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Academic literature on the topic 'Stream graphs'

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Published: 25 May 2024

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Journal articles on the topic "Stream graphs"

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Ashrafi-Payaman, Nosratali, Mohammad Reza Kangavari, and Amir Mohammad Fander. "A new method for graph stream summarization based on both the structure and concepts." Open Engineering 9, no. 1 (November 2, 2019): 500–511. http://dx.doi.org/10.1515/eng-2019-0060.

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AbstractGraph datasets are common in many application domains and for which their graphs are usually massive. One solution to process such massive graphs is summarization. There are two kinds of graphs, stationary and stream. For stationary graphs, a number of summarization algorithms are available while for graph stream there is no a comprehensive summarization method that summarizes a graph stream based on the structure, vertex attributes or both with varying contributions. This is because of challenges of graph stream, which are volume of data and changing of data over time. In this paper, we propose a method based on sliding-window model for which summarizes a graph stream based on a combination of the structure and vertex attributes. We proposed a new structure for summary graphs and also proposed new methods for comparing two summary graphs. To the best of our knowledge, this is the first method that summarizes a graph stream based on both the structure and vertex attributes with varying contributions. Through extensive experiments on real dataset of Amazon co-purchasing products, we have demonstrated the performance of the proposed method.
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Malik, Avinash, and David Gregg. "Orchestrating stream graphs using model checking." ACM Transactions on Architecture and Code Optimization 10, no. 3 (September 16, 2013): 1–25. http://dx.doi.org/10.1145/2512435.

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Du, Zhihui, Oliver Alvarado Rodriguez, Joseph Patchett, and David A. Bader. "Interactive Graph Stream Analytics in Arkouda." Algorithms 14, no. 8 (July 21, 2021): 221. http://dx.doi.org/10.3390/a14080221.

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Data from emerging applications, such as cybersecurity and social networking, can be abstracted as graphs whose edges are updated sequentially in the form of a stream. The challenging problem of interactive graph stream analytics is the quick response of the queries on terabyte and beyond graph stream data from end users. In this paper, a succinct and efficient double index data structure is designed to build the sketch of a graph stream to meet general queries. A single pass stream model, which includes general sketch building, distributed sketch based analysis algorithms and regression based approximation solution generation, is developed, and a typical graph algorithm—triangle counting—is implemented to evaluate the proposed method. Experimental results on power law and normal distribution graph streams show that our method can generate accurate results (mean relative error less than 4%) with a high performance. All our methods and code have been implemented in an open source framework, Arkouda, and are available from our GitHub repository, Bader-Research. This work provides the large and rapidly growing Python community with a powerful way to handle terabyte and beyond graph stream data using their laptops.
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Wang, Ruize, Zhongyu Wei, Piji Li, Qi Zhang, and Xuanjing Huang. "Storytelling from an Image Stream Using Scene Graphs." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 05 (April 3, 2020): 9185–92. http://dx.doi.org/10.1609/aaai.v34i05.6455.

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Visual storytelling aims at generating a story from an image stream. Most existing methods tend to represent images directly with the extracted high-level features, which is not intuitive and difficult to interpret. We argue that translating each image into a graph-based semantic representation, i.e., scene graph, which explicitly encodes the objects and relationships detected within image, would benefit representing and describing images. To this end, we propose a novel graph-based architecture for visual storytelling by modeling the two-level relationships on scene graphs. In particular, on the within-image level, we employ a Graph Convolution Network (GCN) to enrich local fine-grained region representations of objects on scene graphs. To further model the interaction among images, on the cross-images level, a Temporal Convolution Network (TCN) is utilized to refine the region representations along the temporal dimension. Then the relation-aware representations are fed into the Gated Recurrent Unit (GRU) with attention mechanism for story generation. Experiments are conducted on the public visual storytelling dataset. Automatic and human evaluation results indicate that our method achieves state-of-the-art.
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Ni, Xiang, Jing Li, Mo Yu, Wang Zhou, and Kun-Lung Wu. "Generalizable Resource Allocation in Stream Processing via Deep Reinforcement Learning." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 857–64. http://dx.doi.org/10.1609/aaai.v34i01.5431.

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This paper considers the problem of resource allocation in stream processing, where continuous data flows must be processed in real time in a large distributed system. To maximize system throughput, the resource allocation strategy that partitions the computation tasks of a stream processing graph onto computing devices must simultaneously balance workload distribution and minimize communication. Since this problem of graph partitioning is known to be NP-complete yet crucial to practical streaming systems, many heuristic-based algorithms have been developed to find reasonably good solutions. In this paper, we present a graph-aware encoder-decoder framework to learn a generalizable resource allocation strategy that can properly distribute computation tasks of stream processing graphs unobserved from training data. We, for the first time, propose to leverage graph embedding to learn the structural information of the stream processing graphs. Jointly trained with the graph-aware decoder using deep reinforcement learning, our approach can effectively find optimized solutions for unseen graphs. Our experiments show that the proposed model outperforms both METIS, a state-of-the-art graph partitioning algorithm, and an LSTM-based encoder-decoder model, in about 70% of the test cases.
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Declerck, Philippe. "DETECTION OF CHANGES BY OBSERVER IN TIMED EVENT GRAPHS AND TIME STREAM EVENT GRAPHS." IFAC Proceedings Volumes 40, no. 6 (2007): 49–54. http://dx.doi.org/10.3182/20070613-3-fr-4909.00011.

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Llaves, Alejandro, Oscar Corcho, Peter Taylor, and Kerry Taylor. "Enabling RDF Stream Processing for Sensor Data Management in the Environmental Domain." International Journal on Semantic Web and Information Systems 12, no. 4 (October 2016): 1–21. http://dx.doi.org/10.4018/ijswis.2016100101.

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This paper presents a generic approach to integrate environmental sensor data efficiently, allowing the detection of relevant situations and events in near real-time through continuous querying. Data variety is addressed with the use of the Semantic Sensor Network ontology for observation data modelling, and semantic annotations for environmental phenomena. Data velocity is handled by distributing sensor data messaging and serving observations as RDF graphs on query demand. The stream processing engine presented in the paper, morph-streams++, provides adapters for different data formats and distributed processing of streams in a cluster. An evaluation of different configurations for parallelization and semantic annotation parameters proves that the described approach reduces the average latency of message processing in some cases.
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Mathieu, Claire, and Michel de Rougemont. "Large very dense subgraphs in a stream of edges." Network Science 9, no. 4 (December 2021): 403–24. http://dx.doi.org/10.1017/nws.2021.17.

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AbstractWe study the detection and the reconstruction of a large very dense subgraph in a social graph with n nodes and m edges given as a stream of edges, when the graph follows a power law degree distribution, in the regime when $m=O(n. \log n)$ . A subgraph S is very dense if it has $\Omega(|S|^2)$ edges. We uniformly sample the edges with a Reservoir of size $k=O(\sqrt{n}.\log n)$ . Our detection algorithm checks whether the Reservoir has a giant component. We show that if the graph contains a very dense subgraph of size $\Omega(\sqrt{n})$ , then the detection algorithm is almost surely correct. On the other hand, a random graph that follows a power law degree distribution almost surely has no large very dense subgraph, and the detection algorithm is almost surely correct. We define a new model of random graphs which follow a power law degree distribution and have large very dense subgraphs. We then show that on this class of random graphs we can reconstruct a good approximation of the very dense subgraph with high probability. We generalize these results to dynamic graphs defined by sliding windows in a stream of edges.
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Ajwani, Deepak, Shoukat Ali, Kostas Katrinis, Cheng-Hong Li, Alfred J. Park, John P. Morrison, and Eugen Schenfeld. "Generating synthetic task graphs for simulating stream computing systems." Journal of Parallel and Distributed Computing 73, no. 10 (October 2013): 1362–74. http://dx.doi.org/10.1016/j.jpdc.2013.06.002.

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Li, Yan, Tingjian Ge, and Cindy Chen. "Data stream event prediction based on timing knowledge and state transitions." Proceedings of the VLDB Endowment 13, no. 10 (June 2020): 1779–92. http://dx.doi.org/10.14778/3401960.3401973.

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We study a practical problem of predicting the upcoming events in data streams using a novel approach. Treating event time orders as relationship types between event entities, we build a dynamic knowledge graph and use it to predict future event timing. A unique aspect of this knowledge graph embedding approach for prediction is that we enhance conventional knowledge graphs with the notion of "states"---in what we call the ephemeral state nodes---to characterize the state of a data stream over time. We devise a complete set of methods for learning relevant events, for building the event-order graph stream from the original data stream, for embedding and prediction, and for theoretically bounding the complexity. We evaluate our approach with four real world stream datasets and find that our method results in high precision and recall values for event timing prediction, ranging between 0.7 and nearly 1, significantly outperforming baseline approaches. Moreover, due to our choice of efficient translation-based embedding, the overall throughput that the stream system can handle, including continuous graph building, training, and event predictions, is over one thousand to sixty thousand tuples per second even on a personal computer---which is especially important in resource constrained environments, including edge computing.
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Dissertations / Theses on the topic "Stream graphs"

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Gillani, Syed. "Semantically-enabled stream processing and complex event processing over RDF graph streams." Thesis, Lyon, 2016. http://www.theses.fr/2016LYSES055/document.

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Résumé en français non fourni par l'auteur
There is a paradigm shift in the nature and processing means of today’s data: data are used to being mostly static and stored in large databases to be queried. Today, with the advent of new applications and means of collecting data, most applications on the Web and in enterprises produce data in a continuous manner under the form of streams. Thus, the users of these applications expect to process a large volume of data with fresh low latency results. This has resulted in the introduction of Data Stream Processing Systems (DSMSs) and a Complex Event Processing (CEP) paradigm – both with distinctive aims: DSMSs are mostly employed to process traditional query operators (mostly stateless), while CEP systems focus on temporal pattern matching (stateful operators) to detect changes in the data that can be thought of as events. In the past decade or so, a number of scalable and performance intensive DSMSs and CEP systems have been proposed. Most of them, however, are based on the relational data models – which begs the question for the support of heterogeneous data sources, i.e., variety of the data. Work in RDF stream processing (RSP) systems partly addresses the challenge of variety by promoting the RDF data model. Nonetheless, challenges like volume and velocity are overlooked by existing approaches. These challenges require customised optimisations which consider RDF as a first class citizen and scale the processof continuous graph pattern matching. To gain insights into these problems, this thesis focuses on developing scalable RDF graph stream processing, and semantically-enabled CEP systems (i.e., Semantic Complex Event Processing, SCEP). In addition to our optimised algorithmic and data structure methodologies, we also contribute to the design of a new query language for SCEP. Our contributions in these two fields are as follows: • RDF Graph Stream Processing. We first propose an RDF graph stream model, where each data item/event within streams is comprised of an RDF graph (a set of RDF triples). Second, we implement customised indexing techniques and data structures to continuously process RDF graph streams in an incremental manner. • Semantic Complex Event Processing. We extend the idea of RDF graph stream processing to enable SCEP over such RDF graph streams, i.e., temporalpattern matching. Our first contribution in this context is to provide a new querylanguage that encompasses the RDF graph stream model and employs a set of expressive temporal operators such as sequencing, kleene-+, negation, optional,conjunction, disjunction and event selection strategies. Based on this, we implement a scalable system that employs a non-deterministic finite automata model to evaluate these operators in an optimised manner. We leverage techniques from diverse fields, such as relational query optimisations, incremental query processing, sensor and social networks in order to solve real-world problems. We have applied our proposed techniques to a wide range of real-world and synthetic datasets to extract the knowledge from RDF structured data in motion. Our experimental evaluations confirm our theoretical insights, and demonstrate the viability of our proposed methods
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Rannou, Léo. "Temporal Connectivity and Path Computation for Stream Graph." Electronic Thesis or Diss., Sorbonne université, 2020. http://www.theses.fr/2020SORUS418.

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Les données structurelles et les données temporelles ont, pendant longtemps, été analysées séparément. De nombreux réseaux complexes contiennent une dimension temporelle, comme les contacts entre individus ou les transactions financières. La théorie des graphes fournit un large ensemble d'outils pour modéliser et analyser les connexions entre entités. Malheureusement, cette approche ne prend pas compte la nature temporelle des interactions. La théorie des stream graphs est un formalisme permettant de modéliser les réseaux dynamiques dans lesquels les nœuds et/ou les liens arrivent et/ou partent au fil du temps. Plusieurs concepts théoriques tels que les composantes connexes dans les stream graphs ont été définis récemment, mais aucun algorithme n'a été proposé pour les calculer. De plus, la complexité algorithmique de ces problèmes est inconnue, ainsi que les connaissances qu'ils peuvent apporter sur les stream graphs de terrain. Dans cette thèse, nous proposons plusieurs solutions pour le calcul de notions de connectivité et de chemins dans les stream graphs. Nous présentons également des représentations alternatives - des structures de données conçues pour faciliter certains calculs - stream graphs. Nous fournissons également des implémentations et comparons expérimentalement nos méthodes sur une grande variété de cas pratiques. Nous montrons que ces concepts apportent beaucoup d'informations sur les caractéristiques de ces ensembles de données. Straph, une bibliothèque python, a été développée afin de disposer d'une ressource fiable afin de manipuler, analyser et visualiser les stream graphs
For a long time, structured data and temporal data have been analysed separately. Many real world complex networks have a temporal dimension, such as contacts between individuals or financial transactions. Graph theory provides a wide set of tools to model and analyze static connections between entities. Unfortunately, this approach does not take into account the temporal nature of interactions. Stream graph theory is a formalism to model highly dynamic networks in which nodes and/or links arrive and/or leave over time. The number of applications of stream graph theory has risen rapidly, along with the number of theoretical concepts and algorithms to compute them. Several theoretical concepts such as connected components and temporal paths in stream graphs were defined recently, but no algorithm was provided to compute them. Moreover, the algorithmic complexities of these problems are unknown, as well as the insight they may shed on real-world stream graphs of interest. In this thesis, we present several solutions to compute notions of connectivity and path concepts in stream graphs. We also present alternative representations - data structures designed to facilitate specific computations - of stream graphs. We provide implementations and experimentally compare our methods in a wide range of practical cases. We show that these concepts indeed give much insight on features of large-scale datasets. Straph, a python library, was developed in order to have a reliable library for manipulating, analysing and visualising stream graphs, to design algorithms and models, and to rapidly evaluate them
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Faleiros, Thiago de Paulo. "Propagação em grafos bipartidos para extração de tópicos em fluxo de documentos textuais." Universidade de São Paulo, 2016. http://www.teses.usp.br/teses/disponiveis/55/55134/tde-10112016-105854/.

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Tratar grandes quantidades de dados é uma exigência dos modernos algoritmos de mineração de texto. Para algumas aplicações, documentos são constantemente publicados, o que demanda alto custo de armazenamento em longo prazo. Então, é necessário criar métodos de fácil adaptação para uma abordagem que considere documentos em fluxo, e que analise os dados em apenas um passo sem requerer alto custo de armazenamento. Outra exigência é a de que essa abordagem possa explorar heurísticas a fim de melhorar a qualidade dos resultados. Diversos modelos para a extração automática das informações latentes de uma coleção de documentos foram propostas na literatura, dentre eles destacando-se os modelos probabilísticos de tópicos. Modelos probabilísticos de tópicos apresentaram bons resultados práticos, sendo estendidos para diversos modelos com diversos tipos de informações inclusas. Entretanto, descrever corretamente esses modelos, derivá-los e em seguida obter o apropriado algoritmo de inferência são tarefas difíceis, exigindo um tratamento matemático rigoroso para as descrições das operações efetuadas no processo de descoberta das dimensões latentes. Assim, para a elaboração de um método simples e eficiente para resolver o problema da descoberta das dimensões latentes, é necessário uma apropriada representação dos dados. A hipótese desta tese é a de que, usando a representação de documentos em grafos bipartidos, é possível endereçar problemas de aprendizado de máquinas, para a descoberta de padrões latentes em relações entre objetos, por exemplo nas relações entre documentos e palavras, de forma simples e intuitiva. Para validar essa hipótese, foi desenvolvido um arcabouço baseado no algoritmo de propagação de rótulos utilizando a representação em grafos bipartidos. O arcabouço, denominado PBG (Propagation in Bipartite Graph), foi aplicado inicialmente para o contexto não supervisionado, considerando uma coleção estática de documentos. Em seguida, foi proposta uma versão semissupervisionada, que considera uma pequena quantidade de documentos rotulados para a tarefa de classificação transdutiva. E por fim, foi aplicado no contexto dinâmico, onde se considerou fluxo de documentos textuais. Análises comparativas foram realizadas, sendo que os resultados indicaram que o PBG é uma alternativa viável e competitiva para tarefas nos contextos não supervisionado e semissupervisionado.
Handling large amounts of data is a requirement for modern text mining algorithms. For some applications, documents are published constantly, which demand a high cost for long-term storage. So it is necessary easily adaptable methods for an approach that considers documents flow, and be capable of analyzing the data in one step without requiring the high cost of storage. Another requirement is that this approach can exploit heuristics in order to improve the quality of results. Several models for automatic extraction of latent information in a collection of documents have been proposed in the literature, among them probabilistic topic models are prominent. Probabilistic topic models achieve good practical results, and have been extended to several models with different types of information included. However, properly describe these models, derive them, and then get appropriate inference algorithms are difficult tasks, requiring a rigorous mathematical treatment for descriptions of operations performed in the latent dimensions discovery process. Thus, for the development of a simple and efficient method to tackle the problem of latent dimensions discovery, a proper representation of the data is required. The hypothesis of this thesis is that by using bipartite graph for representation of textual data one can address the task of latent patterns discovery, present in the relationships between documents and words, in a simple and intuitive way. For validation of this hypothesis, we have developed a framework based on label propagation algorithm using the bipartite graph representation. The framework, called PBG (Propagation in Bipartite Graph) was initially applied to the unsupervised context for a static collection of documents. Then a semi-supervised version was proposed which need only a small amount of labeled documents to the transductive classification task. Finally, it was applied in the dynamic context in which flow of textual data was considered. Comparative analyzes were performed, and the results indicated that the PBG is a viable and competitive alternative for tasks in the unsupervised and semi-supervised contexts.
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Arnoux, Thibaud. "Prédiction d'interactions dans les flots de liens. Combiner les caractéristiques structurelles et temporelles." Electronic Thesis or Diss., Sorbonne université, 2018. http://www.theses.fr/2018SORUS229.

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Le formalisme des flots de liens représente une approche permettant de conserver la dynamique du système tout en fournissant un cadre d'étude solide pour appréhender le comportement du système. Un flot de liens est une série de triplets (t,u,v) indiquant qu'une interaction a eu lieu entre u et v au temps t. La forte importance de la dynamique du système dans la prédiction dans les flots de liens la place au carrefour de la prédiction de liens dans les graphes et de la prédiction de séries temporelles. Nous allons explorer différentes formalisations du problème de la prédiction dans les flots de liens. Dans la suite nous nous intéresserons à la prédiction de l'activité, c'est-à-dire prédire le nombre d'interactions apparaissant dans le futur pour chaque paire de nœuds durant une certaine période. Nous introduisons le protocole développé, permettant de combiner de manière cohérente les caractéristiques des données afin d'effectuer la prédiction de l'activité. Nous étudions le comportement de notre protocole sur diverses expériences sur quatre jeux de données et évaluons la qualité de chaque prédiction. Nous étudierons comment l'utilisation de classes de nœuds permet de préserver la diversité des types de liens prédits tout en améliorant la prédiction. Notre objectif est de définir un cadre de prédiction général permettant des études approfondies de la relation entre caractéristiques structurelles et temporelles dans les tâches de prédiction
The link stream formalism represent an approach allowing to capture the system dynamic while providing a framework to understand the system's behavior. A link stream is a sequence of triplet (t,u,v) indicating that an interaction occurred between u and v at time t. The importance of the system's dynamic during the prediction places it at the crossroads of link prediction in graphs and time series prediction. We will explore several formalizations of the problem of prediction in link streams. In the following we will study the activity prediction, that is to say predicting the number of interactions occurring in the future between each pair of nodes during a given period. We introduce the protocol, allowing to combine the data characteristics to predict the activity. We study the behavior of our protocol during several experiments on four datasets et evaluate the prediction quality. We will look at how the introduction of pair of nodes classes allows to preserve the link diversity in the prediction while improving the prediction. Our goal is to define a general prediction framework allowing in-depth studies of the relationship between temporal and structural characteristics in prediction tasks
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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.

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Les graphes sont des objets mathématiques qui permettent de modéliser des interactions ou connexions entre entités de types variés. Un graphe peut représenter par exemple un réseau social qui connecte les utilisateurs entre eux, un réseau de transport comme le métro où les stations sont connectées entre elles, ou encore un cerveau avec les milliards de neurones en interaction qu'il contient. Depuis quelques années, la forte dynamicité de ces structures a été mise en évidence, ainsi que l'importance de prendre en compte l'évolution temporelle de ces réseaux pour en comprendre le fonctionnement. Alors que de nombreux concepts et algorithmes ont été développés sur les graphes pour décrire des structures de réseaux statiques, il reste encore beaucoup à faire pour formaliser et développer des algorithmes pertinents pour décrire la dynamique des réseaux réels. Cette thèse vise à mieux comprendre comment sont structurés les graphes massifs qui sont issus du monde réel et à développer des outils pour étendre notre compréhension à des structures évoluant dans le temps. Il a été montré que ces graphes ont des propriétés particulières, qui les distinguent des graphes théoriques ou tirés aléatoirement. Exploiter ces propriétés permet alors de concevoir des algorithmes pour résoudre certains problèmes difficiles beaucoup plus rapidement sur ces instances que dans le cas général. La thèse se focalise sur les cliques, qui sont des groupes d'éléments tous connectés entre eux. Nous étudions l'énumération des cliques dans les graphes statiques et temporels et la détection de communautés qu'elles permettent de mettre en œuvre. Les communautés d'un graphe sont des ensembles de sommets tels qu'au sein d'une communauté, les sommets interagissent fortement entre eux, et peu avec le reste du graphe. Leur étude aide à comprendre les propriétés structurelles et fonctionnelles des réseaux. Nous évaluons nos algorithmes sur des graphes massifs issus du monde réel, ouvrant ainsi de nouvelles perspectives pour comprendre les interactions au sein de ces réseaux. Nous travaillons d'abord sur des graphes, sans tenir compte de la composante temporelle des interactions. Nous commençons par utiliser la méthode de détection de communautés par percolation de cliques, en mettant en évidence ses limites en mémoire, qui empêchent de l'appliquer à des graphes trop massifs. En introduisant un algorithme de résolution approchée du problème, nous dépassons cette limite. Puis, nous améliorons l'énumération des cliques maximales dans le cas des graphes particuliers dits bipartis. Ils correspondent à des interactions entre des groupes de sommets de type différent, par exemple des liens entre des personnes et du contenu consulté, la participation à des événements, etc. Ensuite, nous considérons des interactions qui ont lieu au cours du temps, grâce au formalisme des flots de liens. Nous cherchons à étendre les algorithmes présentés en première partie, pour exploiter leurs avantages dans l'étude des interactions temporelles. Nous fournissons un nouvel algorithme d'énumération des cliques maximales dans les flots de liens, beaucoup plus efficace que l'état de l'art sur des jeux de données massifs. Enfin, nous nous intéressons aux communautés dans les flots de liens par percolation de cliques, en développant une extension de la méthode utilisée sur les graphes. Les résultats montrent une amélioration significative par rapport à l'état de l'art, et nous analysons les communautés obtenues pour fournir des informations pertinentes sur l'organisation des interactions temporelles dans les flots de liens. Mon travail de thèse a permis d’apporter de nouvelles réflexions sur l’étude des réseaux massifs issus du monde réel. Cela montre l'importance d'explorer le potentiel des graphes dans un contexte réel, et pourrait contribuer à l'émergence de solutions novatrices pour les défis complexes de notre société moderne
Graphs 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
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Wang, Changliang. "Continuous subgraph pattern search over graph streams /." View abstract or full-text, 2009. http://library.ust.hk/cgi/db/thesis.pl?CSED%202009%20WANG.

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Navarin, Nicolò <1984&gt. "Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6578/1/navarin_nicolo_tesi.pdf.

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In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.
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Navarin, Nicolò <1984&gt. "Learning with Kernels on Graphs: DAG-based kernels, data streams and RNA function prediction." Doctoral thesis, Alma Mater Studiorum - Università di Bologna, 2014. http://amsdottorato.unibo.it/6578/.

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In many application domains data can be naturally represented as graphs. When the application of analytical solutions for a given problem is unfeasible, machine learning techniques could be a viable way to solve the problem. Classical machine learning techniques are defined for data represented in a vectorial form. Recently some of them have been extended to deal directly with structured data. Among those techniques, kernel methods have shown promising results both from the computational complexity and the predictive performance point of view. Kernel methods allow to avoid an explicit mapping in a vectorial form relying on kernel functions, which informally are functions calculating a similarity measure between two entities. However, the definition of good kernels for graphs is a challenging problem because of the difficulty to find a good tradeoff between computational complexity and expressiveness. Another problem we face is learning on data streams, where a potentially unbounded sequence of data is generated by some sources. There are three main contributions in this thesis. The first contribution is the definition of a new family of kernels for graphs based on Directed Acyclic Graphs (DAGs). We analyzed two kernels from this family, achieving state-of-the-art results from both the computational and the classification point of view on real-world datasets. The second contribution consists in making the application of learning algorithms for streams of graphs feasible. Moreover,we defined a principled way for the memory management. The third contribution is the application of machine learning techniques for structured data to non-coding RNA function prediction. In this setting, the secondary structure is thought to carry relevant information. However, existing methods considering the secondary structure have prohibitively high computational complexity. We propose to apply kernel methods on this domain, obtaining state-of-the-art results.
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Reyes, Juan C. (Juan Carlos) 1980. "A graph editing framework for the StreamIt language." Thesis, Massachusetts Institute of Technology, 2004. http://hdl.handle.net/1721.1/17980.

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Thesis (M. Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2004.
Includes bibliographical references (leaves 55-56).
A programming language is more useful if it provides a level of abstraction that makes programming more intuitive and also allows the development of tools that take advantage of the language's internal representation. StreamIt, a language for the development of streaming applications, has a hierarchical and structural nature that lends itself to a graphical programming tool. I created a prototype StreamIt Graph Editor (SGE) to facilitate the development of streaming applications using StreamIt. The SGE provides intuitive visualization tools that allow developers to work more efficiently by automating certain processes. Thus, the programmer can focus more on design issues than on low level details that slow down the development process.
by Juan C. Reyes.
M.Eng.
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Karczmarek, Michal 1977. "Constrained and phased scheduling of synchronous data flow graphs for StreamIt language." Thesis, Massachusetts Institute of Technology, 2002. http://hdl.handle.net/1721.1/87333.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, February 2003.
Includes bibliographical references (p. 107-109).
by Michal Karczmarek.
S.M.
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Books on the topic "Stream graphs"

1

Lees, Timothy. On context stream tuples and higher-order context flow graphs. Edinburgh: University of Edinburgh Department ofComputer Science, 1990.

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Kirk, Andy. Stream Graph. 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2016. http://dx.doi.org/10.4135/9781529777154.

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Lees, Timothy. On context streams and the boundaries of context flow graphs. Edinburgh: University of Edinburgh Department of Computer Science, 1990.

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Koivunen-Niemi, Laura. Learn to Create a Stream Graph in R With Data From Our World in Data (2018). 1 Oliver’s Yard, 55 City Road, London EC1Y 1SP United Kingdom: SAGE Publications, Ltd., 2021. http://dx.doi.org/10.4135/9781529772005.

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Hart, Susan J. Entering adulthood.: A curriculum for grades 9-12. Santa Cruz, CA: Network Publications, 1990.

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Hart, Susan J. Entering adulthood.: A curriculum for grades 9-12. Santa Cruz, CA: Network Publications, 1990.

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Graphiscape - New York. Crans-Près-Céligny: RotoVision, 2003.

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Child, Nancy Fraser. A comparison of the English language and reading achievement of French immersion students with transfer and English stream students (grades 3-6). Regina, Sask: Research Centre, Saskatchewan School Trustees Association, 1987.

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Dianne, Schilling, ed. Less student stress, more school success: Strategies and activities for creating optimal learning environments, grades K-12. Austin, Tex: Pro-Ed, 2010.

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Castellucci, Cecil. The plain Janes. New York, N.Y: DC Comics, 2007.

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Book chapters on the topic "Stream graphs"

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Ganguly, Sumit. "Data Stream Algorithms via Expander Graphs." In Algorithms and Computation, 52–63. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-92182-0_8.

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Schiller, Benjamin, Sven Jager, Kay Hamacher, and Thorsten Strufe. "StreaM - A Stream-Based Algorithm for Counting Motifs in Dynamic Graphs." In Algorithms for Computational Biology, 53–67. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-21233-3_5.

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Parmentier, P., T. Viard, B. Renoust, and J. F. Baffier. "Introducing Multilayer Stream Graphs and Layer Centralities." In Complex Networks and Their Applications VIII, 684–96. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-36683-4_55.

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Rutten, J. J. M. M. "An Application of Stream Calculus to Signal Flow Graphs." In Formal Methods for Components and Objects, 276–91. Berlin, Heidelberg: Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-30101-1_13.

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Rannou, Léo, Clémence Magnien, and Matthieu Latapy. "Strongly Connected Components in Stream Graphs: Computation and Experimentations." In Complex Networks & Their Applications IX, 568–80. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-65347-7_47.

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Nguyen, Quan, Peter Eades, and Seok-Hee Hong. "StreamEB: Stream Edge Bundling." In Graph Drawing, 400–413. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-36763-2_36.

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Latapy, Matthieu, Clémence Magnien, and Tiphaine Viard. "Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks." In Computational Social Sciences, 49–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-23495-9_3.

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Latapy, Matthieu, Clémence Magnien, and Tiphaine Viard. "Weighted, Bipartite, or Directed Stream Graphs for the Modeling of Temporal Networks." In Computational Social Sciences, 49–64. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-30399-9_3.

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Raab, Marius, Mark Wernsdorfer, Emanuel Kitzelmann, and Ute Schmid. "From Sensorimotor Graphs to Rules: An Agent Learns from a Stream of Experience." In Artificial General Intelligence, 333–39. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22887-2_39.

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Goh, Alwyn, G. S. Poh, and David C. L. Ngo. "Loss-Tolerant Stream Authentication via Configurable Integration of One-Time Signatures and Hash-Graphs." In Communications and Multimedia Security. Advanced Techniques for Network and Data Protection, 239–51. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-45184-6_20.

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Conference papers on the topic "Stream graphs"

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Shirui Pan, Xingquan Zhu, Chengqi Zhang, and P. S. Yu. "Graph stream classification using labeled and unlabeled graphs." In 2013 29th IEEE International Conference on Data Engineering (ICDE 2013). IEEE, 2013. http://dx.doi.org/10.1109/icde.2013.6544842.

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Kakkad, Vasvi, Andrew E. Santosa, and Bernhard Scholz. "Migrating operator placement for compositional stream graphs." In the 15th ACM international conference. New York, New York, USA: ACM Press, 2012. http://dx.doi.org/10.1145/2387238.2387261.

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Amma, Keigo, Shunsuke Wada, Kanto Nakayama, Yuki Akamatsu, Yuichi Yaguchi, and Keitaro Naruse. "Visualization of spread of topic words on Twitter using stream graphs and relational graphs." In 2014 Joint 7th International Conference on Soft Computing and Intelligent Systems (SCIS) and 15th International Symposium on Advanced Intelligent Systems (ISIS). IEEE, 2014. http://dx.doi.org/10.1109/scis-isis.2014.7044759.

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Amini, Lisa, Jorge Lepre, and Martin Kienzle. "Distributed stream control for self-managing media processing graphs." In the seventh ACM international conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/319878.319905.

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Chavez, Nidia Yadira Vaquera, and Trilce Estrada. "PASCAL-G: a Probabilistic Stream Clustering Analysis on Graphs." In 2021 IEEE International Conference on Big Data (Big Data). IEEE, 2021. http://dx.doi.org/10.1109/bigdata52589.2021.9671750.

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Nguyen, Dong, and Jongeun Lee. "Communication-aware mapping of stream graphs for multi-GPU platforms." In CGO '16: 14th Annual IEEE/ACM International Symposium on Code Generation and Optimization. New York, NY, USA: ACM, 2016. http://dx.doi.org/10.1145/2854038.2854055.

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Hu, Binbin, Zhengwei Wu, Jun Zhou, Ziqi Liu, Zhigang Huangfu, Zhiqiang Zhang, and Chaochao Chen. "MERIT: Learning Multi-level Representations on Temporal Graphs." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/288.

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Recently, representation learning on temporal graphs has drawn increasing attention, which aims at learning temporal patterns to characterize the evolving nature of dynamic graphs in real-world applications. Despite effectiveness, these methods commonly ignore the individual- and combinatorial-level patterns derived from different types of interactions (e.g.,user-item), which are at the heart of the representation learning on temporal graphs. To fill this gap, we propose MERIT, a novel multi-level graph attention network for inductive representation learning on temporal graphs.We adaptively embed the original timestamps to a higher, continuous dimensional space for learn-ing individual-level periodicity through Personalized Time Encoding (PTE) module. Furthermore, we equip MERIT with Continuous time and Con-text aware Attention (Coco-Attention) mechanism which chronologically locates most relevant neighbors by jointly capturing multi-level context on temporal graphs. Finally, MERIT performs multiple aggregations and propagations to explore and exploit high-order structural information for down-stream tasks. Extensive experiments on four public datasets demonstrate the effectiveness of MERITon both (inductive / transductive) link prediction and node classification task.
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Lee, Geon, Minyoung Choe, and Kijung Shin. "HashNWalk: Hash and Random Walk Based Anomaly Detection in Hyperedge Streams." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/296.

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Sequences of group interactions, such as emails, online discussions, and co-authorships, are ubiquitous; and they are naturally represented as a stream of hyperedges (i.e., sets of nodes). Despite its broad potential applications, anomaly detection in hypergraphs (i.e., sets of hyperedges) has received surprisingly little attention, compared to anomaly detection in graphs. While it is tempting to reduce hypergraphs to graphs and apply existing graph-based methods, according to our experiments, taking higher-order structures of hypergraphs into consideration is worthwhile. We propose HashNWalk, an incremental algorithm that detects anomalies in a stream of hyperedges. It maintains and updates a constant-size summary of the structural and temporal information about the input stream. Using the summary, which is the form of a proximity matrix, HashNWalk measures the anomalousness of each new hyperedge as it appears. HashNWalk is (a) Fast: it processes each hyperedge in near real-time and billions of hyperedges within a few hours, (b) Space Efficient: the size of the maintained summary is a user-specific constant, (c) Effective: it successfully detects anomalous hyperedges in real-world hypergraphs.
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Akili, Samira, and Matthias Weidlich. "MuSE Graphs for Flexible Distribution of Event Stream Processing in Networks." In SIGMOD/PODS '21: International Conference on Management of Data. New York, NY, USA: ACM, 2021. http://dx.doi.org/10.1145/3448016.3457318.

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Middendorf, Lars, and Christian Haubelt. "Scheduling of Recursive and Dynamic Data-Flow Graphs Using Stream Rewriting." In 2014 International Symposium on Computer Architecture and High Performance Computing Workshop (SBAC-PADW). IEEE, 2014. http://dx.doi.org/10.1109/sbac-padw.2014.7.

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Reports on the topic "Stream graphs"

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Tercek, Michael. Climate monitoring in the Mediterranean Coast Network 2020: Cabrillo National Monument. National Park Service, September 2022. http://dx.doi.org/10.36967/2294406.

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This report details the climate (temperature, precipitation, drought, and streamflow) of Cabrillo National Monument during 2020. Gridded GIS maps are used to present broad-brush conditions across the region for each month. This regional presentation is compared to detailed data from weather stations in the Global Historical Climatology Network (GHCN), automated meteorological stations, and data from USGS stream gages. Prior to analysis, a set of data quality control checks were performed by the author in accordance with an established National Park Service protocol. Readers can download the data used in this report and make custom climate graphs and tables of their own by visiting www.ClimateAnalyzer.org.
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Tercek, Michael. Climate monitoring in the Mediterranean Coast Network 2020: Santa Monica Mountains National Recreation Area. National Park Service, September 2022. http://dx.doi.org/10.36967/2294435.

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This report details the climate (temperature, precipitation, drought, and streamflow) of Santa Monica Mountains National Recreation Area (NRA) during 2020. Gridded GIS maps are used to present broad-brush conditions across the region for each month. This regional presentation is compared to detailed data from Remote Automated Weather Stations (RAWS), weather stations in Global Historical Climatology Network (GHCN), and data from USGS stream gages. Prior to analysis, a set of data quality control checks were performed by the authors in accordance with an established National Park Service protocol (Rocky Mountain Climate Working Group 2010). Key points summarizing calendar year 2020 are presented as bullets in the Conclusion section at the end of the report. There was significant spatial and month to month variability in the data that provided exceptions to these generalizations, so an examination of the regional maps is key to understanding the climatic patterns for 2020. Readers can download the data used in this report and make custom climate graphs and tables of their own by visiting www.ClimateAnalyzer.org.
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Fait, Aaron, Grant Cramer, and Avichai Perl. Towards improved grape nutrition and defense: The regulation of stilbene metabolism under drought. United States Department of Agriculture, May 2014. http://dx.doi.org/10.32747/2014.7594398.bard.

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The goals of the present research proposal were to elucidate the physiological and molecular basis of the regulation of stilbene metabolism in grape, against the background of (i) grape metabolic network behavior in response to drought and of (ii) varietal diversity. The specific objectives included the study of the physiology of the response of different grape cultivars to continuous WD; the characterization of the differences and commonalities of gene network topology associated with WD in berry skin across varieties; the study of the metabolic response of developing berries to continuous WD with specific attention to the stilbene compounds; the integration analysis of the omics data generated; the study of isolated drought-associated stress factors on the regulation of stilbene biosynthesis in plantaand in vitro. Background to the topic Grape quality has a complex relationship with water input. Regulated water deficit (WD) is known to improve wine grapes by reducing the vine growth (without affecting fruit yield) and boosting sugar content (Keller et al. 2008). On the other hand, irregular rainfall during the summer can lead to drought-associated damage of fruit developmental process and alter fruit metabolism (Downey et al., 2006; Tarara et al., 2008; Chalmers et al., 792). In areas undergoing desertification, WD is associated with high temperatures. This WD/high temperature synergism can limit the areas of grape cultivation and can damage yields and fruit quality. Grapes and wine are the major source of stilbenes in human nutrition, and multiple stilbene-derived compounds, including isomers, polymers and glycosylated forms, have also been characterized in grapes (Jeandet et al., 2002; Halls and Yu, 2008). Heterologous expression of stilbenesynthase (STS) in a variety of plants has led to an enhanced resistance to pathogens, but in others the association has not been proven (Kobayashi et al., 2000; Soleas et al., 1995). Tomato transgenic plants harboring a grape STS had increased levels of resveratrol, ascorbate, and glutathione at the expense of the anthocyanin pathways (Giovinazzo et al. 2005), further emphasizing the intermingled relation among secondary metabolic pathways. Stilbenes are are induced in green and fleshy parts of the berries by biotic and abiotic elicitors (Chong et al., 2009). As is the case for other classes of secondary metabolites, the biosynthesis of stilbenes is not very well understood, but it is known to be under tight spatial and temporal control, which limits the availability of these compounds from plant sources. Only very few studies have attempted to analyze the effects of different environmental components on stilbene accumulation (Jeandet et al., 1995; Martinez-Ortega et al., 2000). Targeted analyses have generally shown higher levels of resveratrol in the grape skin (induced), in seeded varieties, in varieties of wine grapes, and in dark-skinned varieties (Gatto et al., 2008; summarized by Bavaresco et al., 2009). Yet, the effect of the grape variety and the rootstock on stilbene metabolism has not yet been thoroughly investigated (Bavaresco et al., 2009). The study identified a link between vine hydraulic behavior and physiology of stress with the leaf metabolism, which the PIs believe can eventually lead to the modifications identified in the developing berries that interested the polyphenol metabolism and its regulation during development and under stress. Implications are discussed below.
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Mikhaleva, E., E. Babikova, G. Bezhashvili, M. Ilina, and I. Samkova. VALUE STREAM PROGRAM. Sverdlovsk Regional Medical College, December 2022. http://dx.doi.org/10.12731/er0618.03122022.

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In order to increase the efficiency of the work of a medical organization, it is necessary to train medical workers, employees of medical organizations, students of educational organizations in the techniques and methods of lean production, followed by the application of the acquired skills directly at the workplace in a medical organization. The purpose of the training under the program is to acquire new competencies necessary to perform professional tasks using lean manufacturing tools - mapping the value stream to ensure maximum operational efficiency of production processes. The program provides for independent work: mapping the value stream of the current, ideal and target states of the process, analysis of the value stream of the current state of the process (problem identification: spaghetti method, pyramid of problems, graph-links, summary table of root causes and contribution of the solution), development of a plan measures to achieve the target state of the process.
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Beckett-Brown, C. E., A. M. McDonald, and M. B. McClenaghan. Discovering a porphyry deposit using tourmaline: a case study from Yukon. Natural Resources Canada/CMSS/Information Management, 2023. http://dx.doi.org/10.4095/331349.

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As the exploration for porphyry Cu-Au-Mo deposits has become increasingly challenging, the development of more effective techniques directed at detecting buried deposits has become critical. One methodology is to focus on key minerals, one of which is tourmaline, a robust, ubiquitous mineral in most mineralized porphyry systems. Overall, a combination of physical and chemical characteristics including 1) macro-color, 2) morphology, 3) inclusion populations, and 4) trace-element compositions are useful in discriminating between porphyry- versus non-porphyry-derived (or related) tourmaline in surficial sediments (Beckett-Brown 2022). These features are applied to tourmaline obtained from stream sediment samples (n = 22) from 16 streams derived from the unglaciated terrain proximal to the Casino calc-alkaline porphyry Cu-Au-Mo deposit (Yukon Territory, Canada). The obtained tourmaline occurs as two distinct morphologies: 1) individual blocky to prismatic sub- to euhedral grains (Type 1), 2) aggregates of radiating prismatic to acicular sub- to euhedral grains (Type 2). Type 1 grains display trace-element contents that reflect mixed origins including a mineralized porphyry origin as well metamorphic and pegmatitic (background) environments. Type 2 grains almost exclusively exhibit porphyry-derived trace-element chemistries (i.e., high Sr/Pb ~150 avg. and relatively low Zn/Cu ~2.5 avg. values). In Canadian Creek, that directly drains from the Casino deposit, samples closest to the deposit contain &amp;gt;70% porphyry-derived tourmaline, while other streams in the region from unprospective drainage basins contain no porphyry-derived tourmaline. At the most distal sample site in Canadian Creek, ~20 km downstream from Casino, nearly 30% of the recovered tourmaline in the stream sediments is porphyry-related. This method has potential to be a strong indicator of prospectivity and applicable for exploration for porphyry Cu-Au-Mo systems in both unglaciated and glaciated terrains.
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Werne, R. The Contact Stress Sensor _ Proposal to the FY2020 Technology Tech Mat Grants Program. Office of Scientific and Technical Information (OSTI), February 2020. http://dx.doi.org/10.2172/1598958.

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Holub, Oleksandr, Mykhailo Moiseienko, and Natalia Moiseienko. Fluid Flow Modelling in Houdini. [б. в.], November 2020. http://dx.doi.org/10.31812/123456789/4128.

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The modern educational environment in the field of physics and information technology ensures the widespread use of visualization software for successful and deep memorization of material. There are many software for creating graphic objects for presentations and demonstrations, the most popular of which were analyzed. The work is devoted to the visualization of liquids with different viscosity parameters. The article describes the development of a fluid model in the form of a particle stream. The proposed methodology involves using the Houdini application to create interactive models. The developed model can be used in the educational process in the field of information technology.
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Schwarte, Kirk A., and James R. Russell. Grazing Management Effects on the Sward and Physical Characteristics Relative to Streams in Cool-Season Grass Pastures. Ames (Iowa): Iowa State University, January 2009. http://dx.doi.org/10.31274/ans_air-180814-71.

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Light. L52011 Development of Fieldable Magnet and Digital MIVC Stress Measurement Techniques. Chantilly, Virginia: Pipeline Research Council International, Inc. (PRCI), April 2002. http://dx.doi.org/10.55274/r0010147.

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The objective of this project was to start the process of making the MIVC technique simple and cost effective for use in the field by reducing both the size of the required sensors and instrumentation and the calibration requirements. Three steps were taken to meet this objective: (1) design and build a small, lightweight magnetic circuit that can be handled by a single technician, (2) reduce the number and size of the instruments used to perform the MIVC measurements, and (3) investigate the variability of the MIVC calibration curves among different pipe grades and the feasibility of establishing a universal calibration curve or family of curves.
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Firon, Nurit, Prem Chourey, Etan Pressman, Allen Hartwell, and Kenneth J. Boote. Molecular Identification and Characterization of Heat-Stress-Responsive Microgametogenesis Genes in Tomato and Sorghum - A Feasibility Study. United States Department of Agriculture, October 2007. http://dx.doi.org/10.32747/2007.7591741.bard.

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Exposure to higher than optimal temperatures - heat-stress (HS) - is becoming increasingly common to all crop plants worldwide. Heat stress coinciding with microgametogenesis, especially during the post-meiotic phase that is marked by starch biosynthesis, is often associated with starch-deficient pollen and male sterility and ultimately, greatly reduced crop yields. The molecular basis for the high sensitivity of developing pollen grains, on one hand, and factors involved in pollen heat-tolerance, on the other, is poorly understood. The long-term goal of this project is to provide a better understanding of the genes that control pollen quality under heat-stress conditions. The specific objectives of this project were: (1) Determination of the threshold heat stress temperature(s) that affects tomato and sorghum pollen quality whether: a) Chronic mild heat stress conditions (CMHS), or b) Acute heat stress (AHS). (2) Isolation of heat-responsive, microgametogenesis-specific sequences. During our one-year feasibility project, we have accomplished the proposed objectives as follows: Objectrive 1: We have determined the threshold HS conditions in tomato and sorghum. This was essential for achieving the 2nd objective, since our accumulated experience (both Israeli and US labs) indicate that when temperature is raised too high above "threshold HS levels" it may cause massive death of the developing pollen grains. Above-threshold conditions have additional major disadvantages including the "noise" caused by induced expression of genes involved in cell death and masking of the differences between heatsensitive and heat-tolerant pollen grains. Two different types of HS conditions were determined: a) Season-long CMHS conditions: 32/26°C day/night temperatures confirmed in tomato and 36/26°C day maximum/night minimum temperatures in sorghum. b) Short-term AHS: In tomato, 2 hour exposure to 42-45°C (at 7 to 3 days before anthesis) followed by transfer to 28/22±2oC day/night temperatures until flower opening and pollen maturation, caused 50% reduced germinating pollen in the heat-sensitive 3017 cv.. In sorghum, 36/26°C day/night temperatures 10 to 5 days prior to panicle emergence, occurring at 35 days after sowing (DAS) in cv. DeKalb28E, produced starch-deficient and sterile pollen. Objective 2: We have established protocols for the high throughput transcriptomic approach, cDNA-AFLP, for identifying and isolating genes exhibiting differential expression in developing microspores exposed to either ambient or HS conditions and created a databank of HS-responsivemicrogametogenesis-expressed genes. A subset of differentially displayed Transcript-Derived Fragments (TDFs) that were cloned and sequenced (35 & 23 TDFs in tomato and sorghum, respectively) show close sequence similarities with metabolic genes, genes involved in regulation of carbohydrate metabolism, genes implicated in thermotolerance (heat shock proteins), genes involved in long chain fatty acids elongation, genes involved in proteolysis, in oxidation-reduction, vesicle-mediated transport, cell division and transcription factors. T-DNA-tagged Arabidopsis mutants for part of these genes were obtained to be used for their functional analysis. These studies are planned for a continuation project. Following functional analyses of these genes under HS – a valuable resource of genes, engaged in the HS-response of developing pollen grains, that could be modulated for the improvement of pollen quality under HS in both dicots and monocots and/or used to look for natural variability of such genes for selecting heat-tolerant germplasm - is expected.
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