id stringlengths 9 16 | title stringlengths 4 278 | categories listlengths 1 13 | abstract stringlengths 3 4.08k | filtered_category_membership dict |
|---|---|---|---|---|
2501.06478 | Speech Recognition for Automatically Assessing Afrikaans and isiXhosa
Preschool Oral Narratives | [
"eess.AS",
"cs.CL",
"cs.SD"
] | We develop automatic speech recognition (ASR) systems for stories told by Afrikaans and isiXhosa preschool children. Oral narratives provide a way to assess children's language development before they learn to read. We consider a range of prior child-speech ASR strategies to determine which is best suited to this uniqu... | {
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2501.06480 | Flash Window Attention: speedup the attention computation for Swin
Transformer | [
"cs.CV"
] | To address the high resolution of image pixels, the Swin Transformer introduces window attention. This mechanism divides an image into non-overlapping windows and restricts attention computation to within each window, significantly enhancing computational efficiency. To further optimize this process, one might consider... | {
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2501.06481 | Focus-N-Fix: Region-Aware Fine-Tuning for Text-to-Image Generation | [
"cs.CV"
] | Text-to-image (T2I) generation has made significant advances in recent years, but challenges still remain in the generation of perceptual artifacts, misalignment with complex prompts, and safety. The prevailing approach to address these issues involves collecting human feedback on generated images, training reward mode... | {
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2501.06485 | A Diffusive Data Augmentation Framework for Reconstruction of Complex
Network Evolutionary History | [
"cs.AI"
] | The evolutionary processes of complex systems contain critical information regarding their functional characteristics. The generation time of edges provides insights into the historical evolution of various networked complex systems, such as protein-protein interaction networks, ecosystems, and social networks. Recover... | {
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2501.06488 | NVS-SQA: Exploring Self-Supervised Quality Representation Learning for
Neurally Synthesized Scenes without References | [
"cs.CV",
"cs.AI",
"cs.HC",
"cs.MM",
"eess.IV"
] | Neural View Synthesis (NVS), such as NeRF and 3D Gaussian Splatting, effectively creates photorealistic scenes from sparse viewpoints, typically evaluated by quality assessment methods like PSNR, SSIM, and LPIPS. However, these full-reference methods, which compare synthesized views to reference views, may not fully ca... | {
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2501.06490 | Sequential Classification of Aviation Safety Occurrences with Natural
Language Processing | [
"cs.CL",
"cs.LG"
] | Safety is a critical aspect of the air transport system given even slight operational anomalies can result in serious consequences. To reduce the chances of aviation safety occurrences, accidents and incidents are reported to establish the root cause, propose safety recommendations etc. However, analysis narratives of ... | {
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2501.06491 | Improving Requirements Classification with SMOTE-Tomek Preprocessing | [
"cs.SE",
"cs.AI",
"cs.SY",
"eess.SY"
] | This study emphasizes the domain of requirements engineering by applying the SMOTE-Tomek preprocessing technique, combined with stratified K-fold cross-validation, to address class imbalance in the PROMISE dataset. This dataset comprises 969 categorized requirements, classified into functional and non-functional types.... | {
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2501.06492 | A New Flexible Train-Test Split Algorithm, an approach for choosing
among the Hold-out, K-fold cross-validation, and Hold-out iteration | [
"cs.LG"
] | Artificial Intelligent transformed industries, like engineering, medicine, finance. Predictive models use supervised learning, a vital Machine learning subset. Crucial for model evaluation, cross-validation includes re-substitution, hold-out, and K-fold. This study focuses on improving the accuracy of ML algorithms acr... | {
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2501.06493 | Whole-Body Integrated Motion Planning for Aerial Manipulators | [
"cs.RO"
] | Efficient motion planning for Aerial Manipulators (AMs) is essential for tackling complex manipulation tasks, yet achieving coupled trajectory planning remains challenging. In this work, we propose, to the best of our knowledge, the first whole-body integrated motion planning framework for aerial manipulators, which is... | {
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2501.06494 | TopoFormer: Integrating Transformers and ConvLSTMs for Coastal
Topography Prediction | [
"eess.SP",
"cs.AI"
] | This paper presents \textit{TopoFormer}, a novel hybrid deep learning architecture that integrates transformer-based encoders with convolutional long short-term memory (ConvLSTM) layers for the precise prediction of topographic beach profiles referenced to elevation datums, with a particular focus on Mean Low Water Spr... | {
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2501.06496 | Analyzing the Role of Context in Forecasting with Large Language Models | [
"cs.CL",
"cs.IR"
] | This study evaluates the forecasting performance of recent language models (LLMs) on binary forecasting questions. We first introduce a novel dataset of over 600 binary forecasting questions, augmented with related news articles and their concise question-related summaries. We then explore the impact of input prompts w... | {
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2501.06497 | PASS: Presentation Automation for Slide Generation and Speech | [
"cs.CL",
"cs.AI"
] | In today's fast-paced world, effective presentations have become an essential tool for communication in both online and offline meetings. The crafting of a compelling presentation requires significant time and effort, from gathering key insights to designing slides that convey information clearly and concisely. However... | {
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2501.06505 | Online Algorithm for Aggregating Experts' Predictions with Unbounded
Quadratic Loss | [
"cs.LG"
] | We consider the problem of online aggregation of expert predictions with the quadratic loss function. We propose an algorithm for aggregating expert predictions which does not require a prior knowledge of the upper bound on the losses. The algorithm is based on the exponential reweighing of expert losses. | {
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2501.06506 | Resource Allocation under the Latin Square Constraint | [
"cs.GT",
"cs.AI",
"cs.MA"
] | A Latin square is an $n \times n$ matrix filled with $n$ distinct symbols, each of which appears exactly once in each row and exactly once in each column. We introduce a problem of allocating $n$ indivisible items among $n$ agents over $n$ rounds while satisfying the Latin square constraint. This constraint ensures tha... | {
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2501.06510 | Cooperative Optimal Output Tracking for Discrete-Time Multiagent
Systems: Stabilizing Policy Iteration Frameworks and Analysis | [
"eess.SY",
"cs.SY"
] | In this paper, two model-free optimal output tracking frameworks based on policy iteration for discrete-time multi-agent systems are proposed. First, we establish a framework of stabilizing policy iteration that can start from any initial feedback control policy, relaxing the dependence of traditional policy iteration ... | {
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2501.06514 | Neural Codec Source Tracing: Toward Comprehensive Attribution in
Open-Set Condition | [
"cs.SD",
"cs.AI",
"eess.AS"
] | Current research in audio deepfake detection is gradually transitioning from binary classification to multi-class tasks, referred as audio deepfake source tracing task. However, existing studies on source tracing consider only closed-set scenarios and have not considered the challenges posed by open-set conditions. In ... | {
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2501.06521 | Fine-tuning Large Language Models for Improving Factuality in Legal
Question Answering | [
"cs.CL"
] | Hallucination, or the generation of incorrect or fabricated information, remains a critical challenge in large language models (LLMs), particularly in high-stake domains such as legal question answering (QA). In order to mitigate the hallucination rate in legal QA, we first introduce a benchmark called LegalHalBench an... | {
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2501.06524 | Multi-View Factorizing and Disentangling: A Novel Framework for
Incomplete Multi-View Multi-Label Classification | [
"cs.CV"
] | Multi-view multi-label classification (MvMLC) has recently garnered significant research attention due to its wide range of real-world applications. However, incompleteness in views and labels is a common challenge, often resulting from data collection oversights and uncertainties in manual annotation. Furthermore, the... | {
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2501.06527 | Scaffolding Creativity: Integrating Generative AI Tools and Real-world
Experiences in Business Education | [
"cs.AI",
"cs.HC"
] | This case study explores the integration of Generative AI tools and real-world experiences in business education. Through a study of an innovative undergraduate course, we investigate how AI-assisted learning, combined with experiential components, impacts students' creative processes and learning outcomes. Our finding... | {
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2501.06528 | Safe Circumnavigation of a Hostile Target Using Range-Based Measurements | [
"cs.RO",
"cs.SY",
"eess.SY"
] | Robotic systems are frequently deployed in missions that are dull, dirty, and dangerous, where ensuring their safety is of paramount importance when designing stabilizing controllers to achieve their desired goals. This paper addresses the problem of safe circumnavigation around a hostile target by a nonholonomic robot... | {
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2501.06532 | Determination of galaxy photometric redshifts using Conditional
Generative Adversarial Networks (CGANs) | [
"astro-ph.IM",
"astro-ph.CO",
"cs.AI"
] | Accurate and reliable photometric redshifts determination is one of the key aspects for wide-field photometric surveys. Determination of photometric redshift for galaxies, has been traditionally solved by use of machine-learning and artificial intelligence techniques trained on a calibration sample of galaxies, where b... | {
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2501.06533 | DivTrackee versus DynTracker: Promoting Diversity in Anti-Facial
Recognition against Dynamic FR Strategy | [
"cs.CV",
"cs.CR"
] | The widespread adoption of facial recognition (FR) models raises serious concerns about their potential misuse, motivating the development of anti-facial recognition (AFR) to protect user facial privacy. In this paper, we argue that the static FR strategy, predominantly adopted in prior literature for evaluating AFR ef... | {
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2501.06534 | Dynamic Causal Structure Discovery and Causal Effect Estimation | [
"stat.ML",
"cs.LG"
] | To represent the causal relationships between variables, a directed acyclic graph (DAG) is widely utilized in many areas, such as social sciences, epidemics, and genetics. Many causal structure learning approaches are developed to learn the hidden causal structure utilizing deep-learning approaches. However, these appr... | {
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2501.06536 | Dispersion Measures as Predictors of Lexical Decision Time, Word
Familiarity, and Lexical Complexity | [
"cs.CL"
] | Various measures of dispersion have been proposed to paint a fuller picture of a word's distribution in a corpus, but only little has been done to validate them externally. We evaluate a wide range of dispersion measures as predictors of lexical decision time, word familiarity, and lexical complexity in five diverse la... | {
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2501.06540 | CeViT: Copula-Enhanced Vision Transformer in multi-task learning and
bi-group image covariates with an application to myopia screening | [
"cs.CV",
"math.ST",
"stat.AP",
"stat.ME",
"stat.TH"
] | We aim to assist image-based myopia screening by resolving two longstanding problems, "how to integrate the information of ocular images of a pair of eyes" and "how to incorporate the inherent dependence among high-myopia status and axial length for both eyes." The classification-regression task is modeled as a novel 4... | {
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2501.06545 | Energy-Aware Resource Allocation for Energy Harvesting Powered Wireless
Sensor Nodes | [
"cs.IT",
"eess.SP",
"math.IT"
] | Low harvested energy poses a significant challenge to sustaining continuous communication in energy harvesting (EH)-powered wireless sensor networks. This is mainly due to intermittent and limited power availability from radio frequency signals. In this paper, we introduce a novel energy-aware resource allocation probl... | {
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2501.06546 | Natural Language Supervision for Low-light Image Enhancement | [
"cs.CV",
"cs.AI"
] | With the development of deep learning, numerous methods for low-light image enhancement (LLIE) have demonstrated remarkable performance. Mainstream LLIE methods typically learn an end-to-end mapping based on pairs of low-light and normal-light images. However, normal-light images under varying illumination conditions s... | {
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2501.06550 | CoreNet: Conflict Resolution Network for Point-Pixel Misalignment and
Sub-Task Suppression of 3D LiDAR-Camera Object Detection | [
"cs.CV"
] | Fusing multi-modality inputs from different sensors is an effective way to improve the performance of 3D object detection. However, current methods overlook two important conflicts: point-pixel misalignment and sub-task suppression. The former means a pixel feature from the opaque object is projected to multiple point ... | {
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2501.06552 | When xURLLC Meets NOMA: A Stochastic Network Calculus Perspective | [
"eess.SP",
"cs.IT",
"cs.SY",
"eess.SY",
"math.IT"
] | The advent of next-generation ultra-reliable and low-latency communications (xURLLC) presents stringent and unprecedented requirements for key performance indicators (KPIs). As a disruptive technology, non-orthogonal multiple access (NOMA) harbors the potential to fulfill these stringent KPIs essential for xURLLC. Howe... | {
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2501.06553 | VASparse: Towards Efficient Visual Hallucination Mitigation for Large
Vision-Language Model via Visual-Aware Sparsification | [
"cs.CV"
] | Large Vision-Language Models (LVLMs) may produce outputs that are unfaithful to reality, also known as visual hallucinations (VH), which significantly impedes their real-world usage. To alleviate VH, various decoding strategies have been proposed to enhance visual information. However, many of these methods may require... | {
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2501.06554 | Hierarchical Reinforcement Learning for Optimal Agent Grouping in
Cooperative Systems | [
"cs.LG",
"cs.AI",
"cs.MA"
] | This paper presents a hierarchical reinforcement learning (RL) approach to address the agent grouping or pairing problem in cooperative multi-agent systems. The goal is to simultaneously learn the optimal grouping and agent policy. By employing a hierarchical RL framework, we distinguish between high-level decisions of... | {
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2501.06557 | A Survey on Spoken Italian Datasets and Corpora | [
"cs.CL",
"cs.AI",
"cs.DL"
] | Spoken language datasets are vital for advancing linguistic research, Natural Language Processing, and speech technology. However, resources dedicated to Italian, a linguistically rich and diverse Romance language, remain underexplored compared to major languages like English or Mandarin. This survey provides a compreh... | {
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2501.06561 | Where to Go Next Day: Multi-scale Spatial-Temporal Decoupled Model for
Mid-term Human Mobility Prediction | [
"cs.AI"
] | Predicting individual mobility patterns is crucial across various applications. While current methods mainly focus on predicting the next location for personalized services like recommendations, they often fall short in supporting broader applications such as traffic management and epidemic control, which require longe... | {
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2501.06562 | Discrete Speech Unit Extraction via Independent Component Analysis | [
"eess.AS",
"cs.AI",
"cs.LG",
"cs.SD"
] | Self-supervised speech models (S3Ms) have become a common tool for the speech processing community, leveraging representations for downstream tasks. Clustering S3M representations yields discrete speech units (DSUs), which serve as compact representations for speech signals. DSUs are typically obtained by k-means clust... | {
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2501.06564 | Natural Language Processing and Deep Learning Models to Classify Phase
of Flight in Aviation Safety Occurrences | [
"cs.CL",
"cs.LG"
] | The air transport system recognizes the criticality of safety, as even minor anomalies can have severe consequences. Reporting accidents and incidents play a vital role in identifying their causes and proposing safety recommendations. However, the narratives describing pre-accident events are presented in unstructured ... | {
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2501.06566 | Cooperative Aerial Robot Inspection Challenge: A Benchmark for
Heterogeneous Multi-UAV Planning and Lessons Learned | [
"cs.RO",
"cs.SY",
"eess.SY"
] | We propose the Cooperative Aerial Robot Inspection Challenge (CARIC), a simulation-based benchmark for motion planning algorithms in heterogeneous multi-UAV systems. CARIC features UAV teams with complementary sensors, realistic constraints, and evaluation metrics prioritizing inspection quality and efficiency. It offe... | {
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2501.06570 | Aster: Enhancing LSM-structures for Scalable Graph Database | [
"cs.DB"
] | There is a proliferation of applications requiring the management of large-scale, evolving graphs under workloads with intensive graph updates and lookups. Driven by this challenge, we introduce Poly-LSM, a high-performance key-value storage engine for graphs with the following novel techniques: (1) Poly-LSM is embedde... | {
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2501.06571 | Active Rule Mining for Multivariate Anomaly Detection in Radio Access
Networks | [
"cs.LG",
"cs.AI"
] | Multivariate anomaly detection finds its importance in diverse applications. Despite the existence of many detectors to solve this problem, one cannot simply define why an obtained anomaly inferred by the detector is anomalous. This reasoning is required for network operators to understand the root cause of the anomaly... | {
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2501.06572 | Physics-Informed Neuro-Evolution (PINE): A Survey and Prospects | [
"cs.NE",
"cs.CE",
"cs.LG"
] | Deep learning models trained on finite data lack a complete understanding of the physical world. On the other hand, physics-informed neural networks (PINNs) are infused with such knowledge through the incorporation of mathematically expressible laws of nature into their training loss function. By complying with physica... | {
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2501.06573 | Modeling the residual queue and queue-dependent capacity in a static
traffic assignment problem | [
"eess.SY",
"cs.SY"
] | The residual queue during a given study period (e.g., peak hour) is an important feature that should be considered when solving a traffic assignment problem under equilibrium for strategic traffic planning. Although studies have focused extensively on static or quasi-dynamic traffic assignment models considering the re... | {
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2501.06577 | Transforming Social Science Research with Transfer Learning: Social
Science Survey Data Integration with AI | [
"cs.AI"
] | Large-N nationally representative surveys, which have profoundly shaped American politics scholarship, represent related but distinct domains -a key condition for transfer learning applications. These surveys are related through their shared demographic, party identification, and ideological variables, yet differ in th... | {
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2501.06581 | Recommending the right academic programs: An interest mining approach
using BERTopic | [
"cs.LG",
"cs.CY",
"cs.IR"
] | Prospective students face the challenging task of selecting a university program that will shape their academic and professional careers. For decision-makers and support services, it is often time-consuming and extremely difficult to match personal interests with suitable programs due to the vast and complex catalogue ... | {
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2501.06582 | ACORD: An Expert-Annotated Retrieval Dataset for Legal Contract Drafting | [
"cs.CL"
] | Information retrieval, specifically contract clause retrieval, is foundational to contract drafting because lawyers rarely draft contracts from scratch; instead, they locate and revise the most relevant precedent. We introduce the Atticus Clause Retrieval Dataset (ACORD), the first retrieval benchmark for contract draf... | {
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2501.06583 | Optimizing wheel loader performance: an end-to-end approach | [
"cs.CE",
"cs.SY",
"eess.SY"
] | Wheel loaders in mines and construction sites repeatedly load soil from a pile to load receivers. This task presents a challenging optimization problem since each loading's performance depends on the pile state, which depends on previous loadings. We investigate an end-to-end optimization approach considering future lo... | {
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2501.06585 | Boundary-enhanced time series data imputation with long-term dependency
diffusion models | [
"cs.LG",
"cs.SI"
] | Data imputation is crucial for addressing challenges posed by missing values in multivariate time series data across various fields, such as healthcare, traffic, and economics, and has garnered significant attention. Among various methods, diffusion model-based approaches show notable performance improvements. However,... | {
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2501.06588 | A Tight VC-Dimension Analysis of Clustering Coresets with Applications | [
"cs.CG",
"cs.DS",
"cs.LG"
] | We consider coresets for $k$-clustering problems, where the goal is to assign points to centers minimizing powers of distances. A popular example is the $k$-median objective $\sum_{p}\min_{c\in C}dist(p,C)$. Given a point set $P$, a coreset $\Omega$ is a small weighted subset that approximates the cost of $P$ for all c... | {
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2501.06589 | Ladder-residual: parallelism-aware architecture for accelerating large
model inference with communication overlapping | [
"cs.LG",
"cs.CL",
"cs.DC"
] | Large language model inference is both memory-intensive and time-consuming, often requiring distributed algorithms to efficiently scale. Various model parallelism strategies are used in multi-gpu training and inference to partition computation across multiple devices, reducing memory load and computation time. However,... | {
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2501.06590 | ChemAgent: Self-updating Library in Large Language Models Improves
Chemical Reasoning | [
"cs.CL",
"cs.AI"
] | Chemical reasoning usually involves complex, multi-step processes that demand precise calculations, where even minor errors can lead to cascading failures. Furthermore, large language models (LLMs) encounter difficulties handling domain-specific formulas, executing reasoning steps accurately, and integrating code effec... | {
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2501.06591 | Exploring Pose-Based Anomaly Detection for Retail Security: A Real-World
Shoplifting Dataset and Benchmark | [
"cs.CV",
"cs.AI"
] | Shoplifting poses a significant challenge for retailers, resulting in billions of dollars in annual losses. Traditional security measures often fall short, highlighting the need for intelligent solutions capable of detecting shoplifting behaviors in real time. This paper frames shoplifting detection as an anomaly detec... | {
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2501.06597 | EmoXpt: Analyzing Emotional Variances in Human Comments and
LLM-Generated Responses | [
"cs.LG",
"cs.CL",
"cs.HC"
] | The widespread adoption of generative AI has generated diverse opinions, with individuals expressing both support and criticism of its applications. This study investigates the emotional dynamics surrounding generative AI by analyzing human tweets referencing terms such as ChatGPT, OpenAI, Copilot, and LLMs. To further... | {
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2501.06598 | ChartCoder: Advancing Multimodal Large Language Model for Chart-to-Code
Generation | [
"cs.AI"
] | Multimodal Large Language Models (MLLMs) have demonstrated remarkable capabilities in chart understanding tasks. However, interpreting charts with textual descriptions often leads to information loss, as it fails to fully capture the dense information embedded in charts. In contrast, parsing charts into code provides l... | {
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2501.06602 | A Comparative Performance Analysis of Classification and Segmentation
Models on Bangladeshi Pothole Dataset | [
"cs.CV"
] | The study involves a comprehensive performance analysis of popular classification and segmentation models, applied over a Bangladeshi pothole dataset, being developed by the authors of this research. This custom dataset of 824 samples, collected from the streets of Dhaka and Bogura performs competitively against the ex... | {
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2501.06603 | Preconditioned Sharpness-Aware Minimization: Unifying Analysis and a
Novel Learning Algorithm | [
"cs.LG"
] | Targeting solutions over `flat' regions of the loss landscape, sharpness-aware minimization (SAM) has emerged as a powerful tool to improve generalizability of deep neural network based learning. While several SAM variants have been developed to this end, a unifying approach that also guides principled algorithm design... | {
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2501.06605 | RoboHorizon: An LLM-Assisted Multi-View World Model for Long-Horizon
Robotic Manipulation | [
"cs.RO"
] | Efficient control in long-horizon robotic manipulation is challenging due to complex representation and policy learning requirements. Model-based visual reinforcement learning (RL) has shown great potential in addressing these challenges but still faces notable limitations, particularly in handling sparse rewards and c... | {
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2501.06608 | Dual-Modality Representation Learning for Molecular Property Prediction | [
"cs.LG",
"q-bio.QM"
] | Molecular property prediction has attracted substantial attention recently. Accurate prediction of drug properties relies heavily on effective molecular representations. The structures of chemical compounds are commonly represented as graphs or SMILES sequences. Recent advances in learning drug properties commonly empl... | {
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2501.06625 | Guided Code Generation with LLMs: A Multi-Agent Framework for Complex
Code Tasks | [
"cs.AI"
] | Large Language Models (LLMs) have shown remarkable capabilities in code generation tasks, yet they face significant limitations in handling complex, long-context programming challenges and demonstrating complex compositional reasoning abilities. This paper introduces a novel agentic framework for ``guided code generati... | {
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2501.06628 | Quantifying Relational Exploration in Cultural Heritage Knowledge Graphs
with LLMs: A Neuro-Symbolic Approach | [
"cs.AI"
] | This paper introduces a neuro-symbolic approach for relational exploration in cultural heritage knowledge graphs, leveraging Large Language Models (LLMs) for explanation generation and a novel mathematical framework to quantify the interestingness of relationships. We demonstrate the importance of interestingness measu... | {
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2501.06635 | A Reduced Order Iterative Linear Quadratic Regulator (ILQR) Technique
for the Optimal Control of Nonlinear Partial Differential Equations | [
"eess.SY",
"cs.SY"
] | In this paper, we introduce a reduced order model-based reinforcement learning (MBRL) approach, utilizing the Iterative Linear Quadratic Regulator (ILQR) algorithm for the optimal control of nonlinear partial differential equations (PDEs). The approach proposes a novel modification of the ILQR technique: it uses the Me... | {
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2501.06636 | Dual use issues in the field of Natural Language Generation | [
"cs.CL"
] | This report documents the results of a recent survey in the SIGGEN community, focusing on Dual Use issues in Natural Language Generation (NLG). SIGGEN is the Special Interest Group (SIG) of the Association for Computational Linguistics (ACL) for researchers working on NLG. The survey was prompted by the ACL executive b... | {
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2501.06638 | Scaling Down Semantic Leakage: Investigating Associative Bias in Smaller
Language Models | [
"cs.CL"
] | Semantic leakage is a phenomenon recently introduced by Gonen et al. (2024). It refers to a situation in which associations learnt from the training data emerge in language model generations in an unexpected and sometimes undesired way. Prior work has focused on leakage in large language models (7B+ parameters). In thi... | {
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2501.06639 | Enhancing Path Planning Performance through Image Representation
Learning of High-Dimensional Configuration Spaces | [
"cs.RO",
"cs.AI"
] | This paper presents a novel method for accelerating path-planning tasks in unknown scenes with obstacles by utilizing Wasserstein Generative Adversarial Networks (WGANs) with Gradient Penalty (GP) to approximate the distribution of waypoints for a collision-free path using the Rapidly-exploring Random Tree algorithm. O... | {
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2501.06641 | A Permutation-Free Length 3 Decimal Check Digit Code | [
"cs.IT",
"math.CO",
"math.IT"
] | In 1969 J. Verhoeff provided the first examples of a decimal error detecting code using a single check digit to provide protection against all single, transposition and adjacent twin errors. The three codes he presented are length 3-digit codes with 2 information digits. Existence of a 4-digit code would imply the exis... | {
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2501.06642 | Common Sense Is All You Need | [
"cs.AI"
] | Artificial intelligence (AI) has made significant strides in recent years, yet it continues to struggle with a fundamental aspect of cognition present in all animals: common sense. Current AI systems, including those designed for complex tasks like autonomous driving, problem-solving challenges such as the Abstraction ... | {
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2501.06645 | FocalPO: Enhancing Preference Optimizing by Focusing on Correct
Preference Rankings | [
"cs.CL",
"cs.AI"
] | Efficient preference optimization algorithms such as Direct Preference Optimization (DPO) have become a popular approach in aligning large language models (LLMs) with human preferences. These algorithms implicitly treat the LLM as a reward model, and focus on training it to correct misranked preference pairs. However, ... | {
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2501.06650 | SafeSplit: A Novel Defense Against Client-Side Backdoor Attacks in Split
Learning | [
"cs.CR",
"cs.DC",
"cs.LG"
] | Split Learning (SL) is a distributed deep learning approach enabling multiple clients and a server to collaboratively train and infer on a shared deep neural network (DNN) without requiring clients to share their private local data. The DNN is partitioned in SL, with most layers residing on the server and a few initial... | {
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2501.06651 | Parking Space Detection in the City of Granada | [
"cs.CV"
] | This paper addresses the challenge of parking space detection in urban areas, focusing on the city of Granada. Utilizing aerial imagery, we develop and apply semantic segmentation techniques to accurately identify parked cars, moving cars and roads. A significant aspect of our research is the creation of a proprietary ... | {
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2501.06653 | Theoretical Characterization of Effect of Masks in Snapshot Compressive
Imaging | [
"cs.IT",
"eess.IV",
"math.IT",
"stat.AP"
] | Snapshot compressive imaging (SCI) refers to the recovery of three-dimensional data cubes-such as videos or hyperspectral images-from their two-dimensional projections, which are generated by a special encoding of the data with a mask. SCI systems commonly use binary-valued masks that follow certain physical constraint... | {
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2501.06655 | Personalized Preference Fine-tuning of Diffusion Models | [
"cs.LG",
"cs.CV"
] | RLHF techniques like DPO can significantly improve the generation quality of text-to-image diffusion models. However, these methods optimize for a single reward that aligns model generation with population-level preferences, neglecting the nuances of individual users' beliefs or values. This lack of personalization lim... | {
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2501.06659 | TWIX: Automatically Reconstructing Structured Data from Templatized
Documents | [
"cs.DB",
"cs.CV"
] | Many documents, that we call templatized documents, are programmatically generated by populating fields in a visual template. Effective data extraction from these documents is crucial to supporting downstream analytical tasks. Current data extraction tools often struggle with complex document layouts, incur high latenc... | {
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2501.06660 | MapGS: Generalizable Pretraining and Data Augmentation for Online
Mapping via Novel View Synthesis | [
"cs.CV",
"cs.RO"
] | Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability. However, recent advancements often overlook cross-sensor configuration generalization, leading to performance degradation when models are deployed on vehicles with different camera intrinsics an... | {
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2501.06661 | Learning dynamical systems with hit-and-run random feature maps | [
"cs.LG",
"physics.data-an",
"stat.ME",
"stat.ML"
] | We show how random feature maps can be used to forecast dynamical systems with excellent forecasting skill. We consider the tanh activation function and judiciously choose the internal weights in a data-driven manner such that the resulting features explore the nonlinear, non-saturated regions of the activation functio... | {
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2501.06662 | The Magnitude of Categories of Texts Enriched by Language Models | [
"math.CT",
"cs.CL"
] | The purpose of this article is twofold. Firstly, we use the next-token probabilities given by a language model to explicitly define a $[0,1]$-enrichment of a category of texts in natural language, in the sense of Bradley, Terilla, and Vlassopoulos. We consider explicitly the terminating conditions for text generation a... | {
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2501.06663 | Ultra Memory-Efficient On-FPGA Training of Transformers via
Tensor-Compressed Optimization | [
"cs.LG",
"cs.AR",
"cs.CL"
] | Transformer models have achieved state-of-the-art performance across a wide range of machine learning tasks. There is growing interest in training transformers on resource-constrained edge devices due to considerations such as privacy, domain adaptation, and on-device scientific machine learning. However, the significa... | {
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2501.06669 | Challenging reaction prediction models to generalize to novel chemistry | [
"cs.LG",
"physics.chem-ph"
] | Deep learning models for anticipating the products of organic reactions have found many use cases, including validating retrosynthetic pathways and constraining synthesis-based molecular design tools. Despite compelling performance on popular benchmark tasks, strange and erroneous predictions sometimes ensue when using... | {
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2501.06670 | A Geometric Analysis-Based Safety Assessment Framework for MASS Route
Decision-Making in Restricted Waters | [
"eess.SY",
"cs.SY"
] | To enhance the safety of Maritime Autonomous Surface Ships (MASS) navigating in restricted waters, this paper aims to develop a geometric analysis-based route safety assessment (GARSA) framework, specifically designed for their route decision-making in irregularly shaped waterways. Utilizing line and point geometric el... | {
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2501.06678 | Imbalanced Medical Image Segmentation with Pixel-dependent Noisy Labels | [
"cs.CV",
"cs.AI"
] | Accurate medical image segmentation is often hindered by noisy labels in training data, due to the challenges of annotating medical images. Prior research works addressing noisy labels tend to make class-dependent assumptions, overlooking the pixel-dependent nature of most noisy labels. Furthermore, existing methods ty... | {
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2501.06679 | Coordinated Deliverable Energy Flexibility from EV Aggregators in
Distribution Networks | [
"eess.SY",
"cs.SY"
] | This paper presents a coordinated framework to optimize electric vehicle (EV) charging considering grid constraints and system uncertainties. The proposed framework consists of two optimization models. In particular, the distribution system operator (DSO) solves the first model to optimize the amount of deliverable ene... | {
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2501.06680 | Application of Vision-Language Model to Pedestrians Behavior and Scene
Understanding in Autonomous Driving | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] | Autonomous driving (AD) has experienced significant improvements in recent years and achieved promising 3D detection, classification, and localization results. However, many challenges remain, e.g. semantic understanding of pedestrians' behaviors, and downstream handling for pedestrian interactions. Recent studies in a... | {
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2501.06682 | Generative AI in Education: From Foundational Insights to the Socratic
Playground for Learning | [
"cs.AI"
] | This paper explores the synergy between human cognition and Large Language Models (LLMs), highlighting how generative AI can drive personalized learning at scale. We discuss parallels between LLMs and human cognition, emphasizing both the promise and new perspectives on integrating AI systems into education. After exam... | {
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2501.06685 | Tab-Shapley: Identifying Top-k Tabular Data Quality Insights | [
"cs.LG",
"stat.ML"
] | We present an unsupervised method for aggregating anomalies in tabular datasets by identifying the top-k tabular data quality insights. Each insight consists of a set of anomalous attributes and the corresponding subsets of records that serve as evidence to the user. The process of identifying these insight blocks is c... | {
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2501.06686 | Understanding and Mitigating Membership Inference Risks of Neural
Ordinary Differential Equations | [
"cs.CR",
"cs.LG"
] | Neural ordinary differential equations (NODEs) are an emerging paradigm in scientific computing for modeling dynamical systems. By accurately learning underlying dynamics in data in the form of differential equations, NODEs have been widely adopted in various domains, such as healthcare, finance, computer vision, and l... | {
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2501.06689 | TAPO: Task-Referenced Adaptation for Prompt Optimization | [
"cs.CL"
] | Prompt engineering can significantly improve the performance of large language models (LLMs), with automated prompt optimization (APO) gaining significant attention due to the time-consuming and laborious nature of manual prompt design. However, much of the existing work in APO overlooks task-specific characteristics, ... | {
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2501.06692 | PGP-SAM: Prototype-Guided Prompt Learning for Efficient Few-Shot Medical
Image Segmentation | [
"cs.CV",
"cs.AI"
] | The Segment Anything Model (SAM) has demonstrated strong and versatile segmentation capabilities, along with intuitive prompt-based interactions. However, customizing SAM for medical image segmentation requires massive amounts of pixel-level annotations and precise point- or box-based prompt designs. To address these c... | {
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2501.06693 | Vid2Sim: Realistic and Interactive Simulation from Video for Urban
Navigation | [
"cs.CV",
"cs.RO"
] | Sim-to-real gap has long posed a significant challenge for robot learning in simulation, preventing the deployment of learned models in the real world. Previous work has primarily focused on domain randomization and system identification to mitigate this gap. However, these methods are often limited by the inherent con... | {
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2501.06695 | DVM: Towards Controllable LLM Agents in Social Deduction Games | [
"cs.AI"
] | Large Language Models (LLMs) have advanced the capability of game agents in social deduction games (SDGs). These games rely heavily on conversation-driven interactions and require agents to infer, make decisions, and express based on such information. While this progress leads to more sophisticated and strategic non-pl... | {
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2501.06697 | Mamba-MOC: A Multicategory Remote Object Counting via State Space Model | [
"cs.CV",
"cs.AI"
] | Multicategory remote object counting is a fundamental task in computer vision, aimed at accurately estimating the number of objects of various categories in remote images. Existing methods rely on CNNs and Transformers, but CNNs struggle to capture global dependencies, and Transformers are computationally expensive, wh... | {
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2501.06699 | Large Language Models, Knowledge Graphs and Search Engines: A Crossroads
for Answering Users' Questions | [
"cs.AI",
"cs.IR",
"cs.SC"
] | Much has been discussed about how Large Language Models, Knowledge Graphs and Search Engines can be combined in a synergistic manner. A dimension largely absent from current academic discourse is the user perspective. In particular, there remain many open questions regarding how best to address the diverse information ... | {
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2501.06700 | Average Reward Reinforcement Learning for Wireless Radio Resource
Management | [
"cs.IT",
"cs.LG",
"cs.NI",
"eess.SP",
"math.IT"
] | In this paper, we address a crucial but often overlooked issue in applying reinforcement learning (RL) to radio resource management (RRM) in wireless communications: the mismatch between the discounted reward RL formulation and the undiscounted goal of wireless network optimization. To the best of our knowledge, we are... | {
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2501.06701 | Sequential Portfolio Selection under Latent Side Information-Dependence
Structure: Optimality and Universal Learning Algorithms | [
"q-fin.MF",
"cs.IT",
"cs.LG",
"math.IT",
"math.PR",
"q-fin.PM"
] | This paper investigates the investment problem of constructing an optimal no-short sequential portfolio strategy in a market with a latent dependence structure between asset prices and partly unobservable side information, which is often high-dimensional. The results demonstrate that a dynamic strategy, which forms a p... | {
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2501.06704 | Fine-tuning ChatGPT for Automatic Scoring of Written Scientific
Explanations in Chinese | [
"cs.AI",
"cs.CL"
] | The development of explanations for scientific phenomena is essential in science assessment, but scoring student-written explanations remains challenging and resource-intensive. Large language models (LLMs) have shown promise in addressing this issue, particularly in alphabetic languages like English. However, their ap... | {
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} |
2501.06705 | Quantum Data Sketches | [
"cs.DB",
"quant-ph"
] | Recent advancements in quantum technologies, particularly in quantum sensing and simulation, have facilitated the generation and analysis of inherently quantum data. This progress underscores the necessity for developing efficient and scalable quantum data management strategies. This goal faces immense challenges due t... | {
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} |
2501.06706 | AIOpsLab: A Holistic Framework to Evaluate AI Agents for Enabling
Autonomous Clouds | [
"cs.AI",
"cs.DC",
"cs.MA",
"cs.SE"
] | AI for IT Operations (AIOps) aims to automate complex operational tasks, such as fault localization and root cause analysis, to reduce human workload and minimize customer impact. While traditional DevOps tools and AIOps algorithms often focus on addressing isolated operational tasks, recent advances in Large Language ... | {
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} |
2501.06707 | ELIZA Reanimated: The world's first chatbot restored on the world's
first time sharing system | [
"cs.AI",
"cs.CY",
"cs.SC"
] | ELIZA, created by Joseph Weizenbaum at MIT in the early 1960s, is usually considered the world's first chatbot. It was developed in MAD-SLIP on MIT's CTSS, the world's first time-sharing system, on an IBM 7094. We discovered an original ELIZA printout in Prof. Weizenbaum's archives at MIT, including an early version of... | {
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} |
2501.06708 | Evaluating Sample Utility for Data Selection by Mimicking Model Weights | [
"cs.LG",
"cs.AI"
] | Foundation models are trained on large-scale web-crawled datasets, which often contain noise, biases, and irrelevant information. This motivates the use of data selection techniques, which can be divided into model-free variants -- relying on heuristic rules and downstream datasets -- and model-based, e.g., using influ... | {
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} |
2501.06710 | Multi-task Visual Grounding with Coarse-to-Fine Consistency Constraints | [
"cs.CV",
"cs.AI"
] | Multi-task visual grounding involves the simultaneous execution of localization and segmentation in images based on textual expressions. The majority of advanced methods predominantly focus on transformer-based multimodal fusion, aiming to extract robust multimodal representations. However, ambiguity between referring ... | {
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} |
2501.06713 | MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation | [
"cs.AI"
] | The growing demand for efficient and lightweight Retrieval-Augmented Generation (RAG) systems has highlighted significant challenges when deploying Small Language Models (SLMs) in existing RAG frameworks. Current approaches face severe performance degradation due to SLMs' limited semantic understanding and text process... | {
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} |
2501.06714 | F3D-Gaus: Feed-forward 3D-aware Generation on ImageNet with
Cycle-Consistent Gaussian Splatting | [
"cs.CV"
] | This paper tackles the problem of generalizable 3D-aware generation from monocular datasets, e.g., ImageNet. The key challenge of this task is learning a robust 3D-aware representation without multi-view or dynamic data, while ensuring consistent texture and geometry across different viewpoints. Although some baseline ... | {
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} |
2501.06715 | ZNO-Eval: Benchmarking reasoning capabilities of large language models
in Ukrainian | [
"cs.CL",
"cs.AI"
] | As the usage of large language models for problems outside of simple text understanding or generation increases, assessing their abilities and limitations becomes crucial. While significant progress has been made in this area over the last few years, most research has focused on benchmarking English, leaving other lang... | {
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} |
2501.06718 | DRDT3: Diffusion-Refined Decision Test-Time Training Model | [
"cs.LG"
] | Decision Transformer (DT), a trajectory modeling method, has shown competitive performance compared to traditional offline reinforcement learning (RL) approaches on various classic control tasks. However, it struggles to learn optimal policies from suboptimal, reward-labeled trajectories. In this study, we explore the ... | {
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} |
2501.06719 | Hierarchical Sampling-based Planner with LTL Constraints and Text
Prompting | [
"cs.RO",
"cs.SY",
"eess.SY"
] | This project introduces a hierarchical planner integrating Linear Temporal Logic (LTL) constraints with natural language prompting for robot motion planning. The framework decomposes maps into regions, generates directed graphs, and converts them into transition systems for high-level planning. Text instructions are tr... | {
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} |
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