检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

来源：评论

学校读者我要写书评

暂无评论

On-orbit measurements of the ISS atmosphere by the vehicle cabin atmosphere monitor

On-orbit measurements of the ISS atmosphere by the vehicle c...

引用

41st International Conference on Environmental Systems 2011, ICES 2011

作者： Darrach, M.R. Chutjian, A. Bornstein, B.J. Croonquist, A.P. Garkanian, V. Haemmerle, V.R. Hofman, J. Heinrichs, W.M. Karmon, D. Kenny, J. Kidd, R.D. Lee, S. MacAskill, J.A. Madzunkov, S.M. Mandrake, L. Rust, T.M. Schaefer, R.T. Thomas, J.L. Toomarian, N. Jet Propulsion Laboratory California Institute of Technology Pasadena CA 91109 United States Atomic and Molecular Physics Group JPL/Caltech United States Instrument Autonomy Group JPL/Caltech United States MicroDevices Group JPL/CalTech United States Optical Communications Group JPL/Caltech United States Processing Algorithms and Calibration Engineering JPL/Caltech United States Inst System Engineering JPL/Caltech United States Instruments and Science Data Systems JPL/Caltech United States Instrument Integration and Test JPL/Caltech United States Planetary Chemistry and Astrobiology JPL/Caltech United States High Capacity Computing and Modeling JPL/Caltech United States Machine Learning and Instrument Autonomy Group JPL/Caltech United States SpaceX 1 Rocket Rd Hawthorne CA 90250 United States Advanced Computer Systems and Technology JPL/Caltech United States Advanced Instrument Concepts JPL/Caltech United States

ISBN: (纸本)9781600869488

We report on trace gas and major atmospheric constituents results obtained by the Vehicle Cabin Atmosphere Monitor (VCAM) during operations aboard the International Space Station (ISS). VCAM is an autonomous environmental monitor based on a miniature gas chromatograph/mass spectrometer. It was flown to the ISS on shuttle mission STS-131 and commenced operations on 6/10/10. VCAM provides measurements of ppb-to-ppm levels of volatile trace-gas constituents, and of the atmospheric major constituents (nitrogen, oxygen, argon, and carbon dioxide) in a space vehicle or station. It is designed to operate autonomously and maintenance-free, approximately once per day, with a self-contained gas supply sufficient for a one-year lifetime. VCAM is designed to detect and identify 90% of the target compounds at their 180-day Spacecraft Maximum Allowable Concentration levels. © 2011 by the American Institute of Aeronautics and Astronautics, Inc.

关键词： Gases

来源：评论

学校读者我要写书评

暂无评论

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

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

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

SSRN

引用

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

来源：评论

学校读者我要写书评

暂无评论

Design and Implementation of a data Governance Framework and Platform: A Case Study of a National Research Organization of Thailand

Design and Implementation of a Data Governance Framework and...

引用

International Joint Conference on Computer science and Software Engineering (JCSSE)

作者： Sapa Chanyachatchawan Krich Nasingkun Patipat Tumsangthong Porntiwa Chata Marut Buranarach Monsak Socharoentum Leveraging Technology Solutions Section National Electronics and Computer Technology Center Bangkok Thailand Strategic Analytics Networks with Machine Learning and AI Research National Electronics and Computer Technology Center Bangkok Thailand Data Science and Analytics Research Group National Electronics and Computer Technology Center Bangkok Thailand Digital Government Development Agency Bangkok Thailand

In the current era of extensive data usage across industries, data collection, preservation, utilization, and organization has become more challenging and nuanced because it is necessary to consider critical concerns such as data security, privacy, and legal issues, apart from efficiency issues. As a result, Thai government initiated the idea and effort to implement data governance throughout the government agency. This paper showcases the implementation of data governance in a governmental research organization with highly diverse structured and unstructured data. The implementation follows international standards and the guidelines of the Digital Government Development Agency (DGA). The executives set up the working body, including the data Governance Council and data Stewards, responsible for setting up and deploying policies and regulations. Creating awareness and the necessary infrastructure are the main focuses in the first-year phase. The metadata was designed to extend DGA's version and match the organization's unique requirements. A data catalog platform was developed accordingly. We organized activities to boost employee awareness and participation, including advertising and data catalog platform training. By the end of the first year of implementation, every organization unit had registered at least one data record into the data catalog.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Heat flux for semi-local machine-learning potentials

arXiv

引用

arXiv 2023年

作者： Langer, Marcel F. Knoop, Florian Carbogno, Christian Scheffler, Matthias Rupp, Matthias Machine Learning Group Technische Universität Berlin Berlin10587 Germany Bifold - Berlin Institute for the Foundations of Learning and Data Berlin Germany The Nomad Laboratory The Fhi of the Max-Planck-Gesellschaft Iris Adlershof The Humboldt Universität zu Berlin Germany Linköping University LinköpingSE-581 83 Sweden Department of Computer and Information Science University of Konstanz Konstanz78464 Germany Belvaux Luxembourg

The Green-Kubo (GK) method is a rigorous framework for heat transport simulations in materials. However, it requires an accurate description of the potential-energy surface and carefully converged statistics. machine-learning potentials can achieve the accuracy of first-principles simulations while allowing to reach well beyond their simulation time and length scales at a fraction of the cost. In this paper, we explain how to apply the GK approach to the recent class of message-passing machine-learning potentials, which iteratively consider semi-local interactions beyond the initial interaction cutoff. We derive an adapted heat flux formulation that can be implemented using automatic differentiation without compromising computational efficiency. The approach is demonstrated and validated by calculating the thermal conductivity of zirconium dioxide across temperatures. Copyright © 2023, The Authors. All rights reserved.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Automated Diagnosis of Cardiovascular Diseases from Cardiac Magnetic Resonance Imaging Using Deep learning Models: A Review

arXiv

引用

arXiv 2022年

作者： Jafari, Mahboobeh Shoeibi, Afshin Khodatars, Marjane Ghassemi, Navid Moridian, Parisa Delfan, Niloufar Alizadehsani, Roohallah Khosravi, Abbas Ling, Sai Ho Zhang, Yu-Dong Wang, Shui-Hua Gorriz, Juan M. Rokny, Hamid Alinejad Acharya, U. Rajendra Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain Department of Medical Engineering Mashhad Branch Islamic Azad University Mashhad Iran Faculty of Computer Engineering Dept. of Artificial Intelligence Engineering K. N. Toosi University of Technology Tehran Iran Deakin University VIC3217 Australia Australia School of Computing and Mathematical Sciences University of Leicester Leicester United Kingdom Department of Psychiatry University of Cambridge United Kingdom BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Research Centre Macquarie University Sydney2109 Australia Ngee Ann Polytechnic Singapore599489 Singapore Dept. of Biomedical Informatics and Medical Engineering Asia University Taichung Taiwan Dept. of Biomedical Engineering School of Science and Technology Singapore University of Social Sciences Singapore

In recent years, cardiovascular diseases (CVDs) have become one of the leading causes of mortality globally. CVDs appear with minor symptoms and progressively get worse. The majority of people experience symptoms such as exhaustion, shortness of breath, ankle swelling, fluid retention, and other symptoms when starting CVD. Coronary artery disease (CAD), arrhythmia, cardiomyopathy, congenital heart defect (CHD), mitral regurgitation, and angina are the most common CVDs. Clinical methods such as blood tests, electrocardiography (ECG) signals, and medical imaging are the most effective methods used for the detection of CVDs. Among the diagnostic methods, cardiac magnetic resonance imaging (CMR) is increasingly used to diagnose, monitor the disease, plan treatment and predict CVDs. Coupled with all the advantages of CMR data, CVDs diagnosis is challenging for physicians due to many slices of data, low contrast, etc. To address these issues, deep learning (DL) techniques have been employed to the diagnosis of CVDs using CMR data, and much research is currently being conducted in this field. This review provides an overview of the studies performed in CVDs detection using CMR images and DL techniques. The introduction section examined CVDs types, diagnostic methods, and the most important medical imaging techniques. In the following, investigations to detect CVDs using CMR images and the most significant DL methods are presented. Another section discussed the challenges in diagnosing CVDs from CMR data. Next, the discussion section discusses the results of this review, and future work in CVDs diagnosis from CMR images and DL techniques are outlined. The most important findings of this study are presented in the conclusion section. © 2022, CC BY-NC-ND.

关键词： Electrocardiography

来源：评论

学校读者我要写书评

暂无评论

Preemptively Pruning Clever-Hans Strategies in Deep Neural Networks

arXiv

引用

arXiv 2023年

作者： Linhardt, Lorenz Müller, Klaus-Robert Montavon, Grégoire Machine Learning Group Technische Universität Berlin Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data - BIFOLD Berlin10587 Germany Department of Artificial Intelligence Korea University Seoul136-713 Korea Republic of Max Planck Institute for Informatics Saarbrücken66123 Germany Google DeepMind Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin14195 Germany

Robustness has become an important consideration in deep learning. With the help of explainable AI, mismatches between an explained model's decision strategy and the user's domain knowledge (e.g. Clever Hans effects) have been identified as a starting point for improving faulty models. However, it is less clear what to do when the user and the explanation agree. In this paper, we demonstrate that acceptance of explanations by the user is not a guarantee for a machine learning model to be robust against Clever Hans effects, which may remain undetected. Such hidden flaws of the model can nevertheless be mitigated, and we demonstrate this by contributing a new method, Explanation-Guided Exposure Minimization (EGEM), that preemptively prunes variations in the ML model that have not been the subject of positive explanation feedback. Experiments demonstrate that our approach leads to models that strongly reduce their reliance on hidden Clever Hans strategies, and consequently achieve higher accuracy on new data. Copyright © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

Transition State Search and Geometry Relaxation throughout Chemical Compound Space with Quantum machine learning

arXiv

引用

arXiv 2022年

作者： Heinen, Stefan Von Rudorff, Guido Falk Von Lilienfeld, O. Anatole University of Vienna Faculty of Physics Kolingasse 14-16 WienAT-1090 Austria Vector Institute for Artificial Intelligence ON TorontoM5S 1M1 Canada Departments of Chemistry Materials Science and Engineering and Physics University of Toronto St. George Campus ON Toronto Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

We use energies and forces predicted within response operator based quantum machine learning (OQML) to perform geometry optimization and transition state search calculations with legacy optimizers. For randomly sampled initial coordinates of small organic query molecules we report systematic improvement of equilibrium and transition state geometry output as training set sizes increase. Out-of-sample SN2 reactant complexes and transition state geometries have been predicted using the LBFGS and the QST2 algorithm with an RMSD of 0.16 and 0.4 Å - after training on up to 200 reactant complexes relaxations and transition state search trajectories from the QMrxn20 data-set, respectively. For geometry optimizations, we have also considered relaxation paths up to 5'500 constitutional isomers with sum formula C7H10O2from the QM9-database. Using the resulting OQML models with an LBFGS optimizer reproduces the minimum geometry with an RMSD of 0.14 Å. For converged equilibrium and transition state geometries subsequent vibrational normal mode frequency analysis indicates deviation from MP2 reference results by on average 14 and 26 cm-1, respectively. While the numerical cost for OQML predictions is negligible in comparison to DFT or MP2, the number of steps until convergence is typically larger in either case. The success rate for reaching convergence, however, improves systematically with training set size, underscoring OQML's potential for universal applicability. © 2022, CC BY.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Adaptive atomic basis sets

arXiv

引用

arXiv 2024年

作者： Khan, Danish Ach, Maximillian L. von Lilienfeld, O. Anatole Chemical Physics Theory Group Department of Chemistry University of Toronto St. George Campus TorontoON Canada Vector Institute for Artificial Intelligence TorontoONM5S 1M1 Canada Department of Physics University of Toronto St. George Campus TorontoON Canada Munich Germany Acceleration Consortium University of Toronto TorontoON Canada Department of Materials Science and Engineering University of Toronto St. George Campus TorontoON Canada Machine Learning Group Technische Universität Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany Berlin Institute for the Foundations of Learning and Data Berlin10587 Germany

Atomic basis sets are widely employed within quantum mechanics based simulations of matter. We introduce a machine learning model that adapts the basis set to the local chemical environment of each atom, prior to the start of self consistent field (SCF) calculations. In particular, as a proof of principle and because of their historic popularity, we have studied the Gaussian type orbitals from the Pople basis set, i.e. the STO-3G, 3-21G, 6-31G and 6-31G*. We adapt the basis by scaling the variance of the radial Gaussian functions leading to contraction or expansion of the atomic orbitals. A data set of optimal scaling factors for C, H, O, N and F were obtained by variational minimization of the Hartree-Fock (HF) energy of the smallest 2500 organic molecules from the QM9 database. Kernel ridge regression based machine learning (ML) prediction errors of the change in scaling decay rapidly with training set size, typically reaching less than 1% for training set size 2000. Overall, we find systematically lower variance, and consequently the larger training efficiencies, when going from hydrogen to carbon to nitrogen to oxygen. Using the scaled basis functions obtained from the ML model, we conducted HF calculations for the subsequent 30’000 molecules in QM9. In comparison to the corresponding default Pople basis set results we observed improved energetics in up to 99% of all cases. With respect to the larger basis set 6-311G(2df,2pd), atomization energy errors are lowered on average by ∼31, 107, 11, and 11 kcal/mol for STO-3G, 3-21G, 6-31G and 6-31G*, respectively — with negligible computational overhead. We illustrate the high transferability of adaptive basis sets for larger out-of-domain molecules relevant to addiction, diabetes, pain, aging. Generally, deviation from cc-pvQZ based HF results for counter-poise corrected atomization energies, HOMO-LUMO gaps, and dipole moments also decreases — in line with the variational principle. © 2024, CC BY.

关键词： Quantum theory

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：