id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.09050 | Generating Realistic Synthetic Head Rotation Data for Extended Reality
using Deep Learning | [
"cs.CV",
"cs.AI"
] | Extended Reality is a revolutionary method of delivering multimedia content to users. A large contributor to its popularity is the sense of immersion and interactivity enabled by having real-world motion reflected in the virtual experience accurately and immediately. This user motion, mainly caused by head rotations, i... | {
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2501.09051 | Polyp detection in colonoscopy images using YOLOv11 | [
"cs.CV",
"cs.AI"
] | Colorectal cancer (CRC) is one of the most commonly diagnosed cancers all over the world. It starts as a polyp in the inner lining of the colon. To prevent CRC, early polyp detection is required. Colonosopy is used for the inspection of the colon. Generally, the images taken by the camera placed at the tip of the endos... | {
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2501.09052 | Continual Test-Time Adaptation for Single Image Defocus Deblurring via
Causal Siamese Networks | [
"eess.IV",
"cs.LG"
] | Single image defocus deblurring (SIDD) aims to restore an all-in-focus image from a defocused one. Distribution shifts in defocused images generally lead to performance degradation of existing methods during out-of-distribution inferences. In this work, we gauge the intrinsic reason behind the performance degradation, ... | {
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2501.09055 | SHYI: Action Support for Contrastive Learning in High-Fidelity
Text-to-Image Generation | [
"cs.CV"
] | In this project, we address the issue of infidelity in text-to-image generation, particularly for actions involving multiple objects. For this we build on top of the CONFORM framework which uses Contrastive Learning to improve the accuracy of the generated image for multiple objects. However the depiction of actions wh... | {
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2501.09056 | Decompose-ToM: Enhancing Theory of Mind Reasoning in Large Language
Models through Simulation and Task Decomposition | [
"cs.CL",
"cs.AI"
] | Theory of Mind (ToM) is the ability to understand and reflect on the mental states of others. Although this capability is crucial for human interaction, testing on Large Language Models (LLMs) reveals that they possess only a rudimentary understanding of it. Although the most capable closed-source LLMs have come close ... | {
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2501.09064 | Generative diffusion model with inverse renormalization group flows | [
"cond-mat.stat-mech",
"cond-mat.dis-nn",
"cs.LG",
"physics.app-ph",
"physics.bio-ph"
] | Diffusion models represent a class of generative models that produce data by denoising a sample corrupted by white noise. Despite the success of diffusion models in computer vision, audio synthesis, and point cloud generation, so far they overlook inherent multiscale structures in data and have a slow generation proces... | {
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2501.09080 | Average-Reward Reinforcement Learning with Entropy Regularization | [
"cs.LG",
"cs.AI"
] | The average-reward formulation of reinforcement learning (RL) has drawn increased interest in recent years due to its ability to solve temporally-extended problems without discounting. Independently, RL algorithms have benefited from entropy-regularization: an approach used to make the optimal policy stochastic, thereb... | {
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2501.09081 | Inferring Transition Dynamics from Value Functions | [
"cs.LG",
"cs.AI"
] | In reinforcement learning, the value function is typically trained to solve the Bellman equation, which connects the current value to future values. This temporal dependency hints that the value function may contain implicit information about the environment's transition dynamics. By rearranging the Bellman equation, w... | {
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2501.09086 | Salient Information Preserving Adversarial Training Improves Clean and
Robust Accuracy | [
"cs.CV"
] | In this work we introduce Salient Information Preserving Adversarial Training (SIP-AT), an intuitive method for relieving the robustness-accuracy trade-off incurred by traditional adversarial training. SIP-AT uses salient image regions to guide the adversarial training process in such a way that fragile features deemed... | {
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2501.09089 | Physics-Aware POD-Based Learning for Ab initio QEM-Galerkin Simulations
of Periodic Nanostructures | [
"physics.comp-ph",
"cond-mat.mtrl-sci",
"cs.CE"
] | Quantum nanostructures offer crucial applications in electronics, photonics, materials, drugs, etc. For accurate design and analysis of nanostructures and materials, simulations of the Schrodinger or Schrodinger-like equation are always needed. For large nanostructures, these eigenvalue problems can be computationally ... | {
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2501.09092 | SteLLA: A Structured Grading System Using LLMs with RAG | [
"cs.CL",
"cs.AI",
"cs.CY"
] | Large Language Models (LLMs) have shown strong general capabilities in many applications. However, how to make them reliable tools for some specific tasks such as automated short answer grading (ASAG) remains a challenge. We present SteLLA (Structured Grading System Using LLMs with RAG) in which a) Retrieval Augmented ... | {
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2501.09096 | Self Pre-training with Adaptive Mask Autoencoders for Variable-Contrast
3D Medical Imaging | [
"eess.IV",
"cs.CV"
] | The Masked Autoencoder (MAE) has recently demonstrated effectiveness in pre-training Vision Transformers (ViT) for analyzing natural images. By reconstructing complete images from partially masked inputs, the ViT encoder gathers contextual information to predict the missing regions. This capability to aggregate context... | {
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2501.09101 | Relation U-Net | [
"eess.IV",
"cs.CV"
] | Towards clinical interpretations, this paper presents a new ''output-with-confidence'' segmentation neural network with multiple input images and multiple output segmentation maps and their pairwise relations. A confidence score of the test image without ground-truth can be estimated from the difference among the estim... | {
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2501.09102 | Tracking the Takes and Trajectories of English-Language News Narratives
across Trustworthy and Worrisome Websites | [
"cs.SI",
"cs.AI",
"cs.CY",
"cs.LG"
] | Understanding how misleading and outright false information enters news ecosystems remains a difficult challenge that requires tracking how narratives spread across thousands of fringe and mainstream news websites. To do this, we introduce a system that utilizes encoder-based large language models and zero-shot stance ... | {
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2501.09103 | Similarity-Quantized Relative Difference Learning for Improved Molecular
Activity Prediction | [
"cs.LG"
] | Accurate prediction of molecular activities is crucial for efficient drug discovery, yet remains challenging due to limited and noisy datasets. We introduce Similarity-Quantized Relative Learning (SQRL), a learning framework that reformulates molecular activity prediction as relative difference learning between structu... | {
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2501.09104 | A Non-autoregressive Model for Joint STT and TTS | [
"cs.SD",
"cs.AI",
"eess.AS"
] | In this paper, we take a step towards jointly modeling automatic speech recognition (STT) and speech synthesis (TTS) in a fully non-autoregressive way. We develop a novel multimodal framework capable of handling the speech and text modalities as input either individually or together. The proposed model can also be trai... | {
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2501.09106 | Physical Layer Security in FAS-aided Wireless Powered NOMA Systems | [
"cs.IT",
"eess.SP",
"math.IT"
] | The rapid evolution of communication technologies and the emergence of sixth-generation (6G) networks have introduced unprecedented opportunities for ultra-reliable, low-latency, and energy-efficient communication. However, the integration of advanced technologies like non-orthogonal multiple access (NOMA) and wireless... | {
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2501.09107 | Rethinking Post-Training Quantization: Introducing a Statistical
Pre-Calibration Approach | [
"cs.LG"
] | As Large Language Models (LLMs) become increasingly computationally complex, developing efficient deployment strategies, such as quantization, becomes crucial. State-of-the-art Post-training Quantization (PTQ) techniques often rely on calibration processes to maintain the accuracy of these models. However, while these ... | {
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2501.09112 | Mantis Shrimp: Exploring Photometric Band Utilization in Computer Vision
Networks for Photometric Redshift Estimation | [
"astro-ph.IM",
"cs.AI"
] | We present Mantis Shrimp, a multi-survey deep learning model for photometric redshift estimation that fuses ultra-violet (GALEX), optical (PanSTARRS), and infrared (UnWISE) imagery. Machine learning is now an established approach for photometric redshift estimation, with generally acknowledged higher performance in are... | {
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2501.09114 | Generative Medical Image Anonymization Based on Latent Code Projection
and Optimization | [
"cs.CV",
"cs.AI"
] | Medical image anonymization aims to protect patient privacy by removing identifying information, while preserving the data utility to solve downstream tasks. In this paper, we address the medical image anonymization problem with a two-stage solution: latent code projection and optimization. In the projection stage, we ... | {
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2501.09116 | Deep Distance Map Regression Network with Shape-aware Loss for
Imbalanced Medical Image Segmentation | [
"eess.IV",
"cs.CV"
] | Small object segmentation, like tumor segmentation, is a difficult and critical task in the field of medical image analysis. Although deep learning based methods have achieved promising performance, they are restricted to the use of binary segmentation mask. Inspired by the rigorous mapping between binary segmentation ... | {
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2501.09117 | Multi-Class Traffic Assignment using Multi-View Heterogeneous Graph
Attention Networks | [
"cs.LG"
] | Solving traffic assignment problem for large networks is computationally challenging when conventional optimization-based methods are used. In our research, we develop an innovative surrogate model for a traffic assignment when multi-class vehicles are involved. We do so by employing heterogeneous graph neural networks... | {
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2501.09126 | Augmenting Human-Annotated Training Data with Large Language Model
Generation and Distillation in Open-Response Assessment | [
"cs.CL",
"cs.CY",
"cs.LG"
] | Large Language Models (LLMs) like GPT-4o can help automate text classification tasks at low cost and scale. However, there are major concerns about the validity and reliability of LLM outputs. By contrast, human coding is generally more reliable but expensive to procure at scale. In this study, we propose a hybrid solu... | {
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2501.09127 | Multilingual LLMs Struggle to Link Orthography and Semantics in
Bilingual Word Processing | [
"cs.CL"
] | Bilingual lexical processing is shaped by the complex interplay of phonological, orthographic, and semantic features of two languages within an integrated mental lexicon. In humans, this is evident in the ease with which cognate words - words similar in both orthographic form and meaning (e.g., blind, meaning "sightles... | {
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2501.09129 | Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1
Radiometric Terrain Corrected SAR Backscatter Product | [
"cs.CV",
"cs.LG",
"eess.IV"
] | Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions. Despite S... | {
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2501.09134 | Benchmarking Robustness of Contrastive Learning Models for Medical
Image-Report Retrieval | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.IR",
"cs.LG"
] | Medical images and reports offer invaluable insights into patient health. The heterogeneity and complexity of these data hinder effective analysis. To bridge this gap, we investigate contrastive learning models for cross-domain retrieval, which associates medical images with their corresponding clinical reports. This s... | {
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2501.09136 | Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG | [
"cs.AI",
"cs.CL",
"cs.IR"
] | Large Language Models (LLMs) have revolutionized artificial intelligence (AI) by enabling human like text generation and natural language understanding. However, their reliance on static training data limits their ability to respond to dynamic, real time queries, resulting in outdated or inaccurate outputs. Retrieval A... | {
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2501.09137 | Gradient Descent Converges Linearly to Flatter Minima than Gradient Flow
in Shallow Linear Networks | [
"cs.LG",
"math.OC",
"stat.ML"
] | We study the gradient descent (GD) dynamics of a depth-2 linear neural network with a single input and output. We show that GD converges at an explicit linear rate to a global minimum of the training loss, even with a large stepsize -- about $2/\textrm{sharpness}$. It still converges for even larger stepsizes, but may ... | {
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2501.09138 | Few-Shot Adaptation of Training-Free Foundation Model for 3D Medical
Image Segmentation | [
"cs.CV"
] | Vision foundation models have achieved remarkable progress across various image analysis tasks. In the image segmentation task, foundation models like the Segment Anything Model (SAM) enable generalizable zero-shot segmentation through user-provided prompts. However, SAM primarily trained on natural images, lacks the d... | {
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2501.09143 | Reducing real-time complexity via sub-control Lyapunov functions: from
theory to experiments | [
"eess.SY",
"cs.SY",
"math.OC"
] | The techniques to design control Lyapunov functions (CLF), along with a proper stabilizing feedback, possibly in the presence of constraints, often provide control laws that are too complex for proper implementation online, especially when an optimization problem is involved. In this work, we show how to acquire an alt... | {
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2501.09146 | Towards Federated Multi-Armed Bandit Learning for Content Dissemination
using Swarm of UAVs | [
"cs.LG",
"cs.NI"
] | This paper introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical content access in communities of users that are communication-isolated during diverse types of disaster scenarios. The proposed architecture leverages a hybrid network of stationary anchor UAVs and m... | {
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2501.09154 | Towards Multilingual LLM Evaluation for Baltic and Nordic languages: A
study on Lithuanian History | [
"cs.CL",
"cs.AI"
] | In this work, we evaluated Lithuanian and general history knowledge of multilingual Large Language Models (LLMs) on a multiple-choice question-answering task. The models were tested on a dataset of Lithuanian national and general history questions translated into Baltic, Nordic, and other languages (English, Ukrainian,... | {
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2501.09155 | VCRScore: Image captioning metric based on V\&L Transformers, CLIP, and
precision-recall | [
"cs.CV",
"cs.CL"
] | Image captioning has become an essential Vision & Language research task. It is about predicting the most accurate caption given a specific image or video. The research community has achieved impressive results by continuously proposing new models and approaches to improve the overall model's performance. Nevertheless,... | {
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2501.09158 | Evaluating GenAI for Simplifying Texts for Education: Improving Accuracy
and Consistency for Enhanced Readability | [
"cs.CL"
] | Generative artificial intelligence (GenAI) holds great promise as a tool to support personalized learning. Teachers need tools to efficiently and effectively enhance content readability of educational texts so that they are matched to individual students reading levels, while retaining key details. Large Language Model... | {
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2501.09160 | AutoLoop: Fast Visual SLAM Fine-tuning through Agentic Curriculum
Learning | [
"cs.RO",
"cs.AI",
"cs.LG"
] | Current visual SLAM systems face significant challenges in balancing computational efficiency with robust loop closure handling. Traditional approaches require careful manual tuning and incur substantial computational overhead, while learning-based methods either lack explicit loop closure capabilities or implement the... | {
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2501.09162 | A Vessel Bifurcation Landmark Pair Dataset for Abdominal CT Deformable
Image Registration (DIR) Validation | [
"cs.CV",
"physics.med-ph"
] | Deformable image registration (DIR) is an enabling technology in many diagnostic and therapeutic tasks. Despite this, DIR algorithms have limited clinical use, largely due to a lack of benchmark datasets for quality assurance during development. To support future algorithm development, here we introduce our first-of-it... | {
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2501.09163 | Towards Understanding Extrapolation: a Causal Lens | [
"cs.LG",
"cs.AI",
"stat.ML"
] | Canonical work handling distribution shifts typically necessitates an entire target distribution that lands inside the training distribution. However, practical scenarios often involve only a handful of target samples, potentially lying outside the training support, which requires the capability of extrapolation. In th... | {
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2501.09164 | The Veln(ia)s is in the Details: Evaluating LLM Judgment on Latvian and
Lithuanian Short Answer Matching | [
"cs.CL",
"cs.AI"
] | In this work, we address the challenge of evaluating large language models (LLMs) on the short answer matching task for Latvian and Lithuanian languages. We introduce novel datasets consisting of 502 Latvian and 690 Lithuanian question-answer pairs. For each question-answer pair, we generated matched and non-matched an... | {
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2501.09166 | Attention is All You Need Until You Need Retention | [
"cs.LG",
"cs.AI"
] | This work introduces a novel Retention Layer mechanism for Transformer based architectures, addressing their inherent lack of intrinsic retention capabilities. Unlike human cognition, which can encode and dynamically recall symbolic templates, Generative Pretrained Transformers rely solely on fixed pretrained weights a... | {
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2501.09167 | Embodied Scene Understanding for Vision Language Models via MetaVQA | [
"cs.CV",
"cs.RO"
] | Vision Language Models (VLMs) demonstrate significant potential as embodied AI agents for various mobility applications. However, a standardized, closed-loop benchmark for evaluating their spatial reasoning and sequential decision-making capabilities is lacking. To address this, we present MetaVQA: a comprehensive benc... | {
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2501.09171 | Generative AI Takes a Statistics Exam: A Comparison of Performance
between ChatGPT3.5, ChatGPT4, and ChatGPT4o-mini | [
"stat.OT",
"cs.LG"
] | Many believe that use of generative AI as a private tutor has the potential to shrink access and achievement gaps between students and schools with abundant resources versus those with fewer resources. Shrinking the gap is possible only if paid and free versions of the platforms perform with the same accuracy. In this ... | {
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2501.09173 | Formalising the intentional stance 2: a coinductive approach | [
"math.OC",
"cs.SY",
"eess.SY",
"math.PR"
] | Given a stochastic process with inputs and outputs, how might its behaviour be related to pursuit of a goal? We model this using 'transducers', objects that capture only the external behaviour of a system and not its internal state. A companion paper summarises our results for cognitive scientists; the current paper gi... | {
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2501.09174 | Short-time Variational Mode Decomposition | [
"cs.IT",
"math.IT"
] | Variational mode decomposition (VMD) and its extensions like Multivariate VMD (MVMD) decompose signals into ensembles of band-limited modes with narrow central frequencies. These methods utilize Fourier transformations to shift signals between time and frequency domains. However, since Fourier transformations span the ... | {
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2501.09178 | Enhancing Graph Representation Learning with Localized Topological
Features | [
"cs.LG",
"cs.SI"
] | Representation learning on graphs is a fundamental problem that can be crucial in various tasks. Graph neural networks, the dominant approach for graph representation learning, are limited in their representation power. Therefore, it can be beneficial to explicitly extract and incorporate high-order topological and geo... | {
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2501.09182 | A Blockchain-Enabled Approach to Cross-Border Compliance and Trust | [
"cs.AI",
"cs.CR",
"cs.CY",
"cs.SE"
] | As artificial intelligence (AI) systems become increasingly integral to critical infrastructure and global operations, the need for a unified, trustworthy governance framework is more urgent that ever. This paper proposes a novel approach to AI governance, utilizing blockchain and distributed ledger technologies (DLT) ... | {
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2501.09185 | Cancer-Net PCa-Seg: Benchmarking Deep Learning Models for Prostate
Cancer Segmentation Using Synthetic Correlated Diffusion Imaging | [
"eess.IV",
"cs.CV"
] | Prostate cancer (PCa) is the most prevalent cancer among men in the United States, accounting for nearly 300,000 cases, 29% of all diagnoses and 35,000 total deaths in 2024. Traditional screening methods such as prostate-specific antigen (PSA) testing and magnetic resonance imaging (MRI) have been pivotal in diagnosis,... | {
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2501.09186 | Guiding Retrieval using LLM-based Listwise Rankers | [
"cs.IR",
"cs.AI"
] | Large Language Models (LLMs) have shown strong promise as rerankers, especially in ``listwise'' settings where an LLM is prompted to rerank several search results at once. However, this ``cascading'' retrieve-and-rerank approach is limited by the bounded recall problem: relevant documents not retrieved initially are pe... | {
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2501.09187 | Patch-aware Vector Quantized Codebook Learning for Unsupervised Visual
Defect Detection | [
"cs.CV",
"cs.AI",
"cs.LG"
] | Unsupervised visual defect detection is critical in industrial applications, requiring a representation space that captures normal data features while detecting deviations. Achieving a balance between expressiveness and compactness is challenging; an overly expressive space risks inefficiency and mode collapse, impairi... | {
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2501.09189 | Testing Noise Assumptions of Learning Algorithms | [
"cs.LG",
"cs.DS"
] | We pose a fundamental question in computational learning theory: can we efficiently test whether a training set satisfies the assumptions of a given noise model? This question has remained unaddressed despite decades of research on learning in the presence of noise. In this work, we show that this task is tractable and... | {
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2501.09192 | Estimation-Aware Trajectory Optimization with Set-Valued Measurement
Uncertainties | [
"math.OC",
"cs.RO",
"cs.SY",
"eess.SY"
] | In this paper, we present an optimization-based framework for generating estimation-aware trajectories in scenarios where measurement (output) uncertainties are state-dependent and set-valued. The framework leverages the concept of regularity for set-valued output maps. Specifically, we demonstrate that, for output-reg... | {
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2501.09194 | Grounding Text-to-Image Diffusion Models for Controlled High-Quality
Image Generation | [
"cs.CV",
"cs.AI"
] | Text-to-image (T2I) generative diffusion models have demonstrated outstanding performance in synthesizing diverse, high-quality visuals from text captions. Several layout-to-image models have been developed to control the generation process by utilizing a wide range of layouts, such as segmentation maps, edges, and hum... | {
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2501.09198 | Combining Movement Primitives with Contraction Theory | [
"cs.RO"
] | This paper presents a modular framework for motion planning using movement primitives. Central to the approach is Contraction Theory, a modular stability tool for nonlinear dynamical systems. The approach extends prior methods by achieving parallel and sequential combinations of both discrete and rhythmic movements, wh... | {
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2501.09203 | Unified Few-shot Crack Segmentation and its Precise 3D Automatic
Measurement in Concrete Structures | [
"cs.CV",
"cs.RO"
] | Visual-Spatial Systems has become increasingly essential in concrete crack inspection. However, existing methods often lacks adaptability to diverse scenarios, exhibits limited robustness in image-based approaches, and struggles with curved or complex geometries. To address these limitations, an innovative framework fo... | {
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2501.09209 | Surgical Visual Understanding (SurgVU) Dataset | [
"cs.CV"
] | Owing to recent advances in machine learning and the ability to harvest large amounts of data during robotic-assisted surgeries, surgical data science is ripe for foundational work. We present a large dataset of surgical videos and their accompanying labels for this purpose. We describe how the data was collected and s... | {
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2501.09211 | Fuzzy Integration of Data Lake Tables | [
"cs.DB",
"cs.IR"
] | Data integration is an important step in any data science pipeline where the objective is to unify the information available in different datasets for comprehensive analysis. Full Disjunction, which is an associative extension of the outer join operator, has been shown to be an effective operator for integrating datase... | {
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2501.09213 | FineMedLM-o1: Enhancing the Medical Reasoning Ability of LLM from
Supervised Fine-Tuning to Test-Time Training | [
"cs.CL"
] | Recent advancements in large language models (LLMs) have shown promise in medical applications such as disease diagnosis and treatment planning. However, most existing medical LLMs struggle with the advanced reasoning required for complex clinical scenarios, such as differential diagnosis or personalized treatment sugg... | {
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2501.09214 | Boosting Short Text Classification with Multi-Source Information
Exploration and Dual-Level Contrastive Learning | [
"cs.CL"
] | Short text classification, as a research subtopic in natural language processing, is more challenging due to its semantic sparsity and insufficient labeled samples in practical scenarios. We propose a novel model named MI-DELIGHT for short text classification in this work. Specifically, it first performs multi-source i... | {
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2501.09217 | Adaptive Law-Based Transformation (ALT): A Lightweight Feature
Representation for Time Series Classification | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] | Time series classification (TSC) is fundamental in numerous domains, including finance, healthcare, and environmental monitoring. However, traditional TSC methods often struggle with the inherent complexity and variability of time series data. Building on our previous work with the linear law-based transformation (LLT)... | {
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2501.09218 | Interpretable Droplet Digital PCR Assay for Trustworthy Molecular
Diagnostics | [
"q-bio.QM",
"cs.AI"
] | Accurate molecular quantification is essential for advancing research and diagnostics in fields such as infectious diseases, cancer biology, and genetic disorders. Droplet digital PCR (ddPCR) has emerged as a gold standard for achieving absolute quantification. While computational ddPCR technologies have advanced signi... | {
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2501.09219 | A Simple Graph Contrastive Learning Framework for Short Text
Classification | [
"cs.CL"
] | Short text classification has gained significant attention in the information age due to its prevalence and real-world applications. Recent advancements in graph learning combined with contrastive learning have shown promising results in addressing the challenges of semantic sparsity and limited labeled data in short t... | {
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2501.09221 | ASCENT-ViT: Attention-based Scale-aware Concept Learning Framework for
Enhanced Alignment in Vision Transformers | [
"cs.CV",
"cs.LG"
] | As Vision Transformers (ViTs) are increasingly adopted in sensitive vision applications, there is a growing demand for improved interpretability. This has led to efforts to forward-align these models with carefully annotated abstract, human-understandable semantic entities - concepts. Concepts provide global rationales... | {
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2501.09223 | Foundations of Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | This is a book about large language models. As indicated by the title, it primarily focuses on foundational concepts rather than comprehensive coverage of all cutting-edge technologies. The book is structured into four main chapters, each exploring a key area: pre-training, generative models, prompting techniques, and ... | {
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2501.09229 | Tessellated Linear Model for Age Prediction from Voice | [
"cs.LG",
"cs.SD",
"eess.AS"
] | Voice biometric tasks, such as age estimation require modeling the often complex relationship between voice features and the biometric variable. While deep learning models can handle such complexity, they typically require large amounts of accurately labeled data to perform well. Such data are often scarce for biometri... | {
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2501.09238 | Mono-Forward: Backpropagation-Free Algorithm for Efficient Neural
Network Training Harnessing Local Errors | [
"cs.LG"
] | Backpropagation is the standard method for achieving state-of-the-art accuracy in neural network training, but it often imposes high memory costs and lacks biological plausibility. In this paper, we introduce the Mono-Forward algorithm, a purely local layerwise learning method inspired by Hinton's Forward-Forward frame... | {
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2501.09239 | AI-based Identity Fraud Detection: A Systematic Review | [
"cs.AI"
] | With the rapid development of digital services, a large volume of personally identifiable information (PII) is stored online and is subject to cyberattacks such as Identity fraud. Most recently, the use of Artificial Intelligence (AI) enabled deep fake technologies has significantly increased the complexity of identity... | {
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2501.09240 | Task Vectors in In-Context Learning: Emergence, Formation, and Benefit | [
"cs.LG"
] | In-context learning is a remarkable capability of transformers, referring to their ability to adapt to specific tasks based on a short history or context. Previous research has found that task-specific information is locally encoded within models, though their emergence and functionality remain unclear due to opaque pr... | {
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2501.09254 | Clone-Robust AI Alignment | [
"cs.LG",
"cs.AI",
"cs.GT"
] | A key challenge in training Large Language Models (LLMs) is properly aligning them with human preferences. Reinforcement Learning with Human Feedback (RLHF) uses pairwise comparisons from human annotators to train reward functions and has emerged as a popular alignment method. However, input datasets in RLHF are not ne... | {
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2501.09258 | Delayed Fusion: Integrating Large Language Models into First-Pass
Decoding in End-to-end Speech Recognition | [
"cs.CL",
"cs.SD",
"eess.AS"
] | This paper presents an efficient decoding approach for end-to-end automatic speech recognition (E2E-ASR) with large language models (LLMs). Although shallow fusion is the most common approach to incorporate language models into E2E-ASR decoding, we face two practical problems with LLMs. (1) LLM inference is computation... | {
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2501.09259 | OpticFusion: Multi-Modal Neural Implicit 3D Reconstruction of
Microstructures by Fusing White Light Interferometry and Optical Microscopy | [
"cs.CV",
"physics.app-ph",
"physics.ins-det",
"physics.optics"
] | White Light Interferometry (WLI) is a precise optical tool for measuring the 3D topography of microstructures. However, conventional WLI cannot capture the natural color of a sample's surface, which is essential for many microscale research applications that require both 3D geometry and color information. Previous meth... | {
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2501.09262 | On the convergence rate of noisy Bayesian Optimization with Expected
Improvement | [
"stat.ML",
"cs.LG",
"math.OC"
] | Expected improvement (EI) is one of the most widely used acquisition functions in Bayesian optimization (BO). Despite its proven success in applications for decades, important open questions remain on the theoretical convergence behaviors and rates for EI. In this paper, we contribute to the convergence theory of EI in... | {
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2501.09265 | Perspective Transition of Large Language Models for Solving Subjective
Tasks | [
"cs.CL",
"cs.AI"
] | Large language models (LLMs) have revolutionized the field of natural language processing, enabling remarkable progress in various tasks. Different from objective tasks such as commonsense reasoning and arithmetic question-answering, the performance of LLMs on subjective tasks is still limited, where the perspective on... | {
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2501.09267 | Are Open-Vocabulary Models Ready for Detection of MEP Elements on
Construction Sites | [
"cs.CV",
"cs.RO"
] | The construction industry has long explored robotics and computer vision, yet their deployment on construction sites remains very limited. These technologies have the potential to revolutionize traditional workflows by enhancing accuracy, efficiency, and safety in construction management. Ground robots equipped with ad... | {
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2501.09268 | Knowledge Distillation for Image Restoration : Simultaneous Learning
from Degraded and Clean Images | [
"cs.CV",
"eess.IV"
] | Model compression through knowledge distillation has seen extensive application in classification and segmentation tasks. However, its potential in image-to-image translation, particularly in image restoration, remains underexplored. To address this gap, we propose a Simultaneous Learning Knowledge Distillation (SLKD) ... | {
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2501.09273 | ThinTact:Thin Vision-Based Tactile Sensor by Lensless Imaging | [
"cs.RO"
] | Vision-based tactile sensors have drawn increasing interest in the robotics community. However, traditional lens-based designs impose minimum thickness constraints on these sensors, limiting their applicability in space-restricted settings. In this paper, we propose ThinTact, a novel lensless vision-based tactile senso... | {
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2501.09274 | Large Language Model is Secretly a Protein Sequence Optimizer | [
"cs.LG",
"cs.AI",
"q-bio.QM"
] | We consider the protein sequence engineering problem, which aims to find protein sequences with high fitness levels, starting from a given wild-type sequence. Directed evolution has been a dominating paradigm in this field which has an iterative process to generate variants and select via experimental feedback. We demo... | {
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2501.09275 | MagnetDB: A Longitudinal Torrent Discovery Dataset with IMDb-Matched
Movies and TV Shows | [
"cs.CY",
"cs.MM",
"cs.NI",
"cs.SI"
] | BitTorrent remains a prominent channel for illicit distribution of copyrighted material, yet the supply side of such content remains understudied. We introduce MagnetDB, a longitudinal dataset of torrents discovered through the BitTorrent DHT between 2018 and 2024, containing more than 28.6 million torrents and metadat... | {
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2501.09277 | Bias for Action: Video Implicit Neural Representations with Bias
Modulation | [
"cs.CV"
] | We propose a new continuous video modeling framework based on implicit neural representations (INRs) called ActINR. At the core of our approach is the observation that INRs can be considered as a learnable dictionary, with the shapes of the basis functions governed by the weights of the INR, and their locations governe... | {
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2501.09278 | Text-guided Synthetic Geometric Augmentation for Zero-shot 3D
Understanding | [
"cs.CV"
] | Zero-shot recognition models require extensive training data for generalization. However, in zero-shot 3D classification, collecting 3D data and captions is costly and laborintensive, posing a significant barrier compared to 2D vision. Recent advances in generative models have achieved unprecedented realism in syntheti... | {
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2501.09279 | Text Semantics to Flexible Design: A Residential Layout Generation
Method Based on Stable Diffusion Model | [
"cs.AI"
] | Flexibility in the AI-based residential layout design remains a significant challenge, as traditional methods like rule-based heuristics and graph-based generation often lack flexibility and require substantial design knowledge from users. To address these limitations, we propose a cross-modal design approach based on ... | {
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2501.09281 | SoccerSynth-Detection: A Synthetic Dataset for Soccer Player Detection | [
"cs.CV"
] | In soccer video analysis, player detection is essential for identifying key events and reconstructing tactical positions. The presence of numerous players and frequent occlusions, combined with copyright restrictions, severely restricts the availability of datasets, leaving limited options such as SoccerNet-Tracking an... | {
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2501.09283 | Free-Knots Kolmogorov-Arnold Network: On the Analysis of Spline Knots
and Advancing Stability | [
"cs.LG"
] | Kolmogorov-Arnold Neural Networks (KANs) have gained significant attention in the machine learning community. However, their implementation often suffers from poor training stability and heavy trainable parameter. Furthermore, there is limited understanding of the behavior of the learned activation functions derived fr... | {
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2501.09284 | SEAL: Entangled White-box Watermarks on Low-Rank Adaptation | [
"cs.AI",
"cs.CR"
] | Recently, LoRA and its variants have become the de facto strategy for training and sharing task-specific versions of large pretrained models, thanks to their efficiency and simplicity. However, the issue of copyright protection for LoRA weights, especially through watermark-based techniques, remains underexplored. To a... | {
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2501.09289 | Control Barrier Function-Based Safety Filters: Characterization of
Undesired Equilibria, Unbounded Trajectories, and Limit Cycles | [
"math.OC",
"cs.SY",
"eess.SY"
] | This paper focuses on safety filters designed based on Control Barrier Functions (CBFs): these are modifications of a nominal stabilizing controller typically utilized in safety-critical control applications to render a given subset of states forward invariant. The paper investigates the dynamical properties of the clo... | {
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2501.09290 | Interoceptive Robots for Convergent Shared Control in Collaborative
Construction Work | [
"cs.RO"
] | Building autonomous mobile robots (AMRs) with optimized efficiency and adaptive capabilities-able to respond to changing task demands and dynamic environments-is a strongly desired goal for advancing construction robotics. Such robots can play a critical role in enabling automation, reducing operational carbon footprin... | {
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2501.09291 | LAVCap: LLM-based Audio-Visual Captioning using Optimal Transport | [
"cs.MM",
"cs.AI",
"cs.SD",
"eess.AS"
] | Automated audio captioning is a task that generates textual descriptions for audio content, and recent studies have explored using visual information to enhance captioning quality. However, current methods often fail to effectively fuse audio and visual data, missing important semantic cues from each modality. To addre... | {
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2501.09292 | To Retrieve or Not to Retrieve? Uncertainty Detection for Dynamic
Retrieval Augmented Generation | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Retrieval-Augmented Generation equips large language models with the capability to retrieve external knowledge, thereby mitigating hallucinations by incorporating information beyond the model's intrinsic abilities. However, most prior works have focused on invoking retrieval deterministically, which makes it unsuitable... | {
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2501.09294 | Efficient Few-Shot Medical Image Analysis via Hierarchical Contrastive
Vision-Language Learning | [
"cs.CV",
"cs.CL"
] | Few-shot learning in medical image classification presents a significant challenge due to the limited availability of annotated data and the complex nature of medical imagery. In this work, we propose Adaptive Vision-Language Fine-tuning with Hierarchical Contrastive Alignment (HiCA), a novel framework that leverages t... | {
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2501.09298 | Physics-informed deep learning for infectious disease forecasting | [
"cs.LG",
"q-bio.QM"
] | Accurate forecasting of contagious illnesses has become increasingly important to public health policymaking, and better prediction could prevent the loss of millions of lives. To better prepare for future pandemics, it is essential to improve forecasting methods and capabilities. In this work, we propose a new infecti... | {
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2501.09302 | Creating Virtual Environments with 3D Gaussian Splatting: A Comparative
Study | [
"cs.CV",
"cs.GR",
"cs.HC"
] | 3D Gaussian Splatting (3DGS) has recently emerged as an innovative and efficient 3D representation technique. While its potential for extended reality (XR) applications is frequently highlighted, its practical effectiveness remains underexplored. In this work, we examine three distinct 3DGS-based approaches for virtual... | {
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2501.09304 | Finding the Trigger: Causal Abductive Reasoning on Video Events | [
"cs.CV",
"cs.LG"
] | This paper introduces a new problem, Causal Abductive Reasoning on Video Events (CARVE), which involves identifying causal relationships between events in a video and generating hypotheses about causal chains that account for the occurrence of a target event. To facilitate research in this direction, we create two new ... | {
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} |
2501.09305 | Domain-conditioned and Temporal-guided Diffusion Modeling for
Accelerated Dynamic MRI Reconstruction | [
"eess.IV",
"cs.CV",
"physics.med-ph"
] | Purpose: To propose a domain-conditioned and temporal-guided diffusion modeling method, termed dynamic Diffusion Modeling (dDiMo), for accelerated dynamic MRI reconstruction, enabling diffusion process to characterize spatiotemporal information for time-resolved multi-coil Cartesian and non-Cartesian data. Methods: The... | {
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} |
2501.09307 | RoboReflect: Robotic Reflective Reasoning for Grasping
Ambiguous-Condition Objects | [
"cs.RO"
] | As robotic technology rapidly develops, robots are being employed in an increasing number of fields. However, due to the complexity of deployment environments or the prevalence of ambiguous-condition objects, the practical application of robotics still faces many challenges, leading to frequent errors. Traditional meth... | {
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} |
2501.09309 | Understanding Mental Health Content on Social Media and Its Effect
Towards Suicidal Ideation | [
"cs.CY",
"cs.AI",
"cs.CL"
] | This review underscores the critical need for effective strategies to identify and support individuals with suicidal ideation, exploiting technological innovations in ML and DL to further suicide prevention efforts. The study details the application of these technologies in analyzing vast amounts of unstructured social... | {
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} |
2501.09310 | A Study of In-Context-Learning-Based Text-to-SQL Errors | [
"cs.CL",
"cs.AI",
"cs.SE"
] | Large language models (LLMs) have been adopted to perform text-to-SQL tasks, utilizing their in-context learning (ICL) capability to translate natural language questions into structured query language (SQL). However, such a technique faces correctness problems and requires efficient repairing solutions. In this paper, ... | {
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} |
2501.09311 | Shape-Based Single Object Classification Using Ensemble Method
Classifiers | [
"cs.CV",
"cs.AI",
"cs.CL"
] | Nowadays, more and more images are available. Annotation and retrieval of the images pose classification problems, where each class is defined as the group of database images labelled with a common semantic label. Various systems have been proposed for content-based retrieval, as well as for image classification and in... | {
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} |
2501.09316 | SOP-Agent: Empower General Purpose AI Agent with Domain-Specific SOPs | [
"cs.AI"
] | Despite significant advancements in general-purpose AI agents, several challenges still hinder their practical application in real-world scenarios. First, the limited planning capabilities of Large Language Models (LLM) restrict AI agents from effectively solving complex tasks that require long-horizon planning. Second... | {
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} |
2501.09320 | Cooperative Decentralized Backdoor Attacks on Vertical Federated
Learning | [
"cs.LG",
"cs.CR"
] | Federated learning (FL) is vulnerable to backdoor attacks, where adversaries alter model behavior on target classification labels by embedding triggers into data samples. While these attacks have received considerable attention in horizontal FL, they are less understood for vertical FL (VFL), where devices hold differe... | {
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} |
2501.09321 | Soft Knowledge Distillation with Multi-Dimensional Cross-Net Attention
for Image Restoration Models Compression | [
"cs.CV"
] | Transformer-based encoder-decoder models have achieved remarkable success in image-to-image transfer tasks, particularly in image restoration. However, their high computational complexity-manifested in elevated FLOPs and parameter counts-limits their application in real-world scenarios. Existing knowledge distillation ... | {
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} |
2501.09324 | Safety-Critical Control for Discrete-time Stochastic Systems with
Flexible Safe Bounds using Affine and Quadratic Control Barrier Functions | [
"eess.SY",
"cs.SY"
] | This paper presents a safe controller synthesis of discrete-time stochastic systems using Control Barrier Functions (CBFs). The proposed condition allows the design of a safe controller synthesis that ensures system safety while avoiding the conservative bounds of safe probabilities. In particular, this study focuses o... | {
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} |
2501.09326 | Algorithm for Semantic Network Generation from Texts of Low Resource
Languages Such as Kiswahili | [
"cs.CL"
] | Processing low-resource languages, such as Kiswahili, using machine learning is difficult due to lack of adequate training data. However, such low-resource languages are still important for human communication and are already in daily use and users need practical machine processing tasks such as summarization, disambig... | {
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} |
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