id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.07589 | Overhead-free User-side Recommender Systems | [
"cs.IR",
"cs.AI",
"cs.DB",
"cs.DL"
] | Traditionally, recommendation algorithms have been designed for service developers. But recently, a new paradigm called user-side recommender systems has been proposed. User-side recommender systems are built and used by end users, in sharp contrast to traditional provider-side recommender systems. Even if the official... | {
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2411.07590 | Multiple noncooperative targets encirclement by relative distance-based
positioning and neural antisynchronization control | [
"cs.RO"
] | From prehistoric encirclement for hunting to GPS orbiting the earth for positioning, target encirclement has numerous real world applications. However, encircling multiple non-cooperative targets in GPS-denied environments remains challenging. In this work, multiple targets encirclement by using a minimum of two taskin... | {
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2411.07591 | Overcoming the Curse of Dimensionality in Reinforcement Learning Through
Approximate Factorization | [
"cs.LG"
] | Reinforcement Learning (RL) algorithms are known to suffer from the curse of dimensionality, which refers to the fact that large-scale problems often lead to exponentially high sample complexity. A common solution is to use deep neural networks for function approximation; however, such approaches typically lack theoret... | {
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2411.07592 | Longitudinal dynamic modelling and control for a quad-tilt rotor UAV | [
"eess.SY",
"cs.SY"
] | Tilt rotor aircraft combine the benefits of both helicopters and fixed wing aircraft, this makes them popular for a variety of applications, including Search and Rescue and VVIP transport. However, due to the multiple flight modes, significant challenges with regards to the control system design are experienced. The ma... | {
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2411.07593 | Robust control for uncertain air-to-air missile systems | [
"eess.SY",
"cs.SY"
] | Air-to-air missiles are used on many modern military combat aircraft for self-defence. It is imperative for the pilots using the weapons that the missiles hit their target first time. The important goals for a missile control system to achieve are minimising the time constant, overshoot, and settling time of the missil... | {
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2411.07594 | Modelling and Control of Subsonic Missile for Air-to-Air Interception | [
"eess.SY",
"cs.SY"
] | Subsonic missiles play an important role in modern air-to-air combat scenarios - utilized by the F-35 Lightning II - but require complex Guidance, Navigation and Control systems to manoeuvre with 30G's of acceleration to intercept successfully. Challenges with mathematically modelling and controlling such a dynamic sys... | {
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2411.07595 | Entropy Controllable Direct Preference Optimization | [
"cs.LG",
"cs.AI",
"cs.CL"
] | In the post-training of large language models (LLMs), Reinforcement Learning from Human Feedback (RLHF) is an effective approach to achieve generation aligned with human preferences. Direct Preference Optimization (DPO) allows for policy training with a simple binary cross-entropy loss without a reward model. The objec... | {
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2411.07598 | Problem-Oriented Segmentation and Retrieval: Case Study on Tutoring
Conversations | [
"cs.CL",
"cs.AI"
] | Many open-ended conversations (e.g., tutoring lessons or business meetings) revolve around pre-defined reference materials, like worksheets or meeting bullets. To provide a framework for studying such conversation structure, we introduce Problem-Oriented Segmentation & Retrieval (POSR), the task of jointly breaking dow... | {
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2411.07600 | Decision Feedback In-Context Symbol Detection over Block-Fading Channels | [
"cs.IT",
"cs.LG",
"eess.SP",
"math.IT",
"stat.ML"
] | Pre-trained Transformers, through in-context learning (ICL), have demonstrated exceptional capabilities to adapt to new tasks using example prompts \textit{without model update}. Transformer-based wireless receivers, where prompts consist of the pilot data in the form of transmitted and received signal pairs, have show... | {
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2411.07601 | SegQC: a segmentation network-based framework for multi-metric
segmentation quality control and segmentation error detection in volumetric
medical images | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Quality control of structures segmentation in volumetric medical images is important for identifying segmentation errors in clinical practice and for facilitating model development. This paper introduces SegQC, a novel framework for segmentation quality estimation and segmentation error detection. SegQC computes an est... | {
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2411.07602 | Circuit Complexity Bounds for RoPE-based Transformer Architecture | [
"cs.LG",
"cs.AI",
"cs.CC",
"cs.CL"
] | Characterizing the express power of the Transformer architecture is critical to understanding its capacity limits and scaling law. Recent works provide the circuit complexity bounds to Transformer-like architecture. On the other hand, Rotary Position Embedding ($\mathsf{RoPE}$) has emerged as a crucial technique in mod... | {
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2411.07603 | $\mathscr{H}_2$ Model Reduction for Linear Quantum Systems | [
"quant-ph",
"cs.SY",
"eess.SY"
] | In this paper, an $\mathscr{H}_2$ norm-based model reduction method for linear quantum systems is presented, which can obtain a physically realizable model with a reduced order for closely approximating the original system. The model reduction problem is described as an optimization problem, whose objective is taken as... | {
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2411.07606 | Optimizing Service Function Chain Mapping in Network Function
Virtualization through Simultaneous NF Decomposition and VNF Placement | [
"cs.NI",
"cs.AI"
] | Network function virtualization enables network operators to implement new services through a process called service function chain mapping. The concept of Service Function Chain (SFC) is introduced to provide complex services, which is an ordered set of Network Functions (NF). The network functions of an SFC can be de... | {
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2411.07607 | CJST: CTC Compressor based Joint Speech and Text Training for
Decoder-Only ASR | [
"eess.AS",
"cs.LG",
"cs.SD"
] | CTC compressor can be an effective approach to integrate audio encoders to decoder-only models, which has gained growing interest for different speech applications. In this work, we propose a novel CTC compressor based joint speech and text training (CJST) framework for decoder-only ASR. CJST matches speech and text mo... | {
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2411.07608 | Quantum Information-Empowered Graph Neural Network for Hyperspectral
Change Detection | [
"cs.CV",
"eess.IV"
] | Change detection (CD) is a critical remote sensing technique for identifying changes in the Earth's surface over time. The outstanding substance identifiability of hyperspectral images (HSIs) has significantly enhanced the detection accuracy, making hyperspectral change detection (HCD) an essential technology. The dete... | {
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2411.07611 | Multimodal Clinical Reasoning through Knowledge-augmented Rationale
Generation | [
"cs.CL",
"cs.AI"
] | Clinical rationales play a pivotal role in accurate disease diagnosis; however, many models predominantly use discriminative methods and overlook the importance of generating supportive rationales. Rationale distillation is a process that transfers knowledge from large language models (LLMs) to smaller language models ... | {
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2411.07612 | A Joint Prediction Method of Multi-Agent to Reduce Collision Rate | [
"cs.RO"
] | Predicting future motions of road participants is an important task for driving autonomously. Most existing models excel at predicting the marginal trajectory of a single agent, but predicting joint trajectories for multiple agents that are consistent within a scene remains a challenge. Previous research has often focu... | {
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2411.07618 | Direct Preference Optimization Using Sparse Feature-Level Constraints | [
"cs.AI",
"cs.CL"
] | The alignment of large language models (LLMs) with human preferences remains a key challenge. While post-training techniques like Reinforcement Learning from Human Feedback (RLHF) and Direct Preference Optimization (DPO) have achieved notable success, they often introduce computational inefficiencies and training insta... | {
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2411.07619 | Artificial Intelligence for Biomedical Video Generation | [
"cs.CV"
] | As a prominent subfield of Artificial Intelligence Generated Content (AIGC), video generation has achieved notable advancements in recent years. The introduction of Sora-alike models represents a pivotal breakthrough in video generation technologies, significantly enhancing the quality of synthesized videos. Particular... | {
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2411.07621 | Mix from Failure: Confusion-Pairing Mixup for Long-Tailed Recognition | [
"cs.CV"
] | Long-tailed image recognition is a computer vision problem considering a real-world class distribution rather than an artificial uniform. Existing methods typically detour the problem by i) adjusting a loss function, ii) decoupling classifier learning, or iii) proposing a new multi-head architecture called experts. In ... | {
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2411.07623 | Annotating Constructions with UD: the experience of the Italian
Constructicon | [
"cs.CL"
] | The paper descirbes a first attempt of linking the Italian constructicon to UD resources | {
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2411.07625 | Unraveling the Connections between Flow Matching and Diffusion
Probabilistic Models in Training-free Conditional Generation | [
"cs.CV"
] | Training-free conditional generation aims to leverage the unconditional diffusion models to implement the conditional generation, where flow-matching (FM) and diffusion probabilistic models (DPMs) are two mature unconditional diffusion models that achieve high-quality generation. Two questions were asked in this paper:... | {
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2411.07627 | Leveraging Previous Steps: A Training-free Fast Solver for Flow
Diffusion | [
"cs.CV"
] | Flow diffusion models (FDMs) have recently shown potential in generation tasks due to the high generation quality. However, the current ordinary differential equation (ODE) solver for FDMs, e.g., the Euler solver, still suffers from slow generation since ODE solvers need many number function evaluations (NFE) to keep h... | {
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2411.07634 | Exploring Multi-Agent Reinforcement Learning for Unrelated Parallel
Machine Scheduling | [
"cs.AI",
"cs.LG",
"cs.MA",
"cs.NE"
] | Scheduling problems pose significant challenges in resource, industry, and operational management. This paper addresses the Unrelated Parallel Machine Scheduling Problem (UPMS) with setup times and resources using a Multi-Agent Reinforcement Learning (MARL) approach. The study introduces the Reinforcement Learning envi... | {
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2411.07635 | Breaking the Low-Rank Dilemma of Linear Attention | [
"cs.CV"
] | The Softmax attention mechanism in Transformer models is notoriously computationally expensive, particularly due to its quadratic complexity, posing significant challenges in vision applications. In contrast, linear attention provides a far more efficient solution by reducing the complexity to linear levels. However, c... | {
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2411.07636 | Node Reliability: Approximation, Upper Bounds, and Applications to
Network Robustness | [
"eess.SY",
"cs.SY",
"math.PR"
] | This paper discusses the reliability of a graph in which the links are perfectly reliable but the nodes may fail with certain probability p. Calculating graph node reliability is an NP-Hard problem. We introduce an efficient and accurate Monte Carlo method and a stochastic approximation for the node reliability polynom... | {
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2411.07640 | Reducing Conservativeness of Controlled-Invariant Safe Sets by
Introducing a Novel Synthesis of Control Barrier Certificates | [
"eess.SY",
"cs.SY"
] | Finding a controlled-invariant safe set for a given system with state and control constraints plays an important role in safety-critical systems. Current methods typically produce conservative solutions. In this paper, we introduce a method to generate controlled-invariant safe sets for nonlinear polynomial control-aff... | {
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2411.07641 | Top-$n\sigma$: Not All Logits Are You Need | [
"cs.LG"
] | Large language models (LLMs) typically employ greedy decoding or low-temperature sampling for reasoning tasks, reflecting a perceived trade-off between diversity and accuracy. We challenge this convention by introducing top-$n\sigma$, a novel sampling method that operates directly on pre-softmax logits by leveraging a ... | {
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2411.07642 | Safety Filter Design for Articulated Frame Steering Vehicles In the
Presence of Actuator Dynamics Using High-Order Control Barrier Functions | [
"eess.SY",
"cs.SY"
] | Articulated Frame Steering (AFS) vehicles are widely used in heavy-duty industries, where they often operate near operators and laborers. Therefore, designing safe controllers for AFS vehicles is essential. In this paper, we develop a Quadratic Program (QP)-based safety filter that ensures feasibility for AFS vehicles ... | {
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2411.07643 | xCG: Explainable Cell Graphs for Survival Prediction in Non-Small Cell
Lung Cancer | [
"cs.CV",
"cs.LG"
] | Understanding how deep learning models predict oncology patient risk can provide critical insights into disease progression, support clinical decision-making, and pave the way for trustworthy and data-driven precision medicine. Building on recent advances in the spatial modeling of the tumor microenvironment using grap... | {
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2411.07644 | Human Arm Pose Estimation with a Shoulder-worn Force-Myography Device
for Human-Robot Interaction | [
"cs.RO"
] | Accurate human pose estimation is essential for effective Human-Robot Interaction (HRI). By observing a user's arm movements, robots can respond appropriately, whether it's providing assistance or avoiding collisions. While visual perception offers potential for human pose estimation, it can be hindered by factors like... | {
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2411.07649 | Maritime Search and Rescue Missions with Aerial Images: A Survey | [
"cs.CV"
] | The speed of response by search and rescue teams at sea is of vital importance, as survival may depend on it. Recent technological advancements have led to the development of more efficient systems for locating individuals involved in a maritime incident, such as the use of Unmanned Aerial Vehicles (UAVs) equipped with... | {
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2411.07650 | Understanding Audiovisual Deepfake Detection: Techniques, Challenges,
Human Factors and Perceptual Insights | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM",
"cs.SD",
"eess.IV"
] | Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impersonation, phishing, slandering, or spreading misinformation. Despite extensive research on unimodal deep... | {
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2411.07654 | Spike Talk in Power Electronic Grids -- Leveraging Post Moore's
Computing Laws | [
"cs.ET",
"cs.AI",
"cs.NE",
"cs.SY",
"eess.SY"
] | Emerging distributed generation demands highly reliable and resilient coordinating control in microgrids. To improve on these aspects, spiking neural network is leveraged, as a grid-edge intelligence tool to establish a talkative infrastructure, Spike Talk, expediting coordination in next-generation microgrids without ... | {
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2411.07656 | Mitigating Bias in Queer Representation within Large Language Models: A
Collaborative Agent Approach | [
"cs.CL",
"cs.MA"
] | Large Language Models (LLMs) often perpetuate biases in pronoun usage, leading to misrepresentation or exclusion of queer individuals. This paper addresses the specific problem of biased pronoun usage in LLM outputs, particularly the inappropriate use of traditionally gendered pronouns ("he," "she") when inclusive lang... | {
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2411.07658 | Advancing Sustainability via Recommender Systems: A Survey | [
"cs.IR",
"cs.CY"
] | Human behavioral patterns and consumption paradigms have emerged as pivotal determinants in environmental degradation and climate change, with quotidian decisions pertaining to transportation, energy utilization, and resource consumption collectively precipitating substantial ecological impacts. Recommender systems, wh... | {
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2411.07660 | HMIL: Hierarchical Multi-Instance Learning for Fine-Grained Whole Slide
Image Classification | [
"cs.CV"
] | Fine-grained classification of whole slide images (WSIs) is essential in precision oncology, enabling precise cancer diagnosis and personalized treatment strategies. The core of this task involves distinguishing subtle morphological variations within the same broad category of gigapixel-resolution images, which present... | {
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2411.07663 | Is Graph Convolution Always Beneficial For Every Feature? | [
"cs.LG",
"cs.SI"
] | Graph Neural Networks (GNNs) have demonstrated strong capabilities in processing structured data. While traditional GNNs typically treat each feature dimension equally during graph convolution, we raise an important question: Is the graph convolution operation equally beneficial for each feature? If not, the convolutio... | {
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2411.07664 | Evaluating the Generation of Spatial Relations in Text and Image
Generative Models | [
"cs.CV"
] | Understanding spatial relations is a crucial cognitive ability for both humans and AI. While current research has predominantly focused on the benchmarking of text-to-image (T2I) models, we propose a more comprehensive evaluation that includes \textit{both} T2I and Large Language Models (LLMs). As spatial relations are... | {
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2411.07672 | Rethinking Structure Learning For Graph Neural Networks | [
"cs.LG"
] | To improve the performance of Graph Neural Networks (GNNs), Graph Structure Learning (GSL) has been extensively applied to reconstruct or refine original graph structures, effectively addressing issues like heterophily, over-squashing, and noisy structures. While GSL is generally thought to improve GNN performance, it ... | {
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2411.07679 | Safe Exploitative Play with Untrusted Type Beliefs | [
"cs.LG",
"cs.GT"
] | The combination of the Bayesian game and learning has a rich history, with the idea of controlling a single agent in a system composed of multiple agents with unknown behaviors given a set of types, each specifying a possible behavior for the other agents. The idea is to plan an agent's own actions with respect to thos... | {
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2411.07681 | What Do Learning Dynamics Reveal About Generalization in LLM Reasoning? | [
"cs.LG"
] | Despite the remarkable capabilities of modern large language models (LLMs), the mechanisms behind their problem-solving abilities remain elusive. In this work, we aim to better understand how the learning dynamics of LLM finetuning shapes downstream generalization. Our analysis focuses on reasoning tasks, whose problem... | {
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2411.07684 | AI enhanced diagnosis of Peyronies disease a novel approach using
Computer Vision | [
"eess.IV",
"cs.AI",
"cs.CV"
] | This study presents an innovative AI-driven tool for diagnosing Peyronie's Disease (PD), a condition that affects between 0.3% and 13.1% of men worldwide. Our method uses key point detection on both images and videos to measure penile curvature angles, utilizing advanced computer vision techniques. This tool has demons... | {
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2411.07685 | Fast Disentangled Slim Tensor Learning for Multi-view Clustering | [
"cs.CV",
"cs.AI"
] | Tensor-based multi-view clustering has recently received significant attention due to its exceptional ability to explore cross-view high-order correlations. However, most existing methods still encounter some limitations. (1) Most of them explore the correlations among different affinity matrices, making them unscalabl... | {
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2411.07686 | Data-Driven Graph Switching for Cyber-Resilient Control in Microgrids | [
"eess.SY",
"cs.AI",
"cs.SY"
] | Distributed microgrids are conventionally dependent on communication networks to achieve secondary control objectives. This dependence makes them vulnerable to stealth data integrity attacks (DIAs) where adversaries may perform manipulations via infected transmitters and repeaters to jeopardize stability. This paper pr... | {
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2411.07688 | Enhancing Ultra High Resolution Remote Sensing Imagery Analysis with
ImageRAG | [
"cs.CV",
"cs.AI"
] | Ultra High Resolution (UHR) remote sensing imagery (RSI) (e.g. 100,000 $\times$ 100,000 pixels or more) poses a significant challenge for current Remote Sensing Multimodal Large Language Models (RSMLLMs). If choose to resize the UHR image to standard input image size, the extensive spatial and contextual information th... | {
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2411.07690 | World Models: The Safety Perspective | [
"cs.AI"
] | With the proliferation of the Large Language Model (LLM), the concept of World Models (WM) has recently attracted a great deal of attention in the AI research community, especially in the context of AI agents. It is arguably evolving into an essential foundation for building AI agent systems. A WM is intended to help t... | {
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2411.07691 | New Emerged Security and Privacy of Pre-trained Model: a Survey and
Outlook | [
"cs.AI"
] | Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on various tasks, such as neural language processing, computer vision, and more. Despite their powerful capabilities, pre-trained models have also sp... | {
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2411.07699 | RINO: Accurate, Robust Radar-Inertial Odometry with Non-Iterative
Estimation | [
"cs.RO"
] | Precise localization and mapping are critical for achieving autonomous navigation in self-driving vehicles. However, ego-motion estimation still faces significant challenges, particularly when GNSS failures occur or under extreme weather conditions (e.g., fog, rain, and snow). In recent years, scanning radar has emerge... | {
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2411.07700 | Test Where Decisions Matter: Importance-driven Testing for Deep
Reinforcement Learning | [
"cs.LG"
] | In many Deep Reinforcement Learning (RL) problems, decisions in a trained policy vary in significance for the expected safety and performance of the policy. Since RL policies are very complex, testing efforts should concentrate on states in which the agent's decisions have the highest impact on the expected outcome. In... | {
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2411.07708 | Emotion Classification of Children Expressions | [
"cs.CV"
] | This paper proposes a process for a classification model for the facial expressions. The proposed process would aid in specific categorisation of children's emotions from 2 emotions namely 'Happy' and 'Sad'. Since the existing emotion recognition systems algorithms primarily train on adult faces, the model developed is... | {
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2411.07711 | OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous
Driving Framework | [
"cs.LG",
"cs.RO"
] | The integration of Large Language Models (LLMs) into autonomous driving systems offers promising enhancements in environmental understanding and decision-making. However, the substantial computational demands of deploying LLMs locally on vehicles render this approach unfeasible for real-world automotive applications. T... | {
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2411.07715 | Training Data for Large Language Model | [
"cs.AI"
] | In 2022, with the release of ChatGPT, large-scale language models gained widespread attention. ChatGPT not only surpassed previous models in terms of parameters and the scale of its pretraining corpus but also achieved revolutionary performance improvements through fine-tuning on a vast amount of high-quality, human-an... | {
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2411.07719 | EMPERROR: A Flexible Generative Perception Error Model for Probing
Self-Driving Planners | [
"cs.RO",
"cs.CV",
"cs.LG"
] | To handle the complexities of real-world traffic, learning planners for self-driving from data is a promising direction. While recent approaches have shown great progress, they typically assume a setting in which the ground-truth world state is available as input. However, when deployed, planning needs to be robust to ... | {
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2411.07722 | Is Cognition consistent with Perception? Assessing and Mitigating
Multimodal Knowledge Conflicts in Document Understanding | [
"cs.AI"
] | Multimodal large language models (MLLMs) have shown impressive capabilities in document understanding, a rapidly growing research area with significant industrial demand in recent years. As a multimodal task, document understanding requires models to possess both perceptual and cognitive abilities. However, current MLL... | {
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2411.07724 | Convergence Rate Analysis of LION | [
"cs.LG",
"math.OC"
] | The LION (evoLved sIgn mOmeNtum) optimizer for deep neural network training was found by Google via program search, with the simple sign update yet showing impressive performance in training large scale networks. Although previous studies have investigated its convergence properties, a comprehensive analysis, especiall... | {
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2411.07725 | ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost
Volume-based Flow Prediction | [
"cs.CV"
] | Vision-based semantic occupancy and flow prediction plays a crucial role in providing spatiotemporal cues for real-world tasks, such as autonomous driving. Existing methods prioritize higher accuracy to cater to the demands of these tasks. In this work, we strive to improve performance by introducing a series of target... | {
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2411.07728 | No-Reference Point Cloud Quality Assessment via Graph Convolutional
Network | [
"cs.CV",
"cs.AI",
"eess.IV"
] | Three-dimensional (3D) point cloud, as an emerging visual media format, is increasingly favored by consumers as it can provide more realistic visual information than two-dimensional (2D) data. Similar to 2D plane images and videos, point clouds inevitably suffer from quality degradation and information loss through mul... | {
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2411.07729 | Exploring the loss landscape of regularized neural networks via convex
duality | [
"cs.LG"
] | We discuss several aspects of the loss landscape of regularized neural networks: the structure of stationary points, connectivity of optimal solutions, path with nonincreasing loss to arbitrary global optimum, and the nonuniqueness of optimal solutions, by casting the problem into an equivalent convex problem and consi... | {
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2411.07739 | Unlocking Legal Knowledge with Multi-Layered Embedding-Based Retrieval | [
"cs.AI",
"cs.IR"
] | This work addresses the challenge of capturing the complexities of legal knowledge by proposing a multi-layered embedding-based retrieval method for legal and legislative texts. Creating embeddings not only for individual articles but also for their components (paragraphs, clauses) and structural groupings (books, titl... | {
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2411.07740 | 3D Focusing-and-Matching Network for Multi-Instance Point Cloud
Registration | [
"cs.CV"
] | Multi-instance point cloud registration aims to estimate the pose of all instances of a model point cloud in the whole scene. Existing methods all adopt the strategy of first obtaining the global correspondence and then clustering to obtain the pose of each instance. However, due to the cluttered and occluded objects i... | {
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2411.07742 | Efficient 3D Perception on Multi-Sweep Point Cloud with Gumbel Spatial
Pruning | [
"cs.CV"
] | This paper studies point cloud perception within outdoor environments. Existing methods face limitations in recognizing objects located at a distance or occluded, due to the sparse nature of outdoor point clouds. In this work, we observe a significant mitigation of this problem by accumulating multiple temporally conse... | {
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2411.07747 | Constraint Learning for Parametric Point Cloud | [
"cs.CV"
] | Parametric point clouds are sampled from CAD shapes, and have become increasingly prevalent in industrial manufacturing. However, most existing point cloud learning methods focus on the geometric features, such as developing efficient convolution operations, overlooking the important attribute of constraints inherent i... | {
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2411.07750 | LapGSR: Laplacian Reconstructive Network for Guided Thermal
Super-Resolution | [
"eess.IV",
"cs.CV"
] | In the last few years, the fusion of multi-modal data has been widely studied for various applications such as robotics, gesture recognition, and autonomous navigation. Indeed, high-quality visual sensors are expensive, and consumer-grade sensors produce low-resolution images. Researchers have developed methods to comb... | {
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2411.07751 | SAV-SE: Scene-aware Audio-Visual Speech Enhancement with Selective State
Space Model | [
"cs.SD",
"cs.AI",
"cs.CV",
"cs.MM",
"eess.AS"
] | Speech enhancement plays an essential role in various applications, and the integration of visual information has been demonstrated to bring substantial advantages. However, the majority of current research concentrates on the examination of facial and lip movements, which can be compromised or entirely inaccessible in... | {
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2411.07753 | Spatially Regularized Graph Attention Autoencoder Framework for
Detecting Rainfall Extremes | [
"cs.LG"
] | We introduce a novel Graph Attention Autoencoder (GAE) with spatial regularization to address the challenge of scalable anomaly detection in spatiotemporal rainfall data across India from 1990 to 2015. Our model leverages a Graph Attention Network (GAT) to capture spatial dependencies and temporal dynamics in the data,... | {
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2411.07758 | AdaSemiCD: An Adaptive Semi-Supervised Change Detection Method Based on
Pseudo-Label Evaluation | [
"cs.CV"
] | Change Detection (CD) is an essential field in remote sensing, with a primary focus on identifying areas of change in bi-temporal image pairs captured at varying intervals of the same region by a satellite. The data annotation process for the CD task is both time-consuming and labor-intensive. To make better use of the... | {
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2411.07759 | Optimizing Traffic Signal Control using High-Dimensional State
Representation and Efficient Deep Reinforcement Learning | [
"eess.SY",
"cs.AI",
"cs.SY"
] | In reinforcement learning-based (RL-based) traffic signal control (TSC), decisions on the signal timing are made based on the available information on vehicles at a road intersection. This forms the state representation for the RL environment which can either be high-dimensional containing several variables or a low-di... | {
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2411.07760 | Navigation with QPHIL: Quantizing Planner for Hierarchical Implicit
Q-Learning | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Offline Reinforcement Learning (RL) has emerged as a powerful alternative to imitation learning for behavior modeling in various domains, particularly in complex navigation tasks. An existing challenge with Offline RL is the signal-to-noise ratio, i.e. how to mitigate incorrect policy updates due to errors in value est... | {
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2411.07762 | ASER: Activation Smoothing and Error Reconstruction for Large Language
Model Quantization | [
"cs.LG",
"cs.AI"
] | Quantization stands as a pivotal technique for large language model (LLM) serving, yet it poses significant challenges particularly in achieving effective low-bit quantization. The limited numerical mapping makes the quantized model produce a non-trivial error, bringing out intolerable performance degration. This paper... | {
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2411.07763 | Spider 2.0: Evaluating Language Models on Real-World Enterprise
Text-to-SQL Workflows | [
"cs.CL",
"cs.AI",
"cs.DB"
] | Real-world enterprise text-to-SQL workflows often involve complex cloud or local data across various database systems, multiple SQL queries in various dialects, and diverse operations from data transformation to analytics. We introduce Spider 2.0, an evaluation framework comprising 632 real-world text-to-SQL workflow p... | {
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2411.07765 | Novel View Synthesis with Pixel-Space Diffusion Models | [
"cs.CV"
] | Synthesizing a novel view from a single input image is a challenging task. Traditionally, this task was approached by estimating scene depth, warping, and inpainting, with machine learning models enabling parts of the pipeline. More recently, generative models are being increasingly employed in novel view synthesis (NV... | {
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2411.07770 | A Theoretical Analysis of Recommendation Loss Functions under Negative
Sampling | [
"cs.IR"
] | Loss functions like Categorical Cross Entropy (CCE), Binary Cross Entropy (BCE), and Bayesian Personalized Ranking (BPR) are commonly used in training Recommender Systems (RSs) to differentiate positive items - those interacted with by users - and negative items. While prior works empirically showed that CCE outperform... | {
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2411.07772 | Automatic Album Sequencing | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.MM",
"cs.SD",
"eess.AS"
] | Album sequencing is a critical part of the album production process. Recently, a data-driven approach was proposed that sequences general collections of independent media by extracting the narrative essence of the items in the collections. While this approach implies an album sequencing technique, it is not widely acce... | {
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2411.07773 | Likelihood as a Performance Gauge for Retrieval-Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Recent work finds that retrieval-augmented generation with large language models is prone to be influenced by the order of retrieved documents in the context. However, the lack of in-depth analysis limits the use of this phenomenon for prompt engineering in practice. In this study, we posit that likelihoods serve as an... | {
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2411.07781 | RedCode: Risky Code Execution and Generation Benchmark for Code Agents | [
"cs.SE",
"cs.AI"
] | With the rapidly increasing capabilities and adoption of code agents for AI-assisted coding, safety concerns, such as generating or executing risky code, have become significant barriers to the real-world deployment of these agents. To provide comprehensive and practical evaluations on the safety of code agents, we pro... | {
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2411.07784 | Interaction Asymmetry: A General Principle for Learning Composable
Abstractions | [
"cs.LG",
"cs.CV"
] | Learning disentangled representations of concepts and re-composing them in unseen ways is crucial for generalizing to out-of-domain situations. However, the underlying properties of concepts that enable such disentanglement and compositional generalization remain poorly understood. In this work, we propose the principl... | {
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2411.07794 | Feature Fusion Transferability Aware Transformer for Unsupervised Domain
Adaptation | [
"cs.CV",
"cs.AI"
] | Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from labeled source domains to improve performance on the unlabeled target domains. While Convolutional Neural Networks (CNNs) have been dominant in previous UDA methods, recent research has shown promise in applying Vision Transformers (ViTs) ... | {
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2411.07795 | InvisMark: Invisible and Robust Watermarking for AI-generated Image
Provenance | [
"cs.CR",
"cs.AI"
] | The proliferation of AI-generated images has intensified the need for robust content authentication methods. We present InvisMark, a novel watermarking technique designed for high-resolution AI-generated images. Our approach leverages advanced neural network architectures and training strategies to embed imperceptible ... | {
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2411.07796 | PatchCTG: Patch Cardiotocography Transformer for Antepartum Fetal Health
Monitoring | [
"cs.AI",
"cs.LG"
] | Antepartum Cardiotocography (CTG) is vital for fetal health monitoring, but traditional methods like the Dawes-Redman system are often limited by high inter-observer variability, leading to inconsistent interpretations and potential misdiagnoses. This paper introduces PatchCTG, a transformer-based model specifically de... | {
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2411.07799 | Horticultural Temporal Fruit Monitoring via 3D Instance Segmentation and
Re-Identification using Point Clouds | [
"cs.CV",
"cs.RO"
] | Robotic fruit monitoring is a key step toward automated agricultural production systems. Robots can significantly enhance plant and temporal fruit monitoring by providing precise, high-throughput assessments that overcome the limitations of traditional manual methods. Fruit monitoring is a challenging task due to the s... | {
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2411.07800 | Kernel-based retrieval models for hyperspectral image data optimized
with Kernel Flows | [
"cs.LG",
"cs.CE",
"stat.ME"
] | Kernel-based statistical methods are efficient, but their performance depends heavily on the selection of kernel parameters. In literature, the optimization studies on kernel-based chemometric methods is limited and often reduced to grid searching. Previously, the authors introduced Kernel Flows (KF) to learn kernel pa... | {
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2411.07802 | Large-scale Remote Sensing Image Target Recognition and Automatic
Annotation | [
"cs.CV"
] | This paper presents a method for object recognition and automatic labeling in large-area remote sensing images called LRSAA. The method integrates YOLOv11 and MobileNetV3-SSD object detection algorithms through ensemble learning to enhance model performance. Furthermore, it employs Poisson disk sampling segmentation te... | {
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2411.07805 | Effects of charging and discharging capabilities on trade-offs between
model accuracy and computational efficiency in pumped thermal electricity
storage | [
"eess.SY",
"cs.SY"
] | The increasing need for energy storage solutions to balance variable renewable energy sources has highlighted the potential of Pumped Thermal Electricity Storage (PTES). In this paper, we investigate the trade-offs between model accuracy and computational efficiency in PTES systems. We evaluate a range of PTES models, ... | {
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2411.07806 | Federated Low-Rank Adaptation with Differential Privacy over Wireless
Networks | [
"cs.LG",
"cs.CR",
"eess.SP"
] | Fine-tuning large pre-trained foundation models (FMs) on distributed edge devices presents considerable computational and privacy challenges. Federated fine-tuning (FedFT) mitigates some privacy issues by facilitating collaborative model training without the need to share raw data. To lessen the computational burden on... | {
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2411.07814 | Community Research Earth Digital Intelligence Twin (CREDIT) | [
"cs.AI",
"physics.ao-ph"
] | Recent advancements in artificial intelligence (AI) for numerical weather prediction (NWP) have significantly transformed atmospheric modeling. AI NWP models outperform traditional physics-based systems, such as the Integrated Forecast System (IFS), across several global metrics while requiring fewer computational reso... | {
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2411.07815 | Reliable-loc: Robust sequential LiDAR global localization in large-scale
street scenes based on verifiable cues | [
"cs.RO",
"cs.CV"
] | Wearable laser scanning (WLS) system has the advantages of flexibility and portability. It can be used for determining the user's path within a prior map, which is a huge demand for applications in pedestrian navigation, collaborative mapping, augmented reality, and emergency rescue. However, existing LiDAR-based globa... | {
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2411.07816 | Dual-Criterion Model Aggregation in Federated Learning: Balancing Data
Quantity and Quality | [
"cs.LG"
] | Federated learning (FL) has become one of the key methods for privacy-preserving collaborative learning, as it enables the transfer of models without requiring local data exchange. Within the FL framework, an aggregation algorithm is recognized as one of the most crucial components for ensuring the efficacy and securit... | {
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2411.07820 | Query Optimization for Parametric Knowledge Refinement in
Retrieval-Augmented Large Language Models | [
"cs.CL",
"cs.IR"
] | We introduce the Extract-Refine-Retrieve-Read (ERRR) framework, a novel approach designed to bridge the pre-retrieval information gap in Retrieval-Augmented Generation (RAG) systems through query optimization tailored to meet the specific knowledge requirements of Large Language Models (LLMs). Unlike conventional query... | {
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2411.07826 | Efficient Federated Finetuning of Tiny Transformers with
Resource-Constrained Devices | [
"cs.LG",
"cs.AI",
"cs.DC"
] | In recent years, Large Language Models (LLMs) through Transformer structures have dominated many machine learning tasks, especially text processing. However, these models require massive amounts of data for training and induce high resource requirements, particularly in terms of the large number of Floating Point Opera... | {
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2411.07828 | Suite-IN: Aggregating Motion Features from Apple Suite for Robust
Inertial Navigation | [
"cs.LG"
] | With the rapid development of wearable technology, devices like smartphones, smartwatches, and headphones equipped with IMUs have become essential for applications such as pedestrian positioning. However, traditional pedestrian dead reckoning (PDR) methods struggle with diverse motion patterns, while recent data-driven... | {
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2411.07830 | Singularity-Avoidance Control of Robotic Systems with Model Mismatch and
Actuator Constraints | [
"eess.SY",
"cs.RO",
"cs.SY"
] | Singularities, manifesting as special configuration states, deteriorate robot performance and may even lead to a loss of control over the system. This paper addresses the kinematic singularity concerns in robotic systems with model mismatch and actuator constraints through control barrier functions (CBFs). We propose a... | {
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2411.07832 | Dynamical-VAE-based Hindsight to Learn the Causal Dynamics of
Factored-POMDPs | [
"cs.LG",
"stat.ML"
] | Learning representations of underlying environmental dynamics from partial observations is a critical challenge in machine learning. In the context of Partially Observable Markov Decision Processes (POMDPs), state representations are often inferred from the history of past observations and actions. We demonstrate that ... | {
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} |
2411.07833 | Robust Adaptive Safe Robotic Grasping with Tactile Sensing | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Robotic grasping requires safe force interaction to prevent a grasped object from being damaged or slipping out of the hand. In this vein, this paper proposes an integrated framework for grasping with formal safety guarantees based on Control Barrier Functions. We first design contact force and force closure constraint... | {
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} |
2411.07834 | Towards Vision Mixture of Experts for Wildlife Monitoring on the Edge | [
"cs.CV"
] | The explosion of IoT sensors in industrial, consumer and remote sensing use cases has come with unprecedented demand for computing infrastructure to transmit and to analyze petabytes of data. Concurrently, the world is slowly shifting its focus towards more sustainable computing. For these reasons, there has been a rec... | {
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} |
2411.07837 | FRUGAL: Memory-Efficient Optimization by Reducing State Overhead for
Scalable Training | [
"cs.LG"
] | With the increase in the number of parameters in large language models, the process of pre-training and fine-tuning increasingly demands larger volumes of GPU memory. A significant portion of this memory is typically consumed by the optimizer state. To overcome this challenge, recent approaches such as low-rank adaptat... | {
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} |
2411.07841 | Federated Learning for Discrete Optimal Transport with Large Population
under Incomplete Information | [
"cs.AI"
] | Optimal transport is a powerful framework for the efficient allocation of resources between sources and targets. However, traditional models often struggle to scale effectively in the presence of large and heterogeneous populations. In this work, we introduce a discrete optimal transport framework designed to handle la... | {
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} |
2411.07843 | Chain Association-based Attacking and Shielding Natural Language
Processing Systems | [
"cs.CL",
"cs.AI"
] | Association as a gift enables people do not have to mention something in completely straightforward words and allows others to understand what they intend to refer to. In this paper, we propose a chain association-based adversarial attack against natural language processing systems, utilizing the comprehension gap betw... | {
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} |
2411.07845 | Ethical Concern Identification in NLP: A Corpus of ACL Anthology Ethics
Statements | [
"cs.CL",
"cs.AI",
"cs.CY"
] | What ethical concerns, if any, do LLM researchers have? We introduce EthiCon, a corpus of 1,580 ethical concern statements extracted from scientific papers published in the ACL Anthology. We extract ethical concern keywords from the statements and show promising results in automating the concern identification process.... | {
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} |
2411.07848 | NL-SLAM for OC-VLN: Natural Language Grounded SLAM for Object-Centric
VLN | [
"cs.RO",
"cs.CV"
] | Landmark-based navigation (e.g. go to the wooden desk) and relative positional navigation (e.g. move 5 meters forward) are distinct navigation challenges solved very differently in existing robotics navigation methodology. We present a new dataset, OC-VLN, in order to distinctly evaluate grounding object-centric natura... | {
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} |
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