id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.05215 | Watermarking across Modalities for Content Tracing and Generative AI | [
"cs.CR",
"cs.AI",
"cs.LG"
] | Watermarking embeds information into digital content like images, audio, or text, imperceptible to humans but robustly detectable by specific algorithms. This technology has important applications in many challenges of the industry such as content moderation, tracing AI-generated content, and monitoring the usage of AI... | {
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2502.05218 | FactorGCL: A Hypergraph-Based Factor Model with Temporal Residual
Contrastive Learning for Stock Returns Prediction | [
"q-fin.ST",
"cs.AI",
"cs.LG"
] | As a fundamental method in economics and finance, the factor model has been extensively utilized in quantitative investment. In recent years, there has been a paradigm shift from traditional linear models with expert-designed factors to more flexible nonlinear machine learning-based models with data-driven factors, aim... | {
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2502.05219 | Enabling External Scrutiny of AI Systems with Privacy-Enhancing
Technologies | [
"cs.CY",
"cs.AI",
"cs.CR"
] | This article describes how technical infrastructure developed by the nonprofit OpenMined enables external scrutiny of AI systems without compromising sensitive information. Independent external scrutiny of AI systems provides crucial transparency into AI development, so it should be an integral component of any appro... | {
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2502.05220 | Aero-LLM: A Distributed Framework for Secure UAV Communication and
Intelligent Decision-Making | [
"cs.CR",
"cs.AI"
] | Increased utilization of unmanned aerial vehicles (UAVs) in critical operations necessitates secure and reliable communication with Ground Control Stations (GCS). This paper introduces Aero-LLM, a framework integrating multiple Large Language Models (LLMs) to enhance UAV mission security and operational efficiency. Unl... | {
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2502.05221 | Blackout DIFUSCO | [
"math.OC",
"cs.AI"
] | This study explores the integration of Blackout Diffusion into the DIFUSCO framework for combinatorial optimization, specifically targeting the Traveling Salesman Problem (TSP). Inspired by the success of discrete-time diffusion models (D3PM) in maintaining structural integrity, we extend the paradigm to a continuous-t... | {
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2502.05222 | VistaFlow: Photorealistic Volumetric Reconstruction with Dynamic
Resolution Management via Q-Learning | [
"cs.CV",
"cs.GR"
] | We introduce VistaFlow, a scalable three-dimensional imaging technique capable of reconstructing fully interactive 3D volumetric images from a set of 2D photographs. Our model synthesizes novel viewpoints through a differentiable rendering system capable of dynamic resolution management on photorealistic 3D scenes. We ... | {
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2502.05223 | KDA: A Knowledge-Distilled Attacker for Generating Diverse Prompts to
Jailbreak LLMs | [
"cs.CR",
"cs.AI",
"cs.CL",
"cs.LG"
] | Jailbreak attacks exploit specific prompts to bypass LLM safeguards, causing the LLM to generate harmful, inappropriate, and misaligned content. Current jailbreaking methods rely heavily on carefully designed system prompts and numerous queries to achieve a single successful attack, which is costly and impractical for ... | {
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2502.05224 | A Survey on Backdoor Threats in Large Language Models (LLMs): Attacks,
Defenses, and Evaluations | [
"cs.CR",
"cs.AI"
] | Large Language Models (LLMs) have achieved significantly advanced capabilities in understanding and generating human language text, which have gained increasing popularity over recent years. Apart from their state-of-the-art natural language processing (NLP) performance, considering their widespread usage in many indus... | {
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2502.05225 | BitAbuse: A Dataset of Visually Perturbed Texts for Defending Phishing
Attacks | [
"cs.CR",
"cs.AI"
] | Phishing often targets victims through visually perturbed texts to bypass security systems. The noise contained in these texts functions as an adversarial attack, designed to deceive language models and hinder their ability to accurately interpret the content. However, since it is difficult to obtain sufficient phishin... | {
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2502.05227 | Robotouille: An Asynchronous Planning Benchmark for LLM Agents | [
"cs.RO",
"cs.AI",
"cs.CL"
] | Effective asynchronous planning, or the ability to efficiently reason and plan over states and actions that must happen in parallel or sequentially, is essential for agents that must account for time delays, reason over diverse long-horizon tasks, and collaborate with other agents. While large language model (LLM) agen... | {
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2502.05228 | Multi-Objective Mobile Damped Wave Algorithm (MOMDWA): A Novel Approach
For Quantum System Control | [
"quant-ph",
"cs.AI",
"cs.SY"
] | In this paper, we introduce a novel multi-objective optimization algorithm, the Multi-Objective Mobile Damped Wave Algorithm (MOMDWA), specifically designed to address complex quantum control problems. Our approach extends the capabilities of the original Mobile Damped Wave Algorithm (MDWA) by incorporating multiple ob... | {
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2502.05229 | L2GNet: Optimal Local-to-Global Representation of Anatomical Structures
for Generalized Medical Image Segmentation | [
"cs.CV"
] | Continuous Latent Space (CLS) and Discrete Latent Space (DLS) models, like AttnUNet and VQUNet, have excelled in medical image segmentation. In contrast, Synergistic Continuous and Discrete Latent Space (CDLS) models show promise in handling fine and coarse-grained information. However, they struggle with modeling long... | {
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2502.05230 | DiffNMR2: NMR Guided Sampling Acquisition Through Diffusion Model
Uncertainty | [
"q-bio.QM",
"cs.AI"
] | Nuclear Magnetic Resonance (NMR) spectrometry uses electro-frequency pulses to probe the resonance of a compound's nucleus, which is then analyzed to determine its structure. The acquisition time of high-resolution NMR spectra remains a significant bottleneck, especially for complex biological samples such as proteins.... | {
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2502.05231 | Thin ring wing as a means of flow improvement upstream of a propeller | [
"physics.flu-dyn",
"cs.AI"
] | There are numerous devices currently known with the purpose of reducing the irregularity of the flow upstream of the propeller and to decrease by that means the propeller-induced vibration and noise. Many of these devices are wing-shaped vortex-generators that affect the flow with their induced (i.e. passive) longitudi... | {
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2502.05232 | Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | Modern systems for automatic speech recognition, including the RNN-Transducer and Attention-based Encoder-Decoder (AED), are designed so that the encoder is not required to alter the time-position of information from the audio sequence into the embedding; alignment to the final text output is processed during decoding.... | {
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2502.05233 | Efficient Knowledge Feeding to Language Models: A Novel Integrated
Encoder-Decoder Architecture | [
"cs.CL",
"cs.IR"
] | This paper introduces a novel approach to efficiently feeding knowledge to language models (LLMs) during prediction by integrating retrieval and generation processes within a unified framework. While the Retrieval-Augmented Generation (RAG) model addresses gaps in LLMs' training data and knowledge limits, it is hindere... | {
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2502.05234 | Optimizing Temperature for Language Models with Multi-Sample Inference | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Multi-sample aggregation strategies, such as majority voting and best-of-N sampling, are widely used in contemporary large language models (LLMs) to enhance predictive accuracy across various tasks. A key challenge in this process is temperature selection, which significantly impacts model performance. Existing approac... | {
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2502.05236 | Koel-TTS: Enhancing LLM based Speech Generation with Preference
Alignment and Classifier Free Guidance | [
"cs.SD",
"cs.AI",
"cs.LG",
"eess.AS"
] | While autoregressive speech token generation models produce speech with remarkable variety and naturalness, their inherent lack of controllability often results in issues such as hallucinations and undesired vocalizations that do not conform to conditioning inputs. We introduce Koel-TTS, a suite of enhanced encoder-dec... | {
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2502.05237 | PSM-SQL: Progressive Schema Learning with Multi-granularity Semantics
for Text-to-SQL | [
"cs.DB",
"cs.AI"
] | It is challenging to convert natural language (NL) questions into executable structured query language (SQL) queries for text-to-SQL tasks due to the vast number of database schemas with redundancy, which interferes with semantic learning, and the domain shift between NL and SQL. Existing works for schema linking focus... | {
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2502.05239 | Enhancing Knowledge Graph Construction: Evaluating with Emphasis on
Hallucination, Omission, and Graph Similarity Metrics | [
"cs.CL",
"cs.AI"
] | Recent advancements in large language models have demonstrated significant potential in the automated construction of knowledge graphs from unstructured text. This paper builds upon our previous work [16], which evaluated various models using metrics like precision, recall, F1 score, triple matching, and graph matching... | {
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2502.05240 | Survey on AI-Generated Media Detection: From Non-MLLM to MLLM | [
"cs.CV"
] | The proliferation of AI-generated media poses significant challenges to information authenticity and social trust, making reliable detection methods highly demanded. Methods for detecting AI-generated media have evolved rapidly, paralleling the advancement of Multimodal Large Language Models (MLLMs). Current detection ... | {
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2502.05242 | SEER: Self-Explainability Enhancement of Large Language Models'
Representations | [
"cs.CL",
"cs.AI",
"cs.CV",
"cs.LG"
] | Explaining the hidden representations of Large Language Models (LLMs) is a perspective to understand LLMs' underlying inference logic and improve their reliability in application scenarios. However, previous methods introduce external ''black-box'' modules to explain ''black-box'' LLMs, increasing the potential uncerta... | {
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2502.05244 | Probabilistic Artificial Intelligence | [
"cs.AI",
"cs.LG"
] | Artificial intelligence commonly refers to the science and engineering of artificial systems that can carry out tasks generally associated with requiring aspects of human intelligence, such as playing games, translating languages, and driving cars. In recent years, there have been exciting advances in learning-based, d... | {
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2502.05246 | Optimizing Wealth by a Game within Cellular Automata | [
"cs.GT",
"cs.MA"
] | The objective is to find a Cellular Automata (CA) rule that can evolve 2D patterns that are optimal with respect to a global fitness function. The global fitness is defined as the sum of local computed utilities. A utility or value function computes a score depending on the states in the local neighborhood. First the m... | {
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2502.05248 | Evaluating Personality Traits in Large Language Models: Insights from
Psychological Questionnaires | [
"cs.CL",
"cs.AI",
"cs.MA"
] | Psychological assessment tools have long helped humans understand behavioural patterns. While Large Language Models (LLMs) can generate content comparable to that of humans, we explore whether they exhibit personality traits. To this end, this work applies psychological tools to LLMs in diverse scenarios to generate pe... | {
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2502.05252 | GSM-Infinite: How Do Your LLMs Behave over Infinitely Increasing Context
Length and Reasoning Complexity? | [
"cs.CL",
"cs.AI"
] | Long-context large language models (LLMs) have recently shown strong performance in information retrieval and long-document QA. However, to tackle the most challenging intellectual problems, LLMs must reason effectively in long and complex contexts (e.g., frontier mathematical research). Studying how LLMs handle increa... | {
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2502.05253 | LLMs Can Teach Themselves to Better Predict the Future | [
"cs.CL",
"cs.AI"
] | We present an outcome-driven fine-tuning framework that enhances the forecasting capabilities of large language models (LLMs) without relying on human-curated reasoning samples. Our method leverages model self-play to generate pairs of diverse reasoning trajectories and probabilistic forecasts for a set of diverse ques... | {
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2502.05255 | Incivility and Contentiousness Spillover between COVID-19 and Climate
Science Engagement | [
"cs.SI",
"cs.CY",
"physics.soc-ph"
] | Affective polarization and its accompanying cleavage-based sorting drives incivility and contentiousness around climate change and other science-related issues. Looking at the COVID-19 period, we study cross-domain spillover of incivility and contentiousness in public engagements with climate change and climate science... | {
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2502.05256 | Learned Offline Query Planning via Bayesian Optimization | [
"cs.DB"
] | Analytics database workloads often contain queries that are executed repeatedly. Existing optimization techniques generally prioritize keeping optimization cost low, normally well below the time it takes to execute a single instance of a query. If a given query is going to be executed thousands of times, could it be wo... | {
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2502.05264 | Quantum automated learning with provable and explainable trainability | [
"quant-ph",
"cs.AI",
"cs.LG"
] | Machine learning is widely believed to be one of the most promising practical applications of quantum computing. Existing quantum machine learning schemes typically employ a quantum-classical hybrid approach that relies crucially on gradients of model parameters. Such an approach lacks provable convergence to global mi... | {
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2502.05271 | RobotMover: Learning to Move Large Objects by Imitating the Dynamic
Chain | [
"cs.RO"
] | Moving large objects, such as furniture, is a critical capability for robots operating in human environments. This task presents significant challenges due to two key factors: the need to synchronize whole-body movements to prevent collisions between the robot and the object, and the under-actuated dynamics arising fro... | {
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2502.05273 | Principles and Components of Federated Learning Architectures | [
"cs.LG"
] | Federated learning, also known as FL, is a machine learning framework in which a significant amount of clients (such as mobile devices or whole enterprises) collaborate to collaboratively train a model while keeping decentralized training data, all overseen by a central server (such as a service provider). There are ad... | {
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2502.05275 | Interpretable Failure Detection with Human-Level Concepts | [
"cs.CV"
] | Reliable failure detection holds paramount importance in safety-critical applications. Yet, neural networks are known to produce overconfident predictions for misclassified samples. As a result, it remains a problematic matter as existing confidence score functions rely on category-level signals, the logits, to detect ... | {
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2502.05277 | Invizo: Arabic Handwritten Document Optical Character Recognition
Solution | [
"cs.CV"
] | Converting images of Arabic text into plain text is a widely researched topic in academia and industry. However, recognition of Arabic handwritten and printed text presents difficult challenges due to the complex nature of variations of the Arabic script. This work proposes an end-to-end solution for recognizing Arabic... | {
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2502.05282 | Homeomorphism Prior for False Positive and Negative Problem in Medical
Image Dense Contrastive Representation Learning | [
"cs.CV",
"cs.AI"
] | Dense contrastive representation learning (DCRL) has greatly improved the learning efficiency for image-dense prediction tasks, showing its great potential to reduce the large costs of medical image collection and dense annotation. However, the properties of medical images make unreliable correspondence discovery, brin... | {
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2502.05286 | Fairness and Sparsity within Rashomon sets: Enumeration-Free Exploration
and Characterization | [
"cs.LG"
] | We introduce an enumeration-free method based on mathematical programming to precisely characterize various properties such as fairness or sparsity within the set of "good models", known as Rashomon set. This approach is generically applicable to any hypothesis class, provided that a mathematical formulation of the mod... | {
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2502.05290 | Switch-based Independent Antagonist Actuation with a Single Motor for a
Soft Exosuit | [
"cs.RO"
] | The use of a cable-driven soft exosuit poses challenges with regards to the mechanical design of the actuation system, particularly when used for actuation along multiple degrees of freedom (DoF). The simplest general solution requires the use of two actuators to be capable of inducing movement along one DoF. However, ... | {
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2502.05291 | Can LLMs Rank the Harmfulness of Smaller LLMs? We are Not There Yet | [
"cs.CL"
] | Large language models (LLMs) have become ubiquitous, thus it is important to understand their risks and limitations. Smaller LLMs can be deployed where compute resources are constrained, such as edge devices, but with different propensity to generate harmful output. Mitigation of LLM harm typically depends on annotatin... | {
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2502.05292 | Drone Detection and Tracking with YOLO and a Rule-based Method | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Drones or unmanned aerial vehicles are traditionally used for military missions, warfare, and espionage. However, the usage of drones has significantly increased due to multiple industrial applications involving security and inspection, transportation, research purposes, and recreational drone flying. Such an increased... | {
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2502.05295 | GST-UNet: Spatiotemporal Causal Inference with Time-Varying Confounders | [
"cs.LG",
"stat.ME"
] | Estimating causal effects from spatiotemporal data is a key challenge in fields such as public health, social policy, and environmental science, where controlled experiments are often infeasible. However, existing causal inference methods relying on observational data face significant limitations: they depend on strong... | {
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2502.05300 | Parameter Symmetry Breaking and Restoration Determines the Hierarchical
Learning in AI Systems | [
"cs.LG",
"cond-mat.dis-nn",
"cs.AI",
"stat.ML"
] | The dynamics of learning in modern large AI systems is hierarchical, often characterized by abrupt, qualitative shifts akin to phase transitions observed in physical systems. While these phenomena hold promise for uncovering the mechanisms behind neural networks and language models, existing theories remain fragmented,... | {
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2502.05301 | Decentralized Online Ensembles of Gaussian Processes for Multi-Agent
Systems | [
"cs.LG",
"cs.MA",
"eess.SP",
"stat.ML"
] | Flexible and scalable decentralized learning solutions are fundamentally important in the application of multi-agent systems. While several recent approaches introduce (ensembles of) kernel machines in the distributed setting, Bayesian solutions are much more limited. We introduce a fully decentralized, asymptotically ... | {
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2502.05305 | Online Covariance Estimation in Nonsmooth Stochastic Approximation | [
"stat.ML",
"cs.LG",
"math.OC"
] | We consider applying stochastic approximation (SA) methods to solve nonsmooth variational inclusion problems. Existing studies have shown that the averaged iterates of SA methods exhibit asymptotic normality, with an optimal limiting covariance matrix in the local minimax sense of H\'ajek and Le Cam. However, no method... | {
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2502.05307 | Training Set Reconstruction from Differentially Private Forests: How
Effective is DP? | [
"cs.LG",
"cs.CR"
] | Recent research has shown that machine learning models are vulnerable to privacy attacks targeting their training data. Differential privacy (DP) has become a widely adopted countermeasure, as it offers rigorous privacy protections. In this paper, we introduce a reconstruction attack targeting state-of-the-art $\vare... | {
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2502.05309 | Learning the Geometric Mechanics of Robot Motion Using Gaussian Mixtures | [
"cs.RO"
] | Data-driven models of robot motion constructed using principles from Geometric Mechanics have been shown to produce useful predictions of robot motion for a variety of robots. For robots with a useful number of DoF, these geometric mechanics models can only be constructed in the neighborhood of a gait. Here we show how... | {
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2502.05310 | Oracular Programming: A Modular Foundation for Building LLM-Enabled
Software | [
"cs.PL",
"cs.AI"
] | Large Language Models have proved surprisingly effective at solving a wide range of tasks from just a handful of examples. However, their lack of reliability and modularity limits their capacity to tackle large problems that require many steps of reasoning. In response, researchers have proposed advanced pipelines that... | {
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2502.05311 | ParquetDB: A Lightweight Python Parquet-Based Database | [
"cs.DB",
"physics.data-an"
] | Traditional data storage formats and databases often introduce complexities and inefficiencies that hinder rapid iteration and adaptability. To address these challenges, we introduce ParquetDB, a Python-based database framework that leverages the Parquet file format's optimized columnar storage. ParquetDB offers effici... | {
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2502.05312 | Towards the Development of Balanced Synthetic Data for Correcting
Grammatical Errors in Arabic: An Approach Based on Error Tagging Model and
Synthetic Data Generating Model | [
"cs.CL",
"cs.AI"
] | Synthetic data generation is widely recognized as a way to enhance the quality of neural grammatical error correction (GEC) systems. However, current approaches often lack diversity or are too simplistic to generate the wide range of grammatical errors made by humans, especially for low-resource languages such as Arabi... | {
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2502.05315 | AI/ML-Based Automatic Modulation Recognition: Recent Trends and Future
Possibilities | [
"cs.LG"
] | We present a review of high-performance automatic modulation recognition (AMR) models proposed in the literature to classify various Radio Frequency (RF) modulation schemes. We replicated these models and compared their performance in terms of accuracy across a range of signal-to-noise ratios. To ensure a fair comparis... | {
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2502.05318 | Diagonal Symmetrization of Neural Network Solvers for the Many-Electron
Schr\"odinger Equation | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Incorporating group symmetries into neural networks has been a cornerstone of success in many AI-for-science applications. Diagonal groups of isometries, which describe the invariance under a simultaneous movement of multiple objects, arise naturally in many-body quantum problems. Despite their importance, diagonal gro... | {
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2502.05320 | Towards Fine-grained Renal Vasculature Segmentation: Full-Scale
Hierarchical Learning with FH-Seg | [
"cs.CV"
] | Accurate fine-grained segmentation of the renal vasculature is critical for nephrological analysis, yet it faces challenges due to diverse and insufficiently annotated images. Existing methods struggle to accurately segment intricate regions of the renal vasculature, such as the inner and outer walls, arteries and lesi... | {
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2502.05321 | Using Federated Machine Learning in Predictive Maintenance of Jet
Engines | [
"cs.LG"
] | The goal of this paper is to predict the Remaining Useful Life (RUL) of turbine jet engines using a federated machine learning framework. Federated Learning enables multiple edge devices/nodes or servers to collaboratively train a shared model without sharing sensitive data, thus preserving data privacy and security. B... | {
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2502.05325 | From Counterfactuals to Trees: Competitive Analysis of Model Extraction
Attacks | [
"cs.LG",
"cs.CR"
] | The advent of Machine Learning as a Service (MLaaS) has heightened the trade-off between model explainability and security. In particular, explainability techniques, such as counterfactual explanations, inadvertently increase the risk of model extraction attacks, enabling unauthorized replication of proprietary models.... | {
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2502.05330 | Multi-Class Segmentation of Aortic Branches and Zones in Computed
Tomography Angiography: The AortaSeg24 Challenge | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Multi-class segmentation of the aorta in computed tomography angiography (CTA) scans is essential for diagnosing and planning complex endovascular treatments for patients with aortic dissections. However, existing methods reduce aortic segmentation to a binary problem, limiting their ability to measure diameters across... | {
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2502.05331 | Fine-Tuned LLMs are "Time Capsules" for Tracking Societal Bias Through
Books | [
"cs.CL"
] | Books, while often rich in cultural insights, can also mirror societal biases of their eras - biases that Large Language Models (LLMs) may learn and perpetuate during training. We introduce a novel method to trace and quantify these biases using fine-tuned LLMs. We develop BookPAGE, a corpus comprising 593 fictional bo... | {
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2502.05332 | Removing Neural Signal Artifacts with Autoencoder-Targeted Adversarial
Transformers (AT-AT) | [
"cs.LG"
] | Electromyogenic (EMG) noise is a major contamination source in EEG data that can impede accurate analysis of brain-specific neural activity. Recent literature on EMG artifact removal has moved beyond traditional linear algorithms in favor of machine learning-based systems. However, existing deep learning-based filtrati... | {
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2502.05333 | A Tutorial On Intersectionality in Fair Rankings | [
"cs.CY",
"cs.IR",
"cs.LG"
] | We address the critical issue of biased algorithms and unfair rankings, which have permeated various sectors, including search engines, recommendation systems, and workforce management. These biases can lead to discriminatory outcomes in a data-driven world, especially against marginalized and underrepresented groups. ... | {
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2502.05334 | Geometric Machine Learning on EEG Signals | [
"cs.LG"
] | Brain-computer interfaces (BCIs) offer transformative potential, but decoding neural signals presents significant challenges. The core premise of this paper is built around demonstrating methods to elucidate the underlying low-dimensional geometric structure present in high-dimensional brainwave data in order to assist... | {
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2502.05335 | Towards Foundational Models for Dynamical System Reconstruction:
Hierarchical Meta-Learning via Mixture of Experts | [
"cs.LG"
] | As foundational models reshape scientific discovery, a bottleneck persists in dynamical system reconstruction (DSR): the ability to learn across system hierarchies. Many meta-learning approaches have been applied successfully to single systems, but falter when confronted with sparse, loosely related datasets requiring ... | {
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2502.05343 | Towards Wearable Interfaces for Robotic Caregiving | [
"cs.RO"
] | Physically assistive robots in home environments can enhance the autonomy of individuals with impairments, allowing them to regain the ability to conduct self-care and household tasks. Individuals with physical limitations may find existing interfaces challenging to use, highlighting the need for novel interfaces that ... | {
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2502.05344 | RAG-Verus: Repository-Level Program Verification with LLMs using
Retrieval Augmented Generation | [
"cs.SE",
"cs.AI"
] | Scaling automated formal verification to real-world projects requires resolving cross-module dependencies and global contexts, which are challenges overlooked by existing function-centric methods. We introduce RagVerus, a framework that synergizes retrieval-augmented generation with context-aware prompting to automate ... | {
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2502.05345 | Estimating Voltage Drop: Models, Features and Data Representation
Towards a Neural Surrogate | [
"cs.AR",
"cs.AI"
] | Accurate estimation of voltage drop (IR drop) in modern Application-Specific Integrated Circuits (ASICs) is highly time and resource demanding, due to the growing complexity and the transistor density in recent technology nodes. To mitigate this challenge, we investigate how Machine Learning (ML) techniques, including ... | {
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2502.05346 | Probabilistic Subspace Manifolds for Contextual Inference in Large
Language Models | [
"cs.CL"
] | Representing token embeddings as probability distributions over learned manifolds allows for more flexible contextual inference, reducing representational rigidity while enhancing semantic granularity. Comparative evaluations demonstrate that probabilistic embeddings improve neighborhood consistency and decrease redund... | {
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2502.05349 | Contextual Scenario Generation for Two-Stage Stochastic Programming | [
"math.OC",
"cs.LG"
] | Two-stage stochastic programs (2SPs) are important tools for making decisions under uncertainty. Decision-makers use contextual information to generate a set of scenarios to represent the true conditional distribution. However, the number of scenarios required is a barrier to implementing 2SPs, motivating the problem o... | {
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2502.05351 | Deep Generative model that uses physical quantities to generate and
retrieve solar magnetic active regions | [
"astro-ph.SR",
"cs.LG",
"stat.ML"
] | Deep generative models have shown immense potential in generating unseen data that has properties of real data. These models learn complex data-generating distributions starting from a smaller set of latent dimensions. However, generative models have encountered great skepticism in scientific domains due to the disconn... | {
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2502.05352 | ITBench: Evaluating AI Agents across Diverse Real-World IT Automation
Tasks | [
"cs.AI",
"cs.DC",
"cs.MA"
] | Realizing the vision of using AI agents to automate critical IT tasks depends on the ability to measure and understand effectiveness of proposed solutions. We introduce ITBench, a framework that offers a systematic methodology for benchmarking AI agents to address real-world IT automation tasks. Our initial release tar... | {
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2502.05360 | Curse of Dimensionality in Neural Network Optimization | [
"cs.LG",
"math.OC",
"stat.ML"
] | The curse of dimensionality in neural network optimization under the mean-field regime is studied. It is demonstrated that when a shallow neural network with a Lipschitz continuous activation function is trained using either empirical or population risk to approximate a target function that is $r$ times continuously di... | {
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2502.05364 | Hypencoder: Hypernetworks for Information Retrieval | [
"cs.IR",
"cs.LG"
] | The vast majority of retrieval models depend on vector inner products to produce a relevance score between a query and a document. This naturally limits the expressiveness of the relevance score that can be employed. We propose a new paradigm, instead of producing a vector to represent the query we produce a small neur... | {
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2502.05365 | Low Dimensional Koopman Generalized Eigenfunctions Representation: An
Approach to Address Koopman High-Dimensionality Problem | [
"eess.SY",
"cs.SY"
] | This Paper introduces a methodology to achieve a lower dimensional Koopman quasi linear representation of nonlinear dynamics using Koopman generalized eigenfunctions. The methodology is presented for the analytically derived Koopman formulation of rigid body dynamics but can be generalized to any data-driven or analyti... | {
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2502.05367 | Detecting APT Malware Command and Control over HTTP(S) Using Contextual
Summaries | [
"cs.CR",
"cs.LG",
"cs.NI"
] | Advanced Persistent Threats (APTs) are among the most sophisticated threats facing critical organizations worldwide. APTs employ specific tactics, techniques, and procedures (TTPs) which make them difficult to detect in comparison to frequent and aggressive attacks. In fact, current network intrusion detection systems ... | {
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2502.05368 | Otter: Generating Tests from Issues to Validate SWE Patches | [
"cs.SE",
"cs.LG"
] | While there has been plenty of work on generating tests from existing code, there has been limited work on generating tests from issues. A correct test must validate the code patch that resolves the issue. In this work, we focus on the scenario where the code patch does not exist yet. This approach supports two major u... | {
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2502.05369 | DobLIX: A Dual-Objective Learned Index for Log-Structured Merge Trees | [
"cs.DB",
"cs.LG",
"math.OC"
] | In this paper, we introduce DobLIX, a dual-objective learned index specifically designed for Log-Structured Merge(LSM) tree-based key-value stores. Although traditional learned indexes focus exclusively on optimizing index lookups, they often overlook the impact of data access from storage, resulting in performance bot... | {
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2502.05370 | fMoE: Fine-Grained Expert Offloading for Large Mixture-of-Experts
Serving | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Large Language Models (LLMs) have gained immense success in revolutionizing various applications, including content generation, search and recommendation, and AI-assisted operation. To reduce high training costs, Mixture-of-Experts (MoE) architecture has become a popular backbone for modern LLMs. However, despite the b... | {
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2502.05371 | Cumulant Structures of Entanglement Entropy | [
"math-ph",
"cs.IT",
"math.IT",
"math.MP",
"quant-ph"
] | We present a new method to derive exact cumulant expressions of any order of von Neumann entropy over Hilbert-Schmidt ensemble. The new method uncovers hidden cumulant structures that decouple each cumulant in a summation-free manner into its lower-order joint cumulants involving families of ancillary statistics. Impor... | {
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2502.05372 | Active Learning of Model Discrepancy with Bayesian Experimental Design | [
"cs.LG"
] | Digital twins have been actively explored in many engineering applications, such as manufacturing and autonomous systems. However, model discrepancy is ubiquitous in most digital twin models and has significant impacts on the performance of using those models. In recent years, data-driven modeling techniques have been ... | {
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2502.05374 | Towards LLM Unlearning Resilient to Relearning Attacks: A
Sharpness-Aware Minimization Perspective and Beyond | [
"cs.LG",
"cs.CL"
] | The LLM unlearning technique has recently been introduced to comply with data regulations and address the safety and ethical concerns of LLMs by removing the undesired data-model influence. However, state-of-the-art unlearning methods face a critical vulnerability: they are susceptible to ``relearning'' the removed inf... | {
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2502.05376 | BCQ: Block Clustered Quantization for 4-bit (W4A4) LLM Inference | [
"cs.LG"
] | Post-training quantization (PTQ) is a promising approach to reducing the storage and computational requirements of large language models (LLMs) without additional training cost. Recent PTQ studies have primarily focused on quantizing only weights to sub-8-bits while maintaining activations at 8-bits or higher. Accurate... | {
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2502.05378 | NextBestPath: Efficient 3D Mapping of Unseen Environments | [
"cs.CV",
"cs.RO"
] | This work addresses the problem of active 3D mapping, where an agent must find an efficient trajectory to exhaustively reconstruct a new scene. Previous approaches mainly predict the next best view near the agent's location, which is prone to getting stuck in local areas. Additionally, existing indoor datasets are insu... | {
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2502.05383 | Is attention all you need to solve the correlated electron problem? | [
"cond-mat.str-el",
"cond-mat.mes-hall",
"cs.AI"
] | The attention mechanism has transformed artificial intelligence research by its ability to learn relations between objects. In this work, we explore how a many-body wavefunction ansatz constructed from a large-parameter self-attention neural network can be used to solve the interacting electron problem in solids. By a ... | {
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2502.05384 | Demonstrating CavePI: Autonomous Exploration of Underwater Caves by
Semantic Guidance | [
"cs.RO"
] | Enabling autonomous robots to safely and efficiently navigate, explore, and map underwater caves is of significant importance to water resource management, hydrogeology, archaeology, and marine robotics. In this work, we demonstrate the system design and algorithmic integration of a visual servoing framework for semant... | {
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} |
2502.05387 | Coarse-to-Fine Structure-Aware Artistic Style Transfer | [
"cs.CV",
"cs.AI"
] | Artistic style transfer aims to use a style image and a content image to synthesize a target image that retains the same artistic expression as the style image while preserving the basic content of the content image. Many recently proposed style transfer methods have a common problem; that is, they simply transfer the ... | {
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2502.05389 | The Role of Prosody in Spoken Question Answering | [
"cs.CL"
] | Spoken language understanding research to date has generally carried a heavy text perspective. Most datasets are derived from text, which is then subsequently synthesized into speech, and most models typically rely on automatic transcriptions of speech. This is to the detriment of prosody--additional information carrie... | {
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2502.05390 | Learning Task Representations from In-Context Learning | [
"cs.CL",
"cs.LG"
] | Large language models (LLMs) have demonstrated remarkable proficiency in in-context learning (ICL), where models adapt to new tasks through example-based prompts without requiring parameter updates. However, understanding how tasks are internally encoded and generalized remains a challenge. To address some of the empir... | {
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2502.05391 | Beyond and Free from Diffusion: Invertible Guided Consistency Training | [
"cs.CV"
] | Guidance in image generation steers models towards higher-quality or more targeted outputs, typically achieved in Diffusion Models (DMs) via Classifier-free Guidance (CFG). However, recent Consistency Models (CMs), which offer fewer function evaluations, rely on distilling CFG knowledge from pretrained DMs to achieve g... | {
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2502.05392 | Open Challenges in Time Series Anomaly Detection: An Industry
Perspective | [
"cs.LG"
] | Current research in time-series anomaly detection is using definitions that miss critical aspects of how anomaly detection is commonly used in practice. We list several areas that are of practical relevance and that we believe are either under-investigated or missing entirely from the current discourse. Based on an inv... | {
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2502.05395 | Hierarchical Lexical Manifold Projection in Large Language Models: A
Novel Mechanism for Multi-Scale Semantic Representation | [
"cs.CL"
] | The integration of structured hierarchical embeddings into transformer-based architectures introduces a refined approach to lexical representation, ensuring that multi-scale semantic relationships are preserved without compromising computational efficiency. A projection mechanism that maps tokens onto a structured mani... | {
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2502.05396 | A Novel Convolutional-Free Method for 3D Medical Imaging Segmentation | [
"eess.IV",
"cs.CV"
] | Segmentation of 3D medical images is a critical task for accurate diagnosis and treatment planning. Convolutional neural networks (CNNs) have dominated the field, achieving significant success in 3D medical image segmentation. However, CNNs struggle with capturing long-range dependencies and global context, limiting th... | {
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2502.05397 | Imitation Learning from a Single Temporally Misaligned Video | [
"cs.LG"
] | We examine the problem of learning sequential tasks from a single visual demonstration. A key challenge arises when demonstrations are temporally misaligned due to variations in timing, differences in embodiment, or inconsistencies in execution. Existing approaches treat imitation as a distribution-matching problem, al... | {
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2502.05398 | Probabilistic Foundations for Metacognition via Hybrid-AI | [
"cs.AI"
] | Metacognition is the concept of reasoning about an agent's own internal processes, and it has recently received renewed attention with respect to artificial intelligence (AI) and, more specifically, machine learning systems. This paper reviews a hybrid-AI approach known as "error detecting and correcting rules" (EDCR) ... | {
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2502.05400 | Dynamic Noise Preference Optimization for LLM Self-Improvement via
Synthetic Data | [
"cs.CL"
] | Although LLMs have achieved significant success, their reliance on large volumes of human-annotated data has limited their potential for further scaling. In this situation, utilizing self-generated synthetic data has become crucial for fine-tuning LLMs without extensive human annotation. However, current methods often ... | {
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2502.05402 | Convolutional Deep Colorization for Image Compression: A Color Grid
Based Approach | [
"cs.CV",
"cs.AI"
] | The search for image compression optimization techniques is a topic of constant interest both in and out of academic circles. One method that shows promise toward future improvements in this field is image colorization since image colorization algorithms can reduce the amount of color data that needs to be stored for a... | {
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} |
2502.05403 | Analyzing public sentiment to gauge key stock events and determine
volatility in conjunction with time and options premiums | [
"cs.LG"
] | Analyzing stocks and making higher accurate predictions on where the price is heading continues to become more and more challenging therefore, we designed a new financial algorithm that leverages social media sentiment analysis to enhance the prediction of key stock earnings and associated volatility. Our model integra... | {
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} |
2502.05407 | The Complexity of Learning Sparse Superposed Features with Feedback | [
"cs.LG",
"cs.AI",
"stat.ML"
] | The success of deep networks is crucially attributed to their ability to capture latent features within a representation space. In this work, we investigate whether the underlying learned features of a model can be efficiently retrieved through feedback from an agent, such as a large language model (LLM), in the form o... | {
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} |
2502.05409 | Vision-in-the-loop Simulation for Deep Monocular Pose Estimation of UAV
in Ocean Environment | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO",
"cs.SY",
"eess.SY"
] | This paper proposes a vision-in-the-loop simulation environment for deep monocular pose estimation of a UAV operating in an ocean environment. Recently, a deep neural network with a transformer architecture has been successfully trained to estimate the pose of a UAV relative to the flight deck of a research vessel, ove... | {
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"cs.SY": 1
} |
2502.05414 | Graph-based Molecular In-context Learning Grounded on Morgan
Fingerprints | [
"cs.LG",
"cs.CL"
] | In-context learning (ICL) effectively conditions large language models (LLMs) for molecular tasks, such as property prediction and molecule captioning, by embedding carefully selected demonstration examples into the input prompt. This approach avoids the computational overhead of extensive pertaining and fine-tuning. H... | {
"Other": 0,
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"cs.SY": 0
} |
2502.05415 | Show-o Turbo: Towards Accelerated Unified Multimodal Understanding and
Generation | [
"cs.CV",
"cs.AI"
] | There has been increasing research interest in building unified multimodal understanding and generation models, among which Show-o stands as a notable representative, demonstrating great promise for both text-to-image and image-to-text generation. The inference of Show-o involves progressively denoising image tokens an... | {
"Other": 0,
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"cs.SY": 0
} |
2502.05416 | Deep Generative Models with Hard Linear Equality Constraints | [
"cs.LG"
] | While deep generative models~(DGMs) have demonstrated remarkable success in capturing complex data distributions, they consistently fail to learn constraints that encode domain knowledge and thus require constraint integration. Existing solutions to this challenge have primarily relied on heuristic methods and often ig... | {
"Other": 0,
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} |
2502.05423 | LRA-GNN: Latent Relation-Aware Graph Neural Network with Initial and
Dynamic Residual for Facial Age Estimation | [
"cs.CV"
] | Face information is mainly concentrated among facial key points, and frontier research has begun to use graph neural networks to segment faces into patches as nodes to model complex face representations. However, these methods construct node-to-node relations based on similarity thresholds, so there is a problem that s... | {
"Other": 0,
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"cs.SY": 0
} |
2502.05424 | SAMGPT: Text-free Graph Foundation Model for Multi-domain Pre-training
and Cross-domain Adaptation | [
"cs.CL",
"cs.AI"
] | Graphs are able to model interconnected entities in many online services, supporting a wide range of applications on the Web. This raises an important question: How can we train a graph foundational model on multiple source domains and adapt to an unseen target domain? A major obstacle is that graphs from different dom... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2502.05425 | Toward Copyright Integrity and Verifiability via Multi-Bit Watermarking
for Intelligent Transportation Systems | [
"cs.CR",
"cs.CL"
] | Intelligent transportation systems (ITS) use advanced technologies such as artificial intelligence to significantly improve traffic flow management efficiency, and promote the intelligent development of the transportation industry. However, if the data in ITS is attacked, such as tampering or forgery, it will endanger ... | {
"Other": 0,
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"cs.SY": 0
} |
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