id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.01941 | Can LLMs Maintain Fundamental Abilities under KV Cache Compression? | [
"cs.CL",
"cs.AI"
] | This paper investigates an under-explored challenge in large language models (LLMs): the impact of KV cache compression methods on LLMs' fundamental capabilities. While existing methods achieve impressive compression ratios on long-context benchmarks, their effects on core model capabilities remain understudied. We pre... | {
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2502.01942 | Boundary-Driven Table-Filling with Cross-Granularity Contrastive
Learning for Aspect Sentiment Triplet Extraction | [
"cs.CL",
"cs.AI"
] | The Aspect Sentiment Triplet Extraction (ASTE) task aims to extract aspect terms, opinion terms, and their corresponding sentiment polarity from a given sentence. It remains one of the most prominent subtasks in fine-grained sentiment analysis. Most existing approaches frame triplet extraction as a 2D table-filling pro... | {
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2502.01943 | DAMA: Data- and Model-aware Alignment of Multi-modal LLMs | [
"cs.CV"
] | Direct Preference Optimization (DPO) has shown effectiveness in aligning multi-modal large language models (MLLM) with human preferences. However, existing methods exhibit an imbalanced responsiveness to the data of varying hardness, tending to overfit on the easy-to-distinguish data while underfitting on the hard-to-d... | {
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2502.01946 | HeRCULES: Heterogeneous Radar Dataset in Complex Urban Environment for
Multi-session Radar SLAM | [
"cs.RO",
"cs.CV"
] | Recently, radars have been widely featured in robotics for their robustness in challenging weather conditions. Two commonly used radar types are spinning radars and phased-array radars, each offering distinct sensor characteristics. Existing datasets typically feature only a single type of radar, leading to the develop... | {
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2502.01949 | LAYOUTDREAMER: Physics-guided Layout for Text-to-3D Compositional Scene
Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Recently, the field of text-guided 3D scene generation has garnered significant attention. High-quality generation that aligns with physical realism and high controllability is crucial for practical 3D scene applications. However, existing methods face fundamental limitations: (i) difficulty capturing complex relations... | {
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2502.01951 | On the Emergence of Position Bias in Transformers | [
"cs.LG"
] | Recent studies have revealed various manifestations of position bias in transformer architectures, from the "lost-in-the-middle" phenomenon to attention sinks, yet a comprehensive theoretical understanding of how attention masks and positional encodings shape these biases remains elusive. This paper introduces a novel ... | {
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2502.01953 | Local minima of the empirical risk in high dimension: General theorems
and convex examples | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | We consider a general model for high-dimensional empirical risk minimization whereby the data $\mathbf{x}_i$ are $d$-dimensional isotropic Gaussian vectors, the model is parametrized by $\mathbf{\Theta}\in\mathbb{R}^{d\times k}$, and the loss depends on the data via the projection $\mathbf{\Theta}^\mathsf{T}\mathbf{x}_... | {
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2502.01954 | Constrained belief updates explain geometric structures in transformer
representations | [
"cs.LG"
] | What computational structures emerge in transformers trained on next-token prediction? In this work, we provide evidence that transformers implement constrained Bayesian belief updating -- a parallelized version of partial Bayesian inference shaped by architectural constraints. To do this, we integrate the model-agnost... | {
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2502.01956 | DHP: Discrete Hierarchical Planning for Hierarchical Reinforcement
Learning Agents | [
"cs.RO",
"cs.AI",
"cs.LG"
] | In this paper, we address the challenge of long-horizon visual planning tasks using Hierarchical Reinforcement Learning (HRL). Our key contribution is a Discrete Hierarchical Planning (DHP) method, an alternative to traditional distance-based approaches. We provide theoretical foundations for the method and demonstrate... | {
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2502.01959 | MATCNN: Infrared and Visible Image Fusion Method Based on Multi-scale
CNN with Attention Transformer | [
"cs.CV"
] | While attention-based approaches have shown considerable progress in enhancing image fusion and addressing the challenges posed by long-range feature dependencies, their efficacy in capturing local features is compromised by the lack of diverse receptive field extraction techniques. To overcome the shortcomings of exis... | {
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2502.01960 | MPIC: Position-Independent Multimodal Context Caching System for
Efficient MLLM Serving | [
"cs.LG"
] | The context caching technique is employed to accelerate the Multimodal Large Language Model (MLLM) inference by prevailing serving platforms currently. However, this approach merely reuses the Key-Value (KV) cache of the initial sequence of prompt, resulting in full KV cache recomputation even if the prefix differs sli... | {
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2502.01961 | Hierarchical Consensus Network for Multiview Feature Learning | [
"cs.CV",
"cs.LG"
] | Multiview feature learning aims to learn discriminative features by integrating the distinct information in each view. However, most existing methods still face significant challenges in learning view-consistency features, which are crucial for effective multiview learning. Motivated by the theories of CCA and contrast... | {
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2502.01962 | Memory Efficient Transformer Adapter for Dense Predictions | [
"cs.CV"
] | While current Vision Transformer (ViT) adapter methods have shown promising accuracy, their inference speed is implicitly hindered by inefficient memory access operations, e.g., standard normalization and frequent reshaping. In this work, we propose META, a simple and fast ViT adapter that can improve the model's memor... | {
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2502.01968 | Token Cleaning: Fine-Grained Data Selection for LLM Supervised
Fine-Tuning | [
"cs.CL",
"cs.AI"
] | Recent studies show that in supervised fine-tuning (SFT) of large language models (LLMs), data quality matters more than quantity. While most data cleaning methods concentrate on filtering entire samples, the quality of individual tokens within a sample can vary significantly. After pre-training, even in high-quality s... | {
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2502.01969 | Mitigating Object Hallucinations in Large Vision-Language Models via
Attention Calibration | [
"cs.CV",
"cs.AI"
] | Large Vision-Language Models (LVLMs) exhibit impressive multimodal reasoning capabilities but remain highly susceptible to object hallucination, where models generate responses that are not factually aligned with the visual content. Recent works attribute this issue to an inherent bias of LVLMs where vision token atten... | {
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2502.01971 | Bottom-Up Reputation Promotes Cooperation with Multi-Agent Reinforcement
Learning | [
"cs.MA"
] | Reputation serves as a powerful mechanism for promoting cooperation in multi-agent systems, as agents are more inclined to cooperate with those of good social standing. While existing multi-agent reinforcement learning methods typically rely on predefined social norms to assign reputations, the question of how a popula... | {
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2502.01972 | Layer Separation: Adjustable Joint Space Width Images Synthesis in
Conventional Radiography | [
"eess.IV",
"cs.AI",
"cs.CV",
"cs.LG"
] | Rheumatoid arthritis (RA) is a chronic autoimmune disease characterized by joint inflammation and progressive structural damage. Joint space width (JSW) is a critical indicator in conventional radiography for evaluating disease progression, which has become a prominent research topic in computer-aided diagnostic (CAD) ... | {
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2502.01976 | CITER: Collaborative Inference for Efficient Large Language Model
Decoding with Token-Level Routing | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.PF"
] | Large language models have achieved remarkable success in various tasks but suffer from high computational costs during inference, limiting their deployment in resource-constrained applications. To address this issue, we propose a novel CITER (Collaborative Inference with Token-lEvel Routing) framework that enables eff... | {
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2502.01977 | AutoGUI: Scaling GUI Grounding with Automatic Functionality Annotations
from LLMs | [
"cs.CV"
] | User interface understanding with vision-language models has received much attention due to its potential for enabling next-generation software automation. However, existing UI datasets either only provide large-scale context-free element annotations or contextualized functional descriptions for elements at a much smal... | {
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2502.01979 | Gradient-Regularized Latent Space Modulation in Large Language Models
for Structured Contextual Synthesis | [
"cs.CL"
] | Generating structured textual content requires mechanisms that enforce coherence, stability, and adherence to predefined constraints while maintaining semantic fidelity. Conventional approaches often rely on rule-based heuristics or fine-tuning strategies that lack flexibility and generalizability across diverse tasks.... | {
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2502.01980 | Generative Data Mining with Longtail-Guided Diffusion | [
"cs.LG",
"cs.AI"
] | It is difficult to anticipate the myriad challenges that a predictive model will encounter once deployed. Common practice entails a reactive, cyclical approach: model deployment, data mining, and retraining. We instead develop a proactive longtail discovery process by imagining additional data during training. In parti... | {
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2502.01983 | Diagrammatics of information | [
"math-ph",
"cs.IT",
"math.IT",
"math.MP"
] | We introduce a diagrammatic perspective for Shannon entropy created by the first author and Mikhail Khovanov and connect it to information theory and mutual information. We also give two complete proofs that the $5$-term dilogarithm deforms to the $4$-term infinitesimal dilogarithm. | {
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2502.01984 | Efficient Covering Using Reed--Solomon Codes | [
"cs.IT",
"eess.SP",
"math.IT"
] | We propose an efficient algorithm to find a Reed-Solomon (RS) codeword at a distance within the covering radius of the code from any point in its ambient Hamming space. To the best of the authors' knowledge, this is the first attempt of its kind to solve the covering problem for RS codes. The proposed algorithm leverag... | {
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2502.01985 | Ilargi: a GPU Compatible Factorized ML Model Training Framework | [
"cs.LG",
"cs.DC"
] | The machine learning (ML) training over disparate data sources traditionally involves materialization, which can impose substantial time and space overhead due to data movement and replication. Factorized learning, which leverages direct computation on disparate sources through linear algebra (LA) rewriting, has emerge... | {
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2502.01986 | DCT-Mamba3D: Spectral Decorrelation and Spatial-Spectral Feature
Extraction for Hyperspectral Image Classification | [
"cs.CV",
"eess.IV"
] | Hyperspectral image classification presents challenges due to spectral redundancy and complex spatial-spectral dependencies. This paper proposes a novel framework, DCT-Mamba3D, for hyperspectral image classification. DCT-Mamba3D incorporates: (1) a 3D spectral-spatial decorrelation module that applies 3D discrete cosin... | {
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2502.01987 | Online Adaptive Traversability Estimation through Interaction for
Unstructured, Densely Vegetated Environments | [
"cs.RO"
] | Navigating densely vegetated environments poses significant challenges for autonomous ground vehicles. Learning-based systems typically use prior and in-situ data to predict terrain traversability but often degrade in performance when encountering out-of-distribution elements caused by rapid environmental changes or no... | {
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2502.01988 | ReMiDi: Reconstruction of Microstructure Using a Differentiable
Diffusion MRI Simulator | [
"eess.IV",
"cs.GR",
"cs.LG",
"physics.med-ph"
] | We propose ReMiDi, a novel method for inferring neuronal microstructure as arbitrary 3D meshes using a differentiable diffusion Magnetic Resonance Imaging (dMRI) simulator. We first implemented in PyTorch a differentiable dMRI simulator that simulates the forward diffusion process using a finite-element method on an in... | {
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2502.01989 | T-SCEND: Test-time Scalable MCTS-enhanced Diffusion Model | [
"cs.LG"
] | We introduce Test-time Scalable MCTS-enhanced Diffusion Model (T-SCEND), a novel framework that significantly improves diffusion model's reasoning capabilities with better energy-based training and scaling up test-time computation. We first show that na\"ively scaling up inference budget for diffusion models yields mar... | {
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2502.01990 | Rethinking Timesteps Samplers and Prediction Types | [
"cs.LG",
"cs.CV"
] | Diffusion models suffer from the huge consumption of time and resources to train. For example, diffusion models need hundreds of GPUs to train for several weeks for a high-resolution generative task to meet the requirements of an extremely large number of iterations and a large batch size. Training diffusion models bec... | {
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2502.01991 | Can LLMs Assist Annotators in Identifying Morality Frames? -- Case Study
on Vaccination Debate on Social Media | [
"cs.CL",
"cs.AI",
"cs.CY",
"cs.HC",
"cs.SI"
] | Nowadays, social media is pivotal in shaping public discourse, especially on polarizing issues like vaccination, where diverse moral perspectives influence individual opinions. In NLP, data scarcity and complexity of psycholinguistic tasks, such as identifying morality frames, make relying solely on human annotators co... | {
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2502.01992 | FinRLlama: A Solution to LLM-Engineered Signals Challenge at FinRL
Contest 2024 | [
"q-fin.TR",
"cs.LG"
] | In response to Task II of the FinRL Challenge at ACM ICAIF 2024, this study proposes a novel prompt framework for fine-tuning large language models (LLM) with Reinforcement Learning from Market Feedback (RLMF). Our framework incorporates market-specific features and short-term price dynamics to generate more precise tr... | {
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} |
2502.01993 | One Diffusion Step to Real-World Super-Resolution via Flow Trajectory
Distillation | [
"cs.CV"
] | Diffusion models (DMs) have significantly advanced the development of real-world image super-resolution (Real-ISR), but the computational cost of multi-step diffusion models limits their application. One-step diffusion models generate high-quality images in a one sampling step, greatly reducing computational overhead a... | {
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2502.01995 | Theoretical and Practical Analysis of Fr\'echet Regression via
Comparison Geometry | [
"stat.ML",
"cs.AI",
"cs.LG"
] | Fr\'echet regression extends classical regression methods to non-Euclidean metric spaces, enabling the analysis of data relationships on complex structures such as manifolds and graphs. This work establishes a rigorous theoretical analysis for Fr\'echet regression through the lens of comparison geometry which leads to ... | {
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2502.01998 | Data Guard: A Fine-grained Purpose-based Access Control System for Large
Data Warehouses | [
"cs.DB"
] | The last few years have witnessed a spate of data protection regulations in conjunction with an ever-growing appetite for data usage in large businesses, thus presenting significant challenges for businesses to maintain compliance. To address this conflict, we present Data Guard - a fine-grained, purpose-based access c... | {
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2502.02002 | The Ball-Proximal (="Broximal") Point Method: a New Algorithm,
Convergence Theory, and Applications | [
"math.OC",
"cs.LG",
"stat.ML"
] | Non-smooth and non-convex global optimization poses significant challenges across various applications, where standard gradient-based methods often struggle. We propose the Ball-Proximal Point Method, Broximal Point Method, or Ball Point Method (BPM) for short - a novel algorithmic framework inspired by the classical P... | {
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2502.02004 | Wavelet-based Positional Representation for Long Context | [
"cs.CL"
] | In the realm of large-scale language models, a significant challenge arises when extrapolating sequences beyond the maximum allowable length. This is because the model's position embedding mechanisms are limited to positions encountered during training, thus preventing effective representation of positions in longer se... | {
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2502.02007 | Reasoning Bias of Next Token Prediction Training | [
"cs.CL",
"cs.LG"
] | Since the inception of Large Language Models (LLMs), the quest to efficiently train them for superior reasoning capabilities has been a pivotal challenge. The dominant training paradigm for LLMs is based on next token prediction (NTP). Alternative methodologies, called Critical Token Prediction (CTP), focused exclusive... | {
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2502.02009 | LLMSecConfig: An LLM-Based Approach for Fixing Software Container
Misconfigurations | [
"cs.SE",
"cs.AI",
"cs.CR",
"cs.LG"
] | Security misconfigurations in Container Orchestrators (COs) can pose serious threats to software systems. While Static Analysis Tools (SATs) can effectively detect these security vulnerabilities, the industry currently lacks automated solutions capable of fixing these misconfigurations. The emergence of Large Language ... | {
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2502.02013 | Layer by Layer: Uncovering Hidden Representations in Language Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | From extracting features to generating text, the outputs of large language models (LLMs) typically rely on their final layers, following the conventional wisdom that earlier layers capture only low-level cues. However, our analysis shows that intermediate layers can encode even richer representations, often improving p... | {
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2502.02014 | Analytical Lyapunov Function Discovery: An RL-based Generative Approach | [
"cs.LG",
"cs.AI",
"cs.SC",
"cs.SY",
"eess.SY"
] | Despite advances in learning-based methods, finding valid Lyapunov functions for nonlinear dynamical systems remains challenging. Current neural network approaches face two main issues: challenges in scalable verification and limited interpretability. To address these, we propose an end-to-end framework using transform... | {
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2502.02015 | The Wisdom of Intellectually Humble Networks | [
"cs.SI"
] | People's collectively held beliefs can have significant social implications, including on democratic processes and policies. Unfortunately, as people interact with peers to form and update their beliefs, various cognitive and social biases can hinder their collective wisdom. In this paper, we probe whether and how the ... | {
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2502.02016 | A Periodic Bayesian Flow for Material Generation | [
"cs.LG",
"cs.AI"
] | Generative modeling of crystal data distribution is an important yet challenging task due to the unique periodic physical symmetry of crystals. Diffusion-based methods have shown early promise in modeling crystal distribution. More recently, Bayesian Flow Networks were introduced to aggregate noisy latent variables, re... | {
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} |
2502.02017 | Multi-Domain Graph Foundation Models: Robust Knowledge Transfer via
Topology Alignment | [
"cs.SI",
"cs.AI",
"cs.LG"
] | Recent advances in CV and NLP have inspired researchers to develop general-purpose graph foundation models through pre-training across diverse domains. However, a fundamental challenge arises from the substantial differences in graph topologies across domains. Additionally, real-world graphs are often sparse and prone ... | {
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2502.02018 | Dual Ensembled Multiagent Q-Learning with Hypernet Regularizer | [
"cs.MA",
"cs.LG"
] | Overestimation in single-agent reinforcement learning has been extensively studied. In contrast, overestimation in the multiagent setting has received comparatively little attention although it increases with the number of agents and leads to severe learning instability. Previous works concentrate on reducing overestim... | {
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2502.02020 | Causal bandits with backdoor adjustment on unknown Gaussian DAGs | [
"cs.LG",
"stat.ME"
] | The causal bandit problem aims to sequentially learn the intervention that maximizes the expectation of a reward variable within a system governed by a causal graph. Most existing approaches assume prior knowledge of the graph structure, or impose unrealistically restrictive conditions on the graph. In this paper, we a... | {
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} |
2502.02021 | Multi-illuminant Color Constancy via Multi-scale Illuminant Estimation
and Fusion | [
"cs.CV",
"eess.IV"
] | Multi-illuminant color constancy methods aim to eliminate local color casts within an image through pixel-wise illuminant estimation. Existing methods mainly employ deep learning to establish a direct mapping between an image and its illumination map, which neglects the impact of image scales. To alleviate this problem... | {
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2502.02024 | UD-Mamba: A pixel-level uncertainty-driven Mamba model for medical image
segmentation | [
"eess.IV",
"cs.CV"
] | Recent advancements have highlighted the Mamba framework, a state-space model known for its efficiency in capturing long-range dependencies with linear computational complexity. While Mamba has shown competitive performance in medical image segmentation, it encounters difficulties in modeling local features due to the ... | {
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2502.02026 | ContinuouSP: Generative Model for Crystal Structure Prediction with
Invariance and Continuity | [
"cs.LG",
"cond-mat.mtrl-sci"
] | The discovery of new materials using crystal structure prediction (CSP) based on generative machine learning models has become a significant research topic in recent years. In this paper, we study invariance and continuity in the generative machine learning for CSP. We propose a new model, called ContinuouSP, which eff... | {
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2502.02027 | From Fog to Failure: How Dehazing Can Harm Clear Image Object Detection | [
"cs.CV",
"cs.AI"
] | This study explores the challenges of integrating human visual cue-based dehazing into object detection, given the selective nature of human perception. While human vision adapts dynamically to environmental conditions, computational dehazing does not always enhance detection uniformly. We propose a multi-stage framewo... | {
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2502.02028 | Fine-tuning Language Models for Recipe Generation: A Comparative
Analysis and Benchmark Study | [
"cs.CL",
"cs.AI"
] | This research presents an exploration and study of the recipe generation task by fine-tuning various very small language models, with a focus on developing robust evaluation metrics and comparing across different language models the open-ended task of recipe generation. This study presents extensive experiments with mu... | {
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2502.02029 | MORPH-LER: Log-Euclidean Regularization for Population-Aware Image
Registration | [
"cs.CV",
"cs.LG"
] | Spatial transformations that capture population-level morphological statistics are critical for medical image analysis. Commonly used smoothness regularizers for image registration fail to integrate population statistics, leading to anatomically inconsistent transformations. Inverse consistency regularizers promote geo... | {
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} |
2502.02032 | Heteroscedastic Double Bayesian Elastic Net | [
"stat.ME",
"cs.AI",
"stat.ML"
] | In many practical applications, regression models are employed to uncover relationships between predictors and a response variable, yet the common assumption of constant error variance is frequently violated. This issue is further compounded in high-dimensional settings where the number of predictors exceeds the sample... | {
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2502.02033 | On Iso-Dual MDS Codes From Elliptic Curves | [
"cs.IT",
"math.IT"
] | For a linear code $C$ over a finite field, if its dual code $C^{\perp}$ is equivalent to itself, then the code $C$ is said to be {\it isometry-dual}. In this paper, we first confirm a conjecture about the isometry-dual MDS elliptic codes proposed by Han and Ren. Subsequently, two constructions of isometry-dual maximum ... | {
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2502.02034 | Improving Wireless Federated Learning via Joint Downlink-Uplink
Beamforming over Analog Transmission | [
"cs.IT",
"eess.SP",
"math.IT"
] | Federated learning (FL) over wireless networks using analog transmission can efficiently utilize the communication resource but is susceptible to errors caused by noisy wireless links. In this paper, assuming a multi-antenna base station, we jointly design downlink-uplink beamforming to maximize FL training convergence... | {
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2502.02036 | From Human Hands to Robotic Limbs: A Study in Motor Skill Embodiment for
Telemanipulation | [
"cs.RO",
"cs.AI"
] | This paper presents a teleoperation system for controlling a redundant degree of freedom robot manipulator using human arm gestures. We propose a GRU-based Variational Autoencoder to learn a latent representation of the manipulator's configuration space, capturing its complex joint kinematics. A fully connected neural ... | {
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2502.02040 | M2R2: Mixture of Multi-Rate Residuals for Efficient Transformer
Inference | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Residual transformations enhance the representational depth and expressive power of large language models (LLMs). However, applying static residual transformations across all tokens in auto-regressive generation leads to a suboptimal trade-off between inference efficiency and generation fidelity. Existing methods, incl... | {
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} |
2502.02046 | Contextual Memory Reweaving in Large Language Models Using Layered
Latent State Reconstruction | [
"cs.CL"
] | Memory retention challenges in deep neural architectures have ongoing limitations in the ability to process and recall extended contextual information. Token dependencies degrade as sequence length increases, leading to a decline in coherence and factual consistency across longer outputs. A structured approach is intro... | {
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} |
2502.02047 | AmaSQuAD: A Benchmark for Amharic Extractive Question Answering | [
"cs.CL"
] | This research presents a novel framework for translating extractive question-answering datasets into low-resource languages, as demonstrated by the creation of the AmaSQuAD dataset, a translation of SQuAD 2.0 into Amharic. The methodology addresses challenges related to misalignment between translated questions and ans... | {
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} |
2502.02048 | Efficient Domain Adaptation of Multimodal Embeddings using Constrastive
Learning | [
"cs.LG",
"cs.CL",
"cs.CV"
] | Recent advancements in machine learning (ML), natural language processing (NLP), and foundational models have shown promise for real-life applications in critical, albeit compute-constrainted fields like healthcare. In such areas, combining foundational models with supervised ML offers potential for automating tasks ... | {
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} |
2502.02050 | RECCS: Realistic Cluster Connectivity Simulator for Synthetic Network
Generation | [
"cs.SI"
] | The limited availability of useful ground-truth communities in real-world networks presents a challenge to evaluating and selecting a "best" community detection method for a given network or family of networks. The use of synthetic networks with planted ground-truths is one way to address this challenge. While several ... | {
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2502.02051 | Sound Judgment: Properties of Consequential Sounds Affecting
Human-Perception of Robots | [
"cs.RO",
"cs.HC",
"cs.SD",
"eess.AS"
] | Positive human-perception of robots is critical to achieving sustained use of robots in shared environments. One key factor affecting human-perception of robots are their sounds, especially the consequential sounds which robots (as machines) must produce as they operate. This paper explores qualitative responses from 1... | {
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2502.02052 | Multimaterial topology optimization for finite strain elastoplasticity:
theory, methods, and applications | [
"cs.CE",
"math.OC"
] | Plasticity is inherent to many engineering materials such as metals. While it can degrade the load-carrying capacity of structures via material yielding, it can also protect structures through plastic energy dissipation. To fully harness plasticity, here we present the theory, method, and application of a topology opti... | {
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2502.02054 | RAPID: Robust and Agile Planner Using Inverse Reinforcement Learning for
Vision-Based Drone Navigation | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.LG"
] | This paper introduces a learning-based visual planner for agile drone flight in cluttered environments. The proposed planner generates collision-free waypoints in milliseconds, enabling drones to perform agile maneuvers in complex environments without building separate perception, mapping, and planning modules. Learnin... | {
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2502.02060 | CH-MARL: Constrained Hierarchical Multiagent Reinforcement Learning for
Sustainable Maritime Logistics | [
"cs.AI",
"cs.MA"
] | Addressing global challenges such as greenhouse gas emissions and resource inequity demands advanced AI-driven coordination among autonomous agents. We propose CH-MARL (Constrained Hierarchical Multiagent Reinforcement Learning), a novel framework that integrates hierarchical decision-making with dynamic constraint enf... | {
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2502.02061 | Reason4Rec: Large Language Models for Recommendation with Deliberative
User Preference Alignment | [
"cs.IR"
] | While recent advancements in aligning Large Language Models (LLMs) with recommendation tasks have shown great potential and promising performance overall, these aligned recommendation LLMs still face challenges in complex scenarios. This is primarily due to the current alignment approach focusing on optimizing LLMs to ... | {
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2502.02063 | CASIM: Composite Aware Semantic Injection for Text to Motion Generation | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Recent advances in generative modeling and tokenization have driven significant progress in text-to-motion generation, leading to enhanced quality and realism in generated motions. However, effectively leveraging textual information for conditional motion generation remains an open challenge. We observe that current ap... | {
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2502.02066 | Anticipate & Act : Integrating LLMs and Classical Planning for Efficient
Task Execution in Household Environments | [
"cs.RO",
"cs.CL",
"cs.LG"
] | Assistive agents performing household tasks such as making the bed or cooking breakfast often compute and execute actions that accomplish one task at a time. However, efficiency can be improved by anticipating upcoming tasks and computing an action sequence that jointly achieves these tasks. State-of-the-art methods fo... | {
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2502.02067 | AdaptBot: Combining LLM with Knowledge Graphs and Human Input for
Generic-to-Specific Task Decomposition and Knowledge Refinement | [
"cs.RO",
"cs.AI",
"cs.CL",
"cs.LG"
] | Embodied agents assisting humans are often asked to complete a new task in a new scenario. An agent preparing a particular dish in the kitchen based on a known recipe may be asked to prepare a new dish or to perform cleaning tasks in the storeroom. There may not be sufficient resources, e.g., time or labeled examples, ... | {
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2502.02068 | Robust and Secure Code Watermarking for Large Language Models via
ML/Crypto Codesign | [
"cs.CR",
"cs.CL",
"cs.LG"
] | This paper introduces RoSeMary, the first-of-its-kind ML/Crypto codesign watermarking framework that regulates LLM-generated code to avoid intellectual property rights violations and inappropriate misuse in software development. High-quality watermarks adhering to the detectability-fidelity-robustness tri-objective are... | {
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2502.02069 | LoRA-TTT: Low-Rank Test-Time Training for Vision-Language Models | [
"cs.CV"
] | The rapid advancements in vision-language models (VLMs), such as CLIP, have intensified the need to address distribution shifts between training and testing datasets. Although prior Test-Time Training (TTT) techniques for VLMs have demonstrated robust performance, they predominantly rely on tuning text prompts, a proce... | {
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2502.02071 | Sequential Multi-objective Multi-agent Reinforcement Learning Approach
for Predictive Maintenance | [
"eess.SY",
"cs.SY"
] | Existing predictive maintenance (PdM) methods typically focus solely on whether to replace system components without considering the costs incurred by inspection. However, a well-considered approach should be able to minimize Remaining Useful Life (RUL) at engine replacement while maximizing inspection interval. To ach... | {
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2502.02072 | ASCenD-BDS: Adaptable, Stochastic and Context-aware framework for
Detection of Bias, Discrimination and Stereotyping | [
"cs.CL",
"cs.AI",
"cs.CY"
] | The rapid evolution of Large Language Models (LLMs) has transformed natural language processing but raises critical concerns about biases inherent in their deployment and use across diverse linguistic and sociocultural contexts. This paper presents a framework named ASCenD BDS (Adaptable, Stochastic and Context-aware f... | {
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2502.02074 | Rethinking stance detection: A theoretically-informed research agenda
for user-level inference using language models | [
"cs.CL"
] | Stance detection has emerged as a popular task in natural language processing research, enabled largely by the abundance of target-specific social media data. While there has been considerable research on the development of stance detection models, datasets, and application, we highlight important gaps pertaining to (i... | {
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2502.02076 | Position Paper: Building Trust in Synthetic Data for Clinical AI | [
"cs.LG",
"cs.CV"
] | Deep generative models and synthetic medical data have shown significant promise in addressing key challenges in healthcare, such as privacy concerns, data bias, and the scarcity of realistic datasets. While research in this area has grown rapidly and demonstrated substantial theoretical potential, its practical adopti... | {
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2502.02079 | Online Clustering of Dueling Bandits | [
"cs.LG",
"cs.AI"
] | The contextual multi-armed bandit (MAB) is a widely used framework for problems requiring sequential decision-making under uncertainty, such as recommendation systems. In applications involving a large number of users, the performance of contextual MAB can be significantly improved by facilitating collaboration among m... | {
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2502.02083 | Improving Power Plant CO2 Emission Estimation with Deep Learning and
Satellite/Simulated Data | [
"cs.CV",
"eess.IV"
] | CO2 emissions from power plants, as significant super emitters, contribute substantially to global warming. Accurate quantification of these emissions is crucial for effective climate mitigation strategies. While satellite-based plume inversion offers a promising approach, challenges arise from data limitations and the... | {
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2502.02085 | A New Rejection Sampling Approach to $k$-$\mathtt{means}$++ With
Improved Trade-Offs | [
"cs.DS",
"cs.LG"
] | The $k$-$\mathtt{means}$++ seeding algorithm (Arthur & Vassilvitskii, 2007) is widely used in practice for the $k$-means clustering problem where the goal is to cluster a dataset $\mathcal{X} \subset \mathbb{R} ^d$ into $k$ clusters. The popularity of this algorithm is due to its simplicity and provable guarantee of ... | {
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2502.02088 | IPO: Iterative Preference Optimization for Text-to-Video Generation | [
"cs.CV",
"cs.AI"
] | Video foundation models have achieved significant advancement with the help of network upgrade as well as model scale-up. However, they are still hard to meet requirements of applications due to unsatisfied generation quality. To solve this problem, we propose to align video foundation models with human preferences fro... | {
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2502.02091 | Efficient Dynamic Scene Editing via 4D Gaussian-based Static-Dynamic
Separation | [
"cs.CV"
] | Recent 4D dynamic scene editing methods require editing thousands of 2D images used for dynamic scene synthesis and updating the entire scene with additional training loops, resulting in several hours of processing to edit a single dynamic scene. Therefore, these methods are not scalable with respect to the temporal di... | {
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2502.02092 | Sum of Squared Extended {\eta}-{\mu} and {\kappa}-{\mu} RVs: A New
Framework Applied to FR3 and Sub-THz Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | The analysis of systems operating in future frequency ranges calls for a proper statistical channel characterization through generalized fading models. In this paper, we adopt the Extended {\eta}-{\mu} and {\kappa}-{\mu} models to characterize the propagation in FR3 and the sub-THz band, respectively. For these models,... | {
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2502.02095 | LongDPO: Unlock Better Long-form Generation Abilities for LLMs via
Critique-augmented Stepwise Information | [
"cs.CL"
] | Long-form generation is crucial for academic writing papers and repo-level code generation. Despite this, current models, including GPT-4o, still exhibit unsatisfactory performance. Existing methods that utilize preference learning with outcome supervision often fail to provide detailed feedback for extended contexts. ... | {
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2502.02096 | Dual-Flow: Transferable Multi-Target, Instance-Agnostic Attacks via
In-the-wild Cascading Flow Optimization | [
"cs.CV"
] | Adversarial attacks are widely used to evaluate model robustness, and in black-box scenarios, the transferability of these attacks becomes crucial. Existing generator-based attacks have excellent generalization and transferability due to their instance-agnostic nature. However, when training generators for multi-target... | {
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2502.02097 | VerteNet -- A Multi-Context Hybrid CNN Transformer for Accurate
Vertebral Landmark Localization in Lateral Spine DXA Images | [
"cs.CV"
] | Lateral Spine Image (LSI) analysis is important for medical diagnosis, treatment planning, and detailed spinal health assessments. Although modalities like Computed Tomography and Digital X-ray Imaging are commonly used, Dual Energy X-ray Absorptiometry (DXA) is often preferred due to lower radiation exposure, seamless... | {
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2502.02100 | Topic Modeling in Marathi | [
"cs.CL",
"cs.LG"
] | While topic modeling in English has become a prevalent and well-explored area, venturing into topic modeling for Indic languages remains relatively rare. The limited availability of resources, diverse linguistic structures, and unique challenges posed by Indic languages contribute to the scarcity of research and applic... | {
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2502.02103 | Neural Networks Learn Distance Metrics | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Neural networks may naturally favor distance-based representations, where smaller activations indicate closer proximity to learned prototypes. This contrasts with intensity-based approaches, which rely on activation magnitudes. To test this hypothesis, we conducted experiments with six MNIST architectural variants cons... | {
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2502.02104 | Concept-Aware Latent and Explicit Knowledge Integration for Enhanced
Cognitive Diagnosis | [
"cs.LG"
] | Cognitive diagnosis can infer the students' mastery of specific knowledge concepts based on historical response logs. However, the existing cognitive diagnostic models (CDMs) represent students' proficiency via a unidimensional perspective, which can't assess the students' mastery on each knowledge concept comprehensiv... | {
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2502.02109 | Causally-informed Deep Learning towards Explainable and Generalizable
Outcomes Prediction in Critical Care | [
"cs.LG",
"cs.AI"
] | Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney injury, acute myocardial infarction, or circulatory failure. DL models have proven to be powerful tools for various tasks but come with the co... | {
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2502.02112 | The Induced Matching Distance: A Novel Topological Metric with
Applications in Robotics | [
"math.AT",
"cs.RO"
] | This paper introduces the induced matching distance, a novel topological metric designed to compare discrete structures represented by a symmetric non-negative function. We apply this notion to analyze agent trajectories over time. We use dynamic time warping to measure trajectory similarity and compute the 0-dimension... | {
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2502.02118 | BRIDLE: Generalized Self-supervised Learning with Quantization | [
"cs.LG",
"cs.CV"
] | Self-supervised learning has been a powerful approach for learning meaningful representations from unlabeled data across various domains, reducing the reliance on large labeled datasets. Inspired by BERT's success in capturing deep bidirectional contexts in natural language processing, similar frameworks have been adap... | {
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2502.02121 | BILBO: BILevel Bayesian Optimization | [
"cs.LG",
"stat.ML"
] | Bilevel optimization is characterized by a two-level optimization structure, where the upper-level problem is constrained by optimal lower-level solutions, and such structures are prevalent in real-world problems. The constraint by optimal lower-level solutions poses significant challenges, especially in noisy, constra... | {
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} |
2502.02129 | Deep Neural Cellular Potts Models | [
"cs.LG",
"q-bio.QM"
] | The cellular Potts model (CPM) is a powerful computational method for simulating collective spatiotemporal dynamics of biological cells. To drive the dynamics, CPMs rely on physics-inspired Hamiltonians. However, as first principles remain elusive in biology, these Hamiltonians only approximate the full complexity of r... | {
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} |
2502.02132 | How Memory in Optimization Algorithms Implicitly Modifies the Loss | [
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
] | In modern optimization methods used in deep learning, each update depends on the history of previous iterations, often referred to as memory, and this dependence decays fast as the iterates go further into the past. For example, gradient descent with momentum has exponentially decaying memory through exponentially aver... | {
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} |
2502.02133 | Synthesis of Model Predictive Control and Reinforcement Learning: Survey
and Classification | [
"eess.SY",
"cs.AI",
"cs.LG",
"cs.SY"
] | The fields of MPC and RL consider two successful control techniques for Markov decision processes. Both approaches are derived from similar fundamental principles, and both are widely used in practical applications, including robotics, process control, energy systems, and autonomous driving. Despite their similarities,... | {
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} |
2502.02135 | Standard Neural Computation Alone Is Insufficient for Logical
Intelligence | [
"cs.AI",
"cs.LG"
] | Neural networks, as currently designed, fall short of achieving true logical intelligence. Modern AI models rely on standard neural computation-inner-product-based transformations and nonlinear activations-to approximate patterns from data. While effective for inductive learning, this architecture lacks the structural ... | {
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} |
2502.02140 | An Information-Theoretic Analysis of Thompson Sampling with Infinite
Action Spaces | [
"stat.ML",
"cs.LG"
] | This paper studies the Bayesian regret of the Thompson Sampling algorithm for bandit problems, building on the information-theoretic framework introduced by Russo and Van Roy (2015). Specifically, it extends the rate-distortion analysis of Dong and Van Roy (2018), which provides near-optimal bounds for linear bandits. ... | {
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} |
2502.02144 | DOC-Depth: A novel approach for dense depth ground truth generation | [
"cs.CV",
"cs.RO"
] | Accurate depth information is essential for many computer vision applications. Yet, no available dataset recording method allows for fully dense accurate depth estimation in a large scale dynamic environment. In this paper, we introduce DOC-Depth, a novel, efficient and easy-to-deploy approach for dense depth generatio... | {
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} |
2502.02145 | Risk-Aware Driving Scenario Analysis with Large Language Models | [
"cs.AI",
"cs.CL",
"cs.RO"
] | Large Language Models (LLMs) can capture nuanced contextual relationships, reasoning, and complex problem-solving. By leveraging their ability to process and interpret large-scale information, LLMs have shown potential to address domain-specific challenges, including those in autonomous driving systems. This paper prop... | {
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} |
2502.02150 | On the Guidance of Flow Matching | [
"cs.CV",
"cs.LG"
] | Flow matching has shown state-of-the-art performance in various generative tasks, ranging from image generation to decision-making, where guided generation is pivotal. However, the guidance of flow matching is more general than and thus substantially different from that of its predecessor, diffusion models. Therefore, ... | {
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} |
2502.02153 | Vulnerability Mitigation for Safety-Aligned Language Models via
Debiasing | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Safety alignment is an essential research topic for real-world AI applications. Despite the multifaceted nature of safety and trustworthiness in AI, current safety alignment methods often focus on a comprehensive notion of safety. By carefully assessing models from the existing safety-alignment methods, we found that, ... | {
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} |
2502.02163 | Progressive Correspondence Regenerator for Robust 3D Registration | [
"cs.CV"
] | Obtaining enough high-quality correspondences is crucial for robust registration. Existing correspondence refinement methods mostly follow the paradigm of outlier removal, which either fails to correctly identify the accurate correspondences under extreme outlier ratios, or select too few correct correspondences to sup... | {
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} |
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