id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.15604 | Advanced Control Strategy to Compensate Power Sharing Error and DC
Circulating Current in Parallel Single-Phase Inverters | [
"eess.SY",
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] | This paper proposes an advanced control strategy to eliminate both current sharing error and DC circulating current caused by line impedance mismatched and measurement errors in islanded AC microgrid system. The proposed adaptive virtual impedance scheme is developed with the aid of a low bandwidth communication and a ... | {
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2412.15605 | Don't Do RAG: When Cache-Augmented Generation is All You Need for
Knowledge Tasks | [
"cs.CL"
] | Retrieval-augmented generation (RAG) has gained traction as a powerful approach for enhancing language models by integrating external knowledge sources. However, RAG introduces challenges such as retrieval latency, potential errors in document selection, and increased system complexity. With the advent of large languag... | {
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2412.15606 | Multi-modal Agent Tuning: Building a VLM-Driven Agent for Efficient Tool
Usage | [
"cs.AI",
"cs.CV"
] | The advancement of large language models (LLMs) prompts the development of multi-modal agents, which are used as a controller to call external tools, providing a feasible way to solve practical tasks. In this paper, we propose a multi-modal agent tuning method that automatically generates multi-modal tool-usage data an... | {
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2412.15607 | Short-Term Forecasting of Thermostatic and Residential Loads Using Long
Short-Term Memory Recurrent Neural Networks | [
"eess.SY",
"cs.SY"
] | Internet of Things (IoT) devices in smart grids enable intelligent energy management for grid managers and personalized energy services for consumers. Investigating a smart grid with IoT devices requires a simulation framework with IoT devices modeling. However, there lack comprehensive study on the modeling of IoT dev... | {
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2412.15608 | Robust Dynamic Edge Service Placement Under Spatio-Temporal Correlated
Demand Uncertainty | [
"math.OC",
"cs.SY",
"eess.SY"
] | Edge computing allows Service Providers (SPs) to enhance user experience by placing their services closer to the network edge. Determining the optimal provisioning of edge resources to meet the varying and uncertain demand cost-effectively is a critical task for SPs. This paper introduces a novel two-stage multi-period... | {
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2412.15610 | A Fusion Approach of Dependency Syntax and Sentiment Polarity for
Feature Label Extraction in Commodity Reviews | [
"cs.CL",
"cs.AI"
] | This study analyzes 13,218 product reviews from JD.com, covering four categories: mobile phones, computers, cosmetics, and food. A novel method for feature label extraction is proposed by integrating dependency parsing and sentiment polarity analysis. The proposed method addresses the challenges of low robustness in ex... | {
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2412.15614 | Technical Report for ICML 2024 TiFA Workshop MLLM Attack Challenge:
Suffix Injection and Projected Gradient Descent Can Easily Fool An MLLM | [
"cs.CR",
"cs.CV"
] | This technical report introduces our top-ranked solution that employs two approaches, \ie suffix injection and projected gradient descent (PGD) , to address the TiFA workshop MLLM attack challenge. Specifically, we first append the text from an incorrectly labeled option (pseudo-labeled) to the original query as a suff... | {
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2412.15616 | Microservices-Based Framework for Predictive Analytics and Real-time
Performance Enhancement in Travel Reservation Systems | [
"cs.IT",
"cs.AI",
"cs.CE",
"cs.LG",
"math.IT"
] | The paper presents a framework of microservices-based architecture dedicated to enhancing the performance of real-time travel reservation systems using the power of predictive analytics. Traditional monolithic systems are bad at scaling and performing with high loads, causing backup resources to be underutilized along ... | {
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2412.15618 | 3D Shape Tokenization | [
"cs.CV",
"cs.GR"
] | We introduce Shape Tokens, a 3D representation that is continuous, compact, and easy to incorporate into machine learning models. Shape Tokens act as conditioning vectors that represent shape information in a 3D flow-matching model. The flow-matching model is trained to approximate probability density functions corresp... | {
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2412.15619 | Understanding Individual Agent Importance in Multi-Agent System via
Counterfactual Reasoning | [
"cs.AI",
"cs.MA"
] | Explaining multi-agent systems (MAS) is urgent as these systems become increasingly prevalent in various applications. Previous work has proveided explanations for the actions or states of agents, yet falls short in understanding the black-boxed agent's importance within a MAS and the overall team strategy. To bridge t... | {
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2412.15620 | Modeling Autonomous Shifts Between Focus State and Mind-Wandering Using
a Predictive-Coding-Inspired Variational RNN Model | [
"q-bio.NC",
"cs.AI"
] | The current study investigates possible neural mechanisms underling autonomous shifts between focus state and mind-wandering by conducting model simulation experiments. On this purpose, we modeled perception processes of continuous sensory sequences using our previous proposed variational RNN model which was developed ... | {
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2412.15622 | TouchASP: Elastic Automatic Speech Perception that Everyone Can Touch | [
"eess.AS",
"cs.CL",
"eess.SP"
] | Large Automatic Speech Recognition (ASR) models demand a vast number of parameters, copious amounts of data, and significant computational resources during the training process. However, such models can merely be deployed on high-compute cloud platforms and are only capable of performing speech recognition tasks. This ... | {
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2412.15623 | JailPO: A Novel Black-box Jailbreak Framework via Preference
Optimization against Aligned LLMs | [
"cs.CR",
"cs.AI"
] | Large Language Models (LLMs) aligned with human feedback have recently garnered significant attention. However, it remains vulnerable to jailbreak attacks, where adversaries manipulate prompts to induce harmful outputs. Exploring jailbreak attacks enables us to investigate the vulnerabilities of LLMs and further guides... | {
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2412.15628 | Can Input Attributions Interpret the Inductive Reasoning Process in
In-Context Learning? | [
"cs.CL"
] | Interpreting the internal process of neural models has long been a challenge. This challenge remains relevant in the era of large language models (LLMs) and in-context learning (ICL); for example, ICL poses a new issue of interpreting which example in the few-shot examples contributed to identifying/solving the task. T... | {
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2412.15632 | A New Method to Capturing Compositional Knowledge in Linguistic Space | [
"cs.CV"
] | Compositional understanding allows visual language models to interpret complex relationships between objects, attributes, and relations in images and text. However, most existing methods often rely on hard negative examples and fine-tuning, which can overestimate improvements and are limited by the difficulty of obtain... | {
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2412.15637 | CrackUDA: Incremental Unsupervised Domain Adaptation for Improved Crack
Segmentation in Civil Structures | [
"cs.CV"
] | Crack segmentation plays a crucial role in ensuring the structural integrity and seismic safety of civil structures. However, existing crack segmentation algorithms encounter challenges in maintaining accuracy with domain shifts across datasets. To address this issue, we propose a novel deep network that employs increm... | {
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2412.15639 | Tacit Learning with Adaptive Information Selection for Cooperative
Multi-Agent Reinforcement Learning | [
"cs.MA",
"cs.AI",
"cs.LG"
] | In multi-agent reinforcement learning (MARL), the centralized training with decentralized execution (CTDE) framework has gained widespread adoption due to its strong performance. However, the further development of CTDE faces two key challenges. First, agents struggle to autonomously assess the relevance of input infor... | {
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2412.15646 | CustomTTT: Motion and Appearance Customized Video Generation via
Test-Time Training | [
"cs.CV"
] | Benefiting from large-scale pre-training of text-video pairs, current text-to-video (T2V) diffusion models can generate high-quality videos from the text description. Besides, given some reference images or videos, the parameter-efficient fine-tuning method, i.e. LoRA, can generate high-quality customized concepts, e.g... | {
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2412.15647 | Variable Metric Evolution Strategies for High-dimensional
Multi-Objective Optimization | [
"cs.NE"
] | We design a class of variable metric evolution strategies well suited for high-dimensional problems. We target problems with many variables, not (necessarily) with many objectives. The construction combines two independent developments: efficient algorithms for scaling covariance matrix adaptation to high dimensions, a... | {
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2412.15650 | Beyond Human Data: Aligning Multimodal Large Language Models by
Iterative Self-Evolution | [
"cs.LG"
] | Human preference alignment can greatly enhance Multimodal Large Language Models (MLLMs), but collecting high-quality preference data is costly. A promising solution is the self-evolution strategy, where models are iteratively trained on data they generate. However, current techniques still rely on human- or GPT-annotat... | {
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2412.15652 | Error-driven Data-efficient Large Multimodal Model Tuning | [
"cs.CL"
] | Large Multimodal Models (LMMs) have demonstrated impressive performance across numerous academic benchmarks. However, fine-tuning still remains essential to achieve satisfactory performance on downstream tasks, while the task-specific tuning samples are usually not readily available or expensive and time-consuming to o... | {
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2412.15655 | MathSpeech: Leveraging Small LMs for Accurate Conversion in Mathematical
Speech-to-Formula | [
"cs.CL",
"cs.AI"
] | In various academic and professional settings, such as mathematics lectures or research presentations, it is often necessary to convey mathematical expressions orally. However, reading mathematical expressions aloud without accompanying visuals can significantly hinder comprehension, especially for those who are hearin... | {
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2412.15657 | Synthetic Tabular Data Generation for Imbalanced Classification: The
Surprising Effectiveness of an Overlap Class | [
"cs.LG"
] | Handling imbalance in class distribution when building a classifier over tabular data has been a problem of long-standing interest. One popular approach is augmenting the training dataset with synthetically generated data. While classical augmentation techniques were limited to linear interpolation of existing minority... | {
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2412.15660 | Adaptable and Precise: Enterprise-Scenario LLM Function-Calling
Capability Training Pipeline | [
"cs.AI",
"cs.CL",
"cs.SE"
] | Enterprises possess a vast array of API assets scattered across various functions, forming the backbone of existing business processes. By leveraging these APIs as functional tools, enterprises can design diverse, scenario-specific agent applications, driven by on-premise function-calling models as the core engine. How... | {
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2412.15664 | SCENIC: Scene-aware Semantic Navigation with Instruction-guided Control | [
"cs.CV"
] | Synthesizing natural human motion that adapts to complex environments while allowing creative control remains a fundamental challenge in motion synthesis. Existing models often fall short, either by assuming flat terrain or lacking the ability to control motion semantics through text. To address these limitations, we i... | {
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2412.15666 | A survey on FPGA-based accelerator for ML models | [
"cs.AR",
"cs.LG"
] | This paper thoroughly surveys machine learning (ML) algorithms acceleration in hardware accelerators, focusing on Field-Programmable Gate Arrays (FPGAs). It reviews 287 out of 1138 papers from the past six years, sourced from four top FPGA conferences. Such selection underscores the increasing integration of ML and FPG... | {
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2412.15668 | Adaptive Hierarchical Graph Cut for Multi-granularity
Out-of-distribution Detection | [
"cs.CV"
] | This paper focuses on a significant yet challenging task: out-of-distribution detection (OOD detection), which aims to distinguish and reject test samples with semantic shifts, so as to prevent models trained on in-distribution (ID) data from producing unreliable predictions. Although previous works have made decent su... | {
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2412.15670 | BS-LDM: Effective Bone Suppression in High-Resolution Chest X-Ray Images
with Conditional Latent Diffusion Models | [
"eess.IV",
"cs.CV"
] | Lung diseases represent a significant global health challenge, with Chest X-Ray (CXR) being a key diagnostic tool due to their accessibility and affordability. Nonetheless, the detection of pulmonary lesions is often hindered by overlapping bone structures in CXR images, leading to potential misdiagnoses. To address th... | {
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2412.15673 | Learning Group Interactions and Semantic Intentions for Multi-Object
Trajectory Prediction | [
"cs.CV"
] | Effective modeling of group interactions and dynamic semantic intentions is crucial for forecasting behaviors like trajectories or movements. In complex scenarios like sports, agents' trajectories are influenced by group interactions and intentions, including team strategies and opponent actions. To this end, we propos... | {
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2412.15674 | PersonaMagic: Stage-Regulated High-Fidelity Face Customization with
Tandem Equilibrium | [
"cs.CV"
] | Personalized image generation has made significant strides in adapting content to novel concepts. However, a persistent challenge remains: balancing the accurate reconstruction of unseen concepts with the need for editability according to the prompt, especially when dealing with the complex nuances of facial features. ... | {
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2412.15677 | AI-generated Image Quality Assessment in Visual Communication | [
"cs.CV",
"cs.AI"
] | Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality assessment (IQA) algorithms primarily focus on low-level visual perception, while existing IQA works on AIGIs overemphasize the generated conten... | {
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2412.15678 | Multi-Pair Temporal Sentence Grounding via Multi-Thread Knowledge
Transfer Network | [
"cs.CV"
] | Given some video-query pairs with untrimmed videos and sentence queries, temporal sentence grounding (TSG) aims to locate query-relevant segments in these videos. Although previous respectable TSG methods have achieved remarkable success, they train each video-query pair separately and ignore the relationship between d... | {
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2412.15679 | High-Dimensional Bayesian Optimisation with Large-Scale Constraints via
Latent Space Gaussian Processes | [
"cs.CE"
] | Design optimisation offers the potential to develop lightweight aircraft structures with reduced environmental impact. Due to the high number of design variables and constraints, these challenges are typically addressed using gradient-based optimisation methods to maintain efficiency. However, this approach often resul... | {
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2412.15681 | Asynchronous Vector Consensus over Matrix-Weighted Networks | [
"eess.SY",
"cs.SY"
] | We study the distributed consensus of state vectors in a discrete-time multi-agent network with matrix edge weights using stochastic matrix convergence theory. We present a distributed asynchronous time update model wherein one randomly selected agent updates its state vector at a time by interacting with its neighbors... | {
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2412.15683 | Variability Need Not Imply Error: The Case of Adequate but Semantically
Distinct Responses | [
"cs.CL"
] | With the broader use of language models (LMs) comes the need to estimate their ability to respond reliably to prompts (e.g., are generated responses likely to be correct?). Uncertainty quantification tools (notions of confidence and entropy, i.a.) can be used to that end (e.g., to reject a response when the model is `u... | {
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2412.15687 | GraphDOP: Towards skilful data-driven medium-range weather forecasts
learnt and initialised directly from observations | [
"physics.ao-ph",
"cs.LG"
] | We introduce GraphDOP, a new data-driven, end-to-end forecast system developed at the European Centre for Medium-Range Weather Forecasts (ECMWF) that is trained and initialised exclusively from Earth System observations, with no physics-based (re)analysis inputs or feedbacks. GraphDOP learns the correlations between ob... | {
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2412.15689 | DOLLAR: Few-Step Video Generation via Distillation and Latent Reward
Optimization | [
"cs.CV"
] | Diffusion probabilistic models have shown significant progress in video generation; however, their computational efficiency is limited by the large number of sampling steps required. Reducing sampling steps often compromises video quality or generation diversity. In this work, we introduce a distillation method that co... | {
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2412.15690 | Theory of Mixture-of-Experts for Mobile Edge Computing | [
"cs.LG"
] | In mobile edge computing (MEC) networks, mobile users generate diverse machine learning tasks dynamically over time. These tasks are typically offloaded to the nearest available edge server, by considering communication and computational efficiency. However, its operation does not ensure that each server specializes in... | {
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2412.15691 | Exploiting Multimodal Spatial-temporal Patterns for Video Object
Tracking | [
"cs.CV"
] | Multimodal tracking has garnered widespread attention as a result of its ability to effectively address the inherent limitations of traditional RGB tracking. However, existing multimodal trackers mainly focus on the fusion and enhancement of spatial features or merely leverage the sparse temporal relationships between ... | {
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2412.15695 | Hypergraph clustering using Ricci curvature: an edge transport
perspective | [
"cs.LG",
"cs.SI",
"stat.ML"
] | In this paper, we introduce a novel method for extending Ricci flow to hypergraphs by defining probability measures on the edges and transporting them on the line expansion. This approach yields a new weighting on the edges, which proves particularly effective for community detection. We extensively compare this method... | {
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2412.15698 | Concept Boundary Vectors | [
"cs.LG"
] | Machine learning models are trained with relatively simple objectives, such as next token prediction. However, on deployment, they appear to capture a more fundamental representation of their input data. It is of interest to understand the nature of these representations to help interpret the model's outputs and to ide... | {
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2412.15700 | AIR: Unifying Individual and Collective Exploration in Cooperative
Multi-Agent Reinforcement Learning | [
"cs.AI",
"cs.LG",
"cs.MA"
] | Exploration in cooperative multi-agent reinforcement learning (MARL) remains challenging for value-based agents due to the absence of an explicit policy. Existing approaches include individual exploration based on uncertainty towards the system and collective exploration through behavioral diversity among agents. Howev... | {
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2412.15701 | Collaborative Gym: A Framework for Enabling and Evaluating Human-Agent
Collaboration | [
"cs.AI",
"cs.CL",
"cs.HC"
] | Recent advancements in language models (LMs) have sparked growing interest in developing LM agents. While fully autonomous agents could excel in many scenarios, numerous use cases inherently require them to collaborate with humans due to humans' latent preferences, domain expertise, or need for control. To facilitate t... | {
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2412.15703 | MacLight: Multi-scene Aggregation Convolutional Learning for Traffic
Signal Control | [
"cs.MA",
"cs.AI",
"cs.LG"
] | Reinforcement learning methods have proposed promising traffic signal control policy that can be trained on large road networks. Current SOTA methods model road networks as topological graph structures, incorporate graph attention into deep Q-learning, and merge local and global embeddings to improve policy. However, g... | {
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2412.15712 | Contrastive Learning for Task-Independent SpeechLLM-Pretraining | [
"cs.CL",
"cs.HC"
] | Large language models (LLMs) excel in natural language processing but adapting these LLMs to speech processing tasks efficiently is not straightforward. Direct task-specific fine-tuning is limited by overfitting risks, data requirements, and computational costs. To address these challenges, we propose a scalable, two-s... | {
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2412.15714 | AutoLife: Automatic Life Journaling with Smartphones and LLMs | [
"cs.AI",
"cs.CL",
"cs.HC"
] | This paper introduces a novel mobile sensing application - life journaling - designed to generate semantic descriptions of users' daily lives. We present AutoLife, an automatic life journaling system based on commercial smartphones. AutoLife only inputs low-cost sensor data (without photos or audio) from smartphones an... | {
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2412.15716 | Towards Secure AI-driven Industrial Metaverse with NFT Digital Twins | [
"cs.CR",
"cs.AI",
"cs.HC"
] | The rise of the industrial metaverse has brought digital twins (DTs) to the forefront. Blockchain-powered non-fungible tokens (NFTs) offer a decentralized approach to creating and owning these cloneable DTs. However, the potential for unauthorized duplication, or counterfeiting, poses a significant threat to the securi... | {
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2412.15720 | Switching Frequency as FPGA Monitor: Studying Degradation and Ageing
Prognosis at Large Scale | [
"cs.AR",
"cs.SY",
"eess.SY"
] | The growing deployment of unhardened embedded devices in critical systems demands the monitoring of hardware ageing as part of predictive maintenance. In this paper, we study degradation on a large deployment of 298 naturally aged FPGAs operating in the European XFEL particle accelerator. We base our statistical analys... | {
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2412.15721 | Safe Spaces or Toxic Places? Content Moderation and Social Dynamics of
Online Eating Disorder Communities | [
"cs.SI",
"cs.CY",
"cs.HC"
] | Social media platforms have become critical spaces for discussing mental health concerns, including eating disorders. While these platforms can provide valuable support networks, they may also amplify harmful content that glorifies disordered cognition and self-destructive behaviors. While social media platforms have i... | {
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2412.15726 | Fine-tuning Whisper on Low-Resource Languages for Real-World
Applications | [
"cs.CL",
"eess.AS"
] | This paper presents a new approach to fine-tuning OpenAI's Whisper model for low-resource languages by introducing a novel data generation method that converts sentence-level data into a long-form corpus, using Swiss German as a case study. Non-sentence-level data, which could improve the performance of long-form audio... | {
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2412.15728 | fluke: Federated Learning Utility frameworK for Experimentation and
research | [
"cs.LG",
"cs.AI"
] | Since its inception in 2016, Federated Learning (FL) has been gaining tremendous popularity in the machine learning community. Several frameworks have been proposed to facilitate the development of FL algorithms, but researchers often resort to implementing their algorithms from scratch, including all baselines and exp... | {
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2412.15734 | The Role of Recurrency in Image Segmentation for Noisy and Limited
Sample Settings | [
"cs.CV",
"cs.LG"
] | The biological brain has inspired multiple advances in machine learning. However, most state-of-the-art models in computer vision do not operate like the human brain, simply because they are not capable of changing or improving their decisions/outputs based on a deeper analysis. The brain is recurrent, while these mode... | {
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2412.15735 | Prompt-based Unifying Inference Attack on Graph Neural Networks | [
"cs.LG"
] | Graph neural networks (GNNs) provide important prospective insights in applications such as social behavior analysis and financial risk analysis based on their powerful learning capabilities on graph data. Nevertheless, GNNs' predictive performance relies on the quality of task-specific node labels, so it is common pra... | {
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2412.15739 | VORD: Visual Ordinal Calibration for Mitigating Object Hallucinations in
Large Vision-Language Models | [
"cs.CV"
] | Large Vision-Language Models (LVLMs) have made remarkable developments along with the recent surge of large language models. Despite their advancements, LVLMs have a tendency to generate plausible yet inaccurate or inconsistent information based on the provided source content. This phenomenon, also known as ``hallucina... | {
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2412.15740 | From Model Based to Learned Regularization in Medical Image
Registration: A Comprehensive Review | [
"eess.IV",
"cs.CV"
] | Image registration is fundamental in medical imaging applications, such as disease progression analysis or radiation therapy planning. The primary objective of image registration is to precisely capture the deformation between two or more images, typically achieved by minimizing an optimization problem. Due to its inhe... | {
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2412.15745 | Dynamic Learning Rate Decay for Stochastic Variational Inference | [
"cs.CE"
] | Like many optimization algorithms, Stochastic Variational Inference (SVI) is sensitive to the choice of the learning rate. If the learning rate is too small, the optimization process may be slow, and the algorithm might get stuck in local optima. On the other hand, if the learning rate is too large, the algorithm may o... | {
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2412.15748 | Critique of Impure Reason: Unveiling the reasoning behaviour of medical
Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Background: Despite the current ubiquity of Large Language Models (LLMs) across the medical domain, there is a surprising lack of studies which address their reasoning behaviour. We emphasise the importance of understanding reasoning behaviour as opposed to high-level prediction accuracies, since it is equivalent to ex... | {
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2412.15750 | Extracting Interpretable Task-Specific Circuits from Large Language
Models for Faster Inference | [
"cs.LG"
] | Large Language Models (LLMs) have shown impressive performance across a wide range of tasks. However, the size of LLMs is steadily increasing, hindering their application on computationally constrained environments. On the other hand, despite their general capabilities, there are many situations where only one specific... | {
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2412.15752 | Sparse Point Clouds Assisted Learned Image Compression | [
"cs.CV",
"eess.IV"
] | In the field of autonomous driving, a variety of sensor data types exist, each representing different modalities of the same scene. Therefore, it is feasible to utilize data from other sensors to facilitate image compression. However, few techniques have explored the potential benefits of utilizing inter-modality corre... | {
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2412.15756 | Probabilistic Latent Variable Modeling for Dynamic Friction
Identification and Estimation | [
"cs.RO",
"cs.LG",
"cs.SY",
"eess.SY"
] | Precise identification of dynamic models in robotics is essential to support control design, friction compensation, output torque estimation, etc. A longstanding challenge remains in the identification of friction models for robotic joints, given the numerous physical phenomena affecting the underlying friction dynamic... | {
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2412.15757 | FTISS Adaptive Bearing-Only Formation Tracking Control with Unknown
Disturbance Rejection | [
"eess.SY",
"cs.SY"
] | This paper proposes a finite-time input-to-state stable (FTISS) bearing-only formation control law that rejects unknown constant disturbances. Unlike existing finite-time bearing-based formation control laws, which typically rely on the availability of a global coordinate frame and some information about the disturbanc... | {
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2412.15758 | Function Space Diversity for Uncertainty Prediction via Repulsive
Last-Layer Ensembles | [
"cs.LG"
] | Bayesian inference in function space has gained attention due to its robustness against overparameterization in neural networks. However, approximating the infinite-dimensional function space introduces several challenges. In this work, we discuss function space inference via particle optimization and present practical... | {
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2412.15759 | ASPIRE: Assistive System for Performance Evaluation in IR | [
"cs.IR"
] | Information Retrieval (IR) evaluation involves far more complexity than merely presenting performance measures in a table. Researchers often need to compare multiple models across various dimensions, such as the Precision-Recall trade-off and response time, to understand the reasons behind the varying performance of sp... | {
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2412.15772 | Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease
Detection based on Spontaneous Speech | [
"cs.CL",
"cs.AI"
] | Alzheimer's Disease (AD) is a significant and growing public health concern. Investigating alterations in speech and language patterns offers a promising path towards cost-effective and non-invasive early detection of AD on a large scale. Large language models (LLMs), such as GPT, have enabled powerful new possibilitie... | {
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2412.15776 | On the optimal growth of autocatalytic subnetworks: A Mathematical
Optimization Approach | [
"math.OC",
"cs.CE"
] | Chemical reaction networks (CRNs) are essential for modeling and analyzing complex systems across fields, from biochemistry to economics. Autocatalytic reaction network -- networks where certain species catalyze their own production -- are particularly significant for understanding self-replication dynamics in biologic... | {
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2412.15785 | Learning from Impairment: Leveraging Insights from Clinical Linguistics
in Language Modelling Research | [
"cs.CL"
] | This position paper investigates the potential of integrating insights from language impairment research and its clinical treatment to develop human-inspired learning strategies and evaluation frameworks for language models (LMs). We inspect the theoretical underpinnings underlying some influential linguistically motiv... | {
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2412.15790 | GraphSeqLM: A Unified Graph Language Framework for Omic Graph Learning | [
"q-bio.QM",
"cs.AI",
"cs.LG"
] | The integration of multi-omic data is pivotal for understanding complex diseases, but its high dimensionality and noise present significant challenges. Graph Neural Networks (GNNs) offer a robust framework for analyzing large-scale signaling pathways and protein-protein interaction networks, yet they face limitations i... | {
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2412.15797 | Ensembling Large Language Models with Process Reward-Guided Tree Search
for Better Complex Reasoning | [
"cs.CL"
] | Despite recent advances in large language models, open-source models often struggle to consistently perform well on complex reasoning tasks. Existing ensemble methods, whether applied at the token or output levels, fail to address these challenges. In response, we present Language model Ensemble with Monte Carlo Tree S... | {
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2412.15798 | Diffusion-Based Conditional Image Editing through Optimized Inference
with Guidance | [
"cs.CV"
] | We present a simple but effective training-free approach for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our goal is to generate an image that aligns with the target task while preserving the structure and background of a source image. To this end, we derive the represent... | {
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2412.15800 | Variance of the sum of independent quantum computing errors | [
"quant-ph",
"cs.DC",
"cs.IT",
"math.IT"
] | The sum of quantum computing errors is the key element both for the estimation and control of errors in quantum computing and for its statistical study. In this article we analyze the sum of two independent quantum computing errors, $X_1$ and $X_2$, and we obtain the formula of the variance of the sum of these errors: ... | {
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2412.15801 | Bi-directional Mapping of Morphology Metrics and 3D City Blocks for
Enhanced Characterization and Generation of Urban Form | [
"cs.CE",
"cs.AI"
] | Urban morphology, examining city spatial configurations, links urban design to sustainability. Morphology metrics play a fundamental role in performance-driven computational urban design (CUD) which integrates urban form generation, performance evaluation and optimization. However, a critical gap remains between perfor... | {
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2412.15803 | WebLLM: A High-Performance In-Browser LLM Inference Engine | [
"cs.LG",
"cs.AI"
] | Advancements in large language models (LLMs) have unlocked remarkable capabilities. While deploying these models typically requires server-grade GPUs and cloud-based inference, the recent emergence of smaller open-source models and increasingly powerful consumer devices have made on-device deployment practical. The web... | {
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2412.15808 | Deep learning joint extremes of metocean variables using the SPAR model | [
"stat.ML",
"cs.LG",
"stat.ME"
] | This paper presents a novel deep learning framework for estimating multivariate joint extremes of metocean variables, based on the Semi-Parametric Angular-Radial (SPAR) model. When considered in polar coordinates, the problem of modelling multivariate extremes is transformed to one of modelling an angular density, and ... | {
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2412.15810 | Tutorial Problems for Nonsmooth Dynamics and Optimal Control: Ski
Jumping and Accelerating a Bike Without Pedaling | [
"eess.SY",
"cs.SY",
"math.OC"
] | Nonsmooth phenomena, such as abrupt changes, impacts, and switching behaviors, frequently arise in real-world systems and present significant challenges for traditional optimal control methods, which typically assume smoothness and differentiability. These phenomena introduce numerical challenges in both simulation and... | {
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2412.15813 | Cross-Modal Few-Shot Learning with Second-Order Neural Ordinary
Differential Equations | [
"cs.CV"
] | We introduce SONO, a novel method leveraging Second-Order Neural Ordinary Differential Equations (Second-Order NODEs) to enhance cross-modal few-shot learning. By employing a simple yet effective architecture consisting of a Second-Order NODEs model paired with a cross-modal classifier, SONO addresses the significant c... | {
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2412.15818 | Precision ICU Resource Planning: A Multimodal Model for Brain Surgery
Outcomes | [
"eess.IV",
"cs.CV",
"q-bio.NC"
] | Although advances in brain surgery techniques have led to fewer postoperative complications requiring Intensive Care Unit (ICU) monitoring, the routine transfer of patients to the ICU remains the clinical standard, despite its high cost. Predictive Gradient Boosted Trees based on clinical data have attempted to optimiz... | {
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2412.15819 | Robustness-enhanced Myoelectric Control with GAN-based Open-set
Recognition | [
"cs.CV",
"cs.HC",
"eess.SP"
] | Electromyography (EMG) signals are widely used in human motion recognition and medical rehabilitation, yet their variability and susceptibility to noise significantly limit the reliability of myoelectric control systems. Existing recognition algorithms often fail to handle unfamiliar actions effectively, leading to sys... | {
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2412.15821 | $\pi$-yalli: un nouveau corpus pour le nahuatl | [
"cs.CL",
"cs.AI"
] | The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by aroun... | {
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2412.15822 | S$^2$DN: Learning to Denoise Unconvincing Knowledge for Inductive
Knowledge Graph Completion | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Inductive Knowledge Graph Completion (KGC) aims to infer missing facts between newly emerged entities within knowledge graphs (KGs), posing a significant challenge. While recent studies have shown promising results in inferring such entities through knowledge subgraph reasoning, they suffer from (i) the semantic incons... | {
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} |
2412.15826 | Using matrix-product states for time-series machine learning | [
"stat.ML",
"cs.LG",
"quant-ph"
] | Matrix-product states (MPS) have proven to be a versatile ansatz for modeling quantum many-body physics. For many applications, and particularly in one-dimension, they capture relevant quantum correlations in many-body wavefunctions while remaining tractable to store and manipulate on a classical computer. This has mot... | {
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2412.15828 | Measuring Cross-Modal Interactions in Multimodal Models | [
"cs.LG"
] | Integrating AI in healthcare can greatly improve patient care and system efficiency. However, the lack of explainability in AI systems (XAI) hinders their clinical adoption, especially in multimodal settings that use increasingly complex model architectures. Most existing XAI methods focus on unimodal models, which fai... | {
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2412.15831 | Enriching Social Science Research via Survey Item Linking | [
"cs.DL",
"cs.CL"
] | Questions within surveys, called survey items, are used in the social sciences to study latent concepts, such as the factors influencing life satisfaction. Instead of using explicit citations, researchers paraphrase the content of the survey items they use in-text. However, this makes it challenging to find survey item... | {
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2412.15835 | Enhancing Generalized Few-Shot Semantic Segmentation via Effective
Knowledge Transfer | [
"cs.CV"
] | Generalized few-shot semantic segmentation (GFSS) aims to segment objects of both base and novel classes, using sufficient samples of base classes and few samples of novel classes. Representative GFSS approaches typically employ a two-phase training scheme, involving base class pre-training followed by novel class fine... | {
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2412.15837 | Traffic-Rule-Compliant Trajectory Repair via Satisfiability Modulo
Theories and Reachability Analysis | [
"cs.RO",
"cs.AI"
] | Complying with traffic rules is challenging for automated vehicles, as numerous rules need to be considered simultaneously. If a planned trajectory violates traffic rules, it is common to replan a new trajectory from scratch. We instead propose a trajectory repair technique to save computation time. By coupling satisfi... | {
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2412.15838 | Align Anything: Training All-Modality Models to Follow Instructions with
Language Feedback | [
"cs.AI",
"cs.CL"
] | Reinforcement learning from human feedback (RLHF) has proven effective in enhancing the instruction-following capabilities of large language models; however, it remains underexplored in the cross-modality domain. As the number of modalities increases, aligning all-modality models with human intentions -- such as instru... | {
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2412.15844 | Efficient Curation of Invertebrate Image Datasets Using Feature
Embeddings and Automatic Size Comparison | [
"cs.CV"
] | The amount of image datasets collected for environmental monitoring purposes has increased in the past years as computer vision assisted methods have gained interest. Computer vision applications rely on high-quality datasets, making data curation important. However, data curation is often done ad-hoc and the methods u... | {
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2412.15845 | Multi-dimensional Visual Prompt Enhanced Image Restoration via
Mamba-Transformer Aggregation | [
"cs.CV"
] | Recent efforts on image restoration have focused on developing "all-in-one" models that can handle different degradation types and levels within single model. However, most of mainstream Transformer-based ones confronted with dilemma between model capabilities and computation burdens, since self-attention mechanism qua... | {
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2412.15846 | Improving Quantization-aware Training of Low-Precision Network via Block
Replacement on Full-Precision Counterpart | [
"cs.LG"
] | Quantization-aware training (QAT) is a common paradigm for network quantization, in which the training phase incorporates the simulation of the low-precision computation to optimize the quantization parameters in alignment with the task goals. However, direct training of low-precision networks generally faces two obsta... | {
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2412.15847 | Image Quality Assessment: Enhancing Perceptual Exploration and
Interpretation with Collaborative Feature Refinement and Hausdorff distance | [
"eess.IV",
"cs.CV"
] | Current full-reference image quality assessment (FR-IQA) methods often fuse features from reference and distorted images, overlooking that color and luminance distortions occur mainly at low frequencies, whereas edge and texture distortions occur at high frequencies. This work introduces a pioneering training-free FR-I... | {
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2412.15848 | Stalling in Space: Attractor Analysis for any Algorithm | [
"cs.NE"
] | Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisation algorithms. Local optima networks (LONs) have been especially dominant in the literature and capture an approximation of local optima and their... | {
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2412.15853 | Semi-Supervised Adaptation of Diffusion Models for Handwritten Text
Generation | [
"cs.CV"
] | The generation of images of realistic looking, readable handwritten text is a challenging task which is referred to as handwritten text generation (HTG). Given a string and examples from a writer, the goal is to synthesize an image depicting the correctly spelled word in handwriting with the calligraphic style of the d... | {
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} |
2412.15859 | PyBOP: A Python package for battery model optimisation and
parameterisation | [
"eess.SY",
"cs.SY"
] | The Python Battery Optimisation and Parameterisation (PyBOP) package provides methods for estimating and optimising battery model parameters, offering both deterministic and stochastic approaches with example workflows to assist users. PyBOP enables parameter identification from data for various battery models, includi... | {
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} |
2412.15861 | On Robust Cross Domain Alignment | [
"stat.ML",
"cs.LG"
] | The Gromov-Wasserstein (GW) distance is an effective measure of alignment between distributions supported on distinct ambient spaces. Calculating essentially the mutual departure from isometry, it has found vast usage in domain translation and network analysis. It has long been shown to be vulnerable to contamination i... | {
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} |
2412.15862 | MarkovType: A Markov Decision Process Strategy for Non-Invasive
Brain-Computer Interfaces Typing Systems | [
"cs.LG"
] | Brain-Computer Interfaces (BCIs) help people with severe speech and motor disabilities communicate and interact with their environment using neural activity. This work focuses on the Rapid Serial Visual Presentation (RSVP) paradigm of BCIs using noninvasive electroencephalography (EEG). The RSVP typing task is a recurs... | {
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} |
2412.15863 | Bayesian Optimization for Unknown Cost-Varying Variable Subsets with
No-Regret Costs | [
"cs.LG"
] | Bayesian Optimization (BO) is a widely-used method for optimizing expensive-to-evaluate black-box functions. Traditional BO assumes that the learner has full control over all query variables without additional constraints. However, in many real-world scenarios, controlling certain query variables may incur costs. There... | {
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} |
2412.15866 | The common ground of DAE approaches. An overview of diverse DAE
frameworks emphasizing their commonalities | [
"math.CA",
"cs.LG"
] | We analyze different approaches to differential-algebraic equations with attention to the implemented rank conditions of various matrix functions. These conditions are apparently very different and certain rank drops in some matrix functions actually indicate a critical solution behavior. We look for common ground by c... | {
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} |
2412.15867 | IRGS: Inter-Reflective Gaussian Splatting with 2D Gaussian Ray Tracing | [
"cs.CV"
] | In inverse rendering, accurately modeling visibility and indirect radiance for incident light is essential for capturing secondary effects. Due to the absence of a powerful Gaussian ray tracer, previous 3DGS-based methods have either adopted a simplified rendering equation or used learnable parameters to approximate in... | {
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} |
2412.15876 | AI-in-the-loop: The future of biomedical visual analytics applications
in the era of AI | [
"cs.HC",
"cs.AI",
"cs.GR"
] | AI is the workhorse of modern data analytics and omnipresent across many sectors. Large Language Models and multi-modal foundation models are today capable of generating code, charts, visualizations, etc. How will these massive developments of AI in data analytics shape future data visualizations and visual analytics w... | {
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} |
2412.15877 | Approximate State Abstraction for Markov Games | [
"cs.GT",
"cs.AI",
"cs.MA"
] | This paper introduces state abstraction for two-player zero-sum Markov games (TZMGs), where the payoffs for the two players are determined by the state representing the environment and their respective actions, with state transitions following Markov decision processes. For example, in games like soccer, the value of a... | {
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} |
2412.15888 | IMPLY-based Approximate Full Adders for Efficient Arithmetic Operations
in Image Processing and Machine Learning | [
"cs.ET",
"cs.LG"
] | To overcome the performance limitations in modern computing, such as the power wall, emerging computing paradigms are gaining increasing importance. Approximate computing offers a promising solution by substantially enhancing energy efficiency and reducing latency, albeit with a trade-off in accuracy. Another emerging ... | {
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} |
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