id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.14850 | On the locality bias and results in the Long Range Arena | [
"cs.CL",
"cs.AI"
] | The Long Range Arena (LRA) benchmark was designed to evaluate the performance of Transformer improvements and alternatives in long-range dependency modeling tasks. The Transformer and its main variants performed poorly on this benchmark, and a new series of architectures such as State Space Models (SSMs) gained some tr... | {
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2501.14851 | JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning
in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.LO"
] | Logical reasoning is a critical component of Large Language Models (LLMs), and substantial research efforts in recent years have aimed to enhance their deductive reasoning capabilities. However, existing deductive reasoning benchmarks, which are crucial for evaluating and advancing LLMs, are inadequate due to their lac... | {
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2501.14856 | Noise-conditioned Energy-based Annealed Rewards (NEAR): A Generative
Framework for Imitation Learning from Observation | [
"cs.RO",
"cs.AI"
] | This paper introduces a new imitation learning framework based on energy-based generative models capable of learning complex, physics-dependent, robot motion policies through state-only expert motion trajectories. Our algorithm, called Noise-conditioned Energy-based Annealed Rewards (NEAR), constructs several perturbed... | {
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2501.14859 | Dynamic Adaptation of LoRA Fine-Tuning for Efficient and Task-Specific
Optimization of Large Language Models | [
"cs.CL",
"cs.LG"
] | This paper presents a novel methodology of fine-tuning for large language models-dynamic LoRA. Building from the standard Low-Rank Adaptation framework, this methodology further adds dynamic adaptation mechanisms to improve efficiency and performance. The key contribution of dynamic LoRA lies within its adaptive weight... | {
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2501.14861 | A Deep-Unfolding-Optimized Coordinate-Descent Data-Detector ASIC for
mmWave Massive MIMO | [
"cs.IT",
"eess.SP",
"math.IT"
] | We present a 22 nm FD-SOI (fully depleted silicon-on-insulator) application-specific integrated circuit (ASIC) implementation of a novel soft-output Gram-domain block coordinate descent (GBCD) data detector for massive multi-user (MU) multiple-input multiple-output (MIMO) systems. The ASIC simultaneously addresses the ... | {
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2501.14877 | DrawEduMath: Evaluating Vision Language Models with Expert-Annotated
Students' Hand-Drawn Math Images | [
"cs.CL",
"cs.CV"
] | In real-world settings, vision language models (VLMs) should robustly handle naturalistic, noisy visual content as well as domain-specific language and concepts. For example, K-12 educators using digital learning platforms may need to examine and provide feedback across many images of students' math work. To assess the... | {
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2501.14883 | Verify with Caution: The Pitfalls of Relying on Imperfect Factuality
Metrics | [
"cs.CL",
"cs.LG"
] | Improvements in large language models have led to increasing optimism that they can serve as reliable evaluators of natural language generation outputs. In this paper, we challenge this optimism by thoroughly re-evaluating five state-of-the-art factuality metrics on a collection of 11 datasets for summarization, retrie... | {
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2501.14885 | Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis:
Integrating Radial Basis Function Networks with Explainable AI | [
"cs.CV"
] | Skin cancer is one of the most prevalent and potentially life-threatening diseases worldwide, necessitating early and accurate diagnosis to improve patient outcomes. Conventional diagnostic methods, reliant on clinical expertise and histopathological analysis, are often time-intensive, subjective, and prone to variabil... | {
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2501.14889 | Iterative Feature Space Optimization through Incremental Adaptive
Evaluation | [
"cs.LG"
] | Iterative feature space optimization involves systematically evaluating and adjusting the feature space to improve downstream task performance. However, existing works suffer from three key limitations:1) overlooking differences among data samples leads to evaluation bias; 2) tailoring feature spaces to specific machin... | {
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2501.14892 | Causal Graphs Meet Thoughts: Enhancing Complex Reasoning in
Graph-Augmented LLMs | [
"cs.AI",
"cs.CL"
] | In knowledge-intensive tasks, especially in high-stakes domains like medicine and law, it is critical not only to retrieve relevant information but also to provide causal reasoning and explainability. Large language models (LLMs) have achieved remarkable performance in natural language understanding and generation task... | {
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2501.14894 | Improving reliability of uncertainty-aware gaze estimation with
probability calibration | [
"cs.CV"
] | Current deep learning powered appearance based uncertainty-aware gaze estimation models produce inconsistent and unreliable uncertainty estimation that limits their adoptions in downstream applications. In this study, we propose a workflow to improve the accuracy of uncertainty estimation using probability calibration ... | {
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2501.14896 | Glissando-Net: Deep sinGLe vIew category level poSe eStimation ANd 3D
recOnstruction | [
"cs.CV"
] | We present a deep learning model, dubbed Glissando-Net, to simultaneously estimate the pose and reconstruct the 3D shape of objects at the category level from a single RGB image. Previous works predominantly focused on either estimating poses(often at the instance level), or reconstructing shapes, but not both. Glissan... | {
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2501.14905 | Measuring and Mitigating Hallucinations in Vision-Language Dataset
Generation for Remote Sensing | [
"cs.CV"
] | Vision language models have achieved impressive results across various fields. However, adoption in remote sensing remains limited, largely due to the scarcity of paired image-text data. To bridge this gap, synthetic caption generation has gained interest, traditionally relying on rule-based methods that use metadata o... | {
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2501.14906 | What is a Relevant Signal-to-Noise Ratio for Numerical Differentiation? | [
"eess.SY",
"cs.SY"
] | In applications that involve sensor data, a useful measure of signal-to-noise ratio (SNR) is the ratio of the root-mean-squared (RMS) signal to the RMS sensor noise. The present paper shows that, for numerical differentiation, the traditional SNR is ineffective. In particular, it is shown that, for a harmonic signal wi... | {
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2501.14910 | A cluster mean approach for topology optimization of natural frequencies
and bandgaps with simple/multiple eigenfrequencies | [
"cs.CE",
"math.OC"
] | This study presents a novel approach utilizing cluster means to address the non-differentiability issue arising from multiple eigenvalues in eigenfrequency and bandgap optimization. By constructing symmetric functions of repeated eigenvalues -- including cluster mean, p-norm and KS functions -- the study confirms their... | {
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2501.14912 | Feasible Learning | [
"cs.LG",
"cs.AI"
] | We introduce Feasible Learning (FL), a sample-centric learning paradigm where models are trained by solving a feasibility problem that bounds the loss for each training sample. In contrast to the ubiquitous Empirical Risk Minimization (ERM) framework, which optimizes for average performance, FL demands satisfactory per... | {
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2501.14914 | Light3R-SfM: Towards Feed-forward Structure-from-Motion | [
"cs.CV",
"cs.LG"
] | We present Light3R-SfM, a feed-forward, end-to-end learnable framework for efficient large-scale Structure-from-Motion (SfM) from unconstrained image collections. Unlike existing SfM solutions that rely on costly matching and global optimization to achieve accurate 3D reconstructions, Light3R-SfM addresses this limitat... | {
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2501.14917 | Self-reflecting Large Language Models: A Hegelian Dialectical Approach | [
"cs.CL",
"cs.HC",
"cs.LG"
] | Investigating NLP through a philosophical lens has recently caught researcher's eyes as it connects computational methods with classical schools of philosophy. This paper introduces a philosophical approach inspired by the Hegelian Dialectic for LLMs' self-reflection, utilizing a self-dialectical approach to emulate in... | {
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2501.14918 | 3D/2D Registration of Angiograms using Silhouette-based Differentiable
Rendering | [
"cs.CV"
] | We present a method for 3D/2D registration of Digital Subtraction Angiography (DSA) images to provide valuable insight into brain hemodynamics and angioarchitecture. Our approach formulates the registration as a pose estimation problem, leveraging both anteroposterior and lateral DSA views and employing differentiable ... | {
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2501.14921 | Achieving uniform side information gain with multilevel lattice codes
over the ring of integers | [
"cs.IT",
"math.IT"
] | The index coding problem aims to optimise broadcast communication by taking advantage of receiver-side information to improve transmission efficiency. In this letter, we explore the application of Construction $\pi_A$ lattices to index coding. We introduce a coding scheme, named \textit{CRT lattice index coding}, using... | {
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2501.14922 | Search results diversification in competitive search | [
"cs.IR",
"cs.GT"
] | In Web retrieval, there are many cases of competition between authors of Web documents: their incentive is to have their documents highly ranked for queries of interest. As such, the Web is a prominent example of a competitive search setting. Past work on competitive search focused on ranking functions based solely on ... | {
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2501.14926 | Interpretability in Parameter Space: Minimizing Mechanistic Description
Length with Attribution-based Parameter Decomposition | [
"cs.LG",
"stat.ML"
] | Mechanistic interpretability aims to understand the internal mechanisms learned by neural networks. Despite recent progress toward this goal, it remains unclear how best to decompose neural network parameters into mechanistic components. We introduce Attribution-based Parameter Decomposition (APD), a method that direct... | {
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2501.14928 | Decision Making in Changing Environments: Robustness, Query-Based
Learning, and Differential Privacy | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT",
"math.ST",
"stat.ML",
"stat.TH"
] | We study the problem of interactive decision making in which the underlying environment changes over time subject to given constraints. We propose a framework, which we call \textit{hybrid Decision Making with Structured Observations} (hybrid DMSO), that provides an interpolation between the stochastic and adversarial ... | {
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2501.14929 | Motion-enhancement to Echocardiography Segmentation via Inserting a
Temporal Attention Module: An Efficient, Adaptable, and Scalable Approach | [
"cs.CV",
"cs.AI"
] | Cardiac anatomy segmentation is essential for clinical assessment of cardiac function and disease diagnosis to inform treatment and intervention. In performing segmentation, deep learning (DL) algorithms improved accuracy significantly compared to traditional image processing approaches. More recently, studies showed t... | {
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2501.14932 | Explaining Categorical Feature Interactions Using Graph Covariance and
LLMs | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Modern datasets often consist of numerous samples with abundant features and associated timestamps. Analyzing such datasets to uncover underlying events typically requires complex statistical methods and substantial domain expertise. A notable example, and the primary data focus of this paper, is the global synthetic d... | {
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2501.14933 | Conformal Inference of Individual Treatment Effects Using Conditional
Density Estimates | [
"stat.ML",
"cs.LG"
] | In an era where diverse and complex data are increasingly accessible, the precise prediction of individual treatment effects (ITE) becomes crucial across fields such as healthcare, economics, and public policy. Current state-of-the-art approaches, while providing valid prediction intervals through Conformal Quantile Re... | {
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2501.14934 | Temporal Binding Foundation Model for Material Property Recognition via
Tactile Sequence Perception | [
"cs.RO",
"cs.AI"
] | Robots engaged in complex manipulation tasks require robust material property recognition to ensure adaptability and precision. Traditionally, visual data has been the primary source for object perception; however, it often proves insufficient in scenarios where visibility is obstructed or detailed observation is neede... | {
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2501.14936 | Context-Aware Neural Gradient Mapping for Fine-Grained Instruction
Processing | [
"cs.CL",
"cs.AI"
] | The integration of contextual embeddings into the optimization processes of large language models is an advancement in natural language processing. The Context-Aware Neural Gradient Mapping framework introduces a dynamic gradient adjustment mechanism, incorporating contextual embeddings directly into the optimization p... | {
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2501.14939 | Principal Graph Encoder Embedding and Principal Community Detection | [
"cs.SI",
"stat.ML"
] | In this paper, we introduce the concept of principal communities and propose a principal graph encoder embedding method that concurrently detects these communities and achieves vertex embedding. Given a graph adjacency matrix with vertex labels, the method computes a sample community score for each community, ranking t... | {
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2501.14940 | CASE-Bench: Context-Aware SafEty Benchmark for Large Language Models | [
"cs.CL",
"cs.AI"
] | Aligning large language models (LLMs) with human values is essential for their safe deployment and widespread adoption. Current LLM safety benchmarks often focus solely on the refusal of individual problematic queries, which overlooks the importance of the context where the query occurs and may cause undesired refusal ... | {
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2501.14941 | On the Optimality of Gaussian Code-books for Signaling over a Two-Users
Weak Gaussian Interference Channel | [
"cs.IT",
"math.IT",
"math.PR"
] | This article shows that the capacity region of a 2-users weak Gaussian interference channel is achieved using Gaussian code-books. The approach relies on traversing the boundary in incremental steps. Starting from a corner point with Gaussian code-books, and relying on calculus of variation, it is shown that the end po... | {
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2501.14942 | Force-Based Robotic Imitation Learning: A Two-Phase Approach for
Construction Assembly Tasks | [
"cs.RO",
"cs.AI"
] | The drive for efficiency and safety in construction has boosted the role of robotics and automation. However, complex tasks like welding and pipe insertion pose challenges due to their need for precise adaptive force control, which complicates robotic training. This paper proposes a two-phase system to improve robot le... | {
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2501.14945 | MATCHA:Towards Matching Anything | [
"cs.CV"
] | Establishing correspondences across images is a fundamental challenge in computer vision, underpinning tasks like Structure-from-Motion, image editing, and point tracking. Traditional methods are often specialized for specific correspondence types, geometric, semantic, or temporal, whereas humans naturally identify ali... | {
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2501.14948 | HECLIP: Histology-Enhanced Contrastive Learning for Imputation of
Transcriptomics Profiles | [
"cs.CE",
"q-bio.QM"
] | Histopathology, particularly hematoxylin and eosin (H\&E) staining, plays a critical role in diagnosing and characterizing pathological conditions by highlighting tissue morphology. However, H\&E-stained images inherently lack molecular information, requiring costly and resource-intensive methods like spatial transcrip... | {
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2501.14951 | E-Gen: Leveraging E-Graphs to Improve Continuous Representations of
Symbolic Expressions | [
"cs.LG",
"cs.CL",
"cs.SC"
] | As vector representations have been pivotal in advancing natural language processing (NLP), some prior research has concentrated on creating embedding techniques for mathematical expressions by leveraging mathematically equivalent expressions. While effective, these methods are limited by the training data. In this wor... | {
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2501.14954 | MISCON: A Mission-Driven Conversational Consultant for Pre-Venture
Entrepreneurs in Food Deserts | [
"cs.AI",
"cs.CL",
"cs.IR"
] | This work-in-progress report describes MISCON, a conversational consultant being developed for a public mission project called NOURISH. With MISCON, aspiring small business owners in a food-insecure region and their advisors in Community-based organizations would be able to get information, recommendation and analysis ... | {
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2501.14956 | ExPerT: Effective and Explainable Evaluation of Personalized Long-Form
Text Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Evaluating personalized text generated by large language models (LLMs) is challenging, as only the LLM user, i.e., prompt author, can reliably assess the output, but re-engaging the same individuals across studies is infeasible. This paper addresses the challenge of evaluating personalized text generation by introducin... | {
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2501.14959 | The Curious Case of Arbitrariness in Machine Learning | [
"cs.LG",
"cs.AI"
] | Algorithmic modelling relies on limited information in data to extrapolate outcomes for unseen scenarios, often embedding an element of arbitrariness in its decisions. A perspective on this arbitrariness that has recently gained interest is multiplicity-the study of arbitrariness across a set of "good models", i.e., th... | {
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2501.14960 | LLM4DistReconfig: A Fine-tuned Large Language Model for Power
Distribution Network Reconfiguration | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Power distribution networks are evolving due to the integration of DERs and increased customer participation. To maintain optimal operation, minimize losses, and meet varying load demands, frequent network reconfiguration is necessary. Traditionally, the reconfiguration task relies on optimization software and expert o... | {
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2501.14964 | Personalized Layer Selection for Graph Neural Networks | [
"cs.LG"
] | Graph Neural Networks (GNNs) combine node attributes over a fixed granularity of the local graph structure around a node to predict its label. However, different nodes may relate to a node-level property with a different granularity of its local neighborhood, and using the same level of smoothing for all nodes can be d... | {
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2501.14970 | AI-driven Wireless Positioning: Fundamentals, Standards,
State-of-the-art, and Challenges | [
"eess.SP",
"cs.AI",
"cs.LG"
] | Wireless positioning technologies hold significant value for applications in autonomous driving, extended reality (XR), unmanned aerial vehicles (UAVs), and more. With the advancement of artificial intelligence (AI), leveraging AI to enhance positioning accuracy and robustness has emerged as a field full of potential. ... | {
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2501.14971 | Automatic Link Selection in Multi-Channel Multiple Access with Link
Failures | [
"eess.SY",
"cs.SY"
] | This paper focuses on the problem of automatic link selection in multi-channel multiple access control using bandit feedback. In particular, a controller assigns multiple users to multiple channels in a time slotted system, where in each time slot at most one user can be assigned to a given channel and at most one chan... | {
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2501.14976 | A review of annotation classification tools in the educational domain | [
"cs.CL",
"cs.DL"
] | An annotation consists of a portion of information that is associated with a piece of content in order to explain something about the content or to add more information. The use of annotations as a tool in the educational field has positive effects on the learning process. The usual way to use this instrument is to pro... | {
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2501.14980 | A Deep State Space Model for Rainfall-Runoff Simulations | [
"cs.LG",
"cs.AI",
"physics.ao-ph"
] | The classical way of studying the rainfall-runoff processes in the water cycle relies on conceptual or physically-based hydrologic models. Deep learning (DL) has recently emerged as an alternative and blossomed in hydrology community for rainfall-runoff simulations. However, the decades-old Long Short-Term Memory (LSTM... | {
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2501.14981 | The Muddy Waters of Modeling Empathy in Language: The Practical Impacts
of Theoretical Constructs | [
"cs.CL"
] | Conceptual operationalizations of empathy in NLP are varied, with some having specific behaviors and properties, while others are more abstract. How these variations relate to one another and capture properties of empathy observable in text remains unclear. To provide insight into this, we analyze the transfer performa... | {
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2501.14984 | The Cloud and Flock Polynomials of q-Matroids | [
"math.CO",
"cs.IT",
"math.IT"
] | We show that the Whitney function of a q-matroid can be determined from the cloud and flock polynomials associated to the cyclic flats. These polynomials capture information about the corank (resp., nullity) of certain spaces whose cyclic core (resp., closure) is the given cyclic flat. Going one step further, we prove ... | {
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2501.14985 | DepressionX: Knowledge Infused Residual Attention for Explainable
Depression Severity Assessment | [
"cs.LG"
] | In today's interconnected society, social media platforms have become an important part of our lives, where individuals virtually express their thoughts, emotions, and moods. These expressions offer valuable insights into their mental health. This paper explores the use of platforms like Facebook, $\mathbb{X}$ (formerl... | {
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2501.14991 | Advances in Set Function Learning: A Survey of Techniques and
Applications | [
"cs.LG"
] | Set function learning has emerged as a crucial area in machine learning, addressing the challenge of modeling functions that take sets as inputs. Unlike traditional machine learning that involves fixed-size input vectors where the order of features matters, set function learning demands methods that are invariant to pe... | {
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2501.14992 | Extensive Exploration in Complex Traffic Scenarios using Hierarchical
Reinforcement Learning | [
"cs.LG",
"cs.RO"
] | Developing an automated driving system capable of navigating complex traffic environments remains a formidable challenge. Unlike rule-based or supervised learning-based methods, Deep Reinforcement Learning (DRL) based controllers eliminate the need for domain-specific knowledge and datasets, thus providing adaptability... | {
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2501.14994 | Robust Cross-Etiology and Speaker-Independent Dysarthric Speech
Recognition | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | In this paper, we present a speaker-independent dysarthric speech recognition system, with a focus on evaluating the recently released Speech Accessibility Project (SAP-1005) dataset, which includes speech data from individuals with Parkinson's disease (PD). Despite the growing body of research in dysarthric speech rec... | {
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2501.14995 | GreenAuto: An Automated Platform for Sustainable AI Model Design on Edge
Devices | [
"cs.LG"
] | We present GreenAuto, an end-to-end automated platform designed for sustainable AI model exploration, generation, deployment, and evaluation. GreenAuto employs a Pareto front-based search method within an expanded neural architecture search (NAS) space, guided by gradient descent to optimize model exploration. Pre-trai... | {
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2501.14997 | Causal Discovery via Bayesian Optimization | [
"cs.LG",
"stat.ML"
] | Existing score-based methods for directed acyclic graph (DAG) learning from observational data struggle to recover the causal graph accurately and sample-efficiently. To overcome this, in this study, we propose DrBO (DAG recovery via Bayesian Optimization)-a novel DAG learning framework leveraging Bayesian optimization... | {
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2501.14998 | Federated Retrieval Augmented Generation for Multi-Product Question
Answering | [
"cs.CL"
] | Recent advancements in Large Language Models and Retrieval-Augmented Generation have boosted interest in domain-specific question-answering for enterprise products. However, AI Assistants often face challenges in multi-product QA settings, requiring accurate responses across diverse domains. Existing multi-domain RAG-Q... | {
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2501.14999 | VideoPure: Diffusion-based Adversarial Purification for Video
Recognition | [
"cs.CV"
] | Recent work indicates that video recognition models are vulnerable to adversarial examples, posing a serious security risk to downstream applications. However, current research has primarily focused on adversarial attacks, with limited work exploring defense mechanisms. Furthermore, due to the spatial-temporal complexi... | {
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2501.15000 | MDEval: Evaluating and Enhancing Markdown Awareness in Large Language
Models | [
"cs.CL",
"cs.IR"
] | Large language models (LLMs) are expected to offer structured Markdown responses for the sake of readability in web chatbots (e.g., ChatGPT). Although there are a myriad of metrics to evaluate LLMs, they fail to evaluate the readability from the view of output content structure. To this end, we focus on an overlooked y... | {
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2501.15001 | What if Eye...? Computationally Recreating Vision Evolution | [
"cs.AI",
"cs.CV",
"cs.NE",
"q-bio.NC"
] | Vision systems in nature show remarkable diversity, from simple light-sensitive patches to complex camera eyes with lenses. While natural selection has produced these eyes through countless mutations over millions of years, they represent just one set of realized evolutionary paths. Testing hypotheses about how environ... | {
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2501.15005 | Towards Distributed Backdoor Attacks with Network Detection in
Decentralized Federated Learning | [
"cs.LG"
] | Distributed backdoor attacks (DBA) have shown a higher attack success rate than centralized attacks in centralized federated learning (FL). However, it has not been investigated in the decentralized FL. In this paper, we experimentally demonstrate that, while directly applying DBA to decentralized FL, the attack succes... | {
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2501.15007 | Controllable Protein Sequence Generation with LLM Preference
Optimization | [
"cs.AI",
"cs.CE",
"q-bio.QM"
] | Designing proteins with specific attributes offers an important solution to address biomedical challenges. Pre-trained protein large language models (LLMs) have shown promising results on protein sequence generation. However, to control sequence generation for specific attributes, existing work still exhibits poor func... | {
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2501.15008 | HuGDiffusion: Generalizable Single-Image Human Rendering via 3D Gaussian
Diffusion | [
"cs.CV"
] | We present HuGDiffusion, a generalizable 3D Gaussian splatting (3DGS) learning pipeline to achieve novel view synthesis (NVS) of human characters from single-view input images. Existing approaches typically require monocular videos or calibrated multi-view images as inputs, whose applicability could be weakened in real... | {
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2501.15013 | An Information-Theoretic Efficient Capacity Region for Multi-User
Interference Channel | [
"cs.IT",
"math.IT"
] | We investigate the capacity region of multi-user interference channels (IC), where each user encodes multiple sub-user components. By unifying chain-rule decomposition with the Entropy Power Inequality (EPI), we reason that single-user Gaussian codebooks suffice to achieve optimal performance, thus obviating any need f... | {
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2501.15014 | On Accelerating Edge AI: Optimizing Resource-Constrained Environments | [
"cs.LG",
"cs.AI",
"cs.NE"
] | Resource-constrained edge deployments demand AI solutions that balance high performance with stringent compute, memory, and energy limitations. In this survey, we present a comprehensive overview of the primary strategies for accelerating deep learning models under such constraints. First, we examine model compression ... | {
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2501.15017 | SPOCK 2.0: Update to the FeatureClassifier in the Stability of Planetary
Orbital Configurations Klassifier | [
"astro-ph.EP",
"astro-ph.IM",
"cs.LG"
] | The Stability of Planetary Orbital Configurations Klassifier (SPOCK) package collects machine learning models for predicting the stability and collisional evolution of compact planetary systems. In this paper we explore improvements to SPOCK's binary stability classifier (FeatureClassifier), which predicts orbital stab... | {
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2501.15019 | Utilizing Graph Neural Networks for Effective Link Prediction in
Microservice Architectures | [
"cs.LG"
] | Managing microservice architectures in distributed systems is complex and resource intensive due to the high frequency and dynamic nature of inter service interactions. Accurate prediction of these future interactions can enhance adaptive monitoring, enabling proactive maintenance and resolution of potential performanc... | {
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2501.15021 | AKVQ-VL: Attention-Aware KV Cache Adaptive 2-Bit Quantization for
Vision-Language Models | [
"cs.CL"
] | Vision-language models (VLMs) show remarkable performance in multimodal tasks. However, excessively long multimodal inputs lead to oversized Key-Value (KV) caches, resulting in significant memory consumption and I/O bottlenecks. Previous KV quantization methods for Large Language Models (LLMs) may alleviate these issue... | {
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2501.15022 | Using Large Language Models for education managements in Vietnamese with
low resources | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs), such as GPT-4, Gemini 1.5, Claude 3.5 Sonnet, and Llama3, have demonstrated significant advancements in various NLP tasks since the release of ChatGPT in 2022. Despite their success, fine-tuning and deploying LLMs remain computationally expensive, especially in resource-constrained environ... | {
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2501.15030 | OptiSeq: Ordering Examples On-The-Fly for In-Context Learning | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.PF"
] | Developers using LLMs and LLM-based agents in their applications have provided plenty of anecdotal evidence that in-context-learning (ICL) is fragile. In this paper, we show that in addition to the quantity and quality of examples, the order in which the in-context examples are listed in the prompt affects the output o... | {
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2501.15034 | Divergence-Augmented Policy Optimization | [
"cs.LG",
"cs.AI",
"stat.ML"
] | In deep reinforcement learning, policy optimization methods need to deal with issues such as function approximation and the reuse of off-policy data. Standard policy gradient methods do not handle off-policy data well, leading to premature convergence and instability. This paper introduces a method to stabilize policy ... | {
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2501.15035 | Semi-supervised Anomaly Detection with Extremely Limited Labels in
Dynamic Graphs | [
"cs.LG"
] | Semi-supervised graph anomaly detection (GAD) has recently received increasing attention, which aims to distinguish anomalous patterns from graphs under the guidance of a moderate amount of labeled data and a large volume of unlabeled data. Although these proposed semi-supervised GAD methods have achieved great success... | {
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2501.15038 | Adaptive Client Selection in Federated Learning: A Network Anomaly
Detection Use Case | [
"cs.LG",
"cs.AI"
] | Federated Learning (FL) has become a widely used approach for training machine learning models on decentralized data, addressing the significant privacy concerns associated with traditional centralized methods. However, the efficiency of FL relies on effective client selection and robust privacy preservation mechanisms... | {
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2501.15040 | Complementary Subspace Low-Rank Adaptation of Vision-Language Models for
Few-Shot Classification | [
"cs.CV"
] | Vision language model (VLM) has been designed for large scale image-text alignment as a pretrained foundation model. For downstream few shot classification tasks, parameter efficient fine-tuning (PEFT) VLM has gained much popularity in the computer vision community. PEFT methods like prompt tuning and linear adapter ha... | {
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2501.15042 | SCCD: A Session-based Dataset for Chinese Cyberbullying Detection | [
"cs.CL"
] | The rampant spread of cyberbullying content poses a growing threat to societal well-being. However, research on cyberbullying detection in Chinese remains underdeveloped, primarily due to the lack of comprehensive and reliable datasets. Notably, no existing Chinese dataset is specifically tailored for cyberbullying det... | {
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2501.15043 | Prompt-Aware Controllable Shadow Removal | [
"cs.CV"
] | Shadow removal aims to restore the image content in shadowed regions. While deep learning-based methods have shown promising results, they still face key challenges: 1) uncontrolled removal of all shadows, or 2) controllable removal but heavily relies on precise shadow region masks. To address these issues, we introduc... | {
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2501.15045 | Towards Robust Unsupervised Attention Prediction in Autonomous Driving | [
"cs.CV",
"cs.AI"
] | Robustly predicting attention regions of interest for self-driving systems is crucial for driving safety but presents significant challenges due to the labor-intensive nature of obtaining large-scale attention labels and the domain gap between self-driving scenarios and natural scenes. These challenges are further exac... | {
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2501.15046 | Evaluating Hallucination in Large Vision-Language Models based on
Context-Aware Object Similarities | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Despite their impressive performance on multi-modal tasks, large vision-language models (LVLMs) tend to suffer from hallucinations. An important type is object hallucination, where LVLMs generate objects that are inconsistent with the images shown to the model. Existing works typically attempt to quantify object halluc... | {
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2501.15048 | YouTube Recommendations Reinforce Negative Emotions: Auditing
Algorithmic Bias with Emotionally-Agentic Sock Puppets | [
"cs.SI",
"cs.CY"
] | Personalized recommendation algorithms, like those on YouTube, significantly shape online content consumption. These systems aim to maximize engagement by learning users' preferences and aligning content accordingly but may unintentionally reinforce impulsive and emotional biases. Using a sock-puppet audit methodology,... | {
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2501.15051 | Abstractive Text Summarization for Bangla Language Using NLP and Machine
Learning Approaches | [
"cs.CL"
] | Text summarization involves reducing extensive documents to short sentences that encapsulate the essential ideas. The goal is to create a summary that effectively conveys the main points of the original text. We spend a significant amount of time each day reading the newspaper to stay informed about current events both... | {
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2501.15052 | Graph-Based Cross-Domain Knowledge Distillation for Cross-Dataset
Text-to-Image Person Retrieval | [
"cs.CV",
"cs.AI",
"cs.MM"
] | Video surveillance systems are crucial components for ensuring public safety and management in smart city. As a fundamental task in video surveillance, text-to-image person retrieval aims to retrieve the target person from an image gallery that best matches the given text description. Most existing text-to-image person... | {
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2501.15053 | Exploring the impact of Optimised Hyperparameters on Bi-LSTM-based
Contextual Anomaly Detector | [
"cs.LG",
"cs.AI"
] | The exponential growth in the usage of Internet of Things in daily life has caused immense increase in the generation of time series data. Smart homes is one such domain where bulk of data is being generated and anomaly detection is one of the many challenges addressed by researchers in recent years. Contextual anomaly... | {
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2501.15054 | An Attempt to Unraveling Token Prediction Refinement and Identifying
Essential Layers of Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This research aims to unravel how large language models (LLMs) iteratively refine token predictions (or, in a general sense, vector predictions). We utilized a logit lens technique to analyze the model's token predictions derived from intermediate representations. Specifically, we focused on how LLMs access and use inf... | {
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2501.15055 | Group Ligands Docking to Protein Pockets | [
"q-bio.BM",
"cs.AI"
] | Molecular docking is a key task in computational biology that has attracted increasing interest from the machine learning community. While existing methods have achieved success, they generally treat each protein-ligand pair in isolation. Inspired by the biochemical observation that ligands binding to the same target p... | {
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2501.15056 | Feedback-Aware Monte Carlo Tree Search for Efficient Information Seeking
in Goal-Oriented Conversations | [
"cs.AI",
"cs.CL",
"cs.HC",
"cs.LG"
] | The ability to identify and acquire missing information is a critical component of effective decision making and problem solving. With the rise of conversational artificial intelligence (AI) systems, strategically formulating information-seeking questions becomes crucial and demands efficient methods to guide the searc... | {
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2501.15057 | Predictive Modeling and Uncertainty Quantification of Fatigue Life in
Metal Alloys using Machine Learning | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Recent advancements in machine learning-based methods have demonstrated great potential for improved property prediction in material science. However, reliable estimation of the confidence intervals for the predicted values remains a challenge, due to the inherent complexities in material modeling. This study introduce... | {
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2501.15058 | KETA: Kinematic-Phrases-Enhanced Text-to-Motion Generation via
Fine-grained Alignment | [
"cs.CV"
] | Motion synthesis plays a vital role in various fields of artificial intelligence. Among the various conditions of motion generation, text can describe motion details elaborately and is easy to acquire, making text-to-motion(T2M) generation important. State-of-the-art T2M techniques mainly leverage diffusion models to g... | {
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2501.15061 | PolaFormer: Polarity-aware Linear Attention for Vision Transformers | [
"cs.CV",
"cs.AI"
] | Linear attention has emerged as a promising alternative to softmax-based attention, leveraging kernelized feature maps to reduce complexity from quadratic to linear in sequence length. However, the non-negative constraint on feature maps and the relaxed exponential function used in approximation lead to significant inf... | {
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2501.15062 | Exact Fit Attention in Node-Holistic Graph Convolutional Network for
Improved EEG-Based Driver Fatigue Detection | [
"cs.LG"
] | EEG-based fatigue monitoring can effectively reduce the incidence of related traffic accidents. In the past decade, with the advancement of deep learning, convolutional neural networks (CNN) have been increasingly used for EEG signal processing. However, due to the data's non-Euclidean characteristics, existing CNNs ma... | {
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2501.15063 | Cross-modal Context Fusion and Adaptive Graph Convolutional Network for
Multimodal Conversational Emotion Recognition | [
"cs.CL"
] | Emotion recognition has a wide range of applications in human-computer interaction, marketing, healthcare, and other fields. In recent years, the development of deep learning technology has provided new methods for emotion recognition. Prior to this, many emotion recognition methods have been proposed, including multim... | {
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2501.15065 | Task Arithmetic in Trust Region: A Training-Free Model Merging Approach
to Navigate Knowledge Conflicts | [
"cs.LG",
"cs.AI"
] | Multi-task model merging offers an efficient solution for integrating knowledge from multiple fine-tuned models, mitigating the significant computational and storage demands associated with multi-task training. As a key technique in this field, Task Arithmetic (TA) defines task vectors by subtracting the pre-trained mo... | {
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2501.15067 | CG-RAG: Research Question Answering by Citation Graph
Retrieval-Augmented LLMs | [
"cs.IR",
"cs.LG"
] | Research question answering requires accurate retrieval and contextual understanding of scientific literature. However, current Retrieval-Augmented Generation (RAG) methods often struggle to balance complex document relationships with precise information retrieval. In this paper, we introduce Contextualized Graph Retri... | {
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2501.15068 | An Atomic Skill Library Construction Method for Data-Efficient Embodied
Manipulation | [
"cs.RO"
] | Embodied manipulation is a fundamental ability in the realm of embodied artificial intelligence. Although current embodied manipulation models show certain generalizations in specific settings, they struggle in new environments and tasks due to the complexity and diversity of real-world scenarios. The traditional end-t... | {
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2501.15070 | Unifying Prediction and Explanation in Time-Series Transformers via
Shapley-based Pretraining | [
"cs.LG"
] | In this paper, we propose ShapTST, a framework that enables time-series transformers to efficiently generate Shapley-value-based explanations alongside predictions in a single forward pass. Shapley values are widely used to evaluate the contribution of different time-steps and features in a test sample, and are commonl... | {
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} |
2501.15071 | Gaze-based Task Decomposition for Robot Manipulation in Imitation
Learning | [
"cs.RO"
] | In imitation learning for robotic manipulation, decomposing object manipulation tasks into multiple sub-tasks is essential. This decomposition enables the reuse of learned skills in varying contexts and the combination of acquired skills to perform novel tasks, rather than merely replicating demonstrated motions. Gaze ... | {
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2501.15073 | SpatioTemporal Learning for Human Pose Estimation in Sparsely-Labeled
Videos | [
"cs.CV"
] | Human pose estimation in videos remains a challenge, largely due to the reliance on extensive manual annotation of large datasets, which is expensive and labor-intensive. Furthermore, existing approaches often struggle to capture long-range temporal dependencies and overlook the complementary relationship between tempo... | {
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} |
2501.15074 | PatentLMM: Large Multimodal Model for Generating Descriptions for Patent
Figures | [
"cs.CV",
"cs.AI"
] | Writing comprehensive and accurate descriptions of technical drawings in patent documents is crucial to effective knowledge sharing and enabling the replication and protection of intellectual property. However, automation of this task has been largely overlooked by the research community. To this end, we introduce Pate... | {
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} |
2501.15076 | Cryptanalysis via Machine Learning Based Information Theoretic Metrics | [
"cs.CR",
"cs.IT",
"cs.LG",
"math.IT"
] | The fields of machine learning (ML) and cryptanalysis share an interestingly common objective of creating a function, based on a given set of inputs and outputs. However, the approaches and methods in doing so vary vastly between the two fields. In this paper, we explore integrating the knowledge from the ML domain to ... | {
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} |
2501.15077 | NetChain: Authenticated Blockchain Top-k Graph Data Queries and its
Application in Asset Management | [
"cs.CR",
"cs.DB"
] | As a valuable digital resource, graph data is an important data asset, which has been widely utilized across various fields to optimize decision-making and enable smarter solutions. To manage data assets, blockchain is widely used to enable data sharing and trading, but it cannot supply complex analytical queries. vCha... | {
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} |
2501.15078 | Impact-resistant, autonomous robots inspired by tensegrity architecture | [
"cs.RO"
] | Future robots will navigate perilous, remote environments with resilience and autonomy. Researchers have proposed building robots with compliant bodies to enhance robustness, but this approach often sacrifices the autonomous capabilities expected of rigid robots. Inspired by tensegrity architecture, we introduce a tens... | {
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} |
2501.15079 | Salvaging Forbidden Treasure in Medical Data: Utilizing Surrogate
Outcomes and Single Records for Rare Event Modeling | [
"stat.ME",
"cs.LG",
"stat.AP",
"stat.ML"
] | The vast repositories of Electronic Health Records (EHR) and medical claims hold untapped potential for studying rare but critical events, such as suicide attempt. Conventional setups often model suicide attempt as a univariate outcome and also exclude any ``single-record'' patients with a single documented encounter d... | {
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} |
2501.15081 | Can Large Language Models Be Trusted as Black-Box Evolutionary
Optimizers for Combinatorial Problems? | [
"cs.NE",
"cs.AI"
] | Evolutionary computation excels in complex optimization but demands deep domain knowledge, restricting its accessibility. Large Language Models (LLMs) offer a game-changing solution with their extensive knowledge and could democratize the optimization paradigm. Although LLMs possess significant capabilities, they may n... | {
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} |
2501.15084 | Hierarchical Pattern Decryption Methodology for Ransomware Detection
Using Probabilistic Cryptographic Footprints | [
"cs.CR",
"cs.AI"
] | The increasing sophistication of encryption-based ransomware has demanded innovative approaches to detection and mitigation, prompting the development of a hierarchical framework grounded in probabilistic cryptographic analysis. By focusing on the statistical characteristics of encryption patterns, the proposed methodo... | {
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} |
2501.15085 | Data Center Cooling System Optimization Using Offline Reinforcement
Learning | [
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SY"
] | The recent advances in information technology and artificial intelligence have fueled a rapid expansion of the data center (DC) industry worldwide, accompanied by an immense appetite for electricity to power the DCs. In a typical DC, around 30~40% of the energy is spent on the cooling system rather than on computer ser... | {
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} |
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