id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2411.09593 | SMILE-UHURA Challenge -- Small Vessel Segmentation at Mesoscopic Scale
from Ultra-High Resolution 7T Magnetic Resonance Angiograms | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The human brain receives nutrients and oxygen through an intricate network of blood vessels. Pathology affecting small vessels, at the mesoscopic scale, represents a critical vulnerability within the cerebral blood supply and can lead to severe conditions, such as Cerebral Small Vessel Diseases. The advent of 7 Tesla M... | {
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2411.09595 | LLaMA-Mesh: Unifying 3D Mesh Generation with Language Models | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV"
] | This work explores expanding the capabilities of large language models (LLMs) pretrained on text to generate 3D meshes within a unified model. This offers key advantages of (1) leveraging spatial knowledge already embedded in LLMs, derived from textual sources like 3D tutorials, and (2) enabling conversational 3D gener... | {
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2411.09598 | Assessing the Performance of the DINOv2 Self-supervised Learning Vision
Transformer Model for the Segmentation of the Left Atrium from MRI Images | [
"eess.IV",
"cs.CV"
] | Accurate left atrium (LA) segmentation from pre-operative scans is crucial for diagnosing atrial fibrillation, treatment planning, and supporting surgical interventions. While deep learning models are key in medical image segmentation, they often require extensive manually annotated data. Foundation models trained on l... | {
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2411.09600 | Latency Optimization in LEO Satellite Communications with Hybrid Beam
Pattern and Interference Control | [
"cs.IT",
"cs.LG",
"math.IT"
] | The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimizatio... | {
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2411.09601 | Accelerating Knowledge Graph and Ontology Engineering with Large
Language Models | [
"cs.AI"
] | Large Language Models bear the promise of significant acceleration of key Knowledge Graph and Ontology Engineering tasks, including ontology modeling, extension, modification, population, alignment, as well as entity disambiguation. We lay out LLM-based Knowledge Graph and Ontology Engineering as a new and coming area ... | {
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2411.09603 | Smart Automation in Luxury Leather Shoe Polishing: A Human Centric
Robotic Approach | [
"cs.RO"
] | The polishing of luxury leather shoes is a delicate, labor intensive process traditionally performed by skilled craftsmen. Footwear companies aim to automate parts of this process to enhance quality, productivity, and operator well-being, but the unique nature of luxury shoe production presents challenges. This paper i... | {
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2411.09604 | Local-Global Attention: An Adaptive Mechanism for Multi-Scale Feature
Integration | [
"cs.CV",
"cs.AI"
] | In recent years, attention mechanisms have significantly enhanced the performance of object detection by focusing on key feature information. However, prevalent methods still encounter difficulties in effectively balancing local and global features. This imbalance hampers their ability to capture both fine-grained deta... | {
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2411.09607 | Initial Nugget Evaluation Results for the TREC 2024 RAG Track with the
AutoNuggetizer Framework | [
"cs.IR",
"cs.CL"
] | This report provides an initial look at partial results from the TREC 2024 Retrieval-Augmented Generation (RAG) Track. We have identified RAG evaluation as a barrier to continued progress in information access (and more broadly, natural language processing and artificial intelligence), and it is our hope that we can co... | {
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2411.09612 | The Moral Foundations Weibo Corpus | [
"cs.CL",
"cs.LG"
] | Moral sentiments expressed in natural language significantly influence both online and offline environments, shaping behavioral styles and interaction patterns, including social media selfpresentation, cyberbullying, adherence to social norms, and ethical decision-making. To effectively measure moral sentiments in natu... | {
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2411.09613 | PTR: Precision-Driven Tool Recommendation for Large Language Models | [
"cs.CL",
"cs.AI"
] | By augmenting Large Language Models (LLMs) with external tools, their capacity to solve complex problems has been significantly enhanced. However, despite ongoing advancements in the parsing capabilities of LLMs, incorporating all available tools simultaneously in the prompt remains impractical due to the vast number o... | {
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2411.09618 | MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust
Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI | [
"physics.med-ph",
"cs.LG"
] | White matter alterations are increasingly implicated in neurological diseases and their progression. International-scale studies use diffusion-weighted magnetic resonance imaging (DW-MRI) to qualitatively identify changes in white matter microstructure and connectivity. Yet, quantitative analysis of DW-MRI data is hind... | {
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2411.09623 | Vision-based Manipulation of Transparent Plastic Bags in Industrial
Setups | [
"cs.RO",
"cs.AI",
"cs.CV"
] | This paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, aligning with the Industry 4.0 paradigm. Industry 4.0, driven by data, connectivity, analytics, and robotics, promises enhanced accessibility and sustainability through... | {
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2411.09625 | Local deployment of large-scale music AI models on commodity hardware | [
"cs.SD",
"cs.LG",
"eess.AS"
] | We present the MIDInfinite, a web application capable of generating symbolic music using a large-scale generative AI model locally on commodity hardware. Creating this demo involved porting the Anticipatory Music Transformer, a large language model (LLM) pre-trained on the Lakh MIDI dataset, to the Machine Learning Com... | {
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2411.09627 | One-Shot Manipulation Strategy Learning by Making Contact Analogies | [
"cs.RO",
"cs.AI",
"cs.CV"
] | We present a novel approach, MAGIC (manipulation analogies for generalizable intelligent contacts), for one-shot learning of manipulation strategies with fast and extensive generalization to novel objects. By leveraging a reference action trajectory, MAGIC effectively identifies similar contact points and sequences of ... | {
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2411.09635 | Counterfactual Uncertainty Quantification of Factual Estimand of
Efficacy from Before-and-After Treatment Repeated Measures Randomized
Controlled Trials | [
"stat.ML",
"cs.LG"
] | The ideal estimand for comparing treatment $Rx$ with a control $C$ is the $\textit{counterfactual}$ efficacy $Rx:C$, the expected differential outcome between $Rx$ and $C$ if each patient were given $\textit{both}$. One hundred years ago, Neyman (1923a) proved unbiased $\textit{point estimation}$ of counterfactual effi... | {
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2411.09636 | Nash equilibrium seeking for a class of quadratic-bilinear Wasserstein
distributionally robust games | [
"math.OC",
"cs.MA",
"cs.SY",
"eess.SY"
] | We consider a class of Wasserstein distributionally robust Nash equilibrium problems, where agents construct heterogeneous data-driven Wasserstein ambiguity sets using private samples and radii, in line with their individual risk-averse behaviour. By leveraging relevant properties of this class of games, we show that e... | {
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2411.09639 | MCCE: Missingness-aware Causal Concept Explainer | [
"cs.LG"
] | Causal concept effect estimation is gaining increasing interest in the field of interpretable machine learning. This general approach explains the behaviors of machine learning models by estimating the causal effect of human-understandable concepts, which represent high-level knowledge more comprehensibly than raw inpu... | {
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2411.09642 | On the Limits of Language Generation: Trade-Offs Between Hallucination
and Mode Collapse | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.DS",
"stat.ML"
] | Specifying all desirable properties of a language model is challenging, but certain requirements seem essential. Given samples from an unknown language, the trained model should produce valid strings not seen in training and be expressive enough to capture the language's full richness. Otherwise, outputting invalid str... | {
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2411.09643 | Modular Fault Diagnosis Framework for Complex Autonomous Driving Systems | [
"cs.RO"
] | Fault diagnosis is crucial for complex autonomous mobile systems, especially for modern-day autonomous driving (AD). Different actors, numerous use cases, and complex heterogeneous components motivate a fault diagnosis of the system and overall system integrity. AD systems are composed of many heterogeneous components,... | {
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2411.09644 | Neural Operators Can Play Dynamic Stackelberg Games | [
"math.OC",
"cs.LG",
"cs.NA",
"math.NA",
"math.PR",
"q-fin.CP"
] | Dynamic Stackelberg games are a broad class of two-player games in which the leader acts first, and the follower chooses a response strategy to the leader's strategy. Unfortunately, only stylized Stackelberg games are explicitly solvable since the follower's best-response operator (as a function of the control of the l... | {
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2411.09645 | How do Machine Learning Models Change? | [
"cs.SE",
"cs.LG"
] | The proliferation of Machine Learning (ML) models and their open-source implementations has transformed Artificial Intelligence research and applications. Platforms like Hugging Face (HF) enable the development, sharing, and deployment of these models, fostering an evolving ecosystem. While previous studies have examin... | {
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2411.09648 | Med-Bot: An AI-Powered Assistant to Provide Accurate and Reliable
Medical Information | [
"cs.AI",
"cs.LG",
"cs.NE"
] | This paper introduces Med-Bot, an AI-powered chatbot designed to provide users with accurate and reliable medical information. Utilizing advanced libraries and frameworks such as PyTorch, Chromadb, Langchain and Autogptq, Med-Bot is built to handle the complexities of natural language understanding in a healthcare cont... | {
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2411.09653 | How to implement the Bayes' formula in the age of ML? | [
"eess.SY",
"cs.SY",
"math.OC"
] | This chapter contains a self-contained introduction to the significance of Bayes' formula in the context of nonlinear filtering problems. Both discrete-time and continuous-time settings of the problem are considered in a unified manner. In control theory, the focus on optimization-based solution approaches is stressed ... | {
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2411.09658 | Motion Before Action: Diffusing Object Motion as Manipulation Condition | [
"cs.RO"
] | Inferring object motion representations from observations enhances the performance of robotic manipulation tasks. This paper introduces a new paradigm for robot imitation learning that generates action sequences by reasoning about object motion from visual observations. We propose MBA (Motion Before Action), a novel mo... | {
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2411.09661 | Adaptive Decoding via Latent Preference Optimization | [
"cs.CL"
] | During language model decoding, it is known that using higher temperature sampling gives more creative responses, while lower temperatures are more factually accurate. However, such models are commonly applied to general instruction following, which involves both creative and fact seeking tasks, using a single fixed te... | {
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2411.09666 | Evaluating 5G Networks for U-Space Applications: Insights from Dense
Urban Measurement Campaign | [
"cs.IT",
"eess.SP",
"math.IT"
] | This paper examines the communication performance of unmanned aerial vehicles (UAVs) in dense urban environments, specifically in Benidorm, Spain. Through a comprehensive measurement campaign, we assessed key performance indicators (KPIs) relating to received signal strength and quality as well as rate across various l... | {
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2411.09675 | Citation Sentiment Reflects Multiscale Sociocultural Norms | [
"cs.SI"
] | Modern science is formally structured around scholarly publication, where scientific knowledge is canonized through citation. Precisely how citations are given and accrued can provide information about the value of discovery, the history of scientific ideas, the structure of fields, and the space or scope of inquiry. Y... | {
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2411.09678 | NeuralDEM -- Real-time Simulation of Industrial Particulate Flows | [
"cs.LG",
"cs.AI"
] | Advancements in computing power have made it possible to numerically simulate large-scale fluid-mechanical and/or particulate systems, many of which are integral to core industrial processes. Among the different numerical methods available, the discrete element method (DEM) provides one of the most accurate representat... | {
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2411.09683 | Towards a Classification of Open-Source ML Models and Datasets for
Software Engineering | [
"cs.SE",
"cs.AI",
"cs.LG"
] | Background: Open-Source Pre-Trained Models (PTMs) and datasets provide extensive resources for various Machine Learning (ML) tasks, yet these resources lack a classification tailored to Software Engineering (SE) needs. Aims: We apply an SE-oriented classification to PTMs and datasets on a popular open-source ML reposit... | {
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2411.09686 | Conditional regression for the Nonlinear Single-Variable Model | [
"stat.ML",
"cs.LG"
] | Several statistical models for regression of a function $F$ on $\mathbb{R}^d$ without the statistical and computational curse of dimensionality exist, for example by imposing and exploiting geometric assumptions on the distribution of the data (e.g. that its support is low-dimensional), or strong smoothness assumptions... | {
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2411.09688 | Squeezed Attention: Accelerating Long Context Length LLM Inference | [
"cs.CL"
] | Emerging Large Language Model (LLM) applications require long input prompts to perform complex downstream tasks like document analysis and code generation. For these long context length applications, the length of the input prompt poses a significant challenge in terms of inference efficiency since the inference costs ... | {
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2411.09689 | LLM Hallucination Reasoning with Zero-shot Knowledge Test | [
"cs.AI",
"cs.CL"
] | LLM hallucination, where LLMs occasionally generate unfaithful text, poses significant challenges for their practical applications. Most existing detection methods rely on external knowledge, LLM fine-tuning, or hallucination-labeled datasets, and they do not distinguish between different types of hallucinations, which... | {
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2411.09691 | Advancing Fine-Grained Visual Understanding with Multi-Scale Alignment
in Multi-Modal Models | [
"cs.CV"
] | Multi-modal large language models (MLLMs) have achieved remarkable success in fine-grained visual understanding across a range of tasks. However, they often encounter significant challenges due to inadequate alignment for fine-grained knowledge, which restricts their ability to accurately capture local details and atta... | {
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2411.09693 | CropCraft: Inverse Procedural Modeling for 3D Reconstruction of Crop
Plants | [
"cs.CV"
] | The ability to automatically build 3D digital twins of plants from images has countless applications in agriculture, environmental science, robotics, and other fields. However, current 3D reconstruction methods fail to recover complete shapes of plants due to heavy occlusion and complex geometries. In this work, we pre... | {
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2411.09694 | A Bayesian Optimization Approach to Machine Translation Reranking | [
"cs.CL"
] | Reranking a list of candidates from a machine translation system with an external scoring model and returning the highest-scoring candidate remains a simple and effective method for improving the overall output quality. Translation scoring models continue to grow in size, with the best models being comparable to genera... | {
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2411.09702 | On the Surprising Effectiveness of Attention Transfer for Vision
Transformers | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NE"
] | Conventional wisdom suggests that pre-training Vision Transformers (ViT) improves downstream performance by learning useful representations. Is this actually true? We investigate this question and find that the features and representations learned during pre-training are not essential. Surprisingly, using only the atte... | {
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2411.09703 | MagicQuill: An Intelligent Interactive Image Editing System | [
"cs.CV"
] | Image editing involves a variety of complex tasks and requires efficient and precise manipulation techniques. In this paper, we present MagicQuill, an integrated image editing system that enables swift actualization of creative ideas. Our system features a streamlined yet functionally robust interface, allowing for the... | {
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2411.09705 | Residual Multi-Task Learner for Applied Ranking | [
"cs.IR",
"cs.LG"
] | Modern e-commerce platforms rely heavily on modeling diverse user feedback to provide personalized services. Consequently, multi-task learning has become an integral part of their ranking systems. However, existing multi-task learning methods encounter two main challenges: some lack explicit modeling of task relationsh... | {
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2411.09706 | AI-Driven Feedback Loops in Digital Technologies: Psychological Impacts
on User Behaviour and Well-Being | [
"cs.CY",
"cs.AI",
"cs.HC"
] | The rapid spread of digital technologies has produced data-driven feedback loops, wearable devices, social media networks, and mobile applications that shape user behavior, motivation, and mental well-being. While these systems encourage self-improvement and the development of healthier habits through real-time feedbac... | {
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2411.09707 | Decoding Fatigue Levels of Pilots Using EEG Signals with Hybrid Deep
Neural Networks | [
"eess.SP",
"cs.HC",
"cs.LG"
] | The detection of pilots' mental states is critical, as abnormal mental states have the potential to cause catastrophic accidents. This study demonstrates the feasibility of using deep learning techniques to classify different fatigue levels, specifically a normal state, low fatigue, and high fatigue. To the best of our... | {
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2411.09709 | Feature Selection via Dynamic Graph-based Attention Block in MI-based
EEG Signals | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Brain-computer interface (BCI) technology enables direct interaction between humans and computers by analyzing brain signals. Electroencephalogram (EEG) is one of the non-invasive tools used in BCI systems, providing high temporal resolution for real-time applications. However, EEG signals are often affected by a low s... | {
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2411.09712 | Digital Twin-Assisted Space-Air-Ground Integrated Multi-Access Edge
Computing for Low-Altitude Economy: An Online Decentralized Optimization
Approach | [
"eess.SY",
"cs.GT",
"cs.SY"
] | The emergence of space-air-ground integrated multi-access edge computing (SAGIMEC) networks opens a significant opportunity for the rapidly growing low altitude economy (LAE), facilitating the development of various applications by offering efficient communication and computing services. However, the heterogeneous natu... | {
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2411.09714 | Machine learning approaches to explore important features behind bird
flight modes | [
"q-bio.QM",
"cs.LG"
] | Birds exhibit a variety of flight styles, primarily classified as flapping, which is characterized by rapid up-and-down wing movements, and soaring, which involves gliding with wings outstretched. Each species usually performs specific flight styles, and this has been argued in terms of morphological and physiological ... | {
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2411.09717 | Integrating Fuzzy Set Theory with Pandora Temporal Fault Trees for
Dynamic Failure Analysis of Complex Systems | [
"eess.SY",
"cs.SY",
"math.PR"
] | Pandora temporal fault tree, as one notable extension of the fault tree, introduces temporal gates and temporal laws. Pandora Temporal Fault Tree(TFT) enhances the capability of fault trees and enables the modeling of system failure behavior that depends on sequences. The calculation of system failure probability in Pa... | {
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2411.09718 | NFRs in Medical Imaging | [
"cs.SE",
"cs.AI",
"cs.LG"
] | The diagnostic imaging departments are under great pressure due to a growing workload. The number of required scans is growing and there is a shortage of qualified labor. AI solutions for medical imaging applications have shown great potential. However, very few diagnostic imaging models have been approved for hospital... | {
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2411.09720 | Early-Scheduled Handover Preparation in 5G NR Millimeter-Wave Systems | [
"cs.LG",
"cs.IT",
"math.IT"
] | The handover (HO) procedure is one of the most critical functions in a cellular network driven by measurements of the user channel of the serving and neighboring cells. The success rate of the entire HO procedure is significantly affected by the preparation stage. As massive Multiple-Input Multiple-Output (MIMO) system... | {
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2411.09722 | Iterative Batch Reinforcement Learning via Safe Diversified Model-based
Policy Search | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Batch reinforcement learning enables policy learning without direct interaction with the environment during training, relying exclusively on previously collected sets of interactions. This approach is, therefore, well-suited for high-risk and cost-intensive applications, such as industrial control. Learned policies are... | {
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2411.09723 | Towards Neural Foundation Models for Vision: Aligning EEG, MEG, and fMRI
Representations for Decoding, Encoding, and Modality Conversion | [
"cs.CV",
"cs.AI"
] | This paper presents a novel approach towards creating a foundational model for aligning neural data and visual stimuli across multimodal representationsof brain activity by leveraging contrastive learning. We used electroencephalography (EEG), magnetoencephalography (MEG), and functional magnetic resonance imaging (fMR... | {
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2411.09726 | Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring | [
"stat.AP",
"cs.LG",
"stat.ME"
] | Thermal comfort is essential for well-being in urban spaces, especially as cities face increasing heat from urbanization and climate change. Existing thermal comfort models usually overlook temporal dynamics alongside spatial dependencies. We address this problem by introducing a spatio-temporal jump model that cluster... | {
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2411.09728 | Physics-informed neural networks (PINNs) for numerical model error
approximation and superresolution | [
"cs.LG",
"cs.NA",
"math.NA",
"stat.CO"
] | Numerical modeling errors are unavoidable in finite element analysis. The presence of model errors inherently reflects both model accuracy and uncertainty. To date there have been few methods for explicitly quantifying errors at points of interest (e.g. at finite element nodes). The lack of explicit model error approxi... | {
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2411.09730 | SureMap: Simultaneous Mean Estimation for Single-Task and Multi-Task
Disaggregated Evaluation | [
"cs.LG",
"cs.AI",
"stat.AP",
"stat.ML"
] | Disaggregated evaluation -- estimation of performance of a machine learning model on different subpopulations -- is a core task when assessing performance and group-fairness of AI systems. A key challenge is that evaluation data is scarce, and subpopulations arising from intersections of attributes (e.g., race, sex, ag... | {
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2411.09731 | To bootstrap or to rollout? An optimal and adaptive interpolation | [
"cs.LG",
"math.ST",
"stat.ML",
"stat.TH"
] | Bootstrapping and rollout are two fundamental principles for value function estimation in reinforcement learning (RL). We introduce a novel class of Bellman operators, called subgraph Bellman operators, that interpolate between bootstrapping and rollout methods. Our estimator, derived by solving the fixed point of the ... | {
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2411.09734 | Modeling AdaGrad, RMSProp, and Adam with Integro-Differential Equations | [
"cs.LG",
"cs.NA",
"math.NA",
"math.OC"
] | In this paper, we propose a continuous-time formulation for the AdaGrad, RMSProp, and Adam optimization algorithms by modeling them as first-order integro-differential equations. We perform numerical simulations of these equations to demonstrate their validity as accurate approximations of the original algorithms. Our ... | {
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2411.09749 | Adversarial Attacks Using Differentiable Rendering: A Survey | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Differentiable rendering methods have emerged as a promising means for generating photo-realistic and physically plausible adversarial attacks by manipulating 3D objects and scenes that can deceive deep neural networks (DNNs). Recently, differentiable rendering capabilities have evolved significantly into a diverse lan... | {
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2411.09751 | Analyzing the AI Nudification Application Ecosystem | [
"cs.HC",
"cs.CV"
] | Given a source image of a clothed person (an image subject), AI-based nudification applications can produce nude (undressed) images of that person. Moreover, not only do such applications exist, but there is ample evidence of the use of such applications in the real world and without the consent of an image subject. St... | {
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2411.09758 | Partial Multi-View Clustering via Meta-Learning and Contrastive Feature
Alignment | [
"cs.CV",
"cs.LG"
] | Partial multi-view clustering (PVC) presents significant challenges practical research problem for data analysis in real-world applications, especially when some views of the data are partially missing. Existing clustering methods struggle to handle incomplete views effectively, leading to suboptimal clustering perform... | {
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2411.09763 | Evaluating the Predictive Capacity of ChatGPT for Academic Peer Review
Outcomes Across Multiple Platforms | [
"cs.DL",
"cs.CL"
] | While previous studies have demonstrated that Large Language Models (LLMs) can predict peer review outcomes to some extent, this paper builds on that by introducing two new contexts and employing a more robust method - averaging multiple ChatGPT scores. The findings that averaging 30 ChatGPT predictions, based on revie... | {
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2411.09764 | ModelPredictiveControl.jl: advanced process control made easy in Julia | [
"eess.SY",
"cs.SY"
] | Proprietary closed-source software is still the norm in advanced process control. Transparency and reproducibility are key aspects of scientific research. Free and open-source toolkit can contribute to the development, sharing and advancement of new and efficient control approaches, and the industrial sector will certa... | {
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2411.09766 | NACNet: A Histology Context-aware Transformer Graph Convolution Network
for Predicting Treatment Response to Neoadjuvant Chemotherapy in Triple
Negative Breast Cancer | [
"cs.CV",
"q-bio.QM"
] | Neoadjuvant chemotherapy (NAC) response prediction for triple negative breast cancer (TNBC) patients is a challenging task clinically as it requires understanding complex histology interactions within the tumor microenvironment (TME). Digital whole slide images (WSIs) capture detailed tissue information, but their giga... | {
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2411.09767 | Deep Learning for Fetal Inflammatory Response Diagnosis in the Umbilical
Cord | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Inflammation of the umbilical cord can be seen as a result of ascending intrauterine infection or other inflammatory stimuli. Acute fetal inflammatory response (FIR) is characterized by infiltration of the umbilical cord by fetal neutrophils, and can be associated with neonatal sepsis or fetal inflammatory response syn... | {
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2411.09772 | Beyond Static Tools: Evaluating Large Language Models for Cryptographic
Misuse Detection | [
"cs.CR",
"cs.LG"
] | The use of Large Language Models (LLMs) in software development is rapidly growing, with developers increasingly relying on these models for coding assistance, including security-critical tasks. Our work presents a comprehensive comparison between traditional static analysis tools for cryptographic API misuse detection... | {
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2411.09776 | Combining Machine Learning Defenses without Conflicts | [
"cs.CR",
"cs.LG"
] | Machine learning (ML) defenses protect against various risks to security, privacy, and fairness. Real-life models need simultaneous protection against multiple different risks which necessitates combining multiple defenses. But combining defenses with conflicting interactions in an ML model can be ineffective, incurrin... | {
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2411.09779 | Variational methods for Learning Multilevel Genetic Algorithms using the
Kantorovich Monad | [
"q-bio.PE",
"cs.NE",
"math.CT"
] | Levels of selection and multilevel evolutionary processes are essential concepts in evolutionary theory, and yet there is a lack of common mathematical models for these core ideas. Here, we propose a unified mathematical framework for formulating and optimizing multilevel evolutionary processes and genetic algorithms o... | {
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2411.09783 | Exploring the Use of Autonomous Unmanned Vehicles for Supporting Power
Grid Operations | [
"eess.SY",
"cs.SY"
] | This paper explores the use of autonomous unmanned vehicles to support power grid operations. With built-in batteries and the capability to carry additional battery energy storage, the rising number of autonomous vehicles can represent a substantial amount of capacity that is currently underutilized in the power grid. ... | {
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2411.09784 | Reinforced Disentanglers on Random Unitary Circuits | [
"quant-ph",
"cond-mat.dis-nn",
"cond-mat.stat-mech",
"cs.LG"
] | We search for efficient disentanglers on random Clifford circuits of two-qubit gates arranged in a brick-wall pattern, using the proximal policy optimization (PPO) algorithm \cite{schulman2017proximalpolicyoptimizationalgorithms}. Disentanglers are defined as a set of projective measurements inserted between consecutiv... | {
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2411.09787 | ART-Rx: A Proportional-Integral-Derivative (PID) Controlled Adaptive
Real-Time Threshold Receiver for Molecular Communication | [
"cs.ET",
"cs.SY",
"eess.SY"
] | Molecular communication (MC) in microfluidic channels faces significant challenges in signal detection due to the stochastic nature of molecule propagation and dynamic, noisy environments. Conventional detection methods often struggle under varying channel conditions, leading to high bit error rates (BER) and reduced c... | {
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2411.09788 | AI-Driven Human-Autonomy Teaming in Tactical Operations: Proposed
Framework, Challenges, and Future Directions | [
"cs.HC",
"cs.AI",
"cs.CY"
] | Artificial Intelligence (AI) techniques, particularly machine learning techniques, are rapidly transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach, focusing on how it empowers human decision-making in ... | {
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2411.09789 | Can EEG resting state data benefit data-driven approaches for
motor-imagery decoding? | [
"eess.SP",
"cs.HC",
"cs.LG",
"q-bio.NC"
] | Resting-state EEG data in neuroscience research serve as reliable markers for user identification and reveal individual-specific traits. Despite this, the use of resting-state data in EEG classification models is limited. In this work, we propose a feature concatenation approach to enhance decoding models' generalizati... | {
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2411.09798 | Video Denoising in Fluorescence Guided Surgery | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Fluorescence guided surgery (FGS) is a promising surgical technique that gives surgeons a unique view of tissue that is used to guide their practice by delineating tissue types and diseased areas. As new fluorescent contrast agents are developed that have low fluorescent photon yields, it becomes increasingly important... | {
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2411.09802 | Modeling human decomposition: a Bayesian approach | [
"cs.LG"
] | Environmental and individualistic variables affect the rate of human decomposition in complex ways. These effects complicate the estimation of the postmortem interval (PMI) based on observed decomposition characteristics. In this work, we develop a generative probabilistic model for decomposing human remains based on P... | {
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2411.09803 | Using a Single-Parity-Check to Reduce the Guesswork of Guessing Codeword
Decoding | [
"cs.IT",
"math.IT"
] | Guessing Codeword Decoding (GCD) is a recently proposed soft-input forward error correction decoder for arbitrary binary linear codes. Inspired by recent proposals that leverage binary linear codebook structure to reduce the number of queries made by Guessing Random Additive Noise Decoding (GRAND), for binary linear co... | {
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2411.09804 | Fair Resource Allocation in Weakly Coupled Markov Decision Processes | [
"cs.LG"
] | We consider fair resource allocation in sequential decision-making environments modeled as weakly coupled Markov decision processes, where resource constraints couple the action spaces of $N$ sub-Markov decision processes (sub-MDPs) that would otherwise operate independently. We adopt a fairness definition using the ge... | {
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2411.09807 | Evaluating Loss Landscapes from a Topology Perspective | [
"cs.LG",
"cs.AI"
] | Characterizing the loss of a neural network with respect to model parameters, i.e., the loss landscape, can provide valuable insights into properties of that model. Various methods for visualizing loss landscapes have been proposed, but less emphasis has been placed on quantifying and extracting actionable and reproduc... | {
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2411.09810 | Robustness Assessment of Static Structures for Efficient Object Handling | [
"cs.RO"
] | This work establishes a solution to the problem of assessing the robustness of multi-object assemblies to external forces. Our physically-grounded approach handles arbitrary static structures made from rigid objects of any shape and mass distribution without relying on heuristics or approximations. The result is a meth... | {
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2411.09812 | Edge Caching Optimization with PPO and Transfer Learning for Dynamic
Environments | [
"cs.NI",
"cs.LG",
"cs.SY",
"eess.SY"
] | This paper addresses the challenge of edge caching in dynamic environments, where rising traffic loads strain backhaul links and core networks. We propose a Proximal Policy Optimization (PPO)-based caching strategy that fully incorporates key file attributes such as size, lifetime, importance, and popularity, while als... | {
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2411.09813 | Can Features for Phishing URL Detection Be Trusted Across Diverse
Datasets? A Case Study with Explainable AI | [
"cs.CR",
"cs.LG"
] | Phishing has been a prevalent cyber threat that manipulates users into revealing sensitive private information through deceptive tactics, designed to masquerade as trustworthy entities. Over the years, proactively detection of phishing URLs (or websites) has been established as an widely-accepted defense approach. In l... | {
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2411.09816 | Learning Parameter Sharing with Tensor Decompositions and Sparsity | [
"cs.LG"
] | Large neural networks achieve remarkable performance, but their size hinders deployment on resource-constrained devices. While various compression techniques exist, parameter sharing remains relatively unexplored. This paper introduces Fine-grained Parameter Sharing (FiPS), a novel algorithm that leverages the relation... | {
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2411.09820 | WelQrate: Defining the Gold Standard in Small Molecule Drug Discovery
Benchmarking | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | While deep learning has revolutionized computer-aided drug discovery, the AI community has predominantly focused on model innovation and placed less emphasis on establishing best benchmarking practices. We posit that without a sound model evaluation framework, the AI community's efforts cannot reach their full potentia... | {
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2411.09821 | Automatic Classification of General Movements in Newborns | [
"cs.LG",
"cs.CV"
] | General movements (GMs) are spontaneous, coordinated body movements in infants that offer valuable insights into the developing nervous system. Assessed through the Prechtl GM Assessment (GMA), GMs are reliable predictors for neurodevelopmental disorders. However, GMA requires specifically trained clinicians, who are l... | {
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2411.09822 | A Self-Supervised Model for Multi-modal Stroke Risk Prediction | [
"cs.CV",
"cs.AI"
] | Predicting stroke risk is a complex challenge that can be enhanced by integrating diverse clinically available data modalities. This study introduces a self-supervised multimodal framework that combines 3D brain imaging, clinical data, and image-derived features to improve stroke risk prediction prior to onset. By leve... | {
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2411.09823 | Architect: Generating Vivid and Interactive 3D Scenes with Hierarchical
2D Inpainting | [
"cs.CV"
] | Creating large-scale interactive 3D environments is essential for the development of Robotics and Embodied AI research. Current methods, including manual design, procedural generation, diffusion-based scene generation, and large language model (LLM) guided scene design, are hindered by limitations such as excessive hum... | {
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2411.09826 | Evaluating Gender Bias in Large Language Models | [
"cs.CL"
] | Gender bias in artificial intelligence has become an important issue, particularly in the context of language models used in communication-oriented applications. This study examines the extent to which Large Language Models (LLMs) exhibit gender bias in pronoun selection in occupational contexts. The analysis evaluates... | {
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2411.09827 | The Good, The Efficient and the Inductive Biases: Exploring Efficiency
in Deep Learning Through the Use of Inductive Biases | [
"cs.LG"
] | The emergence of Deep Learning has marked a profound shift in machine learning, driven by numerous breakthroughs achieved in recent years. However, as Deep Learning becomes increasingly present in everyday tools and applications, there is a growing need to address unresolved challenges related to its efficiency and sus... | {
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2411.09834 | A Benchmark for Long-Form Medical Question Answering | [
"cs.CL",
"cs.AI"
] | There is a lack of benchmarks for evaluating large language models (LLMs) in long-form medical question answering (QA). Most existing medical QA evaluation benchmarks focus on automatic metrics and multiple-choice questions. While valuable, these benchmarks fail to fully capture or assess the complexities of real-world... | {
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2411.09837 | Real-time Adapting Routing (RAR): Improving Efficiency Through
Continuous Learning in Software Powered by Layered Foundation Models | [
"cs.LG",
"cs.AI",
"cs.MA"
] | To balance the quality and inference cost of a Foundation Model (FM, such as large language models (LLMs)) powered software, people often opt to train a routing model that routes requests to FMs with different sizes and capabilities. Existing routing models rely on learning the optimal routing decision from carefully c... | {
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2411.09838 | OneNet: A Channel-Wise 1D Convolutional U-Net | [
"eess.IV",
"cs.CV"
] | Many state-of-the-art computer vision architectures leverage U-Net for its adaptability and efficient feature extraction. However, the multi-resolution convolutional design often leads to significant computational demands, limiting deployment on edge devices. We present a streamlined alternative: a 1D convolutional enc... | {
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2411.09842 | FedRewind: Rewinding Continual Model Exchange for Decentralized
Federated Learning | [
"cs.LG"
] | In this paper, we present FedRewind, a novel approach to decentralized federated learning that leverages model exchange among nodes to address the issue of data distribution shift. Drawing inspiration from continual learning (CL) principles and cognitive neuroscience theories for memory retention, FedRewind implements ... | {
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2411.09844 | Deep Autoencoders for Unsupervised Anomaly Detection in Wildfire
Prediction | [
"cs.LG",
"cs.AI"
] | Wildfires pose a significantly increasing hazard to global ecosystems due to the climate crisis. Due to its complex nature, there is an urgent need for innovative approaches to wildfire prediction, such as machine learning. This research took a unique approach, differentiating from classical supervised learning, and ad... | {
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2411.09847 | Towards a Fairer Non-negative Matrix Factorization | [
"cs.LG",
"stat.ML"
] | Topic modeling, or more broadly, dimensionality reduction, techniques provide powerful tools for uncovering patterns in large datasets and are widely applied across various domains. We investigate how Non-negative Matrix Factorization (NMF) can introduce bias in the representation of data groups, such as those defined ... | {
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2411.09849 | Self-Supervised Radio Pre-training: Toward Foundational Models for
Spectrogram Learning | [
"eess.SP",
"cs.AI",
"cs.LG",
"cs.NI"
] | Foundational deep learning (DL) models are general models, trained on large, diverse, and unlabelled datasets, typically using self-supervised learning techniques have led to significant advancements especially in natural language processing. These pretrained models can be fine-tuned for related downstream tasks, offer... | {
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} |
2411.09850 | Enhancing Diffusion Posterior Sampling for Inverse Problems by
Integrating Crafted Measurements | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Diffusion models have emerged as a powerful foundation model for visual generation. With an appropriate sampling process, it can effectively serve as a generative prior to solve general inverse problems. Current posterior sampling based methods take the measurement (i.e., degraded image sample) into the posterior sampl... | {
"Other": 0,
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"cs.SY": 0
} |
2411.09851 | SymbolFit: Automatic Parametric Modeling with Symbolic Regression | [
"hep-ex",
"cs.LG",
"physics.data-an"
] | We introduce SymbolFit, a framework that automates parametric modeling by using symbolic regression to perform a machine-search for functions that fit the data, while simultaneously providing uncertainty estimates in a single run. Traditionally, constructing a parametric model to accurately describe binned data has bee... | {
"Other": 0,
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} |
2411.09852 | InterFormer: Towards Effective Heterogeneous Interaction Learning for
Click-Through Rate Prediction | [
"cs.IR",
"cs.AI",
"cs.LG"
] | Click-through rate (CTR) prediction, which predicts the probability of a user clicking an ad, is a fundamental task in recommender systems. The emergence of heterogeneous information, such as user profile and behavior sequences, depicts user interests from different aspects. A mutually beneficial integration of heterog... | {
"Other": 0,
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"cs.NE": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.09853 | KULCQ: An Unsupervised Keyword-based Utterance Level Clustering Quality
Metric | [
"cs.CL",
"cs.LG"
] | Intent discovery is crucial for both building new conversational agents and improving existing ones. While several approaches have been proposed for intent discovery, most rely on clustering to group similar utterances together. Traditional evaluation of these utterance clusters requires intent labels for each utteranc... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2411.09854 | Fair Secretaries with Unfair Predictions | [
"cs.LG",
"cs.DS"
] | Algorithms with predictions is a recent framework for decision-making under uncertainty that leverages the power of machine-learned predictions without making any assumption about their quality. The goal in this framework is for algorithms to achieve an improved performance when the predictions are accurate while maint... | {
"Other": 1,
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} |
2411.09856 | InvestESG: A multi-agent reinforcement learning benchmark for studying
climate investment as a social dilemma | [
"cs.LG",
"cs.CY",
"cs.MA",
"econ.GN",
"q-fin.EC"
] | InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of Environmental, Social, and Governance (ESG) disclosure mandates on corporate climate investments. The benchmark models an intertemporal social dilemma where companies balance short-term profit losses from climate mi... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.09858 | One Leaf Reveals the Season: Occlusion-Based Contrastive Learning with
Semantic-Aware Views for Efficient Visual Representation | [
"cs.CV"
] | This paper proposes a scalable and straightforward pre-training paradigm for efficient visual conceptual representation called occluded image contrastive learning (OCL). Our OCL approach is simple: we randomly mask patches to generate different views within an image and contrast them among a mini-batch of images. The c... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2411.09863 | Face De-identification: State-of-the-art Methods and Comparative Studies | [
"cs.CV",
"cs.CR"
] | The widespread use of image acquisition technologies, along with advances in facial recognition, has raised serious privacy concerns. Face de-identification usually refers to the process of concealing or replacing personal identifiers, which is regarded as an effective means to protect the privacy of facial images. A s... | {
"Other": 0,
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} |
2411.09866 | Power Allocation for Compute-and-Forward over Fading Channels | [
"cs.IT",
"math.IT"
] | Compute-and-forward (CF) is a relaying strategy which allows the relay to decode a linear combination of the transmitted messages. This work studies the optimal power allocation problem for the CF scheme in fast fading channels for maximizing the symmetric computation rate, which is a non-convex optimization problem wi... | {
"Other": 0,
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"cs.SY": 0
} |
2411.09868 | Phase Transitions with Structured Sparsity | [
"cs.IT",
"eess.SP",
"math.IT"
] | In the field of signal processing, phase transition phenomena have recently attracted great attention. Donoho's work established the signal recovery threshold using indicators such as restricted isotropy (RIP) and incoherence and proved that phase transition phenomena occur in compressed sampling. Nevertheless, the pha... | {
"Other": 0,
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"cs.NE": 0,
"cs.RO": 0,
"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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