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arXiv 2021年

作者： Zhu, Daiwei Kahanamoku-Meyer, Gregory D. Lewis, Laura Noel, Crystal Katz, Or Harraz, Bahaa Wang, Qingfeng Risinger, Andrew Feng, Lei Biswas, Debopriyo Egan, Laird Gheorghiu, Alexandru Nam, Yunseong Vidick, Thomas Vazirani, Umesh Yao, Norman Y. Cetina, Marko Monroe, Christopher Joint Quantum Institute Departments of Physics and Electrical and Computer Engineering University of Maryland College ParkMD20742 United States Joint Center for Quantum Information and Computer Science NIST University of Maryland College ParkMD20742 United States Department of Physics University of California BerkeleyCA94720 United States Materials Sciences Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Computing and Mathematical Sciences California Institute of Technology CA91125 United States Division of Physics Mathematics and Astronomy California Institute of Technology CA91125-0001 United States Duke Quantum Center Department of Physics Duke University DurhamNC27708 United States Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States IonQ Inc. College ParkMD20740 United States Institute for Theoretical Studies ETH Zürich CH 8001 Switzerland Chemical Physics Program Institute for Physical Science and Technology University of Maryland College ParkMD20742 United States Departments of Electrical and Computer Engineering University of Maryland College ParkMD20742 United States

Interaction is a powerful resource for quantum computation. It can be utilized in applications ranging from the verification of quantum algorithms all the way to verifying quantum mechanics itself. Here, we present the first implementation of interactive protocols for proofs of quantumness — which when suitably scaled promise the efficient verification of quantum computational advantage. The key feature that distinguishes these protocols from existing demonstrations of quantum advantage is that the classical verification is efficient and scales polynomially rather than exponentially with the number of qubits. The experimental implementation of such interactive protocols requires the ability to independently measure subsets of qubits in the middle of a quantum circuit and to continue coherent evolution afterwards. This is achieved by spatially isolating target qubits via shuttling, and opens the door to a range of quantum interactive protocols as well as new information processing architectures. Copyright © 2021, The Authors. All rights reserved.

关键词： Qubits

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Dimensional reduction using conditional entropy for incomplete information systems 1

引用

15th International Conference on Parallel Computing Technologies, PaCT 2019

作者： Deris, Mustafa Mat Senan, Norhalina Abdullah, Zailani Mamat, Rabiei Handaga, Bana Faculty of Computer Science and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Faculty of Entrepreneurship and Business Universiti Malaysia Kelantan Pengkalan Chepa Kota BharuKelantan16100 Malaysia Faculty of Informatics and Applied Mathematics University of Malaysia Terengganu Kuala TerengganuTerengganu21030 Malaysia Program Studi Informatika Universitas Muhammadiah Surakarta SurakartaCentral Java57162 Indonesia

ISBN: (数字)9783030256364

ISBN: (纸本)9783030256357

Dimension reduction approach is one of the main data reduction approaches in order to reduce the storage and processing time while maintaining the integrity of the original data. A wide range of dimension reduction approaches are based on classical approaches such as PCA and Bayer’s, and machine learning approaches such as clustering, and feature selection techniques. However, many of the approaches do not consider the incomplete information systems where some attribute values are missing or incomplete. Only few studies were proposed for the problem in incomplete information systems due to its complexities, specifically on attribute selection. The most popular approaches is based on probability theory to replace missing values with the most common values, or remove the missing objects from the information systems. However, it needs to know the probability distribution of data in advance. To overcome these issues, we propose a new approach based on conditional entropy to reduce dimensionality. The results show that the proposed approach achieves better data reduction with higher accuracy for objects and dimensionality reduction in incomplete information systems. © 2019, Springer Nature Switzerland AG.

关键词： Data reduction

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Will artificial intelligence supersede earth system and climate models?

arXiv

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arXiv 2021年

作者： Irrgang, Christopher Boers, Niklas Sonnewald, Maike Barnes, Elizabeth A. Kadow, Christopher Staneva, Joanna Saynisch-Wagner, Jan Helmholtz Centre Potsdam German Research Centre for Geosciences GFZ Potsdam Germany Department of Mathematics and Computer Science Free University of Berlin Germany Potsdam Institute for Climate Impact Research Potsdam Germany Department of Mathematics and Global Systems Institute University of Exeter United Kingdom Program in Atmospheric and Oceanic Sciences Princeton University PrincetonNJ08540 United States NOAA OAR Geophysical Fluid Dynamics Laboratory Ocean and Cryosphere Division PrincetonNJ08540 United States University of Washington School of Oceanography SeattleWA United States Colorado State University Fort Collins United States German Climate Computing Center DKRZ Hamburg Germany Helmholtz-Zentrum Geesthacht Center for Material and Coastal Research HZG Geesthacht Germany

We outline a perspective of an entirely new research branch in Earth and climate sciences, where deep neural networks and Earth system models are dismantled as individual methodological approaches and reassembled as learning, self-validating, and interpretable Earth system model-network hybrids. Following this path, we coin the term"Neural Earth System Modelling" (NESYM) and highlight the necessity of a transdisciplinary discussion platform, bringing together Earth and climate scientists, big data analysts, and AI experts. We examine the concurrent potential and pitfalls of Neural Earth System Modelling and discuss the open question whether artificial intelligence will not only infuse Earth system modelling, but ultimately render them obsolete. © 2021, CC BY.

关键词： Climate models

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Simultaneous dimensionality and complexity model selection for spectral graph clustering

arXiv

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arXiv 2019年

作者： Yang, Congyuan Priebe, Carey E. Park, Youngser Marchette, David J. Department of Electrical and Computer Engineering Johns Hopkins University Department of Applied Math and Statistics Johns Hopkins University Center for Imaging Science Johns Hopkins University Applied Mathematics and Data Analytics Group Naval Surface Warfare Center Dahlgren Division

Our problem of interest is to cluster vertices of a graph by identifying underlying community structure. Among various vertex clustering approaches, spectral clustering is one of the most popular methods because it is easy to implement while often outperforming more traditional clustering algorithms. However, there are two inherent model selection problems in spectral clustering, namely estimating both the embedding dimension and number of clusters. This paper attempts to address the issue by establishing a novel model selection framework specifically for vertex clustering on graphs under a stochastic block model. The first contribution is a probabilistic model which approximates the distribution of the extended spectral embedding of a graph. The model is constructed based on a theoretical result of asymptotic normality of the informative part of the embedding, and on a simulation result providing a conjecture for the limiting behavior of the redundant part of the embedding. The second contribution is a simultaneous model selection framework. In contrast with the traditional approaches, our model selection procedure estimates embedding dimension and number of clusters simultaneously. Based on our conjectured distributional model, a theorem on the consistency of the estimates of model parameters is stated and proven. The theorem provides statistical support for the validity of our method. Heuristic algorithms via the simultaneous model selection framework for vertex clustering are proposed, with good performance shown in the experiment on synthetic data and on the real application of connectome analysis. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

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BTEX exposure and its body burden pose differential risks for asthma and its phenotypic clusters

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Allergy 2023年第7期78卷 2036-2040页

作者： Yuan-Ting Hsu Chao-Chien Wu Chin-Chou Wang Wen-Yu Chung Chau-Chyun Sheu Yi-Hsin Yang Ming-Yen Cheng Ruay-Sheng Lai Sum-Yee Leung Chi-Cheng Lin Yu-Feng Wei Ching-Hsiung Lin Sheng-Hao Lin Jeng-Yuan Hsu Wei-Chang Huang Chia-Cheng Tseng Yung-Fa Lai Meng-Hsuan Cheng Huang-Chi Chen Chih-Jen Yang Chian-Heng Su Chien-Jen Wang Shih-Chang Hsu Chih-Hsing Hung Chon-Lin Lee Ming-Shyan Huang Shau-Ku Huang National Institute of Environmental Health Sciences National Health Research Institutes Miaoli Taiwan. Department of Internal Medicine Division of Pulmonary and Critical Care Medicine Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine Kaohsiung Taiwan. Department of Public Health Kaohsiung Medical University Kaohsiung Taiwan. Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan. Department of Internal Medicine Division of Pulmonary and Critical Care Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan. Department of Internal Medicine School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan. National Institute of Cancer Research National Health Research Institutes Tainan Taiwan. Department of Mathematics Hong Kong Baptist University Hong Kong China. Division of Chest Medicine Kaohsiung Veterans General Hospital Kaohsiung Taiwan. Department of Internal Medicine Chest Division Antai Medical Care Cooperation Antai Tian-Sheng Memorial Hospital Pingtung Taiwan. Department of Internal Medicine E-Da Cancer Hospital I-Shou University Kaohsiung Taiwan. Department of Internal Medicine Division of Chest Medicine Changhua Christian Hospital Changhua Taiwan. Institute of Genomics and Bioinformatics National Chung Hsing University Taichung Taiwan. Department of Recreation and Holistic Wellness MingDao University Changhua Taiwan. Department of Medical Research Division of Clinical Research Taichung Veterans General Hospital Taichung Taiwan. Department of Internal Medicine Division of Chest Medicine Taichung Veterans General Hospital Taichung Taiwan. Department of Medical Technology Jen-Teh Junior College of Medicine Nursing and Management Miaoli Taiwan. School of Medicine Chung Shan Medical University Taichung Taiwan. Ph.D. Program in Translational Medicine National Chung Hsing Universi

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Classification of datasets with imputed missing values: does imputation quality matter?

arXiv

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arXiv 2022年

作者： Shadbahr, Tolou Roberts, Michael Stanczuk, Jan Gilbey, Julian Teare, Philip Dittmer, Sören Thorpe, Matthew Torné, Ramon Viñas Sala, Evis Lió, Pietro Patel, Mishal Rudd, James H.F. Mirtti, Tuomas Rannikko, Antti Sakari Aston, John A.D. Tang, Jing Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Preller, Jacobus Stanczuk, Jan Babar, Judith Sánchez, Lorena Escudero Wassin, Marcel Holzer, Markus Walton, Nicholas Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Department of Mathematics University of Manchester Manchester United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom Department of Radiology University of Cambridge Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pathology University of Helsinki Helsinki University Hospital Finland iCAN-Digital Precision Cancer Medicine Flagship Helsinki Finland Department of Urology University of Helsinki Helsinki University Hospital Helsinki Finland Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Faculty of Mathematics University of Vienna Austria Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete, imputed, samples. The focus of the machine learning researcher is then to optimise the downstream classification performance. In this study, we highlight that it is imperative to consider the quality of the imputation. We demonstrate how the commonly used measures for assessing quality are flawed and propose a new class of discrepancy scores which focus on how well the method recreates the overall distribution of the data. To conclude, we highlight the compromised interpretability of classifier models trained using poorly imputed data. All code and data used in this paper are also released publicly at [inserted upon publication]. © 2022, CC BY.

关键词： Classification (of information)

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The Multi-modality Cell Segmentation Challenge: Towards Universal Solutions

arXiv

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arXiv 2023年

作者： Ma, Jun Xie, Ronald Ayyadhury, Shamini Ge, Cheng Gupta, Anubha Gupta, Ritu Gu, Song Zhang, Yao Lee, Gihun Kim, Joonkee Lou, Wei Li, Haofeng Upschulte, Eric Dickscheid, Timo de Almeida, José Guilherme Wang, Yixin Han, Lin Yang, Xin Labagnara, Marco Gligorovski, Vojislav Scheder, Maxime Rahi, Sahand Jamal Kempster, Carly Pollitt, Alice Espinosa, Leon Mignot, Tâm Middeke, Jan Moritz Eckardt, Jan-Niklas Li, Wangkai Li, Zhaoyang Cai, Xiaochen Bai, Bizhe Greenwald, Noah F. Van Valen, David Weisbart, Erin Cimini, Beth A. Cheung, Trevor Brück, Oscar Bader, Gary D. Wang, Bo Peter Munk Cardiac Centre University Health Network TorontoON Canada Department of Laboratory Medicine and Pathobiology University of Toronto TorontoON Canada Vector Institute TorontoON Canada Department of Molecular Genetics University of Toronto TorontoON Canada Donnelly Centre University of Toronto TorontoON Canada Princess Margaret Cancer Centre University Health Network TorontoON Canada School of Medicine and Pharmacy Ocean University of China Qingdao China New Delhi India Laboratory Oncology Dr. BRA-IRCH All India Institute of Medical Sciences New Delhi India Department of Image Reconstruction Nanjing Anke Medical Technology Co. Ltd. Nanjing China Shanghai Artificial Intelligence Laboratory Shanghai China Graduate School of AI KAIST Seoul Korea Republic of Shenzhen Research Institute of Big Data Shenzhen China Shenzhen China Helmholtz AI Research Center Jülich Jülich Germany Faculty of Mathematics and Natural Sciences Institute of Computer Science Heinrich Heine University Düsseldorf Düsseldorf Germany Hinxton United Kingdom Champalimaud Foundation - Centre for the Unknown Lisbon Portugal Department of Bioengineering Stanford University Palo AltoCA United States Tandon School of Engineering New York University New YorkNY United States School of Biomedical Engineering Health Science Center Shenzhen University Shenzhen China Lausanne Switzerland School of Biological Sciences University of Reading Reading United Kingdom Laboratoire de Chimie Bactérienne CNRS Université Aix Marseille UMR Institut de Microbiologie de la Méditerranée Marseille France Department of Internal Medicine I University Hospital Dresden Technical University Dresden Dresden Germany Else Kroener Fresenius Center for Digital Health Technical University Dresden Dresden Germany Department of Automation University of Science and Technology of China Hefei China Institute of Advanced Technology University of Science and Technology of China Hefei Chi

Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different experimental settings. Here, we present a multi-modality cell segmentation benchmark, comprising over 1500 labeled images derived from more than 50 diverse biological experiments. The top participants developed a Transformer-based deep-learning algorithm that not only exceeds existing methods but can also be applied to diverse microscopy images across imaging platforms and tissue types without manual parameter adjustments. This benchmark and the improved algorithm offer promising avenues for more accurate and versatile cell analysis in microscopy imaging. © 2023, CC BY-NC-ND.

关键词： Deep learning

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DeePMD-kit v2: A software package for Deep Potential models

arXiv

引用

arXiv 2023年

作者： Zeng, Jinzhe Zhang, Duo Lu, Denghui Mo, Pinghui Li, Zeyu Chen, Yixiao Rynik, Marián Huang, Li'ang Li, Ziyao Shi, Shaochen Wang, Yingze Ye, Haotian Tuo, Ping Yang, Jiabin Ding, Ye Li, Yifan Tisi, Davide Zeng, Qiyu Bao, Han Xia, Yu Huang, Jiameng Muraoka, Koki Wang, Yibo Chang, Junhan Yuan, Fengbo Bore, Sigbjørn Løland Cai, Chun Lin, Yinnian Wang, Bo Xu, Jiayan Zhu, Jia-Xin Luo, Chenxing Zhang, Yuzhi Goodall, Rhys E.A. Liang, Wenshuo Singh, Anurag Kumar Yao, Sikai Zhang, Jingchao Wentzcovitch, Renata Han, Jiequn Liu, Jie Jia, Weile York, Darrin M. Weinan, E. Car, Roberto Zhang, Linfeng Wang, Han Laboratory for Biomolecular Simulation Research Institute for Quantitative Biomedicine Department of Chemistry and Chemical Biology Rutgers University PiscatawayNJ08854 United States AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China HEDPS CAPT College of Engineering Peking University Beijing100871 China College of Electrical and Information Engineering Hunan University Changsha China Yuanpei College Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08540 United States Department of Experimental Physics Comenius University Mlynská Dolina F2 Bratislava842 48 Slovakia Center for Quantum Information Institute for Interdisciplinary Information Sciences Tsinghua University Beijing100084 China Center for Data Science Peking University Beijing100871 China ByteDance Research Zhonghang Plaza No. 43 North 3rd Ring West Road Haidian District Beijing China College of Chemistry and Molecular Engineering Peking University Beijing100871 China Baidu Inc. Beijing China Key Laboratory of Structural Biology of Zhejiang Province School of Life Sciences Westlake University Zhejiang Hangzhou China Westlake AI Therapeutics Lab Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Hangzhou China Department of Chemistry Princeton University PrincetonNJ08544 United States SISSA Scuola Internazionale Superiore di Studi Avanzati Trieste34136 Italy Laboratory of Computational Science and Modeling Institute of Materials École Polytechnique Fédérale de Lausanne Lausanne1015 Switzerland Department of Physics National University of Defense Technology Hunan Changsha410073 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineerin

DeePMD-kit is a powerful open-source software package that facilitates molecular dynamics simulations using machine learning potentials (MLP) known as Deep Potential (DP) models. This package, which was released in 2017, has been widely used in the fields of physics, chemistry, biology, and material science for studying atomistic systems. The current version of DeePMD-kit offers numerous advanced features such as DeepPot-SE, attention-based and hybrid descriptors, the ability to fit tensile properties, type embedding, model deviation, Deep Potential - Range Correction (DPRc), Deep Potential Long Range (DPLR), GPU support for customized operators, model compression, non-von Neumann molecular dynamics (NVNMD), and improved usability, including documentation, compiled binary packages, graphical user interfaces (GUI), and application programming interfaces (API). This article presents an overview of the current major version of the DeePMD-kit package, highlighting its features and technical details. Additionally, the article benchmarks the accuracy and efficiency of different models and discusses ongoing developments. © 2023, CC BY-NC-ND.

关键词： Molecular dynamics

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Testing viable f(R) models with the angular-diameter distance to compact quasar cores

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Physical Review D 2019年第10期99卷 103505-103505页

作者： Joseph Sultana Fulvio Melia Demosthenes Kazanas Department of Mathematics Faculty of Science University of Malta Msida MSD2080 Malta Department of Physics The Applied Math Program and Department of Astronomy The University of Arizona Tucson Arizona 85721 USA Astrophysics Science Division NASA/Goddard Space Flight Center Greenbelt Maryland 20771 USA

We consider here some popular f(R) models generally viewed as possible alternatives to the existence of dark energy in General Relativity. For each of these, we compute the redshift zmax at which the angular diameter distance dA(z) is expected to reach its maximum value. This turning point in dA(z) was recently measured in a model-independent way using compact quasar cores and was found to occur at zmax=1.70±0.20. We compare the predictions of zmax for the f(R) models with this observed value to test their viability at a deeper level than has been attempted thus far, thereby quantifying an important observational difference between such modified gravity scenarios and standard Lambda Cold Dark Matter (ΛCDM) cosmology. Our results show that, while the most popular f(R) models today are consistent with this measurement to within 1σ, the turning point zmax will allow us to prioritize these alternative gravity theories as the measurement precision continues to improve, particularly with regard to how well they mitigate the tension between the predictions of ΛCDM and the observations. For example, while the Hu-Sawicki version of f(R) increases this tension, the Starobinky model reduces it.

关键词： Alternative gravity theories Cosmological parameters Relativistic aspects of cosmology

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Quantum circuits for exact unitary t-designs and applications to higher-order randomized benchmarking

arXiv

引用

arXiv 2021年

作者： Nakata, Yoshifumi Zhao, Da Okuda, Takayuki Bannai, Eiichi Suzuki, Yasunari Tamiya, Shiro Heya, Kentaro Yan, Zhiguang Zuo, Kun Tamate, Shuhei Tabuchi, Yutaka Nakamura, Yasunobu Photon Science Center Graduate School of Engineering University of Tokyo Bunkyo-ku Tokyo113–8656 Japan JST PRESTO 4–1–8 Honcho Kawaguchi Saitama332–0012 Japan School of Mathematical Sciences Shanghai Jiao Tong University Shanghai400240 China Department of Mathematics Hiroshima University 1-3-1 Kagamiyama Higashihiroshima739-8562 Japan Fukuoka819-0385 Japan Mathematics Division National Center for Theoretical Sciences National Taiwan University Taipei10617 Taiwan NTT Computer and Data Science Laboratories NTT Corporation Musashino180-8585 Japan Department of Applied Physics Graduate School of Engineering University of Tokyo 7-3-1 Hongo Bynkyo-ku Tokyo113-8656 Japan University of Tokyo Meguro-ku Tokyo153-8904 Japan Wako Saitama351-0198 Japan

A unitary t-design is a powerful tool in quantum information science and fundamental physics. Despite its usefulness, only approximate implementations were known for general t. In this paper, we provide for the first time quantum circuits that generate exact unitary t-designs for any t on an arbitrary number of qubits. Our construction is inductive and is of practical use in small systems. We then introduce a t-th order generalization of randomized benchmarking (t-RB) as an application of exact 2t-designs. We particularly study the 2-RB in detail and show that it reveals self-adjointness of quantum noise, a new metric related to the feasibility of quantum error correction (QEC). We numerically demonstrate that the 2-RB in one- and two-qubit systems is feasible, and experimentally characterize background noise of a superconducting qubit by the 2-RB. It is shown from the experiment that interactions with adjacent qubits induce the noise that may result in an obstacle toward a realization of QEC. © 2021, CC BY.

关键词： Benchmarking

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