Resource usage analysis of logic programs via abstract interpretation using sized types

We present a novel general resource analysis for logic programs based on sized types. Sized types are representations that incorporate structural (shape) information and allow expressing both lower and upper bounds on the size of a set of terms and their subterms at any position and depth. They also allow relating the sizes of terms and subterms occurring at different argument positions in logic predicates. Using these sized types, the resource analysis can infer both lower and upper bounds on the resources used by all the procedures in a program as functions on input term (and subterm) sizes, overcoming limitations of existing resource analyses and enhancing their precision. Our new resource analysis has been developed within the abstract interpretation framework, as an extension of the sized types abstract domain, and has been integrated into the Ciao preprocessor, CiaoPP. The abstract domain operations are integrated with the setting up and solving of recurrence equations for inferring both size and resource usage functions. We show that the analysis is an improvement over the previous resource analysis present in CiaoPP and compares well in power to state of the art systems.

Cooperative Multi-robot Patrolling: A study of distributed approaches based on mathematical models of game theory to protect infrastructures

El principio de Teoría de Juegos permite desarrollar modelos estocásticos de patrullaje multi-robot para proteger infraestructuras criticas. La protección de infraestructuras criticas representa un gran reto para los países al rededor del mundo, principalmente después de los ataques terroristas llevados a cabo la década pasada. En este documento el termino infraestructura hace referencia a aeropuertos, plantas nucleares u otros instalaciones. El problema de patrullaje se define como la actividad de patrullar un entorno determinado para monitorear cualquier actividad o sensar algunas variables ambientales. En esta actividad, un grupo de robots debe visitar un conjunto de puntos de interés definidos en un entorno en intervalos de tiempo irregulares con propósitos de seguridad. Los modelos de partullaje multi-robot son utilizados para resolver este problema. Hasta el momento existen trabajos que resuelven este problema utilizando diversos principios matemáticos. Los modelos de patrullaje multi-robot desarrollados en esos trabajos representan un gran avance en este campo de investigación. Sin embargo, los modelos con los mejores resultados no son viables para aplicaciones de seguridad debido a su naturaleza centralizada y determinista. Esta tesis presenta cinco modelos de patrullaje multi-robot distribuidos e impredecibles basados en modelos matemáticos de aprendizaje de Teoría de Juegos. El objetivo del desarrollo de estos modelos está en resolver los inconvenientes presentes en trabajos preliminares. Con esta finalidad, el problema de patrullaje multi-robot se formuló utilizando conceptos de Teoría de Grafos, en la cual se definieron varios juegos en cada vértice de un grafo. Los modelos de patrullaje multi-robot desarrollados en este trabajo de investigación se han validado y comparado con los mejores modelos disponibles en la literatura. Para llevar a cabo tanto la validación como la comparación se ha utilizado un simulador de patrullaje y un grupo de robots reales. Los resultados experimentales muestran que los modelos de patrullaje desarrollados en este trabajo de investigación trabajan mejor que modelos de trabajos previos en el 80% de 150 casos de estudio. Además de esto, estos modelos cuentan con varias características importantes tales como distribución, robustez, escalabilidad y dinamismo. Los avances logrados con este trabajo de investigación dan evidencia del potencial de Teoría de Juegos para desarrollar modelos de patrullaje útiles para proteger infraestructuras. ABSTRACT Game theory principle allows to developing stochastic multi-robot patrolling models to protect critical infrastructures. Critical infrastructures protection is a great concern for countries around the world, mainly due to terrorist attacks in the last decade. In this document, the term infrastructures includes airports, nuclear power plants, and many other facilities. The patrolling problem is defined as the activity of traversing a given environment to monitoring any activity or sensing some environmental variables If this activity were performed by a fleet of robots, they would have to visit some places of interest of an environment at irregular intervals of time for security purposes. This problem is solved using multi-robot patrolling models. To date, literature works have been solved this problem applying various mathematical principles.The multi-robot patrolling models developed in those works represent great advances in this field. However, the models that obtain the best results are unfeasible for security applications due to their centralized and predictable nature. This thesis presents five distributed and unpredictable multi-robot patrolling models based on mathematical learning models derived from Game Theory. These multi-robot patrolling models aim at overcoming the disadvantages of previous work. To this end, the multi-robot patrolling problem was formulated using concepts of Graph Theory to represent the environment. Several normal-form games were defined at each vertex of a graph in this formulation. The multi-robot patrolling models developed in this research work have been validated and compared with best ranked multi-robot patrolling models in the literature. Both validation and comparison were preformed by using both a patrolling simulator and real robots. Experimental results show that the multirobot patrolling models developed in this research work improve previous ones in as many as 80% of 150 cases of study. Moreover, these multi-robot patrolling models rely on several features to highlight in security applications such as distribution, robustness, scalability, and dynamism. The achievements obtained in this research work validate the potential of Game Theory to develop patrolling models to protect infrastructures.

The TRANSPLANTA collection of Arabidopsis lines: a resource for functional analysis of transcription factors based on their conditional over-expression

Transcription factors (TFs) are key regulators of gene expression in all organisms. In eukaryotes, TFs are often represented by functionally redundant members of large gene families. Overexpression might prove a means to unveil the biological functions of redundant TFs; however, constitutive overexpression of TFs frequently causes severe developmental defects, preventing their functional characterization. Conditional overexpression strategies help to overcome this problem. Here, we report on the TRANSPLANTA collection of Arabidopsis lines, each expressing one of 949 TFs under the control of a β–estradiol-inducible promoter. Thus far, 1636 independent homozygous lines, representing an average of 2.6 lines for every TF, have been produced for the inducible expression of 634 TFs. Along with a GUS-GFP reporter, randomly selected TRANSPLANTA lines were tested and confirmed for conditional transgene expression upon β–estradiol treatment. As a proof of concept for the exploitation of this resource, β–estradiol-induced proliferation of root hairs, dark-induced senescence, anthocyanin accumulation and dwarfism were observed in lines conditionally expressing full-length cDNAs encoding RHD6, WRKY22, MYB123/TT2 and MYB26, respectively, in agreement with previously reported phenotypes conferred by these TFs. Further screening performed with other TRANSPLANTA lines allowed the identification of TFs involved in different plant biological processes, illustrating that the collection is a powerful resource for the functional characterization of TFs. For instance, ANAC058 and a TINY/AP2 TF were identified as modulators of ABA-mediated germination potential, and RAP2.10/DEAR4 was identified as a regulator of cell death in the hypocotyl–root transition zone. Seeds of TRANSPLANTA lines have been deposited at the Nottingham Arabidopsis Stock Centre for further distribution.

A game theory based strategy for reducing energy consumption in cognitive WSN

Wireless sensor networks (WSNs) are one of the most important users of wireless communication technologies in the coming years and some challenges in this area must be addressed for their complete development. Energy consumption and spectrum availability are two of the most severe constraints of WSNs due to their intrinsic nature. The introduction of cognitive capabilities into these networks has arisen to face the issue of spectrum scarcity but could be used to face energy challenges too due to their new range of communication possibilities. In this paper a new strategy based on game theory for cognitive WSNs is discussed. The presented strategy improves energy consumption by taking advantage of the new change-communication-channel capability. Based on game theory, the strategy decides when to change the transmission channel depending on the behavior of the rest of the network nodes. The strategy presented is lightweight but still has higher energy saving rates as compared to noncognitive networks and even to other strategies based on scheduled spectrum sensing. Simulations are presented for several scenarios that demonstrate energy saving rates of around 65% as compared to WSNs without cognitive techniques.

Resource allocation in GMD and SVD-based MIMO System

Singular-value decomposition (SVD)-based multiple-input multiple output (MIMO) systems, where the whole MIMO channel is decomposed into a number of unequally weighted single-input single-output (SISO) channels, have attracted a lot of attention in the wireless community. The unequal weighting of the SISO channels has led to intensive research on bit- and power allocation even in MIMO channel situation with poor scattering conditions identified as the antennas correlation effect. In this situation, the unequal weighting of the SISO channels becomes even much stronger. In comparison to the SVD-assisted MIMO transmission, geometric mean decomposition (GMD)-based MIMO systems are able to compensate the drawback of weighted SISO channels when using SVD, where the decomposition result is nearly independent of the antennas correlation effect. The remaining interferences after the GMD-based signal processing can be easily removed by using dirty paper precoding as demonstrated in this work. Our results show that GMD-based MIMO transmission has the potential to significantly simplify the bit and power loading processes and outperforms the SVD-based MIMO transmission as long as the same QAM-constellation size is used on all equally-weighted SISO channels.

Optimizing the reliability and resource efficiency of MapReduce-based systems

Debido al gran incremento de datos digitales que ha tenido lugar en los últimos años, ha surgido un nuevo paradigma de computación paralela para el procesamiento eficiente de grandes volúmenes de datos. Muchos de los sistemas basados en este paradigma, también llamados sistemas de computación intensiva de datos, siguen el modelo de programación de Google MapReduce. La principal ventaja de los sistemas MapReduce es que se basan en la idea de enviar la computación donde residen los datos, tratando de proporcionar escalabilidad y eficiencia. En escenarios libres de fallo, estos sistemas generalmente logran buenos resultados. Sin embargo, la mayoría de escenarios donde se utilizan, se caracterizan por la existencia de fallos. Por tanto, estas plataformas suelen incorporar características de tolerancia a fallos y fiabilidad. Por otro lado, es reconocido que las mejoras en confiabilidad vienen asociadas a costes adicionales en recursos. Esto es razonable y los proveedores que ofrecen este tipo de infraestructuras son conscientes de ello. No obstante, no todos los enfoques proporcionan la misma solución de compromiso entre las capacidades de tolerancia a fallo (o de manera general, las capacidades de fiabilidad) y su coste. Esta tesis ha tratado la problemática de la coexistencia entre fiabilidad y eficiencia de los recursos en los sistemas basados en el paradigma MapReduce, a través de metodologías que introducen el mínimo coste, garantizando un nivel adecuado de fiabilidad. Para lograr esto, se ha propuesto: (i) la formalización de una abstracción de detección de fallos; (ii) una solución alternativa a los puntos únicos de fallo de estas plataformas, y, finalmente, (iii) un nuevo sistema de asignación de recursos basado en retroalimentación a nivel de contenedores. Estas contribuciones genéricas han sido evaluadas tomando como referencia la arquitectura Hadoop YARN, que, hoy en día, es la plataforma de referencia en la comunidad de los sistemas de computación intensiva de datos. En la tesis se demuestra cómo todas las contribuciones de la misma superan a Hadoop YARN tanto en fiabilidad como en eficiencia de los recursos utilizados. ABSTRACT Due to the increase of huge data volumes, a new parallel computing paradigm to process big data in an efficient way has arisen. Many of these systems, called dataintensive computing systems, follow the Google MapReduce programming model. The main advantage of these systems is based on the idea of sending the computation where the data resides, trying to provide scalability and efficiency. In failure-free scenarios, these frameworks usually achieve good results. However, these ones are not realistic scenarios. Consequently, these frameworks exhibit some fault tolerance and dependability techniques as built-in features. On the other hand, dependability improvements are known to imply additional resource costs. This is reasonable and providers offering these infrastructures are aware of this. Nevertheless, not all the approaches provide the same tradeoff between fault tolerant capabilities (or more generally, reliability capabilities) and cost. In this thesis, we have addressed the coexistence between reliability and resource efficiency in MapReduce-based systems, looking for methodologies that introduce the minimal cost and guarantee an appropriate level of reliability. In order to achieve this, we have proposed: (i) a formalization of a failure detector abstraction; (ii) an alternative solution to single points of failure of these frameworks, and finally (iii) a novel feedback-based resource allocation system at the container level. Finally, our generic contributions have been instantiated for the Hadoop YARN architecture, which is the state-of-the-art framework in the data-intensive computing systems community nowadays. The thesis demonstrates how all our approaches outperform Hadoop YARN in terms of reliability and resource efficiency.

System theory based hazard analysis applied to the process industry

Chemical process accidents still occur and cost billions of dollars and, what is worse, many human lives. That means that traditional hazard analysis techniques are not enough mainly owing to the increase of complexity and size of chemical plants. In the last years, a new hazard analysis technique has been developed, changing the focus from reliability to system theory and showing promising results in other industries such as aeronautical and nuclear. In this paper, we present an approach for the application of STAMP and STPA analysis developed by Leveson in 2011 to the process industry.