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Identification of fishing ground hotspot of traditional purse seine fisher at northern waters of Aceh – A community-based data collection approach

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IOP Conference Series: Earth and Environmental Science 2021年第1期674卷

作者： H A Haridhi S Rizal M Nanda Muhammad C R Wilson Department of Marine Sciences Faculty of Marine and Fisheries Syiah Kuala University Banda Aceh Indonesia Marine and Fisheries Research Center Syiah Kuala University Banda Aceh Indonesia Doctoral Program of Mathematics and Applied Science Graduate School Syiah Kuala University Banda Aceh Indonesia Asian Development Bank Mandaluyong Phillippines

Fishing grounds are among the most important key of information kept by the fishermen. Understanding the fishing ground locations are considered important as an evaluation of the fishing effort being reached by the fishermen as well as understanding the pressure being received to the fish stock at the surrounding water. The recent study has shown that a community-based data collection approach was found successful in recording and analyzing the fishing ground locations and its properties. One of the few projects implementing this concept is the Community-Based Bathymetric Survey (CBBS), which was realized by the Body of Rehabilitation and Reconstruction (BRR) Nanggroe Aceh Darussalam (NAD) – Nias with funding from the Asian Development Bank (ADB). As many as 151 GPS-transducer recording devices were installed to the fishermen boats along the coast of Aceh, with 45 devices were installed to the traditional purse seine fisher at the northern water of Aceh. These devices were configured to automatically record locations each time the boat moved 30 m. Other important information, such as water depth, time, speed, and sea surface temperature (SST) were recorded as well. This study aims to examine the fishing ground and its fishing hotspot of the traditional purse seine fisher in the northern waters of Aceh. The data collection was performed between June 2007 and May 2009 and gathered as many as 922 data sets consist of 6,170,648 data points, from the 45 purse seine boats. Trough the data, we are able to identify the typical fishing activities of purse seiner and as many as 1,619 data points were being detected. The results indicate that there are five main regions of the fishing ground hotspot in the northern waters of Aceh, i.e. 1. Western offshore of Lhok Nga, 2. Northern offshore of Ulee Lheue, 3. Western offshore of Aceh Islands, 4. Eastern offshore of Aceh Island, and 5. Northern offshore of Krueng Raya. Among the fishing ground hotspot analyzed, the Western offshor

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Towards realistic simulations of human cough: effect of droplet emission duration and spread angle

arXiv

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arXiv 2022年

作者： Li, Mogeng Chong, Kai Leong Ng, Chong Shen Bahl, Prateek de Silva, Charitha M. Verzicco, Roberto Doolan, Con MacIntyre, C. Raina Lohse, Detlef Physics of Fluids Group Max Planck Center for Complex Fluid Dynamics J. M. Burgers Center for Fluid Dynamics and MESA+ Research Institute Department of Science and Technology University of Twente Enschede7500AE Netherlands Max Planck Institute for Dynamics and Self-Organisation Göttingen37077 Germany Shanghai Key Laboratory of Mechanics in Energy Engineering Shanghai Institute of Applied Mathematics and Mechanics School of Mechanics and Engineering Science Shanghai University Shanghai200072 China School of Mechanical and Manufacturing Engineering UNSW Sydney KensingtonNSW2052 Australia Dipartimento di Ingegneria Industriale University of Rome 'Tor Vergata' Roma00133 Italy Gran Sasso Science Institute Viale F. Crispi L'Aquila7 67100 Italy Biosecurity Program The Kirby Institute UNSW Sydney KensingtonNSW2052 Australia College of Public Service & Community Solutions College of Health Solutions Arizona State University PhoenixAZ United States

Human respiratory events, such as coughing and sneezing, play an important role in the host-to-host airborne transmission of diseases. Thus, there has been a substantial effort in understanding these processes: various analytical or numerical models have been developed to describe them, but their validity has not been fully assessed due to the difficulty of a direct comparison with real human exhalations. In this study, we report a unique comparison between datasets that have both detailed measurements of a real human cough using spirometer and particle tracking velocimetry, and direct numerical simulation at similar conditions. By examining the experimental data, we find that the injection velocity at the mouth is not uni-directional. Instead, the droplets are injected into various directions, with their trajectories forming a cone shape in space. Furthermore, we find that the period of droplet emissions is much shorter than that of the cough: experimental results indicate that the droplets with an initial diameter 10µm are emitted within the first 0.05 s, whereas the cough duration is closer to 1 s. These two features (the spread in the direction of injection velocity and the short duration of droplet emission) are incorporated into our direct numerical simulation, leading to an improved agreement with the experimental measurements. Thus, to have accurate representations of human expulsions in respiratory models, it is imperative to include parametrisation of these two features. © 2022, CC BY.

关键词： COVID-19

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Manifold learning-based methods for analyzing single-cell RNA-sequencing data

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Current Opinion in Systems Biology 2018年 7卷 36-46页

作者： Moon, Kevin R. Stanley, Jay S. Burkhardt, Daniel van Dijk, David Wolf, Guy Krishnaswamy, Smita Department of Genetics Yale University New Haven CT United States Applied Mathematics Program Yale University New Haven CT United States Computational Biology and Bioinformatics Program Yale University New Haven CT United States Department of Computer Science Yale University New Haven CT United States

Recent advances in single-cell RNA sequencing technologies enable deep insights into cellular development, gene regulation, and phenotypic diversity by measuring gene expression for thousands of cells in a single experiment. While these technologies hold great potential for improving our understanding of cellular states and progression, they also pose new challenges and require advanced mathematical and algorithmic tools to extract underlying biological signals. In this review, we cover one of the most promising avenues of research into unlocking the potential of scRNA-seq data: the field of manifold learning, and the related manifold assumption in data analysis. Manifold learning provides a powerful structure for algorithmic approaches to process the data, extract its dynamics, and infer patterns in it. In particular, we cover manifold learning-based methods for denoising the data, revealing gene interactions, extracting pseudotime progressions with model fitting, visualizing the cellular state space via dimensionality reduction, and clustering the data. © 2017 Published by Elsevier Ltd.

关键词： Data mining Gene regulatory networks Imputation Manifold learning Pseudotime trends Single cell RNA sequencing

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Geometry based data generation 32

Geometry based data generation

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32nd Conference on Neural Information Processing Systems, NeurIPS 2018

作者： Lindenbaum, Ofir Wolf, Guy Stanley, Jay S. Krishnaswamy, Smita Applied Mathematics Program Yale University New HavenCT06511 United States Computational Biology and Bioinformatics Program Yale University New HavenCT06510 United States Departments of Genetics and Computer Science Yale University New HavenCT06510 United States

We propose a new type of generative model for high-dimensional data that learns a manifold geometry of the data, rather than density, and can generate points evenly along this manifold. This is in contrast to existing generative models that represent data density, and are strongly affected by noise and other artifacts of data collection. We demonstrate how this approach corrects sampling biases and artifacts, thus improves several downstream data analysis tasks, such as clustering and classification. Finally, we demonstrate that this approach is especially useful in biology where, despite the advent of single-cell technologies, rare subpopulations and gene-interaction relationships are affected by biased sampling. We show that SUGAR can generate hypothetical populations, and it is able to reveal intrinsic patterns and mutual-information relationships between genes on a single-cell RNA sequencing dataset of hematopoiesis. © 2018 Curran Associates Inc..All rights reserved.

关键词： Genes

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How SGD selects the global minima in over-parameterized learning: a dynamical stability perspective 18

How SGD selects the global minima in over-parameterized lear...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Lei Wu Chao Ma Weinan E. School of Mathematical Sciences Peking University Beijing P.R. China Program in Applied and Computational Mathematics Princeton University Princeton NJ Department of Mathematics and Program in Applied and Computational Mathematics Princeton University Princeton NJ and Beijing Institute of Big Data Research Beijing P.R. China

The question of which global minima are accessible by a stochastic gradient decent (SGD) algorithm with specific learning rate and batch size is studied from the perspective of dynamical stability. The concept of non-uniformity is introduced, which, together with sharpness, characterizes the stability property of a global minimum and hence the accessibility of a particular SGD algorithm to that global minimum. In particular, this analysis shows that learning rate and batch size play different roles in minima selection. Extensive empirical results seem to correlate well with the theoretical findings and provide further support to these claims.

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Isotope effects in liquid water via deep potential molecular dynamics

arXiv

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arXiv 2019年

作者： Ko, Hsin-Yu Zhang, Linfeng Santra, Biswajit Wang, Han Weinan, E. DiStasio, Robert A. Cara, Roberto Department of Chemistry Princeton University PrincetonNJ08544 United States Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States Department of Physics Temple University PhiladelphiaPA19122 United States Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Huayuan Road 6 Beijing100088 China Department of Mathematics Princeton University PrincetonNJ08544 United States Department of Chemistry and Chemical Biology Cornell University IthacaNY14853 United States Department of Physics Princeton Institute for the Science and Technology of Materials Princeton University PrincetonNJ08544 United States

A comprehensive microscopic understanding of ambient liquid water is a major challenge for ab initio simulations as it simultaneously requires an accurate quantum mechanical description of the underlying potential energy surface (PES) as well as extensive sampling of configuration space. Due to the presence of light atoms (e.g., H or D), nuclear quantum fluctuations lead to observable changes in the structural properties of liquid water (e.g., isotope effects), and therefore provide yet another challenge for ab initio approaches. In this work, we demonstrate that the combination of dispersion-inclusive hybrid density functional theory (DFT), the Feynman discretized path-integral (PI) approach, and machine learning (ML) constitutes a versatile ab initio based framework that enables extensive sampling of both thermal and nuclear quantum fluctuations on a quite accurate underlying PES. In particular, we employ the recently developed deep potential molecular dynamics (DPMD) model - a neural-network representation of the ab initio PES - in conjunction with a PI approach based on the generalized Langevin equation (PIGLET) to investigate how isotope effects influence the structural properties of ambient liquid H2O and D2O. Through a detailed analysis of the interference differential cross sections as well as several radial and angular distribution functions, we demonstrate that this approach can furnish a semi-quantitative prediction of these subtle isotope effects. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep neural networks

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Chemical Electron Tomography at Lower Dose and Higher Resolution

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Microscopy and Microanalysis 2024年第SUPPLEMENT_1期30卷

作者： Hovden, Robert Schwartz, Jonathan Sung, Suk Hyun Di, Zichao Wendy Jiang, Yi Manassa, Jason Pietryga, Jacob Qian, Yiwen Cho, Min Gee Rowell, Jonathan L Zheng, Huihuo Robinson, Richard D Gu, Junsi Kirilin, Alexey Rozeveld, Steve Ercius, Peter Scott, Mary Department of Materials Science and Engineering University of Michigan Ann Arbor MI Applied Physics Program University of Michigan Ann Arbor MI Chan Zuckerberg Initiative Redwood City CA Rowland Institute at Harvard Cambridge MA. Mathematics and Computer Science Division Argonne National Laboratory Lemont IL Advanced Photon Source Facility Argonne National Laboratory Lemont IL Department of Materials Science and Engineering U.C. Berkeley Berkeley CA National Center for Electron Microscopy Molecular Foundry Berkeley CA Department of Chemistry and Chemical Biology Cornell University Ithaca NY Argonne Leadership Computing Facility Argonne National Laboratory Lemont IL Dow Chemical Co. Terneuzen Netherlands Dow Chemical Co. Midland MI

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Stationary frequencies and mixing times for neutral drift processes with spatial structure

Stationary frequencies and mixing times for neutral drift pr...

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作者： McAvoy, Alex Adlam, Ben Allen, Benjamin Nowak, Martin A. Program for Evolutionary Dynamics CambridgeMA02138 United States School of Engineering and Applied Sciences CambridgeMA02138 United States Department of Organismic and Evolutionary Biology CambridgeMA02138 United States Department of Mathematics Harvard University CambridgeMA02138 United States Department of Mathematics Emmanuel College BostonMA02115 United States

We study a general setting of neutral evolution in which the population is of finite, constant size and can have spatial structure. Mutation leads to different genetic types (traits), which can be discrete or continuous. Under minimal assumptions, we show that the marginal trait distributions of the evolutionary process, which specify the probability that any given individual has a certain trait, all converge to the stationary distribution of the mutation process. In particular, the stationary frequencies of traits in the population are independent of its size, spatial structure and evolutionary update rule, and these frequencies can be calculated by evaluating a simple stochastic process describing a population of size one (i.e. the mutation process itself). We conclude by analysing mixing times, which characterize rates of convergence of the mutation process along the lineages, in terms of demographic variables of the evolutionary process. © 2018 The Author(s) Published by the Royal Society. All rights reserved.

关键词： Random processes

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MONGE-AMPÈRE FLOW FOR GENERATIVE MODELING

arXiv

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arXiv 2018年

作者： Zhang, Linfeng Weinan, E. Wang, Lei Program in Applied and Computational Mathematics Princeton University United States Department of Mathematics and Program in Applied and Computational Mathematics Princeton University United States Center for Data Science Peking University Beijing Institute of Big Data Research Beijing100871 China Institute of Physics Chinese Academy of Sciences Beijing100190 China

We present a deep generative model, named Monge-Ampère flow, which builds on continuous-time gradient flow arising from the Monge-Ampère equation in optimal transport theory. The generative map from the latent space to the data space follows a dynamical system, where a learnable potential function guides a compressible fluid to flow towards the target density distribution. Training of the model amounts to solving an optimal control problem. The Monge-Ampère flow has tractable likelihoods and supports efficient sampling and inference. One can easily impose symmetry constraints in the generative model by designing suitable scalar potential functions. We apply the approach to unsupervised density estimation of the MNIST dataset and variational calculation of the two-dimensional Ising model at the critical point. This approach brings insights and techniques from Monge-Ampère equation, optimal transport, and fluid dynamics into reversible flow-based generative models. Copyright © 2018, The Authors. All rights reserved.

关键词： Statistical mechanics

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Inverting the markovian projection, with an application to local stochastic volatility models

arXiv

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arXiv 2019年

作者： Lacker, Daniel Shkolnikov, Mykhaylo Zhang, Jiacheng Ieor Department Columbia University New YorkNY10027 United States Orfe Department Bendheim Center for Finance Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 Orfe Department Princeton University PrincetonNJ08544 United States

We study two-dimensional stochastic differential equations (SDEs) of McKean- Vlasov type in which the conditional distribution of the second component of the solution given the first enters the equation for the first component of the solution. Such SDEs arise when one tries to invert the Markovian projection developed in [Gyö86], typically to produce an It o process with the fixed-time marginal distributions of a given one-dimensional diffusion but richer dynamical features. We prove the strong existence of stationary solutions for these SDEs, as well as their strong uniqueness in an important special case. Variants of the SDEs discussed in this paper enjoy frequent application in the calibration of local stochastic volatility models in finance, despite the very limited theoretical understanding. Copyright © 2019, The Authors. All rights reserved.

关键词： Stochastic systems

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