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arXiv 2021年

作者： Zhang, Chunyi Tang, Fujie Chen, Mohan Zhang, Linfeng Qiu, Diana Y. Perdew, John P. Klein, Michael L. Wu, Xifan Department of Physics Temple University PhiladelphiaPA19122 United States HEDPS Center for Applied Physics and Technology College of Engineering Peking University Beijing100871 China Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Department of Chemistry Temple University PhiladelphiaPA19122 United States Institute for Computational Molecular Science Temple University PhiladelphiaPA19122 United States

Within the framework of Kohn-Sham density functional theory (DFT), the ability to provide good predictions of water properties by employing a strongly constrained and appropriately normed (SCAN) functional has been extensively demonstrated in recent years. Here, we further advance the modeling of water by building a more accurate model on the fourth rung of Jacob’s ladder with the hybrid functional, SCAN0. In particular, we carry out both classical and Feynman path-integral molecular dynamics calculations of water with the SCAN0 functional and the isobaric-isothermal ensemble. In order to generate the equilibrated structure of water, a deep neural network potential is trained from the atomic potential energy surface based on ab initio data obtained from SCAN0 DFT calculations. For the electronic properties of water, a separate deep neural network potential is trained using the Deep Wannier method based on the maximally localized Wannier functions of the equilibrated trajectory at the SCAN0 level. The structural, dynamic, and electric properties of water were analyzed. The hydrogen-bond structures, density, infrared spectra, diffusion coefficients, and dielectric constants of water, in the electronic ground state, are computed using a large simulation box and long simulation time. For the properties involving electronic excitations, we apply the GW approximation within many-body perturbation theory to calculate the quasiparticle density of states and bandgap of water. Compared to the SCAN functional, mixing exact exchange mitigates the self-interaction error in the meta-generalized-gradient approximation and further softens liquid water towards the experimental direction. For most of the water properties, the SCAN0 functional shows a systematic improvement over the SCAN functional. © 2021, CC BY.

关键词： Deep neural networks

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Nonrigid registration using Gaussian processes and local likelihood estimation

arXiv

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arXiv 2020年

作者： Wiens, Ashton Kleiber, William Nychka, Douglas Barnhart, Katherine R. Department of Applied Mathematics University of Colorado BoulderCO United States Department of Applied Mathematics and Statistics Colorado School of Mines GoldenCO United States Cooperative Institute for Research in Environmental Sciences University of Colorado BoulderCO United States Department of Geological Sciences University of Colorado BoulderCO United States US Geological Survey Landslide Hazards Program 1711 Illinois St. GoldenCO80401 United States

Surface registration, the task of aligning several multidimensional point sets, is a necessary task in many scientific fields. In this work, a novel statistical approach is developed to solve the problem of nonrigid registration. While the application of an affine transformation results in rigid registration, using a general nonlinear function to achieve nonrigid registration is necessary when the point sets require deformations that change over space. The use of a local likelihood-based approach using windowed Gaussian processes provides a flexible way to accurately estimate the nonrigid deformation. This strategy also makes registration of massive data sets feasible by splitting the data into many subsets. The estimation results yield spatially-varying local rigid registration parameters. Gaussian process surface models are then fit to the parameter fields, allowing prediction of the transformation parameters at unestimated locations, specifically at observation locations in the unregistered data set. Applying these transformations results in a global, nonrigid registration. A penalty on the transformation parameters is included in the likelihood objective function. Combined with smoothing of the local estimates from the surface models, the nonrigid registration model can prevent the problem of overfitting. The efficacy of the nonrigid registration method is tested in two simulation studies, varying the number of windows and number of points, as well as the type of deformation. The nonrigid method is applied to a pair of massive remote sensing elevation data sets exhibiting complex geological terrain, with improved accuracy and uncertainty quantification in a cross validation study versus two rigid registration methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Uncertainty analysis

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Cellular organization in lab-evolved and extant multicellular species obeys a maximum entropy law

arXiv

引用

arXiv 2021年

作者： Day, Thomas C. Höhn, Stephanie S. Zamani-Dahaj, Seyed A. Yanni, David Burnetti, Anthony Pentz, Jennifer Honerkamp-Smith, Aurelia R. Wioland, Hugo Sleath, Hannah R. Ratcliff, William C. Goldstein, Raymond E. Yunker, Peter J. School of Physics Georgia Institute of Technology AtlantaGA United States Department of Applied Mathematics and Theoretical Physics Centre for Mathematical Sciences University of Cambridge Cambridge United Kingdom School of Biological Sciences Georgia Institute of Technology AtlantaGA United States Quantitative Biosciences Graduate Program Georgia Institute of Technology AtlantaGA United States Department of Molecular Biology Umeå University Umeå Sweden

The prevalence of multicellular organisms is due in part to their ability to form complex structures. How cells pack in these structures is a fundamental biophysical issue, underlying their functional properties. However, much remains unknown about how cell packing geometries arise, and how they are affected by random noise during growth - especially absent developmental programs. Here, we quantify the statistics of cellular neighborhoods of two different multicellular eukaryotes: lab-evolved "snowflake" yeast and the green alga Volvox carteri. We find that despite large differences in cellular organization, the free space associated with individual cells in both organisms closely fits a modified gamma distribution, consistent with maximum entropy predictions originally developed for granular materials. This 'entropic' cellular packing ensures a degree of predictability despite noise, facilitating parent-offspring fidelity even in the absence of developmental regulation. Together with simulations of diverse growth morphologies, these results suggest that gamma-distributed cell neighborhood sizes are a general feature of multicellularity, arising from conserved statistics of cellular packing. © 2021, CC BY.

关键词： Cytology

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Temperature-resilient anapole modes associated with TE polarization in semiconductor nanowire

arXiv

引用

arXiv 2022年

作者： Thakore, Vaibhav Ala-Nissila, Tapio Karttunen, Mikko Department of Applied Mathematics Western University 1151 Richmond Street LondonONN6A 5B7 Canada QTF Center of Excellence Department of Applied Physics Aalto University School of Science Aalto EspooFIN-00076 Finland Department of Physics Brown University ProvidenceRI02912-1843 United States Interdisciplinary Centre for Mathematical Modelling Department of Mathematical Sciences Loughborough University LoughboroughLE11 3TU United Kingdom Department of Physics and Astronomy The University of Western Ontario 1151 Richmond Street LondonONN6A 3K7 Canada The Centre of Advanced Materials and Biomaterials Research The University of Western Ontario 1151 Richmond Street LondonONN6A 5B7 Canada Department of Chemistry The University of Western Ontario 1151 Richmond Street LondonONN6A 5B7 Canada

Polarization-dependent scattering anisotropy of cylindrical nanowires has numerous potential applications in, for example, nanoantennas, photothermal therapy, thermophotovoltaics, catalysis, sensing, optical filters and switches. In all these applications, temperature-dependent material properties play an important role and often adversely impact performance depending on the dominance of either radiative or dissipative damping. Here, we employ numerical modeling based on Mie scattering theory to investigate and compare the temperature and polarization-dependent optical anisotropy of metallic (gold, Au) nanowires with indirect (silicon, Si) and direct (gallium arsenide, GaAs) bandgap semiconducting nanowires. Results indicate that plasmonic scattering resonances in semiconductors, within the absorption band, deteriorate with an increase in temperature whereas those occurring away from the absorption band strengthen as a result of the increase in phononic contribution. Indirect-bandgap thin (20 nm) Si nanowires present low absorption efficiencies for both the transverse electric (TE, E⊥) and magnetic (TM, E) modes, and high scattering efficiencies for the TM mode at shorter wavelengths making them suitable as highly efficient scatterers. Temperature-resilient higher-order anapole modes with their characteristic high absorption and low scattering efficiencies are also observed in the semiconductor nanowires (r = 125 - 30 nm) for the TE polarization. Herein, the GaAs nanowires present 3 - 7 times greater absorption efficiencies compared to the Si nanowires making them especially suitable for temperature-resilient applications such as scanning near-field optical microscopy (SNOM), localized heating, non-invasive sensing or detection that require strong localization of energy in the near field. Copyright © 2022, The Authors. All rights reserved.

关键词： Gallium arsenide

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A general semi-parametric elliptical distribution model for semi-supervised learning

引用

Journal of Nonparametric Statistics 2025年第2期37卷

作者： Chin-Tsang Chiang Sheng-Hsin Fan Ming-Yueh Huang Jen-Chieh Teng Alvin Lim a Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan b Department of Mathematics National Taiwan University Taipei Taiwan c Institute of Statistical Science Academia Sinica Taipei Taiwan d Data Science Degree Program National Taiwan University Taipei Taiwan e Data Science Department Measured Inc. Austin TX USAf Goizueta Business School Emory University Atlanta GA USA

ABSTRACTThis research proposes a novel semi-parametric elliptical distribution model for application in semi-supervised learning tasks. We use labelled and unlabelled data to develop a pseudo maximum likelihood method for estimation and classification. The proposed estimator outperforms the estimator based solely on labelled data and achieves the semi-parametric efficiency bound with a suitable size of unlabelled data. We efficiently maximise the objective function by utilising low-dimensional groupwise pseudo-likelihood functions in a block coordinate descent manner while ensuring numerical stability and convergence through appropriate bandwidth selectors and initial parameter estimates. Additionally, the study comprehensively investigates the impact of labelled and unlabelled data on the pseudo maximum likelihood estimator and classifier. Simulation studies and empirical data applications illustrate the superiority of our methodology.

关键词： elliptical distribution posterior group probability pseudo maximum likelihood method semi-parametric efficiency semi-supervised learning

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Topological data analysis of black hole images

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Physical Review D 2022年第2期106卷 023017-023017页

作者： Pierre Christian Chi-kwan Chan Anthony Hsu Feryal Özel Dimitrios Psaltis Iniyan Natarajan Physics Department Fairfield University 1073 N Benson Road Fairfield Connecticut 06824 USA Department of Astronomy University of Arizona 933 North Cherry Avenue Tucson Arizona 85721 USA Data Science Institute University of Arizona 1230 N. Cherry Avenue Tucson Arizona 85721 USA Program in Applied Mathematics University of Arizona 617 N. Santa Rita Tucson Arizona 85721 USA Department of Computer Science University of Arizona 1040 4th Street Tucson Arizona 85721 USA Wits Centre for Astrophysics University of the Witwatersrand 1 Jan Smuts Avenue Braamfontein Johannesburg 2050 South Africa South African Radio Astronomy Observatory Observatory 7925 Cape Town South Africa

Features such as photon rings, jets, or hot spots can leave particular topological signatures in a black hole image. As such, topological data analysis can be used to characterize images resulting from high-resolution observations (synthetic or real) of black holes in the electromagnetic sector. We demonstrate that persistent homology allows for this characterization to be made automatically by counting the number of connected components and one-dimensional holes. Further, persistent homology also allows for the distance between connected components or the diameter of holes to be extracted from the image. In order to apply persistent homology on synthetic black hole images, we also introduce metronization, a new algorithm to prepare black hole images in a form that is suitable for topological analysis.

关键词： Astronomical black holes

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mathematics for cryo-electron microscopy

Mathematics for cryo-electron microscopy

引用

2018 International Congress of Mathematicians, ICM 2018

作者： Singer, Amit Department of Mathematics Program in Applied and Computational Mathematic Princeton University United States

ISBN: (纸本)9789813272934

Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method for biological macromolecules. Cryo-EM was selected by Nature Methods as Method of the Year 2015, large scale investments in cryo-EM facilities are being made all over the world, and the Nobel Prize in Chemistry 2017 was awarded to Jacques Dubochet, Joachim Frank and Richard Henderson "for developing cryo-electron microscopy for the high-resolution structure determination of biomolecules in solution". This paper focuses on the mathematical principles underlying existing algorithms for structure determination using single particle cryo-EM. © ICM *** rights reserved.

关键词： X ray crystallography

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Prevalence of comorbidities and their impact on survival among older adults with the five most common cancers in Taiwan: a population study

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Scientific Reports 2023年第1期13卷 1-15页

作者： Chien, Li-Hsin Tseng, Tzu-Jui Chen, Tzu-Yu Chen, Chung-Hsing Chen, Chia-Yu Jiang, Hsin-Fang Tsai, Fang-Yu Ku, Hsiu-Ying Jiang, Shih Sheng Hsiung, Chao A. Liu, Tsang-Wu Chang, I-Shou Institute of Population Health Sciences National Health Research Institutes 35 Keyan Road Miaoli County Zhunan 35053 Taiwan Department of Applied Mathematics Chung-Yuan Christian University Chong-Li Taiwan Center of Biomedical Resources National Health Research Institutes 35 Keyan Road Miaoli County Zhunan 35053 Taiwan Department of Bachelor’s Degree Program for Indigenous Peoples in Senior Health and Care Management National Taitung University 369 Section 2 University Road Taitung County Taitung City 95092 Taiwan Master Program in Biomedicine National Taitung University 369 Section 2 University Road Taitung County Taitung City 95092 Taiwan National Institute of Cancer Research National Health Research Institutes 35 Keyan Road Miaoli County Zhunan 35053 Taiwan

Because of the cancer incidence increase and population aging in Taiwan, we aimed to assess the cancer prevalence, to summarize the comorbidities of older patients with the five most common cancers (i.e., breast, colorectal, liver, lung, and oral), and to develop a Taiwan cancer comorbidity index (TCCI) for studying their actual prognosis. The linkage of the Taiwan Cancer Registry, Cause of Death Database, and National Health Insurance Research Database was used. We followed the standard statistical learning steps to obtain a survival model with good discriminatory accuracy in predicting death due to noncancer causes, from which we obtained the TCCI and defined comorbidity levels. We reported the actual prognosis by age, stage, and comorbidity level. In Taiwan, cancer prevalence nearly doubled in 2004–2014, and comorbidities were common among older patients. Stage was the major predictor of patients' actual prognoses. For localized and regional breast, colorectal, and oral cancers, comorbidities correlated with noncancer-related deaths. Compared with the US, the chances of dying from comorbidities in Taiwan were lower and the chances of dying from cancer were higher for breast, colorectal, and male lung cancers. These actual prognoses could help clinicians and patients in treatment decision-making and help policymakers in resource planning. © 2023, The Author(s).

关键词： Cancer epidemiology Cancer epidemiology Geriatrics

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The nanotoxicity assessment of cube-like iron nitride magnetic nanoparticles at the organismal level of nematodeCaenorhabditis elegans

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Nanotoxicology 2022年第4期16卷 472-483页

作者： Greici Gubert Priscila Gubert Jana Messias Sandes Julia Bornhorst Luiz Carlos Alves Caroline Brandão Quines Dante Homero Mosca a Departamento de Física Universidade Federal do Paraná Curitiba Brazilb Instituto Federal Catarinense Rio do Sul Brazil c Graduate Program in Pure and Applied Chemistry POSQUIPA. Federal University of Western Bahia Barreiras Brazild Immunopathology Laboratory Keizo Asami LIKA. Federal University of Pernambuco Recife Brazil d Immunopathology Laboratory Keizo Asami LIKA. Federal University of Pernambuco Recife Brazil e Department of Food Chemistry Institute of Nutritional Science University of Potsdam Nuthetal Germanyf Food Chemistry Faculty of Mathematics and Natural Sciences University of Wuppertal Wuppertal Germany g Oswaldo Cruz Foundation Aggeu Magalhães Institute Department of Virology and Experimental Therapy Recife Brazil h Postgraduating Program in Biochemistry Federal University of Pampa (UNIPAMPA) Uruguaiana Brazil a Departamento de Física Universidade Federal do Paraná Curitiba Brazil

Magnetic nanoparticles (NPs) are suitable candidates for various medical and biological applications, despite some concerns that they may have negative impacts on human health. In this study, the toxicity effects of magnetic NPs consisting of α”-Fe16N2captured and bioaccumulated by the nematodeCaenorhabditis elegans(C. elegans) in the early larval stage are evaluated. The choice of α”-Fe16N2NPs is based on their good structural stability when stored in saline solution and high magnetic performance. The uptake and bioaccumulation of α”-Fe16N2NPs in intestinal cells ofC. eleganswas evidenced by transmission electron microscopy. After exposure to NPs up to 40 mg mL−1,C. eleganslarval development, survival, feeding behavior, defecation cycles, movement and reproduction were monitored.C. eleganssurvival and other monitored behavioral evolutions do not show significant changes, except for a slight statistical reduction in the reproductive profile. Therefore, the present results are promising and very encouraging for investigations of applications of α”-Fe16N2NPs in the biomedical area.

关键词： Nanotoxicology C. elegans Nanoparticles α”-Fe16N2 iron nitride

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Isotope effects in molecular structures and electronic properties of liquid water via deep potential molecular dynamics based on the SCAN functional

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Physical Review B 2020年第21期102卷 214113-214113页

作者： Jianhang Xu Chunyi Zhang Linfeng Zhang Mohan Chen Biswajit Santra Xifan Wu Department of Physics Temple University Philadelphia Pennsylvania 19122 USA Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA CAPT HEDPS College of Engineering Peking University Beijing 100871 China Institute for Computational Molecular Science Temple University Philadelphia Pennsylvania 19122 USA

Feynman path-integral deep potential molecular dynamics (PI-DPMD) calculations have been employed to study both light (H2O) and heavy water (D2O) within the isothermalisobaric ensemble. In particular, the deep neural network is trained based on ab initio data obtained from the strongly constrained and appropriately normed (SCAN) exchange-correlation functional. Because of the lighter mass of hydrogen than deuteron, the properties of light water are more influenced by nuclear quantum effect than those of heavy water. Clear isotope effects are observed and analyzed in terms of hydrogen-bond structure and electronic properties of water that are closely associated with experimental observables. The molecular structures of both liquid H2O and D2O agree well with the data extracted from scattering experiments. The delicate isotope effects on radial distribution functions and angular distribution functions are well reproduced as well. Our approach demonstrates that deep neural network combined with SCAN functional based ab initio molecular dynamics provides an accurate theoretical tool for modeling water and its isotope effects.

关键词： Atomic & molecular structure Electronic structure Isotope effect Water Ab initio calculations Density functional theory Machine learning Molecular dynamics Path-integral methods

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