id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.13185 | Move-in-2D: 2D-Conditioned Human Motion Generation | [
"cs.CV"
] | Generating realistic human videos remains a challenging task, with the most effective methods currently relying on a human motion sequence as a control signal. Existing approaches often use existing motion extracted from other videos, which restricts applications to specific motion types and global scene matching. We p... | {
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2412.13187 | HandsOnVLM: Vision-Language Models for Hand-Object Interaction
Prediction | [
"cs.CV",
"cs.LG"
] | How can we predict future interaction trajectories of human hands in a scene given high-level colloquial task specifications in the form of natural language? In this paper, we extend the classic hand trajectory prediction task to two tasks involving explicit or implicit language queries. Our proposed tasks require exte... | {
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2412.13188 | StreetCrafter: Street View Synthesis with Controllable Video Diffusion
Models | [
"cs.CV"
] | This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data. Recent advancements in neural scene representation have achieved notable success in rendering high-quality autonomous driving scenes, but the performance significantly degrades as the viewpoint deviates from the training tr... | {
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2412.13190 | MotionBridge: Dynamic Video Inbetweening with Flexible Controls | [
"cs.CV"
] | By generating plausible and smooth transitions between two image frames, video inbetweening is an essential tool for video editing and long video synthesis. Traditional works lack the capability to generate complex large motions. While recent video generation techniques are powerful in creating high-quality results, th... | {
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2412.13193 | GaussTR: Foundation Model-Aligned Gaussian Transformer for
Self-Supervised 3D Spatial Understanding | [
"cs.CV"
] | 3D Semantic Occupancy Prediction is fundamental for spatial understanding as it provides a comprehensive semantic cognition of surrounding environments. However, prevalent approaches primarily rely on extensive labeled data and computationally intensive voxel-based modeling, restricting the scalability and generalizabi... | {
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2412.13194 | Proposer-Agent-Evaluator(PAE): Autonomous Skill Discovery For Foundation
Model Internet Agents | [
"cs.LG",
"cs.AI",
"cs.CV"
] | The vision of a broadly capable and goal-directed agent, such as an Internet-browsing agent in the digital world and a household humanoid in the physical world, has rapidly advanced, thanks to the generalization capability of foundation models. Such a generalist agent needs to have a large and diverse skill repertoire,... | {
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2412.13195 | CoMPaSS: Enhancing Spatial Understanding in Text-to-Image Diffusion
Models | [
"cs.CV"
] | Text-to-image diffusion models excel at generating photorealistic images, but commonly struggle to render accurate spatial relationships described in text prompts. We identify two core issues underlying this common failure: 1) the ambiguous nature of spatial-related data in existing datasets, and 2) the inability of cu... | {
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2412.13196 | ExBody2: Advanced Expressive Humanoid Whole-Body Control | [
"cs.RO",
"cs.AI",
"cs.LG"
] | This paper enables real-world humanoid robots to maintain stability while performing expressive motions like humans do. We propose ExBody2, a generalized whole-body tracking framework that can take any reference motion inputs and control the humanoid to mimic the motion. The model is trained in simulation with Reinforc... | {
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2412.13197 | Stochastic Analysis of Retention Time of Coupled Memory Topology | [
"cs.ET",
"cs.CE",
"physics.app-ph"
] | Recently, it has been experimentally demonstrated that individual memory units coupled in certain topology can provide the intended performance. However, experimental or simulation based evaluation of different coupled memory topologies and materials are costly and time consuming. In this paper, inspired by Glauber dyn... | {
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2412.13200 | Forward and Inverse Simulation of Pseudo-Two-Dimensional Model of
Lithium-Ion Batteries Using Neural Networks | [
"physics.comp-ph",
"cs.LG"
] | In this work, we address the challenges posed by the high nonlinearity of the Butler-Volmer (BV) equation in forward and inverse simulations of the pseudo-two-dimensional (P2D) model using the physics-informed neural network (PINN) framework. The BV equation presents significant challenges for PINNs, primarily due to t... | {
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2412.13204 | Optimizing Age of Information in Internet of Vehicles Over Error-Prone
Channels | [
"cs.IT",
"cs.NI",
"math.IT"
] | In the Internet of Vehicles (IoV), Age of Information (AoI) has become a vital performance metric for evaluating the freshness of information in communication systems. Although many studies aim to minimize the average AoI of the system through optimized resource scheduling schemes, they often fail to adequately conside... | {
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2412.13205 | Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval | [
"cs.IR",
"cs.CL",
"cs.LG"
] | Text Retrieval (TR) involves finding and retrieving text-based content relevant to a user's query from a large repository, with applications in real-world scenarios such as legal document retrieval. While most existing studies focus on English, limited work addresses Japanese contexts. In this paper, we introduce a new... | {
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2412.13211 | ManiSkill-HAB: A Benchmark for Low-Level Manipulation in Home
Rearrangement Tasks | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | High-quality benchmarks are the foundation for embodied AI research, enabling significant advancements in long-horizon navigation, manipulation and rearrangement tasks. However, as frontier tasks in robotics get more advanced, they require faster simulation speed, more intricate test environments, and larger demonstrat... | {
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2412.13212 | An introduction to reservoir computing | [
"cs.ET",
"cond-mat.dis-nn",
"cond-mat.mtrl-sci",
"cs.AI",
"physics.app-ph",
"quant-ph"
] | There is a growing interest in the development of artificial neural networks that are implemented in a physical system. A major challenge in this context is that these networks are difficult to train since training here would require a change of physical parameters rather than simply of coefficients in a computer progr... | {
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2412.13217 | Alternative Channel Charting Techniques in Cellular Wireless
Communications | [
"eess.SY",
"cs.SY"
] | We investigate the use of conventional angle of arrival (AoA) algorithms the Bartlett's algorithm, the Minimum Variance Distortion Response (MVDR or Capon) algorithm, and the Minimum Norm algorithm for estimating the AoA $\theta$ together with our previously introduced algorithms linear regression (LR), inverse of the ... | {
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2412.13223 | Generative modeling of protein ensembles guided by crystallographic
electron densities | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Proteins are dynamic, adopting ensembles of conformations. The nature of this conformational heterogenity is imprinted in the raw electron density measurements obtained from X-ray crystallography experiments. Fitting an ensemble of protein structures to these measurements is a challenging, ill-posed inverse problem. We... | {
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2412.13224 | Physics-model-guided Worst-case Sampling for Safe Reinforcement Learning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Real-world accidents in learning-enabled CPS frequently occur in challenging corner cases. During the training of deep reinforcement learning (DRL) policy, the standard setup for training conditions is either fixed at a single initial condition or uniformly sampled from the admissible state space. This setup often over... | {
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2412.13227 | Cross-table Synthetic Tabular Data Detection | [
"cs.LG",
"cs.DB",
"cs.NE"
] | Detecting synthetic tabular data is essential to prevent the distribution of false or manipulated datasets that could compromise data-driven decision-making. This study explores whether synthetic tabular data can be reliably identified ''in the wild''-meaning across different generators, domains, and table formats. Thi... | {
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2412.13228 | TSEML: A task-specific embedding-based method for few-shot
classification of cancer molecular subtypes | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | Molecular subtyping of cancer is recognized as a critical and challenging upstream task for personalized therapy. Existing deep learning methods have achieved significant performance in this domain when abundant data samples are available. However, the acquisition of densely labeled samples for cancer molecular subtype... | {
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2412.13229 | Training Verification-Friendly Neural Networks via Neuron Behavior
Consistency | [
"cs.LG",
"cs.AI"
] | Formal verification provides critical security assurances for neural networks, yet its practical application suffers from the long verification time. This work introduces a novel method for training verification-friendly neural networks, which are robust, easy to verify, and relatively accurate. Our method integrates n... | {
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2412.13231 | C2F-TP: A Coarse-to-Fine Denoising Framework for Uncertainty-Aware
Trajectory Prediction | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Accurately predicting the trajectory of vehicles is critically important for ensuring safety and reliability in autonomous driving. Although considerable research efforts have been made recently, the inherent trajectory uncertainty caused by various factors including the dynamic driving intends and the diverse driving ... | {
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2412.13232 | Content-aware Balanced Spectrum Encoding in Masked Modeling for Time
Series Classification | [
"cs.LG"
] | Due to the superior ability of global dependency, transformer and its variants have become the primary choice in Masked Time-series Modeling (MTM) towards time-series classification task. In this paper, we experimentally analyze that existing transformer-based MTM methods encounter with two under-explored issues when d... | {
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2412.13235 | Logic-Constrained Shortest Paths for Flight Planning | [
"cs.AI",
"cs.DM"
] | The Logic-Constrained Shortest Path Problem (LCSP) combines a one-to-one shortest path problem with satisfiability constraints imposed on the routing graph. This setting arises in flight planning, where air traffic control (ATC) authorities are enforcing a set of traffic flow restrictions (TFRs) on aircraft routes in o... | {
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2412.13236 | COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated
Sample Weighting Mechanism | [
"cs.LG",
"cs.AI"
] | Early exiting is an effective paradigm for improving the inference efficiency of pre-trained language models (PLMs) by dynamically adjusting the number of executed layers for each sample. However, in most existing works, easy and hard samples are treated equally by each classifier during training, which neglects the te... | {
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2412.13237 | Optimized two-stage AI-based Neural Decoding for Enhanced Visual
Stimulus Reconstruction from fMRI Data | [
"eess.IV",
"cs.CV",
"cs.LG",
"q-bio.NC"
] | AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity, measured through functional MRI (fMRI), into latent hierarchical representations. Traditionally, ridge linear models transform fMRI into a latent space, which is then decoded using latent diffusion models (LDM... | {
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2412.13238 | SafeDrive: Knowledge- and Data-Driven Risk-Sensitive Decision-Making for
Autonomous Vehicles with Large Language Models | [
"cs.AI",
"cs.ET",
"cs.RO"
] | Recent advancements in autonomous vehicles (AVs) use Large Language Models (LLMs) to perform well in normal driving scenarios. However, ensuring safety in dynamic, high-risk environments and managing safety-critical long-tail events remain significant challenges. To address these issues, we propose SafeDrive, a knowled... | {
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2412.13240 | Enhancing Internet of Things Security throughSelf-Supervised Graph
Neural Networks | [
"cs.LG",
"cs.AI",
"cs.CR"
] | With the rapid rise of the Internet of Things (IoT), ensuring the security of IoT devices has become essential. One of the primary challenges in this field is that new types of attacks often have significantly fewer samples than more common attacks, leading to unbalanced datasets. Existing research on detecting intrusi... | {
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2412.13243 | In-Context Learning Distillation for Efficient Few-Shot Fine-Tuning | [
"cs.CL"
] | We applied few-shot in-context learning on the OPT-1.3B model for the natural language inference task and employed knowledge distillation to internalize the context information, reducing model parameter from 1.3B to 125M and achieving a size reduction from 2.5GB to 0.25GB. Compared to using in-context learning alone on... | {
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2412.13244 | iRBSM: A Deep Implicit 3D Breast Shape Model | [
"cs.CV"
] | We present the first deep implicit 3D shape model of the female breast, building upon and improving the recently proposed Regensburg Breast Shape Model (RBSM). Compared to its PCA-based predecessor, our model employs implicit neural representations; hence, it can be trained on raw 3D breast scans and eliminates the nee... | {
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2412.13268 | JudgeBlender: Ensembling Judgments for Automatic Relevance Assessment | [
"cs.IR"
] | The effective training and evaluation of retrieval systems require a substantial amount of relevance judgments, which are traditionally collected from human assessors -- a process that is both costly and time-consuming. Large Language Models (LLMs) have shown promise in generating relevance labels for search tasks, off... | {
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2412.13273 | CompactFlowNet: Efficient Real-time Optical Flow Estimation on Mobile
Devices | [
"cs.CV"
] | We present CompactFlowNet, the first real-time mobile neural network for optical flow prediction, which involves determining the displacement of each pixel in an initial frame relative to the corresponding pixel in a subsequent frame. Optical flow serves as a fundamental building block for various video-related tasks, ... | {
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2412.13276 | GPgym: A Remote Service Platform with Gaussian Process Regression for
Online Learning | [
"cs.LG"
] | Machine learning is now widely applied across various domains, including industry, engineering, and research. While numerous mature machine learning models have been open-sourced on platforms like GitHub, their deployment often requires writing scripts in specific programming languages, such as Python, C++, or MATLAB. ... | {
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2412.13280 | Political Fact-Checking Efforts are Constrained by Deficiencies in
Coverage, Speed, and Reach | [
"cs.SI",
"cs.CY"
] | Fact-checking has been promoted as a key method for combating political misinformation. Comparing the spread of election-related misinformation narratives along with their relevant political fact-checks, this study provides the most comprehensive assessment to date of the real-world limitations faced by political fact-... | {
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2412.13281 | Generative Optimization: A Perspective on AI-Enhanced Problem Solving in
Engineering | [
"cs.CE"
] | The field of engineering is shaped by the tools and methods used to solve problems. Optimization is one such class of powerful, robust, and effective engineering tools proven over decades of use. Within just a few years, generative artificial intelligence (GenAI) has risen as another promising tool for general-purpose ... | {
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2412.13283 | Enhancing Persona Classification in Dialogue Systems: A Graph Neural
Network Approach | [
"cs.CL"
] | In recent years, Large Language Models (LLMs) gain considerable attention for their potential to enhance personalized experiences in virtual assistants and chatbots. A key area of interest is the integration of personas into LLMs to improve dialogue naturalness and user engagement. This study addresses the challenge of... | {
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2412.13286 | Posterior Mean Matching: Generative Modeling through Online Bayesian
Inference | [
"cs.LG",
"cs.AI",
"stat.ML"
] | This paper introduces posterior mean matching (PMM), a new method for generative modeling that is grounded in Bayesian inference. PMM uses conjugate pairs of distributions to model complex data of various modalities like images and text, offering a flexible alternative to existing methods like diffusion models. PMM mod... | {
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2412.13292 | Hint Marginalization for Improved Reasoning in Large Language Models | [
"cs.CL"
] | Large Language Models (LLMs) have exhibited an impressive capability to perform reasoning tasks, especially if they are encouraged to generate a sequence of intermediate steps. Reasoning performance can be improved by suitably combining multiple LLM responses, generated either in parallel in a single query, or via sequ... | {
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2412.13294 | Image registration is a geometric deep learning task | [
"cs.CV",
"cs.LG"
] | Data-driven deformable image registration methods predominantly rely on operations that process grid-like inputs. However, applying deformable transformations to an image results in a warped space that deviates from a rigid grid structure. Consequently, data-driven approaches with sequential deformations have to apply ... | {
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2412.13299 | In-context learning for medical image segmentation | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Annotation of medical images, such as MRI and CT scans, is crucial for evaluating treatment efficacy and planning radiotherapy. However, the extensive workload of medical professionals limits their ability to annotate large image datasets, posing a bottleneck for AI applications in medical imaging. To address this, we ... | {
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2412.13303 | FastVLM: Efficient Vision Encoding for Vision Language Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Scaling the input image resolution is essential for enhancing the performance of Vision Language Models (VLMs), particularly in text-rich image understanding tasks. However, popular visual encoders such as ViTs become inefficient at high resolutions due to the large number of tokens and high encoding latency caused by ... | {
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2412.13305 | Scene Modeling of Autonomous Vehicles Avoiding Stationary and Moving
Vehicles on Narrow Roads | [
"cs.RO"
] | Navigating narrow roads with oncoming vehicles is a significant challenge that has garnered considerable public interest. These scenarios often involve sections that cannot accommodate two moving vehicles simultaneously due to the presence of stationary vehicles or limited road width. Autonomous vehicles must therefore... | {
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2412.13307 | Evolution of the "long tail" concept for scientific data | [
"cs.DL",
"cs.SI"
] | This review paper explores the evolution of discussions about "long-tail" scientific data in the scholarly literature. The "long-tail" concept, originally used to explain trends in digital consumer goods, was first applied to scientific data in 2007 to refer to a vast array of smaller, heterogeneous data collections th... | {
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2412.13312 | Automated Phytosensing: Ozone Exposure Classification Based on Plant
Electrical Signals | [
"cs.LG",
"eess.SP"
] | In our project WatchPlant, we propose to use a decentralized network of living plants as air-quality sensors by measuring their electrophysiology to infer the environmental state, also called phytosensing. We conducted in-lab experiments exposing ivy (Hedera helix) plants to ozone, an important pollutant to monitor, an... | {
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2412.13317 | Predictive Probability Density Mapping for Search and Rescue Using An
Agent-Based Approach with Sparse Data | [
"cs.AI",
"cs.SY",
"eess.SY"
] | Predicting the location where a lost person could be found is crucial for search and rescue operations with limited resources. To improve the precision and efficiency of these predictions, simulated agents can be created to emulate the behavior of the lost person. Within this study, we introduce an innovative agent-bas... | {
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2412.13321 | LossLens: Diagnostics for Machine Learning through Loss Landscape Visual
Analytics | [
"cs.LG"
] | Modern machine learning often relies on optimizing a neural network's parameters using a loss function to learn complex features. Beyond training, examining the loss function with respect to a network's parameters (i.e., as a loss landscape) can reveal insights into the architecture and learning process. While the loca... | {
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2412.13324 | BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised
Anomaly Detection | [
"cs.CV",
"cs.AI",
"cs.CR"
] | Image anomaly detection (IAD) is essential in applications such as industrial inspection, medical imaging, and security. Despite the progress achieved with deep learning models like Deep Semi-Supervised Anomaly Detection (DeepSAD), these models remain susceptible to backdoor attacks, presenting significant security cha... | {
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2412.13328 | Expansion Span: Combining Fading Memory and Retrieval in Hybrid State
Space Models | [
"cs.CL",
"cs.LG"
] | The "state" of State Space Models (SSMs) represents their memory, which fades exponentially over an unbounded span. By contrast, Attention-based models have "eidetic" (i.e., verbatim, or photographic) memory over a finite span (context size). Hybrid architectures combine State Space layers with Attention, but still can... | {
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2412.13333 | Beyond Accuracy: On the Effects of Fine-tuning Towards Vision-Language
Model's Prediction Rationality | [
"cs.LG"
] | Vision-Language Models (VLMs), such as CLIP, have already seen widespread applications. Researchers actively engage in further fine-tuning VLMs in safety-critical domains. In these domains, prediction rationality is crucial: the prediction should be correct and based on valid evidence. Yet, for VLMs, the impact of fine... | {
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2412.13335 | Experience of Training a 1.7B-Parameter LLaMa Model From Scratch | [
"cs.CL",
"cs.AI"
] | Pretraining large language models is a complex endeavor influenced by multiple factors, including model architecture, data quality, training continuity, and hardware constraints. In this paper, we share insights gained from the experience of training DMaS-LLaMa-Lite, a fully open source, 1.7-billion-parameter, LLaMa-ba... | {
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2412.13337 | Unveiling the Secret Recipe: A Guide For Supervised Fine-Tuning Small
LLMs | [
"cs.LG",
"cs.AI",
"stat.ML"
] | The rise of large language models (LLMs) has created a significant disparity: industrial research labs with their computational resources, expert teams, and advanced infrastructures, can effectively fine-tune LLMs, while individual developers and small organizations face barriers due to limited resources. In this paper... | {
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2412.13341 | Concept-ROT: Poisoning Concepts in Large Language Models with Model
Editing | [
"cs.LG",
"cs.CR"
] | Model editing methods modify specific behaviors of Large Language Models by altering a small, targeted set of network weights and require very little data and compute. These methods can be used for malicious applications such as inserting misinformation or simple trojans that result in adversary-specified behaviors whe... | {
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2412.13350 | A Novel Machine Learning Classifier Based on Genetic Algorithms and Data
Importance Reformatting | [
"cs.LG",
"cs.AI",
"cs.NE"
] | In this paper, a novel classification algorithm that is based on Data Importance (DI) reformatting and Genetic Algorithms (GA) named GADIC is proposed to overcome the issues related to the nature of data which may hinder the performance of the Machine Learning (ML) classifiers. GADIC comprises three phases which are da... | {
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2412.13356 | Wind Speed Forecasting Based on Data Decomposition and Deep Learning
Models: A Case Study of a Wind Farm in Saudi Arabia | [
"cs.LG"
] | With industrial and technological development and the increasing demand for electric power, wind energy has gradually become the fastest-growing and most environmentally friendly new energy source. Nevertheless, wind power generation is always accompanied by uncertainty due to the wind speed's volatility. Wind speed fo... | {
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2412.13359 | Multi-Agent Motion Planning For Differential Drive Robots Through
Stationary State Search | [
"cs.RO",
"cs.AI",
"cs.MA"
] | Multi-Agent Motion Planning (MAMP) finds various applications in fields such as traffic management, airport operations, and warehouse automation. In many of these environments, differential drive robots are commonly used. These robots have a kinodynamic model that allows only in-place rotation and movement along their ... | {
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2412.13364 | Bringing Multimodality to Amazon Visual Search System | [
"cs.CV"
] | Image to image matching has been well studied in the computer vision community. Previous studies mainly focus on training a deep metric learning model matching visual patterns between the query image and gallery images. In this study, we show that pure image-to-image matching suffers from false positives caused by matc... | {
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2412.13365 | Quantitative Predictive Monitoring and Control for Safe Human-Machine
Interaction | [
"cs.AI",
"cs.HC",
"cs.SY",
"eess.SY"
] | There is a growing trend toward AI systems interacting with humans to revolutionize a range of application domains such as healthcare and transportation. However, unsafe human-machine interaction can lead to catastrophic failures. We propose a novel approach that predicts future states by accounting for the uncertainty... | {
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2412.13369 | Multiple Mean-Payoff Optimization under Local Stability Constraints | [
"cs.AI"
] | The long-run average payoff per transition (mean payoff) is the main tool for specifying the performance and dependability properties of discrete systems. The problem of constructing a controller (strategy) simultaneously optimizing several mean payoffs has been deeply studied for stochastic and game-theoretic models. ... | {
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2412.13370 | Inverse design of anisotropic microstructures using physics-augmented
neural networks | [
"cs.CE"
] | Composite materials often exhibit mechanical anisotropy owing to the material properties or geometrical configurations of the microstructure. This makes their inverse design a two-fold problem. First, we must learn the type and orientation of anisotropy and then find the optimal design parameters to achieve the desired... | {
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2412.13372 | Sum-of-Squares Programming for Ma-Trudinger-Wang Regularity of Optimal
Transport Maps | [
"math.OC",
"cs.AI",
"cs.LG",
"math.DG"
] | For a given ground cost, approximating the Monge optimal transport map that pushes forward a given probability measure onto another has become a staple in several modern machine learning algorithms. The fourth-order Ma-Trudinger-Wang (MTW) tensor associated with this ground cost function provides a notion of curvature ... | {
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2412.13375 | Extending LLMs to New Languages: A Case Study of Llama and Persian
Adaptation | [
"cs.CL"
] | Large language models (LLMs) have made great progress in classification and text generation tasks. However, they are mainly trained on English data and often struggle with low-resource languages. In this study, we explore adding a new language, i.e., Persian, to Llama (a model with a limited understanding of Persian) u... | {
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2412.13376 | Targeted View-Invariant Adversarial Perturbations for 3D Object
Recognition | [
"cs.CV",
"cs.AI",
"cs.CR",
"eess.IV"
] | Adversarial attacks pose significant challenges in 3D object recognition, especially in scenarios involving multi-view analysis where objects can be observed from varying angles. This paper introduces View-Invariant Adversarial Perturbations (VIAP), a novel method for crafting robust adversarial examples that remain ef... | {
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2412.13377 | DateLogicQA: Benchmarking Temporal Biases in Large Language Models | [
"cs.CL",
"cs.AI"
] | This paper introduces DateLogicQA, a benchmark with 190 questions covering diverse date formats, temporal contexts, and reasoning types. We propose the Semantic Integrity Metric to assess tokenization quality and analyse two biases: Representation-Level Bias, affecting embeddings, and Logical-Level Bias, influencing re... | {
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2412.13378 | SummExecEdit: A Factual Consistency Benchmark in Summarization with
Executable Edits | [
"cs.CL"
] | Detecting factual inconsistencies in summarization is critical, yet existing benchmarks lack the necessary challenge and interpretability for robust evaluation. In this paper, we introduce SummExecEdit, a novel benchmark leveraging executable edits to assess models on their ability to both detect factual errors and pro... | {
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2412.13380 | Voter Priming Campaigns: Strategies, Equilibria, and Algorithms | [
"cs.GT",
"cs.AI"
] | Issue salience is a major determinant in voters' decisions. Candidates and political parties campaign to shift salience to their advantage - a process termed priming. We study the dynamics, strategies and equilibria of campaign spending for voter priming in multi-issue multi-party settings. We consider both parliamenta... | {
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2412.13381 | An Automated Explainable Educational Assessment System Built on LLMs | [
"cs.CL"
] | In this demo, we present AERA Chat, an automated and explainable educational assessment system designed for interactive and visual evaluations of student responses. This system leverages large language models (LLMs) to generate automated marking and rationale explanations, addressing the challenge of limited explainabi... | {
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2412.13386 | An Exploratory Study of ML Sketches and Visual Code Assistants | [
"cs.SE",
"cs.AI",
"cs.HC"
] | This paper explores the integration of Visual Code Assistants in Integrated Development Environments (IDEs). In Software Engineering, whiteboard sketching is often the initial step before coding, serving as a crucial collaboration tool for developers. Previous studies have investigated patterns in SE sketches and how t... | {
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2412.13388 | Catalysts of Conversation: Examining Interaction Dynamics Between Topic
Initiators and Commentors in Alzheimer's Disease Online Communities | [
"cs.CY",
"cs.CL",
"cs.LG",
"stat.AP"
] | Informal caregivers (e.g.,family members or friends) of people living with Alzheimers Disease and Related Dementias (ADRD) face substantial challenges and often seek informational or emotional support through online communities. Understanding the factors that drive engagement within these platforms is crucial, as it ca... | {
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2412.13389 | Marigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion | [
"cs.CV",
"cs.LG"
] | Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings and tend to struggle when applied to images outside the training domain or when the available depth measurements are sparse, i... | {
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2412.13390 | Phase Robustness Analysis for Structured Perturbations in MIMO LTI
Systems | [
"eess.SY",
"cs.SY"
] | The stability of interconnected linear time-invariant systems using singular values and the small gain theorem has been studied for many decades. The methods of mu-analysis and synthesis has been extensively developed to provide robustness guarantees for a plant subject to structured perturbations, with components in t... | {
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2412.13393 | MMHMR: Generative Masked Modeling for Hand Mesh Recovery | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Reconstructing a 3D hand mesh from a single RGB image is challenging due to complex articulations, self-occlusions, and depth ambiguities. Traditional discriminative methods, which learn a deterministic mapping from a 2D image to a single 3D mesh, often struggle with the inherent ambiguities in 2D-to-3D mapping. To add... | {
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2412.13394 | Distribution Shifts at Scale: Out-of-distribution Detection in Earth
Observation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Training robust deep learning models is critical in Earth Observation, where globally deployed models often face distribution shifts that degrade performance, especially in low-data regions. Out-of-distribution (OOD) detection addresses this challenge by identifying inputs that differ from in-distribution (ID) data. Ho... | {
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2412.13395 | Enhancing Talk Moves Analysis in Mathematics Tutoring through Classroom
Teaching Discourse | [
"cs.CL"
] | Human tutoring interventions play a crucial role in supporting student learning, improving academic performance, and promoting personal growth. This paper focuses on analyzing mathematics tutoring discourse using talk moves - a framework of dialogue acts grounded in Accountable Talk theory. However, scaling the collect... | {
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2412.13398 | Pattern Matching in AI Compilers and its Formalization (Extended
Version) | [
"cs.PL",
"cs.LG"
] | PyPM is a Python-based domain specific language (DSL) for building rewrite-based optimization passes on machine learning computation graphs. Users define individual optimizations by writing (a) patterns that match subgraphs of a computation graph and (b) corresponding rules which replace a matched subgraph with an opti... | {
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2412.13401 | Zero-Shot Low Light Image Enhancement with Diffusion Prior | [
"cs.CV"
] | Balancing aesthetic quality with fidelity when enhancing images from challenging, degraded sources is a core objective in computational photography. In this paper, we address low light image enhancement (LLIE), a task in which dark images often contain limited visible information. Diffusion models, known for their powe... | {
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2412.13405 | What Human-Horse Interactions may Teach us About Effective Human-AI
Interactions | [
"cs.HC",
"cs.AI"
] | This article explores human-horse interactions as a metaphor for understanding and designing effective human-AI partnerships. Drawing on the long history of human collaboration with horses, we propose that AI, like horses, should complement rather than replace human capabilities. We move beyond traditional benchmarks s... | {
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2412.13408 | Lightweight yet Fine-grained: A Graph Capsule Convolutional Network with
Subspace Alignment for Shared-account Sequential Recommendation | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Shared-account Sequential Recommendation (SSR) aims to provide personalized recommendations for accounts shared by multiple users with varying sequential preferences. Previous studies on SSR struggle to capture the fine-grained associations between interactions and different latent users within the shared account's hyb... | {
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2412.13419 | Exploring Transformer-Augmented LSTM for Temporal and Spatial Feature
Learning in Trajectory Prediction | [
"cs.RO",
"cs.CV",
"cs.LG"
] | Accurate vehicle trajectory prediction is crucial for ensuring safe and efficient autonomous driving. This work explores the integration of Transformer based model with Long Short-Term Memory (LSTM) based technique to enhance spatial and temporal feature learning in vehicle trajectory prediction. Here, a hybrid model t... | {
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2412.13420 | BotSim: LLM-Powered Malicious Social Botnet Simulation | [
"cs.SI"
] | Social media platforms like X(Twitter) and Reddit are vital to global communication. However, advancements in Large Language Model (LLM) technology give rise to social media bots with unprecedented intelligence. These bots adeptly simulate human profiles, conversations, and interactions, disseminating large amounts of ... | {
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2412.13422 | Generating Diverse Hypotheses for Inductive Reasoning | [
"cs.AI",
"cs.SE"
] | Inductive reasoning - the process of inferring general rules from a small number of observations - is a fundamental aspect of human intelligence. Recent works suggest that large language models (LLMs) can engage in inductive reasoning by sampling multiple hypotheses about the rules and selecting the one that best expla... | {
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2412.13426 | Safeguarding System Prompts for LLMs | [
"cs.CR",
"cs.AI"
] | Large language models (LLMs) are increasingly utilized in applications where system prompts, which guide model outputs, play a crucial role. These prompts often contain business logic and sensitive information, making their protection essential. However, adversarial and even regular user queries can exploit LLM vulnera... | {
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2412.13432 | Large Language Model Enhanced Recommender Systems: Taxonomy, Trend,
Application and Future | [
"cs.IR",
"cs.AI"
] | Large Language Model (LLM) has transformative potential in various domains, including recommender systems (RS). There have been a handful of research that focuses on empowering the RS by LLM. However, previous efforts mainly focus on LLM as RS, which may face the challenge of intolerant inference costs by LLM. Recently... | {
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2412.13435 | Lightweight Safety Classification Using Pruned Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | In this paper, we introduce a novel technique for content safety and prompt injection classification for Large Language Models. Our technique, Layer Enhanced Classification (LEC), trains a Penalized Logistic Regression (PLR) classifier on the hidden state of an LLM's optimal intermediate transformer layer. By combining... | {
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2412.13437 | Deploying Foundation Model Powered Agent Services: A Survey | [
"cs.DC",
"cs.AI"
] | Foundation model (FM) powered agent services are regarded as a promising solution to develop intelligent and personalized applications for advancing toward Artificial General Intelligence (AGI). To achieve high reliability and scalability in deploying these agent services, it is essential to collaboratively optimize co... | {
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2412.13439 | Rare Event Detection in Imbalanced Multi-Class Datasets Using an Optimal
MIP-Based Ensemble Weighting Approach | [
"cs.LG"
] | To address the challenges of imbalanced multi-class datasets typically used for rare event detection in critical cyber-physical systems, we propose an optimal, efficient, and adaptable mixed integer programming (MIP) ensemble weighting scheme. Our approach leverages the diverse capabilities of the classifier ensemble o... | {
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2412.13441 | FlashVTG: Feature Layering and Adaptive Score Handling Network for Video
Temporal Grounding | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Text-guided Video Temporal Grounding (VTG) aims to localize relevant segments in untrimmed videos based on textual descriptions, encompassing two subtasks: Moment Retrieval (MR) and Highlight Detection (HD). Although previous typical methods have achieved commendable results, it is still challenging to retrieve short v... | {
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2412.13442 | Communication-Efficient Personalized Federal Graph Learning via Low-Rank
Decomposition | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Federated graph learning (FGL) has gained significant attention for enabling heterogeneous clients to process their private graph data locally while interacting with a centralized server, thus maintaining privacy. However, graph data on clients are typically non-IID, posing a challenge for a single model to perform wel... | {
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2412.13443 | DarkIR: Robust Low-Light Image Restoration | [
"cs.CV",
"eess.IV"
] | Photography during night or in dark conditions typically suffers from noise, low light and blurring issues due to the dim environment and the common use of long exposure. Although Deblurring and Low-light Image Enhancement (LLIE) are related under these conditions, most approaches in image restoration solve these tasks... | {
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2412.13446 | Toward an Insider Threat Education Platform: A Theoretical Literature
Review | [
"cs.CR",
"cs.AI",
"cs.CY",
"cs.HC",
"cs.SI"
] | Insider threats (InTs) within organizations are small in number but have a disproportionate ability to damage systems, information, and infrastructure. Existing InT research studies the problem from psychological, technical, and educational perspectives. Proposed theories include research on psychological indicators, m... | {
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2412.13447 | Low Time Complexity Near-Field Channel and Position Estimations | [
"cs.IT",
"eess.SP",
"math.IT"
] | With the application of high-frequency communication and extremely large MIMO (XL-MIMO), the near-field effect has become increasingly apparent. The near-field channel estimation and position estimation problems both rely on the Angle of Arrival (AoA) and the Curvature of Arrival (CoA) estimation. However, in the near-... | {
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2412.13452 | ConDo: Continual Domain Expansion for Absolute Pose Regression | [
"cs.CV",
"cs.AI"
] | Visual localization is a fundamental machine learning problem. Absolute Pose Regression (APR) trains a scene-dependent model to efficiently map an input image to the camera pose in a pre-defined scene. However, many applications have continually changing environments, where inference data at novel poses or scene condit... | {
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} |
2412.13454 | Pre-training a Density-Aware Pose Transformer for Robust LiDAR-based 3D
Human Pose Estimation | [
"cs.CV",
"cs.AI"
] | With the rapid development of autonomous driving, LiDAR-based 3D Human Pose Estimation (3D HPE) is becoming a research focus. However, due to the noise and sparsity of LiDAR-captured point clouds, robust human pose estimation remains challenging. Most of the existing methods use temporal information, multi-modal fusion... | {
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} |
2412.13461 | Look Inside for More: Internal Spatial Modality Perception for 3D
Anomaly Detection | [
"cs.CV",
"cs.AI",
"eess.IV"
] | 3D anomaly detection has recently become a significant focus in computer vision. Several advanced methods have achieved satisfying anomaly detection performance. However, they typically concentrate on the external structure of 3D samples and struggle to leverage the internal information embedded within samples. Inspire... | {
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} |
2412.13463 | FlexPose: Pose Distribution Adaptation with Limited Guidance | [
"cs.CV",
"cs.AI"
] | Numerous well-annotated human key-point datasets are publicly available to date. However, annotating human poses for newly collected images is still a costly and time-consuming progress. Pose distributions from different datasets share similar pose hinge-structure priors with different geometric transformations, such a... | {
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} |
2412.13464 | GenX: Mastering Code and Test Generation with Execution Feedback | [
"cs.SE",
"cs.CL"
] | Recent advancements in language modeling have enabled the translation of natural language into code, and the use of execution feedback to improve code generation. However, these methods often rely heavily on pre-existing test cases, which may not always be available or comprehensive. In this work, we propose a novel ap... | {
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} |
2412.13466 | Federated Unlearning Model Recovery in Data with Skewed Label
Distributions | [
"cs.LG"
] | In federated learning, federated unlearning is a technique that provides clients with a rollback mechanism that allows them to withdraw their data contribution without training from scratch. However, existing research has not considered scenarios with skewed label distributions. Unfortunately, the unlearning of a clien... | {
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} |
2412.13467 | Transducer Tuning: Efficient Model Adaptation for Software Tasks Using
Code Property Graphs | [
"cs.SE",
"cs.AI",
"cs.CL"
] | Large language models have demonstrated promising performance across various software engineering tasks. While fine-tuning is a common practice to adapt these models for downstream tasks, it becomes challenging in resource-constrained environments due to increased memory requirements from growing trainable parameters i... | {
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} |
2412.13469 | Enabling Region-Specific Control via Lassos in Point-Based Colorization | [
"cs.CV",
"cs.GR"
] | Point-based interactive colorization techniques allow users to effortlessly colorize grayscale images using user-provided color hints. However, point-based methods often face challenges when different colors are given to semantically similar areas, leading to color intermingling and unsatisfactory results-an issue we r... | {
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} |
2412.13471 | Gradual Vigilance and Interval Communication: Enhancing Value Alignment
in Multi-Agent Debates | [
"cs.AI",
"cs.CL"
] | In recent years, large language models have shown exceptional performance in fulfilling diverse human needs. However, their training data can introduce harmful content, underscoring the necessity for robust value alignment. Mainstream methods, which depend on feedback learning and supervised training, are resource-inte... | {
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} |
2412.13472 | SocialED: A Python Library for Social Event Detection | [
"cs.LG",
"cs.DL",
"cs.SI"
] | SocialED is a comprehensive, open-source Python library designed to support social event detection (SED) tasks, integrating 19 detection algorithms and 14 diverse datasets. It provides a unified API with detailed documentation, offering researchers and practitioners a complete solution for event detection in social med... | {
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} |
2412.13474 | Planning Human-Robot Co-manipulation with Human Motor Control Objectives
and Multi-component Reaching Strategies | [
"cs.RO",
"cs.SY",
"eess.SY"
] | For successful goal-directed human-robot interaction, the robot should adapt to the intentions and actions of the collaborating human. This can be supported by musculoskeletal or data-driven human models, where the former are limited to lower-level functioning such as ergonomics, and the latter have limited generalizab... | {
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"cs.SY": 1
} |
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