Search

As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Many scientific problems focus on observed patterns of change or on how to design a system to achieve particular dynamics. Those problems often require fitting differential equation models to target trajectories. Fitting such models can be difficult because each evaluation of the fit must calculate the distance between the model and target patterns at numerous points along a trajectory. The gradient of the fit with respect to the model parameters can be challenging to compute. Recent technical advances in automatic differentiation through numerical differential equation solvers potentially change the fitting process into a relatively easy problem, opening up new possibilities to study dynamics. However, application of the new tools to real data may fail to achieve a good fit. This article illustrates how to overcome a variety of common challenges, using the classic ecological data for oscillations in hare and lynx populations. Models include simple ordinary differential equations (ODEs) and neural ordinary differential equations (NODEs), which use artificial neural networks to estimate the derivatives of differential equation systems. Comparing the fits obtained with ODEs versus NODEs, representing small and large parameter spaces, and changing the number of variable dimensions provide insight into the geometry of the observed and model trajectories. To analyze the quality of the models for predicting future observations, a Bayesian-inspired preconditioned stochastic gradient Langevin dynamics (pSGLD) calculation of the posterior distribution of predicted model trajectories clarifies the tendency for various models to underfit or overfit the data. Coupling fitted differential equation systems with pSGLD sampling provides a powerful way to study the properties of optimization surfaces, raising an analogy with mutation-selection dynamics on fitness landscapes.

Capture fisheries production is decreasing due to natural resources or weather conditions, so other resources are needed to support fisheries production. One alternative is to increase the production of aquaculture commodities through seawater, freshwater, or brackish water cultivation. Various potentials for developing aquaculture have been developed in various regions, including the North Sulawesi region. Grouping aquaculture commodity production according to container type is very important to maintain and increase aquaculture production. This research aims to cluster aquaculture production in the North Sulawesi area using the K-Means method using Euclidean, Manhattan, and Minkowsky distances. The results of the research obtained three clusters, namely the first cluster, the region with the highest production of pond container types, namely the Sitaro Islands, and for second cluster consisting of 13 regions that have variations in production ranging from low to high for the types of floating net containers and ponds, while third cluster is the region Bitung with moderate production for pond types. It is hoped that this research can help related agencies to create policies to increase the production potential of aquaculture.

As the dimensionality, throughput and complexity of cytometry data increases, so does the demand for user-friendly, interactive analysis tools that leverage high-performance machine learning frameworks. Here we introduce FlowAtlas: an interactive web application that enables dimensionality reduction of cytometry data without down-sampling and that is compatible with datasets stained with non-identical panels. FlowAtlas bridges the user-friendly environment of FlowJo and computational tools in Julia developed by the scientific machine learning community, eliminating the need for coding and bioinformatics expertise. New population discovery and detection of rare populations in FlowAtlas is intuitive and rapid. We demonstrate the capabilities of FlowAtlas using a human multi-tissue, multi-donor immune cell dataset, highlighting key immunological findings. FlowAtlas is available at https://github.com/gszep/FlowAtlas.jl.git.

This paper presents novel free open-source software JuliaCAP for symbolic analysis of linear time-invariant electric circuits in the complex domain of the Unilateral Laplace Transform or Phasor Transform. Modified Nodal Analysis (MNA) has been chosen for formulation of circuit equations in JuliaCAP. JuliaCAP has been developed by Kristina Rajković, Glorija Došlo, Nikola Radojević, Tamara Petković and Ivana Stanojević, at the University of Belgrade - School of Electrical Engineering, Belgrade, Serbia. JuliaCAP is written in Julia, a free open-source (MIT licensed) programming language. Whereas Julia is a relatively new language, there will be some words about it. As a free opensource software package, the underlining algorithm is revealed in full detail, so anybody can get a better understanding of the used methods. We offer JuliaCAP as a useful tool for solving linear time-invariant electric circuits. The future directives should be the improvement of simplifying expressions and the absence of the possibility to solve electric circuits in the complex domain of the Unilateral Laplace Transform. Also, it is shown through examples how this software operates. Electric circuits that are used in examples are Riordan gyrator network, 3rd order Butterworth Low-pass Filter, Subtractor and Adder.

The open-source code PSSFSS for analysis and design of polarization selective surfaces (PSSs), and frequency selective surfaces (FSSs) is presented, beginning with an introduction to the Julia programming language in which the code is written. Analysis methods and algorithms used in PSSFSS are described, highlighting features of Julia that make it attractive for developing this type of application. Usage examples illustrate the code's ease of use, speed, and accuracy. [ABSTRACT FROM AUTHOR]

How do cellular regulatory networks solve the challenges of life? This article presents computer software to study that question, focusing on how transcription factor networks transform internal and external inputs into cellular response outputs. The example challenge concerns maintaining a circadian rhythm of molecular concentrations. The system must buffer intrinsic stochastic fluctuations in molecular concentrations and entrain to an external circadian signal that appears and disappears randomly. The software optimizes a stochastic differential equation of transcription factor protein dynamics and the associated mRNAs that produce those transcription factors. The cellular network takes as inputs the concentrations of the transcription factors and produces as outputs the transcription rates of the mRNAs that make the transcription factors. An artificial neural network encodes the cellular input-output function, allowing efficient search for solutions to the complex stochastic challenge. Several good solutions are discovered, measured by the probability distribution for the tracking deviation between the stochastic cellular circadian trajectory and the deterministic external circadian pattern. The solutions differ significantly from each other, showing that overparameterized cellular networks may solve a given challenge in a variety of ways. The computation method provides a major advance in its ability to find transcription factor network dynamics that can solve environmental challenges. The article concludes by drawing an analogy between overparameterized cellular networks and the dense and deeply connected overparameterized artificial neural networks that have succeeded so well in deep learning. Understanding how overparameterized networks solve challenges may provide insight into the evolutionary design of cellular regulation. [ABSTRACT FROM AUTHOR]

The Anaerobic Digestion Model 1 is a system of differential equations that was developed by an International Water Association task group to describe the processes of anaerobic digestion. An implementation of the Anaerobic Digestion Model 1 (ADM1) must be as computationally fast and flexible as possible. The ADM1jl package was designed with those requirements in mind, exploiting the Julia Programming Language’s computational speed to create a programme that is both fast (between 15 and 800 times faster than other implementations tested, depending on the implementation being compared to) and user friendly.

Transcription factors (TFs) affect the production of mRNAs. In essence, the TFs form a large computational network that controls many aspects of cellular function. This article introduces a computational method to optimize TF networks. The method extends recent advances in artificial neural network optimization. In a simple example, computational optimization discovers a four-dimensional TF network that maintains a circadian rhythm over many days, successfully buffering strong stochastic perturbations in molecular dynamics and entraining to an external day-night signal that randomly turns on and off at intervals of several days. This work highlights the similar challenges in understanding how computational TF and neural networks gain information and improve performance.

Computer modeling is one of the most common approaches to the analysis of thin-walled shell structures stress-strain state analysis. It requires considerable time costs and high-performance hardware, especially when it is necessary to conduct a comparative analysis of various shell configurations. In this paper, we propose the use of deep learning methods to improve performance of this process. The purpose of work is to develop methods for high-performance computer simulation of thin-walled shell structures using deep neural networks, allowing to take into account geometric and physical properties of the structure as well as the load applied to it. A training approach and deep neural network architecture were developed to perform computer modeling of the stress-strain state of the shell. To form a training dataset, a computational experiment was carried out to simulate 3904 different configurations of doubly curved shallow shells that differ in linear dimensions, curvature radii, and materials used. Based on this dataset, 30 deep neural networks with different architectures were trained. To determine the optimal architecture in terms of modeling accuracy, mean absolute percentage error with clipping near-zero samples was calculated for each of the neural networks based on the test dataset. A network has been developed that allows calculating the stress-strain state of different shell configurations under an arbitrary uniformly distributed load. This is the first solution in the field of shell neural network modeling that allows us to vary the applied load, geometric and physical parameters of the shell and obtain calculation results at an arbitrary point of its middle surface. Performance measurements were carried out which show that the developed neural network allows simulating the stress-strain state of a shell structure 2117 times faster compared to the duration of solving the same problem by classical simulation. The modeling error using the network is at an acceptable level. An original architecture of a neural network for modeling the stress-strain state of shells was proposed which, through minor modifications, can be adapted for high-performance modeling of other building structure types. In accordance with the described architecture, a deep neural network was trained which reduces the computation time by several orders of magnitude. The results obtained are of high practical importance for researchers in the field of thin-walled shells modeling since they allow us to significantly reduce the time costs associated with conducting computational experiments. One of the possible applications for developed solution is prototyping of various shell configurations. Once prototyping is complete, the most efficient shell configurations can be explored in detail using classical computer simulation techniques.

The Julia programming language is a specialized language for scientific computing. It is relatively new, so most of the libraries for it are in the active development stage. In this article, the authors consider the possibilities of the language in the field of mathematical statistics. Special emphasis is placed on the technical component, in particular, the process of installing and configuring the software environment is described in detail. Since users of the Julia language are often not professional programmers, technical issues in setting up the software environment can cause difficulties that prevent them from quickly mastering the basic features of the language. The article also describes some features of Julia that distinguish it from other popular languages used for scientific computing. The third part of the article provides an overview of the two main libraries for mathematical statistics. The emphasis is again on the technical side in order to give the reader an idea of the general possibilities of the language in the field of mathematical statistics.

The Anaerobic Digestion Model 1 is the quasi-industry standard for modelling anaerobic digestion, and it has seen several new implementations in recent years. It is assumed that these implementations would give the same results; however, a thorough comparison of these implementations has never been reported. This paper considers four different implementations of ADM1: one in Julia, one in Java, and two in Python. The Julia code is a de novo implementation of the ODE formulation of ADM1 that is reported here for the first time. The existing Java and Python codes implement the more common DAE formulation. Therefore, this paper also examines how DAE implementations compare to ODE implementations in terms of computational speed as well as solutions returned. As expected, the ODE and DAE forms both return comparable solutions. However, contrary to popular belief, the Julia ODE implementation is faster than the DAE implementations, namely by one to three orders of magnitude of compute time, depending on the simulation scenario and the reference implementation used for comparison. [ABSTRACT FROM AUTHOR]

Hyperbolic complex numbers are used in the description of hyperbolic spaces. One of the well-known examples of such spaces is the Minkowski space, which plays a leading role in the problems of the special theory of relativity and electrodynamics. However, such numbers are not very common in different programming languages. Of interest is the implementation of hyperbolic complex in scientific programming languages, in particular, in the Julia language. The Julia language is based on the concept of multiple dispatch. This concept is an extension of the concept of polymorphism for object-oriented programming languages. To implement hyperbolic complex numbers, the multiple dispatching approach of the Julia language was used. The result is a library that implements hyperbolic numbers. Based on the results of the study, we can conclude that the concept of multiple dispatching in scientific programming languages is convenient and natural.

Abstract Many scientific problems focus on observed patterns of change or on how to design a system to achieve particular dynamics. Those problems often require fitting differential equation models to target trajectories. Fitting such models can be difficult because each evaluation of the fit must calculate the distance between the model and target patterns at numerous points along a trajectory. The gradient of the fit with respect to the model parameters can be challenging to compute. Recent technical advances in automatic differentiation through numerical differential equation solvers potentially change the fitting process into a relatively easy problem, opening up new possibilities to study dynamics. However, application of the new tools to real data may fail to achieve a good fit. This article illustrates how to overcome a variety of common challenges, using the classic ecological data for oscillations in hare and lynx populations. Models include simple ordinary differential equations (ODEs) and neural ordinary differential equations (NODEs), which use artificial neural networks to estimate the derivatives of differential equation systems. Comparing the fits obtained with ODEs versus NODEs, representing small and large parameter spaces, and changing the number of variable dimensions provide insight into the geometry of the observed and model trajectories. To analyze the quality of the models for predicting future observations, a Bayesian‐inspired preconditioned stochastic gradient Langevin dynamics (pSGLD) calculation of the posterior distribution of predicted model trajectories clarifies the tendency for various models to underfit or overfit the data. Coupling fitted differential equation systems with pSGLD sampling provides a powerful way to study the properties of optimization surfaces, raising an analogy with mutation‐selection dynamics on fitness landscapes.

We present an implementation of the Atiyah-Bott residue formula for M ‾ 0 , m (P n , d). We use this implementation to compute a large number of Gromov-Witten invariants of genus 0, including intersection numbers of rational curves on general complete intersections. We also compute some numbers of rational contact curves satisfying suitable Schubert conditions. Our computations confirm known predictions made by Mirror Symmetry. The code we developed for these problems is publicly available and can also be used for other types of computations. [ABSTRACT FROM AUTHOR]

Bu çalışmada, Julia programlama dili tabanlı bir tanımlayıcı sistem dili ve amaca yönelik hızlı ve etkili sistem benzetimlerine ve çevrimiçi ve çevrimdışı çözümlemelerine olanak sağlayan bir benzetim ortamı geliştirilmiştir. Geliştirilen benzetim ortamında ayrık zamanlı ya da sürekli zamanlı, statik ya da dinamik sistemlerin benzetimleri mümkündür. Özellikle, adi, rastgele adi, rassal, cebirsel, gecikmeli türev denklemleri ve ayrık fark denklemleri gibi çok farklı denklem türleri ile modellenen dinamik sistemlerin benzetimi yapılabilmektedir. Benzetim sırasında modelin bağlantıları üzerinden akan veri çevrimiçi ve çevrimdışı olarak işlenebilmekte ve özelleşmiş çözümlemeler yapılabilmektedir. Bu çözümlemelerin, standart Julia kütüphanesi ya da çeşitli Julia paketleri kullanılarak kolaylıkla tanımlanabilecek eklentiler ile de zenginleştirilmesi mümkündür. Benzetim model bileşenlerinin bireysel ve örnekleme zaman aralıklarında eşzamanlı ve paralel evrilmesi ile yapılır. Bileşenlerin birbirinden bağımsız evrilmesi farklı matematiksel denklemler ile ifade edilen bileşenlerden oluşan modellerin benzetimine olanak sağlarken; bileşenlerin eşzamanlı ve paralel evrilmesi ise benzetim hızını artırmaktadır.

Forest fires lead to the serious damage of ecological state and national economy of the country. This problem is especially relevant for Siberians. According to Greenpeace, Siberian forest fires in 2019 reached record levels in the entire history of observation in terms of burning area and the amount of carbon dioxide emitted into the atmosphere. It leads not only to a deterioration in the health of Siberians, but also to environmental problems of the region. Note that the large-scale fire-prevention measures entails enormous financial costs. Therefore, economical, ecological and mathematical modeling of the situations, arose in forest fires countering, becomes actual. The paper is devoted to optimal control problem of forest fires fighting. Its prototype is the well-known Parks model. To investigate the model, we apply the modern programming language Julia, which is designed to mathematical calculations and numerical studies. We made an extensive computational experiment in this model and a numerical analysis of corresponding optimal control problems. The obtained results were examined both on the adequacy of the model, and on the possibility of using the Julia language and the included solvers of mathematical problems.

In this study, a Julia programming language based system description language and simulation environment that enables fast and effective system simulations together with online and offline data analysis is introduced. In the simulation environment developed, it is possible to simulate discrete time or continuous time, static or dynamical systems. In particular, it is possible to simulate dynamical systems modeled by different types of equations, such as the ordinary differential, random ordinary differential, stochastic differential, differential-algebraic, delayed differential equations, and discrete-time difference equations. During the simulation, the data flowing through the links of the model can be processed online and offline, and specialized analysis can be performed. These analyzes can also be enriched with plugins that can be easily defined using the standard Julia library or various Julia packages. The simulation is performed by evolving the model components individually and parallelly between sampling time intervals. The independent evolution of the components allows the simulation of the models consisting of the components represented by different mathematical equations, while the parallel evolution of components increases the simulation speed. [ABSTRACT FROM AUTHOR]

Goal: This paper aims to implement a periodic capacitated vehicle routing problem with simulated annealing algorithm using a real-life industrial distribution problem and to recommend it to industry practitioners. The authors aimed to achieve high-performance solutions by coding a manually solved industrial problem and thus solving a real-life vehicle routing problem using Julia language and simulated annealing algorithm. Design / Methodology / Approach: The vehicle routing problem (VRP) that is a widely studied combinatorial optimization and integer programming problem, aims to design optimal tours for a fleet of vehicles serving a given set of customers at different locations. The simulated annealing algorithm is used for periodic capacitated vehicle routing problem. Julia is a state-of-art scientific computation language. Therefore, a Julia programming language toolbox developed for logistic optimization is used. Results: The results are compared to savings algorithms from Matlab in terms of solution quality and time. It is seen that the simulated annealing algorithm with Julia gives better solution quality in reasonable simulation time compared to the constructive savings algorithm. Limitations of the investigation: The data of the company is obtained from 12 periods with a history of four years. About the capacitated vehicle routing problem, the homogenous fleet with 3000 meters/vehicle is used. Then, the simulated annealing design parameters are chosen rule-of-thumb. Therefore, better performance can be obtained by optimizing the simulated annealing parameters. Practical implications: In this study, a furniture roving parts manufacturing company that have 30 customers in Denizli, an industrial city in the west part of Turkey, is investigated. Before the scheduling implementation with Julia, the company has no effective and efficient planning as they have been using spreadsheet programs for vehicle scheduling solutions. In this study, the solutions with Julia are used in practice for the distribution with higher utilization rate and minimum number of vehicles. The simulated annealing and savings algorithms are compared in terms of solution time and performance. The savings algorithm has produced better solution time, the simulated annealing approach has minimum total distance objective value, minimum number of required vehicles, and maximum vehicle utilization rate for the whole model. Thus, this paper can contribute to small scale business management in the sense of presenting a digitalization solution for the vehicle scheduling solution. Also, Julia application of simulated annealing for vehicle scheduling is demonstrated that can help both academicians and practitioners in organizations, mainly in logistics and distribution problems. Originality / Value: The main contribution of this study is a new solution method to capacitated vehicle routing problems for a real-life industrial problem using the advantages of the high-level computing language Julia and a meta-heuristic algorithm, the simulated annealing method. Keywords: Capacitated vehicle routing problem, Simulated annealing algorithm, Julia programming language.

Adaptive cubic regularization (ARC) and trust-region (TR) methods use modified linear systems to compute their steps. The modified systems consist in adding some multiple of the identity matrix (or a well-chosen positive definite matrix) to the Hessian to obtain a sufficiently positive definite linear system, the so called shifted system. This type of system was first proposed by Levenberg and Marquardt. Some trial and error is often involved to obtain a specified value for this shift parameter. We provide an efficient unified implementation to track the shift parameter; our implementation encompasses many ARC and TR variants. [ABSTRACT FROM AUTHOR]

A new software package for the Julia language, CountTimeSeries.jl, is under review, which provides likelihood based methods for integer-valued time series. The package’s functionalities are showcased in a simulation study on finite sample properties of Maximum Likelihood (ML) estimation and three real-life data applications. First, the number of newly infected COVID-19 patients is predicted. Then, previous findings on the need for overdispersion and zero inflation are reviewed in an application on animal submissions in New Zealand. Further, information criteria are used for model selection to investigate patterns in corporate insolvencies in Rhineland-Palatinate. Theoretical background and implementation details are described, and complete code for all applications is provided online. The CountTimeSeries package is available at the general Julia package registry.

The conservation field is experiencing a rapid increase in the amount, variety, and quality of spatial data that can help us understand species movement and landscape connectivity patterns. As interest grows in more dynamic representations of movement potential, modelers are often limited by the capacity of their analytic tools to handle these datasets. Technology developments in software and high-performance computing are rapidly emerging in many fields, but uptake within conservation may lag, as our tools or our choice of computing language can constrain our ability to keep pace. We recently updated Circuitscape, a widely used connectivity analysis tool developed by Brad McRae and Viral Shah, by implementing it in Julia, a high-performance computing language. In this initial re-code (Circuitscape 5.0) and later updates, we improved computational efficiency and parallelism, achieving major speed improvements, and enabling assessments across larger extents or with higher resolution data. Here, we reflect on the benefits to conservation of strengthening collaborations with computer scientists, and extract examples from a collection of 572 Circuitscape applications to illustrate how through a decade of repeated investment in the software, applications have been many, varied, and increasingly dynamic. Beyond empowering continued innovations in dynamic connectivity, we expect that faster run times will play an important role in facilitating co-production of connectivity assessments with stakeholders, increasing the likelihood that connectivity science will be incorporated in land use decisions.

This note explains how MPI may be used with the Julia programming language. An example of a simple Monte Carlo study is presented, with code. The code is intended to serve as a general purpose template for more relevant applications. A second example shows how the template code may be adapted to perform a Monte Carlo study of the properties of an approximate Bayesian computing estimator of actual research interest. All of the code is available at . [ABSTRACT FROM AUTHOR]