7 research outputs found

    Prediction of Large Scale Spatio-temporal Traffic Flow Data with New Graph Convolution Model

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    Prompt and accurate prediction of traffic flow is quite useful. It will help traffic administrator to analyze the road occupancy status and formulate dynamic and flexible traffic control in advance to improve the road capacity. It can also provide more precise navigation guidance for the road users in future. However, it is hard to predict spatiotemporal traffic flow data in large scale promptly with high accuracy caused by complex interrelation and nonlinear dynamic nature. With development of deep learning and other technologies, many prediction networks could predict traffic flow with accumulated historical data in time series. In consideration of the regional characteristics of traffic flow, the emerging Graph Convolutional Network (GCN) model is systematically introduced with representative applications. Those successful applications provide a possible way to contribute fast and proper traffic control strategies that could relieve traffic pressure, reduce potential conflict, fasten emergency response, etc

    Moduli Stabilisation and the Holographic Swampland

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    We investigate whether Swampland constraints on the low-energy dynamics of weakly coupled, moduli-stabilised string vacua in AdS can be related to inconsistencies of their putative holographic duals or, more generally, recast in terms of CFT data. We find that various swampland consistency constraints are equivalent to a negativity condition on the sign of certain mixed anomalous dimensions. This condition is similar to well-established CFT positivity bounds arising from causality and unitarity, but not known to hold in general. The studied scenarios include LVS, KKLT, and both perturbative and racetrack stabilisation. Interestingly, the LVS vacuum (with Δφ=8.038\Delta_{\varphi} = 8.038) also appears to live very close to a critical value (Δφ=8\Delta_{\varphi} = 8) where the anomalous dimensions change sign. We finally point out an intriguing connection to the Swampland Distance Conjecture, both in its original and refined versions.Comment: 37 pages + references, 4 figures; v2 added references and corrected typo

    Parameter Synthesis for Markov Models

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    Markov chain analysis is a key technique in reliability engineering. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often not---or only partially---known. This motivates considering parametric models with transitions labeled with functions over parameters. Whereas traditional Markov chain analysis evaluates a reliability metric for a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given reliability or performance specification φ\varphi. Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise reliability? This paper presents various analysis algorithms for parametric Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy φ\varphi?, (b) which regions satisfy φ\varphi and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values. We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications.Comment: 38 page

    A new Nested Graph Model for Data Integration

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    Despite graph data gained increasing interest in several fields, no data model suitable for both querying and integrating differently structured graph and (semi)structured data has been currently conceived. The lack of operators allowing combinations of (multiple) graphs in current graph query languages (graph joins), and on graph data structure allowing neither data integration nor nested multidimensional representations (graph nesting) are a possible motivation. In order to make such data integration possible, this thesis proposes a novel model (General Semistructured data Model) allowing the representation of both graphs and arbitrarily nested contents (e.g., one node can be contained by more than just one parent node), thus allowing the definition of a nested graph model, where both vertices and edges may include (overlapping) graphs. We provide two graph joins algorithms (Graph Conjunctive Equijoin Algorithm and Graph Conjunctive Less-equal Algorithm) and one graph nesting algorithm (Two HOp Separated Patterns). Their evaluation on top of our secondary memory representation showed the inefficiency of existing query languages’ query plan on top of their respective data models (relational, graph and document-oriented). In all three algorithms, the enhancement was possible by using an adjacency list graph representation, thus reducing the cost of joining the vertices with their respective outgoing (or ingoing) edges, and by associating hash values to both vertices and edges. As a secondary outcome of this thesis, a general data integration scenario is provided where both graph data and other semistructured and structured data could be represented and integrated into the General Semistructured data Model. A new query language outlines the feasibility of this approach (General Semistructured Query Language) over the former data model, also allowing to express both graph joins and graph nestings. This language is also capable of representing both traversal and data manipulation operators

    Stratégies de génération de tests à partir de modÚles UML/OCL interprétés en logique du premier ordre et systÚme de contraintes.

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    This thesis describes an automatic test generation process from models.This process uses two modelling languages, UML4MBT and OCL4MBT, created specificallyfor tests generation. Theses languages are derived from UML and OCL. Therefore the behaviours,the structure and the initial state of the system are described by the class diagram, the objectdiagram and the state-chart.To generate tests, the evolution of the model is encoded with a transition system. Consequently,to construct a test is to find transition sequences that rely the initial state of the system to thestates described by the test targets.The sequence are obtained by the resolution of animation scenarios. This resolution is executedby SMT provers and CSP solvers. To create the scenario, two dedicated meta-models, UML4MBTand CSP4MBT have been established. Theses meta-models associate first order logic formulas withthe test notions.7 strategies have been developed to improve the tests generation time. A strategy is responsiblefor the selection and the prioritization of the scenarios. A strategy is built upon the properties ofthe solvers and provers and the specification of our encoding process. Moreover the process canalso be paralleled to get better performance. 5 strategies employ only a prover and 2 make theprover collaborate with a solver.Finally the interest of this process has been evaluated through a list of benchmark on variouscases studies.Les travaux prĂ©sentĂ©s dans cette thĂšse proposent une mĂ©thode de gĂ©nĂ©ration automatique de tests Ă  partir de modĂšles.Cette mĂ©thode emploie deux langages de modĂ©lisations UML4MBT et OCL4MBT qui ont Ă©tĂ© spĂ©cifiquement dĂ©rivĂ©es d’ UML et OCL pour la gĂ©nĂ©ration de tests. Ainsi les comportements, la structure et l’état initial du systĂšme sont dĂ©crits au travers des diagrammes de classes, d’objets et d’états-transitions.Pour gĂ©nĂ©rer des tests, l’évolution du modĂšle est reprĂ©sente sous la forme d’un systĂšme de transitions. Ainsi la construction de tests est Ă©quivalente Ă  la dĂ©couverte de sĂ©quences de transitions qui relient l’Žétat initial du systĂšme Ă  des Ă©tats validant les cibles de test.Ces sĂ©quences sont obtenues par la rĂ©solution de scĂ©narios d’animations par des prouveurs SMT et solveurs CSP. Pour crĂ©er ces scĂ©narios, des mĂ©ta-modĂšles UML4MBT et CSP4MBT regroupant formules logiques et notions liĂ©es aux tests ont Ă©tĂ© Ă©tablies pour chacun des outils.Afin d’optimiser les temps de gĂ©nĂ©rations, des stratĂ©gies ont Ă©tĂ© dĂ©veloppĂ© pour sĂ©lectionner et hiĂ©rarchiser les scĂ©narios Ă  rĂ©soudre. Ces stratĂ©gies s’appuient sur la parallĂ©lisation, les propriĂ©tĂ©s des solveurs et des prouveurs et les caractĂ©ristiques de nos encodages pour optimiser les performances. 5 stratĂ©gies emploient uniquement un prouveur et 2 stratĂ©gies reposent sur une collaboration du prouveur avec un solveur.Finalement l’intĂ©rĂȘt de cette nouvelle mĂ©thode Ă  Ă©tĂ© validĂ©e sur des cas d’études grĂące Ă  l’implĂ©mentation rĂ©alisĂ©e
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