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GarQ: An efficient scheduling data structure for advance reservations of grid resources

In Grids, users may require assurance for completing their jobs on shared resources. Such guarantees can only be provided by reserving resources in advance. However, if many reservation requests arrive at a resource simultaneously, the overhead of providing such service due to adding, deleting and searching, will be significant. An efficient data structure for managing these reservations plays an important role in order to minimise the time required for searching available resources, adding and deleting reservations. In this paper, we present new approaches to advance reservation in order to deal with the limitations of the existing data structures, such as Segment Tree and Calendar Queue in similar problems. We propose a Grid advanced reservation Queue (GarQ), which is a new data structure that improves some weaknesses of the aforementioned data structures. We demonstrate the superiority of the proposed structure by conducting a detailed performance evaluation on real workload traces.

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 counting have been developed so far, but most of 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 to minutes. 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 that would otherwise require image merging for accurate analysis, whereas Learn123s evolutionary algorithm can adapt counting parameters to specific sets of images of different experimental settings.

Search Strategies for Subgraph Isomorphism Algorithms

Searching for subgraph isomorphisms is an essential problem in pattern matching. Most of the algorithms use a branch-and-bound method to sequentially assign pattern nodes to compatible nodes in the target graph. It is well known that the order in which nodes are assigned, a so-called search strategy, influences drastically the size of the search space. In this article we investigate the impact of various search strategies on the efficiency of two algorithms, the first being the Ullmanns algorithm and the second one the recently proposed improvement of Ullmanns algorithm. From the large set of proposed orders we find the most successful ones by thorough testing on a large database of graphs.

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 counts.

Exploratory equivalence in graphs: Definition and algorithms

Motivated by improving the efficiency of pattern matching on graphs, we define a new kind of equivalence on graph vertices. Since it can be used in various graph algorithms that explore graphs, we call it exploratory equivalence. The equivalence is based on graph automorphisms. Because many similar equivalences exist (some also based on automorphisms), we argue that this one is novel. For each graph, there are many possible exploratory equivalences, but for improving the efficiency of the exploration, some are better than others. To this end, we define a goal function that models the reduction of the search space in such algorithms. We describe two greedy algorithms for the underlying optimization problem. One is based directly on the definition using a straightforward greedy criterion, whereas the second one uses several practical speedups and a different greedy criterion. Finally, we demonstrate the huge impact of exploratory equivalence on a real application, i.e., graph grammar parsing.

Improvements to Ullmann's Algorithm for the Subgraph Isomorphism Problem

The subgraph isomorphism problem is one of the most important problems for pattern recognition in graphs. Its applications are found in many different disciplines, including chemistry, medicine, and social network analysis. Because of the -completeness of the problem, the existing exact algorithms exhibit an exponential worst-case running time. In this paper, we propose several improvements to the well-known Ullmanns algorithm for the problem. The improvements lower the time consumption as well as the space requirements of the algorithm. We experimentally demonstrate the efficiency of our improvement by comparing it to another set of improvements called FocusSearch, as well as other state-of-the-art algorithms, namely VF2 and LAD.

Symmetry-Compressible Graphs

This article describes an alternative representation of graphs, using symmetries. We define the class of graphs that are compressed using this representation as symmetry-compressible graphs. This class of graphs is extended into the class of near symmetry-compressible graphs, which includes many more graphs arising in practical applications. To demonstrate the practical potential of the proposed concepts, an empirical evaluation of two algorithms is given.

Chapter Three - Adaptation and Evaluation of the Simplex Algorithm for a Data-Flow Architecture

Abstract The main goal of this chapter is to present a novel adaptation of the classical simplex algorithm for a data-flow architecture. Due to the recent reemergence of the data-flow paradigm, most of the algorithms need to be reengineered in order to use the features of the new platform. By exploring various possibilities of implementations and by extensive empirical testing we manage to show the suitability of the data-flow paradigm for the simplex algorithm, as well as pinpoint the strengths and some of the weaknesses of the used architecture.

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.

Maximum exploratory equivalence in trees

Many practical problems are modeled with networks and graphs. Their exploration is of significant importance, and several graph-exploration algorithms already exist. In this paper, we focus on a type of vertex equivalence, called exploratory equivalence, which has a great potential to speed up such algorithms. It is an equivalence based on graph automorphisms and can, for example, help us in solving the subgraph isomorphism problem, which is a well-known NP-hard problem. In particular, if a given pattern graph has nontrivial automorphisms, then each of its nontrivial exploratory equivalent classes gives rise to a set of constraints to prune the search space of solutions. In the paper, we define the maximum exploratory equivalence problem. We show that the defined problem is at least as hard the graph isomorphism problem. Additionally, we present a polynomial-time algorithm for solving the problem when the input is restricted to tree graphs. Furthermore, we show that for trees, a maximum exploratory equivalent partition leads to a globally optimal set of subgraph isomorphism constraints, whereas this is not necessarily the case for general graphs.