"Tonga" : A Novel Toolbox for Straightforward Bioimage Analysis

Techniques to acquire and analyze biological images are central to life science. However, the workflow downstream of imaging can be complex and involve several tools, leading to creation of very specialized scripts and pipelines that are difficult to reproduce by other users. Although many commercial and open-source software are available, non-expert users are often challenged by a knowledge gap in setting up analysis pipelines and selecting correct tools for extracting data from images. Moreover, a significant share of everyday image analysis requires simple tools, such as precise segmentation, cell counting, and recording of fluorescent intensities. Hence, there is a need for user-friendly platforms for everyday image analysis that do not require extensive prior knowledge on bioimage analysis or coding. We set out to create a bioimage analysis software that has a straightforward interface and covers common analysis tasks such as object segmentation and analysis, in a practical, reproducible, and modular fashion. We envision our software being useful for analysis of cultured cells, histological sections, and high-content data.Peer reviewe

On the characterization of magnetic reconnection in global MHD simulations

The conventional definition of reconnection rate as the electric field parallel to an x-line is problematic in global MHD simulations for several reasons: the x-line itself may be hard to find in a non-trivial geometry such as at the magnetopause, and the lack of realistic resistivity modelling leaves us without reliable non-convective electric field. In this article we describe reconnection characterization methods that avoid those problems and are practical to apply in global MHD simulations. We propose that the reconnection separator line can be identified as the region where magnetic field lines of different topological properties meet, rather than by local considerations. The global convection associated with reconnection is then quantified by calculating the transfer of mass, energy or magnetic field across the boundary of closed and open field line regions. The extent of the diffusion region is determined from the destruction of electromagnetic energy, given by the divergence of the Poynting vector. Integrals of this energy conversion provide a way to estimate the total reconnection efficiency

Dual decomposition scheme for resource allocation problem in networks with moving nodes

We consider a two-level, two criteria, optimization problem of resource allocation in communication networks, which consists of maximizing the total network utility, i.e., the fee paid by the consumers, and minimizing the costs of implementing these resources. In this paper we present a new dual iterative method for solving this problem, which enables us to utilize its decomposable structure via sequential solution of families of one-dimensional problems. We compare our new dual method to known methods for solving this problem. In general, we give a new promising approach in the paper to solve resource allocation problems in communication networks. Moreover, we suggest a new way to adjust the basic model to networks with moving nodes. We present numerical results about the computational efficiency of the methods considered. © 2012 IEEE

Optimization of wireless networks performance: An approach based on a partial penalty method

© 2017, North Atlantic University Union. All rights reserved.We study an optimization problem for a wireless telecommunication network stated as a generalized transportation problem (TP), where m (the number of “sellers”) is the number of network providers, and n (the number of “buyers”) is the number of connections established at a given time moment. Since in practice initial data of such problems are, generally speaking, inexact and/or vary rather quickly, it is more important to obtain an approximate solution of the problem (with a prescribed accuracy) within a reasonable time interval rather than to solve it precisely (but in a longer time). We propose to solve this problem by a technique that explores the idea of penalty functions, namely, the so-called Partial Penalty Method (PPM, for short). As distinct from exact solution methods for TP (e.g., the method of potentials), our approach allows us to further extend the class of considered problems by including to it TP with nonlinear objective functions. As an example, we consider a TP, where the objective function (expenses connected with resource allocation) is such that the price of the unit amount of the resource is not constant but depends on the total purchase size. In addition, we study the limit behavior of solutions to TP whose data are subject to fading disturbances. Since in our approach the initial point is not necessarily admissible, we use an approximate solution of each problem as the initial point for the next one. As expected, under certain requirements to disturbances the sequence of solutions to “disturbed” problems tends to a solution of the limit problem. We prove experimentally that PPM is more efficient than the usual variant of the Penalty Function Method (the Full Penalty Method, or FPM). The preference of PPM over FPM is more evident for n much greater than m

Dual methods for optimal allocation of total network resources

© 2016, North Atlantic University Union NAUN. All rights reserved.We consider a general problem of optimal allocation of a homogeneous resource (bandwidth) in a wireless communication network, which is decomposed into several zones (clusters). The network manager must satisfy different users requirements. However, they may vary essentially from time to time. This makes the fixed allocation rules inefficient and requires certain adjustment procedure for each selected time period. Besides, sometimes users requirements may exceed the local network capacity in some zones, hence the network manager can buy additional volumes of this resource. This approach leads to a constrained convex optimization problem. We discuss several ways to find a solution of this problem, which exploit its special features. We suggest the dual Lagrangian method to be applied to selected constraints. This in particular enables us to replace the initial problem with one-dimensional dual one. We consider the case of the affine cost (utility) functions, when each calculation of the value of the dual function requires solution of a special linear programming problem. We can also utilize the zonal resource decomposition approach, which leads to a sequence of onedimensional optimization problems. The results of the numerical experiments confirm the preferences of the first method

Vector resource allocation problems in communication networks

We consider a problem of optimal allocation of a homogeneous resource in spatially distributed systems such as communication networks, where both utilities of users and network expenses must be taken into account. The network is divided into zones which leads to a two-level vector optimization problem and involves non-differentiable functions whose values are computed algorithmically. We propose several approaches to find a solution. Also, new simple subgradient type methods for non-differentiable Pareto optimization problems are suggested. Their performance is illustrated by computational results on test problems. © 2013 IFIP

Optimisation problems for control of distributed resources

We consider a two-level optimisation problem of resource allocation in communication networks, which is based on profit maximisation of the network subject to capacity constraints. The cost function of the upper level problem involves a sum of non-differentiable functions whose values are computed algorithmically. The corresponding solution methods utilise duality theory and decomposition technique for optimisation problems. Copyright © 2011 Inderscience Enterprises Ltd

Optimization problems for distribution of resources in spatial systems

We consider a problem of optimal allocation of a homogeneous resource in spatially distributed systems such as communication networks, where both utilities of consumers and network expenses must be taken into account. This approach leads to a two-objective optimization problem, which involves non-differentiable functions whose values are computed algorithmically. We propose several approaches to define a solution and to construct corresponding solution methods for such problems. In particular, new subgradient methods for non-differentiable Pareto optimization problems are suggested. Their work is illustrated by computational results on test problems