id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.05850 | Cooperative SQL Generation for Segmented Databases By Using
Multi-functional LLM Agents | [
"cs.CL"
] | Text-to-SQL task aims to automatically yield SQL queries according to user text questions. To address this problem, we propose a Cooperative SQL Generation framework based on Multi-functional Agents (CSMA) through information interaction among large language model (LLM) based agents who own part of the database schema ... | {
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2412.05852 | Evolving Algebraic Multigrid Methods Using Grammar-Guided Genetic
Programming | [
"cs.CE",
"cs.AI",
"cs.NA",
"math.NA"
] | Multigrid methods despite being known to be asymptotically optimal algorithms, depend on the careful selection of their individual components for efficiency. Also, they are mostly restricted to standard cycle types like V-, F-, and W-cycles. We use grammar rules to generate arbitrary-shaped cycles, wherein the smoother... | {
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2412.05853 | Unsupervised Multi-Parameter Inverse Solving for Reducing Ring Artifacts
in 3D X-Ray CBCT | [
"eess.IV",
"cs.CV"
] | Ring artifacts are prevalent in 3D cone-beam computed tomography (CBCT) due to non-ideal responses of X-ray detectors, severely degrading imaging quality and reliability. Current state-of-the-art (SOTA) ring artifact reduction (RAR) algorithms rely on extensive paired CT samples for supervised learning. While effective... | {
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2412.05861 | Depression detection from Social Media Bangla Text Using Recurrent
Neural Networks | [
"cs.HC",
"cs.CL",
"cs.LG"
] | Emotion artificial intelligence is a field of study that focuses on figuring out how to recognize emotions, especially in the area of text mining. Today is the age of social media which has opened a door for us to share our individual expressions, emotions, and perspectives on any event. We can analyze sentiment on soc... | {
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2412.05862 | Domain-Specific Translation with Open-Source Large Language Models:
Resource-Oriented Analysis | [
"cs.CL"
] | In this work, we compare the domain-specific translation performance of open-source autoregressive decoder-only large language models (LLMs) with task-oriented machine translation (MT) models. Our experiments focus on the medical domain and cover four language pairs with varied resource availability: English-to-French,... | {
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2412.05864 | CardOOD: Robust Query-driven Cardinality Estimation under
Out-of-Distribution | [
"cs.DB",
"cs.AI"
] | Query-driven learned estimators are accurate, flexible, and lightweight alternatives to traditional estimators in query optimization. However, existing query-driven approaches struggle with the Out-of-distribution (OOD) problem, where the test workload distribution differs from the training workload, leading to perform... | {
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2412.05865 | Studying the Cycle Complexity of DNA Synthesis | [
"cs.IT",
"math.IT"
] | Storing data in DNA is being explored as an efficient solution for archiving and in-object storage. Synthesis time and cost remain challenging, significantly limiting some applications at this stage. In this paper we investigate efficient synthesis, as it relates to cyclic synchronized synthesis technologies, such as p... | {
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2412.05868 | Automated Extraction and Creation of FBS Design Reasoning Knowledge
Graphs from Structured Data in Product Catalogues Lacking Contextual
Information | [
"cs.IR",
"cs.AI",
"cs.CL"
] | Ontology-based knowledge graphs (KG) are desirable for effective knowledge management and reuse in various decision making scenarios, including design. Creating and populating extensive KG based on specific ontological models can be highly labour and time-intensive unless automated processes are developed for knowledge... | {
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2412.05871 | MID: A Comprehensive Shore-Based Dataset for Multi-Scale Dense Ship
Occlusion and Interaction Scenarios | [
"cs.CV"
] | This paper introduces the Maritime Ship Navigation Behavior Dataset (MID), designed to address challenges in ship detection within complex maritime environments using Oriented Bounding Boxes (OBB). MID contains 5,673 images with 135,884 finely annotated target instances, supporting both supervised and semi-supervised l... | {
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2412.05873 | AC-LIO: Towards Asymptotic and Consistent Convergence in LiDAR-Inertial
Odometry | [
"cs.RO"
] | Existing LiDAR-Inertial Odometry (LIO) frameworks typically utilize prior state trajectories derived from IMU integration to compensate for the motion distortion within LiDAR frames, and demonstrate outstanding accuracy and stability in regular low-speed and smooth scenes. However, in high-speed or intense motion scena... | {
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2412.05876 | MG-3D: Multi-Grained Knowledge-Enhanced 3D Medical Vision-Language
Pre-training | [
"cs.CV",
"cs.AI"
] | 3D medical image analysis is pivotal in numerous clinical applications. However, the scarcity of labeled data and limited generalization capabilities hinder the advancement of AI-empowered models. Radiology reports are easily accessible and can serve as weakly-supervised signals. However, large-scale vision-language pr... | {
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2412.05878 | Matrix Pre-orthogonal-Matching Pursuit as a Fundamental AI Algorithm | [
"cs.IT",
"math.IT"
] | We develop a framework for efficient sparse representation and approximation in artificial intelligence called Matrix Pre-orthogonal Matching Pursuit. By leveraging matrix structures and pre-orthogonalization, the method enhances convergence and reduces computational complexity. This approach offers potential benefits ... | {
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2412.05881 | 3D-Consistent Image Inpainting with Diffusion Models | [
"cs.CV",
"cs.AI"
] | We address the problem of 3D inconsistency of image inpainting based on diffusion models. We propose a generative model using image pairs that belong to the same scene. To achieve the 3D-consistent and semantically coherent inpainting, we modify the generative diffusion model by incorporating an alternative point of vi... | {
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2412.05882 | Towards Modeling Data Quality and Machine Learning Model Performance | [
"cs.LG",
"cs.AI"
] | Understanding the effect of uncertainty and noise in data on machine learning models (MLM) is crucial in developing trust and measuring performance. In this paper, a new model is proposed to quantify uncertainties and noise in data on MLMs. Using the concept of signal-to-noise ratio (SNR), a new metric called determini... | {
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2412.05883 | Understanding the Impact of Graph Reduction on Adversarial Robustness in
Graph Neural Networks | [
"cs.LG",
"cs.CR"
] | As Graph Neural Networks (GNNs) become increasingly popular for learning from large-scale graph data across various domains, their susceptibility to adversarial attacks when using graph reduction techniques for scalability remains underexplored. In this paper, we present an extensive empirical study to investigate the ... | {
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2412.05888 | MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model
Trained with a Single GPU in Just One Day | [
"cs.CV"
] | Medical image segmentation involves partitioning medical images into meaningful regions, with a focus on identifying anatomical structures or abnormalities. It has broad applications in healthcare, and deep learning methods have enabled significant advancements in automating this process. Recently, the introduction of ... | {
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2412.05892 | PBI-Attack: Prior-Guided Bimodal Interactive Black-Box Jailbreak Attack
for Toxicity Maximization | [
"cs.CR",
"cs.AI"
] | Understanding the vulnerabilities of Large Vision Language Models (LVLMs) to jailbreak attacks is essential for their responsible real-world deployment. Most previous work requires access to model gradients, or is based on human knowledge (prompt engineering) to complete jailbreak, and they hardly consider the interact... | {
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2412.05893 | doScenes: An Autonomous Driving Dataset with Natural Language
Instruction for Human Interaction and Vision-Language Navigation | [
"cs.CV",
"cs.AI"
] | Human-interactive robotic systems, particularly autonomous vehicles (AVs), must effectively integrate human instructions into their motion planning. This paper introduces doScenes, a novel dataset designed to facilitate research on human-vehicle instruction interactions, focusing on short-term directives that directly ... | {
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2412.05894 | FedRBE -- a decentralized privacy-preserving federated batch effect
correction tool for omics data based on limma | [
"q-bio.QM",
"cs.CR",
"cs.DC",
"cs.LG"
] | Batch effects in omics data obscure true biological signals and constitute a major challenge for privacy-preserving analyses of distributed patient data. Existing batch effect correction methods either require data centralization, which may easily conflict with privacy requirements, or lack support for missing values a... | {
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2412.05896 | XKV: Personalized KV Cache Memory Reduction for Long-Context LLM
Inference | [
"cs.LG",
"cs.CL"
] | Recently the generative Large Language Model (LLM) has achieved remarkable success in numerous applications. Notably its inference generates output tokens one-by-one, leading to many redundant computations. The widely-used KV-Cache framework makes a compromise between time and space complexities. However, caching data ... | {
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2412.05897 | Detecting Discrepancies Between AI-Generated and Natural Images Using
Uncertainty | [
"cs.CV",
"cs.AI"
] | In this work, we propose a novel approach for detecting AI-generated images by leveraging predictive uncertainty to mitigate misuse and associated risks. The motivation arises from the fundamental assumption regarding the distributional discrepancy between natural and AI-generated images. The feasibility of distinguish... | {
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2412.05899 | Accelerating Video Diffusion Models via Distribution Matching | [
"cs.CV"
] | Generative models, particularly diffusion models, have made significant success in data synthesis across various modalities, including images, videos, and 3D assets. However, current diffusion models are computationally intensive, often requiring numerous sampling steps that limit their practical application, especiall... | {
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2412.05901 | Thermal Image-based Fault Diagnosis in Induction Machines via
Self-Organized Operational Neural Networks | [
"cs.LG",
"cs.CV",
"eess.IV"
] | Condition monitoring of induction machines is crucial to prevent costly interruptions and equipment failure. Mechanical faults such as misalignment and rotor issues are among the most common problems encountered in industrial environments. To effectively monitor and detect these faults, a variety of sensors, including ... | {
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2412.05906 | Reinforcement Learning for a Discrete-Time Linear-Quadratic Control
Problem with an Application | [
"stat.ML",
"cs.LG",
"math.OC"
] | We study the discrete-time linear-quadratic (LQ) control model using reinforcement learning (RL). Using entropy to measure the cost of exploration, we prove that the optimal feedback policy for the problem must be Gaussian type. Then, we apply the results of the discrete-time LQ model to solve the discrete-time mean-va... | {
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2412.05908 | GBR: Generative Bundle Refinement for High-fidelity Gaussian Splatting
and Meshing | [
"cs.CV"
] | Gaussian splatting has gained attention for its efficient representation and rendering of 3D scenes using continuous Gaussian primitives. However, it struggles with sparse-view inputs due to limited geometric and photometric information, causing ambiguities in depth, shape, and texture. we propose GBR: Generative Bun... | {
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2412.05916 | Paraphrase-Aligned Machine Translation | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated significant capabilities in machine translation. However, their translation quality is sometimes questioned, as the generated outputs may deviate from expressions typically used by native speakers. These deviations often arise from differences in sentence structure between... | {
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2412.05921 | Learning Cluster Representatives for Approximate Nearest Neighbor Search | [
"cs.IR"
] | Developing increasingly efficient and accurate algorithms for approximate nearest neighbor search is a paramount goal in modern information retrieval. A primary approach to addressing this question is clustering, which involves partitioning the dataset into distinct groups, with each group characterized by a representa... | {
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2412.05926 | BiDM: Pushing the Limit of Quantization for Diffusion Models | [
"cs.CV"
] | Diffusion models (DMs) have been significantly developed and widely used in various applications due to their excellent generative qualities. However, the expensive computation and massive parameters of DMs hinder their practical use in resource-constrained scenarios. As one of the effective compression approaches, qua... | {
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2412.05929 | Enhanced 3D Generation by 2D Editing | [
"cs.CV"
] | Distilling 3D representations from pretrained 2D diffusion models is essential for 3D creative applications across gaming, film, and interior design. Current SDS-based methods are hindered by inefficient information distillation from diffusion models, which prevents the creation of photorealistic 3D contents. Our resea... | {
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2412.05934 | Heuristic-Induced Multimodal Risk Distribution Jailbreak Attack for
Multimodal Large Language Models | [
"cs.CR",
"cs.AI"
] | With the rapid advancement of multimodal large language models (MLLMs), concerns regarding their security have increasingly captured the attention of both academia and industry. Although MLLMs are vulnerable to jailbreak attacks, designing effective multimodal jailbreak attacks poses unique challenges, especially given... | {
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2412.05936 | Linear codes associated to symmetric determinantal varieties; General
case | [
"math.AG",
"cs.IT",
"math.IT"
] | The study of linear codes over a finite field of odd cardinality, derived from determinantal varieties obtained from symmetric matrices of bounded rank, was initiated in a recent paper by the authors. There, one found the minimum distance of the code obtained from evaluating homogeneous linear functions at all symmetri... | {
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2412.05937 | Accelerating Manufacturing Scale-Up from Material Discovery Using
Agentic Web Navigation and Retrieval-Augmented AI for Process Engineering
Schematics Design | [
"cs.LG",
"cs.AI",
"cs.IR",
"cs.MA"
] | Process Flow Diagrams (PFDs) and Process and Instrumentation Diagrams (PIDs) are critical tools for industrial process design, control, and safety. However, the generation of precise and regulation-compliant diagrams remains a significant challenge, particularly in scaling breakthroughs from material discovery to indus... | {
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2412.05938 | Accurate Multi-Category Student Performance Forecasting at Early Stages
of Online Education Using Neural Networks | [
"cs.LG",
"cs.CY"
] | The ability to accurately predict and analyze student performance in online education, both at the outset and throughout the semester, is vital. Most of the published studies focus on binary classification (Fail or Pass) but there is still a significant research gap in predicting students' performance across multiple c... | {
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2412.05939 | Exploring Multi-Grained Concept Annotations for Multimodal Large
Language Models | [
"cs.CV",
"cs.CL",
"cs.LG"
] | Multimodal Large Language Models (MLLMs) excel in vision--language tasks by pre-training solely on coarse-grained concept annotations (e.g., image captions). We hypothesize that integrating fine-grained concept annotations (e.g., object labels and object regions) will further improve performance, as both data granulari... | {
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2412.05940 | Digital Modeling of Massage Techniques and Reproduction by Robotic Arms | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper explores the digital modeling and robotic reproduction of traditional Chinese medicine (TCM) massage techniques. We adopt an adaptive admittance control algorithm to optimize force and position control, ensuring safety and comfort. The paper analyzes key TCM techniques from kinematic and dynamic perspectives... | {
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2412.05942 | Finite-Graph-Cover-Based Analysis of Factor Graphs in Classical and
Quantum Information Processing Systems | [
"cs.IT",
"math.IT"
] | In this thesis, we leverage finite graph covers to analyze the SPA and the Bethe partition function for both S-FGs and DE-FGs. There are two main contributions in this thesis. The first main contribution concerns a special class of S-FGs where the partition function of each S-FG equals the permanent of a nonnegative ... | {
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2412.05943 | Adversarial Transferability in Deep Denoising Models: Theoretical
Insights and Robustness Enhancement via Out-of-Distribution Typical Set
Sampling | [
"cs.CV"
] | Deep learning-based image denoising models demonstrate remarkable performance, but their lack of robustness analysis remains a significant concern. A major issue is that these models are susceptible to adversarial attacks, where small, carefully crafted perturbations to input data can cause them to fail. Surprisingly, ... | {
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2412.05947 | Materials-Discovery Workflows Guided by Symbolic Regression: Identifying
Acid-Stable Oxides for Electrocatalysis | [
"cond-mat.mtrl-sci",
"cs.AI"
] | The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on... | {
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2412.05951 | When Vision Models Meet Parameter Efficient Look-Aside Adapters Without
Large-Scale Audio Pretraining | [
"cs.SD",
"cs.CV",
"cs.LG",
"eess.AS"
] | Recent studies show that pretrained vision models can boost performance in audio downstream tasks. To enhance the performance further, an additional pretraining stage with large scale audio data is typically required to infuse audio specific knowledge into the vision model. However, such approaches require extensive au... | {
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2412.05956 | Uncertainty-Aware Capacity Expansion for Real-World DER Deployment via
End-to-End Network Integration | [
"eess.SY",
"cs.SY"
] | The deployment of distributed energy resource (DER) devices plays a critical role in distribution grids, offering multiple value streams, including decarbonization, provision of ancillary services, non-wire alternatives, and enhanced grid flexibility. However, existing research on capacity expansion suffers from two ma... | {
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2412.05957 | A Two-Stage AI-Powered Motif Mining Method for Efficient Power System
Topological Analysis | [
"eess.SY",
"cs.SY"
] | Graph motif, defined as the microstructure that appears repeatedly in a large graph, reveals important topological characteristics of the large graph and has gained increasing attention in power system analysis regarding reliability, vulnerability and resiliency. However, searching motifs within the large-scale power s... | {
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2412.05961 | FOF-X: Towards Real-time Detailed Human Reconstruction from a Single
Image | [
"cs.CV"
] | We introduce FOF-X for real-time reconstruction of detailed human geometry from a single image. Balancing real-time speed against high-quality results is a persistent challenge, mainly due to the high computational demands of existing 3D representations. To address this, we propose Fourier Occupancy Field (FOF), an eff... | {
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2412.05964 | A Cross-Validation Study of Turkish Sentiment Analysis Datasets and
Tools | [
"cs.CL"
] | In recent years, sentiment analysis has gained increasing significance, prompting researchers to explore datasets in various languages, including Turkish. However, the limited availability of Turkish datasets has led to their multifaceted usage in different studies, yielding diverse outcomes. To overcome this challenge... | {
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2412.05967 | Language hooks: a modular framework for augmenting LLM reasoning that
decouples tool usage from the model and its prompt | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Prompting and fine-tuning have emerged as two competing paradigms for augmenting language models with new capabilities, such as the use of tools. Prompting approaches are quick to set up but rely on providing explicit demonstrations of each tool's usage in the model's prompt, thus coupling tool use to the task at hand ... | {
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2412.05968 | LVS-Net: A Lightweight Vessels Segmentation Network for Retinal Image
Analysis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The analysis of retinal images for the diagnosis of various diseases is one of the emerging areas of research. Recently, the research direction has been inclined towards investigating several changes in retinal blood vessels in subjects with many neurological disorders, including dementia. This research focuses on dete... | {
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2412.05969 | Efficient Semantic Splatting for Remote Sensing Multi-view Segmentation | [
"cs.CV"
] | In this paper, we propose a novel semantic splatting approach based on Gaussian Splatting to achieve efficient and low-latency. Our method projects the RGB attributes and semantic features of point clouds onto the image plane, simultaneously rendering RGB images and semantic segmentation results. Leveraging the explici... | {
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2412.05976 | Lightweight Spatial Embedding for Vision-based 3D Occupancy Prediction | [
"cs.CV"
] | Occupancy prediction has garnered increasing attention in recent years for its comprehensive fine-grained environmental representation and strong generalization to open-set objects. However, cumbersome voxel features and 3D convolution operations inevitably introduce large overheads in both memory and computation, obst... | {
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2412.05980 | Anti-Reference: Universal and Immediate Defense Against Reference-Based
Generation | [
"cs.CV"
] | Diffusion models have revolutionized generative modeling with their exceptional ability to produce high-fidelity images. However, misuse of such potent tools can lead to the creation of fake news or disturbing content targeting individuals, resulting in significant social harm. In this paper, we introduce Anti-Referenc... | {
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2412.05983 | Chimera: Improving Generalist Model with Domain-Specific Experts | [
"cs.CV"
] | Recent advancements in Large Multi-modal Models (LMMs) underscore the importance of scaling by increasing image-text paired data, achieving impressive performance on general tasks. Despite their effectiveness in broad applications, generalist models are primarily trained on web-scale datasets dominated by natural image... | {
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2412.05984 | Nested Diffusion Models Using Hierarchical Latent Priors | [
"cs.CV"
] | We introduce nested diffusion models, an efficient and powerful hierarchical generative framework that substantially enhances the generation quality of diffusion models, particularly for images of complex scenes. Our approach employs a series of diffusion models to progressively generate latent variables at different s... | {
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2412.05994 | PIG: Physics-Informed Gaussians as Adaptive Parametric Mesh
Representations | [
"cs.LG",
"cs.AI"
] | The approximation of Partial Differential Equations (PDEs) using neural networks has seen significant advancements through Physics-Informed Neural Networks (PINNs). Despite their straightforward optimization framework and flexibility in implementing various PDEs, PINNs often suffer from limited accuracy due to the spec... | {
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2412.05996 | Paddy Disease Detection and Classification Using Computer Vision
Techniques: A Mobile Application to Detect Paddy Disease | [
"cs.CV"
] | Plant diseases significantly impact our food supply, causing problems for farmers, economies reliant on agriculture, and global food security. Accurate and timely plant disease diagnosis is crucial for effective treatment and minimizing yield losses. Despite advancements in agricultural technology, a precise and early ... | {
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2412.06000 | Does RLHF Scale? Exploring the Impacts From Data, Model, and Method | [
"cs.CL",
"cs.LG"
] | This study explores the scaling properties of Reinforcement Learning from Human Feedback (RLHF) in Large Language Models (LLMs). Although RLHF is considered an important step in post-training of LLMs, its scaling potential is still largely unknown. We systematically analyze key components in the RLHF framework--model s... | {
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2412.06003 | Enhancing Content Representation for AR Image Quality Assessment Using
Knowledge Distillation | [
"cs.CV",
"eess.IV"
] | Augmented Reality (AR) is a major immersive media technology that enriches our perception of reality by overlaying digital content (the foreground) onto physical environments (the background). It has far-reaching applications, from entertainment and gaming to education, healthcare, and industrial training. Nevertheless... | {
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2412.06009 | 1-800-SHARED-TASKS at RegNLP: Lexical Reranking of Semantic Retrieval
(LeSeR) for Regulatory Question Answering | [
"cs.CL",
"cs.IR",
"cs.LG"
] | This paper presents the system description of our entry for the COLING 2025 RegNLP RIRAG (Regulatory Information Retrieval and Answer Generation) challenge, focusing on leveraging advanced information retrieval and answer generation techniques in regulatory domains. We experimented with a combination of embedding model... | {
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2412.06011 | TopoCellGen: Generating Histopathology Cell Topology with a Diffusion
Model | [
"eess.IV",
"cs.CV"
] | Accurately modeling multi-class cell topology is crucial in digital pathology, as it provides critical insights into tissue structure and pathology. The synthetic generation of cell topology enables realistic simulations of complex tissue environments, enhances downstream tasks by augmenting training data, aligns more ... | {
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2412.06014 | Post-hoc Probabilistic Vision-Language Models | [
"cs.CV",
"cs.LG"
] | Vision-language models (VLMs), such as CLIP and SigLIP, have found remarkable success in classification, retrieval, and generative tasks. For this, VLMs deterministically map images and text descriptions to a joint latent space in which their similarity is assessed using the cosine similarity. However, a deterministic ... | {
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2412.06015 | siForest: Detecting Network Anomalies with Set-Structured Isolation
Forest | [
"cs.LG",
"cs.CR"
] | As cyber threats continue to evolve in sophistication and scale, the ability to detect anomalous network behavior has become critical for maintaining robust cybersecurity defenses. Modern cybersecurity systems face the overwhelming challenge of analyzing billions of daily network interactions to identify potential thre... | {
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2412.06016 | Track4Gen: Teaching Video Diffusion Models to Track Points Improves
Video Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | While recent foundational video generators produce visually rich output, they still struggle with appearance drift, where objects gradually degrade or change inconsistently across frames, breaking visual coherence. We hypothesize that this is because there is no explicit supervision in terms of spatial tracking at the ... | {
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2412.06018 | Imputation Matters: A Deeper Look into an Overlooked Step in
Longitudinal Health and Behavior Sensing Research | [
"stat.ME",
"cs.AI",
"cs.HC",
"cs.LG"
] | Longitudinal passive sensing studies for health and behavior outcomes often have missing and incomplete data. Handling missing data effectively is thus a critical data processing and modeling step. Our formative interviews with researchers working in longitudinal health and behavior passive sensing revealed a recurring... | {
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2412.06028 | FlexDiT: Dynamic Token Density Control for Diffusion Transformer | [
"cs.CV"
] | Diffusion Transformers (DiT) deliver impressive generative performance but face prohibitive computational demands due to both the quadratic complexity of token-based self-attention and the need for extensive sampling steps. While recent research has focused on accelerating sampling, the structural inefficiencies of DiT... | {
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2412.06029 | Latent-Reframe: Enabling Camera Control for Video Diffusion Model
without Training | [
"cs.CV"
] | Precise camera pose control is crucial for video generation with diffusion models. Existing methods require fine-tuning with additional datasets containing paired videos and camera pose annotations, which are both data-intensive and computationally costly, and can disrupt the pre-trained model distribution. We introduc... | {
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2412.06033 | Can Generative AI Solve Your In-Context Learning Problem? A Martingale
Perspective | [
"stat.ML",
"cs.AI",
"cs.CL",
"cs.LG"
] | This work is about estimating when a conditional generative model (CGM) can solve an in-context learning (ICL) problem. An in-context learning (ICL) problem comprises a CGM, a dataset, and a prediction task. The CGM could be a multi-modal foundation model; the dataset, a collection of patient histories, test results, a... | {
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2412.06035 | Teleoperation of Continuum Instruments: Investigation of Linear vs.
Angular Commands through Task-Priority Analysis | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper addresses the challenge of teleoperating continuum instruments for minimally invasive surgery (MIS). We develop and adopt a novel task-priority-based kinematic formulation to quantitatively investigate teleoperation commands for continuum instruments under remote center of motion (RCM) constraints. Using red... | {
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2412.06038 | Vision Transformer-based Semantic Communications With Importance-Aware
Quantization | [
"eess.SP",
"cs.CV",
"cs.IT",
"math.IT"
] | Semantic communications provide significant performance gains over traditional communications by transmitting task-relevant semantic features through wireless channels. However, most existing studies rely on end-to-end (E2E) training of neural-type encoders and decoders to ensure effective transmission of these semanti... | {
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2412.06040 | The AI Double Standard: Humans Judge All AIs for the Actions of One | [
"cs.AI",
"cs.CY",
"cs.ET",
"cs.HC"
] | Robots and other artificial intelligence (AI) systems are widely perceived as moral agents responsible for their actions. As AI proliferates, these perceptions may become entangled via the moral spillover of attitudes towards one AI to attitudes towards other AIs. We tested how the seemingly harmful and immoral actions... | {
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2412.06041 | PyPOD-GP: Using PyTorch for Accelerated Chip-Level Thermal Simulation of
the GPU | [
"cs.CE"
] | The rising demand for high-performance computing (HPC) has made full-chip dynamic thermal simulation in many-core GPUs critical for optimizing performance and extending device lifespans. Proper orthogonal decomposition (POD) with Galerkin projection (GP) has shown to offer high accuracy and massive runtime improvements... | {
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2412.06044 | Cloud Platforms for Developing Generative AI Solutions: A Scoping Review
of Tools and Services | [
"cs.DC",
"cs.AI",
"cs.CY"
] | Generative AI is transforming enterprise application development by enabling machines to create content, code, and designs. These models, however, demand substantial computational power and data management. Cloud computing addresses these needs by offering infrastructure to train, deploy, and scale generative AI models... | {
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2412.06045 | Dilated Balanced Cross Entropy Loss for Medical Image Segmentation | [
"eess.IV",
"cs.CV",
"cs.LG"
] | A novel method for tackling the problem of imbalanced data in medical image segmentation is proposed in this work. In balanced cross entropy (CE) loss, which is a type of weighted CE loss, the weight assigned to each class is the in-verse of the class frequency. These balancing weights are expected to equalize the effe... | {
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2412.06049 | MIMO Detection under Hardware Impairments: Data Augmentation With
Boosting | [
"eess.SP",
"cs.IT",
"math.IT"
] | This paper addresses a data detection problem for multiple-input multiple-output (MIMO) communication systems with hardware impairments. To facilitate maximum likelihood (ML) data detection without knowledge of nonlinear and unknown hardware impairments, we develop novel likelihood function (LF) estimation methods base... | {
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2412.06060 | Steering Large Language Models to Evaluate and Amplify Creativity | [
"cs.CL",
"cs.AI"
] | Although capable of generating creative text, Large Language Models (LLMs) are poor judges of what constitutes "creativity". In this work, we show that we can leverage this knowledge of how to write creatively in order to better judge what is creative. We take a mechanistic approach that extracts differences in the int... | {
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2412.06061 | Curse of Attention: A Kernel-Based Perspective for Why Transformers Fail
to Generalize on Time Series Forecasting and Beyond | [
"cs.LG",
"cs.AI"
] | The application of transformer-based models on time series forecasting (TSF) tasks has long been popular to study. However, many of these works fail to beat the simple linear residual model, and the theoretical understanding of this issue is still limited. In this work, we propose the first theoretical explanation of t... | {
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2412.06063 | On Socially Fair Low-Rank Approximation and Column Subset Selection | [
"cs.LG",
"cs.DS",
"stat.ML"
] | Low-rank approximation and column subset selection are two fundamental and related problems that are applied across a wealth of machine learning applications. In this paper, we study the question of socially fair low-rank approximation and socially fair column subset selection, where the goal is to minimize the loss ov... | {
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2412.06064 | Implicit Delta Learning of High Fidelity Neural Network Potentials | [
"physics.chem-ph",
"cs.LG"
] | Neural network potentials (NNPs) offer a fast and accurate alternative to ab-initio methods for molecular dynamics (MD) simulations but are hindered by the high cost of training data from high-fidelity Quantum Mechanics (QM) methods. Our work introduces the Implicit Delta Learning (IDLe) method, which reduces the need ... | {
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2412.06069 | Fuzzy Norm-Explicit Product Quantization for Recommender Systems | [
"cs.IR",
"cs.AI"
] | As the data resources grow, providing recommendations that best meet the demands has become a vital requirement in business and life to overcome the information overload problem. However, building a system suggesting relevant recommendations has always been a point of debate. One of the most cost-efficient techniques i... | {
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2412.06071 | KaSA: Knowledge-Aware Singular-Value Adaptation of Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | The increasing sizes of large language models (LLMs) result in significant computational overhead and memory usage when adapting these models to specific tasks or domains. Various parameter-efficient fine-tuning (PEFT) methods have been devised to mitigate these challenges by training a small set of parameters for the ... | {
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2412.06072 | PAC codes with Bounded-Complexity Sequential Decoding: Pareto
Distribution and Code Design | [
"cs.IT",
"cs.CC",
"math.IT"
] | Recently, a novel variation of polar codes known as polarization-adjusted convolutional (PAC) codes has been introduced by Ar{\i}kan. These codes significantly outperform conventional polar and convolutional codes, particularly for short codeword lengths, and are shown to operate very close to the optimal bounds. It ha... | {
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2412.06075 | Hyperspectral Image Spectral-Spatial Feature Extraction via Tensor
Principal Component Analysis | [
"cs.CV",
"cs.LG"
] | This paper addresses the challenge of spectral-spatial feature extraction for hyperspectral image classification by introducing a novel tensor-based framework. The proposed approach incorporates circular convolution into a tensor structure to effectively capture and integrate both spectral and spatial information. Buil... | {
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2412.06078 | Mixture-of-PageRanks: Replacing Long-Context with Real-Time, Sparse
GraphRAG | [
"cs.IR",
"cs.LG"
] | Recent advances have extended the context window of frontier LLMs dramatically, from a few thousand tokens up to millions, enabling entire books and codebases to fit into context. However, the compute costs of inferencing long-context LLMs are massive and often prohibitive in practice. RAG offers an efficient and effec... | {
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2412.06079 | Learning from Snapshots of Discrete and Continuous Data Streams | [
"cs.LG"
] | Imagine a smart camera trap selectively clicking pictures to understand animal movement patterns within a particular habitat. These "snapshots", or pieces of data captured from a data stream at adaptively chosen times, provide a glimpse of different animal movements unfolding through time. Learning a continuous-time pr... | {
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2412.06080 | GVDepth: Zero-Shot Monocular Depth Estimation for Ground Vehicles based
on Probabilistic Cue Fusion | [
"cs.CV",
"cs.AI",
"cs.RO"
] | Generalizing metric monocular depth estimation presents a significant challenge due to its ill-posed nature, while the entanglement between camera parameters and depth amplifies issues further, hindering multi-dataset training and zero-shot accuracy. This challenge is particularly evident in autonomous vehicles and mob... | {
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2412.06082 | Are foundation models for computer vision good conformal predictors? | [
"cs.CV"
] | Recent advances in self-supervision and constrastive learning have brought the performance of foundation models to unprecedented levels in a variety of tasks. Fueled by this progress, these models are becoming the prevailing approach for a wide array of real-world vision problems, including risk-sensitive and high-stak... | {
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2412.06087 | Ethnography and Machine Learning: Synergies and New Directions | [
"cs.LG",
"cs.AI",
"cs.CY",
"stat.ME"
] | Ethnography (social scientific methods that illuminate how people understand, navigate and shape the real world contexts in which they live their lives) and machine learning (computational techniques that use big data and statistical learning models to perform quantifiable tasks) are each core to contemporary social sc... | {
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2412.06088 | A4-Unet: Deformable Multi-Scale Attention Network for Brain Tumor
Segmentation | [
"cs.CV",
"cs.AI"
] | Brain tumor segmentation models have aided diagnosis in recent years. However, they face MRI complexity and variability challenges, including irregular shapes and unclear boundaries, leading to noise, misclassification, and incomplete segmentation, thereby limiting accuracy. To address these issues, we adhere to an out... | {
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2412.06089 | GraPE: A Generate-Plan-Edit Framework for Compositional T2I Synthesis | [
"cs.CV"
] | Text-to-image (T2I) generation has seen significant progress with diffusion models, enabling generation of photo-realistic images from text prompts. Despite this progress, existing methods still face challenges in following complex text prompts, especially those requiring compositional and multi-step reasoning. Given s... | {
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2412.06090 | Trust No AI: Prompt Injection Along The CIA Security Triad | [
"cs.CR",
"cs.AI",
"cs.LG"
] | The CIA security triad - Confidentiality, Integrity, and Availability - is a cornerstone of data and cybersecurity. With the emergence of large language model (LLM) applications, a new class of threat, known as prompt injection, was first identified in 2022. Since then, numerous real-world vulnerabilities and exploits ... | {
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2412.06091 | A Linear-Time Algorithm for the Closest Vector Problem of Triangular
Lattices | [
"cs.CR",
"cs.IT",
"math.IT"
] | Fuzzy Extractor (FE) and Fuzzy Signature (FS) are useful schemes for generating cryptographic keys from fuzzy data such as biometric features. Several techniques have been proposed to implement FE and FS for fuzzy data in an Euclidean space, such as facial feature vectors, that use triangular lattice-based error correc... | {
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2412.06095 | Measuring Grammatical Diversity from Small Corpora: Derivational Entropy
Rates, Mean Length of Utterances, and Annotation Invariance | [
"cs.CL",
"cs.FL",
"cs.IT",
"math.IT"
] | In many fields, such as language acquisition, neuropsychology of language, the study of aging, and historical linguistics, corpora are used for estimating the diversity of grammatical structures that are produced during a period by an individual, community, or type of speakers. In these cases, treebanks are taken as re... | {
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2412.06097 | Order Theory in the Context of Machine Learning: an application | [
"cs.CV",
"cs.AI",
"math.CT"
] | The paper ``Tropical Geometry of Deep Neural Networks'' by L. Zhang et al. introduces an equivalence between integer-valued neural networks (IVNN) with activation $\text{ReLU}_{t}$ and tropical rational functions, which come with a map to polytopes. Here, IVNN refers to a network with integer weights but real biases, a... | {
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2412.06099 | DECO: Life-Cycle Management of Enterprise-Grade Chatbots | [
"cs.SE",
"cs.AI"
] | Software engineers frequently grapple with the challenge of accessing disparate documentation and telemetry data, including Troubleshooting Guides (TSGs), incident reports, code repositories, and various internal tools developed by multiple stakeholders. While on-call duties are inevitable, incident resolution becomes ... | {
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} |
2412.06101 | Self-supervised cost of transport estimation for multimodal path
planning | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Autonomous robots operating in real environments are often faced with decisions on how best to navigate their surroundings. In this work, we address a particular instance of this problem: how can a robot autonomously decide on the energetically optimal path to follow given a high-level objective and information about t... | {
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"cs.SY": 0
} |
2412.06102 | Synthesizing Document Database Queries using Collection Abstractions | [
"cs.DB",
"cs.PL",
"cs.SE"
] | Document databases are increasingly popular in various applications, but their queries are challenging to write due to the flexible and complex data model underlying document databases. This paper presents a synthesis technique that aims to generate document database queries from input-output examples automatically. A ... | {
"Other": 1,
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} |
2412.06105 | Fully Distributed Online Training of Graph Neural Networks in Networked
Systems | [
"cs.LG",
"cs.DC"
] | Graph neural networks (GNNs) are powerful tools for developing scalable, decentralized artificial intelligence in large-scale networked systems, such as wireless networks, power grids, and transportation networks. Currently, GNNs in networked systems mostly follow a paradigm of `centralized training, distributed execut... | {
"Other": 1,
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} |
2412.06106 | Enhanced Computationally Efficient Long LoRA Inspired Perceiver
Architectures for Auto-Regressive Language Modeling | [
"cs.CL",
"cs.LG"
] | The Transformer architecture has revolutionized the Natural Language Processing field and is the backbone of Large Language Models (LLMs). The Transformer uses the attention mechanism that computes the pair-wise similarity between its input tokens to produce latent vectors that are able to understand the semantic meani... | {
"Other": 0,
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} |
2412.06107 | Infusing Prompts with Syntax and Semantics | [
"cs.CL"
] | Despite impressive success, language models often generate outputs with flawed linguistic structure. We analyze the effect of directly infusing various kinds of syntactic and semantic information into large language models. To demonstrate the value of our proposals, we focus on the translation of natural language queri... | {
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} |
2412.06112 | PowerMamba: A Deep State Space Model and Comprehensive Benchmark for
Time Series Prediction in Electric Power Systems | [
"cs.LG",
"cs.SY",
"eess.SY"
] | The electricity sector is undergoing substantial transformations due to the rising electrification of demand, enhanced integration of renewable energy resources, and the emergence of new technologies. These changes are rendering the electric grid more volatile and unpredictable, making it difficult to maintain reliable... | {
"Other": 0,
"cs.AI": 0,
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"cs.SD": 0,
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"cs.SY": 1
} |
2412.06113 | Privacy-Preserving Large Language Models: Mechanisms, Applications, and
Future Directions | [
"cs.CR",
"cs.AI"
] | The rapid advancement of large language models (LLMs) has revolutionized natural language processing, enabling applications in diverse domains such as healthcare, finance and education. However, the growing reliance on extensive data for training and inference has raised significant privacy concerns, ranging from data ... | {
"Other": 0,
"cs.AI": 1,
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"cs.SY": 0
} |
2412.06116 | How Accurate is the Positioning in VR? Using Motion Capture and Robotics
to Compare Positioning Capabilities of Popular VR Headsets | [
"cs.RO",
"cs.HC"
] | In this paper, we introduce a new methodology for assessing the positioning accuracy of virtual reality (VR) headsets, utilizing a cooperative industrial robot to simulate user head trajectories in a reproducible manner. We conduct a comprehensive evaluation of two popular VR headsets, i.e., Meta Quest 2 and Meta Quest... | {
"Other": 0,
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"cs.RO": 1,
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"cs.SY": 0
} |
2412.06124 | Spiking Neural Networks for Radio Frequency Interference Detection in
Radio Astronomy | [
"cs.NE",
"astro-ph.IM"
] | Spiking Neural Networks (SNNs) promise efficient spatio-temporal data processing owing to their dynamic nature. This paper addresses a significant challenge in radio astronomy, Radio Frequency Interference (RFI) detection, by reformulating it as a time-series segmentation task inherently suited for SNN execution. Autom... | {
"Other": 0,
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} |
2412.06126 | UCB algorithms for multi-armed bandits: Precise regret and adaptive
inference | [
"math.ST",
"cs.IT",
"cs.LG",
"math.IT",
"stat.ML",
"stat.TH"
] | Upper Confidence Bound (UCB) algorithms are a widely-used class of sequential algorithms for the $K$-armed bandit problem. Despite extensive research over the past decades aimed at understanding their asymptotic and (near) minimax optimality properties, a precise understanding of their regret behavior remains elusive. ... | {
"Other": 0,
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"cs.SY": 0
} |
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