id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.11673 | Randomized Kaczmarz Methods with Beyond-Krylov Convergence | [
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"math.OC",
"stat.ML"
] | Randomized Kaczmarz methods form a family of linear system solvers which converge by repeatedly projecting their iterates onto randomly sampled equations. While effective in some contexts, such as highly over-determined least squares, Kaczmarz methods are traditionally deemed secondary to Krylov subspace methods, since... | {
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2501.11689 | Randomness, exchangeability, and conformal prediction | [
"cs.LG",
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"stat.ML",
"stat.TH"
] | This paper continues development of the functional theory of randomness, a modification of the algorithmic theory of randomness getting rid of unspecified additive constants. It introduces new kinds of confidence predictors, including randomness predictors (the most general confidence predictors based on the assumption... | {
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2501.11695 | Spatially-Delineated Domain-Adapted AI Classification: An Application
for Oncology Data | [
"cs.LG",
"cs.AI"
] | Given multi-type point maps from different place-types (e.g., tumor regions), our objective is to develop a classifier trained on the source place-type to accurately distinguish between two classes of the target place-type based on their point arrangements. This problem is societally important for many applications, su... | {
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2501.11699 | Power Ramp-Rate Control via Power Regulation for Storageless
Grid-Connected Photovoltaic Systems | [
"eess.SY",
"cs.SY"
] | Photovoltaic Power Ramp-Rate Control (PRRC) constitutes a key ancillary service for future power systems. Although its implementation through the installation of storage systems or irradiance sensors has been widely investigated, fewer studies have explored the power curtailment approach. The latter lacks efficiency, a... | {
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2501.11704 | Ultra-High Reliability by Predictive Interference Management Using
Extreme Value Theory | [
"eess.SY",
"cs.SY"
] | Ultra-reliable low-latency communications (URLLC) require innovative approaches to modeling channel and interference dynamics, extending beyond traditional average estimates to encompass entire statistical distributions, including rare and extreme events that challenge achieving ultra-reliability performance regions. I... | {
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2501.11705 | Human services organizations and the responsible integration of AI:
Considering ethics and contextualizing risk(s) | [
"cs.CY",
"cs.AI"
] | This paper examines the responsible integration of artificial intelligence (AI) in human services organizations (HSOs), proposing a nuanced framework for evaluating AI applications across multiple dimensions of risk. The authors argue that ethical concerns about AI deployment -- including professional judgment displace... | {
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2501.11706 | Trustformer: A Trusted Federated Transformer | [
"cs.LG",
"cs.CR"
] | Transformers, a cornerstone of deep-learning architectures for sequential data, have achieved state-of-the-art results in tasks like Natural Language Processing (NLP). Models such as BERT and GPT-3 exemplify their success and have driven the rise of large language models (LLMs). However, a critical challenge persists: ... | {
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2501.11711 | Leveraging graph neural networks and mobility data for COVID-19
forecasting | [
"cs.LG",
"cs.SI"
] | The COVID-19 pandemic has victimized over 7 million people to date, prompting diverse research efforts. Spatio-temporal models combining mobility data with machine learning have gained attention for disease forecasting. Here, we explore Graph Convolutional Recurrent Network (GCRN) and Graph Convolutional Long Short-Ter... | {
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2501.11712 | YouLeQD: Decoding the Cognitive Complexity of Questions and Engagement
in Online Educational Videos from Learners' Perspectives | [
"cs.CL"
] | Questioning is a fundamental aspect of education, as it helps assess students' understanding, promotes critical thinking, and encourages active engagement. With the rise of artificial intelligence in education, there is a growing interest in developing intelligent systems that can automatically generate and answer ques... | {
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2501.11714 | The Transition from Centralized Machine Learning to Federated Learning
for Mental Health in Education: A Survey of Current Methods and Future
Directions | [
"cs.CY",
"cs.LG"
] | Research has increasingly explored the application of artificial intelligence (AI) and machine learning (ML) within the mental health domain to enhance both patient care and healthcare provider efficiency. Given that mental health challenges frequently emerge during early adolescence -- the critical years of high schoo... | {
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2501.11715 | GL-ICNN: An End-To-End Interpretable Convolutional Neural Network for
the Diagnosis and Prediction of Alzheimer's Disease | [
"cs.CV",
"cs.AI"
] | Deep learning methods based on Convolutional Neural Networks (CNNs) have shown great potential to improve early and accurate diagnosis of Alzheimer's disease (AD) dementia based on imaging data. However, these methods have yet to be widely adopted in clinical practice, possibly due to the limited interpretability of de... | {
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2501.11720 | Prediction of Lung Metastasis from Hepatocellular Carcinoma using the
SEER Database | [
"q-bio.TO",
"cs.LG"
] | Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality, with lung metastases being the most common site of distant spread and significantly worsening prognosis. Despite the growing availability of clinical and demographic data, predictive models for lung metastasis in HCC remain limited in scope ... | {
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2501.11721 | Explain-Query-Test: Self-Evaluating LLMs Via Explanation and
Comprehension Discrepancy | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have demonstrated remarkable proficiency in generating detailed and coherent explanations of complex concepts. However, the extent to which these models truly comprehend the concepts they articulate remains unclear. To assess the level of comprehension of a model relative to the content it ... | {
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2501.11729 | SeRpEnt: Selective Resampling for Expressive State Space Models | [
"cs.LG",
"cs.CV"
] | State Space Models (SSMs) have recently enjoyed a rise to prominence in the field of deep learning for sequence modeling, especially as an alternative to Transformers. Their success stems from avoiding two well-known drawbacks of attention-based models: quadratic complexity with respect to the sequence length and inabi... | {
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2501.11730 | Transformer Vibration Forecasting for Advancing Rail Safety and
Maintenance 4.0 | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Maintaining railway axles is critical to preventing severe accidents and financial losses. The railway industry is increasingly interested in advanced condition monitoring techniques to enhance safety and efficiency, moving beyond traditional periodic inspections toward Maintenance 4.0. This study introduces a robust... | {
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2501.11733 | Mobile-Agent-E: Self-Evolving Mobile Assistant for Complex Tasks | [
"cs.CL",
"cs.CV"
] | Smartphones have become indispensable in modern life, yet navigating complex tasks on mobile devices often remains frustrating. Recent advancements in large multimodal model (LMM)-based mobile agents have demonstrated the ability to perceive and act in mobile environments. However, current approaches face significant l... | {
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2501.11734 | MedicoSAM: Towards foundation models for medical image segmentation | [
"eess.IV",
"cs.CV"
] | Medical image segmentation is an important analysis task in clinical practice and research. Deep learning has massively advanced the field, but current approaches are mostly based on models trained for a specific task. Training such models or adapting them to a new condition is costly due to the need for (manually) lab... | {
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2501.11739 | Episodic memory in AI agents poses risks that should be studied and
mitigated | [
"cs.AI",
"cs.CY"
] | Most current AI models have little ability to store and later retrieve a record or representation of what they do. In human cognition, episodic memories play an important role in both recall of the past as well as planning for the future. The ability to form and use episodic memories would similarly enable a broad rang... | {
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2501.11740 | PIR Over Wireless Channels: Achieving Privacy With Public Responses | [
"cs.IT",
"math.IT"
] | In this paper, we address the problem of Private Information Retrieval (PIR) over a public Additive White Gaussian Noise (AWGN) channel. In such a setup, the server's responses are visible to other servers. Thus, a curious server can listen to the other responses, compromising the user's privacy. Indeed, previous works... | {
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2501.11741 | FaceQSORT: a Multi-Face Tracking Method based on Biometric and
Appearance Features | [
"cs.CV"
] | Tracking multiple faces is a difficult problem, as there may be partially occluded or lateral faces. In multiple face tracking, association is typically based on (biometric) face features. However, the models used to extract these face features usually require frontal face images, which can limit the tracking performan... | {
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2501.11742 | Force-Aware Autonomous Robotic Surgery | [
"cs.RO"
] | This work demonstrates the benefits of using tool-tissue interaction forces in the design of autonomous systems in robot-assisted surgery (RAS). Autonomous systems in surgery must manipulate tissues of different stiffness levels and hence should apply different levels of forces accordingly. We hypothesize that this abi... | {
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2501.11743 | Non-Reversible Langevin Algorithms for Constrained Sampling | [
"cs.LG",
"math.PR",
"stat.CO"
] | We consider the constrained sampling problem where the goal is to sample from a target distribution on a constrained domain. We propose skew-reflected non-reversible Langevin dynamics (SRNLD), a continuous-time stochastic differential equation with skew-reflected boundary. We obtain non-asymptotic convergence rate of S... | {
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2501.11745 | Personalized Federated Learning for Cellular VR: Online Learning and
Dynamic Caching | [
"cs.IT",
"cs.LG",
"math.IT"
] | Delivering an immersive experience to virtual reality (VR) users through wireless connectivity offers the freedom to engage from anywhere at any time. Nevertheless, it is challenging to ensure seamless wireless connectivity that delivers real-time and high-quality videos to the VR users. This paper proposes a field of ... | {
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2501.11746 | SILO: Solving Inverse Problems with Latent Operators | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Consistent improvement of image priors over the years has led to the development of better inverse problem solvers. Diffusion models are the newcomers to this arena, posing the strongest known prior to date. Recently, such models operating in a latent space have become increasingly predominant due to their efficiency. ... | {
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2501.11747 | Optimizing Pretraining Data Mixtures with LLM-Estimated Utility | [
"cs.CL",
"cs.AI"
] | Large Language Models improve with increasing amounts of high-quality training data. However, leveraging larger datasets requires balancing quality, quantity, and diversity across sources. After evaluating nine baseline methods under both compute- and data-constrained scenarios, we find token-count heuristics outperfor... | {
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2501.11752 | Are generative models fair? A study of racial bias in dermatological
image generation | [
"cs.CV"
] | Racial bias in medicine, such as in dermatology, presents significant ethical and clinical challenges. This is likely to happen because there is a significant underrepresentation of darker skin tones in training datasets for machine learning models. While efforts to address bias in dermatology have focused on improving... | {
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2501.11755 | A generalizable 3D framework and model for self-supervised learning in
medical imaging | [
"eess.IV",
"cs.CV"
] | Current self-supervised learning methods for 3D medical imaging rely on simple pretext formulations and organ- or modality-specific datasets, limiting their generalizability and scalability. We present 3DINO, a cutting-edge SSL method adapted to 3D datasets, and use it to pretrain 3DINO-ViT: a general-purpose medical i... | {
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2501.11757 | An Information Geometric Approach to Local Information Privacy with
Applications to Max-lift and Local Differential Privacy | [
"cs.IT",
"math.IT"
] | We study an information-theoretic privacy mechanism design, where an agent observes useful data $Y$ and wants to reveal the information to a user. Since the useful data is correlated with the private data $X$, the agent uses a privacy mechanism to produce disclosed data $U$ that can be released. We assume that the agen... | {
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2501.11758 | A Review Paper of the Effects of Distinct Modalities and ML Techniques
to Distracted Driving Detection | [
"cs.CV",
"stat.ML"
] | Distracted driving remains a significant global challenge with severe human and economic repercussions, demanding improved detection and intervention strategies. While previous studies have extensively explored single-modality approaches, recent research indicates that these systems often fall short in identifying comp... | {
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2501.11759 | Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented
Generation in Recommender Systems | [
"cs.IR"
] | This study presents Poison-RAG, a framework for adversarial data poisoning attacks targeting retrieval-augmented generation (RAG)-based recommender systems. Poison-RAG manipulates item metadata, such as tags and descriptions, to influence recommendation outcomes. Using item metadata generated through a large language m... | {
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2501.11762 | Disentangling stellar atmospheric parameters in astronomical spectra
using Generative Adversarial Neural Networks | [
"astro-ph.IM",
"astro-ph.GA",
"astro-ph.SR",
"cs.LG"
] | A method based on Generative Adversaria! Networks (GANs) is developed for disentangling the physical (effective temperature and gravity) and chemical (metallicity, overabundance of a-elements with respect to iron) atmospheric properties in astronomical spectra. Using a projection of the stellar spectra, commonly called... | {
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2501.11765 | Is logical analysis performed by transformers taking place in
self-attention or in the fully connected part? | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Transformers architecture apply self-attention to tokens represented as vectors, before a fully connected (neuronal network) layer. These two parts can be layered many times. Traditionally, self-attention is seen as a mechanism for aggregating information before logical operations are performed by the fully connected l... | {
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2501.11770 | The Value of Nothing: Multimodal Extraction of Human Values Expressed by
TikTok Influencers | [
"cs.CL",
"cs.CY",
"cs.SI"
] | Societal and personal values are transmitted to younger generations through interaction and exposure. Traditionally, children and adolescents learned values from parents, educators, or peers. Nowadays, social platforms serve as a significant channel through which youth (and adults) consume information, as the main medi... | {
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2501.11773 | Can Bayesian Neural Networks Make Confident Predictions? | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | Bayesian inference promises a framework for principled uncertainty quantification of neural network predictions. Barriers to adoption include the difficulty of fully characterizing posterior distributions on network parameters and the interpretability of posterior predictive distributions. We demonstrate that under a d... | {
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2501.11776 | EfficientVITON: An Efficient Virtual Try-On Model using Optimized
Diffusion Process | [
"cs.CV"
] | Would not it be much more convenient for everybody to try on clothes by only looking into a mirror ? The answer to that problem is virtual try-on, enabling users to digitally experiment with outfits. The core challenge lies in realistic image-to-image translation, where clothing must fit diverse human forms, poses, and... | {
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2501.11779 | Glinthawk: A Two-Tiered Architecture for Offline LLM Inference | [
"cs.LG",
"cs.DC",
"cs.PF"
] | We introduce Glinthawk, an architecture for offline Large Language Model (LLM) inference. By leveraging a two-tiered structure, Glinthawk optimizes the utilization of the high-end accelerators ("Tier 1") by offloading the attention mechanism to lower-end compute tier ("Tier 2"). This separation allows the memory demand... | {
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2501.11782 | Human-AI Collaborative Game Testing with Vision Language Models | [
"cs.HC",
"cs.AI"
] | As modern video games become increasingly complex, traditional manual testing methods are proving costly and inefficient, limiting the ability to ensure high-quality game experiences. While advancements in Artificial Intelligence (AI) offer the potential to assist human testers, the effectiveness of AI in truly enhanci... | {
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2501.11784 | Generating visual explanations from deep networks using implicit neural
representations | [
"cs.CV"
] | Explaining deep learning models in a way that humans can easily understand is essential for responsible artificial intelligence applications. Attribution methods constitute an important area of explainable deep learning. The attribution problem involves finding parts of the network's input that are the most responsible... | {
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2501.11786 | Synthetic Data Can Mislead Evaluations: Membership Inference as Machine
Text Detection | [
"cs.CL",
"cs.CR",
"cs.LG"
] | Recent work shows membership inference attacks (MIAs) on large language models (LLMs) produce inconclusive results, partly due to difficulties in creating non-member datasets without temporal shifts. While researchers have turned to synthetic data as an alternative, we show this approach can be fundamentally misleading... | {
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2501.11788 | OciorABA: Improved Error-Free Asynchronous Byzantine Agreement via
Partial Vector Agreement | [
"cs.DC",
"cs.CR",
"cs.IT",
"math.IT"
] | In this work, we propose an error-free, information-theoretically secure multi-valued asynchronous Byzantine agreement (ABA) protocol, called OciorABA. This protocol achieves ABA consensus on an $\ell$-bit message with an expected communication complexity of $O(n\ell + n^3 \log q )$ bits and an expected round complexit... | {
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2501.11790 | Benchmarking Large Language Models via Random Variables | [
"cs.CL",
"cs.AI"
] | Recent studies have raised concerns about the reliability of current mathematical benchmarks, highlighting issues such as simplistic design and potential data contamination. Therefore, creating a reliable benchmark that effectively evaluates the genuine capabilities of large language models (LLMs) in mathematical reaso... | {
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2501.11795 | Provably effective detection of effective data poisoning attacks | [
"cs.CR",
"cs.CV",
"cs.LG",
"stat.ML"
] | This paper establishes a mathematically precise definition of dataset poisoning attack and proves that the very act of effectively poisoning a dataset ensures that the attack can be effectively detected. On top of a mathematical guarantee that dataset poisoning is identifiable by a new statistical test that we call the... | {
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2501.11799 | Policy-Adaptable Methods For Resolving Normative Conflicts Through
Argumentation and Graph Colouring | [
"cs.AI",
"cs.LO",
"math.LO"
] | In a multi-agent system, one may choose to govern the behaviour of an agent by imposing norms, which act as guidelines for how agents should act either all of the time or in given situations. However, imposing multiple norms on one or more agents may result in situations where these norms conflict over how the agent sh... | {
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2501.11800 | TFLOP: Table Structure Recognition Framework with Layout Pointer
Mechanism | [
"cs.CV"
] | Table Structure Recognition (TSR) is a task aimed at converting table images into a machine-readable format (e.g. HTML), to facilitate other applications such as information retrieval. Recent works tackle this problem by identifying the HTML tags and text regions, where the latter is used for text extraction from the t... | {
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2501.11803 | Automating High Quality RT Planning at Scale | [
"cs.HC",
"cs.LG",
"cs.RO"
] | Radiotherapy (RT) planning is complex, subjective, and time-intensive. Advances in artificial intelligence (AI) promise to improve its precision, efficiency, and consistency, but progress is often limited by the scarcity of large, standardized datasets. To address this, we introduce the Automated Iterative RT Planning ... | {
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2501.11813 | Utilising Deep Learning to Elicit Expert Uncertainty | [
"cs.LG",
"stat.OT"
] | Recent work [ 14 ] has introduced a method for prior elicitation that utilizes records of expert decisions to infer a prior distribution. While this method provides a promising approach to eliciting expert uncertainty, it has only been demonstrated using tabular data, which may not entirely represent the information us... | {
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2501.11815 | CogMorph: Cognitive Morphing Attacks for Text-to-Image Models | [
"cs.CV"
] | The development of text-to-image (T2I) generative models, that enable the creation of high-quality synthetic images from textual prompts, has opened new frontiers in creative design and content generation. However, this paper reveals a significant and previously unrecognized ethical risk inherent in this technology and... | {
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2501.11817 | Toward Effective Digraph Representation Learning: A Magnetic Adaptive
Propagation based Approach | [
"cs.LG",
"cs.AI",
"cs.DB",
"cs.SI"
] | The $q$-parameterized magnetic Laplacian serves as the foundation of directed graph (digraph) convolution, enabling this kind of digraph neural network (MagDG) to encode node features and structural insights by complex-domain message passing. As a generalization of undirected methods, MagDG shows superior capability in... | {
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2501.11818 | Group-Agent Reinforcement Learning with Heterogeneous Agents | [
"cs.LG"
] | Group-agent reinforcement learning (GARL) is a newly arising learning scenario, where multiple reinforcement learning agents study together in a group, sharing knowledge in an asynchronous fashion. The goal is to improve the learning performance of each individual agent. Under a more general heterogeneous setting where... | {
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2501.11820 | Comparative Analysis of Control Strategies for Position Regulation in DC
Servo Motors | [
"eess.SY",
"cs.SY"
] | A servomotor is a closed-loop system designed for precise movement control, utilizing position feedback to achieve accurate final positions. Due to the ability to deliver higher power output and operate at enhanced speeds, DC servo motors are considered ideal for applications requiring precision and performance. This r... | {
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2501.11823 | Toward Scalable Graph Unlearning: A Node Influence Maximization based
Approach | [
"cs.LG",
"cs.AI",
"cs.DB",
"cs.SI"
] | Machine unlearning, as a pivotal technology for enhancing model robustness and data privacy, has garnered significant attention in prevalent web mining applications, especially in thriving graph-based scenarios. However, most existing graph unlearning (GU) approaches face significant challenges due to the intricate int... | {
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2501.11827 | PXGen: A Post-hoc Explainable Method for Generative Models | [
"cs.LG",
"cs.AI"
] | With the rapid growth of generative AI in numerous applications, explainable AI (XAI) plays a crucial role in ensuring the responsible development and deployment of generative AI technologies. XAI has undergone notable advancements and widespread adoption in recent years, reflecting a concerted push to enhance the tran... | {
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2501.11828 | Fact-Preserved Personalized News Headline Generation | [
"cs.CL",
"cs.AI"
] | Personalized news headline generation, aiming at generating user-specific headlines based on readers' preferences, burgeons a recent flourishing research direction. Existing studies generally inject a user interest embedding into an encoderdecoder headline generator to make the output personalized, while the factual co... | {
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2501.11830 | ShadowGenes: Leveraging Recurring Patterns within Computational Graphs
for Model Genealogy | [
"cs.LG",
"cs.CR"
] | Machine learning model genealogy enables practitioners to determine which architectural family a neural network belongs to. In this paper, we introduce ShadowGenes, a novel, signature-based method for identifying a given model's architecture, type, and family. Our method involves building a computational graph of the m... | {
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2501.11833 | Is your LLM trapped in a Mental Set? Investigative study on how mental
sets affect the reasoning capabilities of LLMs | [
"cs.CL",
"cs.AI"
] | In this paper, we present an investigative study on how Mental Sets influence the reasoning capabilities of LLMs. LLMs have excelled in diverse natural language processing (NLP) tasks, driven by advancements in parameter-efficient fine-tuning (PEFT) and emergent capabilities like in-context learning (ICL). For complex ... | {
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2501.11834 | PDA Construction via Union of Cartesian Product Cache Configurations for
Coded Caching | [
"cs.IT",
"math.IT"
] | Caching is an efficient technique to reduce peak traffic by storing popular content in local caches. Placement delivery array (PDA) proposed by Yan et al. is a combinatorial structure to design coded caching schemes with uncoded placement and one-shot linear delivery. By taking the $m$-fold Cartesian product of a small... | {
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2501.11835 | Hybrid Adaptive Modeling using Neural Networks Trained with Nonlinear
Dynamics Based Features | [
"cs.LG",
"nlin.AO"
] | Accurate models are essential for design, performance prediction, control, and diagnostics in complex engineering systems. Physics-based models excel during the design phase but often become outdated during system deployment due to changing operational conditions, unknown interactions, excitations, and parametric drift... | {
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2501.11836 | Data-driven Detection and Evaluation of Damages in Concrete Structures:
Using Deep Learning and Computer Vision | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Structural integrity is vital for maintaining the safety and longevity of concrete infrastructures such as bridges, tunnels, and walls. Traditional methods for detecting damages like cracks and spalls are labor-intensive, time-consuming, and prone to human error. To address these challenges, this study explores advance... | {
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2501.11839 | Supervised Learning for Analog and RF Circuit Design: Benchmarks and
Comparative Insights | [
"cs.LG",
"cs.AI",
"cs.AR"
] | Automating analog and radio-frequency (RF) circuit design using machine learning (ML) significantly reduces the time and effort required for parameter optimization. This study explores supervised ML-based approaches for designing circuit parameters from performance specifications across various circuit types, including... | {
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2501.11841 | Survey on Monocular Metric Depth Estimation | [
"cs.CV"
] | Monocular Depth Estimation (MDE) is a fundamental computer vision task underpinning applications such as spatial understanding, 3D reconstruction, and autonomous driving. While deep learning-based MDE methods can predict relative depth from a single image, their lack of metric scale information often results in scale i... | {
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2501.11842 | Harnessing Rydberg Atomic Receivers: From Quantum Physics to Wireless
Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | The intrinsic integration of Rydberg atomic receivers into wireless communication systems is proposed, by harnessing the principles of quantum physics in wireless communications. More particularly, we conceive a pair of Rydberg atomic receivers, one incorporates a local oscillator (LO), referred to as an LO-dressed rec... | {
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2501.11847 | A Survey on Memory-Efficient Large-Scale Model Training in AI for
Science | [
"cs.LG",
"cs.AI"
] | Scientific research faces high costs and inefficiencies with traditional methods, but the rise of deep learning and large language models (LLMs) offers innovative solutions. This survey reviews LLM applications across scientific fields such as biology, medicine, chemistry, and meteorology, underscoring their role in ad... | {
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2501.11849 | Network-informed Prompt Engineering against Organized Astroturf
Campaigns under Extreme Class Imbalance | [
"cs.CL",
"cs.AI",
"cs.SI"
] | Detecting organized political campaigns is of paramount importance in fighting against disinformation on social media. Existing approaches for the identification of such organized actions employ techniques mostly from network science, graph machine learning and natural language processing. Their ultimate goal is to ana... | {
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2501.11851 | Challenges in Expanding Portuguese Resources: A View from Open
Information Extraction | [
"cs.CL"
] | Open Information Extraction (Open IE) is the task of extracting structured information from textual documents, independent of domain. While traditional Open IE methods were based on unsupervised approaches, recently, with the emergence of robust annotated datasets, new data-based approaches have been developed to achie... | {
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2501.11852 | Cross-Entropy Attacks to Language Models via Rare Event Simulation | [
"cs.CL",
"cs.CR",
"cs.LG"
] | Black-box textual adversarial attacks are challenging due to the lack of model information and the discrete, non-differentiable nature of text. Existing methods often lack versatility for attacking different models, suffer from limited attacking performance due to the inefficient optimization with word saliency ranking... | {
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2501.11854 | WaveNet-SF: A Hybrid Network for Retinal Disease Detection Based on
Wavelet Transform in the Spatial-Frequency Domain | [
"eess.IV",
"cs.CV"
] | Retinal diseases are a leading cause of vision impairment and blindness, with timely diagnosis being critical for effective treatment. Optical Coherence Tomography (OCT) has become a standard imaging modality for retinal disease diagnosis, but OCT images often suffer from issues such as speckle noise, complex lesion sh... | {
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2501.11855 | A New Construction Structure on Coded Caching with Linear
Subpacketization: Non-Half-Sum Disjoint Packing | [
"cs.IT",
"math.IT"
] | Coded caching is a promising technique to effectively reduce peak traffic by using local caches and the multicast gains generated by these local caches. We prefer to design a coded caching scheme with the subpacketization $F$ and transmission load $R$ as small as possible since these are the key metrics for evaluating ... | {
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2501.11858 | EmbodiedEval: Evaluate Multimodal LLMs as Embodied Agents | [
"cs.CV",
"cs.CL"
] | Multimodal Large Language Models (MLLMs) have shown significant advancements, providing a promising future for embodied agents. Existing benchmarks for evaluating MLLMs primarily utilize static images or videos, limiting assessments to non-interactive scenarios. Meanwhile, existing embodied AI benchmarks are task-speci... | {
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2501.11860 | Bayesian Despeckling of Structured Sources | [
"cs.IT",
"cs.LG",
"math.IT",
"stat.AP"
] | Speckle noise is a fundamental challenge in coherent imaging systems, significantly degrading image quality. Over the past decades, numerous despeckling algorithms have been developed for applications such as Synthetic Aperture Radar (SAR) and digital holography. In this paper, we aim to establish a theoretically groun... | {
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2501.11866 | Evaluating multiple models using labeled and unlabeled data | [
"cs.LG",
"cs.CY"
] | It remains difficult to evaluate machine learning classifiers in the absence of a large, labeled dataset. While labeled data can be prohibitively expensive or impossible to obtain, unlabeled data is plentiful. Here, we introduce Semi-Supervised Model Evaluation (SSME), a method that uses both labeled and unlabeled data... | {
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2501.11869 | Saturation in Snapshot Compressive Imaging | [
"eess.IV",
"cs.IT",
"math.IT",
"stat.AP"
] | Snapshot Compressive Imaging (SCI) maps three-dimensional (3D) data cubes, such as videos or hyperspectral images, into two-dimensional (2D) measurements via optical modulation, enabling efficient data acquisition and reconstruction. Recent advances have shown the potential of mask optimization to enhance SCI performan... | {
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2501.11870 | Coarse-to-Fine Lightweight Meta-Embedding for ID-Based Recommendation | [
"cs.IR",
"cs.AI"
] | The state-of-the-art recommendation systems have shifted the attention to efficient recommendation, e.g., on-device recommendation, under memory constraints. To this end, the existing methods either focused on the lightweight embeddings for both users and items, or involved on-device systems enjoying the compact embedd... | {
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2501.11873 | Demons in the Detail: On Implementing Load Balancing Loss for Training
Specialized Mixture-of-Expert Models | [
"cs.LG",
"cs.CL"
] | This paper revisits the implementation of $\textbf{L}$oad-$\textbf{b}$alancing $\textbf{L}$oss (LBL) when training Mixture-of-Experts (MoEs) models. Specifically, LBL for MoEs is defined as $N_E \sum_{i=1}^{N_E} f_i p_i$, where $N_E$ is the total number of experts, $f_i$ represents the frequency of expert $i$ being sel... | {
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2501.11876 | FNIN: A Fourier Neural Operator-based Numerical Integration Network for
Surface-form-gradients | [
"cs.CV"
] | Surface-from-gradients (SfG) aims to recover a three-dimensional (3D) surface from its gradients. Traditional methods encounter significant challenges in achieving high accuracy and handling high-resolution inputs, particularly facing the complex nature of discontinuities and the inefficiencies associated with large-sc... | {
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2501.11877 | From Drafts to Answers: Unlocking LLM Potential via Aggregation
Fine-Tuning | [
"cs.CL",
"cs.AI"
] | Scaling data and model size has been proven effective for boosting the performance of large language models. In addition to training-time scaling, recent studies have revealed that increasing test-time computational resources can further improve performance. In this work, we introduce Aggregation Fine-Tuning (AFT), a s... | {
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2501.11880 | Community-Aware Temporal Walks: Parameter-Free Representation Learning
on Continuous-Time Dynamic Graphs | [
"cs.LG",
"cs.AI"
] | Dynamic graph representation learning plays a crucial role in understanding evolving behaviors. However, existing methods often struggle with flexibility, adaptability, and the preservation of temporal and structural dynamics. To address these issues, we propose Community-aware Temporal Walks (CTWalks), a novel framewo... | {
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2501.11881 | Channel Resolvability Using Multiplicative Weight Update Algorithm | [
"cs.IT",
"math.IT"
] | We study the channel resolvability problem, which is used to prove strong converse of identification via channel. Channel resolvability has been solved by only random coding in the literature. We prove channel resolvability using the multiplicative weight update algorithm. This is the first approach to channel resolvab... | {
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2501.11883 | An Improved Lower Bound on Oblivious Transfer Capacity Using
Polarization and Interaction | [
"cs.IT",
"math.IT"
] | We consider the oblivious transfer (OT) capacities of noisy channels against the passive adversary; this problem has not been solved even for the binary symmetric channel (BSC). In the literature, the general construction of OT has been known only for generalized erasure channels (GECs); for the BSC, we convert the cha... | {
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2501.11884 | Fast Underwater Scene Reconstruction using Multi-View Stereo and
Physical Imaging | [
"cs.CV"
] | Underwater scene reconstruction poses a substantial challenge because of the intricate interplay between light and the medium, resulting in scattering and absorption effects that make both depth estimation and rendering more complex. While recent Neural Radiance Fields (NeRF) based methods for underwater scenes achieve... | {
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2501.11885 | Med-R$^2$: Crafting Trustworthy LLM Physicians through Retrieval and
Reasoning of Evidence-Based Medicine | [
"cs.CL"
] | In recent years, Large Language Models (LLMs) have exhibited remarkable capabilities in clinical scenarios. However, despite their potential, existing works face challenges when applying LLMs to medical settings. Strategies relying on training with medical datasets are highly cost-intensive and may suffer from outdated... | {
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2501.11887 | Connection-Coordination Rapport (CCR) Scale: A Dual-Factor Scale to
Measure Human-Robot Rapport | [
"cs.RO",
"cs.HC"
] | Robots, particularly in service and companionship roles, must develop positive relationships with people they interact with regularly to be successful. These positive human-robot relationships can be characterized as establishing "rapport," which indicates mutual understanding and interpersonal connection that form the... | {
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2501.11893 | DynoSAM: Open-Source Smoothing and Mapping Framework for Dynamic SLAM | [
"cs.RO"
] | Traditional Visual Simultaneous Localization and Mapping (vSLAM) systems focus solely on static scene structures, overlooking dynamic elements in the environment. Although effective for accurate visual odometry in complex scenarios, these methods discard crucial information about moving objects. By incorporating this i... | {
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2501.11895 | Contrastive Masked Autoencoders for Character-Level Open-Set Writer
Identification | [
"cs.CV",
"cs.LG"
] | In the realm of digital forensics and document authentication, writer identification plays a crucial role in determining the authors of documents based on handwriting styles. The primary challenge in writer-id is the "open-set scenario", where the goal is accurately recognizing writers unseen during the model training.... | {
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2501.11896 | Systematic Abductive Reasoning via Diverse Relation Representations in
Vector-symbolic Architecture | [
"cs.AI"
] | In abstract visual reasoning, monolithic deep learning models suffer from limited interpretability and generalization, while existing neuro-symbolic approaches fall short in capturing the diversity and systematicity of attributes and relation representations. To address these challenges, we propose a Systematic Abducti... | {
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2501.11898 | Highly Efficient Rotation-Invariant Spectral Embedding for Scalable
Incomplete Multi-View Clustering | [
"cs.LG"
] | Incomplete multi-view clustering presents significant challenges due to missing views. Although many existing graph-based methods aim to recover missing instances or complete similarity matrices with promising results, they still face several limitations: (1) Recovered data may be unsuitable for spectral clustering, as... | {
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2501.11899 | LASER: Lip Landmark Assisted Speaker Detection for Robustness | [
"cs.CV",
"cs.LG"
] | Active Speaker Detection (ASD) aims to identify speaking individuals in complex visual scenes. While humans can easily detect speech by matching lip movements to audio, current ASD models struggle to establish this correspondence, often misclassifying non-speaking instances when audio and lip movements are unsynchroniz... | {
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2501.11900 | Panoramic Interests: Stylistic-Content Aware Personalized Headline
Generation | [
"cs.CL",
"cs.AI"
] | Personalized news headline generation aims to provide users with attention-grabbing headlines that are tailored to their preferences. Prevailing methods focus on user-oriented content preferences, but most of them overlook the fact that diverse stylistic preferences are integral to users' panoramic interests, leading t... | {
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2501.11901 | Enhancing Adversarial Transferability via Component-Wise Augmentation
Method | [
"cs.CV"
] | Deep Neural Networks (DNNs) are highly vulnerable to adversarial examples, which pose significant challenges in security-sensitive applications. Among various adversarial attack strategies, input transformation-based attacks have demonstrated remarkable effectiveness in enhancing adversarial transferability. However, e... | {
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2501.11903 | Finding the nearest bounded-real port-Hamiltonian system | [
"math.OC",
"cs.NA",
"cs.SY",
"eess.SY",
"math.NA"
] | In this paper, we consider linear time-invariant continuous control systems which are bounded real, also known as scattering passive. Our main theoretical contribution is to show the equivalence between such systems and port-Hamiltonian (PH) systems whose factors satisfy certain linear matrix inequalities. Based on thi... | {
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2501.11905 | Phase Transitions in Phase-Only Compressed Sensing | [
"cs.IT",
"eess.SP",
"math.IT"
] | The goal of phase-only compressed sensing is to recover a structured signal $\mathbf{x}$ from the phases $\mathbf{z} = {\rm sign}(\mathbf{\Phi}\mathbf{x})$ under some complex-valued sensing matrix $\mathbf{\Phi}$. Exact reconstruction of the signal's direction is possible: we can reformulate it as a linear compressed s... | {
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} |
2501.11906 | Multi-source Multi-level Multi-token Ethereum Dataset and Benchmark
Platform | [
"cs.CE"
] | This paper introduces 3MEthTaskforce (https://3meth.github.io), a multi-source, multi-level, and multi-token Ethereum dataset addressing the limitations of single-source datasets. Integrating over 300 million transaction records, 3,880 token profiles, global market indicators, and Reddit sentiment data from 2014-2024, ... | {
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} |
2501.11909 | Bridging the Communication Gap: Evaluating AI Labeling Practices for
Trustworthy AI Development | [
"cs.AI"
] | As artificial intelligence (AI) becomes integral to economy and society, communication gaps between developers, users, and stakeholders hinder trust and informed decision-making. High-level AI labels, inspired by frameworks like EU energy labels, have been proposed to make the properties of AI models more transparent. ... | {
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} |
2501.11911 | Integrate Temporal Graph Learning into LLM-based Temporal Knowledge
Graph Model | [
"cs.IR"
] | Temporal Knowledge Graph Forecasting (TKGF) aims to predict future events based on the observed events in history. Recently, Large Language Models (LLMs) have exhibited remarkable capabilities, generating significant research interest in their application for reasoning over temporal knowledge graphs (TKGs). Existing LL... | {
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} |
2501.11914 | LuxVeri at GenAI Detection Task 1: Inverse Perplexity Weighted Ensemble
for Robust Detection of AI-Generated Text across English and Multilingual
Contexts | [
"cs.CL",
"cs.AI"
] | This paper presents a system developed for Task 1 of the COLING 2025 Workshop on Detecting AI-Generated Content, focusing on the binary classification of machine-generated versus human-written text. Our approach utilizes an ensemble of models, with weights assigned according to each model's inverse perplexity, to enhan... | {
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} |
2501.11915 | Stabilizing Optimal Control for Nonlinear Stochastic Systems: A
Parametric Gradient-Based Approach | [
"math.OC",
"cs.SY",
"eess.SY"
] | This study proposes a method for designing stabilizing suboptimal controllers for nonlinear stochastic systems. These systems include time-invariant stochastic parameters that represent uncertainty of dynamics, posing two key difficulties in optimal control. Firstly, the time-invariant stochastic nature violates the pr... | {
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} |
2501.11916 | Generating with Fairness: A Modality-Diffused Counterfactual Framework
for Incomplete Multimodal Recommendations | [
"cs.IR"
] | Incomplete scenario is a prevalent, practical, yet challenging setting in Multimodal Recommendations (MMRec), where some item modalities are missing due to various factors. Recently, a few efforts have sought to improve the recommendation accuracy by exploring generic structures from incomplete data. However, two signi... | {
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} |
2501.11918 | LuxVeri at GenAI Detection Task 3: Cross-Domain Detection of
AI-Generated Text Using Inverse Perplexity-Weighted Ensemble of Fine-Tuned
Transformer Models | [
"cs.CL",
"cs.AI"
] | This paper presents our approach for Task 3 of the GenAI content detection workshop at COLING-2025, focusing on Cross-Domain Machine-Generated Text (MGT) Detection. We propose an ensemble of fine-tuned transformer models, enhanced by inverse perplexity weighting, to improve classification accuracy across diverse text d... | {
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"cs.SD": 0,
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} |
2501.11919 | Improving Fine-Tuning with Latent Cluster Correction | [
"cs.LG"
] | The existence of salient semantic clusters in the latent spaces of a neural network during training strongly correlates its final accuracy on classification tasks. This paper proposes a novel fine-tuning method that boosts performance by optimising the formation of these latent clusters, using the Louvain community det... | {
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} |
2501.11921 | Goal-oriented Transmission Scheduling: Structure-guided DRL with a
Unified Dual On-policy and Off-policy Approach | [
"cs.IT",
"cs.AI",
"cs.LG",
"cs.SY",
"eess.SP",
"eess.SY",
"math.IT"
] | Goal-oriented communications prioritize application-driven objectives over data accuracy, enabling intelligent next-generation wireless systems. Efficient scheduling in multi-device, multi-channel systems poses significant challenges due to high-dimensional state and action spaces. We address these challenges by derivi... | {
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"cs.SY": 1
} |
2501.11923 | Progressive Cross Attention Network for Flood Segmentation using
Multispectral Satellite Imagery | [
"cs.CV",
"cs.LG"
] | In recent years, the integration of deep learning techniques with remote sensing technology has revolutionized the way natural hazards, such as floods, are monitored and managed. However, existing methods for flood segmentation using remote sensing data often overlook the utility of correlative features among multispec... | {
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} |
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