id stringlengths 9 16 | title stringlengths 4 278 | categories stringlengths 5 104 | abstract stringlengths 6 4.09k |
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2502.09793 | Noise Controlled CT Super-Resolution with Conditional Diffusion Model | cs.CV | Improving the spatial resolution of CT images is a meaningful yet challenging
task, often accompanied by the issue of noise amplification. This article
introduces an innovative framework for noise-controlled CT super-resolution
utilizing the conditional diffusion model. The model is trained on hybrid
datasets, combin... |
2502.09794 | Reconstruction of frequency-localized functions from pointwise samples
via least squares and deep learning | math.CA cs.LG | Recovering frequency-localized functions from pointwise data is a fundamental
task in signal processing. We examine this problem from an
approximation-theoretic perspective, focusing on least squares and deep
learning-based methods. First, we establish a novel recovery theorem for least
squares approximations using t... |
2502.09795 | Vision-based Geo-Localization of Future Mars Rotorcraft in Challenging
Illumination Conditions | cs.CV cs.RO | Planetary exploration using aerial assets has the potential for unprecedented
scientific discoveries on Mars. While NASA's Mars helicopter Ingenuity proved
flight in Martian atmosphere is possible, future Mars rotocrafts will require
advanced navigation capabilities for long-range flights. One such critical
capabilit... |
2502.09797 | A Survey on LLM-based News Recommender Systems | cs.IR cs.AI | News recommender systems play a critical role in mitigating the information
overload problem. In recent years, due to the successful applications of large
language model technologies, researchers have utilized Discriminative Large
Language Models (DLLMs) or Generative Large Language Models (GLLMs) to improve
the perf... |
2502.09799 | Co-designing Large Language Model Tools for Project-Based Learning with
K12 Educators | cs.HC cs.AI cs.CY | The emergence of generative AI, particularly large language models (LLMs),
has opened the door for student-centered and active learning methods like
project-based learning (PBL). However, PBL poses practical implementation
challenges for educators around project design and management, assessment, and
balancing studen... |
2502.09804 | Acute Lymphoblastic Leukemia Diagnosis Employing YOLOv11, YOLOv8,
ResNet50, and Inception-ResNet-v2 Deep Learning Models | eess.IV cs.AI cs.CV cs.LG | Thousands of individuals succumb annually to leukemia alone. As artificial
intelligence-driven technologies continue to evolve and advance, the question
of their applicability and reliability remains unresolved. This study aims to
utilize image processing and deep learning methodologies to achieve
state-of-the-art re... |
2502.09805 | Towards Patient-Specific Surgical Planning for Bicuspid Aortic Valve
Repair: Fully Automated Segmentation of the Aortic Valve in 4D CT | eess.IV cs.CV | The bicuspid aortic valve (BAV) is the most prevalent congenital heart defect
and may require surgery for complications such as stenosis, regurgitation, and
aortopathy. BAV repair surgery is effective but challenging due to the
heterogeneity of BAV morphology. Multiple imaging modalities can be employed to
assist the... |
2502.09806 | Prioritized Ranking Experimental Design Using Recommender Systems in
Two-Sided Platforms | econ.EM cs.IR cs.SI stat.ME | Interdependencies between units in online two-sided marketplaces complicate
estimating causal effects in experimental settings. We propose a novel
experimental design to mitigate the interference bias in estimating the total
average treatment effect (TATE) of item-side interventions in online two-sided
marketplaces. ... |
2502.09809 | AgentGuard: Repurposing Agentic Orchestrator for Safety Evaluation of
Tool Orchestration | cs.CR cs.AI | The integration of tool use into large language models (LLMs) enables agentic
systems with real-world impact. In the meantime, unlike standalone LLMs,
compromised agents can execute malicious workflows with more consequential
impact, signified by their tool-use capability. We propose AgentGuard, a
framework to autono... |
2502.09810 | $\Lambda$CDM and early dark energy in latent space: a data-driven
parametrization of the CMB temperature power spectrum | astro-ph.CO astro-ph.IM cs.LG | Finding the best parametrization for cosmological models in the absence of
first-principle theories is an open question. We propose a data-driven
parametrization of cosmological models given by the disentangled 'latent'
representation of a variational autoencoder (VAE) trained to compress cosmic
microwave background ... |
2502.09812 | Face Deepfakes -- A Comprehensive Review | cs.CV cs.LG | In recent years, remarkable advancements in deep-fake generation technology
have led to unprecedented leaps in its realism and capabilities. Despite these
advances, we observe a notable lack of structured and deep analysis deepfake
technology. The principal aim of this survey is to contribute a thorough
theoretical a... |
2502.09813 | Suture Thread Modeling Using Control Barrier Functions for Autonomous
Surgery | cs.RO cs.SY eess.SY | Automating surgical systems enhances precision and safety while reducing
human involvement in high-risk environments. A major challenge in automating
surgical procedures like suturing is accurately modeling the suture thread, a
highly flexible and compliant component. Existing models either lack the
accuracy needed f... |
2502.09814 | INJONGO: A Multicultural Intent Detection and Slot-filling Dataset for
16 African Languages | cs.CL | Slot-filling and intent detection are well-established tasks in
Conversational AI. However, current large-scale benchmarks for these tasks
often exclude evaluations of low-resource languages and rely on translations
from English benchmarks, thereby predominantly reflecting Western-centric
concepts. In this paper, we ... |
2502.09815 | Statistical Coherence Alignment for Large Language Model Representation
Learning Through Tensor Field Convergence | cs.CL | Representation learning plays a central role in structuring internal
embeddings to capture the statistical properties of language, influencing the
coherence and contextual consistency of generated text. Statistical Coherence
Alignment is introduced as a method to enforce structured token representations
through tenso... |
2502.09817 | Vector Linear Secure Aggregation | cs.IT math.IT | The secure summation problem, where $K$ users wish to compute the sum of
their inputs at a server while revealing nothing about all $K$ inputs beyond
the desired sum, is generalized in two aspects - first, the desired function is
an arbitrary linear function (multiple linear combinations) of the $K$ inputs
instead of... |
2502.09818 | On the robustness of multimodal language model towards distractions | cs.CV | Although vision-language models (VLMs) have achieved significant success in
various applications such as visual question answering, their resilience to
prompt variations remains an under-explored area. Understanding how
distractions affect VLMs is crucial for improving their real-world
applicability, as inputs could ... |
2502.09819 | A Solver-Aided Hierarchical Language for LLM-Driven CAD Design | cs.CV cs.AI cs.GR cs.LG cs.PL | Large language models (LLMs) have been enormously successful in solving a
wide variety of structured and unstructured generative tasks, but they struggle
to generate procedural geometry in Computer Aided Design (CAD). These
difficulties arise from an inability to do spatial reasoning and the necessity
to guide a mode... |
2502.09822 | ATM-Net: Adaptive Termination and Multi-Precision Neural Networks for
Energy-Harvested Edge Intelligence | cs.LG | ATM-Net is a novel neural network architecture tailored for energy-harvested
IoT devices, integrating adaptive termination points with multi-precision
computing. It dynamically adjusts computational precision (32/8/4-bit) and
network depth based on energy availability via early exit points. An
energy-aware task sched... |
2502.09824 | PUGS: Perceptual Uncertainty for Grasp Selection in Underwater
Environments | cs.RO cs.CV | When navigating and interacting in challenging environments where sensory
information is imperfect and incomplete, robots must make decisions that
account for these shortcomings. We propose a novel method for quantifying and
representing such perceptual uncertainty in 3D reconstruction through occupancy
uncertainty e... |
2502.09826 | Safe Reinforcement Learning-based Control for Hydrogen Diesel Dual-Fuel
Engines | eess.SY cs.SY | The urgent energy transition requirements towards a sustainable future
stretch across various industries and are a significant challenge facing
humanity. Hydrogen promises a clean, carbon-free future, with the opportunity
to integrate with existing solutions in the transportation sector. However,
adding hydrogen to e... |
2502.09827 | Data and Decision Traceability for SDA TAP Lab's Prototype Battle
Management System | cs.IR cs.CR | Space Protocol is applying the principles derived from MITRE and NIST's
Supply Chain Traceability: Manufacturing Meta-Framework (NIST IR 8536) to a
complex multi party system to achieve introspection, auditing, and replay of
data and decisions that ultimately lead to a end decision. The core goal of
decision traceabi... |
2502.09829 | Efficient Evaluation of Multi-Task Robot Policies With Active Experiment
Selection | cs.RO cs.AI cs.LG | Evaluating learned robot control policies to determine their physical
task-level capabilities costs experimenter time and effort. The growing number
of policies and tasks exacerbates this issue. It is impractical to test every
policy on every task multiple times; each trial requires a manual environment
reset, and ea... |
2502.09831 | Learning Fair Policies for Infectious Diseases Mitigation using Path
Integral Control | cs.LG math.OC | Infectious diseases pose major public health challenges to society,
highlighting the importance of designing effective policies to reduce economic
loss and mortality. In this paper, we propose a framework for sequential
decision-making under uncertainty to design fairness-aware disease mitigation
policies that incorp... |
2502.09832 | Algorithmic contiguity from low-degree conjecture and applications in
correlated random graphs | stat.ML cs.DS cs.LG math.PR math.ST stat.TH | In this paper, assuming a natural strengthening of the low-degree conjecture,
we provide evidence of computational hardness for two problems: (1) the
(partial) matching recovery problem in the sparse correlated Erd\H{o}s-R\'enyi
graphs $\mathcal G(n,q;\rho)$ when the edge-density $q=n^{-1+o(1)}$ and the
correlation $... |
2502.09838 | HealthGPT: A Medical Large Vision-Language Model for Unifying
Comprehension and Generation via Heterogeneous Knowledge Adaptation | cs.CV cs.AI | We present HealthGPT, a powerful Medical Large Vision-Language Model
(Med-LVLM) that integrates medical visual comprehension and generation
capabilities within a unified autoregressive paradigm. Our bootstrapping
philosophy is to progressively adapt heterogeneous comprehension and generation
knowledge to pre-trained ... |
2502.09843 | MuDoC: An Interactive Multimodal Document-grounded Conversational AI
System | cs.AI cs.HC cs.MM | Multimodal AI is an important step towards building effective tools to
leverage multiple modalities in human-AI communication. Building a multimodal
document-grounded AI system to interact with long documents remains a
challenge. Our work aims to fill the research gap of directly leveraging
grounded visuals from docu... |
2502.09844 | Solving Empirical Bayes via Transformers | cs.LG stat.ML | This work applies modern AI tools (transformers) to solving one of the oldest
statistical problems: Poisson means under empirical Bayes (Poisson-EB) setting.
In Poisson-EB a high-dimensional mean vector $\theta$ (with iid coordinates
sampled from an unknown prior $\pi$) is estimated on the basis of
$X=\mathrm{Poisson... |
2502.09846 | Robust Event-Triggered Integrated Communication and Control with Graph
Information Bottleneck Optimization | cs.MA | Integrated communication and control serves as a critical ingredient in
Multi-Agent Reinforcement Learning. However, partial observability limitations
will impair collaboration effectiveness, and a potential solution is to
establish consensus through well-calibrated latent variables obtained from
neighboring agents. ... |
2502.09849 | A Survey on Human-Centered Evaluation of Explainable AI Methods in
Clinical Decision Support Systems | cs.LG cs.HC | Explainable AI (XAI) has become a crucial component of Clinical Decision
Support Systems (CDSS) to enhance transparency, trust, and clinical adoption.
However, while many XAI methods have been proposed, their effectiveness in
real-world medical settings remains underexplored. This paper provides a survey
of human-cen... |
2502.09850 | Elastic Representation: Mitigating Spurious Correlations for Group
Robustness | cs.LG | Deep learning models can suffer from severe performance degradation when
relying on spurious correlations between input features and labels, making the
models perform well on training data but have poor prediction accuracy for
minority groups. This problem arises especially when training data are limited
or imbalance... |
2502.09854 | Efficient Multitask Learning in Small Language Models Through
Upside-Down Reinforcement Learning | cs.CL cs.AI cs.LG | In this work, we demonstrate that small language models (SLMs), specifically
a 100M parameter GPT-2 model, can achieve competitive performance in multitask
prompt generation tasks while requiring only a fraction of the computational
resources needed by large language models (LLMs). Through a novel combination
of upsi... |
2502.09858 | Automated Hypothesis Validation with Agentic Sequential Falsifications | cs.LG cs.AI cs.CL q-bio.QM | Hypotheses are central to information acquisition, decision-making, and
discovery. However, many real-world hypotheses are abstract, high-level
statements that are difficult to validate directly. This challenge is further
intensified by the rise of hypothesis generation from Large Language Models
(LLMs), which are pr... |
2502.09860 | Gradient GA: Gradient Genetic Algorithm for Drug Molecular Design | q-bio.BM cs.CE cs.LG stat.ML | Molecular discovery has brought great benefits to the chemical industry.
Various molecule design techniques are developed to identify molecules with
desirable properties. Traditional optimization methods, such as genetic
algorithms, continue to achieve state-of-the-art results across multiple
molecular design benchma... |
2502.09861 | A Scoresheet for Explainable AI | cs.AI cs.MA cs.SE | Explainability is important for the transparency of autonomous and
intelligent systems and for helping to support the development of appropriate
levels of trust. There has been considerable work on developing approaches for
explaining systems and there are standards that specify requirements for
transparency. However... |
2502.09863 | Solvable Dynamics of Self-Supervised Word Embeddings and the Emergence
of Analogical Reasoning | cs.LG cs.CL stat.ML | The remarkable success of large language models relies on their ability to
implicitly learn structured latent representations from the pretraining corpus.
As a simpler surrogate for representation learning in language modeling, we
study a class of solvable contrastive self-supervised algorithms which we term
quadrati... |
2502.09866 | How Users Who are Blind or Low Vision Play Mobile Games: Perceptions,
Challenges, and Strategies | cs.HC cs.AI cs.CY cs.LG | As blind and low-vision (BLV) players engage more deeply with games,
accessibility features have become essential. While some research has explored
tools and strategies to enhance game accessibility, the specific experiences of
these players with mobile games remain underexamined. This study addresses this
gap by inv... |
2502.09870 | A Taxonomy of Linguistic Expressions That Contribute To Anthropomorphism
of Language Technologies | cs.HC cs.AI cs.CL | Recent attention to anthropomorphism -- the attribution of human-like
qualities to non-human objects or entities -- of language technologies like
LLMs has sparked renewed discussions about potential negative impacts of
anthropomorphism. To productively discuss the impacts of this anthropomorphism
and in what contexts... |
2502.09872 | Learning to Calibrate for Reliable Visual Fire Detection | cs.CV cs.LG | Fire is characterized by its sudden onset and destructive power, making early
fire detection crucial for ensuring human safety and protecting property. With
the advancement of deep learning, the application of computer vision in fire
detection has significantly improved. However, deep learning models often
exhibit a ... |
2502.09873 | Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal | cs.CV | Diffusion models have demonstrated remarkable success in image restoration
tasks. However, their multi-step denoising process introduces significant
computational overhead, limiting their practical deployment. Furthermore,
existing methods struggle to effectively remove severe JPEG artifact,
especially in highly comp... |
2502.09874 | FrGNet: A fourier-guided weakly-supervised framework for nuclear
instance segmentation | cs.CV cs.AI | Nuclear instance segmentation has played a critical role in pathology image
analysis. The main challenges arise from the difficulty in accurately
segmenting instances and the high cost of precise mask-level annotations for
fully-supervised training.In this work, we propose a fourier guidance framework
for solving the... |
2502.09877 | Stretching Rubber, Not Budgets: Accurate Parking Utilization on a
Shoestring | eess.SY cs.SY | Effective parking management is essential for ensuring accessibility, safety,
and convenience in master-planned communities, particularly in active adult
neighborhoods experiencing rapid growth. Accurately assessing parking
utilization is a crucial first step in planning for future demand, but data
collection methods... |
2502.09880 | Interpretable Early Warnings using Machine Learning in an Online
Game-experiment | physics.soc-ph cs.LG cs.SI nlin.AO stat.ML | Stemming from physics and later applied to other fields such as ecology, the
theory of critical transitions suggests that some regime shifts are preceded by
statistical early warning signals. Reddit's r/place experiment, a large-scale
social game, provides a unique opportunity to test these signals consistently
acros... |
2502.09884 | Nonasymptotic CLT and Error Bounds for Two-Time-Scale Stochastic
Approximation | cs.LG cs.AI | We consider linear two-time-scale stochastic approximation algorithms driven
by martingale noise. Recent applications in machine learning motivate the need
to understand finite-time error rates, but conventional stochastic
approximation analysis focus on either asymptotic convergence in distribution
or finite-time bo... |
2502.09885 | Comprehensive Review of Neural Differential Equations for Time Series
Analysis | cs.LG cs.AI | Time series modeling and analysis has become critical in various domains.
Conventional methods such as RNNs and Transformers, while effective for
discrete-time and regularly sampled data, face significant challenges in
capturing the continuous dynamics and irregular sampling patterns inherent in
real-world scenarios.... |
2502.09886 | Video2Policy: Scaling up Manipulation Tasks in Simulation through
Internet Videos | cs.RO cs.AI cs.LG | Simulation offers a promising approach for cheaply scaling training data for
generalist policies. To scalably generate data from diverse and realistic
tasks, existing algorithms either rely on large language models (LLMs) that may
hallucinate tasks not interesting for robotics; or digital twins, which require
careful... |
2502.09888 | An Efficient Large Recommendation Model: Towards a Resource-Optimal
Scaling Law | cs.IR | The pursuit of scaling up recommendation models confronts intrinsic tensions
between expanding model capacity and preserving computational tractability.
While prior studies have explored scaling laws for recommendation systems,
their resource-intensive paradigms -- often requiring tens of thousands of A100
GPU hours ... |
2502.09889 | Evaluating and Improving Graph-based Explanation Methods for Multi-Agent
Coordination | cs.MA cs.AI cs.LG cs.RO | Graph Neural Networks (GNNs), developed by the graph learning community, have
been adopted and shown to be highly effective in multi-robot and multi-agent
learning. Inspired by this successful cross-pollination, we investigate and
characterize the suitability of existing GNN explanation methods for explaining
multi-a... |
2502.09890 | Symmetry-Preserving Diffusion Models via Target Symmetrization | cs.LG | Diffusion models are powerful tools for capturing complex distributions, but
modeling data with inherent symmetries, such as molecular structures, remains
challenging. Equivariant denoisers are commonly used to address this, but they
introduce architectural complexity and optimization challenges, including noisy
grad... |
2502.09891 | ArchRAG: Attributed Community-based Hierarchical Retrieval-Augmented
Generation | cs.IR cs.AI | Retrieval-Augmented Generation (RAG) has proven effective in integrating
external knowledge into large language models (LLMs) for question-answer (QA)
tasks. The state-of-the-art RAG approaches often use the graph data as the
external data since they capture the rich semantic information and link
relationships betwee... |
2502.09893 | Dynamic-Computed Tomography Angiography for Cerebral Vessel Templates
and Segmentation | physics.med-ph cs.CV | Background: Computed Tomography Angiography (CTA) is crucial for
cerebrovascular disease diagnosis. Dynamic CTA is a type of imaging that
captures temporal information about the We aim to develop and evaluate two
segmentation techniques to segment vessels directly on CTA images: (1) creating
and registering populatio... |
2502.09897 | Artificial Intelligence in Spectroscopy: Advancing Chemistry from
Prediction to Generation and Beyond | cs.AI cs.LG | The rapid advent of machine learning (ML) and artificial intelligence (AI)
has catalyzed major transformations in chemistry, yet the application of these
methods to spectroscopic and spectrometric data, referred to as Spectroscopy
Machine Learning (SpectraML), remains relatively underexplored. Modern
spectroscopic te... |
2502.09898 | Optimal lower Lipschitz bounds for ReLU layers, saturation, and phase
retrieval | cs.LG cs.NA math.FA math.NA | The injectivity of ReLU layers in neural networks, the recovery of vectors
from clipped or saturated measurements, and (real) phase retrieval in
$\mathbb{R}^n$ allow for a similar problem formulation and characterization
using frame theory. In this paper, we revisit all three problems with a unified
perspective and d... |
2502.09900 | Thompson Sampling for Repeated Newsvendor | cs.LG | In this paper, we investigate the performance of Thompson Sampling (TS) for
online learning with censored feedback, focusing primarily on the classic
repeated newsvendor model--a foundational framework in inventory
management--and demonstrating how our techniques can be naturally extended to a
broader class of proble... |
2502.09903 | The Ann Arbor Architecture for Agent-Oriented Programming | cs.AI cs.HC cs.SE | In this paper, we reexamine prompt engineering for large language models
through the lens of automata theory. We argue that language models function as
automata and, like all automata, should be programmed in the languages they
accept, a unified collection of all natural and formal languages. Therefore,
traditional s... |
2502.09905 | Towards personalised assessment of abdominal aortic aneurysm structural
integrity | cs.CE | Abdominal aortic aneurysm (AAA) is a life-threatening condition involving the
permanent dilation of the aorta, often detected incidentally through imaging
for some other condition. The standard clinical approach to managing AAA
follows a one-size-fits-all model based on aneurysm size and growth rate,
leading to under... |
2502.09906 | Insect-Foundation: A Foundation Model and Large Multimodal Dataset for
Vision-Language Insect Understanding | cs.CV | Multimodal conversational generative AI has shown impressive capabilities in
various vision and language understanding through learning massive text-image
data. However, current conversational models still lack knowledge about visual
insects since they are often trained on the general knowledge of
vision-language dat... |
2502.09913 | AutoS$^2$earch: Unlocking the Reasoning Potential of Large Models for
Web-based Source Search | cs.AI cs.HC | Web-based management systems have been widely used in risk control and
industrial safety. However, effectively integrating source search capabilities
into these systems, to enable decision-makers to locate and address the hazard
(e.g., gas leak detection) remains a challenge. While prior efforts have
explored using w... |
2502.09918 | Dual Control for Interactive Autonomous Merging with Model Predictive
Diffusion | cs.RO cs.SY eess.SY math.OC | Interactive decision-making is essential in applications such as autonomous
driving, where the agent must infer the behavior of nearby human drivers while
planning in real-time. Traditional predict-then-act frameworks are often
insufficient or inefficient because accurate inference of human behavior
requires a contin... |
2502.09919 | AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on
AI-READI Dataset | cs.LG cs.AI | Diabetes is a chronic metabolic disorder characterized by persistently high
blood glucose levels (BGLs), leading to severe complications such as
cardiovascular disease, neuropathy, and retinopathy. Predicting BGLs enables
patients to maintain glucose levels within a safe range and allows caregivers
to take proactive ... |
2502.09920 | Machine Learning for Phase Estimation in Satellite-to-Earth Quantum
Communication | quant-ph cs.AI eess.SP | A global continuous-variable quantum key distribution (CV-QKD) network can be
established using a series of satellite-to-Earth channels. Increased
performance in such a network is provided by performing coherent measurement of
the optical quantum signals using a real local oscillator, calibrated locally
by encoding k... |
2502.09923 | Self-Consistent Model-based Adaptation for Visual Reinforcement Learning | cs.CV cs.LG | Visual reinforcement learning agents typically face serious performance
declines in real-world applications caused by visual distractions. Existing
methods rely on fine-tuning the policy's representations with hand-crafted
augmentations. In this work, we propose Self-Consistent Model-based Adaptation
(SCMA), a novel ... |
2502.09925 | TaskGalaxy: Scaling Multi-modal Instruction Fine-tuning with Tens of
Thousands Vision Task Types | cs.CV cs.AI | Multimodal visual language models are gaining prominence in open-world
applications, driven by advancements in model architectures, training
techniques, and high-quality data. However, their performance is often limited
by insufficient task-specific data, leading to poor generalization and biased
outputs. Existing ef... |
2502.09926 | Robust Anomaly Detection via Tensor Chidori Pseudoskeleton Decomposition | cs.LG | Anomaly detection plays a critical role in modern data-driven applications,
from identifying fraudulent transactions and safeguarding network
infrastructure to monitoring sensor systems for irregular patterns. Traditional
approaches, such as distance, density, or cluster-based methods, face
significant challenges whe... |
2502.09927 | Granite Vision: a lightweight, open-source multimodal model for
enterprise Intelligence | cs.CV cs.AI | We introduce Granite Vision, a lightweight large language model with vision
capabilities, specifically designed to excel in enterprise use cases,
particularly in visual document understanding. Our model is trained on a
comprehensive instruction-following dataset, including document-related tasks,
such as content extr... |
2502.09928 | Deep Tree Tensor Networks for Image Recognition | cs.CV cs.AI | Originating in quantum physics, tensor networks (TNs) have been widely
adopted as exponential machines and parameter decomposers for recognition
tasks. Typical TN models, such as Matrix Product States (MPS), have not yet
achieved successful application in natural image processing. When employed,
they primarily serve ... |
2502.09931 | TransGUNet: Transformer Meets Graph-based Skip Connection for Medical
Image Segmentation | cs.CV cs.AI | Skip connection engineering is primarily employed to address the semantic gap
between the encoder and decoder, while also integrating global dependencies to
understand the relationships among complex anatomical structures in medical
image segmentation. Although several models have proposed transformer-based
approache... |
2502.09932 | AffectSRNet : Facial Emotion-Aware Super-Resolution Network | cs.CV | Facial expression recognition (FER) systems in low-resolution settings face
significant challenges in accurately identifying expressions due to the loss of
fine-grained facial details. This limitation is especially problematic for
applications like surveillance and mobile communications, where low image
resolution is... |
2502.09933 | MIR-Bench: Benchmarking LLM's Long-Context Intelligence via Many-Shot
In-Context Inductive Reasoning | cs.AI cs.CL cs.LG | Inductive Reasoning (IR), the ability to summarize rules from examples and
apply on new ones, has long been viewed as a primal ability for general
intelligence and widely studied by cognitive science and AI researchers. Many
benchmarks have been proposed to measure such ability for Large Language Models
(LLMs); howev... |
2502.09934 | Fused Partial Gromov-Wasserstein for Structured Objects | cs.LG | Structured data, such as graphs, are vital in machine learning due to their
capacity to capture complex relationships and interactions. In recent years,
the Fused Gromov-Wasserstein (FGW) distance has attracted growing interest
because it enables the comparison of structured data by jointly accounting for
feature sim... |
2502.09935 | Precise Parameter Localization for Textual Generation in Diffusion
Models | cs.CV | Novel diffusion models can synthesize photo-realistic images with integrated
high-quality text. Surprisingly, we demonstrate through attention activation
patching that only less than 1% of diffusion models' parameters, all contained
in attention layers, influence the generation of textual content within the
images. B... |
2502.09937 | Tradeoffs in Processing Queries and Supporting Updates over an
ML-Enhanced R-tree | cs.DB cs.LG | Machine Learning (ML) techniques have been successfully applied to design
various learned database index structures for both the one- and
multi-dimensional spaces. Particularly, a class of traditional
multi-dimensional indexes has been augmented with ML models to design
ML-enhanced variants of their traditional count... |
2502.09939 | Temporal Scale and Shift Invariant Automatic Event Recognition using the
Mellin Transform | cs.CV | The Spatio-temporal holographic correlator combines the traditional 2D
optical image correlation techniques with inhomogeneously broadened arrays of
cold atoms to achieve 3D time-space correlation to realize automatic event
recognition at an ultra-high speed. Here we propose a method to realize such
event recognition... |
2502.09940 | A Preliminary Exploration with GPT-4o Voice Mode | cs.CL cs.SD eess.AS | With the rise of multimodal large language models, GPT-4o stands out as a
pioneering model, driving us to evaluate its capabilities. This report assesses
GPT-4o across various tasks to analyze its audio processing and reasoning
abilities. We find that GPT-4o exhibits strong knowledge in audio, speech, and
music under... |
2502.09941 | A Lightweight and Effective Image Tampering Localization Network with
Vision Mamba | cs.CV cs.CR | Current image tampering localization methods primarily rely on Convolutional
Neural Networks (CNNs) and Transformers. While CNNs suffer from limited local
receptive fields, Transformers offer global context modeling at the expense of
quadratic computational complexity. Recently, the state space model Mamba has
emerge... |
2502.09944 | Self-Supervised Learning for Neural Topic Models with
Variance-Invariance-Covariance Regularization | cs.LG cs.CL | In our study, we propose a self-supervised neural topic model (NTM) that
combines the power of NTMs and regularized self-supervised learning methods to
improve performance. NTMs use neural networks to learn latent topics hidden
behind the words in documents, enabling greater flexibility and the ability to
estimate mo... |
2502.09947 | Analyzing Patient Daily Movement Behavior Dynamics Using Two-Stage
Encoding Model | cs.AI cs.LG | In the analysis of remote healthcare monitoring data, time series
representation learning offers substantial value in uncovering deeper patterns
of patient behavior, especially given the fine temporal granularity of the
data. In this study, we focus on a dataset of home activity records from people
living with Dement... |
2502.09952 | Using MRNet to Predict Lunar Rock Categories Detected by Chang'e 5 Probe | cs.CV cs.AI | China's Chang'e 5 mission has been a remarkable success, with the chang'e 5
lander traveling on the Oceanus Procellarum to collect images of the lunar
surface. Over the past half century, people have brought back some lunar rock
samples, but its quantity does not meet the need for research. Under current
circumstance... |
2502.09954 | On Space Folds of ReLU Neural Networks | cs.LG cs.NE | Recent findings suggest that the consecutive layers of ReLU neural networks
can be understood geometrically as space folding transformations of the input
space, revealing patterns of self-similarity. In this paper, we present the
first quantitative analysis of this space folding phenomenon in ReLU neural
networks. Ou... |
2502.09955 | Diverse Inference and Verification for Advanced Reasoning | cs.AI | Reasoning LLMs such as OpenAI o1, o3 and DeepSeek R1 have made significant
progress in mathematics and coding, yet find challenging advanced tasks such as
International Mathematical Olympiad (IMO) combinatorics problems, Abstraction
and Reasoning Corpus (ARC) puzzles, and Humanity's Last Exam (HLE) questions.
We use ... |
2502.09956 | KGGen: Extracting Knowledge Graphs from Plain Text with Language Models | cs.CL cs.AI cs.IR cs.LG | Recent interest in building foundation models for KGs has highlighted a
fundamental challenge: knowledge-graph data is relatively scarce. The
best-known KGs are primarily human-labeled, created by pattern-matching, or
extracted using early NLP techniques. While human-generated KGs are in short
supply, automatically e... |
2502.09960 | Global-Local Interface for On-Demand Teleoperation | cs.RO | Teleoperation is a critical method for human-robot interface, holds
significant potential for enabling robotic applications in industrial and
unstructured environments. Existing teleoperation methods have distinct
strengths and limitations in flexibility, range of workspace and precision. To
fuse these advantages, we... |
2502.09963 | Generating on Generated: An Approach Towards Self-Evolving Diffusion
Models | cs.CV | Recursive Self-Improvement (RSI) enables intelligence systems to autonomously
refine their capabilities. This paper explores the application of RSI in
text-to-image diffusion models, addressing the challenge of training collapse
caused by synthetic data. We identify two key factors contributing to this
collapse: the ... |
2502.09967 | VicKAM: Visual Conceptual Knowledge Guided Action Map for Weakly
Supervised Group Activity Recognition | cs.CV | Existing weakly supervised group activity recognition methods rely on object
detectors or attention mechanisms to capture key areas automatically. However,
they overlook the semantic information associated with captured areas, which
may adversely affect the recognition performance. In this paper, we propose a
novel f... |
2502.09969 | Data Valuation using Neural Networks for Efficient Instruction
Fine-Tuning | cs.LG cs.AI cs.CL | Influence functions provide crucial insights into model training, but
existing methods suffer from large computational costs and limited
generalization. Particularly, recent works have proposed various metrics and
algorithms to calculate the influence of data using language models, which do
not scale well with large ... |
2502.09970 | Universal Machine Learning Interatomic Potentials are Ready for Solid
Ion Conductors | cond-mat.mtrl-sci cs.LG | With the rapid development of energy storage technology, high-performance
solid-state electrolytes (SSEs) have become critical for next-generation
lithium-ion batteries. These materials require high ionic conductivity,
excellent electrochemical stability, and good mechanical properties to meet the
demands of electric... |
2502.09971 | Conditional Latent Coding with Learnable Synthesized Reference for Deep
Image Compression | cs.CV cs.AI | In this paper, we study how to synthesize a dynamic reference from an
external dictionary to perform conditional coding of the input image in the
latent domain and how to learn the conditional latent synthesis and coding
modules in an end-to-end manner. Our approach begins by constructing a
universal image feature di... |
2502.09974 | Has My System Prompt Been Used? Large Language Model Prompt Membership
Inference | cs.AI cs.CR | Prompt engineering has emerged as a powerful technique for optimizing large
language models (LLMs) for specific applications, enabling faster prototyping
and improved performance, and giving rise to the interest of the community in
protecting proprietary system prompts. In this work, we explore a novel
perspective on... |
2502.09977 | LaRA: Benchmarking Retrieval-Augmented Generation and Long-Context LLMs
-- No Silver Bullet for LC or RAG Routing | cs.CL cs.AI | Effectively incorporating external knowledge into Large Language Models
(LLMs) is crucial for enhancing their capabilities and addressing real-world
needs. Retrieval-Augmented Generation (RAG) offers an effective method for
achieving this by retrieving the most relevant fragments into LLMs. However,
the advancements ... |
2502.09978 | RoadFed: A Multimodal Federated Learning System for Improving Road
Safety | cs.CE | Internet of Things (IoTs) have been widely applied in Collaborative
Intelligent Transportation Systems (C-ITS) for the prevention of road
accidents. As one of the primary causes of road accidents in C-ITS, the
efficient detection and early alarm of road hazards are of paramount
importance. Given the importance, exten... |
2502.09980 | V2V-LLM: Vehicle-to-Vehicle Cooperative Autonomous Driving with
Multi-Modal Large Language Models | cs.CV cs.RO | Current autonomous driving vehicles rely mainly on their individual sensors
to understand surrounding scenes and plan for future trajectories, which can be
unreliable when the sensors are malfunctioning or occluded. To address this
problem, cooperative perception methods via vehicle-to-vehicle (V2V)
communication hav... |
2502.09981 | Exploring Neural Granger Causality with xLSTMs: Unveiling Temporal
Dependencies in Complex Data | cs.LG | Causality in time series can be difficult to determine, especially in the
presence of non-linear dependencies. The concept of Granger causality helps
analyze potential relationships between variables, thereby offering a method to
determine whether one time series can predict-Granger cause-future values of
another. Al... |
2502.09985 | On Volume Minimization in Conformal Regression | stat.ML cs.LG | We study the question of volume optimality in split conformal regression, a
topic still poorly understood in comparison to coverage control. Using the fact
that the calibration step can be seen as an empirical volume minimization
problem, we first derive a finite-sample upper-bound on the excess volume loss
of the in... |
2502.09990 | X-Boundary: Establishing Exact Safety Boundary to Shield LLMs from
Multi-Turn Jailbreaks without Compromising Usability | cs.CR cs.AI cs.CL cs.CV cs.LG | Despite the rapid development of safety alignment techniques for LLMs,
defending against multi-turn jailbreaks is still a challenging task. In this
paper, we conduct a comprehensive comparison, revealing that some existing
defense methods can improve the robustness of LLMs against multi-turn
jailbreaks but compromise... |
2502.09992 | Large Language Diffusion Models | cs.CL cs.LG | Autoregressive models (ARMs) are widely regarded as the cornerstone of large
language models (LLMs). We challenge this notion by introducing LLaDA, a
diffusion model trained from scratch under the pre-training and supervised
fine-tuning (SFT) paradigm. LLaDA models distributions through a forward data
masking process... |
2502.09993 | Navigating Label Ambiguity for Facial Expression Recognition in the Wild | cs.CV | Facial expression recognition (FER) remains a challenging task due to label
ambiguity caused by the subjective nature of facial expressions and noisy
samples. Additionally, class imbalance, which is common in real-world datasets,
further complicates FER. Although many studies have shown impressive
improvements, they ... |
2502.09994 | Decision Information Meets Large Language Models: The Future of
Explainable Operations Research | cs.AI | Operations Research (OR) is vital for decision-making in many industries.
While recent OR methods have seen significant improvements in automation and
efficiency through integrating Large Language Models (LLMs), they still
struggle to produce meaningful explanations. This lack of clarity raises
concerns about transpa... |
2502.09998 | Estimation of the Learning Coefficient Using Empirical Loss | stat.ML cs.LG | The learning coefficient plays a crucial role in analyzing the performance of
information criteria, such as the Widely Applicable Information Criterion
(WAIC) and the Widely Applicable Bayesian Information Criterion (WBIC), which
Sumio Watanabe developed to assess model generalization ability. In regular
statistical ... |
2502.10001 | EmbBERT-Q: Breaking Memory Barriers in Embedded NLP | cs.CL cs.AR cs.DC cs.LG | Large Language Models (LLMs) have revolutionized natural language processing,
setting new standards across a wide range of applications. However, their
relevant memory and computational demands make them impractical for deployment
on technologically-constrained tiny devices such as wearable devices and
Internet-of-Th... |
2502.10003 | SciClaimHunt: A Large Dataset for Evidence-based Scientific Claim
Verification | cs.CL | Verifying scientific claims presents a significantly greater challenge than
verifying political or news-related claims. Unlike the relatively broad
audience for political claims, the users of scientific claim verification
systems can vary widely, ranging from researchers testing specific hypotheses
to everyday users ... |
2502.10011 | InterGridNet: An Electric Network Frequency Approach for Audio Source
Location Classification Using Convolutional Neural Networks | cs.SD cs.LG eess.AS | A novel framework, called InterGridNet, is introduced, leveraging a shallow
RawNet model for geolocation classification of Electric Network Frequency (ENF)
signatures in the SP Cup 2016 dataset. During data preparation, recordings are
sorted into audio and power groups based on inherent characteristics, further
divid... |
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