id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.09082 | CoSER: Coordinating LLM-Based Persona Simulation of Established Roles | cs.CL cs.AI | Role-playing language agents (RPLAs) have emerged as promising applications
of large language models (LLMs). However, simulating established characters
presents a challenging task for RPLAs, due to the lack of authentic character
datasets and nuanced evaluation methods using such data. In this paper, we
present CoSER... |
2502.09083 | Show Me the Work: Fact-Checkers' Requirements for Explainable Automated
Fact-Checking | cs.HC cs.AI cs.CL | The pervasiveness of large language models and generative AI in online media
has amplified the need for effective automated fact-checking to assist
fact-checkers in tackling the increasing volume and sophistication of
misinformation. The complex nature of fact-checking demands that automated
fact-checking systems pro... |
2502.09084 | Application of Tabular Transformer Architectures for Operating System
Fingerprinting | cs.CR cs.LG cs.NI | Operating System (OS) fingerprinting is essential for network management and
cybersecurity, enabling accurate device identification based on network traffic
analysis. Traditional rule-based tools such as Nmap and p0f face challenges in
dynamic environments due to frequent OS updates and obfuscation techniques.
While ... |
2502.09085 | Multi-user Visible Light Communications with Probabilistic Constellation
Shaping and Precoding | eess.SY cs.SY | This paper proposes a joint design of probabilistic constellation shaping
(PCS) and precoding to enhance the sum-rate performance of multi-user visible
light communications (VLC) broadcast channels subject to signal amplitude
constraint. In the proposed design, the transmission probabilities of bipolar
$M$-pulse ampl... |
2502.09086 | A Hybrid Model for Few-Shot Text Classification Using Transfer and
Meta-Learning | cs.CL | With the continuous development of natural language processing (NLP)
technology, text classification tasks have been widely used in multiple
application fields. However, obtaining labeled data is often expensive and
difficult, especially in few-shot learning scenarios. To solve this problem,
this paper proposes a few... |
2502.09088 | Unsupervised Anomaly Detection on Implicit Shape representations for
Sarcopenia Detection | cs.CV cs.LG | Sarcopenia is an age-related progressive loss of muscle mass and strength
that significantly impacts daily life. A commonly studied criterion for
characterizing the muscle mass has been the combination of 3D imaging and
manual segmentations. In this paper, we instead study the muscles' shape. We
rely on an implicit n... |
2502.09089 | Semantic Ads Retrieval at Walmart eCommerce with Language Models
Progressively Trained on Multiple Knowledge Domains | cs.IR | Sponsored search in e-commerce poses several unique and complex challenges.
These challenges stem from factors such as the asymmetric language structure
between search queries and product names, the inherent ambiguity in user search
intent, and the vast volume of sparse and imbalanced search corpus data. The
role of ... |
2502.09093 | From Visuals to Vocabulary: Establishing Equivalence Between Image and
Text Token Through Autoregressive Pre-training in MLLMs | cs.CV | While MLLMs perform well on perceptual tasks, they lack precise multimodal
alignment, limiting performance. To address this challenge, we propose Vision
Dynamic Embedding-Guided Pretraining (VDEP), a hybrid autoregressive training
paradigm for MLLMs. Utilizing dynamic embeddings from the MLP following the
visual enco... |
2502.09097 | A Hybrid Transformer Model for Fake News Detection: Leveraging Bayesian
Optimization and Bidirectional Recurrent Unit | cs.CL | In this paper, we propose an optimized Transformer model that integrates
Bayesian algorithms with a Bidirectional Gated Recurrent Unit (BiGRU), and
apply it to fake news classification for the first time. First, we employ the
TF-IDF method to extract features from news texts and transform them into
numeric representa... |
2502.09100 | Logical Reasoning in Large Language Models: A Survey | cs.AI cs.CL | With the emergence of advanced reasoning models like OpenAI o3 and
DeepSeek-R1, large language models (LLMs) have demonstrated remarkable
reasoning capabilities. However, their ability to perform rigorous logical
reasoning remains an open question. This survey synthesizes recent advancements
in logical reasoning with... |
2502.09104 | One-shot Federated Learning Methods: A Practical Guide | cs.LG cs.AI | One-shot Federated Learning (OFL) is a distributed machine learning paradigm
that constrains client-server communication to a single round, addressing
privacy and communication overhead issues associated with multiple rounds of
data exchange in traditional Federated Learning (FL). OFL demonstrates the
practical poten... |
2502.09106 | Scaling Law for Stochastic Gradient Descent in Quadratically
Parameterized Linear Regression | cs.LG | In machine learning, the scaling law describes how the model performance
improves with the model and data size scaling up. From a learning theory
perspective, this class of results establishes upper and lower generalization
bounds for a specific learning algorithm. Here, the exact algorithm running
using a specific m... |
2502.09110 | Pulling Back the Curtain: Unsupervised Adversarial Detection via
Contrastive Auxiliary Networks | cs.CV | Deep learning models are widely employed in safety-critical applications yet
remain susceptible to adversarial attacks -- imperceptible perturbations that
can significantly degrade model performance. Conventional defense mechanisms
predominantly focus on either enhancing model robustness or detecting
adversarial inpu... |
2502.09111 | DenseSplat: Densifying Gaussian Splatting SLAM with Neural Radiance
Prior | cs.CV | Gaussian SLAM systems excel in real-time rendering and fine-grained
reconstruction compared to NeRF-based systems. However, their reliance on
extensive keyframes is impractical for deployment in real-world robotic
systems, which typically operate under sparse-view conditions that can result
in substantial holes in th... |
2502.09120 | The influence of visual and linguistic cues on ignorance inference in
Vision-Language Models | cs.CL | This study explored how Vision-Language Models (VLMs) process ignorance
implicatures with visual and linguistic cues. Particularly, we focused on the
effects of contexts (precise and approximate contexts) and modifier types (bare
numerals, superlative, and comparative modifiers), which were considered
pragmatic and s... |
2502.09122 | Improving Deep Regression with Tightness | cs.LG cs.AI cs.CV | For deep regression, preserving the ordinality of the targets with respect to
the feature representation improves performance across various tasks. However,
a theoretical explanation for the benefits of ordinality is still lacking. This
work reveals that preserving ordinality reduces the conditional entropy
$H(Z|Y)$ ... |
2502.09125 | Automatic Pruning via Structured Lasso with Class-wise Information | cs.CV cs.AI | Most pruning methods concentrate on unimportant filters of neural networks.
However, they face the loss of statistical information due to a lack of
consideration for class-wise data. In this paper, from the perspective of
leveraging precise class-wise information for model pruning, we utilize
structured lasso with gu... |
2502.09128 | A Novel Dialect-Aware Framework for the Classification of Arabic
Dialects and Emotions | cs.CL cs.LG | Arabic is one of the oldest languages still in use today. As a result,
several Arabic-speaking regions have developed dialects that are unique to
them. Dialect and emotion recognition have various uses in Arabic text
analysis, such as determining an online customer's origin based on their
comments. Furthermore, intel... |
2502.09130 | Finite-Time Analysis of Discrete-Time Stochastic Interpolants | cs.LG | The stochastic interpolant framework offers a powerful approach for
constructing generative models based on ordinary differential equations (ODEs)
or stochastic differential equations (SDEs) to transform arbitrary data
distributions. However, prior analyses of this framework have primarily focused
on the continuous-t... |
2502.09131 | A Stochastic Fundamental Lemma with Reduced Disturbance Data
Requirements | eess.SY cs.SY math.OC | Recently, the fundamental lemma by Willems et. al has been extended towards
stochastic LTI systems subject to process disturbances. Using this lemma
requires previously recorded data of inputs, outputs, and disturbances. In this
paper, we exploit causality concepts of stochastic control to propose a variant
of the st... |
2502.09135 | Interpreting and Steering Protein Language Models through Sparse
Autoencoders | cs.LG q-bio.BM | The rapid advancements in transformer-based language models have
revolutionized natural language processing, yet understanding the internal
mechanisms of these models remains a significant challenge. This paper explores
the application of sparse autoencoders (SAE) to interpret the internal
representations of protein ... |
2502.09137 | Trust Me, I Know the Way: Predictive Uncertainty in the Presence of
Shortcut Learning | cs.LG | The correct way to quantify predictive uncertainty in neural networks remains
a topic of active discussion. In particular, it is unclear whether the
state-of-the art entropy decomposition leads to a meaningful representation of
model, or epistemic, uncertainty (EU) in the light of a debate that pits
ignorance against... |
2502.09140 | Replay-free Online Continual Learning with Self-Supervised MultiPatches | cs.LG cs.CV | Online Continual Learning (OCL) methods train a model on a non-stationary
data stream where only a few examples are available at a time, often leveraging
replay strategies. However, usage of replay is sometimes forbidden, especially
in applications with strict privacy regulations. Therefore, we propose
Continual Mult... |
2502.09142 | LLM-Driven Augmented Reality Puppeteer: Controller-Free Voice-Commanded
Robot Teleoperation | cs.HC cs.RO | The integration of robotics and augmented reality (AR) presents
transformative opportunities for advancing human-robot interaction (HRI) by
improving usability, intuitiveness, and accessibility. This work introduces a
controller-free, LLM-driven voice-commanded AR puppeteering system, enabling
users to teleoperate a ... |
2502.09143 | Feature-based Graph Attention Networks Improve Online Continual Learning | cs.CV cs.LG | Online continual learning for image classification is crucial for models to
adapt to new data while retaining knowledge of previously learned tasks. This
capability is essential to address real-world challenges involving dynamic
environments and evolving data distributions. Traditional approaches
predominantly employ... |
2502.09148 | Multimodal HIE Lesion Segmentation in Neonates: A Comparative Study of
Loss Functions | cs.CV | Segmentation of Hypoxic-Ischemic Encephalopathy (HIE) lesions in neonatal MRI
is a crucial but challenging task due to diffuse multifocal lesions with
varying volumes and the limited availability of annotated HIE lesion datasets.
Using the BONBID-HIE dataset, we implemented a 3D U-Net with optimized
preprocessing, au... |
2502.09150 | Shortcut Learning Susceptibility in Vision Classifiers | cs.LG cs.CV | Shortcut learning, where machine learning models exploit spurious
correlations in data instead of capturing meaningful features, poses a
significant challenge to building robust and generalizable models. This
phenomenon is prevalent across various machine learning applications, including
vision, natural language proc... |
2502.09151 | Regularization can make diffusion models more efficient | cs.LG math.ST stat.ML stat.TH | Diffusion models are one of the key architectures of generative AI. Their
main drawback, however, is the computational costs. This study indicates that
the concept of sparsity, well known especially in statistics, can provide a
pathway to more efficient diffusion pipelines. Our mathematical guarantees
prove that spar... |
2502.09152 | Vertical Federated Continual Learning via Evolving Prototype Knowledge | cs.LG cs.NE | Vertical Federated Learning (VFL) has garnered significant attention as a
privacy-preserving machine learning framework for sample-aligned feature
federation. However, traditional VFL approaches do not address the challenges
of class and feature continual learning, resulting in catastrophic forgetting
of knowledge fr... |
2502.09155 | Use of Air Quality Sensor Network Data for Real-time Pollution-Aware POI
Suggestion | cs.IR | This demo paper presents AirSense-R, a privacy-preserving mobile application
that provides real-time, pollution-aware recommendations for points of interest
(POIs) in urban environments. By combining real-time air quality monitoring
data with user preferences, the proposed system aims to help users make
health-consci... |
2502.09156 | Improving TCM Question Answering through Tree-Organized Self-Reflective
Retrieval with LLMs | cs.CL | Objectives: Large language models (LLMs) can harness medical knowledge for
intelligent question answering (Q&A), promising support for auxiliary diagnosis
and medical talent cultivation. However, there is a deficiency of highly
efficient retrieval-augmented generation (RAG) frameworks within the domain of
Traditional... |
2502.09164 | E-MD3C: Taming Masked Diffusion Transformers for Efficient Zero-Shot
Object Customization | cs.CV cs.LG | We propose E-MD3C ($\underline{E}$fficient $\underline{M}$asked
$\underline{D}$iffusion Transformer with Disentangled $\underline{C}$onditions
and $\underline{C}$ompact $\underline{C}$ollector), a highly efficient
framework for zero-shot object image customization. Unlike prior works reliant
on resource-intensive Une... |
2502.09166 | Integrated Sensing and Communication with Distributed Rate-Limited
Helpers | cs.IT math.IT | This paper studies integrated sensing and communication (ISAC) systems with
two rate-limited helpers who observe the channel state sequence and the
feedback sequence, respectively. Depending on the timing of compressing and
using the state information, our proposed coding scheme gives an inner bound of
the capacity-c... |
2502.09168 | Musical Heritage Historical Entity Linking | cs.CL | Linking named entities occurring in text to their corresponding entity in a
Knowledge Base (KB) is challenging, especially when dealing with historical
texts. In this work, we introduce Musical Heritage named Entities Recognition,
Classification and Linking (MHERCL), a novel benchmark consisting of manually
annotated... |
2502.09170 | LimSim Series: An Autonomous Driving Simulation Platform for Validation
and Enhancement | cs.RO | Closed-loop simulation environments play a crucial role in the validation and
enhancement of autonomous driving systems (ADS). However, certain challenges
warrant significant attention, including balancing simulation accuracy with
duration, reconciling functionality with practicality, and establishing
comprehensive e... |
2502.09172 | LOB-Bench: Benchmarking Generative AI for Finance -- an Application to
Limit Order Book Data | cs.LG cs.CE q-fin.CP q-fin.TR | While financial data presents one of the most challenging and interesting
sequence modelling tasks due to high noise, heavy tails, and strategic
interactions, progress in this area has been hindered by the lack of consensus
on quantitative evaluation paradigms. To address this, we present LOB-Bench, a
benchmark, impl... |
2502.09173 | Two-Stage Representation Learning for Analyzing Movement Behavior
Dynamics in People Living with Dementia | cs.LG cs.AI | In remote healthcare monitoring, time series representation learning reveals
critical patient behavior patterns from high-frequency data. This study
analyzes home activity data from individuals living with dementia by proposing
a two-stage, self-supervised learning approach tailored to uncover low-rank
structures. Th... |
2502.09175 | FLAME: Flexible LLM-Assisted Moderation Engine | cs.CR cs.AI cs.CL | The rapid advancement of Large Language Models (LLMs) has introduced
significant challenges in moderating user-model interactions. While LLMs
demonstrate remarkable capabilities, they remain vulnerable to adversarial
attacks, particularly ``jailbreaking'' techniques that bypass content safety
measures. Current conten... |
2502.09180 | A Machine Learning Approach to Sensor Substitution for Non-Prehensile
Manipulation | cs.RO | Mobile manipulators are increasingly deployed in complex environments,
requiring diverse sensors to perceive and interact with their surroundings.
However, equipping every robot with every possible sensor is often impractical
due to cost and physical constraints. A critical challenge arises when robots
with differing... |
2502.09183 | RefineCoder: Iterative Improving of Large Language Models via Adaptive
Critique Refinement for Code Generation | cs.CL cs.AI | Code generation has attracted increasing attention with the rise of Large
Language Models (LLMs). Many studies have developed powerful code LLMs by
synthesizing code-related instruction data and applying supervised fine-tuning.
However, these methods are limited by teacher model distillation and ignore the
potential ... |
2502.09184 | Array-Fed RIS: Validation of Friis-Based Modeling Using Full-Wave
Simulations | eess.SP cs.SY eess.SY | Space-fed large antenna arrays offer superior efficiency, simplicity, and
reductions in size, weight, power, and cost (SWaP-C) compared to
constrained-feed systems. Historically, horn antennas have been used for space
feeding, but they suffer from limitations such as bulky designs, low aperture
efficiency ($\approx 5... |
2502.09188 | Matina: A Large-Scale 73B Token Persian Text Corpus | cs.CL cs.AI | Text corpora are essential for training models used in tasks like
summarization, translation, and large language models (LLMs). While various
efforts have been made to collect monolingual and multilingual datasets in many
languages, Persian has often been underrepresented due to limited resources for
data collection ... |
2502.09192 | Thinking beyond the anthropomorphic paradigm benefits LLM research | cs.CL | Anthropomorphism, or the attribution of human traits to technology, is an
automatic and unconscious response that occurs even in those with advanced
technical expertise. In this position paper, we analyze hundreds of thousands
of computer science research articles from the past decade and present
empirical evidence o... |
2502.09193 | Generalizability through Explainability: Countering Overfitting with
Counterfactual Examples | cs.LG | Overfitting is a well-known issue in machine learning that occurs when a
model struggles to generalize its predictions to new, unseen data beyond the
scope of its training set. Traditional techniques to mitigate overfitting
include early stopping, data augmentation, and regularization. In this work, we
demonstrate th... |
2502.09194 | XAInomaly: Explainable and Interpretable Deep Contractive Autoencoder
for O-RAN Traffic Anomaly Detection | cs.IT math.IT | Generative Artificial Intelligence (AI) techniques have become integral part
in advancing next generation wireless communication systems by enabling
sophisticated data modeling and feature extraction for enhanced network
performance. In the realm of open radio access networks (O-RAN), characterized
by their disaggreg... |
2502.09198 | Understanding High-Dimensional Bayesian Optimization | cs.LG | Recent work reported that simple Bayesian optimization methods perform well
for high-dimensional real-world tasks, seemingly contradicting prior work and
tribal knowledge. This paper investigates the 'why'. We identify fundamental
challenges that arise in high-dimensional Bayesian optimization and explain why
recent ... |
2502.09202 | Faster than real-time detection of shot boundaries, sampling structure
and dynamic keyframes in video | cs.CV | The detection of shot boundaries (hardcuts and short dissolves), sampling
structure (progressive / interlaced / pulldown) and dynamic keyframes in a
video are fundamental video analysis tasks which have to be done before any
further high-level analysis tasks. We present a novel algorithm which does all
these analysis... |
2502.09203 | Revisiting Euclidean Alignment for Transfer Learning in EEG-Based
Brain-Computer Interfaces | cs.HC cs.LG | Due to the non-stationarity and large individual differences of EEG signals,
EEG-based brain-computer interfaces (BCIs) usually need subject-specific
calibration to tailor the decoding algorithm for each new subject, which is
time-consuming and user-unfriendly, hindering their real-world applications.
Transfer learni... |
2502.09204 | Logical Lease Litigation: Prolog and LLMs for Rental Law Compliance in
New York | cs.AI cs.LO | Legal cases require careful logical reasoning following the laws, whereas
interactions with non-technical users must be in natural language. As an
application combining logical reasoning using Prolog and natural language
processing using large language models (LLMs), this paper presents a novel
approach and system, L... |
2502.09205 | Counterfactual Explanations as Plans | cs.AI cs.LO | There has been considerable recent interest in explainability in AI,
especially with black-box machine learning models. As correctly observed by the
planning community, when the application at hand is not a single-shot decision
or prediction, but a sequence of actions that depend on observations, a richer
notion of e... |
2502.09206 | Efficient OWL2QL Meta-reasoning Using ASP-based Hybrid Knowledge Bases | cs.LO cs.AI cs.SC | Metamodeling refers to scenarios in ontologies in which classes and roles can
be members of classes or occur in roles. This is a desirable modelling feature
in several applications, but allowing it without restrictions is problematic
for several reasons, mainly because it causes undecidability. Therefore,
practical l... |
2502.09209 | On LLM-generated Logic Programs and their Inference Execution Methods | cs.AI | Large Language Models (LLMs) trained on petabytes of data are highly
compressed repositories of a significant proportion of the knowledge
accumulated and distilled so far. In this paper we study techniques to elicit
this knowledge in the form of several classes of logic programs, including
propositional Horn clauses,... |
2502.09211 | Visual Graph Question Answering with ASP and LLMs for Language Parsing | cs.AI cs.CV cs.LO | Visual Question Answering (VQA) is a challenging problem that requires to
process multimodal input. Answer-Set Programming (ASP) has shown great
potential in this regard to add interpretability and explainability to modular
VQA architectures. In this work, we address the problem of how to integrate ASP
with modules f... |
2502.09212 | LP-LM: No Hallucinations in Question Answering with Logic Programming | cs.AI cs.CL | Large language models (LLMs) are able to generate human-like responses to
user queries. However, LLMs exhibit inherent limitations, especially because
they hallucinate. This paper introduces LP-LM, a system that grounds answers to
questions in known facts contained in a knowledge base (KB), facilitated
through semant... |
2502.09213 | Neuro-Symbolic Contrastive Learning for Cross-domain Inference | cs.LG cs.CL | Pre-trained language models (PLMs) have made significant advances in natural
language inference (NLI) tasks, however their sensitivity to textual
perturbations and dependence on large datasets indicate an over-reliance on
shallow heuristics. In contrast, inductive logic programming (ILP) excels at
inferring logical r... |
2502.09215 | Architecture for Simulating Behavior Mode Changes in Norm-Aware
Autonomous Agents | cs.LO cs.AI | This paper presents an architecture for simulating the actions of a
norm-aware intelligent agent whose behavior with respect to norm compliance is
set, and can later be changed, by a human controller. Updating an agent's
behavior mode from a norm-abiding to a riskier one may be relevant when the
agent is involved in ... |
2502.09216 | Mind the Gaps: Logical English, Prolog, and Multi-agent Systems for
Autonomous Vehicles | cs.AI cs.CL cs.LO cs.MA | In this paper, we present a modular system for representing and reasoning
with legal aspects of traffic rules for autonomous vehicles. We focus on a
subset of the United Kingdom's Highway Code (HC) related to junctions. As human
drivers and automated vehicles (AVs) will interact on the roads, especially in
urban envi... |
2502.09218 | Data2Concept2Text: An Explainable Multilingual Framework for Data
Analysis Narration | cs.LO cs.AI | This paper presents a complete explainable system that interprets a set of
data, abstracts the underlying features and describes them in a natural
language of choice. The system relies on two crucial stages: (i) identifying
emerging properties from data and transforming them into abstract concepts, and
(ii) convertin... |
2502.09219 | Abduction of Domain Relationships from Data for VQA | cs.LO cs.AI cs.LG | In this paper, we study the problem of visual question answering (VQA) where
the image and query are represented by ASP programs that lack domain data. We
provide an approach that is orthogonal and complementary to existing knowledge
augmentation techniques where we abduce domain relationships of image
constructs fro... |
2502.09220 | Graphical Conditions for the Existence, Unicity and Number of Regular
Models | cs.LO cs.AI cs.DM | The regular models of a normal logic program are a particular type of partial
(i.e. 3-valued) models which correspond to stable partial models with minimal
undefinedness. In this paper, we explore graphical conditions on the dependency
graph of a finite ground normal logic program to analyze the existence, unicity
an... |
2502.09221 | Pearce's Characterisation in an Epistemic Domain | cs.AI cs.LO cs.PL | Answer-set programming (ASP) is a successful problem-solving approach in
logic-based AI. In ASP, problems are represented as declarative logic programs,
and solutions are identified through their answer sets. Equilibrium logic (EL)
is a general-purpose nonmonotonic reasoning formalism, based on a monotonic
logic call... |
2502.09222 | ASP-driven User-interaction with Clinguin | cs.AI cs.HC cs.LO cs.SE | We present clinguin, a system for ASP-driven user interface design. Clinguin
streamlines the development of user interfaces for ASP developers by letting
them build interactive prototypes directly in ASP, eliminating the need for
separate frontend languages. To this end, clinguin uses a few dedicated
predicates to de... |
2502.09223 | A Prolog Program for Bottom-up Evaluation | cs.PL cs.DB cs.LO | This short paper describes a simple and intuitive Prolog program, a
metainterpreter, that computes the bottom up meaning of a simple positive Horn
clause definition. It involves a simple transformation of the object program
rules into metarules, which are then used by a metainterpreter to compute
bottom up the model ... |
2502.09224 | Order-Sorted Intensional Logic: Expressing Subtyping Polymorphism with
Typing Assertions and Quantification over Concepts | cs.AI cs.LO | Subtyping, also known as subtype polymorphism, is a concept extensively
studied in programming language theory, delineating the substitutability
relation among datatypes. This property ensures that programs designed for
supertype objects remain compatible with their subtypes.
In this paper, we explore the capabilit... |
2502.09226 | Generating Causally Compliant Counterfactual Explanations using ASP | cs.AI | This research is focused on generating achievable counterfactual
explanations. Given a negative outcome computed by a machine learning model or
a decision system, the novel CoGS approach generates (i) a counterfactual
solution that represents a positive outcome and (ii) a path that will take us
from the negative outc... |
2502.09228 | Computational methods for Dynamic Answer Set Programming | cs.AI cs.FL cs.LO | In our daily lives and industrial settings, we often encounter dynamic
problems that require reasoning over time and metric constraints. These include
tasks such as scheduling, routing, and production sequencing. Dynamic logics
have traditionally addressed these needs but often lack the flexibility and
integration re... |
2502.09230 | Relating Answer Set Programming and Many-sorted Logics for Formal
Verification | cs.LO cs.AI cs.PL | Answer Set Programming (ASP) is an important logic programming paradigm
within the field of Knowledge Representation and Reasoning. As a concise,
human-readable, declarative language, ASP is an excellent tool for developing
trustworthy (especially, artificially intelligent) software systems. However,
formally verifyi... |
2502.09231 | Answer Set Counting and its Applications | cs.CL cs.LO | We have focused on Answer Set Programming (ASP), more specifically, answer
set counting, exploring both exact and approximate methodologies. We developed
an exact ASP counter, sharpASP, which utilizes a compact encoding for
propositional formulas, significantly enhancing efficiency compared to existing
methods that o... |
2502.09232 | Logical foundations of Smart Contracts | cs.LO cs.AI | Nowadays, sophisticated domains are emerging which require appropriate
formalisms to be specified accurately in order to reason about them. One such
domain is constituted of smart contracts that have emerged in cyber physical
systems as a way of enforcing formal agreements between components of these
systems. Smart c... |
2502.09233 | Commonsense Reasoning-Aided Autonomous Vehicle Systems | cs.AI | Autonomous Vehicle (AV) systems have been developed with a strong reliance on
machine learning techniques. While machine learning approaches, such as deep
learning, are extremely effective at tasks that involve observation and
classification, they struggle when it comes to performing higher level
reasoning about situ... |
2502.09235 | Hybrid Answer Set Programming: Foundations and Applications | cs.AI cs.LO | Answer Set Programming (ASP) is a powerful tool for solving real-world
problems. However, many problems involve numeric values and complex constraints
beyond the capabilities of standard ASP solvers. Hybrid solvers like CLINGCON
and CLINGO[DL] address this by using specialized methods for specific
constraints. Howeve... |
2502.09237 | Reliable Conversational Agents under ASP Control that Understand Natural
Language | cs.LO cs.CL | Efforts have been made to make machines converse like humans in the past few
decades. The recent techniques of Large Language Models (LLMs) make it possible
to have human-like conversations with machines, but LLM's flaws of lacking
understanding and reliability are well documented. We believe that the best way
to eli... |
2502.09238 | OpenBench: A New Benchmark and Baseline for Semantic Navigation in Smart
Logistics | cs.RO | The increasing demand for efficient last-mile delivery in smart logistics
underscores the role of autonomous robots in enhancing operational efficiency
and reducing costs. Traditional navigation methods, which depend on
high-precision maps, are resource-intensive, while learning-based approaches
often struggle with g... |
2502.09241 | Safety Evaluation of Human Arm Operations Using IMU Sensors with a
Spring-Damper-Mass Predictive Model | cs.RO | This paper presents a novel approach to real-time safety monitoring in
human-robot collaborative manufacturing environments through a wrist-mounted
Inertial Measurement Unit (IMU) system integrated with a Predictive Safety
Model (PSM). The proposed system extends previous PSM implementations through
the adaptation of... |
2502.09242 | From large language models to multimodal AI: A scoping review on the
potential of generative AI in medicine | cs.AI | Generative artificial intelligence (AI) models, such as diffusion models and
OpenAI's ChatGPT, are transforming medicine by enhancing diagnostic accuracy
and automating clinical workflows. The field has advanced rapidly, evolving
from text-only large language models for tasks such as clinical documentation
and decisi... |
2502.09244 | Memristor-Based Meta-Learning for Fast mmWave Beam Prediction in
Non-Stationary Environments | cs.IT math.IT | Traditional machine learning techniques have achieved great success in
improving data-rate performance and reducing latency in millimeter wave
(mmWave) communications. However, these methods still face two key challenges:
(i) their reliance on large-scale paired data for model training and tuning
which limits perform... |
2502.09245 | You Do Not Fully Utilize Transformer's Representation Capacity | cs.LG cs.CL | In contrast to RNNs, which compress previous tokens into a single hidden
state, Transformers can attend to all previous tokens directly. However,
standard Transformers only use representations from the immediately preceding
layer. In this paper, we show that this design choice causes representation
collapse and leads... |
2502.09247 | The Joint Entity-Relation Extraction Model Based on Span and Interactive
Fusion Representation for Chinese Medical Texts with Complex Semantics | cs.CL cs.AI | Joint entity-relation extraction is a critical task in transforming
unstructured or semi-structured text into triplets, facilitating the
construction of large-scale knowledge graphs, and supporting various downstream
applications. Despite its importance, research on Chinese text, particularly
with complex semantics i... |
2502.09252 | On the Importance of Embedding Norms in Self-Supervised Learning | cs.LG | Self-supervised learning (SSL) allows training data representations without a
supervised signal and has become an important paradigm in machine learning.
Most SSL methods employ the cosine similarity between embedding vectors and
hence effectively embed data on a hypersphere. While this seemingly implies
that embeddi... |
2502.09254 | AnomalyGFM: Graph Foundation Model for Zero/Few-shot Anomaly Detection | cs.LG cs.AI | Graph anomaly detection (GAD) aims to identify abnormal nodes that differ
from the majority of the nodes in a graph, which has been attracting
significant attention in recent years. Existing generalist graph models have
achieved remarkable success in different graph tasks but struggle to generalize
to the GAD task. T... |
2502.09256 | DynSegNet:Dynamic Architecture Adjustment for Adversarial Learning in
Segmenting Hemorrhagic Lesions from Fundus Images | cs.CV cs.AI | The hemorrhagic lesion segmentation plays a critical role in ophthalmic
diagnosis, directly influencing early disease detection, treatment planning,
and therapeutic efficacy evaluation. However, the task faces significant
challenges due to lesion morphological variability, indistinct boundaries, and
low contrast with... |
2502.09257 | Bandit Multiclass List Classification | cs.LG cs.AI stat.ML | We study the problem of multiclass list classification with (semi-)bandit
feedback, where input examples are mapped into subsets of size $m$ of a
collection of $K$ possible labels, and the feedback consists of the predicted
labels which lie in the set of true labels of the given example. Our main
result is for the $(... |
2502.09263 | Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple
Architectures Meet Excellence | cs.LG | Message-passing Graph Neural Networks (GNNs) are often criticized for their
limited expressiveness, issues like over-smoothing and over-squashing, and
challenges in capturing long-range dependencies, while Graph Transformers (GTs)
are considered superior due to their global attention mechanisms. Literature
frequently... |
2502.09268 | GEVRM: Goal-Expressive Video Generation Model For Robust Visual
Manipulation | cs.RO cs.LG | With the rapid development of embodied artificial intelligence, significant
progress has been made in vision-language-action (VLA) models for general robot
decision-making. However, the majority of existing VLAs fail to account for the
inevitable external perturbations encountered during deployment. These
perturbatio... |
2502.09269 | Memory-based Ensemble Learning in CMR Semantic Segmentation | cs.CV | Existing models typically segment either the entire 3D frame or 2D slices
independently to derive clinical functional metrics from ventricular
segmentation in cardiac cine sequences. While performing well overall, they
struggle at the end slices. To address this, we leverage spatial continuity to
extract global uncer... |
2502.09271 | LiSA: Leveraging Link Recommender to Attack Graph Neural Networks via
Subgraph Injection | cs.LG cs.AI | Graph Neural Networks (GNNs) have demonstrated remarkable proficiency in
modeling data with graph structures, yet recent research reveals their
susceptibility to adversarial attacks. Traditional attack methodologies, which
rely on manipulating the original graph or adding links to artificially created
nodes, often pr... |
2502.09274 | FLARES: Fast and Accurate LiDAR Multi-Range Semantic Segmentation | cs.CV | 3D scene understanding is a critical yet challenging task in autonomous
driving, primarily due to the irregularity and sparsity of LiDAR data, as well
as the computational demands of processing large-scale point clouds. Recent
methods leverage the range-view representation to improve processing
efficiency. To mitigat... |
2502.09278 | ConsistentDreamer: View-Consistent Meshes Through Balanced Multi-View
Gaussian Optimization | cs.CV | Recent advances in diffusion models have significantly improved 3D
generation, enabling the use of assets generated from an image for embodied AI
simulations. However, the one-to-many nature of the image-to-3D problem limits
their use due to inconsistent content and quality across views. Previous models
optimize a 3D... |
2502.09280 | Adaptive Multi-Objective Bayesian Optimization for Capacity Planning of
Hybrid Heat Sources in Electric-Heat Coupling Systems of Cold Regions | eess.SY cs.NE cs.SY | The traditional heat-load generation pattern of combined heat and power
generators has become a problem leading to renewable energy source (RES) power
curtailment in cold regions, motivating the proposal of a planning model for
alternative heat sources. The model aims to identify non-dominant capacity
allocation sche... |
2502.09282 | FE-LWS: Refined Image-Text Representations via Decoder Stacking and
Fused Encodings for Remote Sensing Image Captioning | cs.CV cs.HC cs.LG | Remote sensing image captioning aims to generate descriptive text from remote
sensing images, typically employing an encoder-decoder framework. In this
setup, a convolutional neural network (CNN) extracts feature representations
from the input image, which then guide the decoder in a sequence-to-sequence
caption gene... |
2502.09284 | SparQLe: Speech Queries to Text Translation Through LLMs | cs.CL cs.AI | With the growing influence of Large Language Models (LLMs), there is
increasing interest in integrating speech representations with them to enable
more seamless multi-modal processing and speech understanding. This study
introduces a novel approach that leverages self-supervised speech
representations in combination ... |
2502.09285 | EmoAssist: Emotional Assistant for Visual Impairment Community | cs.CV cs.CY | The rapid advancement of large multi-modality models (LMMs) has significantly
propelled the integration of artificial intelligence into practical
applications. Visual Question Answering (VQA) systems, which can process
multi-modal data including vision, text, and audio, hold great potential for
assisting the Visual I... |
2502.09287 | An Uncertainty Principle for Linear Recurrent Neural Networks | cs.LG | We consider linear recurrent neural networks, which have become a key
building block of sequence modeling due to their ability for stable and
effective long-range modeling. In this paper, we aim at characterizing this
ability on a simple but core copy task, whose goal is to build a linear filter
of order $S$ that app... |
2502.09290 | Dynamic Rolling Horizon Optimization for Network-Constrained V2X Value
Stacking of Electric Vehicles Under Uncertainties | math.OC cs.LG cs.SY eess.SY | Electric vehicle (EV) coordination can provide significant benefits through
vehicle-to-everything (V2X) by interacting with the grid, buildings, and other
EVs. This work aims to develop a V2X value-stacking framework, including
vehicle-to-building (V2B), vehicle-to-grid (V2G), and energy trading, to
maximize economic... |
2502.09291 | Joint Attention Mechanism Learning to Facilitate Opto-physiological
Monitoring during Physical Activity | eess.SP cs.LG | Opto-physiological monitoring is a non-contact technique for measuring
cardiac signals, i.e., photoplethysmography (PPG). Quality PPG signals directly
lead to reliable physiological readings. However, PPG signal acquisition
procedures are often accompanied by spurious motion artefacts (MAs), especially
during low-to-... |
2502.09294 | Indeterminacy in Affective Computing: Considering Meaning and Context in
Data Collection Practices | cs.AI | Automatic Affect Prediction (AAP) uses computational analysis of input data
such as text, speech, images, and physiological signals to predict various
affective phenomena (e.g., emotions or moods). These models are typically
constructed using supervised machine-learning algorithms, which rely heavily on
labeled train... |
2502.09296 | A Physics-Informed Deep Learning Model for MRI Brain Motion Correction | cs.CV physics.med-ph | Background: MRI is crucial for brain imaging but is highly susceptible to
motion artifacts due to long acquisition times. This study introduces
PI-MoCoNet, a physics-informed motion correction network that integrates
spatial and k-space information to remove motion artifacts without explicit
motion parameter estimati... |
2502.09297 | When do neural networks learn world models? | cs.LG | Humans develop world models that capture the underlying generation process of
data. Whether neural networks can learn similar world models remains an open
problem. In this work, we provide the first theoretical results for this
problem, showing that in a multi-task setting, models with a low-degree bias
provably reco... |
2502.09298 | Convex Is Back: Solving Belief MDPs With Convexity-Informed Deep
Reinforcement Learning | cs.LG | We present a novel method for Deep Reinforcement Learning (DRL),
incorporating the convex property of the value function over the belief space
in Partially Observable Markov Decision Processes (POMDPs). We introduce hard-
and soft-enforced convexity as two different approaches, and compare their
performance against s... |
2502.09299 | Moving Matter: Efficient Reconfiguration of Tile Arrangements by a
Single Active Robot | cs.CG cs.DS cs.RO | We consider the problem of reconfiguring a two-dimensional connected grid
arrangement of passive building blocks from a start configuration to a goal
configuration, using a single active robot that can move on the tiles, remove
individual tiles from a given location and physically move them to a new
position by walki... |
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