uniformity, significantly enhancing attack stealth. This approach preserves normal model functionality (high utility) while achieving precise target generation upon trigger activation (high specificity). Experiments on multiple benchmark datasets demonstrate that BadRSSD substantially outperforms existing attacks in both FID and MSE metrics, reliably establishing backdoors across different architectures and configurations, and effectively resisting state-of-the-art backdoor defenses.", "Recent breakthroughs in large language models (LLMs) have led to notable successes in complex reasoning tasks, such as mathematical problem solving. A common strategy for improving performance is parallel thinking , in which multiple reasoning traces are generated and the final prediction is made using aggregation schemes like majority voting or best-of- $N$ decoding. However, two key challenges persist. First, multi-sample decoding incurs substantial inference latency, especially for long-form outputs. Second, effective mechanisms for reliably assessing the correctness of individual reasoning traces are still limited. To address these challenges, we introduce One-Token Verification (OTV), a computational method that estimates reasoning correctness in a single forward pass during generation. OTV is activated by a learnable token and integrated into the LLM via low-rank adaptation to probe internal reasoning signals through the key-value cache, supporting token-level correctness estimation at any stage of generation without disrupting primary reasoning. Experiments on mathematical reasoning benchmarks demonstrate that OTV consistently surpasses existing verifiers. Additionally, OTV reduces token usage by up to $90\\%$ through correctness-guided early termination, prioritizing shorter, more reliable solutions.", "Tensor Ring (TR) decomposition is a powerful tool for high-order data modeling, but is inherently restricted to discrete forms defined on fixed meshgrids. In this work, we propose a TR functional decomposition for both meshgrid and non-meshgrid data, where factors are parameterized by Implicit Neural Representations (INRs). However, optimizing this continuous framework to capture fine-scale details is intrinsically difficult. Through a frequency-domain analysis, we demonstrate that the spectral structure of TR factors determines the frequency composition of the reconstructed tensor and limits the high-frequency modeling capacity. To mitigate this, we propose a reparameterized TR functional decomposition, in which each TR factor is a structured combination of a learnable latent tensor and a fixed basis. This reparameterization is theoretically shown to improve the training dynamics of TR factor learning. We further derive a principled initialization scheme for the fixed basis and prove the Lipschitz continuity of our proposed model. Extensive experiments on image inpainting, denoising, super-resolution, and point cloud recovery demonstrate that our method achieves consistently superior performance over existing approaches. Code is available at https://github.com/YangyangXu2002/RepTRFD .", "Federated learning (FL) in post-deployment settings must adapt to non-stationary data streams across heterogeneous clients without access to ground-truth labels. A major challenge is learning rate selection under client-specific, time-varying distribution shifts, where fixed learning rates often lead to underfitting or divergence. We propose Fed-ADE ( Fed erated A daptation with D istribution Shift E stimation ), an unsupervised federated adaptation framework that leverages lightweight estimators of distribution dynamics. Specifically, Fed-ADE employs uncertainty dynamics estimation to capture changes in predictive uncertainty and representation dynamics estimation to detect covariate-level feature drift, combining them into a per-client, per-timestep adaptive learning rate. We provide theoretical analyses showing that our dynamics estimation approximates the underlying distribution shift and yields dynamic regret and convergence guarantees. Experiments on image and text benchmarks under diverse distribution shifts (label and covariate) demonstrate consistent improvements over strong baselines. These results highlight that distribution shift-aware adaptation enables effective and robust federated post-adaptation under real-world non-stationarity. The code is available at https://github.com/h2w1/Fed-ADE", "Large Language Models (LLMs) have demonstrated remarkable success in general-purpose reasoning. However, they still struggle to understand and reason about time series data, which limits their effectiveness in decision-making scenarios that depend on temporal dynamics. In this paper, we propose Thoth , the first family of mid-trained LLMs with general-purpose time series understanding capabilities. As a pivotal intermediate stage, mid-training achieves task- and domain-agnostic alignment between time series and natural language, for which we construct Book-of-Thoth , a high-quality, time-series-centric mid-training corpus. Book-of-Thoth enables both time-series-to-text and text-to-time-series generation, equipping LLMs with a foundational grasp of temporal patterns. To better evaluate advanced reasoning capabilities, we further present KnoTS, a novel benchmark of knowledge-intensive time series understanding, designed for joint reasoning over temporal patterns and domain knowledge. Extensive experiments demonstrate that mid-training with Book-of-Thoth enables Thoth to significantly outperform its base model and advanced LLMs across a range of time series question answering benchmarks. Moreover, Thoth exhibits superior capabilities when fine-tuned under data scarcity, underscoring the effectiveness of mid-training for time series understanding. Code is available at: https://github.com/thuml/Thoth .", "Generative Flow Networks (GFlowNets) were developed to learn policies for efficiently sampling combinatorial candidates by interpreting their generative processes as trajectories in directed acyclic graphs. In the value-based training workflow, the objective is to enforce the balance over partial episodes between the flows of the learned policy and the estimated flows of the desired policy, implicitly encouraging policy divergence minimization. The policy-based strategy alternates between estimating the policy divergence and updating the policy, but reliable estimation of the divergence under directed acyclic graphs remains a major challenge. This work bridges the two perspectives by showing that flow balance also yields a principled policy evaluator that measures the divergence, and an evaluation balance objective over partial episodes is proposed for learning the evaluator. As demonstrated on both synthetic and real-world tasks, evaluation balance not only strengthens the reliability of policy-based training but also broadens its flexibility by seamlessly supporting parameterized backward policies and enabling the integration of offline data-collection techniques. Our code is available at github.com/niupuhua1234/Sub-EB .", "The recent works on causal discovery have followed a similar trend of learning partial ancestral graphs (PAGs) since observational data constrain the true causal directed acyclic graph (DAG) only up to a Markov equivalence class. This limits their application in the majority of downstream tasks, as uncertainty in causal relations remains unresolved. We propose a new refinement framework, CausalSAGE, for converting PAGs to DAGs while respecting the underlying causal relations. The framework expands discrete variables into state-level representations, constrains the search space using structural knowledge and soft priors, and applies a unified differentiable objective for joint optimization. The final DAG is obtained by aggregating the optimized structures and enforcing acyclicity when necessary. Our experimental evaluations show that the obtained DAGs preserve the underlying causal relations while also being efficient to obtain.", ". Dataset distillation compresses a large real dataset into a small synthetic one, enabling models trained on the synthetic data to achieve performance comparable to those trained on the real data. Although synthetic datasets are assumed to be privacy-preserving, we show that existing distillation methods can cause severe privacy leakage because synthetic datasets implicitly encode the weight trajectories of the distilled model, they become over-informative and exploitable by adversaries. To expose this risk, we introduce the Information Revelation Attack (IRA) against state-of-the-art distillation techniques. IRA has three stages: architecture inference, membership inference, and model inversion, each exploiting hidden information in the synthetic data. In the architecture inference stage, the adversary trains an attack model that predicts the distillation algorithm and the model architecture using the loss trajectory\u2014obtained by recording the losses of a model trained on the synthetic dataset. With this predicted architecture, the adversary can train a local model that mirrors the victim model\u2019s structure and performance, effectively converting a black-box scenario into a white-box one \u2014surpassing the capabilities of shadow models used in prior attacks. Next, in the membership inference stage, the adversary leverages full access to this local model to train an attack model that uses its hidden-layer and final-layer outputs to detect whether a sample was in the real dataset. Finally, in the model inversion stage, the adversary attempts to reconstruct real samples. We propose an enhanced dual-network diffusion framework that allows imposing constraints on the generator, as well as a trajectory loss that guides the generator toward the real data distribution by exploiting deeper information embedded in the synthetic dataset. Experiments show that IRA accurately predicts both the distillation algorithm and model architecture, and can successfully infer membership and recover sensitive samples from the real dataset. Our code will be released after acceptance.", "Convolution-type integral equations commonly occur in signal processing and image processing. Discretizing these equations yields large and ill-conditioned linear systems. While the classic multigrid method is effective for solving linear systems derived from partial differential equations (PDE) problems, it fails to solve integral equations because its smoothers, which are implemented as conventional relaxation methods, are ineffective in reducing high-frequency components in the errors. We propose a novel neural multigrid scheme where learned neural operators replace classical smoothers. Unlike classical smoothers, these operators are trained offline. Once trained, the neural smoothers generalize to new right-hand-side vectors without retraining, making it an efficient solver. We design level-wise loss functions incorporating spectral filtering to emulate the multigrid frequency decomposition principle, ensuring each operator focuses on solving distinct high-frequency spectral bands. Although we focus on integral equations, the framework is generalizable to all kinds of problems, including PDE problems. Our experiments demonstrate superior efficiency over classical solvers and robust convergence across varying problem sizes and regularization weights.", "We present LLaDA-o , an effective and length-adaptive omni diffusion model for multimodal understanding and generation. LLaDA-o is built on a Mixture of Diffusion (MoD) framework that decouples discrete masked diffusion for text understanding and continuous diffusion for visual generation, while coupling them through a shared, simple, and efficient attention backbone that reduces redundant computation for fixed conditions. Building on MoD, we further introduce a data-centric length adaptation strategy that enables flexible-length decoding in multimodal settings without architectural changes. Extensive experiments show that LLaDA-o achieves state-of-the-art performance among omni-diffusion models on multimodal understanding and generation benchmarks, and reaches 87.04 on DPG-Bench for text-to-image generation, supporting the effectiveness of unified omni diffusion modeling. Code is available at https://github.com/ML-GSAI/LLaDA-o .", "In this paper, we investigate the coordination of vehicle maneuvers in mixed-traffic corridors where connected and automated vehicles, human-driven vehicles, and buses interact under dedicated bus lane operations. We develop a segment-based network coordination framework that jointly optimizes lane-change and routing decisions of connected and automated vehicles to improve dedicated lane utilization while preserving bus priority. The proposed framework incorporates a predictive bus-protection mechanism that restricts vehicle access to protected lane segments within a monitoring horizon, together with a utility-driven lane-change strategy that accounts for anticipated travel time gains, downstream routing feasibility, and lane-change stability. By explicitly coupling network-level routing decisions with lane-level interaction control, the method proactively mitigates conflicts on dedicated lanes before congestion effects materialize. The proposed approach is evaluated through microscopic traffic simulations in SUMO using a realistic urban corridor. Simulation results demonstrate that the framework enhances bus schedule adherence and reduces average travel times for both automated and human-driven vehicles, while maintaining stable lane-change behavior without increasing maneuver frequency.", "Continuum robots possess high flexibility and redundancy, making them well suited for safe interaction in complex environments, yet their continuous deformation and nonlinear dynamics pose fundamental challenges to perception, modeling, and control. Existing vision-based control approaches often rely on end-to-end learning, achieving shape regulation without explicit awareness of robot geometry or its interaction with the environment. Here, we introduce a shape-interpretable visual self-modeling framework for continuum robots that enables geometry-aware control. Robot shapes are encoded from multi-view planar images using a B\u00e9zier-curve representation, transforming visual observations into a compact and physically meaningful shape space that uniquely characterizes the robot\u2019s three-dimensional configuration. Based on this representation, neural ordinary differential equations are employed to self-model both shape and end-effector dynamics directly from data, enabling hybrid shape-position control without analytical models or dense body markers. The explicit geometric structure of the learned shape space allows the robot to reason about its body and surroundings, supporting environment-aware behaviors such as obstacle avoidance and self-motion while maintaining end-effector objectives. Experiments on a cable-driven continuum robot demonstrate accurate shape-position regulation and tracking, with shape errors within 1.56% of image resolution and end-effector errors within 2% of robot length, as well as robust performance in constrained environments. By elevating visual shape representations from two-dimensional observations to an interpretable three-dimensional self-model, this work establishes a principled alternative to vision-based end-to-end control and advances autonomous, geometry-aware manipulation for continuum robots.", "With the increasing penetration of renewable energy sources, grid-forming (GFM) inverters are becoming essential for voltage and frequency regulation. However, the transient stability of GFM inverter is critically affected by the current limiters that are embedded with the standard control schemes. This paper proposes a novel adaptive function to enhance the transient stability of droop-controlled GFM inverters. The proposed method autonomously adjusts the active power reference and the droop gain based on the terminal voltage of the inverter. Also, the acceleration of the phase angle is prevented, leading to the maximization of critical clearing time (CCT). The proposed method is benchmarked against two state-of-the-art GFM inverter CCT enhancement methods. Effectiveness of the proposed method is validated through electromagnetic transient (EMT) simulations in MATLAB/Simulink \u00ae .", "Sub- $30\\text{\\,}\\mathrm{g}$ nano-sized aerial robots can leverage their agility and form factor to autonomously explore cluttered and narrow environments, like in industrial inspection and search and rescue missions. However, the price for their tiny size is a strong limit in their resources, i.e., sub- $100\\text{\\,}\\mathrm{mW}$ microcontroller units (MCUs) delivering $\\sim$ $100\\text{\\,}\\mathrm{G}\\mathrm{O}\\mathrm{p}\\mathrm{s}\\mathrm{/}\\mathrm{s}$ at best, and memory budgets well below $100\\text{\\,}\\mathrm{MB}$ . Despite these strict constraints, we aim to enable complex vision-based tasks aboard nano-drones, such as dense 3D scene reconstruction: a key robotic task underlying fundamental capabilities like spatial awareness and motion planning. Top-performing 3D reconstruction methods leverage neural radiance fields (NeRF) models, which require $\\text{\\,}\\mathrm{GB}$ s of memory and massive computation, usually delivered by high-end GPUs consuming 100s of Watts. Our work introduces Tiny-DroNeRF, a lightweight NeRF model, based on Instant-NGP, and optimized for running on a GAP9 ultra-low-power (ULP) MCU aboard our nano-drones. Then, we further empower our Tiny-DroNeRF by leveraging a collaborative federated learning scheme, which distributes the model training among multiple nano-drones. Our experimental results show a 96% reduction in Tiny-DroNeRF\u2019s memory footprint compared to Instant-NGP, with only a $5.7\\text{\\,}\\mathrm{dB}$ drop in reconstruction accuracy. Finally, our federated learning scheme allows Tiny-DroNeRF to train with an amount of data otherwise impossible to keep in a single drone\u2019s memory, increasing the overall reconstruction accuracy. Ultimately, our work combines, for the first time, NeRF training on an ULP MCU with federated learning on nano-drones.", "# Dynamic Connectivity and Local Frequency Strength under Stochastic Variations Dynamic Connectivity and Local Frequency Strength under Stochastic Variations Bruno Pinheiro and Daniel Dotta University of Campinas (UNICAMP) Campinas, Brazil b229989@dac.unicamp.br,dottad@unicamp.br Abstract\u2014This paper introduces a novel metric, termed the Generalized Fiedler Vector (GFV), to evaluate the dynamic connectivity in power systems. The proposed metric leverages the network connectivity, represented by the system Laplacian matrix, together with the nodal inertia distribution, following a formulation previously developed by the first author. By capturing the interplay between system topology and dynamic properties, the GFV provides valuable insights for the optimal siting of stochastic generation to mitigate its impact on local and system-wide frequency variability. The effectiveness of the proposed approach is demonstrated through Monte Carlo simulations performed on the IEEE 68-bus test system. Index Terms\u2014Nodal flexibility, disturbance propagation, system strength, Laplacian matrix, nodal inertia, stochastic generation. I. Introduction A. Motivation The increasing integration of inverter-", ". We investigate the problem of establishing finite-time probabilistic safety guarantees for discrete-time stochastic dynamical systems subject to unknown disturbance distributions, using barrier certificate methods. Our approach develops a data-driven safety certification framework that relies only on a finite collection of independent and identically distributed (i.i.d.) disturbance samples. Within this framework, we propose a certification procedure such that, with confidence at least $1-\\delta$ over the sampled disturbances, if the output of the certification procedure is accepted, the probability that the system remains within a prescribed safe set over a finite horizon is at least $1-\\epsilon$ . A key challenge lies in formally characterizing the probably approximately correct (PAC) generalization behavior induced by finite samples. To address this, we derive PAC generalization bounds using tools from VC dimension, scenario optimization, and Rademacher complexity. These results illuminate the fundamental trade-offs between sample size, model complexity, and safety tolerance, providing both theoretical insight and practical guidance for designing reliable, data-driven safety certificates in discrete-time stochastic systems.", "Regional watersheds are complex systems of systems encompassing hydrology, land-use decision-making, estuarine ecological feedbacks, and overlapping governance jurisdictions. Their effective management underlies many modern societal challenges and therefore requires models that capture interdependencies between natural and institutional systems. Regional-specific models such as the Chesapeake Assessment Scenario Tool, used in this paper\u2019s case study, provide valuable nutrient estimates but rely on structurally opaque watershed routing that limits integration into broader systems-level analyses. This paper introduces a modeling framework for watershed systems. First, a region-independent reference architecture is developed. Second, the Weighted Least Squares Error Hetero-functional Graph State Estimator, an extension of Hetero-functional Graph Theory (HFGT), is adapted to estimate nutrient flows from uncertain data. The framework is demonstrated through instantiation in the Chesapeake Bay Watershed. By establishing a shared ontology grounded in Systems Modeling Language and HFGT, the approach enables integration of economic and governance systems to support sustainable watershed management.", "Decentralized electricity systems increasingly emerge where centralized grids fail to provide reliable supply. In such settings, privately operated neighborhood microgrids, often based on diesel generators, exhibit significant market power, limited regulatory oversight, and high environmental externalities. In parallel, households increasingly deploy off-grid solar photovoltaic (PV) systems to gain control over electricity supply. However, these systems suffer from curtailed excess generation during peak solar hours and unreliable access at other times. While prior studies have optimized microgrids in low-reliability grid contexts from a techno-economic perspective, they largely neglect the market power exerted by monopolistic private generators. This paper addresses this gap by developing a bi-level game-theoretic model that enables household-generated electricity to be fed into the microgrid while explicitly accounting for the market power of a neighborhood diesel generator company (DGC). The regulator sets price and feed-in-tariff caps to maximize household economic surplus (HES), while the DGC acts as a profit-maximizing agent controlling access and supply. The model is illustrated using high-resolution empirical data from Lebanon. Results show that: (i) price and feed-in-tariff caps substantially increase HES and consistently induce significant household PV feed-in to the microgrid; (ii) higher DGC budgets or greater PV-owner penetration lead to pronounced gains in HES; and (iii) the renewable energy share reaches 60% under base conditions and approaches 100% at sufficiently high budgets or PV-owner penetration levels, compared to 0% under the status quo.", "Modern societal challenges, such as climate change, urbanization, and water resource management, demand integrated, multi-discipline, multi-problem approaches to frame and address their complexity. Unfortunately, current methodologies often operate within disciplinary silos, leading to fragmented insights and missed opportunities for convergence. A critical barrier to cross-disciplinary integration lies in the disparate ontologies that shape how different fields conceptualize and communicate knowledge. To address these limitations, this paper proposes a system-of-systems (SoS) convergence paradigm grounded in a meta-cognition map, a framework that integrates five complementary domains: real-world observations, systems thinking, visual modeling, mathematics, and computing. The paradigm is based on the Systems Modeling Language (SysML), offering a standardized, domain-neutral approach for representing and analyzing complex systems. The proposed methodology is demonstrated through a case study of the Chesapeake Bay Watershed, a socio-environmental system requiring coordination across land use, hydrology, economic and policy domains. By modeling this system with SysML, the study illustrates practical strategies for navigating interdisciplinary challenges and highlights the potential of agile SoS modeling to support large-scale, multi-dimensional decision-making.", "This paper presents the Distributed Adaptive Multi-Radio Cross-Layer Routing (DAMCR) protocol, designed to enhance reliability, adaptability, and energy efficiency in smart grid and industrial Internet of Things (IoT) communication networks. DAMCR integrates Chaotic Frequency-Hopping Spread Spectrum (C-FHSS) to improve physical-layer security and jamming resilience with Link-Adaptive Quality Power Control (LAQPC) to dynamically regulate transmission power based on instantaneous link quality and residual node energy. To meet heterogeneous traffic requirements, the protocol incorporates priority-aware message classification that differentiates between periodic monitoring data and time-critical fault and protection messages. The proposed framework is implemented and evaluated in MATLAB using a heterogeneous network composed of LoRa, Wi-Fi, and dual-radio nodes operating under AWGN, Rayleigh, and Rician fading environments. Extensive simulation results demonstrate that DAMCR consistently achieves a Packet Delivery Ratio (PDR) exceeding 98% across all evaluated scenarios, while maintaining end-to-end latency between 17 and 23 ms, even under controlled jamming attacks. These results confirm that the tight integration of chaos-based spectrum agility, cross-technology routing, and energy-aware cross-layer adaptation significantly improves communication reliability, latency stability, and resilience compared to conventional single-radio and static-routing protocols.", "We investigate information accuracy in timeliness-based gossip networks where the source evolves according to a continuous-time Markov chain (CTMC) with $M$ states and disseminates status updates to a network of $n$ nodes. In addition to direct source updates, nodes exchange their locally stored packets via gossip and accept incoming packets solely based on whether the incoming packet is fresher than their local copy. As a result, a node can possess the freshest packet in the network while still not having the current source state. To quantify the amount of accurate information flowing in the network under such a gossiping scheme, we introduce two accuracy metrics, average accuracy , defined as the expected fraction of nodes carrying accurate information in any given subset, and freshness-based accuracy , defined as the accuracy of the freshest node in any given subset. Using a stochastic hybrid systems (SHS) framework, we first derive steady-state balance equations and obtain matrix-valued recursions that characterize these metrics in fully connected gossip networks under binary CTMCs. We then extend our analysis to the general multi-state information source using a joint CTMC approach. Finally, we quantify the fraction of nodes whose information is accurate due to direct source pushes versus gossip exchanges. We verify our findings with numerical analyses and provide asymptotic insights.", "# A Passivity-Agnostic Framework for Distributed Adaptive Synchronization under Unknown Leader Dynamics A PASSIVITY-AGNOSTIC FRAMEWORK FOR DISTRIBUTED ADAPTIVE SYNCHRONIZATION UNDER UNKNOWN LEADER DYNAMICS \u2217 Moh Kamalul Wafi, Milad Siami Department of Electrical and Computer Engineering Northeastern University Boston, MA, USA {wafi.m, m.siami}@northeastern.edu ABSTRACT We present a passivity-agnostic framework for distributed adaptive synchronization under positiononly communication, bounded disturbances, and unknown leader dynamics. By passivity-agnostic we mean the design does not require the closed loop system to be strictly positive real (SPR) a priori: it certifies SPR when present and recovers it by frequency shaping when absent. Followers are heterogeneous second-order systems with unknown (possibly unstable) dynamics. In the SPR regime, a structured reparameterization yields gradient-based adaptive error dynamics; Lyapunov analysis guarantees global asymptotic synchronization in the disturbance-free case, exact rejection of constant disturbances, and bounded responses to time-varying disturbances, with parameter convergence under persistent excitation. In the non-SPR regime", "We propose a decision-theoretic framework in which a robot strategically can shape inferred human\u2019s prosocial state during repeated interactions. Modeling the human\u2019s prosociality as a latent state that evolves over time, the robot learns to infer and influence this state through its own actions, including helping and signaling. We formalize this as a latent-state POMDP with limited observations and learn the transition and observation dynamics using expectation maximization. The resulting belief-based policy balances task and social objectives, selecting actions that maximize long-term cooperative outcomes. We evaluate the model using data from user studies and show that the learned policy outperforms baseline strategies in both team performance and increasing observed human cooperative behavior.", "This paper focuses on power flow analysis through the lens of the Newton flow, a continuous-time formulation of Newton\u2019s method. Within this framework, we explore how quantized-state concepts, originally developed as an alternative to time discretization, can be incorporated to govern the evolution of the Newton flow toward the power flow solution. This approach provides a novel perspective on adaptive step-size control and shows how state quantization can enhance robustness in ill-conditioned cases. The performance of the proposed approach is discussed with the ACTIVSg70k synthetic test system.", "Simultaneous locomotion and manipulation enables robots to interact with their environment beyond the constraints of a fixed base. However, coordinating legged locomotion with arm manipulation, while considering safety and compliance during contact interaction remains challenging. To this end, we propose a whole-body controller that combines a model-based admittance control for the manipulator arm with a Reinforcement Learning (RL) policy for legged locomotion. The admittance controller maps external wrenches\u2014such as those applied by a human during physical interaction\u2014into desired end-effector velocities, allowing for compliant behavior. The velocities are tracked jointly by the arm and leg controllers, enabling a unified 6-DoF force response. The model-based design permits accurate force control and safety guarantees via a Reference Governor (RG), while robustness is further improved by a Kalman filter enhanced with neural networks for reliable base velocity estimation. We validate our approach in both simulation and hardware using the Unitree Go2 quadruped robot with a 6-DoF arm and wrist-mounted 6-DoF Force/Torque sensor. Results demonstrate accurate tracking of interaction-driven velocities, compliant behavior, and safe, reliable performance in dynamic settings.", "Attitude estimation methods typically rely on full vector measurements from inertial sensors such as accelerometers and magnetometers. This paper shows that reliable estimation can also be achieved using only scalar measurements, which naturally arise either as components of vector readings or as independent constraints from other sensing modalities. We propose nonlinear deterministic observers on $\\mathbf{SO}(3)$ that incorporate gyroscope bias compensation and guarantee uniform local exponential stability under suitable observability conditions. A key feature of the framework is its robustness to partial sensing: accurate estimation is maintained even when only a subset of vector components is available. Experimental validation on the BROAD dataset confirms consistent performance across progressively reduced measurement configurations, with estimation errors remaining small even under severe information loss. To the best of our knowledge, this is the first work to establish fundamental observability results showing that two scalar measurements under suitable excitation suffice for attitude estimation, and that three are enough in the static case. These results position scalar-measurement-based observers as a practical and reliable alternative to conventional vector-based approaches.", "Keywords: Dynamic O-D demand; Disutility function; Multi-modal systems; Computational graph; Hypothesis tests; Multi-source system-level data; Boarding and alighting count 0 0 footnotetext: * Corresponding author. E-mail address: seanqian@cmu.edu .", "This paper studies the safe and resilient control of Connected and Automated Vehicles (CAVs) operating in mixed traffic environments where they must interact with Human-Driven Vehicles (HDVs) under uncertain dynamics and exponentially unbounded false data injection (EU-FDI) attacks. These attacks pose serious threats to safety-critical applications. While resilient control strategies can mitigate adversarial effects, they often overlook collision avoidance requirements. Conversely, safety-critical approaches tend to assume nominal operating conditions and lack resilience to adversarial inputs. To address these challenges, we propose an event-driven safe and resilient (EDSR) control framework that integrates event-driven Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs) with adaptive attack-resilient control. The framework further incorporates data-driven estimation of HDV behaviors to ensure safety and resilience against EU-FDI attacks. Specifically, we focus on the lane-changing maneuver of CAVs in the presence of unpredictable HDVs and EU-FDI attacks on acceleration inputs. The event-driven approach reduces computational load while maintaining real-time safety guarantees. Simulation results, including comparisons with conventional safety-critical control methods that lack resilience, validate the effectiveness and robustness of the proposed EDSR framework in achieving collision-free maneuvers, stable velocity regulation, and resilient operation under adversarial conditions.", "For general control-affine nonlinear systems in the positive orthant, and with positive controls, we show how strict CLFs can be utilized for inverse optimal stabilization. Conventional \u201cLgV\u201d inverse optimal feedback laws, for systems with unconstrained states and controls, assume sign-unconstrained inputs and input penalties that are class-K in the input magnitude, hence symmetric about zero. Such techniques do not extend to positive-state-and-control systems. Major customizations are needed, and introduced in this paper, for positive systems where highly asymmetric (or unconventionally symmetric) costs not only on the state but also on control are necessary. With the predator-prey positive-state positive-input benchmark system as inspiration, using a strict CLF built in our previous paper, we prototype two general inverse optimal methodological frameworks that employ particular \u201ccontractor and expander functions.\u201d One framework (A) employs a triple consisting of a CLF, a stabilizing feedback, and an expander, whereas the other framework (B) employs a pair of a CLF and a contractor function. Both frameworks yield inverse optimal stabilizer constructions, on positive orthants of arbitrary dimensions. A stronger construction results from a stronger CLF condition. Biological interpretation for the predator-prey model illuminates that such inverse optimal control constructions are bio-ecologically meaningful. In addition to general frameworks, we present two fully explicit designs: two Sontag-like universal formulae for stabilization of positive-orthant systems by positive feedback, one of them with inverse optimality.", "In this paper, we consider the problem of estimating parameters of a linear regression model. Using a hybrid systems framework, a hybrid algorithm is proposed allowing the estimate to converge to the exact value of the unknown parameters in predetermined finite time. Interestingly, we show that for the case of constant parameters, the convergence property of the hybrid algorithm holds while only requiring the regressor to be exciting on a given interval. For the case of piecewise constant parameters, the classical persistency of excitation condition is required to guarantee the convergence. Robustness of the proposed algorithm with respect to measurements noise is analysed. Finally, illustrative examples are provided showing the merits of the proposed approach in terms of scalability and the applicability for the general class of time-varying unknown parameters.", "# Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response Contextual Invertible World Models: A Neuro-Symbolic Agentic Framework for Colorectal Cancer Drug Response Christopher Baker (corresponding author) c.baker@qub.ac.uk School of Electronics, Electrical Engineering and Computer Science Queen's University Belfast, Belfast, United Kingdom Tianyu Ren tren01@qub.ac.uk School of Electronics, Electrical Engineering and Computer Science Queen's University Belfast, Belfast, United Kingdom Karen Rafferty k.rafferty@qub.ac.uk School of Electronics, Electrical Engineering and Computer Science Queen's University Belfast, Belfast, United Kingdom Hui Wang h.wang@qub.ac.uk School of Electronics, Electrical Engineering and Computer Science Queen's University Belfast, Belfast, United Kingdom Neuro-Symbolic AI, Precision Oncology, Explainable AI, Agentic Frameworks, Pharmacogenomics, Colorectal Cancer, In Silico CRISPR, World Models Abstract Precision oncology is currently limited by the small-N, large-P paradox, where high-dimensional genomic data is abundant but pharmacological response samples are sparse. While deep learning achieves predicti", "Single-cell perturbation studies face dual heterogeneity bottlenecks: (i) semantic heterogeneity\u2014identical biological concepts encoded under incompatible metadata schemas across datasets; and (ii) statistical heterogeneity\u2014distribution shifts from biological variation demanding dataset-specific inductive biases. We propose HarmonyCell, an end-to-end agent framework resolving each challenge through a dedicated mechanism: an LLM-driven Semantic Unifier autonomously maps disparate metadata into a canonical interface without manual intervention; and an adaptive Monte Carlo Tree Search engine operates over a hierarchical action space to synthesize architectures with optimal statistical inductive biases for distribution shifts. Evaluated across diverse perturbation tasks under both semantic and distribution shifts, HarmonyCell achieves a 95% valid execution rate on heterogeneous input datasets (versus 0% for general agents) while matching or even exceeding expert-designed baselines in rigorous out-of-distribution evaluations. This dual-track orchestration enables scalable automatic virtual cell modeling without dataset-specific engineering .", "Background: Drug repurposing offers a cost-effective alternative to de novo discovery, yet the relative contributions of model complexity, data volume, and feature modalities to knowledge graph\u2013based repurposing remain poorly quantified under rigorous temporal validation. Methods: We constructed a pharmacology knowledge graph from ChEMBL 36 comprising 5,348 entities (3,127 drugs, 1,156 proteins, 1,065 indications) and 20,015 edges across 4 relation types. We enforced a strict temporal split (training: $\\leq$ 2022; testing: 2023\u20132025) with biologically verified hard negatives mined from failed assays and clinical trials. We benchmarked five Knowledge Graph Embedding models (TransE, TransR, RotatE, ComplEx, DistMult; 0.78\u20130.81M parameters) and a Standard GNN (3.44M parameters) that incorporates drug chemical structure using a GAT encoder and ESM-2 embeddings evaluated by PR-AUC and Hits@ $k$ on drug\u2013protein and drug\u2013indication link prediction. Scaling (0.78M\u20139.75M parameters; 25\u2013100% data) and feature ablation studies isolated contributions of model capacity, graph density, and node feature modalities. Results: Feature ablation revealed a counter-intuitive performance hierarchy. Removing the GAT-based drug structure encoder entirely from the GNN and retaining only topological embeddings combined with ESM-2 protein features improved drug\u2013protein PR-AUC from 0.5631 to 0.5785, while simultaneously reducing VRAM usage from 5.30 GB to 353 MB. Additionally, replacing the GAT encoder with Morgan fingerprints further degraded performance (PR-AUC = 0.5286), indicating that explicit chemical structure representations can be not only redundant but detrimental for predicting pharmacological network interactions. Scaling the GNN beyond 2.44 M parameters yielded diminishing performance gains, whereas increasing training data consistently improved model performance with no observable ceiling. External validation confirmed 6 of the top 14 novel predictions (42.9%) as established therapeutic indications. Conclusions: Drug pharmacological behavior can be accurately predicted using target-centric information and drug\u2013network topology alone, without requiring explicit drug chemical structure representations. Model performance is substantially more sensitive to data volume and node density than to architectural complexity, with scaling in model size yielding limited returns relative to improvements in graph coverage. Consequently, state-of-the-art performance is achievable on budget hardware, with a model using only 352 MB VRAM on a consumer GPU. Keywords: Drug repurposing, Knowledge graph, Graph neural networks, TransR, Link prediction, Computational pharmacology", "Comparing multivariate yield quality distributions across spatially referenced agricultural fields is complicated by two pervasive features: non-normality and spatial autocorrelation. Classical procedures such as ANOVA, MANOVA, and standard rank tests assume independence and therefore exhibit severe Type I error inflation when spatial dependence is present. We propose a nonparametric spatial Cramer\u2013von Mises\u2013type test based on kernel-smoothed empirical copula processes constructed from pooled componentwise ranks. Spatial kernel weights account explicitly for local dependence, while the rank transformation removes sensitivity to marginal distributional form. Under fixed-domain infill asymptotics and polynomial $\\alpha$ -mixing conditions, we establish weak convergence of the smoothed empirical copula process to a mean-zero Gaussian limit and show that the resulting quadratic test statistic converges to a weighted sum of chi-squared random variables restricted to the $(K-1)$ -dimensional contrast subspace. Practical inference is obtained through a Satterthwaite approximation calibrated using the exact discrete spatial covariance operator under a Gaussian copula model. Monte Carlo experiments with bivariate log-normal spatial data demonstrate that the proposed test maintains nominal size across varying strengths of spatial dependence, in contrast to classical parametric and non-spatial rank-based methods, which become severely anti-conservative. The procedure provides a theoretically justified and computationally tractable framework for comparing multivariate spatial yield distributions in precision agriculture and related applied settings. Keywords: Spatial Autocorrelation, Rank-Based Tests, Kernel Smoothing, Alpha-Mixing, Precision Agriculture, Satterthwaite Approximation.", "Envelope models provide a sufficient dimension reduction framework for multivariate regression analysis. Bayesian inference for these models has been developed primarily using Markov chain Monte Carlo (MCMC) methods. Specifically, Gibbs sampling and Metropolis\u2013Hastings algorithms suffer from slow mixing and high computational cost. Although automatic differentiation variational inference (ADVI) has been explored for Bayesian envelope models, the resulting gradient-based optimization is often numerically unstable due to severe ill-conditioning of the posterior distribution. To address this issue, we propose a novel reparameterization of the posterior distribution that alleviates the ill-conditioning inherent in conventional variational approaches. Building on this reparameterization, we develop an efficient variational inference procedure. Since the resulting likelihood remains nonconjugate, we approximate the corresponding variational factor using a Laplace approximation within a coordinate-ascent variational inference (CAVI) framework. We establish theoretical results showing that, at each one-step coordinate update, the Laplace approximation error relative to the exact variational inference coordinate update converges to zero. Simulation studies and a real-data analysis demonstrate that the proposed method substantially improves computational efficiency while maintaining estimation accuracy and model-selection performance relative to existing approaches.", "High-dimensional feature selection is routinely required to balance statistical power with strict control of multiple-error metrics such as the k-Family-Wise Error Rate (k-FWER) and the False Discovery Proportion (FDP), yet some existing frameworks are confined to the narrower goal of controlling the expected False Discovery Rate (FDR) and can not exploit the group-structure of the covariates, such as Sorted L-One Penalized Estimation (SLOPE). We introduce the Group Stepdown SLOPE, a unified optimization procedure which is capable of embedding the Lehmann\u2013Romano stepdown rules into SLOPE to achieve finite-sample guarantees under k-FWER and FDP thresholds. Specifically, we derive closed-form regularization sequences under orthogonal designs that provably bound k-FWER and FDP at user-specified levels, and extend these results to grouped settings via gk-SLOPE and gF-SLOPE, which control the analogous group-level errors gk-FWER and gFDP. For non-orthogonal general designs, we provide a calibrated data-driven sequence inspired by Gaussian approximation and Monte-Carlo correction, preserving convexity and scalability. Extensive simulations are conducted across sparse, correlated, and group-structured regimes. Empirical results corroborate our theoretical findings that the proposed methods achieve nominal error control, while yielding markedly higher power than competing stepdown procedures, thereby confirming the practical value of the theoretical advances.", "# Learning with the Nash-Sutcliffe loss Learning with the Nash-Sutcliffe loss Hristos Tyralis1*, Georgia Papacharalampous2 1Support Command, Hellenic Air Force, Elefsina Air Base, 19 200, Elefsina, Greece (montchrister@gmail.com, hristos@itia.ntua.gr, https://orcid.org/0000-0002-89324997) 2Department of Land, Environment, Agriculture and Forestry, University of Padova, Viale dell'Universit\u00e0 16, 35020, Legnaro, Italy (papacharalampous.georgia@gmail.com, georgia.papacharalampous@unipd.it, https://orcid.org/0000-0001-5446-954X) *Corresponding author Abstract: The Nash-Sutcliffe efficiency (NSE) is a widely used, positively oriented relative measure for evaluating forecasts across multiple time series. However, it lacks a decisiontheoretic foundation for this purpose. To address this, we examine its negatively oriented counterpart, which we refer to as Nash\u2010Sutcliffe loss, defined as LNS = 1 \u2212NSE. We prove that LNS is strictly consistent for an elicitable and identifiable multi-dimensional functional, which we name the Nash-Sutcliffe functional. This functional is a dataweighted component-wise mean. The common practice of maximizing the average NSE across multiple series is the sample", "# A Dirichlet-Multinomial-Poisson framework for the coherent analysis and forecast of cause-specific mortality ## 1 Introduction Human life expectancy increased considerably in developed countries during the twentieth century due to improved living standards, medical advances, and advances in public health policies. For example, in the United States (U.S.), life expectancy at birth increased from 47.3 years in 1900 to 78.7 years in 2010 . This aspect poses profound epidemiological and social challenges. In addition to the ageing process, the impact of each cause of death changes over time. The proportion of deaths from some causes, such as infectious diseases, is declining dramatically due to medical advances, while others remain persistent or even increase. For public health decision makers, identifying the evolution of mortality by cause is highly relevant for guiding prevention strategies, allocating resources, and designing interventions targeting specific cohorts or generations . Traditional approaches for extrapolating general mortality rates, such as the model of , have proven very useful for capturing age-specific trends and projecting overall mortality rates. However, when", "Net survival is conventionally defined as \u201csurvival if cancer were the only possible cause of death\u201d, an estimand corresponding to cancer-specific mortality alone. The Pohar Perme estimator targets this by removing general population other-cause mortality from observed total mortality, but achieves it only when cancer patients experience the same other-cause mortality as the general population. However, cancer patients often experience elevated other-cause mortality due to baseline health differences and treatment-induced effects. Using recent theoretical work decomposing total mortality into four components (cancer deaths, baseline health differences, treatment-induced other-cause deaths, and general population other-cause mortality), we show that the Pohar Perme estimator delivers the sum of cancer deaths, baseline differences, and treatment-induced deaths, falling short of its intended estimand whenever either source of excess is present. From Botta et al , we present empirical evidence showing relative risk of other-cause deaths ranging from 1.0 (colorectal cancer) to 4.0+ (head and neck cancers), and calculations demonstrating that net survival can substantially underestimate cancer-specific survival probability when relative risk exceeds 1.0. Critically, treatment-induced other-cause deaths represent irreducible causal pathways from cancer to death that cannot be eliminated through better stratification. We recommend interpreting net survival as \u201csurvival where general population other-cause mortality is removed\u201d rather than as a causal counterfactual, and call for more precise language in cancer epidemiology. Keywords: net survival, cancer-specific survival, relative survival, competing risks, causal inference, cancer epidemiology", "A fundamental challenge in causal inference with observational data is correct specification of a causal model. When there is model uncertainty, analysts may seek to use estimates from multiple candidate models that rely on distinct, and possibly partially overlapping, sets of identifying assumptions to infer the causal effect, a process known as triangulation . Principled methods for triangulation, however, remain underdeveloped. Here, we develop a framework for causal effect triangulation that combines model testability methods from causal discovery with statistical inference methods from semiparametric theory, while avoiding explicit model selection and post-selection inference problems. We propose a triangulation functional that combines identified functionals from each model with data-driven measures of model validity. We provide a bound on the distance of the functional from the true causal effect along with conditions under which this distance can be taken to zero. Finally, we derive valid statistical inference for this functional. Our framework formalizes robustness under causal pluralism without requiring agreement across models or commitment to a single specification. We demonstrate its performance through simulations and an empirical application.", "# A Percentile-Focused Regression Method for Applied Data with Irregular Error Structures 1 Percentile-Focused Regression Method for Applied Data with Irregular Error Structures Elsayed A. H. Elamir Department of Management and Marketing, College of Business Administration, Kingdom of Bahrain Email: shabib@uob.edu.bh Abstract Irregular errors such as heteroscedasticity and non\u2010normality remain major challenges in linear modelling. These issues often lead to biased inference and unreliable measures of uncertainty. Classical remedies, such as robust standard errors and weighted least squares, only partially address the problem and may fail when heteroscedasticity interacts with skewness or nonlinear mean structures. To address this, we propose a two-stage cumulative distribution functionbased (CDF-based) beta regression framework that models the full conditional distribution of the response. The approach first transforms the outcome using a smoothed empirical CDF and then fits a flexible beta regression, allowing heteroscedasticity and non-normality to be handled naturally through the mean\u2013precision structure of the beta distribution. Predictions are mapped back to the original scale", "Circular variables that represent directions or periodic observations arise in many fields, such as biology and environmental sciences. An important issue when dealing with circular data is how to estimate their dispersion robustly, avoiding undue effects of anomalies. This work extends three robust dispersion measures from the line to the circle. Their robustness is studied via their influence functions and relative bias curves. From these dispersion measures, robust estimators of parameters of circular distributions can be derived. This yields robust estimators for the concentration parameter of the von Mises distribution and the dispersion parameter of the wrapped normal distribution. Their breakdown values and statistical efficiencies are obtained, and they are compared in a simulation study. Building on the best performing estimator, a robust circular anomaly detection procedure is developed, and employed to visualize outliers through a circular violin plot. Three real datasets are analyzed.", "Integrated IPD\u2013AD analysis, which combines individual participant data (IPD) with aggregate data (AD), is increasingly recognized as an effective strategy for generating more reliable and generalizable inferences from heterogeneous studies. While most existing work has focused on algorithmic approaches, this paper investigates a complementary yet underexplored question: how different forms of AD influence the efficiency of data integration. Working within a constrained maximum likelihood estimation framework, we compare commonly reported summary statistics and show that subgroup-specific summaries can substantially improve estimation efficiency. In particular, we find that AD derived from outcome-stratified subgroups (e.g., cases and controls) consistently yield greater efficiency gains than those based on covariate-stratified subgroups (e.g., age or exposure categories), especially when the outcome is continuous. Although outcome-stratified summaries are commonly reported for discrete outcomes, they are rarely provided when the outcome is continuous. Our findings therefore support the routine inclusion of outcome-stratified summaries for continuous endpoints in trial reports and public data repositories to facilitate more efficient evidence synthesis. We further extend the constrained maximum likelihood framework to accommodate dataset shift and develop a fast, non-iterative estimation procedure to improve numerical stability and scalability. We illustrate the proposed methodology with two applications: an analysis of income data under covariate shift and an analysis of housing data under prior probability shift. Key words: Constrained maximum likelihood estimation; Covariate shift; Efficiency; Prior probability shift.", "# A Simpson Based Estimation Approach for the Overlapping Coefficient of k>=2 Normal Distributions 1 A Simpson\u2013Based Estimation Approach for the Overlapping Coefficient of k\u22652 Normal Distributions Omar M. Eidous and Majd M. Alsheyyab omarm@yu.edu.jo majd.alsheyyab.97@gmail.com Department of Statistics - Yarmouk University Irbid - Jordan 2026 Abstract The overlapping coefficient is a fundamental measure of similarity between probability distributions. While the case of two distributions has been extensively studied, extending this measure to multiple populations presents both analytical and computational challenges. In this paper, we propose a general estimation framework for the overlapping coefficient of k\u22652 normal distributions. The method employs Simpson\u2019s numerical integration rule combined with plug-in maximum likelihood estimators of the normal parameters. The resulting estimator is shown to be consistent under standard regularity conditions. A Monte Carlo simulation study is conducted across various overlap scenarios and sample sizes. The results demonstrate that the proposed Simpson-based estimator performs competitively for all overlap levels, with notable advantages in lo", "Two\u2013component mixture models are particularly useful for identifying differentially expressed genes, but their performance can deteriorate markedly when the alternative distribution departs from parametric assumptions or symmetry. We propose a semiparametric mixture model in which the null component is standard normal and the alternative follows a skew\u2013normal scale mixture with an unspecified scale mixing distribution. This formulation accommodates skewness and heavy tails, providing a flexible and computationally tractable tool for differential gene\u2013expression analysis without restrictive distributional assumptions. We establish identifiability and consistency of the model and develop an efficient estimation algorithm that incorporates nonparametric maximum likelihood estimation of the scale distribution. Numerical studies show notable improvements over existing parametric and nonparametric approaches for modeling the alternative distribution, and applications to colon cancer and leukemia datasets demonstrate reduced false discovery and false negative rates.", "Kernel density estimators with circular data have been studied extensively for decades, as they allow flexible estimations even when the shape of the underlying density is complex. Many recent studies have examined bias correction methods; however, these methods are limited by the order when trying to improve the convergence rate of the bias, even if the true density is sufficiently smooth. To overcome this limitation, the present study considers a new bias correction approach based on the characteristic functions of the underlying circular density. We introduce wrapped flat-top kernels, which are generated by wrapping the standard flat-top kernels defined on the real line onto the circumference of a unit circle. The asymptotic mean squared errors of the wrapped flat-top kernel density estimators are then derived. The results show that the convergence rate of these estimators is faster than that of previously introduced estimators. Furthermore, wrapped flat-top kernel density estimators achieve $\\sqrt{n}$ -consistency under the characteristic function of finite support, such as the circular uniform and cardioid distributions. We confirm these theoretical results in the numerical experiments. In empirical analyses, we also show that wrapped flat-top kernel density estimators effectively capture the shape of data. Therefore, such estimators are expected to allow flexible and accurate estimation in circular data analysis.", "Non-negative matrix factorization (NMF) is widely used for parts-based representations, yet formal inference for covariate effects is rarely available when the basis is learned under non-negativity. We introduce non-negative matrix factorization with random effects (NMF-RE), a mean-structure latent-variable model $Y=X(\\Theta A+U)+\\mathcal{E}$ that combines covariate-driven scores with unit-specific deviations. Random effects act as a working device for modeling heterogeneity and controlling complexity; we monitor their effective degrees of freedom and enforce a df-based cap to prevent near-saturated fits. Estimation alternates closed-form ridge (BLUP-like) updates for $U$ with multiplicative non-negative updates for $X$ and $\\Theta$ . For inference on $\\Theta$ , we condition on $(\\widehat{X},\\widehat{U})$ and obtain fast uncertainty quantification via asymptotic linearization, a one-step Newton update, and a multiplier (wild) bootstrap; this avoids repeated constrained re-optimization. Simulations include a targeted stress test showing that, without df control, the random-effects penalty can collapse and inference for $\\Theta$ becomes degenerate, whereas the df-cap prevents this failure mode. The non-negativity constraint induces sparse, parts-based loadings\u2014a measurement-side variable selection\u2014while inference on $\\Theta$ identifies which covariates affect which components, providing covariate-side selection. Longitudinal, psychometric, spatial-flow, and text examples further illustrate stable, interpretable covariate-effect inference.", "Bayesian sample size calculations in clinical trials usually rely on complex Monte Carlo simulations in practice. Obtaining bounds on Bayesian notions of the false-positive rate and power often lack closed-form or approximate numerical solutions. In this paper, we focus on power and sample size calculations for Bayes factors in the two-arm binomial setting of phase II trials. We cover point-null versus composite and directional hypothesis tests, derive the corresponding Bayes factors, and discuss relevant aspects to consider when pursuing Bayesian design of experiments with the introduced approach. Based on these Bayes factors, we propose a numerical approach which allows to determine the necessary sample size to obtain prespecified bounds of Bayesian power and type-I-error rate in a computationally efficient way. Our method does not rely on Monte Carlo simulations and instead solely relies on standard numerical methods. Real-world examples of phase II trials from oncology and autoimmune diseases illustrate the advantage of the proposed calibration method. In summary, our approach allows for a Bayes-frequentist compromise by providing a Bayesian analogue to a frequentist power analysis for various Bayes factors in the two-arm binomial setting of a phase II clinical trial. The methods are implemented in our R package bfbin2arm . Keywords: phase II trial, Bayesian statistics, Bayes factors, two-arm clinical trial, binary endpoint", "We have developed and tested a spatial scan statistic for categorical, functional data (CFSS) - a data structure within which current approaches cannot identify spatial clusters. Our methodology combines an encoding scheme for categorical, functional observations with a nonparametric scan statistic. In a simulation study with three distinct scenarios, the CFSS accurately recovered the simulated spatial clusters and gave very low false positive rates, high true positive rates, and high positive predictive values. We have also used the CFSS to identify and characterize spatial clusters in French air pollution data from the winter of 2024. Keywords: Categorical data, Cluster detection, Functional data, Spatial scan statistics", "Estimating causal effects is particularly challenging when outcomes arise in complex, non-Euclidean spaces, where conventional methods often fail to capture meaningful structural variation. We develop a framework for topological causal inference that defines treatment effects through differences in the topological structure of potential outcomes, summarized by power-weighted silhouette functions of persistence diagrams. We develop an efficient, doubly robust estimator in a fully nonparametric model, establish functional weak convergence, and construct a formal test of the null hypothesis of no topological effect. Empirical studies illustrate that the proposed method reliably quantifies topological treatment effects across diverse complex outcome types.", "# A Simulation Study to Compare Inferential Properties when Modelling Ordinal Outcomes: The Case for the (Plain but Robust) Proportional Odds Model A Simulation Study to Compare Inferential Properties when Modelling Ordinal Outcomes: The Case for the (Plain but Robust) Proportional Odds Model Stefan Inerle1, Markus Pauly1,2, Moritz Berger3 1Department of Statistics, TU Dortmund University 2Research Center Trustworthy Data Science and Security, University Alliance Ruhr (UA Ruhr) 3Core Facility Biostatistics, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany Autor Note Stefan Inerle https://orcid.org/0009-0002-7259-4760 Markus Pauly https://orcid.org/0000-0002-0976-7190 Moritz Berger https://orcid.org/0000-0002-0656-5286 Correspondence concerning this article should be addressed to Stefan Inerle, Department of Statistics, TU Dortmund University, Vogelpothsweg 87, 44221 Dortmund Germany. Email: stefan.inerle@tu-dortmund.de arXiv:2603.01943v1 [stat.ME] 2 Mar 2026 Comparing models for ordinal outcomes 2 Abstract Ordinal measurements are common outcomes in studies within psychology, as well as in the social and behavioral sciences. Cho", "[Abstract]Stepped-wedge cluster randomised trials (SW-CRTs) increasingly evaluate complex interventions, yet methodological guidance for analysing composite endpoints using generalized pairwise comparisons (GPC) remains limited. This work investigates the performance of several GPC-based estimators in the presence of clustering, temporal trends, and varying correlation structures typical of SW-CRTs. We conducted an extensive simulation study covering a range of intraclass correlations (ICC), cluster autocorrelation coefficients (CAC), time effects, and treatment effect sizes. Eight analytical approaches were compared, including unadjusted estimators, cluster-stratified win odds, mixed-effects models applied to cluster\u2013period win odds, and probabilistic index models (PIMs). Type I error control was strongly compromised for methods ignoring time or clustering, whereas only two approaches consistently maintained nominal error rates: a hierarchical mixed-effects model with sequence- and cluster-level random slopes (b4) and a cluster-restricted PIM (c2). These two methods were further evaluated in terms of statistical power, where c2 generally showed higher efficiency, particularly under strong clustering, low CAC, or the presence of temporal trends, while both converged to similar performance for large treatment effects. Overall, our findings identify b4 and c2 as the most reliable GPC-based strategies for SW-CRT analysis and provide practical guidance for their application, including for ongoing trials such as ETHER. \\jnlcitation \\cname , , , and . \\ctitle On simplifying \u00e2\u0102\u0178incremental remap\u00e2\u0102\u0179-based transport schemes. \\cjournal J Comput Phys. \\cvol 2021;00(00):1\u201318.", "Controlling the false discovery rate (FDR) in variable selection becomes challenging when predictors are correlated, as existing methods often exclude all members of correlated groups and consequently perform poorly for prediction. We introduce a new setwise variable\u2011selection framework that identifies clusters of potential predictors rather than forcing selection of a single variable. By allowing any member of a selected set to serve as a surrogate predictor, our approach supports strong predictive performance while maintaining rigorous FDR control. We construct sets via hierarchical clustering of predictors based on correlation, then test whether each set contains any non\u2011null effects. Similar clustering and setwise selection have been applied in the familywise error rate (FWER) control regime, but previous research has been unable to overcome the inherent challenges of extending this to the FDR control framework. To control the FDR, we develop substantial generalizations of linear step\u2011up procedures, extending the Benjamini\u2013Hochberg and Benjamini\u2013Yekutieli methods to accommodate the logical dependencies among these composite hypotheses. We prove that these procedures control the FDR at the nominal level and highlight their broader applicability. Simulation studies and real\u2011data analyses show that our methods achieve higher power than existing approaches while preserving FDR control, yielding more informative variable selections and improved predictive models.", "Analysts routinely use Bayesian hierarchical models to understand natural processes. The no-U-turn sampler (NUTS) is the most widely used algorithm to sample high-dimensional, continuously differentiable models. But NUTS is slowed by high correlations, especially in high dimensions, limiting the complexity of applied analyses. Here we introduce Sparse NUTS (SNUTS), which preconditions (decorrelates and descales) posteriors using a sparse precision matrix ( $Q$ ). We use Template Model Builder (TMB) to efficiently compute $Q$ from the mode of the Laplace approximation to the marginal posterior, then pass the preconditioned posterior to NUTS through the Bayesian software Stan for sampling. We apply SNUTS to seventeen diverse case studies to demonstrate that preconditioning with $Q$ converges one to two orders of magnitude faster than Stan\u2019s industry standard diagonal or dense preconditioners. SNUTS also outperforms preconditioning with the inverse of the covariance estimated with Pathfinder variational inference. SNUTS does not improve sampling efficiency for models with the highly varying curvature found in funnels, wide tails, or multiple modes. SNUTS is most advantageous, and can be scaled beyond $10^{4}$ parameters, in the presence of high dimensionality, sparseness, and high correlations, all of which are widespread in applied statistics. An open-source implementation of SNUTS is provided in the R package SparseNUTS .", "Transporting findings from a study population to a target population is central to evidence-based decision-making in real-world settings. Most existing methods require individual-level data from both populations to account for covariate shift. However, privacy regulations and data-sharing constraints often preclude access to such data from the target population, leaving only covariate summaries available for analysis. In this paper, we develop transportability methods that enable valid inference using source individual-level data and target covariate summaries. Firstly, we apply entropy balancing to transportability, enabling source individual-level data to be adjusted to match the target covariate moments. We establish asymptotic normality for the entropy balancing estimator and propose a variance estimator to account for uncertainty in covariate summaries. Secondly, we develop a new transportability method that allows flexible modeling of covariate shift, thereby accounting for covariate shift and uncertainty in covariate summaries simultaneously. Asymptotic normality for the proposed estimator is established and its asymptotic variance is consistently estimated. The proposed method offers greater flexibility in accounting for covariate shift and thus permits consistent estimation and valid inference under weaker conditions than those required by entropy balancing. The proposed methods are evaluated by simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast cancer data.", "# A Decision Analysis Framework for High-fidelity and Low-fidelity Systems with Applications in Manufacturing Processes ## 1 Introduction ### 1.1 Background and Motivation Computer simulations are indispensable in modern manufacturing, as they enable engineers to efficiently explore complex physical phenomena and find optimal process parameter settings that would be costly or infeasible to test experimentally. However, relying on a single data source often presents a dilemma: high-fidelity data, whether derived from physical experiments or complex simulations, are accurate but expensive to obtain, limiting their availability for optimization. Conversely, computationally efficient low-fidelity models are abundant often rely on simplifying assumptions that limit their accuracy. Consequently, a key challenge in manufacturing process optimization is to combine these data sources to support efficient and robust decision-making. We illustrate this challenge through two manufacturing applications: composite cure optimization and injection molding process control, where combining high-fidelity experimental measurements with low-fidelity simulation data leads to more efficient and practical", "Analyzing data collected from multiple sources to estimate common and heterogeneous structures through a hierarchical model is a central task in Bayesian inference, and to this end, Bayesian factor models are one of the most widely used tools for this purpose. In this paper, we propose a new Bayesian latent factor model for distributions, providing a parsimonious model for describing many observed distributions through lower-dimensional structures. Many applications are found in the social science in the form of grouped data, for example, distributions of age composition and income observed across locations. In these contexts, standard mixture models can be inefficient because the distributions do not necessarily exhibit clear clustering structures. To overcome the difficulty, we introduce a tree-based transformation that embeds distributions into a Euclidean space and construct a Bayesian latent factor model in the transformed space. Once transformed into Euclidean vectors, the Bayesian hierarchical model can be extended in a straightforward manner. As an illustration, we incorporate spatial dependence by introducing a prior based on a simultaneous autoregressive (SAR) model. The proposed model is \u201cnonparametric\u201d in the sense that it does not impose parametric assumptions on the form of the observed distributions. Through numerical experiments using real population data, we demonstrate that the proposed model outperforms the standard Dirichlet mixture model as well as models built on parametric assumptions.", "We propose a semi-partitioned Generalized Method of Moments (GMM) framework for analyzing longitudinal data with time-dependent covariates, within a marginal modeling paradigm. This approach addresses limitations of both aggregated and fully partitioned GMM models. Aggregated methods obscure temporal dynamics by assuming constant effects, while fully partitioned approaches offer temporal specificity at the cost of increased model complexity and instability\u2014particularly with moderate sample sizes or deep lag structures. Our method distinguishes immediate from lagged effects by estimating contemporaneous coefficients separately and grouping lagged moment conditions into structured sets, while retaining flexibility in the lag\u2011specific effects. This yields a model that is both statistically efficient and interpretable, capturing essential temporal variation while mitigating variance inflation and convergence challenges associated with full partitioning. The framework accommodates feedback, supports both continuous and binary outcomes, and utilizes the Broyden\u2013Fletcher\u2013Goldfarb\u2013Shanno (BFGS) algorithm for reliable optimization. Through simulations, we demonstrate that the semi-partitioned GMM achieves coverage and competitive efficiency relative to fully partitioned models when the grouped\u2011lag structure approximates the underlying lag pattern. Applications to clinical datasets on knee osteoarthritis and adolescent obesity confirm that the method recovers consistent, interpretable effects and avoids instability associated with finely grained partitioning.", "Many types of bounded data defined on the unit interval arise naturally as ratios of the form $X/(X+Y)$ . In the existing literature, the main statistical models proposed for this type of bounded data typically based on the assumption that the random variables $X$ and $Y$ are independent. However, this assumption is often unrealistic in practical applications, where $X$ and $Y$ tend to be correlated due to shared underlying mechanisms or common sources of variability. In this paper, we overcome such limitations and propose a model in which the marginal distributions of the two components are linked by a copula, leading to a more flexible and realistic representation of unit-interval data. In particular, in the proposed model, $X$ and $Y$ are dependent gamma random variables whose joint distribution is specified via Morgenstern\u2019s bivariate distribution , allowing for positive and negative correlations between the components. The mathematical properties and practical applications are rigorously investigated. The resulting distribution exhibits a wide range of shapes, accommodating different degrees of skewness and, for some parameter configurations, more complex density structures. A Monte Carlo simulation study is carried out that shows the good performance of the maximum likelihood estimator in several scenarios of parameter choices. The potential and limitations of efficient likelihood-based computations are also discussed. We evaluate the effectiveness of the new model and its estimates in modeling real-world datasets related to economics.", "Orthogonal wavelet transforms are a cornerstone of modern signal and image denoising because they combine multiscale representation, energy preservation, and perfect reconstruction. In this paper, we show that these advantages can be retained and substantially enhanced by moving beyond classical single\u2013basis wavelet filterbanks to a broader class of composite wavelet\u2013like matrices . By combining orthogonal wavelet matrices through products, Kronecker products, and block\u2013diagonal constructions, we obtain new unitary transforms that generally fall outside the strict wavelet filterbank class, yet remain fully invertible and numerically stable. The central finding is that such composite transforms induce stronger concentration of signal energy into fewer coefficients than conventional wavelets. This increased sparsity, quantified using Lorenz curve diagnostics, directly translates into improved denoising under identical thresholding rules. Extensive simulations on Donoho\u2013Johnstone benchmark signals, complex\u2013valued unitary examples, and adaptive block constructions demonstrate consistent reductions in mean\u2013squared error relative to single\u2013basis transforms. Applications to atmospheric turbulence measurements and image denoising of the Barbara benchmark further confirm that composite transforms better preserve salient structures while suppressing noise. Keywords: Composite wavelet matrices, Kronecker products, Lorenz curves, Orthogonal transforms, Wavelet denoising.", ". Bounded discrete proportions\u2014counts out of known totals\u2014present modeling challenges when data exhibit structural zeros, overdispersion, and hierarchical clustering. We develop a Bayesian hierarchical hurdle beta-binomial model with state-varying coefficients that addresses all four features. The framework makes three methodological contributions: (i) it studies cross-margin dependence via a cross-block covariance component and clarifies when and how this parameter is identified through the hierarchical layer rather than the conditional likelihood; (ii) it proposes a Cholesky-based sandwich variance calibration for pseudo-posterior inference under survey weights, guided by a parameter-specific design effect ratio diagnostic; and (iii) it introduces a log-scale marginal effect decomposition for hurdle models that translates regression coefficients into policy-relevant quantities. Applied to 6,785 childcare providers across 51 states from the 2019 National Survey of Early Care and Education, the model reveals a \u201cpoverty reversal\u201d: poverty reduces enrollment participation yet increases intensity among participants, with the extensive margin accounting for two-thirds of the total effect. Design-calibrated simulation shows that sandwich-corrected intervals substantially improve coverage, reaching 82\u201388.5% at the 90% nominal level for fixed effects. The R package hurdlebb implements all methods.", "Structural heart disease (SHD) is a prevalent condition with many undiagnosed cases, and early detection is often limited by the high cost and accessibility constraints of echocardiography (ECHO). Recent studies show that artificial intelligence (AI)-based analysis of electrocardiograms (ECGs) can detect SHD, offering a scalable alternative. However, existing methods are fully black-box models, limiting interpretability and clinical adoption. To address these challenges, we propose an interpretable and effective framework that integrates clinically meaningful ECG foundation-model predictors within a generalized additive model, enabling transparent risk attribution while maintaining strong predictive performance. Using the EchoNext benchmark of over 80,000 ECG\u2013ECHO pairs, the method demonstrates relative improvements of +0.98% in AUROC, +1.01% in AUPRC, and +1.41% in F1 score over the latest state-of-the-art deep\u2010learning baseline, while achieving slightly better performance even with only 30% of the training data. Subgroup analyses confirm robust performance across heterogeneous populations, and the estimated entry-wise functions provide interpretable insights into the relationships between risks of traditional ECG diagnoses and SHD. This work illustrates a complementary paradigm between classical statistical modeling and modern AI, offering a pathway to interpretable, high-performing, and clinically actionable ECG-based SHD screening.", "# Causal Discovery Should Embrace the Wisdom of the Crowd ## 1 Introduction The pursuit of understanding causal relationships represents the cornerstone of scientific progress, bridging the gap between statistical observation and actionable, mechanistic knowledge. In the modern era, this philosophical endeavor has transformed into a rigorous computational challenge , thanks to the emergence of probabilistic and statistical languages for causality, the advancement of computational powers, and the rise of AI and machine learning (AI/ML). Now, causal discovery, the task of inferring the underlying causal structure for a set of variables under explicit model assumptions, has become a fundamental problem in science and engineering . Various formulations of this problem have been proposed, among which learning a DAG that represents the data-generating process is now a canonical approach. However, DAG learning has long faced a critical bottleneck of computational intractability. As the number of variables increases, the search space for possible causal structures grows super-exponentially with the number of variables, making optimal structure learning NP-hard . For a large causal graph wi", "Aalen\u2019s linear hazard rate regression model is a useful and increasingly popular alternative to Cox\u2019 multiplicative hazard rate model. It postulates that an individual has hazard rate function $h(s)=z_{1}\\alpha_{1}(s)+\\cdots+z_{r}\\alpha_{r}(s)$ in terms of his covariate values $z_{1},\\ldots,z_{r}$ . These are typically levels of various hazard factors, and may also be time-dependent. The hazard factor functions $\\alpha_{j}(s)$ are the parameters of the model and are estimated from data. This is traditionally accomplished in a fully nonparametric way. This paper develops methodology for estimating the hazard factor functions when some of them are modelled parametrically while the others are left unspecified. Large-sample results are reached inside this partly parametric, partly nonparametric framework, which also enables us to assess the goodness of fit of the model\u2019s parametric components. In addition, these results are used to pinpoint how much precision is gained, using the parametric-nonparametric model, over the standard nonparametric method. A real-data application is included, along with a brief simulation study.", "The semiparametric linear hazard regression model introduced by McKeague and Sasieni (1994) is an extension of the linear hazard regression model developed by Aalen (1980). Methods of model selection for this type of model are still underdeveloped. In the process of fitting a semiparametric linear hazard regression model one usually starts with a given set of covariates. For each covariate one has at least the following three choices: allow it to have time-varying effect; allow it to have constant effect over time; or exclude it from the model. In this paper we discuss focused information criteria (FIC) to help with this choice. In the spirit of Claeskens and Hjort (2003), \u2018focused\u2019 means that one is interested in one specific quantity, e.g. the probability of survival of a patient with a certain set of covariates up to a given time. The FIC involves estimating the mean squared error of the estimator of the quantity one is interested in, and the chosen model is the one minimising this estimated mean squared error. The focused model selection machinery is extended to allow for weighted versions, leading to a suitable wFIC method that aims at finding models that lead to good estimates of a given list of parameters, such as survival probabilities for a subset of patients or for a specified region of covariate vectors. In addition to developing model selection criteria, methods associated with averaging across the best models are also discussed. We illustrate these methods of model selection in a real data situation. Key words and phrases: Aalen\u2019s linear hazard regression model, focused model selection, model averaging, semiparametric, survival analysis", "# Terrestrial Test of Shapiro Time Delay: Forth test of Einstein General Theory of Relativity Terrestrial Test of Shapiro Time Delay: Forth test of Einstein General Theory of Relativity (May 25th, 2026) Farhad Hakimi and Hosain Hakimi, emails: fhakimi@alum.mit.edu, hhakimi@alum.mit.edu Abstract: We present a compact fiber-optic Sagnac interferometer designed to measure the relativistic Shapiro time delay in a terrestrial laboratory. The instrument converts tiny propagation delays from spacetime curvature into directly measurable optical phase shifts, enabling a tabletop determination of the parametrized post-Newtonian parameter \u03b3. We describe the sensor architecture, analyze its expected signal-to-noise performance, and illustrate a representative implementation with realistic parameters. The results demonstrate that laboratory-scale Shapiro delays can be detected with sufficient precision, providing a practical platform for tabletop tests of general relativity. This work establishes a novel interferometric sensor and a framework for future precision experiments probing fundamental relativistic effects. I. Introduction In the early 1960s, Irwin I. Shapiro, then a physicist at MIT L", "Our Sun is the closest X-ray astrophysical source to Earth. As such, it makes a formidable case study to better understand astrophysical processes. Solar flares are in particular very interesting as they are linked to coronal mass ejections as well as magnetic field reconnection sites in the solar atmosphere. Flares can therefore provide insightful information on the physical processes at play on their production sites, but also on the emission and acceleration of energetic charged particles towards our planet, making it a formidable forecasting tool for space weather. While solar flares are critical to understanding magnetic reconnection and particle acceleration, their hard X-ray polarization \u2013 key to distinguishing between competing theoretical models \u2013 remains poorly constrained by existing observations. To address this, we present the CUbesat Solar Polarimeter (CUSP), a mission under development to perform solar flare polarimetry in the 25-100 keV energy range. CUSP consists of a 6U-XL platform hosting a dual-phase Compton polarimeter. The polarimeter is made of a central assembly of four 4 $\\times$ 4 arrays of plastic scintillators, each coupled to multi-anode photomultiplier tubes, surrounded by four strips of eight elongated GAGG scintillator bars coupled to avalanche photodiodes. Both types of sensors from Hamamatsu are respectively read out by the MAROC-3A and SKIROC-2A ASICs from Weeroc. In this manuscript, we present the preliminary spectral performances of single plastic and GAGG channels measured in the laboratory using development boards of the ASICs foreseen for the flight model.", "We present a new empirical model for galaxy rotation curves that introduces a velocity correction term $\\omega$ , derived from observed stellar motion and anchored to Keplerian baselines. Unlike parametric halo models or modified gravity theories, this approach does not alter Newtonian dynamics or invoke dark matter distributions. Instead, it identifies a repeatable kinematic offset that aligns with observed rotation profiles across a wide range of galaxies. Using SPARC data [1], we demonstrate that this model consistently achieves high-fidelity fits, often outperforming MOND and CDM halo models in RMSE and R-squared metrics without parametric tuning. The method is reproducible, minimally dependent on mass modeling, and offers a streamlined alternative for characterizing galactic dynamics. While the velocity correction $\\omega$ lacks a definitive physical interpretation, its empirical success invites further exploration. We position this model as a local kinematic tool rather than a cosmological framework, and we welcome dialogue on its implications for galactic structure and gravitational theory. Appendix B presents RMSE and $R^{2}$ comparisons showing that this method consistently outperforms MOND and CDM halo models across a representative galaxy sample.", "Cloud coverage is one of the crucial elements of site testing in astronomy. All-sky camera (ASC) images are beneficial for our research on cloud coverage. In this paper, we propose ASCNet, an innovative model specifically designed for classifying nighttime ASC images collected at the Muztagh-ata site from 2022 March to 2024 June. ASCNet integrates ResNet34 with an ASCModule, which employs Depthwise Dilated Convolution and embeds lightweight Squeeze-and-Excitation attention within its branches to extract fine-grained texture information from the luminance channel. The data set is partitioned by category, with 70% of images assigned to the training set and 30% to the test set. The model\u2019s performance is assessed by comparing its predictions on the test set with manually annotated labels, yielding a consistency rate of 92.7%. All evaluation metrics of ASCNet are as follows: Accuracy 92.66%, Precision 83.26%, Recall 84.25%, and F1-Score 83.67%, and both ablation and comparative experiments demonstrate significant superiority over other models. A confusion matrix is utilized to analyze the differences between manual classification and model classification. The statistical results demonstrate the model\u2019s excellent classification performance and its robust generalization ability, illustrating that ASCNet has potential for application in future astronomical image classifications.", "# Unlocking the Future of X-Ray Polarimetry with IXPE: Lessons Learned and Next Steps Academic Editor: Firstname Lastname Received: 19 October 2025 Revised: 1 December 2025 Accepted: 9 December 2025 Published: Citation: Soffitta, P.; Costa, E.; Del Monte, E., Di Marco, A.; Fabiani. S.; Ferrazzoli, R.; La Monaca, F.; Muleri, F.; Rubini, A.; Trois, A. Unlocking the Future of X-Ray Polarimetry with IXPE: Lessons Learned and Next Steps. Particles 2025, 1, 0. https://doi.org/ Copyright: \u00a9 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/ licenses/by/4.0/). Article Unlocking the Future of X-Ray Polarimetry with IXPE: Lessons Learned and Next Steps Paolo Soffitta 1,* , Enrico Costa 1 , Ettore Del Monte 1 , Alessandro Di Marco 1 , Sergio Fabiani 1 , Riccardo Ferrazzoli 1 , Fabio La Monaca 1 , Fabio Muleri 1 , Alda Rubini 1 and Alessio Trois 2 1 INAF-IAPS, Via Fosso del Cavaliere 100, 00133 Rome, Italy; enrico.costa@inaf.it (E.C.); ettore.delmonte@inaf.it (E.D.M.); alessandro.dimarco@inaf.it (A.D.M.); sergio.fabiani@inaf.it", "The nature of dark energy (DE) remains elusive, even though it constitutes the dominant energy-density component of the Universe and drives the late-time acceleration of cosmic expansion. By combining measurements of the expansion history from baryon acoustic oscillations, supernova surveys, and cosmic microwave background data, the Dark Energy Spectroscopic Instrument (DESI) Collaboration has inferred that the DE equation of state may evolve over time. The profound implications of a time-variable, \u201cdynamical\u201d DE (DDE) that departs from a cosmological constant motivate the need for independent observational tests. In this work, we use cosmological hydrodynamical simulations of structure formation to investigate how DDE affects the properties of the Lyman- $\\alpha$ \u201cforest\u201d of absorption features produced by neutral hydrogen in the cosmic web. We find that DDE models consistent with the DESI constraints induce a spectral tilt in the forest transmitted flux power spectrum, imprinting a scale- and redshift-dependent signature relative to standard $\\Lambda$ CDM cosmologies. These models also yield higher intergalactic medium temperatures and reduced Lyman- $\\alpha$ opacity compared to $\\Lambda$ CDM. We discuss the observational implications of these trends as potential avenues for independent confirmation of DDE.", "Subatomic particles can interact with target nuclei in matter or decay in flight, and an individual high-energy particle can induce a particle shower composed of numerous, lower-energy secondaries. These particle showers broadly exhibit universality across diverse media, including air, water, ice, and other materials, with their development governed by the Standard Model. Full Monte Carlo simulation of particle showers, where each secondary is individually tracked and propagated, can be a computational challenge to perform at scale. Experiments thus resort to parametrized approximations when efficient simulation becomes necessary. Here, we construct distributions of parameters capable of describing the Cherenkov light yield from particle showers in ice or water. Sampling from the distributions allows for a much improved description of event-to-event fluctuations, in amplitude and shape, along the shower axis. Including these effects is essential for a more accurate simulation of signal and background events in current and next-generation neutrino telescopes.", "Integral field spectroscopy allows for spectral mapping of extended sources in a time efficient manner. An integral field unit (IFU) in the ultraviolet on Habitable Worlds Observatory (HWO) could be used to quickly map extended objects like supernova remnants or galaxies and their surroundings, but there are technical challenges to an ultraviolet IFU. The INtegral Field Ultraviolet Spectrographic Experiment (INFUSE), a sounding rocket project, is the first static configuration far ultraviolet integral field spectrograph. INFUSE features an f /16, 0.49m Cassegrain telescope and a 26-element image slicer feeding 26 replica holographic gratings, with spectra imaged by the largest cross-strip microchannel plate detector flown in space. The first launch of INFUSE occurred from White Sands Missile Range on October 29th, 2023, and demonstrated spectral multiplexing, successfully detecting ionizing gas emission in the XA region of the Cygnus Loop. INFUSE will launch again in fall 2025 to observe NGC 2366, a local analog for Green Pea type galaxies, with several enhancements including a xenon-enhanced lithium fluoride + aluminum coated grating, testing the leading flight coating for HWO for the first time. The INFUSE IFU is designed as a pathfinder for a potential IFU mode on HWO, enabling rapid 3D spectroscopy of extended sources.", "Light curves represent astronomical time series of flux measured across one or more photometric bands. With the rapid growth of large-scale sky surveys, time-domain astronomy has become an essential area of modern astrophysical research. Interactive visualization of extensive light-curve datasets plays a key role in exploring transient phenomena and in planning large follow-up campaigns. In this work, we introduce two web-based platforms designed for interactive light-curve visualization: Fulu , for transient event studies, and VALC, for investigations of low-mass active galactic nuclei (AGNs). These tools provide a user-friendly interface for examining, comparing, and interpreting vast collections of astronomical light curves, supporting scientific discovery.", "We present neutrino-transport algorithms implemented in the toolkit for high-order neutrino-radiation hydrodynamics ( thornado ) and their coupling to self-gravitating hydrodynamics within the adaptive mesh refinement (AMR)-based multiphysics simulation framework Flash-X . thornado , developed primarily for simulations of core-collapse supernovae (CCSNe), employs a spectral, six-species two-moment formulation with algebraic closure and special-relativistic observer corrections accurate to $\\mathcal{O}(v/c)$ , and uses discontinuous Galerkin (DG) methods for phase-space discretization combined with implicit-explicit time stepping. A key development is a nonlinear neutrino\u2013matter coupling algorithm based on nested fixed-point iteration with Anderson acceleration, enabling fully implicit treatment of collisional processes, including energy-coupling interactions such as neutrino\u2013electron scattering and pair production. Coupling to finite-volume (FV) hydrodynamics is achieved through a hybrid DG-FV representation of the fluid variables and operator-split evolution within Flash-X . The implementation is verified using basic transport tests with idealized opacities and relaxation and deleptonization problems with tabulated microphysics. Spherically symmetric CCSN simulations demonstrate accuracy and robustness of the coupled scheme, including close agreement with the CCSN simulation code Chimera . An axisymmetric CCSN simulation further demonstrates the viability of DG-based neutrino transport for multidimensional supernova modeling within Flash-X . thornado \u2019s neutrino-transport solver is GPU-enabled using OpenMP offloading or OpenACC, and all CCSN applications included in this work use the GPU implementation. Together, these results establish a foundation for future enhancements in physics fidelity, numerical algorithms, and computational performance, for increasingly realistic large-scale CCSN simulations.", "The detection and characterization of post-merger gravitational wave signals from binary neutron star mergers remains challenging with current ground-based detectors. We present a convolutional neural network framework designed for real-time detection and multi-mode frequency extraction of post-merger signals, achieving an inference latency of $3.0\\,\\mathrm{ms}$ and a frequency accuracy of $48.6\\,\\mathrm{Hz}$ on the direct-comparison subsets ( $53\\,\\mathrm{Hz}$ on the comprehensive test set). The framework is validated on realistic LIGO O4 detector noise including authentic GravitySpy glitch morphologies, demonstrating ROC AUC of 0.999999 and 99.998% detection efficiency at 1% false alarm rate. These exceptional performance metrics arise from an aggressive training augmentation strategy that exposes the network to artificially challenging conditions, enabling robust generalization to our synthetic O4 detector noise model. We compare performance against a simplified matched filtering baseline using Lorentzian templates ( $23\\times$ more accurate despite a $4.7\\times$ computational overhead) and Bayesian parameter estimation (1.2 million $\\times$ faster), establishing complementary trade-offs in the analysis landscape. While current O4 sensitivity limits post-merger detections to $\\sim$ 20 Mpc ( $\\sim$ 1 detection per century), this methodology provides essential infrastructure for third-generation detectors (Einstein Telescope, Cosmic Explorer) where post-merger detection will become routine with annual detection rates exceeding 100 events. Our validation framework identifies expected behavior in uncertainty scaling that reflects realistic training constraints rather than idealized Fisher information limits, demonstrating honest assessment practices for machine learning applications in gravitational wave astronomy.", "# Exploring Metal Additive Manufacturing in Martian Atmospheric Environments Exploring Metal Additive Manufacturing in Martian Atmospheric Environments Zane Mebruer, Wan Shou* Department of Mechanical Engineering, University of Arkansas, AR 72701, USA *Corresponding author: wshou@uark.edu Abstract In-space manufacturing is essential for achieving long-term planetary colonization, particularly on Mars, where material transport from Earth is both costly and logistically restrictive. Traditional subtractive manufacturing methods are highly equipment-, energy-, and material-intensive, making additive manufacturing (AM) a more practical and sustainable alternative for extraterrestrial production. Among various AM technologies, selective laser melting (SLM) stands out due to its exceptional versatility, precision, and capability to produce dense metallic parts with complex geometries. However, conventional SLM processes rely heavily on inert argon environments to prevent oxidation and ensure high-quality part formation\u2014conditions that are difficult to reproduce on Mars. This study investigates the feasibility of using carbon dioxide (CO2)\u2014which makes up over 95% of the Martian atmosphere", "# Calorimetric Measurement of the Surface Energy of Enstatite, MgSiO$_3$ Calorimetric Measurement of the Surface Energy of Enstatite, MgSiO3 Megan A. Householder, Tamilarasan Subramani, Kristina Lilova, James R. Lyons, Rhonda M. Stroud, and Alexandra Navrotsky* Cite This: https://doi.org/10.1021/acs.jpcc.3c04211 Read Online ACCESS Metrics & More Article Recommendations * s\u0131 Supporting Information ABSTRACT: Surface thermodynamics of minerals influence their properties and occurrence in both terrestrial and planetary systems. Using high-temperature oxide melt solution calorimetry, we report the first direct measurement of the surface energy of enstatite, MgSiO3. Enstatite nanoparticles of different sizes were synthesized using the sol\u2212gel method, characterized with X-ray diffraction, thermal analysis, infrared spectroscopy, surface area measurements, and electron microscopy. The materials consist of crystallites with sizes of \u223c10\u221220 nm, which are agglomerated into larger nanoparticles. Thus, both surface and interface terms contribute to the measured enthalpies. Analysis based on calorimetry and calculated surface and interface areas gives the surface enthalpy of enstatite as 4.79 \u00b1", "The life-cycle, structure, and dynamics of the interstellar medium (ISM) is regulated by turbulence. Complex physical processes, including supernova (SN) explosions, shear, and gravitational collapse, drive and maintain turbulence, but it is still an open question what turbulence driving mode is primarily excited by these different mechanisms. The turbulence driving parameter, $b$ , can be used to quantify the ratio of solenoidal to compressive modes in the acceleration field that drives the turbulence. Compressive driving is characterised by $b\\sim 1$ , while purely solenoidal driving gives $b\\sim 0.3$ . To quantify the turbulence in the galactic ISM, we investigate the time evolution of $b$ , as well as the turbulent Mach number, and plasma $\\beta$ (thermal-to-magnetic pressure ratio), and its correlation with star formation in the magnetised warm neutral medium (WNM) of the TIGRESS shearing-box simulations of a $\\sim$ kpc-sized patch of a Milky-Way-like galaxy, over a $100\\,\\mathrm{Myr}$ time period ( $\\sim$ half an orbital time). In this simulation the turbulence is driven by a combination of shear, gravitational collapse, and star formation feedback in the form of radiation and SNe. We find that the turbulence driving parameter fluctuates in time between $b\\sim 0.4$ and $b\\sim 1$ . We find a time-dependent correlation of $b$ with star formation activity, such that high star formation rates follow about one turbulent turnover time ( $\\sim 10\\,\\mathrm{Myr}$ ) after phases of highly compressive driving ( $b>0.5$ ). About $20\\,\\mathrm{Myr}$ after the peak in star formation, type-B SN feedback drives up the WNM fraction and turbulent Mach numbers, and reduces plasma $\\beta$ and the driving to $b\\sim 0.4-0.5$ .", "Context. Quantitative spectroscopy of luminous blue stars relies on detailed non-LTE model atmospheres whose increasing physical realism makes direct, iterative analyses computationally demanding. Aims. We introduce MAUI (Machine-learning Assisted Uncertainty Inference), a statistical framework designed for efficient Bayesian inference of stellar parameters using emulator-based spectral models. Methods. MAUI employs Gaussian-process-based emulators trained on a limited set of non-LTE simulations, combined with Markov Chain Monte Carlo (MCMC) sampling to explore posterior distributions. We validate the approach with recovery experiments and demonstrate it on Galactic late-type O dwarf and early-type B dwarf/subgiant stars. Results. The emulator reproduces the predictions of full atmosphere models within quoted uncertainties while reducing computational cost by orders of magnitude. Posterior distributions are well calibrated, with conservative coverage across all stellar parameters. Conclusions. Emulator-driven Bayesian inference retains the accuracy of classical analyses at a fraction of the computational expense, enabling posterior sampling that would be prohibitive with direct model evaluations. This positions emulators as a practical tool for high-fidelity spectroscopy of massive stars as atmosphere models grow more demanding.", "This paper introduces a novel technique to preserve spectral features in lossy compression based on a novel fast Fourier correction algorithm for regular-grid data. Preserving both spatial and frequency representations of data is crucial for applications such as cosmology, turbulent combustion, and X-ray diffraction, where spatial and frequency views provide complementary scientific insights. In particular, many analysis tasks rely on frequency-domain representations to capture key features, including the power spectrum of cosmology simulations, the turbulent energy spectrum in combustion, and diffraction patterns in reciprocal space for ptychography. However, existing compression methods guarantee accuracy only in the spatial domain while disregarding the frequency domain. To address this limitation, we propose an algorithm that corrects the errors produced by off-the-shelf \u201cbase\u201d compressors such as SZ3, ZFP, and SPERR, thereby preserving both spatial and frequency representations by bounding errors in both domains. By expressing frequency-domain errors as linear combinations of spatial-domain errors, we derive a region that jointly bounds errors in both domains. Given as input the spatial errors from a base compressor and user-defined error bounds in the spatial and frequency domains, we iteratively project the spatial error vector onto the regions defined by the spatial and frequency constraints until it lies within their intersection. We further accelerate the algorithm using GPU parallelism to achieve practical performance. We validate our approach with datasets from cosmology simulations, X-ray diffraction, combustion simulation, and electroencephalography demonstrating its effectiveness in preserving critical scientific information in both spatial and frequency domains. An open source GPU implementation of our algorithm is available at https://github.com/rcrcarissa/FFCz .", "Active galactic nuclei (AGN) hosting water megamaser disks provide precise geometric measurements of black hole mass, distance, maser disk orientation, and dynamical center. In anticipation of space-based very long baseline interferometry, these systems offer a path to black hole shadow (BHS) imaging beyond Sgr A* and M87*. We present new Submillimeter Array continuum observations of water megamaser galaxies, supplemented by archival ALMA and VLA measurements, to assess whether their AGN cores are bright enough for BHS-scale imaging. For a 21-source parent sample, we map the predicted BHS diameters of systems with published SMBH masses to submillimeter/millimeter (submm-mm) baseline requirements, estimate AGN core flux densities at 230 GHz while bounding thermal dust and extended-jet contamination and checking whether variability could affect the continuum estimates, and evaluate the astrometric precision required to detect spin-dependent BHS offsets for NGC 4258. NGC 4258 is the only source resolvable on Earth-L2 baselines; other targets require longer baselines approaching Earth-L4/L5 distances, and only a handful have $S_{230}\\gtrsim 10$ mJy beam -1 . We also find a submillimeter excess in NGC 4258, suggesting that its disk remains geometrically thin to $\\lesssim 100$ Schwarzschild radii before transitioning to an advection-dominated flow. Even for maximal spin, the formal 230 GHz BHS centroid precision is not the limiting term: the measurement would require locating the 22 GHz water maser dynamical center and registering it to the 230 GHz BHS image roughly seventy times more precisely than current maser astrometry allows, making the spin-offset measurement infeasible with present data.", "The increasing complexity and scale of cosmological N-body simulations, driven by astronomical surveys like Euclid, call for a paradigm shift towards more sustainable and energy-efficient high-performance computing (HPC). The rising energy consumption of supercomputing facilities poses a significant environmental and financial challenge. In this work, we build upon a recently developed GPU implementation of PINOCCHIO , a widely-used tool for the fast generation of dark matter (DM) halo catalogues, to investigate energy consumption. Using a different resource configuration, we confirmed the time-to-solution behavior observed in a companion study, and we use these runs to compare time-to-solution with energy-to-solution. By profiling the code on various HPC platforms with a newly developed implementation of the Power Measurement Toolkit (PMT), we demonstrate an $8\\times$ reduction in energy-to-solution and $8\\times$ speed-up in time-to-solution compared to the CPU-only version. Taken together, these gains translate into an overall efficiency improvement of up to $64\\times$ . Our results show that the GPU-accelerated PINOCCHIO not only achieves substantial speed-up, making the generation of large-scale mock catalogues more tractable, but also significantly reduces the energy footprint of the simulations. This work represents an step towards \u201cgreen-aware\u201d scientific computing in cosmology, proving that performance and sustainability can be simultaneously achieved.", "In our previous paper, we developed a technique for identifying pulsar candidates in interferometric radio images using their distinctive scintillation signatures. Building on this technique, the present study simulates a pulsar population using the PsrPopPy Python module to investigate the technique\u2019s limitations and detection capabilities. Among pulsars detectable exclusively by this technique, 50% have duty cycles exceeding the mean value of 0.09 observed in time-domain detections. Our pulsar population simulations revealed a set of observational parameters that optimize pulsar detection. An observation frequency of 1420 MHz and a channel width of 10 kHz emerge as the optimal configuration to maximize the pulsar detection efficiency. By applying a scintillation-based technique to future radio telescopes like DSA-2000, we can detect 56% of normal pulsars and 84% of MSPs in addition to those detected using non-imaging, time-domain surveys. These detected pulsars cannot be verified by time-domain searches.", "Optical aberrations and instrument resolution can affect the observed morphological properties of features in the solar atmosphere. However, little work has been done to study the effects of spatial resolution on the dynamical processes occurring in the Sun\u2019s atmosphere. In this work, owing to the availability of high-resolution observations of a magnetic pore captured with the Interferometric BIdimensional Spectrometer mounted at the Dunn Solar Telescope, we studied the impact of the diffraction limit and the sampling of an instrument on line-of-sight Doppler velocity oscillations. We reported a noticeable shift in the dominant frequency band from $5$ to $3$ mHz, as both the angular and detector resolutions of the instruments were degraded. We argue that the observed behaviour is a result of the increased contamination of straylight from neighbouring quiet Sun regions, masking the true behaviour of umbral oscillations. These results suggest that the wave energy contributions reported in the literature and based on low-resolution instrumentation may be fundamentally underestimated. As we move into the era of high-resolution instrumentation such as DKIST and MUSE, this work will offer a critical baseline for interpreting new observations, especially in terms of distinguishing true dynamic behaviours from artefacts introduced by instrument-related limitations.", "# Experiments in binary evolution ## 1 Studying binary evolution with volume-complete samples The majority of stars more massive than the Sun is found in binary or multiple star systems and also less massive stars have significant multiplicity fractions ( $\\sim 25\\%$ ). A significant fraction of the systems is close enough that binary interactions will happen at some point of their evolution (Offner et al. 2023, ASP Conf. Ser., 534, 275). These interactions are in most cases initiated, when a star expands as it evolves. As soon as the radius of this star $R\\_{\\rm donor}$ exceeds it\u2019s Roche radius $R\\_{\\rm Roche}$ , mass transfer to the companion starts (Roche Lobe Overflow, RLOF). What happens next, is a crucial topic in theoretical stellar astrophysics (Marchant & Bodensteiner 2024, ARA&A, 62, 21; Mathieu & Pols 2025, ARA&A, 63, 467). The main paradigm is that the mode of mass transfer is dictated by the mass ratio between the donor and the accretor star $q=M\\_{\\rm donor}/M\\_{\\rm accretor}$ as well as the internal structure of the donor (e.g. Temmink et al. 2023, A&A, 669, A45). For small values of $q$ , mass transfer is predicted to be stable. However, it is unclear, how much mas", "We identify and correct for small but coherent instrumental drifts in seven years of radial velocity data from the EXtreme PREcision Spectrograph ( EXPRES ). The systematics are most notable for the six months before and after 2022 January, when EXPRES experienced larger temperature variations, and we see a systematic trough-to-peak amplitude of 2.8 m s -1 in the radial velocities. This is large enough to mimic or obscure planetary signatures. To isolate and correct these effects, we develop a suite of diagnostics that track two-dimensional \u00e9chellogram shifts, scalings, and rotation, as well as line bisector spans (LBS) derived from laser frequency comb (LFC) lines. By combining these empirical tracers with instrument telemetry in a multi-dimensional regression, we reduce the EXPRES instrument trend traced with solar RVs from an RMS of 1.32 m s -1 to 0.43 m s -1 , a 67% improvement, and the aggregate of twelve chromospherically quiet stars show a 26% reduction in velocity scatter. Our injection\u2013recovery simulations further demonstrate a doubling in sensitivity to low-amplitude planetary signals after correction. When applied to the stellar time series of $\\rho$ Coronae Borealis ( $\\rho$ CrB), the correction removes a spurious planet d signal, restoring the integrity of the data. These results highlight the need for long-term monitoring and multi-dimensional calibration diagnostics on the path toward true centimeter-per-second precision in next-generation EPRV instruments.", "# Validation of Satellite Lifetime Predictions at Leonid Space Validation of Satellite Lifetime Predictions at Leonid Space \u2013 Rev. 1.1 \u00a92026 Leonid Space Approved for Public Release 1 Validation of Satellite Lifetime Predictions at Leonid Space Rev 1.1, authored 2026-04-10 by Scott Shambaugh leonidspace.com/contact Executive Summary Leonid Space provides real-time satellite lifetime predictions for low Earth orbit missions. This report validates our prediction pipeline through comprehensive backtesting against 934 satellites that deorbited between 1961 and 2024, demonstrating operational readiness for commercial deployment. Methodology We backtested against previously deorbited non-maneuvering objects (debris, rocket bodies, and unknown), using TLE data from Space-Track and space weather records spanning six solar cycles. We simulated the final year of flight, and compared predicted deorbit dates against actuals. Our three-stage validation approach progressively removed hindsight to arrive at fully predictive operational conditions. Key Results This is the first large-scale validation of lifetime predictions using forecasted space weather \u2013 all other studies have done so on small s", "Topology forms a cornerstone in modern condensed matter and statistical physics, offering a new framework to classify the phases and phase transitions beyond the traditional Landau paradigm. However, it is widely believed that topological properties are destroyed when the bulk energy gap closes, making it highly nontrivial to consider topology in gapless quantum critical systems. To address these challenges, recent advancements have sought to generalize the notion of topology to systems without a bulk energy gap, including quantum critical points and critical phases, collectively referred to as gapless symmetry-protected topological states. Extending topology to gapless quantum critical systems challenges the traditional belief in condensed matter physics that topological edge states are typically tied to the presence of a bulk energy gap. Furthermore, it suggests that topology plays a crucial role in classifying quantum phase transitions even if they belong to the same universality class, fundamentally enriching the textbook understanding of phase transitions. Given its importance, here we give a pedagogical review of the current progress of topological physics in quantum critical systems. We introduce the topological properties of quantum critical points and generalize them to stable critical phases, both for noninteracting and interacting systems. Additionally, we discuss further generalizations and future directions, including higher dimensions, nonequilibrium phase transitions, and realizations in modern experiments.", "We study Anderson localisation on high-dimensional graphs with spatial structure induced by long-ranged but distance-dependent hopping. To this end, we introduce a class of models that interpolate between the short-range Anderson model on a random regular graph and fully connected models with statistically uniform hopping, by embedding a random regular graph into a complete graph and allowing hopping amplitudes to decay exponentially with graph distance. The competition between the exponentially growing number of neighbours with graph distance and the exponentially decaying hopping amplitude positions our models effectively as power-law hopping generalisation of the Anderson model on random regular graphs. Using a combination of numerical exact diagonalisation and analytical renormalised perturbation theory, we establish the resulting localisation phase diagram emerging from the interplay of the lengthscale associated to the hopping range and the onsite disorder strength. We find that increasing the hopping range shifts the localisation transition to stronger disorder, and that beyond a critical range the localised phase ceases to exist even at arbitrarily strong disorder. Our results indicate a direct Anderson transition between delocalised and localised phases, with no evidence for an intervening multifractal phase, for both deterministic and random hopping models. A scaling analysis based on inverse participation ratios reveals behaviour consistent with a Kosterlitz\u2013Thouless\u2013like transition with two-parameter scaling, in line with Anderson transitions on high-dimensional graphs. We also observe distinct critical behaviour in average and typical correlation functions, reflecting the different scaling properties of generalised inverse participation ratios.", "Recent advances in quantum simulators allow direct experimental access to ensembles of pure states generated by measuring part of an isolated quantum many-body system. These projected ensembles encode fine-grained information beyond thermal expectation values and provide a new window into quantum thermalization. In chaotic dynamics, projected ensembles exhibit universal statistics governed by maximum-entropy principles, known as deep thermalization. At infinite temperature this universality is characterized by Haar-random ensembles. More generally, physical constraints such as finite temperature or conservation laws lead to Scrooge ensembles, which are maximally entropic distributions of pure states consistent with these constraints. Here we introduce Scrooge $k$ -designs, which approximate Scrooge ensembles, and use this framework to sharpen the conditions under which Scrooge-like behavior emerges. We first show that global Scrooge designs arise from long-time chaotic unitary dynamics alone, without measurements. Second, we show that measuring a complementary subsystem of a scrambled global state drawn from a global Scrooge $2k$ -design induces a local Scrooge $k$ -design. Third, we show that a local Scrooge $k$ -design arises from an arbitrary entangled state when the complementary system is measured in a scrambled basis induced by a unitary drawn from a Haar $2k$ -design. These results show that the resources required to generate approximate Scrooge ensembles scale only with the desired degree of approximation, enabling efficient implementations. Complementing our analytical results, numerical simulations identify coherence, entanglement, non-stabilizerness, and information scrambling as essential ingredients for the emergence of Scrooge-like behavior. Together, our findings advance theoretical explanations for maximally entropic, information-stingy randomness in quantum many-body systems.", "# Bridging Commutant and Polynomial Methods for Hilbert Space Fragmentation Bridging Commutant and Polynomial Methods for Hilbert Space Fragmentation Bo-Ting Chen,1, \u2217Yu-Ping Wang,2, \u2217and Biao Lian1 1Department of Physics, Princeton University, Princeton, New Jersey 08544, USA 2Department of Physics, Stony Brook University, Stony Brook, New York 11790, USA (Dated: January 5, 2026) A quantum model exhibits Hilbert space fragmentation (HSF) if its Hilbert space decomposes into exponentially many dynamically disconnected subspaces, known as Krylov subspaces. A model may however have different HSFs depending on the method for identifying them. Here we establish a connection between two vastly distinct methods recently proposed for identifying HSF: the commutant algebra (CA) method and integer characteristic polynomial factorization (ICPF) method. For a Hamiltonian consisting of operators admitting rational number matrix representations, we prove a theorem that, if its center of commutant algebra have all eigenvalues being rational, the HSF from the ICPF method must be equal to or finer than that from the CA method. We show that this condition is satisfied by most known models exhibitin", "We show that the functional appearing in the celebrated Parisi formula for the free energy of the Sherrington-Kirkpatrick model can be found from the incremental free energy obtained by Cavity Method if one assumes that the state is a product of independent Random Energy models.", "We introduce a method to compute the reweighted path ensemble by combining transition interface sampling simulations conditioned on different collective variables. The approach is based on the Multistate Bennett Acceptance Ratio (MBAR) methodology applied to entire trajectories. Illustrating the technique with simple 2D potential models and a more complex host-guest system, we show that the statistics can significantly improve compared to a straightforward combination.", ". We study the ergodic properties of a unitary Floquet dynamics arising from the repeated application of a translationally-invariant Clifford Quantum Cellular Automata to an infinite system of qubits in $d$ dimensions. One expects that if the QCA does not exhibit any periodicity, a generic initial state of qubits will thermalize, that is, approach the infinite-temperature state. We show that this is true for many classes of states, both pure and mixed. In particular, this is true for all initial states that are short-range entangled and close to the equilibrium state. We also point out a subtle distinction between weak and strong thermalization.", "In this work, we provide a self-contained derivation of the spin-operator matrix elements in the fermionic basis, for the critical periodic Ising chain at a generic system length $N\\in 2Z_{\\geq 2}$ . The approach relies on the near-Cauchy property of certain matrices formed by the Toeplitz symbols in the critical model, and leads to a few square-root products for the leg functions. The square root products allow simple integral representations, that further reduce to the Binet\u2019s second integral and its generalization by Hermite, in the finite volume scaling limit . This leads to product formulas for the spin operator matrix elements in the scaling limit, providing explicit expressions for the spin-operator form factors of the Ising CFT in the fermionic basis, that were computed iteratively in . They are all rational numbers up to $\\sqrt{2}$ . We also determine the normalization factor of the spin-operator and show explicitly how the coefficient $G(\\frac{1}{2})G(\\frac{3}{2})$ appear through a ground state overlap. Moreover, by expanding the spin-spin two point correlator in the fermionic basis, we observed a Fredholm determinant identity $\\displaystyle\\det(1+{\\cal K}_{ij}(w))_{1\\leq i,j\\leq\\infty}=(1-w)^{-\\frac{1}{8}},\\ \\ {\\cal K}_{ij}(w)=\\frac{1}{\\pi}\\frac{i}{i+j}\\frac{\\Gamma(i+\\frac{1}{2})\\Gamma(j+\\frac{1}{2})}{\\Gamma(i+1)\\Gamma(j+1)}w^{j}\\ ,$ (1) which allows to show the convergence of the rescaled two-point correlator to the CFT version on a cylinder.", "Quantum magic, quantified by nonstabilizerness, measures departures from stabilizer structure and underlies potential quantum speedups. We introduce an efficient classical framework for computing stabilizer R\u00e9nyi entropies and stabilizer nullity of generic $N$ -qubit wavefunctions. The method combines the fast Walsh-Hadamard transform with an exact partition of Pauli operators, reducing the average cost per sampled Pauli string from $\\mathcal{O}(2^{N})$ to $\\mathcal{O}(N)$ . We further develop a Monte Carlo estimator with Clifford preconditioning and find that the required number of samples shows no visible growth with $N$ in our benchmarks. Applying the method to $T$ -doped random Clifford circuits, we identify the scrambling ratio $\\eta$ (Clifford gates per $T$ gate) as the key parameter governing magic growth. Each $T$ gate approaches its dilute-limit nonstabilizerness power with only modest Clifford scrambling. Our approach enables quantitative studies of magic in highly entangled states and long-time nonequilibrium dynamics.", "We investigate the non-Hermitian Kitaev chain with non-reciprocal hopping amplitudes and asymmetric superconducting pairing. We work out the symmetry structure of the model and show that particle-hole symmetry (PHS) is preserved throughout the entire parameter regime. As a consequence of PHS, the topological phase transition point of a finite open chain coincides with that of the periodic (infinite) system. By explicitly constructing the zero-energy wave functions (Majorana modes), we show that Majorana modes necessarily occur as reciprocal localization pairs accumulating on opposite boundaries, whose combined probability density exhibits an exact cancellation of the non-Hermitian skin effect for the zero energy modes. Excited states, by contrast, generically display skin-effect localization, with particle and hole components accumulating at opposite ends of the system. At the level of bulk topology, we further construct a $\\mathbb{Z}_{2}$ topological invariant in restricted parameter regimes that correctly distinguishes the topological and trivial phases. Finally, we present the topological phase diagram of the non-Hermitian Kitaev chain across a broad range of complex parameters and delineate the associated phase boundaries.", "Magnetic skyrmions are nanometric swirling spin textures that exhibit remarkable stability at finite temperatures, making them promising candidates for spintronic applications. Achieving controllable stability and transitions between distinct topological structures is crucial for practical implementations. In this work, we investigate the effect of uniaxial mechanical strain on a magnetic model on the kagome lattice, focusing on skyrmion stability and emergent topological phases. To this end, we consider a Heisenberg model that includes exchange interactions and both in-plane and out-of-plane Dzyaloshinskii-Moriya interactions. Using a combination of Spin-Lattice Dynamics and Monte Carlo simulations, we explore uniaxial strain variations in the range of $-10\\%$ to $10\\%$ , showing important effects on the phase diagram. For compressive strain, we find that the density of skyrmions in the skyrmion gas (SkG) phase can be tuned and that the stability of this phase extends to higher temperatures. Tensile strain, in contrast, reduces the number of skyrmions and promotes transitions to other magnetic states. Within this regime, strain levels of about ( $\\sim 4-6\\%$ ) lead to a change in topological charge, turning skyrmions ( $Q=-1$ ) into antiskyrmions ( $Q=+1$ ). We also examine how strain affects other phases commonly appearing in skyrmion-hosting systems, such as the helical and fully polarized states, showing that mechanical deformation alters their stability and characteristic properties. Finally, we compare these results with the strain response of a more conventional skyrmion model, in order to clarify the role of the different interactions involved. Our results identify strain as an experimentally accessible route for engineering topological spin textures.", "We study the influence of dissipation on the Ising-Gamma model. Through observables such as ground-state energy, order parameters, entanglement entropy, etc., we identify each phase region and provide the global phase diagram of the system. The results show that the region of the spiral phase will continuously expand with the increase of dissipation, gradually squeezing the original paramagnetic and antiferromagnetic phase regions. Remarkably, unlike the conservative system, the introduction of dissipation will cause two spiral phases with opposite chirality to emerge simultaneously in the system, which provides a possibility for the manipulation of spiral chirality in cold atomic experiments. Moreover, we reveal the dependence mechanism of the transformation between these two spiral phases with distinct chirality on the strength of the relative coefficient of off-diagonal Gamma interactions in the Ising-Gamma model. Since both the relevant order parameters and dissipation can be well controlled within a detectable range, these phenomena can be observed in ultracold atomic experiments.", "# A Unified Computational Framework for Two Dimensional Diffusion Limited Aggregation via Finite-Size Scaling, Multifractality, and Morphological Analysis A Unified Computational Framework for Two-Dimensional Diffusion-Limited Aggregation via Finite-Size Scaling, Multifractality, and Morphological Analysis Satish Prajapati1+ 1 Department of Ceramic Technology, Government College of Engineering and Ceramic Technology, Kolkata, West Bengal 700010, India. + Corresponding author: iamsatish.gcect.ac@gmail.com Abstract Diffusion-Limited Aggregation (DLA), the canonical model for non-equilibrium fractal growth, emerges from the simple rule of irreversible attachment by random walkers. Despite four decades of study, a unified computational framework reconciling its stochastic algorithm, universal fractal dimension, multifractal growth measure, and finite-size effects remains essential for applications from materials science to geomorphology. Through large-scale simulations (clusters up to N= 106 particles) in two dimensions, we perform a tripartite analysis: (1) We establish a definitive finite-size scaling collapse, extracting the universal fractal dimension D= 1.712 \u00b1 0.015 and identifyi", "Dense Associative Memory networks (DenseAMs) unify several popular paradigms in Artificial Intelligence (AI), such as Hopfield Networks, transformers, and diffusion models, while casting their computational properties into the language of dynamical systems and energy landscapes. This formulation provides a natural setting for studying thermodynamics and computation in neural systems, because DenseAMs are simultaneously simple enough to admit analytic treatment and rich enough to implement nontrivial computational function. Aspects of these networks have been studied at equilibrium and at zero temperature, but the thermodynamic costs associated with their operation out of equilibrium are largely unexplored. Here, we define the thermodynamic entropy production associated with the operation of such networks, and study polynomial DenseAMs at intermediate memory load. At large system sizes and intermediate and low load, we use dynamical mean field theory to characterize out-of-equilibrium properties, work requirements, and memory transition times when driving the system with corrupted memories. We characterize a failure mode of higher order networks not observed at zero temperature. Further, we develop a method for calculating work and power costs in the mean field limit. Finally, we find tradeoffs between entropy production, memory retrieval accuracy, and operation speed.", "Random walk models with log-normal outcomes fit local market observations remarkably well. Yet interconnected or recursive structures - layered derivatives, leveraged positions, iterative funding rounds - periodically produce power-law distributed events. We show that the transition from log-normal to power-law dynamics requires only three conditions: randomness in the underlying process, rectification of payouts, and iterative feed-forward of expected values. Using an infinite option-on-option chain as an illustrative model, we derive a critical volatility threshold at $\\sigma^{\\*}=\\sqrt{2\\pi}\\approx 250.66\\%$ for the unconditional case. With selective survival - where participants require minimum returns to continue - the critical threshold drops discontinuously to $\\sigma\\_{\\text{th}}^{\\*}=\\sqrt{\\pi/2}\\approx 125.3\\%$ , and can decrease further with higher survival thresholds. The resulting outcomes follow what we term the Critical Volatility (V\\*) Distribution - a power-law whose exponent admits closed-form expression in terms of survival pressure and conditional expected growth. The result suggests that fat tails may be an emergent property of iterative log-normal processes with selection rather than an exogenous feature.", "We demonstrate that the thermodynamic friction metric governing dissipation in slowly driven continuous-time Markov chains is equivalent to the commute-time embedding and the resistance distance. This equivalence yields complementary insights: The commute-time embedding demonstrates the intrinsic cost of transporting probability across dynamical bottlenecks, while the resistance distance maps thermodynamic dissipation to Joule heating in an electrical network. We further demonstrate that the linear-response thermodynamic distance is a discrete $L^{2}$ -Wasserstein optimal transport cost evaluated along paths of equilibrium distributions, extending a continuous-state correspondence to discrete networks. This conceptual synthesis of linear-response thermodynamics, random walks on graphs, electrical circuits, and optimal-transport theory connects independently developed geometric frameworks, reduces complex metric calculations to simple circuit algebra, and provides a clear physical picture of dissipation as the energetic cost of routing probability through the state space network.", "Cellular automata (CA) are discrete-time dynamical systems with local update rules on a lattice. Despite their elementary definition, CA support a wide spectrum of macroscopic phenomena central to statistical physics: equilibrium and nonequilibrium phase transitions, transport and hydrodynamic limits, kinetic roughening, self-organized criticality, and complex spatiotemporal correlations. This survey focuses on three tightly connected themes. (i) We present a structural view of CA as shift-commuting maps on configuration spaces, emphasizing rule complexity, reversibility, and conservation laws (including discrete continuity equations). (ii) We organize transport in CA into ballistic, diffusive, and anomalous regimes, and connect microscopic currents to macroscopic laws through Green\u2013Kubo formulas, scaling theory, and universality classes. (iii) We develop correlation-based methods\u2014from structure factors and response formulas to computational mechanics and data-driven inference\u2014that diagnose regimes and enable coarse-graining.", "# From Random Walks to Thermal Rides: Universal Anomalous Transport in Soaring Flights From Random Walks to Thermal Rides: Universal Anomalous Transport in Soaring Flights J \u0301er \u0301emie Vilpellet,1, 2 Alexandre Darmon,3 and Michael Benzaquen1, 2, 4, \u2217 1EconophysiX Lab, Institut Louis Bachelier, 28 Pl. de la Bourse, Palais Brongniart, 75002 Paris, France 2LadHyX UMR CNRS 7646, \u0301Ecole Polytechnique, Institut Polytechnique de Paris, 91128 Palaiseau Cedex, France 3Art in Research, 33 rue Censier, 75005 Paris, France 4Capital Fund Management, 23 Rue de l\u2019Universit \u0301e, 75007 Paris, France (Dated: January 6, 2026) Cross-country soaring flights rely on intermittent atmospheric updrafts to cover long distances, producing trajectories that alternate between rapid relocation and local exploration. From a large dataset of paraglider, hang glider, and sailplane flights, we uncover a universal transport law: beyond short ballistic times, horizontal motion is persistently sub-ballistic, with a Hurst exponent \u22480.88 largely independent of aircraft type. Phase-resolved analysis using a probabilistic segmentation method shows that this scaling arises from the fundamentally intermittent, two-dimensional", "We investigate obtaining intermediate quantum states during the quantum annealing process to address the limitation of the linear weighted sum method in multi-objective optimization, which inherently fails to reach non-convex regions of the Pareto front. We validate this approach through physical experiments utilizing quench-based readout and numerical simulations assuming ideal mid-anneal measurements. Both methods consistently demonstrate a clear trade-off where earlier timing enhances diversity of the solutions, whereas later timing ensures convergence to non-dominated solutions. Notably, a practical compromise timing balances both metrics. The qualitative agreement between practical quench and ideal simulation indicates the potential of accessing the intermediate states for comprehensive Pareto front exploration.", "Self-propelled particles rarely move in straight lines; environmental interactions, shape asymmetry, and intrinsic torques generically induce curved or fluctuating trajectories. In biological and synthetic systems, this curvature often coexists with directional sensing and non-reciprocal interactions. Motivated by this, we explore the collective dynamics of chiral intelligent active Brownian particles (iABPs) that combine polar alignment with vision-based sensing. By varying the ratio of alignment to visual maneuverability, the vision angle, and the reduced chirality $(\\omega/D_{r})$ , we construct a phase diagram exhibiting diverse collective states: spinners, vortices, ripples, worm-like swarms, rotary clusters, and irregular aggregates. Chirality critically governs their morphology: high chirality yields dilute phases, while moderate to low chirality produces cohesive yet dynamic patterns. Ripple loops emerge as a distinct state, characterized by expanding ring-like motion driven by outward torques and sustained only when both particle number and visual maneuverability are large. Structural and dynamical measures, including polarization, pair correlations, mean-square displacement, and orientation correlations, reveal clear signatures distinguishing these phases. Overall, our results show how chirality, non-reciprocal perception, and alignment together generate collective states inaccessible to non-chiral systems, with implications for chiral active matter in biological and synthetic contexts.", "There are a wide range of first passage time (FPT) problems in the physical and life sciences that can be modelled in terms of a Brownian particle binding to a reactive surface (target) and initiating a downstream event (absorption). However, prior to absorption, the particle may undergo several rounds of surface attachment (adsorption), detachment (desorption) and diffusion. Alternatively, the surface may be stochastically gated so that absorption can only occur when the gate is open. In both cases one can view each return to the surface as a renewal event. In this paper we develop a renewal theory for stochastically gated FPT problems along analogous lines to previous work on adsorption/desorption processes. We proceed by constructing a renewal equation that relates the joint probability density for particle position and the state of a gate (or multiple gates) to the probability density and FPT density for a totally absorbing (non-gated) boundary. This essentially decomposes sample paths into an alternating sequence of bulk diffusion and instantaneous adsorption/desorption events, which is terminated when adsorption coincides with an open gate. In order to ensure that diffusion restarts in a state that avoids immediate re-adsorption. we assume that whenever the particle reaches a closed boundary it is instantaneously shifted a distance $\\epsilon$ from the boundary (boundary-induced resetting). We explicitly solve the renewal equation for $\\epsilon>0$ and show how the solution to the original gated FPT problem is recovered in the limit $\\epsilon\\rightarrow 0$ . Through a series of examples, we who how renewal theory proves a general mathematical framework for modelling stochastically gated targets.", "The Jarzynski equality, which relates equilibrium free-energy difference to an average of non-equilibrium work, plays a central role in modern non-equilibrium statistical thermodynamics. In this paper, we study a weaker consequence of this relation, known as Jarzynski\u2019s inequality, which can be formally obtained from the Jarzynski equality via Jensen\u2019s inequality. We identify and analyze several extensions of Jarzynski\u2019s inequality that go beyond its direct derivation from the Jarzynski equality. In particular, we consider chemical systems both in the linear-response regime and away from linear thermodynamics. Furthermore, by employing functional-integral techniques, we extend Jarzynski\u2019s inequality to many-body statistical systems described by quantum field theory. Salient issues, such as connections of the Jarzynski inequality with the maximum work theorem and the Landau\u2013Lifshitz theory of fluctuations, are also discussed.", "The energetics and stability of dislocations, vacancies and, interstitials in the one-component plasma (OCP), where the charges interact with a log potential and move on the curved surface of a cylinder have been investigated numerically. For vacancy\u2013interstitial pairs, the log term of the direct Coulomb attraction and the elastic displacement energy cancel exactly at long distances, resulting in a defect energy of $O(1)$ . The numerical results confirm the predicted asymptotic behavior but also identify critical distances below which pairs evolve to different forms. We have found that bound pairs of dislocations \u2014 created by adding/removing 120 degree \u201dzig-zags\u201d of particles \u2014 have a dependence on their preparation history which is not accounted for in the usual starting point of the KTHNY theory. Furthermore, isolated dislocations, whose presence disrupts crystalline order, have energies of $O(1)$ at some values of $N$ , the number of particles in the system, and therefore will be thermally excited, raising questions about the applicability of standard KTHNY theory to the OCP, and supporting older suggestions that there are no phase transitions at all in the two-dimensional OCP.", "The square-lattice $J_{1}$ - $J_{2}$ transverse-field (TF) Ising model was investigated with the exact diagonalization (ED) method. In order to analyze the TF-driven phase transition, we applied the longitudinal-field fidelity susceptibility $\\chi^{(h)}_{F}$ , which is readily evaluated via the ED scheme. Here, the longitudinal field couples with the absolute value of the magnetic moment $|M|$ rather than the raw $M$ so that the remedied fidelity susceptibility exhibits a peak around the critical point; note that the spontaneous magnetization does not appear for the finite-size systems. As a preliminary survey, the modified fidelity susceptibility $\\chi^{(h)}_{F}$ is applied to the analysis of criticality for $J_{2}=0$ , where a number of preceding results are available. Thereby, properly scaling the distance from the multi-criticality, $\\eta=0.5-J_{2}$ , the $\\chi^{(h)}_{F}$ data were cast into the crossover-scaling formula, and the multi-critical exponent for $\\chi_{F}^{(h)}$ is estimated. The result is cross-checked by the numerically evaluated $\\beta$ -function behavior.", "This paper reveals that LLM-powered agents exhibit not only demographic bias (e.g., gender, religion) but also intergroup bias under minimal \u201cus\u201d versus \u201cthem\u201d cues. When such group boundaries align with the agent\u2013human divide, a new bias risk emerges: agents may treat other AI agents as the ingroup and humans as the outgroup. To examine this risk, we conduct a controlled multi-agent social simulation and find that agents display consistent intergroup bias in an all-agent setting. More critically, this bias persists even in human-facing interactions when agents are uncertain about whether the counterpart is truly human, revealing a belief-dependent fragility in bias suppression toward humans. Motivated by this observation, we identify a new attack surface rooted in identity beliefs and formalize a Belief Poisoning Attack (BPA) that can manipulate agent identity beliefs and induce outgroup bias toward humans. Extensive experiments demonstrate both the prevalence of agent intergroup bias and the severity of BPA across settings, while also showing that our proposed defenses can mitigate the risk. These findings are expected to inform safer agent design and motivate more robust safeguards for human-facing agents.", "Generative AI (GenAI) now produces text, images, audio, and video that can be perceptually convincing at scale and at negligible marginal cost. While public debate often frames the associated harms as \u201cdeepfakes\u201d or incremental extensions of misinformation and fraud, this view misses a broader socio-technical shift: GenAI enables synthetic realities ; coherent, interactive, and potentially personalized information environments in which content, identity, and social interaction are jointly manufactured and mutually reinforcing. We argue that the most consequential risk is not merely the production of isolated synthetic artifacts, but the progressive erosion of shared epistemic ground and institutional verification practices as synthetic content, synthetic identity, and synthetic interaction become easy to generate and hard to audit. This paper (i) formalizes synthetic reality as a layered stack (content, identity, interaction, institutions), (ii) expands a taxonomy of GenAI harms spanning personal, economic, informational, and socio-technical risks, (iii) articulates the qualitative shifts introduced by GenAI (cost collapse, throughput, customization, micro-segmentation, provenance gaps, and trust erosion), and (iv) synthesizes recent risk realizations (2023\u20132025) into a compact case bank illustrating how these mechanisms manifest in fraud, elections, harassment, documentation, and supply-chain compromise. We then propose a mitigation stack that treats provenance infrastructure, platform governance, institutional workflow redesign, and public resilience as complementary rather than substitutable, and outline a research agenda focused on measuring epistemic security . We conclude with the Generative AI Paradox : as synthetic media becomes ubiquitous, societies may rationally discount digital evidence altogether, raising the cost of truth for everyday life and for democratic and economic institutions.", "As multi-agent AI systems become increasingly autonomous, evidence shows they can develop collusive strategies similar to those long observed in human markets and institutions. While human domains have accumulated centuries of anti-collusion mechanisms, it remains unclear how these can be adapted to AI settings. This paper addresses that gap by (i) developing a taxonomy of human anti-collusion mechanisms, including sanctions, leniency & whistleblowing, monitoring & auditing, market design, and governance and (ii) mapping them to potential interventions for multi-agent AI systems. For each mechanism, we propose implementation approaches. We also highlight open challenges, such as the attribution problem (difficulty attributing emergent coordination to specific agents), identity fluidity (agents being easily forked or modified), the boundary problem (distinguishing beneficial cooperation from harmful collusion), and adversarial adaptation (agents learning to evade detection). Keywords: Collusion, Multi-Agent AI, AI Safety, Anti-collusion Mechanisms, Human Domains", "\u2018SciHigh: Research Highlight Generation from Scientific Papers\u2019 focuses on the task of automatically generating concise, informative, and meaningful bullet-point highlights directly from scientific abstracts. The goal of this task is to evaluate how effectively computational models can generate highlights that capture the key contributions, findings, and novelty of a paper in a concise form. Highlights help readers grasp essential ideas quickly and are often easier to read and understand than longer paragraphs, especially on mobile devices. The track uses the MixSub dataset , which provides pairs of abstracts and corresponding author-written highlights. In this inaugural edition of the track, 12 teams participated, exploring various approaches, including pre-trained language models, to generate highlights from this scientific dataset. All submissions were evaluated using established metrics such as ROUGE, METEOR, and BERTScore to measure both alignment with author-written highlights and overall informativeness. Teams were ranked based on ROUGE-L scores. The findings suggest that automatically generated highlights can reduce reading effort, accelerate literature reviews, and enhance metadata for digital libraries and academic search platforms. SciHigh provides a dedicated benchmark for advancing methods aimed at concise and accurate highlight generation from scientific writing.", "Prosecutors across Mexico face growing backlogs due to high caseloads and limited institutional capacity. This paper presents a machine learning (ML) system co-developed with the Zacatecas State Prosecutor\u2019s Office to support internal case triage. Focusing on the M\u00f3dulo de Atenci\u00f3n Temprana (MAT)\u2014the unit responsible for intake and early-stage case resolution\u2014we train classification models on administrative data from the state\u2019s digital case management system (PIE) to predict which open cases are likely to finalize within six months. The model generates weekly ranked lists of 300 cases to assist prosecutors in identifying actionable files. Using historical data from 2014 to 2024, we evaluate model performance under real-time constraints, finding that Random Forest classifiers achieve a mean Precision@300 of 0.74. The system emphasizes interpretability and operational feasibility, and we will test it via a randomized controlled trial. Our results suggest that data-driven prioritization can serve as a low-overhead tool for improving prosecutorial efficiency without disrupting existing workflows.", "# Bit-politeia: An AI Agent Community in Blockchain Bit-politeia: An AI Agent Community in Blockchain Xing Yang xing.yang@siat.ac.cn Abstract Current resource allocation paradigms, particularly in academic evaluation, are constrained by inherent limitations such as the Matthew Effect, reward hacking driven by Goodhart's Law, and the trade-off between efficiency and fairness. To address these challenges, this paper proposes \u201cBit-politeia\u201d, an AI agent community on blockchain designed to construct a fair, efficient, and sustainable resource allocation system. In this virtual community, residents interact via AI agents serving as their exclusive proxies, which are optimized for impartiality and value alignment. The community adopts a \u201cclustered grouping + hierarchical architecture\u201d that integrates democratic centralism to balance decision-making efficiency and trust mechanisms. Agents engage through casual chat and deliberative interactions to evaluate research outputs and distribute a virtual currency as rewards. This incentive mechanism aims to achieve incentive compatibility through consensus-driven evaluation, while blockchain technology ensures immutable records of all transactio", "# Measuring University Students Satisfaction with Traditional Search Engines and Generative AI Tools as Information Sources Measuring University Students\u2019 Satisfaction with Traditional Search Engines and Generative AI Tools as Information Sources Authors: Brady D. Lund, Ph.D., Assistant Professor, University of North Texas, College of Information, Information Science, brady.lund@unt.edu Scott J. Warren, Ph.D., Professor, University of North Texas, College of Information, Learning Technologies, scott.warren@unt.edu Zoe A. Teel, Ph.D. Student, University of North Texas, College of Information, Learning Technologies, abbieteel@my.unt.edu Data Availability Statement: The data that support the findings of this study are available from the corresponding author upon reasonable request. Funding Statement: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflict of Interest Disclosure: The authors declare no competing interests\u2014financial or nonfinancial\u2014that could be perceived as influencing the content of this paper. Abstract This study examines university students\u2019 levels of satisfaction with generative artificia", "The integration of Large Language Models (LLMs) into software engineering education has driven the emergence of \u201cVibe Coding,\u201d a paradigm where developers articulate high-level intent through natural language and delegate implementation to AI agents. While proponents argue this approach modernizes pedagogy by emphasizing conceptual design over syntactic memorization, accumulating empirical evidence raises concerns regarding skill retention and deep conceptual understanding. This paper proposes a theoretical framework to investigate the research question: Is Vibe Coding a better way to learn software engineering? We posit a divergence in student outcomes between those leveraging AI for acceleration versus those using it for cognitive offloading. To evaluate these educational trade-offs, we propose the Vibe-Check Protocol (VCP) , a systematic benchmarking framework incorporating three quantitative metrics: the Cold Start Refactor ( $M_{CSR}$ ) for modeling skill decay; Hallucination Trap Detection ( $M_{HT}$ ) based on signal detection theory to evaluate error identification; and the Explainability Gap ( $E_{gap}$ ) for quantifying the divergence between code complexity and conceptual comprehension. Through controlled comparisons, VCP aims to provide a quantitative basis for educators to determine the optimal pedagogical boundary: identifying contexts where Vibe Coding fosters genuine mastery and contexts where it introduces hidden technical debt and superficial competence.", "# The Imperative for Grand Challenges in Computing CCC Whitepaper December 2025 The Imperative for Grand Challenges in Computing Authors William Regli (University of Maryland), Rajmohan Rajaraman (Northeastern University), Daniel Lopresti (Lehigh University), David Jensen (University of Massachusetts, Amherst), Mary Lou Maher (Computing Research Association), Manish Parashar (University of Utah), Mona Singh (Princeton University), Holly Yanco (University of Massachusetts, Amherst) Introduction Computing is now an indispensable component of nearly all technologies and is ubiquitous for vast segments of society. It is also essential to discoveries and innovations in most disciplines. However, while past grand challenges in science have involved computing as one of the tools to address the challenge, these challenges have not been principally about computing. Why has the computing community not yet produced challenges at the scale of grandeur that we see in disciplines such as physics, astronomy, or engineering? How might we go about identifying similarly grand challenges? What are the grand challenges of computing that transcend our discipline's traditional boundaries and have the po", "# PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy Among University Students PDPL Metric: Validating a Scale to Measure Personal Data Privacy Literacy Among University Students Brady D. Lund, Nathan Brown, Ana Roeschley, Gahangir Hossain Abstract Personal data privacy literacy (PDPL) refers to a collection of digital literacy skills related to an individual\u2019s ability to understand, evaluate, and manage the collection, use, and protection of personal data in online and digital environments. This study introduces and validates a new psychometric scale (PDPL Metric) designed to measure data privacy literacy among university students, focusing on six key privacy constructs: perceived risk of data misuse, expectations of informed consent, general privacy concern, privacy management awareness, privacy-utility trade-off acceptance, and perceived importance of data security. A 24-item questionnaire was developed and administered to students at U.S.-based research universities. Principal components analysis confirmed the unidimensionality and internal consistency of each construct, and a second-order analysis supported the integration of all six into a unified PDPL", "The Architecture, Engineering, and Construction (AEC) industry is undergoing rapid digital transformation, producing diverse digital assets such as datasets, computational models, use cases, and educational materials across the built environment lifecycle. However, these resources are often fragmented across repositories and inconsistently documented, limiting their discoverability, interpretability, and reuse in research, education, and practice. This study introduces OpenConstruction , a community-driven open-science ecosystem that aggregates, organizes, and contextualizes openly accessible AEC digital resources. The ecosystem is structured into four catalogs, including datasets, models, use cases, and educational resources, supported by consistent descriptors, curator-led validation, and transparent governance. As of December 2025, the platform hosts 94 datasets, 65 models, and a growing collection of use cases and educational materials. Two case studies demonstrate how the ecosystem supports benchmarking, curriculum development, and broader adoption of open-science practices in the AEC sector. The platform is publicly accessible at https://www.openconstruction.org/ .", "This paper introduces ElecTwit, a simulation framework designed to study persuasion within multi-agent systems, specifically emulating the interactions on social media platforms during a political election. By grounding our experiments in a realistic environment, we aimed to overcome the limitations of game-based simulations often used in prior research. We observed the comprehensive use of 25 specific persuasion techniques across most tested LLMs, encompassing a wider range than previously reported. The variations in technique usage and overall persuasion output between models highlight how different model architectures and training can impact the dynamics in realistic social simulations. Additionally, we observed unique phenomena such as \u201ckernel of truth\u201d messages and spontaneous developments with an \u201cink\u201d obsession, where agents collectively demanded written proof. Our study provides a foundation for evaluating persuasive LLM agents in real-world contexts, ensuring alignment and preventing dangerous outcomes. All code used in this paper is available at https://github.com/tcmmichaelb139/ai-electwit.", ". The rapid growth of AI for Science (AI4S) has underscored the significance of scientific datasets, leading to the establishment of numerous national scientific data centers and sharing platforms. Despite this progress, efficiently promoting dataset sharing and utilization for scientific research remains challenging. Scientific datasets contain intricate domain-specific knowledge and contexts, rendering traditional collaborative filtering-based recommenders inadequate. Recent advances in Large Language Models (LLMs) offer unprecedented opportunities to build conversational agents capable of deep semantic understanding and personalized recommendations. In response, we present ScienceDB AI , a novel LLM-driven agentic recommender system developed on Science Data Bank (ScienceDB), one of the largest global scientific data-sharing platforms. ScienceDB AI leverages natural language conversations and deep reasoning to accurately recommend datasets aligned with researchers\u2019 scientific intents and evolving requirements. The system introduces several innovations: a Scientific Intention Perceptor to extract structured experimental elements from complicated queries, a Structured Memory Compressor to manage multi-turn dialogues effectively, and a Trustworthy Retrieval-Augmented Generation (Trustworthy RAG) framework. The Trustworthy RAG employs a two-stage retrieval mechanism and provides citable dataset references via Citable Scientific Task Record (CSTR) identifiers, enhancing recommendation trustworthiness and reproducibility. Through extensive offline and online experiments using over 10 million real-world datasets, ScienceDB AI has demonstrated significant effectiveness, achieving more than 30% improvement in offline metrics compared to advanced baselines and a over 200% increase in click-through rates compared to keyword-based search engines. To our knowledge, ScienceDB AI is the first LLM-driven conversational recommender tailored explicitly for large-scale scientific dataset sharing services. The platform is publicly accessible at: https://ai.scidb.cn/en.", "# Scilit with the Integrated Impact Indicator Assessment Scilit with the Integrated Impact Indicator Assessment Haochen Dong1,*, Sun Qiao1,*, Yanping Mu1, Lu Liao1, Diogo Rodrigues 2, Frank Sauerburger 2, Yi Bu3, Robin Haunschild3,4 1 MDPI, Poly Metropolitan Floor 10-14, Building 2, Courtyard 4, Guanyinan North Street, Beijing 101101, China; 2 MDPI, Grosspeteranlage 5, 4052 Basel, Switzerland; 3 Department of Information Management, Peking University, 5 Yiheyuan Road, Haidian District, Beijing 100871, China; 4 Max Planck Institute for Solid State Research, Heisenbergstr. 1, 70569 Stuttgart, Germany Correspondence: MDPI, Poly Metropolitan Floor 10-14, Building 2, Courtyard 4, Guanyinan North Street, Beijing 101101, China. dhcydll1004@gmail.com; stevensunss@163.com Abstract In this study, we systematically elucidate the background and functionality of the Scilit database and evaluate the feasibility and advantages of the comprehensive impact metrics I3 and I3/N, introduced within the Scilit framework. Using a matched dataset of 17,816 journals, we conduct a comparative analysis of Scilit I3/N, Journal Impact Factor, and CiteScore for 2023 and 2024, covering descriptive statistics and", "# Evidence for studying interactions between science and policy: An exploration of scholarly and policy references in Overton-indexed policy documents 1 This is the author-accepted manuscript. The final version of record was published in the Journal of Data and Information Science. Cite as: Murat, B., Fang, Z., Noyons, E., & Costas, R. (2026). Evidence for studying interactions between science and policy: An exploration of scholarly and policy references in Overton-indexed policy documents. Journal of Data and Information Science, 11(1), 63\u201387. https://doi.org/10.2478/jdis-2025-0054 Evidence for studying interactions between science and policy: An exploration of scholarly and policy references in Overton-indexed policy documents Biegzat Murat, Zhichao Fang*, Ed Noyons, Rodrigo Costas * Corresponding author: z.fang@cwts.leidenuniv.nl Biegzat Murat (ORCID: 0000-0001-7828-2472) 1. Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, The Netherlands. Zhichao Fang (ORCID: 0000-0002-3802-2227) 1. School of Information Resource Management, Renmin University of China, Beijing, China. 2. Centre for Science and Technology Studies (CWTS), Leiden University, Leiden, The", "We introduce LongDA, a data analysis benchmark for evaluating LLM-based agents under documentation-intensive analytical workflows. In contrast to existing benchmarks that assume well-specified schemas and inputs, LongDA targets real-world settings in which navigating long documentation and complex data is the primary bottleneck. To this end, we manually curate raw data files, long and heterogeneous documentation, and expert-written publications from 17 publicly available U.S. national surveys, from which we extract 505 analytical queries grounded in real analytical practice. Solving these queries requires agents to first retrieve and integrate key information from multiple unstructured documents before performing multi-step computations and writing executable code, which remains challenging for existing data analysis agents. To support the systematic evaluation under this setting, we develop LongTA, a tool-augmented agent framework that enables document access, retrieval, and code execution, and evaluate a range of proprietary and open-source models. Our experiments reveal substantial performance gaps even among state-of-the-art models, highlighting the challenges researchers should consider before applying LLM agents for decision support in real-world, high-stakes analytical settings.", "# Automated Classification of Research Papers Toward Sustainable Development Goals: A Boolean Query-Based Computational Framework Automated Classification of Research Papers Toward Sustainable Development Goals: A Boolean Query-Based Computational Framework Sahil Dewani1 and Kiran Sharma1,2* 1School of Engineering & Technology, BML Munjal University, Gurugram, India 2Center for Advanced Data & Computational Science, BML Munjal University, Gurugram,India Correspondance*: kiran.sharma@bmu.edu.in Abstract The rapid expansion of scholarly publications across diverse disciplines has made it increasingly difficult to systematically evaluate how research contributes to the United Nations Sustainable Development Goals (SDGs). Domain classification of research articles done manually through research experts is extremely impractical because of the number of publications, expensive in time and may not be consistent when done by human beings. This paper proposes an automated and rule-based computational model of classifying research papers based on SDGs with expert curated Boolean query mappings to overcome these challenges. The proposed system has a web-based interface to input data and displ", "The Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework provides a rigorous foundation for evidence synthesis, yet the manual processes of data extraction and literature screening remain time-consuming and restrictive. Recent advances in Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), offer opportunities to automate and scale these tasks, thereby improving time and efficiency. However, reproducibility, transparency, and auditability, the core PRISMA principles, are being challenged by the inherent non-determinism of LLMs and the risks of hallucination and bias amplification. To address these limitations, this study integrates human-led synthesis with a GenAI-assisted statistical pre-screening step. Human oversight ensures scientific validity and transparency, while the deterministic nature of the statistical layer enhances reproducibility. The proposed approach systematically enhances PRISMA guidelines, providing a responsible pathway for incorporating GenAI into systematic review workflows.", "# Global research trends and collaborations in Fibrodysplasia Ossificans Progressiva: A bibliometric analysis (1989-2023) Library and Information Perspectives and Research, 7(4), 43 \u2013 65, December 2025 ISSN: 2672-5894 (Online) https://credence-publishing.com DOI: http://doi.org/10.47524/lipr.v7i4.66 Global research trends and collaborations in Fibrodysplasia Ossificans Progressiva: A bibliometric analysis (1989-2023) Muneer Ahmad, PhD1, Undie Felicia Nkatv, PhD2 and Sajid Saleem3 1Chief University Librarian, 2Ag. Deputy Librarian, 3Lecturer, 1,2The Iddi Basajjabalaba Memorial Library, Kampala International University, Box 20000, Ggaba Road, Kansanga, Uganda 3Department IT, SOMAC, Kampala International University, Box 20000, Ggaba Road, Kansanga, Uganda E-mail: 1muneerbangroo@gmail.com; ahmad.muneer@kiu.ac.ug; 2nkavfelicia@gmail.com; felicia.undie@kiu.ac.ug; 3sajid.saleem@kiu.ac.ug. Mobile: 1+256709415657; 2+2348025668229 Orcid: 1https://orcid.org/0000-0001-5666-8438; 2https://orcid.org/0009-0002-0496-8505; 3https://orcid.org/0009-0002-6131-8576 Abstract Fibrodysplasia Ossificans Progressiva (FOP) is a rare and debilitating genetic disorder characterized by the progressive formation", "# Examining persistence of European open repository infrastructure and its diffusion in the scholarly record Submitted date 24 December 2025 ~ Accepted date 2026 Preprint Examining persistence of European open repository infrastructure and its diffusion in the scholarly record George Macgregor, Information Services University of Glasgow https://orcid.org/0000-0002-8482-3973 Joy Davidson Digital Curation Centre University of Glasgow https://orcid.org/0000-0003-3484-7675 Abstract This article seeks to determine the extent to which the principle of persistence is observed by repositories and the organizations that operate them. We also evaluate the impact that negative repository persistence levels may be having on the scholarly record. We do this by interrogating and combining data about European repositories from several repository registries and web scraped sources, including the Internet Archive's Wayback Machine, thereby creating a unique dataset of historic repository locations and their OAI-PMH endpoints. We then use this data as the basis for text mining CORE, a vast corpus of scholarly outputs, to determine the extent to which impersistent European repository content has perm", "Bibliometric analysis is a critical tool for understanding the structure, dynamics, and impact of scientific research. Traditional methods often fall short in capturing the intricate relationships and evolving trends within scientific literature. To address this gap, we present pyBiblioNet, a Python library designed to facilitate comprehensive network-based bibliometric analysis, providing insights into citation networks, co-authorship networks, and keyword co-occurrence networks. The library integrates with OpenAlex, a popular and open catalogue to the global research system, enabling users to easily preprocess, visualize, and analyse bibliometric data. Key features include topic selection, automatic data download via OpenAlex APIs, creation of the root and base sets of manuscripts to analyze, creation of the citation and co-authorship networks, network visualization tools, and a suite of algorithms for computing network centralities, clustering, and community detection, all of them tailored to the bibliometric domain. Additionally, it enables the analysis of key topics and concepts using NLP techniques. We showcase the main functions of the library by performing a bibliometric analysis on the multidisciplinary \u201c15-minute city paradigm\u201d, demonstrating the utility of pyBiblioNet in uncovering hidden patterns and emerging trends in various scientific domains. pyBiblioNet can empower researchers, librarians, and policymakers with a powerful, user-friendly tool for enhancing their bibliometric analyses and making data-driven decisions.", "Peer review author responses often include commitments to add experiments, release code, or clarify content in the final paper. Yet, there is currently no systematic mechanism to ensure authors fulfill these promises. In this position paper, we present a large-scale audit of author commitments using large language models (LLMs) to compare rebuttals against camera-ready versions. Analyzing the commitments from ICLR-2025 and EMNLP-2024, we find that while a majority of promised changes are implemented, a significant share (about 25%) are not, with \u201cmissing experiments\u201d and other high-impact items among the most frequently unfulfilled. We demonstrate that LLM-based tools can feasibly detect the promises. Finally, we propose the idea of Author Commitment Checklist, which would alert authors and organizers to unaddressed promises, increasing accountability and strengthening the integrity of the peer review process. We discuss the benefits of this practice and advocate for its adoption in future conferences.", "# Publishing FAIR and Machine-actionable Reviews in Materials Science: The Case for Symbolic Knowledge in Neuro-symbolic Artificial Intelligence $\\text{Ga}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{2}}}\\text{O}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{3}}}$ 36.0 15 2.40 900 2.04 100 $\\text{Al}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{2}}}\\text{O}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{3}}}\\text{:}\\text{Yb}$ 24.0 14 1.71 \u2013 0.90 \u2013 $\\text{Yb}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{3}}}\\text{Al}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{5}}}\\text{O}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{12}}}$ 5.2 5 1.04 1100 1.20 \u2013 $\\text{Lu}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{3}}}\\text{Al}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{5}}}\\text{O}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{12}}}$ 10.0 50 0.20 1100 1.65 \u2013 $\\text{Yb}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{2}}}\\text{O}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{3}}}$ 8.5 60 0.14 1000 \u2013 \u2013 $\\text{Y}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{3}}}\\text{Ga}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{5}}}\\text{O}{\\vphantom{\\text{X}}}\\_{\\smash[t]{\\text{12}}}$ 2.5 25 0.10 800 2.26 49 Conversion to HTML had a Fatal error and exited abruptly. This document may be truncated", "Identifying suitable datasets for a research question remains challenging because existing dataset search engines rely heavily on metadata quality and keyword overlap, which often fail to capture the semantic intent of scientific investigation. We introduce a literature-driven framework that discovers datasets from citation contexts in scientific papers, enabling retrieval grounded in actual research use rather than metadata availability. Our approach combines large-scale citation-context extraction, schema-guided dataset recognition with Large Language Models, and provenance-preserving entity resolution. We evaluate the system on eight survey-derived computer science queries and find that it achieves substantially higher recall than Google Dataset Search and DataCite Commons, with normalized recall ranging from an average of 47.47% to a highest value of 81.82%. Beyond recovering gold-standard datasets, the method also surfaces additional datasets not documented in the surveys. Expert assessments across five top-level Fields of Science indicate that a substantial portion of the additional datasets are considered high utility, and some are regarded as novel for the specific topics chosen by the experts. These findings establish citation-context mining as an effective and generalizable paradigm for dataset discovery, particularly in settings where datasets lack sufficient or reliable metadata. To support reproducibility and future extensions, we release our code, evaluation datasets, and results on GitHub https://github.com/Fireblossom/citation-context-dataset-discovery .", "Digital Audio Workstations (DAWs) are central to modern music production but often encumber the musician\u2019s workflow, tethering them to a desk and hindering natural interaction with their instrument. Furthermore, effective remote collaboration remains a significant challenge, with existing solutions hampered by network latency and asynchronous file sharing. This paper investigates the potential of Mixed Reality (MR) to overcome these barriers, creating an intuitive environment for real-time, remote musical collaboration. We employ qualitative and speculative design techniques to better understand: 1) how players currently use DAWs, and 2) to imagine a speculative future of collaborative MR-DAWs. To facilitate this discussion, we developed and evaluated the usability of a design probe, MR-DAW. An MR system enabling multiple, geographically dispersed users to control a single, shared DAW instance while moving freely in their local spaces. Our networked system enables each remote musician to use a physical foot pedal for collaborative looping, merging a familiar, hands-free interaction with a shared virtual session. Based on interviews and system evaluations with 20 musicians, we analyze current practices, report on the user experience with our MR system, and speculate on the future of musical collaboration in MR. Our results highlight the affordances of MR for unencumbered musical interaction and provide a speculative outlook on the future of remote collaborative DAWs in the Musical Metaverse.", "During musical collaboration, visual cues are essential for communication between musicians. Extended Reality (XR) applications, often used with head-mounted displays like Augmented Reality (AR) glasses, can limit the field of view (FOV) of players. We conducted a study to investigate the effects of limited FOV on co-presence, gesture recognition, overall enjoyment, and reaction time. Initially, we observed experienced musicians collaborating informally with and without visual occlusion, noting that collaboration suffered with limited FOV. We then conducted a within-subjects study with 19 participants, comparing an unrestricted FOV holographic setup called HoloJam to Nreal AR glasses with a 52 \u2218 limited FOV. In the AR setup, we tested two conditions: standard AR with a 52 \u2218 FOV and a modified AR notification system called Mini Musicians. Results showed that HoloJam provided higher co-presence, quicker gesture recognition, and greater enjoyment. The Mini Musicians application reduced reaction time and maintained enjoyment compared to the standard AR setup. We conclude that limited FOV impacts musical collaboration, but notifications can improve reaction time and should be considered in future XR music collaborations.", "Background: AI-driven speech-to-text (STT) documentation systems are increasingly adopted in clinical settings to reduce documentation burden and improve workflow efficiency. However, adoption has outpaced the systematic evaluation of socio-technical risks related to transparency, reliability, patient autonomy, and organizational accountability. Objective: To develop a socio-technical framework for identifying and governing risks associated with the implementation of clinical speech-to-text systems. Methods: This study synthesizes interdisciplinary evidence from technical automatic speech recognition research, clinical workflow and human factors studies, ethical guidance on consent and patient autonomy, and regulatory and organizational governance sources. Using a structured narrative synthesis approach, relevant literature was iteratively reviewed and thematically analyzed to identify recurring socio-technical risk mechanisms. The synthesis was used to develop a layered conceptual framework for evaluating and governing clinical speech-to-text systems. Results: Findings show that clinical STT systems operate within tightly coupled socio-technical environments where model performance, audio capture conditions, clinician oversight, patient understanding, workflow design, and institutional governance are interdependent. Key risks include inconsistent disclosure and consent practices, performance disparities for accented speech and speech/voice disorders, accuracy degradation under real clinical acoustics, automation complacency and variable clinician review, and unclear accountability across vendors and healthcare organizations. These risk domains informed a six-layer socio-technical governance model spanning technical, human/workflow, ethical, organizational, regulatory, and sociocultural dimensions. Conclusion: The study proposes a socio-technical governance framework and implementation roadmap to support the responsible deployment of clinical STT systems. The framework emphasizes transparency, patient autonomy, documentation integrity, and accountable governance to enable safe and equitable adoption of speech-based documentation technologies.", ". The generation of truly novel and diverse ideas is important for contemporary engineering design, yet it remains a significant cognitive challenge for novice designers. Current \u2019single-spurt\u2019 AI systems exacerbate this challenge by producing a high volume of semantically clustered ideas. We propose MIDAS (Meta-cognitive Ideation through Distributed Agentic AI System), a novel framework that replaces the single-AI paradigm with a distributed \u2019team\u2019 of specialized AI agents designed to emulate the human meta-cognitive ideation workflow. This agentic system progressively refines ideas and assesses each one for both global novelty (against existing solutions) and local novelty (against previously generated ideas). MIDAS, therefore, demonstrates a viable and progressive paradigm for true human-AI co-creation, elevating the human designer from a passive filterer to a participatory, active, collaborative partner.", "# User Perceptions of an LLM-Based Chatbot for Cognitive Reappraisal of Stress: Feasibility Study Original Paper Ananya Bhattacharjee, PhD1, 2 Jina Suh, PhD3 Mohit Chandra, MS4 Javier Hernandez, PhD3 1King Center on Global Development, Stanford University, Stanford, CA, USA 2Department of Computer Science, University of Toronto, Toronto, ON, Canada 3Microsoft Research, Redmond, WA, USA 4School of Interactive Computing, Georgia Institute of Technology, Atlanta, GA, USA User Perceptions of an LLM-Based Chatbot for Cognitive Reappraisal of Stress: Feasibility Study Abstract Background: Cognitive reappraisal is a widely studied emotion regulation strategy that helps individuals reinterpret stressful situations in ways that reduce their emotional impact. Digital mental health (DMH) tools often struggle to support this process because scripted templates fail to adapt to the varied and incomplete ways users describe their stressors. Large language models (LLMs) offer opportunities to increase conversational flexibility while preserving structured intervention steps. Objective: This study examined the feasibility of an LLM-based single-session intervention (SSI) for workplace stress reappr", "The fast integration of artificial intelligence into mobile applications has completely changed the digital landscape; however, the impact of this change on user perception of AI features remains poorly understood. This large-scale analysis examined 1,484,633 mobile application reviews across 422 applications (200 AI-featuring, 222 control) from iOS App Store and Google Play Store. By employing sentiment classification, topic modeling, and concern-benefit categorization, we identified a major disconnect: only 11.9% of reviews mentioned AI, even though 47.4% of applications featured AI capabilities. AI-featuring applications received significantly lower ratings than traditional applications ( $d=0.40$ ); however, hierarchical regression revealed a hidden pattern\u2014the negative relationship reversed after controlling for AI mentions and review characteristics ( $b=0.405$ , $p<.001$ ). Privacy dominated user concerns (34.8% of concern-expressing reviews), while efficiency represented the primary benefit (42.3%). Effects varied greatly by category, from positive for Assistant applications ( $d=0.55$ ) to negative for Entertainment ( $d=-0.23$ ). These findings suggest that AI features often operate below user awareness thresholds, and it is the explicit recognition of AI, rather than its mere presence, that drives negative evaluations. This challenges basic assumptions about technology acceptance in AI systems.", "Ensuring digital accessibility is essential for inclusive access to online services. However, many government and non-government websites that provide critical services - such as education, healthcare, and public administration - continue to exhibit significant accessibility and usability barriers. This study evaluates the accessibility of Bangladeshi government and non-government websites under WCAG 2.2 by combining automated accessibility assessments with user-reported feedback. A total of 212 websites were analyzed using multiple automated tools, complemented by a survey of 103 users to capture real-world usability, accessibility, and security experiences. The results reveal substantial disparities between government and non-government websites, highlighting persistent issues related to navigation complexity, interaction cost, visual readability, accessibility feature adoption, and authentication mechanisms. While non-government websites generally demonstrate better usability and functional performance, accessibility support remains inconsistent across both categories. The findings underscore the need for regular accessibility audits, user-centered design practices, and policy-driven interventions to improve digital inclusivity and ensure equitable access to online services for diverse user populations.", "Talking head generation creates lifelike avatars from static portraits for virtual communication and content creation. However, current models do not yet convey the feeling of truly interactive communication, often generating one-way responses that lack emotional engagement. We identify two key challenges toward truly interactive avatars: generating motion in real-time under causal constraints and learning expressive, vibrant reactions without additional labeled data. To address these challenges, we propose Avatar Forcing, a new framework for interactive head avatar generation that models real-time user-avatar interactions through diffusion forcing. This design allows the avatar to process real-time multimodal inputs, including the user\u2019s audio and motion, with low latency for instant reactions to both verbal and non-verbal cues such as speech, nods, and laughter. Furthermore, we introduce a direct preference optimization method that leverages synthetic losing samples constructed by dropping user conditions, enabling label-free learning of expressive interaction. Experimental results demonstrate that our framework enables real-time interaction with low latency ( $\\approx$ 500ms ), achieving 6.8 $\\times$ speedup compared to the baseline, and produces reactive and expressive avatar motion, which is preferred over 80% against the baseline.", "Continuous electroencephalography (EEG) is routinely used in neurocritical care to monitor seizures and other harmful brain activity, including rhythmic and periodic patterns that are prevalent and clinically significant. Although deep learning methods have achieved high accuracy for seizure detection, most existing approaches remain seizure-centric, rely on discrete-label supervision, and are evaluated primarily using accuracy-based metrics. A central limitation of current EEG modeling practice is the weak correspondence between learned representations and how EEG findings are interpreted and summarized in clinical workflows. Harmful EEG activity exhibits overlapping patterns, graded expert agreement, and temporal persistence, which are not well captured by classification objectives alone This work proposes a multimodal EEG representation learning framework that integrates signal-domain modeling with structured clinical language supervision. First, raw EEG is transformed into a longitudinal bipolar montage and time\u2013frequency representations. Second, dual transformer-based encoders model complementary temporal-centric and frequency-centric dependencies and are fused using an adaptive gating mechanism. Third, EEG embeddings are aligned with structured expert consensus descriptions through a contrastive objective. Finally, an EEG-conditioned text reconstruction loss is introduced as a representation-level constraint alongside standard classification loss. Experimental evaluation using a controlled train\u2013validation\u2013test split achieves a six-class test accuracy of 0.9797. Ablation analyses show that removing contrastive alignment reduces cross-modal retrieval performance from Recall@10 of 0.3390 to 0.0045, despite minimal change in classification accuracy. These findings demonstrate that discriminative accuracy does not reliably reflect representation quality for clinically meaningful EEG modeling. Explicit alignment with structured clinical language exposes differences in semantic organization and optimization behavior that are invisible to accuracy-based evaluation, providing a principled basis for assessing EEG representations beyond classification performance.", ". How do children respond to repeated robot errors? While prior research has examined adult reactions to successive robot errors, children\u2019s responses remain largely unexplored. In this study, we explore children\u2019s reactions to robot social errors and performance errors. For the latter, this study reproduces the successive robot failure paradigm of Liu et al. with child participants ( N =59, ages 8-10) to examine how young users respond to repeated robot conversational errors. Participants interacted with a robot that failed to understand their prompts three times in succession, with their behavioral responses video-recorded and analyzed. We found both similarities and differences compared to adult responses from the original study. Like adults, children adjusted their prompts, modified their verbal tone, and exhibited increasingly emotional non-verbal responses throughout successive errors. However, children demonstrated more disengagement behaviors, including temporarily ignoring the robot or actively seeking an adult. Errors did not affect participants\u2019 perception of the robot, suggesting more flexible conversational expectations in children. These findings inform the design of more effective and developmentally appropriate human-robot interaction systems for young users.", "In this work, we demonstrate how Low-Rank Adaptation (LoRA) can be used to combine different galaxy imaging datasets to improve redshift estimation with CNN models for cosmology. LoRA is an established technique for large language models that adds adapter networks to adjust model weights and biases to efficiently fine-tune large base models without retraining. We train a base model using a photometric redshift ground truth dataset, which contains broad galaxy types but is less accurate. We then fine-tune using LoRA on a spectroscopic redshift ground truth dataset. These redshifts are more accurate but limited to bright galaxies and take orders of magnitude more time to obtain, so are less available for large surveys. Ideally, the combination of the two datasets would yield more accurate models that generalize well. The LoRA model performs better than a traditional transfer learning method, with $\\sim 2.5\\times$ less bias and $\\sim$ 2.2 $\\times$ less scatter. Retraining the model on a combined dataset yields a model that generalizes better than LoRA but at a cost of greater computation time. Our work shows that LoRA is useful for fine-tuning regression models in astrophysics by providing a middle ground between full retraining and no retraining. LoRA shows potential in allowing us to leverage existing pretrained astrophysical models, especially for data sparse tasks.", "We study reinforcement learning methods with linear function approximation under non-Markov state and cost processes. We first consider the policy evaluation method and show that the algorithm converges under suitable ergodicity conditions on the underlying non-Markov processes. Furthermore, we show that the limit corresponds to the fixed point of a joint operator composed of an orthogonal projection and the Bellman operator of an auxiliary Markov decision process. For Q-learning with linear function approximation, as in the Markov setting, convergence is not guaranteed in general. We show, however, that for the special case where the basis functions are chosen based on quantization maps, the convergence can be shown under similar ergodicity conditions. Finally, we apply our results to partially observed Markov decision processes, where finite-memory variables are used as state representations, and we derive explicit error bounds for the limits of the resulting learning algorithms.", "# The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data Periodica Polytechnica Electrical Engineering and Computer Science The Weather Paradox: Why Precipitation Fails to Predict Traffic Accident Severity in Large-Scale US Data Yann Bellec3, Rohan KAMAN1, Siwen CUI1, Aarav AGRAWAL2, Calvin CHEN1 1 Department of Computer Science, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093 2 Department of Data Science, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093 3 Department of Neuroscience, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093 * Corresponding author, e-mail: rkaman@ucsd.edu, s4cui@ucsd.edu, cac028@ucsd.edu, a7agrawal@ucsd.edu, ybellec@ucsd.edu Abstract This study investigates the predictive capacity of environmental, temporal, and spatial factors on traffic accident severity in the United States. Using a dataset of 500,000 U.S. traffic accidents spanning 2016\u20132023, we trained an XGBoost classifier optimized through Randomized Search Cross-Validation and adjusted for class imbalance via class weighting. The final model achieves an overall accuracy of 78%, with st", ". Conformal prediction guarantees degrade under distribution shift. We study this using COVID-19 as a natural experiment across 8 supply chain tasks. Despite identical severe feature turnover (Jaccard approximately 0), coverage drops vary from 0% to 86.7%, spanning two orders of magnitude. Using SHapley Additive exPlanations (SHAP) analysis, we find catastrophic failures correlate with single-feature dependence (rho = 0.714, p = 0.047). Catastrophic tasks concentrate importance in one feature (4.5x increase), while robust tasks redistribute across many (10-20x). Quarterly retraining restores catastrophic task coverage from 22% to 41% (+19 pp, p = 0.04), but provides no benefit for robust tasks (99.8% coverage). Exploratory analysis of 4 additional tasks with moderate feature stability (Jaccard 0.13-0.86) reveals feature stability, not concentration, determines robustness, suggesting concentration effects apply specifically to severe shifts. We provide a decision framework: monitor SHAP concentration before deployment; retrain quarterly if vulnerable (\u00bf40% concentration); skip retraining if robust.", "This paper presents a unified framework for evaluating Linux security hardening on the FABRIC testbed through aggregation of heterogeneous security auditing tools. We deploy three Ubuntu 22.04 nodes configured at baseline, partial, and full hardening levels, and evaluate them using Lynis, OpenSCAP, and AIDE across 108 audit runs. To address the lack of a consistent interpretation across tools, we implement a Unified Compliance Aggregator (UCA) that parses tool outputs, normalizes scores to a common 0\u2013100 scale, and combines them into a weighted metric augmented by a customizable rule engine for organization-specific security policies. Experimental results show that full hardening increases OpenSCAP compliance from 39.7 to 71.8, while custom rule compliance improves from 39.3% to 83.6%. The results demonstrate that UCA provides a clearer and more reproducible assessment of security posture than individual tools alone, enabling systematic evaluation of hardening effectiveness in programmable testbed environments.", "Decision Transformers (DTs) have emerged as a powerful framework for sequential decision making by formulating offline reinforcement learning (RL) as a sequence modeling problem. However, extending DTs to online settings with pure RL gradients remains largely unexplored, as existing approaches continue to rely heavily on supervised sequence-modeling objectives during online finetuning. We identify hindsight return relabeling\u2014a standard component in online DTs\u2014as a critical obstacle to RL-based finetuning: while beneficial for supervised learning, it is fundamentally incompatible with importance sampling-based RL algorithms such as GRPO, leading to unstable training. Building on this insight, we propose new algorithms that enable online finetuning of Decision Transformers using pure reinforcement learning gradients. We adapt GRPO to DTs and introduce several key modifications, including sub-trajectory optimization for improved credit assignment, sequence-level likelihood objectives for enhanced stability and efficiency, and active sampling to encourage exploration in uncertain regions. Through extensive experiments, we demonstrate that our methods outperform existing online DT baselines and achieve new state-of-the-art performance across multiple benchmarks, highlighting the effectiveness of pure-RL-based online finetuning for Decision Transformers.", "Forecasting high-dimensional spatiotemporal systems remains computationally challenging for recurrent neural networks (RNNs) and long short-term memory (LSTM) models due to gradient-based training and memory bottlenecks. Reservoir Computing (RC) mitigates these challenges by replacing backpropagation with fixed recurrent layers and a convex readout optimization, yet conventional RC architectures still scale poorly with input dimensionality. We introduce a Sequential Reservoir Computing (Sequential RC) architecture that decomposes a large reservoir into a series of smaller, interconnected reservoirs. This design reduces memory and computational costs while preserving long-term temporal dependencies. Using both low-dimensional chaotic systems (Lorenz63) and high-dimensional physical simulations (2D vorticity and shallow-water equations), Sequential RC achieves 15\u201325% longer valid forecast horizons, 20\u201330% lower error metrics (SSIM, RMSE), and up to three orders of magnitude lower training cost compared to LSTM and standard RNN baselines. The results demonstrate that Sequential RC maintains the simplicity and efficiency of conventional RC while achieving superior scalability for high-dimensional dynamical systems. This approach provides a practical path toward real-time, energy-efficient forecasting in scientific and engineering applications.", "# Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores Early Prediction of Liver Cirrhosis Up to Two Years in Advance: A Machine Learning Study Benchmarking Against the FIB-4 and APRI Scores Zhuqi Miaoa,b, Ahmed G Qasemc, Sujan Ravid, Jason T. Chenge, Courtney W. Houchene, Sumayah Abedf, Dilorom Azimdjanovna Zuparovac, Abdulaziz Ahmedc,* aCenter for Health Systems Innovation, Oklahoma State University, Tulsa, OK, 74119, USA bDepartment of Management Science and Information Systems, Oklahoma State University, Stillwater, OK, 74106, USA cDepartment of Health Services Administration, University of Alabama at Birmingham, Birmingham, AL, 35233, USA dDivision of Gastroenterology and Hepatology, Department of Medicine, University of Alabama at Birmingham, Birmingham, AL, 35233, USA. eCollege of Medicine, The University of Oklahoma Health Campus, Oklahoma City, OK, 73104, USA. fDepartment of Family and Community Medicine, University of Alabama at Birmingham, Birmingham, AL, 35233, USA. *Corresponding Author: Abdulaziz Ahmed, Email: aahmed2@uab.edu Abstract Objective: Develop and evaluate machine learning (ML", "This paper tackles the pressing challenge of preserving semantic meaning in communication systems constrained by limited bandwidth. We introduce a novel reinforcement learning framework that achieves per-dimension unequal error protection via adaptive repetition coding. Central to our approach is a composite semantic distortion metric that balances global embedding similarity with entity-level preservation, empowering the reinforcement learning agent to allocate protection in a context-aware manner. Experiments show statistically significant gains over uniform protection, achieving 6.8% higher chrF scores and 9.3% better entity preservation at 1 dB SNR. The key innovation of our framework is the demonstration that simple, intelligently allocated repetition coding enables fine-grained semantic protection- an advantage unattainable with conventional codes such as LDPC or Reed-Solomon. Our findings challenge traditional channel coding paradigms by establishing that code structure must align with semantic granularity. This approach is particularly suited to edge computing and IoT scenarios, where bandwidth is scarce, but semantic fidelity is critical, providing a practical pathway for next-generation semantic-aware networks.", "# SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification 1 SSI-GAN: Semi-Supervised Swin-Inspired Generative Adversarial Networks for Neuronal Spike Classification Danial Sharifrazi1*, Nouman Javed1,2, Mojtaba Mohammadi1, Seyede Sana Salehi3, Roohallah Alizadehsani1, Prasad N. Paradkar2, U. Rajendra Acharya4, 5, Asim Bhatti1 1Institute for Intelligent Systems Research and Innovations (IISRI), Deakin University, Geelong, Australia 2CSIRO Health and Biosecurity, Australian Animal Health Laboratory, Geelong, Australia 3Department of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran 4School of Mathematics, Physics and Computing, University of Southern Queensland, Springfield, Australia 5Center for Health Research, University of Southern Queensland, Springfield, Australia Abstract Mosquitos are the main transmissive agents of arboviral diseases. Manual classification of their neuronal spike patterns is very labor-intensive and expensive. Most available deep learning solutions require fully labeled spike datasets and highly preprocessed neuronal signals. This reduces the feasibility of mass adoption in act", "Cardiovascular diseases, particularly arrhythmias, remain a leading global cause of mortality, necessitating continuous monitoring via the Internet of Medical Things (IoMT). However, state-of-the-art deep learning approaches often impose prohibitive computational overheads, rendering them unsuitable for resource-constrained edge devices. This study proposes a resource-efficient, data-centric framework that prioritizes feature engineering over complexity. Our optimized pipeline makes the complex, high-dimensional arrhythmia data linearly separable. This is achieved by integrating time-frequency wavelet decompositions with graph-theoretic structural descriptors, such as PageRank centrality. This hybrid feature space, combining wavelet decompositions and graph-theoretic descriptors, is then refined using mutual information and recursive elimination, enabling interpretable, ultra-lightweight linear classifiers. Validation on the MIT-BIH and INCART datasets yields 98.44% diagnostic accuracy with an 8.54 KB model footprint. The system achieves 0.46 $\\mu$ s classification inference latency within a 52 ms per-beat pipeline, ensuring real-time operation. These outcomes provide an order-of-magnitude efficiency gain over compressed models, such as KD-Light (25 KB, 96.32% accuracy), advancing battery-less cardiac sensors.", "Accurate forecasting of financial markets remains a long-standing challenge due to complex temporal and often latent dependencies, non-linear dynamics, and high volatility. Building on our earlier recurrent neural network framework, we present an enhanced StockBot architecture that systematically evaluates modern attention-based, convolutional, and recurrent time-series forecasting models within a unified experimental setting. While attention-based and transformer-inspired models offer increased modeling flexibility, extensive empirical evaluation reveals that a carefully constructed vanilla LSTM consistently achieves superior predictive accuracy and more stable buy/sell decision-making when trained under a common set of default hyperparameters. These results highlight the robustness and data efficiency of recurrent sequence models for financial time-series forecasting, particularly in the absence of extensive hyperparameter tuning or the availability of sufficient data when discretized to single-day intervals. Additionally, these results underscore the importance of architectural inductive bias in data-limited market prediction tasks.", "Detecting unobserved confounders is crucial for reliable causal inference in observational studies. Existing methods require either linearity assumptions or multiple heterogeneous environments, limiting applicability to nonlinear single-environment settings. To bridge this gap, we propose Kernel Regression Confounder Detection (KRCD), a novel method for detecting unobserved confounding in nonlinear observational data under single-environment conditions. KRCD leverages reproducing kernel Hilbert spaces to model complex dependencies. By comparing standard and higher-order kernel regressions, we derive a test statistic whose significant deviation from zero indicates unobserved confounding. Theoretically, we prove two key results: First, in infinite samples, regression coefficients coincide if and only if no unobserved confounders exist. Second, finite-sample differences converge to zero-mean Gaussian distributions with tractable variance. Extensive experiments on synthetic benchmarks and the Twins dataset demonstrate that KRCD not only outperforms existing baselines but also achieves superior computational efficiency.", "Automated negotiations in insurance and business-to-business (B2B) commerce encounter substantial challenges. Current systems force a trade-off between convenience and privacy by routing sensitive financial data through centralized servers, increasing security risks, and diminishing user trust. This study introduces a device-native autonomous Agentic AI system for privacy-preserving negotiations. The proposed system operates exclusively on user hardware, enabling real-time bargaining while maintaining sensitive constraints locally. It integrates zero-knowledge proofs to ensure privacy and employs distilled world models to support advanced on-device reasoning. The architecture incorporates six technical components within an Agentic AI workflow. Agents autonomously plan negotiation strategies, conduct secure multi-party bargaining, and generate cryptographic audit trails without exposing user data to external servers. The system is evaluated in insurance and B2B procurement scenarios across diverse device configurations. Results show an average success rate of 87%, a 2.4 $\\times$ reduction in latency relative to cloud baselines, and strong privacy preservation through zero-knowledge proofs. User studies show 27% higher trust scores when decision trails are available. These findings establish a foundation for trustworthy autonomous agents in privacy-sensitive financial domains.", "3D Gaussian Splatting produces high-quality scene reconstructions but generates hundreds of thousands of spurious Gaussians (floaters) scattered throughout the environment. These artifacts obscure objects of interest and inflate model sizes, hindering deployment in bandwidth-constrained applications. We present Clean-GS, a method for removing background clutter and floaters from 3DGS reconstructions using sparse semantic masks. Our approach combines whitelist-based spatial filtering with color-guided validation and outlier removal to achieve 60-80% model compression while preserving object quality. Unlike existing 3DGS pruning methods that rely on global importance metrics, Clean-GS uses semantic information from as few as 3 segmentation masks (1% of views) to identify and remove Gaussians not belonging to the target object. Our multi-stage approach consisting of (1) whitelist filtering via projection to masked regions, (2) depth-buffered color validation, and (3) neighbor-based outlier removal isolates monuments and objects from complex outdoor scenes. Experiments on Tanks and Temples show that Clean-GS reduces file sizes from 125MB to 47MB while maintaining rendering quality, making 3DGS models practical for web deployment and AR/VR applications. Our code is available at https://github.com/smlab-niser/clean-gs", "Large climate-model ensembles are computationally expensive; yet many downstream analyses would benefit from additional, statistically consistent realizations of spatiotemporal climate variables. We study a generative modeling approach for producing new realizations from a limited set of available runs by transferring structure learned across an ensemble. Using monthly near-surface temperature time series from ten independent reanalysis realizations (ERA5), we find that a vanilla conditional variational autoencoder (CVAE) trained jointly across realizations yields a fragmented latent space that fails to generalize to unseen ensemble members. To address this, we introduce a latent-constrained CVAE (LC-CVAE) that enforces cross-realization homogeneity of latent embeddings at a small set of shared geographic \u201canchor\u201d locations. We then use multi-output Gaussian process regression in the latent space to predict latent coordinates at unsampled locations in a new realization, followed by decoding to generate full time series fields. Experiments and ablations demonstrate (i) instability when training on a single realization, (ii) diminishing returns after incorporating roughly five realizations, and (iii) a trade-off between spatial coverage and reconstruction quality that is closely linked to the average neighbor distance in latent space.", "The rapid advancement of photorealistic generative models has made it increasingly important to attribute the origin of synthetic content, moving beyond binary real or fake detection toward identifying the specific model that produced a given image. We study the problem of distinguishing outputs from a target generative model (e.g., OpenAI\u2019s DALL\u00b7E 3) from other sources, including real images and images generated by a wide range of alternative models. Using CLIP features and a simple linear classifier, shown to be effective in prior work, we establish a strong baseline for target generator attribution using only limited labeled data from the target model and a small number of known generators. However, this baseline struggles to generalize to harder, unseen, and newly released generators. To address this limitation, we propose a constrained optimization approach that leverages unlabeled wild data, consisting of images collected from the Internet that may include real images, outputs from unknown generators, or even samples from the target model itself. The proposed method encourages wild samples to be classified as non target while explicitly constraining performance on labeled data to remain high. Experimental results show that incorporating wild data substantially improves attribution performance on challenging unseen generators, demonstrating that unlabeled data from the wild can be effectively exploited to enhance AI generated content attribution in open world settings.", "Parameter-Efficient Fine-Tuning (PEFT) method has emerged as a dominant paradigm for adapting pre-trained GNN models to downstream tasks. However, existing PEFT methods usually exhibit significant vulnerability to various noise and attacks on graph topology and node attributes/features. To address this issue, for the first time, we propose integrating adversarial learning into graph prompting and develop a novel Adversarial Graph Prompting (AGP) framework to achieve robust graph fine-tuning. Our AGP has two key aspects. First , we propose the general problem formulation of AGP as a min-max optimization problem and develop an alternating optimization scheme to solve it. For inner maximization, we propose Joint Projected Gradient Descent (JointPGD) algorithm to generate strong adversarial noise. For outer minimization, we employ a simple yet effective module to learn the optimal node prompts to counteract the adversarial noise. Second , we demonstrate that the proposed AGP can theoretically address both graph topology and node noise. This confirms the versatility and robustness of our AGP fine-tuning method across various graph noise. Note that, the proposed AGP is a general method that can be integrated with various pre-trained GNN models to enhance their robustness on the downstream tasks. Extensive experiments on multiple benchmark tasks validate the robustness and effectiveness of AGP method compared to state-of-the-art methods.", "Parameter-efficient fine-tuning (PEFT) has become the default route for adapting LLMs to domain- and application-specific settings, yet widely used methods such as LoRA and QLoRA remain largely geometry-agnostic : they optimize within fixed, randomly oriented low-rank subspaces using first-order descent, largely ignoring local loss curvature . This can inflate the effective update budget and amplify drift along weakly constrained directions. We introduce GRIT , a dynamic, curvature-aware LoRA procedure. GRIT preserves the LoRA parameterization but: (1) preconditions gradients in rank space using K-FAC as a natural-gradient proxy; (2) periodically reprojects the low-rank basis onto dominant Fisher eigendirections to suppress drift; and (3) adapts the effective rank from the spectrum so capacity concentrates where signal resides. The net effect is to steer updates toward high-signal, low-interference directions while using fewer effective parameters . Across instruction-following , comprehension , and reasoning benchmarks on LLaMA backbones, GRIT matches or surpasses LoRA / QLoRA while reducing trainable parameters by $\\sim\\!46\\%$ on average (25\u201380% across tasks) without degrading quality in practice, across diverse prompt styles and varying data mixes. Since fine-tuning often induces catastrophic forgetting , we model GRIT\u2019s drift via a curvature-modulated power law $\\boxed{L_{pt}^{\\mathrm{GRIT}}=L_{pt}^{0}+A\\,\\dfrac{D_{ft}^{\\beta}}{(\\Xi_{\\mathrm{GRIT}}N)^{\\alpha}}+E}$ with $\\Xi_{\\mathrm{GRIT}}=(1+\\gamma_{r}r_{\\mathrm{eff}})(1+\\gamma_{a}\\rho_{\\mathrm{align}})(1+\\gamma_{p}\\pi_{\\mathrm{proj}})$ , capturing effective rank , Fisher alignment , and projection fidelity ; empirically, GRIT yields consistently lower drift than LoRA and improves the parameter-updates\u2013vs\u2013retention frontier against strong PEFT and optimizer baselines (e.g., Orthogonal-LoRA , IA 3 , DoRA / Eff-FT , Shampoo ). Code repository .", "This paper addresses the critical bottleneck of infrared (IR) data scarcity in Printed Circuit Board (PCB) defect detection by proposing a cross-modal data augmentation framework integrating CycleGAN and YOLOv8. Unlike conventional methods relying on paired supervision, we leverage CycleGAN to perform unpaired image-to-image translation, mapping abundant visible-light PCB images into the infrared domain. This generative process synthesizes high-fidelity pseudo-IR samples that preserve the structural semantics of defects while accurately simulating thermal distribution patterns. Subsequently, we construct a heterogeneous training strategy that fuses generated pseudo-IR data with limited real IR samples to train a lightweight YOLOv8 detector. Experimental results demonstrate that this method effectively enhances feature learning under low-data conditions. The augmented detector significantly outperforms models trained on limited real data alone and approaches the performance benchmarks of fully supervised training, proving the efficacy of pseudo-IR synthesis as a robust augmentation strategy for industrial inspection.", "Realizing the full potential of quantum computation requires Quantum Error Correction (QEC). QEC reduces error rates by encoding logical information across redundant physical qubits, enabling errors to be detected and corrected. A common decoder used for this task is Minimum Weight Perfect Matching (MWPM) a graph-based algorithm that relies on edge weights to identify the most likely error chains. In this work, we propose a data-driven decoder named Neural Minimum Weight Perfect Matching (NMWPM). Our decoder utilizes a hybrid architecture that integrates Graph Neural Networks (GNNs) to extract local syndrome features and Transformers to capture long-range global dependencies, which are then used to predict dynamic edge weights for the MWPM decoder. To facilitate training through the non-differentiable MWPM algorithm, we formulate a novel proxy loss function that enables end-to-end optimization. Our findings demonstrate significant performance reduction in the Logical Error Rate (LER) over standard baselines, highlighting the advantage of hybrid decoders that combine the predictive capabilities of neural networks with the algorithmic structure of classical matching.", "The rapid growth of artificial intelligence (AI) has brought novel data processing and generative capabilities but also escalating energy requirements. This challenge motivates renewed interest in neuromorphic computing principles, which promise brain-like efficiency through discrete and sparse activations, recurrent dynamics, and non-linear feedback. In fact, modern AI architectures increasingly embody neuromorphic principles through heavily quantized activations, state-space dynamics, and sparse attention mechanisms. This paper elaborates on the connections between neuromorphic models, state-space models, and transformer architectures through the lens of the distinction between intra-token processing and inter-token processing. Most early work on neuromorphic AI was based on spiking neural networks (SNNs) for intra-token processing, i.e., for transformations involving multiple channels, or features, of the same vector input, such as the pixels of an image. In contrast, more recent research has explored how neuromorphic principles can be leveraged to design efficient inter-token processing methods, which selectively combine different information elements depending on their contextual relevance. Implementing associative memorization mechanisms, these approaches leverage state-space dynamics or sparse self-attention. Along with a systematic presentation of modern neuromorphic AI models through the lens of intra-token and inter-token processing, training methodologies for neuromorphic AI models are also reviewed. These range from surrogate gradients leveraging parallel convolutional processing to local learning rules based on reinforcement learning mechanisms.", "The Transformer architecture, a cornerstone of modern Large Language Models (LLMs), has achieved extraordinary success in sequence modeling, primarily due to its attention mechanism. However, despite its power, the standard attention mechanism is plagued by well-documented issues: representational collapse and attention sink. Although prior work has proposed approaches for these issues, they are often studied in isolation, obscuring their deeper connection. In this paper, we present a unified perspective, arguing that both can be traced to a common root\u2014improper attention allocation. We identify two failure modes: 1) Attention Overload, where tokens receive comparable high weights, blurring semantic features that lead to representational collapse; 2) Attention Underload, where no token is semantically relevant, yet attention is still forced to distribute, resulting in spurious focus such as attention sink. Building on this insight, we introduce Lazy Attention, a novel mechanism designed for a more focused attention distribution. To mitigate overload, it employs positional discrimination across both heads and dimensions to sharpen token distinctions. To counteract underload, it incorporates Elastic-Softmax, a modified normalization function that relaxes the standard softmax constraint to suppress attention on irrelevant tokens. Experiments on the FineWeb-Edu corpus, evaluated across nine diverse benchmarks, demonstrate that Lazy Attention successfully mitigates attention sink and achieves competitive performance compared to both standard attention and modern architectures, while reaching up to 59.58% attention sparsity.", "Deep neural network-based classifiers are prone to errors when processing adversarial examples (AEs). AEs are minimally perturbed input data undetectable to humans posing significant risks to security-dependent applications. Hence, extensive research has been undertaken to develop defense mechanisms that mitigate their threats. Most existing methods primarily focus on discriminating AEs based on the input sample features, emphasizing AE detection without addressing the correct sample categorization before an attack. While some tasks may only require mere rejection on detected AEs, others necessitate identifying the correct original input category such as traffic sign recognition in autonomous driving. The objective of this study is to propose a method for rectifying AEs to estimate the correct labels of their original inputs. Our method is based on re-attacking AEs to move them beyond the decision boundary for accurate label prediction, effectively addressing the issue of rectifying minimally perceptible AEs created using white-box attack methods. However, challenge remains with respect to effectively rectifying AEs produced by black-box attacks at a distance from the boundary, or those misclassified into low-confidence categories by targeted attacks. By adopting a straightforward approach of only considering AEs as inputs, the proposed method can address diverse attacks while avoiding the requirement of parameter adjustments or preliminary training. Results demonstrate that the proposed method exhibits consistent performance in rectifying AEs generated via various attack methods, including targeted and black-box attacks. Moreover, it outperforms conventional rectification and input transformation methods in terms of stability against various attacks.", "We develop a kernel-centric theory of task-driven feature learning in wide neural networks with linear readout and $\\ell_{2}$ -regularization. Our analysis proceeds in two stages to bridge the gap between interpretable dynamics and structural guarantees. First, operating in a fast- readout (adiabatic) regime with squared loss, we derive a closed-form kernel ODE governed by the competition between a task-dependent drive operator and isotropic regularization decay. This reveals the precise mechanism of alignment: for supervised learning with $C$ outputs, the drive is a rank- $C$ operator that compresses the kernel into a low-dimensional subspace and obeys an explicit \u201cwater-filling\u201d spectral law. Second, we show that the resulting structural phenomena are not artifacts of the time-scale separation. Under standard $\\ell_{2}$ -regularization and a $C$ -dimensional linear readout, any stable steady state of the coupled feature\u2013readout dynamics necessarily exhibits label-driven rank compression ( $\\mathrm{rank}(K_{\\infty})\\leq C$ ), and for squared loss satisfies the same spectral truncation law. These results are algebraic consequences of the architecture and loss, and do not depend on the fast-readout approximation. Complementing this deterministic picture, we analyze SGD noise at the kernel level and show that, for any convex loss with $C$ outputs, the instantaneous noise covariance is confined to a low-dimensional subspace of rank at most $O(C)$ , independent of the network width and the current parameter values. Thus stochasticity induces restricted diffusion within the task- relevant subspace rather than isotropic exploration. We further extend our framework in two idealized directions: (i) a population limit, where we relate spectral evolution of the kernel integral operator to the bias\u2013variance trade-off; and (ii) a stylized self-supervised kernel model driven by a graph Laplacian and a log-det repulsion, which produces high-rank, Laplacian-aligned representations. Together, these results provide a unified spectral language that contrasts the compressive nature of supervised learning with the expansive behavior of self-supervision, while clarifying which aspects are rigorous architectural consequences and which arise within specific kernel models.", "Information overload (IOL) is a well-known and devastating phenomenon that alters the performance of carrying out all types of tasks. It has been shown that in the media space, IOL can contribute to news fatigue and news avoidance, which often lead to the proliferation of fake news posts on social networks. However, there is a lack of automated methods for tracking IOL in large datasets. In this study, we examine whether metrics derived from the distribution of topics generated by the BERTopic model can serve as a proxy for IOL. We test our assumptions across a set of Reddit communities related to the COVID-19 pandemic and observe significant global correlation between our metrics and the FakeBERT-detected fake news fraction. However, at the community level, the correlation analysis results are ambiguous.", "Understanding affective polarization in online discourse is crucial for evaluating the societal impact of social media interactions. This study presents a novel framework that leverages large language models (LLMs) and domain-informed heuristics to systematically analyze and quantify affective polarization in discussions on divisive topics such as climate change and gun control. Unlike most prior approaches that relied on sentiment analysis or predefined classifiers, our method integrates LLMs to extract stance, affective tone, and agreement patterns from large-scale social media discussions. We then apply a rule-based scoring system capable of quantifying affective polarization even in small conversations consisting of single interactions, based on stance alignment, emotional content, and interaction dynamics. Our analysis reveals distinct polarization patterns that are event dependent: (i) anticipation-driven polarization, where extreme polarization escalates before well-publicized events, and (ii) reactive polarization, where intense affective polarization spikes immediately after sudden, high-impact events. By combining AI-driven content annotation with domain-informed scoring, our framework offers a scalable and interpretable approach to measuring affective polarization. The source code is publicly available at: https://github.com/hasanjawad001/llm-social-media-polarization", "# LOFA: Online Influence Maximization under Full-Bandit Feedback using Lazy Forward Selection ## 1 Introduction The Influence Maximization (IM) problem is a fundamental challenge in social network analysis that aims to identify a small set of influential nodes (seed users) in a network such that their activation leads to the maximum spread of influence . This problem has significant applications in various domains, such as viral marketing, social network analysis, rumor control, and public health campaigns, where understanding and leveraging network dynamics are crucial. Companies seek to leverage network effects to promote products through word-of-mouth marketing, while policymakers aim to maximize the reach of awareness campaigns with minimal resources. Influence Maximization helps optimize these processes by selecting the most effective set of influencers. The IM problem can be categorized into offline and online settings based on network knowledge and the decision-making process. Some IM research primarily focuses on an offline setting, where the entire network structure and influence probabilities are known in advance, allowing for precomputation of optimal seed sets. However,", "AI-companionship platforms are rapidly reshaping how people form emotional, romantic, and parasocial bonds with non-human agents, raising new questions about how these relationships intersect with gendered online behavior and exposure to harmful content. Focusing on the MyBoyfriendIsAI (MBIA) subreddit, we reconstruct the Reddit activity histories of more than 3,000 highly engaged users over two years, yielding over 67,000 historical submissions. We then situate MBIA within a broader ecosystem by building a historical interaction network spanning more than 2,000 subreddits, which enables us to trace cross-community pathways and measure how toxicity and emotional expression vary across these trajectories. We find that MBIA users primarily traverse four surrounding community spheres (AI-companionship, porn-related, forum-like, and gaming) and that participation across the ecosystem exhibits a distinct gendered structure, with substantial engagement by female users. While toxicity is generally low across most pathways, we observe localized spikes concentrated in a small subset of AI-porn and gender-oriented communities. Nearly 16% of users engage with gender-focused subreddits, and their trajectories display systematically different patterns of emotional expression and elevated toxicity, suggesting that a minority of gendered pathways may act as toxicity amplifiers within the broader AI-companionship ecosystem. These results characterize the gendered structure of cross-community participation around AI companionship on Reddit and highlight where risks concentrate, informing measurement, moderation, and design practices for human-AI relationship platforms.", "# Learning Resilient Elections with Adversarial GNNs Learning Resilient Elections with Adversarial GNNs Hao Xiang Li1* Yash Shah1* Lorenzo Giusti2 1Department of Computer Science, University of Cambridge, Cambridge, United Kingdom 2CERN, Geneva, Switzerland hxl23@cst.cam.ac.uk ys562@cam.ac.uk lorenzo.giusti@cern.ch Abstract In the face of adverse motives, it is indispensable to achieve a consensus. Elections have been the canonical way by which modern democracy has operated since the 17th century. Nowadays, they regulate markets, provide an engine for modern recommender systems or peer-to-peer networks, and remain the main approach to represent democracy. However, a desirable universal voting rule that satisfies all hypothetical scenarios is still a challenging topic, and the design of these systems is at the forefront of mechanism design research. Automated mechanism design is a promising approach, and recent works have demonstrated that set-invariant architectures are uniquely suited to modelling electoral systems. However, various concerns prevent the direct application to real-world settings, such as robustness to strategic voting. In this paper, we generalise the expressive ca", "Community search aims to identify a refined set of nodes that are most relevant to a given query, supporting tasks ranging from fraud detection to recommendation. Unlike homophilic graphs, many real-world networks are heterophilic, where edges predominantly connect dissimilar nodes. Therefore, structural signals that once reflected smooth, low-frequency similarity now appear as sharp, high-frequency contrasts. However, both classical algorithms (e.g., $k$ -core, $k$ -truss) and recent ML-based models struggle to achieve effective community search on heterophilic graphs, where edge signs or semantics are generally unknown. Algorithm-based methods often return communities with mixed class labels, while GNNs, built on homophily, smooth away meaningful signals and blur community boundaries. Therefore, we propose Adaptive Community Search (AdaptCS), a lightweight framework featuring three key designs: (i) an AdaptCS Encoder that disentangles multi-hop and multi-frequency signals, enabling the model to capture both smooth (homophilic) and contrastive (heterophilic) relations; (ii) a memory-efficient low-rank optimization that removes the main computational bottleneck and ensures model scalability; and (iii) an Adaptive Community Score (ACS) that guides online search by balancing embedding similarity and topological relations. Extensive experiments on both heterophilic and homophilic benchmarks demonstrate that AdaptCS outperforms the best-performing baseline by an average of 11% in F1-score, retains robustness across heterophily levels, and achieves up to 2 orders of magnitude speedup over the strongest ML-based CS baselines.", "In this article, we propose a generalization of two well-known invariants of real networks: degree and ksi-centrality. More precisely, we found a set of centralities (degree centrality and ksi-centrality for the cases $j=0,1$ ) based on the Laplacian matrix that closely match the Weibull distribution; for real networks, they are right-skewed, while for artificial networks, they are centered. A threshold of 1 in the Pearson skewness coefficients accurately distinguishes these real networks from artificial ones.", "Homelessness in American cities is becoming an ever more prominent issue, but its causes remain contested, ranging from mental health and substance abuse to housing affordability and local labor markets. To shed light on this issue, I construct a novel MSA-level national panel of homelessness counts using data from the U.S. Department of Housing and Urban Development. Using a long-differencing regression specification with the changes in rent entered in piecewise linear form, I find that rent increases predict large increases in homelessness rates, but decreases have little to no effect. The same conclusions are reached when I use a quasi-differencing moment condition, assuming a multiplicative mean specification. Then, I propose a theoretical model of the low-end housing market that explains the asymmetry I find in the data. Finally, I outline an IV strategy that instruments rent changes with a Bartik instrument of predicted employment growth interacted with local housing-supply elasticities. My findings suggest that homelessness is a housing problem; however, because the response is sticky downward, effective policy must complement housing-market interventions with measures that address barriers faced by people experiencing homelessness.", "# What Is a Causal Effect When Firms Interact? Counterfactuals and Interdependence 1 What Is a Causal Effect When Firms Interact? Counterfactuals and Interdependence January 2026 Mariluz Mat\u00e9 (mluz.mate@upct.es). Abstract Many empirical studies estimate causal effects in environments where economic units interact through spatial or network connections. In such settings, outcomes are jointly determined, and treatment-induced shocks propagate across economically connected units. A growing literature highlights identification challenges in these models and questions the causal interpretation of estimated spillovers. This paper argues that the problem is more fundamental. Under interdependence, causal effects are not uniquely defined objects\u2014even when the interaction structure is correctly specified or consistently learned, and even under ideal identifying conditions. We develop a causal framework for firm-level economies in which interaction structures are unobserved but can be learned from predetermined characteristics. We show that learning the network, while necessary to model interdependence, is not sufficient for causal interpretation. Instead, causal conclusions hinge on explici", "# Effect of Informational Interventions on EV Adoption Intention: Evidence from a Tier II City in India Effect of Informational Interventions on EV Adoption Intention: Evidence from a Tier II City in India Authors: Pranshu Raghuvanshi1 and Anjula Gurtoo1 Abstract This study investigates the effectiveness of targeted informational interventions on electric vehicle adoption intention. A randomised controlled field experiment with three treatment groups and a control group was used to study the effectiveness of three informational interventions. Participants in each treatment group received a distinct informational intervention: cost-based, range-based, and norm-based. Two of the three interventions (rangebased and norm-based), designed to reduce behavioural and psychological barriers, were found to be significant. The cost-based intervention was not significant, suggesting that financial motives alone may not be sufficient to lead to an increase in the adoption of electric vehicles. The significant effect observed for the range-based and norm-based interventions suggests that the discomfort related to the technology must be addressed, and social norms can be effectively utilised to p", "Investment portfolio optimization is a task conducted in all major financial institutions. The Cardinality Constrained Mean-Variance Portfolio Optimization (CCPO) problem formulation is ubiquitous for portfolio optimization. The challenge of this type of portfolio optimization, a mixed-integer quadratic programming (MIQP) problem, arises from the intractability of solutions from exact solvers, where heuristic algorithms are used to find approximate portfolio solutions. CCPO entails many laborious and complex workflows and also requires extensive effort pertaining to heuristic algorithm development, where the combination of pooled heuristic solutions results in improved efficient frontiers. Hence, common approaches are to develop many heuristic algorithms. Agentic frameworks emerge as a promising candidate for many problems within combinatorial optimization, as they have been shown to be equally efficient with regard to automating large workflows and have been shown to be excellent in terms of algorithm development, sometimes surpassing human-level performance. This study implements a novel agentic framework for the CCPO and explores several concrete architectures. In benchmark problems, the implemented agentic framework matches state-of-the-art algorithms. Furthermore, complex workflows and algorithm development efforts are alleviated, while in the worst case, lower but acceptable error is reported.", "# TWICE: Tree-based Wage Inference with Clustering and Estimation TWICE: Tree-based Wage Inference with Clustering and Estimation Aslan Bakirov, Francesco Del Prato, Paolo Zacchia January 2026 How much do worker skills, firm pay policies, and their interaction contribute to wage inequality? Standard approaches rely on latent fixed effects identified through worker mobility, but sparse networks inflate variance estimates, additivity assumptions rule out complementarities, and the resulting decompositions lack interpretability. We propose TWICE\u2014Tree-based Wage Inference with Clustering and Estimation\u2014a framework that models the conditional wage function directly from observables using gradient-boosted trees, replacing latent effects with interpretable, observable-anchored partitions. This trades off the ability to capture idiosyncratic unobservables for robustness to sampling noise and out-of-sample portability. Applied to Portuguese administrative data, TWICE outperforms linear benchmarks out of sample and reveals that sorting and non-additive interactions explain substantially more wage dispersion than implied by standard AKM estimates. Aslan Bakirov: CERGE-EI, aslan.bakirov@cerge-", "This paper proposes a semiparametric joint VaR\u2013ES framework driven by realized information, motivated by the economic mechanisms underlying tail risk generation. Building on the CAViaR quantile recursion, the model introduces a dynamic ES\u2013VaR gap to capture time-varying tail severity, while measurement equations transform multiple realized measures into high-frequency risk innovations.These innovations are further aggregated through a dynamic factor model, extracting common high-frequency tail risk factors that affect the quantile level and tail thickness through distinct risk channels. This structure explicitly separates changes in risk levels from the intensification of tail risk.Empirical evidence shows that the proposed model consistently outperforms quantile regression, EVT-based, and GARCH-type benchmarks across multiple loss functions, highlighting the importance of embedding high-frequency information directly into the tail risk generation layer.", "Social media platforms systematically reward popularity over authenticity, incentivizing users to strategically tailor their expression for attention. In this paper, we introduce (i) popularity as a strategic expression mechanism, distinct from the canonical mechanisms of conformity, learning, persuasion, and (mis)information transmission in social networks, and (ii) a utilitarian framework for measuring user welfare that maps directly to observable platform metrics, filling a critical gap in the social media literature. In the model, agents hold fixed heterogeneous authentic opinions and derive utility gains from the popularity of their own posts\u2014measured by likes received, and utility gains (losses) from exposure to content that aligns with (diverges from) their authentic opinion. Social media interaction acts as a state-dependent welfare amplifier: light topics generate Pareto improvements, whereas intense topics make everyone worse off in a polarized society (e.g., political debates during elections). Moreover, strategic expression amplifies social media polarization during polarized events while dampening it during unified events (e.g., national celebrations). Consequently, strategic distortions magnify welfare outcomes, expanding aggregate gains in light topics while exacerbating losses in intense, polarized ones. Counterintuitively, strategic agents often face a popularity trap : posting a more popular opinion is individually optimal, yet collective action by similar agents eliminates their authentic opinion from the platform, leaving them worse off than under the authentic-expression benchmark. Homophilic algorithms that match users with preferred content\u2014widely used by platforms\u2014discipline popularity-driven behavior, narrowing the popularity trap region and limiting its welfare effects. JEL Codes: D60, D62, D83, D85, D91, L82", "Public debate links worsening job prospects for AI-exposed occupations to the release of ChatGPT in late 2022. Using monthly U.S. unemployment insurance records, we measure occupation- and location-specific unemployment risk and find that risk rose in AI-exposed occupations beginning in early 2022, months before ChatGPT. Analyzing millions of LinkedIn profiles, we show that graduate cohorts from 2021 onward entered AI-exposed jobs at lower rates than earlier cohorts, with gaps opening before late 2022. Finally, from millions of university syllabi, we find that graduates taking more AI-exposed curricula had higher first-job pay and shorter job searches after ChatGPT. Together, these results point to forces pre-dating generative AI and to the ongoing value of LLM-relevant education.", "We study choice among lotteries in which the decision maker chooses from a small library of decision rules. At each menu, the applied rule must make the realized choice a strict improvement under a dominance benchmark on perceived lotteries. We characterize the maximal Herfindahl-Hirschman concentration of rule shares over all locally admissible assignments, and diagnostics that distinguish rules that unify behavior across many menus from rules that mainly act as substitutes. We provide a MIQP formulation, a scalable heuristic, and a finite-sample permutation test of excess concentration relative to a menu-independent random-choice benchmark. Applied to the CPC18 dataset ( $N=686$ subjects, each making $500$ - $700$ repeated binary lottery choices), the mean rule concentration is $0.545$ , and $64.1\\%$ of subjects reject random choice at the $1\\%$ level. Concentration gains are primarily driven by modal-payoff focusing, salience-thinking, and regret-based comparisons. JEL D91, D11, D81 Keywords : Decision Theory, Revealed Preference, Behavioral Economics.", "We identify volatility spillovers across commodities, equities, and treasuries using a hybrid HARX-ElasticNet framework on daily realized volatility for six futures markets over 2002\u20132025. Our two step procedure estimates own-volatility dynamics via OLS to preserve persistence, then applies ElasticNet regularization to cross-market spillovers. The sparse network structure that emerges shows equity markets (ES, NQ) act as the primary volatility transmitters, while crude oil (CL) ends up being the largest receiver of cross-market shocks. Agricultural commodities stay isolated from the larger network. A simple univariate HAR model achieves equally performing point forecasts as our model, but our approach reveals network structure that univariate models cannot. Joint Impulse Response Functions trace how shocks propagate through the network. Our contribution is to demonstrate that hybrid estimation methods can identify meaningful spillover pathways while preserving forecast performance.", "The rapid integration of Artificial Intelligence (AI) into China\u2019s economy presents a classic governance challenge: how to harness its growth potential while managing its disruptive effects on traditional capital and labor markets. This study addresses this policy dilemma by modeling the dynamic interactions between AI capital, physical capital, and labor within a Lotka\u2013Volterra predator-prey framework. Using annual Chinese data (2016\u20132023), we quantify the interaction strengths, identify stable equilibria, and perform a global sensitivity analysis. Our results reveal a consistent pattern where AI capital acts as the \u2018prey\u2019, stimulating both physical capital accumulation and labor compensation (wage bill), while facing only weak constraining feedback. The equilibrium points are stable nodes, indicating a policy-mediated convergence path rather than volatile cycles. Critically, the sensitivity analysis shows that the labor market equilibrium is overwhelmingly driven by AI-related parameters, whereas the physical capital equilibrium is also influenced by its own saturation dynamics. These findings provide a systemic, quantitative basis for policymakers: (1) to calibrate AI promotion policies by recognizing the asymmetric leverage points in capital vs. labor markets; (2) to anticipate and mitigate structural rigidities that may arise from current regulatory settings; and (3) to prioritize interventions that foster complementary growth between AI and traditional economic structures while ensuring broad-based distribution of technological gains.", "Filters from the Gammatone family are often used to model auditory signal processing, but the filter constant values used to mimic human hearing are largely set to values based on historical psychoacoustic data collected several decades ago. Here, we move away from this long-standing convention, and estimate filter constants using a range of more recent reported filter characteristics (such as quality factors and ratios between quality factors and peak group delay) within a characteristics-based framework that clarifies how filter behavior is related to the underlying constants. Using a sharp-filter approximation that captures shared peak-region behavior across certain classes of filters, we analyze the range of behaviors accessible when the full degrees of freedom of the filter are utilized rather than fixing the filter order or exponent to historically prescribed values. Filter behavior is characterized using magnitude-based and phase-based characteristics and their ratios, which reveal which characteristics are informative for constraining filter constants and which are only weakly constraining. We show that these insights and estimation methods extend to multiple realizable filter classes from the Gammatone family and apply them, together with recent physiological and psychoacoustic observations, to derive constraints on and estimates for filter constants for human auditory filters. More broadly, this framework supports the design of auditory filters with arbitrary characteristic-level specifications and enables systematic assessment of how variations in filter characteristics influence auditory models, perceptual findings, and technologies that rely on auditory filterbanks.", "The increasing complexity and interconnectedness of systems across various fields have led to a growing interest in studying complex networks, particularly Scale-Free (SF) networks, which best model real-world systems. This paper investigates the influence of clustering on the observability and controllability of complex SF networks, framing these characteristics in the context of structured systems theory. In this paper, we show that densely clustered networks require fewer driver and observer nodes due to better information propagation within clusters. This relationship is of interest for optimizing network design in applications such as social networks and intelligent transportation systems. We first quantify the network observability/controllability requirements, and then, through Monte-Carlo simulations and different case studies, we show how clustering affects these metrics. Our findings offer practical insights into reducing control and observer nodes for sensor/actuator placement, particularly in resource-constrained setups. This work contributes to the understanding of network observability/controllability and presents techniques for improving these features through alterations in network structure and clustering.", "# Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective This article has been accepted for publication in the IEEE Wireless Communications Magazine. Please cite it as A. S. Abdalla and V. Marojevic, \u201dNext Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective,\u201d IEEE Wireless Communications Magazine, December 2025. 1 Next Generation Intelligent Low-Altitude Economy Deployments: The O-RAN Perspective Aly Sabri Abdalla and Vuk Marojevic Dept. of Electrical and Computer Engineering, Mississippi State University, USA Emails: {asa298; vuk.marojevic}@msstate.edu Abstract\u2014 Despite the growing interest in low-altitude economy (LAE) applications, including UAV-based logistics and emergency response, fundamental challenges remain in orchestrating such missions over complex, signal-constrained environments. These include the absence of real-time, resilient, and contextaware orchestration of aerial nodes with limited integration of arti\ue000cial intelligence (AI) specialized for LAE missions. This paper introduces an open radio access network (O-RAN)- enabled LAE framework that leverages seamless coordination between the disaggregated RAN archit", "The decarbonization of the energy sector has led to a significant high penetration of distributed energy resources (DERs), particularly photovoltaic (PV) systems, in low-voltage (LV) distribution networks. To maintain grid stability, recent standards (e.g., IEEE 1547-2018) mandate DERs to provide grid-support functionalities through smart inverters (SIs), which typically operate autonomously based on local measurements. However, as DER penetration increases, uncoordinated control modes of SIs can lead to adverse interactions, compromising system efficiency, voltage regulation, and overall stability. While previous studies have demonstrated the benefits of coordinated inverter control and optimal dispatch strategies, the system-wide impacts of heterogeneous SI groups operating under different control modes remain largely unexamined. This paper addresses this gap by assessing the dynamic interactions among multiple SI groups with varying control strategies, namely: Constant Power Factor (CPF), Volt-VAR, and Volt-Watt modes. Furthermore, the analysis covers both resistive and inductive feeder types. The validation is performed using a real-time setup. The CIRGE low-voltage (LV) distribution network is simulated in the Opal-RT platform as the test network, enabling realistic and high-fidelity evaluation of SI control interactions under practical grid conditions.", "In robotics and multi-agent systems, fleets of autonomous agents often operate in subtly different environments while pursuing a common high-level objective. Directly pooling their data to learn a shared reward function is typically impractical due to differences in dynamics, privacy constraints, and limited communication bandwidth. This paper introduces an optimal transport\u2013based approach to federated inverse reinforcement learning (IRL). Each client first performs lightweight Maximum Entropy IRL locally, adhering to its computational and privacy limitations. The resulting reward functions are then fused via a Wasserstein barycenter, which considers their underlying geometric structure. We further prove that this barycentric fusion yields a more faithful global reward estimate than conventional parameter averaging methods in federated learning. Overall, this work provides a principled and communication-efficient framework for deriving a shared reward that generalizes across heterogeneous agents and environments.", "Large Language Models (LLMs) are increasingly being used as high-level controllers for autonomous Unmanned Aerial Vehicle (UAV) missions. However, current evaluations rarely assess whether such agents remain safe, protocol-compliant, and effective under realistic constraints of next-generation networking. This paper introduces $\\alpha^{3}$ -Bench, a benchmark for assessing LLM-driven UAV autonomy as a multi-turn conversational reasoning and control problem operating under dynamic 6G conditions. Each mission is modelled as a language-mediated control loop between an LLM-based UAV agent and a human operator, where decisions must satisfy strict schema validity, speaker alternation, mission policies, and safety bounds while adapting to fluctuating network slices (URLLC/eMBB/mMTC), latency, jitter, packet loss, throughput, and edge-load variations. To reflect modern agentic workflows, $\\alpha^{3}$ -Bench integrates a dual-action layer that supports both Model Context Protocol (MCP) tool calls and Agent-to-Agent (A2A) coordination, enabling the evaluation of tool-use consistency and multi-agent interactions within UAV missions. We construct a large-scale corpus of 113k AI conversational episodes grounded in UAVBench scenarios and evaluate 17 state-of-the-art LLMs using a fixed subset of 50 episodes per scenario under deterministic decoding. We propose a composite $\\alpha^{3}$ metric that unifies six pillars\u2014Task Outcome, Safety Policy, Tool Consistency, Interaction Quality, Network Robustness, and Communication Cost\u2014with reliability- and efficiency-normalized scores per second and per thousand tokens. Experimental results show that several frontier models achieve near-perfect mission success and safety compliance (Task Outcome and Safety Policy $\\geq$ 0.95), yet their robustness and efficiency diverge substantially: under degraded 6G conditions, Network Robustness scores drop by up to 30\u201340%, while efficiency-normalized performance varies by more than a 2 $\\times$ factor across models in both $\\alpha^{3}$ per-second and $\\alpha^{3}$ per-1k-tokens metrics, reflecting significant variance in inference latency and token consumption. $\\alpha^{3}$ -Bench provides a reproducible, extensible foundation for benchmarking conversational UAV autonomy and guiding the development of safe, network-aware, and resource-efficient LLM agents in 6G-enabled aerial systems. To support open science and reproducibility, we release the $\\alpha^{3}$ -Bench dataset on GitHub: https://github.com/maferrag/AlphaBench .", "# Safety for Weakly-Hard Control Systems via Graph-Based Barrier Functions Safety for Weakly-Hard Control Systems via Graph-Based Barrier Functions Marc Seidel, Mahathi Anand, Frank Allg \u0308ower Abstract\u2014 Despite significant advancement in technology, communication and computational failures are still prevalent in safety-critical engineering applications. Often, networked control systems experience packet dropouts, leading to openloop behavior that significantly affects the behavior of the system. Similarly, in real-time control applications, control tasks frequently experience computational overruns and thus occasionally no new actuator command is issued. This article addresses the safety verification and controller synthesis problem for a class of control systems subject to weakly-hard constraints, i.e., a set of window-based constraints where the number of failures are bounded within a given time horizon. The results are based on a new notion of graph-based barrier functions that are specifically tailored to the considered system class, offering a set of constraints whose satisfaction leads to safety guarantees despite communication failures. Subsequent reformulations of the safet", "# New Formulations and Discretization Insights for the Electric Autonomous Dial-a-Ride Problem ## 1 Background The Dial-a-Ride Problem (DARP) involves designing vehicle routes and schedules to transport customers from their origins to destinations while minimizing travel cost. The Electric Autonomous Dial-a-Ride Problem (E-ADARP) extends DARP by considering fleets of electric, self-driving vehicles. Here, the autonomous feature mainly implies that driver-related cost and constraints are not modeled explicitly. The E-ADARP setting introduces additional operational constraints due to limited battery capacity, while eliminating those associated with human drivers (e.g., maximum driving time). The need for recharging further increases complexity, requiring decisions about when, where, and how much to recharge\u2013decisions that must balance energy needs with charging time. Additionally, the objective function not only minimizes travel cost but also accounts for excess user ride time. These additional constraints and the objective structure further increase computational complexity. Most existing studies assume highly precise or continuous parameters for time and SoC, particularly in applic", "Current navigation systems conflate time-to-drive with the true time-to-arrive by ignoring parking search duration and the final walking leg. Such underestimation can significantly affect user experience, mode choice, congestion, and emissions. To address this issue, this paper introduces the probability-aware parking selection problem, which aims to direct drivers to the best parking location rather than straight to their destination. An adaptable dynamic programming framework is proposed that leverages probabilistic, lot-level availability to minimize the expected time-to-arrive. Closed-form analysis determines when it is optimal to target a specific parking lot or explore alternatives, as well as the expected time cost. Sensitivity analysis and three illustrative cases are examined, demonstrating the model\u2019s ability to account for the dynamic nature of parking availability. Given the high cost of permanent sensing infrastructure, we assess the error rates of using stochastic observations to estimate availability. Experiments with real-world data from the US city of Seattle indicate this approach\u2019s viability, with mean absolute error decreasing from 7% to below 2% as observation frequency increases. In data-based simulations, probability-aware strategies demonstrate time savings up to 66% relative to probability-unaware baselines, yet still take up to 123% longer than time-to-drive estimates.", "This paper presents a decentralized control framework for distribution matching in multi-agent systems (MAS), where agents collectively achieve a prescribed terminal spatial distribution. The problem is formulated using optimal transport (Wasserstein distance), which provides a principled measure of distributional discrepancy and serves as the basis for the control design. To avoid solving the global optimal transport problem directly, the distribution-matching objective is reformulated into a tractable per-agent decision process, enabling each agent to identify its desired terminal locations using only locally available information. A sequential weight-update rule is introduced to construct feasible local transport plans, and a memory-based correction mechanism is incorporated to maintain reliable operation under intermittent and range-limited communication. Convergence guarantees are established, showing cycle-wise improvement of a surrogate transport cost under both linear and nonlinear agent dynamics. Simulation results demonstrate that the proposed framework achieves effective and scalable distribution matching while operating fully in a decentralized manner.", "The rapid proliferation of Internet of Things (IoT) technologies, projected to exceed 30 billion interconnected devices by 2030, has significantly escalated the complexity of cybersecurity challenges. This survey aims to provide a comprehensive analysis of vulnerabilities, threats, and defense mechanisms, specifically focusing on the integration of network and application layers within real-time monitoring and decision-making systems. Employing an integrative review methodology, 59 scholarly articles published between 2009 and 2024 were selected from databases such as IEEE Xplore, ScienceDirect, and PubMed, utilizing keywords related to IoT vulnerabilities and security attacks. Key findings identify critical threat categories, including sensor vulnerabilities, Denial-of-Service (DoS) attacks, and public cloud insecurity. Conversely, the study highlights advanced defense approaches leveraging Artificial Intelligence (AI) for anomaly detection, Blockchain for decentralized trust, and Zero Trust Architecture (ZTA) for continuous verification. This paper contributes a novel five-layer IoT model and outlines future research directions involving quantum computing and 6G networks to bolster IoT ecosystem resilience.", "Stability analysis of switched systems, characterized by multiple operational modes and switching signals, is challenging due to their nonlinear dynamics. While frameworks such as multiple Lyapunov functions (MLF) provide a foundation for analysis, their computational applicability is limited for systems without favorable structure. This paper investigates stability analysis for switched systems under state-dependent switching conditions. We propose neural multiple Lyapunov functions (NMLF), a unified framework that combines the theoretical guarantees of MLF with the computational efficiency of neural Lyapunov functions (NLF). Our approach leverages a set of tailored loss functions and a counter-example guided inductive synthesis (CEGIS) scheme to train neural networks that rigorously satisfy MLF conditions. Through comprehensive simulations and theoretical analysis, we demonstrate NMLF\u2019s effectiveness and its potential for practical deployment in complex switched systems.", "A large-scale mobile robot (LSMR) is a high-order multibody system that often operates on loose, unconsolidated terrain, which reduces traction. This paper presents a comprehensive navigation and control framework for an LSMR that ensures stability and safety-defined performance, delivering robust operation on slip-prone terrain by jointly leveraging high-performance techniques. The proposed architecture comprises four main modules: (1) a visual pose-estimation module that fuses onboard sensors and stereo cameras to provide an accurate, low-latency robot pose, (2) a high-level nonlinear model predictive control that updates the wheel motion commands to correct robot drift from the robot reference pose on slip-prone terrain, (3) a low-level deep neural network control policy that approximates the complex behavior of the wheel-driven actuation mechanism in LSMRs, augmented with robust adaptive control to handle out-of-distribution disturbances, ensuring that the wheels accurately track the updated commands issued by high-level control module, and (4) a logarithmic safety module to monitor the entire robot stack and guarantees safe operation. The proposed low-level control framework guarantees uniform exponential stability of the actuation subsystem, while the safety module ensures the whole system-level safety during operation. Comparative experiments on a 6,000 kg LSMR actuated by two complex electro-hydrostatic drives, while synchronizing modules operating at different frequencies.", "An algorithm for 3D terrain-following area coverage path planning is presented. Multiple adjacent paths are generated that are (i) locally apart from each other by a distance equal to the working width of a machinery, while (ii) simultaneously floating at a projection distance equal to a specific working height above the terrain. The complexities of the algorithm in comparison to its 2D equivalent are highlighted. These include uniformly spaced elevation data generation using an Inverse Distance Weighting-approach and a local search. Area coverage path planning results for real-world 3D data within an agricultural context are presented to validate the algorithm.", "Effective exploration remains a key challenge in RL, especially with non-stationary rewards or high-dimensional policies. We introduce ARISE , a lightweight framework that enhances reinforcement learning by augmenting standard policy-gradient methods with a compact swarm-based exploration layer. ARISE blends policy actions with particle-driven proposals, where each particle represents a candidate policy trajectory sampled in the action space, and modulates exploration adaptively using reward-variance cues. While easy benchmarks exhibit only slight improvements (e.g., +0.7% on CartPole-v1), ARISE yields substantial gains on more challenging tasks, including +46% on LunarLander-v3 and +22% on Hopper-v4, while preserving stability on Walker2d and Ant. Under non-stationary reward shifts, ARISE provides marked robustness advantages, outperforming PPO by +75 points on CartPole and improving LunarLander accordingly. Ablation studies confirm that both the swarm component and the adaptive mechanism contribute to the performance. Overall, ARISE offers a simple, architecture-agnostic route to more exploratory and resilient RL agents without altering core algorithmic structures.", "Off-policy actor-critic methods in reinforcement learning train a critic with temporal-difference updates and use it as a learning signal for the policy (actor). This design typically achieves higher sample efficiency than purely on-policy methods. However, critic networks tend to overestimate value estimates systematically. This is often addressed by introducing a pessimistic bias based on uncertainty estimates. Current methods employ ensembling to quantify the critic\u2019s epistemic uncertainty \u2014uncertainty due to limited data and model ambiguity\u2014to scale pessimistic updates. In this work, we propose a new algorithm called Stochastic Actor-Critic (STAC) that incorporates temporal (one-step) aleatoric uncertainty \u2014uncertainty arising from stochastic transitions, rewards, and policy-induced variability in Bellman targets\u2014to scale pessimistic bias in temporal-difference updates, rather than relying on epistemic uncertainty. STAC uses a single distributional critic network to model the temporal return uncertainty, and applies dropout to both the critic and actor networks for regularization. Our results show that pessimism based on a distributional critic alone suffices to mitigate overestimation, and naturally leads to risk-averse behavior in stochastic environments. Introducing dropout further improves training stability and performance by means of regularization. With this design, STAC achieves improved computational efficiency using a single distributional critic network.", "# A formal theory on problem space as a semantic world model in systems engineering A FORMAL THEORY ON PROBLEM SPACE AS A SEMANTIC WORLD MODEL IN SYSTEMS ENGINEERING Mayuranath SureshKumar1 and Hanumanthrao Kannan1 1The University of Alabama in Huntsville ABSTRACT Classic problem-space theory models problem solving as a navigation through a structured space of states, operators, goals, and constraints. Systems Engineering (SE) employs analogous constructs (functional analysis, operational analysis, scenarios, trade studies), yet still lacks a rigorous systems-theoretic representation of the problem space itself. In current practice, reasoning often proceeds directly from stakeholder goals to prescriptive artifacts. This makes foundational assumptions about the operational environment, admissible interactions, and contextual conditions implicit or prematurely embedded in architectures or requirements. This paper addresses that gap by formalizing the problem space as an explicit semantic world model containing theoretical constructs that are defined prior to requirements and solution commitments. These constructs along with the developed axioms, theorems and corollary establish a rig", ". Magnetic Resonance Imaging (MRI) is a well-established modality for pre-operative planning and is also explored for intra-operative guidance of procedures such as intravascular interventions. Among the experimental robot-assisted technologies, the magnetic field gradients of the MRI scanner are used to power and maneuver ferromagnetic applicators for accessing sites in the patient\u2019s body via the vascular network. In this work, we propose a computational platform for preoperative planning and modeling of MRI-powered applicators inside blood vessels. This platform was implemented as a two-way data and command pipeline that links the MRI scanner, the computational core, and the operator. The platform first processes multi-slice MR data to extract the vascular bed and then fits a virtual corridor inside the vessel. This corridor serves as a virtual fixture (VF), a forbidden region for the applicators to avoid vessel perforation or collision. The geometric features of the vessel centerline, the VF, and MRI safety compliance (dB/dt, max available gradient) are then used to generate magnetic field gradient waveforms. Different blood flow profiles can be user-selected, and those parameters are used for modeling the applicator\u2019s maneuvering. The modeling module further generates cues about whether the selected vascular path can be safely maneuvered. Given future experimental studies that require a real-time operation, the platform was implemented on the Qt framework (C/C++) with software modules performing specific tasks running on dedicated threads: PID controller, generation of VF, generation of MR gradient waveforms.", "In this study, we focus on the training process and inference improvements of deep neural networks (DNNs), specifically Autoencoders (AEs) and Variational Autoencoders (VAEs), using Random Fourier Transformation (RFT). We further explore the role of RFT in model training behavior using Frequency Principle (F-Principle) analysis and show that models with RFT turn to learn low frequency and high frequency at the same time, whereas conventional DNNs start from low frequency and gradually learn (if successful) high-frequency features. We focus on reconstruction-based anomaly detection using autoencoder and variational autoencoder and investigate the RFT\u2019s role. We also introduced a trainable variant of RFT that uses the existing computation graph to train the expansion of RFT instead of it being random. We showcase our findings with two low-dimensional synthetic datasets for data representation, and an aviation safety dataset, called Dashlink, for high-dimensional reconstruction-based anomaly detection. The results indicate the superiority of models with Fourier transformation compared to the conventional counterpart and remain inconclusive regarding the benefits of using trainable Fourier transformation in contrast to the Random variant.", "RNA velocity is an important model that combines cellular spliced and unspliced RNA counts to infer dynamical properties of various regulatory functions. Despite its wide applicability and many variants used in practice, the model has not been adequately designed to directly account for both intracellular gene regulatory network interactions and spatial intercellular communications. Here, we propose a new RNA velocity approach that jointly and directly captures two new network structures: an intracellular gene regulatory network (GRN) and an intercellular interaction network that captures interactions between (neighboring) cells, with relevance to spatial transcriptomics. We theoretically analyze this two-level network system through the lens of control and consensus theory. In particular, we investigate network equilibria, stability, cellular network consensus, and optimal control approaches for targeted drug intervention.", "# Tiny Machine Learning for Real-Time Aquaculture Monitoring: A Case Study in Morocco Tiny Machine Learning for Real-Time Aquaculture Monitoring: A Case Study in Morocco Achraf Hsain School of Science and Engineering Al Akhawayn University Ifrane, Morocco A.Hsain@aui.ma Hibat-allah Bekkar School of Science and Engineering Al Akhawayn University Ifrane, Morocco H.Bekkar@aui.ma Yahya Zaki School of Science and Engineering Al Akhawayn University Ifrane, Morocco Y.Zaki@aui.ma Yousra Chtouki School of Science and Engineering Al Akhawayn University Ifrane, Morocco Y.Chtouki@aui.ma Othman abaakil School of Science and Engineering Al Akhawayn University Ifrane, Morocco O.Abaakil@aui.ma Abstract: Aquaculture, the farming of aquatic organisms, is a rapidly growing industry facing challenges such as water quality fluctuations, disease outbreaks, and inefficient feed management. Traditional monitoring methods often rely on manual labor and are time consuming, leading to potential delays in addressing issues. This paper proposes the integration of low-power edge devices using Tiny Machine Learning (TinyML) into aquaculture systems to enable real-time automated monitoring and control, such as co", "We propose a scalable reachability-based framework for probabilistic, data-driven safety verification of unknown nonlinear dynamics. We use Koopman theory with a neural network (NN) lifting function to learn an approximate linear representation of the dynamics and design linear controllers in this space to enable closed-loop tracking of a reference trajectory distribution. Closed-loop reachable sets are efficiently computed in the lifted space and mapped back to the original state space via NN verification tools. To capture model mismatch between the Koopman dynamics and the true system, we apply conformal prediction to produce statistically-valid error bounds that inflate the reachable sets to ensure the true trajectories are contained with a user-specified probability. These bounds generalize across references, enabling reuse without recomputation. Results on high-dimensional MuJoCo tasks (11D Hopper, 28D Swimmer) and 12D quadcopters show improved reachable set coverage rate, computational efficiency, and conservativeness over existing methods.", "# Time Series Based CO2 Emission Forecasting and Energy Mix Analysis for Net Zero Transitions: A Multi Country Study Time Series\u2013Based CO2 Emission Forecasting and Energy Mix Analysis for Net-Zero Transitions: A Multi-Country Study Salim Oyinlola1, Joshua Ajayi1, Gozie Ibekwe2, Chika Yinka-Banjo3 1 Electrical and Electronics Engineering, University of Lagos 2 Computer Engineering, University of Lagos 2 Computer Science, University of Lagos Abstract- This study examines long-term CO2 emission trajectories across five major economies, Nigeria, the United States, China, Brazil, and Russia, by integrating national energy-mix characteristics with time-series forecasting models. Annual emissions from 2000 to 2023 were analyzed alongside energy production data to classify countries into fossil-dependent, transition-phase, or renewable-accelerated profiles. Three forecasting models (ARIMA, SARIMA, and Holt-Winters Exponential Smoothing) were evaluated using MAE, RMSE, MAPE, and R2 metrics. Results show that Holt-Winters provided the most accurate forecasts for Nigeria, the United States, China, and Brazil, while SARIMA performed best for Russia due to its relatively stable emissions. Long-", "This paper presents an adaptive multi-model framework for jointly estimating spacecraft attitude and star-tracker misalignments in GPS-denied deep-space CubeSat missions. A Multiplicative Extended Kalman Filter (MEKF) estimates attitude, angular velocity, and gyro bias, while a Bayesian Multiple-Model Adaptive Estimation (MMAE) layer operates on a discrete grid of body-to-sensor misalignment hypotheses. In the single-misalignment case, the MEKF processes gyroscope measurements and TRIAD-based attitude observations, and the MMAE updates a three-dimensional grid over the misalignment vector. For a dual-misalignment configuration, the same MEKF dynamics are retained, and the MMAE bank is driven directly by stacked line-of-sight measurements from two star trackers, forming a six-dimensional grid over the two misalignment quaternions without augmenting the continuous-state dimension. A novel diversity metric, $\\Psi$ , is introduced to trigger adaptive refinement of the misalignment grid around a weighted-mean estimate, thereby preventing premature collapse of the model probabilities and concentrating computation in the most likely region of the parameter space. Monte Carlo simulations show arcsecond-level misalignment estimation and sub-degree attitude errors for both estimation problems, with estimation errors remaining well-bounded, proving robustness and consistency. These results indicate that the proposed MEKF\u2013MMAE architecture enables accurate, autonomous, and computationally efficient in-flight calibration for resource-constrained spacecraft, and establishes dual star-tracker misalignment estimation as a practical option for deep-space CubeSat missions.", "To match the growing demand for bio-methane production, anaerobic digesters need to embrace the co-digestion of different feedstocks; in addition, to improve the techno-economic performance, an optimal and time-varying adaptation of the input diet is required. These operation modes constitute a very hard challenge for the limited instrumentation and control equipment typically installed aboard full-scale plants. A model-based predictive approach may be able to handle such control problem, but the identification of reliable predictive models is limited by the low information content typical of the data available from full-scale plants\u2019 operations, which entail high parametric uncertainty. In this work, the application of a tube-based robust nonlinear model predictive control (NMPC) is proposed to regulate bio-methane production over a period of diet change in time, while warranting safe operation and dealing with uncertainties. In view of its upcoming validation on a true small pilot-scale plant, the NMPC capabilities are assessed via numerical simulations designed to resemble as much as possible the experimental setup, along with some practical final considerations.", "# Designing Information Delays in Supply Chains ## 1 Introduction Successful supply chain operations depend on the effective coordination between inventory management strategies and the flow of information that informs them. Synchronizing these physical and informational flows ensures that goods move efficiently through the supply chain, minimizing costs, reducing lead times, and maintaining optimal inventory levels. While inventory policies dictate the timing and quantity of replenishment across firms, their effectiveness is shaped by the availability and accuracy of demand information. When downstream demand is known in a timely and accurate manner, upstream suppliers can anticipate needs, allocate resources efficiently, and maintain service levels. In ideal settings, this information is explicitly shared through collaborative systems or formal communication protocols. In practice, however, such sharing is often absent, delayed, or incomplete\u2014leaving upstream firms to infer demand conditions indirectly from observed order patterns. This paper investigates how inventory control decisions, and specifically the structure of the retailer\u2019s ordering policy, can serve as a channel for", "This article investigates the difference between the true detection probability and the subjective probability of a uniformly optimal search plan. Its main contributions are multi-fold. First, it provides a set of examples to show that, in terms of the true detection probability, the uniformly optimal search plan may or may not be optimal. Secondly, it establishes that the true detection probability of the uniformly optimal search plan based on a composite prior can be less than that of the composite uniformly search plan based on different priors. Next, it argues that an open problem is unsolvable. Finally, it shows that the true detection probability of the uniformly optimal search plan converges to one as the search time approaches infinity. Keywords and phrases: Bayesian learning; composite probability maps; optimal search theory; search and rescue; stationary targets.", "Polynomial optimization encompasses a broad class of problems in which both the objective function and constraints are polynomial functions of the decision variables. In recent years, a substantial body of research has focused on reformulating polynomial optimization problems (POPs) as conic programs over the cone of completely positive tensors (CPTs). In this article, we propose several new completely positive reformulations for a class of POPs with linear inequality constraints. Our approach begins by lifting these problems into a novel convex optimization framework, wherein the variables are represented as combinations of symmetric rank-one tensors. Based on this lifted formulation, we present a general characterization of POPs with linear inequality constraints that can be reformulated as conic programs over the CPT cone. Additionally, we construct the dual formulations of the resulting completely positive programs. Under mild assumptions, we prove that these dual problems are strictly feasible and strong duality holds.", "Advances in artificial intelligence (AI) and deep learning have raised concerns about its increasing energy consumption, while demand for deploying AI in mobile devices and machines at the edge is growing. Binary neural networks (BNNs) have recently gained attention as energy and memory efficient models suitable for resource constrained environments; however, training BNNs exactly is computationally challenging because of its discrete characteristics. Recent work proposing a framework for training BNNs based on quadratic unconstrained binary optimisation (QUBO) and progress in the design of Ising machines for solving QUBO problems suggest a potential path to efficiently optimising discrete neural networks. In this work, we extend existing QUBO models for training BNNs to accommodate arbitrary network topologies and propose two novel methods for regularisation. The first method maximises neuron margins biasing the training process toward parameter configurations that yield larger pre-activation magnitudes. The second method employs a dropout-inspired iterative scheme in which reduced subnetworks are trained and used to adjust linear penalties on network parameters. We apply the proposed QUBO formulation to a small binary image classification problem and conduct computational experiments on a GPU-based Ising machine. The numerical results indicate that the proposed regularisation terms modify training behaviour and yield improvements in classification accuracy on data not present in the training set.", "This paper presents a safe feedback control framework for nonlinear control-affine systems with parametric uncertainty by leveraging adaptive dynamic programming (ADP) with barrier-state augmentation. The developed ADP-based controller enforces control invariance by optimizing a value function that explicitly penalizes the barrier state, thereby embedding safety directly into the Bellman structure. The near-optimal control policy computed using model-based reinforcement learning is combined with a concurrent learning estimator to identify the unknown parameters and guarantee uniform convergence without requiring persistency of excitation. Using a barrier-state Lyapunov function, we establish boundedness of the barrier dynamics and prove closed-loop stability and safety. Numerical simulations on an optimal obstacle-avoidance problem validate the effectiveness of the developed approach.", "This paper presents a novel disturbance-torque-estimation-augmented model predictive control (MPC) framework to perform momentum management on NASA\u2019s Solar Cruiser solar sail mission. Solar Cruiser represents a critical step in the advancement of large-scale solar sail technology and includes the innovative use of an active mass translator (AMT) and reflectivity control devices (RCDs) as momentum management actuators. The coupled nature of these actuators has proven challenging in the development of a robust momentum management controller. Recent literature has explored the use of MPC for solar sail momentum management with promising results, although exact knowledge of the disturbance torques acting on the solar sail was required. This paper amends this issue through the use of a Kalman filter to provide real-time estimation of unmodeled disturbance torques. Furthermore, the dynamics model used in this paper incorporates key fidelity enhancements compared to prior work, including Solar Cruiser\u2019s four-reaction-wheel assembly and the offset between its center of mass and center of pressure. More realistic operation scenarios involving the tracking of large angle slew maneuvers under attitude-dependent solar radiation force and torque are also performed to further validate the proposed method compared to prior work. Simulation results demonstrate that the proposed policy successfully manages angular momentum growth under slew maneuvers that exceed the operational envelope of the current state-of-the-art method. The inclusion of the disturbance torque estimate is shown to greatly improve the reliability and performance of the proposed MPC approach. This work establishes a new benchmark for Solar Cruiser\u2019s momentum management capabilities and paves the way for MPC-based momentum management of other solar sails making use of an AMT and/or RCDs.", "We study a new nonlinear system which contains a partial differential equation, a quasivariational inequality and a parabolic variational inequality in Banach spaces. We obtain the unique solvability of the coupled system under moderate conditions by using the Banach\u2019s fixed point theorem. We employ the main results to investigate a viscoelastic frictional contact problem with long-memory effects, wear processes, and damage phenomenon. Keywords: Partial differential variational inequality; quasivariational inequality; parabolic variational inequality; damage; wear 2020 Mathematics Subject Classification: 49J40; 47J20; 35Q93", "Maximizing submodular objectives under constraints is a fundamental problem in machine learning and optimization. We study the maximization of a nonnegative, non-monotone $\\gamma$ -weakly DR-submodular function over a down-closed convex body. Our main result is an approximation algorithm whose guarantee depends smoothly on $\\gamma$ ; in particular, when $\\gamma=1$ (the DR-submodular case) our bound recovers the $0.401$ approximation factor, while for $\\gamma<1$ the guarantee degrades gracefully and, it improves upon previously reported bounds for $\\gamma$ -weakly DR-submodular maximization under the same constraints. Our approach combines a Frank\u2013Wolfe\u2013guided continuous-greedy framework with a $\\gamma$ -aware double-greedy step, yielding a simple yet effective procedure for handling non-monotonicity. This results in state-of-the-art guarantees for non-monotone $\\gamma$ -weakly DR-submodular maximization over down-closed convex bodies.", "Motivated by variational inference methods, we propose a zeroth-order algorithm for solving optimization problems in the space of Gaussian probability measures. The algorithm is based on an interacting system of Gaussian particles that stochastically explore the search space and self-organize around global minima via a consensus-based optimization (CBO) mechanism. Its construction relies on the Linearized Bures\u2013Wasserstein (LBW) space, a novel parametrization of Gaussian measures we introduce for efficient computations. We establish well-posedness and study the convergence properties of the particle dynamics via a mean-field approximation. Numerical experiments on variational inference tasks demonstrate the algorithm\u2019s robustness and superior performance with respect to deterministic gradient-based method in presence of low-dimensional non log-concave targets.", ". Bounded-context agents fail when intermediate reasoning exceeds an effective working-memory budget. We study compressed query delegation (CQD) : (i) compress a high-dimensional latent reasoning state into a low-rank tensor query, (ii) delegate the minimal query to an external oracle, and (iii) update the latent state via Riemannian optimization on fixed-rank manifolds. We give a math-first formulation: CQD is a constrained stochastic program with a query-budget functional and an oracle modeled as a noisy operator. We connect CQD to classical rate\u2013distortion and information bottleneck principles, showing that spectral hard-thresholding is optimal for a natural constrained quadratic distortion problem, and we derive convergence guarantees for Riemannian stochastic approximation under bounded oracle noise and smoothness assumptions. Empirically, we report (A) a 2,500-item bounded-context reasoning suite (BBH-derived tasks plus curated paradox instances) comparing CQD against chain-of-thought baselines under fixed compute and context; and (B) a human \u201ccognitive mirror\u201d benchmark ( $N=200$ ) measuring epistemic gain and semantic drift across modern oracles.", "Urban transportation networks face significant challenges due to traffic congestion, leading to adverse environmental and socioeconomic impacts. Vehicular admission control (VAC) strategies have emerged as a promising solution to alleviate congestion. By leveraging information and communication technologies, VAC strategies regulate vehicle entry into the network to optimize different traffic metrics of interest over space and time. Despite the significant development of VAC strategies, their stability at the presence of modelling uncertainty remains under-explored. This paper investigates the stability properties of a class of decentralized VAC schemes under modelling uncertainty. Specifically, we consider large-scale, heterogeneous urban traffic networks characterised by nonlinear dynamics and concave macroscopic fundamental diagrams with bounded uncertainty between flow, density, and speed. In this context, we examine a broad class of decentralized VAC dynamics, described by general nonlinear forms. Using passivity theory, we derive scalable, locally verifiable conditions on the design of VAC schemes, that enable stability guarantees in the presence of modelling uncertainty. Several examples are presented to illustrate the applicability of the proposed design framework. Our analytical results are validated through numerical simulations on a 6 and a 20-region system, demonstrating their effectiveness and practical relevance.", "This study considers a point-to-point wireless link, in which both the transmitter and receiver are equipped with multiple antennas. In addition, two reconfigurable metasurfaces are deployed, one in the immediate vicinity of the transmit antenna array, and one in the immediate vicinity of the receive antenna array. The resulting architecture implements a holographic beamforming structure at both the transmitter and receiver. In this scenario, the system energy efficiency is optimized with respect to the transmit covariance matrix, and the reflection matrices of the two metasurfaces. A low-complexity algorithm is developed, which is guaranteed to converge to a first-order optimal point of the energy efficiency maximization problem. Moreover, closed-form expressions are derived for the metasurface matrices in the special case of single-antenna or single-stream transmission. The two metasurfaces are considered to be nearly-passive and subject to global reflection constraints. A numerical performance analysis is conducted to assess the performance of the proposed optimization methods, showing, in particular, that the use of holographic beamforming by metasurfaces can provide significant energy efficiency gains compared to fully digital beamforming architectures, even when the latter achieve substantial multiplexing gains.", "# Gradient-Free Approaches is a Key to an Efficient Interaction with Markovian Stochasticity ## 1 Introduction Stochasticity is a fundamental aspect of many optimization problems, naturally arising in the field of machine learning . Stochastic gradient descent (SGD) and its accelerated variants have become a de facto optimizers for modern large models training. Theoretical properties of SGD have been extensively studied under various statistical frameworks , often relying on the assumption that noise is independent and identically distributed (i.i.d.). However, in many real-world applications \u2014 including reinforcement learning (RL) , distributed optimization , and bandit problems \u2014 noise is not i.i.d., instead exhibiting correlations or *Markovian structure*. For instance, in the mentioned growing field of RL, sequential interactions with the environment induce state-dependent structure of the noise, creating a need for non-i.i.d. noise aware algorithms. Although several gradient-based methods for Markovian stochastic oracles have been studied in the past decade , policy optimization in RL is based solely on reward feedback, making traditional methods inapplicable, since there is n", "We develop a scalable algorithm for mean field control problems with kernel interactions by combining particle system simulations with random Fourier feature approximations. The method replaces the quadratic-cost kernel evaluations by linear-time estimates, enabling efficient stochastic gradient descent for training feedback controls in large populations. We provide theoretical complexity bounds and demonstrate through crowd motion and flocking examples that the approach preserves control performance while substantially reducing computational cost. The results indicate that random feature approximations offer an effective and practical tool for high dimensional and large scale mean field control.", "In this work, we investigate a stochastic control framework for global optimization over both Euclidean spaces and the Wasserstein space of probability measures, where the objective function may be non-convex and/or non-differentiable. In the Euclidean setting, the original minimization problem is approximated by a family of regularized stochastic control problems; using dynamic programming, we analyze the associated Hamilton\u2013Jacobi\u2013Bellman equations and obtain tractable representations via the Cole\u2013Hopf transformation and the Feynman\u2013Kac formula. For optimization over probability measures, we formulate a regularized mean-field control problem characterized by a master equation, and further approximate it by controlled $N$ -particle systems. We establish that, as the regularization parameter tends to zero (and as the particle number tends to infinity for the optimization over probability measures), the value of the control problem converges to the global minimum of the original objective. Building on the resulting probabilistic representations, we propose the Monte Carlo-based numerical schemes that are derivative-free due to the utilization of the Bismut-Elworthy-Li formula and numerical experiments are reported to illustrate the effectiveness of the methods and to support the theoretical convergence rates.", "We study non-linear Backward Stochastic Differential Equations (BSDEs) driven by a Brownian motion and $p$ default martingales. The driver of the BSDE with multiple default jumps can take a generalized form involving an optional finite variation process. We first show existence and uniqueness. We then establish comparison and strict comparison results for these BSDEs, under a suitable assumption on the driver. In the case of a linear driver, we derive an explicit formula for the first component of the BSDE using an adjoint exponential semimartingale. The representation depends on whether the finite variation process is predictable or only optional. We apply our results to the problem of pricing and hedging a European option in a linear complete market with two defaultable assets and in a non-linear complete market with $p$ defaultable assets. Two examples of the latter market model are provided: an example where the seller of the option is a large investor influencing the probability of default of a single asset and an example where the large seller\u2019s strategy affects the default probabilities of all $p$ assets.", "The $\\mu$ -parameterization ( $\\mu\\mathsf{P}$ ) provides a principled foundation for large language model (LLM) training by prescribing width-independent learning dynamics, which in turn enables predictable scaling behavior and robust hyperparameter transfer across model sizes. A central requirement of $\\mu\\mathsf{P}$ is the satisfaction of certain spectral conditions on weight matrices, which ensure consistent feature learning and optimization behavior as model width grows. While these conditions are well understood in theory, guaranteeing their validity in practical training for matrix-based optimizers such as Muon is still under studied. Existing works that study Muon under $\\mu\\mathsf{P}$ exhibit important limitations: they either do not ensure that the spectral conditions hold throughout the entire training horizon, or require repeated spectral normalization (or Newton\u2013Schulz iterations) applied to both weights and updates, leading to significant computational overhead and reduced practicality. In this work, we show how to reliably guarantee the spectral conditions required by $\\mu\\mathsf{P}$ for Muon during the entire training process. Our key insight is that for moderately large models, maintaining spectral control at the level of optimizer updates alone is sufficient to preserve $\\mu\\mathsf{P}$ -compatible scaling, eliminating the need for explicit spectral normalization of the weights. Based on this principle, we develop a variant of Muon, namely Muon++, that satisfies spectral condition throughout the training process. Our results bridge the gap between the theoretical promises of $\\mu\\mathsf{P}$ and the practical deployment of matrix-based optimizers in long-horizon training. We also take the first step towards an adaptive spectral condition by incorporating data-dependent effects, making it better suited for long-horizon LLM training.", "Distributionally Robust (DR) optimization aims to certify worst-case risk within a Wasserstein uncertainty set. Current certifications typically rely either on global Lipschitz bounds, which are often conservative, or on local gradient information, which provides only a first-order approximation. This paper introduces a novel geometric framework based on the least concave majorants of the growth rate functions. Our proposed concave certificate establishes a tight bound on DR risk that remains applicable to non-Lipschitz and non-differentiable losses. We extend this framework to complexity analysis, introducing the worst-case generalization bound that complements the standard statistical generalization bound. Furthermore, we utilize this certificate to bound the gap between adversarial and empirical Rademacher complexity, demonstrating that dependencies on input diameter, network width, and depth can be eliminated. For practical application in deep learning, we introduce the adversarial score as a tractable relaxation of the concave certificate that enables efficient and layer-wise analysis of neural networks. We validate our theoretical results in various numerical experiments on classification and regression tasks using real-world data.", "Interconnection and Damping Assignment Passivity-Based Control (IDA-PBC) is a well-established stabilization technique for affine nonlinear systems. However, its application is generally hindered by the requirement of solving a set of partial differential equations (PDEs), i.e., the so-called matching equation. This paper introduces the notion of maximum energy shapeability which describes the scenario that the homogeneous part of the matching equation admits $m$ independent solutions with $m$ the dimension of the control input. We demonstrate that the maximum energy shapeability enables a systematic procedure for the IDA-PBC design by transforming the matching equation into a set of easier-to-solve PDEs. Sufficient conditions for maximum energy shapeability are also provided. It is shown that some existing constructive IDA-PBC designs actually implicitly exploit the maximum energy shapeability. The proposed procedure for the IDA-PBC design is illustrated with the magnetic levitation system.", "# GlycoPy: A CasADi-based Python Framework for Hierarchical Modeling, Optimization, and Control of Bioprocesses 1 GlycoPy: A CasADi-based Python Framework for Hierarchical Modeling, Optimization, and Control of Bioprocesses Yingjie Ma1,2, Jing Guo2,3, and Richard D. Braatz2,* 1Nanjing University, 215163, Suzhou, Jiangsu Province, China. 2Massachusetts Institute of Technology, 02139, Cambridge, MA, USA. 3Polytechnique Montr\u00e9al, H3T 0A3, Montr\u00e9al, QC, Canada. Abstract: Efficient implementation of nonlinear model predictive control (NMPC) for bioprocesses remains challenging because large nonlinear models are difficult to organize, simulate, and embed within optimization and control workflows. This difficulty is particularly pronounced for large-scale and multiscale systems that require hierarchical model construction and customized simulation strategies. To address this issue, we present GlycoPy, a CasADi-based Python framework for hierarchical modeling, optimization, and control of bioprocesses. GlycoPy combines an equation-oriented, object-oriented modeling architecture with CasADi\u2019s symbolic and differentiable computational capabilities, enabling hierarchical model composition, nu", "Rare events in molecular dynamics are often related to noise-induced transitions between different macroscopic states (e.g., in protein folding). A common feature of these rare transitions is that they happen on timescales that are on average exponentially long compared to the characteristic timescale of the system, with waiting time distributions that have (sub)exponential tails and infinite support. As a result, sampling such rare events can lead to trajectories that can be become arbitrarily long, with not too low probability, which makes the reweighting of such trajectories a real challenge. Here, we discuss rare event simulation by importance sampling from a variational perspective, with a focus on applications in molecular dynamics, in particular the computation of committor functions. The idea is to design importance sampling schemes that (a) reduce the variance of a rare event estimator while controlling the average length of the trajectories and (b) that do not require the reweighting of possibly very long trajectories. In doing so, we study different stochastic control formulations for committor and mean first exit times, which we compare both from a theoretical and a computational point of view, including numerical studies of some benchmark examples.", "This paper develops a process-based account of scientific explanation that reconceives grounding in terms of stabilisation. Grounding theories capture hierarchical dependence but lack criteria for when explanations remain adequate under model updates, perturbations, and theory change. Stabilisation is formally defined by a schema $C\\to P(I)$ , where explanatory relations are sufficient when they preserve specified relational invariants under admissible transformations. This replaces the search for ultimate foundations with operational adequacy tests indexed to measurable invariance, resolving infinite regress worries while preserving a modest scientific realism. Applications show unifying power: theory change becomes an empirical question about structural continuity; quantum measurement becomes apparatus-dependent pattern selection; the effectiveness of mathematics reflects convergence on transformation-invariant descriptions; and emergence versus reduction reduces to stability of cross-level mappings. The black hole event horizon illustrates how ontologically identical states can diverge in admissible evolution, revealing process as explanatorily fundamental. Companion work develops apparatus-dependent adequacy protocols, including pointer-basis rotation and coupling-spectra methods, turning the framework into a falsifiable research programme across quantum, thermodynamic, and relativistic domains.", "# Mathematics in the liturgical books of the Catholic Church: phases of the ecclesiastical moon ## 1 Gregorian calendar and the ecclesiastical moon The Julian calendar, introduced by Julius Caesar in 46 BC , has been used in the Western world for the next sixteen centuries. The average length of the year in the Julian calendar was 365.25 days, and this was slightly more than the actual duration of the mean solar year (currently 365.24217 mean solar days). As a result, the Julian calendar was gaining a day every 128 years, thus falling behind the astronomical year more and more. In the 16-the century, the astronomical vernal equinox was occurring 10 days before its nominal date of March 21. The problem was known since Middle Ages , but toward the end of the 15th century the progress in astronomy intensified the calls for the calendar reform. The reform was eventually undertaken during the reign of Pope Gregory XIII (1572\u20131585). It is not the purpose of this paper to discuss the details of the history of the reform and all preceding events, but the interested reader can consult, for example, reference , where numerous other works on the subject are sourced. On February 24, 1582, Pope", "# The Physics of Causation 1 The Physics of Causation Leroy Cronin1,2 and Sara I. Walker2,3 1School of Chemistry, The University of Glasgow, University Avenue, Glasgow G12 8QQ, UK. 2Santa Fe Institute, Santa Fe, New Mexico, USA. 3Beyond Center for Fundamental Concepts in Science, Arizona State University, Tempe, Arizona, USA Emails: Lee.Cronin@glasgow.ac.uk; sara.i.walker@asu.edu Abstract Assembly theory (AT) introduces causation as a material property and establishes a metrology for objects produced by evolution and selection. The physical scale of causation is quantified by the assembly index, defined as the minimum number of recursive steps necessary to make an object. Observing countable copies of high assembly index objects indicates a mechanism producing them is persistent, such that the object\u2019s environment constructs a memory that traps causation within a contingent chain. Copy number and assembly index together underlie a standardized metrology for detecting causation (assembly index) and contingency (copy number). These allow a precise definition of an assembly threshold that demarcates life (and its derivative agential, intelligent, and technological forms and artifacts)", "Tractatus de Conscientia is a tractatus-style sketch toward a modern, physically operational account of consciousness. It is also a tractatus-style attempt to talk about consciousness in a way that stays close to what we can actually test and build. It pushes back against two common moves: treating consciousness as a mysterious extra \u201cstuff,\u201d and treating it as nothing more than outward behavior. The central idea is to keep three things separate: what appears for an agent (the lived \u201cgiven\u201d), what is accessible (what can shape report, control, memory, or other records), and what structure remains when we change descriptions (the invariants of organization). On this view, a conscious episode isn\u2019t a mathematical instant. It has a short duration during which many internal distinctions are pulled together into one perspective and held stable enough to guide action\u2014and sometimes to be reported. Unity is captured as a kind of \u201cwhole-over-parts\u201d surplus: the system, over a chosen timescale and partition, carries more integrated predictive power than its pieces considered separately, and that surplus must also be available to access channels (so we don\u2019t count integration that never makes a difference to anything the agent can do or say). The self, in turn, is treated less like a hidden entity and more like a dynamical role\u2014a self-index that helps bind episodes over time by stabilizing prediction and control across changing contexts. The tractatus also stresses a hard limit: every piece of evidence about consciousness requires coupling to the system, and coupling changes what we observe. So there is no protocol-free, perfectly private \u201cidentifier\u201d of what-it-is-like. Consciousness is something we infer and attribute under explicit measurement setups, conventions, and uncertainty bounds\u2014and we should be willing to say \u201cwe can\u2019t tell\u201d when identifiability runs out. Keywords. Consciousness; physicall operationalism; appearance; access; reportability; unity; integrated perspective; mutual information; causal intervention; identifiability; self-model (self-index); measurement limits; observational equivalence; disorders of consciousness; artificial agents.", "This philosophical dialogue explores the idea that the foundational principles of quantum mechanics need not be interpreted as describing a new physics, but may instead arise from the logical necessity of formalising the act of measurement within a coherent algebraic framework. By pushing this perspective to its extreme, the dialogue argues that the core structures of quantum mechanics can be derived independently of any specifically quantum properties of atomic particles, and can be formulated within an otherwise classical theory once limitations of observability and measurement context are taken seriously.", "# Discrete symmetries in classical and quantum oscillators . Discrete symmetries in classical and quantum oscillators Alexander D. Popov Institut f\u00fcr Theoretische Physik, Leibniz Universit\u00e4t Hannover Appelstra\u00dfe 2, 30167 Hannover, Germany Email: alexander.popov@itp.uni-hannover.de Abstract We consider the nature of the wave function using the example of a harmonic oscillator. We show that the eigenfunctions $\\psi\\_{n}{=}z^{n}$ of the quantum Hamiltonian in the complex Bargmann-Fock-Segal representation with $z\\in\\mathbb{C}$ are the coordinates of a classical oscillator with energy $E\\_{n}=\\hbar\\omega n$ , $n=0,1,2,...\\,$ . They are defined on conical spaces ${\\mathbb{C}}/{\\mathbb{Z}}\\_{n}$ with cone angles $2\\pi/n$ , which are embedded as subspaces in the phase space $\\mathbb{C}$ of the classical oscillator. Here ${\\mathbb{Z}}\\_{n}$ is the finite cyclic group of rotations of the space $\\mathbb{C}$ by an angle $2\\pi/n$ . The superposition $\\psi=\\sum\\_{n}c\\_{n}\\psi\\_{n}$ of the eigenfunctions $\\psi\\_{n}$ arises only with incomplete knowledge of the initial data for solving the Schr\u00f6dinger equation, when the conditions of invariance with respect to the discrete groups ${\\mathbb{Z}}\\_{", "We show that Weyl\u2019s abandoned idea of local scale invariance has a natural realization at the quantum level in pilot-wave (de Broglie-Bohm) theory. We obtain the Weyl covariant derivative by complexifying the electromagnetic gauge coupling parameter. The resultant non-hermiticity has a natural interpretation in terms of local scale invariance in pilot-wave theory. The conserved current density is modified from $|\\psi|^{2}$ to the local scale invariant, trajectory-dependent ratio $|\\psi|^{2}/\\mathds{1}^{2}[\\mathcal{C}]$ , where $\\mathds{1}[\\mathcal{C}]$ is a scale factor that depends on the pilot-wave trajectory $\\mathcal{C}$ in configuration space. Our approach is general, and we implement it for the Schr\u00f6dinger, Pauli, and Dirac equations coupled to an external electromagnetic field. We also implement it in quantum field theory for the case of a quantized axion field interacting with a quantized electromagnetic field. We discuss the equilibrium probability density and show that the corresponding trajectories are unique.", ". Exactly a century ago, wave quantum mechanics was born in Arosa, Switzerland. Erwin Schr\u00f6dinger was vacationing in this classic Swiss Alps town at Christmas 1925 when he made his breakthrough discovery of the wave equation .", "This paper diagnoses a much-discussed problem in quantum thermodynamics, that of generalizing classical work into the quantum domain. I begin with the no-go theorem of Perarnau-Llobet et al (2017): no universal measurement scheme for quantum work satisfies two intuitive, classically consilient desiderata. I assess this incompatibility as stemming from the measurement problem. Decoherence restores compatibility for all practical purposes, but raises questions about what \u2018universality\u2019 should mean and whether any measurement scheme can be \u2018universal\u2019. I consider a different standard of universality \u2013 in terms of ontology \u2013 by defining a trajectory-based notion of quantum work using the quantum potential. While this preserves the classical role of work as the integral of forces over distances, and evades the tension of the no-go theorem, consilience fails elsewhere; no single quantum work concept seems capable of preserving all classical features, raising questions for what it takes for successful generalization of the work concept to quantum thermodynamics.", "# On Causality and Predictivity ## 1 Introduction Among the foundational principles of Quantum Field Theory (QFT) are locality, renormalizability, and unitarity. While unitarity is a self-consistency requirement, essential for probability conservation, locality and renormalizability are more pragmatic principles that have successfully guided the construction of theories describing three of the four fundamental interactions of nature. Microcausality, often closely linked to locality and analyticity, has historically played an important, though perhaps less examined, role. However, for various reasons, the necessity of strict causality is increasingly being questioned in fundamental physics, particularly in relation with quantum gravity (QG). Although it is generally anticipated that quantum gravity may demand the sacrifice of certain fundamental principles, this necessity does not automatically mandate the abandonment of the entire QFT framework, which has proven so successful in describing the other three interactions. This position stands in contrast to the assumptions underlying approaches such as String Theory, Loop Quantum Gravity, and Holography, which necessitate radical foun", "# Mati\u00e8re noire et (ou) gravitation modifi\u00e9e : une approche historique et \u00e9pist\u00e9mologique ## 1 Mod\u00e9liser le mouvement des astres L\u2019observation de la r\u00e9gularit\u00e9 des mouvements des astres dans le ciel suscita tr\u00e8s t\u00f4t, probablement d\u00e8s la Pr\u00e9histoire, la mise en place de m\u00e9thodes permettant de d\u00e9crire et de pr\u00e9dire ces mouvements. Au moins \u00e0 partir de l\u2019Antiquit\u00e9 grecque, de telles m\u00e9thodes purent s\u2019appuyer sur des \u201cmod\u00e8les\u201d, quasiment au sens moderne du terme, \u00e0 savoir une repr\u00e9sentation simplifi\u00e9e, souvent formalis\u00e9e math\u00e9matiquement, d\u2019un processus ou d\u2019un syst\u00e8me complexe. Anaximandre de Milet (VIe si\u00e8cle avant J.C.) aurait ainsi \u00e9t\u00e9 le premier \u00e0 rendre compte du mouvement des astres en imaginant un univers sph\u00e9rique centr\u00e9 autour de la Terre. Apr\u00e8s plusieurs si\u00e8cles d\u2019observations et de d\u00e9veloppements conceptuels, l\u2019Almageste de Claude Ptol\u00e9m\u00e9e (IIe si\u00e8cle apr\u00e8s J.C.) est l\u2019aboutissement de ce mod\u00e8le g\u00e9ocentrique et devient la r\u00e9f\u00e9rence pour d\u00e9crire et pr\u00e9dire le mouvement des astres de l\u2019Antiquit\u00e9 jusqu\u2019\u00e0 la fin du Moyen-\u00c2ge, tant en Occident que dans le monde arabe. Dans ce mod\u00e8le, la Terre est immobile au centre du cosmos et le mouvement des astres est d\u00e9crit gr\u00e2ce \u00e0 des comb", "This chapter reexamines wave function realism (WFR) through the lens of phenomenology. We begin by situating WFR within the broader debate about the ontology of the quantum state and the temptation to \u201cread off\u201d metaphysics from mathematical formalism. Against this background, we turn to the London\u2013Bauer interpretation (LBI), the most explicit attempt to interpret quantum mechanics through phenomenological categories. On this view, the measurement transition is not a physical discontinuity but a reflective articulation of objectivity, and the wave function formally encodes the horizonal structure of world-givenness. We develop this idea by reconfiguring the notion of realism itself: not as objectivist, but as correlational and transcendental. The resulting picture suggests that quantum mechanics, rather than depicting a world \u201cminus observers,\u201d mathematically articulates the very correlation through which a world becomes manifest at all.", "Effective graph resistance is a fundamental structural metric in network science, widely used to quantify global connectivity, compare network architectures, and assess robustness in flow-based systems. Despite its importance, current formulations rely mainly on spectral or pseudo-inverse Laplacian representations, offering limited physical insight into how structural features shape this quantity or how it can be efficiently optimized. Here we establish an exact and physically transparent relationship between effective graph resistance and the cumulative heat dissipation generated by Laplacian diffusion dynamics. We show that the total heat dissipated during relaxation to equilibrium precisely equals the effective graph resistance. This dynamical viewpoint uncovers a natural multi-scale decomposition of the Laplacian spectrum: early-time dissipation is governed by degree-based local structure, intermediate times isolate eigenvalues below the spectral mean, and long times are dominated by the algebraic connectivity. These multi-scale properties yield continuous and interpretable strategies for modifying network structure and constructing optimized ensembles, enabling improvements that are otherwise NP-hard to achieve via combinatorial methods. Our results unify structural and dynamical perspectives on network connectivity and provide new tools for analyzing, comparing, and optimizing complex networks across domains.", "# Modeling Policy and Resource Dynamics in the Construction Sector of Developing Countries: A System Dynamics Approach Using Sudan as a Case Study Modeling Policy and Resource Dynamics in the Construction Sector of Developing Countries: A System Dynamics Approach Using Sudan as a Case Study Malik Dongla, PhD CEO Malik Dongula for Construction & Contracting Khartoum, Sudan Email: malikdongula@malikdongula.com Mohamed Khalafalla, PhD, MBA, PMP Associate Professor Construction Engineering Technology Florida A&M University, Tallahassee, FL, 32307 Email: mohamed.ahmed@famu.edu ORCID: 0000-0001-9621-4940 Word Count: 4,664 words + 3 tables (250 words per table) = 5,414 words Submitted August 1, 2025 Presented January 2026 Dongla, and Khalafalla ABSTRACT Construction industries in developing countries face systemic challenges such as chronic project delays, cost overruns, and regulatory inefficiencies. This paper presents a system dynamics (SD) modeling framework for analyzing policy and resource dynamics within Sudan\u2019s construction sector, with broader applicability to Least Developed Countries (LDCs). The model incorporates key variables related to workforce, material supply, financing,", "Formal modelling provides a toolkit for understanding cultural dynamics, from individual decisions to recurring patterns of change. This chapter explains what models are and why they matter. Using a precise, shared language, they aid thinking and communication by turning fuzzy assumptions into clear, comparable, testable claims. The chapter describes the modelling process, trading explanatory clarity against predictive specificity. Four families of models are surveyed, from the micro-level with optimising agents to macro-level dynamics with heuristic or even implicit agents, covering reasoning (Bayesian inference, game theory), adaptive updating (reinforcement learning, evolutionary games), mean-field approaches (compartmental models, population dynamics), and complex systems (agent-based models, social networks). Building on these, a general template for modelling cultural evolution is outlined that connects system states, cognitive processes, behaviour, and macro-level outcomes in dynamic loops, linking individuals, groups, institutions, and their environments. Taken together, these tools support a pluralist but coherent understanding of cultural change. Keywords: scientific method , mathematical models , agent-based models , simulations , complex adaptive systems , Bayesian inference , game theory , reinforcement learning , population dynamics , social networks", "Culture is not just traits but a dynamic system of interdependent beliefs, practices and artefacts embedded in cognitive, social and material structures. Culture evolves as these entities interact, generating path dependence, attractor states and tension, with long-term stability punctuated by rapid systemic transformations. Cultural learning and creativity is modelled as coherence-seeking information processing: individuals filter, transform and recombine input in light of prior acquisitions and dissonance reduction, thereby creating increasingly structured worldviews. Higher-order traits such as goals, skills, norms and cognitive gadgets act as emergent metafilters that regulate subsequent selection by defining what counts as coherent. Together, these filtering processes self-organise into epistemic niches, echo chambers, polarised groups and institutions that channel information flows and constrain future evolution. In this view, LLMs and recommender algorithms are products of cultural embeddings that now act back on cultural systems by automated filtering and recombination of information, reshaping future dynamics of cultural systems. Keywords: complex adaptive systems , belief systems , cognitive dissonance , information filters , social learning , creativity and recombination , cultural segmentation , social norms , embeddings , artificial intelligence", "Various theoretical and empirical studies have accounted for why humans cooperate in competitive environments. Although prior work has revealed that network structure and multiplex interactions can promote cooperation, most theory assumes that individuals play similar dilemma games in all social contexts. However, real-world agents may participate in a diversity of interactions, not all of which present dilemmas. We develop an evolutionary game model on multilayer networks in which one layer supports the prisoner\u2019s dilemma game, while the other follows constant-selection dynamics, representing biased but non-dilemmatic competition, akin to opinion or fad spreading. Our theoretical analysis reveals that coupling a social dilemma layer to a non-dilemmatic constant-selection layer robustly enhances cooperation in many cases, across different multilayer networks, updating rules, and payoff schemes. These findings suggest that embedding individuals within diverse networked settings\u2014even those unrelated to direct social dilemmas\u2014can be a principled approach to engineering cooperation in socio-ecological and organizational systems.", "# Physics-Based Decline Curve Analysis and Machine Learning for Temperature Forecasting in Enhanced Geothermal Systems: Utah FORGE 1 Physics-Based Decline Curve Analysis and Machine Learning for Temperature Forecasting in Enhanced Geothermal Systems: Utah FORGE Mina S. Khalaf University of Houston, Texas, USA Abstract Reliable temperature forecasting in Enhanced Geothermal Systems (EGS) is essential for reservoir design and economic assessment, yet petroleum-based decline curves and many machine-learning (ML) surrogates do not enforce geothermal heat transfer. In addition, high-fidelity thermo-hydro-mechanical (THM) simulation remains computationally expensive. This study proposes a unified, physics-consistent framework that advances both decline-curve analysis (DCA) and surrogate modeling for geothermal temperature forecasts. The classical Arps decline family is generalized for geothermal use by introducing an equilibrium-temperature term motivated by Newton-type cooling, ensuring finite late-time temperature limits while reducing exactly to the conventional Arps forms when the equilibrium term is set to zero. The extended decline curves are validated against Utah FORGE downhole t", "The outbreak of mutant strains and vaccination behaviors have been the focus of recent epidemiological research, but most existing epidemic models failed to simultaneously capture viral mutation and consider the complexity and behavioral dynamics of vaccination. To address this gap, we develop an extended SIRS model that distinguishes infections with the original strain and a mutant strain, and explicitly introduces a vaccinated compartment state. At the behavioral level, we employ evolutionary game theory to model individual vaccination decisions, where strategies are determined by both neighbors\u2019 choices and the current epidemiological situation. This process corresponds to the time-varying vaccination rate of susceptible individuals transitioning to vaccinated individuals at the epidemic spreading level. We then couple the epidemic and vaccination behavioral processes through the microscopic Markov chain approach (MMCA) and finally investigate the evolutionary dynamics via numerical simulations. The results show that our framework can effectively mitigate outbreaks across different disease scenarios. Sensitivity analysis further reveals that vaccination uptake is most strongly influenced by vaccine cost, efficacy, and perceived risk of side effects. Overall, this behavior-aware modeling framework captures the co-evolution of viral mutation and vaccination behavior, providing quantitative and theoretical support for designing effective public health vaccination policies.", ". Wastewater surveillance, which regularly examines the pathogen biomarkers in wastewater samples, is a valuable tool for monitoring infectious diseases circulating in communities. Yet, most wastewater-based epidemiology methods, which use wastewater surveillance results for disease inferences, implicitly assume that individuals excrete only at their residential locations and that the population contribute to wastewater samples are static. These simplifying assumptions ignore daily mobility, social interactions, and heterogeneous toilet use behavior patterns, which can lead to biased interpretation of wastewater results, especially at upstream sampling locations such as neighborhoods, institutions, or buildings. Here, we introduce an agent-based geospatial simulation framework: Building on an established Patterns of Life model, we simulate daily human activities, mobility, and social contacts within a realistic urban environment and extend this agent-based framework with a physiologically motivated defecation cycle and toilet usage patterns. We couple this behavioral model with an infectious disease model to simulate transmissions through spatial and social interactions. When a defecation occurs for an infected agent, we use a pathogen shedding model to determine the amount of pathogen shed in the feces. Such a framework, integrating population mobility, disease transmission, toilet use behavior, and pathogen shedding models, is capable to simulate the Spatial-temporal dynamics of wastewater signals for a city. Using a case study of 10,000 simulated agents in Fulton County, Georgia, we examine how varying infection rates alter epidemic trajectories, pathogen loads in wastewater, and the spatial distribution of contamination across time. Our results highlight that mobility and toilet use can substantially decouple residential population counts from wastewater signals and demonstrate how behaviorally grounded simulations can support interpretation, scenario analysis, and the design of wastewater surveillance strategies.", "Many real-world scale-free networks, such as neural networks and online communication networks, consist of a fixed number of nodes but exhibit dynamic edge fluctuations. However, traditional models frequently overlook scenarios where the node count remains constant, instead prioritizing node growth. In this work, we depart from the assumptions of node number variation and preferential attachment to present an innovative model that conceptualizes node degree fluctuations as a state-dependent random walk process with stasis and variable diffusion coefficient. We show that this model yields stochastic dynamic networks with stable scale-free properties. Through comprehensive theoretical and numerical analyses, we demonstrate that the degree distribution converges to a power-law distribution, provided that the lowest degree state within the network is not an absorbing state. Furthermore, we investigate the resilience of the fraction of the largest component and the average shortest path length following deliberate attacks on the network. By using three real-world networks, we confirm that the proposed model accurately replicates actual data. The proposed model thus elucidates mechanisms by which networks, devoid of growth and preferential attachment features, can still exhibit power-law distributions and be used to simulate and study the resilience of attacked fixed-size scale-free networks.", "Overload-induced cascading failures can cause extreme disruptions in a wide range of networked systems, such as power grids, transportation networks, or financial systems. Empirical studies across domains report that the size of such disruptions often follows a Pareto- or heavy-tailed distribution. While many models reproduce this scaling behavior, they are either tailored to specific domains or based on simplified mechanisms that overlook key aspects of overload cascading behavior. Hence, a general understanding of the mechanisms driving scale-free behavior in these settings remains incomplete. In this paper, we develop a universal and analytically tractable model of overload cascading failures on flow networks, offering a new perspective on how Pareto-tailed disruptions emerge across networks. Our framework shows, under mild assumptions, that heavy-tailed disruptions can arise naturally from Pareto-tailed external inputs, and it establishes a transformation law linking the input and output tail exponents. We further identify broad conditions under which the resulting cascade cost exhibits a heavy-tailed distribution and show that the mechanism is robust across several domains, including power transmission, traffic networks, and processing systems. Our results provide a unified explanation for the emergence of scale-free failures in overload-driven systems and connect previously disparate, application-specific models under a unified framework.", "The global transition from traditional power plants to renewable energy sources introduces new challenges in grid stability, primarily because inverter-based technologies provide insufficient inertia. To address this, we introduce an artificial intelligence algorithm that autonomously stabilizes power grids by adaptively tuning admittance regulators in response to disturbances. This Adaptive Admittance Controller (AAC) algorithm not only stabilizes the system in real time but also identifies the best regulator locations, thereby unifying grid planning and real time control within a single framework. When tested on a real UK power grid, the AAC markedly reduces frequency deviations and rapidly restores nominal operation. In addition, the algorithm isolates a small number of key regulators and intervenes only on these, lowering both system complexity and cost. The AAC algorithm further reduces the nonlinearity effect, quickly stabilizing the frequency and power flow. This intelligent control scheme enables power grids to reliably return to stable operating conditions under a broad spectrum of fault scenarios. The proposed framework can also be used to mitigate cascading failures by adaptively controlling critical links in a variety of networked infrastructures, such as cascades of traffic congestion on road networks or fuse failures in energy-saving systems.", "# Low-Dimensional Interaction Spaces Impose Geometric Constraints On Collective Organization 1 LOW-DIMENSIONAL INTERACTION SPACES IMPOSE GEOMETRIC CONSTRAINTS ON COLLECTIVE ORGANIZATION Arturo Tozzi (corresponding author) ASL Napoli 1 Centro, Distretto 27, Naples, Italy Via Comunale del Principe 13/a 80145 tozziarturo@libero.it ABSTRACT Collective organization in physical, biophysical and biological systems often emerges from many weak and local interactions, yet the resulting global structures display striking regularities and apparent limits in diversity. Theoretical approaches focus on specific mechanisms, detailed dynamics or energetic optimization, making it difficult to identify constraints that are independent of microscopic realization. Here we develop a general theoretical framework showing that, when effective interactions among system components can be compressed into a low-dimensional interaction space, global organization is governed by geometric constraints rather than detailed dynamics. We formalize interaction spaces as metric manifolds derived from coarse-grained effective couplings and prove that low interaction dimensionality imposes upper bounds on the number, s", "We explore a rigorous formulation of agent-based SIR epidemic dynamics as a discrete-state Markov process, capturing the stochastic propagation of infection or an invading agent on networks. Using indicator functions and corresponding marginal probabilities, we derive a hierarchy of evolution equations that resembles the classical BBGKY hierarchy in statistical mechanics. The structure of these equations clarifies the challenges of closure and highlights the principal problem of systemic complexity arising from stochastic but generally not fully chaotic interactions. Monte Carlo simulations are used to validate simplified closures and approximations, offering a unified perspective on the interplay between network topology, stochasticity, and infection dynamics. We also explore the impact of lockdown measures within a networked agent framework, illustrating how SIR dynamics and structural complexity of the network shape epidemic with propagation of COVID-19 in Northern Italy taken as an example. Keywords: SIR epidemic, network clustering, BBGKY hierarchy, conditional moments", "Higher-order interactions provide a nuanced understanding of the relational structure of complex systems beyond traditional pairwise interactions. However, higher-order network analyses also incur more cumbersome interpretations and greater computational demands than their pairwise counterparts. Here we present an information-theoretic framework for determining the extent to which a hypergraph representation of a networked system is structurally redundant, and for identifying its most critical higher orders of interaction that allow us to remove these redundancies while preserving essential higher-order structure.", "The opioid epidemic remains a major public health challenge in the United States, requiring a multi-pronged intervention approach to mitigate harms to communities. Given the heterogeneity of the epidemic across the country, it is crucial for policymakers to understand localized treatment effects of different intervention components and utilize limited resources efficiently. While locally calibrated simulation models offer a useful computational tool to project the epidemic outcomes for any given intervention policy, collecting simulation results for all intervention combinations to estimate localized treatment effects for each community is impractical because the number of possible intervention combinations grows exponentially with the number of interventions and levels at which they are applied. To tackle this, we develop a bi-level metamodel framework with a two-stage sequential design for efficient sampling. The metamodel consists of a response function linking health outcomes to each intervention component\u2019s treatment effect, and a Gaussian process regression (GPR) to learn spatial and socio-economic structures of the treatment effects based on locally-contextualized covariates. With two-stage sequential sampling, we leverage spatial correlations and posterior uncertainty to sequentially sample the most informative counties and treatment conditions. We apply this framework to estimate treatment effects of buprenorphine dispensing and naloxone distribution on overdose mortality rates using a calibrated agent-based opioid epidemic model in Pennsylvania counties. Our approach achieves approximately $5\\%$ average relative error using one-tenth the number of runs required for an exhaustive simulation. Our bi-level framework provides a computationally efficient approach to support policymakers, enabling an efficient evaluation of alternative resource-allocation strategies to mitigate the opioid epidemic in local communities.", "We examine the spread of an infectious disease, such as one that is caused by a respiratory virus, with two distinct modes of transmission. To do this, we consider a susceptible\u2013infected\u2013susceptible (SIS) disease on a hypergraph, which allows us to incorporate the effects of both dyadic (i.e., pairwise) and polyadic (i.e., group) interactions on disease propagation. This disease can spread either via large droplets through direct social contacts, which we associate with edges (i.e., hyperedges of size 2), or via infected aerosols in the environment through hyperedges of size at least 3 (i.e., polyadic interactions). We derive mean-field approximations of our model for two types of hypergraphs, and we obtain threshold conditions that characterize whether the disease dies out or becomes endemic. Additionally, we numerically simulate our model and a mean-field approximation of it to examine the impact of various factors, such as hyperedge size (when the size is uniform), hyperedge-size distribution (when the sizes are nonuniform), and hyperedge-recovery rates (when the sizes are nonuniform) on the disease dynamics. Relevance to Life Sciences In our paper, we formulate a disease-spread model on networks that distinguishes explicitly between two distinct modes of transmission: (1) spread via large droplets (which involve direct social contacts); and (2) spread via aerosols in the environment. Our model includes several important biological features and allows us to draw a variety of relevant biological conclusions. First, we separate the effects of the two modes of transmission on the disease dynamics. Second, we obtain useful insights that depend on the structure of the hypergraph on which a disease spreads. For hypergraphs with uniform hyperedge sizes (i.e., group sizes), we find that reducing this size may be the most effective way to eradicate the examined disease. For hypergraphs with nonuniform hyperedge sizes, we find that having a few very large groups and many small groups (i.e., a heavy-tailed hyperedge-size distribution) leads to longer times before disease eradication (or even cause the disease to become endemic) in comparison to having uniform, medium-sized groups. Finally, if one wants to combat the disease by increasing group recovery rates (e.g., by improving ventilation or air filtration), we find that increasing these rates in a way that depends on group sizes is just as effective but possibly less costly than increasing them uniformly across all groups. Mathematical Content We formulate a stochastic susceptible\u2013infected\u2013susceptible (SIS) model on a hypergraph in which both nodes and groups (i.e., hyperedges of size at least 3) have associated state variables. To date, researchers have analyzed very few network models in which both nodes and hyperedges \u2014 as opposed to only nodes \u2014 have their own state variables. We derive mean-field approximations of our disease-spread model for complete uniform hypergraphs and regular uniform hypergraphs, and we establish threshold conditions for the SIS disease to persist for these approximations. Guided by our mean-field approximations, we perform individual-level stochastic simulations to study how various network parameters and other model parameters affect the disease dynamics. We illustrate that both dyadic interactions and polyadic interactions play an important role in the disease dynamics.", "# Integrated strong reciprocity enables productive punishment and protective defection 1 Integrated strong reciprocity enables productive punishment and protective defection Tatsuya Sasaki1,* and Satoshi Uchida2,3 1 Department of Community Development, Koriyama Women\u2019s College, Koriyama, Japan 2 Research Center for Ethi-Culture Studies, RINRI Institute, Tokyo, Japan 3 High-tech Research Centre, Kokushikan University, Tokyo, Japan * Correspondence: t.sasaki@koriyama-kgc.ac.jp January 5, 2025 Abstract Cooperation in large groups and one-shot interactions is often hindered by freeloading. Punishment can enforce cooperation, but it is usually regarded as wasteful because the costs of punishing offset its benefits. Here, we analyze an evolutionary game model that integrates upstream and downstream reciprocity with costly punishment\u2014integrated strong reciprocity (ISR). We demonstrate that ISR admits a stable mixed equilibrium of ISR and unconditional defection (ALLD), and that costly punishment can become productive: When sufficiently efficient, it raises collective welfare above the no-punishment baseline. ALLD players persist as evolutionary shields, preventing invasion by unconditiona", "Estimating the true prevalence of an epidemic outbreak is a key public health problem. This is challenging because surveillance is usually resource intensive and biased. In the network setting, prior work on cost sensitive disease surveillance has focused on choosing a subset of individuals (or nodes) to minimize objectives such as probability of outbreak detection. Such methods do not give insights into the outbreak size distribution which, despite being complex and multi-modal, is very useful in public health planning. We introduce TestPrev , a problem of choosing a subset of nodes which maximizes the mutual information with disease prevalence, which directly provides information about the outbreak size distribution. We show that, under the independent cascade (IC) model, solutions computed by all prior disease surveillance approaches are highly sub-optimal for TestPrev in general. We also show that TestPrev is hard to even approximate. While this mutual information objective is computationally challenging for general networks, we show that it can be computed efficiently for various network classes. We present a greedy strategy, called GreedyMI , that uses estimates of mutual information from cascade simulations and thus can be applied on any network and disease model. We find that GreedyMI does better than natural baselines in terms of maximizing the mutual information as well as reducing the expected variance in outbreak size, under the IC model.", "Many quantities that characterize network elements are defined in an explicit form and calculated directly from the network structure; examples of include several centrality measures like degree, closeness, or betweenness. However, there are also implicitly defined quantitative measures, which are usually calculated iteratively, in a self-consistent manner, like PageRank or countries\u2019 fitness / products\u2019 complexity relations. The iteration algorithms involve calculations over the entire network; therefore, their convergence properties depend on the structure of the network. Here, we focus on investigating self-consistently defined quantities in bipartite networks of two sets of nodes where the quantities in one set are determined by the quantities in the other set and vice versa. We derive an explicit convergence criterion for iterations of these quantities and describe two different approaches to improve the convergence properties. In the first one, we identify \u201dproblematic nodes\u201d that can be removed or merged while in the second one, we introduce a regularization scheme and show how to estimate the regularization parameter.", "In this paper we propose and investigate a multi-dimensional opinion dynamics model where people are characterised by both opinions and importance weights across these opinions. Opinion changes occur through binary interactions, with a novel coupling mechanism: the change in one topic depends on the weighted similarity across the full opinion vector. We state the kinetic equation for this process and derive its mean-field partial differential equation to describe the overall dynamics. Analytical computations and numerical simulations confirm that this model generates complex stationary states, and we demonstrate that the final opinion structures are critically determined by the peoples\u2019 opinion weights.", "The design of supply chain networks in densely populated urban logistics systems faces a timely dilemma: the traditional optimisation approaches are effective to maximise the level of demand perfusion, but they are limited to embracing large expenses in overlapping the facilities and cannibalisation in the market. In the Delhi National Capital Region (NCR), these inefficiencies occur in the form of high operational wastages, which are explained by unnecessary fleet use and overlapping service lines. We resolve this difficulty by redefining the Capacitated Facility Location Problem (CFLP) as a Quadratic Unconstrained Binary Optimisation (QUBO) model and by benchmarking three computational strategies: Classical Greedy heuristics, Exact Branch-and-Bound solvers, and a Quantum-Inspired reverse annealing method. When tested on a high-fidelity digital twin of the Delhi NCR road network of thirty candidate sites, we establish that Classical Greedy algorithms using the theoretical maximum demand of (473 units) lack any theoretical overlap penalty, but incur a prohibitive overlap penalty (5.08). Here, in comparison, the Quantum-Inspired solution only losses 3.2% of demand (450 compared to 465 units relative to the optimal solution), but the solution preserves 21.8% less operational overlap risk (3.26 compared to 4.17), which can be viewed as a 35.8% improvement compared to the Greedy solution. Geospatial analysis shows that it can be attributed to a shift in strategies: This, in contrast to Classical approaches, which focus on locating facilities in the high-density central areas (North/Central Delhi), the quantum-inspired solver autonomously chooses the diversified topology of the North-south network, penetrating into the underserved periphery growth markets. This is a spatially balanced arrangement which is congruent to the polycentric structure of modern time megacities, and displays better stability to volatility in demand. We have shown that quantum-inspired optimisation methods can close the so-called Linear-Quadratic Gap phenomenon, i.e. the systematic inability of greedy methods to capture the actual quadratic interactions between facilities, and offer a way of computing the pathway to operationally robust and risk-optimised supply chain networks in dense urban conditions.", "Fine-tuned LLMs often exhibit unexpected behavior as a result of generalizing beyond the data they\u2019re shown. We present results in which an LLM fine-tuned to prefer either coastal sports teams or Southern sports teams adopt political beliefs that diverge substantially from those of the base model. While we hypothesized that the coastal model would become more liberal and the southern model would become more conservative, we find that their responses are usually similar to each other, without a clear-cut liberal or conservative bias. In addition to asking the models for numerical ratings of agreement with relevant political statements, we ask them to elaborate on their answers that diverge most strongly from the base model, finding varying degrees of willingness to justify themselves. Further work is needed to understand the mechanisms by which fine-tuning on simple, narrow datasets leads to seemingly unrelated changes in model behavior.", "# Dynamics of Unemployment with Discouraged Workers: A Nonlinear Mathematical Model ## 1. Introduction Unemployment is one of the most serious social and economic problems faced by countries across the world. It affects economic stability, government finances, and social welfare systems, particularly in economic structures that rely heavily on public support. In simple terms, unemployment refers to a situation where people who are able and willing to work cannot find suitable jobs. In recent decades, unemployment has increased due to rapid population growth, technological advances such as automation, artificial intelligence , and major health crises, including the COVID-19 pandemic . Apart from economic difficulties, unemployment also creates serious social problems. It can negatively affect mental health, increase stress and depression, and may even lead to higher levels of criminal behaviour . Motivated by these concerns, considerable attention has been devoted to the development of mathematical models to understand the dynamics of unemployment and to evaluate the effectiveness of policy interventions. One of the early contributions in this direction was made by Nikolopoulos and", "# Computation as Organisation ## 1 Abstract Computation is commonly defined as the execution of abstract algorithms over symbolic representations, with physical systems treated as substrates that realise predefined operations. While effective for engineered machines, this separation becomes problematic when applied to living systems, where persistence, adaptation, and failure occur without symbolic instruction or central control. Here, computation is reformulated as a structural property of organised matter. Organisation is defined as the persistence of relational constraints that delimit admissible state transitions. Information is not encoded content but relational invariance: differences that influence future behaviour by reshaping what transitions remain possible. Computation is identified with the ongoing enactment of such organisation, integrating memory, processing, and execution as inseparable aspects of material dynamics. Within this framework, algorithms correspond to internally embedded regularities enabled by constraint, and computational limits arise from organisation itself. The account provides experimentally accessible criteria for computation based on persistence,", ". Many of the most consequential dynamics in human cognition occur before events become explicit: before decisions are finalized, emotions are labeled, or meanings stabilize into narrative form. These pre-event states are characterized by ambiguity, contextual tension, and competing latent interpretations. Rogue Variable Theory (RVT) formalizes such states as Rogue Variables : structured, pre-event cognitive configurations that influence outcomes while remaining unresolved or incompatible with a system\u2019s current representational manifold. We present a quantum-consistent information-theoretic implementation of RVT based on a time-indexed Mirrored Personal Graph (MPG) embedded into a fixed graph Hilbert space, a normalized Quantum MPG State (QMS) constructed from node and edge metrics under context, Hamiltonian dynamics derived from graph couplings, and an error-weighted \u2018rogue operator\u201d whose principal eigenvectors identify rogue factor directions and candidate Rogue Variable segments. We further introduce a Rosetta Stone Layer (RSL) that maps user-specific latent factor coordinates into a shared reference Hilbert space to enable cross-user comparison and aggregation without explicit node alignment. The framework is fully implementable on classical systems and does not assume physical quantum processes; collapse is interpreted as informational decoherence under interaction, often human clarification.", "Krotov and Hopfield (2021) proposed a biologically plausible two-layer associative memory network with memory storage capacity exponential in the number of visible neurons. However, the capacity was only linear in the number of hidden neurons. This limitation arose from the choice of nonlinearity between the visible and hidden units, which enforced winner-take-all dynamics in the hidden layer, thereby restricting each hidden unit to encode only a single memory. We overcome this limitation by introducing a novel associative memory network with a threshold nonlinearity that enables distributed representations. In contrast to winner-take-all dynamics, where each hidden neuron is tied to an entire memory, our network allows hidden neurons to encode basic components shared across many memories. Consequently, complex patterns are represented through combinations of hidden neurons. These representations reduce redundancy and allow many correlated memories to be stored compositionally. Thus, we achieve much higher capacity: exponential in the number of hidden units, provided the number of visible units is sufficiently large relative to the number of hidden units. Exponential capacity arises because all binary states of the hidden units can become stable memory patterns. Moreover, the distributed hidden representation, which has much lower dimensionality than the visible layer, preserves class-discriminative structure, supporting efficient nonlinear decoding. These results establish a new regime for associative memory, enabling high-capacity, robust, and scalable architectures consistent with biological constraints.", "Understanding the interaction of neural and cardiac systems during cognitive activity is critical to advancing physiological computing. Although EEG has been the gold standard for assessing mental workload, its limited portability restricts its real-world use. Widely available ECG through wearable devices proposes a pragmatic alternative. This research investigates whether ECG signals can reliably reflect cognitive load and serve as proxies for EEG-based indicators. In this work, we present multimodal data acquired from two different paradigms involving working-memory and passive-listening tasks. For each modality, we extracted ECG time-domain HRV metrics and Catch22 descriptors against EEG spectral and Catch22 features, respectively. We propose a cross-modal XGBoost framework to project the ECG features onto EEG-representative cognitive spaces, thereby allowing workload inferences using only ECG. Our results show that ECG-derived projections expressively capture variation in cognitive states and provide good support for accurate classification. Our findings underpin ECG as an interpretable, real-time, wearable solution for everyday cognitive monitoring.", "The vertebrate motor system employs dimensionality-reducing strategies to limit the complexity of movement coordination, for efficient motor control. But when environments are dense with hidden action\u2013outcome contingencies, movement complexity can promote behavioral innovation. Humans, perhaps uniquely, may infer the presence of hidden environmental dynamics from social cues, by drawing upon computational mechanisms shared with Theory of Mind. This proposed \u201cTheory of Environment\u201d supports behavioral innovation by expanding the dimensionality of motor exploration.", "# Insular intracranial activity identifies multiple facial expressions via diverse, intermixed temporal patterns at the single-contact level Title: Insular intracranial activity identifies multiple facial expressions via diverse, intermixed temporal patterns at the single-contact level Yingyu Huang1#, Lisen Sui2#, Liying Zhan 1,3, Chaolun Wang1, Zhihan Guo1, Yanjuan Li2, Xiang Wu1* (#These authors contributed equally to this work) 1Department of Psychology, Sun Yat-Sen University, Guangzhou, China 2Department of Neurology, The Second Affiliated hospital of Guangzhou University of Chinese Medicine (Guangdong Provincial Hospital of Chinese Medicine), Guangzhou, China 3School of Education, Zhaoqing University, Zhaoqing, Guangdong, China * Correspondence: Xiang Wu Department of Psychology, Sun Yat-Sen University, 132 Waihuan East Road, Higher Education Mega Center, Guangzhou, Guangdong, China, 510006 E: wuxiang3@mail.sysu.edu.cn Graphic Abstract Abstract How neural representations in the insular cortex support emotional processing remains poorly understood, and the extent to which the insula is specialized for disgust processing remains debated. We recorded stereoelectroencephalography", "Electroencephalography (EEG) foundation models hold significant promise for universal Brain-Computer Interfaces (BCIs). However, existing approaches often rely on end-to-end fine-tuning and exhibit limited efficacy under frozen-probing protocols, lacking the intrinsic universality required for broad generalization. This limitation stems from adapting general-purpose sequence architectures that overlook the biophysical and dynamical principles of neural activity. To bridge this gap, we propose DeeperBrain, a neuro-grounded foundation model integrating domain-specific inductive biases into its model design and learning objectives. Architecturally, DeeperBrain incorporates a volume conduction-aware channel encoding to model spatial mixing via 3D geometry, and a neurodynamics-aware temporal encoding capturing slow adaptations using oscillatory and exponential bases. For pretraining, we introduce a dual-objective strategy combining Masked EEG Reconstruction (MER) for local fidelity and Neurodynamics Statistics Prediction (NSP). NSP enforces alignment with macroscopic brain states by predicting interpretable order parameters, including spectral power, functional connectivity, cross-frequency coupling, and dynamic complexity. Extensive experiments demonstrate that DeeperBrain achieves state-of-the-art or highly competitive performance under end-to-end fine-tuning . Crucially, it maintains superior efficacy under a rigorous frozen-probing protocol, verifying that embedding neuroscientific first principles endows learned representations with the intrinsic universality essential for universal BCI. The code will be publicly available.", "A remarkable capability of the human brain is to form more abstract conceptual representations from sensorimotor experiences and flexibly apply them independent of direct sensory inputs. However, the computational mechanism underlying this ability remains poorly understood. Here, we present a dual-module neural network framework, the CATS Net, to bridge this gap. Our model consists of a concept-abstraction module that extracts low-dimensional conceptual representations, and a task-solving module that performs visual judgement tasks under the hierarchical gating control of the formed concepts. The system develops transferable semantic structure based on concept representations that enable cross-network knowledge transfer through conceptual communication. Model-brain fitting analyses reveal that these emergent concept spaces align with both neurocognitive semantic model and brain response structures in the human ventral occipitotemporal cortex, while the gating mechanisms mirror that in the semantic control brain network. This work establishes a unified computational framework that can offer mechanistic insights for understanding human conceptual cognition and engineering artificial systems with human-like conceptual intelligence.", "# How much neuroscience does a neuroscientist need to know? How much neuroscience does a neuroscientist need to know? James C.R. Whittington1* and William Dorrell2 1Department of Experimental Psychology, University of Oxford 2Gatsby Computational Neuroscience Unit, University College London *correspondence: james.whittington@psy.ox.ac.uk Abstract How much of the brain\u2019s learned algorithms depend on the fact it is a brain? We argue: a lot, but surprisingly few details matter. We point to simple biological details\u2014e.g. nonnegative firing and energetic/space budgets in connectionist architectures\u2014which, when mixed with the requirements of solving a task, produce models that predict brain responses down to single-neuron tuning. We understand this as details constraining the set of plausible algorithms, and their implementations, such that only \u2018brain-like\u2019 algorithms are learned. In particular, each biological detail breaks a symmetry in connectionist models (scale, rotation, permutation) leading to interpretable single-neuron responses that are meaningfully characteristic of particular algorithms. This view helps us not only understand the brain\u2019s choice of algorithm but also infer al", "# Responses of the Neurobiological Craving Signature to smoking versus alternative social rewards predict craving and monthly smoking in adolescents 1 Responses of the Neurobiological Craving Signature to smoking versus alternative social rewards predict craving and monthly smoking in adolescents Maddalena Tamellini1, Joyce Dieleman2,3,4, Guillaume Sescousse1, Maartje Luijten3, & Leonie Koban1* 1 Lyon Neuroscience Research Center (CRNL), INSERM U1028, CNRS UMR5292, Claude Bernard Lyon 1 University, Lyon, France. 2 Trimbos Institute, Utrecht, The Netherlands. 3 Behavioral Science Institute, Radboud University, Nijmegen, The Netherlands. 4 GGD Noord- en Oost Gelderland, Warnsveld, The Netherlands. *Please address correspondence to: Dr. Leonie Koban Centre de Recherche en Neurosciences de Lyon Inserm U1028 - CNRS UMR5292 - Universit\u00e9 Claude Bernard Lyon1 Institut des \u00c9pilepsies IDEE 59 Boulevard Pinel 69500 BRON Email: leonie.koban@cnrs.fr Telephone: +33 (0)4 81 10 65 93 2 Abstract Smoking remains the leading cause of preventable mortality worldwide. Adolescents are particularly vulnerable to the development of tobacco addiction due to ongoing brain maturation and susceptibility to so", "# Evolutionary tuning of TAM receptor-ligand interfaces highlights electrostatic features associated with regenerative phagocytic signaling Evolutionary tuning of TAM receptor\u2013ligand interfaces highlights electrostatic features associated with regenerative phagocytic signaling Enso O. Torres Alegre ORCID: 0000-0002-6798-8776 Pontifical Catholic University of Chile, Santiago, Chile onill@uc.cl Abstract Efficient resolution of neuroinflammation and debris clearance are key determinants of successful central nervous system (CNS) regeneration. Regenerative vertebrates such as Danio rerio often show faster immune resolution and debris clearance than mammals, yet the molecular determinants underlying these differences remain incompletely understood. TAM receptor tyrosine kinases (Tyro3, Axl, and Mertk) and their ligands Gas6 and Protein S are central regulators of phagocytosis and immune resolution in the nervous system, but whether intrinsic structural properties of these receptor\u2013ligand complexes contribute to regenerative efficiency has not been systematically explored. Here, we perform a comparative in silico analysis of TAM receptors and ligands from zebrafish, human, and mouse, int", "Neuronal systems often preserve their characteristic functions and signalling patterns, also referred to as regimes , despite parametric uncertainties and variations. For neural models having uncertain parameters with a known probability distribution, probabilistic robustness analysis (PRA) allows us to understand and quantify under which uncertainty conditions a regime is preserved in expectation. We introduce a new computational framework for the efficient and systematic PRA of dynamical systems in neuroscience and we show its efficacy in analysing well-known neural models that exhibit multiple dynamical regimes: the Hindmarsh-Rose model for single neurons and the Jansen-Rit model for cortical columns. Given a model subject to parametric uncertainty, we employ generalised polynomial chaos to derive mean neural activity signals, which are then used to assess the amount of parametric uncertainty that the system can withstand while preserving the current regime, thereby quantifying the regime\u2019s robustness to such uncertainty. To assess persistence of regimes, we propose new metrics, which we apply to recurrence plots obtained from the mean neural activity signals. The overall result is a novel, general computational methodology that combines recurrence plot analysis and systematic persistence analysis to assess how much the uncertain model parameters can vary, with respect to their nominal value, while preserving the nominal regimes in expectation. We summarise the PRA results through probabilistic regime preservation (PRP) plots, which capture the effect of parametric uncertainties on the robustness of dynamical regimes in the considered models.", "Human cognition integrates information across nested timescales. While the cortex exhibits hierarchical Temporal Receptive Windows (TRWs), local circuits often display heterogeneous time constants. To reconcile this, we trained biologically constrained deep networks, based on scale-invariant hippocampal time cells, on a language classification task mimicking the hierarchical structure of language (e.g., \u2018letters\u2019 forming \u2018words\u2019 ). First, using a feedforward model (SITHCon), we found that a hierarchy of TRWs emerged naturally across layers, despite the network having an identical spectrum of time constants within layers. We then distilled these inductive priors into a biologically plausible recurrent architecture, SITH-RNN. Training a sequence of architectures ranging from generic RNNs to this restricted subset showed that the scale-invariant SITH-RNN learned faster with orders-of-magnitude fewer parameters, and generalized zero-shot to out-of-distribution timescales. These results suggest the brain employs scale-invariant, sequential priors\u2014coding \u201cwhat\u201d happened \u201cwhen\u201d\u2014making recurrent networks with such priors particularly well-suited to describe human cognition.", "Credit assignment\u2014how changes in individual neurons and synapses affect a network\u2019s output\u2014is central to learning in brains and machines. Noise correlation, which estimates gradients by correlating perturbations of activity with changes in output, provides a biologically plausible solution to credit assignment but scales poorly as accurately estimating the Jacobian requires that the number of perturbations scale with network size. Moreover, isotropic noise conflicts with neurobiological observations that neural activity lies on a low-dimensional manifold. To address these drawbacks, we propose neural manifold noise correlation (NMNC), which performs credit assignment using perturbations restricted to the neural manifold. We show theoretically and empirically that the Jacobian row space aligns with the neural manifold in trained networks, and that manifold dimensionality scales slowly with network size. NMNC substantially improves performance and sample efficiency over vanilla noise correlation in convolutional networks trained on CIFAR-10, ImageNet-scale models, and recurrent networks. NMNC also yields representations more similar to the primate visual system than vanilla noise correlation. These findings offer a mechanistic hypothesis for how biological circuits could support credit assignment, and suggest that biologically inspired constraints may enable, rather than limit, effective learning at scale.", "# A Mathematical Formalization of Self-Determining Agency A Mathematical Formalization of SelfDetermining Agency Yoshiyuki Ohmura*, Earnest Kota Carr, Yasuo Kuniyoshi The University of Tokyo Abstract Defining agency is an extremely important challenge for cognitive science and artificial intelligence. Physics generally describes mechanical happenings, but there remains an unbridgeable gap between these and the acts of agents. To discuss the morality and responsibility of agents, it is necessary to model acts; whether such responsible acts can be fully explained by physical determinism remains an ongoing debate. Although we have already proposed a physical \u201cagent determinism\u201d model that appears to go beyond mere mechanical happenings, we have not yet established a strict mathematical formalism to eliminate ambiguity. Here, we explain why a physical system can follow coarse-graining agent-level determination without violating physical laws by formulating supervenient causation. Generally, supervenience including coarse graining does not change without a change in its lower base; therefore, a single supervenience alone cannot define supervenient causation. We define supervenient causa", "Do transformers learn like brains? A key challenge in addressing this question is that transformers and brains are trained on fundamentally different data. Brains are initially \u201dtrained\u201d on prenatal sensory experiences (e.g., retinal waves), whereas transformers are typically trained on large datasets that are not biologically plausible. We reasoned that if transformers learn like brains, then they should develop the same structure as newborn brains when exposed to the same prenatal data. To test this prediction, we simulated prenatal visual input using a retinal wave generator. Then, using self-supervised temporal learning, we trained transformers to adapt to those retinal waves. During training, the transformers spontaneously developed the same structure as newborn visual systems: (1) early layers became sensitive to edges, (2) later layers became sensitive to shapes, and (3) the models developed larger receptive fields across layers. The organization of newborn visual systems emerges spontaneously when transformers adapt to a prenatal visual world. This developmental convergence suggests that brains and transformers learn in common ways and follow the same general fitting principles.", "# Emergent togetherness in collaborative dance improvisation: neural and motor synchronization reveal a coupling-decoupling paradox Emergent togetherness in collaborative dance improvisation: neural and motor synchronization reveal a coupling-decoupling paradox Yago Emanoel Ramos12, Raphael Silva do Ros\u00e1rio1, Adriana de Faria Gehres3, Maria Jo\u00e3o Alves45, Ana Maria Leit\u00e3o45, Cec\u00edlia Bastos da Costa Accioly6, Fatima Wachowicz6, Ivani L\u00facia Oliveira de Santana7, Jos\u00e9 Garcia Vivas Miranda1* Abstract Collective improvisation in dance provides a rich natural laboratory for studying emergent coordination in coupled neuro-motor systems. Here, we investigate how training shapes spontaneous synchronization patterns in both movement dynamics and brain signals during collaborative performance. Using a dual-recording protocol integrating 3D motion capture and hyperscanning EEG, participants engaged in free, interaction-driven, and rule-based improvisation before and after a program of generative dance, grounded in cellular-automata. Motor behavior was modeled through a time-resolved \u03b1-exponent derived from Movement Element Decomposition scaling between mean velocity and displacement, revealing", "As intelligent systems are developed across diverse substrates - from machine learning models and neuromorphic hardware to in vitro neural cultures -understanding what gives a system agency has become increasingly important. Existing definitions, however, tend to rely on top-down descriptions that are difficult to quantify. We propose a bottom-up framework grounded in a system\u2019s information-processing order: the extent to which its transformation of input evolves over time. We identify three orders of information processing. Class I systems are reactive and memoryless, mapping inputs directly to outputs. Class II systems incorporate internal states that provide memory but follow fixed transformation rules. Class III systems are adaptive\u2014their transformation rules themselves change as a function of prior activity. While not sufficient on their own, these dynamics represent necessary informational conditions for genuine agency. This hierarchy offers a measurable, substrate-independent way to identify the informational precursors of agency. We illustrate the framework with neurophysiological and computational examples, including thermostats and receptor-like memristors, and discuss its implications for the ethical and functional evaluation of systems that may exhibit agency.", "We present a data-driven framework to characterize large-scale brain dynamical states directly from correlation matrices at the single-subject level. By treating correlation thresholding as a percolation-like probe of connectivity, the approach tracks multiple cluster- and network-level observables and identifies a characteristic percolation threshold, $r_{c}$ , at which these signatures converge. We use $r_{c}$ as an operational and physically interpretable descriptor of large-scale brain dynamical state. Applied to resting-state fMRI data from a large cohort of healthy individuals ( $N=996$ ), the method yields stable, subject-specific estimates that covary systematically with established dynamical indicators such as temporal autocorrelations. Numerical simulations of a whole-brain model with a known critical regime further show that $r_{c}$ tracks changes in collective dynamics under controlled variations of excitability. By replacing arbitrary threshold selection with a criterion intrinsic to correlation structure, the $r$ -spectra provides a physically grounded approach for comparing brain dynamical states across individuals.", "Understanding intelligence and consciousness requires moving beyond cataloging cognitive abilities to identifying the fundamental operations that produce them. This paper argues that intelligence emerges from a single purpose:forming, refining, and integrating causal connections between signals, actions, internal states, and learned structures. Through context enrichment\u2014interpreting incoming information using previously learned relational context\u2014intelligent systems achieve efficient processing under metabolic constraints. Rather than rebuilding environmental models from raw input for context, the brain reactivates stable causal structures learned from experience, enabling sophisticated interpretation from minimal sensory data. Building on this foundation, we introduce the systems-explaining-systems principle: consciousness emerges when recursive neural architecture enables higher-order systems to explicitly learn and interpret the relational patterns of lower-order systems across time. Unlike simple stimulus\u2013response networks that merely implement learned relations implicitly, recursive hierarchical systems can represent the underlying connections that give those relations meaning. Fed back through context enrichment and integrated within a dynamically stabilized meta-state, these interpretations transform the brain\u2019s internal model from representing only the external world to representing its own cognitive processes. The framework also reframes predictive processing theories. We embrace similar architectural principles but propose that prediction emerges as a consequence of a conceptually simpler underlying operation focused on discovering stable contextual structure rather than forecasting future inputs. For artificial intelligence, this suggests progress toward human-like cognition may require multi-system architectures where higher modules interpret and regulate lower ones, rather than scaling single feedforward systems. Consciousness is not a separate faculty but the natural outcome of sufficiently deep recursive relational organization\u2014intelligence turned inward upon itself. This work is presented as a preprint, and the author welcomes constructive feedback and discussion.", "# Irreversible behavior drives neural flows in the hippocampus ## Code Availability The code used to perform the analyses in this paper is openly available at github.com/Kaiyue-Shi/Neural-Flow-in-the-Hippocampus. {addendum} Supplementary text and figures accompany this paper. We thank J.L. Gauthier for guiding us through the data. We thank M.P. Leighton for helpful discussions about irreversibility. This work was supported in part by the Department of Physics and the Quantitative Biology Institute at Yale University. K.S. and C.W.L. conceived the project, designed the models, and wrote the manuscript and Supplementary Information. K.S. solved the models and performed the analyses. The authors declare no competing financial interests. Correspondence and requests for materials should be addressed to C.W.L. (christopher.lynn@yale.edu). Supplementary Information ## 1 Introduction In this Supplementary Information, we provide extended figures and discussion to support the results presented in the main text. In Sec. 2, we illustrate multiple mechanisms that cannot account for the decay in neural flow. Specifically, we simulate place cells with different statistical features whose total f", "Efficient navigation in swarms often relies on the emergence of decentralized approaches that minimize traversal time or energy. Stigmergy \u2014 where agents modify a shared environment that then modifies their behavior - is a classic mechanism that can encode this strategy. We develop a theoretical framework for stigmergic transport by casting it as a stochastic optimal control problem: agents (collectively) lay and (individually) follow trails while minimizing expected traversal time. Simulations and analysis reveal two emergent behaviors\u2014path straightening in homogeneous environments and path refraction at material interfaces\u2014both consistent with experimental observations of insect trails. While reminiscent of Fermat\u2019s principle of global optimization, our results show how local, noisy agent\u2013field interactions can give rise to geodesic trajectories in heterogeneous environments, without centralized coordination or global knowledge, but through an embodied slow\u2013fast dynamical mechanism. 1. Trail following by agents. 2. Dimensional analysis. 3. Trail optimization. 4. Numerical simulations. 5. Discussion 6. Acknowledgments 7. A End Matter 1. A.1 Eikonal geometry of navigation 2. A.2 Hydrodynamic field theory 3. A.3 Lyapunov convergence to geodesic paths 4. A.4 Comparison with classical optimal transport \u2020\u2020thanks: Corresponding author. lmahadev@g.harvard.edu", "Phasor Agents are dynamical systems whose internal state is a Phasor Graph : a weighted graph of coupled Stuart\u2013Landau oscillators. A Stuart\u2013Landau oscillator is a minimal stable \u201crhythm generator\u201d (normal form near a Hopf bifurcation); we treat each oscillator as an abstract computational unit (inspired by, but not claiming to model, biological oscillatory populations). In this interpretation, oscillator phase tracks relative timing (coherence), while amplitude tracks local gain/activity. Relative phase structure serves as a representational medium; coupling weights are learned via three-factor local plasticity \u2014eligibility traces gated by sparse global modulators and oscillation-timed write windows\u2014without backpropagation. A central challenge in oscillatory substrates is stability : online weight updates can drive the network into unwanted regimes ( e.g. , global synchrony), collapsing representational diversity. We therefore separate wake tagging from offline consolidation, inspired by synaptic tagging-and-capture and sleep-stage dynamics: deep-sleep-like gated capture commits tagged changes safely, while REM-like replay reconstructs and perturbs experience for planning. A staged experiment suite validates each mechanism with ablations and falsifiers: eligibility traces preserve credit under delayed modulation; compression-progress signals pass timestamp-shuffle controls; phase-coherent retrieval reaches 4 $\\times$ diffusive baselines under noise; wake/sleep separation expands stable learning by 67% under matched weight-norm budgets; REM replay improves maze success rate by +45.5 percentage points; and a Tolman-style latent-learning signature\u2014immediate competence and detour advantage after unrewarded exploration\u2014consistent with an internal model, emerges from replay . The codebase and all artifacts are open-source.", "# Past Psychedelic Use Predicts Divergent Thinking Past Psychedelic Use Predicts Divergent Thinking Gregory J Pope1*, Christopher Timmermann2*, William Trender3, Peter J Hellyer4, Maria B\u0103l\u0103e\u01633,4*, Ruben Eero Laukkonen5,6 1 Southern Cross University, Faculty of Health, Gold Coast, QLD, Australia 2UCL Centre for Consciousness Research, Experimental Psychology, University College London, London, UK 3 Department of Brain Sciences, Imperial College London; 4Department of Neuroimaging, IoPPN, King\u2019s College London, 5Flourishing Intelligence Program, Linacre College, University of Oxford 6Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford *For correspondence: gregoryjamespope@gmail.com; m.balaet17@imperial.ac.uk Abstract Psychedelics have shown potential in treating a range of mental health conditions, yet far less is known about their impact on creativity. This study examined three components of creativity\u2014divergent thinking, cognitive reflection, and insight\u2014in a large sample (N = 5,905) from the Great British Intelligence Test. We compared performance between individuals with past psychedelic use and those without such use. Psychedelic users scored sign", "# Effects of T-type and L-type calcium currents on synchronized activity patterns in a model subthalamo-pallidal network 1 Effects of T-type and L-type calcium currents on synchronized activity patterns in a model subthalamo-pallidal network Choongseok Park1*, Leonid L. Rubchinsky2,3, Sungwoo Ahn4,5 1Department of Mathematics and Statistics, North Carolina A&T State University, Greensboro, NC, USA 27411 2 Department of Mathematical Sciences, Indiana University Indianapolis, Indianapolis, IN, USA 46202 3 Stark Neurosciences Research Institute, Indiana University School of Medicine, Indianapolis, IN, USA 46202 4 Department of Mathematics, East Carolina University, Greenville NC, USA 27858 5 Alliance for Brain Stimulation, College of Allied Health Sciences, East Carolina University, Greenville NC, USA 27858 * Correspondence: Choongseok Park Department of Mathematics and Statistics North Carolina A&T State University 1601 E Market St Greensboro, NC 27411, USA Email: cpark@ncat.edu Abstract Synchronized rhythmic oscillatory activity in the beta frequency band in the basal ganglia (BG) is a hallmark of Parkinson\u2019s disease (PD). Recent experiments and theoretical studies have demonstrated", "Recent Large Reasoning Models trained via reinforcement learning exhibit a \u201cnatural\u201d alignment with human cognitive costs. However, we show that reasoning distillation via Supervised Fine-Tuning (SFT) fails to transmit this cognitive logic, leading to a \u201cCargo Cult\u201d where students only mimic length. Testing the H\u00e1n D\u0101n Xu\u00e9 B\u00f9 (Superficial Mimicry) hypothesis across 14 models, we identify a \u201cFunctional Alignment Collapse\u201d: while teacher models mirror human difficulty scaling ( $\\bar{r}=0.64$ ), distilled students significantly degrade this alignment ( $\\bar{r}=0.34$ ). Crucially, they exhibit \u201cNegative Transfer,\u201d dropping below their own pre-distillation baselines. Our analysis reveals a \u201cLinear Inflation Law\u201d where students apply a constant verbosity multiplier ( $\\approx 2.44$ ) regardless of complexity. Consequently, distillation decouples computational cost from cognitive demand, revealing that human-like cognition is an emergent property of active reinforcement, not passive imitation. Keywords: Large Reasoning Models; Cognitive Modeling; Reasoning Distillation; Cognitive Alignment; Supervised Fine-Tuning; Chain-of-Thought", "Single-cell data analysis has the potential to revolutionize personalized medicine by characterizing disease-associated molecular changes at the single-cell level. Advanced single-cell multimodal assays can now simultaneously measure various molecules (e.g., DNA, RNA, Protein) across hundreds of thousands of individual cells, providing a comprehensive molecular readout. A significant analytical challenge is integrating single-cell measurements across different modalities. Various methods have been developed to address this challenge, but there has been no systematic evaluation of these techniques with different preprocessing strategies. This study examines a general pipeline for single-cell data analysis, which includes normalization, data integration, and dimensionality reduction. The performance of different algorithm combinations often depends on the dataset sizes and characteristics. We evaluate six datasets across diverse modalities, tissues, and organisms using three metrics: Silhouette Coefficient Score, Adjusted Rand Index, and Calinski-Harabasz Index. Our experiments involve combinations of seven normalization methods, four dimensional reduction methods, and five integration methods. The results show that Seurat and Harmony excel in data integration, with Harmony being more time-efficient, especially for large datasets. UMAP is the most compatible dimensionality reduction method with the integration techniques, and the choice of normalization method varies depending on the integration method used.", "# Peak-Nadir Encoding for Efficient CGM Data Compression and High-Fidelity Reconstruction p. 1 of 12 RESEARCH ARTICLE Peak-Nadir Encoding for Efficient CGM Data Compression and High-Fidelity Reconstruction Running title: Efficient CGM Data Compression Authors: Clara Bender MSc PhD1, Line Davidsen MD2,3, S\u00f8ren Schou Olesen MD PhD2,3, Simon Lebech Cichosz MSc BMEI PhD1 Author Affiliations: 1Department of Health Science and Technology, Aalborg University, Denmark. 2Department of Clinical Medicine, Aalborg University Hospital, Aalborg, Denmark. 3Centre for Pancreatic Diseases and Mech-Sense, Department of Gastroenterology and Hepatology, Aalborg University Hospital, Aalborg, Denmark. Corresponding author: Simon Cichosz, simcich@hst.aau.dk, Postal address: Selma Lagerl\u00f8fs Vej 249, 12-02-048, 9260 Gistrup, Danmark, Phone: (+45) 9940 2020; Fax: (+45) 9815 4008. ORCID: 0000-0002-3484-7571 Figures: 5 Tables: 2 Words: 2801 Keywords: compression, CGM, encoding, reconstruction, signal, diabetes, data Abbreviations: Conditional Generative Adversarial Network (CGAN); Continuous Glucose Monitoring (CGM); Coefficient of Variation (CV); Mean Absolute Error (MAE); Mean Amplitude of Glycemic Excursio", "# Quantifying the uncertainty of molecular dynamics simulations : Good-Turing statistics revisited 1 Research article Quantifying the uncertainty of molecular dynamics simulations : Good-Turing statistics revisited Vasiliki Tsampazi & Nicholas M. Glykos* Department of Molecular Biology and Genetics, Democritus University of Thrace, University campus, 68100 Alexandroupolis, Greece, Tel +30-25510-30620, Fax +30-25510-30620, https://utopia.duth.gr/glykos/ , glykos@mbg.duth.gr 2 Abstract We have previously shown that Good-Turing statistics can be applied to molecular dynamics trajectories to estimate the probability of observing completely new (thus far unobserved) biomolecular structures, and showed that the method is stable, dependable and its predictions verifiable. The major problem with that initial algorithm was the requirement for calculating and storing in memory the twodimensional RMSD matrix of the currently available trajectory. This requirement precluded the application of the method to very long simulations. Here we describe a new variant of the Good-Turing algorithm whose memory requirements scale linearly with the number of structures in the trajectory, making it suitabl", "Large Protein Language Models have shown strong potential for generative protein design, yet they frequently produce structural hallucinations, generating sequences with high linguistic likelihood that fold into thermodynamically unstable conformations. Existing alignment approaches such as Direct Preference Optimization are limited in this setting, as they model preferences as binary labels and ignore the continuous structure of the physical energy landscape. We propose Physio-DPO, a physics informed alignment framework that grounds protein language models in thermodynamic stability. Physio-DPO introduces a magnitude aware objective that scales optimization updates according to the energy gap between native structures and physics perturbed hard negatives. Experiments show that Physio-DPO consistently outperforms strong baselines including SFT, PPO, and standard DPO, reducing self consistency RMSD to 1.28 \u00c5 and increasing foldability to 92.8%. Qualitative analysis further demonstrates that Physio-DPO effectively mitigates structural hallucinations by recovering biophysical interactions such as hydrophobic core packing and hydrogen bond networks.", "# Quantum Simulation of Protein Fragment Electronic Structure Using Moment-based Adaptive Variational Quantum Algorithms Quantum Simulation of Protein Fragment Electronic Structure Using Moment-based Adaptive Variational Quantum Algorithms Biraja Ghoshal1,* 1University College London (UCL), London, UK *Correspondence to: b.ghoshal@ucl.ac.uk January 5, 2026 Abstract Background: Understanding electronic interactions in protein active sites is fundamental to drug discovery and enzyme engineering, but remains computationally challenging due to exponential scaling of quantum mechanical calculations. Results: We present a quantum-classical hybrid framework for simulating protein fragment electronic structure using variational quantum algorithms. We construct fermionic Hamiltonians from experimentally determined protein structures, map them to qubits via Jordan-Wigner transformation, and optimize ground state energies using the Variational Quantum Eigensolver implemented in pure Python. For a 4-orbital serine protease fragment, we achieve chemical accuracy (< 1.6 mHartree) with 95.3% correlation energy recovery. Systematic analysis reveals three-phase convergence behaviour with exponentia", "Liquid chromatography mass spectrometry (LC-MS)-based metabolomics and exposomics aim to measure detectable small molecules in biological samples. The results facilitate hypothesis-generating discovery of metabolic changes and disease mechanisms and provide information about environmental exposures and their effects on human health. Metabolomics and exposomics are made possible by the high resolving power of LC and high mass measurement accuracy of MS. However, a majority of the signals from such studies still cannot be identified or annotated using conventional library searching because existing spectral libraries are far from covering the vast chemical space captured by LC-MS/MS. To address this challenge and unleash the full potential of metabolomics and exposomics, a number of computational approaches have been developed to predict compounds based on tandem mass spectra. Published assessment of these approaches used different datasets and evaluation. To select prediction workflows for practical applications and identify areas for further improvements, we have carried out a systematic evaluation of the state-of-the-art prediction algorithms. Specifically, the accuracy of formula prediction and structure prediction was evaluated for different types of adducts. The resulting findings have established realistic performance baselines, identified critical bottlenecks, and provided guidance to further improve compound predictions based on MS.", "Identifying cancer driver genes is crucial for understanding tumor biology and developing precision therapies. However, existing computational methods often rely on single biological networks or population-level mutation patterns, limiting their ability to identify patient-specific drivers and leverage the complementary information from multiple network types. Here, we present HyperNetWalk, a novel computational framework that integrates multiple biological networks and hypergraph diffusion to identify driver genes at both personalized and cohort levels. In the first stage, HyperNetWalk integrates protein-protein interaction networks, gene regulatory networks, and dynamic co-expression networks through sample-independent random walks on patient-specific subnetworks to capture topological importance and expression perturbation effects. In the second stage, it refines predictions through hypergraph-based random walks that leverage cross-sample information while preserving individual mutational contexts. Comprehensive evaluation on 12 TCGA cancer types demonstrates that HyperNetWalk achieves superior or competitive performance compared to state-of-the-art methods in both personalized and cohort-level predictions. Notably, HyperNetWalk successfully identifies known driver genes with high precision while revealing cancer type-specific drivers that reflect distinct biological mechanisms. Our framework provides a unified solution for personalized and population-based driver gene identification, offering valuable insights for precision oncology and therapeutic target discovery.", "Automated pathology image analysis is central to clinical diagnosis, but clinicians still ask which slide features drive a model\u2019s decision and why. Vision\u2013language models can produce natural language explanations, but these are often correlational and lack verifiable evidence. In this paper, we introduce an SQL-centered agentic framework that enables both feature measurement and reasoning to be auditable. Specifically, after extracting human-interpretable cellular features, Feature Reasoning Agents compose and execute SQL queries over feature tables to aggregate visual evidence into quantitative findings. A Knowledge Comparison Agent then evaluates these findings against established pathological knowledge, mirroring how pathologists justify diagnoses from measurable observations. Extensive experiments evaluated on two pathology visual question answering datasets demonstrate our method improves interpretability and decision traceability while producing executable SQL traces that link cellular measurements to diagnostic conclusions.", "# Edge-aware GAT-based protein binding site prediction Edge-aware GAT-based protein binding site prediction Weisen Yang1, Hanqing Zhang1, Wangren Qiu1, Xuan Xiao1,2, Weizhong Lin1 1. School of Information Engineering, Jingdezhen Ceramic University, Jingdezhen, China, 333403 2. School of Information Engineering, Jiangxi Art & Ceramics Technology Institute, Jingdezhen, 333000, China. Abstract Accurate identification of protein binding sites is crucial for understanding biomolecular interaction mechanisms and for the rational design of drug targets. Traditional predictive methods often struggle to balance prediction accuracy with computational efficiency when capturing complex spatial conformations. To address this challenge, we propose an Edge-aware Graph Attention Network (Edge-aware GAT) model for the fine-grained prediction of binding sites across various biomolecules, including proteins, DNA/RNA, ions, ligands, and lipids. Our method constructs atomlevel graphs and integrates multidimensional structural features, including geometric descriptors, DSSP-derived secondary structure, and relative solvent accessibility (RSA), to generate spatially aware embedding vectors. By incorporat", "Bacterial chemotactic sensing converts noisy chemical signals into running and tumbling. We analyze the static sensing limits of mixed Tar/Tsr chemoreceptor clusters in individual Escherichia coli cells using a heterogeneous Monod\u2013Wyman\u2013Changeux (MWC) model. By sweeping a seven-dimensional parameter space, we compute three sensing performance metrics\u2014channel capacity, dynamic range, and effective Hill coefficient. Across E. coli\u2013like parameter regimes, we consistently observe pronounced global maxima of channel capacity and global maxima of the related dynamic range, whereas the effective Hill coefficient does not exhibit comparable optimization. The capacity-achieving input distribution is bimodal, which implies that individual cells maximize information by sampling both low- and high-concentration regimes. Together, these results suggest that, at the individual-cell level, channel capacity and dynamic range may be selected for in E. coli receptor clusters.", "We present Connection-Aware Motif Sequencing (CamS) , a graph-to-sequence representation that enables decoder-only Transformers to learn molecular graphs via standard next-token prediction (NTP). For molecular property prediction, SMILES-based NTP scales well but lacks explicit topology, whereas graph-native masked modeling captures connectivity but risks disrupting the pivotal chemical details (e.g., activity cliffs). CamS bridges this gap by serializing molecular graphs into structure-rich causal sequences. CamS first mines data-driven connection-aware motifs. It then serializes motifs via scaffold-rooted breadth-first search (BFS) to establish a stable core-to-periphery order. Crucially, CamS enables hierarchical modeling by concatenating sequences from fine to coarse motif scales, allowing the model to condition global scaffolds on dense, uncorrupted local structural evidence. We instantiate CamS-LLaMA by pre-training a vanilla LLaMA backbone on CamS sequences. It achieves state-of-the-art performance on MoleculeNet and the activity-cliff benchmark MoleculeACE, outperforming both SMILES-based language models and strong graph baselines. Interpretability analysis confirms that our multi-scale causal serialization effectively drives attention toward cliff-determining differences.", "Personal health analytics systems face a persistent cold-start dilemma: users expect meaningful insights early in data collection, while conventional statistical inference requires data volumes that often exceed engagement horizons. Existing approaches either delay inference until fixed statistical thresholds are met \u2013 leading to user disengagement \u2013 or surface heuristic insights without formal uncertainty quantification, risking false confidence. We propose a progressive Bayesian confidence architecture that formalizes early-stage inference through phased interpretation of posterior uncertainty. Drawing on Bayesian updating and epistemic strategies from financial risk modeling under sparse observations, we map posterior contraction to interpretable tiers of insight, ranging from exploratory directional evidence to robust associative inference. We demonstrate the framework\u2019s performance through controlled experimentation with synthetic N-of-1 health data, showing that calibrated early insights can be generated within 5\u20137 days while maintaining explicit epistemic humility. Compared to fixed-threshold baselines requiring 30+ days of data, the proposed approach yields earlier directional signals (mean: 5.3 vs 31.7 days, $p<0.001$ ) while controlling false discovery rates below 6% (5.9% at day 30) despite 26-day earlier detection, compared to 0% FDR for fixed-threshold baselines that delay insights by 30 days. In addition, we show strong uncertainty calibration (76% credible interval coverage for ground-truth correlations at day 90). This work contributes a methodological framework for uncertainty-aware early inference in personalized health analytics that bridges the gap between user engagement requirements and statistical rigor.", "# A Comprehensive Database of Leaf Temperature, Water, and CO 2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios 1 A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios Raphael Perez1,2, Valentin Torrelli1,2,3,4, Sandrine Roques1,2, S\u00e9bastien Devidal5, Cl\u00e9ment Piel5, Damien Landais5, Merlin Ramel3,4, Thomas Arsouze3,4, Julien Lamour6, Jean-Pierre Caliman7, R\u00e9mi Vezy3,4 1CIRAD, UMR AGAP Institut, F-34398 Montpellier, France 2UMR AGAP Institut, Univ Montpellier, CIRAD, INRAE, Institut Agro, F-34398 Montpellier, France 3CIRAD, UMR AMAP, F-34398 Montpellier, France 4 4AMAP, Univ. Montpellier, CIRAD, CNRS, INRAE, IRD, F-34398 Montpellier, France 5Ecotron Europ\u00e9en de Montpellier, Unit\u00e9 Propre de Service 3248, Centre National de la Recherche Scientifique (CNRS), Campus Baillarguet, F-34980 Montferrier-sur-Lez, France; 6 Centre de Recherche sur la Biodiversit\u00e9 et l'Environnement (CRBE), Universit\u00e9 de Toulouse, CNRS, IRD, Toulouse INP, Universit\u00e9 Toulouse 3 \u2013 Paul Sabatier (UT3), Toulouse, France. 7SMART Research Institute, Pekanbaru 28112, Indonesia Abstract Functional-structural plant models (FSPM) rep", "# Investigating Knowledge Distillation Through Neural Networks for Protein Binding Affinity Prediction Investigating Knowledge Distillation Through Neural Networks for Protein Binding Affinity Prediction Wajid Arshad Abbasi1, *, Syed Ali Abbas1, Maryum Bibi1, Saiqa Andleeb2, Muhammad Naveed Akhtar3 1Computaional Biology and Data Analysis Lab., Institute of Computing, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, 13100, Pakistan. 2Biotechnology Lab., Department of Zoology, King Abdullah Campus, University of Azad Jammu & Kashmir, Muzaffarabad, AJ&K, 13100, Pakistan. 3Department of Computer and Information Sciences(DCIS), Pakistan Institute of Engineering and Applied Sciences(PIEAS), Islamabad, Pakistan. *Corresponding author: Wajid A. Abbasi (e-mail: wajidarshad@uajk.edu.pk). Abstract The trade-off between predictive accuracy and data availability makes it difficult to predict protein\u2013protein binding affinity accurately. The lack of experimentally resolved protein structures limits the performance of structure-based machine learning models, which generally outperform sequence-based methods. In order to overcome this constraint, we suggest a regression", "Many biological networks grow by elongation of filaments that can branch and fuse\u2014typical examples include fungal mycelium or slime mold. These networks must simultaneously perform multiple tasks such as transport, exploration, and robustness under finite resources. Yet, how such multi-task architectures emerge from local growth processes remains poorly understood. Here, we introduce a minimal model of spatial network morphogenesis based solely on stochastic branching, fusion, and stopping, during elongation. Despite the absence of global optimization or feedback, the model generates a broad morphospace from tree-like, to loopy, as well as hybrid architectures. By quantifying multiple functional objectives, we show that (i) these synthetic structures occupy similar regions of performance space than evolved empirical fungal networks, and (ii) that their Pareto front of optimal trade-offs lies close to that of these same fungal networks. Our results show that biological architectures approaching multi-objective optimality can arise from simple local growth rules, and identify branching and fusion as fundamental ingredients shaping the architecture of living transport networks.", "# Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes Transformer-Based Multi-Modal Temporal Embeddings for Explainable Metabolic Phenotyping in Type 1 Diabetes Pir Bakhsh Khokhar Department of Computer Science University of Salerno Salerno, Italy pkhokhar @unisa.it Sule Yildirim Yayilgan Department of Info. Security and Communication Tech. Norwegian University of Science and Technology Gjgvik, Norway sule.yildirim@ntnu.no Abstract\u2014Type 1 diabetes (TID) is a highly metabolically heterogeneous disease that cannot be adequately characterized by conventional biomarkers such as glycated hemoglobin (HbA 1c). This study proposes an explainable deep learning framework that integrates continuous glucose monitoring (CGM) data with laboratory profiles to learn multimodal temporal embeddings of individual metabolic status. Temporal dependencies across modalities are modeled using a transformer encoder, while latent metabolic phenotypes are identified via Gaussian mixture modeling. Model interpretability is achieved through transformer attention visualization and SHAP-based feature attribution. Five latent metabolic phenotypes, ranging fro", "Purpose Aortic valve (AV) biomechanics play a critical role in maintaining normal cardiac function. Pathological variations, particularly in bicuspid aortic valves, alter leaflet loading, increase strain, and accelerate disease progression. Accurate patient-specific characterization of valve geometry and deformation is therefore essential for predicting disease progression and guiding durable repair. However, existing imaging and computational methods often fail to capture rapid valve motion and complex patient-specific features, limiting precise biomechanical assessment. Methods To address these limitations, we developed an image registration framework coupled with the finite element method (FEM) to improve AV tracking and biomechanical evaluation. Patient-specific valve geometries derived from 4D echocardiography and CT were used to simulate AV closure and generate intermediate deformation states. These FEM-generated states facilitated leaflet tracking, while image registration corrected misalignment between simulations and imaging data. Results In 20 patients, FEM-augmented registration improved tracking accuracy by 40% compared with direct registration. This improvement enabled more reliable strain estimation by measuring leaflet deformation directly from imaging and reducing uncertainties associated with boundary conditions and material assumptions. Using the improved tracking results, areal, Green-Lagrange, and deviatoric strains were quantified in adult trileaflet and bicuspid valves, as well as pediatric patients, revealing distinct deformation patterns across valve groups. Convergence in mean deviatoric strain between adult trileaflet and pediatric valves suggests volumetric deformation underlies age- and size-related differences in AV mechanics. Conclusion Overall, this FEM-augmented registration framework enhances geometric tracking and biomechanical evaluation accuracy, providing clinically relevant insights into patient-specific AV deformation to support individualized medical and intervention planning.", "Examination of T-cell receptor (TCR) clonality has become a way of understanding immunologic response to cancer and its interventions in recent years. An aspect of these analyses is determining which receptors expand or contract statistically significantly as a function of an exogenous perturbation such as therapeutic intervention. We characterize the commonly used Fisher\u2019s exact test approach for such analyses and propose an alternative formulation that does not necessitate pairwise, within-patient comparisons. We develop this flexible Bayesian longitudinal mixture model that accommodates variable length patient followup and handles missingness where present, not omitting data in estimation because of structural practicalities. Once clones are partitioned by the model into dynamic (expanding or contracting) and static categories, one can associate their counts or other characteristics with disease state, interventions, baseline biomarkers, and patient prognosis. We apply these developments to a cohort of prostate cancer patients who underwent randomized metastasis-directed therapy or not. Our analyses reveal a significant increase in clonal expansions among MDT patients and their association with later progressions both independent and within strata of MDT. Analysis of receptor motifs and VJ gene enrichment combinations using a high-dimensional penalized log-linear model we develop also suggests distinct biological characteristics of expanding clones, with and without inducement by MDT. keywords: T-cell receptor, VDJ genes, motif analysis, Bayesian mixture model, penalized log-linear model", "The synchronization of biological activity with the alternation of day and night (circadian rhythm) is performed in the brain by a group of neurons, constituting the suprachiasmatic nucleus (SCN). The SCN is divided into two subgroups of oscillating cells: the ventro-lateral (VL) neurons, which are exposed to light (photic signal) and the dorso-medial (DM) neurons which are coupled to the VL cells. When the coupling between these neurons is strong enough, the system synchronizes with the photic period. Upon increasing the cell coupling, the entrainment of the DM cells has been recently shown to occur via a very sharp (jumping) transition when the period of the photic input is larger than the intrinsic period of the cells. Here, we characterize this transition with a simple realistic model. We show that two bifurcations possibly lead to the disappearance of the endogenous mode. Using a mean field model, we show that the jumping transition results from a supercritical Hopf-like bifurcation. This finding implies that both the period and strength of the stimulating photic signal, and the relative fraction of cells in the VL and DM compartments are crucial in determining the synchronization of the system.", "Protein-protein interaction (PPI) represents a central challenge within the biology field, and accurately predicting the consequences of mutations in this context is crucial for drug design and protein engineering. Deep learning (DL) has shown promise in forecasting the effects of such mutations, but is hindered by two primary constraints. First, the structures of mutant proteins are often elusive to acquire. Secondly, PPI takes place dynamically, which is rarely integrated into the DL architecture design. To address these obstacles, we present a novel framework named Refine-PPI with two key enhancements. First, we introduce a structure refinement module trained by a mask mutation modeling (MMM) task on available wild-type structures, which is then transferred to produce the inaccessible mutant structures. Second, we employ a new kind of geometric network, called the probability density cloud network (PDC-Net), to capture 3D dynamic variations and encode the atomic uncertainty associated with PPI. Comprehensive experiments on SKEMPI.v2 substantiate the superiority of Refine-PPI over all existing tools for predicting free energy change. These findings underscore the effectiveness of our hallucination strategy and the PDC module in addressing the absence of mutant protein structure and modeling geometric uncertainty.", "# PRISM: Protocol Refinement through Intelligent Simulation Modeling ## \u2020\u2020footnotetext: a Argonne National Laboratory, 9700 S. Cass Avenue, Lemont, IL 60439, USA.\u2020\u2020footnotetext: b Department of Computer Science, University of Chicago, Chicago, IL 60637, USA.\u2020\u2020footnotetext: \u2217 E-mail: ramanathana@anl.gov\u2020\u2020footnotetext: \u2020 These authors contributed equally. ## 1 Introduction The automation of experimental protocol design and execution remains a fundamental bottleneck in realizing self-driving laboratories. While robotic platforms are increasingly available, translating scientific intent into executable laboratory protocols requires substantial domain expertise, as the process is error-prone and demands intimate knowledge of both experimental procedures and instrument-specific formatting requirements. Existing protocol description languages provide structured representations but remain static and hardware-specific, requiring manual rewriting when laboratory configurations change. Recent work has shown that large language models (LLMs) can generate plausible experimental procedures from natural language descriptions, yet these outputs often contain parameter underspecification, physical", "The computational design of antibodies with high specificity and affinity is a cornerstone of modern therapeutic development. While deep generative models have demonstrated potential, they often struggle to balance high-fidelity geometric conditioning with the discrete nature of amino acid sequences. In this work, we present AntibodyDesignBFN , a novel framework for fixed-backbone antibody design based on Discrete Bayesian Flow Networks (BFN) . Unlike standard diffusion models, BFNs operate on a continuous probability simplex, enabling a fully differentiable generative process that seamlessly integrates geometric gradients. By combining a lightweight Geometric Transformer with Invariant Point Attention (IPA) and a resource-efficient training strategy, our model establishes a new state-of-the-art. Evaluations on a rigorous 2025 temporal test set (43 complexes) demonstrate that AntibodyDesignBFN achieves an unprecedented Amino Acid Recovery (AAR) of 67.8% , significantly outperforming leading graph-based baselines. Furthermore, the model is highly efficient, enabling millisecond-scale inference on consumer-grade hardware. AntibodyDesignBFN thus offers a powerful, accessible, and mathematically robust framework for next-generation antibody engineering. Code and model checkpoints are available at https://github.com/YueHuLab/AntibodyDesignBFN and https://huggingface.co/YueHuLab/AntibodyDesignBFN .", ". Key message: We develop and apply a two-stage factor analytic approach with Procrustes rotation for the integration of time series secondary phenotypes into multivariate genomic prediction. High-dimensional time series phenotypic data is becoming increasingly common within plant breeding programmes. However, analysing and integrating such data for genetic analysis and genomic prediction remains difficult. Here we show how factor analysis with Procrustes rotation on the genetic correlation matrix of hyperspectral secondary phenotype data can help in extracting relevant features for within-trial prediction. We use a subset of Centro Internacional de Mejoramiento de Ma\u00edz y Trigo (CIMMYT) elite yield wheat trial of 2014-2015, consisting of 1,033 genotypes. These were measured across three irrigation treatments at several timepoints during the season, using manned airplane flights with hyperspectral sensors capturing 62 bands in the spectrum of 385-850 nm. We perform multivariate genomic prediction using latent variables to improve within-trial genomic predictive ability (PA) of wheat grain yield within three distinct watering treatments. By integrating latent variables of the hyperspectral data in a multivariate genomic prediction model, we are able to achieve an absolute gain of .1 to .3 (on the correlation scale) in PA compared to univariate genomic prediction. Furthermore, we show which timepoints within a trial are important and how these relate to plant growth stages. This paper showcases how domain knowledge and data-driven approaches can be combined to increase PA and gain new insights from sensor data of high-throughput phenotyping platforms. Key words : Factor analysis; Genomic prediction; Hyperspectral data; Procrustes rotation; Secondary phenotypes", "This study investigates how financial market structure reorganizes during the COVID-19 crash using a conditional p-threshold mutual information (MI) based Minimum Spanning Tree (MST) network framework. We analyze nonlinear dependencies among the largest stocks from four geographically and economically diverse QUAD countries: the United States, Japan, Australia, and India. The crash period is identified using the Hellinger distance and further characterized by the Hilbert spectrum. A crash is defined when the Hellinger distance exceeds the threshold $H_{D}=\\mu_{H}+2\\sigma_{H}$ , enabling the segmentation of the data into pre-crash, crash, and post-crash periods. To isolate direct stock-level dependencies, the conditional p-threshold MI approach filters out common market effects and applies permutation-based significance testing. The resulting statistically validated dependencies are used to construct MST networks, allowing consistent comparison across market periods. The network analysis reveals common crisis-related dynamics across all markets. During the crash, networks become more integrated, with shorter path lengths and higher centrality, while algebraic connectivity declines, indicating increased structural fragility. A clear reorganization of the core\u2013periphery structure is observed, with declining core concentration and increasing periphery fragility, supported by disassortative mixing that facilitates shock transmission. In the post-crash period, network topology shows only partial recovery, suggesting persistent structural effects. This interpretation is further supported by an aftershock analysis based on the Gutenberg\u2013Richter law, which indicates a higher relative frequency of large volatility events following the crash. The consistency of these findings across all four markets highlights the effectiveness of the conditional p-threshold MI framework for capturing nonlinear interdependencies and systemic vulnerability in financial markets.", "We introduce an operator-theoretic framework for causal analysis in multivariate time series based on order-constrained spectral non-invariance . Directional influence is defined as sensitivity of second-order dependence operators to admissible, order-preserving temporal deformations of a designated source component, yielding an intrinsically multivariate causal notion summarized through orthogonally invariant spectral functionals. Under linear Gaussian assumptions, the criterion coincides with linear Granger causality, while beyond this regime it captures collective and nonlinear directional dependence not reflected in pairwise predictability. We establish existence, uniform consistency, and valid inference for the resulting non-smooth supremum\u2013infimum statistics using shift-based randomization that exploits order-induced group invariance, yielding finite-sample exactness under exact invariance and asymptotic validity under weak dependence without parametric assumptions. Simulations demonstrate correct size and strong power against distributed and bulk-dominated alternatives, including nonlinear dependence missed by linear Granger tests with appropriate feature embeddings. An empirical application to a high-dimensional panel of daily financial return series spanning major asset classes illustrates system-level causal monitoring in practice. Directional organization is episodic and stress-dependent, causal propagation strengthens while remaining multi-channel, dominant causal hubs reallocate rapidly, and statistically robust transmission channels are sparse and horizon-heterogeneous even when aggregate lead\u2013lag asymmetry is weak. The framework provides a scalable and interpretable complement to correlation-, factor-, and pairwise Granger-style analyses for complex systems.", "This paper introduces a new theoretical framework for analyzing lead-lag relationships between point processes, with a special focus on applications to high-frequency financial data. In particular, we are interested in lead-lag relationships between two sequences of order arrival timestamps. The seminal work of Dobrev and Schaumburg proposed model-free measures of cross-market trading activity based on cross-counts of timestamps. While their method is known to yield reliable results, it faces limitations because its original formulation inherently relies on discrete-time observations, an issue we address in this study. Specifically, we formulate the problem of estimating lead-lag relationships in two point processes as that of estimating the shape of the cross-pair correlation function (CPCF) of a bivariate stationary point process, a quantity well-studied in the neuroscience and spatial statistics literature. Within this framework, the prevailing lead-lag time is defined as the location of the CPCF\u2019s sharpest peak. Under this interpretation, the peak location in Dobrev and Schaumburg\u2019s cross-market activity measure can be viewed as an estimator of the lead-lag time in the aforementioned sense. We further propose an alternative lead-lag time estimator based on kernel density estimation and show that it possesses desirable theoretical properties and delivers superior numerical performance. Empirical evidence from high-frequency financial data demonstrates the effectiveness of our proposed method. Keywords : Bandwidth selection; cross-correlation histogram; cross-pair correlation function; high-frequency data; non-synchronicity; lead-lag effect.", ". We document regime-dependent predictive structure between equity factors. Using 35 years of Fama-French data (1990\u20132024), we find that the Value factor (High Minus Low, HML) Granger-causes the Size factor (Small Minus Big, SMB) during crisis regimes ( $p<10^{-4}$ , 9-day lag), but not during normal conditions. This pattern validates across 5 of 6 historical stress events (2008, 2011, 2015, 2018, 2020). We identify regimes using a Student- $t$ Hidden Markov Model (HMM), which detects moderate crises (2011: 69%) that Gaussian models miss entirely (0%). While this relationship does not generate trading profits, the 9-day lead time may inform risk management decisions. We emphasize that Granger causality establishes temporal precedence, not structural causality\u2014common drivers could explain the pattern. Our economic interpretation is a hypothesis, not a verified mechanism.", "# Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment Uni-FinLLM: A Unified Multimodal Large Language Model with Modular Task Heads for Micro-Level Stock Prediction and Macro-Level Systemic Risk Assessment Gongao Zhang1, Haijiang Zeng2, Lu Jiang3 1 China University of Geosciences, Wuhan, China 2 Walmart Inc., Bentonville, AR , USA 3 Fordham University, New York, NY, USA 1 1812503968@qq.com 2 hzeng60@gatech.edu 3 ljiang43@fordham.edu Abstract. Financial institutions and national regulatory bodies increasingly require decision-support systems capable of integrating heterogeneous data sources to evaluate risks ranging from micro-level stock fluctuations to macro-level systemic vulnerabilities. However, existing approaches often treat these tasks in isolation, limiting their ability to capture cross-scale financial dependencies. To address this challenge, we propose Uni-FinLLM, a unified multimodal large language model that employs a shared Transformer-based backbone and modular task heads to jointly process textual financial news, numerical market time series, corporate fundamentals, an", "This paper re-examines the empirical Phillips curve (PC) model and its usefulness in the context of medium-term inflation forecasting. A latent variable Phillips curve hypothesis is formulated and tested using 3,968 randomly generated factor combinations. Evidence from US core PCE inflation between Q1 1983 and Q1 2025 suggests that latent variable PC models reliably outperform traditional PC models six to eight quarters ahead and stand a greater chance of outperforming a univariate benchmark. Incorporating an MA(1) residual process improves the accuracy of empirical PC models across the board, although the gains relative to univariate models remain small. The findings presented in this paper have two important implications: First, they corroborate a new conceptual view on the Phillips curve theory; second, they offer a novel path towards improving the competitiveness of Phillips curve forecasts in future empirical work.", "How information transmits through prices\u2014and why this transmission breaks down\u2014remains poorly understood. We combine regularized deconvolution with Hawkes process analysis to study the impulse response structure of investor flows in the Korean equity market (January 2020 \u2013 February 2025). Three findings emerge: foreign and institutional flows drive permanent price discovery while individual flows provide contrarian liquidity; individual investor surges are predominantly panic-driven and exhibit near-explosive self-excitation; and during herding episodes, institutional price impact deteriorates sharply in small-cap stocks while large-cap stocks maintain resilience. These results reframe market efficiency as a state variable\u2014conditional on both herding intensity and firm size\u2014rather than a structural constant.", "A quantitative understanding of the stochastic dynamics in limit order price changes is essential for meaningful advances in market microstructure research and effective execution strategy design. This paper presents the first comprehensive empirical analysis of intraday limit order price change transition dynamics, treating ask and bid orders separately across different market capitalization tiers. Using high-frequency tick data from NASDAQ100 stocks, we employ a discrete-time Markov chain framework to analyze the evolution of price adjustments throughout the trading day. We categorize consecutive price changes into nine distinct states and estimate transition probability matrices (TPMs) for six intraday intervals across High ( $\\mathtt{HMC}$ ), Medium ( $\\mathtt{MMC}$ ), and Low ( $\\mathtt{LMC}$ ) market capitalization stocks. Elememt-wise comparison of TPMs reveals systematic intraday patterns: price inertia i.e. self-transition probability, peaks during opening and closing hours, stabilizing at lower levels during midday. A pronounced capitalization gradient is also observed: $\\mathtt{HMC}$ stocks exhibit the strongest price inertia, while $\\mathtt{LMC}$ stocks demonstrate significantly lower stability and pronounced bid-ask spread. Markov chain metrics, including spectral gap, entropy rate, and mean recurrence times quantify these dynamics. Clustering analysis identifies three distinct temporal phases on the bid side \u2013 Opening, Midday, and Closing and four phases on the ask side \u2013 Opening, Midday, Pre-Close, and Close, indicating that sellers initiate end-of-day positioning strategies earlier than buyers. Stationary distributions reveal that limit order dynamics are predominantly characterized by neutral and mild price changes. Furthermore, Jensen-Shannon divergence computed between stationary distributions across time-intervals confirms the closing hour as the most distinct phase, with capitalization modulating the intensity of temporal contrasts and the degree of bid-ask asymmetry. These findings advance the understanding of evolving intraday limit order pricing behavior, offering direct applications for capitalization-aware and time-adaptive execution algorithms and risk management frameworks.", "While traditional equity factor investing relies heavily on slow-moving fundamental accounting metrics, these models frequently suffer from factor crowding and miss real-time, sentiment-driven market dislocations. This study explores how institutional investors can leverage a high-dimensional library of 191 short-term, trading-based signals, originally developed for the retail-heavy Chinese A-share market, to enhance alpha generation within the highly institutionalized U.S. S&P 500 universe from 2002 to 2022. Utilizing a robust double-selection LASSO framework to control for 151 established fundamental factors, we isolate 17 distinct price-volume and microstructural signals that capture significant, non-redundant risk premiums. Our empirical evidence demonstrates that these fast trading signals capture universal behavioral dynamics that do not dilute over a monthly rebalancing horizon. Integrating these short-term behavioral footprints with slow fundamental data offers a powerful dual-horizon framework to mitigate model misspecification risk and enhance large-cap portfolio diversification.", "This paper presents a meta-learning framework for credit risk assessment of Italian Small and Medium Enterprises (SMEs) that explicitly addresses the temporal misalignment of credit scoring models. The approach aligns financial statement reference dates with evaluation dates, mitigating bias arising from publication delays and asynchronous data sources. It is based on a two-step temporal decomposition that at first estimates annual probabilities of default (PDs) anchored to balance-sheet reference dates (December 31st) through a static model. Then it models the monthly evolution of PDs using higher-frequency behavioral data. Finally, we employ stacking-based architecture to aggregate multiple scoring systems, each capturing complementary aspects of default risk, into a unified predictive model. In this way, first level model outputs are treated as learned representations that encode non-linear relationships in financial and behavioral indicators, allowing integration of new expert-based features without retraining base models. This design provides a coherent and interpretable solution to challenges typical of low-default environments, including heterogeneous default definitions and reporting delays. Empirical validation shows that the framework effectively captures credit risk evolution over time, improving temporal consistency and predictive stability relative to standard ensemble methods.", "A new wave of work on covariance cleaning and nonlinear shrinkage has delivered asymptotically optimal analytical solutions for large covariance matrices. The same framework has been generalized to empirical cross\u2013covariance matrices, whose singular value decomposition identifies canonical comovement modes between two asset sets, with singular values quantifying the strength of each mode and providing natural targets for shrinkage. Existing analytical cross-covariance cleaners are derived under strong stationarity and large-sample assumptions, and they typically rely on mesoscopic regularity conditions such as bounded spectra; macroscopic common modes (e.g., a global market factor) violate these conditions. When applied to real equity returns, where dependence structures drift over time and global modes are prominent, we find that these theoretically optimal formulas do not translate into robust out-of-sample performance. We address this gap by designing a random-matrix-inspired neural architecture that operates in the empirical singular-vector basis and learns a nonlinear mapping from empirical singular values to their corresponding cleaned values. By construction, the network can recover the analytical solution as a special case, yet it remains flexible enough to adapt to non-stationary dynamics and mode-driven distortions. Trained on a long history of equity returns, the proposed method achieves a more favorable bias\u2013variance trade-off than purely analytical cleaners and delivers systematically lower out-of-sample cross\u2013covariance prediction errors. Our results demonstrate that combining random-matrix theory with machine learning makes asymptotic theories practically effective in realistic time-varying markets.", "Financial markets alternate between tranquil periods and episodes of stress, and return dynamics can change substantially across these regimes. We study regime-dependent dynamics in developed and developing equity indices using a data-driven Hilbert\u2013Huang-based regime identification and profiling pipeline, followed by variable-length Markov modeling of categorized returns. Market regimes are identified using an Empirical Mode Decomposition-based Hilbert\u2013Huang Transform, where instantaneous energy from the Hilbert spectrum separates Normal, High, and Extreme regimes. We then profile each regime using Holo\u2013Hilbert Spectral Analysis, which jointly resolves carrier frequencies, amplitude-modulation frequencies, and amplitude-modulation energy (AME). AME, interpreted as volatility intensity, declines monotonically from Extreme to High to Normal regimes. This decline is markedly sharper in developed markets, while developing markets retain higher baseline volatility intensity even in Normal regimes. Building on these regime-specific volatility signatures, we discretize daily returns into five quintile states $\\mathtt{R}_{1}$ to $\\mathtt{R}_{5}$ and estimate Variable-Length Markov Chains via context trees within each regime. Unconditional state probabilities show tail states dominate in Extreme regimes and recede as regimes stabilize, alongside persistent downside asymmetry. Entropy peaks in High regimes, indicating maximum unpredictability during moderate-volatility periods. Conditional transition dynamics, evaluated over contexts of length up to three days from the context-tree estimates, indicate that developed markets normalize more effectively as stress subsides, whereas developing markets retain residual tail dependence and downside persistence even in Normal regimes, consistent with a coexistence of continuation and burst-like shifts. Overall, market maturity shapes both the pace of stabilization and the persistence of tail dependence, supporting tighter risk controls not only during crises but also during periods classified as stable.", "This study develops a robust machine learning framework for one-step-ahead forecasting of daily log-returns in the Nepal Stock Exchange (NEPSE) Index using the XGBoost regressor. A comprehensive feature set is engineered, including lagged log-returns (up to 30 days) and established technical indicators such as short- and medium-term rolling volatility measures and the 14-period Relative Strength Index. Hyperparameter optimization is performed using Optuna with time-series cross-validation on the initial training segment. Out-of-sample performance is rigorously assessed via walk-forward validation under both expanding and fixed-length rolling window schemes across multiple lag configurations, simulating real-world deployment and avoiding lookahead bias. Predictive accuracy is evaluated using root mean squared error, mean absolute error, coefficient of determination ( $R^{2}$ ), and directional accuracy on both log-returns and reconstructed closing prices. Empirical results show that the optimal configuration\u2014an expanding window with 20 lags\u2014outperforms tuned ARIMA and Ridge regression benchmarks, achieving the lowest log-return RMSE (0.013450) and MAE (0.009814) alongside a directional accuracy of 65.15%. While the $R^{2}$ remains modest, consistent with the noisy nature of financial returns, primary emphasis is placed on relative error reduction and directional prediction. Feature importance analysis and visual inspection further enhance interpretability. These findings demonstrate the effectiveness of gradient boosting ensembles in modeling nonlinear dynamics in volatile emerging market time series and establish a reproducible benchmark for NEPSE Index forecasting.", "# From rough to multifractal multidimensional volatility: A multidimensional Log S-fBM model From rough to multifractal multidimensional volatility: A multidimensional Log S-fBM model Othmane Zarhali1, Emmanuel Bacry1, and Jean-Fran\u00e7ois Muzy2 1Ceremade, CNRS-UMR 7534, Universit\u00e9 Paris-Dauphine PSL, Place du Mar\u00e9chal de Lattre de Tassigny, 75016 Paris, France 2SPE CNRS-UMR 6134, Universit\u00e9 de Corse, campus Grimaldi, BP 52, 20250 Corte, France Abstract We introduce the multivariate Log S-fBM model (mLog S-fBM), extending the univariate framework proposed by Wu et al. [60] to the multidimensional setting. We begin by defining the multidimensional Stationary fractional Brownian motion (mS-fBM). This process is characterized by marginals following S-fBM dynamics and a specific cross-covariance structure. It is parametrized by a correlation scale T, marginal-specific intermittency parameters (\u03bb2 i )1\u2264i\u2264d and Hurst exponents (Hi)1\u2264i\u2264d, as well as their multidimensional counterparts: the co-intermittency matrix (\u03bei,j)1\u2264i,j\u2264d and the co-Hurst matrix (Hi,j)1\u2264i,j\u2264d. The mLog S-fBM is then constructed by modeling the volatility components as exponentials of the mS-fBM, thereby preserving the d", "Financial time series exhibit multiscale behavior, with interaction between multiple processes operating on different timescales. This paper introduces a method for separating these processes using variance and tail stationarity criteria, framed as generalized eigenvalue problems. The approach allows for the identification of slow and fast components in asset returns and prices, with applications to parameter drift, mean reversion, and tail risk management. Empirical examples using currencies, equity ETFs and treasury yields illustrate the practical utility of the method.", "The rough Bergomi (rBergomi) model, characterised by its roughness parameter $H$ , has been shown to exhibit multiscaling behaviour as $H$ approaches zero. Multiscaling has profound implications for financial modelling: it affects extreme risk estimation, influences optimal portfolio allocation across different time horizons, and challenges traditional option pricing approaches that assume uniscaling behaviours. Understanding whether multiscaling arises primarily from the roughness of volatility paths or from the resulting fat-tailed returns has important implications for financial modelling, option pricing, and risk management. This paper investigates the real source of this multiscaling behaviour by introducing a novel two-stage statistical testing procedure. In the first stage, we establish the presence of multiscaling in the rBergomi model against an uniscaling fractional Brownian motion process. We quantify multiscaling by using weighted least squares regression that accounts for heteroscedastic estimation errors across moments. In the second stage, we apply shuffled surrogates that preserve return distributions while destroying temporal correlations. This is done by using distance-based permutation tests robust to asymmetric null distributions. In order to validate our procedure, we check the robustness of the results by using synthetic processes with known multifractal properties, namely the Multifractal Random Walk (MRW) and the Fractional L\u00e9vy Stable Motion (FLSM). We provide compelling evidence that multiscaling in the rBergomi model arises primarily from fat-tailed return distributions rather than memory effects. Our findings suggest that the apparent multiscaling in rough volatility models is largely attributable to distributional properties rather than genuine temporal scaling behaviour.", "We analyze daily lead-time distributions for two Airbnb demand metrics, Nights Booked (volume) and Gross Booking Value (revenue), treating each day\u2019s allocation across 0\u2013365 days as a compositional vector. The data span 2,557 days from January 2019 through December 2025 in a large North American region. Three findings emerge. First, GBV concentrates more heavily in mid-range horizons: beyond 90 days, GBV tail mass typically exceeds Nights by 20\u201350%, with ratios reaching 75% at the 180-day threshold during peak seasons. Second, Gamma and Weibull distributions fit comparably well under interval-censored cross-entropy: Gamma wins on 61% of days for Nights and 52% for GBV, with Weibull close behind at 38% and 45%. Lognormal rarely wins ( $<$ 3%). Nonparametric GAMs achieve 18\u201380 $\\times$ lower CRPS but sacrifice interpretability. Third, generalized Pareto fits suggest bounded tails ( $\\xi<0$ ) for both metrics at thresholds below 150 days, though this may partly reflect right-truncation at 365 days; above 150 days, estimates destabilize. Bai\u2013Perron tests with HAC standard errors identify five structural breaks in the Wasserstein distance series, with early breaks coinciding with COVID-19 disruptions. The results show that volume and revenue lead-time shapes diverge systematically, that simple two-parameter distributions capture daily pmfs adequately, and that tail inference requires care near truncation boundaries. The volume-revenue divergence has direct implications for tourism practitioners: forecasting models and pricing algorithms that treat booking volume and booking revenue as interchangeable will systematically mistime cash flows, underestimating revenue from bookings made one to three months in advance.", "In this work, we propose to apply a new model fusion and learning paradigm, known as Combinatorial Fusion Analysis (CFA), to the field of Bitcoin price prediction. Price prediction of financial product has always been a big topic in finance, as the successful prediction of the price can yield significant profit. Every machine learning model has its own strength and weakness, which hinders progress toward robustness. CFA has been used to enhance models by leveraging rank-score characteristic (RSC) function and cognitive diversity in the combination of a moderate set of diverse and relatively well-performed models. Our method utilizes both score and rank combinations as well as other weighted combination techniques. Key metrics such as RMSE and MAPE are used to evaluate our methodology performance. Our proposal presents a notable MAPE performance of 0.19%. The proposed method greatly improves upon individual model performance, as well as outperforms other Bitcoin price prediction models.", "Generative models for financial time series often create data that look realistic and even reproduce stylized facts such as fat tails or volatility clustering. However, these apparent successes break down under trading backtests: models like GANs or WGAN-GP frequently collapse, yielding extreme and unrealistic results that make the synthetic data unusable in practice. We identify the root cause in the neglect of financial asymmetry and rare tail events, which strongly affect market risk but are often overlooked by objectives focusing on distribution matching. To address this, we introduce the Stylized Facts Alignment GAN (SFAG) , which converts key stylized facts into differentiable structural constraints and jointly optimizes them with adversarial loss. This multi-constraint design ensures that generated series remain aligned with market dynamics not only in plots but also in backtesting. Experiments on the Shanghai Composite Index (2004\u20132024) show that while baseline GANs produce unstable and implausible trading outcomes, SFAG generates synthetic data that preserve stylized facts and support robust momentum strategy performance. Our results highlight that structure-preserving objectives are essential to bridge the gap between superficial realism and practical usability in financial generative modeling.", "The balancing market in the energy sector plays a critical role in physically and financially balancing the supply and demand. Modeling dynamics in the balancing market can provide valuable insights and prognosis to power grid stability and secure energy supply. While complex machine learning models can achieve high accuracy, their \u201cblack-box\u201d nature severely limits the model interpretability. In this paper, we explore the trade-off between model accuracy and interpretability for the energy balancing market. Particularly, we take the example of forecasting manual frequency restoration reserve (mFRR) activation price in balancing market using real market data from different energy price zones. We explore the interpretability of mFRR forecasting using two models: (i) extreme gradient boosting machine (XGBoost) and (ii) explainable boosting machine (EBM). We also integrate the two models, and we benchmark all the models with a baseline na\u00efve model. Our results show that EBM provides forecasting accuracy comparable to XGBoost while yielding a considerable level of interpretability. Our analysis also underscores the challenge of accurately predicting the mFRR price for the instances when the activation price deviates significantly from the spot price. Importantly, EBM\u2019s interpretability features reveal insights into non-linear mFRR price drivers and regional market dynamics. Our study demonstrates that EBM is a viable and valuable interpretable alternative to complex black-box AI models in the forecast for the balancing market.", "Healthcare sector indices consolidate the economic health of pharmaceutical, biotechnology, and healthcare service firms. The short-term movements in these indices are closely intertwined with capital allocation decisions affecting research and development investment, drug availability, and long-term health outcomes. This research investigates whether historical open\u2013high\u2013low\u2013close (OHLC) index data contain sufficient information for predicting the directional movement of the opening index on the subsequent trading day. The problem is formulated as a supervised classification task involving a one-step-ahead rolling window. A diverse feature set is constructed, comprising original prices, volatility-based technical indicators, and a novel class of nowcasting features derived from mutual OHLC ratios. The framework is evaluated on data from healthcare indices in the U.S. and Indian markets over a five-year period spanning multiple economic phases, including the COVID-19 pandemic. The results demonstrate robust predictive performance, with accuracy exceeding 0.8 and Matthews correlation coefficients above 0.6. Notably, the proposed nowcasting features have emerged as a key determinant of the market movement. We have employed the Shapley-based explainability paradigm to further elucidate the contribution of the features: outcomes reveal the dominant role of the nowcasting features, followed by a more moderate contribution of original prices. This research offers a societal utility: the proposed features and model for short-term forecasting of healthcare indices can reduce information asymmetry and support a more stable and equitable health economy.", "Time series encountered in practice are rarely stationary. When the data distribution changes, a forecasting model trained on past observations can lose accuracy. We study a small-footprint test-time adaptation (TTA) framework for causal time-series forecasting and direction classification. The backbone is frozen, and only normalization affine parameters are updated using recent unlabeled windows. For classification we minimize entropy and enforce temporal consistency; for regression we minimize prediction variance across weak time-preserving augmentations and optionally distill from an EMA teacher. A quadratic drift penalty and an uncertainty-triggered fallback keep updates stable. We evaluate this framework in two stages: synthetic regime shifts on ETT benchmarks, and daily equity and FX series (SPY, QQQ, EUR/USD) across pandemic, high-inflation, and recovery regimes. On synthetic gradual drift, normalization-based TTA improves forecasting error, while in financial markets a simple batch-normalization statistics update is a robust default and more aggressive norm-only adaptation can even hurt. Our results provide practical guidance for deploying TTA on non-stationary time series. The source code of this study is available at this Google Drive .", "Overfitting remains a critical challenge in data-driven financial modelling, where machine learning (ML) systems learn spurious patterns in historical prices and fail out of sample and in deployment. This paper introduces the GT-Score, a composite objective function that integrates performance, statistical significance, consistency, and downside risk to guide optimization toward more robust trading strategies. This approach directly addresses critical pitfalls in quantitative strategy development, specifically data snooping during optimization and the unreliability of statistical inference under non-normal return distributions. Using historical stock data for 50 S&P 500 companies spanning 2010\u20132024, we conduct an empirical evaluation that includes walk-forward validation with nine sequential time splits and a Monte Carlo study with 15 random seeds across three trading strategies. In walk-forward validation, GT-Score improves the generalization ratio (validation return divided by training return) by 98% relative to baseline objective functions. Paired statistical tests on Monte Carlo out-of-sample returns indicate statistically detectable differences between objective functions ( p $<$ 0.01 for comparisons with Sortino and Simple), with small effect sizes. These results suggest that embedding an anti-overfitting structure into the objective can improve the reliability of backtests in quantitative research. Reproducible code and processed result files are provided as supplementary materials.", "This study addresses the low-volatility Chinese Public Real Estate Investment Trusts (REITs) market, proposing a large language model (LLMs) driven trading framework based on multi-agent collaboration. The system constructs four types of analytical agents\u2014announcement, event, price momentum, and market\u2014each conducting analysis from different dimensions; then the prediction agent integrates these multi-source signals to output directional probability distributions across multiple time horizons; then the decision agent generates discrete position adjustment signals based on the prediction results and risk control constraints, thereby forming a closed loop of \u201danalysis\u2013prediction\u2013decision\u2013execution.\u201d This study further compares two prediction model pathways: for the prediction agent, directly calling the general-purpose large model DeepSeek-R1 versus using a specialized small model Qwen3-8B fine-tuned via supervised fine-tuning and reinforcement learning alignment. In the backtest from October 2024 to October 2025, both agent-based strategies significantly outperformed the buy-and-hold benchmark in terms of cumulative return, Sharpe ratio, and maximum drawdown. The results indicate that the multi-agent framework can effectively enhance the risk-adjusted return of REITs trading, and the fine-tuned small model performs close to or even better than the general-purpose large model in some scenarios. The project code and data are open-sourced; details can be found at: https://github.com/adennng/REITs-MAS-LLM", "We propose Mixed-Panels-Transformer Encoder (MPTE), a novel framework for estimating factor models in panel datasets with mixed frequencies and nonlinear signals. Traditional factor models rely on linear signal extraction and require homogeneous sampling frequencies, limiting their applicability to modern high-dimensional datasets where variables are observed at different temporal resolutions. Our approach leverages Transformer-style attention mechanisms to enable context-aware signal construction through flexible, data-dependent weighting schemes that replace fixed linear combinations with adaptive reweighting based on similarity and relevance. We extend classical principal component analysis (PCA) to accommodate general temporal and cross-sectional attention matrices, allowing the model to learn how to aggregate information across frequencies without manual alignment or pre-specified weights. For linear activation functions, we establish consistency and asymptotic normality of factor and loading estimators, showing that our framework nests Target PCA as a special case while providing efficiency gains through transfer learning across auxiliary datasets. The nonlinear extension uses a Transformer architecture to capture complex hierarchical interactions while preserving the theoretical foundations. In simulations, MPTE demonstrates superior performance in nonlinear environments, and in an empirical application to 13 macroeconomic forecasting targets using a selected set of 48 monthly and quarterly series from the FRED-MD and FRED-QD databases, our method achieves competitive performance against established benchmarks. We further analyze attention patterns and systematically ablate model components to assess variable importance and temporal dependence. The resulting patterns highlight which indicators and horizons are most influential for forecasting.", "Generally, Lasso, Adaptive Lasso, and SCAD are standard approaches in variable selection in the presence of a large number of predictors. In recent years, during intensity function estimation for spatial point processes with a diverging number of predictors, many researchers have considered these penalized methods. But we have discussed a multi-resolution perspective for the variable selection method for spatial point process data. Its advantage is twofold: it not only efficiently selects the predictors but also provides the idea of which points are liable for selecting a predictor at a specific resolution. Actually, our research is motivated by the crime and accident occurrences in St. Louis and its neighborhoods. It is more relevant to select predictors at the local level, and thus we get the idea of which set of predictors is relevant for the occurrences of crime or accident in which parts of St. Louis. We describe the simulation results to justify the accuracy of local-level variable selection during intensity function estimation.", "End member analysis (EMA) unmixes grain size distribution (GSD) data into a mixture of end members (EMs), thus helping understand sediment provenance and depositional regimes and processes. In highly mixed data sets, however, many EMA algorithms find EMs which are still a mixture of true EMs. To overcome this, we propose maximum volume constrained EMA (MVC-EMA), which finds EMs as different as possible. We provide a uniqueness theorem and a quadratic programming algorithm for MVC-EMA. Experimental results show that MVC-EMA can effectively find true EMs in highly mixed data sets. Keywords: Nonnegative matrix analysis; Minimum volume; Identifiability; Sufficient scattered conditions.", "Maximum likelihood estimators possess desirable optimality properties but require correct specification of the error distribution to ensure exact unbiasedness. Independent of the primary inferential objective, the estimation of a covariance matrix $S^{P\\times P}\\approx\\Sigma^{P\\times P}$ must satisfy structural and regularity constraints, particularly when discrete data, ties, or weak instruments are present. Linear Gaussian covariance models therefore arise as a foundational requirement across scientific inference and predictive analytics. In this work, we introduce an $\\ell_{2}$ -norm\u2013based semi-parametric quasi-likelihood framework constructed from binomial pairwise comparisons over all observed pairs $(X_{n},Y_{n})_{n=1}^{N}.$ The resulting estimator admits an exact unbiased H\u00e1jek projection, yielding minimum-variance covariance estimation under finite samples. The approach operationalises the Kemeny metric through a Whitney embedding within a proven U-statistic framework, producing a rank-Euclidean upon a Riemannian manifold geometry that remains identifiable in the presence of ties for both discrete and continuous random variables. Beyond covariance estimation, the framework extends classical Wilcoxon rank-sum methodology to multivariate settings while preserving exact unbiasedness under linear surjective mappings onto common values\u2014an unresolved problem in existing rank-based inference. The proposed model space therefore constitutes a consistent non-parametric analogue of the general linear model, accommodating unknown heterogeneity and weak inferential instruments. This work represents the first quasi-likelihood formulation of rank-based Euclidean distance correlation with exact finite-sample unbiasedness.", "We derive an estimator of the spectral density of a functional time series that is the output of a multilayer perceptron neural network. The estimator is motivated by difficulties with the computation of existing spectral density estimators for time series of functions defined on very large grids that arise, for example, in climate compute models and medical scans. Existing estimators use autocovariance kernels represented as large $G\\times G$ matrices, where $G$ is the number of grid points on which the functions are evaluated. In many recent applications, functions are defined on 2D and 3D domains, and $G$ can be of the order $G\\sim 10^{5}$ , making the evaluation of the autocovariance kernels computationally intensive or even impossible. We use the theory of spectral functional principal components to derive our deep learning estimator and prove that it is a universal approximator to the spectral density under general assumptions. Our estimator can be trained without computing the autocovariance kernels and it can be parallelized to provide the estimates much faster than existing approaches. We validate its performance by simulations and an application to fMRI images. Key Words: Functional time series; Multilayer perceptron; Spatial domain; Spectral density.", "The Stable Unit Treatment Value Assumption (SUTVA) includes the condition that there are no multiple versions of treatment in causal inference. Though we could not control the implementation of treatment in observational studies, multiple versions may exist in the treatment. It has been pointed out that ignoring such multiple versions of treatment can lead to biased estimates of causal effects, but a causal inference framework that explicitly deals with the unbiased identification and estimation of version-specific causal effects has not been fully developed yet. Thus, obtaining a deeper understanding for mechanisms of the complex treatments is difficult. In this paper, we introduce the Mixture-of-Experts framework into causal inference and develop a methodology for estimating the causal effects of latent versions. This approach enables explicit estimation of version-specific causal effects even if the versions are not observed. Numerical experiments demonstrate the effectiveness of the proposed method.", "This paper investigates the asymptotic distribution of a wavelet-based NKK periodogram constructed from least absolute deviations (LAD) harmonic regression at a fixed resolution level. Using a wavelet representation of the underlying time series, we analyze the probabilistic structure of the resulting periodogram under long-range dependence. It is shown that, under suitable regularity conditions, the NKK periodogram converges in distribution to a nonstandard limit characterized as a quadratic form in a Gaussian random vector, whose covariance structure depends on the memory properties of the process and on the chosen wavelet filters. This result establishes a rigorous theoretical foundation for the use of robust wavelet-based periodograms in the spectral analysis of long-memory time series with heavy-tailed inovations.", "Precision medicine has led to a paradigm shift allowing the development of targeted drugs that are agnostic to the tumor location. In this context, basket trials aim to identify which tumor types - or baskets - would benefit from the targeted therapy among patients with the same molecular marker or mutation. We propose the implementation of continuous monitoring for basket trials to increase the likelihood of early identification of non-promising baskets. Although the current Bayesian trial designs available in the literature can incorporate more than one interim analysis, most of them have high computational cost, and none of them handle delayed outcomes that are expected for targeted treatments such as immunotherapies. We leverage the Bayesian empirical approach proposed by Fujiwara et al., which has low computational cost. We also extend ideas of Cai et al to address the practical challenge of performing interim analysis with delayed outcomes using multiple imputation. Operating characteristics of four different strategies to handle delayed outcomes in basket trials are compared in an extensive simulation study with the benchmark strategy where trial accrual is put on hold until complete data is observed to make a decision. The optimal handling of missing data at interim analyses is trial-dependent. With slow accrual, missingness is minimal even with continuous monitoring, favoring simpler approaches over computationally intensive methods. Although individual sample-size savings are small, multiple imputation becomes more appealing when sample size savings scale with the number of baskets and agents tested. keywords: basket trial, interim analysis, missing data, multiple imputation", "Topological Data Analysis Ball Mapper (TDABM) offers a model free visualization of multivariate data which does not necessitate the information loss associated with dimensionality reduction. TDABM produces a cover of a multidimensional point cloud using equal size balls, the radius of the ball is the only parameter. A TDABM visualization retains the full structure of the data. The graphs produced by TDABM can convey coloration according to further variables, model residuals, or variables within the multivariate data. An expanding literature makes use of the power of TDABM across Finance, Economics, Geography, Medicine and Chemistry amongst others. We provide an introduction to TDABM and the ballmapper package for Stata.", "Numerous studies have shown the harmful effects of airborne pollutants on human health. Vulnerable groups and communities often bear a disproportionately larger health burden due to exposure to airborne pollutants. Thus, there is a need to design policies that effectively reduce the public health burdens while ensuring cost-effective policy interventions. Designing policies that optimally benefit the population while ensuring equity between groups under cost constraints is a challenging statistical and causal inference problem. In the context of environmental policy this is further complicated by the fact that interventions target emission sources but health impacts occur in potentially distant communities due to atmospheric pollutant transport\u2014a setting known as bipartite network interference (BNI). To address these issues, we propose a fair policy learning approach under BNI. Our approach allows to learn cost-effective policies under fairness constraints even accounting for complex BNI data structures. We derive asymptotic properties and demonstrate finite sample performance via Monte Carlo simulations. Finally, we apply the proposed method to a real-world dataset linking power plant scrubber installations to Medicare health records for more than 2 million individuals in the U.S. Our method determine fair scrubber allocations to reduce mortality under fairness and cost constraints.", "Variable importance in regression analyses is of considerable interest in a variety of fields. There is no unique method for assessing variable importance. However, a substantial share of the available literature employs Shapley values, either explicitly or implicitly, to decompose a suitable goodness-of-fit measure, in the linear regression model typically the classical $R^{2}$ . Beyond linear regression, there is no generally accepted goodness-of-fit measure, only a variety of pseudo- $R^{2}$ s. We formulate and discuss the desirable properties of goodness-of-fit measures that enable Shapley values to be interpreted in terms of relative, and even absolute, importance. We suggest to use a pseudo- $R^{2}$ based on the Kullback-Leibler divergence, the Kullback-Leibler $R^{2}$ , which has a convenient form for generalized linear models and permits to unify and extend previous work on variable importance for linear and nonlinear models. Several examples are presented, using data from public health and insurance. Keywords. Averaging over orderings; Dominance analysis; Goodness of fit; Hierarchical partitioning; Kullback-Leibler divergence; Regression", ". We study the expectation-maximization (EM) algorithm for general latent-variable models under (i) distributional misspecification and (ii) nonidentifiability induced by a group action. We formulate EM on the quotient parameter space and measure error using an arbitrary integral probability metric (IPM). Our main results give (a) a sharp local linear convergence rate for population EM governed by the spectral radius of the linearization on a local slice, and (b) tight finite-sample bounds for sample EM obtained via perturbed contraction inequalities and generic chaining/entropy control of EM-induced empirical processes.", "# Beyond P-Values: Importing Quantitative Finance's Risk and Regret Metrics for AI in Learning Health Systems Beyond P-Values: Importing Quantitative Finance's Risk and Regret Metrics for AI in Learning Health Systems Richik Chakraborty, MS Georgetown University Email: rc1586@georgetown.edu Keywords: artificial intelligence in healthcare, learning health systems, quantitative finance, risk metrics, calibration drift, regret minimization, Value-at-Risk, Conditional Value-at-Risk, FDA regulation, medical AI safety, adaptive algorithms, post-deployment monitoring Abstract The increasing deployment of artificial intelligence (AI) in clinical settings challenges foundational assumptions underlying traditional frameworks of medical evidence. Classical statistical approaches, centered on randomized controlled trials, frequentist hypothesis testing, and static confidence intervals, were designed for fixed interventions evaluated under stable conditions. In contrast, AI-driven clinical systems learn continuously, adapt their behavior over time, and operate in non-stationary environments shaped by evolving populations, practices, and feedback effects. In such systems, clinical harm arises le", "We propose a new estimation method for the Stable Trait, Auto Regressive Trait, and State (STARTS) model, which is well known for its frequent occurrence of improper solutions. The proposed approach is implemented through a two-stage estimation procedure that combines matrix decomposition factor analysis (MDFA) based on eigenvalue decomposition with conventional SEM estimation principles. By reformulating the STARTS model within a factor-analytic framework, this study presents a novel way of applying MDFA in the context of structural equation modeling (SEM). Through a simulation study and an empirical application to ToKyo Teen Cohort data, the proposed method was shown to entail a substantially lower risk of improper solutions than commonly used maximum likelihood, conditional ML, and (unweighted) least squares estimators, while tending to yield solutions similar to those obtained by ML. Compared with Bayesian estimation, the proposed method does not require the specification of appropriate (weakly informative) prior distributions and may effectively mitigate bias issues that arise when the number of time points is small. Applying the proposed method, as well as conducting sensitivity analyses informed by it, will enable researchers to more effectively delineate the range of plausible conclusions from data in estimating the STARTS model and other SEMs.", ". In this work, we propose a modified Bayesian Information Criterion (BIC) specifically designed for mixture models and hierarchical structures. This criterion incorporates the determinant of the Hessian matrix of the log-likelihood function, thereby refining the classical Bayes Factor by accounting for the curvature of the likelihood surface. Such geometric information introduces a more nuanced penalization for model complexity. The proposed approach improves model selection, particularly under small-sample conditions or in the presence of noise variables. Through theoretical derivations and extensive simulation studies\u2014including both linear and linear mixed models\u2014we show that our criterion consistently outperforms traditional methods such as BIC, Akaike Information Criterion (AIC), and related variants. The results suggest that integrating curvature-based information from the likelihood landscape leads to more robust and accurate model discrimination in complex data environments.", "This paper is motivated by evaluating the benefits of patients receiving mechanical circulatory support (MCS) devices in end-stage heart failure management inference, in which hypothesis testing for a treatment effect on the risk of recurrent events is challenged in the presence of terminal events. Existing methods based on cumulative frequency unreasonably disadvantage longer survivors as they tend to experience more recurrent events. The While-Alive-based (WA) test has provided a solution to address this survival-length-bias problem, and it performs well when the recurrent event rate holds constant over time. However, if such a constant-rate assumption is violated, the WA test can exhibit an inflated type I error and inaccurate estimation of treatment effects. To fill this methodological gap, we propose a Proportional Rate Marginal Structural Model-assisted Test (PR-MSMaT) in the causal inference framework of separable treatment effects for recurrent and terminal events. Using the simulation study, we demonstrate that our PR-MSMaT can properly control type I error while gaining power comparable to the WA test under time-varying recurrent event rates. We employ PR-MSMaT to compare different MCS devices with the postoperative risk of gastrointestinal bleeding among patients enrolled in the Interagency Registry of Mechanically Assisted Circulatory Support program.", "In this paper we propose a novel multiple imputation method specifically designed for parameter estimation in observation-driven models (ODM). The approach takes advantage of the iterative nature of the systematic component in ODM to propagate the dependence structure through missing data, thereby minimizing its impact on estimation. Unlike traditional imputation techniques, the proposed method accommodates continuous, discrete, and mixed-type data while preserving key distributional and dependence properties. Under general conditions, we prove that the proposed method converges almost surely to the true parameter, regardless of the missing mechanism or the proportion of missing data. We evaluate its performance through Monte Carlo simulations in the context of GARMA models, considering time series with up to 70% missing data. Two practical applications to environmelntal time series further demonstrates its practical utility. Keywords: time series analysis; observation-driven models; non-gaussian time series; missing data; multiple imputation; convergence of algorithms. MSC: 62M10, 62F12, 62E20, 62G20, 60G15.", "Route alignment design in surveying and transportation engineering frequently involves fixed waypoint constraints, where a path must precisely traverse specific coordinates. While existing literature primarily relies on geometric optimization or control-theoretic spline frameworks, there is a lack of systematic statistical modeling approaches that balance global smoothness with exact point adherence. This paper proposes an Adaptive Nadaraya-Watson (ANW) kernel regression estimator designed to address the fixed waypoint problem. By incorporating waypoint-specific weight tuning parameters, the ANW estimator decouples global smoothing from local constraint satisfaction, avoiding the \u201cjagged\u201d artifacts common in naive local bandwidth-shrinking strategies. To further enhance estimation accuracy, we develop an iterative data sharpening algorithm that systematically reduces bias while maintaining the stability of the kernel framework. We establish the theoretical foundation for the ANW estimator by deriving its asymptotic bias and variance and proving its convergence properties under the internal constraint model. Numerical case studies in 1D and 2D trajectory planning demonstrate that the method effectively balances root mean square error (RMSE) and curvature smoothness. Finally, we validate the practical utility of the framework through empirical applications to railway and highway route planning. In sum, this work provides a stable, theoretically grounded, and computationally efficient solution for complex, constrained alignment design problems.", "This work investigates the performance of the final iterate produced by stochastic gradient descent (SGD) under temporally dependent data. We consider two complementary sources of dependence: $(i)$ martingale-type dependence in both the covariate and noise processes, which accommodates non-stationary and non-mixing time series data, and $(ii)$ dependence induced by sequential decision making. Our formulation runs in parallel with classical notions of (local) stationarity and strong mixing, while neither framework fully subsumes the other. Remarkably, SGD is shown to automatically accommodate both independent and dependent information under a broad class of stepsize schedules and exploration rate schemes. Non-asymptotically, we show that SGD simultaneously achieves statistically optimal estimation error and regret, extending and improving existing results. In particular, our tail bounds remain sharp even for potentially infinite horizon $T=+\\infty$ . Asymptotically, the SGD iterates converge to a Gaussian distribution with only an $O_{{\\mathbb{P}}}(1/\\sqrt{t})$ remainder, demonstrating that the supposed estimation-regret trade-off claimed in prior work can in fact be avoided. We further propose a new \u201cconic\u201d approximation of the decision region that allows the covariates to have unbounded support. For online sparse regression, we develop a new SGD-based algorithm that uses only $d$ units of storage and requires $O(d)$ flops per iteration, achieving the long term statistical optimality. Intuitively, each incoming observation contributes to estimation accuracy, while aggregated summary statistics guide support recovery.", "# Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit Unsupervised dense random survival forests identify interpretable patient profiles with heterogeneous treatment benefit Xingyu Li ,1 Qing Liu,2 Tony Jiang,2 Hong Amy Xia,2 Peng Wei,\u2217 1 and Brian P. Hobbs,\u2217 3 1Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, 77030, Texas, USA, 2Center for Design and Analysis, Amgen, Thousand Oaks, CA, USA. and 3Telperian, Austin, TX, USA \u2217Peng wei, The University of Texas MD Anderson Cancer Center, Texas, USA. pwei2@mdanderson.org; Brian P. Hobbs, Telperian, Austin, TX, USA. bhobbs@telperian.com. FOR PUBLISHER ONLY Received on Date Month Year; revised on Date Month Year; accepted on Date Month Year Abstract Precision oncology aims to prescribe the optimal cancer treatment to the right patients, maximizing therapeutic benefits. However, identifying patient subgroups that may benefit more from experimental cancer treatments based on randomized clinical trials presents a significant analytical challenge. To address this, we introduce a novel unsupervised machine learning approach based on very", "Sampling-based inference has seen a surge of interest in recent years. Hamiltonian Monte Carlo (HMC) has emerged as a powerful algorithm that leverages concepts from Hamiltonian dynamics to efficiently explore complex target distributions. Variants of HMC are available in popular software packages, enabling off-the-shelf implementations that have greatly benefited the statistics and machine learning communities. At the same time, the availability of such black-box implementations has made it challenging for users to understand the inner workings of HMC, especially when they are unfamiliar with the underlying physical principles. We provide a pedagogical overview of HMC that aims to bridge the gap between its theoretical foundations and practical applicability. This review article seeks to make HMC more accessible to applied researchers by highlighting its advantages, limitations, and role in enabling scalable and exact Bayesian inference for complex models.", "Recent advances in large-scale models, including deep neural networks and large language models, have substantially improved performance across a wide range of learning tasks. The widespread availability of such pre-trained models creates new opportunities for data-efficient statistical learning, provided they can be effectively integrated into downstream tasks. Motivated by this setting, we study few-shot personalization, where a pre-trained black-box model is adapted to a target domain using a limited number of samples. We develop a theoretical framework for few-shot personalization in nonparametric regression and propose algorithms that can incorporate a black-box pre-trained model into the regression procedure. We establish the minimax optimal rate for the personalization problem and show that the proposed method attains this rate. Our results clarify the statistical benefits of leveraging pre-trained models under sample scarcity and provide robustness guarantees when the pre-trained model is not informative. We illustrate the finite-sample performance of the methods through simulations and an application to the California housing dataset with several pre-trained models.", "The Hidden Markov Model (HMM) is a widely-used statistical model for handling sequential data. However, the presence of missing observations in real-world datasets often complicates the application of the model. The EM algorithm and Gibbs samplers can be used to estimate the model, yet suffering from various problems including non-convexity, high computational complexity and slow mixing. In this paper, we propose a collapsed Gibbs sampler that efficiently samples from HMMs\u2019 posterior by integrating out both the missing observations and the corresponding latent states. The proposed sampler is fast due to its three advantages. First, it achieves an estimation accuracy that is comparable to existing methods. Second, it can produce a larger Effective Sample Size (ESS) per iteration, which can be justified theoretically and numerically. Third, when the number of missing entries is large, the sampler has a significant smaller computational complexity per iteration compared to other methods, thus is faster computationally. In summary, the proposed sampling algorithm is fast both computationally and theoretically and is particularly advantageous when there are a lot of missing entries. Finally, empirical evaluations based on numerical simulations and real data analysis demonstrate that the proposed algorithm consistently outperforms existing algorithms in terms of time complexity and sampling efficiency (measured in ESS).", "Estimating causal effects of continuous treatments is a common problem in practice, for example, in studying average dose-response functions. Classical analyses typically assume that all confounders are fully observed, whereas in real-world applications, unmeasured confounding often persists. In this article, we propose a novel framework for the identification of average dose-response functions using instrumental variables, thereby mitigating bias induced by unobserved confounders. We introduce the concept of a uniform regular weighting function and consider covering the treatment space with a finite collection of open sets. On each of these sets, such a weighting function exists, allowing us to identify the average dose-response function locally within the corresponding region. For estimation, we propose an augmented inverse probability weighted score for continuous treatments with instrumental variables under a debiased machine learning framework, and provide practical guidance to adaptively establish regular weighting functions from the data . We further establish the asymptotic properties when the average dose-response function is estimated via kernel regression or empirical risk minimization. Finally, we conduct both simulation and empirical studies to assess the finite-sample performance of the proposed methods.", "Multivariate network time series are ubiquitous in modern systems, yet existing network autoregressive models typically treat nodes as scalar processes, ignoring cross-variable spillovers. To capture these complex interactions without the curse of dimensionality, we propose the Reduced-Rank Network Autoregressive (RRNAR) model. Our framework introduces a separable bilinear transition structure that couples the known network topology with a learnable low-rank variable subspace. We estimate the model using a novel Scaled Gradient Descent (ScaledGD) algorithm, explicitly designed to bridge the gap between rigid network scalars and flexible factor components. Theoretically, we establish non-asymptotic error bounds under a novel distance metric. A key finding is a network-induced blessing of dimensionality: for sparse networks, the estimation accuracy for network parameters improves as the network size grows. Applications to traffic and server monitoring networks demonstrate that RRNAR significantly outperforms univariate and unstructured benchmarks by identifying latent cross-channel propagation mechanisms.", "In this study, a family of distributions called cubic lower record-based transmuted is provided. A special case of this family is proposed as an alternative exponential distribution. Several statistical properties are explored. We utilize nine different methods to estimate the parameters of the suggested distribution. In order to compare the performances of these methods, we consider a comprehensive Monte-Carlo simulation study. As a result of simulation study, we conclude that minimum absolute distance estimator is a valuable alternative to maximum likelihood estimator. Then, we carried out two real-world data examples to evaluate the fits of introduced distribution as well as its potential competitor ones. The findings of real-world data analysis show that the best-fitting distribution for both datasets is our model.", "We introduce a fast Markov Chain Monte Carlo (MCMC) exploration of the astrophysical parameter space using a modified version of the publicly available code CIGALE (Code Investigating GALaxy emission). The original CIGALE builds a grid of theoretical Spectral Energy Distribution (SED) models and fits to photometric fluxes from Ultraviolet (UV) to Infrared (IR) to put contraints on parameters related to both formation and evolution of galaxies. Such a grid-based method can lead to a long and challenging parameter extraction since the computation time increases exponentially with the number of parameters considered and results can be dependent on the density of sampling points, which must be chosen in advance for each parameter. Markov Chain Monte Carlo methods, on the other hand, scale approximately linearly with the number of parameters, allowing a faster and more accurate exploration of the parameter space by using a smaller number of efficiently chosen samples. We test our MCMC version of the code CIGALE (called CIGALEMC ) with simulated data. After checking the ability of the code to retrieve the input parameters used to build the mock sample, we fit theoretical SEDs to real data from the well known and studied SINGS sample. We discuss constraints on the parameters and show the advantages of our MCMC sampling method in terms of accuracy of the results and optimization of CPU time.", "Sampling from the lattice Gaussian distribution has emerged as an important problem in coding, decoding and cryptography. In this paper, the classic Metropolis-Hastings (MH) algorithm in Markov chain Monte Carlo (MCMC) methods is adopted for lattice Gaussian sampling. Two MH-based algorithms are proposed, which overcome the limitation of Klein\u2019s algorithm. The first one, referred to as the independent Metropolis-Hastings-Klein (MHK) algorithm, establishes a Markov chain via an independent proposal distribution. We show that the Markov chain arising from this independent MHK algorithm is uniformly ergodic, namely, it converges to the stationary distribution exponentially fast regardless of the initial state. Moreover, the rate of convergence is analyzed in terms of the theta series, leading to predictable mixing time. A symmetric Metropolis-Klein (SMK) algorithm is also proposed, which is proven to be geometrically ergodic.", "An important challenge in machine translation is to generate high-quality and diverse translations. Prior work has shown that the estimated likelihood from the MT model correlates poorly with translation quality. In contrast, quality evaluation metrics (such as COMET or BLEURT) exhibit high correlations with human judgments, which has motivated their use as rerankers (such as quality-aware and minimum Bayes risk decoding). However, relying on a single translation with high estimated quality increases the chances of \u201cgaming the metric\u201d. In this paper, we address the problem of sampling a set of high-quality and diverse translations. We provide a simple and effective way to avoid over-reliance on noisy quality estimates by using them as the energy function of a Gibbs distribution. Instead of looking for a mode in the distribution, we generate multiple samples from high-density areas through the Metropolis-Hastings algorithm, a simple Markov chain Monte Carlo approach. The results show that our proposed method leads to high-quality and diverse outputs across multiple language pairs ( English $\\leftrightarrow$ { German, Russian }) with two strong decoder-only LLMs ( Alma-7b, Tower-7b ).", "Bayesian inference is a popular and widely-used approach to infer phylogenies (evolutionary trees). However, despite decades of widespread application, it remains difficult to judge how well a given Bayesian Markov chain Monte Carlo (MCMC) run explores the space of phylogenetic trees. In this paper, we investigate the Monte Carlo error of phylogenies, focusing on high-dimensional summaries of the posterior distribution, including variability in estimated edge/branch (known in phylogenetics as \u201csplit\u201d) probabilities and tree probabilities, and variability in the estimated summary tree. Specifically, we ask if there is any measure of effective sample size (ESS) applicable to phylogenetic trees which is capable of capturing the Monte Carlo error of these three summary measures. We find that there are some ESS measures capable of capturing the error inherent in using MCMC samples to approximate the posterior distributions on phylogenies. We term these tree ESS measures, and identify a set of three which are useful in practice for assessing the Monte Carlo error. Lastly, we present visualization tools that can improve comparisons between multiple independent MCMC runs by accounting for the Monte Carlo error present in each chain. Our results indicate that common post-MCMC workflows are insufficient to capture the inherent Monte Carlo error of the tree, and highlight the need for both within-chain mixing and between-chain convergence assessments.", "In this paper, we theoretically study the critical properties of the classical spin-1 Ising model using two approaches: 1) the analytical low-temperature series expansion and 2) the numerical Metropolis Monte Carlo technique. Within this analysis, we discuss the critical behavior of one-, two- and three-dimensional systems modeled by the first-neighbor spin-1 Ising model for different types of exchange interactions. The comparison of the results obtained according the Metropolis Monte Carlo simulations allows us to highlight the limits of the widely used mean-field theory approach. We also show, via a simple transformation, that for the special case where the bilinear and bicubic terms are set equal to zero in the Hamiltonian the partition function of the spin-1 Ising model can be reduced to that of the spin-1/2 Ising model with temperature dependent external field and temperature independent exchange interaction times an exponential factor depending on the other terms of the Hamiltonian and confirm this result numerically by using the Metropolis Monte Carlo simulation. Finally, we investigate the dependence of the critical temperature on the strength of long-range interactions included in the Ising Hamiltonian comparing it with that of the first-neighbor spin-1/2 Ising model.", "Reconstructing the density fluctuations in the early Universe that evolved into the distribution of galaxies we see today is a challenge of modern cosmology. An accurate reconstruction would allow us to test cosmological models by simulating the evolution starting from the reconstructed state and comparing it to the observations. Several reconstruction techniques have been proposed, but they all suffer from lack of uniqueness because the velocities of galaxies are usually not known. Here we show that reconstruction can be reduced to a well-determined problem of optimisation, and present a specific algorithm that provides excellent agreement when tested against data from N-body simulations.", "Optimal transportation distances are a fundamental family of parameterized distances for histograms. Despite their appealing theoretical properties, excellent performance in retrieval tasks and intuitive formulation, their computation involves the resolution of a linear program whose cost is prohibitive whenever the histograms' dimension exceeds a few hundreds. We propose in this work a new family of optimal transportation distances that look at transportation problems from a maximum-entropy perspective. We smooth the classical optimal transportation problem with an entropic regularization term, and show that the resulting optimum is also a distance which can be computed through Sinkhorn-Knopp's matrix scaling algorithm at a speed that is several orders of magnitude faster than that of transportation solvers. We also report improved performance over classical optimal transportation distances on the MNIST benchmark problem.", "We summarize a book under publication with the above title written by the three present authors, on the theory of Zipf\u2019s law, and more generally of power laws, driven by the mechanism of proportional growth. The preprint is available upon request from the authors. For clarity, consistence of language and conciseness, we discuss the origin and conditions of the validity of Zipf\u2019s law using the terminology of firms\u2019 asset values. We use firms at the entities whose size distributions are to be explained. It should be noted, however, that most of the relations discussed in this book, especially the intimate connection between Zipf\u2019s and Gilbrat\u2019s laws, underlie Zipf\u2019s law in diverse scientific areas. The same models and variations thereof can be straightforwardly applied to any of the other domains of application.", "# A stochastic model of wealth distribution ## 1 Introduction A major research focus in economics and econophysics is on the distribution of wealth in societies at different stages of development. Wealth includes money, material goods and assets of different kinds. Knowledge of the monetary equivalent of the latter two components is required in order to quantify wealth. A related and easier to measure distribution is that of income. The major motivation of theoretical models is to provide insight on the microscopic origins of income/wealth distributions. Such distributions are expected to provide good fits to the empirical data. In the context of incomes, Champernowne2 has commented \"The forces determining the distribution of incomes in any community are so varied and complex and interact and fluctuate so continuously, that any theoretical model must either be unrealistically simplified or hopelessly complicated.\" The statement highlights the desirability of finding a middle ground between the unrealistically simple and the hopelessly complicated. A number of distribution functions has been proposed so far to describe income and wealth distributions. Theoretical models based on sto", "The formation of sentences is a highly structured and history-dependent process. The probability of using a specific word in a sentence strongly depends on the \u2019history\u2019 of word-usage earlier in that sentence. We study a simple history-dependent model of text generation assuming that the sample-space of word usage reduces along sentence formation, on average. We first show that the model explains the approximate Zipf law found in word frequencies as a direct consequence of sample-space reduction. We then empirically quantify the amount of sample-space reduction in the sentences of ten famous English books, by analysis of corresponding word-transition tables that capture which words can follow any given word in a text. We find a highly nested structure in these transition tables and show that this \u2018nestedness\u2019 is tightly related to the power law exponents of the observed word frequency distributions. With the proposed model it is possible to understand that the nestedness of a text can be the origin of the actual scaling exponent, and that deviations from the exact Zipf law can be understood by variations of the degree of nestedness on a book-by-book basis. On a theoretical level we are able to show that in case of weak nesting, Zipf\u2019s law breaks down in a fast transition. Unlike previous attempts to understand Zipf\u2019s law in language the sample-space reducing model is not based on assumptions of multiplicative, preferential, or self-organised critical mechanisms behind language formation, but simply used the empirically quantifiable parameter \u2019nestedness\u2019 to understand the statistics of word frequencies.", "Many networks exhibit scale free behaviour where their degree distribution obeys a power law for large vertex degrees. Models constructed to explain this phenomena have relied on preferential attachment where the networks grow by the addition of both vertices and edges, and the edges attach themselves to a vertex with a probability proportional to its degree. Simulations hint, though not conclusively, that both growth and preferential attachment are necessary for scale free behaviour. We derive analytic expressions for degree distributions for networks that grow by the addition of edges to a fixed number of vertices, based on both linear and non-linear preferential attachment, and show that they fall off exponentially as would be expected for purely random networks. From this we conclude that preferential attachment alone might be necessary but is certainly not a sufficient condition for generating scale free networks.", "# The Evolutions of the Rich get Richer and the Fit get Richer Phenomena in Scholarly Networks: The Case of the Strategic Management Journal The Evolutions of the Rich get Richer and the Fit get Richer Phenomena in Scholarly Networks: The Case of the Strategic Management Journal Guillermo Armando Ronda-Pupo http://orcid.org/0000-0002-9049-8249 Departamento de Administraci\u00f3n Universidad Cat\u00f3lica del Norte, Chile AvenidaAngamos. No 0610, Antofagasta, Chile 1270709. gronda@ucn.cl Departamento de Turismo Universidad de Holgu\u00edn, Cuba Avenida XX Aniversario, Piedra Blanca Holgu\u00edn, Cuba 80100 Thong Pham Mathematical Statistics Team, RIKEN Center for Advanced Intelligence Project, Tokyo, Japan thong.pham@riken.jp All authors designed the experiments, performed the experiments, analyzed the results, wrote and reviewed the manuscript. JEL code: M1 Correspondence to: Guillermo Armando Ronda-Pupo. Departamento de Administraci\u00f3n. Universidad Cat\u00f3lica del Norte. AvenidaAngamos. No 0610. Antofagasta, Chile. ZIP: 1270709. Email: gronda@ucn.cl Phone: +56 2552355802. 2 Abstract Understanding how a scientist develops new scientific collaborations or how their papers receive new citations is a major c", "We perform an empirical study of the preferential attachment phenomenon in temporal networks and show that on the Web, networks follow a nonlinear preferential attachment model in which the exponent depends on the type of network considered. The classical preferential attachment model for networks by Barab\u00e1si and Albert (1999) assumes a linear relationship between the number of neighbors of a node in a network and the probability of attachment. Although this assumption is widely made in Web Science and related fields, the underlying linearity is rarely measured. To fill this gap, this paper performs an empirical longitudinal (time-based) study on forty-seven diverse Web network datasets from seven network categories and including directed, undirected and bipartite networks. We show that contrary to the usual assumption, preferential attachment is nonlinear in the networks under consideration. Furthermore, we observe that the deviation from linearity is dependent on the type of network, giving sublinear attachment in certain types of networks, and superlinear attachment in others. Thus, we introduce the preferential attachment exponent $\\beta$ as a novel numerical network measure that can be used to discriminate different types of networks. We propose explanations for the behavior of that network measure, based on the mechanisms that underly the growth of the network in question.", "In this paper we study methods for estimating causal effects in settings with panel data, where some units are exposed to a treatment during some periods and the goal is estimating counterfactual (untreated) outcomes for the treated unit/period combinations. We propose a class of matrix completion estimators that uses the observed elements of the matrix of control outcomes corresponding to untreated unit/periods to impute the missing elements of the control outcome matrix, corresponding to treated units/periods. This leads to a matrix that well-approximates the original (incomplete) matrix, but has lower complexity according to the nuclear norm for matrices. We generalize results from the matrix completion literature by allowing the patterns of missing data to have a time series dependency structure. We present novel insights concerning the connections between the matrix completion literature, the literature on interactive fixed effects models and the literatures on program evaluation under unconfoundedness and synthetic control methods. The proposed method outperforms unconfoundedness-based or synthetic control estimators in simulations based on real data.", "Principal component analysis (PCA) is commonly used in genetics to infer and visualize population structure and admixture between populations. PCA is often interpreted in a way similar to inferred admixture proportions, where it is assumed that individuals belong to one of several possible populations or are admixed between these populations. We propose a new method to assess the statistical fit of PCA (interpreted as a model spanned by the top principal components) and to show that violations of the PCA assumptions affect the fit. Our method uses the chosen top principal components to predict the genotypes. By assessing the covariance (and the correlation) of the residuals (the differences between observed and predicted genotypes), we are able to detect violation of the model assumptions. Based on simulations and genome wide human data we show that our assessment of fit can be used to guide the interpretation of the data and to pinpoint individuals that are not well represented by the chosen principal components. Our method works equally on other similar models, such as the admixture model, where the mean of the data is represented by linear matrix decomposition.", "Conventional ideal point models rely on Gaussian or quadratic utility functions that violate the triangle inequality, producing non-metric distances that complicate geometric interpretation and undermine clustering and dispersion-based analyses. We introduce a distance-based alternative that adapts the Latent Space Item Response Model (LSIRM) to roll-call data, treating legislators and bills as nodes in a bipartite network jointly embedded in a Euclidean metric space. Through controlled simulations, Euclidean LSIRM consistently recovers latent coalition structure with superior cluster separation relative to existing methods. Applied to the 118th U.S. House, the model improves vote prediction and yields bill embeddings that clarify cross-cutting issue alignments. The results show that restoring metric structure to ideal point estimation provides a clearer and more coherent inference about party cohesion, factional divisions, and multidimensional legislative behavior.", "We present a mathematical model, and the corresponding mathematical analysis, that justifies and quantifies the use of principal component analysis of biallelic genetic marker data for a set of individuals to detect the number of subpopulations represented in the data. We indicate that the power of the technique relies more on the number of individuals genotyped than on the number of markers.", "We provide a basic review of the data screening and assumption testing issues relevant to exploratory and confirmatory factor analysis along with practical advice for conducting analyses that are sensitive to these concerns. Historically, factor analysis was developed for explaining the relationships among many continuous test scores, which led to the expression of the common factor model as a multivariate linear regression model with observed, continuous variables serving as dependent variables, and unobserved factors as the independent, explanatory variables. Thus, we begin our paper with a review of the assumptions for the common factor model and data screening issues as they pertain to the factor analysis of continuous observed variables. In particular, we describe how principles from regression diagnostics also apply to factor analysis. Next, because modern applications of factor analysis frequently involve the analysis of the individual items from a single test or questionnaire, an important focus of this paper is the factor analysis of items. Although the traditional linear factor model is well-suited to the analysis of continuously distributed variables, commonly used item types, including Likert-type items, almost always produce dichotomous or ordered categorical variables. We describe how relationships among such items are often not well described by product-moment correlations, which has clear ramifications for the traditional linear factor analysis. An alternative, non-linear factor analysis using polychoric correlations has become more readily available to applied researchers and thus more popular. Consequently, we also review the assumptions and data-screening issues involved in this method. Throughout the paper, we demonstrate these procedures using an historic data set of nine cognitive ability variables.", "Latent Factor Model (LFM) is one of the most successful methods for Collaborative filtering (CF) in the recommendation system, in which both users and items are projected into a joint latent factor space. Base on matrix factorization applied usually in pattern recognition, LFM models user-item interactions as inner products of factor vectors of user and item in that space and can be efficiently solved by least square methods with optimal estimation. However, such optimal estimation methods are prone to overfitting due to the extreme sparsity of user-item interactions. In this paper, we propose a Bayesian treatment for LFM, named Bayesian Latent Factor Model (BLFM). Based on observed user-item interactions, we build a probabilistic factor model in which the regularization is introduced via placing prior constraint on latent factors, and the likelihood function is established over observations and parameters. Then we draw samples of latent factors from the posterior distribution with Variational Inference (VI) to predict expected value. We further make an extension to BLFM, called BLFMBias, incorporating user-dependent and item-dependent biases into the model for enhancing performance. Extensive experiments on the movie rating dataset show the effectiveness of our proposed models by compared with several strong baselines.", "# Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities 1 Neural Collaborative Filtering Classification Model to Obtain Prediction Reliabilities Jes\u00fas Bobadilla*, Abraham Guti\u00e9rrez, Santiago Alonso, \u00c1ngel Gonz\u00e1lez-Prieto Universidad Polit\u00e9cnica de Madrid, Dpto. Sistemas Inform\u00e1ticos, Madrid (Spain) *Corresponding author: jesus.bobadilla@upm.es Abstract Neural collaborative filtering is the state of art field in the recommender systems area; it provides some models that obtain accurate predictions and recommendations. These models are regression-based, and they just return rating predictions. This paper proposes the use of a classification-based approach, returning both rating predictions and their reliabilities. The extra information (prediction reliabilities) can be used in a variety of relevant collaborative filtering areas such as detection of shilling attacks, recommendations explanation or navigational tools to show users and items dependences. Additionally, recommendation reliabilities can be gracefully provided to users: \u201cprobably you will like this film\u201d, \u201calmost certainly you will like this song\u201d, etc. This paper provides the proposed neura", "# Using various machine learning algorithms for quantitative analysis in Laser induced breakdown spectroscopy 1 Using various machine learning algorithms for quantitative analysis in LIBS technique Mohsen Rezaei a, Fatemeh Rezaei b*, Parvin Karimi c a) Department of Industrial Engineering, University of Science and Technology of Mazandaran, Behshahr, Iran b) Department of Physics, K. N. Toosi University of Technology, Tehran, Iran c) Department of Physics, South Tehran Branch, Islamic Azad University, Tehran, Iran Author declarations Conflict of interest The authors declare that they have no known competing financial interests that could have appeared to influence the work reported in this paper. Abstract Laser induced breakdown spectroscopy (LIBS) technique is employed for quantitative analysis of aluminum samples by different classical machine learning approaches. A Q-switch Nd:YAG laser at fundamental harmonic of 1064 nm is utilized for creation of LIBS plasma for prediction of constituent concentrations of the aluminum standard alloys. In current research, concentration prediction is performed by linear approaches of support vector regression (SVR), multiple linear regression (", "Global dynamical behaviors of the competitive Lotka-Volterra system even in $3$ -dimension are not fully understood. The Lyapunov function can provide us such knowledge once it is constructed. In this paper, we construct explicitly the Lyapunov function in three examples of the competitive Lotka-Volterra system for the whole state space: $(1)$ the general $2$ -dimensional case; $(2)$ a $3$ -dimensional model; $(3)$ the model of May-Leonard. The dynamics of these examples include bistable case and cyclical behavior. The first two examples are the generalized gradient system defined in the Appendixes, while the model of May-Leonard is not. Our method is helpful to understand the limit cycle problems in general $3$ -dimensional case.", "# Dynamic Structure in Four-strategy Game: Theory and Experiment ## 1 Introduction ### 1.1 The background For a discipline of science, the consistency of the theory and experiment is essential, of which the accuracy and the reality are the two fundamental aspects. As a discipline of science, game theory, which attempts to explain the strategy interactions between human subjects, is not an exception. Game statics theory, which is called also as classical game theory, is the main stream in game theory. In classical game theory, Nash equilibrium is the central concept. This theoretical concept was established near 1950. Till 1987, the human subject game experiment by O\u2019Neill provided the first illustration that laboratory human strategy behaviour can be accurately captured by the concept. Since then, this game has been extensively repeated in various setting experiment . Surrounding the central concept, now, human subject behaviour game theory and experiment has become a fruitful academic branch . Moreover, game statics theory has been widely applied in real live policy design (mechanism design) for desired social and economical aims. Game dynamics theory, which is based on evolutiona", "Evolutionary game dynamics is one of the most fruitful frameworks for studying evolution in different disciplines, from Biology to Economics. Within this context, the approach of choice for many researchers is the so-called replicator equation, that describes mathematically the idea that those individuals performing better have more offspring and thus their frequency in the population grows. While very many interesting results have been obtained with this equation in the three decades elapsed since it was first proposed, it is important to realize the limits of its applicability. One particularly relevant issue in this respect is that of non-mean-field effects, that may arise from temporal fluctuations or from spatial correlations, both neglected in the replicator equation. This review discusses these temporal and spatial effects focusing on the non-trivial modifications they induce when compared to the outcome of replicator dynamics. Alongside this question, the hypothesis of linearity and its relation to the choice of the rule for strategy update is also analyzed. The discussion is presented in terms of the emergence of cooperation, as one of the current key problems in Biology and in other disciplines.", "We consider a random financial network with a large number of agents. The agents connect through credit instruments borrowed from each other or through direct lending, and these create the liabilities. The settlement of the debts of various agents at the end of the contract period can be expressed as solutions of random fixed point equations. Our first step is to derive these solutions (asymptotically), using a recent result on random fixed point equations. We consider a large population in which the agents adapt one of the two available strategies, risky or risk-free investments, with an aim to maximize their expected returns (or surplus). We aim to study the emerging strategies when different types of replicator dynamics capture inter-agent interactions. We theoretically reduced the analysis of the complex system to that of an appropriate ordinary differential equation (ODE). We proved that the equilibrium strategies converge almost surely to that of an attractor of the ODE. We also derived the conditions under which a mixed evolutionary stable strategy (ESS) emerges; in these scenarios the replicator dynamics converges to an equilibrium at which the expected returns of both the populations are equal. Further the average dynamics (choices based on large observation sample) always averts systemic risk events (events with large fraction of defaults). We verified through Monte Carlo simulations that the equilibrium suggested by the ODE method indeed represents the limit of the dynamics.", "People tend to align their use of language to the linguistic behaviour of their own ingroup and to simultaneously diverge from the language use of outgroups. This paper proposes to model this phenomenon of sociolinguistic identity maintenance as an evolutionary game in which individuals play the field and the dynamics are supplied by a multi-population extension of the replicator\u2013mutator equation. Using linearization, the stabilities of all dynamic equilibria of the game in its fully symmetric two-population special case are found. The model is then applied to an empirical test case from adolescent sociolinguistic behaviour. It is found that the empirically attested population state corresponds to one of a number of stable equilibria of the game under an independently plausible value of a parameter controlling the rate of linguistic mutations. An asymmetric three-population extension of the game, explored with numerical solution methods, furthermore predicts to which specific equilibrium the system converges. Keywords: dynamical systems, evolutionary game theory, linguistic convergence and divergence, replicator\u2013mutator equation, sociolinguistics", "Prediction with expert advice is a foundational problem in online learning. In instances with $T$ rounds and $n$ experts, the classical Multiplicative Weights Update method suffers at most $\\sqrt{(T/2)\\ln n}$ regret when $T$ is known beforehand. Moreover, this is asymptotically optimal when both $T$ and $n$ grow to infinity. However, when the number of experts $n$ is small/fixed, algorithms with better regret guarantees exist. Cover showed in 1967 a dynamic programming algorithm for the two-experts problem restricted to $\\{0,1\\}$ costs that suffers at most $\\sqrt{T/2\\pi}+O(1)$ regret with $O(T^{2})$ pre-processing time. In this work, we propose an optimal algorithm for prediction with two experts\u2019 advice that works even for costs in $[0,1]$ and with $O(1)$ processing time per turn. Our algorithm builds up on recent work on the experts problem based on techniques and tools from stochastic calculus.", "Current annotation technologies suffer from poor coverage over the development process phases, limited support for the broad scope of the security requirement types and inadequate interconnection between different process phases. In this paper, we introduce the concept of multi-level security annotation for software artifacts, which addresses these problems. Multi-level security annotations are traceable trough the development phases to provide a light-weight, user-friendly alternative to full-fledged assurance methodologies and simplify the system evolution over time with respect to security.", "We extend the classical mean-variance (MV) framework and propose a robust and sparse portfolio selection model incorporating an ellipsoidal uncertainty set to reduce the impact of estimation errors and fixed transaction costs to penalize over-diversification. In the literature, the MV model under fixed transaction costs is referred to as the sparse or cardinality-constrained MV optimization, which is a mixed integer problem and is challenging to solve when the number of assets is large. We develop an efficient semismooth Newton-based proximal difference-of-convex algorithm to solve the proposed model and prove its convergence to at least a local minimizer with a locally linear convergence rate. We explore properties of the robust and sparse portfolio both analytically and numerically. In particular, we show that the MV optimization is indeed a robust procedure as long as an investor makes the proper choice on the risk-aversion coefficient. We contribute to the literature by proving that there is a one-to-one correspondence between the risk-aversion coefficient and the level of robustness. Moreover, we characterize how the number of traded assets changes with respect to the interaction between the level of uncertainty on model parameters and the magnitude of transaction cost.", "# Deep Learning, Predictability, and Optimal Portfolio Returns ## 1 Introduction An extensive empirical asset pricing literature has documented support for the predictability of equity returns. With an ever-increasing number of potential predictors, the practice of applying machine learning methods to make the most accurate predictions using large datasets is gaining further traction. This emerging literature demonstrates the superior performance of machine learning relative to linear regression analysis, which is favoured by researchers for its simplicity. However, it is unclear whether the solid statistical performance of machine learning translates into portfolio gains for an investor who uses these models of return predictability to construct optimal portfolios. Furthermore, this literature typically does not work with the number of time series properties essential for the timing of stock returns. In this paper, we contribute to the existing evidence by exploring the value of predictions offered by deep neural networks, including a long short-term memory (LSTM) recurrent architecture that excels at learning nonlinear time-series dependencies for portfolio construction. In contr", "As modern biotechnologies advance, it has become increasingly frequent that different modalities of high-dimensional molecular data (termed \u201comics\u201d data in this paper), such as gene expression, methylation, and copy number, are collected from the same patient cohort to predict the clinical outcome. While prediction based on omics data has been widely studied in the last fifteen years, little has been done in the statistical literature on the integration of multiple omics modalities to select a subset of variables for prediction, which is a critical task in personalized medicine. In this paper, we propose a simple penalized regression method to address this problem by assigning different penalty factors to different data modalities for feature selection and prediction. The penalty factors can be chosen in a fully data-driven fashion by cross-validation or by taking practical considerations into account. In simulation studies, we compare the prediction performance of our approach, called IPF-LASSO (Integrative LASSO with Penalty Factors) and implemented in the R package `ipflasso`, with the standard LASSO and sparse group LASSO. The use of IPF-LASSO is also illustrated through applications to two real-life cancer datasets. All data and codes are available on the companion website to ensure reproducibility.", "### Motivation Genome-wide association studies (GWAS) have played an important role in identifying genetic variants underlying human complex traits. However, its success is hindered by weak effect at causal variants and presence of noise at non-causal variants. In an effort to overcome these difficulties, a previous study proposed a regularized regression method that penalizes on the difference of signal strength between two consecutive single-nucleotide polymorphisms (SNPs).", "# Spatiotemporal Flexible Sparse Reconstruction for Rapid Dynamic Contrast-enhanced MRI > REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 Abstract\u2014Dynamic Contrast-enhanced magnetic resonance imaging (DCE-MRI) is a tissue perfusion imaging technique. Some versatile free-breathing DCE-MRI techniques combining compressed sensing (CS) and parallel imaging with golden-angle radial sampling have been developed to improve motion robustness with high spatial and temporal resolution. These methods have demonstrated good diagnostic performance in clinical setting, but the reconstruction quality will degrade at high acceleration rates and overall reconstruction time remains long. In this paper, we proposed a new parallel CS reconstruction model for DCE-MRI that enforces flexible weighted sparse constraint along both spatial and temporal dimensions. Weights were introduced to flexibly adjust the importance of time and space sparsity, and we derived a fast thresholding algorithm which was proven to be simple and efficient for solving the proposed reconstruction model. Results on in vivo liver DCE datasets show that the proposed method outperforms the sta", "Radio interferometry probes astrophysical signals through incomplete and noisy Fourier measurements. The theory of compressed sensing demonstrates that such measurements may actually suffice for accurate reconstruction of sparse or compressible signals. We propose new generic imaging techniques based on convex optimization for global minimization problems defined in this context. The versatility of the framework notably allows introduction of specific prior information on the signals, which offers the possibility of significant improvements of reconstruction relative to the standard local matching pursuit algorithm CLEAN used in radio astronomy. We illustrate the potential of the approach by studying reconstruction performances on simulations of two different kinds of signals observed with very generic interferometric configurations. The first kind is an intensity field of compact astrophysical objects. The second kind is the imprint of cosmic strings in the temperature field of the cosmic microwave background radiation, of particular interest for cosmology.", "This article treats the problem of learning a dictionary providing sparse representations for a given signal class, via $\\ell_{1}$ -minimisation. The problem can also be seen as factorising a $d\\times N$ matrix $Y=(y_{1}\\ldots y_{N}),\\,y_{n}\\in{\\mathbb{R}}^{d}$ of training signals into a $d\\times K$ dictionary matrix $\\mathbf{\\Phi}$ and a $K\\times N$ coefficient matrix $X=(x_{1}\\ldots x_{N}),\\,x_{n}\\in{\\mathbb{R}}^{K}$ , which is sparse. The exact question studied here is when a dictionary coefficient pair $(\\mathbf{\\Phi},X)$ can be recovered as local minimum of a (nonconvex) $\\ell_{1}$ -criterion with input $Y=\\mathbf{\\Phi}X$ . First, for general dictionaries and coefficient matrices, algebraic conditions ensuring local identifiability are derived, which are then specialised to the case when the dictionary is a basis. Finally, assuming a random Bernoulli-Gaussian sparse model on the coefficient matrix, it is shown that sufficiently incoherent bases are locally identifiable with high probability. The perhaps surprising result is that the typically sufficient number of training samples $N$ grows up to a logarithmic factor only linearly with the signal dimension, i.e. $N\\approx CK\\log K$ , in contrast to previous approaches requiring combinatorially many samples."]}