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arXiv 2025年

作者： Wu, Peng Li, Haoxuan Zheng, Chunyuan Zeng, Yan Chen, Jiawei Liu, Yang Guo, Ruocheng Zhang, Kun School of Mathematics and Statistics Beijing Technology and Business University China Center for Data Science Peking University China School of Mathematical Sciences Peking University China College of Computer Science and Technology Zhejiang University China Computer Science and Engineering University of California Santa Cruz Bolivia ByteDance Research China Department of Philosophy Carnegie Mellon University United States Machine Learning Department Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

Counterfactual inference aims to estimate the counterfactual outcome at the individual level given knowledge of an observed treatment and the factual outcome, with broad applications in fields such as epidemiology, econometrics, and management science. Previous methods rely on a known structural causal model (SCM) or assume the homogeneity of the exogenous variable and strict monotonicity between the outcome and exogenous variable. In this paper, we propose a principled approach for identifying and estimating the counterfactual outcome. We first introduce a simple and intuitive rank preservation assumption to identify the counterfactual outcome without relying on a known structural causal model. Building on this, we propose a novel ideal loss for theoretically unbiased learning of the counterfactual outcome and further develop a kernel-based estimator for its empirical estimation. Our theoretical analysis shows that the rank preservation assumption is not stronger than the homogeneity and strict monotonicity assumptions, and shows that the proposed ideal loss is convex, and the proposed estimator is unbiased. Extensive semi-synthetic and real-world experiments are conducted to demonstrate the effectiveness of the proposed method. Copyright © 2025, The Authors. All rights reserved.

关键词： Contrastive learning

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Improving the Quality of Diabetic data with Large Language Model-driven Cleaning Techniques

Improving the Quality of Diabetic Data with Large Language M...

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Intelligent Systems and Advanced Applications (ICISAA), International Conference on

作者： Divya Biradar Rahul Dattangire Ruchika Vaidya NagaSuryaShivani Inti Computer Science University of Texas at Arlington Arlington Texas USA Data Engineering Independent Researcher Houston Texas USA Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Computer and Information Science University of Texas at Arlington Arlington Texas USA

ISBN: (数字)9798331539948

ISBN: (纸本)9798331539955

The data-cleaning approach applies the capabilities of large language models to reduce the noise in the extracted and received data from healthcare sources. The aim will be to clean the collected and extracted data by looking up and dealing with the inaccuracies encountered during the project like missing or duplicated records, outliers, and data that will not add any value to the project. The “Diabetes” data set from Kaggle is divided into implementing the combined approach data-cleaning process such as removing duplicate entries, and emptying fields by a Large Language Model, to improve the accuracy is assessed. Three components will increase the reliability of the collected data: 1. Gateway, 2. Storage 3. Cleaning. The outcomes demonstrated the LLMbased approach’s effective impact in correcting and consistent data within the healthcare dataset.

关键词： Adaptation models Large language models Noise Medical services Cleaning data models Diabetes Safety data mining Reliability

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The Role of Technology in Modern Education

The Role of Technology in Modern Education

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Priya Kate Utkarsha Pacharaney Abhay Tale Aditya Barhate Nishad Sheikh Chinmaya Khadse Department of Computer Science and Engineering Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering & Technology Datta Meghe Institute of Higher Education & Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

It can be seen that technology has played a big role in today’s education through the use of smart boards and AI. Some of the advantages are that it affords students better access and opportunity for participation and brings more flexibility and efficiency to the teaching process and allows students to experience learning in new ways. However, there are also disadvantages such as digital divides, screen time, privacy concerns, and technical limitations. After highlighting the implications, the article analyses potential developments such as AI tutors and 5G connectivity as potential frontier technologies for the next era of learning technologies. Concisely, technology is an enhancement resource that should be embraced and complement quality teaching. The appropriate policy and continuous enhancement are the two crucial strategies for the education systems to implement technology to foster students with competencies required in the twenty-first century. Further research is needed regarding such matters as time spent in front of screens, media education, and implementation of new technologies in schools.

关键词： Training Industries Privacy Education Medical services learning (artificial intelligence) Media Stakeholders Security Digital divide

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Meta-Auto-Decoder: A Meta-learning Based Reduced Order Model for Solving Parametric Partial Differential Equations

arXiv

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arXiv 2023年

作者： Ye, Zhanhong Huang, Xiang Liu, Hongsheng Dong, Bin Beijing International Center for Mathematical Research Peking University Beijing China School of Computer Science and Technology University of Science and Technology of China Hefei China Central Software Institute Huawei Technologies Co. Ltd Hangzhou China Center for Machine Learning Research Peking University Beijing China

Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, boundary conditions, shapes of computational domains, etc. Typical reduced order modeling techniques accelarate solution of the parametric PDEs by projecting them onto a linear trial manifold constructed in the offline stage. These methods often need a predefined mesh as well as a series of precomputed solution snapshots, and may struggle to balance between efficiency and accuracy due to the limitation of the linear ansatz. Utilizing the nonlinear representation of neural networks, we propose Meta-Auto-Decoder (MAD) to construct a nonlinear trial manifold, whose best possible performance is measured theoretically by the decoder width. Based on the meta-learning concept, the trial manifold can be learned in a mesh-free and unsupervised way during the pre-training stage. Fast adaptation to new (possibly heterogeneous) PDE parameters is enabled by searching on this trial manifold, and optionally fine-tuning the trial manifold at the same time. Extensive numerical experiments show that the MAD method exhibits faster convergence speed without losing accuracy than other deep learning-based *** Codes 68T07 © 2023, CC BY.

关键词： Partial differential equations

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Smart Healthcare: Harnessing the Power of machine learning for Predictive Analysis

Smart Healthcare: Harnessing the Power of Machine Learning f...

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Engineering and Technology (PICET), Parul International Conference on

作者： Aditya Barhate Praveen Kumar Prateek Verma Nayan Jikar Abhay Tale Vaibhav Hikre Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Computer Science and Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798350369748

ISBN: (纸本)9798350369755

Smart healthcare employs cloud computing, the Internet of Things (IoT), and supercomputers to manage the healthcare system. Medical data storage volume is increasing, necessitating the adoption of machine learning (ML) models for predicting outcomes, particularly in the treatment of chronic illnesses. The trend of care personalization and technological integration is highlighted by the development of ML applications for clinical decision support systems, including real-time predictive analytics. Predictive analytics applications employ ML and Artificial Intelligence (AI) to forecast patient risks, outcomes, and treatment responses. Predictive analytics is more important in healthcare when it comes to detecting Alzheimer's disease, cardiovascular events, radiology, and other conditions. Therefore, the current study highlights the change in illness identification, medical imaging, and personalized medication brought about by ML and Predictive Analytics in Precision Medicine (PM). However, ethical concerns and the deployment of transparent AI models still need more research. This study anticipates a time when healthcare will be quicker, better informed, and more patient-centered by using the insights provided by intelligent healthcare data. By effectively saving patient lives, the use of ML in healthcare ushers in a new era for the industry.

关键词： Ethics Solid modeling Medical services machine learning Radiology Supercomputers Real-time systems Internet of Things Predictive analytics Biomedical imaging

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Towards Symbolic Xai - Explanation Through Human Understandable Logical Relationships between Features

SSRN

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SSRN 2024年

作者： Schnake, Thomas Jafari, Farnoush Rezaei Lederer, Jonas Xiong, Ping Nakajima, Shinichi Gugler, Stefan Montavon, Grégoire Müller, Klaus-Robert Machine Learning Group Technical University of Berlin Berlin10623 Germany Berlin Institute for the Foundations of Learning and Data - BIFOLD Berlin10623 Germany RIKEN Saitama Wakō351-0198 Japan Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institut für Informatik Saarbrücken66123 Germany Institute for Computer Science Free University of Berlin Berlin14195 Germany

Explainable Artificial Intelligence (XAI) plays a crucial role in fostering transparency and trust in AI systems, where traditional XAI approaches typically offer one level of abstraction for explanations, often in the form of heatmaps highlighting single or multiple input features. However, we ask whether abstract reasoning or problem-solving strategies of a model may also be relevant, as these align more closely with how humans approach solutions to problems. We propose a framework, called Symbolic XAI, that attributes relevance to symbolic queries expressing logical relationships between input features, thereby capturing the abstract reasoning behind a model’s predictions. The methodology is built upon a simple yet general multi-order decomposition of model predictions. This decomposition can be specified using higher-order propagation-based relevance methods, such as GNN-LRP, or perturbation-based explanation methods commonly used in XAI. The effectiveness of our framework is demonstrated in the domains of natural language processing (NLP), vision, and quantum chemistry (QC), where abstract symbolic domain knowledge is abundant and of significant interest to users. The Symbolic XAI framework provides an understanding of the model’s decision-making process that is both flexible for customization by the user and human-readable through logical formulas. © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Deep Reinforcement learning In Autonomous Cars To Preventing Road Accident

Deep Reinforcement Learning In Autonomous Cars To Preventing...

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Artificial Intelligence in Education and Industry 4.0 (IDICAIEI), DMIHER International Conference on

作者： Rushikesh Thote Aditya Barhate Abhay Tale Prateek Verma Department of Computer Science and Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India Department of Artificial Intelligence and Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research Wardha Maharashtra India

ISBN: (数字)9798331528713

ISBN: (纸本)9798331528720

The integration of autonomous vehicles (AVs) into modern transportation systems presents both unprecedented opportunities and challenges, particularly in ensuring road safety and reducing accidents. This paper explores the application of Deep Reinforcement learning (DRL) techniques to enhance decision-making processes in AVs, aiming to proactively prevent road accidents. By simulating complex driving environments, DRL algorithms enable AVs to learn optimal driving strategies through continuous interaction and feedback. We introduce a DRL-based framework that incorporates sensor data fusion, real-time environment analysis, and adaptive control mechanisms. Experimental results from simulated urban and highway scenarios demonstrate a significant reduction in collision rates and improved response to unforeseen obstacles compared to traditional rule-based systems. The findings underscore the potential of DRL in fortifying the reliability and safety of autonomous driving technologies.

关键词： Road accidents Reviews Decision making Transportation Transforms Deep reinforcement learning Road safety Safety Reliability Stress

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Coarse-graining conformational dynamics with multi-dimensional generalized Langevin equation: how, when, and why

arXiv

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arXiv 2024年

作者： Xie, Pinchen Qiu, Yunrui Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry University of Wisconsin-Madison MadisonWI53706 United States AI for Science Institute Beijing100080 China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100084 China

A data-driven ab initio generalized Langevin equation (AIGLE) approach is developed to learn and simulate high-dimensional, heterogeneous, coarse-grained conformational dynamics. Constrained by the fluctuation-dissipation theorem, the approach can build coarse-grained models in dynamical consistency with all-atom molecular dynamics. We also propose practical criteria for AIGLE to enforce long-term dynamical consistency. Case studies of a toy polymer, with 20 coarse-grained sites, and the alanine dipeptide, with two dihedral angles, elucidate why one should adopt AIGLE or its Markovian limit for modeling coarse-grained conformational dynamics in practice. © 2024, CC BY.

关键词： Coarse-grained modeling

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Detection of Emergency Vehicles in Traffic and Assign Traffic Free Path Using Deep learning

Detection of Emergency Vehicles in Traffic and Assign Traffi...

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Sentiment Analysis and Deep learning (ICSADL), International Conference on

作者： Sriharsha Vikruthi Tejesh Reddy Singasani V T Ram Pavan Kumar M P V V S D Nagendrudu Ch Raghavendra R. Sahith Dept of CSE B V Raju Institute of Technology Narsapur Medak Telangana India University of Cumberlands Lousiville KY Dept of Computer Science Kakaraparti Bhavanarayana College Vijayawada Andhrapradesh India Department of Artificial Intelligence and Machine Learning Aditya Univeristy Suramplaem India Dept of CSE(Data Science) KKR & KSR Institute of Technology and Sciences Guntur Andhra Pradesh Dept of Computer Science and Engineering CVR College of Engineering Hyderabad Telangana India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

Too much of Traffic jams are in a highly populated country. Sometimes emergency vehicles such as fire-fighter and ambulance get stuck in traffic, putting lives at threat in many cases. Priority should be given to special vehicles and their way should be cleared. However, traffic police find it difficult or sometimes impossible to handle with it. Emergency vehicles have visual and audio warning devices help them to overtake traffic by demanding as little space as possible from other vehicles. Many death happen due to delay in doctors, ambulance and fire engine vehicles while reaching their destinations. The traditional systems didn't provide path free route accurately. Therefore, by using detection of emergency vehicles in traffic and assign traffic free path using deep learning system, emergency vehicles can get free path or fast route by observing the traffic cameras. So, those emergency vehicles can reach the location on time by using RNN system. Therefore, the main objective is to improve this system for better results interms of accuracy, detection rate and precision.

关键词： Deep learning Space vehicles Visualization Sentiment analysis Recurrent neural networks Accuracy Law enforcement Roads Medical services Automobiles

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GAIA: A Global, Multi-modal, Multi-scale Vision-Language dataset for Remote Sensing Image Analysis

arXiv

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arXiv 2025年

作者： Zavras, Angelos Michail, Dimitrios Zhu, Xiao Xiang Demir, Begüm Papoutsis, Ioannis Orion Lab School of Rural Surveying and Geoinformatics Engineering National Technical University of Athens Athens15772 Greece Institute of Astronomy Astrophysics Space Applications and Remote Sensing National Observatory of Athens Athens11810 Greece Department of Informatics and Telematics Harokopio University of Athens Athens17676 Greece Chair of Data Science in Earth Observation Technical University of Munich Munich80333 Germany Munich Center for Machine Learning Munich80333 Germany Faculty of Electrical Engineering and Computer Science Technische Universität Berlin Berlin10623 Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Berlin10623 Germany

The continuous operation of Earth-orbiting satellites generates vast and ever-growing archives of Remote Sensing (RS) images. Natural language presents an intuitive interface for accessing, querying, and interpreting the data from such archives. However, existing Vision-Language Models (VLMs) are predominantly trained on web-scraped, noisy image-text data, exhibiting limited exposure to the specialized domain of RS. This deficiency results in poor performance on RS-specific tasks, as commonly used datasets often lack detailed, scientifically accurate textual descriptions and instead emphasize solely on attributes like date and location. To bridge this critical gap, we introduce GAIA, a novel dataset designed for multi-scale, multi-sensor, and multi-modal RS image analysis. GAIA comprises of 205,150 meticulously curated RS image-text pairs, representing a diverse range of RS modalities associated to different spatial resolutions. Unlike existing vision-language datasets in RS, GAIA specifically focuses on capturing a diverse range of RS applications, providing unique information about environmental changes, natural disasters, and various other dynamic phenomena. The dataset provides a spatially and temporally balanced distribution, spanning across the globe, covering the last 25 years with a balanced temporal distribution of observations. GAIA’s construction involved a two-stage process: (1) targeted web-scraping of images and accompanying text from reputable RS-related sources, and (2) generation of five high-quality, scientifically grounded synthetic captions for each image using carefully crafted prompts that leverage the advanced vision-language capabilities of GPT-4o. We also release an automated processing framework developed for this purpose, enabling the broader research community to generate captions for RS images using the web-crawled image-text data. Our extensive experiments, including fine-tuning of CLIP and BLIP2 models, demonstrate that GAIA significan

关键词： Visual languages

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