44 research outputs found

    Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions

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    With the rise of Systems Biology as a new paradigm for understanding biological processes, the development of quantitative models is no longer restricted to a small circle of theoreticians. The dramatic increase in the number of these models precipitates the need to exchange and reuse both existing and newly created models. The Systems Biology Markup Language (SBML) is a free, open, XML-based format for representing quantitative models of biological interest that advocates the consistent specification of such models and thus facilitates both software development and model exchange.

Principally oriented towards describing systems of biochemical reactions, such as cell signalling pathways, metabolic networks and gene regulation etc., SBML can also be used to encode any kinetic model. SBML offers mechanisms to describe biological components by means of compartments and reacting species, as well as their dynamic behaviour, using reactions, events and arbitrary mathematical rules. SBML also offers all the housekeeping structures needed to ensure an unambiguous understanding of quantitative descriptions.

This is Release 1 of the specification for SBML Level 2 Version 4, describing the structures of the language and the rules used to build a valid model. SBML XML Schema and other related documents and software are also available from the SBML project web site, "http://sbml.org/":http://sbml.org/

    Evolution, testing and configuration of variability intensive systems

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    Tesis descargada desde ResearchGateOne of the key characteristics of software is its ability to be adapted and configured to different scenarios. Recently, software variability has been studied as a first-class concept in different domains ranging from software product lines to pervasive systems. Variability is the ability of a software product to vary depending on different circumstances. Variability intensive systems are those software products where variability management is a core engineering activity. The varying parts of those systems are commonly modeled by us- ing different variability model flavors, being feature modeling one of the most common ones. Feature models were first introduced by Kang et al. back in 1990 and are a compact representation of a set of configurations in a variability intensive system. The large number of configurations that a feature model can encode makes the manual analysis of feature models an error prone and costly task. Then, computer-aided mechanisms appeared as a solution to extract useful information from feature models. This process of extracting information from feature models is known as ÂżAutomated Analysis of Feature modelsÂż that has been one of the main areas of research in the last years where more than thirty analysis operations have been proposed.Premio Extraordinario de Doctorado U

    Development of MEMS Piezoelectric Vibration Energy Harvesters with Wafer-Level Integrated Tungsten Proof-Mass for Ultra Low Power Autonomous Wireless Sensors

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    La gĂ©nĂ©ration d’énergie localisĂ©e et Ă  petite Ă©chelle, par transformation de l’énergie vibratoire disponible dans l’environnement, est une solution attrayante pour amĂ©liorer l’autonomie de certains noeuds de capteurs sans-fil pour l’Internet des objets (IoT). GrĂące Ă  des microdispositifs inertiels rĂ©sonants piĂ©zoĂ©lectriques, il est possible de transformer l’énergie mĂ©canique en Ă©lectricitĂ©. Cette thĂšse prĂ©sente une Ă©tude exhaustive de cette technologie et propose un procĂ©dĂ© pour fabriquer des microgĂ©nĂ©rateurs MEMS offrant des performances surpassant l’état de l’art. On prĂ©sente d’abord une revue complĂšte des limites physiques et technologiques pour identifier le meilleur chemin d’amĂ©lioration. En Ă©valuant les approches proposĂ©es dans la littĂ©rature (gĂ©omĂ©trie, architecture, matĂ©riaux, circuits, etc.), nous suggĂ©rons des mĂ©triques pour comparer l’état de l’art. Ces analyses dĂ©montrent que la limite fondamentale est l’énergie absorbĂ©e par le dispositif, car plusieurs des solutions existantes rĂ©pondent dĂ©jĂ  aux autres limites. Pour un gĂ©nĂ©rateur linĂ©aire rĂ©sonant, l’absorption d’énergie dĂ©pend donc des vibrations disponibles, mais aussi de la masse du dispositif et de son facteur de qualitĂ©. Pour orienter la conception de prototypes, nous avons rĂ©alisĂ© une Ă©tude sur le potentiel des capteurs autonomes dans une automobile. Nous avons Ă©valuĂ© une liste des capteurs prĂ©sents sur un vĂ©hicule pour leur compatibilitĂ© avec cette technologie. Nos mesures de vibrations sur un vĂ©hicule en marche aux emplacements retenus rĂ©vĂšlent que l’énergie disponible pour un dispositif linĂ©aire rĂ©sonant MEMS se situe entre 30 Ă  150 Hz. Celui-ci pourrait produire autour de 1 Ă  10 ÎŒW par gramme. Pour limiter la taille d’un gĂ©nĂ©rateur MEMS pouvant produire 10 ÎŒW, il faut une densitĂ© supĂ©rieure Ă  celle du silicium, ce qui motive l’intĂ©gration du tungstĂšne. L’effet du tungstĂšne sur la sensibilitĂ© du dispositif est Ă©vident, mais nous dĂ©montrons Ă©galement que l’usage de ce matĂ©riau permet de rĂ©duire l’impact de l’amortissement fluidique sur le facteur de qualitĂ© mĂ©canique Qm. En fait, lorsque l’amortissement fluidique domine, ce changement peut amĂ©liorer Qm d’un ordre de grandeur, passant de 103 Ă  104 dans l’air ambiant. Par consĂ©quent, le rendement du dispositif est amĂ©liorĂ© sans utiliser un boĂźtier sous vide. Nous proposons ensuite un procĂ©dĂ© de fabrication qui intĂšgre au niveau de la tranche des masses de tungstĂšne de 500 ÎŒm d’épais. Ce procĂ©dĂ© utilise des approches de collage de tranches et de gravure humide du mĂ©tal en deux Ă©tapes. Nous prĂ©sentons chaque bloc de fabrication rĂ©alisĂ© pour dĂ©montrer la faisabilitĂ© du procĂ©dĂ©, lequel a permis de fabriquer plusieurs prototypes. Ces dispositifs ont Ă©tĂ© testĂ©s en laboratoire, certains dĂ©montrant des performances records en terme de densitĂ© de puissance normalisĂ©e. Notre meilleur design se dĂ©marque par une mĂ©trique de 2.5 mW-s-1/(mm3(m/s2)2), soit le meilleur rĂ©sultat rĂ©pertoriĂ© dans l’état de l’art. Avec un volume de 3.5 mm3, il opĂšre Ă  552.7 Hz et produit 2.7 ÎŒW Ă  1.6 V RMS Ă  partir d’une accĂ©lĂ©ration de 1 m/s2. Ces rĂ©sultats dĂ©montrent que l’intĂ©gration du tungstĂšne dans les microgĂ©nĂ©rateurs MEMS est trĂšs avantageuse et permet de s’approcher davantage des requis des applications rĂ©elles.Small scale and localized power generation, using vibration energy harvesting, is considered as an attractive solution to enhance the autonomy of some wireless sensor nodes used in the Internet of Things (IoT). Conversion of the ambient mechanical energy into electricity is most often done through inertial resonant piezoelectric microdevices. This thesis presents an extensive study of this technology and proposes a process to fabricate MEMS microgenerators with record performances compared to the state of the art. We first present a complete review of the physical and technological limits of this technology to asses the best path of improvement. Reported approaches (geometries, architectures, materials, circuits) are evaluated and figures of merit are proposed to compare the state of the art. These analyses show that the fundamental limit is the absorbed energy, as most proposals to date partially address the other limits. The absorbed energy depends on the level of vibrations available, but also on the mass of the device and its quality factor for a linear resonant generator. To guide design of prototypes, we conducted a study on the potential of autonomous sensors in vehicles. A survey of sensors present on a car was realized to estimate their compatibility with energy harvesting technologies. Vibration measurements done on a running vehicle at relevant locations showed that the energy available for MEMS devices is mostly located in a frequency range of 30 to 150 Hz and could generate power in the range of 1-10 ÎŒW per gram from a linear resonator. To limit the size of a MEMS generator capable of producing 10 ÎŒW, a higher mass density compared to silicon is needed, which motivates the development of a process that incorporates tungsten. Although the effect of tungsten on the device sensitivity is well known, we also demonstrate that it reduces the impact of the fluidic damping on the mechanical quality factor Qm. If fluidic damping is dominant, switching to tungsten can improve Qm by an order of magnitude, going from 103 to 104 in ambient air. As a result, the device efficiency is improved despite the lack of a vacuum package. We then propose a fabrication process flow to integrate 500 ÎŒm thick tungsten masses at the wafer level. This process combines wafer bonding with a 2-step wet metal etching approach. We present each of the fabrication nodes realized to demonstrate the feasibility of the process, which led to the fabrication of several prototypes. These devices are tested in the lab, with some designs demonstrating record breaking performances in term of normalized power density. Our best design is noteworthy for its figure of merit that is around 2.5 mW-s-1/(mm3(m/s2)2), which is the best reported in the state of the art. With a volume of 3.5 mm3, it operates at 552.7 Hz and produces 2.7 ÎŒW at 1.6 V RMS from an acceleration of 1 m/s2. These results therefore show that tungsten integration in MEMS microgenerators is very advantageous, allowing to reduce the gap with needs of current applications

    Exact methods for Bayesian network structure learning and cost function networks

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    Les modĂšles graphiques discrets reprĂ©sentent des fonctions jointes sur de grands ensembles de variables en tant qu'une combinaison de fonctions plus petites. Il existe plusieurs instanciations de modĂšles graphiques, notamment des modĂšles probabilistes et dirigĂ©s comme les rĂ©seaux BayĂ©siens, ou des modĂšles dĂ©terministes et non-dirigĂ©s comme les rĂ©seaux de fonctions de coĂ»ts. Des requĂȘtes comme trouver l'explication la plus probable (MPE) sur un rĂ©seau BayĂ©siens, et son Ă©quivalent, trouver une solution de coĂ»t minimum sur un rĂ©seau de fonctions de coĂ»t, sont toutes les deux des tĂąches d’optimisation combinatoire NP-difficiles. Il existe cependant des techniques de rĂ©solution robustes qui ont une large gamme de domaines d'applications, notamment les rĂ©seaux de rĂ©gulation de gĂšnes, l'analyse de risques et le traitement des images. Dans ce travail, nous contribuons Ă  l'Ă©tat de l'art de l'apprentissage de la structure des rĂ©seaux BayĂ©siens (BNSL), et rĂ©pondons Ă  des requĂȘtes de MPE et de minimisation des coĂ»ts sur les rĂ©seaux BayĂ©siens et les rĂ©seaux de fonctions de coĂ»ts. Pour le BNSL, nous dĂ©couvrons un nouveau point dans l'espace de conception des algorithmes de recherche qui atteint un compromis diffĂ©rent entre la qualitĂ© et la vitesse de l'infĂ©rence. Les algorithmes existants optent soit pour la qualitĂ© maximale de l'infĂ©rence en utilisant la programmation linĂ©aire en nombres entiers (PLNE) et la sĂ©paration et Ă©valuation, soit pour la vitesse de l'infĂ©rence en utilisant la programmation par contraintes (PPC). Nous dĂ©finissons des propriĂ©tĂ©s d'une classe spĂ©ciale d'inĂ©galitĂ©s, qui sont appelĂ©es "les inĂ©galitĂ©s de cluster" et qui mĂšnent Ă  un algorithme avec une qualitĂ© d'infĂ©rence beaucoup plus puissante que celle basĂ©e sur la PPC, et beaucoup plus rapide que celle basĂ©e sur la PLNE. Nous combinons cet algorithme avec des idĂ©es originales pour une propagation renforcĂ©e ainsi qu'une reprĂ©sentation de domaines plus compacte, afin d'obtenir des performances dĂ©passant l'Ă©tat de l'art dans le solveur open source ELSA (Exact Learning of bayesian network Structure using Acyclicity reasoning). Pour les rĂ©seaux de fonctions de coĂ»ts, nous identifions une faiblesse dans l'utilisation de la relaxation continue dans une classe spĂ©cifique de solveurs, y compris le solveur primĂ© "ToulBar2". Nous prouvons que cette faiblesse peut entraĂźner des dĂ©cisions de branchement sous-optimales et montrons comment dĂ©tecter un ensemble maximal de telles dĂ©cisions qui peuvent ensuite ĂȘtre Ă©vitĂ©es par le solveur. Cela permet Ă  ToulBar2 de rĂ©soudre des problĂšmes qui Ă©taient auparavant solvables uniquement par des algorithmes hybrides.Discrete Graphical Models (GMs) represent joint functions over large sets of discrete variables as a combination of smaller functions. There exist several instantiations of GMs, including directed probabilistic GMs like Bayesian Networks (BNs) and undirected deterministic models like Cost Function Networks (CFNs). Queries like Most Probable Explanation (MPE) on BNs and its equivalent on CFNs, which is cost minimisation, are NP-hard, but there exist robust solving techniques which have found a wide range of applications in fields such as bioinformatics, image processing, and risk analysis. In this thesis, we make contributions to the state of the art in learning the structure of BNs, namely the Bayesian Network Structure Learning problem (BNSL), and answering MPE and minimisation queries on BNs and CFNs. For BNSL, we discover a new point in the design space of search algorithms, which achieves a different trade-off between inference strength and speed of inference. Existing algorithms for it opt for either maximal strength of inference, like the algorithms based on Integer Programming (IP) and branch-and-cut, or maximal speed of inference, like the algorithms based on Constraint Programming (CP). We specify properties of a specific class of inequalities, called cluster inequalities, which lead to an algorithm that performs much stronger inference than that based on CP, much faster than that based on IP. We combine this with novel ideas for stronger propagation and more compact domain representations to achieve state-of-the-art performance in the open-source solver ELSA (Exact Learning of bayesian network Structure using Acyclicity reasoning). For CFNs, we identify a weakness in the use of linear programming relaxations by a specific class of solvers, which includes the award-winning open-source ToulBar2 solver. We prove that this weakness can lead to suboptimal branching decisions and show how to detect maximal sets of such decisions, which can then be avoided by the solver. This allows ToulBar2 to tackle problems previously solvable only by hybrid algorithms

    Semantics and Verification of UML Activity Diagrams for Workflow Modelling

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    This thesis defines a formal semantics for UML activity diagrams that is suitable for workflow modelling. The semantics allows verification of functional requirements using model checking. Since a workflow specification prescribes how a workflow system behaves, the semantics is defined and motivated in terms of workflow systems. As workflow systems are reactive and coordinate activities, the defined semantics reflects these aspects. In fact, two formal semantics are defined, which are completely different. Both semantics are defined directly in terms of activity diagrams and not by a mapping of activity diagrams to some existing formal notation. The requirements-level semantics, based on the Statemate semantics of statecharts, assumes that workflow systems are infinitely fast w.r.t. their environment and react immediately to input events (this assumption is called the perfect synchrony hypothesis). The implementation-level semantics, based on the UML semantics of statecharts, does not make this assumption. Due to the perfect synchrony hypothesis, the requirements-level semantics is unrealistic, but easy to use for verification. On the other hand, the implementation-level semantics is realistic, but difficult to use for verification. A class of activity diagrams and a class of functional requirements is identified for which the outcome of the verification does not depend upon the particular semantics being used, i.e., both semantics give the same result. For such activity diagrams and such functional requirements, the requirements-level semantics is as realistic as the implementation-level semantics, even though the requirements-level semantics makes the perfect synchrony hypothesis. The requirements-level semantics has been implemented in a verification tool. The tool interfaces with a model checker by translating an activity diagram into an input for a model checker according to the requirements-level semantics. The model checker checks the desired functional requirement against the input model. If the model checker returns a counterexample, the tool translates this counterexample back into the activity diagram by highlighting a path corresponding to the counterexample. The tool supports verification of workflow models that have event-driven behaviour, data, real time, and loops. Only model checkers supporting strong fairness model checking turn out to be useful. The feasibility of the approach is demonstrated by using the tool to verify some real-life workflow models

    Domain Specific Languages versus Frameworks

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    Denne masteroppgaven sammenlingner domene spesifikke sprÄk med rammeverk. Problemet er undersÞkt ved Ä implementere et domene spesifikt sprÄk og et rammeverk for Ä lage elektroniske internettbutikker. Den fÞrste delen av problemstillingen handler om sammenlingingen av de to teknikkene. Den andre delen tar for seg om det gÄr ann Ä lage en statisk semantisk analysator for et rammeverk. Eksperimentet gjort er beskrevet i detalj. Den fÞrste delen beskriver en domene analyse og designet for bÄde det domene spesifikke sprÄket og for rammeverket. Eksperimentet for den andre delen ser om det gÄr ann Ä modifisere rammeverket slik at det sÞttet statisk semantisk analyse. Masteroppgaven foreslÄr at en betydelig fordel med det domene spesifikke sprÄket er evnen til Ä implementere en domenemodel nÞyaktig. En betydelig fordel for rammeverket forelÄes Ä vÊre at det bÄde er implementert og brukes i et generelt programmeringssprÄk

    Abstract Contract Synthesis and Verification in the Symbolic K Framework

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    [EN] In this article, we propose a symbolic technique that can be used for automatically inferring software contracts from programs that are written in a non-trivial fragment of C, called KernelC, that supports pointer-based structures and heap manipulation. Starting from the semantic definition of KernelC in the K semantic framework, we enrich the symbolic execution facilities recently provided by K with novel capabilities for contract synthesis that are based on abstract subsumption. Roughly speaking, we define an abstract symbolic technique that axiomatically explains the execution of any (modifier) C function by using other (observer) routines in the same program. We implemented our technique in the automated tool KindSpec 2.1, which generates logical axioms that express pre- and postcondition assertions which define the precise input/output behavior of the C routines. Thanks to the integrated support for symbolic execution and deductive verification provided by K, some synthesized axioms that cannot be guaranteed to be correct by construction due to abstraction can finally be verified in our framework with little effort.This work has been partially supported by the EU (FEDER) and Spanish MINECO under grant TIN2015-69175-C4-1-R, and and TIN2013-45732-C4-1-P, and by Generalitat Valenciana PROMETEOII/2015/013. Daniel Pardo was supported by FPU-ME grant FPU14/01830.Alpuente Frasnedo, M.; Pardo Pont, D.; Villanueva GarcĂ­a, A. (2018). Abstract Contract Synthesis and Verification in the Symbolic K Framework. Universitat PolitĂšcnica de ValĂšncia. http://hdl.handle.net/10251/10030

    A constraint-based view

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    Synopsis: This book is an introduction to the syntactic structures that can be found in the Germanic languages. The analyses are couched in the framework of HPSG light, which is a simplified version of HPSG that uses trees to depict analyses rather than complicated attribute value matrices. The book is written for students with basic knowledge about case, constituent tests, and simple phrase structure grammars (advanced BA or MA level) and for researchers with an interest in the Germanic languages and/or an interest in Head-Driven Phrase Structure Grammar/Sign-Based Construction Grammar without having the time to deal with all the details of these theories

    Deep R Programming

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    Deep R Programming is a comprehensive course on one of the most popular languages in data science (statistical computing, graphics, machine learning, data wrangling and analytics). It introduces the base language in-depth and is aimed at ambitious students, practitioners, and researchers who would like to become independent users of this powerful environment. This textbook is a non-profit project. Its online and PDF versions are freely available at . This early draft is distributed in the hope that it will be useful.Comment: Draft: v0.2.1 (2023-04-27
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