id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.06719 | Gaussian Approximation and Multiplier Bootstrap for Stochastic Gradient
Descent | [
"stat.ML",
"cs.LG",
"math.OC",
"math.PR",
"math.ST",
"stat.TH"
] | In this paper, we establish non-asymptotic convergence rates in the central limit theorem for Polyak-Ruppert-averaged iterates of stochastic gradient descent (SGD). Our analysis builds on the result of the Gaussian approximation for nonlinear statistics of independent random variables of Shao and Zhang (2022). Using th... | {
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2502.06722 | HetSwarm: Cooperative Navigation of Heterogeneous Swarm in Dynamic and
Dense Environments through Impedance-based Guidance | [
"cs.RO"
] | With the growing demand for efficient logistics and warehouse management, unmanned aerial vehicles (UAVs) are emerging as a valuable complement to automated guided vehicles (AGVs). UAVs enhance efficiency by navigating dense environments and operating at varying altitudes. However, their limited flight time, battery li... | {
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2502.06725 | AgilePilot: DRL-Based Drone Agent for Real-Time Motion Planning in
Dynamic Environments by Leveraging Object Detection | [
"cs.RO"
] | Autonomous drone navigation in dynamic environments remains a critical challenge, especially when dealing with unpredictable scenarios including fast-moving objects with rapidly changing goal positions. While traditional planners and classical optimisation methods have been extensively used to address this dynamic prob... | {
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2502.06726 | Rough Stochastic Pontryagin Maximum Principle and an Indirect Shooting
Method | [
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"cs.SY",
"eess.SY",
"math.PR"
] | We derive first-order Pontryagin optimality conditions for stochastic optimal control with deterministic controls for systems modeled by rough differential equations (RDE) driven by Gaussian rough paths. This Pontryagin Maximum Principle (PMP) applies to systems following stochastic differential equations (SDE) driven ... | {
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2502.06727 | Application of Artificial Intelligence (AI) in Civil Engineering | [
"cs.AI"
] | Hard computing generally deals with precise data, which provides ideal solutions to problems. However, in the civil engineering field, amongst other disciplines, that is not always the case as real-world systems are continuously changing. Here lies the need to explore soft computing methods and artificial intelligence ... | {
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2502.06728 | FlexDeMo: Decoupled Momentum Optimization for Fully and Hybrid Sharded
Training | [
"cs.LG",
"cs.AI"
] | Training large neural network models requires extensive computational resources, often distributed across several nodes and accelerators. Recent findings suggest that it may be sufficient to only exchange the fast moving components of the gradients, while accumulating momentum locally (Decoupled Momentum, or DeMo). How... | {
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2502.06733 | Dynamic Loss-Based Sample Reweighting for Improved Large Language Model
Pretraining | [
"cs.LG",
"cs.AI"
] | Pretraining large language models (LLMs) on vast and heterogeneous datasets is crucial for achieving state-of-the-art performance across diverse downstream tasks. However, current training paradigms treat all samples equally, overlooking the importance or relevance of individual samples throughout the training process.... | {
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2502.06734 | Se\~norita-2M: A High-Quality Instruction-based Dataset for General
Video Editing by Video Specialists | [
"cs.CV"
] | Recent advancements in video generation have spurred the development of video editing techniques, which can be divided into inversion-based and end-to-end methods. However, current video editing methods still suffer from several challenges. Inversion-based methods, though training-free and flexible, are time-consuming ... | {
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2502.06735 | Enhancing Pneumonia Diagnosis and Severity Assessment through Deep
Learning: A Comprehensive Approach Integrating CNN Classification and
Infection Segmentation | [
"cs.CV"
] | Lung disease poses a substantial global health challenge, with pneumonia being a prevalent concern. This research focuses on leveraging deep learning techniques to detect and assess pneumonia, addressing two interconnected objectives. Initially, Convolutional Neural Network (CNN) models are introduced for pneumonia cla... | {
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2502.06736 | Low-power Spike-based Wearable Analytics on RRAM Crossbars | [
"cs.ET",
"cs.AI",
"cs.AR"
] | This work introduces a spike-based wearable analytics system utilizing Spiking Neural Networks (SNNs) deployed on an In-memory Computing engine based on RRAM crossbars, which are known for their compactness and energy-efficiency. Given the hardware constraints and noise characteristics of the underlying RRAM crossbars,... | {
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2502.06737 | VersaPRM: Multi-Domain Process Reward Model via Synthetic Reasoning Data | [
"cs.LG"
] | Process Reward Models (PRMs) have proven effective at enhancing mathematical reasoning for Large Language Models (LLMs) by leveraging increased inference-time computation. However, they are predominantly trained on mathematical data and their generalizability to non-mathematical domains has not been rigorously studied.... | {
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2502.06738 | Resurrecting saturated LLM benchmarks with adversarial encoding | [
"cs.LG"
] | Recent work showed that small changes in benchmark questions can reduce LLMs' reasoning and recall. We explore two such changes: pairing questions and adding more answer options, on three benchmarks: WMDP-bio, GPQA, and MMLU variants. We find that for more capable models, these predictably reduce performance, essential... | {
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2502.06739 | A note on the physical interpretation of neural PDE's | [
"cs.LG",
"cond-mat.dis-nn",
"physics.comp-ph"
] | We highlight a formal and substantial analogy between Machine Learning (ML) algorithms and discrete dynamical systems (DDS) in relaxation form. The analogy offers a transparent interpretation of the weights in terms of physical information-propagation processes and identifies the model function of the forward ML step w... | {
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2502.06741 | ViSIR: Vision Transformer Single Image Reconstruction Method for Earth
System Models | [
"cs.CV"
] | Purpose: Earth system models (ESMs) integrate the interactions of the atmosphere, ocean, land, ice, and biosphere to estimate the state of regional and global climate under a wide variety of conditions. The ESMs are highly complex, and thus, deep neural network architectures are used to model the complexity and store t... | {
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2502.06742 | Gradient Multi-Normalization for Stateless and Scalable LLM Training | [
"cs.LG",
"cs.AI"
] | Training large language models (LLMs) typically relies on adaptive optimizers like Adam (Kingma & Ba, 2015) which store additional state information to accelerate convergence but incur significant memory overhead. Recent efforts, such as SWAN (Ma et al., 2024) address this by eliminating the need for optimizer states w... | {
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2502.06747 | Wandering around: A bioinspired approach to visual attention through
object motion sensitivity | [
"cs.CV"
] | Active vision enables dynamic visual perception, offering an alternative to static feedforward architectures in computer vision, which rely on large datasets and high computational resources. Biological selective attention mechanisms allow agents to focus on salient Regions of Interest (ROIs), reducing computational de... | {
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2502.06748 | Institutional Preferences in the Laboratory | [
"cs.SI",
"cs.GT"
] | Getting a group to adopt cooperative norms is an enduring challenge. But in real-world settings, individuals don't just passively accept static environments, they act both within and upon the social systems that structure their interactions. Should we expect the dynamism of player-driven changes to the "rules of the ga... | {
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2502.06749 | Incentivizing Desirable Effort Profiles in Strategic Classification: The
Role of Causality and Uncertainty | [
"cs.GT",
"cs.CY",
"cs.LG"
] | We study strategic classification in binary decision-making settings where agents can modify their features in order to improve their classification outcomes. Importantly, our work considers the causal structure across different features, acknowledging that effort in a given feature may affect other features. The main ... | {
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2502.06750 | Accelerating Data Processing and Benchmarking of AI Models for Pathology | [
"cs.CV"
] | Advances in foundation modeling have reshaped computational pathology. However, the increasing number of available models and lack of standardized benchmarks make it increasingly complex to assess their strengths, limitations, and potential for further development. To address these challenges, we introduce a new suite ... | {
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2502.06751 | What makes a good feedforward computational graph? | [
"cs.LG",
"cs.AI",
"cs.SI",
"stat.ML"
] | As implied by the plethora of literature on graph rewiring, the choice of computational graph employed by a neural network can make a significant impact on its downstream performance. Certain effects related to the computational graph, such as under-reaching and over-squashing, may even render the model incapable of le... | {
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2502.06753 | Case for a unified surrogate modelling framework in the age of AI | [
"stat.CO",
"cs.LG"
] | Surrogate models are widely used in natural sciences, engineering, and machine learning to approximate complex systems and reduce computational costs. However, the current landscape lacks standardisation across key stages of the pipeline, including data collection, sampling design, model class selection, evaluation met... | {
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2502.06755 | Sparse Autoencoders for Scientifically Rigorous Interpretation of Vision
Models | [
"cs.CV"
] | To truly understand vision models, we must not only interpret their learned features but also validate these interpretations through controlled experiments. Current approaches either provide interpretable features without the ability to test their causal influence, or enable model editing without interpretable controls... | {
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2502.06756 | SAMRefiner: Taming Segment Anything Model for Universal Mask Refinement | [
"cs.CV"
] | In this paper, we explore a principal way to enhance the quality of widely pre-existing coarse masks, enabling them to serve as reliable training data for segmentation models to reduce the annotation cost. In contrast to prior refinement techniques that are tailored to specific models or tasks in a close-world manner, ... | {
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2502.06759 | Rationalization Models for Text-to-SQL | [
"cs.CL",
"cs.AI",
"cs.DB"
] | We introduce a framework for generating Chain-of-Thought (CoT) rationales to enhance text-to-SQL model fine-tuning. These rationales consist of intermediate SQL statements and explanations, serving as incremental steps toward constructing the final SQL query. The process begins with manually annotating a small set of e... | {
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2502.06760 | Infinite-Horizon Value Function Approximation for Model Predictive
Control | [
"cs.RO"
] | Model Predictive Control has emerged as a popular tool for robots to generate complex motions. However, the real-time requirement has limited the use of hard constraints and large preview horizons, which are necessary to ensure safety and stability. In practice, practitioners have to carefully design cost functions tha... | {
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2502.06761 | When, Where and Why to Average Weights? | [
"cs.LG"
] | Averaging checkpoints along the training trajectory is a simple yet powerful approach to improve the generalization performance of Machine Learning models and reduce training time. Motivated by these potential gains, and in an effort to fairly and thoroughly benchmark this technique, we present an extensive evaluation ... | {
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2502.06764 | History-Guided Video Diffusion | [
"cs.LG",
"cs.CV"
] | Classifier-free guidance (CFG) is a key technique for improving conditional generation in diffusion models, enabling more accurate control while enhancing sample quality. It is natural to extend this technique to video diffusion, which generates video conditioned on a variable number of context frames, collectively ref... | {
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2502.06765 | Are all models wrong? Fundamental limits in distribution-free empirical
model falsification | [
"math.ST",
"cs.LG",
"stat.ML",
"stat.TH"
] | In statistics and machine learning, when we train a fitted model on available data, we typically want to ensure that we are searching within a model class that contains at least one accurate model -- that is, we would like to ensure an upper bound on the model class risk (the lowest possible risk that can be attained b... | {
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2502.06766 | Exploiting Sparsity for Long Context Inference: Million Token Contexts
on Commodity GPUs | [
"cs.CL"
] | There is growing demand for performing inference with hundreds of thousands of input tokens on trained transformer models. Inference at this extreme scale demands significant computational resources, hindering the application of transformers at long contexts on commodity (i.e not data center scale) hardware. To address... | {
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2502.06768 | Train for the Worst, Plan for the Best: Understanding Token Ordering in
Masked Diffusions | [
"cs.LG"
] | In recent years, masked diffusion models (MDMs) have emerged as a promising alternative approach for generative modeling over discrete domains. Compared to autoregressive models (ARMs), MDMs trade off complexity at training time with flexibility at inference time. At training time, they must learn to solve an exponenti... | {
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2502.06770 | Parameter-Dependent Control Lyapunov Functions for Stabilizing Nonlinear
Parameter-Varying Systems | [
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"cs.SY",
"eess.SY"
] | This paper introduces the concept of parameter-dependent (PD) control Lyapunov functions (CLFs) for gainscheduled stabilization of nonlinear parameter-varying (NPV) systems under input constraints. It shows that given a PD-CLF, a locally Lipschitz control law can be constructed by solving a robust quadratic program. Fo... | {
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2502.06771 | Unsupervised Particle Tracking with Neuromorphic Computing | [
"hep-ex",
"cs.ET",
"cs.LG",
"cs.NE"
] | We study the application of a neural network architecture for identifying charged particle trajectories via unsupervised learning of delays and synaptic weights using a spike-time-dependent plasticity rule. In the considered model, the neurons receive time-encoded information on the position of particle hits in a track... | {
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2502.06772 | ReasonFlux: Hierarchical LLM Reasoning via Scaling Thought Templates | [
"cs.CL",
"cs.AI",
"cs.LG"
] | We present that hierarchical LLM reasoning via scaling thought templates can effectively optimize the reasoning search space and outperform the mathematical reasoning capabilities of powerful LLMs like OpenAI o1-preview and DeepSeek V3. We train our ReasonFlux-32B model with only 8 GPUs and introduces three innovations... | {
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2502.06773 | On the Emergence of Thinking in LLMs I: Searching for the Right
Intuition | [
"cs.AI",
"cs.CL",
"cs.LG"
] | Recent AI advancements, such as OpenAI's new models, are transforming LLMs into LRMs (Large Reasoning Models) that perform reasoning during inference, taking extra time and compute for higher-quality outputs. We aim to uncover the algorithmic framework for training LRMs. Methods like self-consistency, PRM, and AlphaZer... | {
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2502.06774 | ENFORCE: Exact Nonlinear Constrained Learning with Adaptive-depth Neural
Projection | [
"cs.LG"
] | Ensuring neural networks adhere to domain-specific constraints is crucial for addressing safety and ethical concerns while also enhancing prediction accuracy. Despite the nonlinear nature of most real-world tasks, existing methods are predominantly limited to affine or convex constraints. We introduce ENFORCE, a neural... | {
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2502.06775 | Enhancing Performance of Explainable AI Models with Constrained Concept
Refinement | [
"cs.LG"
] | The trade-off between accuracy and interpretability has long been a challenge in machine learning (ML). This tension is particularly significant for emerging interpretable-by-design methods, which aim to redesign ML algorithms for trustworthy interpretability but often sacrifice accuracy in the process. In this paper, ... | {
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2502.06776 | Towards Internet-Scale Training For Agents | [
"cs.LG",
"cs.AI"
] | The predominant approach for training web navigation agents gathers human demonstrations for a set of popular websites and hand-written tasks, but it is becoming clear that human data are an inefficient resource. We develop a pipeline to facilitate Internet-scale training for agents without laborious human annotations.... | {
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2502.06777 | Learning an Optimal Assortment Policy under Observational Data | [
"stat.ML",
"cs.LG",
"math.OC",
"math.ST",
"stat.TH"
] | We study the fundamental problem of offline assortment optimization under the Multinomial Logit (MNL) model, where sellers must determine the optimal subset of the products to offer based solely on historical customer choice data. While most existing approaches to learning-based assortment optimization focus on the onl... | {
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2502.06779 | KARST: Multi-Kernel Kronecker Adaptation with Re-Scaling Transmission
for Visual Classification | [
"cs.CV",
"cs.AI"
] | Fine-tuning pre-trained vision models for specific tasks is a common practice in computer vision. However, this process becomes more expensive as models grow larger. Recently, parameter-efficient fine-tuning (PEFT) methods have emerged as a popular solution to improve training efficiency and reduce storage needs by tun... | {
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2502.06781 | Exploring the Limit of Outcome Reward for Learning Mathematical
Reasoning | [
"cs.CL",
"cs.LG"
] | Reasoning abilities, especially those for solving complex math problems, are crucial components of general intelligence. Recent advances by proprietary companies, such as o-series models of OpenAI, have made remarkable progress on reasoning tasks. However, the complete technical details remain unrevealed, and the techn... | {
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2502.06782 | Lumina-Video: Efficient and Flexible Video Generation with Multi-scale
Next-DiT | [
"cs.CV"
] | Recent advancements have established Diffusion Transformers (DiTs) as a dominant framework in generative modeling. Building on this success, Lumina-Next achieves exceptional performance in the generation of photorealistic images with Next-DiT. However, its potential for video generation remains largely untapped, with s... | {
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2502.06784 | RelGNN: Composite Message Passing for Relational Deep Learning | [
"cs.LG",
"cs.AI",
"cs.DB"
] | Predictive tasks on relational databases are critical in real-world applications spanning e-commerce, healthcare, and social media. To address these tasks effectively, Relational Deep Learning (RDL) encodes relational data as graphs, enabling Graph Neural Networks (GNNs) to exploit relational structures for improved pr... | {
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2502.06785 | DeepCrossAttention: Supercharging Transformer Residual Connections | [
"cs.LG"
] | Transformer networks have achieved remarkable success across diverse domains, leveraging a variety of architectural innovations, including residual connections. However, traditional residual connections, which simply sum the outputs of previous layers, can dilute crucial information. This work introduces DeepCrossAtten... | {
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2502.06786 | Matryoshka Quantization | [
"cs.LG",
"cs.AI"
] | Quantizing model weights is critical for reducing the communication and inference costs of large models. However, quantizing models -- especially to low precisions like int4 or int2 -- requires a trade-off in model quality; int2, in particular, is known to severely degrade model quality. Consequently, practitioners are... | {
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2502.06787 | Visual Agentic AI for Spatial Reasoning with a Dynamic API | [
"cs.CV"
] | Visual reasoning -- the ability to interpret the visual world -- is crucial for embodied agents that operate within three-dimensional scenes. Progress in AI has led to vision and language models capable of answering questions from images. However, their performance declines when tasked with 3D spatial reasoning. To tac... | {
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2502.06788 | EVEv2: Improved Baselines for Encoder-Free Vision-Language Models | [
"cs.CV",
"cs.AI"
] | Existing encoder-free vision-language models (VLMs) are rapidly narrowing the performance gap with their encoder-based counterparts, highlighting the promising potential for unified multimodal systems with structural simplicity and efficient deployment. We systematically clarify the performance gap between VLMs using p... | {
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2502.06789 | Information-theoretic Bayesian Optimization: Survey and Tutorial | [
"cs.LG",
"cs.AI",
"cs.IT",
"math.IT",
"stat.ML"
] | Several scenarios require the optimization of non-convex black-box functions, that are noisy expensive to evaluate functions with unknown analytical expression, whose gradients are hence not accessible. For example, the hyper-parameter tuning problem of machine learning models. Bayesian optimization is a class of metho... | {
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2502.06798 | Prompt-Aware Scheduling for Efficient Text-to-Image Inferencing System | [
"cs.LG",
"cs.DC",
"cs.GR"
] | Traditional ML models utilize controlled approximations during high loads, employing faster, but less accurate models in a process called accuracy scaling. However, this method is less effective for generative text-to-image models due to their sensitivity to input prompts and performance degradation caused by large mod... | {
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2502.06800 | Analyzing Geospatial and Socioeconomic Disparities in Breast Cancer
Screening Among Populations in the United States: Machine Learning Approach | [
"cs.LG",
"stat.AP"
] | Breast cancer screening plays a pivotal role in early detection and subsequent effective management of the disease, impacting patient outcomes and survival rates. This study aims to assess breast cancer screening rates nationwide in the United States and investigate the impact of social determinants of health on these ... | {
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2502.06802 | Solving the Content Gap in Roblox Game Recommendations: LLM-Based
Profile Generation and Reranking | [
"cs.IR",
"cs.AI",
"cs.CL",
"cs.LG"
] | With the vast and dynamic user-generated content on Roblox, creating effective game recommendations requires a deep understanding of game content. Traditional recommendation models struggle with the inconsistent and sparse nature of game text features such as titles and descriptions. Recent advancements in large langua... | {
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2502.06803 | Emotion Recognition and Generation: A Comprehensive Review of Face,
Speech, and Text Modalities | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Emotion recognition and generation have emerged as crucial topics in Artificial Intelligence research, playing a significant role in enhancing human-computer interaction within healthcare, customer service, and other fields. Although several reviews have been conducted on emotion recognition and generation as separate ... | {
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2502.06805 | Efficient Diffusion Models: A Survey | [
"cs.LG",
"cs.GR"
] | Diffusion models have emerged as powerful generative models capable of producing high-quality contents such as images, videos, and audio, demonstrating their potential to revolutionize digital content creation. However, these capabilities come at the cost of their significant computational resources and lengthy generat... | {
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2502.06806 | Logits are All We Need to Adapt Closed Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Many commercial Large Language Models (LLMs) are often closed-source, limiting developers to prompt tuning for aligning content generation with specific applications. While these models currently do not provide access to token logits, we argue that if such access were available, it would enable more powerful adaptation... | {
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2502.06807 | Competitive Programming with Large Reasoning Models | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We show that reinforcement learning applied to large language models (LLMs) significantly boosts performance on complex coding and reasoning tasks. Additionally, we compare two general-purpose reasoning models - OpenAI o1 and an early checkpoint of o3 - with a domain-specific system, o1-ioi, which uses hand-engineered ... | {
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2502.06808 | On the Benefits of Attribute-Driven Graph Domain Adaptation | [
"cs.LG",
"cs.AI"
] | Graph Domain Adaptation (GDA) addresses a pressing challenge in cross-network learning, particularly pertinent due to the absence of labeled data in real-world graph datasets. Recent studies attempted to learn domain invariant representations by eliminating structural shifts between graphs. In this work, we show that e... | {
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2502.06809 | Neurons Speak in Ranges: Breaking Free from Discrete Neuronal
Attribution | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Interpreting and controlling the internal mechanisms of large language models (LLMs) is crucial for improving their trustworthiness and utility. Recent efforts have primarily focused on identifying and manipulating neurons by establishing discrete mappings between neurons and semantic concepts. However, such mappings s... | {
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2502.06810 | Emergence of Self-Awareness in Artificial Systems: A Minimalist
Three-Layer Approach to Artificial Consciousness | [
"q-bio.NC",
"cs.AI"
] | This paper proposes a minimalist three-layer model for artificial consciousness, focusing on the emergence of self-awareness. The model comprises a Cognitive Integration Layer, a Pattern Prediction Layer, and an Instinctive Response Layer, interacting with Access-Oriented and Pattern-Integrated Memory systems. Unlike b... | {
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2502.06811 | Aligning Human and Machine Attention for Enhanced Supervised Learning | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Attention, or prioritization of certain information items over others, is a critical element of any learning process, for both humans and machines. Given that humans continue to outperform machines in certain learning tasks, it seems plausible that machine performance could be enriched by aligning machine attention wit... | {
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2502.06812 | Harness Local Rewards for Global Benefits: Effective Text-to-Video
Generation Alignment with Patch-level Reward Models | [
"cs.LG",
"cs.GR"
] | The emergence of diffusion models (DMs) has significantly improved the quality of text-to-video generation models (VGMs). However, current VGM optimization primarily emphasizes the global quality of videos, overlooking localized errors, which leads to suboptimal generation capabilities. To address this issue, we propos... | {
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2502.06813 | Policy Guided Tree Search for Enhanced LLM Reasoning | [
"cs.LG",
"cs.AI"
] | Despite their remarkable capabilities, large language models often struggle with tasks requiring complex reasoning and planning. While existing approaches like Chain-of-Thought prompting and tree search techniques show promise, they are limited by their reliance on predefined heuristics and computationally expensive ex... | {
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2502.06814 | Diffusion Instruction Tuning | [
"cs.LG",
"cs.AI",
"cs.GR"
] | We introduce Lavender, a simple supervised fine-tuning (SFT) method that boosts the performance of advanced vision-language models (VLMs) by leveraging state-of-the-art image generation models such as Stable Diffusion. Specifically, Lavender aligns the text-vision attention in the VLM transformer with the equivalent us... | {
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2502.06815 | Honegumi: An Interface for Accelerating the Adoption of Bayesian
Optimization in the Experimental Sciences | [
"cs.LG",
"cond-mat.mtrl-sci"
] | Bayesian optimization (BO) has emerged as a powerful tool for guiding experimental design and decision-making in various scientific fields, including materials science, chemistry, and biology. However, despite its growing popularity, the complexity of existing BO libraries and the steep learning curve associated with t... | {
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2502.06816 | DeepCell: Multiview Representation Learning for Post-Mapping Netlists | [
"cs.LG",
"cs.AI"
] | Representation learning for post-mapping (PM) netlists is a critical challenge in Electronic Design Automation (EDA), driven by the diverse and complex nature of modern circuit designs. Existing approaches focus on intermediate representations like And-Inverter Graphs (AIGs), limiting their applicability to post-synthe... | {
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2502.06817 | Diffusion-empowered AutoPrompt MedSAM | [
"eess.IV",
"cs.GR",
"cs.LG"
] | MedSAM, a medical foundation model derived from the SAM architecture, has demonstrated notable success across diverse medical domains. However, its clinical application faces two major challenges: the dependency on labor-intensive manual prompt generation, which imposes a significant burden on clinicians, and the absen... | {
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2502.06818 | Globality Strikes Back: Rethinking the Global Knowledge of CLIP in
Training-Free Open-Vocabulary Semantic Segmentation | [
"cs.LG"
] | Recent works modify CLIP to perform open-vocabulary semantic segmentation in a training-free manner (TF-OVSS). In CLIP, patch-wise image representations mainly encode the homogeneous image-level properties and thus are not discriminative enough, hindering its application to the dense prediction task. Previous works mak... | {
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2502.06819 | Functional 3D Scene Synthesis through Human-Scene Optimization | [
"cs.LG",
"cs.GR"
] | This paper presents a novel generative approach that outputs 3D indoor environments solely from a textual description of the scene. Current methods often treat scene synthesis as a mere layout prediction task, leading to rooms with overlapping objects or overly structured scenes, with limited consideration of the pract... | {
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2502.06820 | LoCA: Location-Aware Cosine Adaptation for Parameter-Efficient
Fine-Tuning | [
"cs.LG",
"cs.AI"
] | Low-rank adaptation (LoRA) has become a prevalent method for adapting pre-trained large language models to downstream tasks. However, the simple low-rank decomposition form may constrain the hypothesis space. To address this limitation, we introduce Location-aware Cosine Adaptation (LoCA), a novel frequency-domain para... | {
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2502.06822 | DiffListener: Discrete Diffusion Model for Listener Generation | [
"cs.LG",
"cs.CL",
"cs.GR"
] | The listener head generation (LHG) task aims to generate natural nonverbal listener responses based on the speaker's multimodal cues. While prior work either rely on limited modalities (e.g. audio and facial information) or employ autoregressive approaches which have limitations such as accumulating prediction errors. ... | {
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2502.06823 | CTR-Driven Advertising Image Generation with Multimodal Large Language
Models | [
"cs.LG",
"cs.CV",
"cs.GR",
"cs.IR"
] | In web data, advertising images are crucial for capturing user attention and improving advertising effectiveness. Most existing methods generate background for products primarily focus on the aesthetic quality, which may fail to achieve satisfactory online performance. To address this limitation, we explore the use of ... | {
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2502.06824 | Neural Network-based Vehicular Channel Estimation Performance: Effect of
Noise in the Training Set | [
"cs.LG",
"cs.AI"
] | Vehicular communication systems face significant challenges due to high mobility and rapidly changing environments, which affect the channel over which the signals travel. To address these challenges, neural network (NN)-based channel estimation methods have been suggested. These methods are primarily trained on high s... | {
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2502.06825 | RLOMM: An Efficient and Robust Online Map Matching Framework with
Reinforcement Learning | [
"cs.LG",
"cs.DB"
] | Online map matching is a fundamental problem in location-based services, aiming to incrementally match trajectory data step-by-step onto a road network. However, existing methods fail to meet the needs for efficiency, robustness, and accuracy required by large-scale online applications, making this task still a challen... | {
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2502.06826 | Transferring Graph Neural Networks for Soft Sensor Modeling using
Process Topologies | [
"cs.LG",
"cs.AI"
] | Data-driven soft sensors help in process operations by providing real-time estimates of otherwise hard- to-measure process quantities, e.g., viscosities or product concentrations. Currently, soft sensors need to be developed individually per plant. Using transfer learning, machine learning-based soft sensors could be r... | {
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2502.06827 | Learning to Synthesize Compatible Fashion Items Using Semantic Alignment
and Collocation Classification: An Outfit Generation Framework | [
"cs.LG",
"cs.AI",
"cs.GR"
] | The field of fashion compatibility learning has attracted great attention from both the academic and industrial communities in recent years. Many studies have been carried out for fashion compatibility prediction, collocated outfit recommendation, artificial intelligence (AI)-enabled compatible fashion design, and rela... | {
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2502.06828 | Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding:
Insights from a Large-Scale Longitudinal Study | [
"cs.LG",
"cs.AI"
] | This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) decoding within a causal setting involving a large user group and multiple sessions per participant. We are the first to explore such strategies across a large user group, as... | {
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2502.06829 | Convolution-Based Converter : A Weak-Prior Approach For Modeling
Stochastic Processes Based On Conditional Density Estimation | [
"cs.LG",
"cs.AI"
] | In this paper, a Convolution-Based Converter (CBC) is proposed to develop a methodology for removing the strong or fixed priors in estimating the probability distribution of targets based on observations in the stochastic process. Traditional approaches, e.g., Markov-based and Gaussian process-based methods, typically ... | {
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2502.06830 | OrderFusion: Encoding Orderbook for Probabilistic Intraday Price
Prediction | [
"q-fin.CP",
"cs.AI",
"cs.LG"
] | Efficient and reliable probabilistic prediction of intraday electricity prices is essential to manage market uncertainties and support robust trading strategies. However, current methods often suffer from parameter inefficiencies, as they fail to fully exploit the potential of modeling interdependencies between bids an... | {
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2502.06831 | No Location Left Behind: Measuring and Improving the Fairness of
Implicit Representations for Earth Data | [
"cs.LG",
"cs.AI"
] | Implicit neural representations (INRs) exhibit growing promise in addressing Earth representation challenges, ranging from emissions monitoring to climate modeling. However, existing methods disproportionately prioritize global average performance, whereas practitioners require fine-grained insights to understand biase... | {
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2502.06832 | Optimizing Robustness and Accuracy in Mixture of Experts: A Dual-Model
Approach | [
"cs.LG",
"cs.AI"
] | Mixture of Experts (MoE) have shown remarkable success in leveraging specialized expert networks for complex machine learning tasks. However, their susceptibility to adversarial attacks presents a critical challenge for deployment in robust applications. This paper addresses the critical question of how to incorporate ... | {
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2502.06833 | Entropy Adaptive Decoding: Dynamic Model Switching for Efficient
Inference | [
"cs.LG",
"cs.AI",
"cs.CL"
] | We present Entropy Adaptive Decoding (EAD), a novel approach for efficient language model inference that dynamically switches between different-sized models based on prediction uncertainty. By monitoring rolling entropy in model logit distributions, our method identifies text regions where a smaller model suffices and ... | {
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2502.06834 | A Unified Knowledge-Distillation and Semi-Supervised Learning Framework
to Improve Industrial Ads Delivery Systems | [
"cs.LG",
"cs.AI"
] | Industrial ads ranking systems conventionally rely on labeled impression data, which leads to challenges such as overfitting, slower incremental gain from model scaling, and biases due to discrepancies between training and serving data. To overcome these issues, we propose a Unified framework for Knowledge-Distillation... | {
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2502.06835 | Reinforcement Learning on AYA Dyads to Enhance Medication Adherence | [
"cs.LG"
] | Medication adherence is critical for the recovery of adolescents and young adults (AYAs) who have undergone hematopoietic cell transplantation (HCT). However, maintaining adherence is challenging for AYAs after hospital discharge, who experience both individual (e.g. physical and emotional symptoms) and interpersonal b... | {
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2502.06836 | CAST: Cross Attention based multimodal fusion of Structure and Text for
materials property prediction | [
"cs.LG",
"cond-mat.mtrl-sci",
"cs.AI"
] | Recent advancements in AI have revolutionized property prediction in materials science and accelerating material discovery. Graph neural networks (GNNs) stand out due to their ability to represent crystal structures as graphs, effectively capturing local interactions and delivering superior predictions. However, these ... | {
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2502.06837 | Comparison of CNN-based deep learning architectures for unsteady CFD
acceleration on small datasets | [
"cs.LG",
"physics.flu-dyn"
] | CFD acceleration for virtual nuclear reactors or digital twin technology is a primary goal in the nuclear industry. This study compares advanced convolutional neural network (CNN) architectures for accelerating unsteady computational fluid dynamics (CFD) simulations using small datasets based on a challenging natural c... | {
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2502.06838 | TorchResist: Open-Source Differentiable Resist Simulator | [
"cs.LG"
] | Recent decades have witnessed remarkable advancements in artificial intelligence (AI), including large language models (LLMs), image and video generative models, and embodied AI systems. These advancements have led to an explosive increase in the demand for computational power, challenging the limits of Moore's Law. Op... | {
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2502.06839 | A Hybrid Model for Weakly-Supervised Speech Dereverberation | [
"eess.AS",
"cs.AI",
"cs.SD",
"eess.SP"
] | This paper introduces a new training strategy to improve speech dereverberation systems using minimal acoustic information and reverberant (wet) speech. Most existing algorithms rely on paired dry/wet data, which is difficult to obtain, or on target metrics that may not adequately capture reverberation characteristics ... | {
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2502.06842 | Integrating Generative Artificial Intelligence in ADRD: A Framework for
Streamlining Diagnosis and Care in Neurodegenerative Diseases | [
"cs.CY",
"cs.AI"
] | Healthcare systems are struggling to meet the growing demand for neurological care, with challenges particularly acute in Alzheimer's disease and related dementias (ADRD). While artificial intelligence research has often focused on identifying patterns beyond human perception, implementing such predictive capabilities ... | {
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2502.06843 | Vision-Integrated LLMs for Autonomous Driving Assistance : Human
Performance Comparison and Trust Evaluation | [
"cs.CV",
"cs.AI",
"cs.HC"
] | Traditional autonomous driving systems often struggle with reasoning in complex, unexpected scenarios due to limited comprehension of spatial relationships. In response, this study introduces a Large Language Model (LLM)-based Autonomous Driving (AD) assistance system that integrates a vision adapter and an LLM reasoni... | {
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2502.06844 | Exploring Model Invariance with Discrete Search for Ultra-Low-Bit
Quantization | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models have been increasing in size due to their success in a wide range of applications. This calls for a pressing need to reduce memory usage to make them more accessible. Post-training quantization is a popular technique which uses fewer bits (e.g., 4--8 bits) to represent the model without retraining... | {
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} |
2502.06845 | DiffNMR3: Advancing NMR Resolution Beyond Instrumental Limits | [
"physics.ins-det",
"cs.AI",
"cs.LG"
] | Nuclear Magnetic Resonance (NMR) spectroscopy is a crucial analytical technique used for molecular structure elucidation, with applications spanning chemistry, biology, materials science, and medicine. However, the frequency resolution of NMR spectra is limited by the "field strength" of the instrument. High-field NMR ... | {
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2502.06846 | Prot2Chat: Protein LLM with Early Fusion of Sequence and Structure | [
"cs.LG",
"cs.AI",
"q-bio.BM"
] | Proteins play a pivotal role in living organisms, yet understanding their functions presents significant challenges, including the limited flexibility of classification-based methods, the inability to effectively leverage spatial structural information, and the lack of systematic evaluation metrics for protein Q&A syst... | {
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2502.06847 | A Deep Learning Framework Integrating CNN and BiLSTM for Financial
Systemic Risk Analysis and Prediction | [
"cs.LG",
"cs.CE"
] | This study proposes a deep learning model based on the combination of convolutional neural network (CNN) and bidirectional long short-term memory network (BiLSTM) for discriminant analysis of financial systemic risk. The model first uses CNN to extract local patterns of multidimensional features of financial markets, a... | {
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} |
2502.06848 | Transfer learning in Scalable Graph Neural Network for Improved Physical
Simulation | [
"cs.LG",
"cs.AI"
] | In recent years, Graph Neural Network (GNN) based models have shown promising results in simulating physics of complex systems. However, training dedicated graph network based physics simulators can be costly, as most models are confined to fully supervised training, which requires extensive data generated from traditi... | {
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} |
2502.06849 | Model Fusion via Neuron Transplantation | [
"cs.LG",
"cs.AI"
] | Ensemble learning is a widespread technique to improve the prediction performance of neural networks. However, it comes at the price of increased memory and inference time. In this work we propose a novel model fusion technique called \emph{Neuron Transplantation (NT)} in which we fuse an ensemble of models by transpla... | {
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} |
2502.06851 | Survey on Vision-Language-Action Models | [
"cs.CL",
"cs.AI",
"cs.CV"
] | This paper presents an AI-generated review of Vision-Language-Action (VLA) models, summarizing key methodologies, findings, and future directions. The content is produced using large language models (LLMs) and is intended only for demonstration purposes. This work does not represent original research, but highlights ho... | {
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} |
2502.06852 | EAP-GP: Mitigating Saturation Effect in Gradient-based Automated Circuit
Identification | [
"cs.LG",
"cs.AI"
] | Understanding the internal mechanisms of transformer-based language models remains challenging. Mechanistic interpretability based on circuit discovery aims to reverse engineer neural networks by analyzing their internal processes at the level of computational subgraphs. In this paper, we revisit existing gradient-base... | {
"Other": 0,
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} |
2502.06853 | Native Fortran Implementation of TensorFlow-Trained Deep and Bayesian
Neural Networks | [
"cs.LG",
"cs.AI"
] | Over the past decade, the investigation of machine learning (ML) within the field of nuclear engineering has grown significantly. With many approaches reaching maturity, the next phase of investigation will determine the feasibility and usefulness of ML model implementation in a production setting. Several of the codes... | {
"Other": 0,
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} |
2502.06854 | Can Large Language Models Understand Intermediate Representations? | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Intermediate Representations (IRs) are essential in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. This paper presents a pioneering empirical study to investigate the capabilities of LLMs, including GPT-4, GPT-3, Gemma 2, LLaMA 3.1, and Code Llama, i... | {
"Other": 0,
"cs.AI": 1,
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} |
2502.06855 | Self-Supervised Prompt Optimization | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Well-designed prompts are crucial for enhancing Large language models' (LLMs) reasoning capabilities while aligning their outputs with task requirements across diverse domains. However, manually designed prompts require expertise and iterative experimentation. While existing prompt optimization methods aim to automate ... | {
"Other": 0,
"cs.AI": 1,
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} |
2502.06857 | Gemstones: A Model Suite for Multi-Faceted Scaling Laws | [
"cs.LG",
"cs.AI"
] | Scaling laws are typically fit using a family of models with a narrow range of frozen hyper-parameter choices. In this work we study scaling laws using a wide range of architecture and hyper-parameter choices, and highlight their impact on resulting prescriptions. As a primary artifact of our research, we release the G... | {
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} |
2502.06858 | LLM-Supported Natural Language to Bash Translation | [
"cs.CL",
"cs.AI"
] | The Bourne-Again Shell (Bash) command-line interface for Linux systems has complex syntax and requires extensive specialized knowledge. Using the natural language to Bash command (NL2SH) translation capabilities of large language models (LLMs) for command composition circumvents these issues. However, the NL2SH perform... | {
"Other": 0,
"cs.AI": 1,
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"cs.SY": 0
} |
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