id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.10982 | MedG-KRP: Medical Graph Knowledge Representation Probing | [
"cs.AI"
] | Large language models (LLMs) have recently emerged as powerful tools, finding many medical applications. LLMs' ability to coalesce vast amounts of information from many sources to generate a response-a process similar to that of a human expert-has led many to see potential in deploying LLMs for clinical use. However, m... | {
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2412.10985 | MorphiNet: A Graph Subdivision Network for Adaptive Bi-ventricle Surface
Reconstruction | [
"eess.IV",
"cs.CV"
] | Cardiac Magnetic Resonance (CMR) imaging is widely used for heart modelling and digital twin computational analysis due to its ability to visualize soft tissues and capture dynamic functions. However, the anisotropic nature of CMR images, characterized by large inter-slice distances and misalignments from cardiac motio... | {
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2412.10986 | On Scalable Design for User-Centric Multi-Modal Shared E-Mobility
Systems using MILP and Modified Dijkstra's Algorithm | [
"cs.CE"
] | In the rapidly evolving landscape of urban transportation, shared e-mobility services have emerged as a sustainable solution to meet growing demand for flexible, eco-friendly travel. However, the existing literature lacks a comprehensive multi-modal optimization framework with focus on user preferences and real-world c... | {
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2412.10991 | Navigating Dialectal Bias and Ethical Complexities in Levantine Arabic
Hate Speech Detection | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Social media platforms have become central to global communication, yet they also facilitate the spread of hate speech. For underrepresented dialects like Levantine Arabic, detecting hate speech presents unique cultural, ethical, and linguistic challenges. This paper explores the complex sociopolitical and linguistic l... | {
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2412.10995 | RapidNet: Multi-Level Dilated Convolution Based Mobile Backbone | [
"cs.CV",
"cs.AI"
] | Vision transformers (ViTs) have dominated computer vision in recent years. However, ViTs are computationally expensive and not well suited for mobile devices; this led to the prevalence of convolutional neural network (CNN) and ViT-based hybrid models for mobile vision applications. Recently, Vision GNN (ViG) and CNN h... | {
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2412.10997 | Mask Enhanced Deeply Supervised Prostate Cancer Detection on B-mode
Micro-Ultrasound | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Prostate cancer is a leading cause of cancer-related deaths among men. The recent development of high frequency, micro-ultrasound imaging offers improved resolution compared to conventional ultrasound and potentially a better ability to differentiate clinically significant cancer from normal tissue. However, the featur... | {
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2412.10999 | Cocoa: Co-Planning and Co-Execution with AI Agents | [
"cs.HC",
"cs.AI"
] | We present Cocoa, a system that implements a novel interaction design pattern -- interactive plans -- for users to collaborate with an AI agent on complex, multi-step tasks in a document editor. Cocoa harmonizes human and AI efforts and enables flexible delegation of agency through two actions: Co-planning (where users... | {
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2412.11003 | Optimal Rates for Robust Stochastic Convex Optimization | [
"cs.LG",
"math.OC",
"stat.ML"
] | Machine learning algorithms in high-dimensional settings are highly susceptible to the influence of even a small fraction of structured outliers, making robust optimization techniques essential. In particular, within the $\epsilon$-contamination model, where an adversary can inspect and replace up to an $\epsilon$-frac... | {
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2412.11006 | Entropy-Regularized Process Reward Model | [
"cs.LG",
"cs.CL"
] | Large language models (LLMs) have shown promise in performing complex multi-step reasoning, yet they continue to struggle with mathematical reasoning, often making systematic errors. A promising solution is reinforcement learning (RL) guided by reward models, particularly those focusing on process rewards, which score ... | {
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2412.11007 | FlashSparse: Minimizing Computation Redundancy for Fast Sparse Matrix
Multiplications on Tensor Cores | [
"cs.DC",
"cs.LG"
] | Sparse Matrix-matrix Multiplication (SpMM) and Sampled Dense-dense Matrix Multiplication (SDDMM) are important sparse operators in scientific computing and deep learning. Tensor Core Units (TCUs) enhance modern accelerators with superior computing power, which is promising to boost the performance of matrix operators t... | {
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2412.11008 | Towards Context-aware Convolutional Network for Image Restoration | [
"cs.CV"
] | Image restoration (IR) is a long-standing task to recover a high-quality image from its corrupted observation. Recently, transformer-based algorithms and some attention-based convolutional neural networks (CNNs) have presented promising results on several IR tasks. However, existing convolutional residual building modu... | {
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2412.11009 | Dual Traits in Probabilistic Reasoning of Large Language Models | [
"cs.AI",
"cs.CL",
"cs.CY"
] | We conducted three experiments to investigate how large language models (LLMs) evaluate posterior probabilities. Our results reveal the coexistence of two modes in posterior judgment among state-of-the-art models: a normative mode, which adheres to Bayes' rule, and a representative-based mode, which relies on similarit... | {
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2412.11014 | PromptV: Leveraging LLM-powered Multi-Agent Prompting for High-quality
Verilog Generation | [
"cs.LG",
"cs.AI",
"cs.AR",
"cs.PL",
"cs.SE"
] | Recent advances in agentic LLMs have demonstrated remarkable automated Verilog code generation capabilities. However, existing approaches either demand substantial computational resources or rely on LLM-assisted single-agent prompt learning techniques, which we observe for the first time has a degeneration issue - char... | {
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2412.11016 | A Contextualized BERT model for Knowledge Graph Completion | [
"cs.CL",
"cs.LG"
] | Knowledge graphs (KGs) are valuable for representing structured, interconnected information across domains, enabling tasks like semantic search, recommendation systems and inference. A pertinent challenge with KGs, however, is that many entities (i.e., heads, tails) or relationships are unknown. Knowledge Graph Complet... | {
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2412.11017 | On Distilling the Displacement Knowledge for Few-Shot Class-Incremental
Learning | [
"cs.LG",
"cs.CV"
] | Few-shot Class-Incremental Learning (FSCIL) addresses the challenges of evolving data distributions and the difficulty of data acquisition in real-world scenarios. To counteract the catastrophic forgetting typically encountered in FSCIL, knowledge distillation is employed as a way to maintain the knowledge from learned... | {
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2412.11023 | Exploring Enhanced Contextual Information for Video-Level Object
Tracking | [
"cs.CV"
] | Contextual information at the video level has become increasingly crucial for visual object tracking. However, existing methods typically use only a few tokens to convey this information, which can lead to information loss and limit their ability to fully capture the context. To address this issue, we propose a new vid... | {
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2412.11024 | Exploring Diffusion and Flow Matching Under Generator Matching | [
"cs.LG",
"cs.CV"
] | In this paper, we present a comprehensive theoretical comparison of diffusion and flow matching under the Generator Matching framework. Despite their apparent differences, both diffusion and flow matching can be viewed under the unified framework of Generator Matching. By recasting both diffusion and flow matching unde... | {
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2412.11025 | From Simple to Professional: A Combinatorial Controllable Image
Captioning Agent | [
"cs.CV",
"cs.AI"
] | The Controllable Image Captioning Agent (CapAgent) is an innovative system designed to bridge the gap between user simplicity and professional-level outputs in image captioning tasks. CapAgent automatically transforms user-provided simple instructions into detailed, professional instructions, enabling precise and conte... | {
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2412.11026 | SceneLLM: Implicit Language Reasoning in LLM for Dynamic Scene Graph
Generation | [
"cs.CV",
"cs.AI"
] | Dynamic scenes contain intricate spatio-temporal information, crucial for mobile robots, UAVs, and autonomous driving systems to make informed decisions. Parsing these scenes into semantic triplets <Subject-Predicate-Object> for accurate Scene Graph Generation (SGG) is highly challenging due to the fluctuating spatio-t... | {
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2412.11030 | How to Achieve Justice through Big Data: A Study Based on Credit Card
Cases in Beijing | [
"cs.SI"
] | With the development of intelligence, the combination of big data and judicial practice has become a hot research topic. There are fewer studies on credit card contract disputes related to big data, which makes it difficult to respond to the trend of Big data era. This paper uses the data source of credit card disputes... | {
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2412.11033 | AURORA: Automated Unleash of 3D Room Outlines for VR Applications | [
"cs.CV"
] | Creating realistic VR experiences is challenging due to the labor-intensive process of accurately replicating real-world details into virtual scenes, highlighting the need for automated methods that maintain spatial accuracy and provide design flexibility. In this paper, we propose AURORA, a novel method that leverages... | {
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2412.11034 | SAM-IF: Leveraging SAM for Incremental Few-Shot Instance Segmentation | [
"cs.CV"
] | We propose SAM-IF, a novel method for incremental few-shot instance segmentation leveraging the Segment Anything Model (SAM). SAM-IF addresses the challenges of class-agnostic instance segmentation by introducing a multi-class classifier and fine-tuning SAM to focus on specific target objects. To enhance few-shot learn... | {
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2412.11039 | A Digitalized Atlas for Pulmonary Airway | [
"eess.IV",
"cs.CV"
] | In this work, we proposed AirwayAtlas, which is an end-to-end pipeline for automatic extraction of airway anatomies with lobar, segmental and subsegmental labeling. A compact representation, AirwaySign, is generated based on diverse features of airway branches. Experiments on multi-center datasets validated the effecti... | {
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2412.11041 | Separate the Wheat from the Chaff: A Post-Hoc Approach to Safety
Re-Alignment for Fine-Tuned Language Models | [
"cs.CL"
] | Although large language models (LLMs) achieve effective safety alignment at the time of release, they still face various safety challenges. A key issue is that fine-tuning often compromises the safety alignment of LLMs. To address this issue, we propose a method named IRR (Identify, Remove, and Recalibrate for Safety R... | {
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2412.11044 | Understanding and Mitigating Memorization in Diffusion Models for
Tabular Data | [
"cs.LG"
] | Tabular data generation has attracted significant research interest in recent years, with the tabular diffusion models greatly improving the quality of synthetic data. However, while memorization, where models inadvertently replicate exact or near-identical training data, has been thoroughly investigated in image and t... | {
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2412.11045 | Facial Surgery Preview Based on the Orthognathic Treatment Prediction | [
"cs.CV",
"cs.HC"
] | Orthognathic surgery consultation is essential to help patients understand the changes to their facial appearance after surgery. However, current visualization methods are often inefficient and inaccurate due to limited pre- and post-treatment data and the complexity of the treatment. To overcome these challenges, this... | {
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2412.11047 | Deployment Pipeline from Rockpool to Xylo for Edge Computing | [
"cs.NE",
"cs.AI"
] | Deploying Spiking Neural Networks (SNNs) on the Xylo neuromorphic chip via the Rockpool framework represents a significant advancement in achieving ultra-low-power consumption and high computational efficiency for edge applications. This paper details a novel deployment pipeline, emphasizing the integration of Rockpool... | {
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2412.11050 | RAC3: Retrieval-Augmented Corner Case Comprehension for Autonomous
Driving with Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Understanding and addressing corner cases is essential for ensuring the safety and reliability of autonomous driving systems. Vision-Language Models (VLMs) play a crucial role in enhancing scenario comprehension, yet they face significant challenges, such as hallucination and insufficient real-world grounding, which co... | {
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2412.11051 | DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient
Generative Design in Hybrid Spaces | [
"cs.LG",
"math.OC"
] | We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression. We propose DisCo-DSO (Discrete-Continuous Deep Symbolic Optimization), a novel approach that uses a gen... | {
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2412.11053 | NITRO: LLM Inference on Intel Laptop NPUs | [
"cs.CL",
"cs.AI"
] | Large Language Models (LLMs) have become essential tools in natural language processing, finding large usage in chatbots such as ChatGPT and Gemini, and are a central area of research. A particular area of interest includes designing hardware specialized for these AI applications, with one such example being the neural... | {
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2412.11056 | Overview of TREC 2024 Medical Video Question Answering (MedVidQA) Track | [
"cs.CV"
] | One of the key goals of artificial intelligence (AI) is the development of a multimodal system that facilitates communication with the visual world (image and video) using a natural language query. Earlier works on medical question answering primarily focused on textual and visual (image) modalities, which may be ineff... | {
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2412.11057 | Set-Valued Sensitivity Analysis of Deep Neural Networks | [
"cs.LG",
"cs.AI"
] | This paper proposes a sensitivity analysis framework based on set valued mapping for deep neural networks (DNN) to understand and compute how the solutions (model weights) of DNN respond to perturbations in the training data. As a DNN may not exhibit a unique solution (minima) and the algorithm of solving a DNN may lea... | {
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2412.11058 | SHMT: Self-supervised Hierarchical Makeup Transfer via Latent Diffusion
Models | [
"cs.CV"
] | This paper studies the challenging task of makeup transfer, which aims to apply diverse makeup styles precisely and naturally to a given facial image. Due to the absence of paired data, current methods typically synthesize sub-optimal pseudo ground truths to guide the model training, resulting in low makeup fidelity. A... | {
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2412.11060 | Making Bias Amplification in Balanced Datasets Directional and
Interpretable | [
"cs.CV",
"cs.LG"
] | Most of the ML datasets we use today are biased. When we train models on these biased datasets, they often not only learn dataset biases but can also amplify them -- a phenomenon known as bias amplification. Several co-occurrence-based metrics have been proposed to measure bias amplification between a protected attribu... | {
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2412.11061 | Classification Drives Geographic Bias in Street Scene Segmentation | [
"cs.CV",
"cs.CY",
"cs.LG"
] | Previous studies showed that image datasets lacking geographic diversity can lead to biased performance in models trained on them. While earlier work studied general-purpose image datasets (e.g., ImageNet) and simple tasks like image recognition, we investigated geo-biases in real-world driving datasets on a more compl... | {
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2412.11063 | LAW: Legal Agentic Workflows for Custody and Fund Services Contracts | [
"cs.AI",
"cs.CL",
"cs.SE"
] | Legal contracts in the custody and fund services domain govern critical aspects such as key provider responsibilities, fee schedules, and indemnification rights. However, it is challenging for an off-the-shelf Large Language Model (LLM) to ingest these contracts due to the lengthy unstructured streams of text, limited ... | {
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2412.11065 | Representation learning of dynamic networks | [
"stat.ML",
"cs.LG"
] | This study presents a novel representation learning model tailored for dynamic networks, which describes the continuously evolving relationships among individuals within a population. The problem is encapsulated in the dimension reduction topic of functional data analysis. With dynamic networks represented as matrix-va... | {
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2412.11066 | Learning Robust and Privacy-Preserving Representations via Information
Theory | [
"cs.LG",
"cs.CR"
] | Machine learning models are vulnerable to both security attacks (e.g., adversarial examples) and privacy attacks (e.g., private attribute inference). We take the first step to mitigate both the security and privacy attacks, and maintain task utility as well. Particularly, we propose an information-theoretic framework t... | {
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2412.11067 | CFSynthesis: Controllable and Free-view 3D Human Video Synthesis | [
"cs.CV"
] | Human video synthesis aims to create lifelike characters in various environments, with wide applications in VR, storytelling, and content creation. While 2D diffusion-based methods have made significant progress, they struggle to generalize to complex 3D poses and varying scene backgrounds. To address these limitations... | {
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2412.11068 | RecSys Arena: Pair-wise Recommender System Evaluation with Large
Language Models | [
"cs.IR",
"cs.AI"
] | Evaluating the quality of recommender systems is critical for algorithm design and optimization. Most evaluation methods are computed based on offline metrics for quick algorithm evolution, since online experiments are usually risky and time-consuming. However, offline evaluation usually cannot fully reflect users' pre... | {
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2412.11070 | HC-LLM: Historical-Constrained Large Language Models for Radiology
Report Generation | [
"cs.CV"
] | Radiology report generation (RRG) models typically focus on individual exams, often overlooking the integration of historical visual or textual data, which is crucial for patient follow-ups. Traditional methods usually struggle with long sequence dependencies when incorporating historical information, but large languag... | {
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2412.11072 | Navigating Towards Fairness with Data Selection | [
"cs.LG",
"cs.CY"
] | Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but ... | {
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2412.11073 | Ba-ZebraConf: A Three-Dimension Bayesian Framework for Efficient System
Troubleshooting | [
"eess.SY",
"cs.SY"
] | The proliferation of heterogeneous configurations in distributed systems presents significant challenges in ensuring stability and efficiency. Misconfigurations, driven by complex parameter interdependencies, can lead to critical failures. Group Testing (GT) has been leveraged to expedite troubleshooting by reducing th... | {
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2412.11074 | Adapter-Enhanced Semantic Prompting for Continual Learning | [
"cs.CV",
"cs.LG"
] | Continual learning (CL) enables models to adapt to evolving data streams. A major challenge of CL is catastrophic forgetting, where new knowledge will overwrite previously acquired knowledge. Traditional methods usually retain the past data for replay or add additional branches in the model to learn new knowledge, whic... | {
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2412.11075 | Edge Contrastive Learning: An Augmentation-Free Graph Contrastive
Learning Model | [
"cs.LG"
] | Graph contrastive learning (GCL) aims to learn representations from unlabeled graph data in a self-supervised manner and has developed rapidly in recent years. However, edgelevel contrasts are not well explored by most existing GCL methods. Most studies in GCL only regard edges as auxiliary information while updating n... | {
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2412.11076 | MoRe: Class Patch Attention Needs Regularization for Weakly Supervised
Semantic Segmentation | [
"cs.CV"
] | Weakly Supervised Semantic Segmentation (WSSS) with image-level labels typically uses Class Activation Maps (CAM) to achieve dense predictions. Recently, Vision Transformer (ViT) has provided an alternative to generate localization maps from class-patch attention. However, due to insufficient constraints on modeling su... | {
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2412.11077 | Reason-before-Retrieve: One-Stage Reflective Chain-of-Thoughts for
Training-Free Zero-Shot Composed Image Retrieval | [
"cs.CV"
] | Composed Image Retrieval (CIR) aims to retrieve target images that closely resemble a reference image while integrating user-specified textual modifications, thereby capturing user intent more precisely. Existing training-free zero-shot CIR (ZS-CIR) methods often employ a two-stage process: they first generate a captio... | {
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2412.11080 | Deep Spectral Clustering via Joint Spectral Embedding and Kmeans | [
"cs.LG",
"cs.CV"
] | Spectral clustering is a popular clustering method. It first maps data into the spectral embedding space and then uses Kmeans to find clusters. However, the two decoupled steps prohibit joint optimization for the optimal solution. In addition, it needs to construct the similarity graph for samples, which suffers from t... | {
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2412.11082 | EquiFlow: Equivariant Conditional Flow Matching with Optimal Transport
for 3D Molecular Conformation Prediction | [
"cs.LG",
"physics.chem-ph",
"q-bio.BM"
] | Molecular 3D conformations play a key role in determining how molecules interact with other molecules or protein surfaces. Recent deep learning advancements have improved conformation prediction, but slow training speeds and difficulties in utilizing high-degree features limit performance. We propose EquiFlow, an equiv... | {
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2412.11084 | BarcodeMamba: State Space Models for Biodiversity Analysis | [
"cs.LG",
"q-bio.GN",
"q-bio.QM"
] | DNA barcodes are crucial in biodiversity analysis for building automatic identification systems that recognize known species and discover unseen species. Unlike human genome modeling, barcode-based invertebrate identification poses challenges in the vast diversity of species and taxonomic complexity. Among Transformer-... | {
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2412.11085 | GraphMoRE: Mitigating Topological Heterogeneity via Mixture of
Riemannian Experts | [
"cs.LG",
"cs.AI"
] | Real-world graphs have inherently complex and diverse topological patterns, known as topological heterogeneity. Most existing works learn graph representation in a single constant curvature space that is insufficient to match the complex geometric shapes, resulting in low-quality embeddings with high distortion. This a... | {
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2412.11087 | Leveraging Large Vision-Language Model as User Intent-aware Encoder for
Composed Image Retrieval | [
"cs.IR"
] | Composed Image Retrieval (CIR) aims to retrieve target images from candidate set using a hybrid-modality query consisting of a reference image and a relative caption that describes the user intent. Recent studies attempt to utilize Vision-Language Pre-training Models (VLPMs) with various fusion strategies for addressin... | {
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2412.11088 | Seeing the Forest and the Trees: Solving Visual Graph and Tree Based
Data Structure Problems using Large Multimodal Models | [
"cs.AI",
"cs.CV",
"cs.CY"
] | Recent advancements in generative AI systems have raised concerns about academic integrity among educators. Beyond excelling at solving programming problems and text-based multiple-choice questions, recent research has also found that large multimodal models (LMMs) can solve Parsons problems based only on an image. How... | {
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2412.11090 | Hanprome: Modified Hangeul for Expression of foreign language
pronunciation | [
"cs.CL",
"cs.SD",
"eess.AS"
] | Hangeul was created as a phonetic alphabet and is known to have the best 1:1 correspondence between letters and pronunciation among existing alphabets. In this paper, we examine the possibility of modifying the basic form of Hangeul and using it as a kind of phonetic symbol. The core concept of this approach is to pres... | {
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2412.11091 | Identification Over Binary Noisy Permutation Channels | [
"cs.IT",
"math.IT"
] | We study message identification over the binary noisy permutation channel. For discrete memoryless channels (DMCs), the number of identifiable messages grows doubly exponentially, and the maximum second-order exponent is the Shannon capacity of the DMC. We consider a binary noisy permutation channel where the transmitt... | {
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2412.11095 | Dynamic Graph Attention Networks for Travel Time Distribution Prediction
in Urban Arterial Roads | [
"cs.LG"
] | Effective congestion management along signalized corridors is essential for improving productivity and reducing costs, with arterial travel time serving as a key performance metric. Traditional approaches, such as Coordinated Signal Timing and Adaptive Traffic Control Systems, often lack scalability and generalizabilit... | {
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2412.11098 | Reliably Learn to Trim Multiparametric Quadratic Programs via Constraint
Removal | [
"math.OC",
"cs.SY",
"eess.SY"
] | In a wide range of applications, we are required to rapidly solve a sequence of convex multiparametric quadratic programs (mp-QPs) on resource-limited hardwares. This is a nontrivial task and has been an active topic for decades in control and optimization communities. Observe that the main computational cost of existi... | {
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2412.11100 | DynamicScaler: Seamless and Scalable Video Generation for Panoramic
Scenes | [
"cs.CV"
] | The increasing demand for immersive AR/VR applications and spatial intelligence has heightened the need to generate high-quality scene-level and 360{\deg} panoramic video. However, most video diffusion models are constrained by limited resolution and aspect ratio, which restricts their applicability to scene-level dyna... | {
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2412.11102 | Empowering LLMs to Understand and Generate Complex Vector Graphics | [
"cs.CV"
] | The unprecedented advancements in Large Language Models (LLMs) have profoundly impacted natural language processing but have yet to fully embrace the realm of scalable vector graphics (SVG) generation. While LLMs encode partial knowledge of SVG data from web pages during training, recent findings suggest that semantica... | {
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2412.11104 | ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized
Experiments | [
"cs.LG",
"cs.AI",
"stat.ME"
] | In causal inference, randomized experiment is a de facto method to overcome various theoretical issues in observational study. However, the experimental design requires expensive costs, so an efficient experimental design is necessary. We propose ABC3, a Bayesian active learning policy for causal inference. We show a p... | {
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2412.11105 | Multi-Graph Co-Training for Capturing User Intent in Session-based
Recommendation | [
"cs.IR",
"cs.LG"
] | Session-based recommendation focuses on predicting the next item a user will interact with based on sequences of anonymous user sessions. A significant challenge in this field is data sparsity due to the typically short-term interactions. Most existing methods rely heavily on users' current interactions, overlooking th... | {
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2412.11106 | Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path
Prompted Inversion | [
"eess.IV",
"cs.CV"
] | Virtual staining leverages computer-aided techniques to transfer the style of histochemically stained tissue samples to other staining types. In virtual staining of pathological images, maintaining strict structural consistency is crucial, as these images emphasize structural integrity more than natural images. Even sl... | {
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2412.11108 | Plug-and-Play Priors as a Score-Based Method | [
"eess.IV",
"cs.CV"
] | Plug-and-play (PnP) methods are extensively used for solving imaging inverse problems by integrating physical measurement models with pre-trained deep denoisers as priors. Score-based diffusion models (SBMs) have recently emerged as a powerful framework for image generation by training deep denoisers to represent the s... | {
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2412.11112 | Populating cellular metamaterials on the extrema of attainable
elasticity through neuroevolution | [
"cs.NE",
"cs.CE"
] | The trade-offs between different mechanical properties of materials pose fundamental challenges in engineering material design, such as balancing stiffness versus toughness, weight versus energy-absorbing capacity, and among the various elastic coefficients. Although gradient-based topology optimization approaches have... | {
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2412.11119 | Impact of Adversarial Attacks on Deep Learning Model Explainability | [
"cs.LG",
"cs.AI",
"cs.CV"
] | In this paper, we investigate the impact of adversarial attacks on the explainability of deep learning models, which are commonly criticized for their black-box nature despite their capacity for autonomous feature extraction. This black-box nature can affect the perceived trustworthiness of these models. To address thi... | {
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2412.11120 | Latent Reward: LLM-Empowered Credit Assignment in Episodic Reinforcement
Learning | [
"cs.LG",
"cs.AI"
] | Reinforcement learning (RL) often encounters delayed and sparse feedback in real-world applications, even with only episodic rewards. Previous approaches have made some progress in reward redistribution for credit assignment but still face challenges, including training difficulties due to redundancy and ambiguous attr... | {
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2412.11122 | Paid with Models: Optimal Contract Design for Collaborative Machine
Learning | [
"cs.LG",
"cs.AI",
"cs.GT",
"econ.TH"
] | Collaborative machine learning (CML) provides a promising paradigm for democratizing advanced technologies by enabling cost-sharing among participants. However, the potential for rent-seeking behaviors among parties can undermine such collaborations. Contract theory presents a viable solution by rewarding participants ... | {
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2412.11123 | Hierarchical Bidirectional Transition Dispersion Entropy-based
Lempel-Ziv Complexity and Its Application in Fault-Bearing Diagnosis | [
"physics.data-an",
"cs.LG",
"eess.SP",
"math-ph",
"math.MP"
] | Lempel-Ziv complexity (LZC) is a key measure for detecting the irregularity and complexity of nonlinear time series and has seen various improvements in recent decades. However, existing LZC-based metrics, such as Permutation Lempel-Ziv complexity (PLZC) and Dispersion-Entropy based Lempel-Ziv complexity (DELZC), focus... | {
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2412.11124 | Combating Multimodal LLM Hallucination via Bottom-Up Holistic Reasoning | [
"cs.CV"
] | Recent advancements in multimodal large language models (MLLMs) have shown unprecedented capabilities in advancing various vision-language tasks. However, MLLMs face significant challenges with hallucinations, and misleading outputs that do not align with the input data. While existing efforts are paid to combat MLLM h... | {
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2412.11125 | Feature engineering vs. deep learning for paper section identification:
Toward applications in Chinese medical literature | [
"cs.CL",
"cs.LG"
] | Section identification is an important task for library science, especially knowledge management. Identifying the sections of a paper would help filter noise in entity and relation extraction. In this research, we studied the paper section identification problem in the context of Chinese medical literature analysis, wh... | {
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2412.11127 | Modeling the Heterogeneous Duration of User Interest in Time-Dependent
Recommendation: A Hidden Semi-Markov Approach | [
"cs.IR",
"cs.LG"
] | Recommender systems are widely used for suggesting books, education materials, and products to users by exploring their behaviors. In reality, users' preferences often change over time, leading to studies on time-dependent recommender systems. However, most existing approaches that deal with time information remain pri... | {
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2412.11132 | Entropy conservative and entropy stable solid wall boundary conditions
for the resistive magnetohydrodynamic equations | [
"math.NA",
"cs.CE",
"cs.NA",
"physics.comp-ph"
] | We present a novel technique for imposing non-linear entropy conservative and entropy stable wall boundary conditions for the resistive magnetohydrodynamic equations in the presence of an adiabatic wall or a wall with a prescribed heat entropy flow, addressing three scenarios: electrically insulating walls, thin walls ... | {
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2412.11137 | Decoding Drug Discovery: Exploring A-to-Z In silico Methods for
Beginners | [
"q-bio.QM",
"cs.AI"
] | The drug development process is a critical challenge in the pharmaceutical industry due to its time-consuming nature and the need to discover new drug potentials to address various ailments. The initial step in drug development, drug target identification, often consumes considerable time. While valid, traditional meth... | {
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2412.11138 | Safe Reinforcement Learning using Finite-Horizon Gradient-based
Estimation | [
"cs.LG",
"cs.AI"
] | A key aspect of Safe Reinforcement Learning (Safe RL) involves estimating the constraint condition for the next policy, which is crucial for guiding the optimization of safe policy updates. However, the existing Advantage-based Estimation (ABE) method relies on the infinite-horizon discounted advantage function. This d... | {
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2412.11139 | ViSymRe: Vision-guided Multimodal Symbolic Regression | [
"cs.LG",
"cs.AI",
"cs.SC"
] | Symbolic regression automatically searches for mathematical equations to reveal underlying mechanisms within datasets, offering enhanced interpretability compared to black box models. Traditionally, symbolic regression has been considered to be purely numeric-driven, with insufficient attention given to the potential c... | {
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2412.11142 | AD-LLM: Benchmarking Large Language Models for Anomaly Detection | [
"cs.CL",
"cs.AI"
] | Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural language processing (NLP), AD helps detect issues like spam, misinformation, and unusual user activity. Although large language models (LLMs) ha... | {
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2412.11145 | The Superalignment of Superhuman Intelligence with Large Language Models | [
"cs.CL"
] | We have witnessed superhuman intelligence thanks to the fast development of large language models and multimodal language models. As the application of such superhuman models becomes more and more popular, a critical question arises here: how can we ensure superhuman models are still safe, reliable and aligned well to ... | {
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2412.11146 | Enhancing Multiagent Genetic Network Programming Performance Using
Search Space Reduction | [
"cs.MA"
] | Genetic Network Programming (GNP) is an evolutionary algorithm that extends Genetic Programming (GP). It is typically used in agent control problems. In contrast to GP, which employs a tree structure, GNP utilizes a directed graph structure. During the evolutionary process, the connections between nodes change to disco... | {
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2412.11148 | Redefining Normal: A Novel Object-Level Approach for Multi-Object
Novelty Detection | [
"cs.CV"
] | In the realm of novelty detection, accurately identifying outliers in data without specific class information poses a significant challenge. While current methods excel in single-object scenarios, they struggle with multi-object situations due to their focus on individual objects. Our paper suggests a novel approach: r... | {
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2412.11149 | A Comprehensive Survey of Action Quality Assessment: Method and
Benchmark | [
"cs.CV"
] | Action Quality Assessment (AQA) quantitatively evaluates the quality of human actions, providing automated assessments that reduce biases in human judgment. Its applications span domains such as sports analysis, skill assessment, and medical care. Recent advances in AQA have introduced innovative methodologies, but sim... | {
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2412.11152 | Dual-Schedule Inversion: Training- and Tuning-Free Inversion for Real
Image Editing | [
"cs.CV"
] | Text-conditional image editing is a practical AIGC task that has recently emerged with great commercial and academic value. For real image editing, most diffusion model-based methods use DDIM Inversion as the first stage before editing. However, DDIM Inversion often results in reconstruction failure, leading to unsatis... | {
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2412.11154 | From Easy to Hard: Progressive Active Learning Framework for Infrared
Small Target Detection with Single Point Supervision | [
"cs.CV"
] | Recently, single-frame infrared small target (SIRST) detection with single point supervision has drawn wide-spread attention. However, the latest label evolution with single point supervision (LESPS) framework suffers from instability, excessive label evolution, and difficulty in exerting embedded network performance. ... | {
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2412.11155 | Partial Identifiability in Inverse Reinforcement Learning For Agents
With Non-Exponential Discounting | [
"cs.LG",
"cs.AI"
] | The aim of inverse reinforcement learning (IRL) is to infer an agent's preferences from observing their behaviour. Usually, preferences are modelled as a reward function, $R$, and behaviour is modelled as a policy, $\pi$. One of the central difficulties in IRL is that multiple preferences may lead to the same observed ... | {
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2412.11158 | Early Concept Drift Detection via Prediction Uncertainty | [
"cs.LG"
] | Concept drift, characterized by unpredictable changes in data distribution over time, poses significant challenges to machine learning models in streaming data scenarios. Although error rate-based concept drift detectors are widely used, they often fail to identify drift in the early stages when the data distribution c... | {
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2412.11159 | A Report on Financial Regulations Challenge at COLING 2025 | [
"cs.CE"
] | Financial large language models (FinLLMs) have been applied to various tasks in business, finance, accounting, and auditing. Complex financial regulations and standards are critical to financial services, which LLMs must comply with. However, FinLLMs' performance in understanding and interpreting financial regulations ... | {
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2412.11160 | Means of Hitting Times for Random Walks on Graphs: Connections,
Computation, and Optimization | [
"cs.SI"
] | For random walks on graph $\mathcal{G}$ with $n$ vertices and $m$ edges, the mean hitting time $H_j$ from a vertex chosen from the stationary distribution to vertex $j$ measures the importance for $j$, while the Kemeny constant $\mathcal{K}$ is the mean hitting time from one vertex to another selected randomly accordin... | {
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2412.11161 | Why and How: Knowledge-Guided Learning for Cross-Spectral Image Patch
Matching | [
"cs.CV"
] | Recently, cross-spectral image patch matching based on feature relation learning has attracted extensive attention. However, performance bottleneck problems have gradually emerged in existing methods. To address this challenge, we make the first attempt to explore a stable and efficient bridge between descriptor learni... | {
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2412.11164 | Missing data imputation for noisy time-series data and applications in
healthcare | [
"cs.LG",
"stat.AP"
] | Healthcare time series data is vital for monitoring patient activity but often contains noise and missing values due to various reasons such as sensor errors or data interruptions. Imputation, i.e., filling in the missing values, is a common way to deal with this issue. In this study, we compare imputation methods, inc... | {
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2412.11165 | OTLRM: Orthogonal Learning-based Low-Rank Metric for Multi-Dimensional
Inverse Problems | [
"cs.CV",
"cs.LG"
] | In real-world scenarios, complex data such as multispectral images and multi-frame videos inherently exhibit robust low-rank property. This property is vital for multi-dimensional inverse problems, such as tensor completion, spectral imaging reconstruction, and multispectral image denoising. Existing tensor singular va... | {
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2412.11167 | Cultural Palette: Pluralising Culture Alignment via Multi-agent Palette | [
"cs.CL"
] | Large language models (LLMs) face challenges in aligning with diverse cultural values despite their remarkable performance in generation, which stems from inherent monocultural biases and difficulties in capturing nuanced cultural semantics. Existing methods struggle to adapt to unkown culture after fine-tuning. Inspir... | {
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2412.11168 | PGD-Imp: Rethinking and Unleashing Potential of Classic PGD with Dual
Strategies for Imperceptible Adversarial Attacks | [
"cs.LG",
"cs.CR"
] | Imperceptible adversarial attacks have recently attracted increasing research interests. Existing methods typically incorporate external modules or loss terms other than a simple $l_p$-norm into the attack process to achieve imperceptibility, while we argue that such additional designs may not be necessary. In this pap... | {
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} |
2412.11169 | Design Challenges for Robots in Industrial Applications | [
"cs.RO",
"eess.SP"
] | Nowadays, electric robots play big role in many fields as they can replace humans and/or decrease the amount of load on humans. There are several types of robots that are present in the daily life, some of them are fully controlled by humans while others are programmed to be self-controlled. In addition there are self-... | {
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} |
2412.11170 | Benchmarking and Learning Multi-Dimensional Quality Evaluator for
Text-to-3D Generation | [
"cs.CV"
] | Text-to-3D generation has achieved remarkable progress in recent years, yet evaluating these methods remains challenging for two reasons: i) Existing benchmarks lack fine-grained evaluation on different prompt categories and evaluation dimensions. ii) Previous evaluation metrics only focus on a single aspect (e.g., tex... | {
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} |
2412.11171 | Learning Latent Spaces for Domain Generalization in Time Series
Forecasting | [
"cs.LG"
] | Time series forecasting is vital in many real-world applications, yet developing models that generalize well on unseen relevant domains -- such as forecasting web traffic data on new platforms/websites or estimating e-commerce demand in new regions -- remains underexplored. Existing forecasting models often struggle wi... | {
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} |
2412.11172 | Unpacking the Resilience of SNLI Contradiction Examples to Attacks | [
"cs.CL"
] | Pre-trained models excel on NLI benchmarks like SNLI and MultiNLI, but their true language understanding remains uncertain. Models trained only on hypotheses and labels achieve high accuracy, indicating reliance on dataset biases and spurious correlations. To explore this issue, we applied the Universal Adversarial Att... | {
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} |
2412.11174 | Semi-Supervised Risk Control via Prediction-Powered Inference | [
"cs.LG",
"stat.ML"
] | The risk-controlling prediction sets (RCPS) framework is a general tool for transforming the output of any machine learning model to design a predictive rule with rigorous error rate control. The key idea behind this framework is to use labeled hold-out calibration data to tune a hyper-parameter that affects the error ... | {
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} |
2412.11175 | Knowledge Migration Framework for Smart Contract Vulnerability Detection | [
"cs.CR",
"cs.LG"
] | As a cornerstone of blockchain technology in the 3.0 era, smart contracts play a pivotal role in the evolution of blockchain systems. In order to address the limitations of existing smart contract vulnerability detection models with regard to their generalisation capability, an AF-STip smart contract vulnerability dete... | {
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} |
2412.11177 | A Progressive Transformer for Unifying Binary Code Embedding and
Knowledge Transfer | [
"cs.SE",
"cs.LG"
] | Language model approaches have recently been integrated into binary analysis tasks, such as function similarity detection and function signature recovery. These models typically employ a two-stage training process: pre-training via Masked Language Modeling (MLM) on machine code and fine-tuning for specific tasks. While... | {
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} |
2412.11180 | GNNs-to-MLPs by Teacher Injection and Dirichlet Energy Distillation | [
"cs.LG",
"cs.SI"
] | Graph Neural Networks (GNNs) are fundamental to graph-based learning and excel in node classification tasks. However, GNNs suffer from scalability issues due to the need for multi-hop data during inference, limiting their use in latency-sensitive applications. Recent studies attempt to distill GNNs into multi-layer per... | {
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} |
2412.11183 | OccScene: Semantic Occupancy-based Cross-task Mutual Learning for 3D
Scene Generation | [
"cs.CV"
] | Recent diffusion models have demonstrated remarkable performance in both 3D scene generation and perception tasks. Nevertheless, existing methods typically separate these two processes, acting as a data augmenter to generate synthetic data for downstream perception tasks. In this work, we propose OccScene, a novel mutu... | {
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} |
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