id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.07237 | DrugImproverGPT: A Large Language Model for Drug Optimization with
Fine-Tuning via Structured Policy Optimization | cs.LG cs.CL q-bio.BM stat.ML | Finetuning a Large Language Model (LLM) is crucial for generating results
towards specific objectives. This research delves into the realm of drug
optimization and introduce a novel reinforcement learning algorithm to finetune
a drug optimization LLM-based generative model, enhancing the original drug
across target o... |
2502.07238 | Diffusion Suction Grasping with Large-Scale Parcel Dataset | cs.CV cs.AI | While recent advances in object suction grasping have shown remarkable
progress, significant challenges persist particularly in cluttered and complex
parcel handling scenarios. Two fundamental limitations hinder current
approaches: (1) the lack of a comprehensive suction grasp dataset tailored for
parcel manipulation... |
2502.07239 | Contextual Gesture: Co-Speech Gesture Video Generation through
Context-aware Gesture Representation | cs.CV cs.AI | Co-speech gesture generation is crucial for creating lifelike avatars and
enhancing human-computer interactions by synchronizing gestures with speech.
Despite recent advancements, existing methods struggle with accurately
identifying the rhythmic or semantic triggers from audio for generating
contextualized gesture p... |
2502.07242 | Nonlinear Reed-Solomon codes and nonlinear skew quasi-cyclic codes | cs.IT math.IT math.RA | This article begins with an exploration of nonlinear codes
($\mathbb{F}_q$-linear subspaces of $\mathbb{F}_{q^m}^n$) which are
generalizations of the familiar Reed-Solomon codes. This then leads to a wider
exploration of nonlinear analogues of the skew quasi-cyclic codes of index
$\ell$ first explored in 2010 by Abua... |
2502.07243 | Vevo: Controllable Zero-Shot Voice Imitation with Self-Supervised
Disentanglement | cs.SD cs.AI | The imitation of voice, targeted on specific speech attributes such as timbre
and speaking style, is crucial in speech generation. However, existing methods
rely heavily on annotated data, and struggle with effectively disentangling
timbre and style, leading to challenges in achieving controllable generation,
especia... |
2502.07244 | Linear Transformers as VAR Models: Aligning Autoregressive Attention
Mechanisms with Autoregressive Forecasting | cs.LG cs.AI stat.ML | Autoregressive attention-based time series forecasting (TSF) has drawn
increasing interest, with mechanisms like linear attention sometimes
outperforming vanilla attention. However, deeper Transformer architectures
frequently misalign with autoregressive objectives, obscuring the underlying
VAR structure embedded wit... |
2502.07246 | Robust Indoor Localization in Dynamic Environments: A Multi-source
Unsupervised Domain Adaptation Framework | cs.CV physics.pop-ph | Fingerprint localization has gained significant attention due to its
cost-effective deployment, low complexity, and high efficacy. However,
traditional methods, while effective for static data, often struggle in dynamic
environments where data distributions and feature spaces evolve-a common
occurrence in real-world ... |
2502.07250 | NARCE: A Mamba-Based Neural Algorithmic Reasoner Framework for Online
Complex Event Detection | cs.LG cs.AI | Current machine learning models excel in short-span perception tasks but
struggle to derive high-level insights from long-term observation, a capability
central to understanding complex events (CEs). CEs, defined as sequences of
short-term atomic events (AEs) governed by spatiotemporal rules, are
challenging to detec... |
2502.07254 | Fairness in Multi-Agent AI: A Unified Framework for Ethical and
Equitable Autonomous Systems | cs.MA cs.AI cs.CY | Ensuring fairness in decentralized multi-agent systems presents significant
challenges due to emergent biases, systemic inefficiencies, and conflicting
agent incentives. This paper provides a comprehensive survey of fairness in
multi-agent AI, introducing a novel framework where fairness is treated as a
dynamic, emer... |
2502.07255 | Beyond Confidence: Adaptive Abstention in Dual-Threshold Conformal
Prediction for Autonomous System Perception | cs.RO cs.LG | Safety-critical perception systems require both reliable uncertainty
quantification and principled abstention mechanisms to maintain safety under
diverse operational conditions. We present a novel dual-threshold
conformalization framework that provides statistically-guaranteed uncertainty
estimates while enabling sel... |
2502.07259 | Flat U-Net: An Efficient Ultralightweight Model for Solar Filament
Segmentation in Full-disk H$\alpha$ Images | astro-ph.IM astro-ph.SR cs.CV cs.LG | Solar filaments are one of the most prominent features observed on the Sun,
and their evolutions are closely related to various solar activities, such as
flares and coronal mass ejections. Real-time automated identification of solar
filaments is the most effective approach to managing large volumes of data.
Existing ... |
2502.07263 | Hidden Division of Labor in Scientific Teams Revealed Through 1.6
Million LaTeX Files | cs.SI cs.CL cs.DL | Recognition of individual contributions is fundamental to the scientific
reward system, yet coauthored papers obscure who did what. Traditional
proxies-author order and career stage-reinforce biases, while contribution
statements remain self-reported and limited to select journals. We construct
the first large-scale ... |
2502.07265 | Riemannian Proximal Sampler for High-accuracy Sampling on Manifolds | stat.ML cs.LG math.ST stat.TH | We introduce the Riemannian Proximal Sampler, a method for sampling from
densities defined on Riemannian manifolds. The performance of this sampler
critically depends on two key oracles: the Manifold Brownian Increments (MBI)
oracle and the Riemannian Heat-kernel (RHK) oracle. We establish high-accuracy
sampling guar... |
2502.07266 | When More is Less: Understanding Chain-of-Thought Length in LLMs | cs.AI cs.CL cs.LG | Chain-of-thought (CoT) reasoning enhances the multi-step reasoning
capabilities of large language models (LLMs) by breaking complex tasks into
smaller, manageable sub-tasks. Researchers have been exploring ways to guide
models to generate more complex CoT processes to improve the reasoning ability
of LLMs, such as lo... |
2502.07269 | Exploring Active Data Selection Strategies for Continuous Training in
Deepfake Detection | cs.CV | In deepfake detection, it is essential to maintain high performance by
adjusting the parameters of the detector as new deepfake methods emerge. In
this paper, we propose a method to automatically and actively select the small
amount of additional data required for the continuous training of deepfake
detection models ... |
2502.07272 | GENERator: A Long-Context Generative Genomic Foundation Model | cs.CL q-bio.GN | Advancements in DNA sequencing technologies have significantly improved our
ability to decode genomic sequences. However, the prediction and interpretation
of these sequences remain challenging due to the intricate nature of genetic
material. Large language models (LLMs) have introduced new opportunities for
biologic... |
2502.07273 | Variational Learning Induces Adaptive Label Smoothing | cs.LG cs.AI | We show that variational learning naturally induces an adaptive label
smoothing where label noise is specialized for each example. Such
label-smoothing is useful to handle examples with labeling errors and
distribution shifts, but designing a good adaptivity strategy is not always
easy. We propose to skip this step a... |
2502.07274 | Cost-Efficient Continual Learning with Sufficient Exemplar Memory | cs.LG cs.AI | Continual learning (CL) research typically assumes highly constrained
exemplar memory resources. However, in many real-world scenarios-especially in
the era of large foundation models-memory is abundant, while GPU computational
costs are the primary bottleneck. In this work, we investigate CL in a novel
setting where... |
2502.07276 | Dataset Ownership Verification in Contrastive Pre-trained Models | cs.LG cs.AI cs.CV | High-quality open-source datasets, which necessitate substantial efforts for
curation, has become the primary catalyst for the swift progress of deep
learning. Concurrently, protecting these datasets is paramount for the
well-being of the data owner. Dataset ownership verification emerges as a
crucial method in this ... |
2502.07277 | Enhancing Video Understanding: Deep Neural Networks for Spatiotemporal
Analysis | cs.CV cs.AI | It's no secret that video has become the primary way we share information
online. That's why there's been a surge in demand for algorithms that can
analyze and understand video content. It's a trend going to continue as video
continues to dominate the digital landscape. These algorithms will extract and
classify rela... |
2502.07278 | Articulate That Object Part (ATOP): 3D Part Articulation from Text and
Motion Personalization | cs.CV | We present ATOP (Articulate That Object Part), a novel method based on motion
personalization to articulate a 3D object with respect to a part and its motion
as prescribed in a text prompt. Specifically, the text input allows us to tap
into the power of modern-day video diffusion to generate plausible motion
samples ... |
2502.07279 | Exploratory Diffusion Policy for Unsupervised Reinforcement Learning | cs.LG cs.AI | Unsupervised reinforcement learning (RL) aims to pre-train agents by
exploring states or skills in reward-free environments, facilitating the
adaptation to downstream tasks. However, existing methods often overlook the
fitting ability of pre-trained policies and struggle to handle the
heterogeneous pre-training data,... |
2502.07280 | MIGT: Memory Instance Gated Transformer Framework for Financial
Portfolio Management | cs.LG cs.AI | Deep reinforcement learning (DRL) has been applied in financial portfolio
management to improve returns in changing market conditions. However, unlike
most fields where DRL is widely used, the stock market is more volatile and
dynamic as it is affected by several factors such as global events and investor
sentiment. ... |
2502.07281 | Supervised Contrastive Block Disentanglement | cs.LG | Real-world datasets often combine data collected under different experimental
conditions. This yields larger datasets, but also introduces spurious
correlations that make it difficult to model the phenomena of interest. We
address this by learning two embeddings to independently represent the
phenomena of interest an... |
2502.07282 | Leader-follower formation enabled by pressure sensing in free-swimming
undulatory robotic fish | cs.RO | Fish use their lateral lines to sense flows and pressure gradients, enabling
them to detect nearby objects and organisms. Towards replicating this
capability, we demonstrated successful leader-follower formation swimming using
flow pressure sensing in our undulatory robotic fish ($\mu$Bot/MUBot). The
follower $\mu$Bo... |
2502.07285 | Negative Dependence as a toolbox for machine learning : review and new
developments | stat.ML cs.LG math.PR | Negative dependence is becoming a key driver in advancing learning
capabilities beyond the limits of traditional independence. Recent developments
have evidenced support towards negatively dependent systems as a learning
paradigm in a broad range of fundamental machine learning challenges including
optimization, samp... |
2502.07286 | Small Language Model Makes an Effective Long Text Extractor | cs.CL cs.AI | Named Entity Recognition (NER) is a fundamental problem in natural language
processing (NLP). However, the task of extracting longer entity spans (e.g.,
awards) from extended texts (e.g., homepages) is barely explored. Current NER
methods predominantly fall into two categories: span-based methods and
generation-based... |
2502.07288 | KPIs 2024 Challenge: Advancing Glomerular Segmentation from Patch- to
Slide-Level | cs.CV cs.AI | Chronic kidney disease (CKD) is a major global health issue, affecting over
10% of the population and causing significant mortality. While kidney biopsy
remains the gold standard for CKD diagnosis and treatment, the lack of
comprehensive benchmarks for kidney pathology segmentation hinders progress in
the field. To a... |
2502.07289 | Learning Inverse Laplacian Pyramid for Progressive Depth Completion | cs.CV | Depth completion endeavors to reconstruct a dense depth map from sparse depth
measurements, leveraging the information provided by a corresponding color
image. Existing approaches mostly hinge on single-scale propagation strategies
that iteratively ameliorate initial coarse depth estimates through pixel-level
message... |
2502.07293 | Global Universal Scaling and Ultra-Small Parameterization in Machine
Learning Interatomic Potentials with Super-Linearity | cond-mat.mtrl-sci cs.LG | Using machine learning (ML) to construct interatomic interactions and thus
potential energy surface (PES) has become a common strategy for materials
design and simulations. However, those current models of machine learning
interatomic potential (MLIP) provide no relevant physical constrains, and thus
may owe intrinsi... |
2502.07295 | Treatment Effect Estimation for Exponential Family Outcomes using Neural
Networks with Targeted Regularization | cs.LG | Neural Networks (NNs) have became a natural choice for treatment effect
estimation due to their strong approximation capabilities. Nevertheless, how to
design NN-based estimators with desirable properties, such as low bias and
doubly robustness, still remains a significant challenge. A common approach to
address this... |
2502.07297 | Generation of Drug-Induced Cardiac Reactions towards Virtual Clinical
Trials | cs.LG q-bio.QM | Clinical trials are pivotal in cardiac drug development, yet they often fail
due to inadequate efficacy and unexpected safety issues, leading to significant
financial losses. Using in-silico trials to replace a part of physical clinical
trials, e.g., leveraging advanced generative models to generate drug-influenced
e... |
2502.07299 | Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification | cs.LG cs.AI cs.CL q-bio.GN | The interactions between DNA, RNA, and proteins are fundamental to biological
processes, as illustrated by the central dogma of molecular biology. While
modern biological pre-trained models have achieved great success in analyzing
these macromolecules individually, their interconnected nature remains
under-explored. ... |
2502.07302 | CASC-AI: Consensus-aware Self-corrective AI Agents for Noise Cell
Segmentation | cs.CV | Multi-class cell segmentation in high-resolution gigapixel whole slide images
(WSI) is crucial for various clinical applications. However, training such
models typically requires labor-intensive, pixel-wise annotations by domain
experts. Recent efforts have democratized this process by involving lay
annotators withou... |
2502.07303 | Flow Matching for Collaborative Filtering | cs.IR | Generative models have shown great promise in collaborative filtering by
capturing the underlying distribution of user interests and preferences.
However, existing approaches struggle with inaccurate posterior approximations
and misalignment with the discrete nature of recommendation data, limiting
their expressivene... |
2502.07306 | TRAVEL: Training-Free Retrieval and Alignment for Vision-and-Language
Navigation | cs.CV cs.AI cs.CL cs.LG cs.RO | In this work, we propose a modular approach for the Vision-Language
Navigation (VLN) task by decomposing the problem into four sub-modules that use
state-of-the-art Large Language Models (LLMs) and Vision-Language Models (VLMs)
in a zero-shot setting. Given navigation instruction in natural language, we
first prompt ... |
2502.07307 | CreAgent: Towards Long-Term Evaluation of Recommender System under
Platform-Creator Information Asymmetry | cs.IR | Ensuring the long-term sustainability of recommender systems (RS) emerges as
a crucial issue. Traditional offline evaluation methods for RS typically focus
on immediate user feedback, such as clicks, but they often neglect the
long-term impact of content creators. On real-world content platforms, creators
can strateg... |
2502.07308 | Explicit Codes approaching Generalized Singleton Bound using Expanders | cs.IT cs.CC math.IT | We construct a new family of explicit codes that are list decodable to
capacity and achieve an optimal list size of $O(\frac{1}{\epsilon})$. In
contrast to existing explicit constructions of codes achieving list decoding
capacity, our arguments do not rely on algebraic structure but utilize simple
combinatorial prope... |
2502.07309 | Semi-Supervised Vision-Centric 3D Occupancy World Model for Autonomous
Driving | cs.CV | Understanding world dynamics is crucial for planning in autonomous driving.
Recent methods attempt to achieve this by learning a 3D occupancy world model
that forecasts future surrounding scenes based on current observation. However,
3D occupancy labels are still required to produce promising results.
Considering the... |
2502.07312 | OpenGrok: Enhancing SNS Data Processing with Distilled Knowledge and
Mask-like Mechanisms | cs.LG cs.AI | This report details Lumen Labs' novel approach to processing Social
Networking Service (SNS) data. We leverage knowledge distillation, specifically
a simple distillation method inspired by DeepSeek-R1's CoT acquisition,
combined with prompt hacking, to extract valuable training data from the Grok
model. This data is ... |
2502.07315 | Prompt-Based Document Modifications In Ranking Competitions | cs.IR cs.GT | We study prompting-based approaches with Large Language Models (LLMs) for
modifying documents so as to promote their ranking in a competitive search
setting. Our methods are inspired by prior work on leveraging LLMs as rankers.
We evaluate our approach by deploying it as a bot in previous ranking
competitions and in ... |
2502.07316 | CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction | cs.CL cs.AI | Reasoning is a fundamental capability of Large Language Models. While prior
research predominantly focuses on enhancing narrow skills like math or code
generation, improving performance on many other reasoning tasks remains
challenging due to sparse and fragmented training data. To address this issue,
we propose Code... |
2502.07318 | Beamfocusing Capabilities of a Uniform Linear Array in the Holographic
Regime | cs.IT eess.SP math.IT | The use of multiantenna technologies in the near field offers the possibility
of focusing the energy in spatial regions rather than just in angle. The
objective of this paper is to provide a formal framework that allows to
establish the region in space where this effect can take place and how
efficient this focusing ... |
2502.07319 | Learnable Residual-based Latent Denoising in Semantic Communication | cs.LG cs.IT math.IT | A latent denoising semantic communication (SemCom) framework is proposed for
robust image transmission over noisy channels. By incorporating a learnable
latent denoiser into the receiver, the received signals are preprocessed to
effectively remove the channel noise and recover the semantic information,
thereby enhanc... |
2502.07322 | MEMIT-Merge: Addressing MEMIT's Key-Value Conflicts in Same-Subject
Batch Editing for LLMs | cs.CL cs.LG | As large language models continue to scale up, knowledge editing techniques
that modify models' internal knowledge without full retraining have gained
significant attention. MEMIT, a prominent batch editing algorithm, stands out
for its capability to perform mass knowledge modifications. However, we uncover
a critica... |
2502.07323 | Semantic to Structure: Learning Structural Representations for
Infringement Detection | cs.CV | Structural information in images is crucial for aesthetic assessment, and it
is widely recognized in the artistic field that imitating the structure of
other works significantly infringes on creators' rights. The advancement of
diffusion models has led to AI-generated content imitating artists' structural
creations, ... |
2502.07325 | Long-term simulation of physical and mechanical behaviors using
curriculum-transfer-learning based physics-informed neural networks | cs.LG cs.NA math.NA | This paper proposes a Curriculum-Transfer-Learning based physics-informed
neural network (CTL-PINN) for long-term simulation of physical and mechanical
behaviors. The main innovation of CTL-PINN lies in decomposing long-term
problems into a sequence of short-term subproblems. Initially, the standard
PINN is employed ... |
2502.07326 | PICTS: A Novel Deep Reinforcement Learning Approach for Dynamic P-I
Control in Scanning Probe Microscopy | cond-mat.mtrl-sci cs.LG physics.app-ph | We have developed a Parallel Integrated Control and Training System,
leveraging the deep reinforcement learning to dynamically adjust the control
strategies in real time for scanning probe microscopy techniques.
|
2502.07327 | Generative Ghost: Investigating Ranking Bias Hidden in AI-Generated
Videos | cs.IR cs.CV | With the rapid development of AI-generated content (AIGC), the creation of
high-quality AI-generated videos has become faster and easier, resulting in the
Internet being flooded with all kinds of video content. However, the impact of
these videos on the content ecosystem remains largely unexplored. Video
information ... |
2502.07328 | Music for All: Exploring Multicultural Representations in Music
Generation Models | cs.SD cs.AI cs.CL cs.LG cs.MM | The advent of Music-Language Models has greatly enhanced the automatic music
generation capability of AI systems, but they are also limited in their
coverage of the musical genres and cultures of the world. We present a study of
the datasets and research papers for music generation and quantify the bias and
under-rep... |
2502.07331 | ERANet: Edge Replacement Augmentation for Semi-Supervised Meniscus
Segmentation with Prototype Consistency Alignment and Conditional
Self-Training | cs.CV | Manual segmentation is labor-intensive, and automatic segmentation remains
challenging due to the inherent variability in meniscal morphology, partial
volume effects, and low contrast between the meniscus and surrounding tissues.
To address these challenges, we propose ERANet, an innovative semi-supervised
framework ... |
2502.07332 | The Combined Problem of Online Task Assignment and Lifelong Path Finding
in Logistics Warehouses: A Case Study | cs.MA cs.RO | We study the combined problem of online task assignment and lifelong path
finding, which is crucial for the logistics industries. However, most
literature either (1) focuses on lifelong path finding assuming a given task
assigner, or (2) studies the offline version of this problem where tasks are
known in advance. We... |
2502.07336 | Frequency-selective Dynamic Scattering Arrays for Over-the-air EM
Processing | eess.SP cs.IT math.IT | In this paper, we investigate frequency-selective dynamic scattering array
(DSA), a versatile antenna structure capable of performing joint wave-based
computing and radiation by transitioning signal processing tasks from the
digital domain to the electromagnetic (EM) domain. The numerical results
demonstrate the pote... |
2502.07337 | Neural Flow Samplers with Shortcut Models | cs.LG | Sampling from unnormalized densities is a fundamental task across various
domains. Flow-based samplers generate samples by learning a velocity field that
satisfies the continuity equation, but this requires estimating the intractable
time derivative of the partition function. While importance sampling provides
an app... |
2502.07340 | Aligning Large Language Models to Follow Instructions and Hallucinate
Less via Effective Data Filtering | cs.CL cs.AI | Training LLMs on data containing unfamiliar knowledge during the instruction
tuning stage can encourage hallucinations. To address this challenge, we
introduce NOVA, a novel framework designed to identify high-quality data that
aligns well with the LLM's learned knowledge to reduce hallucinations. NOVA
includes Inter... |
2502.07343 | DEG: Efficient Hybrid Vector Search Using the Dynamic Edge Navigation
Graph | cs.DB | Bimodal data, such as image-text pairs, has become increasingly prevalent in
the digital era. The Hybrid Vector Query (HVQ) is an effective approach for
querying such data and has recently garnered considerable attention from
researchers. It calculates similarity scores for objects represented by two
vectors using a ... |
2502.07344 | Integrating Physics and Data-Driven Approaches: An Explainable and
Uncertainty-Aware Hybrid Model for Wind Turbine Power Prediction | cs.LG cs.AI cs.CE | The rapid growth of the wind energy sector underscores the urgent need to
optimize turbine operations and ensure effective maintenance through early
fault detection systems. While traditional empirical and physics-based models
offer approximate predictions of power generation based on wind speed, they
often fail to c... |
2502.07346 | BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large
Language Models | cs.CL | Previous multilingual benchmarks focus primarily on simple understanding
tasks, but for large language models(LLMs), we emphasize proficiency in
instruction following, reasoning, long context understanding, code generation,
and so on. However, measuring these advanced capabilities across languages is
underexplored. T... |
2502.07347 | Coarse Set Theory: A Mathematical Foundation for Coarse Ethics | cs.AI cs.IT math.IT math.LO math.PR | In ethical decision-making, individuals are often evaluated based on
generalized assessments rather than precise individual performance. This
concept, known as Coarse Ethics (CE), has primarily been discussed in natural
language without a formal mathematical foundation. This paper introduces Coarse
Set Theory (CST) t... |
2502.07350 | KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination
in Multi-Agent Systems | cs.AI | As scaling large language models faces prohibitive costs, multi-agent systems
emerge as a promising alternative, though challenged by static knowledge
assumptions and coordination inefficiencies. We introduces Knowledge-Aware
Bayesian Bandits (KABB), a novel framework that enhances multi-agent system
coordination thr... |
2502.07351 | Multi-Task-oriented Nighttime Haze Imaging Enhancer for Vision-driven
Measurement Systems | cs.CV cs.AI | Salient object detection (SOD) plays a critical role in vision-driven
measurement systems (VMS), facilitating the detection and segmentation of key
visual elements in an image. However, adverse imaging conditions such as haze
during the day, low light, and haze at night severely degrade image quality,
and complicatin... |
2502.07352 | Bridging the Evaluation Gap: Leveraging Large Language Models for Topic
Model Evaluation | cs.CL cs.AI cs.DL | This study presents a framework for automated evaluation of dynamically
evolving topic taxonomies in scientific literature using Large Language Models
(LLMs). In digital library systems, topic modeling plays a crucial role in
efficiently organizing and retrieving scholarly content, guiding researchers
through complex... |
2502.07355 | Performance Bounds and Degree-Distribution Optimization of Finite-Length
BATS Codes | cs.IT math.IT | Batched sparse (BATS) codes were proposed as a reliable communication
solution for networks with packet loss. In the finite-length regime, the error
probability of BATS codes under belief propagation (BP) decoding has been
studied in the literature and can be analyzed by recursive formulae. However,
all existing anal... |
2502.07358 | SymbioSim: Human-in-the-loop Simulation Platform for Bidirectional
Continuing Learning in Human-Robot Interaction | cs.RO | The development of intelligent robots seeks to seamlessly integrate them into
the human world, providing assistance and companionship in daily life and work,
with the ultimate goal of achieving human-robot symbiosis. To realize this
vision, robots must continuously learn and evolve through consistent
interaction and ... |
2502.07360 | Supervised contrastive learning for cell stage classification of animal
embryos | q-bio.QM cs.CV | Video microscopy, when combined with machine learning, offers a promising
approach for studying the early development of in vitro produced (IVP) embryos.
However, manually annotating developmental events, and more specifically cell
divisions, is time-consuming for a biologist and cannot scale up for practical
applica... |
2502.07364 | Effects of Random Edge-Dropping on Over-Squashing in Graph Neural
Networks | cs.LG | Message Passing Neural Networks (MPNNs) are a class of Graph Neural Networks
(GNNs) that leverage the graph topology to propagate messages across
increasingly larger neighborhoods. The message-passing scheme leads to two
distinct challenges: over-smoothing and over-squashing. While several
algorithms, e.g. DropEdge a... |
2502.07365 | LongReD: Mitigating Short-Text Degradation of Long-Context Large
Language Models via Restoration Distillation | cs.CL cs.LG | Large language models (LLMs) have gained extended context windows through
scaling positional encodings and lightweight continual pre-training. However,
this often leads to degraded performance on short-text tasks, while the reasons
for this degradation remain insufficiently explored. In this work, we identify
two pri... |
2502.07368 | Bidirectional Piggybacking Design for Systematic Nodes with
Sub-Packetization $l=2$ | cs.IT math.IT | In 2013, Rashmi et al. proposed the piggybacking design framework to reduce
the repair bandwidth of $(n,k;l)$ MDS array codes with small sub-packetization
$l$ and it has been studied extensively in recent years. In this work, we
propose an explicit bidirectional piggybacking design (BPD) with
sub-packetization $l=2$ ... |
2502.07369 | Uniform Kernel Prober | stat.ML cs.LG math.ST stat.TH | The ability to identify useful features or representations of the input data
based on training data that achieves low prediction error on test data across
multiple prediction tasks is considered the key to multitask learning success.
In practice, however, one faces the issue of the choice of prediction tasks and
the ... |
2502.07371 | Mixed Integer Linear Programming for Active Contact Selection in Deep
Brain Stimulation | eess.SY cs.SY | Deep brain stimulation (DBS) programming remains a complex and time-consuming
process, requiring manual selection of stimulation parameters to achieve
therapeutic effects while minimizing adverse side-effects. This study explores
mathematical optimization for DBS programming, using functional subdivisions of
the subt... |
2502.07372 | USRNet: Unified Scene Recovery Network for Enhancing Traffic Imaging
under Multiple Adverse Weather Conditions | cs.CV | Advancements in computer vision technology have facilitated the extensive
deployment of intelligent transportation systems and visual surveillance
systems across various applications, including autonomous driving, public
safety, and environmental monitoring. However, adverse weather conditions such
as haze, rain, sno... |
2502.07373 | EvoFlow: Evolving Diverse Agentic Workflows On The Fly | cs.LG cs.CL cs.MA cs.NE | The past two years have witnessed the evolution of large language model
(LLM)-based multi-agent systems from labor-intensive manual design to partial
automation (\textit{e.g.}, prompt engineering, communication topology) and
eventually to fully automated design. However, existing agentic automation
pipelines often la... |
2502.07374 | LLMs Can Easily Learn to Reason from Demonstrations Structure, not
content, is what matters! | cs.AI | Large reasoning models (LRMs) tackle complex reasoning problems by following
long chain-of-thoughts (Long CoT) that incorporate reflection, backtracking,
and self-validation. However, the training techniques and data requirements to
elicit Long CoT remain poorly understood. In this work, we find that a Large
Language... |
2502.07377 | Reddit's Appetite: Predicting User Engagement with Nutritional Content | cs.SI cs.CY | The increased popularity of food communities on social media shapes the way
people engage with food-related content. Due to the extensive consequences of
such content on users' eating behavior, researchers have started studying the
factors that drive user engagement with food in online platforms. However,
while most ... |
2502.07380 | Demonstrating Wheeled Lab: Modern Sim2Real for Low-cost, Open-source
Wheeled Robotics | cs.RO | Simulation has been pivotal in recent robotics milestones and is poised to
play a prominent role in the field's future. However, recent robotic advances
often rely on expensive and high-maintenance platforms, limiting access to
broader robotics audiences. This work introduces Wheeled Lab, a framework for
the low-cost... |
2502.07381 | Spatial Degradation-Aware and Temporal Consistent Diffusion Model for
Compressed Video Super-Resolution | cs.CV | Due to limitations of storage and bandwidth, videos stored and transmitted on
the Internet are usually low-quality with low-resolution and compression noise.
Although video super-resolution (VSR) is an efficient technique to enhance
video resolution, relatively VSR methods focus on compressed videos. Directly
applyin... |
2502.07384 | SAGEPhos: Sage Bio-Coupled and Augmented Fusion for Phosphorylation Site
Detection | cs.CE | Phosphorylation site prediction based on kinase-substrate interaction plays a
vital role in understanding cellular signaling pathways and disease mechanisms.
Computational methods for this task can be categorized into
kinase-family-focused and individual kinase-targeted approaches. Individual
kinase-targeted methods ... |
2502.07386 | Parametric type design in the era of variable and color fonts | cs.CL cs.GR | Parametric fonts are programatically defined fonts with variable parameters,
pioneered by Donald Kunth with his MetaFont technology in the 1980s. While
Donald Knuth's ideas in MetaFont and subsequently in MetaPost are often seen as
legacy techniques from the pre-graphical user interface (GUI) era of type
design, rece... |
2502.07388 | UAV-assisted Joint Mobile Edge Computing and Data Collection via
Matching-enabled Deep Reinforcement Learning | cs.NE | Unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) and data
collection (DC) have been popular research issues. Different from existing
works that consider MEC and DC scenarios separately, this paper investigates a
multi-UAV-assisted joint MEC-DC system. Specifically, we formulate a joint
optimization ... |
2502.07389 | FADE: Forecasting for Anomaly Detection on ECG | cs.CV | Cardiovascular diseases, a leading cause of noncommunicable disease-related
deaths, require early and accurate detection to improve patient outcomes.
Taking advantage of advances in machine learning and deep learning, multiple
approaches have been proposed in the literature to address the challenge of
detecting ECG a... |
2502.07391 | Target-Augmented Shared Fusion-based Multimodal Sarcasm Explanation
Generation | cs.CL | Sarcasm is a linguistic phenomenon that intends to ridicule a target (e.g.,
entity, event, or person) in an inherent way. Multimodal Sarcasm Explanation
(MuSE) aims at revealing the intended irony in a sarcastic post using a natural
language explanation. Though important, existing systems overlooked the
significance ... |
2502.07394 | Interpretable Rules for Online Failure Prediction: A Case Study on the
Metro do Porto dataset | cs.LG | Due to their high predictive performance, predictive maintenance applications
have increasingly been approached with Deep Learning techniques in recent
years. However, as in other real-world application scenarios, the need for
explainability is often stated but not sufficiently addressed. This study will
focus on pre... |
2502.07396 | Optimality in importance sampling: a gentle survey | stat.CO cs.CE stat.ML | The performance of the Monte Carlo sampling methods relies on the crucial
choice of a proposal density. The notion of optimality is fundamental to design
suitable adaptive procedures of the proposal density within Monte Carlo
schemes. This work is an exhaustive review around the concept of optimality in
importance sa... |
2502.07397 | Bandit Optimal Transport | stat.ML cs.LG | Despite the impressive progress in statistical Optimal Transport (OT) in
recent years, there has been little interest in the study of the
\emph{sequential learning} of OT. Surprisingly so, as this problem is both
practically motivated and a challenging extension of existing settings such as
linear bandits. This artic... |
2502.07399 | On Iterative Evaluation and Enhancement of Code Quality Using GPT-4o | cs.SE cs.AI | This paper introduces CodeQUEST, a novel framework leveraging Large Language
Models (LLMs) to iteratively evaluate and enhance code quality across multiple
dimensions, including readability, maintainability, efficiency, and security.
The framework is divided into two main components: an Evaluator that assesses
code q... |
2502.07400 | Explainable Multimodal Machine Learning for Revealing Structure-Property
Relationships in Carbon Nanotube Fibers | cond-mat.mtrl-sci cond-mat.soft cs.AI cs.LG physics.data-an | In this study, we propose Explainable Multimodal Machine Learning (EMML),
which integrates the analysis of diverse data types (multimodal data) using
factor analysis for feature extraction with Explainable AI (XAI), for carbon
nanotube (CNT) fibers prepared from aqueous dispersions. This method is a
powerful approach... |
2502.07401 | Enhancing Higher Education with Generative AI: A Multimodal Approach for
Personalised Learning | cs.HC cs.AI | This research explores the opportunities of Generative AI (GenAI) in the
realm of higher education through the design and development of a multimodal
chatbot for an undergraduate course. Leveraging the ChatGPT API for nuanced
text-based interactions and Google Bard for advanced image analysis and
diagram-to-code conv... |
2502.07403 | Extended monocular 3D imaging | cs.CV physics.optics | 3D vision is of paramount importance for numerous applications ranging from
machine intelligence to precision metrology. Despite much recent progress, the
majority of 3D imaging hardware remains bulky and complicated and provides much
lower image resolution compared to their 2D counterparts. Moreover, there are
many ... |
2502.07404 | Human-in-the-Loop Annotation for Image-Based Engagement Estimation:
Assessing the Impact of Model Reliability on Annotation Accuracy | cs.HC cs.AI cs.CV | Human-in-the-loop (HITL) frameworks are increasingly recognized for their
potential to improve annotation accuracy in emotion estimation systems by
combining machine predictions with human expertise. This study focuses on
integrating a high-performing image-based emotion model into a HITL annotation
framework to eval... |
2502.07405 | Coupling Agent-Based Simulations and VR universes: the case of GAMA and
Unity | cs.MA | Agent-based models (ABMs) and video games, including those taking advantage
of virtual reality (VR), have undergone a remarkable parallel evolution,
achieving impressive levels of complexity and sophistication. This paper argues
that while ABMs prioritize scientific analysis and understanding and VR aims
for immersiv... |
2502.07408 | No Data, No Optimization: A Lightweight Method To Disrupt Neural
Networks With Sign-Flips | cs.LG cs.AI cs.CV | Deep Neural Networks (DNNs) can be catastrophically disrupted by flipping
only a handful of sign bits in their parameters. We introduce Deep Neural
Lesion (DNL), a data-free, lightweight method that locates these critical
parameters and triggers massive accuracy drops. We validate its efficacy on a
wide variety of co... |
2502.07409 | MGPATH: Vision-Language Model with Multi-Granular Prompt Learning for
Few-Shot WSI Classification | cs.CV cs.LG | Whole slide pathology image classification presents challenges due to
gigapixel image sizes and limited annotation labels, hindering model
generalization. This paper introduces a prompt learning method to adapt large
vision-language models for few-shot pathology classification. We first extend
the Prov-GigaPath visio... |
2502.07411 | EgoTextVQA: Towards Egocentric Scene-Text Aware Video Question Answering | cs.CV cs.MM | We introduce EgoTextVQA, a novel and rigorously constructed benchmark for
egocentric QA assistance involving scene text. EgoTextVQA contains 1.5K
ego-view videos and 7K scene-text aware questions that reflect real-user needs
in outdoor driving and indoor house-keeping activities. The questions are
designed to elicit ... |
2502.07412 | Mapping the Intellectual Structure of Social Network Research: A
Comparative Bibliometric Analysis | cs.SI | Network science is an interdisciplinary field that transcends traditional
academic boundaries, offering profound insights into complex systems across
disciplines. This study conducts a bibliometric analysis of three leading
journals, Social Networks, Network Science, and the Journal of Complex
Networks, each represen... |
2502.07414 | Sample Weight Averaging for Stable Prediction | cs.LG | The challenge of Out-of-Distribution (OOD) generalization poses a
foundational concern for the application of machine learning algorithms to
risk-sensitive areas. Inspired by traditional importance weighting and
propensity weighting methods, prior approaches employ an independence-based
sample reweighting procedure. ... |
2502.07415 | Quantification of model error for inverse problems in the Weak Neural
Variational Inference framework | stat.ML cs.LG | We present a novel extension of the Weak Neural Variational Inference (WNVI)
framework for probabilistic material property estimation that explicitly
quantifies model errors in PDE-based inverse problems. Traditional approaches
assume the correctness of all governing equations, including potentially
unreliable consti... |
2502.07417 | Fast-COS: A Fast One-Stage Object Detector Based on Reparameterized
Attention Vision Transformer for Autonomous Driving | cs.CV | The perception system is a a critical role of an autonomous driving system
for ensuring safety. The driving scene perception system fundamentally
represents an object detection task that requires achieving a balance between
accuracy and processing speed. Many contemporary methods focus on improving
detection accuracy... |
2502.07418 | Entity Linking using LLMs for Automated Product Carbon Footprint
Estimation | cs.CL | Growing concerns about climate change and sustainability are driving
manufacturers to take significant steps toward reducing their carbon
footprints. For these manufacturers, a first step towards this goal is to
identify the environmental impact of the individual components of their
products. We propose a system leve... |
2502.07422 | MoENAS: Mixture-of-Expert based Neural Architecture Search for jointly
Accurate, Fair, and Robust Edge Deep Neural Networks | cs.LG cs.CV | There has been a surge in optimizing edge Deep Neural Networks (DNNs) for
accuracy and efficiency using traditional optimization techniques such as
pruning, and more recently, employing automatic design methodologies. However,
the focus of these design techniques has often overlooked critical metrics such
as fairness... |
2502.07423 | Towards a Formal Theory of the Need for Competence via Computational
Intrinsic Motivation | cs.AI | Computational models offer powerful tools for formalising psychological
theories, making them both testable and applicable in digital contexts.
However, they remain little used in the study of motivation within psychology.
We focus on the "need for competence", postulated as a key basic human need
within Self-Determi... |
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