id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.02329 | GRAND : Graph Reconstruction from potential partial Adjacency and
Neighborhood Data | [
"cs.CR",
"cs.SI"
] | Cryptographic approaches, such as secure multiparty computation, can be used to compute in a secure manner the function of a distributed graph without centralizing the data of each participant. However, the output of the protocol itself can leak sensitive information about the structure of the original graph. In partic... | {
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2412.02331 | Sample Efficient Robot Learning in Supervised Effect Prediction Tasks | [
"cs.RO",
"cs.AI",
"cs.LG"
] | In self-supervised robot learning, robots actively explore their environments and generate data by acting on entities in the environment. Therefore, an exploration policy is desired that ensures sample efficiency to minimize robot execution costs while still providing accurate learning. For this purpose, the robotic co... | {
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2412.02332 | SimuScope: Realistic Endoscopic Synthetic Dataset Generation through
Surgical Simulation and Diffusion Models | [
"cs.CV",
"eess.IV"
] | Computer-assisted surgical (CAS) systems enhance surgical execution and outcomes by providing advanced support to surgeons. These systems often rely on deep learning models trained on complex, challenging-to-annotate data. While synthetic data generation can address these challenges, enhancing the realism of such data ... | {
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2412.02334 | Reinforcement learning to learn quantum states for Heisenberg scaling
accuracy | [
"quant-ph",
"cs.AI"
] | Learning quantum states is a crucial task for realizing the potential of quantum information technology. Recently, neural approaches have emerged as promising methods for learning quantum states. We propose a meta-learning model that employs reinforcement learning (RL) to optimize the process of learning quantum states... | {
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2412.02335 | An Adaptive Grasping Force Tracking Strategy for Nonlinear and
Time-Varying Object Behaviors | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | Accurate grasp force control is one of the key skills for ensuring successful and damage-free robotic grasping of objects. Although existing methods have conducted in-depth research on slip detection and grasping force planning, they often overlook the issue of adaptive tracking of the actual force to the target force ... | {
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2412.02336 | Amodal Depth Anything: Amodal Depth Estimation in the Wild | [
"cs.CV"
] | Amodal depth estimation aims to predict the depth of occluded (invisible) parts of objects in a scene. This task addresses the question of whether models can effectively perceive the geometry of occluded regions based on visible cues. Prior methods primarily rely on synthetic datasets and focus on metric depth estimati... | {
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2412.02340 | Federated Analytics in Practice: Engineering for Privacy, Scalability
and Practicality | [
"cs.LG"
] | Cross-device Federated Analytics (FA) is a distributed computation paradigm designed to answer analytics queries about and derive insights from data held locally on users' devices. On-device computations combined with other privacy and security measures ensure that only minimal data is transmitted off-device, achieving... | {
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2412.02343 | Multi-Granularity Tibetan Textual Adversarial Attack Method Based on
Masked Language Model | [
"cs.CL",
"cs.CR"
] | In social media, neural network models have been applied to hate speech detection, sentiment analysis, etc., but neural network models are susceptible to adversarial attacks. For instance, in a text classification task, the attacker elaborately introduces perturbations to the original texts that hardly alter the origin... | {
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2412.02344 | UniForm: A Reuse Attention Mechanism Optimized for Efficient Vision
Transformers on Edge Devices | [
"cs.CV"
] | Transformer-based architectures have demonstrated remarkable success across various domains, but their deployment on edge devices remains challenging due to high memory and computational demands. In this paper, we introduce a novel Reuse Attention mechanism, tailored for efficient memory access and computational optimi... | {
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2412.02351 | Dual Exposure Stereo for Extended Dynamic Range 3D Imaging | [
"cs.CV"
] | Achieving robust stereo 3D imaging under diverse illumination conditions is an important however challenging task, due to the limited dynamic ranges (DRs) of cameras, which are significantly smaller than real world DR. As a result, the accuracy of existing stereo depth estimation methods is often compromised by under- ... | {
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2412.02352 | LoRA Diffusion: Zero-Shot LoRA Synthesis for Diffusion Model
Personalization | [
"cs.LG"
] | Low-Rank Adaptation (LoRA) and other parameter-efficient fine-tuning (PEFT) methods provide low-memory, storage-efficient solutions for personalizing text-to-image models. However, these methods offer little to no improvement in wall-clock training time or the number of steps needed for convergence compared to full mod... | {
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2412.02357 | Dynamic Prompt Middleware: Contextual Prompt Refinement Controls for
Comprehension Tasks | [
"cs.HC",
"cs.AI"
] | Effective prompting of generative AI is challenging for many users, particularly in expressing context for comprehension tasks such as explaining spreadsheet formulas, Python code, and text passages. Prompt middleware aims to address this barrier by assisting in prompt construction, but barriers remain for users in exp... | {
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2412.02359 | Realistic Surgical Simulation from Monocular Videos | [
"cs.CV"
] | This paper tackles the challenge of automatically performing realistic surgical simulations from readily available surgical videos. Recent efforts have successfully integrated physically grounded dynamics within 3D Gaussians to perform high-fidelity simulations in well-reconstructed simulation environments from static ... | {
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2412.02366 | GenMix: Effective Data Augmentation with Generative Diffusion Model
Image Editing | [
"cs.CV"
] | Data augmentation is widely used to enhance generalization in visual classification tasks. However, traditional methods struggle when source and target domains differ, as in domain adaptation, due to their inability to address domain gaps. This paper introduces GenMix, a generalizable prompt-guided generative data augm... | {
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2412.02368 | ScImage: How Good Are Multimodal Large Language Models at Scientific
Text-to-Image Generation? | [
"cs.AI",
"cs.CL",
"cs.CV"
] | Multimodal large language models (LLMs) have demonstrated impressive capabilities in generating high-quality images from textual instructions. However, their performance in generating scientific images--a critical application for accelerating scientific progress--remains underexplored. In this work, we address this gap... | {
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2412.02369 | The Impact of Featuring Comments in Online Discussions | [
"cs.CL",
"cs.HC"
] | A widespread moderation strategy by online news platforms is to feature what the platform deems high quality comments, usually called editor picks or featured comments. In this paper, we compare online discussions of news articles in which certain comments are featured, versus discussions in which no comments are featu... | {
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2412.02370 | Trajectory-based Road Autolabeling with Lidar-Camera Fusion in Winter
Conditions | [
"cs.CV"
] | Robust road segmentation in all road conditions is required for safe autonomous driving and advanced driver assistance systems. Supervised deep learning methods provide accurate road segmentation in the domain of their training data but cannot be trusted in out-of-distribution scenarios. Including the whole distributio... | {
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2412.02371 | TSCheater: Generating High-Quality Tibetan Adversarial Texts via Visual
Similarity | [
"cs.CL",
"cs.CR"
] | Language models based on deep neural networks are vulnerable to textual adversarial attacks. While rich-resource languages like English are receiving focused attention, Tibetan, a cross-border language, is gradually being studied due to its abundant ancient literature and critical language strategy. Currently, there ar... | {
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2412.02372 | HERO: Hint-Based Efficient and Reliable Query Optimizer | [
"cs.DB",
"cs.AI",
"cs.LG"
] | We propose a novel model for learned query optimization which provides query hints leading to better execution plans. The model addresses the three key challenges in learned hint-based query optimization: reliable hint recommendation (ensuring non-degradation of query latency), efficient hint exploration, and fast infe... | {
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2412.02373 | Active Negative Loss: A Robust Framework for Learning with Noisy Labels | [
"cs.CV"
] | Deep supervised learning has achieved remarkable success across a wide range of tasks, yet it remains susceptible to overfitting when confronted with noisy labels. To address this issue, noise-robust loss functions offer an effective solution for enhancing learning in the presence of label noise. In this work, we syste... | {
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2412.02376 | Flexible-Antenna Systems: A Pinching-Antenna Perspective | [
"cs.IT",
"eess.SP",
"math.IT"
] | Flexible-antenna systems have recently received significant research interest due to their capability to reconfigure wireless channels intelligently. This paper focuses on a new type of flexible-antenna technology, termed pinching antennas, which can be realized by applying small dielectric particles on a waveguide. An... | {
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2412.02386 | Single-Shot Metric Depth from Focused Plenoptic Cameras | [
"cs.CV"
] | Metric depth estimation from visual sensors is crucial for robots to perceive, navigate, and interact with their environment. Traditional range imaging setups, such as stereo or structured light cameras, face hassles including calibration, occlusions, and hardware demands, with accuracy limited by the baseline between ... | {
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2412.02389 | Fast ground-to-air transition with avian-inspired multifunctional legs | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Most birds can navigate seamlessly between aerial and terrestrial environments. Whereas the forelimbs evolved into wings primarily for flight, the hindlimbs serve diverse functions such as walking, hopping, and leaping, and jumping take-off for transitions into flight. These capabilities have inspired engineers to aim ... | {
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2412.02393 | Bio-inspired visual relative localization for large swarms of UAVs | [
"cs.RO",
"cs.CV"
] | We propose a new approach to visual perception for relative localization of agents within large-scale swarms of UAVs. Inspired by biological perception utilized by schools of sardines, swarms of bees, and other large groups of animals capable of moving in a decentralized yet coherent manner, our method does not rely on... | {
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2412.02395 | Who Walks With You Matters: Perceiving Social Interactions with Groups
for Pedestrian Trajectory Prediction | [
"cs.CV"
] | Understanding and anticipating human movement has become more critical and challenging in diverse applications such as autonomous driving and surveillance. The complex interactions brought by different relations between agents are a crucial reason that poses challenges to this task. Researchers have put much effort int... | {
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2412.02399 | OMENN: One Matrix to Explain Neural Networks | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Deep Learning (DL) models are often black boxes, making their decision-making processes difficult to interpret. This lack of transparency has driven advancements in eXplainable Artificial Intelligence (XAI), a field dedicated to clarifying the reasoning behind DL model predictions. Among these, attribution-based method... | {
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2412.02400 | Four Guiding Principles for Modeling Causal Domain Knowledge: A Case
Study on Brainstorming Approaches for Urban Blight Analysis | [
"cs.CE",
"cs.CL"
] | Urban blight is a problem of high interest for planning and policy making. Researchers frequently propose theories about the relationships between urban blight indicators, focusing on relationships reflecting causality. In this paper, we improve on the integration of domain knowledge in the analysis of urban blight by ... | {
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2412.02402 | RG-SAN: Rule-Guided Spatial Awareness Network for End-to-End 3D
Referring Expression Segmentation | [
"cs.CV"
] | 3D Referring Expression Segmentation (3D-RES) aims to segment 3D objects by correlating referring expressions with point clouds. However, traditional approaches frequently encounter issues like over-segmentation or mis-segmentation, due to insufficient emphasis on spatial information of instances. In this paper, we int... | {
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2412.02403 | 3D Face Reconstruction From Radar Images | [
"cs.CV",
"cs.LG"
] | The 3D reconstruction of faces gains wide attention in computer vision and is used in many fields of application, for example, animation, virtual reality, and even forensics. This work is motivated by monitoring patients in sleep laboratories. Due to their unique characteristics, sensors from the radar domain have adva... | {
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2412.02408 | Leveraging Ensemble-Based Semi-Supervised Learning for Illicit Account
Detection in Ethereum DeFi Transactions | [
"cs.SI",
"cs.LG",
"q-fin.GN"
] | The advent of smart contracts has enabled the rapid rise of Decentralized Finance (DeFi) on the Ethereum blockchain, offering substantial rewards in financial innovation and inclusivity. However, this growth has also introduced significant security risks, including the proliferation of illicit accounts involved in frau... | {
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2412.02410 | A Multi-Agent Framework for Extensible Structured Text Generation in
PLCs | [
"cs.SE",
"cs.AI"
] | Programmable Logic Controllers (PLCs) are microcomputers essential for automating factory operations. Structured Text (ST), a high-level language adhering to the IEC 61131-3 standard, is pivotal for PLCs due to its ability to express logic succinctly and to seamlessly integrate with other languages within the same stan... | {
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2412.02412 | VISTA: A Panoramic View of Neural Representations | [
"cs.LG",
"cs.AI",
"cs.CV"
] | We present VISTA (Visualization of Internal States and Their Associations), a novel pipeline for visually exploring and interpreting neural network representations. VISTA addresses the challenge of analyzing vast multidimensional spaces in modern machine learning models by mapping representations into a semantic 2D spa... | {
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2412.02415 | Knowledge-Enhanced Conversational Recommendation via Transformer-based
Sequential Modelling | [
"cs.IR",
"cs.AI"
] | In conversational recommender systems (CRSs), conversations usually involve a set of items and item-related entities or attributes, e.g., director is a related entity of a movie. These items and item-related entities are often mentioned along the development of a dialog, leading to potential sequential dependencies amo... | {
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2412.02419 | It Takes Two: Real-time Co-Speech Two-person's Interaction Generation
via Reactive Auto-regressive Diffusion Model | [
"cs.SD",
"cs.CV",
"cs.GR",
"cs.MM",
"eess.AS"
] | Conversational scenarios are very common in real-world settings, yet existing co-speech motion synthesis approaches often fall short in these contexts, where one person's audio and gestures will influence the other's responses. Additionally, most existing methods rely on offline sequence-to-sequence frameworks, which a... | {
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2412.02421 | TimeWalker: Personalized Neural Space for Lifelong Head Avatars | [
"cs.CV"
] | We present TimeWalker, a novel framework that models realistic, full-scale 3D head avatars of a person on lifelong scale. Unlike current human head avatar pipelines that capture identity at the momentary level(e.g., instant photography or short videos), TimeWalker constructs a person's comprehensive identity from unstr... | {
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2412.02423 | Time-Series-Informed Closed-loop Learning for Sequential Decision Making
and Control | [
"eess.SY",
"cs.LG",
"cs.SY"
] | Closed-loop performance of sequential decision making algorithms, such as model predictive control, depends strongly on the parameters of cost functions, models, and constraints. Bayesian optimization is a common approach to learning these parameters based on closed-loop experiments. However, traditional Bayesian optim... | {
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2412.02427 | GerPS-Compare: Comparing NER methods for legal norm analysis | [
"cs.CL",
"cs.AI"
] | We apply NER to a particular sub-genre of legal texts in German: the genre of legal norms regulating administrative processes in public service administration. The analysis of such texts involves identifying stretches of text that instantiate one of ten classes identified by public service administration professionals.... | {
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2412.02430 | Transformer-based Koopman Autoencoder for Linearizing Fisher's Equation | [
"math.AP",
"cs.LG",
"math.DS"
] | A Transformer-based Koopman autoencoder is proposed for linearizing Fisher's reaction-diffusion equation. The primary focus of this study is on using deep learning techniques to find complex spatiotemporal patterns in the reaction-diffusion system. The emphasis is on not just solving the equation but also transforming ... | {
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2412.02432 | Improved Localized Machine Unlearning Through the Lens of Memorization | [
"cs.LG"
] | Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is important for key applications, including making the model more accurate by removing outdated, mislabeled, or poisoned data. In this work, we ... | {
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2412.02434 | Fast maximal clique enumeration in weighted temporal networks | [
"cs.SI"
] | Cliques, groups of fully connected nodes in a network, are often used to study group dynamics of complex systems. In real-world settings, group dynamics often have a temporal component. For example, conference attendees moving from one group conversation to another. Recently, maximal clique enumeration methods have bee... | {
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2412.02437 | Reproduction of AdEx dynamics on neuromorphic hardware through data
embedding and simulation-based inference | [
"cs.NE"
] | The development of mechanistic models of physical systems is essential for understanding their behavior and formulating predictions that can be validated experimentally. Calibration of these models, especially for complex systems, requires automated optimization methods due to the impracticality of manual parameter tun... | {
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2412.02439 | Nature versus nurture in galaxy formation: the effect of environment on
star formation with causal machine learning | [
"astro-ph.GA",
"cs.LG",
"stat.ME",
"stat.ML"
] | Understanding how galaxies form and evolve is at the heart of modern astronomy. With the advent of large-scale surveys and simulations, remarkable progress has been made in the last few decades. Despite this, the physical processes behind the phenomena, and particularly their importance, remain far from known, as corre... | {
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2412.02441 | Artificial Expert Intelligence through PAC-reasoning | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Artificial Expert Intelligence (AEI) seeks to transcend the limitations of both Artificial General Intelligence (AGI) and narrow AI by integrating domain-specific expertise with critical, precise reasoning capabilities akin to those of top human experts. Existing AI systems often excel at predefined tasks but struggle ... | {
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2412.02443 | Multi-scale and Multi-path Cascaded Convolutional Network for Semantic
Segmentation of Colorectal Polyps | [
"eess.IV",
"cs.CV"
] | Colorectal polyps are structural abnormalities of the gastrointestinal tract that can potentially become cancerous in some cases. The study introduces a novel framework for colorectal polyp segmentation named the Multi-Scale and Multi-Path Cascaded Convolution Network (MMCC-Net), aimed at addressing the limitations of ... | {
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2412.02447 | Resonance: Learning to Predict Social-Aware Pedestrian Trajectories as
Co-Vibrations | [
"cs.CV"
] | Learning to forecast the trajectories of intelligent agents like pedestrians has caught more researchers' attention. Despite researchers' efforts, it remains a challenge to accurately account for social interactions among agents when forecasting, and in particular, to simulate such social modifications to future trajec... | {
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2412.02448 | UNIFY: Unified Index for Range Filtered Approximate Nearest Neighbors
Search | [
"cs.DS",
"cs.DB"
] | This paper presents an efficient and scalable framework for Range Filtered Approximate Nearest Neighbors Search (RF-ANNS) over high-dimensional vectors associated with attribute values. Given a query vector $q$ and a range $[l, h]$, RF-ANNS aims to find the approximate $k$ nearest neighbors of $q$ among data whose attr... | {
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2412.02449 | BYE: Build Your Encoder with One Sequence of Exploration Data for
Long-Term Dynamic Scene Understanding | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | Dynamic scene understanding remains a persistent challenge in robotic applications. Early dynamic mapping methods focused on mitigating the negative influence of short-term dynamic objects on camera motion estimation by masking or tracking specific categories, which often fall short in adapting to long-term scene chang... | {
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2412.02454 | Gracefully Filtering Backdoor Samples for Generative Large Language
Models without Retraining | [
"cs.CL",
"cs.AI",
"cs.CR"
] | Backdoor attacks remain significant security threats to generative large language models (LLMs). Since generative LLMs output sequences of high-dimensional token logits instead of low-dimensional classification logits, most existing backdoor defense methods designed for discriminative models like BERT are ineffective f... | {
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2412.02466 | Can ChatGPT capture swearing nuances? Evidence from translating Arabic
oaths | [
"cs.CL"
] | This study sets out to answer one major question: Can ChatGPT capture swearing nuances? It presents an empirical study on the ability of ChatGPT to translate Arabic oath expressions into English. 30 Arabic oath expressions were collected from the literature. These 30 oaths were first translated via ChatGPT and then ana... | {
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2412.02467 | DP-2Stage: Adapting Language Models as Differentially Private Tabular
Data Generators | [
"cs.LG",
"cs.CL",
"cs.CR"
] | Generating tabular data under differential privacy (DP) protection ensures theoretical privacy guarantees but poses challenges for training machine learning models, primarily due to the need to capture complex structures under noisy supervision signals. Recently, pre-trained Large Language Models (LLMs) -- even those a... | {
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2412.02471 | COMET:Combined Matrix for Elucidating Targets | [
"cs.LG"
] | Identifying the interaction targets of bioactive compounds is a foundational element for deciphering their pharmacological effects. Target prediction algorithms equip researchers with an effective tool to rapidly scope and explore potential targets. Here, we introduce the COMET, a multi-technological modular target pre... | {
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2412.02474 | F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain
Information | [
"cs.AI"
] | With the development of society, time series anomaly detection plays an important role in network and IoT services. However, most existing anomaly detection methods directly analyze time series in the time domain and cannot distinguish some relatively hidden anomaly sequences. We attempt to analyze the impact of freque... | {
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2412.02479 | OODFace: Benchmarking Robustness of Face Recognition under Common
Corruptions and Appearance Variations | [
"cs.CV",
"cs.AI",
"cs.CR",
"cs.LG"
] | With the rise of deep learning, facial recognition technology has seen extensive research and rapid development. Although facial recognition is considered a mature technology, we find that existing open-source models and commercial algorithms lack robustness in certain real-world Out-of-Distribution (OOD) scenarios, ra... | {
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2412.02482 | What should a neuron aim for? Designing local objective functions based
on information theory | [
"cs.IT",
"cs.LG",
"cs.NE",
"math.IT"
] | In modern deep neural networks, the learning dynamics of the individual neurons is often obscure, as the networks are trained via global optimization. Conversely, biological systems build on self-organized, local learning, achieving robustness and efficiency with limited global information. We here show how self-organi... | {
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2412.02484 | Vector Optimization with Gaussian Process Bandits | [
"cs.LG",
"stat.AP",
"stat.ML"
] | Learning problems in which multiple conflicting objectives must be considered simultaneously often arise in various fields, including engineering, drug design, and environmental management. Traditional methods for dealing with multiple black-box objective functions, such as scalarization and identification of the Paret... | {
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2412.02492 | The Cost of Consistency: Submodular Maximization with Constant Recourse | [
"cs.DS",
"cs.LG",
"stat.ML"
] | In this work, we study online submodular maximization, and how the requirement of maintaining a stable solution impacts the approximation. In particular, we seek bounds on the best-possible approximation ratio that is attainable when the algorithm is allowed to make at most a constant number of updates per step. We sho... | {
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2412.02493 | RelayGS: Reconstructing Dynamic Scenes with Large-Scale and Complex
Motions via Relay Gaussians | [
"cs.CV"
] | Reconstructing dynamic scenes with large-scale and complex motions remains a significant challenge. Recent techniques like Neural Radiance Fields and 3D Gaussian Splatting (3DGS) have shown promise but still struggle with scenes involving substantial movement. This paper proposes RelayGS, a novel method based on 3DGS, ... | {
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2412.02499 | A Miniature Batteryless Bioelectronic Implant Using One Magnetoelectric
Transducer for Wireless Powering and PWM Backscatter Communication | [
"eess.SY",
"cs.SY"
] | Wireless minimally invasive bioelectronic implants enable a wide range of applications in healthcare, medicine, and scientific research. Magnetoelectric (ME) wireless power transfer (WPT) has emerged as a promising approach for powering miniature bio-implants because of its remarkable efficiency, safety limit, and misa... | {
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2412.02502 | Analysis, Design, and Fabrication of a High-Gain Low-Profile Metasurface
Antenna Using Direct Feeding of Sievenpiper s HIS | [
"physics.app-ph",
"cs.ET",
"cs.SY",
"eess.SP",
"eess.SY"
] | HISs have recently shown the ability to support leaky waves, and to excite plasmonic and HIS resonance frequency modes for use as an antenna. In this paper, we analyzed, designed, and fabricated a TMA by directly feeding edge-located HIS cells through a microstrip feeding network. In contrast to other metasurface anten... | {
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} |
2412.02503 | CA-MoE: Channel-Adapted MoE for Incremental Weather Forecasting | [
"cs.LG",
"physics.ao-ph"
] | Atmospheric science is intricately connected with other fields, e.g., geography and aerospace. Most existing approaches involve training a joint atmospheric and geographic model from scratch, which incurs significant computational costs and overlooks the potential for incremental learning of weather variables across di... | {
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2412.02506 | ROVER: A Multi-Season Dataset for Visual SLAM | [
"cs.RO",
"cs.CV"
] | Robust Simultaneous Localization and Mapping (SLAM) is a crucial enabler for autonomous navigation in natural, unstructured environments such as parks and gardens. However, these environments present unique challenges for SLAM due to frequent seasonal changes, varying light conditions, and dense vegetation. These facto... | {
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2412.02508 | Towards Rich Emotions in 3D Avatars: A Text-to-3D Avatar Generation
Benchmark | [
"cs.AI",
"cs.CV"
] | Producing emotionally dynamic 3D facial avatars with text derived from spoken words (Emo3D) has been a pivotal research topic in 3D avatar generation. While progress has been made in general-purpose 3D avatar generation, the exploration of generating emotional 3D avatars remains scarce, primarily due to the complexitie... | {
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2412.02509 | FCL-ViT: Task-Aware Attention Tuning for Continual Learning | [
"cs.AI"
] | Continual Learning (CL) involves adapting the prior Deep Neural Network (DNN) knowledge to new tasks, without forgetting the old ones. However, modern CL techniques focus on provisioning memory capabilities to existing DNN models rather than designing new ones that are able to adapt according to the task at hand. This ... | {
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2412.02512 | Pre-Deployment Information Sharing: A Zoning Taxonomy for Precursory
Capabilities | [
"cs.CY",
"cs.AI"
] | High-impact and potentially dangerous capabilities can and should be broken down into early warning shots long before reaching red lines. Each of these early warning shots should correspond to a precursory capability. Each precursory capability sits on a spectrum indicating its proximity to a final high-impact capabili... | {
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2412.02520 | Cooperative Cruising: Reinforcement Learning-Based Time-Headway Control
for Increased Traffic Efficiency | [
"cs.MA",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | The proliferation of connected automated vehicles represents an unprecedented opportunity for improving driving efficiency and alleviating traffic congestion. However, existing research fails to address realistic multi-lane highway scenarios without assuming connectivity, perception, and control capabilities that are t... | {
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2412.02525 | LLMForecaster: Improving Seasonal Event Forecasts with Unstructured
Textual Data | [
"cs.LG",
"cs.CL"
] | Modern time-series forecasting models often fail to make full use of rich unstructured information about the time series themselves. This lack of proper conditioning can lead to obvious model failures; for example, models may be unaware of the details of a particular product, and hence fail to anticipate seasonal surge... | {
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2412.02526 | On the lifting degree of girth-8 QC-LDPC codes | [
"cs.IT",
"math.IT"
] | The lifting degree and the deterministic construction of quasi-cyclic low-density parity-check (QC-LDPC) codes have been extensively studied, with many construction methods in the literature, including those based on finite geometry, array-based codes, computer search, and combinatorial techniques. In this paper, we fo... | {
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2412.02528 | Bias Analysis of AI Models for Undergraduate Student Admissions | [
"cs.AI"
] | Bias detection and mitigation is an active area of research in machine learning. This work extends previous research done by the authors to provide a rigorous and more complete analysis of the bias found in AI predictive models. Admissions data spanning six years was used to create an AI model to determine whether a gi... | {
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2412.02529 | Active learning of neural population dynamics using two-photon
holographic optogenetics | [
"q-bio.NC",
"cs.LG",
"stat.ML"
] | Recent advances in techniques for monitoring and perturbing neural populations have greatly enhanced our ability to study circuits in the brain. In particular, two-photon holographic optogenetics now enables precise photostimulation of experimenter-specified groups of individual neurons, while simultaneous two-photon c... | {
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2412.02530 | WEM-GAN: Wavelet transform based facial expression manipulation | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Facial expression manipulation aims to change human facial expressions without affecting face recognition. In order to transform the facial expressions to target expressions, previous methods relied on expression labels to guide the manipulation process. However, these methods failed to preserve the details of facial f... | {
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2412.02531 | Multimodal Remote Sensing Scene Classification Using VLMs and Dual-Cross
Attention Networks | [
"cs.CV"
] | Remote sensing scene classification (RSSC) is a critical task with diverse applications in land use and resource management. While unimodal image-based approaches show promise, they often struggle with limitations such as high intra-class variance and inter-class similarity. Incorporating textual information can enhanc... | {
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2412.02533 | LiDAR-based Registration against Georeferenced Models for Globally
Consistent Allocentric Maps | [
"cs.RO",
"cs.CV"
] | Modern unmanned aerial vehicles (UAVs) are irreplaceable in search and rescue (SAR) missions to obtain a situational overview or provide closeups without endangering personnel. However, UAVs heavily rely on global navigation satellite system (GNSS) for localization which works well in open spaces, but the precision dra... | {
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2412.02535 | Defending Against Diverse Attacks in Federated Learning Through
Consensus-Based Bi-Level Optimization | [
"cs.LG",
"cs.CR",
"cs.MA",
"math.AP"
] | Adversarial attacks pose significant challenges in many machine learning applications, particularly in the setting of distributed training and federated learning, where malicious agents seek to corrupt the training process with the goal of jeopardizing and compromising the performance and reliability of the final model... | {
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2412.02538 | On Privacy, Security, and Trustworthiness in Distributed Wireless Large
AI Models (WLAM) | [
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT"
] | Combining wireless communication with large artificial intelligence (AI) models can open up a myriad of novel application scenarios. In sixth generation (6G) networks, ubiquitous communication and computing resources allow large AI models to serve democratic large AI models-related services to enable real-time applicat... | {
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2412.02539 | Graph-Powered Defense: Controller Area Network Intrusion Detection for
Unmanned Aerial Vehicles | [
"cs.AI"
] | The network of services, including delivery, farming, and environmental monitoring, has experienced exponential expansion in the past decade with Unmanned Aerial Vehicles (UAVs). Yet, UAVs are not robust enough against cyberattacks, especially on the Controller Area Network (CAN) bus. The CAN bus is a general-purpose v... | {
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2412.02542 | Unveiling Concept Attribution in Diffusion Models | [
"cs.CV",
"cs.LG"
] | Diffusion models have shown remarkable abilities in generating realistic and high-quality images from text prompts. However, a trained model remains black-box; little do we know about the role of its components in exhibiting a concept such as objects or styles. Recent works employ causal tracing to localize layers stor... | {
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2412.02545 | ShadowHack: Hacking Shadows via Luminance-Color Divide and Conquer | [
"cs.CV"
] | Shadows introduce challenges such as reduced brightness, texture deterioration, and color distortion in images, complicating a holistic solution. This study presents ShadowHack, a divide-and-conquer strategy that tackles these complexities by decomposing the original task into luminance recovery and color remedy. To br... | {
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2412.02546 | Fractional Order Distributed Optimization | [
"cs.LG"
] | Distributed optimization is fundamental to modern machine learning applications like federated learning, but existing methods often struggle with ill-conditioned problems and face stability-versus-speed tradeoffs. We introduce fractional order distributed optimization (FrODO); a theoretically-grounded framework that in... | {
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2412.02547 | Interaction Identification of a Heterogeneous NDS with
Quadratic-Bilinear Subsystems | [
"cs.MA",
"cs.SY",
"eess.SY",
"math.DS"
] | This paper attacks time-domain identification for the interaction parameters of a heterogeneous networked dynamic system (NDS), with each of its subsystems being described by a continuous-time descriptor quadratic-bilinear time-invariant (QBTI) model. No restrictions are put on the sampling rate. Explicit formulas are ... | {
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2412.02548 | Plug-and-Play Half-Quadratic Splitting for Ptychography | [
"eess.IV",
"cs.LG"
] | Ptychography is a coherent diffraction imaging method that uses phase retrieval techniques to reconstruct complex-valued images. It achieves this by sequentially illuminating overlapping regions of a sample with a coherent beam and recording the diffraction pattern. Although this addresses traditional imaging system ch... | {
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2412.02549 | Patent-CR: A Dataset for Patent Claim Revision | [
"cs.CL"
] | This paper presents Patent-CR, the first dataset created for the patent claim revision task in English. It includes both initial patent applications rejected by patent examiners and the final granted versions. Unlike normal text revision tasks that predominantly focus on enhancing sentence quality, such as grammar corr... | {
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2412.02563 | Semantic Tokens in Retrieval Augmented Generation | [
"cs.CL",
"cs.AI"
] | Retrieval-Augmented Generation (RAG) architectures have recently garnered significant attention for their ability to improve truth grounding and coherence in natural language processing tasks. However, the reliability of RAG systems in producing accurate answers diminishes as the volume of data they access increases. E... | {
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2412.02565 | SJTU:Spatial judgments in multimodal models towards unified segmentation
through coordinate detection | [
"cs.CV"
] | Despite significant advances in vision-language understanding, implementing image segmentation within multimodal architectures remains a fundamental challenge in modern artificial intelligence systems. Existing vision-language models, which primarily rely on backbone architectures or CLIP-based embedding learning, demo... | {
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2412.02568 | Segmentation of Coronary Artery Stenosis in X-ray Angiography using
Mamba Models | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Coronary artery disease stands as one of the primary contributors to global mortality rates. The automated identification of coronary artery stenosis from X-ray images plays a critical role in the diagnostic process for coronary heart disease. This task is challenging due to the complex structure of coronary arteries, ... | {
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2412.02570 | TAB-Fields: A Maximum Entropy Framework for Mission-Aware Adversarial
Planning | [
"cs.RO",
"cs.AI",
"cs.LG",
"cs.MA",
"cs.SY",
"eess.SY"
] | Autonomous agents operating in adversarial scenarios face a fundamental challenge: while they may know their adversaries' high-level objectives, such as reaching specific destinations within time constraints, the exact policies these adversaries will employ remain unknown. Traditional approaches address this challenge ... | {
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2412.02573 | Remote Sensing Temporal Vision-Language Models: A Comprehensive Survey | [
"cs.CV"
] | Temporal image analysis in remote sensing has traditionally centered on change detection, which identifies regions of change between images captured at different times. However, change detection remains limited by its focus on visual-level interpretation, often lacking contextual or descriptive information. The rise of... | {
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2412.02574 | Generating Critical Scenarios for Testing Automated Driving Systems | [
"cs.RO",
"cs.AI",
"cs.SE"
] | Autonomous vehicles (AVs) have demonstrated significant potential in revolutionizing transportation, yet ensuring their safety and reliability remains a critical challenge, especially when exposed to dynamic and unpredictable environments. Real-world testing of an Autonomous Driving System (ADS) is both expensive and r... | {
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2412.02575 | Copy-Move Forgery Detection and Question Answering for Remote Sensing
Image | [
"cs.CV",
"cs.MM"
] | This paper introduces the task of Remote Sensing Copy-Move Question Answering (RSCMQA). Unlike traditional Remote Sensing Visual Question Answering (RSVQA), RSCMQA focuses on interpreting complex tampering scenarios and inferring relationships between objects. Based on the practical needs of national defense security a... | {
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2412.02578 | Private Linear Regression with Differential Privacy and PAC Privacy | [
"cs.LG",
"cs.CR"
] | Linear regression is a fundamental tool for statistical analysis, which has motivated the development of linear regression methods that satisfy provable privacy guarantees so that the learned model reveals little about any one data point used to construct it. Most existing privacy-preserving linear regression methods r... | {
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2412.02579 | Factored space models: Towards causality between levels of abstraction | [
"cs.AI"
] | Causality plays an important role in understanding intelligent behavior, and there is a wealth of literature on mathematical models for causality, most of which is focused on causal graphs. Causal graphs are a powerful tool for a wide range of applications, in particular when the relevant variables are known and at the... | {
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2412.02581 | Mobile Cell-Free Massive MIMO with Multi-Agent Reinforcement Learning: A
Scalable Framework | [
"cs.IT",
"eess.SP",
"math.IT"
] | Cell-free massive multiple-input multiple-output (mMIMO) offers significant advantages in mobility scenarios, mainly due to the elimination of cell boundaries and strong macro diversity. In this paper, we examine the downlink performance of cell-free mMIMO systems equipped with mobile-APs utilizing the concept of unman... | {
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2412.02588 | Explainable CTR Prediction via LLM Reasoning | [
"cs.IR",
"cs.AI"
] | Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hindered by reliance on a post-hoc paradigm, wherein explanation generators are trained independently of the underlying recommender models. Th... | {
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2412.02589 | MedTet: An Online Motion Model for 4D Heart Reconstruction | [
"cs.CV"
] | We present a novel approach to reconstruction of 3D cardiac motion from sparse intraoperative data. While existing methods can accurately reconstruct 3D organ geometries from full 3D volumetric imaging, they cannot be used during surgical interventions where usually limited observed data, such as a few 2D frames or 1D ... | {
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} |
2412.02592 | OCR Hinders RAG: Evaluating the Cascading Impact of OCR on
Retrieval-Augmented Generation | [
"cs.CV"
] | Retrieval-augmented Generation (RAG) enhances Large Language Models (LLMs) by integrating external knowledge to reduce hallucinations and incorporate up-to-date information without retraining. As an essential part of RAG, external knowledge bases are commonly built by extracting structured data from unstructured PDF do... | {
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2412.02594 | PrefixLLM: LLM-aided Prefix Circuit Design | [
"cs.AR",
"cs.AI"
] | Prefix circuits are fundamental components in digital adders, widely used in digital systems due to their efficiency in calculating carry signals. Synthesizing prefix circuits with minimized area and delay is crucial for enhancing the performance of modern computing systems. Recently, large language models (LLMs) have ... | {
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} |
2412.02595 | Nemotron-CC: Transforming Common Crawl into a Refined Long-Horizon
Pretraining Dataset | [
"cs.CL"
] | Recent English Common Crawl datasets like FineWeb-Edu and DCLM achieved significant benchmark gains via aggressive model-based filtering, but at the cost of removing 90% of data. This limits their suitability for long token horizon training, such as 15T tokens for Llama 3.1. In this paper, we show how to achieve better... | {
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} |
2412.02596 | Class-wise Autoencoders Measure Classification Difficulty And Detect
Label Mistakes | [
"cs.LG",
"cs.CV"
] | We introduce a new framework for analyzing classification datasets based on the ratios of reconstruction errors between autoencoders trained on individual classes. This analysis framework enables efficient characterization of datasets on the sample, class, and entire dataset levels. We define reconstruction error ratio... | {
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} |
2412.02601 | MERGE: Multi-faceted Hierarchical Graph-based GNN for Gene Expression
Prediction from Whole Slide Histopathology Images | [
"cs.CV"
] | Recent advances in Spatial Transcriptomics (ST) pair histology images with spatially resolved gene expression profiles, enabling predictions of gene expression across different tissue locations based on image patches. This opens up new possibilities for enhancing whole slide image (WSI) prediction tasks with localized ... | {
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2412.02602 | CEGI: Measuring the trade-off between efficiency and carbon emissions
for SLMs and VLMs | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | This paper analyzes the performance of Small Language Models (SLMs) and Vision Language Models (VLMs) and evaluates the trade-off between model performance and carbon emissions across 4 essential tasks: Image Captioning, Visual Question Answering (VQA), Dialogue Summarization and Text-to-SQL conversion. Various SLMs an... | {
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} |
2412.02604 | Fairness-Regulated Dense Subgraph Discovery | [
"cs.SI"
] | Dense subgraph discovery (DSD) is a key graph mining primitive with myriad applications including finding densely connected communities which are diverse in their vertex composition. In such a context, it is desirable to extract a dense subgraph that provides fair representation of the diverse subgroups that constitute... | {
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} |
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