id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.02002 | HMM-LSTM Fusion Model for Economic Forecasting | [
"cs.LG",
"econ.EM",
"stat.ME"
] | This paper explores the application of Hidden Markov Models (HMM) and Long Short-Term Memory (LSTM) neural networks for economic forecasting, focusing on predicting CPI inflation rates. The study explores a new approach that integrates HMM-derived hidden states and means as additional features for LSTM modeling, aiming... | {
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2501.02003 | SurfPatch: Enabling Patch Matching for Exploratory Stream Surface
Visualization | [
"cs.GR",
"cs.CV"
] | Unlike their line-based counterparts, surface-based techniques have yet to be thoroughly investigated in flow visualization due to their significant placement, speed, perception, and evaluation challenges. This paper presents SurfPatch, a novel framework supporting exploratory stream surface visualization. To begin wit... | {
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2501.02004 | General Information Metrics for Improving AI Model Training Efficiency | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT"
] | To address the growing size of AI model training data and the lack of a universal data selection methodology-factors that significantly drive up training costs -- this paper presents the General Information Metrics Evaluation (GIME) method. GIME leverages general information metrics from Objective Information Theory (O... | {
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2501.02006 | Multi-Task Semantic Communication With Graph Attention-Based Feature
Correlation Extraction | [
"cs.LG",
"cs.AI"
] | Multi-task semantic communication can serve multiple learning tasks using a shared encoder model. Existing models have overlooked the intricate relationships between features extracted during an encoding process of tasks. This paper presents a new graph attention inter-block (GAI) module to the encoder/transmitter of a... | {
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} |
2501.02007 | TART: Token-based Architecture Transformer for Neural Network
Performance Prediction | [
"cs.LG",
"cs.AI"
] | In the realm of neural architecture design, achieving high performance is largely reliant on the manual expertise of researchers. Despite the emergence of Neural Architecture Search (NAS) as a promising technique for automating this process, current NAS methods still require human input to expand the search space and c... | {
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} |
2501.02008 | Integrated Strategy for Urban Traffic Optimization: Prediction, Adaptive
Signal Control, and Distributed Communication via Messaging | [
"eess.SY",
"cs.SY",
"eess.SP"
] | This work introduces an integrated approach to optimizing urban traffic by combining predictive modeling of vehicle flow, adaptive traffic signal control, and a modular integration architecture through distributed messaging. Using real-time data from various sensors, the system anticipates traffic fluctuations and dyna... | {
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2501.02009 | Cross-model Transferability among Large Language Models on the Platonic
Representations of Concepts | [
"cs.CL",
"cs.AI"
] | Understanding the inner workings of Large Language Models (LLMs) is a critical research frontier. Prior research has shown that a single LLM's concept representations can be captured as steering vectors (SVs), enabling the control of LLM behavior (e.g., towards generating harmful content). Our work takes a novel approa... | {
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2501.02010 | Explainable Neural Networks with Guarantees: A Sparse Estimation
Approach | [
"cs.LG"
] | Balancing predictive power and interpretability has long been a challenging research area, particularly in powerful yet complex models like neural networks, where nonlinearity obstructs direct interpretation. This paper introduces a novel approach to constructing an explainable neural network that harmonizes predictive... | {
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2501.02012 | Information Subtraction: Learning Representations for Conditional
Entropy | [
"cs.LG"
] | The representations of conditional entropy and conditional mutual information are significant in explaining the unique effects among variables. While previous studies based on conditional contrastive sampling have effectively removed information regarding discrete sensitive variables, they have not yet extended their s... | {
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2501.02014 | Machine Learning-Based Differential Diagnosis of Parkinson's Disease
Using Kinematic Feature Extraction and Selection | [
"cs.LG",
"cs.AI"
] | Parkinson's disease (PD), the second most common neurodegenerative disorder, is characterized by dopaminergic neuron loss and the accumulation of abnormal synuclein. PD presents both motor and non-motor symptoms that progressively impair daily functioning. The severity of these symptoms is typically assessed using the ... | {
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2501.02015 | KANS: Knowledge Discovery Graph Attention Network for Soft Sensing in
Multivariate Industrial Processes | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SP",
"eess.SY"
] | Soft sensing of hard-to-measure variables is often crucial in industrial processes. Current practices rely heavily on conventional modeling techniques that show success in improving accuracy. However, they overlook the non-linear nature, dynamics characteristics, and non-Euclidean dependencies between complex process v... | {
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} |
2501.02016 | ST-HCSS: Deep Spatio-Temporal Hypergraph Convolutional Neural Network
for Soft Sensing | [
"cs.LG",
"cs.AI",
"eess.SP"
] | Higher-order sensor networks are more accurate in characterizing the nonlinear dynamics of sensory time-series data in modern industrial settings by allowing multi-node connections beyond simple pairwise graph edges. In light of this, we propose a deep spatio-temporal hypergraph convolutional neural network for soft se... | {
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} |
2501.02017 | Rephotography in the Digital Era: Mass Rephotography and re.photos, the
Web Portal for Rephotography | [
"cs.CV"
] | Since the beginning of rephotography in the middle of the 19th century, techniques in registration, conservation, presentation, and sharing of rephotographs have come a long way. Here, we will present existing digital approaches to rephotography and discuss future approaches and requirements for digital mass rephotogra... | {
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2501.02018 | Safeguarding Large Language Models in Real-time with Tunable
Safety-Performance Trade-offs | [
"cs.CL",
"cs.AI",
"cs.CR",
"cs.LG"
] | Large Language Models (LLMs) have been shown to be susceptible to jailbreak attacks, or adversarial attacks used to illicit high risk behavior from a model. Jailbreaks have been exploited by cybercriminals and blackhat actors to cause significant harm, highlighting the critical need to safeguard widely-deployed models.... | {
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} |
2501.02019 | Benchmarking Constraint-Based Bayesian Structure Learning Algorithms:
Role of Network Topology | [
"cs.LG",
"cs.AI",
"q-bio.MN"
] | Modeling the associations between real world entities from their multivariate cross-sectional profiles can provide cues into the concerted working of these entities as a system. Several techniques have been proposed for deciphering these associations including constraint-based Bayesian structure learning (BSL) algorith... | {
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} |
2501.02020 | Enhancing Uncertainty Modeling with Semantic Graph for Hallucination
Detection | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) are prone to hallucination with non-factual or unfaithful statements, which undermines the applications in real-world scenarios. Recent researches focus on uncertainty-based hallucination detection, which utilizes the output probability of LLMs for uncertainty calculation and does not rely ... | {
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2501.02021 | Weakly Supervised Learning on Large Graphs | [
"cs.LG",
"cs.AI"
] | Graph classification plays a pivotal role in various domains, including pathology, where images can be represented as graphs. In this domain, images can be represented as graphs, where nodes might represent individual nuclei, and edges capture the spatial or functional relationships between them. Often, the overall lab... | {
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2501.02024 | Model Checking in Medical Imaging for Tumor Detection and Segmentation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Recent advancements in model checking have demonstrated significant potential across diverse applications, particularly in signal and image analysis. Medical imaging stands out as a critical domain where model checking can be effectively applied to design and evaluate robust frameworks. These frameworks facilitate auto... | {
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2501.02025 | RealDiffFusionNet: Neural Controlled Differential Equation Informed
Multi-Head Attention Fusion Networks for Disease Progression Modeling Using
Real-World Data | [
"cs.LG",
"cs.CV",
"q-bio.QM"
] | This paper presents a novel deep learning-based approach named RealDiffFusionNet incorporating Neural Controlled Differential Equations (Neural CDE) - time series models that are robust in handling irregularly sampled data - and multi-head attention to align relevant multimodal context (image data, time invariant data,... | {
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} |
2501.02026 | Recursive Decomposition of Logical Thoughts: Framework for Superior
Reasoning and Knowledge Propagation in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.LO"
] | Enhancing the reasoning capabilities of Large Language Models remains a critical challenge in artificial intelligence. We introduce RDoLT, Recursive Decomposition of Logical Thought prompting, a novel framework that significantly boosts LLM reasoning performance. RDoLT is built on three key innovations: (1) recursively... | {
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2501.02029 | Spot Risks Before Speaking! Unraveling Safety Attention Heads in Large
Vision-Language Models | [
"cs.LG",
"cs.AI",
"cs.CR",
"cs.CV"
] | With the integration of an additional modality, large vision-language models (LVLMs) exhibit greater vulnerability to safety risks (e.g., jailbreaking) compared to their language-only predecessors. Although recent studies have devoted considerable effort to the post-hoc alignment of LVLMs, the inner safety mechanisms r... | {
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2501.02030 | Detecting Music Performance Errors with Transformers | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Beginner musicians often struggle to identify specific errors in their performances, such as playing incorrect notes or rhythms. There are two limitations in existing tools for music error detection: (1) Existing approaches rely on automatic alignment; therefore, they are prone to errors caused by small deviations betw... | {
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} |
2501.02031 | CarbonChat: Large Language Model-Based Corporate Carbon Emission
Analysis and Climate Knowledge Q&A System | [
"cs.CL",
"cs.AI"
] | As the impact of global climate change intensifies, corporate carbon emissions have become a focal point of global attention. In response to issues such as the lag in climate change knowledge updates within large language models, the lack of specialization and accuracy in traditional augmented generation architectures ... | {
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} |
2501.02032 | Dynamic Feature Fusion: Combining Global Graph Structures and Local
Semantics for Blockchain Fraud Detection | [
"cs.CR",
"cs.AI",
"cs.SE"
] | The advent of blockchain technology has facilitated the widespread adoption of smart contracts in the financial sector. However, current fraud detection methodologies exhibit limitations in capturing both global structural patterns within transaction networks and local semantic relationships embedded in transaction dat... | {
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2501.02035 | 3D Cloud reconstruction through geospatially-aware Masked Autoencoders | [
"cs.CV",
"cs.AI"
] | Clouds play a key role in Earth's radiation balance with complex effects that introduce large uncertainties into climate models. Real-time 3D cloud data is essential for improving climate predictions. This study leverages geostationary imagery from MSG/SEVIRI and radar reflectivity measurements of cloud profiles from C... | {
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2501.02036 | Deep Clustering via Community Detection | [
"cs.LG",
"cs.AI",
"cs.SI"
] | Deep clustering is an essential task in modern artificial intelligence, aiming to partition a set of data samples into a given number of homogeneous groups (i.e., clusters). Even though many Deep Neural Network (DNN) backbones and clustering strategies have been proposed for the task, achieving increasingly improved pe... | {
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2501.02038 | Architecture for Trajectory-Based Fishing Ship Classification with AIS
Data | [
"cs.LG",
"cs.AI"
] | This paper proposes a data preparation process for managing real-world kinematic data and detecting fishing vessels. The solution is a binary classification that classifies ship trajectories into either fishing or non-fishing ships. The data used are characterized by the typical problems found in classic data mining ap... | {
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2501.02039 | An Investigation into Value Misalignment in LLM-Generated Texts for
Cultural Heritage | [
"cs.CL",
"cs.AI"
] | As Large Language Models (LLMs) become increasingly prevalent in tasks related to cultural heritage, such as generating descriptions of historical monuments, translating ancient texts, preserving oral traditions, and creating educational content, their ability to produce accurate and culturally aligned texts is being i... | {
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2501.02040 | A Separable Self-attention Inspired by the State Space Model for
Computer Vision | [
"cs.CV",
"cs.AI"
] | Mamba is an efficient State Space Model (SSM) with linear computational complexity. Although SSMs are not suitable for handling non-causal data, Vision Mamba (ViM) methods still demonstrate good performance in tasks such as image classification and object detection. Recent studies have shown that there is a rich theore... | {
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2501.02041 | MRG: A Multi-Robot Manufacturing Digital Scene Generation Method Using
Multi-Instance Point Cloud Registration | [
"cs.CV",
"cs.AI"
] | A high-fidelity digital simulation environment is crucial for accurately replicating physical operational processes. However, inconsistencies between simulation and physical environments result in low confidence in simulation outcomes, limiting their effectiveness in guiding real-world production. Unlike the traditiona... | {
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2501.02042 | Towards Robust and Accurate Stability Estimation of Local Surrogate
Models in Text-based Explainable AI | [
"cs.LG",
"cs.CR"
] | Recent work has investigated the concept of adversarial attacks on explainable AI (XAI) in the NLP domain with a focus on examining the vulnerability of local surrogate methods such as Lime to adversarial perturbations or small changes on the input of a machine learning (ML) model. In such attacks, the generated explan... | {
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2501.02043 | Modeling COVID-19 spread in the USA using metapopulation SIR models
coupled with graph convolutional neural networks | [
"stat.ML",
"cs.LG",
"math.DS",
"q-bio.PE"
] | Graph convolutional neural networks (GCNs) have shown tremendous promise in addressing data-intensive challenges in recent years. In particular, some attempts have been made to improve predictions of Susceptible-Infected-Recovered (SIR) models by incorporating human mobility between metapopulations and using graph appr... | {
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2501.02044 | Advancing Pancreatic Cancer Prediction with a Next Visit Token
Prediction Head on top of Med-BERT | [
"cs.CL",
"cs.AI"
] | Background: Recently, numerous foundation models pretrained on extensive data have demonstrated efficacy in disease prediction using Electronic Health Records (EHRs). However, there remains some unanswered questions on how to best utilize such models especially with very small fine-tuning cohorts. Methods: We utilized ... | {
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2501.02045 | METAGENE-1: Metagenomic Foundation Model for Pandemic Monitoring | [
"q-bio.GN",
"cs.AI",
"cs.CL",
"cs.LG"
] | We pretrain METAGENE-1, a 7-billion-parameter autoregressive transformer model, which we refer to as a metagenomic foundation model, on a novel corpus of diverse metagenomic DNA and RNA sequences comprising over 1.5 trillion base pairs. This dataset is sourced from a large collection of human wastewater samples, proces... | {
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2501.02048 | DreamMask: Boosting Open-vocabulary Panoptic Segmentation with Synthetic
Data | [
"cs.CV"
] | Open-vocabulary panoptic segmentation has received significant attention due to its applicability in the real world. Despite claims of robust generalization, we find that the advancements of previous works are attributed mainly on trained categories, exposing a lack of generalization to novel classes. In this paper, we... | {
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2501.02059 | Active Learning Enables Extrapolation in Molecular Generative Models | [
"cs.LG",
"cond-mat.mtrl-sci",
"physics.chem-ph"
] | Although generative models hold promise for discovering molecules with optimized desired properties, they often fail to suggest synthesizable molecules that improve upon the known molecules seen in training. We find that a key limitation is not in the molecule generation process itself, but in the poor generalization c... | {
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2501.02063 | AGGA: A Dataset of Academic Guidelines for Generative AI and Large
Language Models | [
"cs.CL",
"cs.CY"
] | This study introduces AGGA, a dataset comprising 80 academic guidelines for the use of Generative AIs (GAIs) and Large Language Models (LLMs) in academic settings, meticulously collected from official university websites. The dataset contains 188,674 words and serves as a valuable resource for natural language processi... | {
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2501.02064 | ArtCrafter: Text-Image Aligning Style Transfer via Embedding Reframing | [
"cs.CV",
"cs.AI"
] | Recent years have witnessed significant advancements in text-guided style transfer, primarily attributed to innovations in diffusion models. These models excel in conditional guidance, utilizing text or images to direct the sampling process. However, despite their capabilities, direct conditional guidance approaches of... | {
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2501.02066 | RadHop-Net: A Lightweight Radiomics-to-Error Regression for False
Positive Reduction In MRI Prostate Cancer Detection | [
"cs.CV",
"eess.IV"
] | Clinically significant prostate cancer (csPCa) is a leading cause of cancer death in men, yet it has a high survival rate if diagnosed early. Bi-parametric MRI (bpMRI) reading has become a prominent screening test for csPCa. However, this process has a high false positive (FP) rate, incurring higher diagnostic costs an... | {
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2501.02068 | The interplay between domain specialization and model size: a case study
in the legal domain | [
"cs.CL",
"cs.AI"
] | Scaling laws for language models so far focused on finding the compute-optimal model size and token count for training from scratch. However, achieving this optimal balance requires significant compute resources due to the extensive data demands when training models from randomly-initialized weights. Continual pre-trai... | {
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2501.02069 | Counterfactual Explanation for Auto-Encoder Based Time-Series Anomaly
Detection | [
"cs.LG"
] | The complexity of modern electro-mechanical systems require the development of sophisticated diagnostic methods like anomaly detection capable of detecting deviations. Conventional anomaly detection approaches like signal processing and statistical modelling often struggle to effectively handle the intricacies of compl... | {
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2501.02071 | Laws of thermodynamics for exponential families | [
"cond-mat.stat-mech",
"cs.LG",
"math.ST",
"stat.TH"
] | We develop the laws of thermodynamics in terms of general exponential families. By casting learning (log-loss minimization) problems in max-entropy and statistical mechanics terms, we translate thermodynamics results to learning scenarios. We extend the well-known way in which exponential families characterize thermody... | {
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2501.02077 | Chance Constrained PDE-Constrained Optimal Design Strategies Under
High-Dimensional Uncertainty | [
"cs.CE"
] | This study presents an advanced computational framework for the optimal design of thermal insulation components in buildings, utilizing silica aerogel porous materials. The framework aims to achieve superior thermal insulation while maintaining structural integrity of the component under stress concentrations. A multip... | {
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2501.02080 | AI-Powered Cow Detection in Complex Farm Environments | [
"cs.CV"
] | Animal welfare has become a critical issue in contemporary society, emphasizing our ethical responsibilities toward animals, particularly within livestock farming. The advent of Artificial Intelligence (AI) technologies, specifically computer vision, offers an innovative approach to monitoring and enhancing animal welf... | {
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2501.02086 | Instruction-Following Pruning for Large Language Models | [
"cs.CL"
] | With the rapid scaling of large language models (LLMs), structured pruning has become a widely used technique to learn efficient, smaller models from larger ones, delivering superior performance compared to training similarly sized models from scratch. In this paper, we move beyond the traditional static pruning approa... | {
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2501.02087 | Beyond CVaR: Leveraging Static Spectral Risk Measures for Enhanced
Decision-Making in Distributional Reinforcement Learning | [
"cs.LG",
"stat.ML"
] | In domains such as finance, healthcare, and robotics, managing worst-case scenarios is critical, as failure to do so can lead to catastrophic outcomes. Distributional Reinforcement Learning (DRL) provides a natural framework to incorporate risk sensitivity into decision-making processes. However, existing approaches fa... | {
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2501.02089 | On the Statistical Complexity for Offline and Low-Adaptive Reinforcement
Learning with Structures | [
"cs.LG",
"cs.AI"
] | This article reviews the recent advances on the statistical foundation of reinforcement learning (RL) in the offline and low-adaptive settings. We will start by arguing why offline RL is the appropriate model for almost any real-life ML problems, even if they have nothing to do with the recent AI breakthroughs that use... | {
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2501.02090 | Applying Text Mining to Analyze Human Question Asking in Creativity
Research | [
"cs.CL"
] | Creativity relates to the ability to generate novel and effective ideas in the areas of interest. How are such creative ideas generated? One possible mechanism that supports creative ideation and is gaining increased empirical attention is by asking questions. Question asking is a likely cognitive mechanism that allows... | {
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2501.02094 | SMTL: A Stratified Logic for Expressive Multi-Level Temporal
Specifications | [
"eess.SY",
"cs.SY"
] | We present Stratified Metric Temporal Logic (SMTL), a novel formalism for specifying and verifying properties of complex cyber-physical systems that exhibit behaviors across multiple temporal and abstraction scales. SMTL extends existing temporal logics by incorporating a stratification operator, enabling the associati... | {
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2501.02104 | Equivalence of Informations Characterizes Bregman Divergences | [
"cs.IT",
"math.IT"
] | Bregman divergences are a class of distance-like comparison functions which play fundamental roles in optimization, statistics, and information theory. One important property of Bregman divergences is that they cause two useful formulations of information content (in the sense of variability or non-uniformity) in a wei... | {
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2501.02105 | Learning Fricke signs from Maass form Coefficients | [
"math.NT",
"cs.LG",
"hep-th",
"stat.ML"
] | In this paper, we conduct a data-scientific investigation of Maass forms. We find that averaging the Fourier coefficients of Maass forms with the same Fricke sign reveals patterns analogous to the recently discovered "murmuration" phenomenon, and that these patterns become more pronounced when parity is incorporated as... | {
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2501.02107 | Online Detection of Water Contamination Under Concept Drift | [
"cs.LG",
"cs.AI"
] | Water Distribution Networks (WDNs) are vital infrastructures, and contamination poses serious public health risks. Harmful substances can interact with disinfectants like chlorine, making chlorine monitoring essential for detecting contaminants. However, chlorine sensors often become unreliable and require frequent cal... | {
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} |
2501.02111 | How Your Location Relates to Health: Variable Importance and
Interpretable Machine Learning for Environmental and Sociodemographic Data | [
"cs.LG"
] | Health outcomes depend on complex environmental and sociodemographic factors whose effects change over location and time. Only recently has fine-grained spatial and temporal data become available to study these effects, namely the MEDSAT dataset of English health, environmental, and sociodemographic information. Levera... | {
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2501.02112 | Siamese Networks for Cat Re-Identification: Exploring Neural Models for
Cat Instance Recognition | [
"cs.CV",
"cs.AI"
] | Street cats in urban areas often rely on human intervention for survival, leading to challenges in population control and welfare management. In April 2023, Hello Inc., a Chinese urban mobility company, launched the Hello Street Cat initiative to address these issues. The project deployed over 21,000 smart feeding stat... | {
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2501.02114 | Relaxation-assisted reverse annealing on nonnegative/binary matrix
factorization | [
"quant-ph",
"cond-mat.stat-mech",
"cs.AI",
"cs.LG"
] | Quantum annealing has garnered significant attention as meta-heuristics inspired by quantum physics for combinatorial optimization problems. Among its many applications, nonnegative/binary matrix factorization stands out for its complexity and relevance in unsupervised machine learning. The use of reverse annealing, a ... | {
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} |
2501.02116 | Humanoid Locomotion and Manipulation: Current Progress and Challenges in
Control, Planning, and Learning | [
"cs.RO"
] | Humanoid robots have great potential to perform various human-level skills. These skills involve locomotion, manipulation, and cognitive capabilities. Driven by advances in machine learning and the strength of existing model-based approaches, these capabilities have progressed rapidly, but often separately. Therefore, ... | {
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2501.02117 | Fastest mixing reversible Markov chain on friendship graph: Trade-off
between transition probabilities among friends and convergence rate | [
"cs.IT",
"math.IT"
] | A long-standing goal of social network research has been to alter the properties of network to achieve the desired outcome. In doing so, DeGroot's consensus model has served as the popular choice for modeling the information diffusion and opinion formation in social networks. Achieving a trade-off between the cost asso... | {
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} |
2501.02127 | How do Humans take an Object from a Robot: Behavior changes observed in
a User Study | [
"cs.RO"
] | To facilitate human-robot interaction and gain human trust, a robot should recognize and adapt to changes in human behavior. This work documents different human behaviors observed while taking objects from an interactive robot in an experimental study, categorized across two dimensions: pull force applied and handednes... | {
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2501.02132 | A hybrid marketplace of ideas | [
"cs.CY",
"cs.AI",
"cs.ET"
] | The convergence of humans and artificial intelligence systems introduces new dynamics into the cultural and intellectual landscape. Complementing emerging cultural evolution concepts such as machine culture, AI agents represent a significant techno-sociological development, particularly within the anthropological study... | {
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2501.02135 | AVTrustBench: Assessing and Enhancing Reliability and Robustness in
Audio-Visual LLMs | [
"cs.CV",
"cs.AI"
] | With the rapid advancement of Multi-modal Large Language Models (MLLMs), several diagnostic benchmarks have recently been developed to assess these models' multi-modal reasoning proficiency. However, these benchmarks are restricted to assessing primarily the visual aspect and do not examine the holistic audio-visual (A... | {
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2501.02138 | Effective LLM-Driven Code Generation with Pythoness | [
"cs.PL",
"cs.AI",
"cs.SE"
] | The advent of large language models (LLMs) has paved the way for a new era of programming tools with both significant capabilities and risks, as the generated code lacks guarantees of correctness and reliability. Developers using LLMs currently face the difficult task of optimizing, integrating, and maintaining code ge... | {
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2501.02140 | Tree-NET: Enhancing Medical Image Segmentation Through Efficient
Low-Level Feature Training | [
"eess.IV",
"cs.CV"
] | This paper introduces Tree-NET, a novel framework for medical image segmentation that leverages bottleneck feature supervision to enhance both segmentation accuracy and computational efficiency. While previous studies have employed bottleneck feature supervision, their applications have largely been limited to the trai... | {
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2501.02143 | SafeAug: Safety-Critical Driving Data Augmentation from Naturalistic
Datasets | [
"cs.CV",
"cs.LG"
] | Safety-critical driving data is crucial for developing safe and trustworthy self-driving algorithms. Due to the scarcity of safety-critical data in naturalistic datasets, current approaches primarily utilize simulated or artificially generated images. However, there remains a gap in authenticity between these generated... | {
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2501.02144 | Establishing baselines for generative discovery of inorganic crystals | [
"cond-mat.mtrl-sci",
"cs.AI",
"physics.chem-ph"
] | Generative artificial intelligence offers a promising avenue for materials discovery, yet its advantages over traditional methods remain unclear. In this work, we introduce and benchmark two baseline approaches - random enumeration of charge-balanced prototypes and data-driven ion exchange of known compounds - against ... | {
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2501.02146 | Plasma-CycleGAN: Plasma Biomarker-Guided MRI to PET Cross-modality
Translation Using Conditional CycleGAN | [
"cs.CV",
"cs.AI",
"q-bio.NC"
] | Cross-modality translation between MRI and PET imaging is challenging due to the distinct mechanisms underlying these modalities. Blood-based biomarkers (BBBMs) are revolutionizing Alzheimer's disease (AD) detection by identifying patients and quantifying brain amyloid levels. However, the potential of BBBMs to enhance... | {
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2501.02147 | Exploring Secure Machine Learning Through Payload Injection and FGSM
Attacks on ResNet-50 | [
"cs.CR",
"cs.LG"
] | This paper investigates the resilience of a ResNet-50 image classification model under two prominent security threats: Fast Gradient Sign Method (FGSM) adversarial attacks and malicious payload injection. Initially, the model attains a 53.33% accuracy on clean images. When subjected to FGSM perturbations, its overall a... | {
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2501.02149 | Attribute-Based Robotic Grasping with Data-Efficient Adaptation | [
"cs.RO",
"cs.AI"
] | Robotic grasping is one of the most fundamental robotic manipulation tasks and has been the subject of extensive research. However, swiftly teaching a robot to grasp a novel target object in clutter remains challenging. This paper attempts to address the challenge by leveraging object attributes that facilitate recogni... | {
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2501.02151 | From Images to Detection: Machine Learning for Blood Pattern
Classification | [
"cs.CV",
"stat.AP"
] | Bloodstain Pattern Analysis (BPA) helps us understand how bloodstains form, with a focus on their size, shape, and distribution. This aids in crime scene reconstruction and provides insight into victim positions and crime investigation. One challenge in BPA is distinguishing between different types of bloodstains, such... | {
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2501.02152 | Table as Thought: Exploring Structured Thoughts in LLM Reasoning | [
"cs.AI",
"cs.CL"
] | Large language models' reasoning abilities benefit from methods that organize their thought processes, such as chain-of-thought prompting, which employs a sequential structure to guide the reasoning process step-by-step. However, existing approaches focus primarily on organizing the sequence of thoughts, leaving struct... | {
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2501.02153 | Resolving the Exploration-Exploitation Dilemma in Evolutionary
Algorithms: A Novel Human-Centered Framework | [
"cs.NE",
"math.OC"
] | Evolutionary Algorithms (EAs) are powerful tools for tackling complex computational problems, yet effectively managing the exploitation and exploration dynamics -- crucial for robust search navigation -- remains a persistent challenge for EA designers, and leads to the so-called exploration-exploitation dilemma. In thi... | {
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2501.02156 | The Race to Efficiency: A New Perspective on AI Scaling Laws | [
"cs.LG",
"cs.AI",
"cs.PF"
] | As large-scale AI models expand, training becomes costlier and sustaining progress grows harder. Classical scaling laws (e.g., Kaplan et al. (2020), Hoffmann et al. (2022)) predict training loss from a static compute budget yet neglect time and efficiency, prompting the question: how can we balance ballooning GPU fleet... | {
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2501.02157 | Personalized Graph-Based Retrieval for Large Language Models | [
"cs.CL"
] | As large language models (LLMs) evolve, their ability to deliver personalized and context-aware responses offers transformative potential for improving user experiences. Existing personalization approaches, however, often rely solely on user history to augment the prompt, limiting their effectiveness in generating tail... | {
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2501.02158 | Joint Optimization for 4D Human-Scene Reconstruction in the Wild | [
"cs.CV"
] | Reconstructing human motion and its surrounding environment is crucial for understanding human-scene interaction and predicting human movements in the scene. While much progress has been made in capturing human-scene interaction in constrained environments, those prior methods can hardly reconstruct the natural and div... | {
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2501.02166 | ROLO-SLAM: Rotation-Optimized LiDAR-Only SLAM in Uneven Terrain with
Ground Vehicle | [
"cs.RO",
"cs.CV"
] | LiDAR-based SLAM is recognized as one effective method to offer localization guidance in rough environments. However, off-the-shelf LiDAR-based SLAM methods suffer from significant pose estimation drifts, particularly components relevant to the vertical direction, when passing to uneven terrains. This deficiency typica... | {
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2501.02167 | Generating Multimodal Images with GAN: Integrating Text, Image, and
Style | [
"cs.CV"
] | In the field of computer vision, multimodal image generation has become a research hotspot, especially the task of integrating text, image, and style. In this study, we propose a multimodal image generation method based on Generative Adversarial Networks (GAN), capable of effectively combining text descriptions, refere... | {
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2501.02169 | The Integration of Blockchain and Artificial Intelligence for Secure
Healthcare Systems | [
"cs.CY",
"cs.AI"
] | Verisign reported a 125 percent increase in data breaches within the healthcare sector in the United States during 2022, with 18.2 million patient records being impacted. Growing healthcare data volumes and diversification mean that medical information is becoming more valuable. Many Health Centers use various technolo... | {
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2501.02172 | Multifractal Terrain Generation for Evaluating Autonomous Off-Road
Ground Vehicles | [
"cs.RO"
] | We present a multifractal artificial terrain generation method that uses the 3D Weierstrass-Mandelbrot function to control roughness. By varying the fractal dimension used in terrain generation across three different values, we generate 60 unique off-road terrains. We use gradient maps to categorize the roughness of ea... | {
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2501.02173 | The Efficiency vs. Accuracy Trade-off: Optimizing RAG-Enhanced LLM
Recommender Systems Using Multi-Head Early Exit | [
"cs.IR",
"cs.LG"
] | The deployment of Large Language Models (LLMs) in recommender systems for predicting Click-Through Rates (CTR) necessitates a delicate balance between computational efficiency and predictive accuracy. This paper presents an optimization framework that combines Retrieval-Augmented Generation (RAG) with an innovative mul... | {
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2501.02174 | TACTIC: Task-Agnostic Contrastive pre-Training for Inter-Agent
Communication | [
"cs.MA"
] | The "sight range dilemma" in cooperative Multi-Agent Reinforcement Learning (MARL) presents a significant challenge: limited observability hinders team coordination, while extensive sight ranges lead to distracted attention and reduced performance. While communication can potentially address this issue, existing method... | {
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2501.02176 | Molecule-dynamic-based Aging Clock and Aging Roadmap Forecast with
Sundial | [
"q-bio.QM",
"cs.LG"
] | Addressing the unavoidable bias inherent in supervised aging clocks, we introduce Sundial, a novel framework that models molecular dynamics through a diffusion field, capturing both the population-level aging process and the individual-level relative aging order. Sundial enables unbiasedestimation of biological age and... | {
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2501.02178 | The Application of Large Language Models in Recommendation Systems | [
"cs.IR"
] | The integration of Large Language Models into recommendation frameworks presents key advantages for personalization and adaptability of experiences to the users. Classic methods of recommendations, such as collaborative filtering and content-based filtering, are seriously limited in the solution of cold-start problems,... | {
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2501.02180 | Phase Retrieval by Quaternionic Reweighted Amplitude Flow on Image
Reconstruction | [
"cs.CV",
"math.CV"
] | Quaternionic signal processing provides powerful tools for efficiently managing color signals by preserving the intrinsic correlations among signal dimensions through quaternion algebra. In this paper, we address the quaternionic phase retrieval problem by systematically developing novel algorithms based on an amplitud... | {
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2501.02181 | SMDP-Based Dynamic Batching for Improving Responsiveness and Energy
Efficiency of Batch Services | [
"cs.DC",
"cs.LG",
"cs.SY",
"eess.SY"
] | For servers incorporating parallel computing resources, batching is a pivotal technique for providing efficient and economical services at scale. Parallel computing resources exhibit heightened computational and energy efficiency when operating with larger batch sizes. However, in the realm of online services, the adop... | {
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2501.02182 | AdaMixup: A Dynamic Defense Framework for Membership Inference Attack
Mitigation | [
"cs.LG",
"cs.AI"
] | Membership inference attacks have emerged as a significant privacy concern in the training of deep learning models, where attackers can infer whether a data point was part of the training set based on the model's outputs. To address this challenge, we propose a novel defense mechanism, AdaMixup. AdaMixup employs adapti... | {
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2501.02184 | Model-Free and Real-Time Bioinspired Unicycle-Based Source Seeking:
Differential Wheeled Robotic Experiments | [
"cs.RO",
"math.OC"
] | Bioinspred robots aimed at source-seeking are often studied, and their controls designed, using unicycle modeling and formulation. This is true not only for model-based controllers, but also for model-free, real-time control methods such as extremum seeking control (ESC). In this paper, we propose a unicycle-based ESC ... | {
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2501.02187 | An Efficient Quadratic Penalty Method for a Class of Graph Clustering
Problems | [
"math.OC",
"cs.SI"
] | Community-based graph clustering is one of the most popular topics in the analysis of complex social networks. This type of clustering involves grouping vertices that are considered to share more connections, whereas vertices in different groups share fewer connections. A successful clustering result forms densely conn... | {
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2501.02189 | Benchmark Evaluations, Applications, and Challenges of Large Vision
Language Models: A Survey | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG",
"cs.RO"
] | Multimodal Vision Language Models (VLMs) have emerged as a transformative technology at the intersection of computer vision and natural language processing, enabling machines to perceive and reason about the world through both visual and textual modalities. For example, models such as CLIP, Claude, and GPT-4V demonstra... | {
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2501.02191 | On LLM-Enhanced Mixed-Type Data Imputation with High-Order Message
Passing | [
"cs.LG",
"cs.SI"
] | Missing data imputation, which aims to impute the missing values in the raw datasets to achieve the completeness of datasets, is crucial for modern data-driven models like large language models (LLMs) and has attracted increasing interest over the past decades. Despite its importance, existing solutions for missing dat... | {
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2501.02192 | EvoPath: Evolutionary Meta-path Discovery with Large Language Models for
Complex Heterogeneous Information Networks | [
"cs.SI"
] | Heterogeneous Information Networks (HINs) encapsulate diverse entity and relation types, with meta-paths providing essential meta-level semantics for knowledge reasoning, although their utility is constrained by discovery challenges. While Large Language Models (LLMs) offer new prospects for meta-path discovery due to ... | {
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2501.02194 | Ensemble-based Deep Multilayer Community Search | [
"cs.SI"
] | Multilayer graphs, consisting of multiple interconnected layers, are widely used to model diverse relationships in the real world. A community is a cohesive subgraph that offers valuable insights for analyzing (multilayer) graphs. Recently, there has been an emerging trend focused on searching query-driven communities ... | {
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2501.02196 | CPTuning: Contrastive Prompt Tuning for Generative Relation Extraction | [
"cs.CL",
"cs.AI"
] | Generative relation extraction (RE) commonly involves first reformulating RE as a linguistic modeling problem easily tackled with pre-trained language models (PLM) and then fine-tuning a PLM with supervised cross-entropy loss. Although having achieved promising performance, existing approaches assume only one determini... | {
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} |
2501.02197 | Majorization-Minimization Dual Stagewise Algorithm for Generalized Lasso | [
"stat.ML",
"cs.LG",
"stat.CO"
] | The generalized lasso is a natural generalization of the celebrated lasso approach to handle structural regularization problems. Many important methods and applications fall into this framework, including fused lasso, clustered lasso, and constrained lasso. To elevate its effectiveness in large-scale problems, extensiv... | {
"Other": 0,
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} |
2501.02198 | Fresh-CL: Feature Realignment through Experts on Hypersphere in
Continual Learning | [
"cs.LG",
"cs.CV"
] | Continual Learning enables models to learn and adapt to new tasks while retaining prior knowledge. Introducing new tasks, however, can naturally lead to feature entanglement across tasks, limiting the model's capability to distinguish between new domain data. In this work, we propose a method called Feature Realignment... | {
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} |
2501.02199 | Can ChatGPT implement finite element models for geotechnical engineering
applications? | [
"math.NA",
"cs.AI",
"cs.NA"
] | This study assesses the capability of ChatGPT to generate finite element code for geotechnical engineering applications from a set of prompts. We tested three different initial boundary value problems using a hydro-mechanically coupled formulation for unsaturated soils, including the dissipation of excess pore water pr... | {
"Other": 1,
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"cs.NE": 0,
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"cs.SY": 0
} |
2501.02200 | Learning Evolution via Optimization Knowledge Adaptation | [
"cs.NE",
"cs.AI",
"cs.CV",
"cs.LG"
] | Evolutionary algorithms (EAs) maintain populations through evolutionary operators to discover diverse solutions for complex tasks while gathering valuable knowledge, such as historical population data and fitness evaluations. However, traditional EAs face challenges in dynamically adapting to expanding knowledge bases,... | {
"Other": 0,
"cs.AI": 1,
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} |
2501.02201 | Accounting for Focus Ambiguity in Visual Questions | [
"cs.CV"
] | No existing work on visual question answering explicitly accounts for ambiguity regarding where the content described in the question is located in the image. To fill this gap, we introduce VQ-FocusAmbiguity, the first VQA dataset that visually grounds each region described in the question that is necessary to arrive a... | {
"Other": 0,
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"cs.SD": 0,
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} |
2501.02205 | Digital Twin Calibration with Model-Based Reinforcement Learning | [
"cs.LG"
] | This paper presents a novel methodological framework, called the Actor-Simulator, that incorporates the calibration of digital twins into model-based reinforcement learning for more effective control of stochastic systems with complex nonlinear dynamics. Traditional model-based control often relies on restrictive struc... | {
"Other": 0,
"cs.AI": 0,
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} |
2501.02207 | Self-Supervised Learning for Detecting AI-Generated Faces as Anomalies | [
"cs.CV"
] | The detection of AI-generated faces is commonly approached as a binary classification task. Nevertheless, the resulting detectors frequently struggle to adapt to novel AI face generators, which evolve rapidly. In this paper, we describe an anomaly detection method for AI-generated faces by leveraging self-supervised le... | {
"Other": 0,
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"cs.CL": 0,
"cs.CR": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.02208 | Robust Multi-Dimensional Scaling via Accelerated Alternating Projections | [
"stat.ML",
"cs.LG",
"math.OC"
] | We consider the robust multi-dimensional scaling (RMDS) problem in this paper. The goal is to localize point locations from pairwise distances that may be corrupted by outliers. Inspired by classic MDS theories, and nonconvex works for the robust principal component analysis (RPCA) problem, we propose an alternating pr... | {
"Other": 0,
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"cs.CL": 0,
"cs.CR": 0,
"cs.CV": 0,
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"cs.MA": 0,
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} |
2501.02211 | Examining the Robustness of Homogeneity Bias to Hyperparameter
Adjustments in GPT-4 | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Vision-Language Models trained on massive collections of human-generated data often reproduce and amplify societal stereotypes. One critical form of stereotyping reproduced by these models is homogeneity bias-the tendency to represent certain groups as more homogeneous than others. We investigate how this bias responds... | {
"Other": 0,
"cs.AI": 0,
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"cs.CR": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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