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arXiv 2022年

作者： Hou, Wenzheng Xu, Qianqian Yang, Zhiyong Bao, Shilong He, Yuan Huang, Qingming Key Laboratory of Intelligent Information Processing Institute of Computing Technology CAS Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China State Key Laboratory of Information Security Institute of Information Engineering CAS Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Alibaba Group Beijing China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China Artificial Intelligence Research Center Peng Cheng Laboratory Shenzhen China

It is well-known that deep learning models are vulnerable to adversarial examples. Existing studies of adversarial training have made great progress against this challenge. As a typical trait, they often assume that the class distribution is overall balanced. However, long-tail datasets are ubiquitous in a wide spectrum of applications, where the amount of head class instances is larger than the tail classes. Under such a scenario, AUC is a much more reasonable metric than accuracy since it is insensitive toward class distribution. Motivated by this, we present an early trial to explore adversarial training methods to optimize AUC. The main challenge lies in that the positive and negative examples are tightly coupled in the objective function. As a direct result, one cannot generate adversarial examples without a full scan of the dataset. To address this issue, based on a concavity regularization scheme, we reformulate the AUC optimization problem as a saddle point problem, where the objective becomes an instance-wise function. This leads to an end-to-end training protocol. Furthermore, we provide a convergence guarantee of the proposed algorithm. Our analysis differs from the existing studies since the algorithm is asked to generate adversarial examples by calculating the gradient of a min-max problem. Finally, the extensive experimental results show the performance and robustness of our algorithm in three long-tail datasets. © 2022, CC BY.

关键词： Deep learning

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Physicochemical signatures of nanoparticle-dependent complement activation

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Computational science and Discovery 2014年第1期7卷 015003页

作者： Thomas, Dennis G. Chikkagoudar, Satish Heredia-Langner, Alejandro Tardiff, Mark F. Xu, Zhixiang Hourcade, Dennis E. Pham, Christine T. N. Lanza, Gregory M. Weinberger, Kilian Q. Baker, Nathan A. Knowledge Discovery and Informatics Pacific Northwest National Laboratory RichlandWA99352 United States Applied Statistics and Computational Modeling Group Pacific Northwest National Laboratory RichlandWA99352 United States Department of Computer Science and Engineering Washington University in St. Louis St. LouisMO63130 United States Division of Rheumatology Department of Medicine Washington University School of Medicine St. LouisMO63110 United States Division of Cardiology Department of Medicine Washington University School of Medicine St. LouisMO63110 United States

Nanoparticles are potentially powerful therapeutic tools that have the capacity to target drug payloads and imaging agents. However, some nanoparticles can activate complement, a branch of the innate immune system, and cause adverse side-effects. Recently, we employed an in vitro hemolysis assay to measure the serum complement activity of perfluorocarbon nanoparticles that differed by size, surface charge, and surface chemistry, quantifying the nanoparticle-dependent complement activity using a metric called Residual Hemolytic Activity (RHA). In the present work, we have used a decision tree learning algorithm to derive the rules for estimating nanoparticle-dependent complement response based on the data generated from the hemolytic assay studies. Our results indicate that physicochemical properties of nanoparticles, namely, size, polydispersity index, zeta potential, and mole percentage of the active surface ligand of a nanoparticle, can serve as good descriptors for prediction of nanoparticle-dependent complement activation in the decision tree modeling framework. © 2014 IOP Publishing Ltd.

关键词： Nanoparticles

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CloudBrain-NMR: An Intelligent Cloud Computing Platform for NMR Spectroscopy Processing, Reconstruction and Analysis

arXiv

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arXiv 2023年

作者： Guo, Di Li, Sijin Liu, Jun Tu, Zhangren Qiu, Tianyu Xu, Jingjing Feng, Liubin Lin, Donghai Hong, Qing Lin, Meijin Lin, Yanqin Qu, Xiaobo School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361005 China The Key Laboratory for Chemical Biology of Fujian Province MOE Key Laboratory of Spectrochemical Analysis & Instrumentation College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China The China Mobile Group Xiamen361005 China

Nuclear Magnetic Resonance (NMR) spectroscopy has served as a powerful analytical tool for studying molecular structure and dynamics in chemistry and biology. However, the processing of raw data acquired from NMR spectrometers and subsequent quantitative analysis involves various specialized tools, which necessitates comprehensive knowledge in programming and NMR. Particularly, the emerging deep learning tools is hard to be widely used in NMR due to the sophisticated setup of computation. Thus, NMR processing is not an easy task for chemist and biologists. In this work, we present CloudBrain-NMR, an intelligent online cloud computing platform designed for NMR data reading, processing, reconstruction, and quantitative analysis. The platform is conveniently accessed through a web browser, eliminating the need for any program installation on the user side. CloudBrain-NMR uses parallel computing with graphics processing units and central processing units, resulting in significantly shortened computation time. Furthermore, it incorporates state-of-the-art deep learning-based algorithms offering comprehensive functionalities that allow users to complete the entire processing procedure without relying on additional software. This platform has empowered NMR applications with advanced artificial intelligence processing. CloudBrain-NMR is openly accessible for free usage at https://***/***. © 2023, CC BY.

关键词： Deep learning

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A generative artificial intelligence framework based on a molecular diffusion model for the design of metal–organic frameworks for carbon capture

arXiv

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arXiv 2023年

作者： Park, Hyun Yan, Xiaoli Zhu, Ruijie Huerta, Eliu A. Chaudhuri, Santanu Cooper, Donny Foster, Ian Tajkhorshid, Emad Data Science and Learning Division Argonne National Laboratory LemontIL60439 United States Theoretical and Computational Biophysics Group NIH Resource Center for Macromolecular Modeling and Visualization Beckman Institute for Advanced Science and Technology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Center for Biophysics and Quantitative Biology University of Illinois at Urbana-Champaign UrbanaIL61801 United States Multiscale Materials and Manufacturing Lab University of Illinois Chicago ChicagoIL60607 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Computer Science University of Chicago ChicagoIL60637 United States Department of Physics University of Illinois at Urbana-Champaign UrbanaIL61801 United States Computational Science and Engineering Data Science and AI Department TotalEnergies EP Research & Technology USA LLC HoustonTX77002 United States Department of Biochemistry University of Illinois at Urbana-Champaign UrbanaIL61801 United States

Metal–organic frameworks (MOFs) exhibit great promise for CO2 capture. However, finding the best performing materials poses computational and experimental grand challenges in view of the vast chemical space of potential building blocks. Here, we introduce GHP-MOFassemble, a generative artificial intelligence (AI), high performance framework for the rational and accelerated design of MOFs with high CO2 adsorption capacity and synthesizable linkers. GHP-MOFassemble generates novel linkers, assembled with one of three pre-selected metal nodes (Cu paddlewheel, Zn paddlewheel, Zn tetramer) into MOFs in a primitive cubic topology. GHP-MOFassemble screens and validates AI-generated MOFs for uniqueness, synthesizability, structural validity, uses molecular dynamics simulations to study their stability and chemical consistency, and crystal graph neural networks and Grand Canonical Monte Carlo simulations to quantify their CO2 adsorption capacities. We present the top six AI-generated MOFs with CO2 capacities greater than 2m molg−1, i.e., higher than 96.9% of structures in the hypothetical MOF dataset. Copyright © 2023, The Authors. All rights reserved.

关键词： Carbon dioxide

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Large scale report generation in data consolidation environments of banks

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Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of science and Technology (Natural science Edition) 2012年第SUPPL.1期40卷 5-8页

作者： Qin, Xiongpai Zhou, Xiaoyun Wu, Zhongxin Yang, Hongzhi Wang, Wei Ministry of Education Key Lab of Data Engineering and Knowledge Engineering Renmin University of China Beijing 100872 China Information School Renmin University of China Beijing 100872 China Computer Science Department Jiangsu Normal University Xuzhou 221116 Jiangsu China Beijing Nantian Software Co. Ltd. Beijing 100085 China

To generate large number of reports in a limited time window, four techniques were proposed, including ROLAP&SQL, Shared Scanning, Hadoop based Solution, and MOLAP&Cube Sharding, an algorithm that performs in memory aggregation was designed for the second solution. The experiment results show that all techniques except ROLAP&SQL can meet the time window constraint, the Hadoop based solution is a promising technique owe to its highly scalability. Considering maturity of the techniques and their performance, we put MOLAP&Cube Sharding into practice while keeping an eye on Hadoop for future adoption.

关键词： Custom report data consolidation Hadoop Large bank MOLAP ROLAP

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Waters network's potential to transform environmental engineering education

Waters network's potential to transform environmental engine...

引用

114th Annual ASEE Conference and Exposition, 2007

作者： Eschenbach, Elizabeth Montgomery, Jami Johnson, James Brus, Chris Carlson, Patricia Giammar, Dan Grauer, Bette Hotaling, Liesl Oguntimein, Gbekeloluwa Safferman, Steven Wentling, Tim Humboldt State University United States Department of Environmental Resources Engineering Humboldt State University United States WATERS Network - CLEANER Project Office Network Howard University United States Department of Environmental Engineering College of Engineering Architecture and Computer Sciences Howard University United States American Academy of Environmental Engineers United States University of Iowa United States Program University of Iowa United States Rose-Hulman Institute of Technology United States Department of Rhetoric Rose-Hulman Institute of Technology United States National Research Council U. S. Air Force United States NASA's Classroom of the Future Wheeling WV United States Washington University United States Department of Energy Environmental and Chemical Engineering Washington University St. Louis United States Environmental Studies Program Center for Materials Innovation United States McPherson High School United States McPherson High School McPherson KS United States Stevens Institute of Technology United States Stevens Institute of Technology United States Morgan State University United States Michigan State University United States Biosystems and Agricultural Engineering Department Michigan State University United States University of Dayton United States Education Planning Committee National Center for Supercomputing Applications United States Department of Information Science Graduate School of Library and Information Science National Center for Supercomputing Applications University or Illinois United States Knowledge and Learning Systems Group NCSA United States

The WATERS Network (WATer and Environmental Research Systems Network) will be an integrated real-time distributed observing system which will enable academic and government scientists, engineers, educators, and practitioners to advance effective management of our nation's water resources by understanding human interactions with water and the natural and built environment. WATERS will provide easily accessible real time environmental data as well as analysis tools to engineers, scientists, educators, K- graduate students and policymakers so they can better understand how water quantity, quality and related components of the hydrologic cycle are impacted by natural and human influences. The WATERS strategic plan will be completed by July 2007 and requires constituent input. This paper describes the draft Conceptual Design and the Education Plan of WATERS Network. This paper will highlight the potential impact of WATERS on undergraduate and graduate environmental engineering education in order to elicit input from the environmental engineering education community on how WATERS Network could better meet the future needs of undergraduate and graduate students and educators. More information can be found at ***. © American Society for engineering Education, 2007.

关键词： Water resources

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XCloud-VIP: Virtual Peak Enables Highly Accelerated NMR Spectroscopy and Faithful Quantitative Measures

arXiv

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arXiv 2021年

作者： Guo, Di Tu, Zhangren Guo, Yi Zhou, Yirong Wang, Jian Wang, Zi Qiu, Tianyu Xiao, Min Chen, Yinran Feng, Liubin Huang, Yuqing Lin, Donghai Hong, Qing Goldbourt, Amir Lin, Meijin Qu, Xiaobo The School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China Department of Electronic Science Biomedical Intelligent Cloud R&D Center Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China School of Opto-Electronic and Communication Engineering Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Xiamen University Xiamen361102 China College of Chemistry and Chemical Engineering Key Laboratory for Chemical Biology of Fujian Province High-field NMR Center Xiamen University Xiamen361005 China China Mobile Group Xiamen361005 China School of Chemistry Tel Aviv University Tel Aviv6997801 Israel Department of Applied Marine Physics and Engineering College of Ocean and Earth Sciences Xiamen University Xiamen361102 China

—Nuclear Magnetic Resonance (NMR) spectroscopy is an important bio-engineering tool to determine the metabolic concentrations, molecule structures and so on. The data acquisition time, however, is very long in multi-dimensional NMR. To accelerate data acquisition, non-uniformly sampling is an effective way but may encounter severe spectral distortions and unfaithful quantitative measures when the acceleration factor is high. By modelling the acquired signal as the superimposed exponentials, we proposed a virtual peak (VIP) approach to self-adapt the prior spectral information, such as the resonance frequency and peak lineshape, and then feed these information into the reconstruction. The proposed method is further implemented with cloud computing to facilitate online, open, and easy access. Results on simulated and experimental data demonstrate that, compared with the low-rank Hankel matrix method, the new approach reconstructs high-fidelity NMR spectra from highly undersampled data and achieves more accurate quantification. The maximum quantitative errors of distances between nuclear pairs and concentrations of metabolites in mixtures have been reduced by 61.1% and 57.7%, respectively. Copyright © 2021, The Authors. All rights reserved.

关键词： Nuclear magnetic resonance spectroscopy

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BPRIM: An integrated framework for business process management and risk management

引用

computers in Industry 2019年 113卷

作者： Lamine, Elyes Thabet, Rafika Sienou, Amadou Bork, Dominik Fontanili, Franck Pingaud, Herve Toulouse University ISIS Institut National Universitaire Champollion Rue Firmin-OulèsCastres81104 France Toulouse University IMT Mines Albi Department of Industrial Engineering Route de Teillet Albi Cedex 981013 France abamix Consulting – Business Information Systems Unit Stuttgart Germany MARS Laboratory University of Sousse ISITCom Hammam Sousse Tunisia University of Vienna Faculty of Computer Science Research Group Knowledge Engineering Währingerstrasse 29 Vienna1090 Austria Toulouse University CNRS LGC Toulouse Cedex 04F-31432 France

Enterprise engineering deals with the design of processes which aim to improve the structure and efficiency of business organizations. It develops approaches based on modeling techniques, particularly on business process modeling, to ensure the quality and the global consistency of enterprise strategies and expectations. Nowadays, risk consideration in enterprise engineering is a growing concern since the business environment is becoming more and more competitive, complex, and unpredictable. To face this concern, a paradigm named risk-aware business process management (R-BPM) has recently emerged. It seeks to integrate the two traditionally isolated fields of risk management and business process management. Despite the significant benefits that can arise from the use of R-BPM, it suffers from a lack of solid scientific foundations and dedicated tooling. This present research work contributes to bridging that gap in a twofold way: (i) by establishing the BPRIM business process-risk integrated method framework, and (ii) by designing a dedicated tool, named ADOBPRIM which supports the efficient application of the BPRIM framework. This paper first comprehensively presents the foundation of BPRIM which is based on three main components and, secondly, its dedicated tool ADOBPRIM which was designed using the ADOxx meta-modeling platform. An evaluation with a real case study in the health care domain shows the relevance of the methodological framework. © 2019 Elsevier B.V.

关键词： Risk management

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A Review of Multimodal Explainable Artificial Intelligence: Past, Present and Future

arXiv

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arXiv 2024年

作者： Sun, Shilin An, Wenbin Tian, Feng Nan, Fang Liu, Qidong Liu, Jun Shah, Nazaraf Chen, Ping School of Computer Science and Technology Xi’an Jiaotong University Xi’an710049 China Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi’an Jiaotong University Xi’an710049 China Faculty of Electronic and Information Engineering Xi’an Jiaotong University Xi’an710049 China Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi’an Jiaotong University Xi’an710049 China Institute for Future Transport and Cities Coventry University Priory Street CoventryCV1 5FB United Kingdom Department of Engineering University of Massachusetts Boston BostonMA02125 United States

Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-box" nature of AI models. To address these concerns, eXplainable AI (XAI) has emerged with a focus on transparency and interpretability to enhance human understanding and trust in AI decision-making processes. In the context of multimodal data fusion and complex reasoning scenarios, the proposal of Multimodal eXplainable AI (MXAI) integrates multiple modalities for prediction and explanation tasks. Meanwhile, the advent of Large Language Models (LLMs) has led to remarkable breakthroughs in natural language processing, yet their complexity has further exacerbated the issue of MXAI. To gain key insights into the development of MXAI methods and provide crucial guidance for building more transparent, fair, and trustworthy AI systems, we review the MXAI methods from a historical perspective and categorize them across four eras: traditional machine learning, deep learning, discriminative foundation models, and generative LLMs. We also review evaluation metrics and datasets used in MXAI research, concluding with a discussion of future challenges and directions. A project related to this review has been created at https://***/ShilinSun/mxai_review. Copyright © 2024, The Authors. All rights reserved.

关键词： data fusion

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Positioning Using Wireless Networks: Applications, Recent Progress and Future Challenges

arXiv

引用

arXiv 2024年

作者： Yang, Yang Chen, Mingzhe Blankenship, Yufei Lee, Jemin Ghassemlooy, Zabih Cheng, Julian Mao, Shiwen The School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China The Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral GablesFL United States The Ericsson SchaumburgIL United States School of Electrical and Electronic Engineering Yonsei University Seoul Korea Republic of The Optical Communications Research Group Faculty of Engineering and Environment Northumbria University Newcastle upon TyneNE1 8ST United Kingdom The School of Engineering The University of British Columbia KelownaBC Canada The Wireless Engineering Research and Education Center Auburn University AuburnAL United States

Positioning has recently received considerable attention as a key enabler in emerging applications such as extended reality, unmanned aerial vehicles and smart environments. These applications require both data communication and high-precision positioning, and thus they are particularly well-suited to be offered in wireless networks (WNs). The purpose of this paper is to provide a comprehensive overview of existing works and new trends in the field of positioning techniques from both the academic and industrial perspectives. The paper provides a comprehensive overview of positioning in WNs, covering the background, applications, measurements, state-of-the-art technologies and future challenges. The paper outlines the applications of positioning from the perspectives of public facilities, enterprises and individual users. We investigate the key performance indicators and measurements of positioning systems, followed by the review of the key enabler techniques such as artificial intelligence/large models and adaptive systems. Next, we discuss a number of typical wireless positioning technologies. We extend our overview beyond the academic progress, to include the standardization efforts, and finally, we provide insight into the challenges that remain. The comprehensive overview of exisitng efforts and new trends in the field of positioning from both the academic and industrial communities would be a useful reference to researchers in the field. Copyright © 2024, The Authors. All rights reserved.

关键词： Wireless networks

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