id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.03830 | MeshConv3D: Efficient convolution and pooling operators for triangular
3D meshes | [
"cs.CV",
"cs.GR"
] | Convolutional neural networks (CNNs) have been pivotal in various 2D image analysis tasks, including computer vision, image indexing and retrieval or semantic classification. Extending CNNs to 3D data such as point clouds and 3D meshes raises significant challenges since the very basic convolution and pooling operators... | {
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2501.03832 | Three-dimensional attention Transformer for state evaluation in
real-time strategy games | [
"cs.LG",
"cs.AI"
] | Situation assessment in Real-Time Strategy (RTS) games is crucial for understanding decision-making in complex adversarial environments. However, existing methods remain limited in processing multi-dimensional feature information and temporal dependencies. Here we propose a tri-dimensional Space-Time-Feature Transforme... | {
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2501.03833 | Sequence Reconstruction for the Single-Deletion Single-Substitution
Channel | [
"cs.IT",
"math.IT"
] | The central problem in sequence reconstruction is to find the minimum number of distinct channel outputs required to uniquely reconstruct the transmitted sequence. According to Levenshtein's work in 2001, this number is determined by the size of the maximum intersection between the error balls of any two distinct input... | {
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2501.03835 | TACLR: A Scalable and Efficient Retrieval-based Method for Industrial
Product Attribute Value Identification | [
"cs.CL",
"cs.AI",
"cs.IR"
] | Product Attribute Value Identification (PAVI) involves identifying attribute values from product profiles, a key task for improving product search, recommendations, and business analytics on e-commerce platforms. However, existing PAVI methods face critical challenges, such as inferring implicit values, handling out-of... | {
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2501.03836 | SCC-YOLO: An Improved Object Detector for Assisting in Brain Tumor
Diagnosis | [
"eess.IV",
"cs.AI",
"cs.CV"
] | Brain tumors can result in neurological dysfunction, alterations in cognitive and psychological states, increased intracranial pressure, and the occurrence of seizures, thereby presenting a substantial risk to human life and health. The You Only Look Once(YOLO) series models have demonstrated superior accuracy in objec... | {
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2501.03838 | LM-Net: A Light-weight and Multi-scale Network for Medical Image
Segmentation | [
"cs.CV"
] | Current medical image segmentation approaches have limitations in deeply exploring multi-scale information and effectively combining local detail textures with global contextual semantic information. This results in over-segmentation, under-segmentation, and blurred segmentation boundaries. To tackle these challenges, ... | {
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2501.03839 | MedFocusCLIP : Improving few shot classification in medical datasets
using pixel wise attention | [
"eess.IV",
"cs.CV"
] | With the popularity of foundational models, parameter efficient fine tuning has become the defacto approach to leverage pretrained models to perform downstream tasks. Taking inspiration from recent advances in large language models, Visual Prompt Tuning, and similar techniques, learn an additional prompt to efficiently... | {
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2501.03840 | Machine learning applications in archaeological practices: a review | [
"cs.LG"
] | Artificial intelligence and machine learning applications in archaeology have increased significantly in recent years, and these now span all subfields, geographical regions, and time periods. The prevalence and success of these applications have remained largely unexamined, as recent reviews on the use of machine lear... | {
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2501.03841 | OmniManip: Towards General Robotic Manipulation via Object-Centric
Interaction Primitives as Spatial Constraints | [
"cs.RO"
] | The development of general robotic systems capable of manipulating in unstructured environments is a significant challenge. While Vision-Language Models(VLM) excel in high-level commonsense reasoning, they lack the fine-grained 3D spatial understanding required for precise manipulation tasks. Fine-tuning VLM on robotic... | {
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2501.03843 | BERTopic for Topic Modeling of Hindi Short Texts: A Comparative Study | [
"cs.IR",
"cs.CL",
"cs.LG"
] | As short text data in native languages like Hindi increasingly appear in modern media, robust methods for topic modeling on such data have gained importance. This study investigates the performance of BERTopic in modeling Hindi short texts, an area that has been under-explored in existing research. Using contextual emb... | {
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2501.03847 | Diffusion as Shader: 3D-aware Video Diffusion for Versatile Video
Generation Control | [
"cs.CV",
"cs.AI",
"cs.GR"
] | Diffusion models have demonstrated impressive performance in generating high-quality videos from text prompts or images. However, precise control over the video generation process, such as camera manipulation or content editing, remains a significant challenge. Existing methods for controlled video generation are typic... | {
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2501.03848 | Semise: Semi-supervised learning for severity representation in medical
image | [
"eess.IV",
"cs.CV"
] | This paper introduces SEMISE, a novel method for representation learning in medical imaging that combines self-supervised and supervised learning. By leveraging both labeled and augmented data, SEMISE addresses the challenge of data scarcity and enhances the encoder's ability to extract meaningful features. This integr... | {
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2501.03850 | Partitioning Strategies for Parallel Computation of Flexible Skylines | [
"cs.DB"
] | While classical skyline queries identify interesting data within large datasets, flexible skylines introduce preferences through constraints on attribute weights, and further reduce the data returned. However, computing these queries can be time-consuming for large datasets. We propose and implement a parallel computat... | {
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2501.03853 | Leveraging time and parameters for nonlinear model reduction methods | [
"math.NA",
"cs.LG",
"cs.NA"
] | In this paper, we consider model order reduction (MOR) methods for problems with slowly decaying Kolmogorov $n$-widths as, e.g., certain wave-like or transport-dominated problems. To overcome this Kolmogorov barrier within MOR, nonlinear projections are used, which are often realized numerically using autoencoders. The... | {
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2501.03854 | Comparison of Integration Methods for Cut Elements | [
"cs.CE"
] | Using an interface inserted in a background mesh is an alternative way of constructing a complex geometrical shape with a relative low meshing efforts. However, this process may require special treatment of elements cut by the interface. Our study focuses on comparing the integration of cut elements defined by implicit... | {
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2501.03855 | BabyLMs for isiXhosa: Data-Efficient Language Modelling in a
Low-Resource Context | [
"cs.CL"
] | The BabyLM challenge called on participants to develop sample-efficient language models. Submissions were pretrained on a fixed English corpus, limited to the amount of words children are exposed to in development (<100m). The challenge produced new architectures for data-efficient language modelling, which outperforme... | {
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2501.03857 | Progressive Document-level Text Simplification via Large Language Models | [
"cs.CL"
] | Research on text simplification has primarily focused on lexical and sentence-level changes. Long document-level simplification (DS) is still relatively unexplored. Large Language Models (LLMs), like ChatGPT, have excelled in many natural language processing tasks. However, their performance on DS tasks is unsatisfacto... | {
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2501.03858 | Symmetry and Generalisation in Machine Learning | [
"cs.LG",
"stat.ML"
] | This work is about understanding the impact of invariance and equivariance on generalisation in supervised learning. We use the perspective afforded by an averaging operator to show that for any predictor that is not equivariant, there is an equivariant predictor with strictly lower test risk on all regression problems... | {
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2501.03859 | A Synergistic Framework for Learning Shape Estimation and Shape-Aware
Whole-Body Control Policy for Continuum Robots | [
"cs.RO"
] | In this paper, we present a novel synergistic framework for learning shape estimation and a shape-aware whole-body control policy for tendon-driven continuum robots. Our approach leverages the interaction between two Augmented Neural Ordinary Differential Equations (ANODEs) -- the Shape-NODE and Control-NODE -- to achi... | {
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2501.03863 | Improving Dialectal Slot and Intent Detection with Auxiliary Tasks: A
Multi-Dialectal Bavarian Case Study | [
"cs.CL"
] | Reliable slot and intent detection (SID) is crucial in natural language understanding for applications like digital assistants. Encoder-only transformer models fine-tuned on high-resource languages generally perform well on SID. However, they struggle with dialectal data, where no standardized form exists and training ... | {
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2501.03865 | Truthful mechanisms for linear bandit games with private contexts | [
"cs.LG",
"cs.GT"
] | The contextual bandit problem, where agents arrive sequentially with personal contexts and the system adapts its arm allocation decisions accordingly, has recently garnered increasing attention for enabling more personalized outcomes. However, in many healthcare and recommendation applications, agents have private prof... | {
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2501.03870 | Add Noise, Tasks, or Layers? MaiNLP at the VarDial 2025 Shared Task on
Norwegian Dialectal Slot and Intent Detection | [
"cs.CL"
] | Slot and intent detection (SID) is a classic natural language understanding task. Despite this, research has only more recently begun focusing on SID for dialectal and colloquial varieties. Many approaches for low-resource scenarios have not yet been applied to dialectal SID data, or compared to each other on the same ... | {
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2501.03874 | Neuromorphic Optical Tracking and Imaging of Randomly Moving Targets
through Strongly Scattering Media | [
"cs.NE",
"cs.CV",
"cs.LG",
"eess.IV"
] | Tracking and acquiring simultaneous optical images of randomly moving targets obscured by scattering media remains a challenging problem of importance to many applications that require precise object localization and identification. In this work we develop an end-to-end neuromorphic optical engineering and computationa... | {
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2501.03875 | ZDySS -- Zero-Shot Dynamic Scene Stylization using Gaussian Splatting | [
"cs.CV"
] | Stylizing a dynamic scene based on an exemplar image is critical for various real-world applications, including gaming, filmmaking, and augmented and virtual reality. However, achieving consistent stylization across both spatial and temporal dimensions remains a significant challenge. Most existing methods are designed... | {
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2501.03877 | Stochastically Constrained Best Arm Identification with Thompson
Sampling | [
"cs.LG"
] | We consider the problem of the best arm identification in the presence of stochastic constraints, where there is a finite number of arms associated with multiple performance measures. The goal is to identify the arm that optimizes the objective measure subject to constraints on the remaining measures. We will explore t... | {
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2501.03879 | CL3DOR: Contrastive Learning for 3D Large Multimodal Models via Odds
Ratio on High-Resolution Point Clouds | [
"cs.CV",
"cs.AI"
] | Recent research has demonstrated that Large Language Models (LLMs) are not limited to text-only tasks but can also function as multimodal models across various modalities, including audio, images, and videos. In particular, research on 3D Large Multimodal Models (3D LMMs) is making notable strides, driven by the potent... | {
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2501.03880 | SELMA3D challenge: Self-supervised learning for 3D light-sheet
microscopy image segmentation | [
"eess.IV",
"cs.CV",
"cs.LG"
] | Recent innovations in light sheet microscopy, paired with developments in tissue clearing techniques, enable the 3D imaging of large mammalian tissues with cellular resolution. Combined with the progress in large-scale data analysis, driven by deep learning, these innovations empower researchers to rapidly investigate ... | {
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2501.03881 | An LSTM-based Test Selection Method for Self-Driving Cars | [
"cs.RO",
"cs.SE"
] | Self-driving cars require extensive testing, which can be costly in terms of time. To optimize this process, simple and straightforward tests should be excluded, focusing on challenging tests instead. This study addresses the test selection problem for lane-keeping systems for self-driving cars. Road segment features, ... | {
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2501.03884 | AlphaPO - Reward shape matters for LLM alignment | [
"cs.CL"
] | Reinforcement Learning with Human Feedback (RLHF) and its variants have made huge strides toward the effective alignment of large language models (LLMs) to follow instructions and reflect human values. More recently, Direct Alignment Algorithms (DAAs) have emerged in which the reward modeling stage of RLHF is skipped b... | {
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2501.03888 | Neural DNF-MT: A Neuro-symbolic Approach for Learning Interpretable and
Editable Policies | [
"cs.AI",
"cs.LG",
"cs.LO"
] | Although deep reinforcement learning has been shown to be effective, the model's black-box nature presents barriers to direct policy interpretation. To address this problem, we propose a neuro-symbolic approach called neural DNF-MT for end-to-end policy learning. The differentiable nature of the neural DNF-MT model ena... | {
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2501.03891 | Superpixel Boundary Correction for Weakly-Supervised Semantic
Segmentation on Histopathology Images | [
"cs.CV"
] | With the rapid advancement of deep learning, computational pathology has made significant progress in cancer diagnosis and subtyping. Tissue segmentation is a core challenge, essential for prognosis and treatment decisions. Weakly supervised semantic segmentation (WSSS) reduces the annotation requirement by using image... | {
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2501.03892 | LEAP: LLM-powered End-to-end Automatic Library for Processing Social
Science Queries on Unstructured Data | [
"cs.DB"
] | Social scientists are increasingly interested in analyzing the semantic information (e.g., emotion) of unstructured data (e.g., Tweets), where the semantic information is not natively present. Performing this analysis in a cost-efficient manner requires using machine learning (ML) models to extract the semantic informa... | {
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2501.03894 | Robust Moving-horizon Estimation for Nonlinear Systems: From Perfect to
Imperfect Optimization | [
"eess.SY",
"cs.SY"
] | Robust stability of moving-horizon estimators is investigated for nonlinear discrete-time systems that are detectable in the sense of incremental input/output-to-state stability and are affected by disturbances. The estimate of a moving-horizon estimator stems from the on-line solution of a least-squares minimization p... | {
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2501.03895 | LLaVA-Mini: Efficient Image and Video Large Multimodal Models with One
Vision Token | [
"cs.CV",
"cs.AI",
"cs.CL"
] | The advent of real-time large multimodal models (LMMs) like GPT-4o has sparked considerable interest in efficient LMMs. LMM frameworks typically encode visual inputs into vision tokens (continuous representations) and integrate them and textual instructions into the context of large language models (LLMs), where large-... | {
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2501.03902 | Explainable Reinforcement Learning via Temporal Policy Decomposition | [
"cs.LG",
"cs.AI"
] | We investigate the explainability of Reinforcement Learning (RL) policies from a temporal perspective, focusing on the sequence of future outcomes associated with individual actions. In RL, value functions compress information about rewards collected across multiple trajectories and over an infinite horizon, allowing a... | {
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2501.03904 | Exploring the Potential of Large Language Models in Public
Transportation: San Antonio Case Study | [
"cs.LG",
"cs.AI",
"cs.IR"
] | The integration of large language models (LLMs) into public transit systems presents a transformative opportunity to enhance urban mobility. This study explores the potential of LLMs to revolutionize public transportation management within the context of San Antonio's transit system. Leveraging the capabilities of LLMs... | {
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2501.03905 | mFabric: An Efficient and Scalable Fabric for Mixture-of-Experts
Training | [
"cs.NI",
"cs.LG"
] | Mixture-of-Expert (MoE) models outperform conventional models by selectively activating different subnets, named \emph{experts}, on a per-token basis. This gated computation generates dynamic communications that cannot be determined beforehand, challenging the existing GPU interconnects that remain \emph{static} during... | {
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2501.03907 | Implicit Coordination using Active Epistemic Inference for Multi-Robot
Systems | [
"cs.RO"
] | A Multi-robot system (MRS) provides significant advantages for intricate tasks such as environmental monitoring, underwater inspections, and space missions. However, addressing potential communication failures or the lack of communication infrastructure in these fields remains a challenge. A significant portion of MRS ... | {
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2501.03910 | HYB-VITON: A Hybrid Approach to Virtual Try-On Combining Explicit and
Implicit Warping | [
"cs.CV"
] | Virtual try-on systems have significant potential in e-commerce, allowing customers to visualize garments on themselves. Existing image-based methods fall into two categories: those that directly warp garment-images onto person-images (explicit warping), and those using cross-attention to reconstruct given garments (im... | {
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2501.03916 | Dolphin: Closed-loop Open-ended Auto-research through Thinking,
Practice, and Feedback | [
"cs.AI",
"cs.CL",
"cs.CV"
] | The scientific research paradigm is undergoing a profound transformation owing to the development of Artificial Intelligence (AI). Recent works demonstrate that various AI-assisted research methods can largely improve research efficiency by improving data analysis, accelerating computation, and fostering novel idea gen... | {
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2501.03922 | Changing almost perfect nonlinear functions on affine subspaces of small
codimensions | [
"math.CO",
"cs.IT",
"math.CA",
"math.IT"
] | In this article, we study algebraic decompositions and secondary constructions of almost perfect nonlinear (APN) functions. In many cases, we establish precise criteria which characterize when certain modifications of a given APN function yield new ones. Furthermore, we show that some of the newly constructed functions... | {
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2501.03923 | Explainable AI model reveals disease-related mechanisms in single-cell
RNA-seq data | [
"q-bio.GN",
"cs.CV",
"cs.LG"
] | Neurodegenerative diseases (NDDs) are complex and lack effective treatment due to their poorly understood mechanism. The increasingly used data analysis from Single nucleus RNA Sequencing (snRNA-seq) allows to explore transcriptomic events at a single cell level, yet face challenges in interpreting the mechanisms under... | {
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2501.03928 | From Newswire to Nexus: Using text-based actor embeddings and
transformer networks to forecast conflict dynamics | [
"cs.CY",
"cs.CL",
"cs.LG"
] | This study advances the field of conflict forecasting by using text-based actor embeddings with transformer models to predict dynamic changes in violent conflict patterns at the actor level. More specifically, we combine newswire texts with structured conflict event data and leverage recent advances in Natural Language... | {
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2501.03930 | Towards Reliable Testing for Multiple Information Retrieval System
Comparisons | [
"cs.IR"
] | Null Hypothesis Significance Testing is the \textit{de facto} tool for assessing effectiveness differences between Information Retrieval systems. Researchers use statistical tests to check whether those differences will generalise to online settings or are just due to the samples observed in the laboratory. Much work h... | {
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2501.03931 | Magic Mirror: ID-Preserved Video Generation in Video Diffusion
Transformers | [
"cs.CV"
] | We present Magic Mirror, a framework for generating identity-preserved videos with cinematic-level quality and dynamic motion. While recent advances in video diffusion models have shown impressive capabilities in text-to-video generation, maintaining consistent identity while producing natural motion remains challengin... | {
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2501.03932 | CoStruction: Conjoint radiance field optimization for urban scene
reconStruction with limited image overlap | [
"cs.CV"
] | Reconstructing the surrounding surface geometry from recorded driving sequences poses a significant challenge due to the limited image overlap and complex topology of urban environments. SoTA neural implicit surface reconstruction methods often struggle in such setting, either failing due to small vision overlap or exh... | {
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2501.03936 | PPTAgent: Generating and Evaluating Presentations Beyond Text-to-Slides | [
"cs.AI",
"cs.CL"
] | Automatically generating presentations from documents is a challenging task that requires accommodating content quality, visual appeal, and structural coherence. Existing methods primarily focus on improving and evaluating the content quality in isolation, overlooking visual appeal and structural coherence, which limit... | {
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2501.03937 | A precise asymptotic analysis of learning diffusion models: theory and
insights | [
"cs.LG",
"cond-mat.dis-nn"
] | In this manuscript, we consider the problem of learning a flow or diffusion-based generative model parametrized by a two-layer auto-encoder, trained with online stochastic gradient descent, on a high-dimensional target density with an underlying low-dimensional manifold structure. We derive a tight asymptotic character... | {
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2501.03939 | Visual question answering: from early developments to recent advances --
a survey | [
"cs.CV",
"cs.MM"
] | Visual Question Answering (VQA) is an evolving research field aimed at enabling machines to answer questions about visual content by integrating image and language processing techniques such as feature extraction, object detection, text embedding, natural language understanding, and language generation. With the growth... | {
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2501.03940 | Not all tokens are created equal: Perplexity Attention Weighted Networks
for AI generated text detection | [
"cs.CL",
"cs.AI"
] | The rapid advancement in large language models (LLMs) has significantly enhanced their ability to generate coherent and contextually relevant text, raising concerns about the misuse of AI-generated content and making it critical to detect it. However, the task remains challenging, particularly in unseen domains or with... | {
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2501.03941 | Synthetic Data Privacy Metrics | [
"cs.LG",
"cs.AI"
] | Recent advancements in generative AI have made it possible to create synthetic datasets that can be as accurate as real-world data for training AI models, powering statistical insights, and fostering collaboration with sensitive datasets while offering strong privacy guarantees. Effectively measuring the empirical priv... | {
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2501.03944 | A GPU Implementation of Multi-Guiding Spark Fireworks Algorithm for
Efficient Black-Box Neural Network Optimization | [
"cs.NE"
] | Swarm intelligence optimization algorithms have gained significant attention due to their ability to solve complex optimization problems. However, the efficiency of optimization in large-scale problems limits the use of related methods. This paper presents a GPU-accelerated version of the Multi-Guiding Spark Fireworks ... | {
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2501.03952 | Localizing AI: Evaluating Open-Weight Language Models for Languages of
Baltic States | [
"cs.CL",
"cs.AI"
] | Although large language models (LLMs) have transformed our expectations of modern language technologies, concerns over data privacy often restrict the use of commercially available LLMs hosted outside of EU jurisdictions. This limits their application in governmental, defence, and other data-sensitive sectors. In this ... | {
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2501.03957 | Vision Language Models as Values Detectors | [
"cs.HC",
"cs.CV"
] | Large Language Models integrating textual and visual inputs have introduced new possibilities for interpreting complex data. Despite their remarkable ability to generate coherent and contextually relevant text based on visual stimuli, the alignment of these models with human perception in identifying relevant elements ... | {
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2501.03961 | Channel Coding based on Skew Polynomials and Multivariate Polynomials | [
"cs.IT",
"eess.SP",
"math.IT"
] | This dissertation considers new constructions and decoding approaches for error-correcting codes based on non-conventional polynomials, with the objective of providing new coding solutions to the applications mentioned above. With skew polynomials, we construct codes that are dual-containing, which is a desired propert... | {
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2501.03964 | A comparative study of uncertainty quantification methods in gust
response analysis of a Lift-Plus-Cruise eVTOL aircraft wing | [
"cs.CE"
] | Wind gusts, being inherently stochastic, can significantly influence the safety and performance of aircraft. This study investigates a three-dimensional uncertainty quantification (UQ) problem to explore how uncertainties in gust and flight conditions affect the structural response of a Lift-Plus-Cruise eVTOL aircraft ... | {
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2501.03967 | Temporal Feature Weaving for Neonatal Echocardiographic Viewpoint Video
Classification | [
"cs.CV"
] | Automated viewpoint classification in echocardiograms can help under-resourced clinics and hospitals in providing faster diagnosis and screening when expert technicians may not be available. We propose a novel approach towards echocardiographic viewpoint classification. We show that treating viewpoint classification as... | {
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2501.03968 | VLM-driven Behavior Tree for Context-aware Task Planning | [
"cs.RO",
"cs.AI",
"cs.CV",
"cs.HC"
] | The use of Large Language Models (LLMs) for generating Behavior Trees (BTs) has recently gained attention in the robotics community, yet remains in its early stages of development. In this paper, we propose a novel framework that leverages Vision-Language Models (VLMs) to interactively generate and edit BTs that addres... | {
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2501.03971 | Impact of Leg Stiffness on Energy Efficiency in One Legged Hopping | [
"cs.RO"
] | In the fields of robotics and biomechanics, the integration of elastic elements such as springs and tendons in legged systems has long been recognized for enabling energy-efficient locomotion. Yet, a significant challenge persists: designing a robotic leg that perform consistently across diverse operating conditions, e... | {
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2501.03972 | MAD-BA: 3D LiDAR Bundle Adjustment -- from Uncertainty Modelling to
Structure Optimization | [
"cs.RO"
] | The joint optimization of sensor poses and 3D structure is fundamental for state estimation in robotics and related fields. Current LiDAR systems often prioritize pose optimization, with structure refinement either omitted or treated separately using representations like signed distance functions or neural networks. Th... | {
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2501.03988 | Semantically Cohesive Word Grouping in Indian Languages | [
"cs.CL"
] | Indian languages are inflectional and agglutinative and typically follow clause-free word order. The structure of sentences across most major Indian languages are similar when their dependency parse trees are considered. While some differences in the parsing structure occur due to peculiarities of a language or its pre... | {
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2501.03989 | (De)-Indexing and the Right to be Forgotten | [
"cs.CY",
"cs.IR"
] | In the digital age, the challenge of forgetfulness has emerged as a significant concern, particularly regarding the management of personal data and its accessibility online. The right to be forgotten (RTBF) allows individuals to request the removal of outdated or harmful information from public access, yet implementing... | {
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2501.03991 | Influences on LLM Calibration: A Study of Response Agreement, Loss
Functions, and Prompt Styles | [
"cs.CL"
] | Calibration, the alignment between model confidence and prediction accuracy, is critical for the reliable deployment of large language models (LLMs). Existing works neglect to measure the generalization of their methods to other prompt styles and different sizes of LLMs. To address this, we define a controlled experime... | {
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2501.03992 | NeuralSVG: An Implicit Representation for Text-to-Vector Generation | [
"cs.CV"
] | Vector graphics are essential in design, providing artists with a versatile medium for creating resolution-independent and highly editable visual content. Recent advancements in vision-language and diffusion models have fueled interest in text-to-vector graphics generation. However, existing approaches often suffer fro... | {
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2501.03995 | RAG-Check: Evaluating Multimodal Retrieval Augmented Generation
Performance | [
"cs.LG",
"cs.CV",
"cs.IR",
"cs.IT",
"math.IT"
] | Retrieval-augmented generation (RAG) improves large language models (LLMs) by using external knowledge to guide response generation, reducing hallucinations. However, RAG, particularly multi-modal RAG, can introduce new hallucination sources: (i) the retrieval process may select irrelevant pieces (e.g., documents, imag... | {
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2501.03999 | WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling
Algorithm for High-Dimensional and Sparse Experiment Settings | [
"cs.LG",
"stat.ML"
] | Aiming for more effective experiment design, such as in video content advertising where different content options compete for user engagement, these scenarios can be modeled as multi-arm bandit problems. In cases where limited interactions are available due to external factors, such as the cost of conducting experiment... | {
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2501.04000 | A Survey on Federated Learning in Human Sensing | [
"cs.LG",
"cs.HC"
] | Human Sensing, a field that leverages technology to monitor human activities, psycho-physiological states, and interactions with the environment, enhances our understanding of human behavior and drives the development of advanced services that improve overall quality of life. However, its reliance on detailed and often... | {
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2501.04001 | Sa2VA: Marrying SAM2 with LLaVA for Dense Grounded Understanding of
Images and Videos | [
"cs.CV"
] | This work presents Sa2VA, the first unified model for dense grounded understanding of both images and videos. Unlike existing multi-modal large language models, which are often limited to specific modalities and tasks, Sa2VA supports a wide range of image and video tasks, including referring segmentation and conversati... | {
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2501.04002 | Extraction Of Cumulative Blobs From Dynamic Gestures | [
"cs.CV"
] | Gesture recognition is a perceptual user interface, which is based on CV technology that allows the computer to interpret human motions as commands, allowing users to communicate with a computer without the use of hands, thus making the mouse and keyboard superfluous. Gesture recognition's main weakness is a light cond... | {
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2501.04003 | Are VLMs Ready for Autonomous Driving? An Empirical Study from the
Reliability, Data, and Metric Perspectives | [
"cs.CV",
"cs.RO"
] | Recent advancements in Vision-Language Models (VLMs) have sparked interest in their use for autonomous driving, particularly in generating interpretable driving decisions through natural language. However, the assumption that VLMs inherently provide visually grounded, reliable, and interpretable explanations for drivin... | {
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2501.04004 | LiMoE: Mixture of LiDAR Representation Learners from Automotive Scenes | [
"cs.CV",
"cs.LG",
"cs.RO"
] | LiDAR data pretraining offers a promising approach to leveraging large-scale, readily available datasets for enhanced data utilization. However, existing methods predominantly focus on sparse voxel representation, overlooking the complementary attributes provided by other LiDAR representations. In this work, we propose... | {
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2501.04005 | LargeAD: Large-Scale Cross-Sensor Data Pretraining for Autonomous
Driving | [
"cs.CV",
"cs.LG",
"cs.RO"
] | Recent advancements in vision foundation models (VFMs) have revolutionized visual perception in 2D, yet their potential for 3D scene understanding, particularly in autonomous driving applications, remains underexplored. In this paper, we introduce LargeAD, a versatile and scalable framework designed for large-scale 3D ... | {
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2501.04006 | Advancing Similarity Search with GenAI: A Retrieval Augmented Generation
Approach | [
"cs.IR"
] | This article introduces an innovative Retrieval Augmented Generation approach to similarity search. The proposed method uses a generative model to capture nuanced semantic information and retrieve similarity scores based on advanced context understanding. The study focuses on the BIOSSES dataset containing 100 pairs of... | {
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2501.04007 | Untapped Potential in Self-Optimization of Hopfield Networks: The
Creativity of Unsupervised Learning | [
"cs.NE",
"nlin.AO"
] | The Self-Optimization (SO) model can be considered as the third operational mode of the classical Hopfield Network (HN), leveraging the power of associative memory to enhance optimization performance. Moreover, is has been argued to express characteristics of minimal agency which, together with its biological plausibil... | {
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2501.04008 | A Generative AI-driven Metadata Modelling Approach | [
"cs.DL",
"cs.AI",
"cs.IR"
] | Since decades, the modelling of metadata has been core to the functioning of any academic library. Its importance has only enhanced with the increasing pervasiveness of Generative Artificial Intelligence (AI)-driven information activities and services which constitute a library's outreach. However, with the rising impo... | {
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2501.04009 | Multi-SpaCE: Multi-Objective Subsequence-based Sparse Counterfactual
Explanations for Multivariate Time Series Classification | [
"cs.NE",
"cs.LG",
"stat.ML"
] | Deep Learning systems excel in complex tasks but often lack transparency, limiting their use in critical applications. Counterfactual explanations, a core tool within eXplainable Artificial Intelligence (XAI), offer insights into model decisions by identifying minimal changes to an input to alter its predicted outcome.... | {
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2501.04012 | FlexCache: Flexible Approximate Cache System for Video Diffusion | [
"cs.MM",
"cs.LG"
] | Text-to-Video applications receive increasing attention from the public. Among these, diffusion models have emerged as the most prominent approach, offering impressive quality in visual content generation. However, it still suffers from substantial computational complexity, often requiring several minutes to generate a... | {
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2501.04014 | AICat: An AI Cataloguing Approach to Support the EU AI Act | [
"cs.DL",
"cs.AI",
"cs.CY"
] | The European Union's Artificial Intelligence Act (AI Act) requires providers and deployers of high-risk AI applications to register their systems into the EU database, wherein the information should be represented and maintained in an easily-navigable and machine-readable manner. Given the uptake of open data and Seman... | {
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2501.04018 | MERCURY: A fast and versatile multi-resolution based global emulator of
compound climate hazards | [
"physics.ao-ph",
"cs.LG",
"stat.AP"
] | High-impact climate damages are often driven by compounding climate conditions. For example, elevated heat stress conditions can arise from a combination of high humidity and temperature. To explore future changes in compounding hazards under a range of climate scenarios and with large ensembles, climate emulators can ... | {
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2501.04023 | Approximation Rates in Fr\'echet Metrics: Barron Spaces, Paley-Wiener
Spaces, and Fourier Multipliers | [
"math.NA",
"cs.IT",
"cs.LG",
"cs.NA",
"math.IT",
"stat.ML"
] | Operator learning is a recent development in the simulation of Partial Differential Equations (PDEs) by means of neural networks. The idea behind this approach is to learn the behavior of an operator, such that the resulting neural network is an (approximate) mapping in infinite-dimensional spaces that is capable of (a... | {
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2501.04038 | Listening and Seeing Again: Generative Error Correction for Audio-Visual
Speech Recognition | [
"cs.MM",
"cs.AI",
"cs.SD",
"eess.AS"
] | Unlike traditional Automatic Speech Recognition (ASR), Audio-Visual Speech Recognition (AVSR) takes audio and visual signals simultaneously to infer the transcription. Recent studies have shown that Large Language Models (LLMs) can be effectively used for Generative Error Correction (GER) in ASR by predicting the best ... | {
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} |
2501.04040 | A Survey on Large Language Models with some Insights on their
Capabilities and Limitations | [
"cs.CL",
"cs.AI",
"cs.LG",
"cs.NE"
] | The rapid advancement of artificial intelligence, particularly with the development of Large Language Models (LLMs) built on the transformer architecture, has redefined the capabilities of natural language processing. These models now exhibit remarkable performance across various language-related tasks, such as text ge... | {
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2501.04046 | Traits of a Leader: User Influence Level Prediction through
Sociolinguistic Modeling | [
"physics.soc-ph",
"cs.AI",
"cs.CY"
] | Recognition of a user's influence level has attracted much attention as human interactions move online. Influential users have the ability to sway others' opinions to achieve some goals. As a result, predicting users' level of influence can help to understand social networks, forecast trends, prevent misinformation, et... | {
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2501.04052 | The Power of Negative Zero: Datatype Customization for Quantized Large
Language Models | [
"cs.LG",
"cs.CL"
] | Large language models (LLMs) have demonstrated remarkable performance across various machine learning tasks, quickly becoming one of the most prevalent AI workloads. Yet the substantial memory requirement of LLMs significantly hinders their deployment for end users. Post-training quantization (PTQ) serves as one of the... | {
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2501.04060 | SFADNet: Spatio-temporal Fused Graph based on Attention Decoupling
Network for Traffic Prediction | [
"cs.LG"
] | In recent years, traffic flow prediction has played a crucial role in the management of intelligent transportation systems. However, traditional prediction methods are often limited by static spatial modeling, making it difficult to accurately capture the dynamic and complex relationships between time and space, thereb... | {
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2501.04061 | Causal Machine Learning Methods for Estimating Personalised Treatment
Effects -- Insights on validity from two large trials | [
"cs.LG",
"stat.ML"
] | Causal machine learning (ML) methods hold great promise for advancing precision medicine by estimating personalized treatment effects. However, their reliability remains largely unvalidated in empirical settings. In this study, we assessed the internal and external validity of 17 mainstream causal heterogeneity ML meth... | {
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2501.04062 | ChronoLLM: A Framework for Customizing Large Language Model for Digital
Twins generalization based on PyChrono | [
"cs.SE",
"cs.AI",
"cs.CE"
] | Recently, the integration of advanced simulation technologies with artificial intelligence (AI) is revolutionizing science and engineering research. ChronoLlama introduces a novel framework that customizes the open-source LLMs, specifically for code generation, paired with PyChrono for multi-physics simulations. This i... | {
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2501.04063 | Fuzzy Information Entropy and Region Biased Matrix Factorization for Web
Service QoS Prediction | [
"cs.LG"
] | Nowadays, there are many similar services available on the internet, making Quality of Service (QoS) a key concern for users. Since collecting QoS values for all services through user invocations is impractical, predicting QoS values is a more feasible approach. Matrix factorization is considered an effective predictio... | {
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2501.04066 | FedKD-hybrid: Federated Hybrid Knowledge Distillation for Lithography
Hotspot Detection | [
"cs.LG",
"cs.AR"
] | Federated Learning (FL) provides novel solutions for machine learning (ML)-based lithography hotspot detection (LHD) under distributed privacy-preserving settings. Currently, two research pipelines have been investigated to aggregate local models and achieve global consensus, including parameter/nonparameter based (als... | {
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2501.04067 | Explainable Time Series Prediction of Tyre Energy in Formula One Race
Strategy | [
"cs.LG",
"cs.AI"
] | Formula One (F1) race strategy takes place in a high-pressure and fast-paced environment where split-second decisions can drastically affect race results. Two of the core decisions of race strategy are when to make pit stops (i.e. replace the cars' tyres) and which tyre compounds (hard, medium or soft, in normal condit... | {
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2501.04068 | Explainable Reinforcement Learning for Formula One Race Strategy | [
"cs.LG",
"cs.AI"
] | In Formula One, teams compete to develop their cars and achieve the highest possible finishing position in each race. During a race, however, teams are unable to alter the car, so they must improve their cars' finishing positions via race strategy, i.e. optimising their selection of which tyre compounds to put on the c... | {
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} |
2501.04070 | More is not always better? Enhancing Many-Shot In-Context Learning with
Differentiated and Reweighting Objectives | [
"cs.LG",
"cs.AI",
"cs.CL"
] | Large language models (LLMs) excel at few-shot in-context learning (ICL) without requiring parameter updates. However, as the number of ICL demonstrations increases from a few to many, performance tends to plateau and eventually decline. We identify two primary causes for this trend: the suboptimal negative log-likelih... | {
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} |
2501.04072 | Multi-armed Bandit and Backbone boost Lin-Kernighan-Helsgaun Algorithm
for the Traveling Salesman Problems | [
"cs.DS",
"cs.AI"
] | The Lin-Kernighan-Helsguan (LKH) heuristic is a classic local search algorithm for the Traveling Salesman Problem (TSP). LKH introduces an $\alpha$-value to replace the traditional distance metric for evaluating the edge quality, which leads to a significant improvement. However, we observe that the $\alpha$-value does... | {
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"cs.SY": 0
} |
2501.04073 | Deep Learning for Ophthalmology: The State-of-the-Art and Future Trends | [
"eess.IV",
"cs.CV"
] | The emergence of artificial intelligence (AI), particularly deep learning (DL), has marked a new era in the realm of ophthalmology, offering transformative potential for the diagnosis and treatment of posterior segment eye diseases. This review explores the cutting-edge applications of DL across a range of ocular condi... | {
"Other": 0,
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"cs.SY": 0
} |
2501.04074 | NeRFs are Mirror Detectors: Using Structural Similarity for Multi-View
Mirror Scene Reconstruction with 3D Surface Primitives | [
"cs.CV"
] | While neural radiance fields (NeRF) led to a breakthrough in photorealistic novel view synthesis, handling mirroring surfaces still denotes a particular challenge as they introduce severe inconsistencies in the scene representation. Previous attempts either focus on reconstructing single reflective objects or rely on s... | {
"Other": 0,
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} |
2501.04099 | Neighbor displacement-based enhanced synthetic oversampling for
multiclass imbalanced data | [
"cs.LG"
] | Imbalanced multiclass datasets pose challenges for machine learning algorithms. These datasets often contain minority classes that are important for accurate prediction. Existing methods still suffer from sparse data and may not accurately represent the original data patterns, leading to noise and poor model performanc... | {
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"cs.NE": 0,
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"cs.SD": 0,
"cs.SI": 0,
"cs.SY": 0
} |
2501.04102 | Enhancing Distribution and Label Consistency for Graph
Out-of-Distribution Generalization | [
"cs.LG",
"cs.AI"
] | To deal with distribution shifts in graph data, various graph out-of-distribution (OOD) generalization techniques have been recently proposed. These methods often employ a two-step strategy that first creates augmented environments and subsequently identifies invariant subgraphs to improve generalizability. Nevertheles... | {
"Other": 0,
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"cs.SY": 0
} |
2501.04104 | Security by Design Issues in Autonomous Vehicles | [
"eess.SY",
"cs.CR",
"cs.SY"
] | As autonomous vehicle (AV) technology advances towards maturity, it becomes imperative to examine the security vulnerabilities within these cyber-physical systems. While conventional cyber-security concerns are often at the forefront of discussions, it is essential to get deeper into the various layers of vulnerability... | {
"Other": 0,
"cs.AI": 0,
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} |
2501.04105 | DeepVIVONet: Using deep neural operators to optimize sensor locations
with application to vortex-induced vibrations | [
"cs.LG",
"math.OC",
"physics.flu-dyn"
] | We introduce DeepVIVONet, a new framework for optimal dynamic reconstruction and forecasting of the vortex-induced vibrations (VIV) of a marine riser, using field data. We demonstrate the effectiveness of DeepVIVONet in accurately reconstructing the motion of an off--shore marine riser by using sparse spatio-temporal m... | {
"Other": 0,
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"cs.SD": 0,
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"cs.SY": 0
} |
2501.04108 | TrojanDec: Data-free Detection of Trojan Inputs in Self-supervised
Learning | [
"cs.CR",
"cs.AI"
] | An image encoder pre-trained by self-supervised learning can be used as a general-purpose feature extractor to build downstream classifiers for various downstream tasks. However, many studies showed that an attacker can embed a trojan into an encoder such that multiple downstream classifiers built based on the trojaned... | {
"Other": 0,
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"cs.SI": 0,
"cs.SY": 0
} |
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