931 resultados para Mixed integer programming


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For every possible spectrum of 2(N)-dimensional density operators, we construct an N-qubit X-state of the same spectrum and maximal genuine multipartite (GM-) concurrence, hence characterizing a global unitary transformation that -constrained to output X-states-maximizes the GM-concurrence of an arbitrary input mixed state of N qubits. We also apply semidefinite programming methods to obtain N-qubit X-states with maximal GM-concurrence for a given purity and to provide an alternative proof of optimality of a recently proposed set of density matrices for the purpose, the so-called X-MEMS. Furthermore, we introduce a numerical strategy to tailor a quantum operation that converts between any two given density matrices using a relatively small number of Kraus operators. We apply our strategy to design short operator-sum representations for the transformation between any given N-qubit mixed state and a corresponding X-MEMS of the same purity.

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Higher education has a responsibility to educate a democratic citizenry and recent research indicates civic engagement is on the decline in the United States. Through a mixed methodological approach, I demonstrate that the potential exists for well structured short-term international service-learning programming to develop college students’ civic identities. Quantitative analysis of questionnaire data, collected from American college students immediately prior to their participation in a short-term service-learning experience in Northern Ireland and again upon their return to the United States, revealed increases in civic accountability, political efficacy, justice oriented citizenship, and service-learning. Subsequent qualitative analysis of interview transcripts, student journals, and field notes suggested that facilitated critical reflection before, during, and after the experience promoted transformational learning. Emergent themes included: (a) responsibilities to others, (b) the value of international service-learning, (c) crosspollination of ideas, (d) stepping outside the daily routine to facilitate divergent thinking, and (e) the necessity of precursory thinking for sustaining transformations in thinking. The first theme, responsibilities to others, was further divided into subthemes of thinking beyond oneself, raising awareness of responsibility to others, and voting responsibly.

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Los sistemas empotrados son cada día más comunes y complejos, de modo que encontrar procesos seguros, eficaces y baratos de desarrollo software dirigidos específicamente a esta clase de sistemas es más necesario que nunca. A diferencia de lo que ocurría hasta hace poco, en la actualidad los avances tecnológicos en el campo de los microprocesadores de los últimos tiempos permiten el desarrollo de equipos con prestaciones más que suficientes para ejecutar varios sistemas software en una única máquina. Además, hay sistemas empotrados con requisitos de seguridad (safety) de cuyo correcto funcionamiento depende la vida de muchas personas y/o grandes inversiones económicas. Estos sistemas software se diseñan e implementan de acuerdo con unos estándares de desarrollo software muy estrictos y exigentes. En algunos casos puede ser necesaria también la certificación del software. Para estos casos, los sistemas con criticidades mixtas pueden ser una alternativa muy valiosa. En esta clase de sistemas, aplicaciones con diferentes niveles de criticidad se ejecutan en el mismo computador. Sin embargo, a menudo es necesario certificar el sistema entero con el nivel de criticidad de la aplicación más crítica, lo que hace que los costes se disparen. La virtualización se ha postulado como una tecnología muy interesante para contener esos costes. Esta tecnología permite que un conjunto de máquinas virtuales o particiones ejecuten las aplicaciones con unos niveles de aislamiento tanto temporal como espacial muy altos. Esto, a su vez, permite que cada partición pueda ser certificada independientemente. Para el desarrollo de sistemas particionados con criticidades mixtas se necesita actualizar los modelos de desarrollo software tradicionales, pues estos no cubren ni las nuevas actividades ni los nuevos roles que se requieren en el desarrollo de estos sistemas. Por ejemplo, el integrador del sistema debe definir las particiones o el desarrollador de aplicaciones debe tener en cuenta las características de la partición donde su aplicación va a ejecutar. Tradicionalmente, en el desarrollo de sistemas empotrados, el modelo en V ha tenido una especial relevancia. Por ello, este modelo ha sido adaptado para tener en cuenta escenarios tales como el desarrollo en paralelo de aplicaciones o la incorporación de una nueva partición a un sistema ya existente. El objetivo de esta tesis doctoral es mejorar la tecnología actual de desarrollo de sistemas particionados con criticidades mixtas. Para ello, se ha diseñado e implementado un entorno dirigido específicamente a facilitar y mejorar los procesos de desarrollo de esta clase de sistemas. En concreto, se ha creado un algoritmo que genera el particionado del sistema automáticamente. En el entorno de desarrollo propuesto, se han integrado todas las actividades necesarias para desarrollo de un sistema particionado, incluidos los nuevos roles y actividades mencionados anteriormente. Además, el diseño del entorno de desarrollo se ha basado en la ingeniería guiada por modelos (Model-Driven Engineering), la cual promueve el uso de los modelos como elementos fundamentales en el proceso de desarrollo. Así pues, se proporcionan las herramientas necesarias para modelar y particionar el sistema, así como para validar los resultados y generar los artefactos necesarios para el compilado, construcción y despliegue del mismo. Además, en el diseño del entorno de desarrollo, la extensión e integración del mismo con herramientas de validación ha sido un factor clave. En concreto, se pueden incorporar al entorno de desarrollo nuevos requisitos no-funcionales, la generación de nuevos artefactos tales como documentación o diferentes lenguajes de programación, etc. Una parte clave del entorno de desarrollo es el algoritmo de particionado. Este algoritmo se ha diseñado para ser independiente de los requisitos de las aplicaciones así como para permitir al integrador del sistema implementar nuevos requisitos del sistema. Para lograr esta independencia, se han definido las restricciones al particionado. El algoritmo garantiza que dichas restricciones se cumplirán en el sistema particionado que resulte de su ejecución. Las restricciones al particionado se han diseñado con una capacidad expresiva suficiente para que, con un pequeño grupo de ellas, se puedan expresar la mayor parte de los requisitos no-funcionales más comunes. Las restricciones pueden ser definidas manualmente por el integrador del sistema o bien pueden ser generadas automáticamente por una herramienta a partir de los requisitos funcionales y no-funcionales de una aplicación. El algoritmo de particionado toma como entradas los modelos y las restricciones al particionado del sistema. Tras la ejecución y como resultado, se genera un modelo de despliegue en el que se definen las particiones que son necesarias para el particionado del sistema. A su vez, cada partición define qué aplicaciones deben ejecutar en ella así como los recursos que necesita la partición para ejecutar correctamente. El problema del particionado y las restricciones al particionado se modelan matemáticamente a través de grafos coloreados. En dichos grafos, un coloreado propio de los vértices representa un particionado del sistema correcto. El algoritmo se ha diseñado también para que, si es necesario, sea posible obtener particionados alternativos al inicialmente propuesto. El entorno de desarrollo, incluyendo el algoritmo de particionado, se ha probado con éxito en dos casos de uso industriales: el satélite UPMSat-2 y un demostrador del sistema de control de una turbina eólica. Además, el algoritmo se ha validado mediante la ejecución de numerosos escenarios sintéticos, incluyendo algunos muy complejos, de más de 500 aplicaciones. ABSTRACT The importance of embedded software is growing as it is required for a large number of systems. Devising cheap, efficient and reliable development processes for embedded systems is thus a notable challenge nowadays. Computer processing power is continuously increasing, and as a result, it is currently possible to integrate complex systems in a single processor, which was not feasible a few years ago.Embedded systems may have safety critical requirements. Its failure may result in personal or substantial economical loss. The development of these systems requires stringent development processes that are usually defined by suitable standards. In some cases their certification is also necessary. This scenario fosters the use of mixed-criticality systems in which applications of different criticality levels must coexist in a single system. In these cases, it is usually necessary to certify the whole system, including non-critical applications, which is costly. Virtualization emerges as an enabling technology used for dealing with this problem. The system is structured as a set of partitions, or virtual machines, that can be executed with temporal and spatial isolation. In this way, applications can be developed and certified independently. The development of MCPS (Mixed-Criticality Partitioned Systems) requires additional roles and activities that traditional systems do not require. The system integrator has to define system partitions. Application development has to consider the characteristics of the partition to which it is allocated. In addition, traditional software process models have to be adapted to this scenario. The V-model is commonly used in embedded systems development. It can be adapted to the development of MCPS by enabling the parallel development of applications or adding an additional partition to an existing system. The objective of this PhD is to improve the available technology for MCPS development by providing a framework tailored to the development of this type of system and by defining a flexible and efficient algorithm for automatically generating system partitionings. The goal of the framework is to integrate all the activities required for developing MCPS and to support the different roles involved in this process. The framework is based on MDE (Model-Driven Engineering), which emphasizes the use of models in the development process. The framework provides basic means for modeling the system, generating system partitions, validating the system and generating final artifacts. The framework has been designed to facilitate its extension and the integration of external validation tools. In particular, it can be extended by adding support for additional non-functional requirements and support for final artifacts, such as new programming languages or additional documentation. The framework includes a novel partitioning algorithm. It has been designed to be independent of the types of applications requirements and also to enable the system integrator to tailor the partitioning to the specific requirements of a system. This independence is achieved by defining partitioning constraints that must be met by the resulting partitioning. They have sufficient expressive capacity to state the most common constraints and can be defined manually by the system integrator or generated automatically based on functional and non-functional requirements of the applications. The partitioning algorithm uses system models and partitioning constraints as its inputs. It generates a deployment model that is composed by a set of partitions. Each partition is in turn composed of a set of allocated applications and assigned resources. The partitioning problem, including applications and constraints, is modeled as a colored graph. A valid partitioning is a proper vertex coloring. A specially designed algorithm generates this coloring and is able to provide alternative partitions if required. The framework, including the partitioning algorithm, has been successfully used in the development of two industrial use cases: the UPMSat-2 satellite and the control system of a wind-power turbine. The partitioning algorithm has been successfully validated by using a large number of synthetic loads, including complex scenarios with more that 500 applications.

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2010 Mathematics Subject Classification: 97D40, 97M10, 97M40, 97N60, 97N80, 97R80

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People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

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People go through their life making all kinds of decisions, and some of these decisions affect their demand for transportation, for example, their choices of where to live and where to work, how and when to travel and which route to take. Transport related choices are typically time dependent and characterized by large number of alternatives that can be spatially correlated. This thesis deals with models that can be used to analyze and predict discrete choices in large-scale networks. The proposed models and methods are highly relevant for, but not limited to, transport applications. We model decisions as sequences of choices within the dynamic discrete choice framework, also known as parametric Markov decision processes. Such models are known to be difficult to estimate and to apply to make predictions because dynamic programming problems need to be solved in order to compute choice probabilities. In this thesis we show that it is possible to explore the network structure and the flexibility of dynamic programming so that the dynamic discrete choice modeling approach is not only useful to model time dependent choices, but also makes it easier to model large-scale static choices. The thesis consists of seven articles containing a number of models and methods for estimating, applying and testing large-scale discrete choice models. In the following we group the contributions under three themes: route choice modeling, large-scale multivariate extreme value (MEV) model estimation and nonlinear optimization algorithms. Five articles are related to route choice modeling. We propose different dynamic discrete choice models that allow paths to be correlated based on the MEV and mixed logit models. The resulting route choice models become expensive to estimate and we deal with this challenge by proposing innovative methods that allow to reduce the estimation cost. For example, we propose a decomposition method that not only opens up for possibility of mixing, but also speeds up the estimation for simple logit models, which has implications also for traffic simulation. Moreover, we compare the utility maximization and regret minimization decision rules, and we propose a misspecification test for logit-based route choice models. The second theme is related to the estimation of static discrete choice models with large choice sets. We establish that a class of MEV models can be reformulated as dynamic discrete choice models on the networks of correlation structures. These dynamic models can then be estimated quickly using dynamic programming techniques and an efficient nonlinear optimization algorithm. Finally, the third theme focuses on structured quasi-Newton techniques for estimating discrete choice models by maximum likelihood. We examine and adapt switching methods that can be easily integrated into usual optimization algorithms (line search and trust region) to accelerate the estimation process. The proposed dynamic discrete choice models and estimation methods can be used in various discrete choice applications. In the area of big data analytics, models that can deal with large choice sets and sequential choices are important. Our research can therefore be of interest in various demand analysis applications (predictive analytics) or can be integrated with optimization models (prescriptive analytics). Furthermore, our studies indicate the potential of dynamic programming techniques in this context, even for static models, which opens up a variety of future research directions.

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The mixed double-decker Eu\[Pc(15C5)4](TPP) (1) was obtained by base-catalysed tetramerisation of 4,5-dicyanobenzo-15-crown-5 using the half-sandwich complex Eu(TPP)(acac) (acac = acetylacetonate), generated in situ, as the template. For comparative studies, the mixed triple-decker complexes Eu2\[Pc(15C5)4](TPP)2 (2) and Eu2\[Pc(15C5)4]2(TPP) (3) were also synthesised by the raise-by-one-story method. These mixed ring sandwich complexes were characterised by various spectroscopic methods. Up to four one-electron oxidations and two one-electron reductions were revealed by cyclic voltammetry (CV) and differential pulse voltammetry (DPV). As shown by electronic absorption and infrared spectroscopy, supramolecular dimers (SM1 and SM3) were formed from the corresponding double-decker 1 and triple-decker 3 in the presence of potassium ions in MeOH/CHCl3.

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Raman spectra were recorded in the range 400–1800 cm−1 for a series of 15 mixed \[tetrakis(4-tert-butylphenyl)porphyrinato](2,3-naphthalocyaninato) rare earth double-deckers M(TBPP)(Nc) (M = Y; La–Lu except Pm) using laser excitation at 632.8 and 785 nm. Comparisons with bis(naphthalocyaninato) rare earth counterparts reveal that the vibrations of the metallonaphthalocyanine M(Nc) fragment dominate the Raman features of M(TBPP)(Nc). When excited with radiation of 632.8 nm, the most intense vibration appears at about 1595 cm−1, due to the naphthalene stretching. These complexes exhibit the marker Raman band for Nc•− as a medium-intense band in the range 1496–1507 cm−1, attributed to the coupling of pyrrole and aza stretching, while the marker Raman band of Nc2− in intermediate-valence Ce(TBPP)(Nc) appears as a strong band at 1493 cm−1 and is due to the isoindole stretchings. By contrast, when excited with radiation of 785 nm that is in close resonance with the main Q absorption band of the naphthalocyanine ligand, the ring radial vibrations at ca 680 and 735 cm−1 for MIII(TBPP)(Nc) are selectively intensified and are the most intense bands. For the cerium double-decker, the most intense vibration also acting as the marker Raman band of Nc2− appears at 1497 cm−1 with contributions from both pyrrole CC and aza CN stretches. The same vibrational modes show weak to medium intensity scattering at 1506–1509 cm−1 for MIII(TBPP)(Nc) and this is the marker Raman band of Nc•− when thus excited. The scatterings due to the Nc breathings, ring radial vibration, aza group stretchings, naphthalene stretchings, benzoisoindole stretchings and the coupling of pyrrole CC and aza CN stretchings in MIII(TBPP)(Nc) are all slightly blue shifted along with the decrease in rare earth ionic radius, confirming the effects of increased ring–ring interactions on the Raman characteristics of naphthalocyanine in the mixed ring double-deckers.

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Timely feedback is a vital component in the learning process. It is especially important for beginner students in Information Technology since many have not yet formed an effective internal model of a computer that they can use to construct viable knowledge. Research has shown that learning efficiency is increased if immediate feedback is provided for students. Automatic analysis of student programs has the potential to provide immediate feedback for students and to assist teaching staff in the marking process. This paper describes a “fill in the gap” programming analysis framework which tests students’ solutions and gives feedback on their correctness, detects logic errors and provides hints on how to fix these errors. Currently, the framework is being used with the Environment for Learning to Programming (ELP) system at Queensland University of Technology (QUT); however, the framework can be integrated into any existing online learning environment or programming Integrated Development Environment (IDE)