id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.07645 | Beyond Behavior Cloning: Robustness through Interactive Imitation and
Contrastive Learning | [
"cs.RO"
] | Behavior cloning (BC) traditionally relies on demonstration data, assuming the demonstrated actions are optimal. This can lead to overfitting under noisy data, particularly when expressive models are used (e.g., the energy-based model in Implicit BC). To address this, we extend behavior cloning into an iterative proces... | {
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2502.07646 | Causal Additive Models with Unobserved Causal Paths and Backdoor Paths | [
"cs.LG",
"stat.ME",
"stat.ML"
] | Causal additive models have been employed as tractable yet expressive frameworks for causal discovery involving hidden variables. State-of-the-art methodologies suggest that determining the causal relationship between a pair of variables is infeasible in the presence of an unobserved backdoor or an unobserved causal pa... | {
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2502.07650 | Guiding Time-Varying Generative Models with Natural Gradients on
Exponential Family Manifold | [
"stat.ML",
"cs.LG"
] | Optimising probabilistic models is a well-studied field in statistics. However, its connection with the training of generative models remains largely under-explored. In this paper, we show that the evolution of time-varying generative models can be projected onto an exponential family manifold, naturally creating a lin... | {
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2502.07656 | A Unifying Framework for Causal Imitation Learning with Hidden
Confounders | [
"cs.LG",
"cs.AI"
] | We propose a general and unifying framework for causal Imitation Learning (IL) with hidden confounders that subsumes several existing confounded IL settings from the literature. Our framework accounts for two types of hidden confounders: (a) those observed by the expert, which thus influence the expert's policy, and (b... | {
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2502.07657 | Private Low-Rank Approximation for Covariance Matrices, Dyson Brownian
Motion, and Eigenvalue-Gap Bounds for Gaussian Perturbations | [
"cs.DS",
"cs.CR",
"cs.LG",
"cs.NA",
"math.NA",
"math.PR"
] | We consider the problem of approximating a $d \times d$ covariance matrix $M$ with a rank-$k$ matrix under $(\varepsilon,\delta)$-differential privacy. We present and analyze a complex variant of the Gaussian mechanism and obtain upper bounds on the Frobenius norm of the difference between the matrix output by this mec... | {
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2502.07658 | IU4Rec: Interest Unit-Based Product Organization and Recommendation for
E-Commerce Platform | [
"cs.IR"
] | Most recommendation systems typically follow a product-based paradigm utilizing user-product interactions to identify the most engaging items for users. However, this product-based paradigm has notable drawbacks for Xianyu~\footnote{Xianyu is China's largest online C2C e-commerce platform where a large portion of the p... | {
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2502.07661 | Partial-Label Learning with Conformal Candidate Cleaning | [
"cs.LG",
"stat.ML"
] | Real-world data is often ambiguous; for example, human annotation produces instances with multiple conflicting class labels. Partial-label learning (PLL) aims at training a classifier in this challenging setting, where each instance is associated with a set of candidate labels and one correct, but unknown, class label.... | {
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2502.07663 | Human Decision-making is Susceptible to AI-driven Manipulation | [
"cs.AI",
"cs.CL",
"cs.CY",
"cs.HC"
] | Artificial Intelligence (AI) systems are increasingly intertwined with daily life, assisting users in executing various tasks and providing guidance on decision-making. This integration introduces risks of AI-driven manipulation, where such systems may exploit users' cognitive biases and emotional vulnerabilities to st... | {
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2502.07677 | Auto-Drafting Police Reports from Noisy ASR Outputs: A Trust-Centered
LLM Approach | [
"cs.CL"
] | Achieving a delicate balance between fostering trust in law enforcement and protecting the rights of both officers and civilians continues to emerge as a pressing research and product challenge in the world today. In the pursuit of fairness and transparency, this study presents an innovative AI-driven system designed t... | {
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2502.07680 | Multiview Point Cloud Registration Based on Minimum Potential Energy for
Free-Form Blade Measurement | [
"cs.CV",
"cs.CG"
] | Point cloud registration is an essential step for free-form blade reconstruction in industrial measurement. Nonetheless, measuring defects of the 3D acquisition system unavoidably result in noisy and incomplete point cloud data, which renders efficient and accurate registration challenging. In this paper, we propose a ... | {
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2502.07683 | exHarmony: Authorship and Citations for Benchmarking the Reviewer
Assignment Problem | [
"cs.IR",
"cs.CL"
] | The peer review process is crucial for ensuring the quality and reliability of scholarly work, yet assigning suitable reviewers remains a significant challenge. Traditional manual methods are labor-intensive and often ineffective, leading to nonconstructive or biased reviews. This paper introduces the exHarmony (eHarmo... | {
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2502.07685 | Matrix3D: Large Photogrammetry Model All-in-One | [
"cs.CV"
] | We present Matrix3D, a unified model that performs several photogrammetry subtasks, including pose estimation, depth prediction, and novel view synthesis using just the same model. Matrix3D utilizes a multi-modal diffusion transformer (DiT) to integrate transformations across several modalities, such as images, camera ... | {
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2502.07687 | Large Language Models as Proxies for Theories of Human Linguistic
Cognition | [
"cs.CL"
] | We consider the possible role of current large language models (LLMs) in the study of human linguistic cognition. We focus on the use of such models as proxies for theories of cognition that are relatively linguistically-neutral in their representations and learning but differ from current LLMs in key ways. We illustra... | {
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2502.07693 | SoK: A Classification for AI-driven Personalized Privacy Assistants | [
"cs.CY",
"cs.AI"
] | To help users make privacy-related decisions, personalized privacy assistants based on AI technology have been developed in recent years. These AI-driven Personalized Privacy Assistants (AI-driven PPAs) can reap significant benefits for users, who may otherwise struggle to make decisions regarding their personal data i... | {
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2502.07694 | Methodology for Identifying Social Groups within a Transactional Graph | [
"cs.SI"
] | Social network analysis is pivotal for organizations aiming to leverage the vast amounts of data generated from user interactions on social media and other digital platforms. These interactions often reveal complex social structures, such as tightly-knit groups based on common interests, which are crucial for enhancing... | {
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2502.07701 | Magic 1-For-1: Generating One Minute Video Clips within One Minute | [
"cs.CV"
] | In this technical report, we present Magic 1-For-1 (Magic141), an efficient video generation model with optimized memory consumption and inference latency. The key idea is simple: factorize the text-to-video generation task into two separate easier tasks for diffusion step distillation, namely text-to-image generation ... | {
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2502.07703 | GaRLIO: Gravity enhanced Radar-LiDAR-Inertial Odometry | [
"cs.RO"
] | Recently, gravity has been highlighted as a crucial constraint for state estimation to alleviate potential vertical drift. Existing online gravity estimation methods rely on pose estimation combined with IMU measurements, which is considered best practice when direct velocity measurements are unavailable. However, with... | {
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2502.07707 | PRVQL: Progressive Knowledge-guided Refinement for Robust Egocentric
Visual Query Localization | [
"cs.CV"
] | Egocentric visual query localization (EgoVQL) focuses on localizing the target of interest in space and time from first-person videos, given a visual query. Despite recent progressive, existing methods often struggle to handle severe object appearance changes and cluttering background in the video due to lacking suffic... | {
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2502.07708 | Global linearization without hyperbolicity | [
"math.DS",
"cs.SY",
"eess.SY"
] | We give a proof of an extension of the Hartman-Grobman theorem to nonhyperbolic but asymptotically stable equilibria of vector fields. Moreover, the linearizing topological conjugacy is (i) defined on the entire basin of attraction if the vector field is complete, and (ii) a $C^{k\geq 1}$ diffeomorphism on the compleme... | {
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2502.07709 | MAGELLAN: Metacognitive predictions of learning progress guide autotelic
LLM agents in large goal spaces | [
"cs.AI"
] | Open-ended learning agents must efficiently prioritize goals in vast possibility spaces, focusing on those that maximize learning progress (LP). When such autotelic exploration is achieved by LLM agents trained with online RL in high-dimensional and evolving goal spaces, a key challenge for LP prediction is modeling on... | {
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2502.07715 | Near-Optimal Sample Complexity in Reward-Free Kernel-Based Reinforcement
Learning | [
"cs.LG"
] | Reinforcement Learning (RL) problems are being considered under increasingly more complex structures. While tabular and linear models have been thoroughly explored, the analytical study of RL under nonlinear function approximation, especially kernel-based models, has recently gained traction for their strong representa... | {
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2502.07717 | Making Language Models Robust Against Negation | [
"cs.CL"
] | Negation has been a long-standing challenge for language models. Previous studies have shown that they struggle with negation in many natural language understanding tasks. In this work, we propose a self-supervised method to make language models more robust against negation. We introduce a novel task, Next Sentence Pol... | {
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2502.07718 | Next-to-minimal weight of toric codes defined over hypersimplices | [
"cs.IT",
"math.AC",
"math.AG",
"math.IT"
] | Toric codes are a type of evaluation codes introduced by J.P. Hansen in 2000. They are produced by evaluating (a vector space composed by) polynomials at the points of $(\mathbb{F}_q^*)^s$, the monomials of these polynomials being related to a certain polytope. Toric codes related to hypersimplices are the result of th... | {
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2502.07721 | TMLC-Net: Transferable Meta Label Correction for Noisy Label Learning | [
"cs.LG",
"cs.AI"
] | The prevalence of noisy labels in real-world datasets poses a significant impediment to the effective deployment of deep learning models. While meta-learning strategies have emerged as a promising approach for addressing this challenge, existing methods often suffer from limited transferability and task-specific design... | {
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2502.07726 | DeepVL: Dynamics and Inertial Measurements-based Deep Velocity Learning
for Underwater Odometry | [
"cs.RO"
] | This paper presents a learned model to predict the robot-centric velocity of an underwater robot through dynamics-aware proprioception. The method exploits a recurrent neural network using as inputs inertial cues, motor commands, and battery voltage readings alongside the hidden state of the previous time-step to outpu... | {
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2502.07728 | Verifying LLM-Generated Code in the Context of Software Verification
with Ada/SPARK | [
"cs.SE",
"cs.AI"
] | Large language models (LLMs) have demonstrated remarkable code generation capabilities, but the correctness of the generated code cannot be inherently trusted. This paper explores the feasibility of using formal software verification, specifically the SPARK framework for Ada, to ensure the reliability of LLM-generated ... | {
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2502.07730 | DOGlove: Dexterous Manipulation with a Low-Cost Open-Source Haptic Force
Feedback Glove | [
"cs.RO"
] | Dexterous hand teleoperation plays a pivotal role in enabling robots to achieve human-level manipulation dexterity. However, current teleoperation systems often rely on expensive equipment and lack multi-modal sensory feedback, restricting human operators' ability to perceive object properties and perform complex manip... | {
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2502.07732 | Economics of Sourcing Human Data | [
"cs.CY",
"cs.AI",
"cs.CL",
"cs.CV",
"cs.HC",
"cs.LG"
] | Progress in AI has relied on human-generated data, from annotator marketplaces to the wider Internet. However, the widespread use of large language models now threatens the quality and integrity of human-generated data on these very platforms. We argue that this issue goes beyond the immediate challenge of filtering AI... | {
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2502.07734 | EdgeEar: Efficient and Accurate Ear Recognition for Edge Devices | [
"cs.CV",
"cs.AI"
] | Ear recognition is a contactless and unobtrusive biometric technique with applications across various domains. However, deploying high-performing ear recognition models on resource-constrained devices is challenging, limiting their applicability and widespread adoption. This paper introduces EdgeEar, a lightweight mode... | {
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2502.07735 | Revisiting Non-Acyclic GFlowNets in Discrete Environments | [
"cs.LG",
"stat.ML"
] | Generative Flow Networks (GFlowNets) are a family of generative models that learn to sample objects from a given probability distribution, potentially known up to a normalizing constant. Instead of working in the object space, GFlowNets proceed by sampling trajectories in an appropriately constructed directed acyclic g... | {
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2502.07737 | Next Block Prediction: Video Generation via Semi-Autoregressive Modeling | [
"cs.CV",
"cs.AI"
] | Next-Token Prediction (NTP) is a de facto approach for autoregressive (AR) video generation, but it suffers from suboptimal unidirectional dependencies and slow inference speed. In this work, we propose a semi-autoregressive (semi-AR) framework, called Next-Block Prediction (NBP), for video generation. By uniformly dec... | {
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2502.07738 | EIQP: Execution-time-certified and Infeasibility-detecting QP Solver | [
"eess.SY",
"cs.SY",
"math.OC"
] | Solving real-time quadratic programming (QP) is a ubiquitous task in control engineering, such as in model predictive control and control barrier function-based QP. In such real-time scenarios, certifying that the employed QP algorithm can either return a solution within a predefined level of optimality or detect QP in... | {
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2502.07739 | HRP: High-Rank Preheating for Superior LoRA Initialization | [
"cs.LG"
] | This paper studies the crucial impact of initialization on the convergence properties of Low-Rank Adaptation (LoRA). We theoretically demonstrate that random initialization, a widely used schema, will likely lead LoRA to random low-rank results, rather than the best low-rank result. While this issue can be mitigated by... | {
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2502.07741 | Advancing climate model interpretability: Feature attribution for Arctic
melt anomalies | [
"cs.LG"
] | The focus of our work is improving the interpretability of anomalies in climate models and advancing our understanding of Arctic melt dynamics. The Arctic and Antarctic ice sheets are experiencing rapid surface melting and increased freshwater runoff, contributing significantly to global sea level rise. Understanding t... | {
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2502.07746 | HiPoNet: A Topology-Preserving Multi-View Neural Network For High
Dimensional Point Cloud and Single-Cell Data | [
"cs.LG",
"math.AT"
] | In this paper, we propose HiPoNet, an end-to-end differentiable neural network for regression, classification, and representation learning on high-dimensional point clouds. Single-cell data can have high dimensionality exceeding the capabilities of existing methods point cloud tailored for 3D data. Moreover, modern sin... | {
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2502.07747 | WHODUNIT: Evaluation benchmark for culprit detection in mystery stories | [
"cs.CL",
"cs.AI"
] | We present a novel data set, WhoDunIt, to assess the deductive reasoning capabilities of large language models (LLM) within narrative contexts. Constructed from open domain mystery novels and short stories, the dataset challenges LLMs to identify the perpetrator after reading and comprehending the story. To evaluate mo... | {
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2502.07749 | Whole-Genome Phenotype Prediction with Machine Learning: Open Problems
in Bacterial Genomics | [
"q-bio.GN",
"cs.LG"
] | How can we identify causal genetic mechanisms that govern bacterial traits? Initial efforts entrusting machine learning models to handle the task of predicting phenotype from genotype return high accuracy scores. However, attempts to extract any meaning from the predictive models are found to be corrupted by falsely id... | {
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2502.07750 | PFedDST: Personalized Federated Learning with Decentralized Selection
Training | [
"cs.LG",
"cs.AI"
] | Distributed Learning (DL) enables the training of machine learning models across multiple devices, yet it faces challenges like non-IID data distributions and device capability disparities, which can impede training efficiency. Communication bottlenecks further complicate traditional Federated Learning (FL) setups. To ... | {
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2502.07751 | CausalGeD: Blending Causality and Diffusion for Spatial Gene Expression
Generation | [
"cs.CV",
"q-bio.GN"
] | The integration of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) data is crucial for understanding gene expression in spatial context. Existing methods for such integration have limited performance, with structural similarity often below 60\%, We attribute this limitation to the failure to con... | {
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2502.07752 | Towards Efficient Optimizer Design for LLM via Structured Fisher
Approximation with a Low-Rank Extension | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Designing efficient optimizers for large language models (LLMs) with low-memory requirements and fast convergence is an important and challenging problem. This paper makes a step towards the systematic design of such optimizers through the lens of structured Fisher information matrix (FIM) approximation. We show that m... | {
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2502.07753 | Direct Ascent Synthesis: Revealing Hidden Generative Capabilities in
Discriminative Models | [
"cs.CV"
] | We demonstrate that discriminative models inherently contain powerful generative capabilities, challenging the fundamental distinction between discriminative and generative architectures. Our method, Direct Ascent Synthesis (DAS), reveals these latent capabilities through multi-resolution optimization of CLIP model rep... | {
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2502.07754 | MeshSplats: Mesh-Based Rendering with Gaussian Splatting Initialization | [
"cs.GR",
"cs.CV"
] | Gaussian Splatting (GS) is a recent and pivotal technique in 3D computer graphics. GS-based algorithms almost always bypass classical methods such as ray tracing, which offers numerous inherent advantages for rendering. For example, ray tracing is able to handle incoherent rays for advanced lighting effects, including ... | {
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2502.07755 | An Advanced NLP Framework for Automated Medical Diagnosis with DeBERTa
and Dynamic Contextual Positional Gating | [
"cs.CL",
"cs.AI"
] | This paper presents a novel Natural Language Processing (NLP) framework for enhancing medical diagnosis through the integration of advanced techniques in data augmentation, feature extraction, and classification. The proposed approach employs back-translation to generate diverse paraphrased datasets, improving robustne... | {
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2502.07758 | Novel computational workflows for natural and biomedical image
processing based on hypercomplex algebras | [
"cs.CV",
"cs.LG"
] | Hypercomplex image processing extends conventional techniques in a unified paradigm encompassing algebraic and geometric principles. This work leverages quaternions and the two-dimensional orthogonal planes split framework (splitting of a quaternion - representing a pixel - into pairs of orthogonal 2D planes) for natur... | {
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2502.07760 | Scalable Fingerprinting of Large Language Models | [
"cs.CR",
"cs.LG"
] | Model fingerprinting has emerged as a powerful tool for model owners to identify their shared model given API access. However, to lower false discovery rate, fight fingerprint leakage, and defend against coalitions of model users attempting to bypass detection, we argue that {\em scalability} is critical, i.e., scaling... | {
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2502.07764 | Polynomial-Time Approximability of Constrained Reinforcement Learning | [
"cs.DS",
"cs.AI",
"cs.LG"
] | We study the computational complexity of approximating general constrained Markov decision processes. Our primary contribution is the design of a polynomial time $(0,\epsilon)$-additive bicriteria approximation algorithm for finding optimal constrained policies across a broad class of recursively computable constraints... | {
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2502.07771 | Breaking Down Bias: On The Limits of Generalizable Pruning Strategies | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.LG"
] | We employ model pruning to examine how LLMs conceptualize racial biases, and whether a generalizable mitigation strategy for such biases appears feasible. Our analysis yields several novel insights. We find that pruning can be an effective method to reduce bias without significantly increasing anomalous model behavior.... | {
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2502.07772 | Automatic Robot Task Planning by Integrating Large Language Model with
Genetic Programming | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Accurate task planning is critical for controlling autonomous systems, such as robots, drones, and self-driving vehicles. Behavior Trees (BTs) are considered one of the most prominent control-policy-defining frameworks in task planning, due to their modularity, flexibility, and reusability. Generating reliable and accu... | {
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2502.07774 | Optimistic Interior Point Methods for Sequential Hypothesis Testing by
Betting | [
"cs.LG"
] | The technique of "testing by betting" frames nonparametric sequential hypothesis testing as a multiple-round game, where a player bets on future observations that arrive in a streaming fashion, accumulates wealth that quantifies evidence against the null hypothesis, and rejects the null once the wealth exceeds a specif... | {
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2502.07776 | Auditing Prompt Caching in Language Model APIs | [
"cs.CL",
"cs.CR",
"cs.LG"
] | Prompt caching in large language models (LLMs) results in data-dependent timing variations: cached prompts are processed faster than non-cached prompts. These timing differences introduce the risk of side-channel timing attacks. For example, if the cache is shared across users, an attacker could identify cached prompts... | {
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2502.07778 | Stay-Positive: A Case for Ignoring Real Image Features in Fake Image
Detection | [
"cs.CV"
] | Detecting AI generated images is a challenging yet essential task. A primary difficulty arises from the detectors tendency to rely on spurious patterns, such as compression artifacts, which can influence its decisions. These issues often stem from specific patterns that the detector associates with the real data distri... | {
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2502.07780 | DarwinLM: Evolutionary Structured Pruning of Large Language Models | [
"cs.LG",
"cs.CL"
] | Large Language Models (LLMs) have achieved significant success across various NLP tasks. However, their massive computational costs limit their widespread use, particularly in real-time applications. Structured pruning offers an effective solution by compressing models and directly providing end-to-end speed improvemen... | {
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2502.07782 | A Flag Decomposition for Hierarchical Datasets | [
"cs.CV"
] | Flag manifolds encode hierarchical nested sequences of subspaces and serve as powerful structures for various computer vision and machine learning applications. Despite their utility in tasks such as dimensionality reduction, motion averaging, and subspace clustering, current applications are often restricted to extrac... | {
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2502.07783 | Curvature Tuning: Provable Training-free Model Steering From a Single
Parameter | [
"cs.LG"
] | The scaling of model size and data size has reshaped the paradigm of AI. As a result, the common protocol to leverage the latest models is to steer them towards a specific downstream task of interest through {\em fine-tuning}. Despite its importance, the main methods for fine-tuning remain limited to full or low-rank a... | {
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2502.07784 | MatSwap: Light-aware material transfers in images | [
"cs.CV",
"cs.GR"
] | We present MatSwap, a method to transfer materials to designated surfaces in an image photorealistically. Such a task is non-trivial due to the large entanglement of material appearance, geometry, and lighting in a photograph. In the literature, material editing methods typically rely on either cumbersome text engineer... | {
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2502.07785 | Pippo: High-Resolution Multi-View Humans from a Single Image | [
"cs.CV",
"cs.GR"
] | We present Pippo, a generative model capable of producing 1K resolution dense turnaround videos of a person from a single casually clicked photo. Pippo is a multi-view diffusion transformer and does not require any additional inputs - e.g., a fitted parametric model or camera parameters of the input image. We pre-train... | {
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2502.07786 | Counterexample Guided Program Repair Using Zero-Shot Learning and
MaxSAT-based Fault Localization | [
"cs.SE",
"cs.AI"
] | Automated Program Repair (APR) for introductory programming assignments (IPAs) is motivated by the large number of student enrollments in programming courses each year. Since providing feedback on IPAs requires substantial time and effort from faculty, personalized feedback often involves suggesting fixes to students' ... | {
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2502.07789 | Do AI assistants help students write formal specifications? A study with
ChatGPT and the B-Method | [
"cs.CY",
"cs.AI"
] | This paper investigates the role of AI assistants, specifically OpenAI's ChatGPT, in teaching formal methods (FM) to undergraduate students, using the B-method as a formal specification technique. While existing studies demonstrate the effectiveness of AI in coding tasks, no study reports on its impact on formal specif... | {
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2502.07790 | Can Generative AI be Egalitarian? | [
"cs.CY",
"cs.AI"
] | The recent explosion of "foundation" generative AI models has been built upon the extensive extraction of value from online sources, often without corresponding reciprocation. This pattern mirrors and intensifies the extractive practices of surveillance capitalism, while the potential for enormous profit has challenged... | {
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2502.07791 | Simple demonstration of different types of coupling in multiphysics
numerical problems | [
"cs.CE",
"physics.comp-ph"
] | Numerical modelling of coupled multiphysics phenomena is becoming an increasingly important subject in applied mathematics. The main challenge in teaching this subject is the complexity of both the mathematical models and their numerical implementation. In this note, a simple demonstrator is proposed that enables demon... | {
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2502.07794 | Regulatory Science Innovation for Generative AI and Large Language
Models in Health and Medicine: A Global Call for Action | [
"cs.CY",
"cs.AI"
] | The integration of generative AI (GenAI) and large language models (LLMs) in healthcare presents both unprecedented opportunities and challenges, necessitating innovative regulatory approaches. GenAI and LLMs offer broad applications, from automating clinical workflows to personalizing diagnostics. However, the non-det... | {
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2502.07800 | neuro2voc: Decoding Vocalizations from Neural Activity | [
"q-bio.NC",
"cs.LG",
"eess.AS"
] | Accurate decoding of neural spike trains and relating them to motor output is a challenging task due to the inherent sparsity and length in neural spikes and the complexity of brain circuits. This master project investigates experimental methods for decoding zebra finch motor outputs (in both discrete syllables and con... | {
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2502.07802 | Movie Weaver: Tuning-Free Multi-Concept Video Personalization with
Anchored Prompts | [
"cs.CV",
"cs.GR",
"cs.LG"
] | Video personalization, which generates customized videos using reference images, has gained significant attention. However, prior methods typically focus on single-concept personalization, limiting broader applications that require multi-concept integration. Attempts to extend these models to multiple concepts often le... | {
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2502.07803 | Reasoning-as-Logic-Units: Scaling Test-Time Reasoning in Large Language
Models Through Logic Unit Alignment | [
"cs.AI",
"cs.LG"
] | Chain-of-Thought (CoT) prompting has shown promise in enhancing the reasoning capabilities of large language models (LLMs) by generating natural language (NL) rationales that lead to the final answer. However, it struggles with numerical computation, which has somehow led to the development of program-aided techniques.... | {
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2502.07806 | Quantum Powered Credit Risk Assessment: A Novel Approach using hybrid
Quantum-Classical Deep Neural Network for Row-Type Dependent Predictive
Analysis | [
"q-fin.CP",
"cs.AI",
"cs.LG"
] | The integration of Quantum Deep Learning (QDL) techniques into the landscape of financial risk analysis presents a promising avenue for innovation. This study introduces a framework for credit risk assessment in the banking sector, combining quantum deep learning techniques with adaptive modeling for Row-Type Dependent... | {
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2502.07807 | CP-Guard+: A New Paradigm for Malicious Agent Detection and Defense in
Collaborative Perception | [
"cs.CR",
"cs.AI",
"cs.CV",
"cs.LG"
] | Collaborative perception (CP) is a promising method for safe connected and autonomous driving, which enables multiple vehicles to share sensing information to enhance perception performance. However, compared with single-vehicle perception, the openness of a CP system makes it more vulnerable to malicious attacks that ... | {
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2502.07809 | Analyzing the Resource Utilization of Lambda Functions on Mobile
Devices: Case Studies on Kotlin and Swift | [
"cs.SE",
"cs.CL",
"cs.PF"
] | With billions of smartphones in use globally, the daily time spent on these devices contributes significantly to overall electricity consumption. Given this scale, even minor reductions in smartphone power use could result in substantial energy savings. This study explores the impact of Lambda functions on resource con... | {
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2502.07811 | CrossVideoMAE: Self-Supervised Image-Video Representation Learning with
Masked Autoencoders | [
"cs.CV"
] | Current video-based Masked Autoencoders (MAEs) primarily focus on learning effective spatiotemporal representations from a visual perspective, which may lead the model to prioritize general spatial-temporal patterns but often overlook nuanced semantic attributes like specific interactions or sequences that define actio... | {
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2502.07812 | Unpaired Image Dehazing via Kolmogorov-Arnold Transformation of Latent
Features | [
"cs.CV"
] | This paper proposes an innovative framework for Unsupervised Image Dehazing via Kolmogorov-Arnold Transformation, termed UID-KAT. Image dehazing is recognized as a challenging and ill-posed vision task that requires complex transformations and interpretations in the feature space. Recent advancements have introduced Ko... | {
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2502.07813 | CryptoX : Compositional Reasoning Evaluation of Large Language Models | [
"cs.CR",
"cs.AI"
] | The compositional reasoning capacity has long been regarded as critical to the generalization and intelligence emergence of large language models LLMs. However, despite numerous reasoning-related benchmarks, the compositional reasoning capacity of LLMs is rarely studied or quantified in the existing benchmarks. In this... | {
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2502.07814 | Satellite Observations Guided Diffusion Model for Accurate
Meteorological States at Arbitrary Resolution | [
"cs.LG",
"cs.AI",
"physics.ao-ph"
] | Accurate acquisition of surface meteorological conditions at arbitrary locations holds significant importance for weather forecasting and climate simulation. Due to the fact that meteorological states derived from satellite observations are often provided in the form of low-resolution grid fields, the direct applicatio... | {
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2502.07815 | Decoding Complexity: Intelligent Pattern Exploration with CHPDA (Context
Aware Hybrid Pattern Detection Algorithm) | [
"cs.CR",
"cs.AI"
] | Detecting sensitive data such as Personally Identifiable Information (PII) and Protected Health Information (PHI) is critical for data security platforms. This study evaluates regex-based pattern matching algorithms and exact-match search techniques to optimize detection speed, accuracy, and scalability. Our benchmarki... | {
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2502.07817 | Temporal Model On Quantum Logic | [
"cs.AI",
"math.LO",
"quant-ph"
] | This paper introduces a unified theoretical framework for modeling temporal memory dynamics, combining concepts from temporal logic, memory decay models, and hierarchical contexts. The framework formalizes the evolution of propositions over time using linear and branching temporal models, incorporating exponential deca... | {
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2502.07819 | Enhancing kidney transplantation through multi-agent kidney exchange
programs: A comprehensive review and optimization models | [
"cs.AI",
"math.OC"
] | This paper presents a comprehensive review of the last two decades of research on Kidney Exchange Programs (KEPs), systematically categorizing and classifying key contributions to provide readers with a structured understanding of advancements in the field. The review highlights the evolution of KEP methodologies and l... | {
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2502.07820 | Low-Rank Compression for IMC Arrays | [
"cs.AR",
"cs.AI"
] | In this study, we address the challenge of low-rank model compression in the context of in-memory computing (IMC) architectures. Traditional pruning approaches, while effective in model size reduction, necessitate additional peripheral circuitry to manage complex dataflows and mitigate dislocation issues, leading to in... | {
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2502.07821 | Amnesia as a Catalyst for Enhancing Black Box Pixel Attacks in Image
Classification and Object Detection | [
"cs.CV",
"cs.AI"
] | It is well known that query-based attacks tend to have relatively higher success rates in adversarial black-box attacks. While research on black-box attacks is actively being conducted, relatively few studies have focused on pixel attacks that target only a limited number of pixels. In image classification, query-based... | {
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2502.07822 | PDM-SSD: Single-Stage Three-Dimensional Object Detector With Point
Dilation | [
"cs.CV",
"cs.AI"
] | Current Point-based detectors can only learn from the provided points, with limited receptive fields and insufficient global learning capabilities for such targets. In this paper, we present a novel Point Dilation Mechanism for single-stage 3D detection (PDM-SSD) that takes advantage of these two representations. Speci... | {
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2502.07823 | Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs | [
"cs.AR",
"cs.AI",
"cs.LG"
] | Embedded Field-Programmable Gate Arrays (eFPGAs) allow for the design of hardware accelerators of edge Machine Learning (ML) applications at a lower power budget compared with traditional FPGA platforms. However, the limited eFPGA logic and memory significantly constrain compute capabilities and model size. As such, ML... | {
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2502.07825 | Pre-Trained Video Generative Models as World Simulators | [
"cs.CV",
"cs.AI"
] | Video generative models pre-trained on large-scale internet datasets have achieved remarkable success, excelling at producing realistic synthetic videos. However, they often generate clips based on static prompts (e.g., text or images), limiting their ability to model interactive and dynamic scenarios. In this paper, w... | {
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2502.07826 | Deep Learning in Automated Power Line Inspection: A Review | [
"cs.CV",
"eess.IV"
] | In recent years, power line maintenance has seen a paradigm shift by moving towards computer vision-powered automated inspection. The utilization of an extensive collection of videos and images has become essential for maintaining the reliability, safety, and sustainability of electricity transmission. A significant fo... | {
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2502.07827 | Implicit Language Models are RNNs: Balancing Parallelization and
Expressivity | [
"cs.LG",
"cs.AI"
] | State-space models (SSMs) and transformers dominate the language modeling landscape. However, they are constrained to a lower computational complexity than classical recurrent neural networks (RNNs), limiting their expressivity. In contrast, RNNs lack parallelization during training, raising fundamental questions about... | {
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2502.07828 | Some things to know about achieving artificial general intelligence | [
"q-bio.NC",
"cs.AI"
] | Current and foreseeable GenAI models are not capable of achieving artificial general intelligence because they are burdened with anthropogenic debt. They depend heavily on human input to provide well-structured problems, architecture, and training data. They cast every problem as a language pattern learning problem and... | {
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2502.07829 | Preference Alignment on Diffusion Model: A Comprehensive Survey for
Image Generation and Editing | [
"cs.CV",
"cs.LG"
] | The integration of preference alignment with diffusion models (DMs) has emerged as a transformative approach to enhance image generation and editing capabilities. Although integrating diffusion models with preference alignment strategies poses significant challenges for novices at this intersection, comprehensive and s... | {
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2502.07830 | Captured by Captions: On Memorization and its Mitigation in CLIP Models | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Multi-modal models, such as CLIP, have demonstrated strong performance in aligning visual and textual representations, excelling in tasks like image retrieval and zero-shot classification. Despite this success, the mechanisms by which these models utilize training data, particularly the role of memorization, remain unc... | {
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2502.07832 | SHARP: Accelerating Language Model Inference by SHaring Adjacent layers
with Recovery Parameters | [
"cs.LG",
"cs.AI"
] | While Large language models (LLMs) have advanced natural language processing tasks, their growing computational and memory demands make deployment on resource-constrained devices like mobile phones increasingly challenging. In this paper, we propose SHARP (SHaring Adjacent Layers with Recovery Parameters), a novel appr... | {
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2502.07834 | MEMHD: Memory-Efficient Multi-Centroid Hyperdimensional Computing for
Fully-Utilized In-Memory Computing Architectures | [
"cs.AR",
"cs.AI",
"cs.LG"
] | The implementation of Hyperdimensional Computing (HDC) on In-Memory Computing (IMC) architectures faces significant challenges due to the mismatch between highdimensional vectors and IMC array sizes, leading to inefficient memory utilization and increased computation cycles. This paper presents MEMHD, a Memory-Efficien... | {
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} |
2502.07835 | Bridging LLM-Generated Code and Requirements: Reverse Generation
technique and SBC Metric for Developer Insights | [
"cs.SE",
"cs.AI"
] | The rise of Large Language Models (LLMs) in software engineering, particularly in code generation, has garnered significant attention. However, assessing the quality of AI-generated code remains a challenge due to the inherent complexity of programming tasks and the lack of robust evaluation metrics that align well wit... | {
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} |
2502.07836 | Advancing Precision Oncology Through Modeling of Longitudinal and
Multimodal Data | [
"q-bio.QM",
"cs.LG"
] | Cancer evolves continuously over time through a complex interplay of genetic, epigenetic, microenvironmental, and phenotypic changes. This dynamic behavior drives uncontrolled cell growth, metastasis, immune evasion, and therapy resistance, posing challenges for effective monitoring and treatment. However, today's data... | {
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2502.07837 | RoboBERT: An End-to-end Multimodal Robotic Manipulation Model | [
"cs.RO",
"cs.LG"
] | Embodied intelligence integrates multiple modalities, enabling agents to understand images, language, and actions simultaneously. However, existing models always depend on additional datasets or extensive pre-training to maximize performance improvements, consuming abundant training time and expensive hardware cost. To... | {
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2502.07838 | NanoVLMs: How small can we go and still make coherent Vision Language
Models? | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs), such as GPT-4V and Llama 3.2 vision, have garnered significant research attention for their ability to leverage Large Language Models (LLMs) in multimodal tasks. However, their potential is constrained by inherent challenges, including proprietary restrictions, substantial computational d... | {
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} |
2502.07839 | Optimal Actuator Attacks on Autonomous Vehicles Using Reinforcement
Learning | [
"cs.RO",
"cs.LG"
] | With the increasing prevalence of autonomous vehicles (AVs), their vulnerability to various types of attacks has grown, presenting significant security challenges. In this paper, we propose a reinforcement learning (RL)-based approach for designing optimal stealthy integrity attacks on AV actuators. We also analyze the... | {
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} |
2502.07840 | TranSplat: Surface Embedding-guided 3D Gaussian Splatting for
Transparent Object Manipulation | [
"cs.CV",
"cs.RO"
] | Transparent object manipulation remains a significant challenge in robotics due to the difficulty of acquiring accurate and dense depth measurements. Conventional depth sensors often fail with transparent objects, resulting in incomplete or erroneous depth data. Existing depth completion methods struggle with interfram... | {
"Other": 0,
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} |
2502.07842 | Column-wise Quantization of Weights and Partial Sums for Accurate and
Efficient Compute-In-Memory Accelerators | [
"cs.AR",
"cs.AI",
"cs.LG"
] | Compute-in-memory (CIM) is an efficient method for implementing deep neural networks (DNNs) but suffers from substantial overhead from analog-to-digital converters (ADCs), especially as ADC precision increases. Low-precision ADCs can reduce this overhead but introduce partial-sum quantization errors degrading accuracy.... | {
"Other": 1,
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} |
2502.07843 | Emotional EEG Classification using Upscaled Connectivity Matrices | [
"cs.LG"
] | In recent studies of emotional EEG classification, connectivity matrices have been successfully employed as input to convolutional neural networks (CNNs), which can effectively consider inter-regional interaction patterns in EEG. However, we find that such an approach has a limitation that important patterns in connect... | {
"Other": 0,
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} |
2502.07844 | The establishment of static digital humans and the integration with
spinal models | [
"eess.IV",
"cs.CV"
] | Adolescent idiopathic scoliosis (AIS), a prevalent spinal deformity, significantly affects individuals' health and quality of life. Conventional imaging techniques, such as X - rays, computed tomography (CT), and magnetic resonance imaging (MRI), offer static views of the spine. However, they are restricted in capturin... | {
"Other": 0,
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"cs.CV": 1,
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} |
2502.07845 | Spread them Apart: Towards Robust Watermarking of Generated Content | [
"cs.CV",
"cs.AI"
] | Generative models that can produce realistic images have improved significantly in recent years. The quality of the generated content has increased drastically, so sometimes it is very difficult to distinguish between the real images and the generated ones. Such an improvement comes at a price of ethical concerns about... | {
"Other": 0,
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} |
2502.07846 | Memory Analysis on the Training Course of DeepSeek Models | [
"cs.PF",
"cs.LG"
] | We present a theoretical analysis of GPU memory consumption during the training of DeepSeek models such as DeepSeek-v2 and DeepSeek-v3. Our primary objective is to clarify the device-level memory requirements associated with various distributed training configurations. Specifically, we examine critical factors influenc... | {
"Other": 1,
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} |
2502.07847 | Technical note on calibrating vision-language models under covariate
shift | [
"cs.CV",
"cs.LG"
] | Despite being a successful example of emerging capability, vision-language foundation models for low-shot vision classification have a limited ability to sufficiently generalize to the target data distribution due to sample poverty, leading to sensitivity to variations in the data. A popular mitigation strategy is fine... | {
"Other": 0,
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} |
2502.07849 | Understanding Classifier-Free Guidance: High-Dimensional Theory and
Non-Linear Generalizations | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Recent studies have raised concerns about the effectiveness of Classifier-Free Guidance (CFG), indicating that in low-dimensional settings, it can lead to overshooting the target distribution and reducing sample diversity. In this work, we demonstrate that in infinite and sufficiently high-dimensional contexts CFG effe... | {
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} |
2502.07850 | Mathematical reasoning and the computer | [
"cs.AI"
] | Computers have already changed the way that humans do mathematics: they enable us to compute efficiently. But will they soon be helping us to reason? And will they one day start reasoning themselves? We give an overview of recent developments in neural networks, computer theorem provers and large language models. | {
"Other": 0,
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} |
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