id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2502.06243 | Multi-Scale Transformer Architecture for Accurate Medical Image
Classification | [
"cs.CV",
"cs.LG"
] | This study introduces an AI-driven skin lesion classification algorithm built on an enhanced Transformer architecture, addressing the challenges of accuracy and robustness in medical image analysis. By integrating a multi-scale feature fusion mechanism and refining the self-attention process, the model effectively extr... | {
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2502.06244 | PiKE: Adaptive Data Mixing for Multi-Task Learning Under Low Gradient
Conflicts | [
"cs.LG"
] | Modern machine learning models are trained on diverse datasets and tasks to improve generalization. A key challenge in multitask learning is determining the optimal data mixing and sampling strategy across different data sources. Prior research in this multi-task learning setting has primarily focused on mitigating gra... | {
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2502.06247 | Advance sharing for stabilizer-based quantum secret sharing schemes | [
"quant-ph",
"cs.CR",
"cs.IT",
"math.IT"
] | In stabilizer-based quantum secret sharing schemes, it is known that some shares can be distributed to participants before a secret is given to the dealer. This distribution is known as advance sharing. It is already known that a set of shares is advance shareable only if it is a forbidden set. However, it was not know... | {
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2502.06249 | Conditioning through indifference in quantum mechanics | [
"quant-ph",
"cs.AI",
"math.PR"
] | We can learn (more) about the state a quantum system is in through measurements. We look at how to describe the uncertainty about a quantum system's state conditional on executing such measurements. We show that by exploiting the interplay between desirability, coherence and indifference, a general rule for conditionin... | {
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2502.06250 | DGNO: A Novel Physics-aware Neural Operator for Solving Forward and
Inverse PDE Problems based on Deep, Generative Probabilistic Modeling | [
"cs.LG",
"math-ph",
"math.MP"
] | Solving parametric partial differential equations (PDEs) and associated PDE-based, inverse problems is a central task in engineering and physics, yet existing neural operator methods struggle with high-dimensional, discontinuous inputs and require large amounts of {\em labeled} training data. We propose the Deep Genera... | {
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2502.06252 | Evaluating Entity Retrieval in Electronic Health Records: a Semantic Gap
Perspective | [
"cs.IR",
"cs.CL"
] | Entity retrieval plays a crucial role in the utilization of Electronic Health Records (EHRs) and is applied across a wide range of clinical practices. However, a comprehensive evaluation of this task is lacking due to the absence of a public benchmark. In this paper, we propose the development and release of a novel be... | {
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2502.06255 | Towards Efficient and Intelligent Laser Weeding: Method and Dataset for
Weed Stem Detection | [
"cs.CV",
"cs.AI"
] | Weed control is a critical challenge in modern agriculture, as weeds compete with crops for essential nutrient resources, significantly reducing crop yield and quality. Traditional weed control methods, including chemical and mechanical approaches, have real-life limitations such as associated environmental impact and ... | {
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2502.06257 | K-ON: Stacking Knowledge On the Head Layer of Large Language Model | [
"cs.CL",
"cs.AI"
] | Recent advancements in large language models (LLMs) have significantly improved various natural language processing (NLP) tasks. Typically, LLMs are trained to predict the next token, aligning well with many NLP tasks. However, in knowledge graph (KG) scenarios, entities are the fundamental units and identifying an ent... | {
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2502.06258 | Emergent Response Planning in LLM | [
"cs.CL",
"cs.LG"
] | In this work, we argue that large language models (LLMs), though trained to predict only the next token, exhibit emergent planning behaviors: $\textbf{their hidden representations encode future outputs beyond the next token}$. Through simple probing, we demonstrate that LLM prompt representations encode global attribut... | {
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2502.06261 | Reducing Variance Caused by Communication in Decentralized Multi-agent
Deep Reinforcement Learning | [
"cs.LG"
] | In decentralized multi-agent deep reinforcement learning (MADRL), communication can help agents to gain a better understanding of the environment to better coordinate their behaviors. Nevertheless, communication may involve uncertainty, which potentially introduces variance to the learning of decentralized agents. In t... | {
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2502.06268 | Spectral-factorized Positive-definite Curvature Learning for NN Training | [
"stat.ML",
"cs.LG"
] | Many training methods, such as Adam(W) and Shampoo, learn a positive-definite curvature matrix and apply an inverse root before preconditioning. Recently, non-diagonal training methods, such as Shampoo, have gained significant attention; however, they remain computationally inefficient and are limited to specific types... | {
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2502.06269 | Progressive Collaborative and Semantic Knowledge Fusion for Generative
Recommendation | [
"cs.IR"
] | With the recent surge in interest surrounding generative paradigms, generative recommendation has increasingly attracted the attention of researchers in the recommendation community. This paradigm generally consists of two stages. In the first stage, pretrained semantic embeddings or collaborative ID embeddings are qua... | {
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2502.06272 | Beyond Batch Learning: Global Awareness Enhanced Domain Adaptation | [
"cs.LG"
] | In domain adaptation (DA), the effectiveness of deep learning-based models is often constrained by batch learning strategies that fail to fully apprehend the global statistical and geometric characteristics of data distributions. Addressing this gap, we introduce 'Global Awareness Enhanced Domain Adaptation' (GAN-DA), ... | {
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2502.06274 | HODDI: A Dataset of High-Order Drug-Drug Interactions for Computational
Pharmacovigilance | [
"cs.LG",
"cs.AI",
"q-bio.MN"
] | Drug-side effect research is vital for understanding adverse reactions arising in complex multi-drug therapies. However, the scarcity of higher-order datasets that capture the combinatorial effects of multiple drugs severely limits progress in this field. Existing resources such as TWOSIDES primarily focus on pairwise ... | {
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2502.06279 | DebateBench: A Challenging Long Context Reasoning Benchmark For Large
Language Models | [
"cs.CL",
"cs.LG"
] | We introduce DebateBench, a novel dataset consisting of an extensive collection of transcripts and metadata from some of the world's most prestigious competitive debates. The dataset consists of British Parliamentary debates from prestigious debating tournaments on diverse topics, annotated with detailed speech-level s... | {
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2502.06280 | IceBerg: Debiased Self-Training for Class-Imbalanced Node Classification | [
"cs.LG"
] | Graph Neural Networks (GNNs) have achieved great success in dealing with non-Euclidean graph-structured data and have been widely deployed in many real-world applications. However, their effectiveness is often jeopardized under class-imbalanced training sets. Most existing studies have analyzed class-imbalanced node cl... | {
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2502.06281 | Application of quantum machine learning using quantum kernel algorithms
on multiclass neuron M type classification | [
"quant-ph",
"cs.LG"
] | The functional characterization of different neuronal types has been a longstanding and crucial challenge. With the advent of physical quantum computers, it has become possible to apply quantum machine learning algorithms to translate theoretical research into practical solutions. Previous studies have shown the advant... | {
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2502.06282 | Jakiro: Boosting Speculative Decoding with Decoupled Multi-Head via MoE | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Speculative decoding (SD) accelerates large language model inference by using a smaller draft model to predict multiple tokens, which are then verified in parallel by the larger target model. However, the limited capacity of the draft model often necessitates tree-based sampling to improve prediction accuracy, where mu... | {
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2502.06283 | On the Expressiveness of Rational ReLU Neural Networks With Bounded
Depth | [
"cs.LG",
"cs.DM"
] | To confirm that the expressive power of ReLU neural networks grows with their depth, the function $F_n = \max \{0,x_1,\ldots,x_n\}$ has been considered in the literature. A conjecture by Hertrich, Basu, Di Summa, and Skutella [NeurIPS 2021] states that any ReLU network that exactly represents $F_n$ has at least $\lceil... | {
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2502.06285 | End-to-End Multi-Microphone Speaker Extraction Using Relative Transfer
Functions | [
"cs.SD",
"cs.AI"
] | This paper introduces a multi-microphone method for extracting a desired speaker from a mixture involving multiple speakers and directional noise in a reverberant environment. In this work, we propose leveraging the instantaneous relative transfer function (RTF), estimated from a reference utterance recorded in the sam... | {
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2502.06287 | CT-UIO: Continuous-Time UWB-Inertial-Odometer Localization Using
Non-Uniform B-spline with Fewer Anchors | [
"cs.RO"
] | Ultra-wideband (UWB) based positioning with fewer anchors has attracted significant research interest in recent years, especially under energy-constrained conditions. However, most existing methods rely on discrete-time representations and smoothness priors to infer a robot's motion states, which often struggle with en... | {
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2502.06288 | Enhancing Ground-to-Aerial Image Matching for Visual Misinformation
Detection Using Semantic Segmentation | [
"cs.CV"
] | The recent advancements in generative AI techniques, which have significantly increased the online dissemination of altered images and videos, have raised serious concerns about the credibility of digital media available on the Internet and distributed through information channels and social networks. This issue partic... | {
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2502.06289 | Is an Ultra Large Natural Image-Based Foundation Model Superior to a
Retina-Specific Model for Detecting Ocular and Systemic Diseases? | [
"eess.IV",
"cs.AI",
"cs.CV"
] | The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.6 million retinal images, has demonstrated high adaptability across clinical applications. Conversely, DINOv2, a general-purpose vision FM p... | {
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2502.06292 | Occupancy-SLAM: An Efficient and Robust Algorithm for Simultaneously
Optimizing Robot Poses and Occupancy Map | [
"cs.RO"
] | Joint optimization of poses and features has been extensively studied and demonstrated to yield more accurate results in feature-based SLAM problems. However, research on jointly optimizing poses and non-feature-based maps remains limited. Occupancy maps are widely used non-feature-based environment representations bec... | {
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2502.06295 | DVFS-Aware DNN Inference on GPUs: Latency Modeling and Performance
Analysis | [
"cs.LG",
"cs.NI"
] | The rapid development of deep neural networks (DNNs) is inherently accompanied by the problem of high computational costs. To tackle this challenge, dynamic voltage frequency scaling (DVFS) is emerging as a promising technology for balancing the latency and energy consumption of DNN inference by adjusting the computing... | {
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2502.06298 | SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual
Questions in Southeast Asia | [
"cs.CL",
"cs.AI"
] | This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenarios. Unlike existing multilingual datasets primarily derived from English translations, these benchmarks are constructed based on real-world ... | {
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2502.06300 | The impact of allocation strategies in subset learning on the expressive
power of neural networks | [
"cs.LG"
] | In traditional machine learning, models are defined by a set of parameters, which are optimized to perform specific tasks. In neural networks, these parameters correspond to the synaptic weights. However, in reality, it is often infeasible to control or update all weights. This challenge is not limited to artificial ne... | {
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2502.06301 | Utilizing Novelty-based Evolution Strategies to Train Transformers in
Reinforcement Learning | [
"cs.LG",
"cs.NE"
] | In this paper, we experiment with novelty-based variants of OpenAI-ES, the NS-ES and NSR-ES algorithms, and evaluate their effectiveness in training complex, transformer-based architectures designed for the problem of reinforcement learning such as Decision Transformers. We also test if we can accelerate the novelty-ba... | {
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2502.06302 | Latent Convergence Modulation in Large Language Models: A Novel Approach
to Iterative Contextual Realignment | [
"cs.CL"
] | Token prediction stability remains a challenge in autoregressive generative models, where minor variations in early inference steps often lead to significant semantic drift over extended sequences. A structured modulation mechanism was introduced to regulate hidden state transitions, ensuring that latent representation... | {
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2502.06307 | Cell Nuclei Detection and Classification in Whole Slide Images with
Transformers | [
"cs.CV"
] | Accurate and efficient cell nuclei detection and classification in histopathological Whole Slide Images (WSIs) are pivotal for digital pathology applications. Traditional cell segmentation approaches, while commonly used, are computationally expensive and require extensive post-processing, limiting their practicality f... | {
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2502.06309 | Analog In-memory Training on General Non-ideal Resistive Elements: The
Impact of Response Functions | [
"cs.LG",
"cs.AR",
"math.OC"
] | As the economic and environmental costs of training and deploying large vision or language models increase dramatically, analog in-memory computing (AIMC) emerges as a promising energy-efficient solution. However, the training perspective, especially its training dynamic, is underexplored. In AIMC hardware, the trainab... | {
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2502.06314 | From Pixels to Components: Eigenvector Masking for Visual Representation
Learning | [
"cs.LG",
"cs.AI",
"cs.CV"
] | Predicting masked from visible parts of an image is a powerful self-supervised approach for visual representation learning. However, the common practice of masking random patches of pixels exhibits certain failure modes, which can prevent learning meaningful high-level features, as required for downstream tasks. We pro... | {
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2502.06316 | Can AI Examine Novelty of Patents?: Novelty Evaluation Based on the
Correspondence between Patent Claim and Prior Art | [
"cs.CL"
] | Assessing the novelty of patent claims is a critical yet challenging task traditionally performed by patent examiners. While advancements in NLP have enabled progress in various patent-related tasks, novelty assessment remains unexplored. This paper introduces a novel challenge by evaluating the ability of large langua... | {
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2502.06323 | A physics-based data-driven model for CO$_2$ gas diffusion electrodes to
drive automated laboratories | [
"cond-mat.mtrl-sci",
"cs.LG"
] | The electrochemical reduction of atmospheric CO$_2$ into high-energy molecules with renewable energy is a promising avenue for energy storage that can take advantage of existing infrastructure especially in areas where sustainable alternatives to fossil fuels do not exist. Automated laboratories are currently being dev... | {
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2502.06324 | UniDemoir\'e: Towards Universal Image Demoir\'eing with Data Generation
and Synthesis | [
"cs.CV",
"cs.AI"
] | Image demoir\'eing poses one of the most formidable challenges in image restoration, primarily due to the unpredictable and anisotropic nature of moir\'e patterns. Limited by the quantity and diversity of training data, current methods tend to overfit to a single moir\'e domain, resulting in performance degradation for... | {
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2502.06327 | Prompt-Driven Continual Graph Learning | [
"cs.LG",
"cs.AI"
] | Continual Graph Learning (CGL), which aims to accommodate new tasks over evolving graph data without forgetting prior knowledge, is garnering significant research interest. Mainstream solutions adopt the memory replay-based idea, ie, caching representative data from earlier tasks for retraining the graph model. However... | {
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2502.06329 | Expect the Unexpected: FailSafe Long Context QA for Finance | [
"cs.CL"
] | We propose a new long-context financial benchmark, FailSafeQA, designed to test the robustness and context-awareness of LLMs against six variations in human-interface interactions in LLM-based query-answer systems within finance. We concentrate on two case studies: Query Failure and Context Failure. In the Query Failur... | {
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2502.06331 | Conformal Prediction Regions are Imprecise Highest Density Regions | [
"stat.ML",
"cs.LG",
"math.PR"
] | Recently, Cella and Martin proved how, under an assumption called consonance, a credal set (i.e. a closed and convex set of probabilities) can be derived from the conformal transducer associated with transductive conformal prediction. We show that the Imprecise Highest Density Region (IHDR) associated with such a creda... | {
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2502.06335 | Microcanonical Langevin Ensembles: Advancing the Sampling of Bayesian
Neural Networks | [
"cs.LG"
] | Despite recent advances, sampling-based inference for Bayesian Neural Networks (BNNs) remains a significant challenge in probabilistic deep learning. While sampling-based approaches do not require a variational distribution assumption, current state-of-the-art samplers still struggle to navigate the complex and highly ... | {
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2502.06336 | DefTransNet: A Transformer-based Method for Non-Rigid Point Cloud
Registration in the Simulation of Soft Tissue Deformation | [
"cs.CV",
"cs.AI"
] | Soft-tissue surgeries, such as tumor resections, are complicated by tissue deformations that can obscure the accurate location and shape of tissues. By representing tissue surfaces as point clouds and applying non-rigid point cloud registration (PCR) methods, surgeons can better understand tissue deformations before, d... | {
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2502.06337 | Accelerating Outlier-robust Rotation Estimation by Stereographic
Projection | [
"cs.CV",
"cs.RO"
] | Rotation estimation plays a fundamental role in many computer vision and robot tasks. However, efficiently estimating rotation in large inputs containing numerous outliers (i.e., mismatches) and noise is a recognized challenge. Many robust rotation estimation methods have been designed to address this challenge. Unfort... | {
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2502.06338 | Zero-shot Depth Completion via Test-time Alignment with Affine-invariant
Depth Prior | [
"cs.CV"
] | Depth completion, predicting dense depth maps from sparse depth measurements, is an ill-posed problem requiring prior knowledge. Recent methods adopt learning-based approaches to implicitly capture priors, but the priors primarily fit in-domain data and do not generalize well to out-of-domain scenarios. To address this... | {
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2502.06341 | Facial Analysis Systems and Down Syndrome | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.LG"
] | The ethical, social and legal issues surrounding facial analysis technologies have been widely debated in recent years. Key critics have argued that these technologies can perpetuate bias and discrimination, particularly against marginalized groups. We contribute to this field of research by reporting on the limitation... | {
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2502.06342 | The exponential distribution of the orders of demonstrative, numeral,
adjective and noun | [
"cs.CL",
"physics.soc-ph"
] | The frequency of the preferred order for a noun phrase formed by demonstrative, numeral, adjective and noun has received significant attention over the last two decades. We investigate the actual distribution of the preferred 24 possible orders. There is no consensus on whether it can be well-fitted by an exponential o... | {
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2502.06343 | Causal Lifting of Neural Representations: Zero-Shot Generalization for
Causal Inferences | [
"cs.LG",
"stat.ML"
] | A plethora of real-world scientific investigations is waiting to scale with the support of trustworthy predictive models that can reduce the need for costly data annotations. We focus on causal inferences on a target experiment with unlabeled factual outcomes, retrieved by a predictive model fine-tuned on a labeled sim... | {
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2502.06348 | AiRacleX: Automated Detection of Price Oracle Manipulations via
LLM-Driven Knowledge Mining and Prompt Generation | [
"cs.CR",
"cs.AI"
] | Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure transactions, yet these oracles are highly vulnerable to manipulation, enabling attackers to exploit smart contract vulnerabilities for unfair asset valuation and financial gain. Detecting such manipulations traditionally relies... | {
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2502.06349 | Provably Near-Optimal Federated Ensemble Distillation with Negligible
Overhead | [
"cs.LG"
] | Federated ensemble distillation addresses client heterogeneity by generating pseudo-labels for an unlabeled server dataset based on client predictions and training the server model using the pseudo-labeled dataset. The unlabeled server dataset can either be pre-existing or generated through a data-free approach. The ef... | {
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2502.06351 | Calibrating LLMs with Information-Theoretic Evidential Deep Learning | [
"cs.LG"
] | Fine-tuned large language models (LLMs) often exhibit overconfidence, particularly when trained on small datasets, resulting in poor calibration and inaccurate uncertainty estimates. Evidential Deep Learning (EDL), an uncertainty-aware approach, enables uncertainty estimation in a single forward pass, making it a promi... | {
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2502.06352 | LANTERN++: Enhanced Relaxed Speculative Decoding with Static Tree
Drafting for Visual Auto-regressive Models | [
"cs.CV"
] | Speculative decoding has been widely used to accelerate autoregressive (AR) text generation. However, its effectiveness in visual AR models remains limited due to token selection ambiguity, where multiple tokens receive similarly low probabilities, reducing acceptance rates. While dynamic tree drafting has been propose... | {
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2502.06354 | Guidance-base Diffusion Models for Improving Photoacoustic Image Quality | [
"cs.CV"
] | Photoacoustic(PA) imaging is a non-destructive and non-invasive technology for visualizing minute blood vessel structures in the body using ultrasonic sensors. In PA imaging, the image quality of a single-shot image is poor, and it is necessary to improve the image quality by averaging many single-shot images. Therefor... | {
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2502.06355 | Fine-tuning Multimodal Transformers on Edge: A Parallel Split Learning
Approach | [
"cs.DC",
"cs.LG"
] | Multimodal transformers integrate diverse data types like images, audio, and text, advancing tasks such as audio-visual understanding and image-text retrieval; yet their high parameterization limits deployment on resource-constrained edge devices. Split Learning (SL), which partitions models at a designated cut-layer t... | {
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2502.06358 | Towards bandit-based prompt-tuning for in-the-wild foundation agents | [
"cs.LG"
] | Prompting has emerged as the dominant paradigm for adapting large, pre-trained transformer-based models to downstream tasks. The Prompting Decision Transformer (PDT) enables large-scale, multi-task offline reinforcement learning pre-training by leveraging stochastic trajectory prompts to identify the target task. Howev... | {
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2502.06359 | Occlusion-Aware Contingency Safety-Critical Planning for Autonomous
Vehicles | [
"cs.RO"
] | Ensuring safe driving while maintaining travel efficiency for autonomous vehicles in dynamic and occluded environments is a critical challenge. This paper proposes an occlusion-aware contingency safety-critical planning approach for real-time autonomous driving in such environments. Leveraging reachability analysis for... | {
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2502.06361 | Weld n'Cut: Automated fabrication of inflatable fabric actuators | [
"cs.RO"
] | Lightweight, durable textile-based inflatable soft actuators are widely used in soft robotics, particularly for wearable robots in rehabilitation and in enhancing human performance in demanding jobs. Fabricating these actuators typically involves multiple steps: heat-sealable fabrics are fused with a heat press, and no... | {
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2502.06362 | Proprioceptive Origami Manipulator | [
"cs.RO"
] | Origami offers a versatile framework for designing morphable structures and soft robots by exploiting the geometry of folds. Tubular origami structures can act as continuum manipulators that balance flexibility and strength. However, precise control of such manipulators often requires reliance on vision-based systems t... | {
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2502.06363 | Improved Regret Analysis in Gaussian Process Bandits: Optimality for
Noiseless Reward, RKHS norm, and Non-Stationary Variance | [
"cs.LG",
"stat.ML"
] | We study the Gaussian process (GP) bandit problem, whose goal is to minimize regret under an unknown reward function lying in some reproducing kernel Hilbert space (RKHS). The maximum posterior variance analysis is vital in analyzing near-optimal GP bandit algorithms such as maximum variance reduction (MVR) and phased ... | {
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2502.06364 | Automatic Identification of Samples in Hip-Hop Music via Multi-Loss
Training and an Artificial Dataset | [
"cs.SD",
"cs.LG",
"eess.AS"
] | Sampling, the practice of reusing recorded music or sounds from another source in a new work, is common in popular music genres like hip-hop and rap. Numerous services have emerged that allow users to identify connections between samples and the songs that incorporate them, with the goal of enhancing music discovery. D... | {
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2502.06367 | FOCUS -- Multi-View Foot Reconstruction From Synthetically Trained Dense
Correspondences | [
"cs.CV"
] | Surface reconstruction from multiple, calibrated images is a challenging task - often requiring a large number of collected images with significant overlap. We look at the specific case of human foot reconstruction. As with previous successful foot reconstruction work, we seek to extract rich per-pixel geometry cues fr... | {
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2502.06374 | Hyperparameters in Score-Based Membership Inference Attacks | [
"cs.LG",
"cs.AI"
] | Membership Inference Attacks (MIAs) have emerged as a valuable framework for evaluating privacy leakage by machine learning models. Score-based MIAs are distinguished, in particular, by their ability to exploit the confidence scores that the model generates for particular inputs. Existing score-based MIAs implicitly as... | {
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2502.06376 | Many-Task Federated Fine-Tuning via Unified Task Vectors | [
"cs.LG",
"cs.CV"
] | Federated Learning (FL) traditionally assumes homogeneous client tasks; however, in real-world scenarios, clients often specialize in diverse tasks, introducing task heterogeneity. To address this challenge, Many-Task FL (MaT-FL) has emerged, enabling clients to collaborate effectively despite task diversity. Existing ... | {
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2502.06379 | Solving Linear-Gaussian Bayesian Inverse Problems with Decoupled
Diffusion Sequential Monte Carlo | [
"cs.LG",
"cs.AI",
"stat.ML"
] | A recent line of research has exploited pre-trained generative diffusion models as priors for solving Bayesian inverse problems. We contribute to this research direction by designing a sequential Monte Carlo method for linear-Gaussian inverse problems which builds on ``decoupled diffusion", where the generative process... | {
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2502.06380 | Structure-preserving contrastive learning for spatial time series | [
"cs.LG",
"cs.CV"
] | Informative representations enhance model performance and generalisability in downstream tasks. However, learning self-supervised representations for spatially characterised time series, like traffic interactions, poses challenges as it requires maintaining fine-grained similarity relations in the latent space. In this... | {
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2502.06387 | How Humans Help LLMs: Assessing and Incentivizing Human Preference
Annotators | [
"cs.LG",
"cs.GT",
"econ.TH"
] | Human-annotated preference data play an important role in aligning large language models (LLMs). In this paper, we investigate the questions of assessing the performance of human annotators and incentivizing them to provide high-quality annotations. The quality assessment of language/text annotation faces two challenge... | {
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2502.06390 | When Data Manipulation Meets Attack Goals: An In-depth Survey of Attacks
for VLMs | [
"cs.CV"
] | Vision-Language Models (VLMs) have gained considerable prominence in recent years due to their remarkable capability to effectively integrate and process both textual and visual information. This integration has significantly enhanced performance across a diverse spectrum of applications, such as scene perception and r... | {
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2502.06392 | TANGLED: Generating 3D Hair Strands from Images with Arbitrary Styles
and Viewpoints | [
"cs.CV",
"cs.GR"
] | Hairstyles are intricate and culturally significant with various geometries, textures, and structures. Existing text or image-guided generation methods fail to handle the richness and complexity of diverse styles. We present TANGLED, a novel approach for 3D hair strand generation that accommodates diverse image inputs ... | {
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2502.06394 | SynthDetoxM: Modern LLMs are Few-Shot Parallel Detoxification Data
Annotators | [
"cs.CL"
] | Existing approaches to multilingual text detoxification are hampered by the scarcity of parallel multilingual datasets. In this work, we introduce a pipeline for the generation of multilingual parallel detoxification data. We also introduce SynthDetoxM, a manually collected and synthetically generated multilingual para... | {
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2502.06395 | AppVLM: A Lightweight Vision Language Model for Online App Control | [
"cs.AI"
] | The utilisation of foundation models as smartphone assistants, termed app agents, is a critical research challenge. These agents aim to execute human instructions on smartphones by interpreting textual instructions and performing actions via the device's interface. While promising, current approaches face significant l... | {
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2502.06398 | Learning Counterfactual Outcomes Under Rank Preservation | [
"cs.LG",
"stat.ML"
] | Counterfactual inference aims to estimate the counterfactual outcome at the individual level given knowledge of an observed treatment and the factual outcome, with broad applications in fields such as epidemiology, econometrics, and management science. Previous methods rely on a known structural causal model (SCM) or a... | {
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2502.06399 | A Linearly Convergent Algorithm for Computing the Petz-Augustin
Information | [
"quant-ph",
"cs.IT",
"math.IT",
"math.OC"
] | We propose an iterative algorithm for computing the Petz-Augustin information of order $\alpha\in(1/2,1)\cup(1,\infty)$. The optimization error is guaranteed to converge at a rate of $O\left(\vert 1-1/\alpha \vert^T\right)$, where $T$ is the number of iterations. Let $n$ denote the cardinality of the input alphabet of ... | {
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2502.06401 | Habitizing Diffusion Planning for Efficient and Effective Decision
Making | [
"cs.LG"
] | Diffusion models have shown great promise in decision-making, also known as diffusion planning. However, the slow inference speeds limit their potential for broader real-world applications. Here, we introduce Habi, a general framework that transforms powerful but slow diffusion planning models into fast decision-making... | {
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2502.06403 | The AI off-switch problem as a signalling game: bounded rationality and
incomparability | [
"cs.LG"
] | The off-switch problem is a critical challenge in AI control: if an AI system resists being switched off, it poses a significant risk. In this paper, we model the off-switch problem as a signalling game, where a human decision-maker communicates its preferences about some underlying decision problem to an AI agent, whi... | {
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2502.06407 | An Automated Machine Learning Framework for Surgical Suturing Action
Detection under Class Imbalance | [
"cs.LG",
"cs.RO"
] | In laparoscopy surgical training and evaluation, real-time detection of surgical actions with interpretable outputs is crucial for automated and real-time instructional feedback and skill development. Such capability would enable development of machine guided training systems. This paper presents a rapid deployment app... | {
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2502.06412 | Toolbox for Developing Physics Informed Neural Networks for Power
Systems Components | [
"eess.SY",
"cs.SY"
] | This paper puts forward the vision of creating a library of neural-network-based models for power system simulations. Traditional numerical solvers struggle with the growing complexity of modern power systems, necessitating faster and more scalable alternatives. Physics-Informed Neural Networks (PINNs) offer promise to... | {
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2502.06415 | Systematic Outliers in Large Language Models | [
"cs.CL",
"cs.AI",
"cs.LG"
] | Outliers have been widely observed in Large Language Models (LLMs), significantly impacting model performance and posing challenges for model compression. Understanding the functionality and formation mechanisms of these outliers is critically important. Existing works, however, largely focus on reducing the impact of ... | {
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2502.06418 | Robust Watermarks Leak: Channel-Aware Feature Extraction Enables
Adversarial Watermark Manipulation | [
"cs.CV",
"cs.CR"
] | Watermarking plays a key role in the provenance and detection of AI-generated content. While existing methods prioritize robustness against real-world distortions (e.g., JPEG compression and noise addition), we reveal a fundamental tradeoff: such robust watermarks inherently improve the redundancy of detectable pattern... | {
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2502.06419 | Occ-LLM: Enhancing Autonomous Driving with Occupancy-Based Large
Language Models | [
"cs.RO"
] | Large Language Models (LLMs) have made substantial advancements in the field of robotic and autonomous driving. This study presents the first Occupancy-based Large Language Model (Occ-LLM), which represents a pioneering effort to integrate LLMs with an important representation. To effectively encode occupancy as input ... | {
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2502.06424 | CS-SHAP: Extending SHAP to Cyclic-Spectral Domain for Better
Interpretability of Intelligent Fault Diagnosis | [
"cs.LG",
"cs.AI"
] | Neural networks (NNs), with their powerful nonlinear mapping and end-to-end capabilities, are widely applied in mechanical intelligent fault diagnosis (IFD). However, as typical black-box models, they pose challenges in understanding their decision basis and logic, limiting their deployment in high-reliability scenario... | {
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2502.06425 | Generating Privacy-Preserving Personalized Advice with Zero-Knowledge
Proofs and LLMs | [
"cs.CR",
"cs.AI"
] | Large language models (LLMs) are increasingly utilized in domains such as finance, healthcare, and interpersonal relationships to provide advice tailored to user traits and contexts. However, this personalization often relies on sensitive data, raising critical privacy concerns and necessitating data minimization. To a... | {
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2502.06427 | Hybrid State-Space and GRU-based Graph Tokenization Mamba for
Hyperspectral Image Classification | [
"cs.CV"
] | Hyperspectral image (HSI) classification plays a pivotal role in domains such as environmental monitoring, agriculture, and urban planning. However, it faces significant challenges due to the high-dimensional nature of the data and the complex spectral-spatial relationships inherent in HSI. Traditional methods, includi... | {
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2502.06428 | CoS: Chain-of-Shot Prompting for Long Video Understanding | [
"cs.CV"
] | Multi-modal Large Language Models (MLLMs) struggle with long videos due to the need for excessive visual tokens. These tokens exceed massively the context length of MLLMs, resulting in filled by redundant task-irrelevant shots. How to select shots is an unsolved critical problem: sparse sampling risks missing key detai... | {
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} |
2502.06430 | Content-Driven Local Response: Supporting Sentence-Level and
Message-Level Mobile Email Replies With and Without AI | [
"cs.HC",
"cs.CL"
] | Mobile emailing demands efficiency in diverse situations, which motivates the use of AI. However, generated text does not always reflect how people want to respond. This challenges users with AI involvement tradeoffs not yet considered in email UIs. We address this with a new UI concept called Content-Driven Local Resp... | {
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2502.06431 | FCVSR: A Frequency-aware Method for Compressed Video Super-Resolution | [
"cs.CV"
] | Compressed video super-resolution (SR) aims to generate high-resolution (HR) videos from the corresponding low-resolution (LR) compressed videos. Recently, some compressed video SR methods attempt to exploit the spatio-temporal information in the frequency domain, showing great promise in super-resolution performance. ... | {
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2502.06432 | Prompt-SID: Learning Structural Representation Prompt via Latent
Diffusion for Single-Image Denoising | [
"cs.CV",
"cs.AI"
] | Many studies have concentrated on constructing supervised models utilizing paired datasets for image denoising, which proves to be expensive and time-consuming. Current self-supervised and unsupervised approaches typically rely on blind-spot networks or sub-image pairs sampling, resulting in pixel information loss and ... | {
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2502.06434 | Rethinking Large-scale Dataset Compression: Shifting Focus From Labels
to Images | [
"cs.CV",
"cs.LG"
] | Dataset distillation and dataset pruning are two prominent techniques for compressing datasets to improve computational and storage efficiency. Despite their overlapping objectives, these approaches are rarely compared directly. Even within each field, the evaluation protocols are inconsistent across various methods, w... | {
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2502.06438 | FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba
Foundation Model | [
"cs.LG",
"cs.AI"
] | Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures and artifacts in long-term monitoring, with applications spanning hospital diagnostics to wearable health devices. Robust EEG analytics have the potential to greatly improve patient care. However, traditional deep learning ... | {
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2502.06439 | Testing software for non-discrimination: an updated and extended audit
in the Italian car insurance domain | [
"cs.SE",
"cs.AI",
"cs.HC",
"cs.LG"
] | Context. As software systems become more integrated into society's infrastructure, the responsibility of software professionals to ensure compliance with various non-functional requirements increases. These requirements include security, safety, privacy, and, increasingly, non-discrimination. Motivation. Fairness in ... | {
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2502.06440 | SIGMA: Sheaf-Informed Geometric Multi-Agent Pathfinding | [
"cs.RO",
"cs.AI",
"cs.MA"
] | The Multi-Agent Path Finding (MAPF) problem aims to determine the shortest and collision-free paths for multiple agents in a known, potentially obstacle-ridden environment. It is the core challenge for robotic deployments in large-scale logistics and transportation. Decentralized learning-based approaches have shown gr... | {
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} |
2502.06443 | Low-dimensional Functions are Efficiently Learnable under Randomly
Biased Distributions | [
"cs.LG",
"stat.ML"
] | The problem of learning single index and multi index models has gained significant interest as a fundamental task in high-dimensional statistics. Many recent works have analysed gradient-based methods, particularly in the setting of isotropic data distributions, often in the context of neural network training. Such stu... | {
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2502.06445 | Benchmarking Vision-Language Models on Optical Character Recognition in
Dynamic Video Environments | [
"cs.CV"
] | This paper introduces an open-source benchmark for evaluating Vision-Language Models (VLMs) on Optical Character Recognition (OCR) tasks in dynamic video environments. We present a curated dataset containing 1,477 manually annotated frames spanning diverse domains, including code editors, news broadcasts, YouTube video... | {
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2502.06452 | SparseFocus: Learning-based One-shot Autofocus for Microscopy with
Sparse Content | [
"cs.CV",
"q-bio.QM"
] | Autofocus is necessary for high-throughput and real-time scanning in microscopic imaging. Traditional methods rely on complex hardware or iterative hill-climbing algorithms. Recent learning-based approaches have demonstrated remarkable efficacy in a one-shot setting, avoiding hardware modifications or iterative mechani... | {
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2502.06453 | MATH-Perturb: Benchmarking LLMs' Math Reasoning Abilities against Hard
Perturbations | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models have demonstrated impressive performance on challenging mathematical reasoning tasks, which has triggered the discussion of whether the performance is achieved by true reasoning capability or memorization. To investigate this question, prior work has constructed mathematical benchmarks when questi... | {
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} |
2502.06460 | Group-CLIP Uncertainty Modeling for Group Re-Identification | [
"cs.CV"
] | Group Re-Identification (Group ReID) aims matching groups of pedestrians across non-overlapping cameras. Unlike single-person ReID, Group ReID focuses more on the changes in group structure, emphasizing the number of members and their spatial arrangement. However, most methods rely on certainty-based models, which cons... | {
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} |
2502.06466 | Inflatable Kirigami Crawlers | [
"cs.RO"
] | Kirigami offers unique opportunities for guided morphing by leveraging the geometry of the cuts. This work presents inflatable kirigami crawlers created by introducing cut patterns into heat-sealable textiles to achieve locomotion upon cyclic pneumatic actuation. Inflating traditional air pouches results in symmetric b... | {
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} |
2502.06468 | Beyond Literal Token Overlap: Token Alignability for Multilinguality | [
"cs.CL"
] | Previous work has considered token overlap, or even similarity of token distributions, as predictors for multilinguality and cross-lingual knowledge transfer in language models. However, these very literal metrics assign large distances to language pairs with different scripts, which can nevertheless show good cross-li... | {
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} |
2502.06469 | Stochastic MPC with Online-optimized Policies and Closed-loop Guarantees | [
"eess.SY",
"cs.SY",
"math.OC"
] | This paper proposes a stochastic model predictive control method for linear systems affected by additive Gaussian disturbances. Closed-loop satisfaction of probabilistic constraints and recursive feasibility of the underlying convex optimization problem is guaranteed. Optimization over feedback policies online increase... | {
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} |
2502.06470 | A Survey of Theory of Mind in Large Language Models: Evaluations,
Representations, and Safety Risks | [
"cs.CL",
"cs.AI"
] | Theory of Mind (ToM), the ability to attribute mental states to others and predict their behaviour, is fundamental to social intelligence. In this paper, we survey studies evaluating behavioural and representational ToM in Large Language Models (LLMs), identify important safety risks from advanced LLM ToM capabilities,... | {
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} |
2502.06472 | KARMA: Leveraging Multi-Agent LLMs for Automated Knowledge Graph
Enrichment | [
"cs.CL",
"cs.AI",
"cs.CE",
"cs.DL"
] | Maintaining comprehensive and up-to-date knowledge graphs (KGs) is critical for modern AI systems, but manual curation struggles to scale with the rapid growth of scientific literature. This paper presents KARMA, a novel framework employing multi-agent large language models (LLMs) to automate KG enrichment through stru... | {
"Other": 1,
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"cs.SD": 0,
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} |
2502.06474 | UniMoD: Efficient Unified Multimodal Transformers with Mixture-of-Depths | [
"cs.CV"
] | Unified multimodal transformers, which handle both generation and understanding tasks within a shared parameter space, have received increasing attention in recent research. Although various unified transformers have been proposed, training these models is costly due to redundant tokens and heavy attention computation.... | {
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} |
2502.06476 | Image Intrinsic Scale Assessment: Bridging the Gap Between Quality and
Resolution | [
"cs.CV"
] | Image Quality Assessment (IQA) measures and predicts perceived image quality by human observers. Although recent studies have highlighted the critical influence that variations in the scale of an image have on its perceived quality, this relationship has not been systematically quantified. To bridge this gap, we introd... | {
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} |
2502.06480 | Logarithmic Regret of Exploration in Average Reward Markov Decision
Processes | [
"cs.LG",
"stat.ML"
] | In average reward Markov decision processes, state-of-the-art algorithms for regret minimization follow a well-established framework: They are model-based, optimistic and episodic. First, they maintain a confidence region from which optimistic policies are computed using a well-known subroutine called Extended Value It... | {
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
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