11,154 research outputs found

    A study of BPS and near-BPS black holes via AdS/CFT

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    In the settings of various AdS/CFT dual pairs, we use results from supersymmetric localiza tion to gain insights into the physics of asymptotically-AdS, BPS black holes in 5 dimensions, and near-BPS black holes in 4 dimensions. We first begin with BPS black holes embedded in the known examples of AdS5/CFT4 dualities. Using the Bethe Ansatz formulation, we compute the superconformal index at large N with arbitrary chemical potentials for all charges and angular momenta, for general N = 1 four-dimensional conformal theories with a holographic dual. We conjecture and bring some evidence that a particular universal contribution to the sum over Bethe vacua dominates the index at large N. For N = 4 SYM, this contribution correctly leads to the entropy of BPS Kerr-Newman black holes in AdS5 × S 5 for arbitrary values of the conserved charges, thus completing the microscopic derivation of their microstates. We also consider theories dual to AdS5 × SE5, where SE5 is a Sasaki-Einstein manifold. We first check our results against the so-called universal black hole. We then explicitly construct the near-horizon geometry of BPS Kerr-Newman black holes in AdS5 × T 1,1 , charged under the baryonic symmetry of the conifold theory and with equal angular momenta. We compute the entropy of these black holes using the attractor mechanism and find complete agreement with field theory predictions. Next, we consider the 3d Chern-Simons matter theory that is holographically dual to massive Type IIA string theory on AdS4 × S 6 . By Kaluza-Klein reducing on S 2 with a background that is dual to the asymptotics of static dyonic BPS black holes in AdS4, we construct a N = 2 supersymmetric gauged quantum mechanics whose ground-state degener acy reproduces the entropy of BPS black holes. We expect its low-lying spectrum to contain information about near-extremal horizons. Interestingly, the model has a large number of statistically-distributed couplings, reminiscent of SYK models

    Beam scanning by liquid-crystal biasing in a modified SIW structure

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    A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium

    Dissecting the mechanisms of transport of herpes simplex virus between Langerhans Cells & dendritic cells in epidermis and dermis following infection of human genital mucosa and skin

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    Herpes Simplex Virus (HSV) is a sexually transmitted infection (STI) that the World Health Organisation (WHO) has deemed a priority for a vaccine. CD8 and CD4T cells are important in the control and clearance of HSV, however no known vaccine has been able to stimulate CD8T cells. The dermal dendritic cells (dDCs) are suspected to play a role. Previously the host lab has shown in human tissue that HSV-1 infection of Langerhans cells (LCs) caused apoptosis and migration of LCs to the dermis, where they were phagocytosed by dDCs (termed HSV viral relay). Very little is known about the mechanisms of this relay. The host lab has also identified a second resident epidermal immune cell, Epi-cDC2s, which are infectable by HSV. This thesis aims to unravel the mechanisms involved in the relay. RNA-seq and cell surface phenotyping on human dDCs subsets showed that was differential chemokine receptor expression. Bead-based immunoassays were used to determine the chemokines produced by HSV-1 infected LCs and Epi-cDC2s,and showed HSV infected LCs produced increased CXCR3 ligands, while HSV infected Epi-cDC2s produced increased CCR5 ligands. The importance of these chemokine axes was investigated using chemotaxis assays. An cyclic immunofluorescent microscopy panel was then developed to investigate whether this migration could be seen in situ in HSV infected foreskin explants. Underneath epidermal foci of infection, there was migration of both cDC1s and cDC2s towards the basement membrane. Under foci of infection there was a greater proportion of cDC2s clustering with LCs. The uptake of HSV infected epidermal cells by the dDC subsets was examined using imaging cytometry. Preliminary results suggest that there were no significant differences between the ability of dDCs to phagocytose HSV infected epidermal cells. Understanding the mechanisms and the role of each dDC subset in the HSV viral relay will determine which dDC subsets are crucial for CD8 and CD4 T cell stimulation

    Antenna Development in Brain-Implantable Biotelemetric Systems for Next-Generation of Human Healthcare

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    In the growing efforts of promoting patients’ life quality through health technology solutions, implantable wireless medical devices (IMDs) have been identified as one of the frontrunners. They are bringing compelling wireless solutions for medical diagnosis and treatment through bio-telemetric systems that deliver real-time transmission of in-body physiological data to an external monitoring/control unit. To set up this bidirectional wireless biomedical communication link for the long- term, the IMDs need small and efficient antennas. Designing antenna-enabled biomedical telemetry is a challenging aim, which must fulfill demanding issues and criteria including miniaturization, appropriate radiation performance, bandwidth enhancement, good impedance matching, and biocompatibility. Overcoming the size restriction mainly depends on the resonant frequency of the required applications. Defined frequency bands for biomedical telemetry systems contain the Medical Implant Communication Service (MICS) operating at the frequency band of 402– 405 MHz, Medical Device Radiocommunication Service (MedRadio) resonating at the frequency ranges of 401– 406 MHz, 413 – 419 MHz, 426 – 432 MHz, 438 – 444 MHz, and 451 – 457 MHz, Wireless Medical Telemetry Service (WMTS) operating at frequency specturms of 1395 to 1400 MHz and 1427 to 1432 MHz, and Industrial, Scientific, and Medical (ISM) bands of 433.1–434.8 MHz, 868–868.6 MHz, 902.8–928.0 MHz, and 2.4–2.48 GHz. On the other hand, a single band antenna may not fulfill all requirements of a bio-telemetry system in either MedRadio, WMTS, or ISM bands. As a result, analyzing dual/multi-band implantable antenna supporting wireless power, data transmission, and control signaling can meet the demand for multitasking biotelemetry systems. In addition, among different antenna structures, PIFA has been found a promising type in terms of size-performance balance in lossy human tissues. To overcome the above-mentioned challenges, this thesis, first, starts with a discussion of antenna radiation in a lossy medium, the requirements of implantable antenna development, and numerical modeling of the human head tissues. In the following discussion, we concentrate on approaching a new design for far-field small antennas integrated into brain-implantable biotelemetric systems that provide attractive features for versatile functions in modern medical applications. To this end, we introduce three different implantable antenna structures including a compact dual-band PIFA, a miniature triple-band PIFA and a small quad-band PIFA for brain care applications. The compelling performance of the proposed antennas is analyzed and discussed with simulation results and the triple-band PIFA is evaluated using simulation outcomes compared with the measurement results of the fabricated prototype. Finally, the first concept and platform of in-body and off-body units are proposed for wireless dopamine monitoring as a brain care application. In addition to the main focus of this thesis, in the second stage, we focus on introducing an equivalent circuit model to the electrical connector-line transition. We present a data fitting technique for two transmission lines characterization independent of the dielectric properties of the substrate materials at the ultra-high frequency band (UHF). This approach is a promising solution for the development of wearable and off-body antennas employing textile materials in biomedical telemetry systems. The approach method is assessed with measurement results of several fabricated transmission lines on different substrate materials

    STEM Education and Retention for Black Women using High-Impact Practices: Historically Black Colleges and Universities vs. Predominantly White Liberal Arts Colleges

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    Black women are significantly underrepresented within the fields of science, technology, engineering, and mathematics (STEM). To address this, the Association of American Colleges & Universities crafted ten high-impact practices to increase student engagement and promote retention. This research paper examines how three specific high-impact practices (learning communities, mentoring, and undergraduate research experience) are utilized in STEM education.This research paper explores and compares the best high impact approaches that successfully teach and retain Black women within the various fields of STEM within the differing academic environments of historically Black colleges & universities ( HBCUs) and predominantly white liberal art colleges (PWLACs). This paper concludes with recommendations for continuous research on Black women who pursue STEM in addition to institutional policies and practices that predominantly white liberal art colleges must do in order to contribute efforts in addressing the large disparity

    Anti-unification and Generalization: A Survey

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    Anti-unification (AU), also known as generalization, is a fundamental operation used for inductive inference and is the dual operation to unification, an operation at the foundation of theorem proving. Interest in AU from the AI and related communities is growing, but without a systematic study of the concept, nor surveys of existing work, investigations7 often resort to developing application-specific methods that may be covered by existing approaches. We provide the first survey of AU research and its applications, together with a general framework for categorizing existing and future developments.Comment: Accepted at IJCAI 2023 - Survey Trac

    The safety and regulatory challenges associated with the geological disposal of the UK’s higher activity radioactive waste in England and Wales

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    The UK’s higher activity waste (HAW) is set to be disposed of in a geological disposal facility (GDF). International consensus is that a GDF provides the most comprehensive means of isolating and containing HAW and its harmful radionuclides, with nations at different stages in their implementation of geological disposal. The maturity of some nations’ disposal programmes (e.g. Finland, Sweden) ensures a regulatory framework for their GDF is well established. The UK is currently engaged in a GDF site selection process, as such it is necessary that the regulatory framework for the geological disposal of its radioactive waste be fully established to meet the unique challenges posed by this first-of-a-kind facility for the UK. The reduced hazard potential and unique features of the GDF may mean the existing framework applied to UK nuclear installations does not proportionately meet the requirements for maintaining worker and public safety and the protection of the environment. The purpose of the work presented in this thesis was to investigate the safety and regulatory challenges associated with the geological disposal of UK HAW. This began by building an understanding of the fundamentals of radioactive waste and geological disposal in the UK and the risks associated with geological disposal. Having investigated the performance of proposed engineered barrier materials for the GDF, a simplified, 1-dimensional risk assessment model was developed for the disposal of spent nuclear fuel (SNF) in a hypothetical geological setting. The model was verified against data provided by Radioactive Waste Management Ltd (RWM Ltd), the UK’s GDF delivery body, and utilised to conduct sensitivity studies, for the purpose of identify factors which could significantly impact on the radiological risk to the public due to the disposal of Spent Nuclear Fuel. Where significant, it was considered whether this might impact on the nature of the regulatory oversight required. The framework applied to nuclear installations in the regulation of nuclear safety, security, environmental protection and safeguards was mapped and analysed for its applicability to GDF-specific challenges. International experience in the regulation of GDFs was drawn upon in order to identify common features. Stakeholder opinion, including members of industry, regulators, waste producers and local interest groups, was also sought, in order to highlight their views on the applicability of the current system of nuclear site licensing to a GDF. This work culminated with a proposal for a regulatory framework, which aims to proportionately address the unique challenges associated with geological disposal.Open Acces

    Deep learning for speech to text transcription for the portuguese language

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    Automatic speech recognition (ASR) is the process of transcribing audio recordings into text, i.e. to transform speech into the respective sequence of words. This process is also commonly known as speechto- text. Machine learning (ML), the ability of machines to learn from examples, is one of the most relevant areas of artificial intelligence in today’s world. Deep learning is a subset of ML which makes use of Deep Neural Networks, a particular type of Artificial Neural Networks (ANNs), which are intended to mimic human neurons, that possess a large number of layers. This dissertation reviews the state-of-the-art on automatic speech recognition throughout time, from early systems which used Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs) to the most up-to-date end-to-end (E2E) deep neural models. Considering the context of the present work, some deep learning algorithms used in state-of-the-art approaches are explained in additional detail. The current work aims to develop an ASR system for the European Portuguese language using deep learning. This is achieved by implementing a pipeline composed of stages responsible for data acquisition, data analysis, data pre-processing, model creation and evaluation of results. With the NVIDIA NeMo framework was possible to implement the QuartzNet15x5 architecture based on 1D time-channel separable convolutions. Following a data-centric methodology, the model developed yielded state-of-the-art Word Error Rate (WER) results of WER = 0.0503; Sumário: Aprendizagem profunda para transcrição de fala para texto para a Língua Portuguesa - O reconhecimento automático de fala (ASR) é o processo de transcrever gravações de áudio em texto, i.e., transformar a fala na respectiva sequência de palavras. Esse processo também é comumente conhecido como speech-to-text. A aprendizagem de máquina (ML), a capacidade das máquinas de aprenderem através de exemplos, é um dos campos mais relevantes da inteligência artificial no mundo atual. Deep learning é um subconjunto de ML que faz uso de Redes Neurais Profundas, um tipo particular de Redes Neurais Artificiais (ANNs), que se destinam a imitar neurónios humanos, que possuem um grande número de camadas Esta dissertação faz uma revisão ao estado da arte do reconhecimento automático de fala ao longo do tempo, desde os primeiros sistemas que usavam Hidden Markov Models (HMMs) e Gaussian Mixture Models (GMMs até sistemas end-to-end (E2E) mais recentes que usam modelos neuronais profundos. Considerando o contexto do presente trabalho, alguns algoritmos de aprendizagem profunda usados em abordagens de ponta são explicados mais detalhadamente. O presente trabalho tem como objetivo desenvolver um sistema ASR para a língua portuguesa europeia utilizando deep learning. Isso é conseguido por meio da implementação de um pipeline composto por etapas responsáveis pela aquisição de dados, análise dos dados, pré-processamento dos dados, criação do modelo e avaliação dos resultados. Com o framework NVIDIA NeMo foi possível implementar a arquitetura QuartzNet15x5 baseada em convoluções 1D separáveis por canal de tempo. Seguindo uma metodologia centrada em dados, o modelo desenvolvido produziu resultados de taxa de erro de palavra (WER) semelhantes aos de estado da arte de WER = 0.0503
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