id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.12668 | NBDI: A Simple and Efficient Termination Condition for Skill Extraction
from Task-Agnostic Demonstrations | [
"cs.LG",
"cs.AI"
] | Intelligent agents are able to make decisions based on different levels of granularity and duration. Recent advances in skill learning enabled the agent to solve complex, long-horizon tasks by effectively guiding the agent in choosing appropriate skills. However, the practice of using fixed-length skills can easily res... | {
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2501.12669 | Information Design for Adaptive Organizations | [
"econ.TH",
"cs.GT",
"cs.SI"
] | This paper examines the optimal design of information sharing in organizations. Organizational performance depends on agents adapting to uncertain external environments while coordinating their actions, where coordination incentives and synergies are modeled as graphs (networks). The equilibrium strategies and the prin... | {
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2501.12670 | Learning Versatile Optimizers on a Compute Diet | [
"cs.LG"
] | Learned optimization has emerged as a promising alternative to hand-crafted optimizers, with the potential to discover stronger learned update rules that enable faster, hyperparameter-free training of neural networks. A critical element for practically useful learned optimizers, that can be used off-the-shelf after met... | {
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2501.12678 | Manifold learning and optimization using tangent space proxies | [
"cs.LG",
"math.OC"
] | We present a framework for efficiently approximating differential-geometric primitives on arbitrary manifolds via construction of an atlas graph representation, which leverages the canonical characterization of a manifold as a finite collection, or atlas, of overlapping coordinate charts. We first show the utility of t... | {
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2501.12681 | Can masking background and object reduce static bias for zero-shot
action recognition? | [
"cs.CV"
] | In this paper, we address the issue of static bias in zero-shot action recognition. Action recognition models need to represent the action itself, not the appearance. However, some fully-supervised works show that models often rely on static appearances, such as the background and objects, rather than human actions. Th... | {
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2501.12689 | EchoLM: Accelerating LLM Serving with Real-time Knowledge Distillation | [
"cs.LG"
] | Large language models (LLMs) have excelled in various applications, yet serving them at scale is challenging due to their substantial resource demands and high latency. Our real-world studies reveal that over 60% of user requests to LLMs have semantically similar counterparts, suggesting the potential for knowledge sha... | {
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2501.12690 | Growth strategies for arbitrary DAG neural architectures | [
"cs.LG",
"cs.AI"
] | Deep learning has shown impressive results obtained at the cost of training huge neural networks. However, the larger the architecture, the higher the computational, financial, and environmental costs during training and inference. We aim at reducing both training and inference durations. We focus on Neural Architectur... | {
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2501.12697 | Combining Knowledge Graph and LLMs for Enhanced Zero-shot Visual
Question Answering | [
"cs.CV"
] | Zero-shot visual question answering (ZS-VQA), an emerged critical research area, intends to answer visual questions without providing training samples. Existing research in ZS-VQA has proposed to leverage knowledge graphs or large language models (LLMs), respectively, as external information sources to help VQA model c... | {
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2501.12698 | Training Dialogue Systems by AI Feedback for Improving Overall Dialogue
Impression | [
"cs.CL"
] | To improve user engagement during conversations with dialogue systems, we must improve individual dialogue responses and dialogue impressions such as consistency, personality, and empathy throughout the entire dialogue. While such dialogue systems have been developing rapidly with the help of large language models (LLM... | {
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2501.12703 | HEPPO: Hardware-Efficient Proximal Policy Optimization -- A Universal
Pipelined Architecture for Generalized Advantage Estimation | [
"cs.AR",
"cs.AI",
"cs.LG"
] | This paper introduces HEPPO, an FPGA-based accelerator designed to optimize the Generalized Advantage Estimation (GAE) stage in Proximal Policy Optimization (PPO). Unlike previous approaches that focused on trajectory collection and actor-critic updates, HEPPO addresses GAE's computational demands with a parallel, pipe... | {
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2501.12705 | The Marginal Importance of Distortions and Alignment in CASSI systems | [
"eess.IV",
"cs.LG",
"physics.comp-ph"
] | This paper introduces a differentiable ray-tracing based model that incorporates aberrations and distortions to render realistic coded hyperspectral acquisitions using Coded-Aperture Spectral Snapshot Imagers (CASSI). CASSI systems can now be optimized in order to fulfill simultaneously several optical design constrain... | {
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2501.12706 | REX: Causal Discovery based on Machine Learning and Explainability
techniques | [
"cs.LG"
] | Explainability techniques hold significant potential for enhancing the causal discovery process, which is crucial for understanding complex systems in areas like healthcare, economics, and artificial intelligence. However, no causal discovery methods currently incorporate explainability into their models to derive caus... | {
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2501.12709 | Practical quantum federated learning and its experimental demonstration | [
"quant-ph",
"cs.AI",
"cs.CR",
"cs.DC"
] | Federated learning is essential for decentralized, privacy-preserving model training in the data-driven era. Quantum-enhanced federated learning leverages quantum resources to address privacy and scalability challenges, offering security and efficiency advantages beyond classical methods. However, practical and scalabl... | {
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2501.12720 | A systematic data characteristic understanding framework towards
physical-sensor big data challenges | [
"cs.IR"
] | Big data present new opportunities for modern society while posing challenges for data scientists. Recent advancements in sensor networks and the widespread adoption of IoT have led to the collection of physical-sensor data on an enormous scale. However, significant challenges arise in high-quality big data analytics. ... | {
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2501.12723 | Anomaly Detection in Double-entry Bookkeeping Data by Federated Learning
System with Non-model Sharing Approach | [
"cs.LG"
] | Anomaly detection is crucial in financial auditing and effective detection often requires obtaining large volumes of data from multiple organizations. However, confidentiality concerns hinder data sharing among audit firms. Although the federated learning (FL)-based approach, FedAvg, has been proposed to address this c... | {
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2501.12728 | A Call for Critically Rethinking and Reforming Data Analysis in
Empirical Software Engineering | [
"cs.SE",
"cs.AI",
"cs.DL"
] | Context: Empirical Software Engineering (ESE) drives innovation in SE through qualitative and quantitative studies. However, concerns about the correct application of empirical methodologies have existed since the 2006 Dagstuhl seminar on SE. Objective: To analyze three decades of SE research, identify mistakes in stat... | {
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2501.12732 | GRAMA: Adaptive Graph Autoregressive Moving Average Models | [
"cs.LG"
] | Graph State Space Models (SSMs) have recently been introduced to enhance Graph Neural Networks (GNNs) in modeling long-range interactions. Despite their success, existing methods either compromise on permutation equivariance or limit their focus to pairwise interactions rather than sequences. Building on the connection... | {
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2501.12735 | Online Preference Alignment for Language Models via Count-based
Exploration | [
"cs.LG"
] | Reinforcement Learning from Human Feedback (RLHF) has shown great potential in fine-tuning Large Language Models (LLMs) to align with human preferences. Existing methods perform preference alignment from a fixed dataset, which can be limited in data coverage, and the resulting reward model is hard to generalize in out-... | {
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2501.12736 | Bad-PFL: Exploring Backdoor Attacks against Personalized Federated
Learning | [
"cs.LG",
"cs.CR",
"cs.CV"
] | Data heterogeneity and backdoor attacks rank among the most significant challenges facing federated learning (FL). For data heterogeneity, personalized federated learning (PFL) enables each client to maintain a private personalized model to cater to client-specific knowledge. Meanwhile, vanilla FL has proven vulnerable... | {
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2501.12737 | Stability and Generalization of Quantum Neural Networks | [
"cs.LG",
"stat.ML"
] | Quantum neural networks (QNNs) play an important role as an emerging technology in the rapidly growing field of quantum machine learning. While their empirical success is evident, the theoretical explorations of QNNs, particularly their generalization properties, are less developed and primarily focus on the uniform co... | {
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2501.12739 | Multiscale Training of Convolutional Neural Networks | [
"cs.LG"
] | Convolutional Neural Networks (CNNs) are the backbone of many deep learning methods, but optimizing them remains computationally expensive. To address this, we explore multiscale training frameworks and mathematically identify key challenges, particularly when dealing with noisy inputs. Our analysis reveals that in the... | {
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2501.12746 | EvidenceMap: Learning Evidence Analysis to Unleash the Power of Small
Language Models for Biomedical Question Answering | [
"cs.CL",
"cs.AI"
] | When addressing professional questions in the biomedical domain, humans typically acquire multiple pieces of information as evidence and engage in multifaceted analysis to provide high-quality answers. Current LLM-based question answering methods lack a detailed definition and learning process for evidence analysis, le... | {
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2501.12747 | Singular leaning coefficients and efficiency in learning theory | [
"stat.ML",
"cs.LG",
"math.AG",
"math.ST",
"stat.TH"
] | Singular learning models with non-positive Fisher information matrices include neural networks, reduced-rank regression, Boltzmann machines, normal mixture models, and others. These models have been widely used in the development of learning machines. However, theoretical analysis is still in its early stages. In this ... | {
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2501.12749 | Estimating the Conformal Prediction Threshold from Noisy Labels | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Conformal Prediction (CP) is a method to control prediction uncertainty by producing a small prediction set, ensuring a predetermined probability that the true class lies within this set. This is commonly done by defining a score, based on the model predictions, and setting a threshold on this score using a validation ... | {
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2501.12751 | Patent Figure Classification using Large Vision-language Models | [
"cs.IR",
"cs.CV",
"cs.LG"
] | Patent figure classification facilitates faceted search in patent retrieval systems, enabling efficient prior art search. Existing approaches have explored patent figure classification for only a single aspect and for aspects with a limited number of concepts. In recent years, large vision-language models (LVLMs) have ... | {
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2501.12752 | Indoor Channel Characterization with Extremely Large Reconfigurable
Intelligent Surfaces at $300$ GHz | [
"cs.IT",
"cs.ET",
"math.IT"
] | The technology of Reconfigurable Intelligent Surfaces (RISs) is lately being considered as a boosting component for various indoor wireless applications, enabling wave propagation control and coverage extension. However, the incorporation of extremely large RISs, as recently being considered for ultra-high capacity ind... | {
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2501.12756 | A topology optimisation framework to design test specimens for one-shot
identification or discovery of material models | [
"cs.CE",
"cond-mat.mtrl-sci"
] | The increasing availability of full-field displacement data from imaging techniques in experimental mechanics is determining a gradual shift in the paradigm of material model calibration and discovery, from using several simple-geometry tests towards a few, or even one single test with complicated geometry. The feasibi... | {
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2501.12761 | Modality Unified Attack for Omni-Modality Person Re-Identification | [
"cs.CV",
"cs.LG"
] | Deep learning based person re-identification (re-id) models have been widely employed in surveillance systems. Recent studies have demonstrated that black-box single-modality and cross-modality re-id models are vulnerable to adversarial examples (AEs), leaving the robustness of multi-modality re-id models unexplored. D... | {
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2501.12764 | Grid-based Submap Joining: An Efficient Algorithm for Simultaneously
Optimizing Global Occupancy Map and Local Submap Frames | [
"cs.RO"
] | Optimizing robot poses and the map simultaneously has been shown to provide more accurate SLAM results. However, for non-feature based SLAM approaches, directly optimizing all the robot poses and the whole map will greatly increase the computational cost, making SLAM problems difficult to solve in large-scale environme... | {
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2501.12766 | NExtLong: Toward Effective Long-Context Training without Long Documents | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) with extended context windows have made significant strides yet remain a challenge due to the scarcity of long documents. Existing methods tend to synthesize long-context data but lack a clear mechanism to reinforce the long-range dependency modeling. To address this limitation, we propose ... | {
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2501.12769 | Urban Priority Pass: Fair Signalized Intersection Management Accounting
For Passenger Needs Through Prioritization | [
"eess.SY",
"cs.SY"
] | Over the past few decades, efforts of road traffic management and practice have predominantly focused on maximizing system efficiency and mitigating congestion from a system perspective. This efficiency-driven approach implies the equal treatment of all vehicles, which often overlooks individual user experiences, broad... | {
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2501.12770 | On Tradeoffs in Learning-Augmented Algorithms | [
"cs.DS",
"cs.AI",
"cs.LG"
] | The field of learning-augmented algorithms has gained significant attention in recent years. These algorithms, using potentially inaccurate predictions, must exhibit three key properties: consistency, robustness, and smoothness. In scenarios where distributional information about predictions is available, a strong expe... | {
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2501.12771 | Non-adaptive Learning of Random Hypergraphs with Queries | [
"cs.IT",
"cs.DM",
"cs.DS",
"cs.LG",
"math.IT",
"stat.ML"
] | We study the problem of learning a hidden hypergraph $G=(V,E)$ by making a single batch of queries (non-adaptively). We consider the hyperedge detection model, in which every query must be of the form: ``Does this set $S\subseteq V$ contain at least one full hyperedge?'' In this model, it is known that there is no ... | {
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2501.12773 | Low-Complexity Channel Estimation for RIS-Assisted Multi-User Wireless
Communications | [
"cs.IT",
"eess.SP",
"math.IT"
] | Reconfigurable intelligent surfaces (RISs) are eminently suitable for improving the reliability of wireless communications by jointly designing the active beamforming at the base station (BS) and the passive beamforming at the RIS. Therefore, the accuracy of channel estimation is crucial for RIS-aided systems. The chal... | {
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2501.12774 | LLMs as Repositories of Factual Knowledge: Limitations and Solutions | [
"cs.CL"
] | LLMs' sources of knowledge are data snapshots containing factual information about entities collected at different timestamps and from different media types (e.g. wikis, social media, etc.). Such unstructured knowledge is subject to change due to updates through time from past to present. Equally important are the inco... | {
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2501.12775 | Regularization, Semi-supervision, and Supervision for a Plausible
Attention-Based Explanation | [
"cs.CL"
] | Attention mechanism is contributing to the majority of recent advances in machine learning for natural language processing. Additionally, it results in an attention map that shows the proportional influence of each input in its decision. Empirical studies postulate that attention maps can be provided as an explanation ... | {
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2501.12776 | Data re-uploading in Quantum Machine Learning for time series:
application to traffic forecasting | [
"quant-ph",
"cs.AI",
"cs.LG",
"cs.NE"
] | Accurate traffic forecasting plays a crucial role in modern Intelligent Transportation Systems (ITS), as it enables real-time traffic flow management, reduces congestion, and improves the overall efficiency of urban transportation networks. With the rise of Quantum Machine Learning (QML), it has emerged a new paradigm ... | {
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2501.12785 | On Generalization and Distributional Update for Mimicking Observations
with Adequate Exploration | [
"stat.ML",
"cs.LG"
] | This paper tackles the efficiency and stability issues in learning from observations (LfO). We commence by investigating how reward functions and policies generalize in LfO. Subsequently, the built-in reinforcement learning (RL) approach in generative adversarial imitation from observation (GAIfO) is replaced with dist... | {
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2501.12789 | Generating Diverse Q&A Benchmarks for RAG Evaluation with DataMorgana | [
"cs.CL",
"cs.IR"
] | Evaluating Retrieval-Augmented Generation (RAG) systems, especially in domain-specific contexts, requires benchmarks that address the distinctive requirements of the applicative scenario. Since real data can be hard to obtain, a common strategy is to use LLM-based methods to generate synthetic data. Existing solutions ... | {
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2501.12793 | Revisit Self-Debugging with Self-Generated Tests for Code Generation | [
"cs.SE",
"cs.AI"
] | Large language models (LLMs) have shown significant advancements in code generation, but still face challenges on tasks beyond their basic capabilities. Recently, the notion of self-debugging has been proposed to boost the performance of code generation by leveraging execution feedback from tests. Despite its promise, ... | {
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2501.12794 | Generation of Standardized E-Learning Contents from Digital Medical
Collections | [
"cs.CL"
] | In this paper, we describe an approach to transforming the huge amount of medical knowledge available in existing online medical collections into standardized learning packages ready to be integrated into the most popular e-learning platforms. The core of our approach is a tool called Clavy, which makes it possible to ... | {
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2501.12796 | Hybrid Losses for Hierarchical Embedding Learning | [
"cs.SD",
"cs.IR",
"cs.LG",
"eess.AS"
] | In traditional supervised learning, the cross-entropy loss treats all incorrect predictions equally, ignoring the relevance or proximity of wrong labels to the correct answer. By leveraging a tree hierarchy for fine-grained labels, we investigate hybrid losses, such as generalised triplet and cross-entropy losses, to e... | {
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2501.12799 | Int2Planner: An Intention-based Multi-modal Motion Planner for
Integrated Prediction and Planning | [
"cs.RO"
] | Motion planning is a critical module in autonomous driving, with the primary challenge of uncertainty caused by interactions with other participants. As most previous methods treat prediction and planning as separate tasks, it is difficult to model these interactions. Furthermore, since the route path navigates ego veh... | {
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2501.12810 | Machine Learning Modeling for Multi-order Human Visual Motion Processing | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Our research aims to develop machines that learn to perceive visual motion as do humans. While recent advances in computer vision (CV) have enabled DNN-based models to accurately estimate optical flow in naturalistic images, a significant disparity remains between CV models and the biological visual system in both arch... | {
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2501.12811 | Unveiling Zero-Space Detection: A Novel Framework for Autonomous
Ransomware Identification in High-Velocity Environments | [
"cs.CR",
"cs.AI"
] | Modern cybersecurity landscapes increasingly demand sophisticated detection frameworks capable of identifying evolving threats with precision and adaptability. The proposed Zero-Space Detection framework introduces a novel approach that dynamically identifies latent behavioral patterns through unsupervised clustering a... | {
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2501.12812 | PSGSL: A Probabilistic Framework Integrating Semantic Scene
Understanding and Gas Sensing for Gas Source Localization | [
"cs.RO"
] | Semantic scene understanding allows a robotic agent to reason about problems in complex ways, using information from multiple and varied sensors to make deductions about a particular matter. As a result, this form of intelligent robotics is capable of performing more complex tasks and achieving more precise results tha... | {
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2501.12815 | Certified Guidance for Planning with Deep Generative Models | [
"cs.LG",
"stat.ML"
] | Deep generative models, such as generative adversarial networks and diffusion models, have recently emerged as powerful tools for planning tasks and behavior synthesis in autonomous systems. Various guidance strategies have been introduced to steer the generative process toward outputs that are more likely to satisfy t... | {
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2501.12823 | To Measure or Not: A Cost-Sensitive, Selective Measuring Environment for
Agricultural Management Decisions with Reinforcement Learning | [
"cs.LG",
"cs.AI"
] | Farmers rely on in-field observations to make well-informed crop management decisions to maximize profit and minimize adverse environmental impact. However, obtaining real-world crop state measurements is labor-intensive, time-consuming and expensive. In most cases, it is not feasible to gather crop state measurements ... | {
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2501.12824 | Enhancing Monocular Depth Estimation with Multi-Source Auxiliary Tasks | [
"cs.CV"
] | Monocular depth estimation (MDE) is a challenging task in computer vision, often hindered by the cost and scarcity of high-quality labeled datasets. We tackle this challenge using auxiliary datasets from related vision tasks for an alternating training scheme with a shared decoder built on top of a pre-trained vision f... | {
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2501.12826 | Open or Closed LLM for Lesser-Resourced Languages? Lessons from Greek | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Natural Language Processing (NLP) for lesser-resourced languages faces persistent challenges, including limited datasets, inherited biases from high-resource languages, and the need for domain-specific solutions. This study addresses these gaps for Modern Greek through three key contributions. First, we evaluate the pe... | {
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2501.12829 | A transformer-based deep q learning approach for dynamic load balancing
in software-defined networks | [
"cs.NI",
"cs.AI",
"cs.ET",
"cs.LG",
"cs.MA"
] | This study proposes a novel approach for dynamic load balancing in Software-Defined Networks (SDNs) using a Transformer-based Deep Q-Network (DQN). Traditional load balancing mechanisms, such as Round Robin (RR) and Weighted Round Robin (WRR), are static and often struggle to adapt to fluctuating traffic conditions, le... | {
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} |
2501.12830 | Orbit-Attitude Predictive Control in the Vicinity of Asteroids with In
Situ Gravity Estimation | [
"eess.SY",
"cs.SY"
] | This paper presents an integrated model-learning predictive control scheme for spacecraft orbit-attitude station-keeping in the vicinity of asteroids. The orbiting probe relies on optical and laser navigation while attitude measurements are provided by star trackers and gyroscopes. The asteroid gravity field inhomogene... | {
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2501.12832 | FDG-Diff: Frequency-Domain-Guided Diffusion Framework for Compressed
Hazy Image Restoration | [
"eess.IV",
"cs.CV"
] | In this study, we reveal that the interaction between haze degradation and JPEG compression introduces complex joint loss effects, which significantly complicate image restoration. Existing dehazing models often neglect compression effects, which limits their effectiveness in practical applications. To address these ch... | {
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2501.12833 | A coupled FE-BE multi-scale method for the dynamics of jointed
structures | [
"eess.SY",
"cs.SY"
] | The damping of built-up structures stems largely from the microscopic dry frictional interactions in the contact interfaces. The accurate prediction of friction damping has been an important scientific aim of the past several decades. Recent research indicates that very good agreement with vibration measurements is to ... | {
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2501.12834 | The Optimization of Random Tree Codes for Limited Computational
Resources | [
"cs.IT",
"cs.CC",
"math.IT"
] | In this paper, we introduce an achievability bound on the frame error rate of random tree code ensembles under a sequential decoding algorithm with a hard computational limit and consider the optimization of the random tree code ensembles over their branching structures/profiles and the decoding measure. Through numeri... | {
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2501.12835 | Adaptive Retrieval Without Self-Knowledge? Bringing Uncertainty Back
Home | [
"cs.CL",
"cs.LG"
] | Retrieval Augmented Generation (RAG) improves correctness of Question Answering (QA) and addresses hallucinations in Large Language Models (LLMs), yet greatly increase computational costs. Besides, RAG is not always needed as may introduce irrelevant information. Recent adaptive retrieval methods integrate LLMs' intrin... | {
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2501.12840 | AMM-Diff: Adaptive Multi-Modality Diffusion Network for Missing Modality
Imputation | [
"cs.CV"
] | In clinical practice, full imaging is not always feasible, often due to complex acquisition protocols, stringent privacy regulations, or specific clinical needs. However, missing MR modalities pose significant challenges for tasks like brain tumor segmentation, especially in deep learning-based segmentation, as each mo... | {
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2501.12844 | GAMED-Snake: Gradient-aware Adaptive Momentum Evolution Deep Snake Model
for Multi-organ Segmentation | [
"cs.CV",
"cs.AI"
] | Multi-organ segmentation is a critical yet challenging task due to complex anatomical backgrounds, blurred boundaries, and diverse morphologies. This study introduces the Gradient-aware Adaptive Momentum Evolution Deep Snake (GAMED-Snake) model, which establishes a novel paradigm for contour-based segmentation by integ... | {
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2501.12851 | ACEBench: Who Wins the Match Point in Tool Usage? | [
"cs.CL"
] | Large Language Models (LLMs) have demonstrated significant potential in decision-making and reasoning, particularly when integrated with various tools to effectively solve complex problems. However, existing benchmarks for evaluating LLMs' tool usage face several limitations: (1) limited evaluation scenarios, often lac... | {
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2501.12853 | Data-and-Semantic Dual-Driven Spectrum Map Construction for 6G Spectrum
Management | [
"cs.LG"
] | Spectrum maps reflect the utilization and distribution of spectrum resources in the electromagnetic environment, serving as an effective approach to support spectrum management. However, the construction of spectrum maps in urban environments is challenging because of high-density connection and complex terrain. Moreov... | {
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2501.12857 | HierPromptLM: A Pure PLM-based Framework for Representation Learning on
Heterogeneous Text-rich Networks | [
"cs.LG"
] | Representation learning on heterogeneous text-rich networks (HTRNs), which consist of multiple types of nodes and edges with each node associated with textual information, is essential for various real-world applications. Given the success of pretrained language models (PLMs) in processing text data, recent efforts hav... | {
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2501.12859 | Monte-Carlo based non-line-of-sight underwater wireless optical
communication channel modeling and system performance analysis under
turbulence | [
"physics.ao-ph",
"cs.IT",
"math.IT"
] | Compared with line-of-sight (LOS) communication, nonline-of-sight (NLOS) underwater wireless optical communication (UWOC) systems have garnered extensive attention because of their heightened suitability for the intricate and dynamic underwater environment. In the NLOS channel, photons can reach the receiver by sea sur... | {
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2501.12860 | CrossDiff: Diffusion Probabilistic Model With Cross-conditional
Encoder-Decoder for Crack Segmentation | [
"cs.CV"
] | Crack Segmentation in industrial concrete surfaces is a challenging task because cracks usually exhibit intricate morphology with slender appearances. Traditional segmentation methods often struggle to accurately locate such cracks, leading to inefficiencies in maintenance and repair processes. In this paper, we propos... | {
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2501.12861 | Hardware Distortion Modeling for Panel Selection in Large Intelligent
Surfaces | [
"eess.SP",
"cs.IT",
"math.IT"
] | Hardware distortion in large intelligent surfaces (LISs) may limit their performance when scaling up such systems. It is of great importance to model the non-ideal effects in their transceivers to study the hardware distortions that can affect their performance. Therefore, we have focused on modeling and studying the e... | {
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2501.12862 | Mutation-Guided LLM-based Test Generation at Meta | [
"cs.SE",
"cs.AI",
"cs.LG"
] | This paper describes Meta's ACH system for mutation-guided LLM-based test generation. ACH generates relatively few mutants (aka simulated faults), compared to traditional mutation testing. Instead, it focuses on generating currently undetected faults that are specific to an issue of concern. From these currently uncaug... | {
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2501.12868 | As Confidence Aligns: Exploring the Effect of AI Confidence on Human
Self-confidence in Human-AI Decision Making | [
"cs.HC",
"cs.AI"
] | Complementary collaboration between humans and AI is essential for human-AI decision making. One feasible approach to achieving it involves accounting for the calibrated confidence levels of both AI and users. However, this process would likely be made more difficult by the fact that AI confidence may influence users' ... | {
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2501.12869 | Drone Carrier: An Integrated Unmanned Surface Vehicle for Autonomous
Inspection and Intervention in GNSS-Denied Maritime Environment | [
"cs.RO",
"cs.AI"
] | This paper introduces an innovative drone carrier concept that is applied in maritime port security or offshore rescue. This system works with a heterogeneous system consisting of multiple Unmanned Aerial Vehicles (UAVs) and Unmanned Surface Vehicles (USVs) to perform inspection and intervention tasks in GNSS-denied or... | {
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2501.12877 | WisdomBot: Tuning Large Language Models with Artificial Intelligence
Knowledge | [
"cs.CL"
] | Large language models (LLMs) have emerged as powerful tools in natural language processing (NLP), showing a promising future of artificial generated intelligence (AGI). Despite their notable performance in the general domain, LLMs have remained suboptimal in the field of education, owing to the unique challenges presen... | {
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2501.12880 | Advanced deep architecture pruning using single filter performance | [
"cs.LG",
"cs.CV"
] | Pruning the parameters and structure of neural networks reduces the computational complexity, energy consumption, and latency during inference. Recently, a novel underlying mechanism for successful deep learning (DL) was presented based on a method that quantitatively measures the single filter performance in each laye... | {
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2501.12881 | Reinforcement learning Based Automated Design of Differential Evolution
Algorithm for Black-box Optimization | [
"cs.NE",
"cs.AI"
] | Differential evolution (DE) algorithm is recognized as one of the most effective evolutionary algorithms, demonstrating remarkable efficacy in black-box optimization due to its derivative-free nature. Numerous enhancements to the fundamental DE have been proposed, incorporating innovative mutation strategies and sophis... | {
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2501.12884 | Learning Graph Node Embeddings by Smooth Pair Sampling | [
"cs.LG",
"cs.AI"
] | Random walk-based node embedding algorithms have attracted a lot of attention due to their scalability and ease of implementation. Previous research has focused on different walk strategies, optimization objectives, and embedding learning models. Inspired by observations on real data, we take a different approach and p... | {
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2501.12886 | Multi-Platform Aggregated Dataset of Online Communities (MADOC) | [
"cs.SI",
"cs.CY",
"physics.soc-ph"
] | The Multi-platform Aggregated Dataset of Online Communities (MADOC) is a comprehensive dataset that facilitates computational social science research by providing FAIR-compliant standardized access to cross-platform analysis of online social dynamics. MADOC aggregates and standardizes data from Bluesky, Koo, Reddit, an... | {
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2501.12892 | Closed-loop robust control of long-term diabetes progression via
physical activity management | [
"eess.SY",
"cs.SY"
] | Large clinical evidence acknowledges the crucial role played by physical activity in delaying the progression of type-2 diabetes. However, the literature lacks control approaches that leverage exercise for type-2 diabetes control and more in general lacks a quantitative assessment of medical guidelines on the recommend... | {
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2501.12894 | Designing and Evaluating an Educational Recommender System with
Different Levels of User Control | [
"cs.IR",
"cs.CY",
"cs.HC"
] | Educational recommender systems (ERSs) play a crucial role in personalizing learning experiences and enhancing educational outcomes by providing recommendations of personalized resources and activities to learners, tailored to their individual learning needs. However, their effectiveness is often diminished by insuffic... | {
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2501.12895 | Test-Time Preference Optimization: On-the-Fly Alignment via Iterative
Textual Feedback | [
"cs.CL"
] | Large language models (LLMs) demonstrate impressive performance but lack the flexibility to adapt to human preferences quickly without retraining. In this work, we introduce Test-time Preference Optimization (TPO), a framework that aligns LLM outputs with human preferences during inference, removing the need to update ... | {
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2501.12896 | Irrational Complex Rotations Empower Low-bit Optimizers | [
"cs.LG"
] | In this paper, we propose a novel optimizer state compression algorithm, namely $\pi$-Quant, which leverages the properties of irrational numbers (e.g., $\pi$) for memory-efficient training. The core idea is based on our mathematical findings, which show that a pair of parameters can be represented by a single rotation... | {
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2501.12898 | DocTTT: Test-Time Training for Handwritten Document Recognition Using
Meta-Auxiliary Learning | [
"cs.CV"
] | Despite recent significant advancements in Handwritten Document Recognition (HDR), the efficient and accurate recognition of text against complex backgrounds, diverse handwriting styles, and varying document layouts remains a practical challenge. Moreover, this issue is seldom addressed in academic research, particular... | {
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2501.12900 | Unified CNNs and transformers underlying learning mechanism reveals
multi-head attention modus vivendi | [
"cs.LG",
"cs.CV"
] | Convolutional neural networks (CNNs) evaluate short-range correlations in input images which progress along the layers, whereas vision transformer (ViT) architectures evaluate long-range correlations, using repeated transformer encoders composed of fully connected layers. Both are designed to solve complex classificati... | {
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2501.12901 | Architectural Fusion Through Contextual Partitioning in Large Language
Models: A Novel Approach to Parameterized Knowledge Integration | [
"cs.CL",
"cs.AI"
] | Contextual Partitioning introduces an innovative approach to enhancing the architectural design of large-scale computational models through the dynamic segmentation of parameters into context-aware regions. This methodology emphasizes the importance of task-specific specialization, achieved through adaptive parameter a... | {
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2501.12902 | Learning to Optimize Joint Chance-constrained Power Dispatch Problems | [
"eess.SY",
"cs.SY"
] | The ever-increasing integration of stochastic renewable energy sources into power systems operation is making the supply-demand balance more challenging. While joint chance-constrained methods are equipped to model these complexities and uncertainties, solving these models using the traditional iterative solvers is tim... | {
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2501.12909 | FilmAgent: A Multi-Agent Framework for End-to-End Film Automation in
Virtual 3D Spaces | [
"cs.CL",
"cs.GR",
"cs.MA"
] | Virtual film production requires intricate decision-making processes, including scriptwriting, virtual cinematography, and precise actor positioning and actions. Motivated by recent advances in automated decision-making with language agent-based societies, this paper introduces FilmAgent, a novel LLM-based multi-agent ... | {
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2501.12910 | PreciseCam: Precise Camera Control for Text-to-Image Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Images as an artistic medium often rely on specific camera angles and lens distortions to convey ideas or emotions; however, such precise control is missing in current text-to-image models. We propose an efficient and general solution that allows precise control over the camera when generating both photographic and art... | {
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} |
2501.12911 | A Selective Homomorphic Encryption Approach for Faster
Privacy-Preserving Federated Learning | [
"cs.CR",
"cs.DC",
"cs.LG"
] | Federated learning is a machine learning method that supports training models on decentralized devices or servers, where each holds its local data, removing the need for data exchange. This approach is especially useful in healthcare, as it enables training on sensitive data without needing to share them. The nature of... | {
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2501.12913 | Set-point control and local stability for flat nonlinear systems using
model-following control | [
"eess.SY",
"cs.SY",
"math.OC"
] | We consider the set-point control problem for nonlinear systems with flat output that are subject to perturbations. The nonlinear dynamics as well as the perturbations are locally Lipschitz. We apply the model-following control (MFC) approach which consists of a model control loop (MCL) for a feedforward generation and... | {
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2501.12914 | A control system framework for counterfactuals: an optimization based
approach | [
"eess.SY",
"cs.SY"
] | Counterfactuals are a concept inherited from the field of logic and in general attain to the existence of causal relations between sentences or events. In particular, this concept has been introduced also in the context of interpretability in artificial intelligence, where counterfactuals refer to the minimum change to... | {
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2501.12916 | Trajectory tracking model-following control using Lyapunov redesign with
output time-derivatives to compensate unmatched uncertainties | [
"eess.SY",
"cs.SY",
"math.OC"
] | We study trajectory tracking for flat nonlinear systems with unmatched uncertainties using the model-following control (MFC) architecture. We apply state feedback linearisation control for the process and propose a simplified implementation of the model control loop which results in a simple model in Brunovsky-form tha... | {
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2501.12919 | Contrastive Language-Structure Pre-training Driven by Materials Science
Literature | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Understanding structure-property relationships is an essential yet challenging aspect of materials discovery and development. To facilitate this process, recent studies in materials informatics have sought latent embedding spaces of crystal structures to capture their similarities based on properties and functionalitie... | {
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2501.12921 | Generalized Orthogonal de Bruijn Sequences | [
"cs.IT",
"math.CO",
"math.IT"
] | A de Bruijn sequence of order $k$ over a finite alphabet is a cyclic sequence with the property that it contains every possible $k$-sequence as a substring exactly once. Orthogonal de Bruijn sequences are collections of de Bruijn sequences of the same order, $k$, satisfying the joint constraint that every $(k+1)$-seque... | {
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2501.12927 | Longitudinal Missing Data Imputation for Predicting Disability Stage of
Patients with Multiple Sclerosis | [
"cs.LG"
] | Multiple Sclerosis (MS) is a chronic disease characterized by progressive or alternate impairment of neurological functions (motor, sensory, visual, and cognitive). Predicting disease progression with a probabilistic and time-dependent approach might help in suggesting interventions that can delay the progression of th... | {
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} |
2501.12931 | DynamicEarth: How Far are We from Open-Vocabulary Change Detection? | [
"cs.CV"
] | Monitoring Earth's evolving land covers requires methods capable of detecting changes across a wide range of categories and contexts. Existing change detection methods are hindered by their dependency on predefined classes, reducing their effectiveness in open-world applications. To address this issue, we introduce ope... | {
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2501.12934 | Correctness Assessment of Code Generated by Large Language Models Using
Internal Representations | [
"cs.SE",
"cs.LG"
] | Ensuring the correctness of code generated by Large Language Models (LLMs) presents a significant challenge in AI-driven software development. Existing approaches predominantly rely on black-box (closed-box) approaches that evaluate correctness post-generation, failing to utilize the rich insights embedded in the LLMs'... | {
"Other": 1,
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} |
2501.12935 | 3D Object Manipulation in a Single Image using Generative Models | [
"cs.CV"
] | Object manipulation in images aims to not only edit the object's presentation but also gift objects with motion. Previous methods encountered challenges in concurrently handling static editing and dynamic generation, while also struggling to achieve fidelity in object appearance and scene lighting. In this work, we int... | {
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} |
2501.12938 | Robust Hypothesis Testing with Abstention | [
"cs.IT",
"math.IT"
] | We study the binary hypothesis testing problem where an adversary may potentially corrupt a fraction of the samples. The detector is, however, permitted to abstain from making a decision if (and only if) the adversary is present. We consider a few natural "contamination models" and characterize for them the trade-off b... | {
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} |
2501.12942 | Offline Critic-Guided Diffusion Policy for Multi-User Delay-Constrained
Scheduling | [
"cs.AI"
] | Effective multi-user delay-constrained scheduling is crucial in various real-world applications, such as instant messaging, live streaming, and data center management. In these scenarios, schedulers must make real-time decisions to satisfy both delay and resource constraints without prior knowledge of system dynamics, ... | {
"Other": 0,
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} |
2501.12943 | Ontology-Enhanced Educational Annotation Activities | [
"cs.CL",
"cs.DL"
] | Information and communications technology and technology-enhanced learning have unquestionably transformed traditional teaching-learning processes and are positioned as key factors to promote quality education, one of the basic sustainable development goals of the 2030 agenda. Document annotation, which was traditional... | {
"Other": 1,
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} |
2501.12946 | Less is More: Simple yet Effective Heuristic Community Detection with
Graph Convolution Network | [
"cs.SI"
] | Community detection is crucial in data mining. Traditional methods primarily focus on graph structure, often neglecting the significance of attribute features. In contrast, deep learning-based approaches incorporate attribute features and local structural information through contrastive learning, improving detection pe... | {
"Other": 0,
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} |
2501.12948 | DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via
Reinforcement Learning | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We introduce our first-generation reasoning models, DeepSeek-R1-Zero and DeepSeek-R1. DeepSeek-R1-Zero, a model trained via large-scale reinforcement learning (RL) without supervised fine-tuning (SFT) as a preliminary step, demonstrates remarkable reasoning capabilities. Through RL, DeepSeek-R1-Zero naturally emerges w... | {
"Other": 0,
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"cs.RO": 0,
"cs.SD": 0,
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"cs.SY": 0
} |
2501.12954 | Punctuation patterns in "Finnegans Wake" by James Joyce are largely
translation-invariant | [
"cs.CL"
] | The complexity characteristics of texts written in natural languages are significantly related to the rules of punctuation. In particular, the distances between punctuation marks measured by the number of words quite universally follow the family of Weibull distributions known from survival analyses. However, the value... | {
"Other": 0,
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} |
2501.12955 | Multifractal hopscotch in "Hopscotch" by Julio Cortazar | [
"cs.CL"
] | Punctuation is the main factor introducing correlations in natural language written texts and it crucially impacts their overall effectiveness, expressiveness, and readability. Punctuation marks at the end of sentences are of particular importance as their distribution can determine various complexity features of writt... | {
"Other": 0,
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} |
2501.12956 | GANQ: GPU-Adaptive Non-Uniform Quantization for Large Language Models | [
"cs.LG",
"cs.AI",
"math.OC"
] | Large Language Models (LLMs) face significant deployment challenges due to their substantial resource requirements. While low-bit quantized weights can reduce memory usage and improve inference efficiency, current hardware lacks native support for mixed-precision General Matrix Multiplication (mpGEMM), resulting in ine... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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