id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.15321 | Figurative-cum-Commonsense Knowledge Infusion for Multimodal Mental
Health Meme Classification | [
"cs.CL",
"cs.SI"
] | The expression of mental health symptoms through non-traditional means, such as memes, has gained remarkable attention over the past few years, with users often highlighting their mental health struggles through figurative intricacies within memes. While humans rely on commonsense knowledge to interpret these complex e... | {
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2501.15322 | Scaling laws for decoding images from brain activity | [
"eess.IV",
"cs.AI",
"cs.LG",
"q-bio.NC"
] | Generative AI has recently propelled the decoding of images from brain activity. How do these approaches scale with the amount and type of neural recordings? Here, we systematically compare image decoding from four types of non-invasive devices: electroencephalography (EEG), magnetoencephalography (MEG), high-field fun... | {
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2501.15324 | A Game-Theoretic Framework for Distributed Load Balancing: Static and
Dynamic Game Models | [
"cs.GT",
"cs.MA"
] | Motivated by applications in job scheduling, queuing networks, and load balancing in cyber-physical systems, we develop and analyze a game-theoretic framework to balance the load among servers in both static and dynamic settings. In these applications, jobs/tasks are often held by selfish entities that do not want to c... | {
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2501.15326 | Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised
Data | [
"cs.CV"
] | We present RASO, a foundation model designed to Recognize Any Surgical Object, offering robust open-set recognition capabilities across a broad range of surgical procedures and object classes, in both surgical images and videos. RASO leverages a novel weakly-supervised learning framework that generates tag-image-text p... | {
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2501.15328 | Physiologically-Informed Predictability of a Teammate's Future Actions
Forecasts Team Performance | [
"q-bio.NC",
"cs.LG"
] | In collaborative environments, a deep understanding of multi-human teaming dynamics is essential for optimizing performance. However, the relationship between individuals' behavioral and physiological markers and their combined influence on overall team performance remains poorly understood. To explore this, we designe... | {
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2501.15337 | Finite Strain Robust Topology Optimization Considering Multiple
Uncertainties | [
"cs.CE",
"math.OC"
] | This paper presents a computational framework for the robust stiffness design of hyperelastic structures at finite deformations subject to various uncertain sources. In particular, the loading, material properties, and geometry uncertainties are incorporated within the topology optimization framework and are modeled by... | {
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2501.15338 | Fairness-aware Contextual Dynamic Pricing with Strategic Buyers | [
"cs.GT",
"cs.LG",
"stat.ML"
] | Contextual pricing strategies are prevalent in online retailing, where the seller adjusts prices based on products' attributes and buyers' characteristics. Although such strategies can enhance seller's profits, they raise concerns about fairness when significant price disparities emerge among specific groups, such as g... | {
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2501.15339 | DER Hosting capacity for distribution networks: definitions, attributes,
use-cases and challenges | [
"eess.SY",
"cs.SY"
] | The rapid adoption of distributed energy resources (DERs) has outpaced grid modernization, leading to capacity limitations that challenge their further integration. Hosting Capacity Assessment (HCA) is a critical tool for evaluating how much DER capacity a grid can handle without breaching operational limits. HCA serve... | {
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2501.15343 | Development and Application of Self-Supervised Machine Learning for
Smoke Plume and Active Fire Identification from the FIREX-AQ Datasets | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) was a field campaign aimed at better understanding the impact of wildfires and agricultural fires on air quality and climate. The FIREX-AQ campaign took place in August 2019 and involved two aircraft and multiple coordinated satellite observat... | {
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2501.15348 | ReInc: Scaling Training of Dynamic Graph Neural Networks | [
"cs.LG",
"cs.DC"
] | Dynamic Graph Neural Networks (DGNNs) have gained widespread attention due to their applicability in diverse domains such as traffic network prediction, epidemiological forecasting, and social network analysis. In this paper, we present ReInc, a system designed to enable efficient and scalable training of DGNNs on larg... | {
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2501.15351 | Fairness in LLM-Generated Surveys | [
"cs.CY",
"cs.LG"
] | Large Language Models (LLMs) excel in text generation and understanding, especially in simulating socio-political and economic patterns, serving as an alternative to traditional surveys. However, their global applicability remains questionable due to unexplored biases across socio-demographic and geographic contexts. T... | {
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2501.15355 | Large Language Models as Theory of Mind Aware Generative Agents with
Counterfactual Reflection | [
"cs.CL",
"cs.AI"
] | Recent studies have increasingly demonstrated that large language models (LLMs) possess significant theory of mind (ToM) capabilities, showing the potential for simulating the tracking of mental states in generative agents. In this study, we propose a novel paradigm called ToM-agent, designed to empower LLMs-based gene... | {
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2501.15356 | Federated Class-Incremental Learning: A Hybrid Approach Using Latent
Exemplars and Data-Free Techniques to Address Local and Global Forgetting | [
"cs.LG"
] | Federated Class-Incremental Learning (FCIL) refers to a scenario where a dynamically changing number of clients collaboratively learn an ever-increasing number of incoming tasks. FCIL is known to suffer from local forgetting due to class imbalance at each client and global forgetting due to class imbalance across clien... | {
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2501.15357 | Structural Symmetry, Multiplicity, and Differentiability of
Eigenfrequencies | [
"cs.CE",
"math.OC"
] | This work investigates the multiplicity and differentiability of eigenfrequencies in structures with various symmetries. In particular, the study explores how the geometric and design variable symmetries affect the distribution of eigenvalues, distinguishing between simple and multiple eigenvalues in 3-D trusses. Moreo... | {
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2501.15361 | Decentralized Low-Rank Fine-Tuning of Large Language Models | [
"cs.LG"
] | While parameter-efficient fine-tuning (PEFT) techniques like Low-Rank Adaptation (LoRA) offer computationally efficient adaptations of Large Language Models (LLMs), their practical deployment often assumes centralized data and training environments. However, real-world scenarios frequently involve distributed, privacy-... | {
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2501.15363 | AI-Driven Secure Data Sharing: A Trustworthy and Privacy-Preserving
Approach | [
"cs.CR",
"cs.CV"
] | In the era of data-driven decision-making, ensuring the privacy and security of shared data is paramount across various domains. Applying existing deep neural networks (DNNs) to encrypted data is critical and often compromises performance, security, and computational overhead. To address these limitations, this researc... | {
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2501.15365 | A Transfer Learning Framework for Anomaly Detection in Multivariate IoT
Traffic Data | [
"cs.LG",
"cs.CR",
"cs.NI"
] | In recent years, rapid technological advancements and expanded Internet access have led to a significant rise in anomalies within network traffic and time-series data. Prompt detection of these irregularities is crucial for ensuring service quality, preventing financial losses, and maintaining robust security standards... | {
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2501.15368 | Baichuan-Omni-1.5 Technical Report | [
"cs.CL",
"cs.SD",
"eess.AS"
] | We introduce Baichuan-Omni-1.5, an omni-modal model that not only has omni-modal understanding capabilities but also provides end-to-end audio generation capabilities. To achieve fluent and high-quality interaction across modalities without compromising the capabilities of any modality, we prioritized optimizing three ... | {
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2501.15369 | iFormer: Integrating ConvNet and Transformer for Mobile Application | [
"cs.CV",
"cs.AI",
"cs.LG"
] | We present a new family of mobile hybrid vision networks, called iFormer, with a focus on optimizing latency and accuracy on mobile applications. iFormer effectively integrates the fast local representation capacity of convolution with the efficient global modeling ability of self-attention. The local interactions are ... | {
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2501.15370 | Scaling Large Vision-Language Models for Enhanced Multimodal
Comprehension In Biomedical Image Analysis | [
"cs.CV",
"cs.AI"
] | Large language models (LLMs) have demonstrated immense capabilities in understanding textual data and are increasingly being adopted to help researchers accelerate scientific discovery through knowledge extraction (information retrieval), knowledge distillation (summarizing key findings and methodologies into concise f... | {
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2501.15371 | Acquiring Submillimeter-Accurate Multi-Task Vision Datasets for
Computer-Assisted Orthopedic Surgery | [
"cs.CV"
] | Advances in computer vision, particularly in optical image-based 3D reconstruction and feature matching, enable applications like marker-less surgical navigation and digitization of surgery. However, their development is hindered by a lack of suitable datasets with 3D ground truth. This work explores an approach to gen... | {
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2501.15373 | Learning-Enhanced Safeguard Control for High-Relative-Degree Systems:
Robust Optimization under Disturbances and Faults | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.SY",
"math.OC",
"nlin.AO"
] | Merely pursuing performance may adversely affect the safety, while a conservative policy for safe exploration will degrade the performance. How to balance the safety and performance in learning-based control problems is an interesting yet challenging issue. This paper aims to enhance system performance with safety guar... | {
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2501.15374 | Evaluating the Effectiveness of XAI Techniques for Encoder-Based
Language Models | [
"cs.CL",
"cs.AI"
] | The black-box nature of large language models (LLMs) necessitates the development of eXplainable AI (XAI) techniques for transparency and trustworthiness. However, evaluating these techniques remains a challenge. This study presents a general evaluation framework using four key metrics: Human-reasoning Agreement (HA), ... | {
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2501.15377 | Fine Tuning without Catastrophic Forgetting via Selective Low Rank
Adaptation | [
"cs.CV"
] | Adapting deep learning models to new domains often requires computationally intensive retraining and risks catastrophic forgetting. While fine-tuning enables domain-specific adaptation, it can reduce robustness to distribution shifts, impacting out-of-distribution (OOD) performance. Pre-trained zero-shot models like CL... | {
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2501.15378 | How to Mitigate Information Loss in Knowledge Graphs for GraphRAG:
Leveraging Triple Context Restoration and Query-Driven Feedback | [
"cs.AI",
"cs.IR"
] | Knowledge Graph (KG)-augmented Large Language Models (LLMs) have recently propelled significant advances in complex reasoning tasks, thanks to their broad domain knowledge and contextual awareness. Unfortunately, current methods often assume KGs to be complete, which is impractical given the inherent limitations of KG ... | {
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2501.15379 | Zero-Shot Interactive Text-to-Image Retrieval via Diffusion-Augmented
Representations | [
"cs.IR",
"cs.AI",
"cs.CV"
] | Interactive Text-to-Image Retrieval (I-TIR) has emerged as a transformative user-interactive tool for applications in domains such as e-commerce and education. Yet, current methodologies predominantly depend on finetuned Multimodal Large Language Models (MLLMs), which face two critical limitations: (1) Finetuning impos... | {
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2501.15383 | Qwen2.5-1M Technical Report | [
"cs.CL"
] | We introduce Qwen2.5-1M, a series of models that extend the context length to 1 million tokens. Compared to the previous 128K version, the Qwen2.5-1M series have significantly enhanced long-context capabilities through long-context pre-training and post-training. Key techniques such as long data synthesis, progressive ... | {
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2501.15384 | MetaOcc: Surround-View 4D Radar and Camera Fusion Framework for 3D
Occupancy Prediction with Dual Training Strategies | [
"cs.CV",
"cs.AI"
] | 3D occupancy prediction is crucial for autonomous driving perception. Fusion of 4D radar and camera provides a potential solution of robust occupancy prediction on serve weather with least cost. How to achieve effective multi-modal feature fusion and reduce annotation costs remains significant challenges. In this work,... | {
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2501.15385 | DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation | [
"cs.CV",
"eess.IV"
] | Cloud segmentation amounts to separating cloud pixels from non-cloud pixels in an image. Current deep learning methods for cloud segmentation suffer from three issues. (a) Constrain on their receptive field due to the fixed size of the convolution kernel. (b) Lack of robustness towards different scenarios. (c) Requirem... | {
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2501.15388 | Guaranteed Multidimensional Time Series Prediction via Deterministic
Tensor Completion Theory | [
"cs.LG"
] | In recent years, the prediction of multidimensional time series data has become increasingly important due to its wide-ranging applications. Tensor-based prediction methods have gained attention for their ability to preserve the inherent structure of such data. However, existing approaches, such as tensor autoregressio... | {
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2501.15389 | CP2M: Clustered-Patch-Mixed Mosaic Augmentation for Aerial Image
Segmentation | [
"cs.CV"
] | Remote sensing image segmentation is pivotal for earth observation, underpinning applications such as environmental monitoring and urban planning. Due to the limited annotation data available in remote sensing images, numerous studies have focused on data augmentation as a means to alleviate overfitting in deep learnin... | {
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2501.15392 | Faster Configuration Performance Bug Testing with Neural Dual-level
Prioritization | [
"cs.SE",
"cs.AI"
] | As software systems become more complex and configurable, more performance problems tend to arise from the configuration designs. This has caused some configuration options to unexpectedly degrade performance which deviates from their original expectations designed by the developers. Such discrepancies, namely configur... | {
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2501.15393 | Diffusion-based Hierarchical Negative Sampling for Multimodal Knowledge
Graph Completion | [
"cs.AI",
"cs.CL"
] | Multimodal Knowledge Graph Completion (MMKGC) aims to address the critical issue of missing knowledge in multimodal knowledge graphs (MMKGs) for their better applications. However, both the previous MMGKC and negative sampling (NS) approaches ignore the employment of multimodal information to generate diverse and high-... | {
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2501.15394 | Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view
4D Radars and Cameras for Omnidirectional Perception | [
"cs.CV"
] | 3D object detection and occupancy prediction are critical tasks in autonomous driving, attracting significant attention. Despite the potential of recent vision-based methods, they encounter challenges under adverse conditions. Thus, integrating cameras with next-generation 4D imaging radar to achieve unified multi-task... | {
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2501.15396 | Foundations of a Knee Joint Digital Twin from qMRI Biomarkers for
Osteoarthritis and Knee Replacement | [
"q-bio.QM",
"cs.CV",
"cs.LG",
"eess.IV",
"stat.AP"
] | This study forms the basis of a digital twin system of the knee joint, using advanced quantitative MRI (qMRI) and machine learning to advance precision health in osteoarthritis (OA) management and knee replacement (KR) prediction. We combined deep learning-based segmentation of knee joint structures with dimensionality... | {
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2501.15398 | How Green are Neural Language Models? Analyzing Energy Consumption in
Text Summarization Fine-tuning | [
"cs.CL"
] | Artificial intelligence systems significantly impact the environment, particularly in natural language processing (NLP) tasks. These tasks often require extensive computational resources to train deep neural networks, including large-scale language models containing billions of parameters. This study analyzes the trade... | {
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2501.15403 | Scaling of hardware-compatible perturbative training algorithms | [
"cs.LG",
"cs.NE",
"math.OC"
] | In this work, we explore the capabilities of multiplexed gradient descent (MGD), a scalable and efficient perturbative zeroth-order training method for estimating the gradient of a loss function in hardware and training it via stochastic gradient descent. We extend the framework to include both weight and node perturba... | {
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2501.15404 | A Neurosymbolic Framework for Geometric Reduction of Binary Forms | [
"cs.AI",
"cs.LG"
] | This paper compares Julia reduction and hyperbolic reduction with the aim of finding equivalent binary forms with minimal coefficients. We demonstrate that hyperbolic reduction generally outperforms Julia reduction, particularly in the cases of sextics and decimics, though neither method guarantees achieving the minima... | {
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2501.15405 | Semantic Layered Embedding Diffusion in Large Language Models for
Multi-Contextual Consistency | [
"cs.CL",
"cs.AI"
] | The Semantic Layered Embedding Diffusion (SLED) mechanism redefines the representation of hierarchical semantics within transformer-based architectures, enabling enhanced contextual consistency across a wide array of linguistic tasks. By introducing a multi-layered diffusion process grounded in spectral analysis, it ac... | {
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2501.15406 | A Token-FCM based risk assessment method for complex engineering designs | [
"cs.CE"
] | Engineering design risks could cause unaffordable losses, and thus risk assessment plays a critical role in engineering design. On the other hand, the high complexity of modern engineering designs makes it difficult to assess risks effectively and accurately due to the complex two-way, dynamic causal-effect risk relati... | {
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2501.15407 | Turn That Frown Upside Down: FaceID Customization via Cross-Training
Data | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Existing face identity (FaceID) customization methods perform well but are limited to generating identical faces as the input, while in real-world applications, users often desire images of the same person but with variations, such as different expressions (e.g., smiling, angry) or angles (e.g., side profile). This lim... | {
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2501.15409 | TdAttenMix: Top-Down Attention Guided Mixup | [
"cs.CV",
"cs.AI"
] | CutMix is a data augmentation strategy that cuts and pastes image patches to mixup training data. Existing methods pick either random or salient areas which are often inconsistent to labels, thus misguiding the training model. By our knowledge, we integrate human gaze to guide cutmix for the first time. Since human att... | {
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2501.15411 | The Potential of Large Language Models in Supply Chain Management:
Advancing Decision-Making, Efficiency, and Innovation | [
"cs.CY",
"cs.CL"
] | The integration of large language models (LLMs) into supply chain management (SCM) is revolutionizing the industry by improving decision-making, predictive analytics, and operational efficiency. This white paper explores the transformative impact of LLMs on various SCM functions, including demand forecasting, inventory... | {
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2501.15415 | OCSU: Optical Chemical Structure Understanding for Molecule-centric
Scientific Discovery | [
"cs.CV"
] | Understanding the chemical structure from a graphical representation of a molecule is a challenging image caption task that would greatly benefit molecule-centric scientific discovery. Variations in molecular images and caption subtasks pose a significant challenge in both image representation learning and task modelin... | {
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2501.15417 | AnyEnhance: A Unified Generative Model with Prompt-Guidance and
Self-Critic for Voice Enhancement | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | We introduce AnyEnhance, a unified generative model for voice enhancement that processes both speech and singing voices. Based on a masked generative model, AnyEnhance is capable of handling both speech and singing voices, supporting a wide range of enhancement tasks including denoising, dereverberation, declipping, su... | {
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2501.15418 | Episodic Novelty Through Temporal Distance | [
"cs.LG",
"cs.AI"
] | Exploration in sparse reward environments remains a significant challenge in reinforcement learning, particularly in Contextual Markov Decision Processes (CMDPs), where environments differ across episodes. Existing episodic intrinsic motivation methods for CMDPs primarily rely on count-based approaches, which are ineff... | {
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2501.15420 | Visual Generation Without Guidance | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Classifier-Free Guidance (CFG) has been a default technique in various visual generative models, yet it requires inference from both conditional and unconditional models during sampling. We propose to build visual models that are free from guided sampling. The resulting algorithm, Guidance-Free Training (GFT), matches ... | {
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2501.15423 | Stroke Lesion Segmentation using Multi-Stage Cross-Scale Attention | [
"eess.IV",
"cs.CV"
] | Precise characterization of stroke lesions from MRI data has immense value in prognosticating clinical and cognitive outcomes following a stroke. Manual stroke lesion segmentation is time-consuming and requires the expertise of neurologists and neuroradiologists. Often, lesions are grossly characterized for their locat... | {
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2501.15425 | An Empirically-parametrized Spatio-Temporal Extended-SIR Model for
Combined Dilution and Vaccination Mitigation for Rabies Outbreaks in Wild
Jackals | [
"cs.IR",
"physics.soc-ph"
] | The transmission of zoonotic diseases between animals and humans poses an increasing threat. Rabies is a prominent example with various instances globally, facilitated by a surplus of meso-predators (commonly, facultative synanthropic species e.g., golden jackals [Canis aureus, hereafter jackals]) thanks to the abundan... | {
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2501.15426 | FAVbot: An Autonomous Target Tracking Micro-Robot with Frequency
Actuation Control | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Robotic autonomy at centimeter scale requires compact and miniaturization-friendly actuation integrated with sensing and neural network processing assembly within a tiny form factor. Applications of such systems have witnessed significant advancements in recent years in fields such as healthcare, manufacturing, and pos... | {
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} |
2501.15427 | OpenCharacter: Training Customizable Role-Playing LLMs with Large-Scale
Synthetic Personas | [
"cs.CL"
] | Customizable role-playing in large language models (LLMs), also known as character generalization, is gaining increasing attention for its versatility and cost-efficiency in developing and deploying role-playing dialogue agents. This study explores a large-scale data synthesis approach to equip LLMs with character gene... | {
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2501.15429 | An Aspect Performance-aware Hypergraph Neural Network for Review-based
Recommendation | [
"cs.IR"
] | Online reviews allow consumers to provide detailed feedback on various aspects of items. Existing methods utilize these aspects to model users' fine-grained preferences for specific item features through graph neural networks. We argue that the performance of items on different aspects is important for making precise r... | {
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2501.15430 | Evaluating Simple Debiasing Techniques in RoBERTa-based Hate Speech
Detection Models | [
"cs.CL"
] | The hate speech detection task is known to suffer from bias against African American English (AAE) dialect text, due to the annotation bias present in the underlying hate speech datasets used to train these models. This leads to a disparity where normal AAE text is more likely to be misclassified as abusive/hateful com... | {
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2501.15431 | Self-supervised Benchmark Lottery on ImageNet: Do Marginal Improvements
Translate to Improvements on Similar Datasets? | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Machine learning (ML) research strongly relies on benchmarks in order to determine the relative effectiveness of newly proposed models. Recently, a number of prominent research effort argued that a number of models that improve the state-of-the-art by a small margin tend to do so by winning what they call a "benchmark ... | {
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2501.15434 | Mitigating Spurious Negative Pairs for Robust Industrial Anomaly
Detection | [
"cs.CV"
] | Despite significant progress in Anomaly Detection (AD), the robustness of existing detection methods against adversarial attacks remains a challenge, compromising their reliability in critical real-world applications such as autonomous driving. This issue primarily arises from the AD setup, which assumes that training ... | {
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2501.15435 | Making Sense Of Distributed Representations With Activation Spectroscopy | [
"cs.LG",
"cs.CV"
] | In the study of neural network interpretability, there is growing evidence to suggest that relevant features are encoded across many neurons in a distributed fashion. Making sense of these distributed representations without knowledge of the network's encoding strategy is a combinatorial task that is not guaranteed to ... | {
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2501.15438 | Cross-Modal Transfer from Memes to Videos: Addressing Data Scarcity in
Hateful Video Detection | [
"cs.CV",
"cs.MM"
] | Detecting hate speech in online content is essential to ensuring safer digital spaces. While significant progress has been made in text and meme modalities, video-based hate speech detection remains under-explored, hindered by a lack of annotated datasets and the high cost of video annotation. This gap is particularly ... | {
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2501.15440 | Dfilled: Repurposing Edge-Enhancing Diffusion for Guided DSM Void
Filling | [
"cs.CV"
] | Digital Surface Models (DSMs) are essential for accurately representing Earth's topography in geospatial analyses. DSMs capture detailed elevations of natural and manmade features, crucial for applications like urban planning, vegetation studies, and 3D reconstruction. However, DSMs derived from stereo satellite imager... | {
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2501.15442 | Overview of the Amphion Toolkit (v0.2) | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Amphion is an open-source toolkit for Audio, Music, and Speech Generation, designed to lower the entry barrier for junior researchers and engineers in these fields. It provides a versatile framework that supports a variety of generation tasks and models. In this report, we introduce Amphion v0.2, the second major relea... | {
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2501.15443 | InfoBFR: Real-World Blind Face Restoration via Information Bottleneck | [
"cs.CV"
] | Blind face restoration (BFR) is a highly challenging problem due to the uncertainty of data degradation patterns. Current BFR methods have realized certain restored productions but with inherent neural degradations that limit real-world generalization in complicated scenarios. In this paper, we propose a plug-and-play ... | {
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2501.15445 | StochSync: Stochastic Diffusion Synchronization for Image Generation in
Arbitrary Spaces | [
"cs.CV",
"cs.AI"
] | We propose a zero-shot method for generating images in arbitrary spaces (e.g., a sphere for 360{\deg} panoramas and a mesh surface for texture) using a pretrained image diffusion model. The zero-shot generation of various visual content using a pretrained image diffusion model has been explored mainly in two directions... | {
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2501.15446 | Token Democracy: The Architectural Limits of Alignment in
Transformer-Based Language Models | [
"cs.CL",
"cs.AI"
] | Modern language models paradoxically combine unprecedented capability with persistent vulnerability in that they can draft poetry yet cannot reliably refuse harmful requests. We reveal this fragility stems not from inadequate training, but from a fundamental architectural limitation: transformers process all tokens as ... | {
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2501.15448 | SQ-DM: Accelerating Diffusion Models with Aggressive Quantization and
Temporal Sparsity | [
"cs.CV",
"cs.AI",
"cs.AR",
"cs.LG"
] | Diffusion models have gained significant popularity in image generation tasks. However, generating high-quality content remains notably slow because it requires running model inference over many time steps. To accelerate these models, we propose to aggressively quantize both weights and activations, while simultaneousl... | {
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2501.15449 | Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active
Learning Framework for 3D Object Detection | [
"cs.CV"
] | To address the annotation burden in LiDAR-based 3D object detection, active learning (AL) methods offer a promising solution. However, traditional active learning approaches solely rely on a small amount of labeled data to train an initial model for data selection, overlooking the potential of leveraging the abundance ... | {
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2501.15450 | FlatTrack: Eye-tracking with ultra-thin lensless cameras | [
"eess.IV",
"cs.CV"
] | Existing eye trackers use cameras based on thick compound optical elements, necessitating the cameras to be placed at focusing distance from the eyes. This results in the overall bulk of wearable eye trackers, especially for augmented and virtual reality (AR/VR) headsets. We overcome this limitation by building a compa... | {
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2501.15451 | STATE ToxiCN: A Benchmark for Span-level Target-Aware Toxicity
Extraction in Chinese Hate Speech Detection | [
"cs.CL"
] | The proliferation of hate speech has caused significant harm to society. The intensity and directionality of hate are closely tied to the target and argument it is associated with. However, research on hate speech detection in Chinese has lagged behind, and existing datasets lack span-level fine-grained annotations. Fu... | {
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2501.15452 | Identifying Critical Tokens for Accurate Predictions in
Transformer-based Medical Imaging Models | [
"cs.CV",
"cs.AI"
] | With the advancements in self-supervised learning (SSL), transformer-based computer vision models have recently demonstrated superior results compared to convolutional neural networks (CNNs) and are poised to dominate the field of artificial intelligence (AI)-based medical imaging in the upcoming years. Nevertheless, s... | {
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2501.15453 | Data-adaptive Safety Rules for Training Reward Models | [
"cs.CL"
] | Reinforcement Learning from Human Feedback (RLHF) is commonly employed to tailor models to human preferences, especially to improve the safety of outputs from large language models (LLMs). Traditionally, this method depends on selecting preferred responses from pairs. However, due to the variability in human opinions a... | {
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2501.15454 | On the Discrimination and Consistency for Exemplar-Free Class
Incremental Learning | [
"cs.CV"
] | Exemplar-free class incremental learning (EF-CIL) is a nontrivial task that requires continuously enriching model capability with new classes while maintaining previously learned knowledge without storing and replaying any old class exemplars. An emerging theory-guided framework for CIL trains task-specific models for ... | {
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2501.15455 | CD-Lamba: Boosting Remote Sensing Change Detection via a Cross-Temporal
Locally Adaptive State Space Model | [
"cs.CV"
] | Mamba, with its advantages of global perception and linear complexity, has been widely applied to identify changes of the target regions within the remote sensing (RS) images captured under complex scenarios and varied conditions. However, existing remote sensing change detection (RSCD) approaches based on Mamba freque... | {
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2501.15458 | Amortized Safe Active Learning for Real-Time Decision-Making: Pretrained
Neural Policies from Simulated Nonparametric Functions | [
"cs.LG"
] | Active Learning (AL) is a sequential learning approach aiming at selecting the most informative data for model training. In many systems, safety constraints appear during data evaluation, requiring the development of safe AL methods. Key challenges of AL are the repeated model training and acquisition optimization requ... | {
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2501.15461 | Mamba-Based Graph Convolutional Networks: Tackling Over-smoothing with
Selective State Space | [
"cs.LG"
] | Graph Neural Networks (GNNs) have shown great success in various graph-based learning tasks. However, it often faces the issue of over-smoothing as the model depth increases, which causes all node representations to converge to a single value and become indistinguishable. This issue stems from the inherent limitations ... | {
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2501.15463 | Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values? | [
"cs.HC",
"cs.AI",
"cs.CL"
] | Existing research primarily evaluates the values of LLMs by examining their stated inclinations towards specific values. However, the "Value-Action Gap," a phenomenon rooted in environmental and social psychology, reveals discrepancies between individuals' stated values and their actions in real-world contexts. To what... | {
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2501.15464 | TractoGPT: A GPT architecture for White Matter Segmentation | [
"cs.CV",
"cs.AI"
] | White matter bundle segmentation is crucial for studying brain structural connectivity, neurosurgical planning, and neurological disorders. White Matter Segmentation remains challenging due to structural similarity in streamlines, subject variability, symmetry in 2 hemispheres, etc. To address these challenges, we prop... | {
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2501.15465 | Geometry of symplectic group and optimal EAQECC codes | [
"quant-ph",
"cs.IT",
"math.IT"
] | A new type of link between geometry of symplectic group and entanglement-assisted (EA) quantum error-correcting codes (EAQECCs) is presented. Relations of symplectic subspaces and quaternary additive codes concerning parameters of EAQECCs are described. Thus, parameters of EA stabilizer codes are revealed in the nomenc... | {
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2501.15469 | CISOL: An Open and Extensible Dataset for Table Structure Recognition in
the Construction Industry | [
"cs.CV"
] | Reproducibility and replicability are critical pillars of empirical research, particularly in machine learning, where they depend not only on the availability of models, but also on the datasets used to train and evaluate those models. In this paper, we introduce the Construction Industry Steel Ordering List (CISOL) da... | {
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2501.15470 | Unveiling the Potential of Multimodal Retrieval Augmented Generation
with Planning | [
"cs.IR",
"cs.MA"
] | Multimodal Retrieval Augmented Generation (MRAG) systems, while promising for enhancing Multimodal Large Language Models (MLLMs), often rely on rigid, single-step retrieval methods. This limitation hinders their ability to effectively address real-world scenarios that demand adaptive information acquisition and query r... | {
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2501.15471 | Dynamic Regressor Extension and Mixing-based Re-design of Adaptive
Observer for Affine Systems | [
"eess.SY",
"cs.SY"
] | The dynamic regressor extension and mixing procedure is employed to redesign a conventional adaptive observer algorithm for affine systems. A reduced-order observer is designed without the construction of the state transition matrix. The dynamics of the regressor are redesigned to incorporate feedback from its extensio... | {
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2501.15478 | LoRAGuard: An Effective Black-box Watermarking Approach for LoRAs | [
"cs.CR",
"cs.LG"
] | LoRA (Low-Rank Adaptation) has achieved remarkable success in the parameter-efficient fine-tuning of large models. The trained LoRA matrix can be integrated with the base model through addition or negation operation to improve performance on downstream tasks. However, the unauthorized use of LoRAs to generate harmful c... | {
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2501.15481 | Query-based versus resource-based cache strategies in tag-based browsing
systems | [
"cs.CL"
] | Tag-based browsing is a popular interaction model for navigating digital libraries. According to this model, users select descriptive tags to filter resources in the collections. Typical implementations of the model are based on inverted indexes. However, these implementations can require a considerable amount of set o... | {
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2501.15484 | PhoTorch: A robust and generalized biochemical photosynthesis model
fitting package based on PyTorch | [
"cs.CE",
"q-bio.QM"
] | Advancements in artificial intelligence (AI) have greatly benefited plant phenotyping and predictive modeling. However, unrealized opportunities exist in leveraging AI advancements in model parameter optimization for parameter fitting in complex biophysical models. This work developed novel software, PhoTorch, for fitt... | {
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2501.15485 | Differentiable Low-computation Global Correlation Loss for Monotonicity
Evaluation in Quality Assessment | [
"eess.IV",
"cs.CV"
] | In this paper, we propose a global monotonicity consistency training strategy for quality assessment, which includes a differentiable, low-computation monotonicity evaluation loss function and a global perception training mechanism. Specifically, unlike conventional ranking loss and linear programming approaches that i... | {
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2501.15486 | FedAlign: Federated Domain Generalization with Cross-Client Feature
Alignment | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.DC"
] | Federated Learning (FL) offers a decentralized paradigm for collaborative model training without direct data sharing, yet it poses unique challenges for Domain Generalization (DG), including strict privacy constraints, non-i.i.d. local data, and limited domain diversity. We introduce FedAlign, a lightweight, privacy-pr... | {
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2501.15487 | Multilevel Browsing of Folksonomy-Based Digital Collections | [
"cs.CL"
] | This paper describes how to extend the usual one-level tag selection navigation paradigm in folksonomy-based digital collections to a multilevel browsing one, according to which it is possible to incrementally narrow down the set of selected objects in a collection by sequentially adding more and more filtering tags. F... | {
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2501.15488 | Qualitative Mechanism Independence | [
"cs.IT",
"math.IT"
] | We define what it means for a joint probability distribution to be compatible with a set of independent causal mechanisms, at a qualitative level -- or, more precisely, with a directed hypergraph ${\mathcal{A}}$, which is the qualitative structure of a probabilistic dependency graph (PDG). When ${\mathcal{A}}$ represen... | {
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2501.15489 | AI in Oncology: Transforming Cancer Detection through Machine Learning
and Deep Learning Applications | [
"cs.AI",
"eess.IV",
"q-bio.QM"
] | Artificial intelligence (AI) has potential to revolutionize the field of oncology by enhancing the precision of cancer diagnosis, optimizing treatment strategies, and personalizing therapies for a variety of cancers. This review examines the limitations of conventional diagnostic techniques and explores the transformat... | {
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2501.15492 | Color Flow Imaging Microscopy Improves Identification of Stress Sources
of Protein Aggregates in Biopharmaceuticals | [
"cs.CV",
"cs.AI"
] | Protein-based therapeutics play a pivotal role in modern medicine targeting various diseases. Despite their therapeutic importance, these products can aggregate and form subvisible particles (SvPs), which can compromise their efficacy and trigger immunological responses, emphasizing the critical need for robust monitor... | {
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2501.15493 | RLER-TTE: An Efficient and Effective Framework for En Route Travel Time
Estimation with Reinforcement Learning | [
"cs.LG"
] | En Route Travel Time Estimation (ER-TTE) aims to learn driving patterns from traveled routes to achieve rapid and accurate real-time predictions. However, existing methods ignore the complexity and dynamism of real-world traffic systems, resulting in significant gaps in efficiency and accuracy in real-time scenarios. A... | {
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2501.15495 | Expert-Free Online Transfer Learning in Multi-Agent Reinforcement
Learning | [
"cs.AI",
"cs.LG",
"cs.MA"
] | Reinforcement Learning (RL) enables an intelligent agent to optimise its performance in a task by continuously taking action from an observed state and receiving a feedback from the environment in form of rewards. RL typically uses tables or linear approximators to map state-action tuples that maximises the reward. Com... | {
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2501.15499 | One Model to Forecast Them All and in Entity Distributions Bind Them | [
"cs.LG",
"cs.SY",
"eess.SY"
] | Probabilistic forecasting in power systems often involves multi-entity datasets like households, feeders, and wind turbines, where generating reliable entity-specific forecasts presents significant challenges. Traditional approaches require training individual models for each entity, making them inefficient and hard to... | {
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"cs.SI": 0,
"cs.SY": 1
} |
2501.15503 | Domain Adaptation from Generated Multi-Weather Images for Unsupervised
Maritime Object Classification | [
"cs.CV"
] | The classification and recognition of maritime objects are crucial for enhancing maritime safety, monitoring, and intelligent sea environment prediction. However, existing unsupervised methods for maritime object classification often struggle with the long-tail data distributions in both object categories and weather c... | {
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} |
2501.15505 | Unveiling the Potential of iMarkers: Invisible Fiducial Markers for
Advanced Robotics | [
"cs.RO",
"cs.CV"
] | Fiducial markers are widely used in various robotics tasks, facilitating enhanced navigation, object recognition, and scene understanding. Despite their advantages for robots and Augmented Reality (AR) applications, they often disrupt the visual aesthetics of environments because they are visible to humans, making them... | {
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} |
2501.15509 | FIT-Print: Towards False-claim-resistant Model Ownership Verification
via Targeted Fingerprint | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Model fingerprinting is a widely adopted approach to safeguard the intellectual property rights of open-source models by preventing their unauthorized reuse. It is promising and convenient since it does not necessitate modifying the protected model. In this paper, we revisit existing fingerprinting methods and reveal t... | {
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} |
2501.15510 | Universal Image Restoration Pre-training via Degradation Classification | [
"cs.CV"
] | This paper proposes the Degradation Classification Pre-Training (DCPT), which enables models to learn how to classify the degradation type of input images for universal image restoration pre-training. Unlike the existing self-supervised pre-training methods, DCPT utilizes the degradation type of the input image as an e... | {
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} |
2501.15513 | TinyLLaVA-Video: A Simple Framework of Small-scale Large Multimodal
Models for Video Understanding | [
"cs.CV"
] | We present the TinyLLaVA-Video, a video understanding model with parameters not exceeding 4B that processes video sequences in a simple manner, without the need for complex architectures, supporting both fps sampling and uniform frame sampling. Our model is characterized by modularity and scalability, allowing training... | {
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} |
2501.15519 | Fuzzy-aware Loss for Source-free Domain Adaptation in Visual Emotion
Recognition | [
"cs.CV",
"cs.LG"
] | Source-free domain adaptation in visual emotion recognition (SFDA-VER) is a highly challenging task that requires adapting VER models to the target domain without relying on source data, which is of great significance for data privacy protection. However, due to the unignorable disparities between visual emotion data a... | {
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} |
2501.15520 | Efficient Self-Supervised Grading of Prostate Cancer Pathology | [
"cs.CV"
] | Prostate cancer grading using the ISUP system (International Society of Urological Pathology) for treatment decisions is highly subjective and requires considerable expertise. Despite advances in computer-aided diagnosis systems, few have handled efficient ISUP grading on Whole Slide Images (WSIs) of prostate biopsies ... | {
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} |
2501.15522 | Estimating Committor Functions via Deep Adaptive Sampling on Rare
Transition Paths | [
"stat.ML",
"cs.LG",
"q-bio.QM"
] | The committor functions are central to investigating rare but important events in molecular simulations. It is known that computing the committor function suffers from the curse of dimensionality. Recently, using neural networks to estimate the committor function has gained attention due to its potential for high-dimen... | {
"Other": 0,
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} |
2501.15529 | UNIDOOR: A Universal Framework for Action-Level Backdoor Attacks in Deep
Reinforcement Learning | [
"cs.LG",
"cs.AI",
"cs.CR"
] | Deep reinforcement learning (DRL) is widely applied to safety-critical decision-making scenarios. However, DRL is vulnerable to backdoor attacks, especially action-level backdoors, which pose significant threats through precise manipulation and flexible activation, risking outcomes like vehicle collisions or drone cras... | {
"Other": 0,
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} |
2501.15536 | Intelligent Surface Assisted Radar Stealth Against Unauthorized ISAC | [
"cs.IT",
"eess.SP",
"math.IT"
] | The integration of radar sensors and communication networks as envisioned for the 6G wireless networks poses significant security risks, e.g., the user position information can be released to an unauthorized dual-functional base station (DFBS). To address this issue, we propose an intelligent surface (IS)-assisted rada... | {
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} |
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