Exploiting immunological metaphors in the development of serial, parallel and distributed learning algorithms

This thesis examines the use of immunological metaphors in building serial, parallel, and distributed learning algorithms. It offers a basic study in the development of biologically-inspired algorithms which merge inspiration from biology with known, standard computing technology to examine robust methods of computing. This thesis begins by detailing key interactions found within the immune system that provide inspiration for the development of a learning system. It then exploits the use of more processing power for the development of faster algorithms. This leads to the exploration of distributed computing resources for the examination of more biologically plausible systems. This thesis offers the following main contributions. The components of the immune system that exhibit the capacity for learning are detailed. A framework for discussing learning algorithms is proposed. Three properties of every learning algorithm-memory, adaptation, and decision-making-are identified for this framework, and traditional learning algorithms are placed in the context of this framework. An investigation into the use of immunological components for learning is provided. This leads to an understanding of these components in terms of the learning framework. A simplification of the Artificial Immune Recognition System (AIRS) immune-inspired learning algorithm is provided by employing affinity-dependent somatic hypermutation. A parallel version of the Clonal Selection Algorithm (CLONALG) immune learning algorithm is developed. It is shown that basic parallel computing techniques can provide computational benefits for this algorithm. Exploring this technology further, a parallel version of AIRS is offered. It is shown that applying these same parallel computing techniques to AIRS, while less scalable than when applied to CLONALG, still provides computational gains. A distributed approach to AIRS is offered, and it is argued that this approach provides a more biologically appealing model. The simple distributed approach is proposed in terms of an initial step toward a more complex, distributed system. Biological immune systems exhibit complex cellular interactions. The mechanisms of these interactions, while often poorly understood, hint at an extremely powerful information processing/problem solving system. This thesis demonstrates how the use of immunological principles coupled with standard computing technology can lead to the development of robust, biologically inspired learning algorithms.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Simulator adaptation at runtime for component-based simulation software

Component-based simulation software can provide many opportunities to compose and configure simulators, resulting in an algorithm selection problem for the user of this software. This thesis aims to automate the selection and adaptation of simulators at runtime in an application-independent manner. Further, it explores the potential of tailored and approximate simulators - in this thesis concretely developed for the modeling language ML-Rules - supporting the effectiveness of the adaptation scheme.Komponenten-basierte Simulationssoftware kann viele Möglichkeiten zur Komposition und Konfiguration von Simulatoren bieten und damit zu einem Konfigurationsproblem für Nutzer dieser Software führen. Das Ziel dieser Arbeit ist die Entwicklung einer generischen und automatisierten Auswahl- und Adaptionsmethode für Simulatoren. Darüber hinaus wird das Potential von spezifischen und approximativen Simulatoren anhand der Modellierungssprache ML-Rules untersucht, welche die Effektivität des entwickelten Adaptionsmechanismus erhöhen können

Software for Exascale Computing - SPPEXA 2016-2019

This open access book summarizes the research done and results obtained in the second funding phase of the Priority Program 1648 "Software for Exascale Computing" (SPPEXA) of the German Research Foundation (DFG) presented at the SPPEXA Symposium in Dresden during October 21-23, 2019. In that respect, it both represents a continuation of Vol. 113 in Springer’s series Lecture Notes in Computational Science and Engineering, the corresponding report of SPPEXA’s first funding phase, and provides an overview of SPPEXA’s contributions towards exascale computing in today's sumpercomputer technology. The individual chapters address one or more of the research directions (1) computational algorithms, (2) system software, (3) application software, (4) data management and exploration, (5) programming, and (6) software tools. The book has an interdisciplinary appeal: scholars from computational sub-fields in computer science, mathematics, physics, or engineering will find it of particular interest

Grid-based methods for chemistry simulations on a quantum computer

First-quantized, grid-based methods for chemistry modeling are a natural and elegant fit for quantum computers. However, it is infeasible to use today’s quantum prototypes to explore the power of this approach because it requires a substantial number of near-perfect qubits. Here, we use exactly emulated quantum computers with up to 36 qubits to execute deep yet resource-frugal algorithms that model 2D and 3D atoms with single and paired particles. A range of tasks is explored, from ground state preparation and energy estimation to the dynamics of scattering and ionization; we evaluate various methods within the split-operator QFT (SO-QFT) Hamiltonian simulation paradigm, including protocols previously described in theoretical papers and our own techniques. While we identify certain restrictions and caveats, generally, the grid-based method is found to perform very well; our results are consistent with the view that first-quantized paradigms will be dominant from the early fault-tolerant quantum computing era onward

Contemporary Robotics

This book book is a collection of 18 chapters written by internationally recognized experts and well-known professionals of the field. Chapters contribute to diverse facets of contemporary robotics and autonomous systems. The volume is organized in four thematic parts according to the main subjects, regarding the recent advances in the contemporary robotics. The first thematic topics of the book are devoted to the theoretical issues. This includes development of algorithms for automatic trajectory generation using redudancy resolution scheme, intelligent algorithms for robotic grasping, modelling approach for reactive mode handling of flexible manufacturing and design of an advanced controller for robot manipulators. The second part of the book deals with different aspects of robot calibration and sensing. This includes a geometric and treshold calibration of a multiple robotic line-vision system, robot-based inline 2D/3D quality monitoring using picture-giving and laser triangulation, and a study on prospective polymer composite materials for flexible tactile sensors. The third part addresses issues of mobile robots and multi-agent systems, including SLAM of mobile robots based on fusion of odometry and visual data, configuration of a localization system by a team of mobile robots, development of generic real-time motion controller for differential mobile robots, control of fuel cells of mobile robots, modelling of omni-directional wheeled-based robots, building of hunter- hybrid tracking environment, as well as design of a cooperative control in distributed population-based multi-agent approach. The fourth part presents recent approaches and results in humanoid and bioinspirative robotics. It deals with design of adaptive control of anthropomorphic biped gait, building of dynamic-based simulation for humanoid robot walking, building controller for perceptual motor control dynamics of humans and biomimetic approach to control mechatronic structure using smart materials

The Origin of Data: Enabling the Determination of Provenance in Multi-institutional Scientific Systems through the Documentation of Processes

The Oxford English Dictionary defines provenance as (i) the fact of coming from some particular source or quarter; origin, derivation. (ii) the history or pedigree of a work of art, manuscript, rare book, etc.; concr., a record of the ultimate derivation and passage of an item through its various owners. In art, knowing the provenance of an artwork lends weight and authority to it while providing a context for curators and the public to understand and appreciate the work’s value. Without such a documented history, the work may be misunderstood, unappreciated, or undervalued. In computer systems, knowing the provenance of digital objects would provide them with greater weight, authority, and context just as it does for works of art. Specifically, if the provenance of digital objects could be determined, then users could understand how documents were produced, how simulation results were generated, and why decisions were made. Provenance is of particular importance in science, where experimental results are reused, reproduced, and verified. However, science is increasingly being done through large-scale collaborations that span multiple institutions, which makes the problem of determining the provenance of scientific results significantly harder. Current approaches to this problem are not designed specifically for multi-institutional scientific systems and their evolution towards greater dynamic and peer-to-peer topologies. Therefore, this thesis advocates a new approach, namely, that through the autonomous creation, scalable recording, and principled organisation of documentation of systems’ processes, the determination of the provenance of results produced by complex multi-institutional scientific systems is enabled. The dissertation makes four contributions to the state of the art. First is the idea that provenance is a query performed over documentation of a system’s past process. Thus, the problem is one of how to collect and collate documentation from multiple distributed sources and organise it in a manner that enables the provenance of a digital object to be determined. Second is an open, generic, shared, principled data model for documentation of processes, which enables its collation so that it provides high-quality evidence that a system’s processes occurred. Once documentation has been created, it is recorded into specialised repositories called provenance stores using a formally specified protocol, which ensures documentation has high-quality characteristics. Furthermore, patterns and techniques are given to permit the distributed deployment of provenance stores. The protocol and patterns are the third contribution. The fourth contribution is a characterisation of the use of documentation of process to answer questions related to the provenance of digital objects and the impact recording has on application performance. Specifically, in the context of a bioinformatics case study, it is shown that six different provenance use cases are answered given an overhead of 13% on experiment run-time. Beyond the case study, the solution has been applied to other applications including fault tolerance in service-oriented systems, aerospace engineering, and organ transplant management

Efficient homology search for genomic sequence databases

Genomic search tools can provide valuable insights into the chemical structure, evolutionary origin and biochemical function of genetic material. A homology search algorithm compares a protein or nucleotide query sequence to each entry in a large sequence database and reports alignments with highly similar sequences. The exponential growth of public data banks such as GenBank has necessitated the development of fast, heuristic approaches to homology search. The versatile and popular blast algorithm, developed by researchers at the US National Center for Biotechnology Information (NCBI), uses a four-stage heuristic approach to efficiently search large collections for analogous sequences while retaining a high degree of accuracy. Despite an abundance of alternative approaches to homology search, blast remains the only method to offer fast, sensitive search of large genomic collections on modern desktop hardware. As a result, the tool has found widespread use with millions of queries posed each day. A significant investment of computing resources is required to process this large volume of genomic searches and a cluster of over 200 workstations is employed by the NCBI to handle queries posed through the organisation's website. As the growth of sequence databases continues to outpace improvements in modern hardware, blast searches are becoming slower each year and novel, faster methods for sequence comparison are required. In this thesis we propose new techniques for fast yet accurate homology search that result in significantly faster blast searches. First, we describe improvements to the final, gapped alignment stages where the query and sequences from the collection are aligned to provide a fine-grain measure of similarity. We describe three new methods for aligning sequences that roughly halve the time required to perform this computationally expensive stage. Next, we investigate improvements to the first stage of search, where short regions of similarity between a pair of sequences are identified. We propose a novel deterministic finite automaton data structure that is significantly smaller than the codeword lookup table employed by ncbi-blast, resulting in improved cache performance and faster search times. We also discuss fast methods for nucleotide sequence comparison. We describe novel approaches for processing sequences that are compressed using the byte packed format already utilised by blast, where four nucleotide bases from a strand of DNA are stored in a single byte. Rather than decompress sequences to perform pairwise comparisons, our innovations permit sequences to be processed in their compressed form, four bases at a time. Our techniques roughly halve average query evaluation times for nucleotide searches with no effect on the sensitivity of blast. Finally, we present a new scheme for managing the high degree of redundancy that is prevalent in genomic collections. Near-duplicate entries in sequence data banks are highly detrimental to retrieval performance, however existing methods for managing redundancy are both slow, requiring almost ten hours to process the GenBank database, and crude, because they simply purge highly-similar sequences to reduce the level of internal redundancy. We describe a new approach for identifying near-duplicate entries that is roughly six times faster than the most successful existing approaches, and a novel approach to managing redundancy that reduces collection size and search times but still provides accurate and comprehensive search results. Our improvements to blast have been integrated into our own version of the tool. We find that our innovations more than halve average search times for nucleotide and protein searches, and have no signifcant effect on search accuracy. Given the enormous popularity of blast, this represents a very significant advance in computational methods to aid life science research

Fast Objective Coupled Planar Illumination Microscopy

Among optical imaging techniques light sheet fluorescence microscopy stands out as one of the most attractive for capturing high-speed biological dynamics unfolding in three dimensions. The technique is potentially millions of times faster than point-scanning techniques such as two-photon microscopy. This potential is especially poignant for neuroscience applications due to the fact that interactions between neurons transpire over mere milliseconds within tissue volumes spanning hundreds of cubic microns. However current-generation light sheet microscopes are limited by volume scanning rate and/or camera frame rate. We begin by reviewing the optical principles underlying light sheet fluorescence microscopy and the origin of these rate bottlenecks. We present an analysis leading us to the conclusion that Objective Coupled Planar Illumination (OCPI) microscopy is a particularly promising technique for recording the activity of large populations of neurons at high sampling rate. We then present speed-optimized OCPI microscopy, the first fast light sheet technique to avoid compromising image quality or photon efficiency. We enact two strategies to develop the fast OCPI microscope. First, we devise a set of optimizations that increase the rate of the volume scanning system to 40 Hz for volumes up to 700 microns thick. Second, we introduce Multi-Camera Image Sharing (MCIS), a technique to scale imaging rate by incorporating additional cameras. MCIS can be applied not only to OCPI but to any widefield imaging technique, circumventing the limitations imposed by the camera. Detailed design drawings are included to aid in dissemination to other research groups. We also demonstrate fast calcium imaging of the larval zebrafish brain and find a heartbeat-induced motion artifact. We recommend a new preprocessing step to remove the artifact through filtering. This step requires a minimal sampling rate of 15 Hz, and we expect it to become a standard procedure in zebrafish imaging pipelines. In the last chapter we describe essential computational considerations for controlling a fast OCPI microscope and processing the data that it generates. We introduce a new image processing pipeline developed to maximize computational efficiency when analyzing these multi-terabyte datasets, including a novel calcium imaging deconvolution algorithm. Finally we provide a demonstration of how combined innovations in microscope hardware and software enable inference of predictive relationships between neurons, a promising complement to more conventional correlation-based analyses