id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.08127 | Fino1: On the Transferability of Reasoning Enhanced LLMs to Finance | cs.CL | Recent advancements in large language models (LLMs) have shown strong general
reasoning abilities, yet their effectiveness in financial reasoning remains
underexplored. In this study, we comprehensively evaluate 16 powerful reasoning
and general LLMs on three complex financial tasks involving financial text,
tabular ... |
2502.08129 | Control Barrier Function-Based Quadratic Programming for SafeOperation
of Tethered UAVs | eess.SY cs.SY | Consider an unmanned aerial vehicle (UAV) physically connected to the ground
station with a tether operating in a space, tasked with performing precise
maneuvers while constrained by the physical limitation of its tether, which
prevents it from flying beyond a maximum allowable length. Violating this
tether constrain... |
2502.08130 | Selective Self-to-Supervised Fine-Tuning for Generalization in Large
Language Models | cs.CL | Fine-tuning Large Language Models (LLMs) on specific datasets is a common
practice to improve performance on target tasks. However, this performance gain
often leads to overfitting, where the model becomes too specialized in either
the task or the characteristics of the training data, resulting in a loss of
generaliz... |
2502.08132 | SS4Rec: Continuous-Time Sequential Recommendation with State Space
Models | cs.IR cs.LG | Sequential recommendation is a key area in the field of recommendation
systems aiming to model user interest based on historical interaction sequences
with irregular intervals. While previous recurrent neural network-based and
attention-based approaches have achieved significant results, they have
limitations in capt... |
2502.08134 | A Survey on Data Curation for Visual Contrastive Learning: Why Crafting
Effective Positive and Negative Pairs Matters | cs.CV | Visual contrastive learning aims to learn representations by contrasting
similar (positive) and dissimilar (negative) pairs of data samples. The design
of these pairs significantly impacts representation quality, training
efficiency, and computational cost. A well-curated set of pairs leads to
stronger representation... |
2502.08136 | In-Context Learning of Linear Dynamical Systems with Transformers: Error
Bounds and Depth-Separation | cs.LG stat.ML | This paper investigates approximation-theoretic aspects of the in-context
learning capability of the transformers in representing a family of noisy
linear dynamical systems. Our first theoretical result establishes an upper
bound on the approximation error of multi-layer transformers with respect to an
$L^2$-testing ... |
2502.08137 | Riemannian Complex Hermit Positive Definite Convolution Network for
Polarimetric SAR Image Classification | cs.CV | Deep learning can learn high-level semantic features in Euclidean space
effectively for PolSAR images, while they need to covert the complex covariance
matrix into a feature vector or complex-valued vector as the network input.
However, the complex covariance matrices are essentially a complex Hermit
positive definit... |
2502.08141 | LowRA: Accurate and Efficient LoRA Fine-Tuning of LLMs under 2 Bits | cs.LG cs.AR cs.CL cs.PF | Fine-tuning large language models (LLMs) is increasingly costly as models
scale to hundreds of billions of parameters, and even parameter-efficient
fine-tuning (PEFT) methods like LoRA remain resource-intensive. We introduce
LowRA, the first framework to enable LoRA fine-tuning below 2 bits per
parameter with minimal... |
2502.08142 | Bridging the Safety Gap: A Guardrail Pipeline for Trustworthy LLM
Inferences | cs.AI | We present Wildflare GuardRail, a guardrail pipeline designed to enhance the
safety and reliability of Large Language Model (LLM) inferences by
systematically addressing risks across the entire processing workflow.
Wildflare GuardRail integrates several core functional modules, including
Safety Detector that identifi... |
2502.08143 | Data-dependent Bounds with $T$-Optimal Best-of-Both-Worlds Guarantees in
Multi-Armed Bandits using Stability-Penalty Matching | cs.LG | Existing data-dependent and best-of-both-worlds regret bounds for multi-armed
bandits problems have limited adaptivity as they are either data-dependent but
not best-of-both-worlds (BOBW), BOBW but not data-dependent or have sub-optimal
$O(\sqrt{T\ln{T}})$ worst-case guarantee in the adversarial regime. To overcome
t... |
2502.08145 | Democratizing AI: Open-source Scalable LLM Training on GPU-based
Supercomputers | cs.LG cs.AI cs.DC | Training and fine-tuning large language models (LLMs) with hundreds of
billions to trillions of parameters requires tens of thousands of GPUs, and a
highly scalable software stack. In this work, we present a novel
four-dimensional hybrid parallel algorithm implemented in a highly scalable,
portable, open-source frame... |
2502.08146 | Knowledge-Guided Wasserstein Distributionally Robust Optimization | cs.LG stat.ME stat.ML | Transfer learning is a popular strategy to leverage external knowledge and
improve statistical efficiency, particularly with a limited target sample. We
propose a novel knowledge-guided Wasserstein Distributionally Robust
Optimization (KG-WDRO) framework that adaptively incorporates multiple sources
of external knowl... |
2502.08148 | ACCESS : A Benchmark for Abstract Causal Event Discovery and Reasoning | cs.AI | Identifying cause-and-effect relationships is critical to understanding
real-world dynamics and ultimately causal reasoning. Existing methods for
identifying event causality in NLP, including those based on Large Language
Models (LLMs), exhibit difficulties in out-of-distribution settings due to the
limited scale and... |
2502.08149 | Generalized Class Discovery in Instance Segmentation | cs.CV cs.AI | This work addresses the task of generalized class discovery (GCD) in instance
segmentation. The goal is to discover novel classes and obtain a model capable
of segmenting instances of both known and novel categories, given labeled and
unlabeled data. Since the real world contains numerous objects with long-tailed
dis... |
2502.08150 | Force Matching with Relativistic Constraints: A Physics-Inspired
Approach to Stable and Efficient Generative Modeling | cs.LG cs.AI cs.CV | This paper introduces Force Matching (ForM), a novel framework for generative
modeling that represents an initial exploration into leveraging special
relativistic mechanics to enhance the stability of the sampling process. By
incorporating the Lorentz factor, ForM imposes a velocity constraint, ensuring
that sample v... |
2502.08151 | Local Differential Privacy is Not Enough: A Sample Reconstruction Attack
against Federated Learning with Local Differential Privacy | cs.CR cs.LG | Reconstruction attacks against federated learning (FL) aim to reconstruct
users' samples through users' uploaded gradients. Local differential privacy
(LDP) is regarded as an effective defense against various attacks, including
sample reconstruction in FL, where gradients are clipped and perturbed.
Existing attacks a... |
2502.08155 | DGSense: A Domain Generalization Framework for Wireless Sensing | cs.LG cs.AI | Wireless sensing is of great benefits to our daily lives. However, wireless
signals are sensitive to the surroundings. Various factors, e.g. environments,
locations, and individuals, may induce extra impact on wireless propagation.
Such a change can be regarded as a domain, in which the data distribution
shifts. A va... |
2502.08158 | Open-Source Factor Graph Optimization Package for GNSS: Examples and
Applications | cs.RO | State estimation methods using factor graph optimization (FGO) have garnered
significant attention in global navigation satellite system (GNSS) research.
FGO exhibits superior estimation accuracy compared with traditional state
estimation methods that rely on least-squares or Kalman filters. However, only
a few FGO l... |
2502.08160 | Vertical Federated Learning in Practice: The Good, the Bad, and the Ugly | cs.LG cs.AI | Vertical Federated Learning (VFL) is a privacy-preserving collaborative
learning paradigm that enables multiple parties with distinct feature sets to
jointly train machine learning models without sharing their raw data. Despite
its potential to facilitate cross-organizational collaborations, the deployment
of VFL sys... |
2502.08161 | MixDec Sampling: A Soft Link-based Sampling Method of Graph Neural
Network for Recommendation | cs.IR cs.AI | Graph neural networks have been widely used in recent recommender systems,
where negative sampling plays an important role. Existing negative sampling
methods restrict the relationship between nodes as either hard positive pairs
or hard negative pairs. This leads to the loss of structural information, and
lacks the m... |
2502.08166 | From Individual Experience to Collective Evidence: A Reporting-Based
Framework for Identifying Systemic Harms | cs.CY cs.LG | When an individual reports a negative interaction with some system, how can
their personal experience be contextualized within broader patterns of system
behavior? We study the incident database problem, where individual reports of
adverse events arrive sequentially, and are aggregated over time. In this work,
our go... |
2502.08167 | DNNs May Determine Major Properties of Their Outputs Early, with Timing
Possibly Driven by Bias | cs.LG cs.CV | This paper argues that deep neural networks (DNNs) mostly determine their
outputs during the early stages of inference, where biases inherent in the
model play a crucial role in shaping this process. We draw a parallel between
this phenomenon and human decision-making, which often relies on fast,
intuitive heuristics... |
2502.08168 | SARChat-Bench-2M: A Multi-Task Vision-Language Benchmark for SAR Image
Interpretation | cs.CL | As a powerful all-weather Earth observation tool, synthetic aperture radar
(SAR) remote sensing enables critical military reconnaissance, maritime
surveillance, and infrastructure monitoring. Although Vision language models
(VLMs) have made remarkable progress in natural language processing and image
understanding, t... |
2502.08169 | CoDynTrust: Robust Asynchronous Collaborative Perception via Dynamic
Feature Trust Modulus | cs.CV | Collaborative perception, fusing information from multiple agents, can extend
perception range so as to improve perception performance. However, temporal
asynchrony in real-world environments, caused by communication delays, clock
misalignment, or sampling configuration differences, can lead to information
mismatches... |
2502.08170 | Learning-Based Design of LQG Controllers in Quantum Coherent Feedback | quant-ph cs.SY eess.SY | In this paper, we propose a differential evolution (DE) algorithm
specifically tailored for the design of Linear-Quadratic-Gaussian (LQG)
controllers in quantum systems. Building upon the foundational DE framework,
the algorithm incorporates specialized modules, including relaxed feasibility
rules, a scheduled penalt... |
2502.08177 | SycEval: Evaluating LLM Sycophancy | cs.AI | Large language models (LLMs) are increasingly applied in educational,
clinical, and professional settings, but their tendency for sycophancy --
prioritizing user agreement over independent reasoning -- poses risks to
reliability. This study introduces a framework to evaluate sycophantic behavior
in ChatGPT-4o, Claude... |
2502.08178 | ParetoRAG: Leveraging Sentence-Context Attention for Robust and
Efficient Retrieval-Augmented Generation | cs.CL | While Retrieval-Augmented Generation (RAG) systems enhance Large Language
Models (LLMs) by incorporating external knowledge, they still face persistent
challenges in retrieval inefficiency and the inability of LLMs to filter out
irrelevant information. We present ParetoRAG, an unsupervised framework that
optimizes RA... |
2502.08180 | Enhancing LLM Character-Level Manipulation via Divide and Conquer | cs.CL cs.AI | Large Language Models (LLMs) have demonstrated strong generalization
capabilities across a wide range of natural language processing (NLP) tasks.
However, they exhibit notable weaknesses in character-level string
manipulation, struggling with fundamental operations such as character
deletion, insertion, and substitut... |
2502.08181 | Latest Advancements Towards Catastrophic Forgetting under Data Scarcity:
A Comprehensive Survey on Few-Shot Class Incremental Learning | cs.LG cs.AI cs.CV | Data scarcity significantly complicates the continual learning problem, i.e.,
how a deep neural network learns in dynamic environments with very few samples.
However, the latest progress of few-shot class incremental learning (FSCIL)
methods and related studies show insightful knowledge on how to tackle the
problem. ... |
2502.08189 | AnyCharV: Bootstrap Controllable Character Video Generation with
Fine-to-Coarse Guidance | cs.CV | Character video generation is a significant real-world application focused on
producing high-quality videos featuring specific characters. Recent
advancements have introduced various control signals to animate static
characters, successfully enhancing control over the generation process.
However, these methods often ... |
2502.08200 | ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for
Long-Tailed Megakaryocyte Classification | cs.CV | Precise classification of megakaryocytes is crucial for diagnosing
myelodysplastic syndromes. Although self-supervised learning has shown promise
in medical image analysis, its application to classifying megakaryocytes in
stained slides faces three main challenges: (1) pervasive background noise that
obscures cellula... |
2502.08202 | Privacy amplification by random allocation | cs.LG | We consider the privacy guarantees of an algorithm in which a user's data is
used in $k$ steps randomly and uniformly chosen from a sequence (or set) of $t$
differentially private steps. We demonstrate that the privacy guarantees of
this sampling scheme can be upper bound by the privacy guarantees of the
well-studied... |
2502.08205 | Wisdom of the Crowds in Forecasting: Forecast Summarization for
Supporting Future Event Prediction | cs.LG cs.CL cs.IR | Future Event Prediction (FEP) is an essential activity whose demand and
application range across multiple domains. While traditional methods like
simulations, predictive and time-series forecasting have demonstrated promising
outcomes, their application in forecasting complex events is not entirely
reliable due to th... |
2502.08206 | Optimizing Asynchronous Federated Learning: A Delicate Trade-Off Between
Model-Parameter Staleness and Update Frequency | cs.LG cs.PF math.OC math.PR | Synchronous federated learning (FL) scales poorly with the number of clients
due to the straggler effect. Algorithms like FedAsync and GeneralizedFedAsync
address this limitation by enabling asynchronous communication between clients
and the central server. In this work, we rely on stochastic modeling to better
under... |
2502.08208 | Exploring Exploration in Bayesian Optimization | cs.LG | A well-balanced exploration-exploitation trade-off is crucial for successful
acquisition functions in Bayesian optimization. However, there is a lack of
quantitative measures for exploration, making it difficult to analyze and
compare different acquisition functions. This work introduces two novel
approaches - observ... |
2502.08209 | Equivariant Masked Position Prediction for Efficient Molecular
Representation | cs.LG cs.AI | Graph neural networks (GNNs) have shown considerable promise in computational
chemistry. However, the limited availability of molecular data raises concerns
regarding GNNs' ability to effectively capture the fundamental principles of
physics and chemistry, which constrains their generalization capabilities. To
addres... |
2502.08211 | Quality over Quantity: Boosting Data Efficiency Through Ensembled
Multimodal Data Curation | cs.LG cs.AI | In an era overwhelmed by vast amounts of data, the effective curation of
web-crawl datasets is essential for optimizing model performance. This paper
tackles the challenges associated with the unstructured and heterogeneous
nature of such datasets. Traditional heuristic curation methods often
inadequately capture com... |
2502.08213 | LLM Modules: Knowledge Transfer from a Large to a Small Model using
Enhanced Cross-Attention | cs.CL cs.LG | In this work, we propose an architecture of LLM Modules that enables the
transfer of knowledge from a large pre-trained model to a smaller model using
an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B
model is frozen and its representations are passed through specially designed
attention l... |
2502.08214 | Unbiased and Error-Detecting Combinatorial Pooling Experiments with
Balanced Constant-Weight Gray Codes for Consecutive Positives Detection | cs.IT math.IT q-bio.QM | Combinatorial pooling schemes have enabled the measurement of thousands of
experiments in a small number of reactions. This efficiency is achieved by
distributing the items to be measured across multiple reaction units called
pools. However, current methods for the design of pooling schemes do not
adequately address ... |
2502.08216 | Deepfake Detection with Spatio-Temporal Consistency and Attention | cs.CV | Deepfake videos are causing growing concerns among communities due to their
ever-increasing realism. Naturally, automated detection of forged Deepfake
videos is attracting a proportional amount of interest of researchers. Current
methods for detecting forged videos mainly rely on global frame features and
under-utili... |
2502.08221 | Take What You Need: Flexible Multi-Task Semantic Communications with
Channel Adaptation | cs.CV cs.IT cs.NI math.IT | The growing demand for efficient semantic communication systems capable of
managing diverse tasks and adapting to fluctuating channel conditions has
driven the development of robust, resource-efficient frameworks. This article
introduces a novel channel-adaptive and multi-task-aware semantic communication
framework b... |
2502.08226 | TRISHUL: Towards Region Identification and Screen Hierarchy
Understanding for Large VLM based GUI Agents | cs.CV cs.AI cs.LG | Recent advancements in Large Vision Language Models (LVLMs) have enabled the
development of LVLM-based Graphical User Interface (GUI) agents under various
paradigms. Training-based approaches, such as CogAgent and SeeClick, struggle
with cross-dataset and cross-platform generalization due to their reliance on
dataset... |
2502.08227 | Enhancing Sample Selection by Cutting Mislabeled Easy Examples | cs.LG | Sample selection is a prevalent approach in learning with noisy labels,
aiming to identify confident samples for training. Although existing sample
selection methods have achieved decent results by reducing the noise rate of
the selected subset, they often overlook that not all mislabeled examples harm
the model's pe... |
2502.08231 | Keep your distance: learning dispersed embeddings on $\mathbb{S}_d$ | cs.LG | Learning well-separated features in high-dimensional spaces, such as text or
image embeddings, is crucial for many machine learning applications. Achieving
such separation can be effectively accomplished through the dispersion of
embeddings, where unrelated vectors are pushed apart as much as possible. By
constrainin... |
2502.08232 | ChemZIP: Accelerated Modeling of Complex Aerothermochemical Interactions
in Novel Turbomachines for Sustainable High-Temperature Chemical Processes | cs.CE | This paper introduces a new platform to accelerate the modeling of complex
aerothermochemical interactions in new turbomachines, turbo-reactors, to
decarbonise chemical processes. While previous work has aerothermally
demonstrated the potential to decarbonize the heat input to the reaction,
optimizing the reaction ef... |
2502.08233 | Plantation Monitoring Using Drone Images: A Dataset and Performance
Review | cs.CV | Automatic monitoring of tree plantations plays a crucial role in agriculture.
Flawless monitoring of tree health helps farmers make informed decisions
regarding their management by taking appropriate action. Use of drone images
for automatic plantation monitoring can enhance the accuracy of the monitoring
process, wh... |
2502.08234 | Learning Human Skill Generators at Key-Step Levels | cs.CV | We are committed to learning human skill generators at key-step levels. The
generation of skills is a challenging endeavor, but its successful
implementation could greatly facilitate human skill learning and provide more
experience for embodied intelligence. Although current video generation models
can synthesis simp... |
2502.08235 | The Danger of Overthinking: Examining the Reasoning-Action Dilemma in
Agentic Tasks | cs.AI | Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving
capabilities, but their effectiveness in interactive environments can be
limited. This paper introduces and analyzes overthinking in LRMs. A phenomenon
where models favor extended internal reasoning chains over environmental
interaction. Thr... |
2502.08244 | FloVD: Optical Flow Meets Video Diffusion Model for Enhanced
Camera-Controlled Video Synthesis | cs.CV | This paper presents FloVD, a novel optical-flow-based video diffusion model
for camera-controllable video generation. FloVD leverages optical flow maps to
represent motions of the camera and moving objects. This approach offers two
key benefits. Since optical flow can be directly estimated from videos, our
approach a... |
2502.08246 | Inference-time sparse attention with asymmetric indexing | cs.CL | Self-attention in transformer models is an incremental associative memory
that maps key vectors to value vectors. One way to speed up self-attention is
to employ GPU-compliant vector search algorithms, yet the standard partitioning
methods yield poor results in this context, because (1) keys and queries follow
differ... |
2502.08253 | Multi-View Oriented GPLVM: Expressiveness and Efficiency | stat.ML cs.LG | The multi-view Gaussian process latent variable model (MV-GPLVM) aims to
learn a unified representation from multi-view data but is hindered by
challenges such as limited kernel expressiveness and low computational
efficiency. To overcome these issues, we first introduce a new duality between
the spectral density and... |
2502.08254 | UniCoRN: Unified Commented Retrieval Network with LMMs | cs.CV | Multimodal retrieval methods have limitations in handling complex,
compositional queries that require reasoning about the visual content of both
the query and the retrieved entities. On the other hand, Large Multimodal
Models (LMMs) can answer with language to more complex visual questions, but
without the inherent a... |
2502.08255 | Principles and Framework for the Operationalisation of Meaningful Human
Control over Autonomous Systems | eess.SY cs.SY | This paper proposes an alignment for the operationalisation of Meaningful
Human Control (MHC) for autonomous systems by proposing operational principles
for MHC and introducing a generic framework for its application. With a
plethora of different seemingly diverging expansions for use of MHC in
practice, this work ai... |
2502.08259 | Balancing optimism and pessimism in offline-to-online learning | cs.LG cs.AI | We consider what we call the offline-to-online learning setting, focusing on
stochastic finite-armed bandit problems. In offline-to-online learning, a
learner starts with offline data collected from interactions with an unknown
environment in a way that is not under the learner's control. Given this data,
the learner... |
2502.08262 | GenIAS: Generator for Instantiating Anomalies in time Series | cs.LG | A recent and promising approach for building time series anomaly detection
(TSAD) models is to inject synthetic samples of anomalies within real data
sets. The existing injection mechanisms have significant limitations - most of
them rely on ad hoc, hand-crafted strategies which fail to capture the natural
diversity ... |
2502.08265 | Exploring the Potential of Large Language Models to Simulate Personality | cs.CL cs.AI | With the advancement of large language models (LLMs), the focus in
Conversational AI has shifted from merely generating coherent and relevant
responses to tackling more complex challenges, such as personalizing dialogue
systems. In an effort to enhance user engagement, chatbots are often designed
to mimic human behav... |
2502.08266 | Dealing with Annotator Disagreement in Hate Speech Classification | cs.CL cs.AI cs.LG | Hate speech detection is a crucial task, especially on social media, where
harmful content can spread quickly. Implementing machine learning models to
automatically identify and address hate speech is essential for mitigating its
impact and preventing its proliferation. The first step in developing an
effective hate ... |
2502.08271 | MoLoRec: A Generalizable and Efficient Framework for LLM-Based
Recommendation | cs.IR | Large Language Models (LLMs) have achieved remarkable success in recent
years, owing to their impressive generalization capabilities and rich world
knowledge. To capitalize on the potential of using LLMs as recommender systems,
mainstream approaches typically focus on two paradigms. The first paradigm
designs multi-d... |
2502.08276 | Higher-order Laplacian dynamics on hypergraphs with cooperative and
antagonistic interactions | eess.SY cs.SY | Laplacian dynamics on a signless graph characterize a class of linear
interactions, where pairwise cooperative interactions between all agents lead
to the convergence to a common state. On a structurally balanced signed graph,
the agents converge to values of the same magnitude but opposite signs
(bipartite consensus... |
2502.08277 | ChorusCVR: Chorus Supervision for Entire Space Post-Click Conversion
Rate Modeling | cs.IR cs.SI | Post-click conversion rate (CVR) estimation is a vital task in many
recommender systems of revenue businesses, e.g., e-commerce and advertising. In
a perspective of sample, a typical CVR positive sample usually goes through a
funnel of exposure to click to conversion. For lack of post-event labels for
un-clicked samp... |
2502.08279 | What Is That Talk About? A Video-to-Text Summarization Dataset for
Scientific Presentations | cs.CL cs.AI cs.CV | Transforming recorded videos into concise and accurate textual summaries is a
growing challenge in multimodal learning. This paper introduces VISTA, a
dataset specifically designed for video-to-text summarization in scientific
domains. VISTA contains 18,599 recorded AI conference presentations paired with
their corre... |
2502.08281 | Redefining Simplicity: Benchmarking Large Language Models from Lexical
to Document Simplification | cs.CL | Text simplification (TS) refers to the process of reducing the complexity of
a text while retaining its original meaning and key information. Existing work
only shows that large language models (LLMs) have outperformed supervised
non-LLM-based methods on sentence simplification. This study offers the first
comprehens... |
2502.08282 | Individualised Treatment Effects Estimation with Composite Treatments
and Composite Outcomes | cs.LG cs.AI | Estimating individualised treatment effect (ITE) -- that is the causal effect
of a set of variables (also called exposures, treatments, actions, policies, or
interventions), referred to as \textit{composite treatments}, on a set of
outcome variables of interest, referred to as \textit{composite outcomes}, for
a unit ... |
2502.08284 | Data Pricing for Graph Neural Networks without Pre-purchased Inspection | cs.GT cs.LG | Machine learning (ML) models have become essential tools in various
scenarios. Their effectiveness, however, hinges on a substantial volume of data
for satisfactory performance. Model marketplaces have thus emerged as crucial
platforms bridging model consumers seeking ML solutions and data owners
possessing valuable ... |
2502.08285 | Fully-Geometric Cross-Attention for Point Cloud Registration | cs.CV | Point cloud registration approaches often fail when the overlap between point
clouds is low due to noisy point correspondences. This work introduces a novel
cross-attention mechanism tailored for Transformer-based architectures that
tackles this problem, by fusing information from coordinates and features at
the supe... |
2502.08287 | CRISP: A Framework for Cryo-EM Image Segmentation and Processing with
Conditional Random Field | eess.IV cs.AI cs.CV | Differentiating signals from the background in micrographs is a critical
initial step for cryogenic electron microscopy (cryo-EM), yet it remains
laborious due to low signal-to-noise ratio (SNR), the presence of contaminants
and densely packed particles of varying sizes. Although image segmentation has
recently been ... |
2502.08297 | BEAM: Bridging Physically-based Rendering and Gaussian Modeling for
Relightable Volumetric Video | cs.GR cs.CV | Volumetric video enables immersive experiences by capturing dynamic 3D
scenes, enabling diverse applications for virtual reality, education, and
telepresence. However, traditional methods struggle with fixed lighting
conditions, while neural approaches face trade-offs in efficiency, quality, or
adaptability for relig... |
2502.08298 | Improving Existing Optimization Algorithms with LLMs | cs.AI cs.CL cs.LG cs.SE | The integration of Large Language Models (LLMs) into optimization has created
a powerful synergy, opening exciting research opportunities. This paper
investigates how LLMs can enhance existing optimization algorithms. Using their
pre-trained knowledge, we demonstrate their ability to propose innovative
heuristic vari... |
2502.08299 | When do they StOP?: A First Step Towards Automatically Identifying Team
Communication in the Operating Room | cs.CV | Purpose: Surgical performance depends not only on surgeons' technical skills
but also on team communication within and across the different professional
groups present during the operation. Therefore, automatically identifying team
communication in the OR is crucial for patient safety and advances in the
development ... |
2502.08301 | Compromising Honesty and Harmlessness in Language Models via Deception
Attacks | cs.CL cs.AI cs.CY | Recent research on large language models (LLMs) has demonstrated their
ability to understand and employ deceptive behavior, even without explicit
prompting. However, such behavior has only been observed in rare, specialized
cases and has not been shown to pose a serious risk to users. Additionally,
research on AI ali... |
2502.08302 | HDT: Hierarchical Discrete Transformer for Multivariate Time Series
Forecasting | cs.LG cs.AI | Generative models have gained significant attention in multivariate time
series forecasting (MTS), particularly due to their ability to generate
high-fidelity samples. Forecasting the probability distribution of multivariate
time series is a challenging yet practical task. Although some recent attempts
have been made... |
2502.08309 | Unlocking Scaling Law in Industrial Recommendation Systems with a
Three-step Paradigm based Large User Model | cs.IR | Recent advancements in autoregressive Large Language Models (LLMs) have
achieved significant milestones, largely attributed to their scalability, often
referred to as the "scaling law". Inspired by these achievements, there has
been a growing interest in adapting LLMs for Recommendation Systems (RecSys) by
reformulat... |
2502.08312 | Word Synchronization Challenge: A Benchmark for Word Association
Responses for LLMs | cs.HC cs.CL | This paper introduces the Word Synchronization Challenge, a novel benchmark
to evaluate large language models (LLMs) in Human-Computer Interaction (HCI).
This benchmark uses a dynamic game-like framework to test LLMs ability to mimic
human cognitive processes through word associations. By simulating complex
human int... |
2502.08317 | Mitigating Hallucinations in Multimodal Spatial Relations through
Constraint-Aware Prompting | cs.CL cs.AI cs.CV | Spatial relation hallucinations pose a persistent challenge in large
vision-language models (LVLMs), leading to generate incorrect predictions about
object positions and spatial configurations within an image. To address this
issue, we propose a constraint-aware prompting framework designed to reduce
spatial relation... |
2502.08319 | MultiProSE: A Multi-label Arabic Dataset for Propaganda, Sentiment, and
Emotion Detection | cs.CL | Propaganda is a form of persuasion that has been used throughout history with
the intention goal of influencing people's opinions through rhetorical and
psychological persuasion techniques for determined ends. Although Arabic ranked
as the fourth most-used language on the internet, resources for propaganda
detection ... |
2502.08321 | Screener: Self-supervised Pathology Segmentation Model for 3D Medical
Images | cs.CV | Accurate segmentation of all pathological findings in 3D medical images
remains a significant challenge, as supervised models are limited to detecting
only the few pathology classes annotated in existing datasets. To address this,
we frame pathology segmentation as an unsupervised visual anomaly segmentation
(UVAS) p... |
2502.08323 | Contextual Compression Encoding for Large Language Models: A Novel
Framework for Multi-Layered Parameter Space Pruning | cs.CL | Context-aware compression techniques have gained increasing attention as
model sizes continue to grow, introducing computational bottlenecks that hinder
efficient deployment. A structured encoding approach was proposed to
selectively eliminate redundant parameter groups while ensuring that
representational fidelity w... |
2502.08324 | Decentralised multi-agent coordination for real-time railway traffic
management | cs.MA | The real-time Railway Traffic Management Problem (rtRTMP) is a challenging
optimisation problem in railway transportation. It involves the efficient
management of train movements while minimising delay propagation caused by
unforeseen perturbations due to, e.g, temporary speed limitations or signal
failures. This pap... |
2502.08326 | Model-Free Counterfactual Subset Selection at Scale | cs.LG cs.DB cs.DS cs.IR | Ensuring transparency in AI decision-making requires interpretable
explanations, particularly at the instance level. Counterfactual explanations
are a powerful tool for this purpose, but existing techniques frequently depend
on synthetic examples, introducing biases from unrealistic assumptions, flawed
models, or ske... |
2502.08331 | Brame: Hierarchical Data Management Framework for Cloud-Edge-Device
Collaboration | cs.DB | In the realm of big data, cloud-edge-device collaboration is prevalent in
industrial scenarios. However, a systematic exploration of the theory and
methodologies related to data management in this field is lacking. This paper
delves into the sub-problem of data storage and scheduling within
cloud-edge-device collabor... |
2502.08332 | Modification and Generated-Text Detection: Achieving Dual Detection
Capabilities for the Outputs of LLM by Watermark | cs.CR cs.AI | The development of large language models (LLMs) has raised concerns about
potential misuse. One practical solution is to embed a watermark in the text,
allowing ownership verification through watermark extraction. Existing methods
primarily focus on defending against modification attacks, often neglecting
other spoof... |
2502.08333 | Foundation Models in Computational Pathology: A Review of Challenges,
Opportunities, and Impact | cs.CV | From self-supervised, vision-only models to contrastive visual-language
frameworks, computational pathology has rapidly evolved in recent years.
Generative AI "co-pilots" now demonstrate the ability to mine subtle,
sub-visual tissue cues across the cellular-to-pathology spectrum, generate
comprehensive reports, and r... |
2502.08336 | Salience-Invariant Consistent Policy Learning for Generalization in
Visual Reinforcement Learning | cs.AI | Generalizing policies to unseen scenarios remains a critical challenge in
visual reinforcement learning, where agents often overfit to the specific
visual observations of the training environment. In unseen environments,
distracting pixels may lead agents to extract representations containing
task-irrelevant informat... |
2502.08337 | Hierarchical Multi-Agent Framework for Carbon-Efficient Liquid-Cooled
Data Center Clusters | cs.LG cs.AI cs.SY eess.SY | Reducing the environmental impact of cloud computing requires efficient
workload distribution across geographically dispersed Data Center Clusters
(DCCs) and simultaneously optimizing liquid and air (HVAC) cooling with time
shift of workloads within individual data centers (DC). This paper introduces
Green-DCC, which... |
2502.08340 | Hierarchical Learning-based Graph Partition for Large-scale Vehicle
Routing Problems | cs.LG cs.AI | Neural solvers based on the divide-and-conquer approach for Vehicle Routing
Problems (VRPs) in general, and capacitated VRP (CVRP) in particular,
integrates the global partition of an instance with local constructions for
each subproblem to enhance generalization. However, during the global partition
phase, miscluste... |
2502.08344 | Energy and Age-Aware MAC for Low-Power Massive IoT | cs.IT math.IT | Efficient multiple access remains a key challenge for emerging Internet of
Things (IoT) networks comprising a large set of devices with sporadic
activation, thus motivating significant research in the last few years. In this
paper, we consider a network wherein IoT sensors capable of energy harvesting
(EH) send updat... |
2502.08346 | Graph Foundation Models for Recommendation: A Comprehensive Survey | cs.IR cs.AI cs.LG | Recommender systems (RS) serve as a fundamental tool for navigating the vast
expanse of online information, with deep learning advancements playing an
increasingly important role in improving ranking accuracy. Among these, graph
neural networks (GNNs) excel at extracting higher-order structural information,
while lar... |
2502.08347 | Hi-End-MAE: Hierarchical encoder-driven masked autoencoders are stronger
vision learners for medical image segmentation | cs.CV | Medical image segmentation remains a formidable challenge due to the label
scarcity. Pre-training Vision Transformer (ViT) through masked image modeling
(MIM) on large-scale unlabeled medical datasets presents a promising solution,
providing both computational efficiency and model generalization for various
downstrea... |
2502.08352 | Sat-DN: Implicit Surface Reconstruction from Multi-View Satellite Images
with Depth and Normal Supervision | cs.CV | With advancements in satellite imaging technology, acquiring high-resolution
multi-view satellite imagery has become increasingly accessible, enabling rapid
and location-independent ground model reconstruction. However, traditional
stereo matching methods struggle to capture fine details, and while neural
radiance fi... |
2502.08353 | Trustworthy GNNs with LLMs: A Systematic Review and Taxonomy | cs.LG cs.AI | With the extensive application of Graph Neural Networks (GNNs) across various
domains, their trustworthiness has emerged as a focal point of research. Some
existing studies have shown that the integration of large language models
(LLMs) can improve the semantic understanding and generation capabilities of
GNNs, which... |
2502.08355 | Loss Landscape Analysis for Reliable Quantized ML Models for Scientific
Sensing | cs.LG | In this paper, we propose a method to perform empirical analysis of the loss
landscape of machine learning (ML) models. The method is applied to two ML
models for scientific sensing, which necessitates quantization to be deployed
and are subject to noise and perturbations due to experimental conditions. Our
method al... |
2502.08356 | Systematic Knowledge Injection into Large Language Models via Diverse
Augmentation for Domain-Specific RAG | cs.CL | Retrieval-Augmented Generation (RAG) has emerged as a prominent method for
incorporating domain knowledge into Large Language Models (LLMs). While RAG
enhances response relevance by incorporating retrieved domain knowledge in the
context, retrieval errors can still lead to hallucinations and incorrect
answers. To rec... |
2502.08363 | Top-Theta Attention: Sparsifying Transformers by Compensated
Thresholding | cs.CL cs.AI | The attention mechanism is essential for the impressive capabilities of
transformer-based Large Language Models (LLMs). However, calculating attention
is computationally intensive due to its quadratic dependency on the sequence
length. We introduce a novel approach called Top-Theta Attention, or simply
Top-$\theta$, ... |
2502.08364 | A Survey on Pre-Trained Diffusion Model Distillations | cs.LG | Diffusion Models~(DMs) have emerged as the dominant approach in Generative
Artificial Intelligence (GenAI), owing to their remarkable performance in tasks
such as text-to-image synthesis. However, practical DMs, such as stable
diffusion, are typically trained on massive datasets and thus usually require
large storage... |
2502.08365 | Towards Principled Multi-Agent Task Agnostic Exploration | cs.LG cs.AI | In reinforcement learning, we typically refer to task-agnostic exploration
when we aim to explore the environment without access to the task specification
a priori. In a single-agent setting the problem has been extensively studied
and mostly understood. A popular approach cast the task-agnostic objective as
maximizi... |
2502.08371 | Unveiling Global Discourse Structures: Theoretical Analysis and NLP
Applications in Argument Mining | cs.CL | Particularly in the structure of global discourse, coherence plays a pivotal
role in human text comprehension and is a hallmark of high-quality text. This
is especially true for persuasive texts, where coherent argument structures
support claims effectively. This paper discusses and proposes methods for
detecting, ex... |
2502.08373 | Uncertainty Aware Human-machine Collaboration in Camouflaged Object
Detection | cs.CV cs.AI | Camouflaged Object Detection (COD), the task of identifying objects concealed
within their environments, has seen rapid growth due to its wide range of
practical applications. A key step toward developing trustworthy COD systems is
the estimation and effective utilization of uncertainty. In this work, we
propose a hu... |
2502.08374 | AdvSwap: Covert Adversarial Perturbation with High Frequency
Info-swapping for Autonomous Driving Perception | cs.CV | Perception module of Autonomous vehicles (AVs) are increasingly susceptible
to be attacked, which exploit vulnerabilities in neural networks through
adversarial inputs, thereby compromising the AI safety. Some researches focus
on creating covert adversarial samples, but existing global noise techniques
are detectable... |
2502.08376 | Enhanced Load Forecasting with GAT-LSTM: Leveraging Grid and Temporal
Features | cs.LG eess.SP | Accurate power load forecasting is essential for the efficient operation and
planning of electrical grids, particularly given the increased variability and
complexity introduced by renewable energy sources. This paper introduces
GAT-LSTM, a hybrid model that combines Graph Attention Networks (GAT) and Long
Short-Term... |
2502.08377 | Not All Frame Features Are Equal: Video-to-4D Generation via Decoupling
Dynamic-Static Features | cs.CV | Recently, the generation of dynamic 3D objects from a video has shown
impressive results. Existing methods directly optimize Gaussians using whole
information in frames. However, when dynamic regions are interwoven with static
regions within frames, particularly if the static regions account for a large
proportion, e... |
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