A generic and highly efficient parallel variant of Borůvka's algorithm

This paper presents (i) a parallel, platformindependent variant of Borůvka's algorithm, an efficient Minimum Spanning Tree (MST) solver, and (ii) a comprehensive comparison of MST-solver implementations, both on multi-core CPU-chips and GPUs. The core of our variant is an effective and explicit contraction of the graph. Our multi-core CPU implementation scales linearly up to 8 threads, whereas the GPU implementation performs considerably better than the optimal number of threads running on the CPU. We also show that our implementations outperform all other parallel MST-solver implementations in (ii), for a broad set of publicly available roadnetwork graphs.info:eu-repo/semantics/publishedVersio

"Transit Data"-based MST Computation

In this work, we present an innovative image recognition technique which is based on the exploitation of transit-data in images or simple photographs of sites of interest. Our objective is to automatically transform real-world images to graphs and, then, compute Minimum Spanning Trees (MST) in them.We apply this framework and present an application which automatically computes efficient construction plans (for escalator or low-emission hot spots) for connecting all points of interest in cultural sites, i.e., archaeological sites, museums, galleries, etc, aiming to to facilitate global physical access to cultural heritage and artistic work and make it accessible to all groups of population

A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

A large number of studies demonstrated that major depressive disorder (MDD) is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST) analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG) sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls

Praktické datové struktury

V této práci implementujeme datové struktury pro uspořádané a neuspořádané slovníky a měříme jejich výkon v hlavní paměti pomocí syntetických i praktických experimentů. Náš průzkum zahrnuje jak obvyklé datové struktury (B-stromy, červeno-černé stromy, splay stromy a hashování), tak exotičtější přístupy (k-splay stromy a k-lesy). Powered by TCPDF (www.tcpdf.org)In this thesis, we implement several data structures for ordered and unordered dictionaries and we benchmark their performance in main memory on synthetic and practical workloads. Our survey includes both well-known data structures (B-trees, red-black trees, splay trees and hashing) and more exotic approaches (k-splay trees and k-forests). Powered by TCPDF (www.tcpdf.org)Department of Applied MathematicsKatedra aplikované matematikyMatematicko-fyzikální fakultaFaculty of Mathematics and Physic

Approches de résolution exacte et approchée en optimisation combinatoire multi-objectif, application au problème de l'arbre couvrant de poids minimal

This thesis deals with several aspects related to solving multi-objective problems, without restriction to the bi-objective case. We consider exact solving, which generates the nondominated set, and approximate solving, which computes an approximation of the nondominated set with a priori guarantee on the quality.We first consider the determination of an explicit representation of the search region. The search region, defined with respect to a set of known feasible points, excludes from the objective space the part which is dominated by these points. Future efforts to find all nondominated points should therefore be concentrated on the search region.Then we review branch and bound and ranking algorithms and we propose a new hybrid approach for the determination of the nondominated set. We show how the proposed method can be adapted to generate an approximation of the nondominated set. This approach is instantiated on the minimum spanning tree problem. We review several properties of this problem which enable us to specialize some procedures of the proposed approach and integrate specific preprocessing rules. This approach is finally supported through experimental results.On s'attache dans cette thèse à plusieurs aspects liés à la résolution de problèmes multi-objectifs, sans se limiter au cas biobjectif. Nous considérons la résolution exacte, dans le sens de la détermination de l'ensemble des points non dominés, ainsi que la résolution approchée dans laquelle on cherche une approximation de cet ensemble dont la qualité est garantie a priori.Nous nous intéressons d'abord au problème de la détermination d'une représentation explicite de la région de recherche. La région de recherche, étant donné un ensemble de points réalisables connus, exclut la partie de l'espace des objectifs que dominent ces points et constitue donc la partie de l'espace des objectifs où les efforts futurs doivent être concentrés dans la perspective de déterminer tous les points non dominés.Puis nous considérons le recours aux algorithmes de séparation et évaluation ainsi qu'aux algorithmes de ranking afin de proposer une nouvelle méthode hybride de détermination de l'ensemble des points non dominés. Nous montrons que celle-ci peut également servir à obtenir une approximation de l'ensemble des points non dominés. Cette méthode est implantée pour le problème de l'arbre couvrant de poids minimal. Les quelques propriétés de ce problème que nous passons en revue nous permettent de spécialiser certaines procédures et d'intégrer des prétraitements spécifiques. L'intérêt de cette approche est alors soutenu à l'aide de résultats expérimentaux

Molecular Bronchiolitis Obliterans Syndrome Risk Monitoring: A Systems-Based Approach

The combination of high throughput omics (i.e. genomics or proteomics) and machine learning offers new possibilities for clinical diagnostics and the detection of biomarkers. One disease for which no reliable prognostic marker has been found yet is bronchiolitis obliterans (BO), a clinical manifestation of chronic rejection after lung transplantation. BO is the major limiting factor for long-term survival after lung transplantation, and manifests as a chronic bronchiolar inammation accompanied by progressive sub-mucosal fibrosis leading to gradual obliteration of the bronchiolar lumen. The resulting reduction in forced expiratory volume per second (FEV 1 ) is defined as the bronchiolitis obliterans syndrome (BOS). As chronic lung transplant failure occurs more frequently than in other organ transplants, molecular markers for early BO and BOS detection are urgently required to adapt the patients immunosuppressive regimen when airway damage is minimal. To achieve this goal, gene expression in bronchial epithelial cells (microarray anaylsis) and on the proteome level in bronchoalveolar lavage fluid (BALF)(mass spectrometry profiling) were monitored. Analysis of the obtained data sets was performed using novel and established methods from the fields of machine learning and statistics. This thesis also introduces a novel clustering algorithm. In the analysis of gene expression microarrays one problem is the unsupervised discovery of stable and biologically relevant patient subgroups. To this end I developed a novel clustering algorithm. This algorithm focuses on the discovery of a set of patient clusters defined by the consistent up- and down-regulation of a subset of genes. Assessment of cluster stability is done using a bootstrap resampling scheme. This makes it possible to rank the genes in accordance with their clusterwise importance. The algorithm was applied to a publicly available B-cell lymphoma microarray data set and compared to other commonly used clustering algorithms