id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.05836 | LegalSeg: Unlocking the Structure of Indian Legal Judgments Through
Rhetorical Role Classification | cs.CL cs.AI cs.IR cs.LG | In this paper, we address the task of semantic segmentation of legal
documents through rhetorical role classification, with a focus on Indian legal
judgments. We introduce LegalSeg, the largest annotated dataset for this task,
comprising over 7,000 documents and 1.4 million sentences, labeled with 7
rhetorical roles.... |
2502.05842 | A Grid-Forming HVDC Series Tapping Converter Using Extended Techniques
of Flex-LCC | eess.SY cs.SY | This paper discusses an extension technology for the previously proposed
Flexible Line-Commutated Converter (Flex LCC) [1]. The proposed extension
involves modifying the arm internal-electromotive-force control, redesigning
the main-circuit parameters, and integrating a low-power coordination strategy.
As a result, t... |
2502.05843 | Training-free Anomaly Event Detection via LLM-guided Symbolic Pattern
Discovery | cs.CV | Anomaly event detection plays a crucial role in various real-world
applications. However, current approaches predominantly rely on supervised
learning, which faces significant challenges: the requirement for extensive
labeled training data and lack of interpretability in decision-making
processes. To address these li... |
2502.05845 | Exploiting the Hidden Capacity of MMC Through Accurate Quantification of
Modulation Indices | eess.SY cs.SY | The modular multilevel converter (MMC) has become increasingly important in
voltage-source converter-based high-voltage direct current (VSC-HVDC) systems.
Direct and indirect modulation are widely used as mainstream modulation
techniques in MMCs. However, due to the challenge of quantitatively evaluating
the operatio... |
2502.05846 | Rapid Detection of High-impedance Arc Faults in Medium Voltage
Electrical Distribution Systems | eess.SY cs.SY | High-impedance arc faults in AC power systems have the potential to lead to
catastrophic accidents. However, significant challenges exist in identifying
these faults because of the much weaker characteristics and variety when
grounded with different surfaces. Addressing a noteworthy gap in prior
research, which large... |
2502.05849 | Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on
Fairness-Related Queries | cs.CL | The generation of incorrect images, such as depictions of people of color in
Nazi-era uniforms by Gemini, frustrated users and harmed Google's reputation,
motivating us to investigate the relationship between accurately reflecting
factuality and promoting diversity and equity. In this study, we focus on 19
real-world... |
2502.05850 | MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep
Learning Acceleration | cs.AR cs.LG | This paper presents a unified framework for codifying and automating
optimization strategies to efficiently deploy deep neural networks (DNNs) on
resource-constrained hardware, such as FPGAs, while maintaining high
performance, accuracy, and resource efficiency. Deploying DNNs on such
platforms involves addressing th... |
2502.05851 | Fairness Driven Slot Allocation Problem in Billboard Advertisement | cs.GT cs.DB cs.MA | In billboard advertisement, a number of digital billboards are owned by an
influence provider, and several commercial houses (which we call advertisers)
approach the influence provider for a specific number of views of their
advertisement content on a payment basis. Though the billboard slot allocation
problem has be... |
2502.05853 | Zak-Transform-Induced Optimal Sequences and Their Applications in OTFS | cs.IT math.IT | This paper introduces a novel finite Zak transform (FZT)-aided framework for
constructing multiple zero-correlation zone (ZCZ) sequence sets with optimal
correlation properties. Specifically, each sequence is perfect with zero
auto-correlation sidelobes, each ZCZ sequence set meets the Tang-Fan-Matsufuji
bound with e... |
2502.05854 | NSPG-Miner: Mining Repetitive Negative Sequential Patterns | cs.DB | Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or
tandem repeat discovery in bioinformatics) can find frequent repetitive
subsequences satisfying gap constraints, which are called positive sequential
patterns with gap constraints (PSPGs). However, classical SPM with gap
constraints cannot fin... |
2502.05855 | DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General
Robot Control | cs.RO cs.CV | Enabling robots to perform diverse tasks across varied environments is a
central challenge in robot learning. While vision-language-action (VLA) models
have shown promise for generalizable robot skills, realizing their full
potential requires addressing limitations in action representation and
efficient training. Cur... |
2502.05857 | Acquisition through My Eyes and Steps: A Joint Predictive Agent Model in
Egocentric Worlds | cs.CV cs.AI cs.LG | This paper addresses the task of learning an agent model behaving like
humans, which can jointly perceive, predict, and act in egocentric worlds.
Previous methods usually train separate models for these three abilities,
leading to information silos among them, which prevents these abilities from
learning from each ot... |
2502.05858 | Let's Have Both! Optimal List-Recoverability via Alphabet Permutation
Codes | cs.IT math.IT | We construct a new family of codes that requires only polynomial randomness
yet achieves $(\rho,\ell,L)$-list-recoverability at a rate within $\epsilon$ of
capacity, with $L \approx \tfrac{\ell}{\epsilon}$. In contrast, every previous
construction using polynomial randomness required an exponentially larger list
size... |
2502.05859 | SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion | cs.CV | Due to the rapid development of panorama cameras, the task of estimating
panorama depth has attracted significant attention from the computer vision
community, especially in applications such as robot sensing and autonomous
driving. However, existing methods relying on different projection formats
often encounter cha... |
2502.05863 | Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education | cs.IR cs.AI cs.MM | In AI-facilitated teaching, leveraging various query styles to interpret
abstract text descriptions is crucial for ensuring high-quality teaching.
However, current retrieval models primarily focus on natural text-image
retrieval, making them insufficiently tailored to educational scenarios due to
the ambiguities in t... |
2502.05864 | Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs
via Node-wise Multi-View Ensemble Distillation | cs.LG | Multiplex graphs, with multiple edge types (graph views) among common nodes,
provide richer structural semantics and better modeling capabilities. Multiplex
Graph Neural Networks (MGNNs), typically comprising view-specific GNNs and a
multi-view integration layer, have achieved advanced performance in various
downstre... |
2502.05867 | Self-Training Large Language Models for Tool-Use Without Demonstrations | cs.CL | Large language models (LLMs) remain prone to factual inaccuracies and
computational errors, including hallucinations and mistakes in mathematical
reasoning. Recent work augmented LLMs with tools to mitigate these
shortcomings, but often requires curated gold tool-use demonstrations. In this
paper, we investigate whet... |
2502.05868 | Norm Augmented Graph AutoEncoders for Link Prediction | cs.LG | Link Prediction (LP) is a crucial problem in graph-structured data. Graph
Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders
(GAEs) being a notable representation. However, our empirical findings reveal
that GAEs' LP performance suffers heavily from the long-tailed node degree
distribution, ... |
2502.05869 | HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action
Recognition | cs.CV | Transformers have demonstrated remarkable performance in skeleton-based human
action recognition, yet their quadratic computational complexity remains a
bottleneck for real-world applications. To mitigate this, linear attention
mechanisms have been explored but struggle to capture the hierarchical
structure of skelet... |
2502.05874 | MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor
Scene Generation | cs.CV cs.AI cs.LG | Controllable 3D scene generation has extensive applications in virtual
reality and interior design, where the generated scenes should exhibit high
levels of realism and controllability in terms of geometry. Scene graphs
provide a suitable data representation that facilitates these applications.
However, current graph... |
2502.05878 | Enhancing Financial Time-Series Forecasting with Retrieval-Augmented
Large Language Models | cs.CL | Stock movement prediction, a critical task in financial time-series
forecasting, relies on identifying and retrieving key influencing factors from
vast and complex datasets. However, traditional text-trained or numeric
similarity-based retrieval methods often struggle to handle the intricacies of
financial data. To a... |
2502.05879 | Enhancing Depression Detection with Chain-of-Thought Prompting: From
Emotion to Reasoning Using Large Language Models | cs.CL cs.AI | Depression is one of the leading causes of disability worldwide, posing a
severe burden on individuals, healthcare systems, and society at large. Recent
advancements in Large Language Models (LLMs) have shown promise in addressing
mental health challenges, including the detection of depression through
text-based anal... |
2502.05883 | NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin | cs.LG cs.AI stat.ML | Real-world sensing challenges such as sensor failures, communication issues,
and power constraints lead to data intermittency. An issue that is known to
undermine the traditional classification task that assumes a continuous data
stream. Previous works addressed this issue by designing bespoke solutions
(i.e. task-sp... |
2502.05884 | Study of Robust Multiuser Scheduling and Power Allocation in Cell-Free
MIMO Networks | cs.IT math.IT | This paper introduces a robust resource allocation framework for the downlink
of cell-free massive multi-input multi-output (CF-mMIMO) networks to address
the effects caused by imperfect channel state information (CSI). In particular,
the proposed robust resource allocation framework includes a robust user
scheduling... |
2502.05887 | MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational
Agents | cs.CL cs.AI | Understanding temporal dynamics is critical for conversational agents,
enabling effective content analysis and informed decision-making. However,
time-aware datasets, particularly for persona-grounded conversations, are still
limited, which narrows their scope and diminishes their complexity. To address
this gap, we ... |
2502.05891 | Room-scale magnetoquasistatic wireless power transfer using a
cavity-based multimode resonator | physics.app-ph cs.SY eess.SY | Magnetoquasistatic wireless power transfer can be used to charge and power
electronic devices such as smartphones and small home appliances. However,
existing coil-based transmitters, which are composed of wire conductors, have a
limited range. Here we show that multimode quasistatic cavity resonance can
provide room... |
2502.05892 | A Distributional Perspective on Word Learning in Neural Language Models | cs.CL cs.AI | Language models (LMs) are increasingly being studied as models of human
language learners. Due to the nascency of the field, it is not well-established
whether LMs exhibit similar learning dynamics to humans, and there are few
direct comparisons between learning trajectories in humans and models. Word
learning trajec... |
2502.05894 | Suppressing Leakage Magnetic Field in Wireless Power Transfer using
Halbach Array-Based Resonators | physics.app-ph cs.SY eess.SY | Wireless power transfer has the potential to seamlessly power electronic
systems, such as electric vehicles, industrial robots, and mobile devices.
However, the leakage magnetic field is a critical bottleneck that limits the
transferable power level, and heavy ferromagnetic shields are needed for
transferring large a... |
2502.05895 | Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in
Personalized Image Generation | cs.CV | Personalized text-to-image generation aims to create images tailored to
user-defined concepts and textual descriptions. Balancing the fidelity of the
learned concept with its ability for generation in various contexts presents a
significant challenge. Existing methods often address this through diverse
fine-tuning pa... |
2502.05902 | Fast Omni-Directional Image Super-Resolution: Adapting the Implicit
Image Function with Pixel and Semantic-Wise Spherical Geometric Priors | cs.CV | In the context of Omni-Directional Image (ODI) Super-Resolution (SR), the
unique challenge arises from the non-uniform oversampling characteristics
caused by EquiRectangular Projection (ERP). Considerable efforts in designing
complex spherical convolutions or polyhedron reprojection offer significant
performance impr... |
2502.05905 | QP-SNN: Quantized and Pruned Spiking Neural Networks | cs.CV | Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to
encode information and operate in an asynchronous event-driven manner, offering
a highly energy-efficient paradigm for machine intelligence. However, the
current SNN community focuses primarily on performance improvement by
developing large-scale... |
2502.05907 | EvoAgent: Agent Autonomous Evolution with Continual World Model for
Long-Horizon Tasks | cs.RO | Completing Long-Horizon (LH) tasks in open-ended worlds is an important yet
difficult problem for embodied agents. Existing approaches suffer from two key
challenges: (1) they heavily rely on experiences obtained from human-created
data or curricula, lacking the ability to continuously update multimodal
experiences, ... |
2502.05908 | Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo | eess.IV cs.CV cs.LG | In image processing, solving inverse problems is the task of finding
plausible reconstructions of an image that was corrupted by some (usually
known) degradation model. Commonly, this process is done using a generative
image model that can guide the reconstruction towards solutions that appear
natural. The success of... |
2502.05911 | GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective
Hallucination Mitigation | cs.CL | Refusal-Aware Instruction Tuning (RAIT) aims to enhance Large Language Models
(LLMs) by improving their ability to refuse responses to questions beyond their
knowledge, thereby reducing hallucinations and improving reliability. Effective
RAIT must address two key challenges: firstly, effectively reject unknown
questi... |
2502.05912 | LpBound: Pessimistic Cardinality Estimation using $\ell_p$-Norms of
Degree Sequences | cs.DB | Cardinality estimation is the problem of estimating the size of the output of
a query, without actually evaluating the query. The cardinality estimator is a
critical piece of a query optimizer, and is often the main culprit when the
optimizer chooses a poor plan.
This paper introduces LpBound, a pessimistic cardina... |
2502.05916 | Adaptive Grasping of Moving Objects in Dense Clutter via Global-to-Local
Detection and Static-to-Dynamic Planning | cs.RO cs.SY eess.SY | Robotic grasping is facing a variety of real-world uncertainties caused by
non-static object states, unknown object properties, and cluttered object
arrangements. The difficulty of grasping increases with the presence of more
uncertainties, where commonly used learning-based approaches struggle to
perform consistentl... |
2502.05917 | Modeling and Beamforming Optimization for Pinching-Antenna Systems | cs.IT math.IT | The Pinching-Antenna SyStem (PASS) is a revolutionary flexible antenna
technology designed to enhance wireless communication by establishing strong
line-of-sight (LoS) links, reducing free-space path loss and enabling antenna
array reconfigurability. PASS uses dielectric waveguides with low propagation
loss for signa... |
2502.05919 | Can Generative Agent-Based Modeling Replicate the Friendship Paradox in
Social Media Simulations? | cs.SI | Generative Agent-Based Modeling (GABM) is an emerging simulation paradigm
that combines the reasoning abilities of Large Language Models with traditional
Agent-Based Modeling to replicate complex social behaviors, including
interactions on social media. While prior work has focused on localized
phenomena such as opin... |
2502.05923 | ARISE: Iterative Rule Induction and Synthetic Data Generation for Text
Classification | cs.CL | We propose ARISE, a framework that iteratively induces rules and generates
synthetic data for text classification. We combine synthetic data generation
and automatic rule induction, via bootstrapping, to iteratively filter the
generated rules and data. We induce rules via inductive generalisation of
syntactic n-grams... |
2502.05924 | Multi-Branch Collaborative Learning Network for Video Quality Assessment
in Industrial Video Search | cs.CV cs.IR | Video Quality Assessment (VQA) is vital for large-scale video retrieval
systems, aimed at identifying quality issues to prioritize high-quality videos.
In industrial systems, low-quality video characteristics fall into four
categories: visual-related issues like mosaics and black boxes, textual issues
from video titl... |
2502.05925 | Sign-Symmetry Learning Rules are Robust Fine-Tuners | cs.LG cs.AI | Backpropagation (BP) has long been the predominant method for training neural
networks due to its effectiveness. However, numerous alternative approaches,
broadly categorized under feedback alignment, have been proposed, many of which
are motivated by the search for biologically plausible learning mechanisms.
Despite... |
2502.05926 | A Generative Framework for Bidirectional Image-Report Understanding in
Chest Radiography | eess.IV cs.CL cs.CV | The rapid advancements in large language models (LLMs) have unlocked their
potential for multimodal tasks, where text and visual data are processed
jointly. However, applying LLMs to medical imaging, particularly for chest
X-rays (CXR), poses significant challenges due to the need for precise
visual-textual alignment... |
2502.05928 | ClinKD: Cross-Modal Clinic Knowledge Distiller For Multi-Task Medical
Images | cs.CV | Med-VQA (Medical Visual Question Answering) is a crucial subtask within the
broader VQA (Visual Question Answering) domain. This task requires a visual
question answering system to analyze the provided image and corresponding
question,offering reasonable analysis and suggestions to assist medical
professionals in mak... |
2502.05931 | Protecting Intellectual Property of EEG-based Neural Networks with
Watermarking | cs.LG cs.AI cs.CR | EEG-based neural networks, pivotal in medical diagnosis and brain-computer
interfaces, face significant intellectual property (IP) risks due to their
reliance on sensitive neurophysiological data and resource-intensive
development. Current watermarking methods, particularly those using abstract
trigger sets, lack rob... |
2502.05932 | Skill Expansion and Composition in Parameter Space | cs.LG cs.AI cs.RO | Humans excel at reusing prior knowledge to address new challenges and
developing skills while solving problems. This paradigm becomes increasingly
popular in the development of autonomous agents, as it develops systems that
can self-evolve in response to new challenges like human beings. However,
previous methods suf... |
2502.05933 | Learning to Substitute Words with Model-based Score Ranking | cs.CL cs.AI | Smart word substitution aims to enhance sentence quality by improving word
choices; however current benchmarks rely on human-labeled data. Since word
choices are inherently subjective, ground-truth word substitutions generated by
a small group of annotators are often incomplete and likely not generalizable.
To circum... |
2502.05934 | Barriers and Pathways to Human-AI Alignment: A Game-Theoretic Approach | cs.AI cs.CC cs.GT cs.LG cs.MA | Under what conditions can capable AI agents efficiently align their actions
with human preferences? More specifically, when they are proficient enough to
collaborate with us, how long does coordination take, and when is it
computationally feasible? These foundational questions of AI alignment help
define what makes a... |
2502.05935 | Interactive Inference: A Neuromorphic Theory of Human-Computer
Interaction | cs.HC cs.IT math.IT | Neuromorphic HCI is a new theoretical approach to designing better UX
inspired by the neurophysiology of the brain. Here, we apply the
neuroscientific theory of Active Inference to HCI, postulating that users
perform Bayesian inference on progress and goal distributions to predict their
next action (Interactive Infer... |
2502.05937 | A Semi-Supervised Text Generation Framework Combining a Deep Transformer
and a GAN | cs.CL cs.AI | This paper introduces a framework that connects a deep generative pre-trained
Transformer language model with a generative adversarial network for
semi-supervised text generation. In other words, the proposed model is first
pre-trained unsupervised on a large and diverse text corpus with 24 layers.
Then a simple GAN ... |
2502.05938 | Energy-Efficient Autonomous Aerial Navigation with Dynamic Vision
Sensors: A Physics-Guided Neuromorphic Approach | cs.RO | Vision-based object tracking is a critical component for achieving autonomous
aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic
Vision Sensors (DVS) or event cameras, inspired by biological vision, offer a
promising alternative to conventional frame-based cameras. These cameras can
detect c... |
2502.05943 | Continual Adaptation for Autonomous Driving with the Mixture of
Progressive Experts Network | cs.RO | Learning-based autonomous driving requires continuous integration of diverse
knowledge in complex traffic , yet existing methods exhibit significant
limitations in adaptive capabilities. Addressing this gap demands autonomous
driving systems that enable continual adaptation through dynamic adjustments to
evolving env... |
2502.05944 | Multi-granular Training Strategies for Robust Multi-hop Reasoning Over
Noisy and Heterogeneous Knowledge Sources | cs.CL | Multi-source multi-hop question answering (QA) represents a challenging task
in natural language processing due to the need for dynamic integration of
heterogeneous knowledge sources and multi-step reasoning. Existing methods
often suffer from cascading errors, insufficient handling of knowledge
conflicts, and comput... |
2502.05945 | "Let the AI conspiracy begin..." Language Model coordination is just one
inference-intervention away | cs.CL cs.AI | In this work, we introduce a straightforward and effective methodology to
steer large language model behaviour capable of bypassing learned alignment
goals. We employ interference-time activation shifting, which is effective
without additional training. Following prior studies, we derive intervention
directions from ... |
2502.05947 | Acceleration Multiple Heads Decoding for LLM via Dynamic Tree Attention | cs.CV cs.CL | Multiple heads decoding accelerates the inference of Large Language Models
(LLMs) by predicting next several tokens simultaneously. It generates and
verifies multiple candidate sequences in parallel via tree attention with a
fixed structure. In this paper, we replace the fixed tree attention with
dynamic tree attenti... |
2502.05949 | Verifying Proportionality in Temporal Voting | cs.GT cs.AI | We study a model of temporal voting where there is a fixed time horizon, and
at each round the voters report their preferences over the available candidates
and a single candidate is selected. Prior work has adapted popular notions of
justified representation as well as voting rules that provide strong
representation... |
2502.05950 | Survival Concept-Based Learning Models | cs.LG cs.AI stat.ML | Concept-based learning enhances prediction accuracy and interpretability by
leveraging high-level, human-understandable concepts. However, existing CBL
frameworks do not address survival analysis tasks, which involve predicting
event times in the presence of censored data -- a common scenario in fields
like medicine ... |
2502.05951 | Cyri: A Conversational AI-based Assistant for Supporting the Human User
in Detecting and Responding to Phishing Attacks | cs.HC cs.AI cs.CR | This work introduces Cyri, an AI-powered conversational assistant designed to
support a human user in detecting and analyzing phishing emails by leveraging
Large Language Models. Cyri has been designed to scrutinize emails for semantic
features used in phishing attacks, such as urgency, and undesirable
consequences, ... |
2502.05954 | Optimization under Attack: Resilience, Vulnerability, and the Path to
Collapse | cs.MA | Optimization is instrumental for improving operations of large-scale
socio-technical infrastructures of Smart Cities, for instance, energy and
traffic systems. In particular, understanding the performance of multi-agent
discrete-choice combinatorial optimization under distributed adversary attacks
is a compelling and... |
2502.05957 | AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents | cs.AI cs.CL | Large Language Model (LLM) Agents have demonstrated remarkable capabilities
in task automation and intelligent decision-making, driving the widespread
adoption of agent development frameworks such as LangChain and AutoGen.
However, these frameworks predominantly serve developers with extensive
technical expertise - a... |
2502.05959 | Ensemble-Tight Second-Order Asymptotics and Exponents for Guessing-Based
Decoding with Abandonment | cs.IT math.IT | This paper considers guessing-based decoders with abandonment for discrete
memoryless channels in which all codewords have the same composition. This
class of decoders rank-orders all input sequences in the codebook's composition
class from ``closest'' to ``farthest'' from the channel output and then queries
them seq... |
2502.05963 | Redefining Robot Generalization Through Interactive Intelligence | cs.LG cs.AI cs.RO | Recent advances in large-scale machine learning have produced high-capacity
foundation models capable of adapting to a broad array of downstream tasks.
While such models hold great promise for robotics, the prevailing paradigm
still portrays robots as single, autonomous decision-makers, performing tasks
like manipula... |
2502.05964 | Revisiting Gradient-based Uncertainty for Monocular Depth Estimation | cs.CV | Monocular depth estimation, similar to other image-based tasks, is prone to
erroneous predictions due to ambiguities in the image, for example, caused by
dynamic objects or shadows. For this reason, pixel-wise uncertainty assessment
is required for safety-critical applications to highlight the areas where the
predict... |
2502.05966 | Detection of Physiological Data Tampering Attacks with Quantum Machine
Learning | quant-ph cs.LG | The widespread use of cloud-based medical devices and wearable sensors has
made physiological data susceptible to tampering. These attacks can compromise
the reliability of healthcare systems which can be critical and
life-threatening. Detection of such data tampering is of immediate need.
Machine learning has been u... |
2502.05967 | $\mu$nit Scaling: Simple and Scalable FP8 LLM Training | cs.LG | Large Language Model training with 8-bit floating point (FP8) formats
promises significant efficiency improvements, but reduced numerical precision
makes training challenging. It is currently possible to train in FP8 only if
one is willing to tune various hyperparameters, reduce model scale, or accept
the overhead of... |
2502.05969 | Asymptotic FDR Control with Model-X Knockoffs: Is Moments Matching
Sufficient? | stat.ML cs.LG math.ST stat.TH | We propose a unified theoretical framework for studying the robustness of the
model-X knockoffs framework by investigating the asymptotic false discovery
rate (FDR) control of the practically implemented approximate knockoffs
procedure. This procedure deviates from the model-X knockoffs framework by
substituting the ... |
2502.05970 | Known Unknowns: Out-of-Distribution Property Prediction in Materials and
Molecules | cs.LG cond-mat.mtrl-sci cs.CE physics.chem-ph | Discovery of high-performance materials and molecules requires identifying
extremes with property values that fall outside the known distribution.
Therefore, the ability to extrapolate to out-of-distribution (OOD) property
values is critical for both solid-state materials and molecular design. Our
objective is to tra... |
2502.05972 | Mechanic Modeling and Nonlinear Optimal Control of Actively Articulated
Suspension of Mobile Heavy-Duty Manipulators | cs.RO | This paper presents the analytic modeling of mobile heavy-duty manipulators
with actively articulated suspension and its optimal control to maximize its
static and dynamic stabilization. By adopting the screw theory formalism, we
consider the suspension mechanism as a rigid multibody composed of two closed
kinematic ... |
2502.05974 | Decision Making in Hybrid Environments: A Model Aggregation Approach | cs.LG stat.ML | Recent work by Foster et al. (2021, 2022, 2023) and Xu and Zeevi (2023)
developed the framework of decision estimation coefficient (DEC) that
characterizes the complexity of general online decision making problems and
provides a general algorithm design principle. These works, however, either
focus on the pure stocha... |
2502.05979 | VFX Creator: Animated Visual Effect Generation with Controllable
Diffusion Transformer | cs.CV | Crafting magic and illusions is one of the most thrilling aspects of
filmmaking, with visual effects (VFX) serving as the powerhouse behind
unforgettable cinematic experiences. While recent advances in generative
artificial intelligence have driven progress in generic image and video
synthesis, the domain of controll... |
2502.05980 | Speech to Speech Translation with Translatotron: A State of the Art
Review | cs.CL cs.AI | A cascade-based speech-to-speech translation has been considered a benchmark
for a very long time, but it is plagued by many issues, like the time taken to
translate a speech from one language to another and compound errors. These
issues are because a cascade-based method uses a combination of methods such as
speech ... |
2502.05982 | HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered
Therapy Using LLM Agents | cs.CL | This paper presents HamRaz, a novel Persian-language mental health dataset
designed for Person-Centered Therapy (PCT) using Large Language Models (LLMs).
Despite the growing application of LLMs in AI-driven psychological counseling,
existing datasets predominantly focus on Western and East Asian contexts,
overlooking... |
2502.05986 | Preventing Rogue Agents Improves Multi-Agent Collaboration | cs.CL cs.MA | Multi-agent systems, where specialized agents collaborate to solve a shared
task hold great potential, from increased modularity to simulating complex
environments. However, they also have a major caveat -- a single agent can
cause the entire system to fail. Consider a simple game where the knowledge to
solve the tas... |
2502.05988 | SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented
Gangue Detection | cs.CV | To address the issues of slow detection speed,low accuracy,difficulty in
deployment on industrial edge devices,and large parameter and computational
requirements in deep learning-based coal gangue target detection methods,we
propose a lightweight coal gangue target detection algorithm based on an
improved YOLOv11.Fir... |
2502.05994 | Diffusion Models for Inverse Problems in the Exponential Family | stat.ML cs.LG | Diffusion models have emerged as powerful tools for solving inverse problems,
yet prior work has primarily focused on observations with Gaussian measurement
noise, restricting their use in real-world scenarios. This limitation persists
due to the intractability of the likelihood score, which until now has only
been a... |
2502.05995 | A Comprehensive Survey on Image Signal Processing Approaches for
Low-Illumination Image Enhancement | cs.CV | The usage of digital content (photos and videos) in a variety of applications
has increased due to the popularity of multimedia devices. These uses include
advertising campaigns, educational resources, and social networking platforms.
There is an increasing need for high-quality graphic information as people
become m... |
2502.05996 | Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement
Learning | cs.RO cs.AI | This paper investigates the application of Deep Reinforcement (DRL) Learning
to address motion control challenges in drones for additive manufacturing (AM).
Drone-based additive manufacturing promises flexible and autonomous material
deposition in large-scale or hazardous environments. However, achieving robust
real-... |
2502.05999 | Pencils to Pixels: A Systematic Study of Creative Drawings across
Children, Adults and AI | cs.HC cs.AI cs.CV | Can we derive computational metrics to quantify visual creativity in drawings
across intelligent agents, while accounting for inherent differences in
technical skill and style? To answer this, we curate a novel dataset consisting
of 1338 drawings by children, adults and AI on a creative drawing task. We
characterize ... |
2502.06004 | Analysis of LLM as a grammatical feature tagger for African American
English | cs.CL cs.AI cs.LG | African American English (AAE) presents unique challenges in natural language
processing (NLP). This research systematically compares the performance of
available NLP models--rule-based, transformer-based, and large language models
(LLMs)--capable of identifying key grammatical features of AAE, namely Habitual
Be and... |
2502.06006 | FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for
Fact-Checking | cs.IR | The field of automated fact-checking increasingly depends on retrieving
web-based evidence to determine the veracity of claims in real-world scenarios.
A significant challenge in this process is not only retrieving relevant
information, but also identifying evidence that can both support and refute
complex claims. Tr... |
2502.06007 | Transformers versus the EM Algorithm in Multi-class Clustering | stat.ML cs.LG | LLMs demonstrate significant inference capacities in complicated machine
learning tasks, using the Transformer model as its backbone. Motivated by the
limited understanding of such models on the unsupervised learning problems, we
study the learning guarantees of Transformers in performing multi-class
clustering of th... |
2502.06011 | Uncertainty Quantification and Causal Considerations for Off-Policy
Decision Making | stat.ML cs.LG | Off-policy evaluation (OPE) is a critical challenge in robust decision-making
that seeks to assess the performance of a new policy using data collected under
a different policy. However, the existing OPE methodologies suffer from several
limitations arising from statistical uncertainty as well as causal
consideration... |
2502.06018 | Kolmogorov-Arnold Fourier Networks | cs.LG cs.AI | Although Kolmogorov-Arnold based interpretable networks (KAN) have strong
theoretical expressiveness, they face significant parameter explosion and
high-frequency feature capture challenges in high-dimensional tasks. To address
this issue, we propose the Kolmogorov-Arnold-Fourier Network (KAF), which
effectively inte... |
2502.06019 | Noise is an Efficient Learner for Zero-Shot Vision-Language Models | cs.CV | Recently, test-time adaptation has garnered attention as a method for tuning
models without labeled data. The conventional modus operandi for adapting
pre-trained vision-language models (VLMs) during test-time primarily focuses on
tuning learnable prompts; however, this approach overlooks potential
distribution shift... |
2502.06020 | Temporal Working Memory: Query-Guided Segment Refinement for Enhanced
Multimodal Understanding | cs.CV cs.MM cs.SD eess.AS | Multimodal foundation models (MFMs) have demonstrated significant success in
tasks such as visual captioning, question answering, and image-text retrieval.
However, these models face inherent limitations due to their finite internal
capacity, which restricts their ability to process extended temporal sequences,
a cru... |
2502.06022 | Nested subspace learning with flags | stat.ML cs.LG | Many machine learning methods look for low-dimensional representations of the
data. The underlying subspace can be estimated by first choosing a dimension
$q$ and then optimizing a certain objective function over the space of
$q$-dimensional subspaces (the Grassmannian). Trying different $q$ yields in
general non-nes... |
2502.06023 | Dual Caption Preference Optimization for Diffusion Models | cs.CV | Recent advancements in human preference optimization, originally developed
for Large Language Models (LLMs), have shown significant potential in improving
text-to-image diffusion models. These methods aim to learn the distribution of
preferred samples while distinguishing them from less preferred ones. However,
exist... |
2502.06025 | Universal point spread function engineering for 3D optical information
processing | physics.optics cs.NE | Point spread function (PSF) engineering has been pivotal in the remarkable
progress made in high-resolution imaging in the last decades. However, the
diversity in PSF structures attainable through existing engineering methods is
limited. Here, we report universal PSF engineering, demonstrating a method to
synthesize ... |
2502.06026 | A Multimodal PDE Foundation Model for Prediction and Scientific Text
Descriptions | cs.LG cs.NA math.NA | Neural networks are one tool for approximating non-linear differential
equations used in scientific computing tasks such as surrogate modeling,
real-time predictions, and optimal control. PDE foundation models utilize
neural networks to train approximations to multiple differential equations
simultaneously and are th... |
2502.06027 | Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models
with Guidance | cs.LG | Drug development is a critical but notoriously resource- and time-consuming
process. In this manuscript, we develop a novel generative artificial
intelligence (genAI) method DiffSMol to facilitate drug development. DiffSmol
generates 3D binding molecules based on the shapes of known ligands. DiffSMol
encapsulates geo... |
2502.06029 | DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations | cs.CV | Pre-trained Vision Transformers now serve as powerful tools for computer
vision. Yet, efficiently adapting them for multiple tasks remains a challenge
that arises from the need to modify the rich hidden representations encoded by
the learned weight matrices, without inducing interference between tasks.
Current parame... |
2502.06031 | A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare
Attacks in IoT Networks | cs.CR cs.LG | Internet of things (IoT) networks, boosted by 6G technology, are transforming
various industries. However, their widespread adoption introduces significant
security risks, particularly in detecting rare but potentially damaging
cyber-attacks. This makes the development of robust IDS crucial for monitoring
network tra... |
2502.06034 | Traveling Waves Integrate Spatial Information Into Spectral
Representations | cs.CV | Traveling waves are widely observed in the brain, but their precise
computational function remains unclear. One prominent hypothesis is that they
enable the transfer and integration of spatial information across neural
populations. However, few computational models have explored how traveling
waves might be harnessed... |
2502.06037 | Investigating Compositional Reasoning in Time Series Foundation Models | cs.LG | Large pre-trained time series foundation models (TSFMs) have demonstrated
promising zero-shot performance across a wide range of domains. However, a
question remains: Do TSFMs succeed solely by memorizing training patterns, or
do they possess the ability to reason? While reasoning is a topic of great
interest in the ... |
2502.06038 | Provably Overwhelming Transformer Models with Designed Inputs | cs.LG cs.AI cs.CC | We develop an algorithm which, given a trained transformer model
$\mathcal{M}$ as input, as well as a string of tokens $s$ of length $n_{fix}$
and an integer $n_{free}$, can generate a mathematical proof that $\mathcal{M}$
is ``overwhelmed'' by $s$, in time and space $\widetilde{O}(n_{fix}^2 +
n_{free}^3)$. We say th... |
2502.06039 | Benchmarking Prompt Engineering Techniques for Secure Code Generation
with GPT Models | cs.SE cs.AI cs.CR | Prompt engineering reduces reasoning mistakes in Large Language Models
(LLMs). However, its effectiveness in mitigating vulnerabilities in
LLM-generated code remains underexplored. To address this gap, we implemented a
benchmark to automatically assess the impact of various prompt engineering
strategies on code secur... |
2502.06042 | Scaling Laws for Forgetting during Finetuning with Pretraining Data
Injection | cs.LG cs.CL | A widespread strategy to obtain a language model that performs well on a
target domain is to finetune a pretrained model to perform unsupervised
next-token prediction on data from that target domain. Finetuning presents two
challenges: (i) if the amount of target data is limited, as in most practical
applications, th... |
2502.06044 | Scalable Differentially Private Bayesian Optimization | stat.ML cs.LG | In recent years, there has been much work on scaling Bayesian Optimization to
high-dimensional problems, for example hyperparameter tuning in large neural
network models. These scalable methods have been successful, finding high
objective values much more quickly than traditional global Bayesian
Optimization or rando... |
2502.06047 | Neural Shortest Path for Surface Reconstruction from Point Clouds | cs.LG | In this paper, we propose the neural shortest path (NSP), a vector-valued
implicit neural representation (INR) that approximates a distance function and
its gradient. The key feature of NSP is to learn the exact shortest path (ESP),
which directs an arbitrary point to its nearest point on the target surface.
The NSP ... |
2502.06049 | LM2: Large Memory Models | cs.CL cs.AI | This paper introduces the Large Memory Model (LM2), a decoder-only
Transformer architecture enhanced with an auxiliary memory module that aims to
address the limitations of standard Transformers in multi-step reasoning,
relational argumentation, and synthesizing information distributed over long
contexts. The propose... |
2502.06051 | Nearly Optimal Sample Complexity of Offline KL-Regularized Contextual
Bandits under Single-Policy Concentrability | cs.LG cs.AI math.ST stat.ML stat.TH | KL-regularized policy optimization has become a workhorse in learning-based
decision making, while its theoretical understanding is still very limited.
Although recent progress has been made towards settling the sample complexity
of KL-regularized contextual bandits, existing sample complexity bounds are
either $\til... |
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