Teses / dissertações sobre o tema "Graphe massifs"
Crie uma referência precisa em APA, MLA, Chicago, Harvard, e outros estilos
Veja os 50 melhores trabalhos (teses / dissertações) para estudos sobre o assunto "Graphe massifs".
Ao lado de cada fonte na lista de referências, há um botão "Adicionar à bibliografia". Clique e geraremos automaticamente a citação bibliográfica do trabalho escolhido no estilo de citação de que você precisa: APA, MLA, Harvard, Chicago, Vancouver, etc.
Você também pode baixar o texto completo da publicação científica em formato .pdf e ler o resumo do trabalho online se estiver presente nos metadados.
Veja as teses / dissertações das mais diversas áreas científicas e compile uma bibliografia correta.
Nabti, Chems Eddine. "Subgraph Isomorphism Search In Massive Graph Data". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1293/document.
Texto completo da fonteQuerying graph data is a fundamental problem that witnesses an increasing interest especially for massive structured data where graphs come as a promising alternative to relational databases for big data modeling. However, querying graph data is different and more complex than querying relational table-based data. The main task involved in querying graph data is subgraph isomorphism search which is an NP-complete problem. Subgraph isomorphism search, is an important problem which is involved in various domains such as pattern recognition, social network analysis, biology, etc. It consists to enumerate the subgraphs of a data graph that match a query graph. The most known solutions of this problem are backtracking-based. They explore a large search space which results in a high computational cost when we deal with massive graph data. To reduce time and memory space complexity of subgraph isomorphism search. We propose to use compressed graphs. In our approach, subgraph isomorphism search is achieved on compressed representations of graphs without decompressing them. Graph compression is performed by grouping vertices into super vertices. This concept is known, in graph theory, as modular decomposition. It is used to generate a tree representation of a graph that highlights groups of vertices that have the same neighbors. With this compression we obtain a substantial reduction of the search space and consequently a significant saving in the processing time. We also propose a novel encoding of vertices that simplifies the filtering of the search space. This new mechanism is called compact neighborhood Index (CNI). A CNI distills all the information around a vertex in a single integer. This simple neighborhood encoding reduces the time complexity of vertex filtering from cubic to quadratic which is considerable for big graphs. We propose also an iterative local global filtering algorithm that relies on the characteristics of CNIs to ensure a global pruning of the search space.We evaluated our approaches on several real-word datasets and compared them with the state of the art algorithms
Bletterer, Arnaud. "Une approche basée graphes pour la modélisation et le traitement de nuages de points massifs issus d’acquisitions de LiDARs terrestres". Thesis, Université Côte d'Azur (ComUE), 2018. http://www.theses.fr/2018AZUR4218/document.
Texto completo da fonteWith the evolution of 3D acquisition devices, point clouds have now become an essential representation of digitized scenes. Recent systems are able to capture several hundreds of millions of points in a single acquisition. As multiple acquisitions are necessary to capture the geometry of large-scale scenes, a historical site for example, we obtain massive point clouds, i.e., composed of billions of points. In this thesis, we are interested in the structuration and manipulation of point clouds from acquisitions generated by terrestrial LiDARs. From the structure of each acquisition, graphs, each representing the local connectivity of the digitized surface, are constructed. Created graphs are then linked together to obtain a global representation of the captured surface. We show that this structure is particularly adapted to the manipulation of the underlying surface of massive point clouds, even on computers with limited memory. Especially, we show that this structure allow to deal with two problems specific to that kind of data. A first one linked to the resampling of point clouds, by generating distributions of good quality in terms of blue noise thanks to a Poisson disk sampling algorithm. Another one connected to the construction of centroidal Voronoi tessellations, allowing to enhance the quality of generated distributions and to reconstruct triangular meshes
Baudin, Alexis. "Cliques statiques et temporelles : algorithmes d'énumération et de détection de communautés". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS609.
Texto completo da fonteGraphs are mathematical objects used to model interactions or connections between entities of various types. A graph can represent, for example, a social network that connects users to each other, a transport network like the metro where stations are connected to each other, or a brain with the billions of interacting neurons it contains. In recent years, the dynamic nature of these structures has been highlighted, as well as the importance of taking into account the temporal evolution of these networks to understand their functioning. While many concepts and algorithms have been developed on graphs to describe static network structures, much remains to be done to formalize and develop relevant algorithms to describe the dynamics of real networks. This thesis aims to better understand how massive graphs are structured in the real world, and to develop tools to extend our understanding to structures that evolve over time. It has been shown that these graphs have particular properties, which distinguish them from theoretical or randomly drawn graphs. Exploiting these properties then enables the design of algorithms to solve certain difficult problems much more quickly on these instances than in the general case. My PhD thesis focuses on cliques, which are groups of elements that are all connected to each other. We study the enumeration of cliques in static and temporal graphs and the detection of communities they enable. The communities of a graph are sets of vertices such that, within a community, the vertices interact strongly with each other, and little with the rest of the graph. Their study helps to understand the structural and functional properties of networks. We are evaluating our algorithms on massive real-world graphs, opening up new perspectives for understanding interactions within these networks. We first work on graphs, without taking into account the temporal component of interactions. We begin by using the clique percolation method of community detection, highlighting its limitations in memory, which prevent it from being applied to graphs that are too massive. By introducing an approximate problem-solving algorithm, we overcome this limitation. Next, we improve the enumeration of maximal cliques in the case of bipartite graphs. These correspond to interactions between groups of vertices of different types, e.g. links between people and viewed content, participation in events, etc. Next, we consider interactions that take place over time, using the link stream formalism. We seek to extend the algorithms presented in the first part, to exploit their advantages in the study of temporal interactions. We provide a new algorithm for enumerating maximal cliques in link streams, which is much more efficient than the state-of-the-art on massive datasets. Finally, we focus on communities in link streams by clique percolation, developing an extension of the method used on graphs. The results show a significant improvement over the state of the art, and we analyze the communities obtained to provide relevant information on the organization of temporal interactions in link streams. My PhD work has provided new insights into the study of massive real-world networks. This shows the importance of exploring the potential of graphs in a real-world context, and could contribute to the emergence of innovative solutions for the complex challenges of our modern society
Hinge, Antoine. "Dessin de graphe distribué par modèle de force : application au Big Data". Thesis, Bordeaux, 2018. http://www.theses.fr/2018BORD0092/document.
Texto completo da fonteGraphs, usually used to model relations between entities, are continually growing mainly because of the internet (social networks for example). Graph visualization (also called drawing) is a fast way of collecting data about a graph. Internet graphs are often stored in a distributed manner, split between several machines interconnected. This thesis aims to develop drawing algorithms to draw very large graphs using the MapReduce paradigm, used for cluster computing. Among graph drawing algorithms, those which rely on a physical model to compute the node placement are generally considered to draw graphs well regardless of the type of graph. We developped two force-directed graph drawing algorithms in the MapReduce paradigm. GDAD, the fist distributed force-directed graph drawing algorithm ever, uses pivots to simplify computations of node interactions. MuGDAD, following GDAD, uses a recursive simplification to draw the original graph, keeping the pivots. We compare these two algorithms with the state of the art to assess their performances
Hernández, Rivas Cecilia Paola. "Managing massive graphs". Tesis, Universidad de Chile, 2014. http://repositorio.uchile.cl/handle/2250/131839.
Texto completo da fonteCon la popularidad de la Web y, mas recientemente, el amplio uso de las redes sociales, la necesidad de procesar y encontrar información en grafos muy grandes impone varios desafíos: Cómo procesar grafos muy grandes e cientemente, dado que probablemente son muy grandes para la memoria disponible, o incluso si la memoria es su ciente, realizar un paso sobre el grafo es todavía caro computacionalmente? Cómo almacenar esos grafos e cientemente, para ser archivados, o para ejecutar algoritmos de grafos? Cómo descubrir información relevante tal como componentes densos, comunidades, u otras estructuras? Se han propuesto tres enfoques para manejar grafos grandes. El primero es usar formatos de grafos comprimidos que permiten consultas de navegación básicas directamentee sobre la estructura comprimida, sin la necesidad de descompresión. Esto permite simular cualquier algoritmo de grafo en memoria principal usando mucho menos espacio que la representación plana. Una segunda línea de investigación se focaliza en usar modelos de stream o semi- stream de datos de manera de procesar secuencialmente, idealmente en un paso sobre el disco, usando una cantidad limitada de memoria principal. La tercera línea es el uso de sistemas distribuidos y paralelos donde la memoria es agregada sobre múltiples unidades de procesamiento para procesar el grafo en paralelo. En esta tesis presentamos varios enfoques para manejar grafos grandes (con arcos sin etiquetas) considerando los tres enfoques. Primero, buscamos por patrones que aparecen en grafos de la Web y redes sociales los que podemos representar en forma compacta, en particular mostramos como generalizar algoritmos para encontrar cliques o bicliques para encontrar sub-estructuras densas que comprimen ambas. Segundo, basado en estos subgrafos densos, proponemos esquemas comprimidos que soportan consultas de vecinos directos y reversos, así como otras consultas mas complejas sobre subgrafos densos. Algunas de las contribuciones combinan técnicas del estado del arte mientras otras incluyen representaciones comprimidas novedosas basadas en estructuras de datos compactas. Encontrar subgrafos densos es una tarea que consume tiempo y espacio, así que proporcionamos algoritmos de streaming and algoritmos de memoria externa para descubrir subgrafos densos, asi como también algoritmos distribuidos para construir las estructuras básicas que usamos para las representaciones comprimidas.
Gillet, Noel. "Optimisation de requêtes sur des données massives dans un environnement distribué". Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0553/document.
Texto completo da fonteDistributed data store are massively used in the actual context of Big Data. In addition to provide data management features, those systems have to deal with an increasing amount of queries sent by distant users in order to process data mining or data visualization operations. One of the main challenge is to evenly distribute the workload of queries between the nodes which compose these system in order to minimize the treatment time. In this thesis, we tackle the problem of query allocation in a distributed environment. We consider that data are replicated and a query can be handle only by a node storing the concerning data. First, near-optimal algorithmic proposals are given when communications between nodes are asynchronous. We also consider that some nodes can be faulty. Second, we study more deeply the impact of data replication on the query treatement. Particularly, we present an algorithm which manage the data replication based on the demand on these data. Combined with our allocation algorithm, we guaranty a near-optimal allocation. Finally, we focus on the impact of data replication when queries are received as a stream by the system. We make an experimental evaluation using the distributed database Apache Cassandra. The experiments confirm the interest of our algorithmic proposals to improve the query treatement compared to the native allocation scheme in Cassandra
Wang, Guan. "STREAMING HYPERGRAPH PARTITION FOR MASSIVE GRAPHS". Kent State University / OhioLINK, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=kent1385097649.
Texto completo da fonteHabi, Abdelmalek. "Search and Aggregation in Big Graphs". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1259/document.
Texto completo da fonteRecent years have witnessed a growing renewed interest in the use of graphs as a reliable means for representing and modeling data. Thereby, graphs enable to ensure efficiency in various fields of computer science, especially for massive data where graphs arise as a promising alternative to relational databases for big data modeling. In this regard, querying data graph proves to be a crucial task to explore the knowledge in these datasets. In this dissertation, we investigate two main problems. In the first part we address the problem of detecting patterns in larger graphs, called the top-k graph pattern matching problem. We introduce a new graph pattern matching model named Relaxed Graph Simulation (RGS), to identify significant matches and to avoid the empty-set answer problem. We formalize and study the top-k matching problem based on two classes of functions, relevance and diversity, for ranking the matches according to the RGS model. We also consider the diversified top-k matching problem, and we propose a diversification function to balance relevance and diversity. Moreover, we provide efficient algorithms based on optimization strategies to compute the top-k and the diversified top-k matches according to the proposed model. The proposed approach is optimal in terms of search time and flexible in terms of applicability. The analyze of the time complexity of the proposed algorithms and the extensive experiments on real-life datasets demonstrate both the effectiveness and the efficiency of these approaches. In the second part, we tackle the problem of graph querying using aggregated search paradigm. We consider this problem for particular types of graphs that are trees, and we deal with the query processing in XML documents. Firstly, we give the motivation behind the use of such a paradigm, and we explain the potential benefits compared to traditional querying approaches. Furthermore, we propose a new method for aggregated tree search, based on approximate tree matching algorithm on several tree fragments, that aims to build, the extent possible, a coherent and complete answer by combining several results. The proposed solutions are shown to be efficient in terms of relevance and quality on different real-life datasets
Jiang, Jiaxin. "Efficient frameworks for keyword search on massive graphs". HKBU Institutional Repository, 2020. https://repository.hkbu.edu.hk/etd_oa/806.
Texto completo da fonteLu, Linyuan Lincoln. "Probabilistic methods in massive graphs and internet computing /". Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2002. http://wwwlib.umi.com/cr/ucsd/fullcit?p3061653.
Texto completo da fonteMa, Zongjie. "Searching on Massive Graphs and Regularizing Deep Learning". Thesis, Griffith University, 2018. http://hdl.handle.net/10072/385875.
Texto completo da fonteThesis (PhD Doctorate)
Doctor of Philosophy (PhD)
Inst Integrated&IntelligentSys
Science, Environment, Engineering and Technology
Full Text
Madduri, Kamesh. "A high-performance framework for analyzing massive complex networks". Diss., Atlanta, Ga. : Georgia Institute of Technology, 2008. http://hdl.handle.net/1853/24712.
Texto completo da fonteCommittee Chair: Bader, David; Committee Member: Berry, Jonathan; Committee Member: Fujimoto, Richard; Committee Member: Saini, Subhash; Committee Member: Vuduc, Richard
Wu, Yubao. "Efficient and Effective Local Algorithms for Analyzing Massive Graphs". Case Western Reserve University School of Graduate Studies / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=case1454451336.
Texto completo da fonteJouili, Salim. "Indexation de masses de documents graphiques : approches structurelles". Phd thesis, Université Nancy II, 2011. http://tel.archives-ouvertes.fr/tel-00597711.
Texto completo da fonteFérey, Nicolas. "Exploration immersive de données génomiques textuelles et factuelles : vers une approche par visual mining". Paris 11, 2006. http://www.theses.fr/2006PA112235.
Texto completo da fonteThis thesis concerns the immersive exploration of textual and factual genomic data. The goal of this work is to design and study new approach for exploring genomic data within an immersive framework (i. E. Of virtual reality). The knowledge about genome is constituted by factual data, coming from structured biological or genomic databanks, and by textual data, namely the unstructured data within the millions publications relating to the research about genome. These data are heterogeneous, huge in quantity, and complex. The stake of this work is to propose visualization and interaction paradigms, which are able to deals with these characteristics. These paradigms must also be adapted to the immersive framework, and must respect the needs of the biologists. We used common points of genomic databanks, to design an original visualization paradigm, where the user is able to choice a translation of the semantic of the genomic data to visual, geometric or topologic properties. We implemented a software prototype in order to test and validate the visualization paradigm within an immersive framework. In this context, we proposed and tested new interaction paradigms, in order to navigate, search and edit the genomic data during the immersive exploration. We used finally this software to lead several experiments of genomic data analysis with biologists, in order to measure the relevance of this visual mining approach on different kinds of genomic data
Sansen, Joris. "La visualisation d’information pour les données massives : une approche par l’abstraction de données". Thesis, Bordeaux, 2017. http://www.theses.fr/2017BORD0636/document.
Texto completo da fonteThe evolution and spread of technologies have led to a real explosion of information and our capacity to generate data and our need to analyze them have never been this strong. Still, the problems raised by such accumulation (storage, computation delays, diversity, speed of gathering/generation, etc. ) is as strong as the data are big, complex and varied. Information visualization,by its ability to summarize and abridge data was naturally established as appropriate approach. However, it does not solve the problem raised by Big Data. Actually, classical visualization techniques are rarely designed to handle such mass of information. Moreover, the problems raised by data storage and computation time have repercussions on the analysis system. For example,the increasing distance between the data and the analyst : the place where the data is stored and the place where the user will perform the analyses arerarely close. In this thesis, we focused on these issues and more particularly on adapting the information visualization techniques for Big Data. First of all focus on relational data : how does the existence of a relation between entity istransmitted and how to improve this transmission for hierarchical data. Then,we focus on multi-variate data and how to handle their complexity for the required computations. Finally, we present the methods we designed to make our techniques compatible with Big Data
Echbarthi, Ghizlane. "Big Graph Processing : Partitioning and Aggregated Querying". Thesis, Lyon, 2017. http://www.theses.fr/2017LYSE1225/document.
Texto completo da fonteWith the advent of the "big data", many repercussions have taken place in all fields of information technology, advocating innovative solutions with the best compromise between cost and accuracy. In graph theory, where graphs provide a powerful modeling support for formalizing problems ranging from the simplest to the most complex, the search for NP-complete or NP-difficult problems is rather directed towards approximate solutions, thus Forward approximation algorithms and heuristics while exact solutions become extremely expensive and impossible to use. In this thesis we discuss two main problems: first, the problem of partitioning graphs is approached from a perspective big data, where massive graphs are partitioned in streaming. We study and propose several models of streaming partitioning and we evaluate their performances both theoretically and empirically. In a second step, we are interested in querying distributed / partitioned graphs. In this context, we study the problem of aggregative search in graphs, which aims to answer queries that interrogate several fragments of graphs and which is responsible for reconstructing the final response such that a Matching approached with the initial query
Castelltort, Arnaud. "Historisation de données dans les bases de données NoSQLorientées graphes". Thesis, Montpellier 2, 2014. http://www.theses.fr/2014MON20076.
Texto completo da fonteThis thesis deals with data historization in the context of graphs. Graph data have been dealt with for many years but their exploitation in information systems, especially in NoSQL engines, is recent. The emerging Big Data and 3V contexts (Variety, Volume, Velocity) have revealed the limits of classical relational databases. Historization, on its side, has been considered for a long time as only linked with technical and backups issues, and more recently with decisional reasons (Business Intelligence). However, historization is now taking more and more importance in management applications.In this framework, graph databases that are often used have received little attention regarding historization. Our first contribution consists in studying the impact of historized data in management information systems. This analysis relies on the hypothesis that historization is taking more and more importance. Our second contribution aims at proposing an original model for managing historization in NoSQL graph databases.This proposition consists on the one hand in elaborating a unique and generic system for representing the history and on the other hand in proposing query features.We show that the system can support both simple and complex queries.Our contributions have been implemented and tested over synthetic and real databases
Deri, Joya A. "Graph Signal Processing: Structure and Scalability to Massive Data Sets". Research Showcase @ CMU, 2016. http://repository.cmu.edu/dissertations/725.
Texto completo da fonteBen, Dhia Imen. "Gestion des grandes masses de données dans les graphes réels". Thesis, Paris, ENST, 2013. http://www.theses.fr/2013ENST0087/document.
Texto completo da fonteIn the last few years, we have been witnessing a rapid growth of networks in a wide range of applications such as social networking, bio-informatics, semantic web, road maps, etc. Most of these networks can be naturally modeled as large graphs. Managing, analyzing, and querying such data has become a very important issue, and, has inspired extensive interest within the database community. In this thesis, we address the problem of efficiently answering distance queries in very large graphs. We propose EUQLID, an efficient algorithm to answer distance queries on very large directed graphs. This algorithm exploits some interesting properties that real-world graphs exhibit. It is based on an efficient variant of the seminal 2-hop algorithm. We conducted an extensive set of experiments against state-of-the-art algorithms which show that our approach outperforms existing approaches and that distance queries can be processed within hundreds of milliseconds on very large real-world directed graphs. We also propose an access control model for social networks which can make use of EUQLID to scale on very large graphs. This model allows users to specify fine-grained privacy policies based on their relations with other users in the network. We describe and demonstrate Primates as a prototype which enforces the proposed access control model and allows users to specify their privacy preferences via a graphical user-friendly interface
Ben, Dhia Imen. "Gestion des grandes masses de données dans les graphes réels". Electronic Thesis or Diss., Paris, ENST, 2013. http://www.theses.fr/2013ENST0087.
Texto completo da fonteIn the last few years, we have been witnessing a rapid growth of networks in a wide range of applications such as social networking, bio-informatics, semantic web, road maps, etc. Most of these networks can be naturally modeled as large graphs. Managing, analyzing, and querying such data has become a very important issue, and, has inspired extensive interest within the database community. In this thesis, we address the problem of efficiently answering distance queries in very large graphs. We propose EUQLID, an efficient algorithm to answer distance queries on very large directed graphs. This algorithm exploits some interesting properties that real-world graphs exhibit. It is based on an efficient variant of the seminal 2-hop algorithm. We conducted an extensive set of experiments against state-of-the-art algorithms which show that our approach outperforms existing approaches and that distance queries can be processed within hundreds of milliseconds on very large real-world directed graphs. We also propose an access control model for social networks which can make use of EUQLID to scale on very large graphs. This model allows users to specify fine-grained privacy policies based on their relations with other users in the network. We describe and demonstrate Primates as a prototype which enforces the proposed access control model and allows users to specify their privacy preferences via a graphical user-friendly interface
Baalbaki, Hussein. "Designing Big Data Frameworks for Quality-of-Data Controlling in Large-Scale Knowledge Graphs". Electronic Thesis or Diss., Sorbonne université, 2023. http://www.theses.fr/2023SORUS697.
Texto completo da fonteKnowledge Graphs (KGs) are the most used representation of structured information about a particular domain consisting of billions of facts in the form of entities (nodes) and relations (edges) between them. Additionally, the semantic type information of the entities is also contained in the KGs. The number of KGs has steadily increased over the past 20 years in a variety of fields, including government, academic research, the biomedical fields, etc. Applications based on machine learning that use KGs include entity linking, question-answering systems, recommender systems, etc. Open KGs are typically produced heuristically, automatically from a variety of sources, including text, photos, and other resources, or are hand-curated. However, these KGs are often incomplete, i.e., there are missing links between the entities and missing links between the entities and their corresponding entity types. In this thesis, we are addressing one of the most challenging issues facing Knowledge Graph Completion (KGC) which is link prediction. General Link Prediction in KGs that include head and tail prediction, triple classification. In recent years, KGE have been trained to represent the entities and relations in the KG in a low-dimensional vector space preserving the graph structure. In most published works such as the translational models, neural network models and others, the triple information is used to generate the latent representation of the entities and relations. In this dissertation, several methods have been proposed for KGC and their effectiveness is shown empirically in this thesis. Firstly, a novel KG embedding model TransModE is proposed for Link Prediction. TransModE projects the contextual information of the entities to modular space, while considering the relation as transition vector that guide the head to the tail entity. Secondly, we worked on building a simple low complexity KGE model, meanwhile preserving its efficiency. KEMA is a novel KGE model among the lowest KGE models in terms of complexity, meanwhile it obtains promising results. Finally, KEMA++ is proposed as an upgrade of KEMA to predict the missing triples in KGs using product arithmetic operation in modular space. The extensive experiments and ablation studies show efficiency of the proposed model, which compete the current state of the art models and set new baselines for KGC. The proposed models establish new way in solving KGC problem other than transitional, neural network, or tensor factorization based approaches. The promising results and observations open up interesting scopes for future research involving exploiting the proposed models in domain-specific KGs such as scholarly data, biomedical data, etc. Furthermore, the link prediction model can be exploited as a base model for the entity alignment task as it considers the neighborhood information of the entities
Gilbert, Frédéric. "Méthodes et modèles pour la visualisation de grandes masses de données multidimensionnelles nominatives dynamiques". Thesis, Bordeaux 1, 2012. http://www.theses.fr/2012BOR14498/document.
Texto completo da fonteSince ten years, informations visualization domain knows a real interest.Recently, with the growing of communications, the research on social networks analysis becomes strongly active. In this thesis, we present results on dynamic social networks analysis. That means that we take into account the temporal aspect of data. We were particularly interested in communities extraction within networks and their evolutions through time. [...]
Bordairon, Marc. "Dimensionnement des massifs en sol renforcé par géosynthétiques". Grenoble INPG, 1986. http://www.theses.fr/1986INPG0113.
Texto completo da fonteScotti, Andrea. "Graph Neural Networks and Learned Approximate Message Passing Algorithms for Massive MIMO Detection". Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-284500.
Texto completo da fonteMassiv MIMO (multiple-input and multiple-output) är en metod som förbättrarprestandan i trådlösa kommunikationssystem genom att ett stort antal antenneranvänds i både sändare och mottagare. I den femte generationens (5G)mobila kommunikationssystem är Massiv MIMO en mycket viktig teknologiför att möta det växande antalet mobilanvändare och tillgodose användarnasbehov. Samtidigt ökar beräkningskomplexiteten för att återfinna den överfördainformationen i en trådlös Massiv MIMO-upplänk när antalet antenner ökar.Faktum är att den optimala ML-detektorn (maximum likelihood) har en beräkningskomplexitetsom ökar exponentiellt med antalet sändare. En av huvudutmaningarnainom detta område är därför att hitta den bästa suboptimalaMIMO-detekteringsalgoritmen med hänsyn till både prestanda och komplexitet.I detta arbete visar vi hur MIMO-detektering kan representeras av ett MarkovRandom Field (MRF) och använder loopy belief-fortplantning (LBP) föratt lösa det motsvarande MAP-slutledningsproblemet (maximum a posteriori).Vi föreslår sedan en ny algoritm (BP-MMSE) som kombinerar LBP ochMMSE (minimum mean square error) för att lösa problemet vid högre modulationsordningarsom QAM-16 (kvadratamplitudsmodulation) och QAM-64.För att undvika komplexiteten med att beräkna MMSE så använder vi oss avgraf neurala nätverk (GNN) för att lära en message-passing algoritm som löserslutledningsproblemet med samma graf. En message-passing algoritm måstegiven en komplett graf utbyta kvadraten av antalet noder meddelanden. För attminska message-passing algoritmers beräkningskomplexitet vet vi att approximativmessage-passing (AMP) kan härledas från LBP i gränsvärdet av storasystem för att lösa MIMO-detektering med oberoende och likafördelade (i.i.d)Gaussiska kanaler. Vi visar sedan hur AMP med dämpning (DAMP) kan vararobust med låg- till mellan-korrelerade kanaler.Avslutningsvis föreslår vi en iterativ djup neuralt nätverk algoritm medlåg beräkningskomplexitet (Pseudo-MMNet) för att lösa MIMO-detektering ikanaler med hög korrelation på bekostnad av online-träning för varje realiseringav kanalen. Pseudo-MMNet är baserad på MMnet som presenteras i [23](istället för AMP) och minskar signifikant online-träningskomplexiteten somgör MMNet orealistisk att använda. Alla föreslagna algoritmer är empirisktutvärderade för stora MIMO-system och högre ordningar av modulation.
Tavares, Mauro. "Etude du comportement tribologique des couples feutres abradables : alliages refractaires massifs à faible et grande vitesse de glissement". Toulouse, INPT, 1987. http://www.theses.fr/1987INPT044G.
Texto completo da fonteTavares, Mauro. "Etude du comportement tribologique des couples feutres abradables alliages réfractaires massifs à faible et grande vitesse de glissement /". Grenoble 2 : ANRT, 1987. http://catalogue.bnf.fr/ark:/12148/cb37610202t.
Texto completo da fonteFarina, Sofia. "A physical interpretation of network laplacian: role of perturbations and masses". Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2018. http://amslaurea.unibo.it/16345/.
Texto completo da fonteBOUGNOUX, ANNE. "Modelisation thermo-hydro-mecanique des massifs fractures a moyenne ou grande echelle simulation micro-macro du comportement mecanique des fractures". Paris, ENMP, 1995. http://www.theses.fr/1995ENMP0583.
Texto completo da fonteABDESSELAM, MALEK. "Structure et fonctionnement d'un karst de montagne sous climat mediterraneen : exemple du djurdjura occidental (grande kabylie algerie)". Besançon, 1995. http://www.theses.fr/1995BESA2068.
Texto completo da fonteSimon, Laurent. "Recherches biogéographiques en forêt de Coucy-Basse (Aisne) : cartographie thématique à grande échelle d'un massif forestier". Paris 1, 1988. http://www.theses.fr/1988PA010507.
Texto completo da fonteSimon, Laurent. "Recherches biogéographiques en forêt de Coucy-Basse, Aisne cartographie thématique à grande échelle d'un massif forestier /". Lille 3 : ANRT, 1989. http://catalogue.bnf.fr/ark:/12148/cb37618548w.
Texto completo da fonteYoon, Hosang. "Two-Dimensional Plasmonics in Massive and Massless Electron Gases". Thesis, Harvard University, 2014. http://nrs.harvard.edu/urn-3:HUL.InstRepos:13070026.
Texto completo da fonteEngineering and Applied Sciences
Perez, Postigo Lorgio Victoriano. "Contribution à l'étude géologique du subbriançonnais entre Arc et Isère. Les massifs du Perron des Encombres et de la Grande Moendaz". Chambéry, 1988. http://www.theses.fr/1988CHAMA002.
Texto completo da fonteAgarwal, Virat. "Algorithm design on multicore processors for massive-data analysis". Diss., Georgia Institute of Technology, 2010. http://hdl.handle.net/1853/34839.
Texto completo da fonteVie, Jill-Jênn. "Modèles de tests adaptatifs pour le diagnostic de connaissances dans un cadre d'apprentissage à grande échelle". Thesis, Université Paris-Saclay (ComUE), 2016. http://www.theses.fr/2016SACLC090/document.
Texto completo da fonteThis thesis studies adaptive tests within learning environments. It falls within educational data mining and learning analytics, where student educational data is processed so as to optimize their learning.Computerized assessments allow us to store and analyze student data easily, in order to provide better tests for future learners. In this thesis, we focus on computerized adaptive testing. Such adaptive tests which can ask a question to the learner, analyze their answer on the fly, and choose the next question to ask accordingly. This process reduces the number of questions to ask to a learner while keeping an accurate measurement of their level. Adaptive tests are today massively used in practice, for example in the GMAT and GRE standardized tests, that are administered to hundreds of thousands of students. Traditionally, models used for adaptive assessment have been mostly summative : they measure or rank effectively examinees, but do not provide any other feedback. Recent advances have focused on formative assessments, that provide more useful feedback for both the learner and the teacher ; hence, they are more useful for improving student learning.In this thesis, we have reviewed adaptive testing models from various research communities. We have compared them qualitatively and quantitatively. Thus, we have proposed an experimental protocol that we have implemented in order to compare the most popular adaptive testing models, on real data. This led us to provide a hybrid model for adaptive cognitive diagnosis, better than existing models for formative assessment on all tried datasets. Finally, we have developed a strategy for asking several questions at the beginning of a test in order to measure the learner more accurately. This system can be applied to the automatic generation of worksheets, for example on a massive online open course (MOOC)
Khelil, Abdallah. "Gestion et optimisation des données massives issues du Web Combining graph exploration and fragmentation for scalable rdf query processing Should We Be Afraid of Querying Billions of Triples in a Graph-Based Centralized System? EXGRAF : Exploration et Fragmentation de Graphes au Service du Traitement Scalable de Requˆetes RDF". Thesis, Chasseneuil-du-Poitou, Ecole nationale supérieure de mécanique et d'aérotechnique, 2020. http://www.theses.fr/2020ESMA0009.
Texto completo da fonteBig Data represents a challenge not only for the socio-economic world but also for scientific research. Indeed, as has been pointed out in several scientific articles and strategic reports, modern computer applications are facing new problems and issues that are mainly related to the storage and the exploitation of data generated by modern observation and simulation instruments. The management of such data represents a real bottleneck which has the effect of slowing down the exploitation of the various data collected not only in the framework of international scientific programs but also by companies, the latter relying increasingly on the analysis of large-scale data. Much of this data is published today on the WEB. Indeed, we are witnessing an evolution of the traditional web, designed basically to manage documents, to a web of data that allows to offer mechanisms for querying semantic information. Several data models have been proposed to represent this information on the Web. The most important is the Resource Description Framework (RDF) which provides a simple and abstract representation of knowledge for resources on the Web. Each semantic Web fact can be encoded with an RDF triple. In order to explore and query structured information expressed in RDF, several query languages have been proposed over the years. In 2008,SPARQL became the official W3C Recommendation language for querying RDF data.The need to efficiently manage and query RDF data has led to the development of new systems specifically designed to process this data format. These approaches can be categorized as centralized that rely on a single machine to manage RDF data and distributed that can combine multiple machines connected with a computer network. Some of these approaches are based on an existing data management system such as Virtuoso and Jena, others relies on an approach specifically designed for the management of RDF triples such as GRIN, RDF3X and gStore. With the evolution ofRDF datasets (e.g. DBPedia) and Sparql, most systems have become obsolete and/or inefficient. For example, no one of existing centralized system is able to manage 1 billion triples provided under the WatDiv benchmark. Distributed systems would allow under certain conditions to improve this point but consequently leads a performance degradation. In this Phd thesis, we propose the centralized system "RDF_QDAG" that allows to find a good compromise between scalability and performance. We propose to combine physical data fragmentation and data graph exploration."RDF_QDAG" supports multiple types of queries based not only on basic graph patterns but also that incorporate filters based on regular expressions and aggregation and sorting functions. "RDF_QDAG" relies on the Volcano execution model, which allows controlling the main memory, avoiding any overflow even if the hardware configuration is limited. To the best of our knowledge, "RDF_QDAG" is the only centralized system that good performance when manage several billion triples. We compared this system with other systems that represent the state of the art in RDF data management: a relational approach (Virtuoso), a graph-based approach (g-Store), an intensive indexing approach (RDF-3X) and two parallel approaches (CliqueSquare and g-Store-D). "RDF_QDAG" surpasses existing systems when it comes to ensuring both scalability and performance
Kirchgessner, Martin. "Fouille et classement d'ensembles fermés dans des données transactionnelles de grande échelle". Thesis, Université Grenoble Alpes (ComUE), 2016. http://www.theses.fr/2016GREAM060/document.
Texto completo da fonteThe recent increase of data volumes raises new challenges for itemset mining algorithms. In this thesis, we focus on transactional datasets (collections of items sets, for example supermarket tickets) containing at least a million transactions over hundreds of thousands items. These datasets usually follow a "long tail" distribution: a few items are very frequent, and most items appear rarely. Such distributions are often truncated by existing itemset mining algorithms, whose results concern only a very small portion of the available items (the most frequents, usually). Thus, existing methods fail to concisely provide relevant insights on large datasets. We therefore introduce a new semantics which is more intuitive for the analyst: browsing associations per item, for any item, and less than a hundred associations at once.To address the items' coverage challenge, our first contribution is the item-centric mining problem. It consists in computing, for each item in the dataset, the k most frequent closed itemsets containing this item. We present an algorithm to solve it, TopPI. We show that TopPI computes efficiently interesting results over our datasets, outperforming simpler solutions or emulations based on existing algorithms, both in terms of run-time and result completeness. We also show and empirically validate how TopPI can be parallelized, on multi-core machines and on Hadoop clusters, in order to speed-up computation on large scale datasets.Our second contribution is CAPA, a framework allowing us to study which existing measures of association rules' quality are relevant to rank results. This concerns results obtained from TopPI or from jLCM, our implementation of a state-of-the-art frequent closed itemsets mining algorithm (LCM). Our quantitative study shows that the 39 quality measures we compare can be grouped into 5 families, based on the similarity of the rankings they produce. We also involve marketing experts in a qualitative study, in order to discover which of the 5 families we propose highlights the most interesting associations for their domain.Our close collaboration with Intermarché, one of our industrial partners in the Datalyse project, allows us to show extensive experiments on real, nation-wide supermarket data. We present a complete analytics workflow addressing this use case. We also experiment on Web data. Our contributions can be relevant in various other fields, thanks to the genericity of transactional datasets.Altogether our contributions allow analysts to discover associations of interest in modern datasets. We pave the way for a more reactive discovery of items' associations in large-scale datasets, whether on highly dynamic data or for interactive exploration systems
Woltering, Matthias [Verfasser]. "Factor Graph-based Receivers for Multi-Carrier Transmission in Two-Way Relaying and Massive Machine Type Communications / Matthias Woltering". Düren : Shaker, 2019. http://d-nb.info/1202218644/34.
Texto completo da fonteJaffré, Marc-Olivier. "Connaissance et optimisation de la prise en charge des patients : la science des réseaux appliquée aux parcours de soins". Thesis, Compiègne, 2018. http://www.theses.fr/2018COMP2445/document.
Texto completo da fonteIn France, the streamlining of means assigned hospitals result in concentration of resources ana growing complexily of heallhcare facilities. Piloting and planning (them turn out to be all the more difficult, thus leading of optimjzation problems. The use of massive data produced by these systems in association with network science an alternative approach for analyzing and improving decision-making support jn healthcare. Method : Various preexisting optimisation are first highblighted based on observations in operating theaters chosen as experirnentai sites. An analysis of merger of two hospitlas also follows as an example of an optimization method by massification. These two steps make it possible to defend an alternative approach that combines the use of big data science of networks data visualization techniques. Two sets of patient data in orthopedic surgery in the ex-Midi-Pyrénées region in France are used to create a network of all sequences of care. The whole is displayed in a visual environment developed in JavaScript allowing a dynamic mining of the graph. Results: Visualizing healthcare sequences in the form of nodes and links graphs has been sel out. The graphs provide an additional perception of' the redundancies of he healthcare pathways. The dynamic character of the graphs also allows their direct rnining. The initial visual approach is supplernented by a series of objcctive measures from the science of networks. Conciusion: Healthcare facilities produce massive data valuable for their analysis and optimization. Data visualizalion together with a framework such as network science gives prelimiaary encouraging indicators uncovering redondant healthcare pathway patterns. Furthev experimentations with various and larger sets of data is required to validate and strengthen these observations and methods
Silva, Flamys Lena do Nascimento 1979. "Aplicação da espectrometria de massas na avaliação da composição química de vinhos e uvas". [s.n.], 2013. http://repositorio.unicamp.br/jspui/handle/REPOSIP/248698.
Texto completo da fonteTese (doutorado) - Universidade Estadual de Campinas, Instituto de Química
Made available in DSpace on 2018-08-22T17:49:00Z (GMT). No. of bitstreams: 1 Silva_FlamysLenadoNascimento_D.pdf: 3948483 bytes, checksum: d08c9b4a732a432656e3795bc2d362dc (MD5) Previous issue date: 2013
Resumo: As variedades de uvas do gênero Vitis vinífera, incluindo a uva Syrah, são amplamente utilizadas na vinificação. O híbrido (Máximo-IAC 138-22), obtida do cruzamento entre Syrah e Seibel 11342 tem mostrado grande capacidade de adaptação ao clima de São Paulo e, aparentemente, produz um vinho de boa qualidade. A primeira parte deste estudo consistiu em comparar a composição volátil no headspace do vinho tinto paulista com outros vinhos originados da casta fina Syrah de diferentes regiões do mundo. Para isso foi empregada a técnica de microextração em fase sólida (SPME) com a cromatografia em fase gasosa acoplada à espectrometria de massas (GC-MS). Na segunda parte foi estudado o perfil fenólico de vinhos empregando a técnica de ionização por eletrospray (ESI) acoplada com a espectrometria de massas de ressonância ciclotrônica de íons com transformada de Fourier (FT-ICR MS) que permitiu a detecção de milhares de compostos polares no vinho sem separação cromatográfica e simples preparo de amostra. Constatou-se que o vinho paulista possui um perfil fenólico similar aos outros vinhos comerciais da uva Syrah. No terceiro e quarto estudos empregou-se a técnica ESI-MS por inserção direta para quantificar os ácidos orgânicos em vinho e em uva. Apesar de o vinho constituir uma matriz complexa, a técnica ESI-MS por inserção direta permitiu quantificar os compostos polares majoritários tais como ácido málico, ácido tartárico e ácido cítrico. Nas uvas Vitis vinífera, Vitis labrusca e híbridos a análise de componentes principais (PCA) mostrou clara distinção entre vinhos de uvas diferentes e o agrupamento do vinho paulista com os vinhos da uva Syrah. O método ESI-MS por inserção direta está sendo proposto pela primeira vez para quantificação de ácidos em vinhos e uvas. O método aqui desenvolvido foi validado segundo as normas do Instituto Nacional de Metrologia, Qualidade e Tecnologia (INMETRO)
Abstract: Varieties of grapes from the Vitis vinifera group incluind the Syrah grape are the most widely used for winemaking. A hybrid grape (Maximum-IAC 138-22) obtained by crossing Syrah and Seibel 11342 grapes has shown great adaptability in São Paulo State, producing apparently a high quality wine. This part first has compared the headspace aroma volatile composition of wine made from the Maximum IAC 138-22 grape with wines made from Syrah varietals originated from different regions of the world. Using static solid-phase microextration (SPME) followed by gas chromatography-mass spectrometry (GC-MS) analysis, main volatile compounds were identifield. Hierarchical clustering analysis (HCA) showed that the wine from the hybrid grape Maximum 138-22 has volatile aroma composition very similar to most high quality Syrah grape wines studied. In the second part the phenolic profile wine using the technique of electrospray ionization (ESI) coupled with mass spectrometry íon cyclotron resonance Fourier transform (FT-ICR MS) that allows detection of thousands of polar compounds in wine without chromatographic separation and simple sample preparation. Was found that the wine paulista has a profile similar phenolic other commercial wines from Syrah grapes. The ESI-MS technique for direct insertion allows us to obtain qualitative and quantitative results without chromatographic separation of wine. In the third and fourth studies employed the technique ESI-MS by direct insertion for quantifying organics acids in wine and grapes. As the wine is a complex matrix, pre concentration and filtration ESI-MS for direct insertion quantify the major polar compounds such as malic acid, tartaric acid and citric acid. In Vitis vinifera grape, Vitis labrusca and hybrid the principal component analysis (PCA) showed a clear distinction between wines from different grapes and wine group in São Paulo with wines from Syrah grapes. The ESI-MS method for direct insertion is first proposed for quantification of acids in wines and grapes. The method ESI-MS by direct insertion is first proposed for quantification acid in wines and grapes. The method developed here was validated according to the standards of the National Health Surveillance Agency and National Institute of Metrology, Quality and Technology (INMETRO)
Doutorado
Quimica Analitica
Doutora em Ciências
Qiao, Wenbao. "GPU component-based neighborhood search for Euclidean graph minimization problems". Thesis, Bourgogne Franche-Comté, 2018. http://www.theses.fr/2018UBFCA020.
Texto completo da fonteIn this thesis, we propose parallel solutions based on current graphics processing unit (GPU) system for two Euclidean graph minimization problems, namely the Euclidean minimum spanning forest/tree (EMSF/EMST) and the travelling salesman problem (TSP). The proposed solutions also solve the bichromatic closest pair (BCP) problem, and follow technique of ``decentralized control, data parallelism, GPU shared memories".We propose a Euclidean K-dimensional nearest neighbourhood search (NNS) technique based on classical Elias' NNS approaches that divide the Euclidean space into congruent and non-overlapping cells where size of points in each cell is bounded. We propose a pruning technique to obtain component-based NNS to find a query point set Q's closest outgoing point within sequential linear time complexity when the data is uniformly distributed. These techniques are used together with two proposed GPU tree traversal algorithms, namely the GPU two-direction Breadth-first search and distributed dynamic linked list, to address the BCP. Based on the BCP solution, a divide and conquer parallel algorithm is implemented for building EMSF and EMST totally on GPU side. The TSP is addressed with different parallel 2-opt local search algorithms, in which we propose a ``multiple K-opt evaluation, multiple K-opt moves" methodology in order to simultaneously execute, without interference, massive 2-/3-opt moves that are globally found on the same TSP tour for many edges. This methodology is explained in details to show how we obtain high performance computing both on GPU and CPU side. We test the proposed solutions and report experimental comparison results against the state-of-the-art algorithms
Ayed, Rihab. "Recherche d’information agrégative dans des bases de graphes distribuées". Thesis, Lyon, 2019. http://www.theses.fr/2019LYSE1305.
Texto completo da fonteIn this research, we are interested in investigating issues related to query evaluation and optimization in the framework of aggregated search. Aggregated search is a new paradigm to access massively distributed information. It aims to produce answers to queries by combining fragments of information from different sources. The queries search for objects (documents) that do not exist as such in the targeted sources, but are built from fragments extracted from the different sources. The sources might not be specified in the query expression, they are dynamically discovered at runtime. In our work, we consider data dependencies to propose a framework for optimizing query evaluation over distributed graph-oriented data sources. For this purpose, we propose an approach for the document indexing/orgranizing process of aggregated search systems. We consider information retrieval systems that are graph oriented (RDF graphs). Using graph relationships, our work is within relational aggregated search where relationships are used to aggregate fragments of information. Our goal is to optimize the access to source of information in a aggregated search system. These sources contain fragments of information that are relevant partially for the query. We aim at minimizing the number of sources to ask, also at maximizing the aggregation operations within a same source. For this, we propose to reorganize the graph database(s) in partitions, dedicated to aggregated queries. We use a semantic or strucutral clustering of RDF predicates. For structural clustering, we propose to use frequent subgraph mining algorithms, we performed for this, a comparative study of their performances. For semantic clustering, we use the descriptive metadata of RDF predicates and apply semantic textual similarity methods to calculate their relatedness. Following the clustering, we define query decomposing rules based on the semantic/structural aspects of RDF predicates
Silva, Neto Otilio Paulo da. "Detecção automática de massas em imagens mamográficas usando particle swarm optimization (PSO) e índice de diversidade funcional". Universidade Federal do Maranhão, 2016. http://tedebc.ufma.br:8080/jspui/handle/tede/298.
Texto completo da fonteBreast cancer is now set on the world stage as the most common among women and the second biggest killer. It is known that diagnosed early, the chance of cure is quite significant, on the other hand, almost late discovery leads to death. Mammography is the most common test that allows early detection of cancer, this procedure can show injury in the early stages also contribute to the discovery and diagnosis of breast lesions. Systems computer aided, have been shown to be very important tools in aid to specialists in diagnosing injuries. This paper proposes a computational methodology to assist in the discovery of mass in dense and nondense breasts. This paper proposes a computational methodology to assist in the discovery of mass in dense and non-dense breasts. Divided into 6 stages, this methodology begins with the acquisition of the acquired breast image Digital Database for Screening Mammography (DDSM). Then the second phase is done preprocessing to eliminate and enhance the image structures. In the third phase is executed targeting with the Particle Swarm Optimization (PSO) to find regions of interest (ROIs) candidates for mass. The fourth stage is reduction of false positives, which is divided into two parts, reduction by distance and clustering graph, both with the aim of removing unwanted ROIs. In the fifth stage are extracted texture features using the functional diversity indicia (FD). Finally, in the sixth phase, the classifier uses support vector machine (SVM) to validate the proposed methodology. The best values found for non-dense breasts, resulted in sensitivity of 96.13%, specificity of 91.17%, accuracy of 93.52%, the taxe of false positives per image 0.64 and acurva free-response receiver operating characteristic (FROC) with 0.98. The best finds for dense breasts hurt with the sensitivity of 97.52%, specificity of 92.28%, accuracy of 94.82% a false positive rate of 0.38 per image and FROC curve 0.99. The best finds with all the dense and non dense breasts Showed 95.36% sensitivity, 89.00% specificity, 92.00% accuracy, 0.75 the rate of false positives per image and 0, 98 FROC curve.
O câncer de mama hoje é configurado no senário mundial como o mais comum entre as mulheres e o segundo que mais mata. Sabe-se que diagnosticado precocemente, a chance de cura é bem significativa, por outro lado, a descoberta tardia praticamente leva a morte. A mamografia é o exame mais comum que permite a descoberta precoce do câncer, esse procedimento consegue mostrar lesões nas fases iniciais, além de contribuir para a descoberta e o diagnóstico de lesões na mama. Sistemas auxiliados por computador, têm-se mostrado ferramentas importantíssimas, no auxilio a especialistas em diagnosticar lesões. Este trabalho propõe uma metodologia computacional para auxiliar na descoberta de massas em mamas densas e não densas. Dividida em 6 fases, esta metodologia se inicia com a aquisição da imagem da mama adquirida da Digital Database for Screening Mammography (DDSM). Em seguida, na segunda fase é feito o pré-processamento para eliminar e realçar as estruturas da imagem. Na terceira fase executa-se a segmentação com o Particle Swarm Optimization (PSO) para encontrar as regiões de interesse (ROIs) candidatas a massa. A quarta fase é a redução de falsos positivos, que se subdivide em duas partes, sendo a redução pela distância e o graph clustering, ambos com o objetivo de remover ROIs indesejadas. Na quinta fase são extraídas as características de textura utilizando os índices de diversidade funcional (FD). Por fim, na sexta fase, utiliza-se o classificador máquina de vetores de suporte (SVM) para validar a metodologia proposta. Os melhores valores achados para as mamas não densas, resultaram na sensibilidade de 96,13%, especificidade de 91,17%, acurácia de 93,52%, a taxe de falsos positivos por imagem de 0,64 e a acurva Free-response Receiver Operating Characteristic (FROC) com 0,98. Os melhores achados para as mamas densas firam com a sensibilidade de 97,52%, especificidade de 92,28%, acurácia de 94,82%, uma taxa de falsos positivos por imagem de 0,38 e a curva FROC de 0,99. Os melhores achados com todas as mamas densas e não densas, apresentaram 95,36% de sensibilidade, 89,00% de especificidade, 92,00% de acurácia, 0,75 a taxa de falsos positivos por imagem e 0,98 a curva FROC.
Moscu, Mircea. "Inférence distribuée de topologie de graphe à partir de flots de données". Thesis, Université Côte d'Azur, 2020. http://www.theses.fr/2020COAZ4081.
Texto completo da fonteThe second decade of the current millennium can be summarized in one short phrase: the advent of data. There has been a surge in the number of data sources: from audio-video streaming, social networks and the Internet of Things, to smartwatches, industrial equipment and personal vehicles, just to name a few. More often than not, these sources form networks in order to exchange information. As a direct consequence, the field of Graph Signal Processing has been thriving and evolving. Its aim: process and make sense of all the surrounding data deluge.In this context, the main goal of this thesis is developing methods and algorithms capable of using data streams, in a distributed fashion, in order to infer the underlying networks that link these streams. Then, these estimated network topologies can be used with tools developed for Graph Signal Processing in order to process and analyze data supported by graphs. After a brief introduction followed by motivating examples, we first develop and propose an online, distributed and adaptive algorithm for graph topology inference for data streams which are linearly dependent. An analysis of the method ensues, in order to establish relations between performance and the input parameters of the algorithm. We then run a set of experiments in order to validate the analysis, as well as compare its performance with that of another proposed method of the literature.The next contribution is in the shape of an algorithm endowed with the same online, distributed and adaptive capacities, but adapted to inferring links between data that interact non-linearly. As such, we propose a simple yet effective additive model which makes use of the reproducing kernel machinery in order to model said nonlinearities. The results if its analysis are convincing, while experiments ran on biomedical data yield estimated networks which exhibit behavior predicted by medical literature.Finally, a third algorithm proposition is made, which aims to improve the nonlinear model by allowing it to escape the constraints induced by additivity. As such, the newly proposed model is as general as possible, and makes use of a natural and intuitive manner of imposing link sparsity, based on the concept of partial derivatives. We analyze this proposed algorithm as well, in order to establish stability conditions and relations between its parameters and its performance. A set of experiments are ran, showcasing how the general model is able to better capture nonlinear links in the data, while the estimated networks behave coherently with previous estimates
Fonseca, Andresa Maíra da. "Cianobactérias e cianotoxinas em áreas recreacionais do Reservatório de Salto Grande, Americana - SP". Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11138/tde-12082014-083903/.
Texto completo da fonteCyanobacteria produce toxic substances which are known as cyanotoxins. Numerous cases of poisoning in humans and animals have been reported in several countries. Several toxic cyanobacteria are planktonic and develop in freshwater environments forming intense blooms under favorable conditions. Cyanobacterial blooms have been observed throughout the year in the Salto Grande reservoir (Americana, SP) that has intense recreational use, besides serves to public water supply, fisheries and crop irrigation. Therefore, evaluate cyanobacterial community and identify the presence of cyanotoxin genes as well as assess the production of toxins in blooms from the Salto Grande reservoir is of fundamental importance to public health agencies to allow safe uses of these water bodies. In this study, three water samples with cyanobacterial bloom were analyzed, which were collected at different periods, in two locations with intense recreational use. Investigations under optical microscope of the samples preserved with Lugol\'s iodine solution identified fifteen cyanobacterial genera, being two of them hitherto unknown to the location (Plantothrix and Komvophorum). Cell counts using the Utermöl technique performed for two water samples showed values exceeding those recommended by the Regulation Nº 2.914 of the Brazilian Ministry of Health, which establish weekly analyzes and sampling of water above 20,000 cells/mL. The genetic potential for production of the toxins cylindrospermopsin, saxitoxin and microcystin was evaluated using total genomic DNA from the environment samples and it was observed PCR amplification of the genes cyrJ, sxtA, sxtI, mcyE and mcyG. The PCR products were sequenced and phylogenetic analyses of amino acid sequences showed that they grouped with homologous sequences of known cyanobacterial producers of the respective toxins. However, the chemical analyzes of LC-MS/MS of the environmental samples searching for the three referred toxins detected only the presence of microcystin. The microcystin variants found were MC-LR and MC-RR. The results of this study contribute to the increase of information on the Salto Grande reservoir, and once again warming to the alarming situation of this reservoir related to public health.
Moutard, Thibaud. "Redshifts photométriques et paramètres physiques des galaxies dans les sondages à grande échelle : contraintes sur l'évolution des galaxies massives". Thesis, Aix-Marseille, 2015. http://www.theses.fr/2015AIXM4782.
Texto completo da fonteThis thesis presents the measurement of the photometric redshifts and physical parameters in the framework of large scale surveys, and their constraint on galaxy evolution. The photometric redshift measurement allows us to study the entire photometric sample. For this reason, the weak lensing signal measurement used in the Euclid mission as a primary cosmological probe will rely on photometric redshift measurements. However, the method is strongly affected by the quality of the photometry. In particular, I show in this thesis how the photometric calibration impacts the photometric redshift precison, in order to constrain the photometric strategy to use in the Euclid mission.Aiming to take into account for observationnal problems, the analysis is done with observationnal data whose photometric configuration is close to the expected Euclid one. These data combine new near-infrared observations conduected to cover the VIPERS spectroscopic survey and the CFHTLS photometry.Using the conclusions of this analysis, I have producted the new photometric catalogue for VIPERS and the associated photometric redshift calalogue.Finally, I used the same photometry to compute the stellar masses of 760,000 galaxies covering 22 square degrees at the limiting magnitude Ks(AB) < 22. This enabled me to study the evolution of the stellar mass function between redshifts z= 0.2 and z = 1.5. We have then shown that the star formation of galaxies with stellar masses around log(M/Msol) = 10.66 is stopped in 2-4 Gyr, while in quiescent low-mass (log(M/Msol) < 9.5) galaxies, the star formation has been stopped 5-10 times faster (approximatelly in 0.4 Gyr)
Larqué, Lionel. "Etude des masses d'eau en Atlantique Sud et de la circulation océanique à grande échelle dans le Bassin argentin". Toulouse 3, 1996. http://www.theses.fr/1996TOU30232.
Texto completo da fonteTran, Viet-Trung. "Sur le passage à l'échelle des systèmes de gestion des grandes masses de données". Phd thesis, École normale supérieure de Cachan - ENS Cachan, 2013. http://tel.archives-ouvertes.fr/tel-00783724.
Texto completo da fonteCorte, Vitor Francisco Dalla. "As estratégias e a organização das indústrias de farinha de trigo e de massas alimentícias do Rio Grande do Sul". Universidade Federal de Santa Maria, 2008. http://repositorio.ufsm.br/handle/1/4531.
Texto completo da fonteO estudo em questão visa identificar as estratégias e a organização das indústrias de farinha de trigo e de massas alimentícias do Rio Grande do Sul. Utiliza-se como base teórica na análise, a cadeia de produção agroindustrial, o modelo estruturaconduta-desempenho (ECD) e as estratégias competitivas de Porter (1980) e de Mintzberg (1988). Para realização da pesquisa, utilizou-se do método descritivo, com dados primários (questionário estruturado aplicado às empresas) e secundários. A opção de restringir o estudo ao Rio Grande do Sul justifica-se por ser o estado um dos pioneiros e atualmente o terceiro maior produtor de farinha de trigo e um dos maiores produtores de massas alimentícias em âmbito nacional. Os resultados mostram que as indústrias de farinha de trigo e de massas alimentícias localizam-se próximas no estado, e na maioria são de médio e pequeno porte. Além disso, verificou-se que tanto o Brasil como o Rio Grande do Sul não são auto-suficientes na produção de trigo, necessitando de importação. O principal destino dos produtos das indústrias é o próprio Estado, mas a integração vertical na cadeia produtiva ainda é baixa. Constatou-se também que a concentração das indústrias cai de 2002 para 2006 e que existem barreiras à entrada importantes para os ingressantes no setor de farinha de trigo, como a escala mínima de produção, e para os produtores de massas alimentícias, a capacidade já instalada das empresas. Em relação às estratégias competitivas de Porter, a produção com custos mais baixos é a que mais se destaca nas empresas produtoras de farinha de trigo, já nas de massas alimentícias é a diferenciação. Entre as tipologias de Mintzberg, a qualidade do produto é considerada, por ambas as indústrias, como sendo o grande diferencial competitivo. O desempenho das indústrias no período analisado piorou, pois houve queda de lucratividade e perda de participação de mercado.