EXPERIMENTAL COMPARISON OF MATRIX ALGORITHMS FOR DATAFLOW COMPUTER ARCHITECTURE

In this paper we draw our attention to several algorithms for the dataflow computer paradigm, where the dataflow computation is used to augment the classical control-flow computation and, hence, strives to obtain an accelerated algorithm. Our main goal is to experimentally explore various dataflow techniques and features, which enable such an acceleration. Our focus is to resolve one of the most important challenges when designing a dataflow algorithm, which is to determine the best possible data choreography in the given context. In order to mitigate this challenge, we systematically enumerate and present possible techniques of various data choreographies. In particular, we focus our interest on the algorithms that use matrices and vectors as the underlaying data structure. We begin with simple algorithms such as matrix and vector multiplication, evaluation of polynomials as well as more advanced ones such as the simplex algorithm for solving linear programs. To evaluate the algorithms we compare their running-times as well as the dataflow resource consumption

SEARCH-TREE SIZE ESTIMATION FOR THE SUBGRAPH ISOMORPHISM PROBLEM

This article addresses the problem of finding patterns in graphs. This is formally defined as the subgraph isomorphism problem and is one of the core problems in theoretical computer science. We consider the counting variation of this problem. The task is to count all instances of the pattern G occurring in a (usually larger) graph H. The vast majority of algorithms for this problem use a variation of backtracking. Most commonly they exhaustively search through the space of all possible monomorphisms between G and H. The size of the search tree depends heavily on the choice of the ordering of vertices of G, which are systematically assigned to the vertices of H. We use a method called heuristic sampling to estimate the size of the search tree for each ordering in advance. We use this estimation to select the most suitable order of vertices of G which minimizes the expected tree size. This approach is empirically evaluated on a set of instances, showing the practical potential of the method

Automatic adaptation of filter sequences for cell counting

Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts

Automatic cell counter for cell viability estimation

Despite several methods that exist in different fields of life sciences, certain biotechnological applications still require microscopic analysis of the samples and in many instances, counting of cells. Some of those are drug delivery, transfection or analysis of mechanism fluorescent probes are used to detect cell viability, efficiency of a specific drug delivery or some other effect. For analysis and quantification of these results it is necessary to either manually or automatically count and analyze microscope images. However, in everyday use many researchers still count cells manually since existing solutions require either some specific knowledge of computer vision and/or manual fine tuning of various parameters. Here we present a new software solution (named CellCounter) for automatic and semi-automatic cell counting of fluorescent microscopic images. This application is specifically designed for counting fluorescently stained cells. The program enables counting of cell nuclei or cell cytoplasm stained with different fluorescent stained. This simplifies image analysis for several biotechnological applications where fluorescent microscopy is used. We present results and validate the presented automatic cell counting program for cell viability application. We give empirical results showing the efficiency of the proposed solution by comparing manual counts with the results returned by automated counting. We also show how the results can be further improved by combining manual and automated

Automatic cell counter for cell viability estimation

Despite several methods that exist in different fields of life sciences, certain biotechnological applications still require microscopic analysis of the samples and in many instances, counting of cells. Some of those are drug delivery, transfection or analysis of mechanism fluorescent probes are used to detect cell viability, efficiency of a specific drug delivery or some other effect. For analysis and quantification of these results it is necessary to either manually or automatically count and analyze microscope images. However, in everyday use many researchers still count cells manually since existing solutions require either some specific knowledge of computer vision and/or manual fine tuning of various parameters. Here we present a new software solution (named CellCounter) for automatic and semi-automatic cell counting of fluorescent microscopic images. This application is specifically designed for counting fluorescently stained cells. The program enables counting of cell nuclei or cell cytoplasm stained with different fluorescent stained. This simplifies image analysis for several biotechnological applications where fluorescent microscopy is used. We present results and validate the presented automatic cell counting program for cell viability application. We give empirical results showing the efficiency of the proposed solution by comparing manual counts with the results returned by automated counting. We also show how the results can be further improved by combining manual and automated

Automatic adaptation of filter sequences for cell counting

Manual cell counting in microscopic images is usually tedious, time consuming and prone to human error. Several programs for automatic cell counting have been developed so far, but most of them demand some specific knowledge of image analysis and/or manual fine tuning of various parameters. Even if a set of filters is found and fine tuned to the specific application, small changes to the image attributes might make the automatic counter very unreliable. The goal of this article is to present a new application that overcomes this problem by learning the set of parameters for each application, thus making it more robust to changes in the input images. The users must provide only a small representative subset of images and their manual count, and the program offers a set of automatic counters learned from the given input. The user can check the counters and choose the most suitable one. The resulting application (which we call Learn123) is specifically tailored to the practitioners, i.e. even though the typical workflow is more complex, the application is easy to use for non-technical experts

Comparison of two automatic cell-counting solutions for fluorescent microscopic images

Cell counting in microscopic images is one of the fundamental analysis tools in life sciences, but is usually tedious, time consuming and prone to human error. Several programs for automatic cell countinghavebeendeveloped sofar,butmostof them demand additional training or data input from the user. Most of them do not allow the users to online monitor the counting results, either. Therefore, we designed two straightforward, simple-to-use cell-counting programs that also allow users to correct the detection results. In this paper, we present the CELLCOUNTER and LEARN123 programs for automatic and semiautomatic counting of objects in fluorescent microscopic images (cells or cell nuclei) with a user-friendly interface. Although CELLCOUNTER is based on predefined and fine-tuned set of filters optimized on sets of chosen experiments, LEARN123 uses an evolutionary algorithm to determine the adapt filter parameters based on a learning set of images. CELLCOUNTER also includes an extension for analysis of overlaying images. The efficiency of both programs was assessed on images of cells stained with different fluorescent dyes by comparing automatically obtained results with results that were manually annotated by an expert. With both programs, the correlation between automatic and manual counting was very high (R2 < 0.9), although CELLCOUNTER had some difficulties processing images with no cells or weakly stained cells, where sometimes the background noise was recognized as an object of interest. Nevertheless, the differences between manual and automatic counting were small compared to variations between experimental repeats. Both programs significantly reduced the time required to process the acquired images from hours tominutes. The programs enable consistent, robust, fast and accurate detection of fluorescent objects and can therefore be applied to a range of different applications in different fields of life sciences where fluorescent labelling is used for quantification of various phenomena. Moreover, CELLCOUNTER overlay extension also enables fast analysis of related images thatwouldotherwise require imagemergingforaccurateanalysis, whereas LEARN123’s evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings

Data replication in grid computing

Grid is a distributed system that enables dynamic aggregation of geographically dislocated computing and data resources. In data grids, data management applications often use data replication to improve data access time and provide better fault tolerance. The goal of this thesis is to study data replication in data grids, present a theoretical basis for the design of new replication methods, and propose a set of new algorithms and methods. In this thesis we present a set of models that include different parameters of a data grid. We prove that data replication is an NP-hard and non-approximable optimization problem. Furthermore, we demonstrate with simulations, that the models describe well quality placements of data. For the formulated optimization problem we develop a set of heuristic algorithms, which we compare on a problem set and extract the best one. Since centralized replication is poorly scalable, we develop a distributed replication method. Using simulations we demonstrate the superiority of this method compared with other existing methods

An experimental evaluation of refinement techniques for the subgraph isomorphism backtracking algorithms

In this paper, we study a well-known computationally hard problem, called the subgraph isomorphism problem where the goal is for a given pattern and target graphs to determine whether the pattern is a subgraph of the target graph. Numerous algorithms for solving the problem exist in the literature and most of them are based on the backtracking approach. Since straightforward backtracking is usually slow, many algorithmic refinement techniques are used in practical algorithms. The main goal of this paper is to study such refinement techniques and to determine their ability to speed up backtracking algorithms. To do this we use a methodology of experimental algorithmics. We perform an experimental evaluation of the techniques and their combinations and, hence, demonstrate their usefulness in practice