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arXiv 2023年

作者： Volpe, Giovanni Wählby, Carolina Tian, Lei Hecht, Michael Yakimovich, Artur Monakhova, Kristina Waller, Laura Sbalzarini, Ivo F. Metzler, Christopher A. Xie, Mingyang Zhang, Kevin Lenton, Isaac C.D. Rubinsztein-Dunlop, Halina Brunner, Daniel Bai, Bijie Ozcan, Aydogan Midtvedt, Daniel Wang, Hao Sladoje, Nataša Lindblad, Joakim Smith, Jason T. Ochoa, Marien Barroso, Margarida Intes, Xavier Qiu, Tong Yu, Li-Yu You, Sixian Liu, Yongtao Ziatdinov, Maxim A. Kalinin, Sergei V. Sheridan, Arlo Manor, Uri Nehme, Elias Goldenberg, Ofri Shechtman, Yoav Moberg, Henrik K. Langhammer, Christoph Špačková, Barbora Helgadottir, Saga Midtvedt, Benjamin Argun, Aykut Thalheim, Tobias Cichos, Frank Bo, Stefano Hubatsch, Lars Pineda, Jesus Manzo, Carlo Bachimanchi, Harshith Selander, Erik Homs-Corbera, Antoni Fränzl, Martin de Haan, Kevin Rivenson, Yair Korczak, Zofia Adiels, Caroline Beck Mijalkov, Mite Veréb, Dániel Chang, Yu-Wei Pereira, Joana B. Matuszewski, Damian Kylberg, Gustaf Sintorn, Ida-Maria Caicedo, Juan C. Cimini, Beth A. Bell, Muyinatu Lediju A. Saraiva, Bruno M. Jacquemet, Guillaume Henriques, Ricardo Ouyang, Wei Le, Trang Gómez-De-Mariscal, Estibaliz Sage, Daniel Muñoz-Barrutia, Arrate Lindqvist, Ebba Josefson Bergman, Johanna Department of Physics University of Gothenburg Gothenburg41296 Sweden Dept. IT and SciLifeLab Uppsala University Sweden Department of Electrical and Computer Engineering Boston University BostonMA02215 United States Department of Biomedical Engineering Boston University BostonMA02215 United States Görlitz Germany Department of Renal Medicine Division of Medicine University College London Royal Free Hospital Campus London United Kingdom Artificial Intelligence for Life Sciences CIC Dorset United Kingdom Roche Pharma International Informatics Roche Diagnostics GmbH Mannheim Germany Department of Electrical Engineering and Computer Sciences UC Berkeley United States Technische Universität Dresden Faculty of Computer Science Dresden Germany Max Planck Institute of Molecular Cell Biology and Genetics Center for Systems Biology Dresden Dresden Germany University of Maryland College Park United States Klosterneuburg3400 Austria School of Mathematics and Physics The University of Queensland Brisbane4072 Australia Australian Research Council Centre of Excellence for Engineered Quantum Systems The University of Queensland Brisbane4072 Australia Département d’Optique P. M. Duffieux Institut FEMTO-ST Université Franche-Comté CNRS UMR 6174 Besançon France Department of Electrical and Computer Engineering University of California Los Angeles Los AngelesCA United States Department of Information Technology Uppsala University Sweden Department of Biomedical Engineering Rensselaer Polytechnic Institute TroyNY12180 United States Elephas 1 Erdman Pl. Madison53705 United States University of Wisconsin–Madison Department of Medical Physics MadisonWI53705 United States Department of Molecular and Cellular Physiology Albany Medical College AlbanyNY12208 United States Research Laboratory of Electronics Electrical Engineering and Computer Science Massachusetts Institute of Technology BostonMA United States Center for Nanophase Materials Sciences Oak R

Through digital imaging, microscopy has evolved from primarily being a means for visual observation of life at the micro- and nano-scale, to a quantitative tool with ever-increasing resolution and throughput. Artificial intelligence, deep neural networks, and machine learning are all niche terms describing computational methods that have gained a pivotal role in microscopy-based research over the past decade. This Roadmap is written collectively by prominent researchers and encompasses selected aspects of how machine learning is applied to microscopy image data, with the aim of gaining scientific knowledge by improved image quality, automated detection, segmentation, classification and tracking of objects, and efficient merging of information from multiple imaging modalities. We aim to give the reader an overview of the key developments and an understanding of possibilities and limitations of machine learning for microscopy. It will be of interest to a wide cross-disciplinary audience in the physical sciences and life sciences. Copyright © 2023, The Authors. All rights reserved.

关键词： Image enhancement

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Model of virus therapy and chemotherapy for cancer

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IOP Conference Series: Earth and Environmental Science 2019年第1期364卷

作者： E Safitri T Tarmizi M Ramli S Wahyuni M Ikhwan Mathematics Graduate Study Program Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Mathematics Universitas Syiah Kuala Banda Aceh 23111 Indonesia Graduate School of Mathematics and Applied Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia

This article discusses the mathematical model of changes in the number of cancer cells as a result of the intervention. Here the effect of combination treatment, in the form of virus injection and chemotherapy on tumor cells, is presented in detail. There are two different tumor cells, uninfected tumor cells and infected tumor cells. Model simulations with different parameters were performed for 700 d. The initial values for the population of virus-infected tumor cells are divided into two categories. The number of tumor cells injected with the virus is ½ (half) and all tumor cells that are not infected. Based on the simulation results, it was concluded that the more the number of tumor cells injected with the virus, the faster the decrease in the number of tumor cell populations. It was also concluded that the reduced population of tumor cells reduces the amount of chemotherapy concentration used.

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Multifunctional hyperuniform cellular networks: Optimality, anisotropy and disorder

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Multifunctional Materials 2018年第1期1卷

作者： Torquato, S. Chen, D. Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry Princeton University PrincetonNJ08544 United States

Disordered hyperuniform heterogeneousmaterials are new, exotic amorphous states of matter that behave like crystals in themanner in which they suppress volume-fraction fluctuations at large length scales, and yet are statistically isotropic with no Bragg peaks. It has recently been shown that disordered hyperuniform dielectric two-dimensional (2D) cellular network solids possess complete photonic band gaps comparable in size to photonic crystals,while at the same timemaintaining statistical isotropy, enabling waveguide geometries not possible with photonic crystals. Motivated by these developments, we explore other functionalities of various 2D ordered and disordered hyperuniform cellular networks, including their effective thermal or electrical conductivities and *** the multifunctionality of a class of such low-density networks by demonstrating that theymaximize or virtually maximize the effective conductivities and elastic moduli. This is accomplished using themachinery of homogenization theory, including optimal bounds and cross-property bounds, and statistical *** rigorously prove that anisotropic networks consisting of sets of intersecting parallel channels in the low-density limit, ordered or disordered, possess optimal effective conductivity tensors. For a variety of different disordered networks,we showthatwhen short-range and long-range order increases, there is an increase in both the effective conductivity and elasticmoduli of the network. Moreover,we demonstrate that the effective conductivity and elasticmoduli of various disordered networks derived from disordered 'stealthy' hyperuniform point patterns possess virtually optimal *** note that the optimal networks for conductivity are also optimal for the fluid permeability associatedwith slow viscous flow through the channels as well as themean survival time associated with diffusioncontrolled reactions in the channels. In summary,we have identified ordered and disor

关键词： Transport properties

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Toward single particle reconstruction without particle picking: Breaking the detection limit

arXiv

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arXiv 2018年

作者： Bendory, Tamir Boumal, Nicolas Leeb, William Levin, Eitan Singer, Amit The School of Electrical Engineering Tel Aviv University Tel Aviv Israel Lausanne Switzerland School of Mathematics University of Minnesota MinneapolisMN United States Department of Computing and Mathematical Sciences California Institute of Technology PasadenaCA United States The Program in Applied and Computational Mathematics Department of Mathematics Princeton University PrincetonNJ United States

Single-particle cryo-electron microscopy (cryo-EM) has recently joined X-ray crystallography and NMR spectroscopy as a high-resolution structural method to resolve biological macromolecules. In a cryo-EM experiment, the microscope produces images called micrographs. Projections of the molecule of interest are embedded in the micrographs at unknown locations, and under unknown viewing directions. Standard imaging techniques first locate these projections (detection) and then reconstruct the 3-D structure from them. Unfortunately, high noise levels hinder detection. When reliable detection is rendered impossible, the standard techniques fail. This is a problem, especially for small molecules. In this paper, we pursue a radically different approach: we contend that the structure could, in principle, be reconstructed directly from the micrographs, without intermediate detection. The aim is to bring small molecules within reach for cryo-EM. To this end, we design an autocorrelation analysis technique that allows to go directly from the micrographs to the sought structures. This involves only one pass over the micrographs, allowing online, streaming processing for large experiments. We show numerical results and discuss challenges that lay ahead to turn this proof-of-concept into a complementary approach to state-of-the-art algorithms. Copyright © 2018, The Authors. All rights reserved.

关键词： X ray crystallography

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Luminosity determination in pp collisions at TeV using the ATLAS detector at the LHC

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The European Physical Journal C 2023年第10期83卷 1-67页

作者： Aad, G. Abbott, B. Abeling, K. Abidi, S. H. Aboulhorma, A. Abramowicz, H. Abreu, H. Abulaiti, Y. Hoffman, A. C. Abusleme Acharya, B. S. Bourdarios, C. Adam Adamczyk, L. Adamek, L. Addepalli, S. V. Adelman, J. Adiguzel, A. Adorni, S. Adye, T. Affolder, A. A. Afik, Y. Agaras, M. N. Agarwala, J. Aggarwal, A. Agheorghiesei, C. Aguilar-Saavedra, J. A. Ahmad, A. Ahmadov, F. Ahmed, W. S. Ahuja, S. Ai, X. Aielli, G. Tamlihat, M. Ait Aitbenchikh, B. Aizenberg, I. Akbiyik, M. Åkesson, T. P. A. Akimov, A. V. Akolkar, N. N. Khoury, K. Al Alberghi, G. L. Albert, J. Albicocco, P. Alderweireldt, S. Aleksa, M. Aleksandrov, I. N. Alexa, C. Alexopoulos, T. Alfonsi, A. Alfonsi, F. Alhroob, M. Ali, B. Ali, S. Aliev, M. Alimonti, G. Alkakhi, W. Allaire, C. Allbrooke, B. M. M. Flores, C. A. Allendes Allport, P. P. Aloisio, A. Alonso, F. Alpigiani, C. Estevez, M. Alvarez Fernandez, A. Alvarez Alviggi, M. G. Aly, M. Coutinho, Y. Amaral Ambler, A. Amelung, C. Amerl, M. Ames, C. G. Amidei, D. Dos Santos, S. P. Amor Amos, K. R. Ananiev, V. Anastopoulos, C. Andeen, T. Anders, J. K. Andrean, S. Y. Andreazza, A. Angelidakis, S. Angerami, A. Anisenkov, A. V. Annovi, A. Antel, C. Anthony, M. T. Antipov, E. Antonelli, M. Antrim, D. J. A. Anulli, F. Aoki, M. Aoki, T. Pozo, J. A. Aparisi Aparo, M. A. Bella, L. Aperio Appelt, C. Aranzabal, N. Ferraz, V. Araujo Arcangeletti, C. Arce, A. T. H. Arena, E. Arguin, J.-F. Argyropoulos, S. Arling, J.-H. Armbruster, A. J. Arnaez, O. Arnold, H. Tame, Z. P. Arrubarrena Artoni, G. Asada, H. Asai, K. Asai, S. Asbah, N. A. Assahsah, J. Assamagan, K. Astalos, R. Atkin, R. J. Atkinson, M. Atlay, N. B. Atmani, H. Atmasiddha, P. A. Augsten, K. Auricchio, S. Auriol, A. D. Austrup, V. A. Avner, G. Avolio, G. Axiotis, K. Azuelos, G. Babal, D. Bachacou, H. Bachas, K. Bachiu, A. Backman, F. Badea, A. Bagnaia, P. Bahmani, M. Bailey, A. J. Bailey, V. R. Baines, J. T. Bakalis, C. Baker, O. K. Bakos, E. Gupta, D. Bakshi Balasubramanian, R. Baldin, E. M. Balek, P. Ballabene, E. Balli, F. Baltes, L. M. Balunas, W. K. Balz, J. CPPM Aix-Marseille Université CNRS/IN2P3 Marseille France Homer L. Dodge Department of Physics and Astronomy University of Oklahoma Norman USA II. Physikalisches Institut Georg-August-Universität Göttingen Göttingen Germany Physics Department Brookhaven National Laboratory Upton USA Faculté des sciences Université Mohammed V Rabat Morocco Raymond and Beverly Sackler School of Physics and Astronomy Tel Aviv University Tel Aviv Israel Department of Physics Technion Israel Institute of Technology Haifa Israel Department of Physics New York University New York USA Departamento de Física Pontificia Universidad Católica de Chile Santiago Chile INFN Gruppo Collegato di Udine Sezione di Trieste Udine Italy ICTP Trieste Italy Department of Physics King’s College London London UK LAPP Université Savoie Mont Blanc CNRS/IN2P3 Annecy France Faculty of Physics and Applied Computer Science AGH University of Science and Technology Krakow Poland Department of Physics University of Toronto Toronto Canada Department of Physics Brandeis University Waltham USA Department of Physics Northern Illinois University DeKalb USA Department of Physics Istanbul University Istanbul Türkiye Département de Physique Nucléaire et Corpusculaire Université de Genève Geneva Switzerland Particle Physics Department Rutherford Appleton Laboratory Didcot UK Santa Cruz Institute for Particle Physics University of California Santa Cruz Santa Cruz USA CERN Geneva Switzerland Institut de Física d’Altes Energies (IFAE) Barcelona Institute of Science and Technology Barcelona Spain INFN Sezione di Pavia Pavia Italy Dipartimento di Fisica Università di Pavia Pavia Italy Institut für Physik Universität Mainz Mainz Germany Department of Physics Alexandru Ioan Cuza University of Iasi Iasi Romania Departamento de Física Teórica y del Cosmos Universidad de Granada Granada Spain Affiliated with an international laboratory covered by a cooperation agreement with CERN Geneva Switzerland

The luminosity determination for the ATLAS detector at the LHC during Run 2 is presented, with pp collisions at a centre-of-mass energy $$\sqrt{s}=13$$ TeV. The absolute luminosity scale is determined using van der Meer beam separation scans during dedicated running periods in each year, and extrapolated to the physics data-taking regime using complementary measurements from several luminosity-sensitive detectors. The total uncertainties in the integrated luminosity for each individual year of data-taking range from 0.9% to 1.1%, and are partially correlated between years. After standard data-quality selections, the full Run 2 pp data sample corresponds to an integrated luminosity of $$140.1\pm 1.2$$ $$\hbox {fb}^{-1}$$ , i.e. an uncertainty of 0.83%. A dedicated sample of low-pileup data recorded in 2017–2018 for precision Standard Model physics measurements is analysed separately, and has an integrated luminosity of $$338.1\pm 3.1$$ $$\hbox {pb}^{-1}$$ .

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Factor Analysis for Spectral Estimation

Factor Analysis for Spectral Estimation

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International Conference on Sampling Theory and Applications

作者： Joakim Anden Amit Singer Program in Applied and Computational Mathematics Princeton University Department of Mathematics and Program in Applied and Computational Mathematics Princeton University

ISBN: (纸本)9781538615669

Power spectrum estimation is an important tool in many applications, such as the whitening of noise. The popular multitaper method enjoys significant success, but fails for short signals with few samples. We propose a statistical model where a signal is given by a random linear combination of fixed, yet unknown, stochastic sources. Given multiple such signals, we estimate the subspace spanned by the power spectra of these fixed sources. Projecting individual power spectrum estimates onto this subspace increases estimation accuracy. We provide accuracy guarantees for this method and demonstrate it on simulated and experimental data from cryo-electron microscopy.

关键词： Spectrum Analysis power spectra Subspace STATIONARY SOURCES Factor analysis Cryoelectron Microscopy Linear Combination statistical models (nuclear) signals Individual Power simulating

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A model of reward-modulated motor learning with parallel cortical and basal ganglia pathways

arXiv

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arXiv 2018年

作者： Pyle, Ryan Rosenbaum, Robert Department of Applied and Computational Mathematics and Statistics University of Notre Dame Notre DameIN United States Interdisciplinary Center for Network Science and Applications University of Notre Dame Notre DameIN United States

Reservoir computing is a biologically inspired class of learning algorithms in which the intrinsic dynamics of a recurrent neural network are mined to produce target time series. Most existing reservoir computing algorithms rely on fully supervised learning rules, which require access to an exact copy of the target response, greatly reducing the utility of the system. Reinforcement learning rules have been developed for reservoir computing, but we find that they fail to converge on complex motor tasks. Current theories of biological motor learning pose that early learning is controlled by dopamine modulated plasticity in the basal ganglia that trains parallel cortical pathways through unsupervised plasticity as a motor task becomes well-learned. We developed a novel learning algorithm for reservoir computing that models the interaction between reinforcement and unsupervised learning observed in experiments. This novel learning algorithm converges on simulated motor tasks on which previous reservoir computing algorithms fail, and reproduces experimental findings that relate Parkinson’s disease and its treatments to motor learning. Hence, incorporating biological theories of motor learning improves the effectiveness and biological relevance of reservoir computing models. Copyright © 2018, The Authors. All rights reserved.

关键词： Reinforcement learning

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An Adaptive Algorithm Employing Continuous Linear Functionals

arXiv

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arXiv 2018年

作者： Ding, Yuhan Hickernell, Fred J. Rugama, Lluís Antoni Jiménez MCA 310 Department of Mathematics Misericordia University 301 Lake St. DallasPA18612 United States Center for Interdisciplinary Scientific Computation Department of Applied Mathematics Illinois Institute of Technology RE 208 10 W. 32nd St. ChicagoIL60616 United States Department of Applied Mathematics Illinois Institute of Technology RE 208 10 W. 32nd United States

Automatic algorithms attempt to provide approximate solutions that differ from exact solutions by no more than a user-specified error tolerance. This paper describes an automatic, adaptive algorithm for approximating the solution to a general linear problem on Hilbert spaces. The algorithm employs continuous linear functionals of the input function, specifically Fourier coefficients. We assume that the Fourier coefficients of the solution decay sufficiently fast, but do not require the decay rate to be known a priori. We also assume that the Fourier coefficients decay steadily, although not necessarily monotonically. Under these assumptions, our adaptive algorithm is shown to produce an approximate solution satisfying the desired error tolerance, without prior knowledge of the norm of the function to be approximated. Moreover, the computational cost of our algorithm is shown to be essentially no worse than that of the optimal algorithm. We provide a numerical experiment to illustrate our algorithm. Copyright © 2018, The Authors. All rights reserved.

关键词： Fourier analysis

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The structure factor of primes

arXiv

引用

arXiv 2018年

作者： Zhang, G. Martelli, F. Torquato, S. Department of Chemistry Princeton University PrincetonNJ08544 Department of Chemistry Department of Physics Princeton Institute for the Science and Technology of Materials Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544

Although the prime numbers are deterministic, they can be viewed, by some measures, as pseudo-random numbers. In this article, we numerically study the pair statistics of the primes using statistical-mechanical methods, especially the structure factor S(k) in an interval M ≤ p ≤ M + L with M large, and L/M smaller than unity. We show that the structure factor of the prime-number configurations in such intervals exhibits well-defined Bragg-like peaks along with a small "diffuse" contribution. This indicates that the primes are appreciably more correlated and ordered than previously thought. Our numerical results definitively suggest an explicit formula for the locations and heights of the peaks. This formula predicts infinitely many peaks in any non-zero interval, similar to the behavior of quasicrystals. However, primes differ from quasicrystals in that the ratio between the location of any two predicted peaks is rational. We also show numerically that the diffuse part decays slowly as M and L increases. This suggests that the diffuse part vanishes in an appropriate infinite-system-size limit. Copyright © 2018, The Authors. All rights reserved.

关键词： Quasicrystals

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Patterns of somatic structural variation in human cancer genomes (vol 578, pg 112, 2020)

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NATURE 2023年第7948期614卷 E38-E38页

作者： Li, Yilong Roberts, Nicola D. Wala, Jeremiah A. Shapira, Ofer Schumacher, Steven E. Kumar, Kiran Khurana, Ekta Waszak, Sebastian Korbel, Jan O. Haber, James E. Imielinski, Marcin Weischenfeldt, Joachim Beroukhim, Rameen Campbell, Peter J. Cancer Genome Project Wellcome Trust Sanger Institute Hinxton UK Totient Inc Cambridge MA USA Wellcome Sanger Institute Wellcome Genome Campus Hinxton UK Department of Haematology University of Cambridge Cambridge UK Cambridge University Hospitals NHS Foundation Trust Cambridge UK Korea Advanced Institute of Science and Technology Daejeon South Korea Department of Zoology Genetics and Physical Anthropology University of Santiago de Compostela Santiago de Compostela Spain Centre for Research in Molecular Medicine and Chronic Diseases (CIMUS) University of Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) University of Vigo Vigo Spain Centre for Research in Molecular Medicine and Chronic Diseases (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain Department of Zoology Genetics and Physical Anthropology (CiMUS) Universidade de Santiago de Compostela Santiago de Compostela Spain The Biomedical Research Centre (CINBIO) Universidade de Vigo Vigo Spain The Broad Institute of Harvard and MIT Cambridge MA USA Bioinformatics and Integrative Genomics Harvard University Cambridge MA USA Department of Cancer Biology Dana-Farber Cancer Institute Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA Department of Medical Oncology Dana-Farber Cancer Institute Boston MA USA Harvard Medical School Boston MA USA Massachusetts General Hospital Center for Cancer Research Charlestown MA USA Dana-Farber Cancer Institute Boston MA USA Department of Biostatistics and Computational Biology Dana-Farber Cancer Institute and Harvard Medical School Boston MA USA Weill Cornell Medical College New York NY USA Department of Physiology and Biophysics Weill Cornell Medicine New York NY USA Institute for Computational Biomedicine Weill Cornell Medicine New York NY USA Controlled Department and Institution New York NY USA Englander Institute for Precision Medicine Weill Cornell Medicine Ne

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