检索结果-内蒙古大学图书馆

Suspended accounts align with the Internet Research Agency misinformation campaign to influence the 2016 US election

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Serafino, Matteo Zhou, Zhenkun Andrade, José S. Bovet, Alexandre Makse, Hernán A. Levich Institute and Physics Departmen City College of New York New YorkNY United States Department of Data Science School of Statistics Capital University of Economics and Business Beijing China Physics Department Universidade Federal do Ceará Ceará Fortaleza Brazil Department of Mathematical Modeling and Machine Learning University of Zurich Zurich Switzerland Digital Society Initiative University of Zurich Zurich Switzerland

The ongoing debate surrounding the impact of the Internet Research Agency’s (IRA) social media campaign during the 2016 U.S. presidential election has largely overshadowed the involvement of other actors. Our analysis brings to light a substantial group of suspended Twitter users, outnumbering the IRA user group by a factor of 60, who align with the ideologies of the IRA campaign. Our study demonstrates that this group of suspended Twitter accounts significantly influenced individuals categorized as undecided or weak supporters, potentially with the aim of swaying their opinions, as indicated by Granger causality. © 2023, CC BY.

关键词：

S3Attention: Improving Long Sequence Attention with Smoothed Skeleton Sketching

学校读者我要写书评

暂无评论

arXiv 2024年

作者： Wang, Xue Zhou, Tian Zhu, Jianqing Liu, Jialin Yuan, Kun Yao, Tao Yin, Wotao Jin, Rong Cai, Han Qin Alibaba Group BellevueWA98004 United States Computer Electrical and Mathematical Science and Engineering Division King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia Department of Statistics and Data Science University of Central Florida OrlandoFL32816 United States Center for Machine Learning Research Peking University Beijing100871 China Antai College of Economics and Management Shanghai Jiao Tong University Shanghai200030 China Meta Menlo ParkCA94025 United States Department of Statistics and Data Science Department of Computer Science University of Central Florida OrlandoFL32816 United States

Attention based models have achieved many remarkable breakthroughs in numerous applications. However, the quadratic complexity of Attention makes the vanilla Attention based models hard to apply to long sequence tasks. Various improved Attention structures are proposed to reduce the computation cost by inducing low rankness and approximating the whole sequence by sub-sequences. The most challenging part of those approaches is maintaining the proper balance between information preservation and computation reduction: the longer sub-sequences used, the better information is preserved, but at the price of introducing more noise and computational costs. In this paper, we propose a smoothed skeleton sketching based Attention structure, coined S3Attention, which significantly improves upon the previous attempts to negotiate this trade-off. S3Attention has two mechanisms to effectively minimize the impact of noise while keeping the linear complexity to the sequence length: a smoothing block to mix information over long sequences and a matrix sketching method that simultaneously selects columns and rows from the input matrix. We verify the effectiveness of S3Attention both theoretically and empirically. Extensive studies over Long Range Arena (LRA) datasets and six time-series forecasting show that S3Attention significantly outperforms both vanilla Attention and other state-of-the-art variants of Attention structures. Copyright © 2024, The Authors. All rights reserved.

关键词： Musculoskeletal system

Invertible Coarse Graining with Physics-Informed Generative Artificial Intelligence

学校读者我要写书评

暂无评论

arXiv 2023年

作者： Zhang, Jun Lin, Xiaohan Weinan, E. Gao, Yi Qin Changping Laboratory Beijing102200 China Beijing National Laboratory for Molecular Sciences College of Chemistry and Molecular Engineering Peking University Beijing100871 China AI for Science Institute Beijing China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing100871 China

Multiscale molecular modeling is widely applied in scientific research of molecular properties over large time and length scales. Two specific challenges are commonly present in multiscale modeling, provided that information between the coarse and fine representations of molecules needs to be properly exchanged: One is to construct coarse grained models by passing information from the fine to coarse levels;the other is to restore finer molecular details given coarse grained configurations. Although these two problems are commonly addressed independently, in this work, we present a theory connecting them, and develop a methodology called Cycle Coarse Graining (CCG) to solve both problems in a unified manner. In CCG, reconstruction can be achieved via a tractable deep generative model, allowing retrieval of fine details from coarse-grained simulations. The reconstruction in turn delivers better coarse-grained models which are informed of the fine-grained physics, and enables calculation of the free energies in a rare-event-free manner. CCG thus provides a systematic way for multiscale molecular modeling, where the finer details of coarse-grained simulations can be efficiently retrieved, and the coarse-grained models can be improved consistently. © 2023, CC BY-NC-SA.

关键词： Coarse-grained modeling

A scalable deep learning approach for solving high-dimensional dynamic optimal transport

学校读者我要写书评

暂无评论

arXiv 2022年

作者： Wan, Wei Zhang, Yuejin Bao, Chenglong Dong, Bin Shi, Zuoqiang School of Mathematics and Physics North China Electric Power University China Department of Mathematical Science Tsinghua University China Yau Mathematical Sciences Center Tsinghua University Yaqi Lake Beijing Institute of Mathematical Sciences and Applications China Beijing International Center for Mathematical Research Peking University Center for Machine Learning Research Peking University China

The dynamic formulation of optimal transport has attracted growing interests in scientific computing and machine learning, and its computation requires to solve a PDE-constrained optimization problem. The classical Eulerian discretization based approaches suffer from the curse of dimensionality, which arises from the approximation of high-dimensional velocity field. In this work, we propose a deep learning based method to solve the dynamic optimal transport in high dimensional space. Our method contains three main ingredients: a carefully designed representation of the velocity field, the discretization of the PDE constraint along the characteristics, and the computation of high dimensional integral by Monte Carlo method in each time step. Specifically, in the representation of the velocity field, we apply the classical nodal basis function in time and the deep neural networks in space domain with the H1-norm regularization. This technique promotes the regularity of the velocity field in both time and space such that the discretization along the characteristic remains to be stable during the training process. Extensive numerical examples have been conducted to test the proposed method. Compared to other solvers of optimal transport, our method could give more accurate results in high dimensional cases and has very good scalability with respect to dimension. Finally, we extend our method to more complicated cases such as crowd motion problem. Copyright © 2022, The Authors. All rights reserved.

关键词： Velocity

data Governance in the Big data Era: A Scalable Framework for Effective Management and Regulatory Compliance

学校读者我要写书评

暂无评论

Data Governance in the Big Data Era: A Scalable Framework fo...

Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Vasanth S G Saravanan Keerthana S Madhu Karthick A Data Science and Applications Indian Institute of Technology Chennai Madras India Department of ECE Sri Sai Ram Institute of Technology Chennai West Tambaram India Department of Artificial Intelligence and Machine Learning St. Joseph's College of Engineering OMR Chennai India Department of CSE (Cyber Security) R.M.K. College of Engineering and Technology Thiruvallur India

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

While big data enhances the capacity of organizations to make choices, the traditional data governance approaches are not productive enough to manage the explosion of volume, variety, and velocity of data. This inadequateness leads to loss of data integrity, inabilities to adhere to legal frameworks and poor use of available data. This study provides a solution by providing an easier way to apply data governance concepts using a framework that is elastic and scalable enough for today's data environment. In addition to this, study helps a firm maintain quality of data, compliance, and interactivity through the use of metadata strategies. This relatively new tool helps to manage data governance more effective and innovatively so that the organization can harness the business aspects within big data through the proper deployment of the data governance structure.

关键词： Scalability data integrity Medical services Big data Metadata Regulation data governance Safety Security Usability

Large language models illuminate a progressive pathway to artificial intelligent healthcare assistant

学校读者我要写书评

暂无评论

Medicine Plus

Medicine Plus 2024年第2期1卷 102-124页

作者： Mingze Yuan Peng Bao Jiajia Yuan Yunhao Shen Zifan Chen Yi Xie Jie Zhao Quanzheng Li Yang Chen Li Zhang Lin Shen Bin Dong Center for Data Science Peking UniversityBeijing 100871China Department of Gastrointestinal Oncology Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education)Peking University Cancer Hospital and Institute Beijing 100142China National Engineering Laboratory for Big Data Analysis and Applications Peking UniversityBeijing 100871China Beijing International Center for Mathematical Research Peking UniversityBeijing 100871China Center for Machine Learning Research Peking University Beijing 100871China National Biomedical Imaging Center Peking UniversityBeijing 100871China Peking University Changsha Institute for Computing and Digital Economy Changsha 410205China Massachusetts General Hospital Boston MA 02114-2696USA Harvard Medical School BostonMA 02115USA

With the rapid development of artificial intelligence,large language models(LLMs)have shown promising capabilities in mimicking human-level language comprehen-sion and *** has sparked significant interest in applying LLMs to enhance various aspects of healthcare,ranging from medical education to clinical decision ***,medicine involves multifaceted data modalities and nuanced reasoning skills,presenting challenges for integrating *** review introduces the fundamental applications of general-purpose and specialized LLMs,demon-strating their utilities in knowledge retrieval,research support,clinical workflow automation,and diagnostic *** the inherent multimodality of medicine,the review emphasizes the multimodal LLMs and discusses their ability to process diverse data types like medical imaging and electronic health records to augment diagnostic *** address LLMs'limitations regarding personalization and complex clinical reasoning,the review further explores the emerging develop-ment of LLM-powered autonomous agents for ***,it summarizes the evaluation methodologies for assessing LLMs'reliability and safety in medical *** have transformative potential in medicine;however,there is a pivotal need for continuous optimizations and ethical oversight before these models can be effectively integrated into clinical practice.

关键词： Large language models Artificial intelligence Medicine Healthcare assistant Prompt engineering In-context learning

An Automatic Analysis of Ultrasound Vocalisations for the Prediction of Interaction Context in Captive Egyptian Fruit Bats

学校读者我要写书评

暂无评论

An Automatic Analysis of Ultrasound Vocalisations for the Pr...

European Signal Processing Conference (EUSIPCO)

作者： Andreas Triantafyllopoulos Alexander Gebhard Manuel Milling Simon Rampp Björn Schuller CHI - Chair of Health Informatics Technical University of Munich MRI Munich Germany EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing Augsburg Germany MCML - Munich Center for Machine Learning Munich Germany MDSI - Munich Data Science Institute Munich Germany GLAM - Group on Language Audio & Music Imperial College London UK

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Prior work in computational bioacoustics has mostly focused on the detection of animal presence in a particular habitat. However, animal sounds contain much richer information than mere presence; among others, they encapsulate the interactions of those animals with other members of their species. Studying these interactions is almost impossible in a naturalistic setting, as the ground truth is often lacking. The use of animals in captivity instead offers a viable alternative pathway. However, most prior works follow a traditional, statistics-based approach to analysing interactions. In the present work, we go beyond this standard framework by attempting to predict the underlying context in interactions between captive Rousettus Aegyptiacus using deep neural networks. We reach an unweighted average recall of over 30% - more than thrice the chance level - and show error patterns that differ from our statistical analysis. This work thus represents an important step towards the automatic analysis of states in animals from sound.

关键词： Ultrasonic imaging Animals Statistical analysis Habitats Europe Signal processing Epidemiology Biomedical monitoring Standards Monitoring

Position: Bayesian Deep learning is Needed in the Age of Large-Scale AI 41

学校读者我要写书评

暂无评论

Position: Bayesian Deep Learning is Needed in the Age of Lar...

41st International Conference on machine learning, ICML 2024

作者： Papamarkou, Theodore Skoularidou, Maria Palla, Konstantina Aitchison, Laurence Arbel, Julyan Dunson, David Filippone, Maurizio Fortuin, Vincent Hennig, Philipp Hernández-Lobato, José Miguel Hubin, Aliaksandr Immer, Alexander Karaletsos, Theofanis Khan, Mohammad Emtiyaz Kristiadi, Agustinus Li, Yingzhen Mandt, Stephan Nemeth, Christopher Osborne, Michael A. Rudner, Tim G.J. Rügamer, David Teh, Yee Whye Welling, Max Wilson, Andrew Gordon Zhang, Ruqi Department of Mathematics The University of Manchester Manchester United Kingdom Eric and Wendy Schmidt Center Broad Institute of MIT and Harvard Cambridge United States Spotify London United Kingdom Computational Neuroscience Unit University of Bristol Bristol United Kingdom Centre Inria de l'Université Grenoble Alpes Grenoble France Department of Statistical Science Duke University United States Statistics Program KAUST Saudi Arabia Helmholtz AI Munich Germany Department of Computer Science Technical University of Munich Munich Germany Munich Center for Machine Learning Munich Germany Tübingen AI Center University of Tübingen Tübingen Germany Department of Engineering University of Cambridge Cambridge United Kingdom Department of Mathematics University of Oslo Oslo Norway Bioinformatics and Applied Statistics Norwegian University of Life Sciences Ås Norway Department of Computer Science ETH Zurich Switzerland Chan Zuckerberg Initiative CA United States Center for Advanced Intelligence Project RIKEN Tokyo Japan Vector Institute Toronto Canada Department of Computing Imperial College London London United Kingdom Department of Computer Science UC Irvine Irvine United States Department of Mathematics and Statistics Lancaster University Lancaster United Kingdom Department of Engineering Science University of Oxford Oxford United Kingdom Center for Data Science New York University New York United States Department of Statistics LMU Munich Munich Germany DeepMind London United Kingdom Department of Statistics University of Oxford Oxford United Kingdom Informatics Institute University of Amsterdam Amsterdam Netherlands Courant Institute of Mathematical Sciences Center for Data Science Computer Science Department New York University New York United States Department of Computer Science Purdue University West Lafayette United States

In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets. However, a broader perspective reveals a multitude of overlooked metrics, tasks, and data types, such as uncertainty, active and continual learning, and scientific data, that demand attention. Bayesian deep learning (BDL) constitutes a promising avenue, offering advantages across these diverse settings. This paper posits that BDL can elevate the capabilities of deep learning. It revisits the strengths of BDL, acknowledges existing challenges, and highlights some exciting research avenues aimed at addressing these obstacles. Looking ahead, the discussion focuses on possible ways to combine large-scale foundation models with BDL to unlock their full potential. Copyright 2024 by the author(s)

关键词： Contrastive learning

From 5G to 6G: The Role of AI, machine learning, and Deep learning in Wireless Systems

学校读者我要写书评

暂无评论

From 5G to 6G: The Role of AI, Machine Learning, and Deep Le...

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

作者： Pradnyawant M. Gote Praveen Kumar Prateek Verma Prajyot Yesankar Adesh Pawar Saniya Saratkar Department of Computer Science & Design Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Computer Science & Medical Engineering Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Machine Learning Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India Department of Artificial Intelligence & Data Science Faculty of Engineering and Technology Datta Meghe Institute of Higher Education and Research (DU) Wardha Maharashtra India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

The swift progression of wireless communication technologies-specifically from 5G to 6G is an approach that could be the most significant revolutionary leap towards changing connectivity and data transmission forever. Additionally, algorithms of machine learning (ML) and deep learning (DL) in 5G and 6G can offer promising advancements toward predictive maintenance, dynamic spectrum management, and enhanced security. The review of the potentially transformative nature of these technologies underlines the need for future directions of research efforts called for in circumventing challenges towards making full capability exploitation of 6G possible.

关键词： 6G mobile communication Wireless communication Deep learning Tactile Internet Technological innovation Sentiment analysis 5G mobile communication Reviews Heuristic algorithms Security