The Localized Union-Of-Balls Bifiltration

We propose an extension of the classical union-of-balls filtration of persistent homology: fixing a point q, we focus our attention to a ball centered at q whose radius is controlled by a second scale parameter. We discuss an absolute variant, where the union is just restricted to the q-ball, and a relative variant where the homology of the q-ball relative to its boundary is considered. Interestingly, these natural constructions lead to bifiltered simplicial complexes which are not k-critical for any finite k. Nevertheless, we demonstrate that these bifiltrations can be computed exactly and efficiently, and we provide a prototypical implementation using the CGAL library. We also argue that some of the recent algorithmic advances for 2-parameter persistence (which usually assume k-criticality for some finite k) carry over to the ?-critical case

Empirical Standards for Software Engineering Research

Empirical Standards are natural-language models of a scientific community's expectations for a specific kind of study (e.g. a questionnaire survey). The ACM SIGSOFT Paper and Peer Review Quality Initiative generated empirical standards for research methods commonly used in software engineering. These living documents, which should be continuously revised to reflect evolving consensus around research best practices, will improve research quality and make peer review more effective, reliable, transparent and fair.Comment: For the complete standards, supplements and other resources, see https://github.com/acmsigsoft/EmpiricalStandard

Power, policies, and algorithms - technologies of surveillance in the European border surveillance regime

This work analyses the emergent European border surveillance regime as part of the European border regime/migratory regime and the power structures this technologogical regime is embedded into, is reproducing and creating. The history, politics, policies and technological characteristics of the border surveillance regime of the EU are analysed through a theoretical framework based in political science, political sociology and surveillance studies

Textual Entanglements: A Performative Approach towards Digital Literature

This thesis conducts a critical investigation into digital literature—a genre of literary expression that is integrated with, and articulated using, digital computing systems and infrastructures. Specifically, it presents a framework for evaluating the expressive capacities of this genre as it relates to particular conceptions of knowledge-making in the contemporary technocultural environment. This framework reveals how the generation of critical knowledge concerning digital literature, as crystallised through a reader’s material engagements with specific works, enacts a ‘performative’ conception of knowing and being, in which the observable world is treated as emerging in the real time of practice—as being articulated through the entanglement of human and nonhuman agencies, rather than existing as a fixed array of passive, unchanging primitives. Digital literature is presented subsequently as a model of this greater performative vision—as a means of evaluating the structures and processes that manifest it, particularly within digital systems, and for assessing its practical and political implications for art and culture more broadly. In so doing, this thesis aims to justify the value of engaging digital literature from a standpoint that is more expressly political, contending not only that these texts are revealing of key processes shaping digital activities, artefacts, and environments, but are enacting alternative vectors of thought and practice concerning them.AHR

Thinking Reality and Time through Film

TABLE OF CONTENTS Introduction by the Editors Part 1 Mental Approaches: On The Nature Of Time, Perception and Images • Susana Viegas, The Integrity Of Gesture In Steve Mcqueen’s Films • Teresa Teixeira, The Image Of Temporality • Claudio Rozzoni, Iconic Consciousness And Perceptual Phantasy. A Husserlian Contribution To Film Image • Przemyslaw Bursztyka, The Phantasmatic Reality. A Phenomenological Study Of The Cinematic Imagination • José Manuel Martins, In Reality: The Ultimate Cinematic Quest • Atėnė Mendelytė, The Filmic Century/ Centuries Of The Mind – Tracing The Beginnings Of The Subjective Cinema • Carlos João Correia, Cinematographic Narrative And Personal Identity • Leighton Grist, Bazin, Style, And Digitization: Ontology, Epistemology, And The New Myth Of Total Cinema Part 2 Ontological Realism And Accesssing Truth Through Film • Joseph Früchtl, Aesthetic-Philosophical-Realism: How Intuition Matters For Ontology And Film • Hyun Kang Kim, The Blue Flower In The Land Of Technology: Film, Time, And Politics In Walter Benjamin • Tatjana Sheplyakova, The Revolutionary Gaze for the Real: Dziga Vertov’s “Kino-Eye” • André Ujica/ Peter Weibel, The Ontology Of Film Images • Christine Reeh, On The Rise Of Solaristic Philosophy • Colin McGinn, Multimodal Theory Of Film Experience Part 3 Unmasking Violence - Trauma and Film • Mirjam Schaub, Violence, Philosophy And Film • Christoph Korn/ Cristina Beckert/ Maria João Madeira, Mask • Sousa Dias, Ralenty As Concept Of Film • Filomena Molder, Green Leaves, Green Sorrows • Vitor Moura, Unexpected Findings and Documentarie

Investigating Randomised Sphere Covers in Supervised Learning

c©This copy of the thesis has been supplied on condition that anyone who consults it is understood to recognise that its copyright rests with the author and that no quotation from the thesis, nor any information derived therefrom, may be published without the author’s prior, written consent. In this thesis, we thoroughly investigate a simple Instance Based Learning (IBL) classifier known as Sphere Cover. We propose a simple Randomized Sphere Cover Classifier (αRSC) and use several datasets in order to evaluate the classification performance of the αRSC classifier. In addition, we analyse the generalization error of the proposed classifier using bias/variance decomposition. A Sphere Cover Classifier may be described from the compression scheme which stipulates data compression as the reason for high generalization performance. We investigate the compression capacity of αRSC using a sample compression bound. The Compression Scheme prompted us to search new compressibility methods for αRSC. As such, we used a Gaussian kernel to investigate further data compression

Learning how to act: making good decisions with machine learning

This thesis is about machine learning and statistical approaches to decision making. How can we learn from data to anticipate the consequence of, and optimally select, interventions or actions? Problems such as deciding which medication to prescribe to patients, who should be released on bail, and how much to charge for insurance are ubiquitous, and have far reaching impacts on our lives. There are two fundamental approaches to learning how to act: reinforcement learning, in which an agent directly intervenes in a system and learns from the outcome, and observational causal inference, whereby we seek to infer the outcome of an intervention from observing the system. The goal of this thesis to connect and unify these key approaches. I introduce causal bandit problems: a synthesis that combines causal graphical models, which were developed for observational causal inference, with multi-armed bandit problems, which are a subset of reinforcement learning problems that are simple enough to admit formal analysis. I show that knowledge of the causal structure allows us to transfer information learned about the outcome of one action to predict the outcome of an alternate action, yielding a novel form of structure between bandit arms that cannot be exploited by existing algorithms. I propose an algorithm for causal bandit problems and prove bounds on the simple regret demonstrating it is close to mini-max optimal and better than algorithms that do not use the additional causal information