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Prior variances and depth un-biased estimators in EEG focal source imaging

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arXiv 2017年

作者： Koulouri, A. Rimpiläinen, V. Brookes, M. Kaipio, J.P. Institute for Computational and Applied Mathematics University of Münster Münster Germany Institute for Biomagnetism and Biosignalanalysis University of Münster Münster Germany Department of Electrical and Electronic Engineering Imperial College London London United Kingdom Department of Mathematics University of Auckland Auckland New Zealand University of Münster Einsteinstrasse 62 Münster Germany

In electroencephalography (EEG) source imaging, the inverse source estimates are depth biased in such a way that their maxima are often close to the sensors. This depth bias can be quantified by inspecting the statistics (mean and covariance) of these estimates. In this paper, we find weighting factors within a Bayesian framework for the used `1/`2 sparsity prior that the resulting maximum a posterior (MAP) estimates do not favour any particular source location. Due to the lack of an analytical expression for the MAP estimate when this sparsity prior is used, we solve the weights indirectly. First, we calculate the Gaussian prior variances that lead to depth un-biased maximum a posterior (MAP) estimates. Subsequently, we approximate the corresponding weight factors in the sparsity prior based on the solved Gaussian prior variances. Finally, we reconstruct focal source configurations using the sparsity prior with the proposed weights and two other commonly used choices of weights that can be found in literature. Copyright © 2017, The Authors. All rights reserved.

关键词： Electrophysiology

Recent developments in the PySCF program package

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arXiv 2020年

作者： Sun, Qiming Zhang, Xing Banerjee, Samragni Bao, Peng Barbry, Marc Blunt, Nick S. Bogdanov, Nikolay A. Booth, George H. Chen, Jia Cui, Zhi-Hao Eriksen, Janus Juul Gao, Yang Guo, Sheng Hermann, Jan Hermes, Matthew R. Koh, Kevin Koval, Peter Lehtola, Susi Li, Zhendong Liu, Junzi Mardirossian, Narbe McClain, James D. Motta, Mario Mussard, Bastien Pham, Hung Q. Pulkin, Artem Purwanto, Wirawan Robinson, Paul J. Ronca, Enrico Sayfutyarova, Elvira Scheurer, Maximilian Schurkus, Henry F. Smith, James E.T. Sun, Chong Sun, Shi-Ning Upadhyay, Shiv Wagner, Lucas K. Wang, Xiao White, Alec Whitfield, James Daniel Williamson, Mark J. Wouters, Sebastian Yang, Jun Yu, Jason M. Zhu, Tianyu Berkelbach, Timothy C. Sharma, Sandeep Sokolov, Alexander Yu. Chan, Garnet Kin-Lic AxiomQuant Investment Management LLC Shanghai200120 China Division of Chemistry and Chemical Engineering California Institute of Technology PasadenaCA91125 United States Department of Chemistry and Biochemistry The Ohio State University ColumbusOH43210 United States Beijing National Laboratory for Molecular Sciences State Key Laboratory for Structural Chemistry of Unstable and Stable Species Institute of Chemistry Chinese Academy of Sciences Beijing100190 China Simbeyond B.V. P.O. Box 513 EindhovenNL-5600 MB Netherlands Department of Chemistry Lensfield Road CambridgeCB2 1EW United Kingdom Max Planck Institute for Solid State Research Heisenbergstraße 1 Stuttgart70569 Germany Department of Physics King's College London Strand LondonWC2R 2LS United Kingdom Department of Physics University of Florida GainesvilleFL32611 United States Quantum Theory Project University of Florida GainesvilleFL32611 United States School of Chemistry University of Bristol Cantock's Close BristolBS8 1TS United Kingdom Division of Engineering and Applied Science California Institute of Technology PasadenaCA91125 United States Google Inc. Mountain ViewCA94043 United States FU Berlin Department of Mathematics and Computer Science Arnimallee 6 Berlin14195 Germany TU Berlin Machine Learning Group Marchstr. 23 Berlin10587 Germany Department of Chemistry Chemical Theory Center Supercomputing Institute University of Minnesota 207 Pleasant Street SE MinneapolisMN55455 United States Department of Chemistry and Biochemistry The University of Notre Dame du Lac 251 Nieuwland Science Hall Notre DameIN46556 United States Simune Atomistics S.L. Avenida Tolosa 76 Donostia-San Sebastian Spain HelsinkiFI-00014 Finland Key Laboratory of Theoretical and Computational Photochemistry Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Chemistry The Johns Hopkins University BaltimoreMD21218 United States AMGEN

PYSCF is a Python-based general-purpose electronic structure platform that both supports first-principles simulations of molecules and solids, as well as accelerates the development of new methodology and complex computational workflows. The present paper explains the design and philosophy behind PYSCF that enables it to meet these twin objectives. With several case studies, we show how users can easily implement their own methods using PYSCF as a development environment. We then summarize the capabilities of PYSCF for molecular and solid-state simulations. Finally, we describe the growing ecosystem of projects that use PYSCF across the domains of quantum chemistry, materials science, machine learning and quantum information science. Copyright © 2020, The Authors. All rights reserved.

关键词： Python

Chang’e 3 lunar mission and upper limit on stochastic background of gravitational wave around the 0.01 Hz band

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arXiv 2017年

作者： Tang, Wenlin Xu, Peng Hu, Songjie Cao, Jianfeng Dong, Peng Bu, Yanlong Chena, Lue Han, Songtao Gong, Xuefei Lie, Wenxiao Ping, Jinsong Lau, Yun-Kau Tang, Geshi Science and Technology on Aerospace Flight Dynamics Laboratory Beijing Aerospace Control Center Beijing China Institute of Applied Mathematics Morningside Center of Mathematics and LESC Institute of Computational Mathematics Academy of Mathematics and System Science Chinese Academy of Sciences Beijing China Department of Aerospace Guidance Navigation and Control School of Astronautics Beihang University Beijing China Beijing Institute of Aerospace Control Devices Beijing China National astronomical observatories Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China

The Doppler tracking data of the Chang’e 3 lunar mission is used to constrain the stochastic background of gravitational wave in cosmology within the 1 mHz to 0.05 Hz frequency band. Our result improves on the upper bound on the energy density of the stochastic background of gravitational wave in the 0.02 Hz to 0.05 Hz band obtained by the Apollo missions, with the improvement reaching almost one order of magnitude at around 0.05 Hz. Detailed noise analysis of the Doppler tracking data is also presented, with the prospect that these noise sources will be mitigated in future Chinese deep space missions. A feasibility study is also undertaken to understand the scientific capability of the Chang’e 4 mission, due to be launched in 2018, in relation to the stochastic gravitational wave background around 0.01 Hz. Copyright © 2017, The Authors. All rights reserved.

关键词： Stochastic systems

Alignments of parity even/odd-only multipoles in CMB

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arXiv 2017年

作者： Aluri, Pavan K. Ralston, John P. Weltman, Amanda Cosmology & Gravity Group Department of Mathematics and Applied Mathematics University of Cape Town Rondebosch7700 South Africa Department of Physics and Astronomy University of Kansas LawrenceKS66045 United States Institute for Advanced Study PrincetonNJ08540 United States Center for Computational Astrophysics Flatiron Institute 162 Fifth Avenue New YorkNY United States

We compare the statistics of parity even and odd multipoles of the cosmic microwave background (CMB) sky from PLANCK full mission temperature measurements. An excess power in odd multipoles compared to even multipoles has previously been found on large angular scales. Motivated by this apparent parity asymmetry, we evaluate directional statistics associated with even compared to odd multipoles, along with their significances. Primary tools are the Power Tensor and Alignment Tensor *** limit our analysis to the first sixty multipoles i.e., l = [2;61]. We find no evidence for statistically unusual alignments of even parity multipoles. More than one independent statistic finds evidence for alignments of anisotropy axes of odd multipoles, with a significance equivalent to 2 or more. The robustness of alignment axes is tested by making galactic cuts and varying the multipole range. Very interestingly, the region spanned by the (a)symmetry axes is found to broadly contain other parity (a)symmetry axes previously observed in the literature. Copyright © 2017, The Authors. All rights reserved.

关键词： Alignment

Single-electron capture in 3-keV/u Ar8+-He collisions

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Physical Review A 2017年第4期95卷 042702-042702页

作者： R. T. Zhang X. L. Zhu X. Y. Li L. Liu S. F. Zhang W. T. Feng D. L. Guo Y. Gao D. M. Zhao J. G. Wang X. Ma Institute of Modern Physics Chinese Academy of Sciences Lanzhou 730000 China Data Center for High Energy Density Physics Institute of Applied Physics and Computational Mathematics Beijing 100088 China Hefei National Laboratory for Physical Sciences at Microscale and Department of Modern Physics University of Science and Technology of China Heifei 230026 China

The electron transfer mechanism was investigated for the single-electron-capture process in 3-keV/u Ar8+-He collisions with cold target recoil ion momentum spectroscopy. The different state-selective electron capture processes were identified for one 1s electron of He capture into 4s, 4p, 4d−4f, and 5s−5p states of Ar8+. We observed that 1s to 4s capture mainly happens at small transverse recoil ion momentum, while 1s to 4p capture mainly happens at large transverse recoil ion momentum. By comparing the measured projectile scattering angle distributions to the theoretical calculations, it is suggested that 1s to 4s and 1s to 4p state-selective capture processes are mainly caused by radial and rotational coupling effects, respectively, and the 4p±1 quantum states are preferred.

关键词： Scattering of atoms, molecules, clusters & ions

On the strong convergence of forward-backward splitting in reconstructing jointly sparse signals

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arXiv 2017年

作者： Dexter, Nick Tran, Hoang Webster, Clayton G. Department of Mathematics Simon Fraser University BurnabyBCV5A 1S6 Canada Department of Computational and Applied Mathematics Oak Ridge National Laboratory Oak RidgeTN37831-6164 United States Oden Institute for Computational Engineering & Sciences The University of Texas at Austin AustinTX78712 United States Behavioral Reinforcement Learning Lab Lirio LLC. KnoxvilleTN37923 United States

We consider the problem of reconstructing an infinite set of sparse, finite-dimensional vectors, that share a common sparsity pattern, from incomplete measurements. This is in contrast to the work [17], where the single vector signal can be infinite-dimensional, and [28], which extends the aforementioned work to the joint sparse recovery of finite number of infinite-dimensional vectors. In our case, to take account of the joint sparsity and promote the coupling of nonvanishing components, we employ a convex relaxation approach with mixed norm penalty 2,1. This paper discusses the computation of the solutions of linear inverse problems with such relaxation by a forward-backward splitting algorithm. However, since the solution matrix possesses infinitely many columns, the arguments of [17] no longer apply. As such, we establish new strong convergence results for the algorithm, in particular when the set of jointly sparse vectors is infinite. Copyright © 2017, The Authors. All rights reserved.

关键词： Relaxation processes

LifeTime and improving European healthcare through cell-based interceptive medicine (vol 587, pg 377, 2020)

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NATURE 2021年第7852期592卷 E8-E8页

作者： Rajewsky, Nikolaus Almouzni, Genevieve Gorski, Stanislaw A. Aerts, Stein Amit, Ido Bertero, Michela G. Bock, Christoph Bredenoord, Annelien L. Cavalli, Giacomo Chiocca, Susanna Clevers, Hans De Strooper, Bart Eggert, Angelika Ellenberg, Jan Fernandez, Xose M. Figlerowicz, Marek Gasser, Susan M. Hubner, Norbert Kjems, Jorgen Knoblich, Jurgen A. Krabbe, Grietje Lichter, Peter Linnarsson, Sten Marine, Jean-Christophe Marioni, John C. Marti-Renom, Marc A. Netea, Mihai G. Nickel, Dorthe Nollmann, Marcelo Novak, Halina R. Parkinson, Helen Piccolo, Stefano Pinheiro, Ines Pombo, Ana Popp, Christian Reik, Wolf Roman-Roman, Sergio Rosenstiel, Philip Schultze, Joachim L. Stegle, Oliver Tanay, Amos Testa, Giuseppe Thanos, Dimitris Theis, Fabian J. Torres-Padilla, Maria-Elena Valencia, Alfonso Vallot, Celine van Oudenaarden, Alexander Vidal, Marie Voet, Thierry Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association Berlin Germany Charité-Universitätsmedizin Berlin Germany Berlin Institute of Health (BIH) Berlin Germany German Center for Cardiovascular Research (DZHK) Partner Site Berlin Berlin Germany Institute for Biology Humboldt University of Berlin Berlin Germany Cardiovascular and Metabolic Sciences Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Department of Pediatric Oncology/Hematology Charité-Universitätsmedizin Berlin Berlin Germany Institut Curie CNRS PSL Research University Sorbonne Université Nuclear Dynamics Unit Equipe Labellisée Ligue contre le cancer Paris France VIB Center for Brain and Disease Research Leuven Belgium Department of Human Genetics KU Leuven Leuven Belgium Department of Neurosciences KU Leuven Leuven Belgium UK Dementia Research Institute at UCL University College London London UK Wellcome Sanger Institute Wellcome Genome Campus Cambridge UK Department of Immunology Weizmann Institute of Science Rehovot Israel Centre for Genomic Regulation (CRG) Barcelona Institute of Science and Technology Barcelona Spain CNAG-CRG Centre for Genomic Regulation Barcelona Institute of Science and Technology Barcelona Spain Universitat Pompeu Fabra Barcelona Spain ICREA Barcelona Spain CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Department of Laboratory Medicine Medical University of Vienna Vienna Austria Ludwig Boltzmann Institute for Rare and Undiagnosed Diseases Vienna Austria Department of Medical Humanities Julius Center for Health Sciences and Primary Care University Medical Center Utrecht Utrecht The Netherlands Institute of Human Genetics UMR 9002 CNRS and University of Montpellier Montpellier France Department of Experimental Oncology IEO European Institute of Oncology IRCCS Milan Italy Department of Oncology and Hemato

Data-driven prediction and origin identification of epidemics in population networks

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arXiv 2017年

作者： Chen, Zhizhong Larson, Karen Bowman, Clark Hadjidoukas, Panagiotis Papadimitriou, Costas Koumoutsakos, Petros Matzavinos, Anastasios Division of Applied Mathematics Brown University ProvidenceRI02912 United States Computational Science and Engineering Laboratory ETH Zürich CH-8092 Switzerland Department of Mechanical Engineering University of Thessaly VolosGR-38334 Greece

While there exist a number of mathematical approaches to modeling the spread of disease on a network, analyzing such systems in the presence of uncertainty introduces significant complexity. In scenarios where system parameters must be inferred from limited observations, general approaches to uncertainty quantification can generate approximate distributions of the unknown parameters, but these methods often become computationally expensive if the underlying disease model is complex. In this paper, we apply the recent massively parallelizable Bayesian uncertainty quantification framework Π4U to a model of a disease spreading on a network of communities, showing that the method can accurately and tractably recover system parameters and select optimal models in this *** Codes 62P10, 92-08 Copyright © 2017, The Authors. All rights reserved.

关键词： Uncertainty analysis

Enhanced Deuteron Coalescence Probability in Jets

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Physical Review Letters 2023年第4期131卷 042301-042301页

作者： S. Acharya D. Adamová A. Adler G. Aglieri Rinella M. Agnello N. Agrawal Z. Ahammed S. Ahmad S. U. Ahn I. Ahuja A. Akindinov M. Al-Turany D. Aleksandrov B. Alessandro H. M. Alfanda R. Alfaro Molina B. Ali A. Alici N. Alizadehvandchali A. Alkin J. Alme G. Alocco T. Alt I. Altsybeev M. N. Anaam C. Andrei A. Andronic V. Anguelov F. Antinori P. Antonioli N. Apadula L. Aphecetche H. Appelshäuser C. Arata S. Arcelli M. Aresti R. Arnaldi J. G. M. C. A. Arneiro I. C. Arsene M. Arslandok A. Augustinus R. Averbeck M. D. Azmi A. Badalà J. Bae Y. W. Baek X. Bai R. Bailhache Y. Bailung A. Balbino A. Baldisseri B. Balis D. Banerjee Z. Banoo R. Barbera F. Barile L. Barioglio M. Barlou G. G. Barnaföldi L. S. Barnby V. Barret L. Barreto C. Bartels K. Barth E. Bartsch N. Bastid S. Basu G. Batigne D. Battistini B. Batyunya D. Bauri J. L. Bazo Alba I. G. Bearden C. Beattie P. Becht D. Behera I. Belikov A. D. C. Bell Hechavarria F. Bellini R. Bellwied S. Belokurova V. Belyaev G. Bencedi S. Beole A. Bercuci Y. Berdnikov A. Berdnikova L. Bergmann M. G. Besoiu L. Betev P. P. Bhaduri A. Bhasin M. A. Bhat B. Bhattacharjee L. Bianchi N. Bianchi J. Bielčík J. Bielčíková J. Biernat A. P. Bigot A. Bilandzic G. Biro S. Biswas N. Bize J. T. Blair D. Blau M. B. Blidaru N. Bluhme C. Blume G. Boca F. Bock T. Bodova A. Bogdanov S. Boi J. Bok L. Boldizsár A. Bolozdynya M. Bombara P. M. Bond G. Bonomi H. Borel A. Borissov A. G. Borquez Carcamo H. Bossi E. Botta Y. E. M. Bouziani L. Bratrud P. Braun-Munzinger M. Bregant M. Broz G. E. Bruno M. D. Buckland D. Budnikov H. Buesching S. Bufalino O. Bugnon P. Buhler Z. Buthelezi S. A. Bysiak M. Cai H. Caines A. Caliva E. Calvo Villar J. M. M. Camacho P. Camerini F. D. M. Canedo M. Carabas A. A. Carballo F. Carnesecchi R. Caron L. A. D. Carvalho J. Castillo Castellanos F. Catalano C. Ceballos Sanchez I. Chakaberia P. Chakraborty S. Chandra S. Chapeland M. Chartier S. Chattopadhyay T. G. Chavez T. Cheng C. Cheshkov B. Cheynis V. Chibante Barroso D. D. Chinellato E. S. Chizzali J. Cho S. Cho P. Chochula P. Chri Université Clermont Auvergne CNRS/IN2P3 LPC Clermont-Ferrand France Nuclear Physics Institute of the Czech Academy of Sciences Husinec-Řež Czech Republic Johann-Wolfgang-Goethe Universität Frankfurt Institut für Informatik Fachbereich Informatik und Mathematik Frankfurt Germany European Organization for Nuclear Research (CERN) Geneva Switzerland Dipartimento DISAT del Politecnico and Sezione INFN Turin Italy INFN Sezione di Bologna Bologna Italy Variable Energy Cyclotron Centre Homi Bhabha National Institute Kolkata India Department of Physics Aligarh Muslim University Aligarh India Korea Institute of Science and Technology Information Daejeon Republic of Korea Faculty of Science P.J. Šafárik University Košice Slovak Republic Affiliated with an institute covered by a cooperation agreement with CERN Research Division and ExtreMe Matter Institute EMMI GSI Helmholtzzentrum für Schwerionenforschung GmbH Darmstadt Germany INFN Sezione di Torino Turin Italy Central China Normal University Wuhan China Instituto de Física Universidad Nacional Autónoma de México Mexico City Mexico Dipartimento di Fisica e Astronomia dell’Università and Sezione INFN Bologna Italy University of Houston Houston Texas United States Department of Physics and Technology University of Bergen Bergen Norway INFN Sezione di Cagliari Cagliari Italy Institut für Kernphysik Johann Wolfgang Goethe-Universität Frankfurt Frankfurt Germany Horia Hulubei National Institute of Physics and Nuclear Engineering Bucharest Romania Westfälische Wilhelms-Universität Münster Institut für Kernphysik Munster Germany Physikalisches Institut Ruprecht-Karls-Universität Heidelberg Heidelberg Germany INFN Sezione di Padova Padova Italy Lawrence Berkeley National Laboratory Berkeley California United States SUBATECH IMT Atlantique Nantes Université CNRS-IN2P3 Nantes France Laboratoire de Physique Subatomique et de Cosmologie Université Grenoble-Alpes CNRS-IN2P3 Grenoble France Universidade de São Pa

The transverse-momentum (pT) spectra and coalescence parameters B2 of (anti)deuterons are measured in p-p collisions at s=13 TeV for the first time in and out of jets. In this measurement, the direction of the leading particle with the highest pT in the event (pTlead>5 GeV/c) is used as an approximation for the jet axis. The event is consequently divided into three azimuthal regions, and the jet signal is obtained as the difference between the toward region, that contains jet fragmentation products in addition to the underlying event (UE), and the transverse region, which is dominated by the UE. The coalescence parameter in the jet is found to be approximately a factor of 10 larger than that in the underlying event. This experimental observation is consistent with the coalescence picture and can be attributed to the smaller average phase-space distance between nucleons in the jet cone as compared with the underlying event. The results presented in this Letter are compared to predictions from a simple nucleon coalescence model, where the phase-space distributions of nucleons are generated using pythia8 with the Monash 2013 tuning, and to predictions from a deuteron production model based on ordinary nuclear reactions with parametrized energy-dependent cross sections tuned on data. The latter model is implemented in pythia8.3. Both models reproduce the observed large difference between in-jet and out-of-jet coalescence parameters, although the almost flat trend of the B2Jet is not reproduced by the models, which instead give a decreasing trend.

关键词： Hadron-hadron interactions