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arXiv 2021年

作者： Wayment-Steele, Hannah K. Kladwang, Wipapat Watkins, Andrew M. Kim, Do Soon Tunguz, Bojan Reade, Walter Demkin, Maggie Romano, Jonathan Wellington-Oguri, Roger Nicol, John J. Gao, Jiayang Onodera, Kazuki Fujikawa, Kazuki Mao, Hanfei Vandewiele, Gilles Tinti, Michele Steenwinckel, Bram Ito, Takuya Noumi, Taiga He, Shujun Ishi, Keiichiro Lee, Youhan Öztürk, Fatih Chiu, Anthony Öztürk, Emin Amer, Karim Fares, Mohamed Participants, Eterna Das, Rhiju Department of Chemistry Stanford University StanfordCA94305 United States Department of Biochemistry Stanford University CA94305 United States NVIDIA Corporation Santa ClaraCA95051 United States Kaggle San FranciscoCA94107 United States Eterna Massive Open Laboratory Department of Computer Science and Engineering State University of New York at Buffalo BuffaloNY14260 United States High-flyer AI Zhejiang Hangzhou310000 China NVIDIA Corporation Minato-ku Tokyo107-0052 Japan DeNA Shibuya-ku Tokyo150-6140 Japan Yanfu Investments Shanghai 200000 China IDLab Ghent University Technologiepark-Zwijnaarde GentB-9052 Belgium College of Life Sciences University of Dundee DundeeDD1 4HN United Kingdom Universal Knowledge Inc. Tokyo150-0013 Japan Keyence Corporation 1-3-14 Higashi-Nakajima Higashi-Yodogawa-ku Osaka533-8555 Japan Department of Chemical Engineering Texas A&M University College StationTX77843 United States Rist Inc Meguro-ku Tokyo153-0063 Japan Kakao Brain Gyeonggi-do Seongnam Korea Republic of H2O Istanbul3400 Turkey Clover Health Hong Kong999077 Hong Kong Afiniti Istanbul3400 Turkey Center for Informatics Science Nile University Sheikh Zayed Giza12588 Egypt National Research Centre Dokki Cairo12622 Egypt Department of Physics Stanford University CA94305 United States

Messenger RNA-based medicines hold immense potential, as evidenced by their rapid deployment as COVID-19 vaccines. However, worldwide distribution of mRNA molecules has been limited by their thermostability, which is fundamentally limited by the intrinsic instability of RNA molecules to a chemical degradation reaction called in-line hydrolysis. Predicting the degradation of an RNA molecule is a key task in designing more stable RNA-based therapeutics. Here, we describe a crowdsourced machine learning competition ("Stanford OpenVaccine") on Kaggle, involving single-nucleotide resolution measurements on 6043 102-130-nucleotide diverse RNA constructs that were themselves solicited through crowdsourcing on the RNA design platform Eterna. The entire experiment was completed in less than 6 months, and 41% of nucleotide-level predictions from the winning model were within experimental error of the ground truth measurement. Furthermore, these models generalized to blindly predicting orthogonal degradation data on much longer mRNA molecules (504-1588 nucleotides) with improved accuracy compared to previously published models. Top teams integrated natural language processing architectures and data augmentation techniques with predictions from previous dynamic programming models for RNA secondary structure. These results indicate that such models are capable of representing in-line hydrolysis with excellent accuracy, supporting their use for designing stabilized messenger RNAs. The integration of two crowdsourcing platforms, one for data set creation and another for machine learning, may be fruitful for other urgent problems that demand scientific discovery on rapid timescales. © 2021, CC BY.

关键词： RNA

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Room-Temperature Charge-to-Spin Conversion from Quasi-2D Electron Gas at SrTiO3-Based Interfaces

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physica status solidi (RRL) – Rapid Research Letters 2023年第6期17卷

作者： Utkarsh Shashank Angshuman Deka Chen Ye Surbhi Gupta Rohit Medwal Rajdeep Singh Rawat Hironori Asada X. Renshaw Wang Yasuhiro Fukuma Department of Physics and Information Technology Faculty of Computer Science and System Engineering Kyushu Institute of Technology 680-4 Kawazu Iizuka 820-8502 Japan Birck Nanotechnology Center School of Electrical and Computer Engineering Purdue University West Lafayette IN 47907 USA School of Physical and Mathematical Sciences Nanyang Technological University 637616 Singapore Natural Sciences and Science Education National Institute of Education Nanyang Technological University 637616 Singapore Department of Physics Indian Institute of Technology Kanpur Kanpur 208016 India Graduate School of Sciences and Technology for Innovation Yamaguchi University 2-16-1 Tokiwadai Ube 755-8611 Japan School of Electrical and Electronic Engineering Nanyang Technological University 639798 Singapore Research Center for Neuromorphic AI hardware Kyushu Institute of Technology Kitakyushu 808-0196 Japan

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TensorFlow quantum: A software framework for quantum machine learning

arXiv

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arXiv 2020年

作者： Broughton, Michael Verdon, Guillaume McCourt, Trevor Martinez, Antonio J. Yoo, Jae Hyeon Isakov, Sergei V. Massey, Philip Halavati, Ramin Niu, Murphy Yuezhen Zlokapa, Alexander Peters, Evan Lockwood, Owen Skolik, Andrea Jerbi, Sofiene Dunjko, Vedran Leib, Martin Streif, Michael Von Dollen, David Chen, Hongxiang Cao, Shuxiang Wiersema, Roeland Huang, Hsin-Yuan McClean, Jarrod R. Babbush, Ryan Boixo, Sergio Bacon, Dave Ho, Alan K. Neven, Hartmut Mohseni, Masoud Google Quantum Ai Mountain ViewCA United States Sandbox Alphabet Mountain ViewCA United States Google Mountain ViewCA United States Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada School of Computer Science University of Waterloo WaterlooONN2L 3G1 Canada Department of Applied Mathematics University of Waterloo WaterlooONN2L 3G1 Canada Department of Mechanical and Mechatronics Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada Division of Physics Mathematics and Astronomy Caltech PasadenaCA91125 United States Fermi National Accelerator Laboratory P.O. Box 500 BataviaIL605010 United States Department of Computer Science Rensselaer Polytechnic Institute TroyNY12180 United States Data:Lab Volkswagen Group Ungererstr. 69 München80805 Germany Leiden University Niels Bohrweg 1 Leiden2333 CA Netherlands Institute for Theoretical Physics University of Innsbruck Technikerstr. 21a InnsbruckA-6020 Austria Institute of Theoretical Physics Staudtstr. 7 Erlangen91058 Germany Volkswagen Group Advanced Technologies San FranciscoCA94108 United States Rahko Ltd. Finsbury Park LondonN4 3JP United Kingdom Department of Computer Science University College London WC1E 6BT United Kingdom Clarendon Laboratory Department of Physics University of Oxford OxfordOX1 3PU United Kingdom Vector Institute MaRS Centre TorontoONM5G 1M1 Canada Department of Physics and Astronomy University of Waterloo ONN2L 3G1 Canada Institute for Quantum Information and Matter Caltech PasadenaCA United States Department of Computing and Mathematical Sciences Caltech PasadenaCA United States

We introduce TensorFlow Quantum (TFQ), an open source library for the rapid prototyping of hybrid quantum-classical models for classical or quantum data. This framework offers high-level abstractions for the design and training of both discriminative and generative quantum models under TensorFlow and supports high-performance quantum circuit simulators. We provide an overview of the software architecture and building blocks through several examples and review the theory of hybrid quantum-classical neural networks. We illustrate TFQ functionalities via several basic applications including supervised learning for quantum classification, quantum control, simulating noisy quantum circuits, and quantum approximate optimization. Moreover, we demonstrate how one can apply TFQ to tackle advanced quantum learning tasks including meta-learning, layerwise learning, Hamiltonian learning, sampling thermal states, variational quantum eigensolvers, classification of quantum phase transitions, generative adversarial networks, and reinforcement learning. We hope this framework provides the necessary tools for the quantum computing and machine learning research communities to explore models of both natural and artificial quantum systems, and ultimately discover new quantum algorithms which could potentially yield a quantum advantage. Copyright © 2020, The Authors. All rights reserved.

关键词： Open source software

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Industry-Scale Orchestrated Federated Learning for Drug Discovery

arXiv

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arXiv 2022年

作者： Oldenhof, Martijn Ács, Gergely Pejó, Balázs Schuffenhauer, Ansgar Holway, Nicholas Sturm, Noé Dieckmann, Arne Fortmeier, Oliver Boniface, Eric Mayer, Clément Gohier, Arnaud Schmidtke, Peter Niwayama, Ritsuya Kopecky, Dieter Mervin, Lewis Rathi, Prakash Chandra Friedrich, Lukas Formanek, András Antal, Peter Rahaman, Jordon Zalewski, Adam Heyndrickx, Wouter Oluoch, Ezron Stößel, Manuel Vančo, Michal Endico, David Gelus, Fabien De Boisfossé, Thaïs Darbier, Adrien Nicollet, Ashley Blottière, Matthieu Telenczuk, Maria Nguyen, Van Tien Martinez, Thibaud Boillet, Camille Moutet, Kelvin Picosson, Alexandre Gasser, Aurélien Djafar, Inal Simon, Antoine Arany, Ádám Simm, Jaak Moreau, Yves Engkvist, Ola Ceulemans, Hugo Marini, Camille Galtier, Mathieu Ku Leuven ESAT-STADIUS Kasteelpark Arenberg 10 Heverlee3001 Belgium BME-HIT CrySyS Lab Budapest Hungary BME-MIT Budapest Hungary Novartis Institutes for BioMedical Research Novartis Campus BaselCH-4002 Switzerland Bayer Ag Leverkusen51373 Germany Substra Foundation Labelia Labs Nantes France Discngine 79 Avenue Ledru Rollin Paris75012 France Institut de Recherches Servier 125 chemin de ronde Croissy-sur-Seine78290 France Boehringer Ingelheim Rcv GmbH & Co Kg Vienna Austria Molecular Ai Discovery Sciences R&d AstraZeneca Cambridge United Kingdom R&d It AstraZeneca Cambridge United Kingdom Molecular Ai Discovery Sciences R&d AstraZeneca Gothenburg Sweden Department of Computer Science and Engineering Chalmers University of Technology Gothenburg Sweden Merck KGaA Global Research & Development Frankfurter Strasse 250 Darmstadt64293 Germany GmbH Staffelseestraße 2 Munich81477 Germany Pillar Biosciences Inc. 9 Strathmore Rd NatickMA01760 United States Janssen Pharmaceutica Nv Turnhoutseweg 30 Beerse2340 Belgium Kubermatic Hamburg Germany Owkin Paris France

To apply federated learning to drug discovery we developed a novel platform in the context of European Innovative Medicines Initiative (IMI) project MELLODDY (grant n°831472), which was comprised of 10 pharmaceutical companies, academic research labs, large industrial companies and startups. The MELLODDY platform was the first industry-scale platform to enable the creation of a global federated model for drug discovery without sharing the confidential data sets of the individual partners. The federated model was trained on the platform by aggregating the gradients of all contributing partners in a cryptographic, secure way following each training iteration. The platform was deployed on an Amazon Web Services (AWS) multi-account architecture running Kubernetes clusters in private subnets. Organisationally, the roles of the different partners were codified as different rights and permissions on the platform and administrated in a decentralized way. The MELLODDY platform generated new scientific discoveries which are described in a companion paper. Copyright © 2022, The Authors. All rights reserved.

关键词： Web services

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BACH: grand challenge on breast cancer histology images

arXiv

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arXiv 2018年

作者： Aresta, Guilherme Araújo, Teresa Kwok, Scotty Chennamsetty, Sai Saketh Safwan, Mohammed Alex, Varghese Marami, Bahram Prastawa, Marcel Chan, Monica Donovan, Michael Fernandez, Gerardo Zeineh, Jack Kohl, Matthias Walz, Christoph Ludwig, Florian Braunewell, Stefan Baust, Maximilian Vu, Quoc Dang Nhat To, Minh Nguyen Kim, Eal Kwak, Jin Tae Galal, Sameh Sanchez-Freire, Veronica Brancati, Nadia Frucci, Maria Riccio, Daniel Wang, Yaqi Sun, Lingling Ma, Kaiqiang Fang, Jiannan Kone, Ismael Boulmane, Lahsen Campilho, Aurélio Eloy, Catarina Polónia, António Aguiar, Paulo INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto4200-465 Portugal Faculty of Engineering of University of Porto Porto4200-465 Portugal Seek AI Limited Hong Kong Bangalore India Gurgaon India Chennai India Center for Computational and Systems Pathology Dpt. of Pathology Icahn School of Medicine at Mount Sinai Mount Sinai Hospital New York United States Konica Minolta Laboratory Europe Munich Germany Institute of Pathology Faculty of Medicine LMU Munich Munich Germany Department of Computer Science and Engineering Sejong University Seoul05006 Korea Republic of Chicago IL United States Naples Italy University of Naples "Federico II" Naples Italy Key Laboratory of RF Circuits and Systems Ministry of Education Hangzhou Dianzi University Hangzhou310018 China Zhejiang Provincial Laboratory of Integrated Circuits Design Hangzhou Dianzi University Hangzhou310018 China p2MIA Research Group LEM2A Lab Faculté des Sciences Université Moulay Ismail Meknes Morocco qLaboratório de Anatomia Patológica Ipatimup Diagnósticos Rua Júilio Amaral de Carvalho 45 Porto4200-135 Portugal Faculdade de Medicina Universidade do Porto Alameda Prof Hernâni Monteiro Porto4200-319 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal Universidade do Porto Rua Alfredo Allen 208 Porto4200-135 Portugal

Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysis by pathologists, in a task that i) is highly time- and cost-consuming and ii) often leads to nonconsensual results. The relevance and potential of automatic classification algorithms using hematoxylin-eosin stained histopathological images has already been demonstrated, but the reported results are still sub-optimal for clinical use. With the goal of advancing the state-of-the-art in automatic classification, the Grand Challenge on BreAst Cancer Histology images (BACH) was organized in conjunction with the 15th International Conference on Image Analysis and Recognition (ICIAR 2018). BACH aimed at the classification and localization of clinically relevant histopathological classes in microscopy and whole-slide images from a large annotated dataset, specifically compiled and made publicly available for the challenge. Following a positive response from the scientific community, a total of 64 submissions, out of 677 registrations, effectively entered the competition. The submitted algorithms improved the state-of-the-art in automatic classification of breast cancer with microscopy images to an accuracy of 87%. Convolutional neuronal networks were the most successful methodology in the BACH challenge. Detailed analysis of the collective results allowed the identification of remaining challenges in the field and recommendations for future developments. The BACH dataset remains publicly available as to promote further improvements to the field of automatic classification in digital pathology. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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Artificial intelligence reveals brain aging patterns in 27,402 individuals without diagnosed cognitive impairment that are linked to genetics, biomedical measures, and cognitive decline

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Alzheimer's & Dementia 2023年第S17期19卷

作者： Ioanna Skampardoni Ilya M. Nasrallah Junhao Wen Yuhan Cui Ahmed Abdulkadir Zhijian Yang Guray Erus Elizabeth Mamourian Ashish Singh Haochang Shou Li Shen Konstantina Nikita Christos Davatzikos iSTAGING consortium School of Electrical and Computer Engineering National Technical University of Athens Athens Greece Centre for Biomedical Image Computing and Analytics University of Pennsylvania Philadelphia PA USA Correspondece Department of Radiology University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Laboratory for Research in Neuroimaging Department of Clinical Neurosciences Lausanne University Hospital (CHUV) and University of Lausanne Lausanne Switzerland 1Artificial Intelligence in Biomedical Imaging Laboratory (AIBIL) Center for and Data Science for Integrated Diagnostics (AI2D) Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Biostatistics Epidemiology & Informatics University of Pennsylvania Philadelphia PA USA University of Pennsylvania Philadelphia PA USA

Background Understanding heterogeneity of structural brain changes in aging may provide insights into susceptibility to neurodegenerative diseases. We characterize the genetics underlying brain structural heterogeneity within cognitively unimpaired (CU) individuals using data-driven machine learning applied to a diverse dataset of 27,402 individuals from 11 neuroimaging studies from the iSTAGING consortium. Method Structural brain morphologic patterns of CU individuals were independently examined in four decade-long intervals spanning ages 45 to 85. Within each interval, Smile-GAN (Yang et al., 2021) was trained on baseline anatomic and white matter hyperintensity (WMH) volumes. Smile-GAN probability scores were used as phenotypes in genome-wide association studies (GWAS). Specifically, we performed multiple linear regressions controlling for confounders (e.g., age) via Plink (Purcell et al., 2007). We observed longitudinal clustering stability across decades, so individuals from adjacent age groups were combined into broader age groups ([45,65), [65,85)) due to the large sample requirement of GWAS. Genomic loci, represented by the top leading single nucleotide polymorphisms (SNPs), were defined considering linkage disequilibrium. We investigated associations of SNPs with clinical traits and mapped them to genes using the GWAS Catalog (Buniello et al., 2019). Result Three structural brain aging patterns, relative to resilient agers (A0), consistent across decades, emerged: A1, or ‘typical’ aging with low atrophy and WMHs, and two ‘advanced’ aging patterns, one showing elevated WMHs and modest atrophy (A2) and the other displaying severe, widespread atrophy and moderate WMH load (A3) (Figure 1). GWAS discovered eight and six genomic loci in [45,65) and [65,85) age groups, respectively (Table 1, Figure 2). The lead SNPs for A1 and A2 were previously associated with several cardiometabolic risk factors, WMHs, and regional brain volumes. Interestingly, rs4843552, previo

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Acceptance sampling in Quality Control: from theory to the Web : In PHP, Python, and gnuplot

Acceptance sampling in Quality Control: from theory to the W...

引用

Iberian Conference on Information Systems and Technologies (CISTI)

作者： Miguel Casquilho Elisabete A. Carolino André Espírito Santo Sandro Carvalho João Luís de Miranda João M. Bordado Department of Chemical Engineering IST Universidade de Lisboa (University of Lisbon) and CERENA Centro de Recursos Naturais e Ambiente (“Centre for Natural Resources and the Environment”) Ave. Rovisco Pais IST South Tower (Bldg. 20) Lisboa (Lisbon) Portugal H&TRC- Health & Technology Research Center ESTeSL- Escola Superior de Tecnologia da Saúde Instituto Politécnico de Lisboa (Polytechnic Institute of Lisbon) Ave. Dom João II lote 4.69.01 Parque das Nações Lisboa (Lisbon) Portugal Dept. of Computer Science and Eng.ing IST Universidade de Lisboa Ave. Rovisco Pais IST Bldg. 09 (Computer Science) Lisboa (Lisbon) Portugal Polytechnic Institute of Cávado and Ave (IPCA) Applied Artificial Intelligence Laboratory (2AI) and Laboratório de Inteligência Artificial e Ciência de Computadores (LIACC) Barcelos Portugal Polytechnic Institute of Portalegre and CERENA Campus Politécnico 10 Portalegre Dept. of Chemical Eng.ing IST Universidade de Lisboa and CERENA Ave. Rovisco Pais IST South Tower (Bldg. 20) Lisboa (Lisbon) Portugal

Quality is nowadays a ubiquitous component in manufacture and many other activities. We present a computation related to Acceptance Sampling, which, together with Statistical Process Control, makes the fundamentals of statistical Quality Control. We provide a webpage where a user’s typical sample from a process can be inserted, leading to “accept” or “reject” vs. given specifications. We have several goals: to offer, on a webpage, the statistical procedure computation, which is otherwise complex; to combine, on a Linux platform, programming languages, PHP and Python, and a graphical utility, gnuplot; and to stress the suggestion of the Web as a computing medium. The webpage is freely accessible to a user just by means of a browser, i.e., installing no software, thus needing no special power or matching operating system, this being an example adaptable to many other problems. The study also draws attention both to the use of the Web for scientific computing, and to the convenience of this use in scientific publications. Web-based computing in general is advocated, this route using the same executable programs as classical computing, which is the core technical difficulty. In our technological era, this still insufficiently explored approach is here made accessible, inviting the sharing between academia and industry.

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Author Correction: The high-dimensional space of human diseases built from diagnosis records and mapped to genetic loci

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Nature computational science 2023年第7期3卷 658页

作者： Gengjie Jia Yu Li Xue Zhong Kanix Wang Milton Pividori Rabab Alomairy Aniello Esposito Hatem Ltaief Chikashi Terao Masato Akiyama Koichi Matsuda David E Keyes Hae Kyung Im Takashi Gojobori Yoichiro Kamatani Michiaki Kubo Nancy J Cox James Evans Xin Gao Andrey Rzhetsky Shenzhen Branch Guangdong Laboratory of Lingnan Modern Agriculture Genome Analysis Laboratory of the Ministry of Agriculture and Rural Affairs Agricultural Genomics Institute at Shenzhen Chinese Academy of Agricultural Sciences Shenzhen China. jiagengjie@***. Computational Bioscience Research Center King Abdullah University of Science and Technology Thuwal Saudi Arabia. Computer Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. Department of Computer Science and Engineering The Chinese University of Hong Kong Hong Kong People's Republic of China. Department of Medicine and Vanderbilt Genetics Institute Vanderbilt University Medical Center Nashville TN US. Department of Medicine Institute of Genomics and Systems Biology Committee on Genomics Genetics and Systems Biology University of Chicago Chicago IL US. Department of Operations Business Analytics and Information Systems University of Cincinnati Cincinnati OH US. Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania Philadelphia PA US. Extreme Computing Research Center Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia. College of Computer Science and Engineering University of Jeddah Jeddah Saudi Arabia. HPE HPC/AI EMEA Research Laboratory Bristol UK. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Clinical Research Center Shizuoka General Hospital Shizuoka Japan. Department of Applied Genetics The School of Pharmaceutical Sciences University of Shizuoka Shizuoka Japan. Department of Ophthalmology Graduate School of Medical Sciences Kyushu University Fukuoka Japan. Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Tokyo Japan. Biological and Environmenta

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Quantum Games and Interactive Tools for Quantum Technologies Outreach and Education

arXiv

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arXiv 2022年

作者： Seskir, Zeki C. Migdal, Piotr Weidner, Carrie Anupam, Aditya Case, Nicky Davis, Noah Decaroli, Chiara Ercan, İlke Foti, Caterina Gora, Pawel Jankiewicz, Klementyna La Cour, Brian R. Malo, Jorge Yago Maniscalco, Sabrina Naeemi, Azad Nita, Laurentiu Parvin, Nassim Scafirimuto, Fabio Sherson, Jacob F. Surer, Elif Wootton, James Yeh, Lia Zabello, Olga Chiofalo, Marilù Karlsruhe Institute of Technology ITAS Karlstraße 11 Karlsruhe76133 Germany Quantum Flytrap ul. Ceglowska 29 Warsaw01-809 Poland Quantum Engineering Technology Laboratories H.H. Wills Physics Laboratory Department of Electrical and Electronic Engineering University of Bristol BristolBS8 1FD United Kingdom Georgia Institute of Technology School of Literature Media and Communication 686 Cherry St NW AtlantaGA30313 United States The University of Texas at Austin Center for Quantum Research Applied Research Laboratories 10000 Burnet Rd AustinTX78758 United States National Quantum Computing Center Oxfordshire DidcotOX11 0GD United Kingdom TU Delft Department of Microelectronics Mekelweg 4 Delft2628 CD Netherlands QPlayLearn Aalto University InstituteQ - The Finnish Quantum Institute Department of Applied Physics AaltoFI-00076 11000 Finland Quantum AI Foundation Sanocka 9/103 Warsaw02-110 Poland University of Pisa Department of Physics Largo Bruno Pontecorvo 3 PisaI-56126 Italy University of Helsinki InstituteQ - The Finnish Quantum Institute Department of Physics Helsinki Finland Georgia Institute of Technology School of Electrical and Computer Engineering 777 Atlantic Drive NW AtlantaGA30332 United States Quarks Interactive Strada Bailor 93a Miercurea-Ciuc Harghita4500 Romania IBM Quantum IBM Research - Zurich Säumerstrasse 4 Rüschlikon8803 Switzerland ScienceAtHome Fuglesangs Allé 4 Aarhus8210 Denmark Aarhus University Institute for Physics and Astronomy Ny Munkegade 120 Aarhus8000 Denmark Middle East Technical University Graduate School of Informatics Department of Modeling and Simulation Üniversiteler Mah. Dumlupınar Blv. No:1 Ankara06800 Turkey University of Oxford Department of Computer Science Wolfson Building Parks Road OxfordOX1 3QD United Kingdom IBM Quantum IBM T. J. Watson Research Center Yorktown HeightsNY10598 United States Offenburg University of Applied Sciences Department of Business and Industrial Eng

In this article, we provide an extensive overview of a wide range of quantum games and interactive tools that have been employed by the community in recent years. The paper presents selected tools, as described by their developers. The list includes Hello Quantum, Hello Qiskit, Particle in a Box, Psi and Delta, QPlayLearn, Virtual Lab by Quantum Flytrap, Quantum Odyssey, scienceAtHome, and The Virtual Quantum Optics Laboratory. Additionally, we present events for quantum game development: hackathons, game jams, and semester projects. Furthermore, we discuss the Quantum Technologies Education for Everyone (QUTE4E) pilot project, which illustrates an effective integration of these interactive tools with quantum outreach and education activities. Finally, we aim at providing guidelines for incorporating quantum games and interactive tools in pedagogic materials to make quantum technologies more accessible for a wider population. Copyright © 2022, The Authors. All rights reserved.

关键词： Quantum optics

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Objective comparison of methods to decode anomalous diffusion

arXiv

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arXiv 2021年

作者： Muñoz-Gil, Gorka Volpe, Giovanni Garcia-March, Miguel Angel Aghion, Erez Argun, Aykut Hong, Chang Beom Bland, Tom Bo, Stefano Conejero, J. Alberto Firbas, Nicolás Orts, Òscar Garibo Gentili, Alessia Huang, Zihan Jeon, Jae-Hyung Kabbech, Hélène Kim, Yeongjin Kowalek, Patrycja Krapf, Diego Loch-Olszewska, Hanna Lomholt, Michael A. Masson, Jean-Baptiste Meyer, Philipp G. Park, Seongyu Requena, Borja Smal, Ihor Song, Taegeun Szwabinski, Janusz Thapa, Samudrajit Verdier, Hippolyte Volpe, Giorgio Widera, Arthur Lewenstein, MacIej Metzler, Ralf Anzo, Carlo 08860 Spain Department of Physics University of Gothenburg Origovägen 6B GothenburgSE-41296 Sweden Instituto Universitario de Matemática Pura y Aplicada Universitat Politècnica de València Spain Max Planck Institute for the Physics of Complex Systems Nöthnitzer Straße 38 DresdenDE-01187 Germany Department of Physics Pohang University of Science and Technology Pohang37673 Korea Republic of Francis Crick Institute 1 Midland Road LondonNW1 1AT United Kingdom Department of Chemistry University College London 20 Gordon Street LondonWC1H 0AJ United Kingdom School of Physics and Electronics Hunan University Changsha410082 China Department of Cell Biology Erasmus Mc Rotterdam Netherlands Faculty of Pure and Applied Mathematics Hugo Steinhaus Center Wroclaw University of Science and Technology Wroclaw Poland Department of Electrical and Computer Engineering Colorado State University Fort CollinsCO80523 United States PhyLife Department of Physics Chemistry and Pharmacy University of Southern Denmark Odense MDK-5230 Denmark Institut Pasteur Decision and Bayesian Computation Lab Paris France Center for Ai and Natural Sciences Korea Institute for Advanced Study Seoul Korea Republic of Institute for Physics and Astronomy University of Potsdam Karl-Liebknecht-Str 24/25 Potsdam-GolmD-14476 Germany Sackler Center for Computational Molecular and Materials Science Tel Aviv University Tel Aviv69978 Israel School of Mechanical Engineering Tel Aviv University Tel Aviv69978 Israel Institut Pasteur Decision and Bayesian Computation Lab Paris France Department of Physics Research Center Optimas Technische Universität Kaiserslautern Kaiserslautern67663 Germany Icrea Pg. Lluís Companys 23 Barcelona08010 Spain C. de la Laura 13 Vic08500 Spain

Deviations from Brownian motion leading to anomalous diffusion are ubiquitously found in transport dynamics, playing a crucial role in phenomena from quantum physics to life sciences. The detection and characterization of anomalous diffusion from the measurement of an individual trajectory are challenging tasks, which traditionally rely on calculating the mean squared displacement of the trajectory. However, this approach breaks down for cases of important practical interest, e.g., short or noisy trajectories, ensembles of heterogeneous trajectories, or non-ergodic processes. Recently, several new approaches have been proposed, mostly building on the ongoing machine-learning revolution. aiming to perform an objective comparison of methods, we gathered the community and organized an open competition, the Anomalous Diffusion challenge (AnDi). Participating teams independently applied their own algorithms to a commonly-defined dataset including diverse conditions. Although no single method performed best across all scenarios, the results revealed clear differences between the various approaches, providing practical advice for users and a benchmark for developers. Copyright © 2021, The Authors. All rights reserved.

关键词： Trajectories

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