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Averages of unlabeled networks: Geometric characterization and asymptotic behavior

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arXiv 2017年

作者： Kolaczyk, Eric D. Lin, Lizhen Rosenberg, Steven Walters, Jackson Xu, Jie Department of Mathematics and Statistics Boston University United States Department of Applied and Computational Mathematics and Statistics University of Notre Dame United States

It is becoming increasingly common to see large collections of network data objects - that is, data sets in which a network is viewed as a fundamental unit of observation. As a result, there is a pressing need to develop network-based analogues of even many of the most basic tools already standard for scalar and vector data. In this paper, our focus is on averages of unlabeled, undirected networks with edge weights. Specifically, we (i) characterize a certain notion of the space of all such networks, (ii) describe key topological and geometric properties of this space relevant to doing probability and statistics thereupon, and (iii) use these properties to establish the asymptotic behavior of a generalized notion of an empirical mean under sampling from a distribution supported on this space. Our results rely on a combination of tools from geometry, probability theory, and statistical shape analysis. In particular, the lack of vertex labeling necessitates working with a quotient space modding out permutations of labels. This results in a nontrivial geometry for the space of unlabeled networks, which in turn is found to have important implications on the types of probabilistic and statistical results that may be obtained and the techniques needed to obtain them.62E20 (Primary) 53C20 (Secondary) Copyright © 2017, The Authors. All rights reserved.

关键词： Asymptotic analysis

First Very Long Baseline Interferometry Detections at 870 µm

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arXiv 2024年

作者： Raymond, Alexander W. Doeleman, Sheperd S. Asada, Keiichi Blackburn, Lindy Bower, Geoffrey C. Bremer, Michael Broguiere, Dominique Chen, Ming-Tang Crew, Geoffrey B. Dornbusch, Sven Fish, Vincent L. García, Roberto Gentaz, Olivier Goddi, Ciriaco Han, Chih-Chiang Hecht, Michael H. Huang, Yau-De Janssen, Michael Keating, Garrett K. Koay, Jun Y.I. Krichbaum, Thomas P. Lo, Wen-Ping Matsushita, Satoki Matthews, Lynn D. Moran, James M. Norton, Timothy J. Patel, Nimesh Pesce, Dominic W. Ramakrishnan, Venkatessh Rottmann, Helge Roy, Alan L. Sánchez, Salvador Tilanus, Remo P.J. Titus, Michael Torne, Pablo Wagner, Jan Weintroub, Jonathan Wielgus, Maciek Young, André Akiyama, Kazunori Albentosa-Ruíz, Ezequiel Alberdi, Antxon Alef, Walter Algaba, Juan Carlos Anantua, Richard Azulay, Rebecca Bach, Uwe Baczko, Anne-Kathrin Ball, David Baloković, Mislav Bandyopadhyay, Bidisha Barrett, John Bauböck, Michi Benson, Bradford A. Bintley, Dan Blundell, Raymond Bouman, Katherine L. Boyce, Hope Brissenden, Roger Britzen, Silke Broderick, Avery E. Bronzwaer, Thomas Bustamante, Sandra Carlstrom, John E. Chael, Andrew Chan, Chi-Kwan Chang, Dominic O. Chatterjee, Koushik Chatterjee, Shami Chen, Yongjun Cheng, Xiaopeng Cho, Ilje Christian, Pierre Conroy, Nicholas S. Conway, John E. Crawford, Thomas M. Cruz-Osorio, Alejandro Cui, Yuzhu Dahale, Rohan Davelaar, Jordy de Laurentis, Mariafelicia Deane, Roger Dempsey, Jessica Desvignes, Gregory Dexter, Jason Dhruv, Vedant Dihingia, Indu K. Dzib, Sergio A. Eatough, Ralph P. Emami, Razieh Falcke, Heino Farah, Joseph Fomalont, Edward Fontana, Anne-Laure Ford, H. Alyson Foschi, Marianna Fraga-Encinas, Raquel Freeman, William T. Friberg, Per Fromm, Christian M. Fuentes, Antonio Galison, Peter Gammie, Charles F. Georgiev, Boris Gold, Roman Gómez-Ruiz, Arturo I. Gómez, José L. Gu, Minfeng Gurwell, Mark Hada, Kazuhiro Haggard, Daryl Hesper, Ronald Heumann, Dirk Ho, Luis C. Ho, Paul Honma, Mareki Huang, Chih-Wei L. Huang, Lei Hughes, David H. Ikeda, Shiro Impellizzeri, C.M. Violette Inoue, Makoto Issaoun, Black Hole Initiative at Harvard University 20 Garden Street CambridgeMA02138 United States Center for Astrophysics Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Institute of Astronomy and Astrophysics Academia Sinica 11F of Astronomy-Mathematics Building AS/NTU No. 1 Sec. 4 Roosevelt Rd. Taipei106216 Taiwan Institute of Astronomy and Astrophysics Academia Sinica 645 N. A’ohoku Place HiloHI96720 United States Department of Physics and Astronomy University of Hawaii at Manoa 2505 Correa Road HonoluluHI96822 United States Institut de Radioastronomie Millimétrique 300 rue de la Piscine Saint Martin d’HèresF-38406 France Massachusetts Institute of Technology Haystack Observatory 99 Millstone Road WestfordMA01886 United States Max-Planck-Institut für Radioastronomie Auf dem Hügel 69 BonnD-53121 Germany Dipartimento di Fisica Università degli Studi di Cagliari SP Monserrato-Sestu km 0.7 MonserratoI-09042 Italy INAF - Osservatorio Astronomico di Cagliari Via della Scienza 5 CA Selargius09047 Italy Radboud University P.O. Box 9010 Nijmegen6500 GL Netherlands Department of Physics National Taiwan University No.1 Sect.4 Roosevelt Rd. Taipei106216 Taiwan Astronomy Department Universidad de Concepción Casilla 160-C Concepción Chile Finnish Centre for Astronomy with ESO University of Turku FI-20014 Finland Aalto University Metsähovi Radio Observatory Metsähovintie 114 KylmäläFI-02540 Finland Institut de Radioastronomie Millimétrique Avenida Divina Pastora 7 Local 20 GranadaE-18012 Spain Steward Observatory Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States National Astronomical Observatory of Japan 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Departament d’Astronomia i Astrofísica Universitat de València C. Dr. Moliner 50 Burjassot ValènciaE-46100 Spain Instituto de Astrofísica de Andalucía-CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Department of Phy

The first very long baseline interferometry (VLBI) detections at 870 µm wavelength (345 GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11 Gλ corresponding to an angular resolution, or fringe spacing, of 19 µas. The Allan deviation of the visibility phase at 870 µm is comparable to that at 1.3 mm on the relevant integration time scales between 2 and 100 s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870 µm. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time. © 2024, CC BY.

关键词： Black holes

Attosecond Time Delay of Retrapped Resonant Ionization

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Physical Review Letters 2018年第10期121卷 103201-103201页

作者： Xiaohong Song Guangluo Shi Guojun Zhang Jingwen Xu Cheng Lin Jing Chen Weifeng Yang Department of Physics College of Science Shantou University Shantou Guangdong 515063 China Key Laboratory of High Energy Density Physics Simulation Center for Applied Physics and Technology Peking University Beijing 100084 China Institute of Applied Physics and Computational Mathematics P.O. Box 8009 Beijing 100088 China Collaborative Innovation Center of Inertial Fusion Sciences and Applications Shanghai Jiao Tong University Shanghai 200240 China

A recent ultrafast pump-probe technique has allowed measurement of time delays during photoemission in a variety of systems ranging from atoms and molecules to solids with unprecedented temporal resolution. However, identifying the underlying physics is still a challenge especially in complicated multichannel above-threshold ionization (ATI) experiments. Here we demonstrate that the time delays of different ionization pathways in ATI can be clearly resolved and extracted with a semiclassical statistical method. The remarkable phase shift of near threshold photoelectrons can be attributed to a temporary retrapping of a photoelectron by the atomic potential in a quasibound state after emerging in the continuum state. This continuum-bound-continuum scattering manifests as a new resonant effect in strong-field photoemission. Our results unify the seemingly opposing quantum Eisenbud-Wigner-Smith time delay and classical Coulomb-induced time delay by highlighting the same physical picture, which holds promise for an intuitive interpretation of time-resolved fundamental electronic processes in strong-field experiments and epistemological reexamination of the quantum-classical correspondence.

关键词： Electronic excitation & ionization Multiphoton or tunneling ionization & excitation Photoemission

Reinforced dynamics for enhanced sampling in large atomic and molecular systems

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arXiv 2017年

作者： Zhang, Linfeng Wang, Han Weinan, E. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Institute of Applied Physics and Computational Mathematics Fenghao East Road 2 Beijing100094 China CAEP Software Center for High Performance Numerical Simulation Huayuan Road 6 Beijing100088 China Department of Mathematics and Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Beijing Institute of Big Data Research Beijing100871 China

A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. The method is illustrated by studying the full-atom, explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables. Copyright © 2017, The Authors. All rights reserved.

关键词： Wave functions

Stock prices forecasting based on wavelet neural networks with PSO 5

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Stock prices forecasting based on wavelet neural networks wi...

5th International Multi-Conference on Engineering and Technology Innovation, IMETI 2016

作者： Wang, Kai-Cheng Yang, Chi-I Chang, Kuei-Fang Ph.D. Program in Mechanical and Aeronautical Engineering Feng-Chia University Taichung40724 Taiwan Department of Applied Mathematics Feng-Chia University Taichung40724 Taiwan

This research examines the forecasting performance of wavelet neural network (WNN) model using published stock data obtained from Financial Times Stock Exchange (FTSE) Taiwan Stock Exchange (TWSE) 50 index, also known as Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX), hereinafter referred to as Taiwan 50. Our WNN model uses particle swarm optimization (PSO) to choose the appropriate initial network values for different companies. The findings come with two advantages. First, the network initial values are automatically selected instead of being a constant. Second, threshold and training data percentage become constant values, because PSO assists with self-adjustment. We can achieve a success rate over 73% without the necessity to manually adjust parameter or create another math model. © The Authors, published by EDP Sciences, 2017.

关键词： Particle swarm optimization (PSO)

Joint analysis of Dark Energy Survey Year 3 data and CMB lensing from SPT and Planck. I. Construction of CMB lensing maps and modeling choices

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Physical Review D 2023年第2期107卷 023529-023529页

作者： Y. Omori E. J. Baxter C. Chang O. Friedrich A. Alarcon O. Alves A. Amon F. Andrade-Oliveira K. Bechtol M. R. Becker G. M. Bernstein J. Blazek L. E. Bleem H. Camacho A. Campos A. Carnero Rosell M. Carrasco Kind R. Cawthon R. Chen A. Choi J. Cordero T. M. Crawford M. Crocce C. Davis J. DeRose S. Dodelson C. Doux A. Drlica-Wagner K. Eckert T. F. Eifler F. Elsner J. Elvin-Poole S. Everett X. Fang A. Ferté P. Fosalba M. Gatti G. Giannini D. Gruen R. A. Gruendl I. Harrison K. Herner H. Huang E. M. Huff D. Huterer M. Jarvis E. Krause N. Kuropatkin P.-F. Leget P. Lemos A. R. Liddle N. MacCrann J. McCullough J. Muir J. Myles A. Navarro-Alsina S. Pandey Y. Park A. Porredon J. Prat M. Raveri R. P. Rollins A. Roodman R. Rosenfeld A. J. Ross E. S. Rykoff C. Sánchez J. Sanchez L. F. Secco I. Sevilla-Noarbe E. Sheldon T. Shin M. A. Troxel I. Tutusaus T. N. Varga N. Weaverdyck R. H. Wechsler W. L. K. Wu B. Yanny B. Yin Y. Zhang J. Zuntz T. M. C. Abbott M. Aguena S. Allam J. Annis D. Bacon B. A. Benson E. Bertin S. Bocquet D. Brooks D. L. Burke J. E. Carlstrom J. Carretero C. L. Chang R. Chown M. Costanzi L. N. da Costa A. T. Crites M. E. S. Pereira T. de Haan J. De Vicente S. Desai H. T. Diehl M. A. Dobbs P. Doel W. Everett I. Ferrero B. Flaugher D. Friedel J. Frieman J. García-Bellido E. Gaztanaga E. M. George T. Giannantonio N. W. Halverson S. R. Hinton G. P. Holder D. L. Hollowood W. L. Holzapfel K. Honscheid J. D. Hrubes D. J. James L. Knox K. Kuehn O. Lahav A. T. Lee M. Lima D. Luong-Van M. March J. J. McMahon P. Melchior F. Menanteau S. S. Meyer R. Miquel L. Mocanu J. J. Mohr R. Morgan T. Natoli S. Padin A. Palmese F. Paz-Chinchón A. Pieres A. A. Plazas Malagón C. Pryke C. L. Reichardt A. K. Romer J. E. Ruhl E. Sanchez K. K. Schaffer M. Schubnell S. Serrano E. Shirokoff M. Smith Z. Staniszewski A. A. Stark E. Suchyta G. Tarle D. Thomas C. To J. D. Vieira J. Weller R. Williamson Department of Astronomy and Astrophysics University of Chicago Chicago Illinois 60637 USA Kavli Institute for Cosmological Physics University of Chicago Chicago Illinois 60637 USA Department of Physics Stanford University 382 Via Pueblo Mall Stanford California 94305 USA Kavli Institute for Particle Astrophysics and Cosmology P. O. Box 2450 Stanford University Stanford California 94305 USA Institute for Astronomy University of Hawai’i 2680 Woodlawn Drive Honolulu Hawaii 96822 USA Kavli Institute for Cosmology University of Cambridge Madingley Road Cambridge CB3 0HA United Kingdom Argonne National Laboratory 9700 South Cass Avenue Lemont Illinois 60439 USA Department of Physics University of Michigan Ann Arbor Michigan 48109 USA Laboratório Interinstitucional de e-Astronomia - LIneA Rua Gal. José Cristino 77 Rio de Janeiro RJ 20921-400 Brazil Physics Department 2320 Chamberlin Hall University of Wisconsin-Madison 1150 University Avenue Madison Wisconsin 53706-1390 USA Department of Physics and Astronomy University of Pennsylvania Philadelphia Pennsylvania 19104 USA Department of Physics Northeastern University Boston Massachusetts 02115 USA Laboratory of Astrophysics École Polytechnique Fédérale de Lausanne (EPFL) Observatoire de Sauverny 1290 Versoix Switzerland High-Energy Physics Division Argonne National Laboratory 9700 South Cass Avenue. Argonne Illinois 60439 USA Instituto de Física Teórica Universidade Estadual Paulista São Paulo 01140-070 Brazil Department of Physics Carnegie Mellon University Pittsburgh Pennsylvania 15312 USA Instituto de Astrofisica de Canarias E-38205 La Laguna Tenerife Spain Universidad de La Laguna Departamento Astrofísica E-38206 La Laguna Tenerife Spain Center for Astrophysical Surveys National Center for Supercomputing Applications 1205 West Clark Street Urbana Illinois 61801 USA Department of Astronomy University of Illinois at Urbana-Champaign 1002 W. Green Street Urbana Illinois 61801 USA Physic

Joint analyses of cross-correlations between measurements of galaxy positions, galaxy lensing, and lensing of the cosmic microwave background (CMB) offer powerful constraints on the large-scale structure of the Universe. In a forthcoming analysis, we will present cosmological constraints from the analysis of such cross-correlations measured using Year 3 data from the Dark Energy Survey (DES), and CMB data from the South Pole Telescope (SPT) and Planck. Here we present two key ingredients of this analysis: (1) an improved CMB lensing map in the SPT-SZ survey footprint and (2) the analysis methodology that will be used to extract cosmological information from the cross-correlation measurements. Relative to previous lensing maps made from the same CMB observations, we have implemented techniques to remove contamination from the thermal Sunyaev Zel’dovich effect, enabling the extraction of cosmological information from smaller angular scales of the cross-correlation measurements than in previous analyses with DES Year 1 data. We describe our model for the cross-correlations between these maps and DES data, and validate our modeling choices to demonstrate the robustness of our analysis. We then forecast the expected cosmological constraints from the galaxy survey-CMB lensing auto and cross-correlations. We find that the galaxy-CMB lensing and galaxy shear-CMB lensing correlations will on their own provide a constraint on S8=σ8Ωm/0.3 at the few percent level, providing a powerful consistency check for the DES-only constraints. We explore scenarios where external priors on shear calibration are removed, finding that the joint analysis of CMB lensing cross-correlations can provide constraints on the shear calibration amplitude at the 5% to 10% level.

关键词： Cosmic microwave background Cosmological parameters Dark matter Evolution of the Universe Gravitational lenses Large scale structure of the Universe

Three dimensions, two microscopes, one code: Automatic differentiation for X-ray nanotomography beyond the depth of focus limit

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arXiv 2019年

作者： Du, Ming Nashed, Youssef S. G. Kandel, Saugat Gursoy, Doga Jacobsen, Chris Department of Materials Science Northwestern University EvanstonIL60208 United States Mathematics and Computer Science Division Argonne National Laboratory LemontIL60439 United States Applied Physics Program Northwestern University EvanstonIL60208 United States Advanced Photon Source Argonne National Laboratory ArgonneIL60439 United States Department of Physics and Astronomy Northwestern University EvanstonIL60208 United States Chemistry of Life Processes Institute Northwestern University EvanstonIL60208 United States

Conventional tomographic reconstruction algorithms assume that one has obtained pure projection images, involving no within-specimen diffraction effects nor multiple scattering. Advances in X-ray nanotomography are leading towards the violation of these assumptions, by combining the high penetration power of X-rays which enables thick specimens to be imaged, with improved spatial resolution which decreases the depth of focus of the imaging system. We describe a reconstruction method where multiple scattering and diffraction effects in thick samples are modeled by multislice propagation, and the 3D object function is retrieved through iterative optimization. We show that the same proposed method works for both full-field microscopy, and for coherent scanning techniques like ptychography. Our implementation utilizes the optimization toolbox and the automatic differentiation capability of the open-source deep learning package TensorFlow, which demonstrates a much straightforward way to solve optimization problems in computational imaging, and endows our program great flexibility and portability. Copyright © 2019, The Authors. All rights reserved.

关键词： computational Imaging

Structure learning from time series with false discovery control

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arXiv 2018年

作者： Pegueroles, Bernat Guillen Vinzamuri, Bhanukiran Shanmugam, Karthikeyan Hedden, Steve Moyer, Jonathan D. Varshney, Kush R. Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States IBM Research Yorktown HeightsNY10598 United States Frederick S. Pardee Center for International Futures Josef Korbel School of International Studies University of Denver DenverCO80210 United States

We consider the Granger causal structure learning problem from time series data. Granger causal algorithms predict a 'Granger causal effect' between two variables by testing if prediction error of one decreases significantly in the absence of the other variable among the predictor covariates. Almost all existing Granger causal algorithms condition on a large number of variables (all but two variables) to test for effects between a pair of variables. We propose a new structure learning algorithm called MMPC-p inspired by the well known MMHC algorithm for non-time series data. We show that under some assumptions, the algorithm provides false discovery rate control. The algorithm is sound and complete when given access to perfect directed information testing oracles. We also outline a novel tester for the linear Gaussian case. We show through our extensive experiments that the MMPC-p algorithm scales to larger problems and has improved statistical power compared to existing state of the art for large sparse graphs. We also apply our algorithm on a global development dataset and validate our findings with subject matter experts. Copyright © 2018, The Authors. All rights reserved.

关键词： Time series

Colossal enhancement of spin transmission through magnon confinement in an antiferromagnet

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arXiv 2025年

作者： Husain, Sajid Ramesh, Maya Li, Xinyan Prokhorenko, Sergei Ojha, Shashank Kumar Ross, Aiden Das, Koushik Zhao, Boyang Park, Hyeon Woo Meisenheimer, Peter Nahas, Yousra Caretta, Lucas Martin, Lane W. Kim, Se Kwon Yao, Zhi Wen, Haidan Salahuddin, Sayeef Chen, Long-Qing Han, Yimo de Sousa, Rogério Bellaiche, Laurent Bibes, Manuel Schlom, Darrell G. Ramesh, Ramamoorthy Department of Materials Science and Engineering University of California BerkeleyCA94720 United States Rice University Materials Science and NanoEngineering HoustonTX United States Department of Materials Science and Engineering Cornell University IthacaNY14853 United States Rice Advanced Materials Institute Rice University HoustonTX77005 United States Smart Ferroic Materials Center Physics Department Institute for Nanoscience and Engineering University of Arkansas FayettevilleAR United States Department of Materials Science and Engineering Materials Research Institute Pennsylvania State University University ParkPA16802 United States Department of Electrical Engineering and Computer Sciences University of California BerkeleyCA94720 United States Department of Chemistry University of California BerkeleyCA94720 United States Materials Science Division Argonne National Laboratory LemontIL60439 United States Department of Physics Korea Advanced Institute of Science and Technology Daejeon Korea Republic of School of Engineering Brown University ProvidenceRI77005 United States Departments of Chemistry and Physics and Astronomy Rice University HoustonTX77005 United States Applied Mathematics and Computational Research Division Lawrence Berkeley National Laboratory CA94720 United States Advanced Photon Source Argonne National Laboratory LemontIL60439 United States Materials Science Division Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Rice Advanced Materials Institute Smalley-Curl Institute Ken Kennedy Institute Rice University HoustonTX77005 United States Department of Physics and Astronomy University of Victoria VictoriaBCV8W 2Y2 Canada Centre for Advanced Materials and Related Technology University of Victoria VictoriaBCV8W 2Y2 Canada Department of Materials Science and Engineering Tel Aviv University Ramat Aviv Tel Aviv6997801 Israel Laboratoire Albert Fert CNRS Thales Université Paris-Saclay Pa

Since Felix Bloch’s introduction of the concept of spin waves in 1930, magnons (the quanta of spin waves) have been extensively studied in a range of materials for spintronics, particularly for non-volatile logic-in-memory devices. Controlling magnons in conventional antiferromagnets and harnessing them in practical applications, however, remains a challenge. In this letter, we demonstrate highly efficient magnon transport in an LaFeO3/BiFeO3/LaFeO3 all-antiferromagnetic system which can be controlled electrically, making it highly desirable for energy-efficient computation. Leveraging spin-orbit-driven spin-charge transduction, we demonstrate that this material architecture permits magnon confinement in ultrathin antiferromagnets, enhancing the output voltage generated by magnon transport by several orders of magnitude, which provides a pathway to enable magnetoelectric memory and logic functionalities. Additionally, its non-volatility enables ultralow-power logic-in-memory processing, where magnonic devices can be efficiently reconfigured via electrically controlled magnon spin currents within magnetoelectric channels. © 2025, CC BY.

关键词： Antiferromagnetism