id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.05836 | LegalSeg: Unlocking the Structure of Indian Legal Judgments Through
Rhetorical Role Classification | [
"cs.CL",
"cs.AI",
"cs.IR",
"cs.LG"
] | In this paper, we address the task of semantic segmentation of legal documents through rhetorical role classification, with a focus on Indian legal judgments. We introduce LegalSeg, the largest annotated dataset for this task, comprising over 7,000 documents and 1.4 million sentences, labeled with 7 rhetorical roles. T... | {
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2502.05842 | A Grid-Forming HVDC Series Tapping Converter Using Extended Techniques
of Flex-LCC | [
"eess.SY",
"cs.SY"
] | This paper discusses an extension technology for the previously proposed Flexible Line-Commutated Converter (Flex LCC) [1]. The proposed extension involves modifying the arm internal-electromotive-force control, redesigning the main-circuit parameters, and integrating a low-power coordination strategy. As a result, the... | {
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2502.05843 | Training-free Anomaly Event Detection via LLM-guided Symbolic Pattern
Discovery | [
"cs.CV"
] | Anomaly event detection plays a crucial role in various real-world applications. However, current approaches predominantly rely on supervised learning, which faces significant challenges: the requirement for extensive labeled training data and lack of interpretability in decision-making processes. To address these limi... | {
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2502.05845 | Exploiting the Hidden Capacity of MMC Through Accurate Quantification of
Modulation Indices | [
"eess.SY",
"cs.SY"
] | The modular multilevel converter (MMC) has become increasingly important in voltage-source converter-based high-voltage direct current (VSC-HVDC) systems. Direct and indirect modulation are widely used as mainstream modulation techniques in MMCs. However, due to the challenge of quantitatively evaluating the operation ... | {
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2502.05846 | Rapid Detection of High-impedance Arc Faults in Medium Voltage
Electrical Distribution Systems | [
"eess.SY",
"cs.SY"
] | High-impedance arc faults in AC power systems have the potential to lead to catastrophic accidents. However, significant challenges exist in identifying these faults because of the much weaker characteristics and variety when grounded with different surfaces. Addressing a noteworthy gap in prior research, which largely... | {
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2502.05849 | Fact-or-Fair: A Checklist for Behavioral Testing of AI Models on
Fairness-Related Queries | [
"cs.CL"
] | The generation of incorrect images, such as depictions of people of color in Nazi-era uniforms by Gemini, frustrated users and harmed Google's reputation, motivating us to investigate the relationship between accurately reflecting factuality and promoting diversity and equity. In this study, we focus on 19 real-world s... | {
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2502.05850 | MetaML-Pro: Cross-Stage Design Flow Automation for Efficient Deep
Learning Acceleration | [
"cs.AR",
"cs.LG"
] | This paper presents a unified framework for codifying and automating optimization strategies to efficiently deploy deep neural networks (DNNs) on resource-constrained hardware, such as FPGAs, while maintaining high performance, accuracy, and resource efficiency. Deploying DNNs on such platforms involves addressing the ... | {
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2502.05851 | Fairness Driven Slot Allocation Problem in Billboard Advertisement | [
"cs.GT",
"cs.DB",
"cs.MA"
] | In billboard advertisement, a number of digital billboards are owned by an influence provider, and several commercial houses (which we call advertisers) approach the influence provider for a specific number of views of their advertisement content on a payment basis. Though the billboard slot allocation problem has been... | {
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2502.05853 | Zak-Transform-Induced Optimal Sequences and Their Applications in OTFS | [
"cs.IT",
"math.IT"
] | This paper introduces a novel finite Zak transform (FZT)-aided framework for constructing multiple zero-correlation zone (ZCZ) sequence sets with optimal correlation properties. Specifically, each sequence is perfect with zero auto-correlation sidelobes, each ZCZ sequence set meets the Tang-Fan-Matsufuji bound with equ... | {
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2502.05854 | NSPG-Miner: Mining Repetitive Negative Sequential Patterns | [
"cs.DB"
] | Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find ... | {
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2502.05855 | DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General
Robot Control | [
"cs.RO",
"cs.CV"
] | Enabling robots to perform diverse tasks across varied environments is a central challenge in robot learning. While vision-language-action (VLA) models have shown promise for generalizable robot skills, realizing their full potential requires addressing limitations in action representation and efficient training. Curre... | {
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2502.05857 | Acquisition through My Eyes and Steps: A Joint Predictive Agent Model in
Egocentric Worlds | [
"cs.CV",
"cs.AI",
"cs.LG"
] | This paper addresses the task of learning an agent model behaving like humans, which can jointly perceive, predict, and act in egocentric worlds. Previous methods usually train separate models for these three abilities, leading to information silos among them, which prevents these abilities from learning from each othe... | {
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2502.05858 | Let's Have Both! Optimal List-Recoverability via Alphabet Permutation
Codes | [
"cs.IT",
"math.IT"
] | We construct a new family of codes that requires only polynomial randomness yet achieves $(\rho,\ell,L)$-list-recoverability at a rate within $\epsilon$ of capacity, with $L \approx \tfrac{\ell}{\epsilon}$. In contrast, every previous construction using polynomial randomness required an exponentially larger list size. ... | {
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2502.05859 | SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion | [
"cs.CV"
] | Due to the rapid development of panorama cameras, the task of estimating panorama depth has attracted significant attention from the computer vision community, especially in applications such as robot sensing and autonomous driving. However, existing methods relying on different projection formats often encounter chall... | {
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2502.05863 | Uni-Retrieval: A Multi-Style Retrieval Framework for STEM's Education | [
"cs.IR",
"cs.AI",
"cs.MM"
] | In AI-facilitated teaching, leveraging various query styles to interpret abstract text descriptions is crucial for ensuring high-quality teaching. However, current retrieval models primarily focus on natural text-image retrieval, making them insufficiently tailored to educational scenarios due to the ambiguities in the... | {
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2502.05864 | Learning Accurate, Efficient, and Interpretable MLPs on Multiplex Graphs
via Node-wise Multi-View Ensemble Distillation | [
"cs.LG"
] | Multiplex graphs, with multiple edge types (graph views) among common nodes, provide richer structural semantics and better modeling capabilities. Multiplex Graph Neural Networks (MGNNs), typically comprising view-specific GNNs and a multi-view integration layer, have achieved advanced performance in various downstream... | {
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2502.05867 | Self-Training Large Language Models for Tool-Use Without Demonstrations | [
"cs.CL"
] | Large language models (LLMs) remain prone to factual inaccuracies and computational errors, including hallucinations and mistakes in mathematical reasoning. Recent work augmented LLMs with tools to mitigate these shortcomings, but often requires curated gold tool-use demonstrations. In this paper, we investigate whethe... | {
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2502.05868 | Norm Augmented Graph AutoEncoders for Link Prediction | [
"cs.LG"
] | Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable representation. However, our empirical findings reveal that GAEs' LP performance suffers heavily from the long-tailed node degree distribution, i.... | {
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2502.05869 | HyLiFormer: Hyperbolic Linear Attention for Skeleton-based Human Action
Recognition | [
"cs.CV"
] | Transformers have demonstrated remarkable performance in skeleton-based human action recognition, yet their quadratic computational complexity remains a bottleneck for real-world applications. To mitigate this, linear attention mechanisms have been explored but struggle to capture the hierarchical structure of skeleton... | {
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2502.05874 | MMGDreamer: Mixed-Modality Graph for Geometry-Controllable 3D Indoor
Scene Generation | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Controllable 3D scene generation has extensive applications in virtual reality and interior design, where the generated scenes should exhibit high levels of realism and controllability in terms of geometry. Scene graphs provide a suitable data representation that facilitates these applications. However, current graph-b... | {
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2502.05878 | Enhancing Financial Time-Series Forecasting with Retrieval-Augmented
Large Language Models | [
"cs.CL"
] | Stock movement prediction, a critical task in financial time-series forecasting, relies on identifying and retrieving key influencing factors from vast and complex datasets. However, traditional text-trained or numeric similarity-based retrieval methods often struggle to handle the intricacies of financial data. To add... | {
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2502.05879 | Enhancing Depression Detection with Chain-of-Thought Prompting: From
Emotion to Reasoning Using Large Language Models | [
"cs.CL",
"cs.AI"
] | Depression is one of the leading causes of disability worldwide, posing a severe burden on individuals, healthcare systems, and society at large. Recent advancements in Large Language Models (LLMs) have shown promise in addressing mental health challenges, including the detection of depression through text-based analys... | {
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2502.05883 | NeuralPrefix: A Zero-shot Sensory Data Imputation Plugin | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Real-world sensing challenges such as sensor failures, communication issues, and power constraints lead to data intermittency. An issue that is known to undermine the traditional classification task that assumes a continuous data stream. Previous works addressed this issue by designing bespoke solutions (i.e. task-spec... | {
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2502.05884 | Study of Robust Multiuser Scheduling and Power Allocation in Cell-Free
MIMO Networks | [
"cs.IT",
"math.IT"
] | This paper introduces a robust resource allocation framework for the downlink of cell-free massive multi-input multi-output (CF-mMIMO) networks to address the effects caused by imperfect channel state information (CSI). In particular, the proposed robust resource allocation framework includes a robust user scheduling a... | {
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2502.05887 | MTPChat: A Multimodal Time-Aware Persona Dataset for Conversational
Agents | [
"cs.CL",
"cs.AI"
] | Understanding temporal dynamics is critical for conversational agents, enabling effective content analysis and informed decision-making. However, time-aware datasets, particularly for persona-grounded conversations, are still limited, which narrows their scope and diminishes their complexity. To address this gap, we in... | {
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2502.05891 | Room-scale magnetoquasistatic wireless power transfer using a
cavity-based multimode resonator | [
"physics.app-ph",
"cs.SY",
"eess.SY"
] | Magnetoquasistatic wireless power transfer can be used to charge and power electronic devices such as smartphones and small home appliances. However, existing coil-based transmitters, which are composed of wire conductors, have a limited range. Here we show that multimode quasistatic cavity resonance can provide room-s... | {
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2502.05892 | A Distributional Perspective on Word Learning in Neural Language Models | [
"cs.CL",
"cs.AI"
] | Language models (LMs) are increasingly being studied as models of human language learners. Due to the nascency of the field, it is not well-established whether LMs exhibit similar learning dynamics to humans, and there are few direct comparisons between learning trajectories in humans and models. Word learning trajecto... | {
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2502.05894 | Suppressing Leakage Magnetic Field in Wireless Power Transfer using
Halbach Array-Based Resonators | [
"physics.app-ph",
"cs.SY",
"eess.SY"
] | Wireless power transfer has the potential to seamlessly power electronic systems, such as electric vehicles, industrial robots, and mobile devices. However, the leakage magnetic field is a critical bottleneck that limits the transferable power level, and heavy ferromagnetic shields are needed for transferring large amo... | {
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2502.05895 | Beyond Fine-Tuning: A Systematic Study of Sampling Techniques in
Personalized Image Generation | [
"cs.CV"
] | Personalized text-to-image generation aims to create images tailored to user-defined concepts and textual descriptions. Balancing the fidelity of the learned concept with its ability for generation in various contexts presents a significant challenge. Existing methods often address this through diverse fine-tuning para... | {
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2502.05902 | Fast Omni-Directional Image Super-Resolution: Adapting the Implicit
Image Function with Pixel and Semantic-Wise Spherical Geometric Priors | [
"cs.CV"
] | In the context of Omni-Directional Image (ODI) Super-Resolution (SR), the unique challenge arises from the non-uniform oversampling characteristics caused by EquiRectangular Projection (ERP). Considerable efforts in designing complex spherical convolutions or polyhedron reprojection offer significant performance improv... | {
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2502.05905 | QP-SNN: Quantized and Pruned Spiking Neural Networks | [
"cs.CV"
] | Brain-inspired Spiking Neural Networks (SNNs) leverage sparse spikes to encode information and operate in an asynchronous event-driven manner, offering a highly energy-efficient paradigm for machine intelligence. However, the current SNN community focuses primarily on performance improvement by developing large-scale m... | {
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2502.05907 | EvoAgent: Agent Autonomous Evolution with Continual World Model for
Long-Horizon Tasks | [
"cs.RO"
] | Completing Long-Horizon (LH) tasks in open-ended worlds is an important yet difficult problem for embodied agents. Existing approaches suffer from two key challenges: (1) they heavily rely on experiences obtained from human-created data or curricula, lacking the ability to continuously update multimodal experiences, an... | {
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2502.05908 | Inverse Problem Sampling in Latent Space Using Sequential Monte Carlo | [
"eess.IV",
"cs.CV",
"cs.LG"
] | In image processing, solving inverse problems is the task of finding plausible reconstructions of an image that was corrupted by some (usually known) degradation model. Commonly, this process is done using a generative image model that can guide the reconstruction towards solutions that appear natural. The success of d... | {
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2502.05911 | GRAIT: Gradient-Driven Refusal-Aware Instruction Tuning for Effective
Hallucination Mitigation | [
"cs.CL"
] | Refusal-Aware Instruction Tuning (RAIT) aims to enhance Large Language Models (LLMs) by improving their ability to refuse responses to questions beyond their knowledge, thereby reducing hallucinations and improving reliability. Effective RAIT must address two key challenges: firstly, effectively reject unknown question... | {
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2502.05912 | LpBound: Pessimistic Cardinality Estimation using $\ell_p$-Norms of
Degree Sequences | [
"cs.DB"
] | Cardinality estimation is the problem of estimating the size of the output of a query, without actually evaluating the query. The cardinality estimator is a critical piece of a query optimizer, and is often the main culprit when the optimizer chooses a poor plan. This paper introduces LpBound, a pessimistic cardinali... | {
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2502.05916 | Adaptive Grasping of Moving Objects in Dense Clutter via Global-to-Local
Detection and Static-to-Dynamic Planning | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Robotic grasping is facing a variety of real-world uncertainties caused by non-static object states, unknown object properties, and cluttered object arrangements. The difficulty of grasping increases with the presence of more uncertainties, where commonly used learning-based approaches struggle to perform consistently ... | {
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2502.05917 | Modeling and Beamforming Optimization for Pinching-Antenna Systems | [
"cs.IT",
"math.IT"
] | The Pinching-Antenna SyStem (PASS) is a revolutionary flexible antenna technology designed to enhance wireless communication by establishing strong line-of-sight (LoS) links, reducing free-space path loss and enabling antenna array reconfigurability. PASS uses dielectric waveguides with low propagation loss for signal ... | {
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2502.05919 | Can Generative Agent-Based Modeling Replicate the Friendship Paradox in
Social Media Simulations? | [
"cs.SI"
] | Generative Agent-Based Modeling (GABM) is an emerging simulation paradigm that combines the reasoning abilities of Large Language Models with traditional Agent-Based Modeling to replicate complex social behaviors, including interactions on social media. While prior work has focused on localized phenomena such as opinio... | {
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2502.05923 | ARISE: Iterative Rule Induction and Synthetic Data Generation for Text
Classification | [
"cs.CL"
] | We propose ARISE, a framework that iteratively induces rules and generates synthetic data for text classification. We combine synthetic data generation and automatic rule induction, via bootstrapping, to iteratively filter the generated rules and data. We induce rules via inductive generalisation of syntactic n-grams, ... | {
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2502.05924 | Multi-Branch Collaborative Learning Network for Video Quality Assessment
in Industrial Video Search | [
"cs.CV",
"cs.IR"
] | Video Quality Assessment (VQA) is vital for large-scale video retrieval systems, aimed at identifying quality issues to prioritize high-quality videos. In industrial systems, low-quality video characteristics fall into four categories: visual-related issues like mosaics and black boxes, textual issues from video titles... | {
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2502.05925 | Sign-Symmetry Learning Rules are Robust Fine-Tuners | [
"cs.LG",
"cs.AI"
] | Backpropagation (BP) has long been the predominant method for training neural networks due to its effectiveness. However, numerous alternative approaches, broadly categorized under feedback alignment, have been proposed, many of which are motivated by the search for biologically plausible learning mechanisms. Despite t... | {
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2502.05926 | A Generative Framework for Bidirectional Image-Report Understanding in
Chest Radiography | [
"eess.IV",
"cs.CL",
"cs.CV"
] | The rapid advancements in large language models (LLMs) have unlocked their potential for multimodal tasks, where text and visual data are processed jointly. However, applying LLMs to medical imaging, particularly for chest X-rays (CXR), poses significant challenges due to the need for precise visual-textual alignment a... | {
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2502.05928 | ClinKD: Cross-Modal Clinic Knowledge Distiller For Multi-Task Medical
Images | [
"cs.CV"
] | Med-VQA (Medical Visual Question Answering) is a crucial subtask within the broader VQA (Visual Question Answering) domain. This task requires a visual question answering system to analyze the provided image and corresponding question,offering reasonable analysis and suggestions to assist medical professionals in makin... | {
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2502.05931 | Protecting Intellectual Property of EEG-based Neural Networks with
Watermarking | [
"cs.LG",
"cs.AI",
"cs.CR"
] | EEG-based neural networks, pivotal in medical diagnosis and brain-computer interfaces, face significant intellectual property (IP) risks due to their reliance on sensitive neurophysiological data and resource-intensive development. Current watermarking methods, particularly those using abstract trigger sets, lack robus... | {
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2502.05932 | Skill Expansion and Composition in Parameter Space | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Humans excel at reusing prior knowledge to address new challenges and developing skills while solving problems. This paradigm becomes increasingly popular in the development of autonomous agents, as it develops systems that can self-evolve in response to new challenges like human beings. However, previous methods suffe... | {
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2502.05933 | Learning to Substitute Words with Model-based Score Ranking | [
"cs.CL",
"cs.AI"
] | Smart word substitution aims to enhance sentence quality by improving word choices; however current benchmarks rely on human-labeled data. Since word choices are inherently subjective, ground-truth word substitutions generated by a small group of annotators are often incomplete and likely not generalizable. To circumve... | {
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2502.05934 | Barriers and Pathways to Human-AI Alignment: A Game-Theoretic Approach | [
"cs.AI",
"cs.CC",
"cs.GT",
"cs.LG",
"cs.MA"
] | Under what conditions can capable AI agents efficiently align their actions with human preferences? More specifically, when they are proficient enough to collaborate with us, how long does coordination take, and when is it computationally feasible? These foundational questions of AI alignment help define what makes an ... | {
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2502.05935 | Interactive Inference: A Neuromorphic Theory of Human-Computer
Interaction | [
"cs.HC",
"cs.IT",
"math.IT"
] | Neuromorphic HCI is a new theoretical approach to designing better UX inspired by the neurophysiology of the brain. Here, we apply the neuroscientific theory of Active Inference to HCI, postulating that users perform Bayesian inference on progress and goal distributions to predict their next action (Interactive Inferen... | {
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2502.05937 | A Semi-Supervised Text Generation Framework Combining a Deep Transformer
and a GAN | [
"cs.CL",
"cs.AI"
] | This paper introduces a framework that connects a deep generative pre-trained Transformer language model with a generative adversarial network for semi-supervised text generation. In other words, the proposed model is first pre-trained unsupervised on a large and diverse text corpus with 24 layers. Then a simple GAN ar... | {
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2502.05938 | Energy-Efficient Autonomous Aerial Navigation with Dynamic Vision
Sensors: A Physics-Guided Neuromorphic Approach | [
"cs.RO"
] | Vision-based object tracking is a critical component for achieving autonomous aerial navigation, particularly for obstacle avoidance. Neuromorphic Dynamic Vision Sensors (DVS) or event cameras, inspired by biological vision, offer a promising alternative to conventional frame-based cameras. These cameras can detect cha... | {
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2502.05943 | Continual Adaptation for Autonomous Driving with the Mixture of
Progressive Experts Network | [
"cs.RO"
] | Learning-based autonomous driving requires continuous integration of diverse knowledge in complex traffic , yet existing methods exhibit significant limitations in adaptive capabilities. Addressing this gap demands autonomous driving systems that enable continual adaptation through dynamic adjustments to evolving envir... | {
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2502.05944 | Multi-granular Training Strategies for Robust Multi-hop Reasoning Over
Noisy and Heterogeneous Knowledge Sources | [
"cs.CL"
] | Multi-source multi-hop question answering (QA) represents a challenging task in natural language processing due to the need for dynamic integration of heterogeneous knowledge sources and multi-step reasoning. Existing methods often suffer from cascading errors, insufficient handling of knowledge conflicts, and computat... | {
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2502.05945 | "Let the AI conspiracy begin..." Language Model coordination is just one
inference-intervention away | [
"cs.CL",
"cs.AI"
] | In this work, we introduce a straightforward and effective methodology to steer large language model behaviour capable of bypassing learned alignment goals. We employ interference-time activation shifting, which is effective without additional training. Following prior studies, we derive intervention directions from ac... | {
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2502.05947 | Acceleration Multiple Heads Decoding for LLM via Dynamic Tree Attention | [
"cs.CV",
"cs.CL"
] | Multiple heads decoding accelerates the inference of Large Language Models (LLMs) by predicting next several tokens simultaneously. It generates and verifies multiple candidate sequences in parallel via tree attention with a fixed structure. In this paper, we replace the fixed tree attention with dynamic tree attention... | {
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2502.05949 | Verifying Proportionality in Temporal Voting | [
"cs.GT",
"cs.AI"
] | We study a model of temporal voting where there is a fixed time horizon, and at each round the voters report their preferences over the available candidates and a single candidate is selected. Prior work has adapted popular notions of justified representation as well as voting rules that provide strong representation g... | {
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2502.05950 | Survival Concept-Based Learning Models | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Concept-based learning enhances prediction accuracy and interpretability by leveraging high-level, human-understandable concepts. However, existing CBL frameworks do not address survival analysis tasks, which involve predicting event times in the presence of censored data -- a common scenario in fields like medicine an... | {
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2502.05951 | Cyri: A Conversational AI-based Assistant for Supporting the Human User
in Detecting and Responding to Phishing Attacks | [
"cs.HC",
"cs.AI",
"cs.CR"
] | This work introduces Cyri, an AI-powered conversational assistant designed to support a human user in detecting and analyzing phishing emails by leveraging Large Language Models. Cyri has been designed to scrutinize emails for semantic features used in phishing attacks, such as urgency, and undesirable consequences, us... | {
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2502.05954 | Optimization under Attack: Resilience, Vulnerability, and the Path to
Collapse | [
"cs.MA"
] | Optimization is instrumental for improving operations of large-scale socio-technical infrastructures of Smart Cities, for instance, energy and traffic systems. In particular, understanding the performance of multi-agent discrete-choice combinatorial optimization under distributed adversary attacks is a compelling and u... | {
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2502.05957 | AutoAgent: A Fully-Automated and Zero-Code Framework for LLM Agents | [
"cs.AI",
"cs.CL"
] | Large Language Model (LLM) Agents have demonstrated remarkable capabilities in task automation and intelligent decision-making, driving the widespread adoption of agent development frameworks such as LangChain and AutoGen. However, these frameworks predominantly serve developers with extensive technical expertise - a s... | {
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2502.05959 | Ensemble-Tight Second-Order Asymptotics and Exponents for Guessing-Based
Decoding with Abandonment | [
"cs.IT",
"math.IT"
] | This paper considers guessing-based decoders with abandonment for discrete memoryless channels in which all codewords have the same composition. This class of decoders rank-orders all input sequences in the codebook's composition class from ``closest'' to ``farthest'' from the channel output and then queries them seque... | {
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2502.05963 | Redefining Robot Generalization Through Interactive Intelligence | [
"cs.LG",
"cs.AI",
"cs.RO"
] | Recent advances in large-scale machine learning have produced high-capacity foundation models capable of adapting to a broad array of downstream tasks. While such models hold great promise for robotics, the prevailing paradigm still portrays robots as single, autonomous decision-makers, performing tasks like manipulati... | {
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2502.05964 | Revisiting Gradient-based Uncertainty for Monocular Depth Estimation | [
"cs.CV"
] | Monocular depth estimation, similar to other image-based tasks, is prone to erroneous predictions due to ambiguities in the image, for example, caused by dynamic objects or shadows. For this reason, pixel-wise uncertainty assessment is required for safety-critical applications to highlight the areas where the predictio... | {
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2502.05966 | Detection of Physiological Data Tampering Attacks with Quantum Machine
Learning | [
"quant-ph",
"cs.LG"
] | The widespread use of cloud-based medical devices and wearable sensors has made physiological data susceptible to tampering. These attacks can compromise the reliability of healthcare systems which can be critical and life-threatening. Detection of such data tampering is of immediate need. Machine learning has been use... | {
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2502.05967 | $\mu$nit Scaling: Simple and Scalable FP8 LLM Training | [
"cs.LG"
] | Large Language Model training with 8-bit floating point (FP8) formats promises significant efficiency improvements, but reduced numerical precision makes training challenging. It is currently possible to train in FP8 only if one is willing to tune various hyperparameters, reduce model scale, or accept the overhead of c... | {
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2502.05969 | Asymptotic FDR Control with Model-X Knockoffs: Is Moments Matching
Sufficient? | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] | We propose a unified theoretical framework for studying the robustness of the model-X knockoffs framework by investigating the asymptotic false discovery rate (FDR) control of the practically implemented approximate knockoffs procedure. This procedure deviates from the model-X knockoffs framework by substituting the tr... | {
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2502.05970 | Known Unknowns: Out-of-Distribution Property Prediction in Materials and
Molecules | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.CE",
"physics.chem-ph"
] | Discovery of high-performance materials and molecules requires identifying extremes with property values that fall outside the known distribution. Therefore, the ability to extrapolate to out-of-distribution (OOD) property values is critical for both solid-state materials and molecular design. Our objective is to train... | {
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2502.05972 | Mechanic Modeling and Nonlinear Optimal Control of Actively Articulated
Suspension of Mobile Heavy-Duty Manipulators | [
"cs.RO"
] | This paper presents the analytic modeling of mobile heavy-duty manipulators with actively articulated suspension and its optimal control to maximize its static and dynamic stabilization. By adopting the screw theory formalism, we consider the suspension mechanism as a rigid multibody composed of two closed kinematic ch... | {
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2502.05974 | Decision Making in Hybrid Environments: A Model Aggregation Approach | [
"cs.LG",
"stat.ML"
] | Recent work by Foster et al. (2021, 2022, 2023) and Xu and Zeevi (2023) developed the framework of decision estimation coefficient (DEC) that characterizes the complexity of general online decision making problems and provides a general algorithm design principle. These works, however, either focus on the pure stochast... | {
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2502.05979 | VFX Creator: Animated Visual Effect Generation with Controllable
Diffusion Transformer | [
"cs.CV"
] | Crafting magic and illusions is one of the most thrilling aspects of filmmaking, with visual effects (VFX) serving as the powerhouse behind unforgettable cinematic experiences. While recent advances in generative artificial intelligence have driven progress in generic image and video synthesis, the domain of controllab... | {
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2502.05980 | Speech to Speech Translation with Translatotron: A State of the Art
Review | [
"cs.CL",
"cs.AI"
] | A cascade-based speech-to-speech translation has been considered a benchmark for a very long time, but it is plagued by many issues, like the time taken to translate a speech from one language to another and compound errors. These issues are because a cascade-based method uses a combination of methods such as speech re... | {
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2502.05982 | HamRaz: A Culture-Based Persian Conversation Dataset for Person-Centered
Therapy Using LLM Agents | [
"cs.CL"
] | This paper presents HamRaz, a novel Persian-language mental health dataset designed for Person-Centered Therapy (PCT) using Large Language Models (LLMs). Despite the growing application of LLMs in AI-driven psychological counseling, existing datasets predominantly focus on Western and East Asian contexts, overlooking c... | {
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2502.05986 | Preventing Rogue Agents Improves Multi-Agent Collaboration | [
"cs.CL",
"cs.MA"
] | Multi-agent systems, where specialized agents collaborate to solve a shared task hold great potential, from increased modularity to simulating complex environments. However, they also have a major caveat -- a single agent can cause the entire system to fail. Consider a simple game where the knowledge to solve the task ... | {
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2502.05988 | SNAT-YOLO: Efficient Cross-Layer Aggregation Network for Edge-Oriented
Gangue Detection | [
"cs.CV"
] | To address the issues of slow detection speed,low accuracy,difficulty in deployment on industrial edge devices,and large parameter and computational requirements in deep learning-based coal gangue target detection methods,we propose a lightweight coal gangue target detection algorithm based on an improved YOLOv11.First... | {
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2502.05994 | Diffusion Models for Inverse Problems in the Exponential Family | [
"stat.ML",
"cs.LG"
] | Diffusion models have emerged as powerful tools for solving inverse problems, yet prior work has primarily focused on observations with Gaussian measurement noise, restricting their use in real-world scenarios. This limitation persists due to the intractability of the likelihood score, which until now has only been app... | {
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2502.05995 | A Comprehensive Survey on Image Signal Processing Approaches for
Low-Illumination Image Enhancement | [
"cs.CV"
] | The usage of digital content (photos and videos) in a variety of applications has increased due to the popularity of multimedia devices. These uses include advertising campaigns, educational resources, and social networking platforms. There is an increasing need for high-quality graphic information as people become mor... | {
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2502.05996 | Motion Control in Multi-Rotor Aerial Robots Using Deep Reinforcement
Learning | [
"cs.RO",
"cs.AI"
] | This paper investigates the application of Deep Reinforcement (DRL) Learning to address motion control challenges in drones for additive manufacturing (AM). Drone-based additive manufacturing promises flexible and autonomous material deposition in large-scale or hazardous environments. However, achieving robust real-ti... | {
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2502.05999 | Pencils to Pixels: A Systematic Study of Creative Drawings across
Children, Adults and AI | [
"cs.HC",
"cs.AI",
"cs.CV"
] | Can we derive computational metrics to quantify visual creativity in drawings across intelligent agents, while accounting for inherent differences in technical skill and style? To answer this, we curate a novel dataset consisting of 1338 drawings by children, adults and AI on a creative drawing task. We characterize tw... | {
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2502.06004 | Analysis of LLM as a grammatical feature tagger for African American
English | [
"cs.CL",
"cs.AI",
"cs.LG"
] | African American English (AAE) presents unique challenges in natural language processing (NLP). This research systematically compares the performance of available NLP models--rule-based, transformer-based, and large language models (LLMs)--capable of identifying key grammatical features of AAE, namely Habitual Be and M... | {
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2502.06006 | FactIR: A Real-World Zero-shot Open-Domain Retrieval Benchmark for
Fact-Checking | [
"cs.IR"
] | The field of automated fact-checking increasingly depends on retrieving web-based evidence to determine the veracity of claims in real-world scenarios. A significant challenge in this process is not only retrieving relevant information, but also identifying evidence that can both support and refute complex claims. Trad... | {
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2502.06007 | Transformers versus the EM Algorithm in Multi-class Clustering | [
"stat.ML",
"cs.LG"
] | LLMs demonstrate significant inference capacities in complicated machine learning tasks, using the Transformer model as its backbone. Motivated by the limited understanding of such models on the unsupervised learning problems, we study the learning guarantees of Transformers in performing multi-class clustering of the ... | {
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2502.06011 | Uncertainty Quantification and Causal Considerations for Off-Policy
Decision Making | [
"stat.ML",
"cs.LG"
] | Off-policy evaluation (OPE) is a critical challenge in robust decision-making that seeks to assess the performance of a new policy using data collected under a different policy. However, the existing OPE methodologies suffer from several limitations arising from statistical uncertainty as well as causal considerations.... | {
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2502.06018 | Kolmogorov-Arnold Fourier Networks | [
"cs.LG",
"cs.AI"
] | Although Kolmogorov-Arnold based interpretable networks (KAN) have strong theoretical expressiveness, they face significant parameter explosion and high-frequency feature capture challenges in high-dimensional tasks. To address this issue, we propose the Kolmogorov-Arnold-Fourier Network (KAF), which effectively integr... | {
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2502.06019 | Noise is an Efficient Learner for Zero-Shot Vision-Language Models | [
"cs.CV"
] | Recently, test-time adaptation has garnered attention as a method for tuning models without labeled data. The conventional modus operandi for adapting pre-trained vision-language models (VLMs) during test-time primarily focuses on tuning learnable prompts; however, this approach overlooks potential distribution shifts ... | {
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2502.06020 | Temporal Working Memory: Query-Guided Segment Refinement for Enhanced
Multimodal Understanding | [
"cs.CV",
"cs.MM",
"cs.SD",
"eess.AS"
] | Multimodal foundation models (MFMs) have demonstrated significant success in tasks such as visual captioning, question answering, and image-text retrieval. However, these models face inherent limitations due to their finite internal capacity, which restricts their ability to process extended temporal sequences, a cruci... | {
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2502.06022 | Nested subspace learning with flags | [
"stat.ML",
"cs.LG"
] | Many machine learning methods look for low-dimensional representations of the data. The underlying subspace can be estimated by first choosing a dimension $q$ and then optimizing a certain objective function over the space of $q$-dimensional subspaces (the Grassmannian). Trying different $q$ yields in general non-neste... | {
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2502.06023 | Dual Caption Preference Optimization for Diffusion Models | [
"cs.CV"
] | Recent advancements in human preference optimization, originally developed for Large Language Models (LLMs), have shown significant potential in improving text-to-image diffusion models. These methods aim to learn the distribution of preferred samples while distinguishing them from less preferred ones. However, existin... | {
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2502.06025 | Universal point spread function engineering for 3D optical information
processing | [
"physics.optics",
"cs.NE"
] | Point spread function (PSF) engineering has been pivotal in the remarkable progress made in high-resolution imaging in the last decades. However, the diversity in PSF structures attainable through existing engineering methods is limited. Here, we report universal PSF engineering, demonstrating a method to synthesize an... | {
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2502.06026 | A Multimodal PDE Foundation Model for Prediction and Scientific Text
Descriptions | [
"cs.LG",
"cs.NA",
"math.NA"
] | Neural networks are one tool for approximating non-linear differential equations used in scientific computing tasks such as surrogate modeling, real-time predictions, and optimal control. PDE foundation models utilize neural networks to train approximations to multiple differential equations simultaneously and are thus... | {
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2502.06027 | Generating 3D Binding Molecules Using Shape-Conditioned Diffusion Models
with Guidance | [
"cs.LG"
] | Drug development is a critical but notoriously resource- and time-consuming process. In this manuscript, we develop a novel generative artificial intelligence (genAI) method DiffSMol to facilitate drug development. DiffSmol generates 3D binding molecules based on the shapes of known ligands. DiffSMol encapsulates geome... | {
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2502.06029 | DiTASK: Multi-Task Fine-Tuning with Diffeomorphic Transformations | [
"cs.CV"
] | Pre-trained Vision Transformers now serve as powerful tools for computer vision. Yet, efficiently adapting them for multiple tasks remains a challenge that arises from the need to modify the rich hidden representations encoded by the learned weight matrices, without inducing interference between tasks. Current paramete... | {
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2502.06031 | A Conditional Tabular GAN-Enhanced Intrusion Detection System for Rare
Attacks in IoT Networks | [
"cs.CR",
"cs.LG"
] | Internet of things (IoT) networks, boosted by 6G technology, are transforming various industries. However, their widespread adoption introduces significant security risks, particularly in detecting rare but potentially damaging cyber-attacks. This makes the development of robust IDS crucial for monitoring network traff... | {
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} |
2502.06034 | Traveling Waves Integrate Spatial Information Into Spectral
Representations | [
"cs.CV"
] | Traveling waves are widely observed in the brain, but their precise computational function remains unclear. One prominent hypothesis is that they enable the transfer and integration of spatial information across neural populations. However, few computational models have explored how traveling waves might be harnessed t... | {
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} |
2502.06037 | Investigating Compositional Reasoning in Time Series Foundation Models | [
"cs.LG"
] | Large pre-trained time series foundation models (TSFMs) have demonstrated promising zero-shot performance across a wide range of domains. However, a question remains: Do TSFMs succeed solely by memorizing training patterns, or do they possess the ability to reason? While reasoning is a topic of great interest in the st... | {
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} |
2502.06038 | Provably Overwhelming Transformer Models with Designed Inputs | [
"cs.LG",
"cs.AI",
"cs.CC"
] | We develop an algorithm which, given a trained transformer model $\mathcal{M}$ as input, as well as a string of tokens $s$ of length $n_{fix}$ and an integer $n_{free}$, can generate a mathematical proof that $\mathcal{M}$ is ``overwhelmed'' by $s$, in time and space $\widetilde{O}(n_{fix}^2 + n_{free}^3)$. We say that... | {
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"cs.SY": 0
} |
2502.06039 | Benchmarking Prompt Engineering Techniques for Secure Code Generation
with GPT Models | [
"cs.SE",
"cs.AI",
"cs.CR"
] | Prompt engineering reduces reasoning mistakes in Large Language Models (LLMs). However, its effectiveness in mitigating vulnerabilities in LLM-generated code remains underexplored. To address this gap, we implemented a benchmark to automatically assess the impact of various prompt engineering strategies on code securit... | {
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} |
2502.06042 | Scaling Laws for Forgetting during Finetuning with Pretraining Data
Injection | [
"cs.LG",
"cs.CL"
] | A widespread strategy to obtain a language model that performs well on a target domain is to finetune a pretrained model to perform unsupervised next-token prediction on data from that target domain. Finetuning presents two challenges: (i) if the amount of target data is limited, as in most practical applications, the ... | {
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} |
2502.06044 | Scalable Differentially Private Bayesian Optimization | [
"stat.ML",
"cs.LG"
] | In recent years, there has been much work on scaling Bayesian Optimization to high-dimensional problems, for example hyperparameter tuning in large neural network models. These scalable methods have been successful, finding high objective values much more quickly than traditional global Bayesian Optimization or random ... | {
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} |
2502.06047 | Neural Shortest Path for Surface Reconstruction from Point Clouds | [
"cs.LG"
] | In this paper, we propose the neural shortest path (NSP), a vector-valued implicit neural representation (INR) that approximates a distance function and its gradient. The key feature of NSP is to learn the exact shortest path (ESP), which directs an arbitrary point to its nearest point on the target surface. The NSP is... | {
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} |
2502.06049 | LM2: Large Memory Models | [
"cs.CL",
"cs.AI"
] | This paper introduces the Large Memory Model (LM2), a decoder-only Transformer architecture enhanced with an auxiliary memory module that aims to address the limitations of standard Transformers in multi-step reasoning, relational argumentation, and synthesizing information distributed over long contexts. The proposed ... | {
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} |
2502.06051 | Nearly Optimal Sample Complexity of Offline KL-Regularized Contextual
Bandits under Single-Policy Concentrability | [
"cs.LG",
"cs.AI",
"math.ST",
"stat.ML",
"stat.TH"
] | KL-regularized policy optimization has become a workhorse in learning-based decision making, while its theoretical understanding is still very limited. Although recent progress has been made towards settling the sample complexity of KL-regularized contextual bandits, existing sample complexity bounds are either $\tilde... | {
"Other": 0,
"cs.AI": 1,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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