On bulk-synchronous distributed-memory parallel processing of relational-database transactions

This paper describes two parallel algorithms for the eÆcient processing of relational database transactions and presents a performance analysis of them. These algorithms are built upon the bulk-synchronous parallel model of computation. The well-de ned structure of this model enabled us to evaluate their performance by using an implementation independent and yet em- pirical approach which includes the e ects of synchronization, communication and computation. The analysis reveals that the algorithm which borrows ideas from optimistic parallel discrete event simulation achieves better performance than the classical approach for synchronizing con- current transactions on a distributed memory system.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

On bulk-synchronous distributed-memory parallel processing of relational-database transactions

This paper describes two parallel algorithms for the eÆcient processing of relational database transactions and presents a performance analysis of them. These algorithms are built upon the bulk-synchronous parallel model of computation. The well-de ned structure of this model enabled us to evaluate their performance by using an implementation independent and yet em- pirical approach which includes the e ects of synchronization, communication and computation. The analysis reveals that the algorithm which borrows ideas from optimistic parallel discrete event simulation achieves better performance than the classical approach for synchronizing con- current transactions on a distributed memory system.Eje: Programación concurrenteRed de Universidades con Carreras en Informática (RedUNCI

Bayesian point processes models with applications in the COVID-19 pandemic

A point process is a set of points randomly located in a space, such as time or abstract spaces. Point process models have found numerous applications in epidemiology, ecology, geophysics, social networks and many other areas. The Poisson process is the most widely known point process. Poisson intensity estimation is a vital task in various applications including medical imaging, astrophysics and network traffic analysis. A Bayesian Additive Regression Trees (BART) scheme for estimating the intensity of inhomogeneous Poisson processes is introduced. The new approach enables full posterior inference of the intensity in a non-parametric regression setting. The performance of the novel scheme is demonstrated through simulation studies on synthetic and real datasets up to five dimensions, and the new scheme is compared with alternative approaches. A drawback of the proposed algorithm is its axis-alignment nature. We discuss this problem and suggest alternative approaches to remedy the drawback. The novel coronavirus disease (COVID-19) has been declared a Global Health Emergency of International Concern with over 557 million cases and 6.36 million deaths as of 3 August 2022 according to the World Health Organization. Understanding the spread of COVID-19 has been the subject of numerous studies, highlighting the significance of reliable epidemic models. We introduce a novel epidemic model using a latent Hawkes process with temporal covariates for modelling the infections. Unlike other Hawkes models, we model the reported cases via a probability distribution driven by the underlying Hawkes process. Modelling the infections via a Hawkes process allows us to estimate by whom an infected individual was infected. We propose a Kernel Density Particle Filter (KDPF) for inference of both latent cases and reproduction number and for predicting new cases in the near future. The computational effort is proportional to the number of infections making it possible to use particle filter-type algorithms, such as the KDPF. We demonstrate the performance of the proposed algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK, and benchmark our model to alternative approaches. We extend the unstructured homogeneously mixing epidemic model considering a finite population stratified by age bands. We model the actual unobserved infections using a latent marked Hawkes process and the reported aggregated infections as random quantities driven by the underlying Hawkes process. We apply a Kernel Density Particle Filter (KDPF) to infer the marked counting process, the instantaneous reproduction number for each age group and forecast the epidemic’s future trajectory in the near future. We demonstrate the performance of the proposed inference algorithm on synthetic data sets and COVID-19 reported cases in various local authorities in the UK. Taking into account the individual heterogeneity in age provides a real-time measurement of interventions and behavioural changes.Open Acces

Evolutionary Computation

This book presents several recent advances on Evolutionary Computation, specially evolution-based optimization methods and hybrid algorithms for several applications, from optimization and learning to pattern recognition and bioinformatics. This book also presents new algorithms based on several analogies and metafores, where one of them is based on philosophy, specifically on the philosophy of praxis and dialectics. In this book it is also presented interesting applications on bioinformatics, specially the use of particle swarms to discover gene expression patterns in DNA microarrays. Therefore, this book features representative work on the field of evolutionary computation and applied sciences. The intended audience is graduate, undergraduate, researchers, and anyone who wishes to become familiar with the latest research work on this field

Comparative Phylogeography, Phylogenetics, and Population Genomics of East African Montane Small Mammals

The Eastern Afromontane region of Africa is characterized by striking levels of endemism and species richness which rank it as a global biodiversity hotspot for diverse plants and animals including mammals, but has been poorly sampled and little studied to date. Using mtDNA and multi-locus nDNA sequence data, genome-wide RAD-Seq SNP data, and morphological data, I identify major cryptic biogeographic patterns within and between 11 co-distributed small mammal species/species groups across the Eastern Afromontane region. I focus on two endemic montane small mammal species complexes, Hylomyscus mice and Sylvisorex shrews, co-distributed across the Albertine Rift (AR) and Kenya Highlands (KH) of the Eastern Afromontane Biodiversity Hotspot. I characterize patterns of phylogeographic structure, demographic history, phylogenetic relationships and undescribed biodiversity across these taxa. Putative independently evolving lineages are inferred using a combination of distribution data, coalescent species delimitation and historical demographic inference. Hypotheses put forward to account for the high diversity of the region include both retention of older palaeo-endemic lineages across major regions in climatically stable refugia, as well as the accumulation of lineages associated with more recent differentiation between allopatric populations separated by unsuitable habitat at the LGM. Populations have persisted since the Pliocene to mid-Pleistocene across a climatic gradient from the AR in the west to the KH in the east for both focal taxa. Deeply divergent and sympatric cryptic lineages, previously unidentified, are strongly supported in both mice and shrews, highlighting the broad temporal scale at which cyclical climatic changes over the last 5 Ma may have contributed to high species diversity and endemism in the Eastern Afromontane Hotspot. Complete genome-wide SNP matrices for Hylomyscus and Sylvisorex are used in population genetic analyses that support lineages not uncovered by the 3-6 locus dataset. Graphs of population splits and admixture support substantial gene flow from AR into KH shrew populations subsequent to isolation that occurred 2.5-3.5 million years earlier, possibly by intermittent colonization. A new species, Hylomyscus kerbispeterhansi, is described from Kenya using combined morphological and multi-locus data sets

Enhancing Portability in High Performance Computing: Designing Fast Scientific Code with Longevity

Portability, an oftentimes sought-after goal in scientific applications, confers a number of possible advantages onto computer code. Portable code will often have greater longevity, enjoy a broader ecosystem, appeal to a wider variety of application developers, and by definition will run on more systems than its pigeonholed counterpart. These advantages come at a cost, however, and a rational approach to balancing costs and benefits requires a systemic evaluation. While the benefits for each application are likely situation-dependent, the costs in terms of resources, including but not limited to time, money, computational power, and memory requirements, are quantifiable. This document will identify strategies for enhancing performance portability on a variety of platforms available to the scientific computing community which will have little or no adverse impact on alternate architectures; this is done by implementing an iterative point solver requiring a high degree of data transfer bandwidth of a type commonly used in high performance applications used for computing a solution to partial differential equations (PDEs). In this thesis, we were able to show significant speed enhancements for architectures as diverse as complex traditional Central Processing Units (CPUs), Graphical Processing Units (GPUs), and Field Programmable Gate Arrays (FPGAs). Employing generalized optimizations on a variety of development frameworks we were able to show as much as a 92.5% reduction on a pipelined architecture (FPGA) while having a negligible impact on alternate architectures, and an 88.6% reduction in execution time on a Single Instruction Multiple Data (SIMD) architecture (GPU/CPU) while also having a negligible impact on alternate architectures. By enforcing these design rules in released versions of scientific code, the code has the potential to be optimally positioned for future advancements in computing architecture as well as being performance portable among existing architectures

Phylogeography and diversification of Taiwanese bats

PhDGene flow is a central evolutionary force that largely determines the level of differentiation among populations of organisms and thus their potential for divergence from each other. Identifying key factors that influence gene flow among populations or closely related taxa can thus provide valuable insights into how new species arise and are maintained. I undertook a comparative study of the factors that have shaped range-wide intraspecific differentiation in four related and broadly co-distributed Taiwanese bat species of the genera Murina and Kerivoula. Bats were sampled from sites across Taiwan and sequenced at two mitochondrial genes as well as genotyped at newly developed and/or existing multi-locus microsatellite markers. To improve phylogeographic inference of existing patterns of population genetic structure, I undertook spatial distribution modeling of the focal species at both the present time and at the Last Glacial Maximum. Genetic data were analysed using traditional and new methods, including Bayesian clustering, coalescent-based estimation of gene flow, and haplotype network reconstruction. My findings revealed contrasting signatures of population subdivision and demographic expansion that appear in part to reflect differences in the altitudinal ranges of the focal taxa. Mitochondrial analyses also revealed a putative sister relationship between two of the Taiwanese endemic taxa - M. gracilis and M. recondita, which - given the fact both are restricted to Taiwan - presents an unusual case of potential non-allopatric divergence. To dissect this divergence process in more detail, I used 454-Pyrosequencing to obtain ten nuclear loci sequences of these two taxa, and a third taxon from mainland Asia, M. eleryi. Based on these loci, Bayesian isolation-migration models provided no strong evidence of post-split gene flow and, therefore, did not support speciation within Taiwan. Instead, the divergence process reconstructed from ncDNA loci was found to be incompatible with the mtDNA tree, with M. recondita showing a sister relationship with M. eleryi. This conflict is best explained by the ancient introgression of mtDNA between the two insular species following their colonization of Taiwan at different times.Overseas Research Students Awards Scheme of the UK and the Taiwanese Ministry of Education, London Central Research Fund and the National Science Council of Taiwa

Beyond 100: The Next Century in Geodesy

This open access book contains 30 peer-reviewed papers based on presentations at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). The meeting was held from July 8 to 18, 2019 in Montreal, Canada, with the theme being the celebration of the centennial of the establishment of the IUGG. The centennial was also a good opportunity to look forward to the next century, as reflected in the title of this volume. The papers in this volume represent a cross-section of present activity in geodesy, and highlight the future directions in the field as we begin the second century of the IUGG. During the meeting, the International Association of Geodesy (IAG) organized one Union Symposium, 6 IAG Symposia, 7 Joint Symposia with other associations, and 20 business meetings. In addition, IAG co-sponsored 8 Union Symposia and 15 Joint Symposia. In total, 3952 participants registered, 437 of them with IAG priority. In total, there were 234 symposia and 18 Workshops with 4580 presentations, of which 469 were in IAG-associated symposia. ; This volume will publish papers based on International Association of Geodesy (IAG) -related presentations made at the International Association of Geodesy at the 27th IUGG General Assembly, Montreal, July 2019. It will include papers associated with all of the IAG and joint symposia from the meeting, which span all aspects of modern geodesy, and linkages to earth and environmental sciences. It continues the long-running IAG Symposia Series

Beyond 100: The Next Century in Geodesy

This open access book contains 30 peer-reviewed papers based on presentations at the 27th General Assembly of the International Union of Geodesy and Geophysics (IUGG). The meeting was held from July 8 to 18, 2019 in Montreal, Canada, with the theme being the celebration of the centennial of the establishment of the IUGG. The centennial was also a good opportunity to look forward to the next century, as reflected in the title of this volume. The papers in this volume represent a cross-section of present activity in geodesy, and highlight the future directions in the field as we begin the second century of the IUGG. During the meeting, the International Association of Geodesy (IAG) organized one Union Symposium, 6 IAG Symposia, 7 Joint Symposia with other associations, and 20 business meetings. In addition, IAG co-sponsored 8 Union Symposia and 15 Joint Symposia. In total, 3952 participants registered, 437 of them with IAG priority. In total, there were 234 symposia and 18 Workshops with 4580 presentations, of which 469 were in IAG-associated symposia. ; This volume will publish papers based on International Association of Geodesy (IAG) -related presentations made at the International Association of Geodesy at the 27th IUGG General Assembly, Montreal, July 2019. It will include papers associated with all of the IAG and joint symposia from the meeting, which span all aspects of modern geodesy, and linkages to earth and environmental sciences. It continues the long-running IAG Symposia Series

Robot Navigation in Human Environments

For the near future, we envision service robots that will help us with everyday chores in home, office, and urban environments. These robots need to work in environments that were designed for humans and they have to collaborate with humans to fulfill their tasks. In this thesis, we propose new methods for communicating, transferring knowledge, and collaborating between humans and robots in four different navigation tasks. In the first application, we investigate how automated services for giving wayfinding directions can be improved to better address the needs of the human recipients. We propose a novel method based on inverse reinforcement learning that learns from a corpus of human-written route descriptions what amount and type of information a route description should contain. By imitating the human teachers' description style, our algorithm produces new route descriptions that sound similarly natural and convey similar information content, as we show in a user study. In the second application, we investigate how robots can leverage background information provided by humans for exploring an unknown environment more efficiently. We propose an algorithm for exploiting user-provided information such as sketches or floor plans by combining a global exploration strategy based on the solution of a traveling salesman problem with a local nearest-frontier-first exploration scheme. Our experiments show that the exploration tours are significantly shorter and that our system allows the user to effectively select the areas that the robot should explore. In the second part of this thesis, we focus on humanoid robots in home and office environments. The human-like body plan allows humanoid robots to navigate in environments and operate tools that were designed for humans, making humanoid robots suitable for a wide range of applications. As localization and mapping are prerequisites for all navigation tasks, we first introduce a novel feature descriptor for RGB-D sensor data and integrate this building block into an appearance-based simultaneous localization and mapping system that we adapt and optimize for the usage on humanoid robots. Our optimized system is able to track a real Nao humanoid robot more accurately and more robustly than existing approaches. As the third application, we investigate how humanoid robots can cover known environments efficiently with their camera, for example for inspection or search tasks. We extend an existing next-best-view approach by integrating inverse reachability maps, allowing us to efficiently sample and check collision-free full-body poses. Our approach enables the robot to inspect as much of the environment as possible. In our fourth application, we extend the coverage scenario to environments that also include articulated objects that the robot has to actively manipulate to uncover obstructed regions. We introduce algorithms for navigation subtasks that run highly parallelized on graphics processing units for embedded devices. Together with a novel heuristic for estimating utility maps, our system allows to find high-utility camera poses for efficiently covering environments with articulated objects. All techniques presented in this thesis were implemented in software and thoroughly evaluated in user studies, simulations, and experiments in both artificial and real-world environments. Our approaches advance the state of the art towards universally usable robots in everyday environments.Roboternavigation in menschlichen Umgebungen In naher Zukunft erwarten wir Serviceroboter, die uns im Haushalt, im Büro und in der Stadt alltägliche Arbeiten abnehmen. Diese Roboter müssen in für Menschen gebauten Umgebungen zurechtkommen und sie müssen mit Menschen zusammenarbeiten um ihre Aufgaben zu erledigen. In dieser Arbeit schlagen wir neue Methoden für die Kommunikation, Wissenstransfer und Zusammenarbeit zwischen Menschen und Robotern bei Navigationsaufgaben in vier Anwendungen vor. In der ersten Anwendung untersuchen wir, wie automatisierte Dienste zur Generierung von Wegbeschreibungen verbessert werden können, um die Beschreibungen besser an die Bedürfnisse der Empfänger anzupassen. Wir schlagen eine neue Methode vor, die inverses bestärkendes Lernen nutzt, um aus einem Korpus von von Menschen geschriebenen Wegbeschreibungen zu lernen, wie viel und welche Art von Information eine Wegbeschreibung enthalten sollte. Indem unser Algorithmus den Stil der Wegbeschreibungen der menschlichen Lehrer imitiert, kann der Algorithmus neue Wegbeschreibungen erzeugen, die sich ähnlich natürlich anhören und einen ähnlichen Informationsgehalt vermitteln, was wir in einer Benutzerstudie zeigen. In der zweiten Anwendung untersuchen wir, wie Roboter von Menschen bereitgestellte Hintergrundinformationen nutzen können, um eine bisher unbekannte Umgebung schneller zu erkunden. Wir schlagen einen Algorithmus vor, der Hintergrundinformationen wie Gebäudegrundrisse oder Skizzen nutzt, indem er eine globale Explorationsstrategie basierend auf der Lösung eines Problems des Handlungsreisenden kombiniert mit einer lokalen Explorationsstrategie. Unsere Experimente zeigen, dass die Erkundungstouren signifikant kürzer werden und dass der Benutzer mit unserem System effektiv die zu erkundenden Regionen spezifizieren kann. Der zweite Teil dieser Arbeit konzentriert sich auf humanoide Roboter in Umgebungen zu Hause und im Büro. Der menschenähnliche Körperbau ermöglicht es humanoiden Robotern, in Umgebungen zu navigieren und Werkzeuge zu benutzen, die für Menschen gebaut wurden, wodurch humanoide Roboter für vielfältige Aufgaben einsetzbar sind. Da Lokalisierung und Kartierung Grundvoraussetzungen für alle Navigationsaufgaben sind, führen wir zunächst einen neuen Merkmalsdeskriptor für RGB-D-Sensordaten ein und integrieren diesen Baustein in ein erscheinungsbasiertes simultanes Lokalisierungs- und Kartierungsverfahren, das wir an die Besonderheiten von humanoiden Robotern anpassen und optimieren. Unser System kann die Position eines realen humanoiden Roboters genauer und robuster verfolgen, als es mit existierenden Ansätzen möglich ist. Als dritte Anwendung untersuchen wir, wie humanoide Roboter bekannte Umgebungen effizient mit ihrer Kamera abdecken können, beispielsweise zu Inspektionszwecken oder zum Suchen eines Gegenstands. Wir erweitern ein bestehendes Verfahren, das die nächstbeste Beobachtungsposition berechnet, durch inverse Erreichbarkeitskarten, wodurch wir kollisionsfreie Ganzkörperposen effizient generieren und prüfen können. Unser Ansatz ermöglicht es dem Roboter, so viel wie möglich von der Umgebung zu untersuchen. In unserer vierten Anwendung erweitern wir dieses Szenario um Umgebungen, die auch bewegbare Gegenstände enthalten, die der Roboter aktiv bewegen muss um verdeckte Regionen zu sehen. Wir führen Algorithmen für Teilprobleme ein, die hoch parallelisiert auf Grafikkarten von eingebetteten Systemen ausgeführt werden. Zusammen mit einer neuen Heuristik zur Schätzung von Nutzenkarten ermöglicht dies unserem System Beobachtungspunkte mit hohem Nutzen zu finden, um Umgebungen mit bewegbaren Objekten effizient zu inspizieren. Alle vorgestellten Techniken wurden in Software implementiert und sorgfältig evaluiert in Benutzerstudien, Simulationen und Experimenten in künstlichen und realen Umgebungen. Unsere Verfahren bringen den Stand der Forschung voran in Richtung universell einsetzbarer Roboter in alltäglichen Umgebungen