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Perspectives on adaptive dynamical systems

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arXiv 2023年

作者： Sawicki, Jakub Berner, Rico Loos, Sarah A.M. Anvari, Mehrnaz Bader, Rolf Barfuss, Wolfram Botta, Nicola Brede, Nuria Franović, Igor Gauthier, Daniel J. Goldt, Sebastian Hajizadeh, Aida Hövel, Philipp Karin, Omer Lorenz-Spreen, Philipp Miehl, Christoph Mölter, Jan Olmi, Simona Schöll, Eckehard Seif, Alireza Tass, Peter A. Volpe, Giovanni Yanchuk, Serhiy Kurths, Jürgen Potsdam Institute for Climate Impact Research Telegrafenberg Potsdam14473 Germany Fachhochschule Nordwestschweiz FHNW Leonhardsstrasse 6 Basel4009 Switzerland Department of Physics Humboldt-Universität zu Berlin Newtonstraße 15 Berlin12489 Germany DAMTP University of Cambridge Wilberforce Road CambridgeCB3 0WA United Kingdom Fraunhofer Institute for Algorithms and Scientific Computing Schloss Birlinghoven Sankt-Augustin53757 Germany Institute of Systematic Musicology University of Hamburg Germany Transdisciplinary Research Area: Sustainable Futures University of Bonn Bonn53113 Germany University of Bonn Bonn53113 Germany Department of Computer Science and Engineering Chalmers University of Technology Göteborg412 96 Sweden Department of Computer Science University of Potsdam An der Bahn 2 Potsdam14476 Germany Scientific Computing Laboratory Center for the Study of Complex Systems Institute of Physics Belgrade University of Belgrade Pregrevica 118 Belgrade11080 Serbia The Ohio State University Department of Physics and Electrical and Computer Engineering 191 West Woodruff Ave ColumbusOH43210 United States ResCon Technologies LLC 1275 Kinnear Rd. Suite 239 ColumbusOH43212 United States Trieste Italy Research Group Comparative Neuroscience Leibniz Institute for Neurobiology Magdeburg Germany Theoretical Physics Saarland University Saarbrücken66123 Germany Center for Biophysics Saarland University Saarbrücken66123 Germany Department of Mathematics Imperial College London United Kingdom Center for Adaptive Rationality Max Planck Institute for Human Development Lentzeallee 94 Berlin14195 Germany Max Planck Institute for Brain Research Max-von-Laue-Straße 4 Frankfurt am Main60438 Germany Technical University of Munich School of Life Sciences Alte Akademie 8 Freising85354 Germany Department of Mathematics School of Computation Information and Technology Technical University of Munich Boltzmannstraße 3 Garching bei München8574

Adaptivity is a dynamical feature that is omnipresent in nature, socio-economics, and technology. For example, adaptive couplings appear in various real-world systems like the power grid, social, and neural networks, and they form the backbone of closed-loop control strategies and machine learning algorithms. In this article, we provide an interdisciplinary perspective on adaptive systems. We reflect on the notion and terminology of adaptivity in different disciplines and discuss which role adaptivity plays for various fields. We highlight common open challenges, and give perspectives on future research directions, looking to inspire interdisciplinary approaches. Copyright © 2023, The Authors. All rights reserved.

关键词： Dynamical systems

MINA: Multilevel knowledge-guided attention for modeling electrocardiography signals

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arXiv 2019年

作者： Hong, Shenda Xiao, Cao Ma, Tengfei Li, Hongyan Sun, Jimeng School of Electronics Engineering and Computer Science Peking University China Key Laboratory of Machine Perception Ministry of Education Peking University China Analytics Center of Excellence IQVIA United States IBM Research United States Department of Computational Science and Engineering Georgia Institute of Technology United States

Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge. By extracting multilevel (beat-, rhythm-A nd frequency-level) domain knowledge features separately, MINA combines the medical knowledge and ECG data via a multilevel attention model, making the learned models highly interpretable. Our experiments showed MINA achieved PR-AUC 0:9436 (outperforming the best baseline by 5:51%) in real world ECG dataset. Finally, MINA also demonstrated robust performance and strong interpretability against signal distortion and noise contamination. Copyright © 2019, The Authors. All rights reserved.

关键词： Electrocardiography

The status of the human gene catalogue

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arXiv 2023年

作者： Amaral, Paulo Carbonell-Sala, Silvia De La Vega, Francisco M. Faial, Tiago Frankish, Adam Gingeras, Thomas Guigo, Roderic Harrow, Jennifer L. Hatzigeorgiou, Artemis G. Johnson, Rory Murphy, Terence D. Pertea, Mihaela Pruitt, Kim D. Pujar, Shashikant Takahashi, Hazuki Ulitsky, Igor Varabyou, Ales Wells, Christine A. Yandell, Mark Carninci, Piero Salzberg, Steven L. INSPER Institute of Education and Research SP São Paulo Brazil Dr. Aiguader 88 Catalonia Barcelona08003 Spain Department of Biomedical Data Science Stanford University School of Medicine StanfordCA United States Tempus Labs Inc. ChicagoIL United States Nature Genetics San FranciscoCA United States European Molecular Biology Laboratory European Bioinformatics Institute Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Department of Functional Genomics Cold Spring Harbor Laboratory Cold Spring HarborNY United States Catalonia Barcelona Spain Centre for Genomics Research Discovery Sciences AstraZeneca Da Vinci Building. Melbourn Science Park RoystonSG8 6HB United Kingdom Universithy of Thessaly Department of Computer Science and Biomedical Informatics Lamia Greece Hellenic Pasteur Institute Athens Greece School of Biology and Environmental Science University College Dublin DublinD04 V1W8 Ireland Conway Institute of Biomedical and Biomolecular Research University College Dublin DublinD04 V1W8 Ireland Department of Medical Oncology Inselspital Bern University Hospital University of Bern Bern3010 Switzerland Department for BioMedical Research University of Bern Bern3008 Switzerland National Center for Biotechnology Information National Library of Medicine National Institutes of Health BethesdaMD20894 United States Center for Computational Biology Johns Hopkins University BaltimoreMD United States Department of Biomedical Engineering Johns Hopkins University BaltimoreMD United States Laboratory for Transcriptome Technology RIKEN Center for Integrative Medical Sciences Kanagawa Yokohama230-0045 Japan Department of Immunology and Regenerative Biology Department of Molecular Neuroscience Weizmann Institute of Science Rehovot76100 Israel Department of Computer Science Johns Hopkins University BaltimoreMD United States Stem Cell Systems Department of Anatomy and Physiology Faculty of Medicine Dentistry and Health Scien

Scientists have been trying to identify all of the genes in the human genome since the initial draft of the genome was published in 2001. Over the intervening years, much progress has been made in identifying protein-coding genes, and the estimated number has shrunk to fewer than 20,000, although the number of distinct protein-coding isoforms has expanded dramatically. The invention of high-throughput RNA sequencing and other technological breakthroughs have led to an explosion in the number of reported non-coding RNA genes, although most of them do not yet have any known function. A combination of recent advances offers a path forward to identifying these functions and towards eventually completing the human gene catalogue. However, much work remains to be done before we have a universal annotation standard that includes all medically significant genes, maintains their relationships with different reference genomes, and describes clinically relevant genetic variants. © 2023, CC BY.

关键词： Genes

Qutrit randomized benchmarking

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arXiv 2020年

作者： Morvan, A. Ramasesh, V.V. Blok, M.S. Kreikebaum, J.M. O'Brien, K. Chen, L. Mitchell, B.K. Naik, R.K. Santiago, D.I. Siddiqi, I. Quantum Nanoelectronics Laboratory Dept. of Physics University of California at Berkeley BerkeleyCA94720 United States Computational Research Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Physics and Astronomy University of Rochester RochesterNY14627 United States Materials Sciences Division Lawrence Berkeley National Lab BerkeleyCA94720 United States Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States

Ternary quantum processors offer significant computational advantages over conventional qubit technologies, leveraging the encoding and processing of quantum information in qutrits (three-level systems). To evaluate and compare the performance of such emerging quantum hardware it is essential to have robust benchmarking methods suitable for a higher-dimensional Hilbert space. We demonstrate extensions of industry standard Randomized Benchmarking (RB) protocols, developed and used extensively for qubits, suitable for ternary quantum logic. Using a superconducting fivequtrit processor, we find a single-qutrit gate infidelity as low as 2.38 × 10-3. Through interleaved RB, we find that this qutrit gate error is largely limited by the native (qubit-like) gate fidelity, and employ simultaneous RB to fully characterize cross-talk errors. Finally, we apply cycle benchmarking to a two-qutrit CSUM gate and obtain a two-qutrit process fidelity of 0.82. Our results demonstrate a RB-based tool to characterize the obtain overall performance of a qutrit processor, and a general approach to diagnose control errors in future qudit hardware. Copyright © 2020, The Authors. All rights reserved.

关键词： Benchmarking

The joint automated repository for various integrated simulations (JARVIS) for data-driven materials design

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arXiv 2020年

作者： Choudhary, Kamal Garrity, Kevin F. Reid, Andrew C.E. DeCost, Brian Biacchi, Adam J. Hight Walker, Angela R. Trautt, Zachary Hattrick-Simpers, Jason Kusne, A. Gilad Centrone, Andrea Davydov, Albert Jiang, Jie Pachter, Ruth Cheon, Gowoon Reed, Evan Agrawal, Ankit Qian, Xiaofeng Sharma, Vinit Zhuang, Houlong Kalinin, Sergei V. Sumpter, Bobby G. Pilania, Ghanshyam Acar, Pinar Mandal, Subhasish Haule, Kristjan Vanderbilt, David Rabe, Karin Tavazza, Francesca Materials Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Theiss Research San diegoCA92037 United States Department of Chemistry and Biochemistry University of Maryland College ParkMD20742 United States Physical Measurement Laboratory National Institute of Standards and Technology GaithersburgMD20899 United States Materials and Manufacturing Directorate Air Force Research Laboratory Wright–Patterson Air Force Base OH45433 United States Department of Materials Science and Engineering Stanford University StanfordCA94305 United States Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Texas A&M University TX77843 United States Joint Institute for Computational Sciences University of Tennessee KnoxvilleTN37996 United States National Institute for Computational Sciences Oak Ridge National Laboratory TN37831 United States School for Engineering of Matter Transport & Energy Arizona state university AZ85287 United States Center for Nanophase Materials Sciences Oak Ridge National Laboratory TN37831 United States Materials Science and Technology Division Los Alamos National Lab Los AlamosNM87545 United States Department of Mechanical Engineering Virginia Tech BlacksburgVA24061 United States Department of Physics and Astronomy Rutgers University PiscatawayNJ08901 United States

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is an integrated infrastructure to accelerate materials discovery and design using density functional theory (DFT), classical force-fields (FF), and machine learning (ML) techniques. JARVIS is motivated by the Materials Genome Initiative (MGI) principles of developing open-access databases and tools to reduce the cost and development time of materials discovery, optimization, and deployment. The major features of JARVIS are: JARVIS-DFT, JARVIS-FF, JARVIS-ML, and JARVIS-Tools. To date, JARVIS consists of ≈ 40,000 materials and ≈ 1 million calculated properties in JARVIS-DFT, ≈ 1,500 materials and ≈ 110 force-fields in JARVIS-FF, and ≈ 25 ML models for material-property predictions in JARVIS-ML, all of which are continuously expanding. JARVIS-Tools provides scripts and workflows for running and analyzing various simulations. We compare our computational data to experiments or high-fidelity computational methods wherever applicable to evaluate error/uncertainty in predictions. In addition to the existing workflows, the infrastructure can support a wide variety of other technologically important applications as part of the data-driven materials design paradigm. The JARVIS datasets and tools are publicly available at the website: https://*** . © 2020, CC BY.

关键词： Density functional theory

Quantum Simulators: Architectures and Opportunities

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PRX Quantum 2021年第1期2卷 017003-017003页

作者： Ehud Altman Kenneth R. Brown Giuseppe Carleo Lincoln D. Carr Eugene Demler Cheng Chin Brian DeMarco Sophia E. Economou Mark A. Eriksson Kai-Mei C. Fu Markus Greiner Kaden R.A. Hazzard Randall G. Hulet Alicia J. Kollár Benjamin L. Lev Mikhail D. Lukin Ruichao Ma Xiao Mi Shashank Misra Christopher Monroe Kater Murch Zaira Nazario Kang-Kuen Ni Andrew C. Potter Pedram Roushan Mark Saffman Monika Schleier-Smith Irfan Siddiqi Raymond Simmonds Meenakshi Singh I.B. Spielman Kristan Temme David S. Weiss Jelena Vučković Vladan Vuletić Jun Ye Martin Zwierlein Department of Physics University of California Berkeley Carolina 94720 USA Department of Electrical and Computer Engineering Department of Physics and Department of Chemistry Duke University Durham North Carolina 27708 USA Center for Computational Quantum Physics Flatiron Institute New York New York 10010 USA Department of Physics Colorado School of Mines Golden Colorado 80401 USA Department of Physics Harvard University Cambridge Massachusetts 02138 USA Department of Physics Chicago University Chicago Illinois 60637 USA Department of Physics and IQUIST University of Illinois Urbana-Champaign Illinois 61801 USA Department of Physics Virgina Tech Blacksburg Virginia 24061 USA Department of Physics University of Wisconsin-Madison Madison Wisconsin 53706 USA Department of Electrical and Computer Engineering and Department of Physics University of Washington Seattle Washington 98195 USA Department of Physics and Astronomy Rice University Houston Texas 77005 USA Joint Quantum Institute and Department of Physics University of Maryland College Park Maryland 20742 USA and IonQ Inc. College Park Maryland 20740 USA Department of Physics and Department of Applied Physics Stanford University Stanford California 94305 USA Department of Physics and Astronomy Purdue University West Lafayette Indiana 47907 USA Google Inc. Santa Barbara California 93101 USA Sandia National Laboratory Albuquerque New Mexico 87123 USA Department of Physics Washington University St. Louis Missouri 63130 USA IBM T.J. Watson Research Center Yorktown Heights New York 10598 USA Department of Chemistry and Chemical Biology Harvard University Cambridge Massachusetts 02138 USA Department of Physics The University of Texas at Austin Austin Texas 78712 USA National Institute of Standards and Technology Boulder Colorado 80305 USA Department of Physics The Pennsylvania State University University Park Pennsylvania 16802 USA Department of Electrical Engineering and Ginzto

Quantum simulators are a promising technology on the spectrum of quantum devices from specialized quantum experiments to universal quantum computers. These quantum devices utilize entanglement and many-particle behavior to explore and solve hard scientific, engineering, and computational problems. Rapid development over the last two decades has produced more than 300 quantum simulators in operation worldwide using a wide variety of experimental platforms. Recent advances in several physical architectures promise a golden age of quantum simulators ranging from highly optimized special purpose simulators to flexible programmable devices. These developments have enabled a convergence of ideas drawn from fundamental physics, computer science, and device engineering. They have strong potential to address problems of societal importance, ranging from understanding vital chemical processes, to enabling the design of new materials with enhanced performance, to solving complex computational problems. It is the position of the community, as represented by participants of the National Science Foundation workshop on “Programmable Quantum Simulators,” that investment in a national quantum simulator program is a high priority in order to accelerate the progress in this field and to result in the first practical applications of quantum machines. Such a program should address two areas of emphasis: (1) support for creating quantum simulator prototypes usable by the broader scientific community, complementary to the present universal quantum computer effort in industry; and (2) support for fundamental research carried out by a blend of multi-investigator, multidisciplinary collaborations with resources for quantum simulator software, hardware, and *** document is a summary from a U.S. National Science Foundation supported workshop held on 16–17 September 2019 in Alexandria, VA. Attendees were charged to identify the scientific and community needs, opportunities, and signi

关键词： Quantum information architectures & platforms Quantum simulation

Confined variational calculation of o-Ps–He scattering properties

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Physical Review A 2020年第4期101卷 042705-042705页

作者： M.-S. Wu J.-Y. Zhang X. Gao Y. Qian H.-H. Xie K. Varga Z.-C. Yan U. Schwingenschlögl State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics Wuhan Institute of Physics and Mathematics Chinese Academy of Sciences Wuhan 430071 China Beijing Computational Science Research Center Beijing 100193 China Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia Department of Computer Science and Technology East China Normal University Shanghai 200062 China LSEC ICMSEC Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China Department of Physics and Astronomy Vanderbilt University Nashville Tennessee 37235 USA Department of Physics University of New Brunswick Fredericton New Brunswick Canada E3B 5A3

High-precision quantum-mechanical calculations have been developed to investigate positronium (Ps) scattering. Positronium scattering experiments are a powerful tool to study positronium-matter interactions, but the theoretical description of these experiments needs better accuracy. We have developed an ab initio confined variational approach that can reach higher collision energy, includes higher orbital momenta and uses small confining radii. Calculation of the Ps–He momentum-transfer cross section shows that the experimental Doppler broadening spectroscopy results are questionable. The energy dependence of the pickoff annihilation rate is also calculated, demonstrating an important role of the so far neglected P-wave contribution.

关键词： Electron & positron scattering Positronium Positrons

Competitive Co-evolution for dynamic constrained optimisation

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arXiv 2019年

作者： Lu, Xiaofen Tang, Ke Menzel, Stefan Yao, Xin Shenzhen Key Laboratory of Computational Intelligence University Key Laboratory of Evolving Intelligent Systems of Guangdong Province Department of Computer Science and Engineering Southern University of Science and Technology Shenzhen518055 China Honda Research Institute Europe Offenbach/Main63073 Germany School of Computer Science University of Birmingham Edgbaston BirminghamB15 2TT United Kingdom

Dynamic constrained optimisation problems (DCOPs) widely exist in the real world due to frequently changing factors influencing the environment. Many dynamic optimisation methods such as diversity-driven methods, memory and prediction methods offer different strategies to deal with environmental changes. However, when DCOPs change very fast or have very limited time for the algorithm to react, the potential of these methods is limited due to time shortage for re-optimisation and adaptation. This is especially true for population-based dynamic optimisation methods, which normally need quite a few fitness evaluations to find a near-optimum solution. To address this issue, this paper proposes to tackle fast-changing DCOPs through a smart combination of offline and online optimisation. The offline optimisation aims to prepare a set of good solutions for all possible environmental changes beforehand. With this solution set, the online optimisation aims to react quickly to each truly happening environmental change by doing optimisation on the set. To find this solution set, this paper further proposes to use competitive co-evolution for offline optimisation by co-evolving candidate solutions and environmental parameters. The experimental studies on a well-known benchmark test set of DCOPs show that the proposed method outperforms existing methods significantly especially when the environment changes very fast. Copyright © 2019, The Authors. All rights reserved.

关键词： Constrained optimization

Onto2Vec: Joint vector-based representation of biological entities and their ontology-based annotations

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arXiv 2018年

作者： Smaili, Fatima Zohra Gao, Xin Hoehndorf, Robert Computer Electrical and Mathematical Sciences and Engineering Division Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal23955 Saudi Arabia

Motivation: Biological knowledge is widely represented in the form of ontology-based annotations: Ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations makes them valuable for developing machine learning, data analysis and knowledge extraction algorithms;notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications. Results: We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies. Our method can be applied to a wide range of bioinformatics research problems such as similarity-based prediction of interactions between proteins, classification of interaction types using supervised learning, or clustering. To evaluate Onto2Vec, we use the Gene Ontology (GO) and jointly produce dense vector representations of proteins, the GO classes to which they are annotated, and the axioms in GO that constrain these classes. First, we demonstrate that Onto2Vec-generated feature vectors can significantly improve prediction of protein-protein interactions in human and yeast. We then illustrate how Onto2Vec representations provide the means for constructing data-driven, trainable semantic similarity measures that can be used to identify particular relations between proteins. Finally, we use an unsupervised clustering approach to identify protein families based on their Enzyme Commission numbers. Our results demonstrate that Onto2Vec can generate high quality feature vectors from biological entities and ontologies. Onto2Vec has the potential to significantly outperform the state-of-the-art in several predictive applications in which ontologies are involved. Copyright © 2018, The Autho

关键词： Gene Ontology