检索结果-内蒙古大学图书馆

arXiv 2019年

作者： Chen, Chen Bai, Wenjia Davies, Rhodri H. Bhuva, Anish N. Manisty, Charlotte Moon, James C. Aung, Nay Lee, Aaron M. Sanghvi, Mihir M. Fung, Kenneth Paiva, Jose Miguel Petersen, Steffen E. Lukaschuk, Elena Piechnik, Stefan K. Neubauer, Stefan Rueckert, Daniel Biomedical Image Analysis Group Department of Computing Imperial College London London United Kingdom Data Science Institute Imperial College London London United Kingdom Department of Medicine Imperial College London London United Kingdom Institutes for Cardiovascular Science University College London London United Kingdom Department of Cardiovascular Imaging Barts Heart Centre St Bartholomew's Hospital London United Kingdom NIHR Biomedical Research Centre at Barts Queen Mary University of London London United Kingdom NIHR BRC Oxford Division of Cardiovascular Medicine Radcliffe Department of Medicine University of Oxford Oxford United Kingdom

Convolutional neural network (CNN) based segmentation methods provide an efficient and automated way for clinicians to assess the structure and function of the heart in cardiac MR images. While CNNs can generally perform the segmentation tasks with high accuracy when training and test images come from the same domain (e.g. same scanner or site), their performance often degrades dramatically on images from different scanners or clinical sites. We propose a simple yet effective way for improving the network generalization ability by carefully designing data normalization and augmentation strategies to accommodate common scenarios in multi-site, multi-scanner clinical imaging data sets. We demonstrate that a neural network trained on a single-site single-scanner dataset from the UK Biobank can be successfully applied to segmenting cardiac MR images across different sites and different scanners without substantial loss of accuracy. Specifically, the method was trained on a large set of 3,975 subjects from the UK Biobank. It was then directly tested on 600 different subjects from the UK Biobank for intra-domain testing and two other sets for cross-domain testing: the ACDC dataset (100 subjects, 1 site, 2 scanners) and the BSCMR-AS dataset (599 subjects, 6 sites, 9 scanners). The proposed method produces promising segmentation results on the UK Biobank test set which are comparable to previously reported values in the literature, while also performing well on cross-domain test sets, achieving a mean Dice metric of 0.90 for the left ventricle, 0.81 for the myocardium and 0.82 for the right ventricle on the ACDC dataset;and 0.89 for the left ventricle, 0.83 for the myocardium on the BSCMR-AS dataset. The proposed method offers a potential solution to improve CNN-based model generalizability for the cross-scanner and cross-site cardiac MR image segmentation task. Copyright © 2019, The Authors. All rights reserved.

关键词： Image segmentation

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Inland port location planning based on user equilibrium 17

Inland port location planning based on user equilibrium

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17th COTA International Conference of Transportation Professionals: Transportation Reform and Change - Equity, Inclusiveness, Sharing, and Innovation, CICTP 2017

作者： Wei, Jingyi Tian, Daxin Rong, Hui Guo, Peng Wang, Wenyang Gong, Jinfeng Wang, Yunpeng Liu, He Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang Univ. Beijing100191 China Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control School of Transportation Science and Engineering Beihang Univ. Beijing100191 China Jiangsu Province Collaborative Innovation Center of Modern Urban Traffic Technologies Nanjing210096 China Automotive Engineering Research Institute China Automotive Technology and Research Center Tianjin300300 China Quartermaster Equipment Institute of the General Logistics Dept. Of CPLA Beijing100010 China

ISBN: (数字)9780784480915

ISBN: (纸本)9780784480915

With stable development of container transportation in shipping industry, the transit shipment from road to railway of inland ports plays a significant role in multimodal transport. This paper presents the method of inland port location planning based on user equilibrium. In the study, the virtual connection simulating the process of transfer in inland ports is conducted to measure the fitness of inland port location through the cargo volume on virtual connection. At last, the methodology was applied into practical experiment with the results showing that the method can locate the suit inland port location. © ASCE.

关键词： Location

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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

arXiv

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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

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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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Building an Aerial-Ground Robotics System for Precision Farming: An Adaptable Solution

arXiv

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arXiv 2019年

作者： Pretto, Alberto Aravecchia, Stéphanie Burgard, Wolfram Chebrolu, Nived Dornhege, Christian Falck, Tillmann Fleckenstein, Freya Fontenla, Alessandra Imperoli, Marco Khanna, Raghav Liebisch, Frank Lottes, Philipp Milioto, Andres Nardi, Daniele Nardi, Sandro Pfeifer, Johannes Popović, Marija Potena, Ciro Pradalier, Cédric Rothacker-Feder, Elisa Sa, Inkyu Schaefer, Alexander Siegwart, Roland Stachniss, Cyrill Walter, Achim Winterhalter, Wera Wu, Xiaolong Nieto, Juan Department of Computer Control and Management Engineering Sapienza University of Rome Rome Italy International Research Lab 2958 Georgia Tech-CNRS Metz France Department of Computer Science University of Freiburg Germany Toyota Research Institute Los Altos United States Photogrammetry & Robotics Lab University of Bonn Germany Robert Bosch GmbH Corporate Research Renningen Germany Osimo Italy Autonomous Systems Lab. Department of Mechanical and Process Engineering ETH Zurich Zürich Switzerland Department of Environmental Systems Science Institute of Agricultural Sciences ETH Zurich Zürich Switzerland IT+Robotics Srl Padova Italy Smart Robotics Lab. Department of Computing Imperial College London London United Kingdom CSIRO Data61 Australia

The application of autonomous robots in agriculture is gaining increasing popularity thanks to the high impact it may have on food security, sustainability, resource use efficiency, reduction of chemical treatments, and the optimization of human effort and yield. With this vision, the Flourish research project aimed to develop an adaptable robotic solution for precision farming that combines the aerial survey capabilities of small autonomous unmanned aerial vehicles (UAVs) with targeted intervention performed by multi-purpose unmanned ground vehicles (UGVs). This paper presents an overview of the scientific and technological advances and outcomes obtained in the project. We introduce multi-spectral perception algorithms and aerial and ground-based systems developed for monitoring crop density, weed pressure, crop nitrogen nutrition status, and to accurately classify and locate weeds. We then introduce the navigation and mapping systems tailored to our robots in the agricultural environment, as well as the modules for collaborative mapping. We finally present the ground intervention hardware, software solutions, and interfaces we implemented and tested in different field conditions and with different crops. We describe a real use case in which a UAV collaborates with a UGV to monitor the field and to perform selective spraying without human intervention. Copyright © 2019, The Authors. All rights reserved.

关键词： Mapping

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Understanding the artificial intelligence clinician and optimal treatment strategies for sepsis in intensive care

arXiv

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arXiv 2019年

作者： Komorowski, Matthieu Celi, Leo A. Badawi, Omar Gordon, Anthony C. Aldo Faisal, A. Department of Surgery and Cancer Imperial College London London United Kingdom Department of Bioengineering Imperial College London London United Kingdom Laboratory of Computational Physiology Harvard-MIT Division of Health Sciences & Technology CambridgeMA United States Beth Israel Deaconess Medical Center BostonMA United States Patient Care Analytic Philips Healthcare BaltimoreMD United States Department of Pharmacy Practice and Science University of Maryland School of Pharmacy BaltimoreMD United States Centre in AI for Healthcare Imperial College London London United Kingdom Department of Computing Imperial College London London United Kingdom MRC London Institute of Medical Sciences London United Kingdom Behaviour Analytics Lab Data Science Institute London United Kingdom

In this document, we explore in more detail our published work (Komorowski, Celi, Badawi, Gordon, & Faisal, 2018) for the benefit of the AI in Healthcare research community. In the above paper, we developed the AI Clinician system, which demonstrated how reinforcement learning could be used to make useful recommendations towards optimal treatment decisions from intensive care data. Since publication a number of authors have reviewed our work (e.g. Abbasi, 2018;Bos, Azoulay, & Martin-Loeches, 2019;Saria, 2018). Given the difference of our framework to previous work, the fact that we are bridging two very different academic communities (intensive care and machine learning) and that our work has impact on a number of other areas with more traditional computer-based approaches (biosignal processing and control, biomedical engineering), we are providing here additional details on our recent publication. We acknowledge the online comments by Jeter et al (https://***/abs/1902.03271). The sections of the present document are structured so as to address some of their questions. For clarity, we label figures from our main Nature Medicine publication as "M", figures from Jeter et al.'s arXiv paper as "J" and figures from our response here as "R". Copyright © 2019, The Authors. All rights reserved.

关键词： Publishing

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A fine-grained perspective on the robustness of global cargo ship transportation networks

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Journal of Geographical sciences 2018年第7期28卷 881-899页

作者：彭澎程诗奋陈金海廖梦迪吴琳刘希亮陆锋 State Key Laboratory of Resources and Environmental Information System Institute of Geographic Sciences and Natural Resources Research CAS University of Chinese Academy of Sciences Navigation Aids Technology Research Center of Jimei University National & Local Joint Engineering Research Center for Marine Navigation Aids Services College of Geomatics Shandong University of Science and Technology Institute of Computing Technology CAS Fujian Collaborative Innovation Center for Big Data Applications in Governments Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application

The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current research mainly focuses on the coarse-grained, holistic cargo ship transportation network while ignoring the structural diversity of different sub-networks. In this paper, we evaluate the robustness of the global cargo ship transportation network based on the most recent Automatic Identification System（AIS） data available. First, we subdivide three typical cargo ship transportation networks（i.e., oil tanker, container ship and bulk carrier） from the original cargo ship transportation network. Then, we design statistical indices based on complex network theory and employ four attack strategies, including random attack and three intentional attacks（i.e., degree-based attack, betweenness-based attack and flux-based attack） to evaluate the robustness of the three typical cargo ship transportation networks. Finally, we compare the integrity of the remaining ports of the network when a small proportion of ports lose their function. The results show that 1） compared with the holistic cargo ship transportation network, the fine-grain-based cargo ship transportation networks can fully reflect the pattern and process of global cargo transportation; 2） different cargo ship networks behave heterogeneously in terms of their robustness, with the container network being the weakest and the bulk carrier network being the strongest; and 3） small-scale intentional attacks may have significant influence on the integrity of the container network but a minor impact on the bulk carrier and oil tanker transportation networks. These conclusions can help improve the decision support capabilities in maritime transportation planning and emergency response and facilitate the establishment of a more reliable maritime transportation ***： The robustness of cargo ship transportation networks is essential to the stability of the world trade system. The current resear

关键词： complex network fine-grained cargo ship transportation network robustness automatic identification system

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First principles calculations of superconducting critical temperature of ThCr2Si2-type structure

arXiv

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arXiv 2019年

作者： Sinaga, Gewinner Senderanto Utimula, Keishu Nakano, Kousuke Hongo, Kenta Maezono, Ryo School of Materials Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science Tsukuba305-0047 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan PRESTO JST Kawaguchi Saitama332-0012 Japan School of Information Science JAIST Asahidai 1-1 Nomi Ishikawa923-1292 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan

High critical temperature (Tc) superconductor has a great potential in many industrial applications. However, discovering a compound having high Tc is still remaining a big challenge for experimental approach due to time-consuming and high cost. In this paper, we investigated the critical temperature (Tc) of several compounds of ThCr2Si2-type structure (space group I4/mmm) since some of them had already been investigated and trusted as the potential candidates for superconductivity. First principle calculation was performed to compute the critical temperature (Tc) based on allen-dynes equation modification of McMillian formula. In order to confirm our calculation scheme, we compared our result with compounds which had been experimentally determined obtained from NIMS database. The result showed a very good agreement with experimental data. Based on this scheme, finally, we found ThCu2Si2 and ThAu2Si2 which were most likely to exhibit a superconductivity around 3.88 K and 4.27. Since, these compounds have not been studied experimentally, then we futher investigated their electronic properties showing non-negative phonons which indicate the dynamical stability of the compounds at ambient condition. Band structure and DOS analysis showed metallic nature where the highest contributor come from d-orbital of rare-earth element and p-orbital of Si emerging a mixture of covalent, ionic and metallic bonds exists in both the compounds. Finally, we calculated the electron phonon coupling coefficient showing a quite large different (27%) leading to the increment of Tc, however, both of compounds possess three dimensional electron-like and hole-like multi-band fermi surface which is related to the phonon mediated which is still under electron-phonon mediated superconductivity. Copyright © 2019, The Authors. All rights reserved.

关键词： Temperature

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New Active Jupiter Family Comet 2008 QZ44: a Discovery with Citizen science

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Research Notes of the AAS 2023年第12期7卷 271-271页

作者： Colin Orion Chandler William J. Oldroyd Chadwick A. Trujillo Henry H. Hsieh Nima Sedaghat Jay K. Kueny William A. Burris Jarod A. DeSpain Kennedy A. Farrell Mark Jesus Mendoza Magbanua Scott S. Sheppard Michele T. Mazzucato Milton K. D. Bosch Tiffany Shaw-Diaz Virgilio Gonano Al Lamperti José A. da Silva Campos Brian L. Goodwin Ivan A. Terentev Charles J. A. Dukes Dept. of Astronomy & the DiRAC Institute University of Washington 3910 15th Ave NE Seattle WA 98195 USA coc123@uw.edu LSST Interdisciplinary Network for Collaboration and Computing 933 N. Cherry Avenue Tucson AZ 85721 USA Dept. of Astronomy & Planetary Science Northern Arizona University P.O. Box 6010 Flagstaff AZ 86011 USA Raw Data Speaks Initiative Planetary Science Institute 1700 East Fort Lowell Rd. Suite 106 Tucson AZ 85719 USA Institute of Astronomy and Astrophysics Academia Sinica P.O. Box 23-141 Taipei 10617 Taiwan University of Arizona Dept. of Astronomy and Steward Observatory 933 North Cherry Avenue Rm. N204 Tucson AZ 85721 USA Lowell Observatory 1400 W Mars Hill Rd Flagstaff AZ 86001 USA Wyant College of Optical Sciences University of Arizona 1630 E. University Blvd. Tucson AZ 85721 USA Dept. of Physics San Diego State University 5500 Campanile Drive San Diego CA 92182 USA Dept. of Laboratory Medicine University of California San Francisco 2340 Sutter Street San Francisco CA 94143 USA Dept. of Terrestrial Magnetism Carnegie Institution for Science 5241 Broad Branch Road. NW Washington DC 20015 USA Active Asteroids Citizen Scientist Royal Astronomical Society Burlington House Piccadilly London W1J 0BQ UK Physical Sciences Group Siena Academy of Sciences Piazzetta Silvio Gigli 2 I-53100 Siena Italy Delaware Valley Amateur Astronomers 112 Pebble Beach Drive Royersford PA 19468 USA Astronomical Society of Southern Africa P.O. Box 9 Observatory 7935 Cape Town South Africa

We report our discovery of cometary activity in the form of a diffuse tail associated with minor planet 2008 QZ44 during two previous orbits: 2008 and 2017. This finding was prompted in part by Active Asteroids, our Z...

We report our discovery of cometary activity in the form of a diffuse tail associated with minor planet 2008 QZ₄₄ during two previous orbits: 2008 and 2017. This finding was prompted in part by Active Asteroids, our Zooniverse-hosted NASA Partner Citizen science program. Participants flagged two UT 2017 July 12 Dark Energy Camera images of 2008 QZ₄₄ as active. Independently, our team identified activity in nine Canada-France-Hawaii Telescope MegaPrime images from UT 2008 November 20. During both apparitions 2008 QZ₄₄ was near its perihelion passage. 2008 QZ₄₄ has a Tisserand parameter with respect to Jupiter of 2.821, placing it in the Jupiter-family comet (JFC) class, and our dynamical integrations confirm this classification. JFCs contain primordial material that informs us about solar system evolution, and help us map the present-day volatile distribution. We note that 2008 QZ₄₄ has previously been classified as a quasi-Hilda comet candidate.

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Making the most of data: Quantum monte carlo post-analysis revisited

arXiv

引用

arXiv 2019年

作者： Ichibha, Tom Hongo, Kenta Maezono, Ryo Thom, Alex J.W. School of Information Science JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Research Center for Advanced Computing Infrastructure JAIST 1-1 Asahidai Nomi Ishikawa923-1292 Japan Center for Materials Research by Information Integration Research and Services Division of Materials Data and Integrated System National Institute for Materials Science 1-2-1 Sengen Tsukuba305-0047 Japan PRESTO Japan Science and Technology Agency 4-1-8 Honcho Kawaguchi-shi Saitama322-0012 Japan Computational Engineering Applications Unit RIKEN 2-1 Hirosawa Wako Saitama351-0198 Japan Department of Chemistry University of Cambridge Lensfield Road CambridgeCB2 1EW United Kingdom

In quantum Monte Carlo (QMC) methods, energy estimators are calculated as the statistical average of the Markov chain sampling of energy estimator along with an associated statistical error. This error estimation is not straightforward and there are several choices of the error estimation methods. We evaluate the performance of three methods, Straatsma, an autoregressive model, and a blocking analysis based on von Neumann's ratio test for randomness, for the energy time-series given by Diffusion Monte Carlo, Full Configuration Interaction Quantum Monte Carlo and Coupled Cluster Monte Carlo methods. From these analyses we describe a hybrid analysis method which provides reliable error estimates for series of all lengths. Equally important is the estimation of the appropriate start point of the equilibrated phase, and two heuristic schemes are tested, establishing that MSER (mean squared error rule) gives reasonable and constant estimations independent of the length of time-series. Copyright © 2019, The Authors. All rights reserved.

关键词： Time series

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