Fault-tolerant interconnection networks for multiprocessor systems

Interconnection networks represent the backbone of multiprocessor systems. A failure in the network, therefore, could seriously degrade the system performance. For this reason, fault tolerance has been regarded as a major consideration in interconnection network design. This thesis presents two novel techniques to provide fault tolerance capabilities to three major networks: the Baseline network, the Benes network and the Clos network. First, the Simple Fault Tolerance Technique (SFT) is presented. The SFT technique is in fact the result of merging two widely known interconnection mechanisms: a normal interconnection network and a shared bus. This technique is most suitable for networks with small switches, such as the Baseline network and the Benes network. For the Clos network, whose switches may be large for the SFT, another technique is developed to produce the Fault-Tolerant Clos (FTC) network. In the FTC, one switch is added to each stage. The two techniques are described and thoroughly analyzed

Statistical Learning for Structured Models: Tree Based Methods and Neural Networks

In this thesis, estimation in regression and classification problems which include low dimensional structures are considered. The underlying question is the following. How well do statistical learn- ing methods perform for models with low dimensional structures? We approach this question using various algorithms in various settings. For our first main contribution, we prove optimal convergence rates in a classification setting using neural networks. While non-optimal rates ex- isted for this problem, we are the first to prove optimal ones. Secondly, we introduce a new tree based algorithm we named random planted forest. It adapts particularly well to models which consist of low dimensional structures. We examine its performance in simulation studies and include some theoretical backing by proving optimal convergence rates in certain settings for a modification of the algorithm. Additionally, a generalized version of the algorithm is included, which can be used in classification settings. In a further contribution, we prove optimal con- vergence rates for the local linear smooth backfitting algorithm. While such rates have already been established, we bring a new simpler perspective to the problem which leads to better understanding and easier interpretation. Additionally, given an estimator in a regression setting, we propose a constraint which leads to a unique decomposition. This decomposition is useful for visualising and interpreting the estimator, in particular if it consits of low dimenional structures

Generation and Applications of Knowledge Graphs in Systems and Networks Biology

The acceleration in the generation of data in the biomedical domain has necessitated the use of computational approaches to assist in its interpretation. However, these approaches rely on the availability of high quality, structured, formalized biomedical knowledge. This thesis has the two goals to improve methods for curation and semantic data integration to generate high granularity biological knowledge graphs and to develop novel methods for using prior biological knowledge to propose new biological hypotheses. The first two publications describe an ecosystem for handling biological knowledge graphs encoded in the Biological Expression Language throughout the stages of curation, visualization, and analysis. Further, the second two publications describe the reproducible acquisition and integration of high-granularity knowledge with low contextual specificity from structured biological data sources on a massive scale and support the semi-automated curation of new content at high speed and precision. After building the ecosystem and acquiring content, the last three publications in this thesis demonstrate three different applications of biological knowledge graphs in modeling and simulation. The first demonstrates the use of agent-based modeling for simulation of neurodegenerative disease biomarker trajectories using biological knowledge graphs as priors. The second applies network representation learning to prioritize nodes in biological knowledge graphs based on corresponding experimental measurements to identify novel targets. Finally, the third uses biological knowledge graphs and develops algorithmics to deconvolute the mechanism of action of drugs, that could also serve to identify drug repositioning candidates. Ultimately, the this thesis lays the groundwork for production-level applications of drug repositioning algorithms and other knowledge-driven approaches to analyzing biomedical experiments

ORTHOGONAL WAVELET FUNCTION FOR COMPRESSION SATELLITE IMAGERY OF PEAT FOREST FIRES

Background: In the process of digital image data representation, constrained the number of data volumes are required. One of the main sources of information in data processing of imagery is satellite imagery. Some applications of remote sensing technology requires a good quality image but in small size. Purpose: This study focuses on image compression is done to reduce the size of the image needs. However, the information contained in the image retained its existence. Method: In this study, using 17 orthogonal wavelet function used to reduce data satellite images of peat forest fires. Then, 17 of these orthogonal wavelet functions are compared with the parameter measurement i.e. PSNR (Peak Signal to Noise Ratio) and compression ratio. The benchmark of image compression is seen from the largest PSNR and large compression ratio Finding: Based on orthogonal wavelet function testing, then the Haar (daubechies 1) wavelet function results obtained has the highest PSNR for all level of decomposition on all test image i.e 50.783 dB for test image 1, 50.954 dB for image 2 and 49.855 dB for image 3. For the highest compression ratio on all test image is a function of wavelet symlet 8 i.e 97.00% for image 1, 97.05% for image 2 and 96.90% for image 3. Originality value: Satellite imagery that has been reduced would contribute to facilitating the processing of data as well as data input for the creation of digital image processing for system detection peat forest fires hotspots

Everywhere, Animals Appear: Species, Race, and the State in Literature from the Raj to Global India

In recent decades, postcolonial writers and critics have started attending to representations of animals in literature from the global south (e.g. Armstrong 2002; Huggan and Tiffin 2010). While the turn toward literary animals has been acknowledged for its potential to complicate how colonial violence dehumanized colonized groups, scholars such as Lucille Desblache have pointed out that animals can also go “unseen” or “unheard” in the service of human narratives about emancipation from colonial dominance if they are treated as allegories for the emancipatory process (Desblache 2016). Critical animal studies, meanwhile, has extensively critiqued how liberal humanism in the West has marginalized animals (Cavell 2009; Nussbaum 2007; Shukin 2009), but the field has only recently started to engage with the Global South (e.g. Sivasundaram 2015). As a contribution to both of these fields, this thesis dwells in the fissures between India’s environmental history and the corpus of Indian literature about animals to reimagine the boundary between humans and animals as a zone of potential resistance against the state’s mandate to control how animals live and die. Over the last 150 years, key shifts in the colonial and postcolonial state’s environmental, administrative, and fiscal policies—such as the founding of the Imperial Forest Service in 1864, constitutional amendments to animal husbandry following the withdrawal of the imperial government in 1947, and the Indian Forest Service’s ongoing engagement with the global conservation movement—have accompanied shifts in the species boundary as a means to secure the state’s control over animals through its legislative and economic mechanisms. Throughout this period, the state has constructed animals variously—and, at times, simultaneously—as deviants, labourers, fetish objects, and commodities. This thesis shows how literary representations of Indian animals by Rudyard Kipling (1893), Bhanu Kapil (1935; 2009), Tania James (2015), Amitabh (1992), and Arjun Dangle (1992) might disrupt and reimagine the totalizing logic of species boundaries constitutive of colonial and postcolonial legislation even as the texts themselves remain unavoidably embedded in the state’s hegemonic paradigms of race, class, caste, gender, and species

Emergent Design

Explorations in Systems Phenomenology in Relation to Ontology, Hermeneutics and the Meta-dialectics of Design SYNOPSIS A Phenomenological Analysis of Emergent Design is performed based on the foundations of General Schemas Theory. The concept of Sign Engineering is explored in terms of Hermeneutics, Dialectics, and Ontology in order to define Emergent Systems and Metasystems Engineering based on the concept of Meta-dialectics. ABSTRACT Phenomenology, Ontology, Hermeneutics, and Dialectics will dominate our inquiry into the nature of the Emergent Design of the System and its inverse dual, the Meta-system. This is an speculative dissertation that attempts to produce a philosophical, mathematical, and theoretical view of the nature of Systems Engineering Design. Emergent System Design, i.e., the design of yet unheard of and/or hitherto non-existent Systems and Metasystems is the focus. This study is a frontal assault on the hard problem of explaining how Engineering produces new things, rather than a repetition or reordering of concepts that already exist. In this work the philosophies of E. Husserl, A. Gurwitsch, M. Heidegger, J. Derrida, G. Deleuze, A. Badiou, G. Hegel, I. Kant and other Continental Philosophers are brought to bear on different aspects of how new technological systems come into existence through the midwifery of Systems Engineering. Sign Engineering is singled out as the most important aspect of Systems Engineering. We will build on the work of Pieter Wisse and extend his theory of Sign Engineering to define Meta-dialectics in the form of Quadralectics and then Pentalectics. Along the way the various ontological levels of Being are explored in conjunction with the discovery that the Quadralectic is related to the possibility of design primarily at the Third Meta-level of Being, called Hyper Being. Design Process is dependent upon the emergent possibilities that appear in Hyper Being. Hyper Being, termed by Heidegger as Being (Being crossed-out) and termed by Derrida as Differance, also appears as the widest space within the Design Field at the third meta-level of Being and therefore provides the most leverage that is needed to produce emergent effects. Hyper Being is where possibilities appear within our worldview. Possibility is necessary for emergent events to occur. Hyper Being possibilities are extended by Wild Being propensities to allow the embodiment of new things. We discuss how this philosophical background relates to meta-methods such as the Gurevich Abstract State Machine and the Wisse Metapattern methods, as well as real-time architectural design methods as described in the Integral Software Engineering Methodology. One aim of this research is to find the foundation for extending the ISEM methodology to become a general purpose Systems Design Methodology. Our purpose is also to bring these philosophical considerations into the practical realm by examining P. Bourdieu’s ideas on the relationship between theoretical and practical reason and M. de Certeau’s ideas on practice. The relationship between design and implementation is seen in terms of the Set/Mass conceptual opposition. General Schemas Theory is used as a way of critiquing the dependence of Set based mathematics as a basis for Design. The dissertation delineates a new foundation for Systems Engineering as Emergent Engineering based on General Schemas Theory, and provides an advanced theory of Design based on the understanding of the meta-levels of Being, particularly focusing upon the relationship between Hyper Being and Wild Being in the context of Pure and Process Being

The Tragedy of the Self:Lectures on Global Hermeneutics

Why do human beings interpret their overall experience in terms of selfhood? How was the notion and sense of self shaped at different times and in different cultures? What sort of problems or paradoxes did these constructions face? These lectures address these and related questions by sketching a roadmap of possible theoretical avenues for conceiving of the self, bringing to the foreground its soteriological implications, while also testing this theoretical outlook against insights offered by various disciplines. Exploring the crosscultural spectrum of possible ways of conceiving of the self invites the more existential question of whether any of these possibilities might offer resources for dealing with the tragedies of today’s world, or maybe even saving it from some of them

Exploring Gender and Sikh Traditions

This volume gathers scholars who focus on gender through a variety of disciplines and approaches to Sikh Studies. The intersections of religion and gender are here explored, based on an understanding that both are socially constructed. Far from being static, as so often presented in world religions textbooks, religious traditions are constantly in flux, responding to historical, cultural and social contexts. So too is ‘the’ Sikh tradition in terms of practices, ideologies, rituals, and notions of identity. We here conclude that ‘a’ Sikh tradition does not exist; instead, there are numerous forms thereof. In this volume, Sikhism is presented as a collection of ‘Sikh traditions’. Gender studies—in line with women’s liberation, masculine and feminist studies have long examined and have long deconstructed the patriarchy, but also move to identify other subordinate-dominant relations between individuals. Indeed, there are numerous forms of discrimination and power structures that simultaneously create a multiplicity of oppression. Intersectionality has become the basis of an increasingly systematized production of contemporary discourses on feminism and gender analysis, as is evidenced by the varied contributions in this volume

Analyzing Traffic and Multicast Switch Issues in an ATM Network.

This dissertation attempts to solve two problems related to an ATM network. First, we consider packetized voice and video sources as the incoming traffic to an ATM multiplexer and propose modeling methods for both individual and aggregated traffic sources. These methods are, then, used to analyze performance parameters such as buffer occupancy, cell loss probability, and cell delay. Results, thus obtained, for different buffer sizes and number of voice and video sources are analyzed and compared with those generated from existing techniques. Second, we study the priority handling feature for time critical services in an ATM multicast switch. For this, we propose a non-blocking copy network and priority handling algorithms. We, then, analyze the copy network using an analytical method and simulation. The analysis utilizes both priority and non-priority cells for two different output reservation schemes. The performance parameters, based on cell delay, delay jitter, and cell loss probability, are studied for different buffer sizes and fan-outs under various input traffic loads. Our results show that the proposed copy network provides a better performance for the priority cells while the performance for the non-priority cells is slightly inferior in comparison with the scenario when the network does not consider priority handling. We also study the fault-tolerant behavior of the copy network, specially for the broadcast banyan network subsection, and present a routing scheme considering the non-blocking property under a specific pattern of connection assignments. A fault tolerant characteristic can be quantified using the full access probability. The computation of the full access probability for a general network is known to be NP-hard. We, therefore, provide a new bounding technique utilizing the concept of minimal cuts to compute full access probability of the copy network. Our study for the fault-tolerant multi-stage interconnection network having either an extra stage or chaining shows that the proposed technique provides tighter bounds as compared to those given by existing approaches. We also apply our bounding method to compute full access probability of the fault-tolerant copy network

Robust input representations for low-resource information extraction

Recent advances in the field of natural language processing were achieved with deep learning models. This led to a wide range of new research questions concerning the stability of such large-scale systems and their applicability beyond well-studied tasks and datasets, such as information extraction in non-standard domains and languages, in particular, in low-resource environments. In this work, we address these challenges and make important contributions across fields such as representation learning and transfer learning by proposing novel model architectures and training strategies to overcome existing limitations, including a lack of training resources, domain mismatches and language barriers. In particular, we propose solutions to close the domain gap between representation models by, e.g., domain-adaptive pre-training or our novel meta-embedding architecture for creating a joint representations of multiple embedding methods. Our broad set of experiments demonstrates state-of-the-art performance of our methods for various sequence tagging and classification tasks and highlight their robustness in challenging low-resource settings across languages and domains.Die jüngsten Fortschritte auf dem Gebiet der Verarbeitung natürlicher Sprache wurden mit Deep-Learning-Modellen erzielt. Dies führte zu einer Vielzahl neuer Forschungsfragen bezüglich der Stabilität solcher großen Systeme und ihrer Anwendbarkeit über gut untersuchte Aufgaben und Datensätze hinaus, wie z. B. die Informationsextraktion für Nicht-Standardsprachen, aber auch Textdomänen und Aufgaben, für die selbst im Englischen nur wenige Trainingsdaten zur Verfügung stehen. In dieser Arbeit gehen wir auf diese Herausforderungen ein und leisten wichtige Beiträge in Bereichen wie Repräsentationslernen und Transferlernen, indem wir neuartige Modellarchitekturen und Trainingsstrategien vorschlagen, um bestehende Beschränkungen zu überwinden, darunter fehlende Trainingsressourcen, ungesehene Domänen und Sprachbarrieren. Insbesondere schlagen wir Lösungen vor, um die Domänenlücke zwischen Repräsentationsmodellen zu schließen, z.B. durch domänenadaptives Vortrainieren oder unsere neuartige Meta-Embedding-Architektur zur Erstellung einer gemeinsamen Repräsentation mehrerer Embeddingmethoden. Unsere umfassende Evaluierung demonstriert die Leistungsfähigkeit unserer Methoden für verschiedene Klassifizierungsaufgaben auf Word und Satzebene und unterstreicht ihre Robustheit in anspruchsvollen, ressourcenarmen Umgebungen in verschiedenen Sprachen und Domänen