id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2412.01193 | Divergent Ensemble Networks: Enhancing Uncertainty Estimation with
Shared Representations and Independent Branching | [
"cs.LG"
] | Ensemble learning has proven effective in improving predictive performance and estimating uncertainty in neural networks. However, conventional ensemble methods often suffer from redundant parameter usage and computational inefficiencies due to entirely independent network training. To address these challenges, we prop... | {
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2412.01195 | Memory-Efficient Training for Deep Speaker Embedding Learning in Speaker
Verification | [
"eess.AS",
"cs.AI",
"cs.SD"
] | Recent speaker verification (SV) systems have shown a trend toward adopting deeper speaker embedding extractors. Although deeper and larger neural networks can significantly improve performance, their substantial memory requirements hinder training on consumer GPUs. In this paper, we explore a memory-efficient training... | {
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2412.01197 | InstantSwap: Fast Customized Concept Swapping across Sharp Shape
Differences | [
"cs.CV",
"cs.AI"
] | Recent advances in Customized Concept Swapping (CCS) enable a text-to-image model to swap a concept in the source image with a customized target concept. However, the existing methods still face the challenges of inconsistency and inefficiency. They struggle to maintain consistency in both the foreground and background... | {
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2412.01199 | TinyFusion: Diffusion Transformers Learned Shallow | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Diffusion Transformers have demonstrated remarkable capabilities in image generation but often come with excessive parameterization, resulting in considerable inference overhead in real-world applications. In this work, we present TinyFusion, a depth pruning method designed to remove redundant layers from diffusion tra... | {
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2412.01202 | Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside
CNN Models | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] | In this paper, we present a Neuron Abandoning Attention Flow (NAFlow) method to address the open problem of visually explaining the attention evolution dynamics inside CNNs when making their classification decisions. A novel cascading neuron abandoning back-propagation algorithm is designed to trace neurons in all laye... | {
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2412.01203 | Domain Adaptive Diabetic Retinopathy Grading with Model Absence and
Flowing Data | [
"cs.CV"
] | Domain shift (the difference between source and target domains) poses a significant challenge in clinical applications, e.g., Diabetic Retinopathy (DR) grading. Despite considering certain clinical requirements, like source data privacy, conventional transfer methods are predominantly model-centered and often struggle ... | {
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2412.01207 | Siamese Machine Unlearning with Knowledge Vaporization and Concentration | [
"cs.LG"
] | In response to the practical demands of the ``right to be forgotten" and the removal of undesired data, machine unlearning emerges as an essential technique to remove the learned knowledge of a fraction of data points from trained models. However, existing methods suffer from limitations such as insufficient methodolog... | {
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2412.01212 | First numerical observation of the Berezinskii-Kosterlitz-Thouless
transition in language models | [
"stat.ML",
"cond-mat.stat-mech",
"cs.CL",
"cs.LG"
] | Several power-law critical properties involving different statistics in natural languages -- reminiscent of scaling properties of physical systems at or near phase transitions -- have been documented for decades. The recent rise of large language models (LLMs) has added further evidence and excitement by providing in... | {
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2412.01213 | GeoTP: Latency-aware Geo-Distributed Transaction Processing in Database
Middlewares (Extended Version) | [
"cs.DB"
] | The widespread adoption of database middleware for supporting distributed transaction processing is prevalent in numerous applications, with heterogeneous data sources deployed across national and international boundaries. However, transaction processing performance significantly drops due to the high network latency b... | {
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2412.01215 | EsurvFusion: An evidential multimodal survival fusion model based on
Gaussian random fuzzy numbers | [
"cs.LG"
] | Multimodal survival analysis aims to combine heterogeneous data sources (e.g., clinical, imaging, text, genomics) to improve the prediction quality of survival outcomes. However, this task is particularly challenging due to high heterogeneity and noise across data sources, which vary in structure, distribution, and con... | {
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2412.01217 | RGBDS-SLAM: A RGB-D Semantic Dense SLAM Based on 3D Multi Level Pyramid
Gaussian Splatting | [
"cs.CV"
] | High-quality reconstruction is crucial for dense SLAM. Recent popular approaches utilize 3D Gaussian Splatting (3D GS) techniques for RGB, depth, and semantic reconstruction of scenes. However, these methods often overlook issues of detail and consistency in different parts of the scene. To address this, we propose RGB... | {
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2412.01218 | FD-LLM: Large Language Model for Fault Diagnosis of Machines | [
"cs.AI",
"cs.LG"
] | Large language models (LLMs) are effective at capturing complex, valuable conceptual representations from textual data for a wide range of real-world applications. However, in fields like Intelligent Fault Diagnosis (IFD), incorporating additional sensor data-such as vibration signals, temperature readings, and operati... | {
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2412.01221 | Assessing GPT Model Uncertainty in Mathematical OCR Tasks via Entropy
Analysis | [
"cs.IT",
"math.IT"
] | This paper investigates the uncertainty of Generative Pre-trained Transformer (GPT) models in extracting mathematical equations from images of varying resolutions and converting them into LaTeX code. We employ concepts of entropy and mutual information to examine the recognition process and assess the model's uncertain... | {
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2412.01223 | PainterNet: Adaptive Image Inpainting with Actual-Token Attention and
Diverse Mask Control | [
"cs.CV",
"cs.AI"
] | Recently, diffusion models have exhibited superior performance in the area of image inpainting. Inpainting methods based on diffusion models can usually generate realistic, high-quality image content for masked areas. However, due to the limitations of diffusion models, existing methods typically encounter problems in ... | {
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2412.01224 | Option Pricing with Convolutional Kolmogorov-Arnold Networks | [
"cs.CE"
] | With the rapid advancement of neural networks, methods for option pricing have evolved significantly. This study employs the Black-Scholes-Merton (B-S-M) model, incorporating an additional variable to improve the accuracy of predictions compared to the traditional Black-Scholes (B-S) model. Furthermore, Convolutional K... | {
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2412.01230 | GraphOTTER: Evolving LLM-based Graph Reasoning for Complex Table
Question Answering | [
"cs.CL"
] | Complex Table Question Answering involves providing accurate answers to specific questions based on intricate tables that exhibit complex layouts and flexible header locations. Despite considerable progress having been made in the LLM era, the reasoning processes of existing methods are often implicit, feeding the enti... | {
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2412.01232 | Variational formulation based on duality to solve partial differential
equations: Use of B-splines and machine learning approximants | [
"math.NA",
"cs.LG",
"cs.NA",
"physics.comp-ph"
] | Many partial differential equations (PDEs) such as Navier--Stokes equations in fluid mechanics, inelastic deformation in solids, and transient parabolic and hyperbolic equations do not have an exact, primal variational structure. Recently, a variational principle based on the dual (Lagrange multiplier) field was propos... | {
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2412.01233 | Best Practices for Large Language Models in Radiology | [
"cs.AI"
] | At the heart of radiological practice is the challenge of integrating complex imaging data with clinical information to produce actionable insights. Nuanced application of language is key for various activities, including managing requests, describing and interpreting imaging findings in the context of clinical data, a... | {
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2412.01234 | Integrating Decision-Making Into Differentiable Optimization Guided
Learning for End-to-End Planning of Autonomous Vehicles | [
"cs.RO"
] | We address the decision-making capability within an end-to-end planning framework that focuses on motion prediction, decision-making, and trajectory planning. Specifically, we formulate decision-making and trajectory planning as a differentiable nonlinear optimization problem, which ensures compatibility with learning-... | {
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2412.01235 | Real-time Traffic Simulation and Management for Large-scale Urban Air
Mobility: Integrating Route Guidance and Collision Avoidance | [
"eess.SY",
"cs.SY"
] | Given the spatial heterogeneity of land use patterns in most cities, large-scale UAM will likely be deployed in specific areas, e.g., inter-transfer traffic between suburbs and city centers. However, large-scale UAM operations connecting multiple origin-destination pairs raise concerns about air traffic safety and effi... | {
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2412.01236 | Confinement Specific Design of SOI Rib Waveguides with Submicron
Dimensions and Single Mode Operation | [
"physics.optics",
"cs.SY",
"eess.SP",
"eess.SY",
"physics.app-ph",
"physics.comp-ph"
] | Full-vectorial finite difference method with perfectly matched layers boundaries is used to identify the single mode operation region of submicron rib waveguides fabricated using sili-con-on-insulator material system. Achieving high mode power confinement factors is emphasized while maintaining the single mode operatio... | {
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2412.01240 | Inspiring the Next Generation of Segment Anything Models:
Comprehensively Evaluate SAM and SAM 2 with Diverse Prompts Towards
Context-Dependent Concepts under Different Scenes | [
"cs.CV"
] | As a foundational model, SAM has significantly influenced multiple fields within computer vision, and its upgraded version, SAM 2, enhances capabilities in video segmentation, poised to make a substantial impact once again. While SAMs (SAM and SAM 2) have demonstrated excellent performance in segmenting context-indepen... | {
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2412.01241 | Quantum Pointwise Convolution: A Flexible and Scalable Approach for
Neural Network Enhancement | [
"cs.LG",
"quant-ph"
] | In this study, we propose a novel architecture, the Quantum Pointwise Convolution, which incorporates pointwise convolution within a quantum neural network framework. Our approach leverages the strengths of pointwise convolution to efficiently integrate information across feature channels while adjusting channel output... | {
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2412.01243 | Schedule On the Fly: Diffusion Time Prediction for Faster and Better
Image Generation | [
"cs.CV",
"cs.AI"
] | Diffusion and flow models have achieved remarkable successes in various applications such as text-to-image generation. However, these models typically rely on the same predetermined denoising schedules during inference for each prompt, which potentially limits the inference efficiency as well as the flexibility when ha... | {
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2412.01244 | Concept Replacer: Replacing Sensitive Concepts in Diffusion Models via
Precision Localization | [
"cs.CV"
] | As large-scale diffusion models continue to advance, they excel at producing high-quality images but often generate unwanted content, such as sexually explicit or violent content. Existing methods for concept removal generally guide the image generation process but can unintentionally modify unrelated regions, leading ... | {
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2412.01245 | Revisiting Generative Policies: A Simpler Reinforcement Learning
Algorithmic Perspective | [
"cs.LG",
"cs.AI"
] | Generative models, particularly diffusion models, have achieved remarkable success in density estimation for multimodal data, drawing significant interest from the reinforcement learning (RL) community, especially in policy modeling in continuous action spaces. However, existing works exhibit significant variations in ... | {
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2412.01246 | Class Distance Weighted Cross Entropy Loss for Classification of Disease
Severity | [
"cs.CV"
] | Assessing disease severity with ordinal classes, where each class reflects increasing severity levels, benefits from loss functions designed for this ordinal structure. Traditional categorical loss functions, like Cross-Entropy (CE), often perform suboptimally in these scenarios. To address this, we propose a novel los... | {
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2412.01248 | Multimodal Fusion Learning with Dual Attention for Medical Imaging | [
"cs.CV"
] | Multimodal fusion learning has shown significant promise in classifying various diseases such as skin cancer and brain tumors. However, existing methods face three key limitations. First, they often lack generalizability to other diagnosis tasks due to their focus on a particular disease. Second, they do not fully leve... | {
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2412.01249 | Data Uncertainty-Aware Learning for Multimodal Aspect-based Sentiment
Analysis | [
"cs.CL"
] | As a fine-grained task, multimodal aspect-based sentiment analysis (MABSA) mainly focuses on identifying aspect-level sentiment information in the text-image pair. However, we observe that it is difficult to recognize the sentiment of aspects in low-quality samples, such as those with low-resolution images that tend to... | {
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2412.01250 | Collaborative Instance Navigation: Leveraging Agent Self-Dialogue to
Minimize User Input | [
"cs.AI"
] | Existing embodied instance goal navigation tasks, driven by natural language, assume human users to provide complete and nuanced instance descriptions prior to the navigation, which can be impractical in the real world as human instructions might be brief and ambiguous. To bridge this gap, we propose a new task, Collab... | {
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2412.01253 | Yi-Lightning Technical Report | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This technical report presents Yi-Lightning, our latest flagship large language model (LLM). It achieves exceptional performance, ranking 6th overall on Chatbot Arena, with particularly strong results (2nd to 4th place) in specialized categories including Chinese, Math, Coding, and Hard Prompts. Yi-Lightning leverages ... | {
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2412.01254 | EmojiDiff: Advanced Facial Expression Control with High Identity
Preservation in Portrait Generation | [
"cs.CV"
] | This paper aims to bring fine-grained expression control to identity-preserving portrait generation. Existing methods tend to synthesize portraits with either neutral or stereotypical expressions. Even when supplemented with control signals like facial landmarks, these models struggle to generate accurate and vivid exp... | {
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2412.01255 | Embryo 2.0: Merging Synthetic and Real Data for Advanced AI Predictions | [
"eess.IV",
"cs.CV"
] | Accurate embryo morphology assessment is essential in assisted reproductive technology for selecting the most viable embryo. Artificial intelligence has the potential to enhance this process. However, the limited availability of embryo data presents challenges for training deep learning models. To address this, we trai... | {
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2412.01256 | NLPrompt: Noise-Label Prompt Learning for Vision-Language Models | [
"cs.CV",
"cs.LG"
] | The emergence of vision-language foundation models, such as CLIP, has revolutionized image-text representation, enabling a broad range of applications via prompt learning. Despite its promise, real-world datasets often contain noisy labels that can degrade prompt learning performance. In this paper, we demonstrate that... | {
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2412.01262 | Do Large Language Models with Reasoning and Acting Meet the Needs of
Task-Oriented Dialogue? | [
"cs.CL",
"cs.AI",
"cs.HC"
] | Large language models (LLMs) gained immense popularity due to their impressive capabilities in unstructured conversations. However, they underperform compared to previous approaches in task-oriented dialogue (TOD), wherein reasoning and accessing external information are crucial. Empowering LLMs with advanced prompting... | {
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2412.01264 | Towards Robust Interpretable Surrogates for Optimization | [
"cs.LG",
"math.OC"
] | An important factor in the practical implementation of optimization models is the acceptance by the intended users. This is influenced among other factors by the interpretability of the solution process. Decision rules that meet this requirement can be generated using the framework for inherently interpretable optimiza... | {
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2412.01265 | Indexing Economic Fluctuation Narratives from Keiki Watchers Survey | [
"cs.CL",
"cs.AI"
] | In this paper, we design indices of economic fluctuation narratives derived from economic surveys. Companies, governments, and investors rely on key metrics like GDP and industrial production indices to predict economic trends. However, they have yet to effectively leverage the wealth of information contained in econom... | {
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2412.01267 | EdgeOAR: Real-time Online Action Recognition On Edge Devices | [
"cs.CV"
] | This paper addresses the challenges of Online Action Recognition (OAR), a framework that involves instantaneous analysis and classification of behaviors in video streams. OAR must operate under stringent latency constraints, making it an indispensable component for real-time feedback for edge computing. Existing method... | {
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2412.01268 | Ponder & Press: Advancing Visual GUI Agent towards General Computer
Control | [
"cs.CV"
] | Most existing GUI agents typically depend on non-vision inputs like HTML source code or accessibility trees, limiting their flexibility across diverse software environments and platforms. Current multimodal large language models (MLLMs), which excel at using vision to ground real-world objects, offer a potential altern... | {
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2412.01269 | CPRM: A LLM-based Continual Pre-training Framework for Relevance
Modeling in Commercial Search | [
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | Relevance modeling between queries and items stands as a pivotal component in commercial search engines, directly affecting the user experience. Given the remarkable achievements of large language models (LLMs) in various natural language processing (NLP) tasks, LLM-based relevance modeling is gradually being adopted w... | {
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2412.01270 | 6DMA-Aided Cell-Free Massive MIMO Communication | [
"cs.IT",
"eess.SP",
"math.IT"
] | In this letter, we propose a six-dimensional movable antenna (6DMA)-aided cell-free massive multiple-input multiple-output (MIMO) system to fully exploit its macro spatial diversity, where a set of distributed access points (APs), each equipped with multiple 6DMA surfaces, cooperatively serve all users in a given area.... | {
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2412.01271 | MuLan: Adapting Multilingual Diffusion Models for Hundreds of Languages
with Negligible Cost | [
"cs.CL",
"cs.AI"
] | In this work, we explore a cost-effective framework for multilingual image generation. We find that, unlike models tuned on high-quality images with multilingual annotations, leveraging text encoders pre-trained on widely available, noisy Internet image-text pairs significantly enhances data efficiency in text-to-image... | {
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2412.01272 | Uncertainty-Aware Artificial Intelligence for Gear Fault Diagnosis in
Motor Drives | [
"eess.SY",
"cs.AI",
"cs.SY"
] | This paper introduces a novel approach to quantify the uncertainties in fault diagnosis of motor drives using Bayesian neural networks (BNN). Conventional data-driven approaches used for fault diagnosis often rely on point-estimate neural networks, which merely provide deterministic outputs and fail to capture the unce... | {
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2412.01273 | AR-Facilitated Safety Inspection and Fall Hazard Detection on
Construction Sites | [
"cs.HC",
"cs.CV"
] | Together with industry experts, we are exploring the potential of head-mounted augmented reality to facilitate safety inspections on high-rise construction sites. A particular concern in the industry is inspecting perimeter safety screens on higher levels of construction sites, intended to prevent falls of people and o... | {
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2412.01276 | Shadow of the (Hierarchical) Tree: Reconciling Symbolic and Predictive
Components of the Neural Code for Syntax | [
"cs.CL"
] | Natural language syntax can serve as a major test for how to integrate two infamously distinct frameworks: symbolic representations and connectionist neural networks. Building on a recent neurocomputational architecture for syntax (ROSE), I discuss the prospects of reconciling the neural code for hierarchical 'vertical... | {
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2412.01277 | Streamlining the Action Dependency Graph Framework: Two Key Enhancements | [
"cs.MA",
"cs.RO"
] | Multi Agent Path Finding (MAPF) is critical for coordinating multiple robots in shared environments, yet robust execution of generated plans remains challenging due to operational uncertainties. The Action Dependency Graph (ADG) framework offers a way to ensure correct action execution by establishing precedence-based ... | {
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2412.01281 | FedPAW: Federated Learning with Personalized Aggregation Weights for
Urban Vehicle Speed Prediction | [
"cs.AI",
"cs.DC"
] | Vehicle speed prediction is crucial for intelligent transportation systems, promoting more reliable autonomous driving by accurately predicting future vehicle conditions. Due to variations in drivers' driving styles and vehicle types, speed predictions for different target vehicles may significantly differ. Existing me... | {
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2412.01282 | Align-KD: Distilling Cross-Modal Alignment Knowledge for Mobile
Vision-Language Model | [
"cs.CV",
"cs.AI"
] | Vision-Language Models (VLMs) bring powerful understanding and reasoning capabilities to multimodal tasks. Meanwhile, the great need for capable aritificial intelligence on mobile devices also arises, such as the AI assistant software. Some efforts try to migrate VLMs to edge devices to expand their application scope. ... | {
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2412.01283 | Big data approach to Kazhdan-Lusztig polynomials | [
"math.RT",
"cs.LG",
"math.CO"
] | We investigate the structure of Kazhdan-Lusztig polynomials of the symmetric group by leveraging computational approaches from big data, including exploratory and topological data analysis, applied to the polynomials for symmetric groups of up to 11 strands. | {
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2412.01284 | MFTF: Mask-free Training-free Object Level Layout Control Diffusion
Model | [
"cs.CV",
"cs.AI"
] | Text-to-image generation models have revolutionized content creation, but diffusion-based vision-language models still face challenges in precisely controlling the shape, appearance, and positional placement of objects in generated images using text guidance alone. Existing global image editing models rely on additiona... | {
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2412.01286 | Self Phase Modulation and Cross Phase Modulation in Nonlinear Silicon
Waveguides for On-Chip Optical Networks -- A Tutorial | [
"physics.optics",
"cs.SY",
"eess.SP",
"eess.SY",
"physics.app-ph",
"physics.comp-ph"
] | Silicon is a nonlinear material and optics based on silicon makes use of these nonlinearities to realize various functionalities required for on-chip communications. This article describes foundations of these nonlinearities in silicon at length. Particularly, self phase modulation and cross phase modulation in the con... | {
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2412.01289 | Enhancing Perception Capabilities of Multimodal LLMs with Training-Free
Fusion | [
"cs.CV",
"cs.AI"
] | Multimodal LLMs (MLLMs) equip language models with visual capabilities by aligning vision encoders with language models. Existing methods to enhance the visual perception of MLLMs often involve designing more powerful vision encoders, which requires exploring a vast design space and re-aligning each potential encoder w... | {
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2412.01290 | Learning Smooth Distance Functions via Queries | [
"cs.LG",
"cs.AI",
"cs.IR",
"stat.ML"
] | In this work, we investigate the problem of learning distance functions within the query-based learning framework, where a learner is able to pose triplet queries of the form: ``Is $x_i$ closer to $x_j$ or $x_k$?'' We establish formal guarantees on the query complexity required to learn smooth, but otherwise general, d... | {
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2412.01291 | Global Estimation of Building-Integrated Facade and Rooftop Photovoltaic
Potential by Integrating 3D Building Footprint and Spatio-Temporal Datasets | [
"cs.IR",
"cs.ET"
] | This research tackles the challenges of estimating Building-Integrated Photovoltaics (BIPV) potential across various temporal and spatial scales, accounting for different geographical climates and urban morphology. We introduce a holistic methodology for evaluating BIPV potential, integrating 3D building footprint mode... | {
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2412.01292 | LSceneLLM: Enhancing Large 3D Scene Understanding Using Adaptive Visual
Preferences | [
"cs.CV"
] | Research on 3D Vision-Language Models (3D-VLMs) is gaining increasing attention, which is crucial for developing embodied AI within 3D scenes, such as visual navigation and embodied question answering. Due to the high density of visual features, especially in large 3D scenes, accurately locating task-relevant visual in... | {
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2412.01293 | SiTSE: Sinhala Text Simplification Dataset and Evaluation | [
"cs.CL"
] | Text Simplification is a task that has been minimally explored for low-resource languages. Consequently, there are only a few manually curated datasets. In this paper, we present a human curated sentence-level text simplification dataset for the Sinhala language. Our evaluation dataset contains 1,000 complex sentences ... | {
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2412.01295 | FedAH: Aggregated Head for Personalized Federated Learning | [
"cs.LG",
"cs.AI",
"cs.DC"
] | Recently, Federated Learning (FL) has gained popularity for its privacy-preserving and collaborative learning capabilities. Personalized Federated Learning (PFL), building upon FL, aims to address the issue of statistical heterogeneity and achieve personalization. Personalized-head-based PFL is a common and effective P... | {
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2412.01296 | I Spy With My Little Eye: A Minimum Cost Multicut Investigation of
Dataset Frames | [
"cs.CV"
] | Visual framing analysis is a key method in social sciences for determining common themes and concepts in a given discourse. To reduce manual effort, image clustering can significantly speed up the annotation process. In this work, we phrase the clustering task as a Minimum Cost Multicut Problem [MP]. Solutions to the M... | {
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2412.01297 | Morphological-Symmetry-Equivariant Heterogeneous Graph Neural Network
for Robotic Dynamics Learning | [
"cs.RO",
"cs.LG"
] | We present a morphological-symmetry-equivariant heterogeneous graph neural network, namely MS-HGNN, for robotic dynamics learning, that integrates robotic kinematic structures and morphological symmetries into a single graph network. These structural priors are embedded into the learning architecture as constraints, en... | {
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2412.01299 | Cross-Modal Visual Relocalization in Prior LiDAR Maps Utilizing
Intensity Textures | [
"cs.CV",
"cs.RO"
] | Cross-modal localization has drawn increasing attention in recent years, while the visual relocalization in prior LiDAR maps is less studied. Related methods usually suffer from inconsistency between the 2D texture and 3D geometry, neglecting the intensity features in the LiDAR point cloud. In this paper, we propose a ... | {
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2412.01300 | Event-Based Tracking Any Point with Motion-Augmented Temporal
Consistency | [
"cs.CV"
] | Tracking Any Point (TAP) plays a crucial role in motion analysis. Video-based approaches rely on iterative local matching for tracking, but they assume linear motion during the blind time between frames, which leads to target point loss under large displacements or nonlinear motion. The high temporal resolution and mot... | {
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2412.01303 | RL2: Reinforce Large Language Model to Assist Safe Reinforcement
Learning for Energy Management of Active Distribution Networks | [
"eess.SY",
"cs.AI",
"cs.SY"
] | As large-scale distributed energy resources are integrated into the active distribution networks (ADNs), effective energy management in ADNs becomes increasingly prominent compared to traditional distribution networks. Although advanced reinforcement learning (RL) methods, which alleviate the burden of complicated mode... | {
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2412.01306 | Multimodal Medical Disease Classification with LLaMA II | [
"cs.AI",
"cs.CV"
] | Medical patient data is always multimodal. Images, text, age, gender, histopathological data are only few examples for different modalities in this context. Processing and integrating this multimodal data with deep learning based methods is of utmost interest due to its huge potential for medical procedure such as diag... | {
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2412.01316 | Long Video Diffusion Generation with Segmented Cross-Attention and
Content-Rich Video Data Curation | [
"cs.CV",
"cs.AI",
"cs.MM"
] | We introduce Presto, a novel video diffusion model designed to generate 15-second videos with long-range coherence and rich content. Extending video generation methods to maintain scenario diversity over long durations presents significant challenges. To address this, we propose a Segmented Cross-Attention (SCA) strate... | {
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2412.01322 | Explainable fault and severity classification for rolling element
bearings using Kolmogorov-Arnold networks | [
"cs.LG",
"cs.AI"
] | Rolling element bearings are critical components of rotating machinery, with their performance directly influencing the efficiency and reliability of industrial systems. At the same time, bearing faults are a leading cause of machinery failures, often resulting in costly downtime, reduced productivity, and, in extreme ... | {
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2412.01324 | Sparse Hierarchical Non-Linear Programming for Inverse Kinematic
Planning and Control with Autonomous Goal Selection | [
"cs.RO"
] | Sparse programming is an important tool in robotics, for example in real-time sparse inverse kinematic control with a minimum number of active joints, or autonomous Cartesian goal selection. However, current approaches are limited to real-time control without consideration of the underlying non-linear problem. This pre... | {
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2412.01330 | The "LLM World of Words" English free association norms generated by
large language models | [
"cs.CL",
"cs.AI"
] | Free associations have been extensively used in cognitive psychology and linguistics for studying how conceptual knowledge is organized. Recently, the potential of applying a similar approach for investigating the knowledge encoded in LLMs has emerged, specifically as a method for investigating LLM biases. However, the... | {
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2412.01331 | Exploring Long-Term Prediction of Type 2 Diabetes Microvascular
Complications | [
"cs.LG",
"cs.CL"
] | Electronic healthcare records (EHR) contain a huge wealth of data that can support the prediction of clinical outcomes. EHR data is often stored and analysed using clinical codes (ICD10, SNOMED), however these can differ across registries and healthcare providers. Integrating data across systems involves mapping betwee... | {
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2412.01335 | A Versatile Influence Function for Data Attribution with
Non-Decomposable Loss | [
"cs.LG",
"stat.ML"
] | Influence function, a technique rooted in robust statistics, has been adapted in modern machine learning for a novel application: data attribution -- quantifying how individual training data points affect a model's predictions. However, the common derivation of influence functions in the data attribution literature is ... | {
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2412.01339 | Negative Token Merging: Image-based Adversarial Feature Guidance | [
"cs.CV",
"cs.AI",
"cs.GR",
"cs.LG",
"stat.ML"
] | Text-based adversarial guidance using a negative prompt has emerged as a widely adopted approach to steer diffusion models away from producing undesired concepts. While useful, performing adversarial guidance using text alone can be insufficient to capture complex visual concepts or avoid specific visual elements like ... | {
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2412.01340 | A 2-step Framework for Automated Literary Translation Evaluation: Its
Promises and Pitfalls | [
"cs.CL"
] | In this work, we propose and evaluate the feasibility of a two-stage pipeline to evaluate literary machine translation, in a fine-grained manner, from English to Korean. The results show that our framework provides fine-grained, interpretable metrics suited for literary translation and obtains a higher correlation with... | {
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2412.01343 | MoTrans: Customized Motion Transfer with Text-driven Video Diffusion
Models | [
"cs.CV"
] | Existing pretrained text-to-video (T2V) models have demonstrated impressive abilities in generating realistic videos with basic motion or camera movement. However, these models exhibit significant limitations when generating intricate, human-centric motions. Current efforts primarily focus on fine-tuning models on a sm... | {
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2412.01344 | Practical Performative Policy Learning with Strategic Agents | [
"cs.LG",
"cs.GT",
"stat.ME",
"stat.ML"
] | This paper studies the performative policy learning problem, where agents adjust their features in response to a released policy to improve their potential outcomes, inducing an endogenous distribution shift. There has been growing interest in training machine learning models in strategic environments, including strate... | {
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2412.01345 | See What You Seek: Semantic Contextual Integration for Cloth-Changing
Person Re-Identification | [
"cs.CV"
] | Cloth-changing person re-identification (CC-ReID) aims to match individuals across multiple surveillance cameras despite variations in clothing. Existing methods typically focus on mitigating the effects of clothing changes or enhancing ID-relevant features but often struggle to capture complex semantic information. In... | {
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2412.01346 | Data-Driven and Stealthy Deactivation of Safety Filters | [
"eess.SY",
"cs.SY"
] | Safety filters ensure that control actions that are executed are always safe, no matter the controller in question. Previous work has proposed a simple and stealthy false-data injection attack for deactivating such safety filters. This attack injects false sensor measurements to bias state estimates toward the interior... | {
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2412.01348 | Hierarchical Object-Oriented POMDP Planning for Object Rearrangement | [
"cs.LG",
"cs.AI",
"cs.RO"
] | We present an online planning framework for solving multi-object rearrangement problems in partially observable, multi-room environments. Current object rearrangement solutions, primarily based on Reinforcement Learning or hand-coded planning methods, often lack adaptability to diverse challenges. To address this limit... | {
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2412.01351 | A multi-criteria decision support system to evaluate the effectiveness
of training courses on citizens' employability | [
"cs.CY",
"cs.AI",
"math.OC"
] | This study examines the impact of lifelong learning on the professional lives of employed and unemployed individuals. Lifelong learning is a crucial factor in securing employment or enhancing one's existing career prospects. To achieve this objective, this study proposes the implementation of a multi-criteria decision ... | {
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2412.01353 | Su-RoBERTa: A Semi-supervised Approach to Predicting Suicide Risk
through Social Media using Base Language Models | [
"cs.HC",
"cs.AI",
"cs.SI"
] | In recent times, more and more people are posting about their mental states across various social media platforms. Leveraging this data, AI-based systems can be developed that help in assessing the mental health of individuals, such as suicide risk. This paper is a study done on suicidal risk assessments using Reddit d... | {
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2412.01354 | Integrative CAM: Adaptive Layer Fusion for Comprehensive Interpretation
of CNNs | [
"cs.CV",
"cs.AI"
] | With the growing demand for interpretable deep learning models, this paper introduces Integrative CAM, an advanced Class Activation Mapping (CAM) technique aimed at providing a holistic view of feature importance across Convolutional Neural Networks (CNNs). Traditional gradient-based CAM methods, such as Grad-CAM and G... | {
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2412.01357 | DeFi: Concepts and Ecosystem | [
"cs.CE"
] | This paper investigates the evolving landscape of decentralized finance (DeFi) by examining its foundational concepts, research trends, and ecosystem. A bibliometric analysis was conducted to identify thematic clusters and track the evolution of DeFi research. Additionally, a thematic review was performed to analyze th... | {
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2412.01363 | Exploring the Robustness of AI-Driven Tools in Digital Forensics: A
Preliminary Study | [
"cs.CV"
] | Nowadays, many tools are used to facilitate forensic tasks about data extraction and data analysis. In particular, some tools leverage Artificial Intelligence (AI) to automatically label examined data into specific categories (\ie, drugs, weapons, nudity). However, this raises a serious concern about the robustness of ... | {
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2412.01365 | Explaining the Unexplained: Revealing Hidden Correlations for Better
Interpretability | [
"cs.LG",
"cs.AI"
] | Deep learning has achieved remarkable success in processing and managing unstructured data. However, its "black box" nature imposes significant limitations, particularly in sensitive application domains. While existing interpretable machine learning methods address some of these issues, they often fail to adequately co... | {
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2412.01369 | Behavior Backdoor for Deep Learning Models | [
"cs.LG",
"cs.AI"
] | The various post-processing methods for deep-learning-based models, such as quantification, pruning, and fine-tuning, play an increasingly important role in artificial intelligence technology, with pre-train large models as one of the main development directions. However, this popular series of post-processing behavior... | {
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2412.01370 | Understanding the World's Museums through Vision-Language Reasoning | [
"cs.CV",
"cs.CL"
] | Museums serve as vital repositories of cultural heritage and historical artifacts spanning diverse epochs, civilizations, and regions, preserving well-documented collections. Data reveal key attributes such as age, origin, material, and cultural significance. Understanding museum exhibits from their images requires rea... | {
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2412.01371 | An overview of diffusion models for generative artificial intelligence | [
"cs.LG",
"cs.AI"
] | This article provides a mathematically rigorous introduction to denoising diffusion probabilistic models (DDPMs), sometimes also referred to as diffusion probabilistic models or diffusion models, for generative artificial intelligence. We provide a detailed basic mathematical framework for DDPMs and explain the main id... | {
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2412.01372 | Research on Cervical Cancer p16/Ki-67 Immunohistochemical Dual-Staining
Image Recognition Algorithm Based on YOLO | [
"cs.AI"
] | The p16/Ki-67 dual staining method is a new approach for cervical cancer screening with high sensitivity and specificity. However, there are issues of mis-detection and inaccurate recognition when the YOLOv5s algorithm is directly applied to dual-stained cell images. This paper Proposes a novel cervical cancer dual-sta... | {
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2412.01373 | Hierarchical VAE with a Diffusion-based VampPrior | [
"cs.LG",
"stat.ML"
] | Deep hierarchical variational autoencoders (VAEs) are powerful latent variable generative models. In this paper, we introduce Hierarchical VAE with Diffusion-based Variational Mixture of the Posterior Prior (VampPrior). We apply amortization to scale the VampPrior to models with many stochastic layers. The proposed app... | {
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2412.01376 | Convolutional Transformer Neural Collaborative Filtering | [
"cs.AI",
"cs.LG"
] | In this study, we introduce Convolutional Transformer Neural Collaborative Filtering (CTNCF), a novel approach aimed at enhancing recommendation systems by effectively capturing high-order structural information in user-item interactions. CTNCF represents a significant advancement over the traditional Neural Collaborat... | {
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2412.01377 | Adapting Large Language Models to Log Analysis with Interpretable Domain
Knowledge | [
"cs.CL",
"cs.SE"
] | The increasing complexity of computer systems necessitates innovative approaches to fault and error management, going beyond traditional manual log analysis. While existing solutions using large language models (LLMs) show promise, they are limited by a gap between natural and domain-specific languages, which restricts... | {
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2412.01378 | A Survey on Deep Neural Networks in Collaborative Filtering
Recommendation Systems | [
"cs.AI",
"cs.LG"
] | This survey provides an examination of the use of Deep Neural Networks (DNN) in Collaborative Filtering (CF) recommendation systems. As the digital world increasingly relies on data-driven approaches, traditional CF techniques face limitations in scalability and flexibility. DNNs can address these challenges by effecti... | {
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2412.01379 | A deformation-based framework for learning solution mappings of PDEs
defined on varying domains | [
"math.NA",
"cs.LG",
"cs.NA"
] | In this work, we establish a deformation-based framework for learning solution mappings of PDEs defined on varying domains. The union of functions defined on varying domains can be identified as a metric space according to the deformation, then the solution mapping is regarded as a continuous metric-to-metric mapping, ... | {
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} |
2412.01380 | Efficient LLM Inference using Dynamic Input Pruning and Cache-Aware
Masking | [
"cs.LG",
"cs.CL"
] | While mobile devices provide ever more compute power, improvements in DRAM bandwidth are much slower. This is unfortunate for large language model (LLM) token generation, which is heavily memory-bound. Previous work has proposed to leverage natural dynamic activation sparsity in ReLU-activated LLMs to reduce effective ... | {
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} |
2412.01383 | Second FRCSyn-onGoing: Winning Solutions and Post-Challenge Analysis to
Improve Face Recognition with Synthetic Data | [
"cs.CV",
"cs.AI",
"cs.CY",
"cs.LG"
] | Synthetic data is gaining increasing popularity for face recognition technologies, mainly due to the privacy concerns and challenges associated with obtaining real data, including diverse scenarios, quality, and demographic groups, among others. It also offers some advantages over real data, such as the large amount of... | {
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} |
2412.01386 | CLASSLA-Express: a Train of CLARIN.SI Workshops on Language Resources
and Tools with Easily Expanding Route | [
"cs.CL"
] | This paper introduces the CLASSLA-Express workshop series as an innovative approach to disseminating linguistic resources and infrastructure provided by the CLASSLA Knowledge Centre for South Slavic languages and the Slovenian CLARIN.SI infrastructure. The workshop series employs two key strategies: (1) conducting work... | {
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} |
2412.01388 | Harnessing Preference Optimisation in Protein LMs for Hit Maturation in
Cell Therapy | [
"cs.LG"
] | Cell and immunotherapy offer transformative potential for treating diseases like cancer and autoimmune disorders by modulating the immune system. The development of these therapies is resource-intensive, with the majority of drug candidates failing to progress beyond laboratory testing. While recent advances in machine... | {
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} |
2412.01389 | Refined Analysis of Federated Averaging's Bias and Federated
Richardson-Romberg Extrapolation | [
"stat.ML",
"cs.LG",
"math.OC"
] | In this paper, we present a novel analysis of FedAvg with constant step size, relying on the Markov property of the underlying process. We demonstrate that the global iterates of the algorithm converge to a stationary distribution and analyze its resulting bias and variance relative to the problem's solution. We provid... | {
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} |
2412.01393 | Machine Learning Analysis of Anomalous Diffusion | [
"cs.LG",
"cond-mat.soft",
"physics.bio-ph",
"physics.data-an"
] | The rapid advancements in machine learning have made its application to anomalous diffusion analysis both essential and inevitable. This review systematically introduces the integration of machine learning techniques for enhanced analysis of anomalous diffusion, focusing on two pivotal aspects: single trajectory charac... | {
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} |
2412.01398 | Holistic Understanding of 3D Scenes as Universal Scene Description | [
"cs.CV",
"cs.RO"
] | 3D scene understanding is a long-standing challenge in computer vision and a key component in enabling mixed reality, wearable computing, and embodied AI. Providing a solution to these applications requires a multifaceted approach that covers scene-centric, object-centric, as well as interaction-centric capabilities. W... | {
"Other": 0,
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} |
2412.01400 | Fire-Image-DenseNet (FIDN) for predicting wildfire burnt area using
remote sensing data | [
"cs.LG",
"cs.AI",
"cs.CE",
"cs.CV"
] | Predicting the extent of massive wildfires once ignited is essential to reduce the subsequent socioeconomic losses and environmental damage, but challenging because of the complexity of fire behaviour. Existing physics-based models are limited in predicting large or long-duration wildfire events. Here, we develop a dee... | {
"Other": 0,
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} |
2412.01402 | ULSR-GS: Ultra Large-scale Surface Reconstruction Gaussian Splatting
with Multi-View Geometric Consistency | [
"cs.CV"
] | While Gaussian Splatting (GS) demonstrates efficient and high-quality scene rendering and small area surface extraction ability, it falls short in handling large-scale aerial image surface extraction tasks. To overcome this, we present ULSR-GS, a framework dedicated to high-fidelity surface extraction in ultra-large-sc... | {
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"cs.SY": 0
} |
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