7,096 research outputs found

    Learning disentangled speech representations

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    A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody. The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions. In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks. This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically

    Targeting Fusion Proteins of HIV-1 and SARS-CoV-2

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    Viruses are disease-causing pathogenic agents that require host cells to replicate. Fusion of host and viral membranes is critical for the lifecycle of enveloped viruses. Studying viral fusion proteins can allow us to better understand how they shape immune responses and inform the design of therapeutics such as drugs, monoclonal antibodies, and vaccines. This thesis discusses two approaches to targeting two fusion proteins: Env from HIV-1 and S from SARS-CoV-2. The first chapter of this thesis is an introduction to viruses with a specific focus on HIV-1 CD4 mimetic drugs and antibodies against SARS-CoV-2. It discusses the architecture of these viruses and fusion proteins and how small molecules, peptides, and antibodies can target these proteins successfully to treat and prevent disease. In addition, a brief overview is included of the techniques involved in structural biology and how it has informed the study of viruses. For the interested reader, chapter 2 contains a review article that serves as a more in-depth introduction for both viruses as well as how the use of structural biology has informed the study of viral surface proteins and neutralizing antibody responses to them. The subsequent chapters provide a body of work divided into two parts. The first part in chapter 3 involves a study on conformational changes induced in the HIV-1 Env protein by CD4-mimemtic drugs using single particle cryo-EM. The second part encompassing chapters 4 and 5 includes two studies on antibodies isolated from convalescent COVID-19 donors. The former involves classification of antibody responses to the SARS-CoV-2 S receptor-binding domain (RBD). The latter discusses an anti-RBD antibody class that binds to a conserved epitope on the RBD and shows cross-binding and cross-neutralization to other coronaviruses in the sarbecovirus subgenus.</p

    Full stack development toward a trapped ion logical qubit

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    Quantum error correction is a key step toward the construction of a large-scale quantum computer, by preventing small infidelities in quantum gates from accumulating over the course of an algorithm. Detecting and correcting errors is achieved by using multiple physical qubits to form a smaller number of robust logical qubits. The physical implementation of a logical qubit requires multiple qubits, on which high fidelity gates can be performed. The project aims to realize a logical qubit based on ions confined on a microfabricated surface trap. Each physical qubit will be a microwave dressed state qubit based on 171Yb+ ions. Gates are intended to be realized through RF and microwave radiation in combination with magnetic field gradients. The project vertically integrates software down to hardware compilation layers in order to deliver, in the near future, a fully functional small device demonstrator. This thesis presents novel results on multiple layers of a full stack quantum computer model. On the hardware level a robust quantum gate is studied and ion displacement over the X-junction geometry is demonstrated. The experimental organization is optimized through automation and compressed waveform data transmission. A new quantum assembly language purely dedicated to trapped ion quantum computers is introduced. The demonstrator is aimed at testing implementation of quantum error correction codes while preparing for larger scale iterations.Open Acces

    Conscience and Consciousness: British Theatre and Human Rights.

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    This research project investigates a paradigm of human rights theatre. Through the lens of performance and theatre-making, this thesis explores how we came to represent, speak about, discuss, and own human rights in Britain. My framework of ‘human rights theatre’ proposes three distinctive features: firstly, such works dramatise real-world issues and highlights the role of the state in endangering its citizens; secondly, ethical ruptures are encountered within and without the drama, and finally, these performances characteristically aspire to produce an activist effect on the collective behaviours of the audience. This thesis interrogates the strategies theatre-makers use to articulate human rights concerns or to animate human rights intent. The selected case-studies for this investigation are ice&fire’s testimonial project, Actors for Human Rights; Badac Theatre; Jonathan Holmes’ work as director of Jericho House; Cardboard Citizens’ youth participation programme, ACT NOW; and Tony Cealy’s Black Men’s Consortium. Deliberately selecting companies and performance events that have received limited critical attention, my methodology constellates case-studies through original interviews, durational observation of creative working methods and proximate descriptions of practice. The thesis is interested in the experience of coming to ‘consciousness’ through human rights theatre, an awakening to the impacts of rights infringements and rights claiming. I explore consciousness as a processual, procedural, and durational happening in these performance events. I explore the ‘æffect’ of activist art and examine the ways in which makers of human rights theatre aim to amplify both affective and effective qualities in their work. My thesis also considers the articulation of activist purpose and the campaigning intent of the selected theatre-makers and explores how their activism is animated in their productions. Through the rich seam of discussion generated by the identification and exploration of the traits of a distinctive human rights theatre, I affirm the generative value of this typological enquiry

    Antibody Targeting of HIV-1 Env: A Structural Perspective

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    A key component of contemporary efforts toward a human immunodeficiency virus 1 (HIV-1) vaccine is the use of structural biology to understand the structural characteristics of antibodies elicited both from human patients and animals immunized with engineered 'immunogens,' or early vaccine candidates. This thesis will report on projects characterizing both types of antibodies against HIV-1. Chapter 1 will introduce relevant topics, including the reasons HIV-1 is particularly capable of evading the immune system in natural infection and after vaccination, the 20+ year history of unsuccessful HIV-1 vaccine large-scale efficacy trials, an introduction to broadly neutralizing antibodies (bNAbs), and a review of common strategies utilized in HIV-1 immunogen design today. Chapter 2 describes the isolation, high-resolution structural characterization, and in vitro resistance profile of a new bNAb, 1-18, that is both very broad and potent, as well as able to restrict HIV-1 escape in vivo. Chapter 3 reports the results of an epitope-focusing immunogen design and immunization experiment carried out in wild type mice, rabbits, and non-human primates where it was shown that B cells targeting the desired epitope were expanded after a single prime immunization with immunogen RC1 or a variant, RC1-4fill. Chapter 4 describes Ab1245, an off-target non-neutralizing monoclonal antibody isolated in a macaque that had been immunized with a series of sequential immunogens after the prime immunization reported in Chapter 3. The antibody structure describes a specific type of distracting response as it binds in a way that causes a large structural change in Env, resulting in the destruction of the neutralizing fusion peptide epitope. Chapter 5 is adapted from a review about how antibodies differentially recognize the viruses HIV-1, SARS-CoV-2, and Zika virus. This review serves as an introduction to the virus SARS-CoV-2, which is the topic of the final chapter, Chapter 6. In this chapter, structures of many neutralizing antibodies isolated from SARS-CoV-2 patients were used to define potentially therapeutic classes of neutralizing receptor-binding domain (RBD) antibodies based on their epitopes and binding profiles

    Graphical scaffolding for the learning of data wrangling APIs

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    In order for students across the sciences to avail themselves of modern data streams, they must first know how to wrangle data: how to reshape ill-organised, tabular data into another format, and how to do this programmatically, in languages such as Python and R. Despite the cross-departmental demand and the ubiquity of data wrangling in analytical workflows, the research on how to optimise the instruction of it has been minimal. Although data wrangling as a programming domain presents distinctive challenges - characterised by on-the-fly syntax lookup and code example integration - it also presents opportunities. One such opportunity is how tabular data structures are easily visualised. To leverage the inherent visualisability of data wrangling, this dissertation evaluates three types of graphics that could be employed as scaffolding for novices: subgoal graphics, thumbnail graphics, and parameter graphics. Using a specially built e-learning platform, this dissertation documents a multi-institutional, randomised, and controlled experiment that investigates the pedagogical effects of these. Our results indicate that the graphics are well-received, that subgoal graphics boost the completion rate, and that thumbnail graphics improve navigability within a command menu. We also obtained several non-significant results, and indications that parameter graphics are counter-productive. We will discuss these findings in the context of general scaffolding dilemmas, and how they fit into a wider research programme on data wrangling instruction

    Structural basis of translational recycling and bacterial ribosome rescue

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    In the last step of gene expression, a messenger RNA (mRNA) sequence is translated into a polypeptide. This highly regulated and dynamic process is carried out by the ribosome, a ribonucleoprotein complex composed of two unequal subunits. The translation cycle is initiated when the small ribosomal subunit (SSU) binds to an mRNA and recognizes the start codon of the open reading frame (ORF). Then the large ribosomal subunit (LSU) joins and the ribosome starts moving along the mRNA. A protein is synthesized until the ribosome reaches a stop codon. A cell needs thousands (prokaryotes) or millions (eukaryotes) of ribosomes for protein production and spends enormous amounts of energy on the assembly of this macromolecular machinery. Therefore, it is crucial to recycle the machinery after each successful round of translation. The recycling step allows release of mRNA, transfer RNA (tRNA) and the synthesized polypeptide from ribosomal subunits and subsequent binding of the next mRNA for protein synthesis. The first part of this dissertation includes studies of the highly conserved and essential ribosome recycling factor ATP binding cassette (ABC) Subfamily E Member 1 (ABCE1). In eukaryotes and archaea, ABCE1 binds the ribosome and in concert with an A-site factor and splits the ribosome into large and small subunits. ABCE1 harbors two nucleotide binding sites (NBSs), which are formed at the interface of two nucleotide binding domains (NBDs). Prior to this work, the ABCE1-bound pre-splitting complex, as well as the ABCE1-bound post-splitting complex, had been visualized by cryo-electron microscopy (cryo-EM) at medium resolution. This structural analysis combined with functional studies led to a model for the mechanism of the splitting event. ATP-binding and the closure of the NBSs lead to repositioning of the iron-sulfur cluster domain, which results in collision with the A-site factor and ribosome splitting. Yet, how conformational changes during the splitting event are triggered and communicated to the NBSs of ABCE1, was not understood. To gain molecular insights into this process, a structure of a fully nucleotide-occluded (closed) state of ABCE1 bound to the archaeal 30S post-splitting complex was solved by cryo-EM. At a resolution of 2.8 Å a detailed molecular analysis of ABCE1 was performed and confirmed by a combination of mutational and functional studies. This allowed to propose a refined model of how the ATPase cycle is linked to ribosome splitting and which role the different domains of ABCE1 play. In eukaryotes, the recycling phase is directly linked to translation initiation via the SSU. After being released from the mRNA 3’ end, the SSU can engage with another or even the same mRNA at the 5’ end. The recycling factor ABCE1 was found to be associated with initiation complexes, but whether it plays a role in initiation was not clear. Using cryo-EM, structures of native ABCE1-containing initiation complexes were solved and intensive 3D classification allowed to distinguish different stages of initiation, during which ABCE1 may play a role. Surprisingly, ABCE1 adopted a previously unknown state for ABC-type ATPases that was termed “hybrid state”. Here, the NBSI is in a half open state with ADP bound and the NBSII is in a closed state with ATP bound. Further, eukaryotic initiation factor 3j (eIF3j) was found to stabilize this hybrid conformation via its N-terminus. Since eIF3j had already been described to assist ABCE1 in ribosome dissociation, in vitro splitting assays were performed demonstrating that eiF3j indeed actively enhances the splitting reaction. On top of this, the high-resolution structure allowed to describe the interaction network of eIF3j with the ribosome, initiation factors (IFs), and ABCE1. Independent of ABCE1, the structures presented here allowed to provide an improved molecular model of the human 43S pre-initiation complex (PIC) and to analyze its sophisticated interaction network. In particular, new molecular insights into the large eIF3 complex encircling the 43S PIC, and the eIF2 ternary complex delivering the initiator tRNA are provided. Equally important as canonical recycling is the recognition and recycling of ribosomes that result from translational failure. Aberrant translation elongation and ribosome stalling can be caused by a plethora of different stresses. In bacterial cells, multiple rescue systems are known such as trans-translation or alternative ribosome rescue factor-mediated termination, which act on ribosome nascent chain complexes with an empty A-site (non-stop complexes). It has been a long standing question how ribosomes that are stalled in the middle of an ORF (no-go complexes) are recognized and recycled. The second part of this dissertation reports a new bacterial rescue system that acts on no-go complexes. In eukaryotes, the concept of ribosome collisions as a trigger for ribosome rescue has been studied extensively. Here, it was found that a similar mechanism exists in bacteria and thus a structural analysis of collided disomes in E. coli and B. subtilis was conducted. In a genetic screen, the endonuclease SmrB was identified as one candidate for a collision sensor. Structural analysis of SmrB-bound disomes elucidated how this rescue factor is recruited to collided ribosomes. Its SMR domain binds to the disome interface between the stalled and the collided ribosome in close proximity to the mRNA and in a position ideal to perform endonucleolytic cleavage. Such cleavage then results in non-stop complexes that can be recycled by the pathways mentioned above. In conclusion, this work provides mechanistic insights into how a cell distinguishes stalled ribosomes from actively translating ribosomes and characterizes a novel ribosome rescue pathway

    Behavioral Economics & Machine Learning Expanding the Field Through a New Lens

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    In this thesis, I investigate central questions in behavioral economics as well as law and economics. I examine well-studied problems through a new methodological lens. The aim is to generate new insights and thus point behavioral scientists to novel analytical tools. To this end, I show how machine learning may be used to build new theories by reducing complexity in experimental economic data. Moreover, I use natural language processing to show how supervised learning can enable the scientific community to expand limited datasets. I also investigate the normative impact of the use of such tools in social science research or decision-making as well as their deficiencies
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