id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.06052 | A Comprehensive Energy Management Application Method considering Smart
Home Occupant Behavior using IoT and Real Big Data | eess.SY cs.SY | One of the most far-reaching use cases of the internet of things is in smart
grid and smart home operation. The smart home concept allows residents to
control, monitor, and manage their energy consumption with minimum loss and
self-involvement. Since each household's lifestyle and energy consumption is
unique, the ma... |
2502.06058 | Regular LDPC codes on BMS wiretap channels: Security bounds | cs.IT math.IT | We improve the secrecy guarantees for transmission over general binary
memoryless symmetric wiretap channels that relies on regular LDPC codes.
Previous works showed that LDPC codes achieve secrecy capacity of some classes
of wiretap channels while leaking $o(n)$ bits of information over $n$ uses of
the channel. In t... |
2502.06060 | Training Language Models for Social Deduction with Multi-Agent
Reinforcement Learning | cs.AI cs.CL cs.LG cs.MA | Communicating in natural language is a powerful tool in multi-agent settings,
as it enables independent agents to share information in partially observable
settings and allows zero-shot coordination with humans. However, most prior
works are limited as they either rely on training with large amounts of human
demonstr... |
2502.06061 | Online Reward-Weighted Fine-Tuning of Flow Matching with Wasserstein
Regularization | cs.LG cs.AI cs.CV stat.ML | Recent advancements in reinforcement learning (RL) have achieved great
success in fine-tuning diffusion-based generative models. However, fine-tuning
continuous flow-based generative models to align with arbitrary user-defined
reward functions remains challenging, particularly due to issues such as policy
collapse fr... |
2502.06062 | Multi-modal Data Fusion and Deep Ensemble Learning for Accurate Crop
Yield Prediction | eess.IV cs.AI | This study introduces RicEns-Net, a novel Deep Ensemble model designed to
predict crop yields by integrating diverse data sources through multimodal data
fusion techniques. The research focuses specifically on the use of synthetic
aperture radar (SAR), optical remote sensing data from Sentinel 1, 2, and 3
satellites,... |
2502.06065 | Benchmarking Prompt Sensitivity in Large Language Models | cs.CL cs.AI cs.IR | Large language Models (LLMs) are highly sensitive to variations in prompt
formulation, which can significantly impact their ability to generate accurate
responses. In this paper, we introduce a new task, Prompt Sensitivity
Prediction, and a dataset PromptSET designed to investigate the effects of
slight prompt variat... |
2502.06067 | Lipschitz-Driven Inference: Bias-corrected Confidence Intervals for
Spatial Linear Models | stat.ML cs.LG stat.ME | Linear models remain ubiquitous in modern spatial applications - including
climate science, public health, and economics - due to their interpretability,
speed, and reproducibility. While practitioners generally report a form of
uncertainty, popular spatial uncertainty quantification methods do not jointly
handle mod... |
2502.06072 | ID policy (with reassignment) is asymptotically optimal for
heterogeneous weakly-coupled MDPs | cs.LG math.OC math.PR | Heterogeneity poses a fundamental challenge for many real-world large-scale
decision-making problems but remains largely understudied. In this paper, we
study the fully heterogeneous setting of a prominent class of such problems,
known as weakly-coupled Markov decision processes (WCMDPs). Each WCMDP consists
of $N$ a... |
2502.06075 | Deconstructing Depression Stigma: Integrating AI-driven Data Collection
and Analysis with Causal Knowledge Graphs | cs.HC cs.CL cs.CY | Mental-illness stigma is a persistent social problem, hampering both
treatment-seeking and recovery. Accordingly, there is a pressing need to
understand it more clearly, but analyzing the relevant data is highly
labor-intensive. Therefore, we designed a chatbot to engage participants in
conversations; coded those con... |
2502.06076 | A Planning Framework for Adaptive Labeling | cs.LG | Ground truth labels/outcomes are critical for advancing scientific and
engineering applications, e.g., evaluating the treatment effect of an
intervention or performance of a predictive model. Since randomly sampling
inputs for labeling can be prohibitively expensive, we introduce an adaptive
labeling framework where ... |
2502.06079 | Debiasing Guidance for Discrete Diffusion with Sequential Monte Carlo | cs.LG | Discrete diffusion models are a class of generative models that produce
samples from an approximated data distribution within a discrete state space.
Often, there is a need to target specific regions of the data distribution.
Current guidance methods aim to sample from a distribution with mass
proportional to $p_0(x_... |
2502.06084 | Physics-Guided Foundation Model for Scientific Discovery: An Application
to Aquatic Science | cs.LG cs.AI cs.NE | Physics-guided machine learning (PGML) has become a prevalent approach in
studying scientific systems due to its ability to integrate scientific theories
for enhancing machine learning (ML) models. However, most PGML approaches are
tailored to isolated and relatively simple tasks, which limits their
applicability to ... |
2502.06086 | Is a Peeled Apple Still Red? Evaluating LLMs' Ability for Conceptual
Combination with Property Type | cs.CL | Conceptual combination is a cognitive process that merges basic concepts,
enabling the creation of complex expressions. During this process, the
properties of combination (e.g., the whiteness of a peeled apple) can be
inherited from basic concepts, newly emerge, or be canceled. However, previous
studies have evaluate... |
2502.06087 | ConMeC: A Dataset for Metonymy Resolution with Common Nouns | cs.CL | Metonymy plays an important role in our daily communication. People naturally
think about things using their most salient properties or commonly related
concepts. For example, by saying "The bus decided to skip our stop today," we
actually mean that the bus driver made the decision, not the bus. Prior work on
metonym... |
2502.06089 | On the Computability of Multiclass PAC Learning | cs.LG stat.ML | We study the problem of computable multiclass learnability within the
Probably Approximately Correct (PAC) learning framework of Valiant (1984). In
the recently introduced computable PAC (CPAC) learning framework of Agarwal et
al. (2020), both learners and the functions they output are required to be
computable. We f... |
2502.06094 | Fair-MoE: Fairness-Oriented Mixture of Experts in Vision-Language Models | cs.CV | Fairness is a fundamental principle in medical ethics. Vision Language Models
(VLMs) have shown significant potential in the medical field due to their
ability to leverage both visual and linguistic contexts, reducing the need for
large datasets and enabling the performance of complex tasks. However, the
exploration ... |
2502.06095 | Rateless Joint Source-Channel Coding, and a Blueprint for 6G Semantic
Communications System Design | cs.IT cs.AI math.IT | This paper introduces rateless joint source-channel coding (rateless JSCC).
The code is rateless in that it is designed and optimized for a continuum of
coding rates such that it achieves a desired distortion for any rate in that
continuum. We further introduce rate-adaptive and stable communication link
operation to... |
2502.06096 | Post-detection inference for sequential changepoint localization | stat.ML cs.AI cs.LG stat.ME | This paper addresses a fundamental but largely unexplored challenge in
sequential changepoint analysis: conducting inference following a detected
change. We study the problem of localizing the changepoint using only the data
observed up to a data-dependent stopping time at which a sequential detection
algorithm $\mat... |
2502.06097 | NLGR: Utilizing Neighbor Lists for Generative Rerank in Personalized
Recommendation Systems | cs.IR cs.AI | Reranking plays a crucial role in modern multi-stage recommender systems by
rearranging the initial ranking list. Due to the inherent challenges of
combinatorial search spaces, some current research adopts an
evaluator-generator paradigm, with a generator generating feasible sequences
and an evaluator selecting the b... |
2502.06099 | Fine-Tuning Federated Learning-Based Intrusion Detection Systems for
Transportation IoT | cs.LG | The rapid advancement of machine learning (ML) and on-device computing has
revolutionized various industries, including transportation, through the
development of Connected and Autonomous Vehicles (CAVs) and Intelligent
Transportation Systems (ITS). These technologies improve traffic management and
vehicle safety, bu... |
2502.06100 | Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text
Recognition | cs.CV eess.SP | Online Handwritten Text Recognition (OLHTR) has gained considerable attention
for its diverse range of applications. Current approaches usually treat OLHTR
as a sequence recognition task, employing either a single trajectory or image
encoder, or multi-stream encoders, combined with a CTC or attention-based
recognitio... |
2502.06101 | RALLRec: Improving Retrieval Augmented Large Language Model
Recommendation with Representation Learning | cs.IR cs.CL | Large Language Models (LLMs) have been integrated into recommendation systems
to enhance user behavior comprehension. The Retrieval Augmented Generation
(RAG) technique is further incorporated into these systems to retrieve more
relevant items and improve system performance. However, existing RAG methods
rely primari... |
2502.06105 | Comprehensive Framework for Evaluating Conversational AI Chatbots | cs.CY cs.AI | Conversational AI chatbots are transforming industries by streamlining
customer service, automating transactions, and enhancing user engagement.
However, evaluating these systems remains a challenge, particularly in
financial services, where compliance, user trust, and operational efficiency
are critical. This paper ... |
2502.06106 | Circuit-tuning: A Mechanistic Approach for Identifying Parameter
Redundancy and Fine-tuning Neural Networks | cs.LG cs.AI cs.CL | The study of mechanistic interpretability aims to reverse-engineer a model to
explain its behaviors. While recent studies have focused on the static
mechanism of a certain behavior, the training dynamics inside a model remain to
be explored. In this work, we develop an interpretable method for fine-tuning
and reveal ... |
2502.06109 | CDM: Contact Diffusion Model for Multi-Contact Point Localization | cs.RO | In this paper, we propose a Contact Diffusion Model (CDM), a novel
learning-based approach for multi-contact point localization. We consider a
robot equipped with joint torque sensors and a force/torque sensor at the base.
By leveraging a diffusion model, CDM addresses the singularity where multiple
pairs of contact ... |
2502.06111 | CSR-Bench: Benchmarking LLM Agents in Deployment of Computer Science
Research Repositories | cs.SE cs.AI cs.LG | The increasing complexity of computer science research projects demands more
effective tools for deploying code repositories. Large Language Models (LLMs),
such as Anthropic Claude and Meta Llama, have demonstrated significant
advancements across various fields of computer science research, including the
automation o... |
2502.06112 | Pcodec: Better Compression for Numerical Sequences | cs.IT cs.DS math.IT | We present Pcodec (Pco), a format and algorithm for losslessly compressing
numerical sequences. Pco's core and most novel component is a binning algorithm
that quickly converges to the true entropy of smoothly, independently, and
identically distributed (SIID) data. To automatically handle more general data,
Pco has ... |
2502.06113 | Towards Bio-inspired Heuristically Accelerated Reinforcement Learning
for Adaptive Underwater Multi-Agents Behaviour | cs.RO cs.SY eess.SY | This paper describes the problem of coordination of an autonomous Multi-Agent
System which aims to solve the coverage planning problem in a complex
environment. The considered applications are the detection and identification
of objects of interest while covering an area. These tasks, which are highly
relevant for sp... |
2502.06114 | A Novel Multi-Teacher Knowledge Distillation for Real-Time Object
Detection using 4D Radar | cs.CV | Accurate 3D object detection is crucial for safe autonomous navigation,
requiring reliable performance across diverse weather conditions. While LiDAR
performance deteriorates in challenging weather, Radar systems maintain their
reliability. Traditional Radars have limitations due to their lack of elevation
data, but ... |
2502.06115 | Task-driven Layerwise Additive Activation Intervention | cs.CL cs.LG | Modern language models (LMs) have significantly advanced generative modeling
in natural language processing (NLP). Despite their success, LMs often struggle
with adaptation to new contexts in real-time applications. A promising approach
to task adaptation is activation intervention, which steers the LMs' generation
p... |
2502.06116 | Event Vision Sensor: A Review | physics.ins-det cs.CV | By monitoring temporal contrast, event-based vision sensors can provide high
temporal resolution and low latency while maintaining low power consumption and
simplicity in circuit structure. These characteristics have garnered
significant attention in both academia and industry. In recent years, the
application of bac... |
2502.06117 | Revisiting Dynamic Graph Clustering via Matrix Factorization | cs.LG cs.AI stat.ML | Dynamic graph clustering aims to detect and track time-varying clusters in
dynamic graphs, revealing the evolutionary mechanisms of complex real-world
dynamic systems. Matrix factorization-based methods are promising approaches
for this task; however, these methods often struggle with scalability and can
be time-cons... |
2502.06118 | Token-Domain Multiple Access: Exploiting Semantic Orthogonality for
Collision Mitigation | cs.IT eess.SP math.IT | Token communications is an emerging generative semantic communication concept
that reduces transmission rates by using context and transformer-based token
processing, with tokens serving as universal semantic units. In this paper, we
propose a semantic multiple access scheme in the token domain, referred to as
ToDMA,... |
2502.06119 | An Appearance Defect Detection Method for Cigarettes Based on
C-CenterNet | cs.CV | Due to the poor adaptability of traditional methods in the cigarette
detection task on the automatic cigarette production line, it is difficult to
accurately identify whether a cigarette has defects and the types of defects;
thus, a cigarette appearance defect detection method based on C-CenterNet is
proposed. This d... |
2502.06123 | Real-Time LiDAR Point Cloud Compression and Transmission for
Resource-constrained Robots | cs.RO | LiDARs are widely used in autonomous robots due to their ability to provide
accurate environment structural information. However, the large size of point
clouds poses challenges in terms of data storage and transmission. In this
paper, we propose a novel point cloud compression and transmission framework
for resource... |
2502.06124 | Foundation Model of Electronic Medical Records for Adaptive Risk
Estimation | cs.LG cs.AI | We developed the Enhanced Transformer for Health Outcome Simulation (ETHOS),
an AI model that tokenizes patient health timelines (PHTs) from EHRs. ETHOS
predicts future PHTs using transformer-based architectures. The Adaptive Risk
Estimation System (ARES) employs ETHOS to compute dynamic and personalized risk
probabi... |
2502.06126 | Graph Pseudotime Analysis and Neural Stochastic Differential Equations
for Analyzing Retinal Degeneration Dynamics and Beyond | cs.LG | Understanding disease progression at the molecular pathway level usually
requires capturing both structural dependencies between pathways and the
temporal dynamics of disease evolution. In this work, we solve the former
challenge by developing a biologically informed graph-forming method to
efficiently construct path... |
2502.06127 | Improved YOLOv5s model for key components detection of power
transmission lines | cs.CV cs.AI | High-voltage transmission lines are located far from the road, resulting in
inconvenient inspection work and rising maintenance costs. Intelligent
inspection of power transmission lines has become increasingly important.
However, subsequent intelligent inspection relies on accurately detecting
various key components.... |
2502.06128 | Intelligent Reconfigurable Optical Wireless Ether | eess.SY cs.SY eess.SP | Optical wireless communication (OWC) uses light for wireless data
transmission, potentially providing faster and more secure communication than
traditional radio-frequency-based techniques like Wi-Fi. However, light's high
directionality and its limited penetration ability restrict the signal
coverage. To address thi... |
2502.06130 | Self-Correcting Decoding with Generative Feedback for Mitigating
Hallucinations in Large Vision-Language Models | cs.CV cs.CL | While recent Large Vision-Language Models (LVLMs) have shown remarkable
performance in multi-modal tasks, they are prone to generating hallucinatory
text responses that do not align with the given visual input, which restricts
their practical applicability in real-world scenarios. In this work, inspired
by the observ... |
2502.06132 | Enhancing Document Key Information Localization Through Data
Augmentation | cs.CV cs.CL | The Visually Rich Form Document Intelligence and Understanding (VRDIU) Track
B focuses on the localization of key information in document images. The goal
is to develop a method capable of localizing objects in both digital and
handwritten documents, using only digital documents for training. This paper
presents a si... |
2502.06134 | Integrating Sequence and Image Modeling in Irregular Medical Time Series
Through Self-Supervised Learning | cs.CV cs.AI | Medical time series are often irregular and face significant missingness,
posing challenges for data analysis and clinical decision-making. Existing
methods typically adopt a single modeling perspective, either treating series
data as sequences or transforming them into image representations for further
classificatio... |
2502.06136 | Graph Neural Networks at a Fraction | cs.LG cs.AI | Graph Neural Networks (GNNs) have emerged as powerful tools for learning
representations of graph-structured data. In addition to real-valued GNNs,
quaternion GNNs also perform well on tasks on graph-structured data. With the
aim of reducing the energy footprint, we reduce the model size while
maintaining accuracy co... |
2502.06138 | Enhanced Hybrid Deep Learning Approach for Botnet Attacks Detection in
IoT Environment | cs.CR cs.CV | Cyberattacks in an Internet of Things (IoT) environment can have significant
impacts because of the interconnected nature of devices and systems. An
attacker uses a network of compromised IoT devices in a botnet attack to carry
out various harmful activities. Detecting botnet attacks poses several
challenges because ... |
2502.06139 | LCIRC: A Recurrent Compression Approach for Efficient Long-form Context
and Query Dependent Modeling in LLMs | cs.CL | While large language models (LLMs) excel in generating coherent and
contextually rich outputs, their capacity to efficiently handle long-form
contexts is limited by fixed-length position embeddings. Additionally, the
computational cost of processing long sequences increases quadratically, making
it challenging to ext... |
2502.06141 | Mixed Reality Outperforms Virtual Reality for Remote Error Resolution in
Pick-and-Place Tasks | cs.RO cs.HC | This study evaluates the performance and usability of Mixed Reality (MR),
Virtual Reality (VR), and camera stream interfaces for remote error resolution
tasks, such as correcting warehouse packaging errors. Specifically, we consider
a scenario where a robotic arm halts after detecting an error, requiring a
remote ope... |
2502.06142 | Linear Bandits with Partially Observable Features | stat.ML cs.LG | We introduce a novel linear bandit problem with partially observable
features, resulting in partial reward information and spurious estimates.
Without proper address for latent part, regret possibly grows linearly in
decision horizon $T$, as their influence on rewards are unknown. To tackle
this, we propose a novel a... |
2502.06145 | Animate Anyone 2: High-Fidelity Character Image Animation with
Environment Affordance | cs.CV | Recent character image animation methods based on diffusion models, such as
Animate Anyone, have made significant progress in generating consistent and
generalizable character animations. However, these approaches fail to produce
reasonable associations between characters and their environments. To address
this limit... |
2502.06146 | Guided Exploration for Efficient Relational Model Learning | cs.LG cs.AI | Efficient exploration is critical for learning relational models in
large-scale environments with complex, long-horizon tasks. Random exploration
methods often collect redundant or irrelevant data, limiting their ability to
learn accurate relational models of the environment. Goal-literal babbling
(GLIB) improves upo... |
2502.06147 | LegalViz: Legal Text Visualization by Text To Diagram Generation | cs.CL | Legal documents including judgments and court orders require highly
sophisticated legal knowledge for understanding. To disclose expert knowledge
for non-experts, we explore the problem of visualizing legal texts with
easy-to-understand diagrams and propose a novel dataset of LegalViz with 23
languages and 7,010 case... |
2502.06148 | Optimizing Knowledge Integration in Retrieval-Augmented Generation with
Self-Selection | cs.CL cs.IR | Retrieval-Augmented Generation (RAG), which integrates external knowledge
into Large Language Models (LLMs), has proven effective in enabling LLMs to
produce more accurate and reliable responses. However, it remains a significant
challenge how to effectively integrate external retrieved knowledge with
internal parame... |
2502.06149 | Reward-Based Collision-Free Algorithm for Trajectory Planning of
Autonomous Robots | cs.RO cs.SY eess.SY | This paper introduces a new mission planning algorithm for autonomous robots
that enables the reward-based selection of an optimal waypoint sequence from a
predefined set. The algorithm computes a feasible trajectory and corresponding
control inputs for a robot to navigate between waypoints while avoiding
obstacles, ... |
2502.06150 | Scaling Public Health Text Annotation: Zero-Shot Learning vs.
Crowdsourcing for Improved Efficiency and Labeling Accuracy | cs.CL | Public health researchers are increasingly interested in using social media
data to study health-related behaviors, but manually labeling this data can be
labor-intensive and costly. This study explores whether zero-shot labeling
using large language models (LLMs) can match or surpass conventional
crowd-sourced annot... |
2502.06151 | Powerformer: A Transformer with Weighted Causal Attention for
Time-series Forecasting | cs.LG cs.AI stat.ML | Transformers have recently shown strong performance in time-series
forecasting, but their all-to-all attention mechanism overlooks the (temporal)
causal and often (temporally) local nature of data. We introduce Powerformer, a
novel Transformer variant that replaces noncausal attention weights with causal
weights that... |
2502.06152 | The Value of Information in Human-AI Decision-making | cs.AI cs.LG | Humans and AIs are often paired on decision tasks with the expectation of
achieving complementary performance, where the combination of human and AI
outperforms either one alone. However, how to improve performance of a human-AI
team is often not clear without knowing more about what particular information
and strate... |
2502.06153 | Low Tensor-Rank Adaptation of Kolmogorov--Arnold Networks | cs.LG cs.AI | Kolmogorov--Arnold networks (KANs) have demonstrated their potential as an
alternative to multi-layer perceptions (MLPs) in various domains, especially
for science-related tasks. However, transfer learning of KANs remains a
relatively unexplored area. In this paper, inspired by Tucker decomposition of
tensors and evi... |
2502.06155 | Efficient-vDiT: Efficient Video Diffusion Transformers With Attention
Tile | cs.CV | Despite the promise of synthesizing high-fidelity videos, Diffusion
Transformers (DiTs) with 3D full attention suffer from expensive inference due
to the complexity of attention computation and numerous sampling steps. For
example, the popular Open-Sora-Plan model consumes more than 9 minutes for
generating a single ... |
2502.06156 | Axial current as the origin of quantum intrinsic orbital angular
momentum | hep-ph cs.SY eess.SY | We show that it is impossible to experimentally observe the quantum intrinsic
orbital angular momentum (IOAM) effect without its axial current. Broadly
speaking, we argue that the spiral or interference characteristics of the axial
current density determine the occurrence of nonlinear or tunneling effects in
any spac... |
2502.06159 | Analysis and Optimization of Robustness in Multiplex Flow Networks
Against Cascading Failures | eess.SY cs.SY | Networked systems are susceptible to cascading failures, where the failure of
an initial set of nodes propagates through the network, often leading to
system-wide failures. In this work, we propose a multiplex flow network model
to study robustness against cascading failures triggered by random failures.
The model is... |
2502.06163 | Scalable k-Means Clustering for Large k via Seeded Approximate
Nearest-Neighbor Search | cs.LG cs.CG stat.ML | For very large values of $k$, we consider methods for fast $k$-means
clustering of massive datasets with $10^7\sim10^9$ points in high-dimensions
($d\geq100$). All current practical methods for this problem have runtimes at
least $\Omega(k^2)$. We find that initialization routines are not a bottleneck
for this case. ... |
2502.06164 | Generalized Temporal Tensor Decomposition with Rank-revealing Latent-ODE | cs.LG stat.ML | Tensor decomposition is a fundamental tool for analyzing multi-dimensional
data by learning low-rank factors to represent high-order interactions. While
recent works on temporal tensor decomposition have made significant progress by
incorporating continuous timestamps in latent factors, they still struggle with
gener... |
2502.06166 | Portable, High-Frequency, and High-Voltage Control Circuits for
Untethered Miniature Robots Driven by Dielectric Elastomer Actuators | cs.RO | In this work, we propose a high-voltage, high-frequency control circuit for
the untethered applications of dielectric elastomer actuators (DEAs). The
circuit board leverages low-voltage resistive components connected in series to
control voltages of up to 1.8 kV within a compact size, suitable for
frequencies ranging... |
2502.06167 | Universal Approximation of Visual Autoregressive Transformers | cs.LG cs.AI cs.CL cs.CV | We investigate the fundamental limits of transformer-based foundation models,
extending our analysis to include Visual Autoregressive (VAR) transformers. VAR
represents a big step toward generating images using a novel, scalable,
coarse-to-fine ``next-scale prediction'' framework. These models set a new
quality bar, ... |
2502.06168 | Dynamic Pricing with Adversarially-Censored Demands | stat.ML cs.LG econ.EM math.OC | We study an online dynamic pricing problem where the potential demand at each
time period $t=1,2,\ldots, T$ is stochastic and dependent on the price.
However, a perishable inventory is imposed at the beginning of each time $t$,
censoring the potential demand if it exceeds the inventory level. To address
this problem,... |
2502.06170 | An Interpretable Implicit-Based Approach for Modeling Local Spatial
Effects: A Case Study of Global Gross Primary Productivity | cs.CV cs.AI cs.LG | In Earth sciences, unobserved factors exhibit non-stationary spatial
distributions, causing the relationships between features and targets to
display spatial heterogeneity. In geographic machine learning tasks,
conventional statistical learning methods often struggle to capture spatial
heterogeneity, leading to unsat... |
2502.06171 | A Data-Efficient Pan-Tumor Foundation Model for Oncology CT
Interpretation | eess.IV cs.CV | Artificial intelligence-assisted imaging analysis has made substantial
strides in tumor diagnosis and management. Here we present PASTA, a pan-tumor
CT foundation model that achieves state-of-the-art performance on 45 of 46
representative oncology tasks -- including lesion segmentation, tumor detection
in plain CT, t... |
2502.06172 | PLATTER: A Page-Level Handwritten Text Recognition System for Indic
Scripts | cs.CV | In recent years, the field of Handwritten Text Recognition (HTR) has seen the
emergence of various new models, each claiming to perform competitively better
than the other in specific scenarios. However, making a fair comparison of
these models is challenging due to inconsistent choices and diversity in test
sets. Fu... |
2502.06173 | Uncertainty-Aware Adaptation of Large Language Models for
Protein-Protein Interaction Analysis | cs.LG cs.AI cs.CL stat.AP stat.ML | Identification of protein-protein interactions (PPIs) helps derive cellular
mechanistic understanding, particularly in the context of complex conditions
such as neurodegenerative disorders, metabolic syndromes, and cancer. Large
Language Models (LLMs) have demonstrated remarkable potential in predicting
protein struc... |
2502.06178 | Bayesian Optimization by Kernel Regression and Density-based Exploration | math.OC cs.LG stat.ML | Bayesian optimization is highly effective for optimizing
expensive-to-evaluate black-box functions, but it faces significant
computational challenges due to the high computational complexity of Gaussian
processes, which results in a total time complexity that is quartic with
respect to the number of iterations. To ad... |
2502.06180 | RideKE: Leveraging Low-Resource, User-Generated Twitter Content for
Sentiment and Emotion Detection in Kenyan Code-Switched Dataset | cs.CL cs.AI | Social media has become a crucial open-access platform for individuals to
express opinions and share experiences. However, leveraging low-resource
language data from Twitter is challenging due to scarce, poor-quality content
and the major variations in language use, such as slang and code-switching.
Identifying tweet... |
2502.06181 | CANeRV: Content Adaptive Neural Representation for Video Compression | cs.CV | Recent advances in video compression introduce implicit neural representation
(INR) based methods, which effectively capture global dependencies and
characteristics of entire video sequences. Unlike traditional and deep learning
based approaches, INR-based methods optimize network parameters from a global
perspective... |
2502.06185 | Discourse-Driven Evaluation: Unveiling Factual Inconsistency in Long
Document Summarization | cs.CL cs.AI | Detecting factual inconsistency for long document summarization remains
challenging, given the complex structure of the source article and long summary
length. In this work, we study factual inconsistency errors and connect them
with a line of discourse analysis. We find that errors are more common in
complex sentenc... |
2502.06186 | Learning the Frequency Dynamics of the Power System Using Higher-order
Dynamic Mode Decomposition | eess.SY cs.SY | The increasing penetration of renewable energy sources, characterised by low
inertia and intermittent disturbances, presents substantial challenges to power
system stability. As critical indicators of system stability, frequency
dynamics and associated oscillatory phenomena have attracted significant
research attenti... |
2502.06189 | Multi-Level Decoupled Relational Distillation for Heterogeneous
Architectures | cs.CV | Heterogeneous distillation is an effective way to transfer knowledge from
cross-architecture teacher models to student models. However, existing
heterogeneous distillation methods do not take full advantage of the dark
knowledge hidden in the teacher's output, limiting their performance.To this
end, we propose a nove... |
2502.06190 | Is Science Inevitable? | cs.DL cs.SI | Using large-scale citation data and a breakthrough metric, the study
systematically evaluates the inevitability of scientific breakthroughs. We find
that scientific breakthroughs emerge as multiple discoveries rather than
singular events. Through analysis of over 40 million journal articles, we
identify multiple disc... |
2502.06192 | Right Time to Learn:Promoting Generalization via Bio-inspired Spacing
Effect in Knowledge Distillation | cs.LG cs.AI | Knowledge distillation (KD) is a powerful strategy for training deep neural
networks (DNNs). Although it was originally proposed to train a more compact
``student'' model from a large ``teacher'' model, many recent efforts have
focused on adapting it to promote generalization of the model itself, such as
online KD an... |
2502.06193 | Can LLMs Replace Human Evaluators? An Empirical Study of LLM-as-a-Judge
in Software Engineering | cs.SE cs.AI | Recently, large language models (LLMs) have been deployed to tackle various
software engineering (SE) tasks like code generation, significantly advancing
the automation of SE tasks. However, assessing the quality of these
LLM-generated code and text remains challenging. The commonly used Pass@k
metric necessitates ex... |
2502.06194 | Multimodal Task Representation Memory Bank vs. Catastrophic Forgetting
in Anomaly Detection | cs.CV | Unsupervised Continuous Anomaly Detection (UCAD) faces significant challenges
in multi-task representation learning, with existing methods suffering from
incomplete representation and catastrophic forgetting. Unlike supervised
models, unsupervised scenarios lack prior information, making it difficult to
effectively d... |
2502.06195 | Calibration of Multiple Asynchronous Microphone Arrays using Hybrid TDOA | cs.SD cs.RO | Accurate calibration of acoustic sensing systems made of multiple
asynchronous microphone arrays is essential for satisfactory performance in
sound source localization and tracking. State-of-the-art calibration methods
for this type of system rely on the time difference of arrival and direction of
arrival measurement... |
2502.06196 | Improved Extrinsic Calibration of Acoustic Cameras via Batch
Optimization | cs.RO cs.SD | Acoustic cameras have found many applications in practice. Accurate and
reliable extrinsic calibration of the microphone array and visual sensors
within acoustic cameras is crucial for fusing visual and auditory measurements.
Existing calibration methods either require prior knowledge of the microphone
array geometry... |
2502.06200 | On the query complexity of sampling from non-log-concave distributions | cs.DS cs.LG stat.ML | We study the problem of sampling from a $d$-dimensional distribution with
density $p(x)\propto e^{-f(x)}$, which does not necessarily satisfy good
isoperimetric conditions.
Specifically, we show that for any $L,M$ satisfying $LM\ge d\ge 5$,
$\epsilon\in \left(0,\frac{1}{32}\right)$, and any algorithm with query
acc... |
2502.06201 | Comparing Image Segmentation Algorithms | cs.CV | This paper presents a novel approach for denoising binary images using
simulated annealing (SA), a global optimization technique that addresses the
inherent challenges of non convex energy functions. Binary images are often
corrupted by noise, necessitating effective restoration methods. We propose an
energy function... |
2502.06204 | Non-literal Understanding of Number Words by Language Models | cs.CL | Humans naturally interpret numbers non-literally, effortlessly combining
context, world knowledge, and speaker intent. We investigate whether large
language models (LLMs) interpret numbers similarly, focusing on hyperbole and
pragmatic halo effects. Through systematic comparison with human data and
computational mode... |
2502.06205 | C-3PO: Compact Plug-and-Play Proxy Optimization to Achieve Human-like
Retrieval-Augmented Generation | cs.CL cs.AI cs.LG | Retrieval-augmented generation (RAG) systems face a fundamental challenge in
aligning independently developed retrievers and large language models (LLMs).
Existing approaches typically involve modifying either component or introducing
simple intermediate modules, resulting in practical limitations and sub-optimal
per... |
2502.06207 | Unveiling the Capabilities of Large Language Models in Detecting
Offensive Language with Annotation Disagreement | cs.CL cs.AI | Large Language Models (LLMs) have become essential for offensive language
detection, yet their ability to handle annotation disagreement remains
underexplored. Disagreement samples, which arise from subjective
interpretations, pose a unique challenge due to their ambiguous nature.
Understanding how LLMs process these... |
2502.06208 | Product gales and Finite state dimension | cs.IT math.IT | In this work, we introduce the notion of product gales, which is the
modification of an $s$-gale such that $k$ separate bets can be placed at each
symbol. The product of the bets placed are taken into the capital function of
the product-gale. We show that Hausdorff dimension can be characterised using
product gales.
... |
2502.06209 | Enhancing Cost Efficiency in Active Learning with Candidate Set Query | cs.LG cs.CV | This paper introduces a cost-efficient active learning (AL) framework for
classification, featuring a novel query design called candidate set query.
Unlike traditional AL queries requiring the oracle to examine all possible
classes, our method narrows down the set of candidate classes likely to include
the ground-tru... |
2502.06210 | Position: Continual Learning Benefits from An Evolving Population over
An Unified Model | cs.LG | Deep neural networks have demonstrated remarkable success in machine
learning; however, they remain fundamentally ill-suited for Continual Learning
(CL). Recent research has increasingly focused on achieving CL without the need
for rehearsal. Among these, parameter isolation-based methods have proven
particularly eff... |
2502.06212 | AVSim -- Realistic Simulation Framework for Airborne and Vector-Borne
Disease Dynamics | eess.SY cs.SY | The COVID-19 pandemic underscored the critical need for rapid epidemic trend
identification and effective intervention strategies to mitigate disease
progression and its socio-economic impact. Concurrent with emerging threats,
endemic diseases like dengue continue to strain healthcare systems,
particularly in populou... |
2502.06215 | LessLeak-Bench: A First Investigation of Data Leakage in LLMs Across 83
Software Engineering Benchmarks | cs.SE cs.AI cs.CL | Large Language Models (LLMs) are widely utilized in software engineering (SE)
tasks, such as code generation and automated program repair. However, their
reliance on extensive and often undisclosed pre-training datasets raises
significant concerns about data leakage, where the evaluation benchmark data is
unintention... |
2502.06217 | Examining False Positives under Inference Scaling for Mathematical
Reasoning | cs.CL cs.AI | Recent advancements in language models have led to significant improvements
in mathematical reasoning across various benchmarks. However, most of these
benchmarks rely on automatic evaluation methods that only compare final answers
using heuristics, without verifying the underlying reasoning steps. This
limitation re... |
2502.06219 | Fully Exploiting Vision Foundation Model's Profound Prior Knowledge for
Generalizable RGB-Depth Driving Scene Parsing | cs.CV | Recent vision foundation models (VFMs), typically based on Vision Transformer
(ViT), have significantly advanced numerous computer vision tasks. Despite
their success in tasks focused solely on RGB images, the potential of VFMs in
RGB-depth driving scene parsing remains largely under-explored. In this
article, we tak... |
2502.06220 | FunduSAM: A Specialized Deep Learning Model for Enhanced Optic Disc and
Cup Segmentation in Fundus Images | cs.CV cs.IR | The Segment Anything Model (SAM) has gained popularity as a versatile image
segmentation method, thanks to its strong generalization capabilities across
various domains. However, when applied to optic disc (OD) and optic cup (OC)
segmentation tasks, SAM encounters challenges due to the complex structures,
low contras... |
2502.06221 | Interaction-aware Conformal Prediction for Crowd Navigation | cs.RO | During crowd navigation, robot motion plan needs to consider human motion
uncertainty, and the human motion uncertainty is dependent on the robot motion
plan. We introduce Interaction-aware Conformal Prediction (ICP) to alternate
uncertainty-aware robot motion planning and decision-dependent human motion
uncertainty ... |
2502.06227 | Unsupervised deep learning for semantic segmentation of multispectral
LiDAR forest point clouds | cs.CV | Point clouds captured with laser scanning systems from forest environments
can be utilized in a wide variety of applications within forestry and plant
ecology, such as the estimation of tree stem attributes, leaf angle
distribution, and above-ground biomass. However, effectively utilizing the data
in such tasks requi... |
2502.06231 | Falsification of Unconfoundedness by Testing Independence of Causal
Mechanisms | stat.ME cs.LG stat.ML | A major challenge in estimating treatment effects in observational studies is
the reliance on untestable conditions such as the assumption of no unmeasured
confounding. In this work, we propose an algorithm that can falsify the
assumption of no unmeasured confounding in a setting with observational data
from multiple... |
2502.06233 | Confidence Improves Self-Consistency in LLMs | cs.CL cs.AI | Self-consistency decoding enhances LLMs' performance on reasoning tasks by
sampling diverse reasoning paths and selecting the most frequent answer.
However, it is computationally expensive, as sampling many of these (lengthy)
paths is required to increase the chances that the correct answer emerges as
the most freque... |
2502.06235 | Conditioning and AGM-like belief change in the Desirability-Indifference
framework | cs.AI math.PR quant-ph | We show how the AGM framework for belief change (expansion, revision,
contraction) can be extended to deal with conditioning in the so-called
Desirability-Indifference framework, based on abstract notions of accepting and
rejecting options, as well as on abstract notions of events. This level of
abstraction allows us... |
2502.06238 | XNet-Enhanced Deep BSDE Method and Numerical Analysis | cs.CE | Solving high-dimensional semilinear parabolic partial differential equations
(PDEs) challenges traditional numerical methods due to the "curse of
dimensionality." Deep learning, particularly through the Deep BSDE method,
offers a promising alternative by leveraging neural networks' capability to
approximate high-dime... |
2502.06239 | Pre-Equalization Aided Grant-Free Massive Access in Massive MIMO System | eess.SP cs.IT math.IT | The spatial diversity and multiplexing advantages of massive
multi-input-multi-output (mMIMO) can significantly improve the capacity of
massive non-orthogonal multiple access (NOMA) in machine type communications.
However, state-of-the-art grant-free massive NOMA schemes for mMIMO systems
require accurate estimation ... |
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