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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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Modeling subgrid-scale force and divergence of heat flux of compressible isotropic turbulence by artificial neural network

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Physical Review Fluids 2019年第10期4卷 104605-104605页

作者： Chenyue Xie Ke Li Chao Ma Jianchun Wang Shenzhen Key Laboratory of Complex Aerospace Flows Center for Complex Flows and Soft Matter Research Department of Mechanics and Aerospace Engineering Southern University of Science and Technology Shenzhen 518055 People's Republic of China Institute of Computational Mathematics and Scientific Engineering Computing Chinese Academy of Sciences Beijing 100190 People's Republic of China The Program in Applied and Computational Mathematics Princeton University Princeton New Jersey 08544 USA

In this paper, the subgrid-scale (SGS) force and the divergence of SGS heat flux of compressible isotropic turbulence are modeled directly by an artificial neural network (ANN), which serves as a data-driven SGS modeling tool for large-eddy simulations (LESs). The unclosed SGS force and divergence of SGS heat flux are modeled based on the local stencil geometry with Galilean invariance. The input features include the first-order and second-order derivatives of filtered velocity and temperature, filtered density, and its first-order derivative. It is shown that the proposed ANN-F7 model shows an advantage over the gradient model in the a priori test. Specifically, the ANN-F7 model gives larger correlation coefficients and smaller relative errors than the gradient model. In an a posteriori analysis, the ANN-F7 model performs better than the dynamic Smagorinsky model (DSM) and dynamic mixed model (DMM) in the prediction of the statistical properties of flow fields at the Taylor microscale Reynolds number Reλ ranging from 180 to 250. The DSM and DMM models lead to the typical tilted spectral distribution of velocity, where low wave numbers are too energy rich, while those near the cutoff are damped too strongly. In contrast, it is shown that the velocity spectrum predicted by the ANN-F7 model almost overlaps with the filtered direct numerical simulation data. Besides, the ANN-F7 model reconstructs the probability density functions of SGS force and divergence of SGS heat flux much better than the DSM and DMM models. An artificial neural network with reasonable physical input features can deepen our understanding of turbulence modeling.

关键词： Compressible flows Turbulence Artificial neural networks

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Sex Differences in Heart Failure Following Acute Coronary Syndromes

JACC: Advances

引用

JACC: Advances 2023年第3期2卷 100294-100294页

作者： Cenko, Edina Manfrini, Olivia Yoon, Jinsung van der Schaar, Mihaela Bergami, Maria Vasiljevic, Zorana Mendieta, Guiomar Stankovic, Goran Vavlukis, Marija Kedev, Sasko Miličić, Davor Badimon, Lina Bugiardini, Raffaele Laboratory of Epidemiological and Clinical Cardiology Department of Medical and Surgical Sciences University of Bologna Bologna Italy IRCCS Azienda Ospedaliero-Universitaria di Bologna Sant'Orsola Hospital Bologna Italy Google Cloud AI Sunnyvale CA United States Department of Electrical and Computer Engineering University of California Los Angeles Los Angeles CA United States Department of Applied Mathematics and Theoretical Physics and Department of Population Health Cambridge Centre for Artificial Intelligence in Medicine University of Cambridge Cambridge United Kingdom Medical Faculty University of Belgrade Belgrade Serbia Centro Nacional de Investigaciones Cardiovasculares Carlos III (CNIC) Madrid Spain Clinic of Cardiology University Clinical Centre of Serbia Belgrade Serbia University Clinic for Cardiology Skopje North Macedonia Faculty of Medicine Ss. Cyril and Methodius University in Skopje Skopje North Macedonia Department for Cardiovascular Diseases University Hospital Center Zagreb University of Zagreb Zagreb Croatia Cardiovascular Research Program ICCC IR-IIB Sant Pau Hospital de la Santa Creu i Sant Pau CiberCV-Institute Carlos III Barcelona Spain

Background: There have been conflicting reports regarding outcomes in women presenting with an acute coronary syndrome (ACS). Objectives: The objective of the study was to examine sex-specific differences in 30-day mortality in patients with ACS and acute heart failure (HF) at the time of presentation. Methods: This was a retrospective study of patients included in the International Survey of Acute Coronary Syndromes-ARCHIVES (ISACS-ARCHIVES;NCT04008173). Acute HF was defined as Killip classes ≥2. Participants were stratified according to ACS presentation: ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation ACS (NSTE-ACS). Differences in 30-day mortality and acute HF presentation at admission between sexes were examined using inverse propensity weighting based on the propensity score. Estimates were compared by test of interaction on the log scale. Results: A total of 87,812 patients were included, of whom 30,922 (35.2%) were women. Mortality was higher in women compared with men in those presenting with STEMI (risk ratio [RR]: 1.65;95% CI: 1.56-1.73) and NSTE-ACS (RR: 1.18;95% CI: 1.09-1.28;P_interaction <0.001). Acute HF was more common in women when compared to men with STEMI (RR: 1.24;95% CI: 1.20-1.29) but not in those with NSTE-ACS (RR: 1.02;95% CI: 0.97-1.08) (P_interaction <0.001). The presence of acute HF increased the risk of mortality for both sexes (odds ratio: 6.60;95% CI: 6.25-6.98). Conclusions: In patients presenting with ACS, mortality is higher in women. The presence of acute HF at hospital presentation increases the risk of mortality in both sexes. Women with STEMI are more likely to present with acute HF and this may, in part, explain sex differences in mortality. These findings may be helpful to improve sex-specific personalized risk stratification. © 2023 The Authors

关键词： acute heart failure outcomes sex differences

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Uniform stability of some large-scale parallel server networks

arXiv

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arXiv 2019年

作者： Hmedi, Hassan Arapostathis, Ari Pang, Guodong Department of Electrical and Computer Engineering The University of Texas at Austin AustinTX78712 United States Department of Computational and Applied Mathematics George R. Brown College of Engineering Rice University HoustonTX77005 United States

In this paper we study the uniform stability properties of two classes of parallel server networks with multiple classes of jobs and multiple server pools of a tree topology. These include a class of networks with a single non-leaf server pool, such as the 'N' and 'M' models, and networks of any tree topology with class-dependent service rates. We show that with √n safety staffing, and no abandonment, in the Halfin-Whitt regime, the diffusion-scaled controlled queueing processes are exponentially ergodic and their invariant probability distributions are tight, uniformly over all stationary Markov controls. We use a unified approach in which the same Lyapunov function is used in the study of the prelimit and diffusion limit. A parameter called the spare capacity (safety staffing) of the network plays a central role in characterizing the stability results: the parameter being positive is necessary and sufficient that the limiting diffusion is uniformly exponentially ergodic over all stationary Markov controls. We introduce the concept of "system-wide work conserving policies", which are defined as policies that minimize the number of idle servers at all times. This is stronger than the so-called joint work conservation. We show that, provided the spare capacity parameter is positive, the diffusion-scaled processes are geometrically ergodic and the invariant distributions are tight, uniformly over all "system-wide work conserving policies". In addition, when the spare capacity is negative we show that the diffusion-scaled processes are transient under any stationary Markov control, and when it is zero, they cannot be positive *** Codes 90B22 (Primary), 60K25, 49L20, 90B36 (Secondary) Copyright © 2019, The Authors. All rights reserved.

关键词： Diffusion

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SI-spreading-based network embedding in static and temporal networks

arXiv

引用

arXiv 2020年

作者： Zhan, Xiu-Xiu Li, Ziyu Masuda, Naoki Holme, Petter Wang, Huijuan Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Mekelweg 4 Delft2628 CD Netherlands Department of Mathematics University at Buffalo State University of New York Buffalo New YorkNY14260-2900 United States Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New YorkNY14260-2900 United States Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8503 Japan

Link prediction can be used to extract missing information, identify spurious interactions as well as forecast network evolution. Network embedding is a methodology to assign coordinates to nodes in a low-dimensional vector space. By embedding nodes into vectors, the link prediction problem can be converted into a similarity comparison task. Nodes with similar embedding vectors are more likely to be connected. Classic network embedding algorithms are random-walk-based. They sample trajectory paths via random walks and generate node pairs from the trajectory paths. The node pair set is further used as the input for a Skip-Gram model, a representative language model that embeds nodes (which are regarded as words) into vectors. In the present study, we propose to replace random walk processes by a spreading process, namely the susceptible-infected (SI) model, to sample paths. Specifically, we propose two SI-spreading-based algorithms, SINE and TSINE, to embed static and temporal networks, respectively. The performance of our algorithms is evaluated by the missing link prediction task in comparison with state-of-the-art static and temporal network embedding algorithms. Results show that SINE and TSINE outperform the baselines across all six empirical datasets. We further find that the performance of SINE is mostly better than TSINE, suggesting that temporal information does not necessarily improve the embedding for missing link prediction. Moreover, we study the effect of the sampling size, quantified as the total length of the trajectory paths, on the performance of the embedding algorithms. The better performance of SINE and TSINE requires a smaller sampling size in comparison with the baseline algorithms. Hence, SI-spreading-based embedding tends to be more applicable to large-scale networks. Copyright © 2020, The Authors. All rights reserved.

关键词： Network embeddings

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Bilinear compressed sensing under known signs via convex programming

arXiv

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arXiv 2019年

作者： Aghasi, Alireza Ahmed, Ali Hand, Paul Joshi, Babhru J. Mack Robinson College of Business GSU United States Department of Electrical Engineering ITU Lahore Pakistan Department of Mathematics and Khoury College of Computing Science Northeastern University United States Department of Computational and Applied Mathematics Rice University United States

We consider the bilinear inverse problem of recovering two vectors, x ∈ RL and w ∈ RL, from their entrywise product. We consider the case where x and w have known signs and are sparse with respect to known dictionaries of size K and N, respectively. Here, K and N may be larger than, smaller than, or equal to L. We introduce `1-BranchHull, which is a convex program posed in the natural parameter space and does not require an approximate solution or initialization in order to be stated or solved. Under the assumptions that x and w satisfy a comparable-effective-sparsity condition and are S1- and S2-sparse with respect to a random dictionary, we present a recovery guarantee in a noisy case. We show that `1-BranchHull is robust to small dense noise with high probability if the number of measurements satisfy L ≥ Ω ((S1 + S2) log2(K + N)). Numerical experiments show that the scaling constant in the theorem is not too large. We also introduce variants of `1-BranchHull for the purposes of tolerating noise and outliers, and for the purpose of recovering piecewise constant signals. We provide an ADMM implementation of these variants and show they can extract piecewise constant behavior from real images. Copyright © 2019, The Authors. All rights reserved.

关键词： Convex optimization

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Multi-frequency phase synchronization

arXiv

引用

arXiv 2019年

作者： Gao, Tingran Zhao, Zhizhen Committee on Computational and Applied Mathematics Department of Statistics University of Chicago ChicagoIL United States Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL United States

We propose a novel formulation for phase synchronization- the statistical problem of jointly estimating alignment angles from noisy pairwise comparisons-as a nonconvex optimization problem that enforces consistency among the pairwise comparisons in multiple frequency channels. Inspired by harmonic retrieval in signal processing, we develop a simple yet efficient two-stage algorithm that leverages the multi-frequency information. We demonstrate in theory and practice that the proposed algorithm significantly outperforms state-of-the-art phase synchronization algorithms, at a mild computational costs incurred by using the extra frequency channels. We also extend our algorithmic framework to general synchronization problems over compact Lie groups. Copyright © 2019, The Authors. All rights reserved.

关键词： Signal processing

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Single-cell stochastic gene expression kinetics with coupled positive-plus-negative feedback

引用

Physical Review E 2019年第5期100卷 052406-052406页

作者： Chen Jia Le Yi Wang George G. Yin Michael Q. Zhang Division of Applied and Computational Mathematics Beijing Computational Science Research Center Beijing 100193 China Department of Mathematics Wayne State University Detroit Michigan 48202 USA Department of Electrical and Computer Engineering Wayne State University Detroit Michigan 48202 USA Department of Biological Sciences Center for Systems Biology University of Texas at Dallas Richardson Texas 75080 USA MOE Key Laboratory of Bioinformatics Center for Synthetic and Systems Biology Tsinghua University Beijing 100084 China

Here we investigate single-cell stochastic gene expression kinetics in a minimal coupled gene circuit with positive-plus-negative feedback. A triphasic stochastic bifurcation is observed upon increasing the ratio of the positive and negative feedback strengths, which reveals a strong synergistic interaction between positive and negative feedback loops. We discover that coupled positive-plus-negative feedback amplifies gene expression mean but reduces gene expression noise over a wide range of feedback strengths when promoter switching is relatively slow, stabilizing gene expression around a relatively high level. In addition, we study two types of macroscopic limits of the discrete chemical master equation model: the Kurtz limit applies to proteins with large burst frequencies and the Lévy limit applies to proteins with large burst sizes. We derive the analytic steady-state distributions of the protein abundance in a coupled gene circuit for both the discrete model and its two macroscopic limits, generalizing the results obtained by Liu et al. [Chaos 26, 043108 (2016)]. We also obtain the analytic time-dependent protein distribution for the classical Friedman-Cai-Xie random bursting model [Friedman, Cai, and Xie, Phys. Rev. Lett. 97, 168302 (2006)]. Our analytic results are further applied to study the structure of gene expression noise in a coupled gene circuit, and a complete decomposition of noise in terms of five different biophysical origins is provided.

关键词： Gene expression Stochastic processes Gene regulatory networks Markovian processes

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Kernel-based parameter estimation of dynamical systems with unknown observation functions

arXiv

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arXiv 2020年

作者： Lindenbaum, Ofir Sagiv, Amir Mishne, Gal Talmon, Ronen Program in Applied Mathematics Yale University 51 Prospect Street New HavenCT06511 United States Department of Applied Physics and Applied Mathematics Columbia University 500 West 120th Street New YorkNY10027 United States Halicioglu Data Science Institute UC San Diego 9500 Gilman Drive MS0555 United States SDSC 215E San DiegoCA92093-0555 United States Faculty of Electrical Engineering Technion - Israel Institute of Technology Haifa32000 Israel

A low-dimensional dynamical system is observed in an experiment as a high-dimensional signal;for example, a video of a chaotic pendulums system. Assuming that we know the dynamical model up to some unknown parameters, can we estimate the underlying system's parameters by measuring its time-evolution only once? The key information for performing this estimation lies in the temporal inter-dependencies between the signal and the model. We propose a kernel-based score to compare these dependencies. Our score generalizes a maximum likelihood estimator for a linear model to a general nonlinear setting in an unknown feature space. We estimate the system's underlying parameters by maximizing the proposed score. We demonstrate the accuracy and efficiency of the method using two chaotic dynamical systems - the double pendulum and the Lorenz'63 model. © 2020, CC BY.

关键词： Maximum likelihood estimation

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Coarse-scale PDEs from fine-scale observations via machine learning

arXiv

引用

arXiv 2019年

作者： Lee, Seungjoon Kooshkbaghi, Mahdi Spiliotis, Konstantinos Siettos, Constantinos I. Kevrekidis, Ioannis G. Department of Chemical and Biomolecular Engineering Johns Hopkins University Program in Applied and Computational Mathematics Princeton University Institute of Mathematics University of Rostock Dipartimento di Matematica e Applicazioni "Renato Caccioppoli" Universitá degli Studi di Napoli Federico II

Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through e.g. atomistic, agent-based or lattice models) based on first principles. Some of these processes can also be successfully modeled at the macroscopic level using e.g. partial differential equations (PDEs) describing the evolution of the right few macroscopic observables (e.g. concentration and momentum fields). Deriving good macroscopic descriptions (the so-called "closure problem") is often a time-consuming process requiring deep understanding/intuition about the system of interest. Recent developments in data science provide alternative ways to effectively extract/learn accurate macroscopic descriptions approximating the underlying microscopic observations. In this paper, we introduce a data-driven framework for the identification of unavailable coarse-scale PDEs from microscopic observations via machine learning algorithms. Specifically, using Gaussian Processes, Artificial Neural Networks, and/or Diffusion Maps, the proposed framework uncovers the relation between the relevant macroscopic space fields and their time evolution (the right-hand-side of the explicitly unavailable macroscopic PDE). Interestingly, several choices equally representative of the data can be discovered. The framework will be illustrated through the data-driven discovery of macroscopic, concentration-level PDEs resulting from a fine-scale, Lattice Boltzmann level model of a reaction/transport process. Once the coarse evolution law is identified, it can be simulated to produce long-term macroscopic predictions. Different features (pros as well as cons) of alternative machine learning algorithms for performing this task (Gaussian Processes and Artificial Neural Networks), are presented and discussed. Copyright © 2019, The Authors. All rights reserved.

关键词： Neural networks

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