Large scale visual search

With the ever-growing amount of image data on the web, much attention has been devoted to large scale image search. It is one of the most challenging problems in computer vision for several reasons. First, it must address various appearance transformations such as changes in perspective, rotation and scale existing in the huge amount of image data. Second, it needs to minimize memory requirements and computational cost when generating image representations. Finally, it needs to construct an efficient index space and a suitable similarity measure to reduce the response time to the users. This thesis aims to provide robust image representations that are less sensitive to above mentioned appearance transformations and are suitable for large scale image retrieval. Although this thesis makes a substantial number of contributions to large scale image retrieval, we also presented additional challenges and future research based on the contributions in this thesis.China Scholarship Council (CSC)Computer Systems, Imagery and Medi

Bayesian techniques for astrophysical inference from gravitational-waves of compact binary coalescences: an application to the Third LIGO-Virgo-KAGRA observing run

A major challenge in gravitational-wave astrophysics is the interpretation of observations, which requires accurate inference of the astrophysical parameters and a rigorous statistical framework. The main focus of this thesis is the analysis of modelled gravitational-wave sources and its enhancement with machine learning and other statistical techniques. Bayesian statistics is at the base of gravitational-wave analysis and interpretation since each observation is unique and can often be assumed to be independent of all others. The unifying thread of this thesis is Bayes’s theorem: how it is routinely leveraged for gravitational-wave analysis, allowing much of the work presented here, and how its use can be extended to develop new analysis techniques. The most notable application of Bayesian statistics in the field is the parameter estimation of compact binary coalescence. Chapter 2 reports the work done to reproduce the first Gravitational-Wave Transients Catalogue (GWTC-1) with the Bayesian Inference Library: bilby. The rigorous comparison between previous GWTC-1 results and the one presented here allowed bilby’s specific tuning towards the gravitational-wave inference problem. Chapter 3, presents the author’s work related to the discovery of the first neutronstar black-hole (NSBH) mergers GW200105 and GW200115, where bilby was used to estimate the parameter of the observed sources. This chapter also illustrates the role of gravitational-wave observations in our understanding of the astrophysical origins of binary sources. Chapter 4 describes a novel effective likelihood method to quantitively compare astrophysical distributions inferred from gravitational-wave observations and distributions obtained with theoretical simulations. This method, which is driven by a Bayesian philosophy, is applied to a set of globular cluster simulations and real data from the third Gravitational-Wave Transients Catalogue (GWTC-3). Chapter 5, presents a novel density estimation tool for parameter estimation products from gravitational-wave observations, based on Gaussian Processes which are a Bayesian machine learning technique. This density estimation method was found to be advantageous over other traditional methods for several gravitational wave applications since we need both the accurate treatment of individual event samples, e.g. standard siren analysis, but also robust propagation of systematics when combining multiple observations, e.g. measure of systematic errors. Finally, Chapter 6 presents a study that makes use of bilby to re-analyse the binary neutron star (BNS) event GW190425, in light of its potential electromagnetic counterpart FRB20190425A, and makes use of a Gaussian Process density estimator to calculate the Bayesian odds of the claimed association. This work is extended by performing a standard siren measurement for GW190425 and its potential host galaxy to determine the value of the Hubble constant

Voicing Kinship with Machines: Diffractive Empathetic Listening to Synthetic Voices in Performance.

This thesis contributes to the field of voice studies by analyzing the design and production of synthetic voices in performance. The work explores six case studies, consisting of different performative experiences of the last decade (2010- 2020) that featured synthetic voice design. It focusses on the political and social impact of synthetic voices, starting from yet challenging the concepts of voice in the machine and voice of the machine. The synthetic voices explored are often playing the role of simulated artificial intelligences, therefore this thesis expands its questions towards technology at large. The analysis of the case studies follows new materialist and posthumanist premises, yet it tries to confute the patriarchal and neoliberal approach towards technological development through feminist and de-colonial approaches, developing a taxonomy for synthetic voices in performance. Chapter 1 introduces terms and explains the taxonomy. Chapter 2 looks at familiar representations of fictional AI. Chapter 3 introduces headphone theatre exploring immersive practices. Chapters 4 and 5 engage with chatbots. Chapter 6 goes in depth exploring Human and Artificial Intelligence interaction, whereas chapter 7 moves slightly towards music production and live art. The body of the thesis includes the work of Pipeline Theatre, Rimini Protokoll, Annie Dorsen, Begüm Erciyas, and Holly Herndon. The analysis is informed by posthumanism, feminism, and performance studies, starting from my own practice as sound designer and singer, looking at aesthetics of reproduction, audience engagement, and voice composition. This thesis has been designed to inspire and provoke practitioners and scholars to explore synthetic voices further, question predominant biases of binarism and acknowledge their importance in redefining technology

Gaze-Based Human-Robot Interaction by the Brunswick Model

We present a new paradigm for human-robot interaction based on social signal processing, and in particular on the Brunswick model. Originally, the Brunswick model copes with face-to-face dyadic interaction, assuming that the interactants are communicating through a continuous exchange of non verbal social signals, in addition to the spoken messages. Social signals have to be interpreted, thanks to a proper recognition phase that considers visual and audio information. The Brunswick model allows to quantitatively evaluate the quality of the interaction using statistical tools which measure how effective is the recognition phase. In this paper we cast this theory when one of the interactants is a robot; in this case, the recognition phase performed by the robot and the human have to be revised w.r.t. the original model. The model is applied to Berrick, a recent open-source low-cost robotic head platform, where the gazing is the social signal to be considered