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  • About
  • The Global ETD Search service is a free service for researchers to find electronic theses and dissertations. This service is provided by the Networked Digital Library of Theses and Dissertations.
    Our metadata is collected from universities around the world. If you manage a university/consortium/country archive and want to be added, details can be found on the NDLTD website.
71

Fotoprodução de kaons e híperons em deutério / Photoproduction of kaons and hyperons in deuterium

Ana Cecilia de Souza Lima 09 September 2002 (has links)
Com o objetivo de investigar a fotoprodução de kaons na região de energias intermediarias, foram realizadas medidas de seção de choque de fotoprodução de híperons utilizando o deutério como alvo, entre 0,50 e 2,95GeV. Para obtenção das medidas foi utilizado o acelerador de elétrons do TJNAF (Thomas Jefferson National Accelerator Facility) localizado na Virginia, USA. Foram obtidas seções de choque de fotoprodução para as reações gama(p, K+)lambda, gama(p, K+)sigmaº, sigma(n, K+)lambda-. Observou-se a estrutura de interferência na proximidade do limiar de produção de \'lambdaº, previsto teoricamente. As seções de choque obtidas foram comparadas com o modelo teórico existente fornecendo informações fundamentais para uma elaboração teórica mais consistente. Foram ainda, observados lambda(1385) com grande resolução abrindo possibilidade de realizar um estudo muito interessante desta produção. / Kaon photoproduction on deuterium was investigated and the differential cross sections were determined using the data obtained in Hall B at TJNAF (Thomas Jefferson National Accelerator Facility). Real photons were produced in the range covered from 0.50 to 2.95GeV. The cross section for kaon production reactions gama(p, K+ ) lambda, gama(p, K+ ) \'sigmaº, sigma(n, K+)lambda- were obtained. The interference structure near the production of lambdaº, predicted theoretically, was revealed. Theoretical model was compared to the cross section obtained and fundamental informations became available in order to improve the consistence of the model existent. At last, lambda(1385) were identified with good resolution conducting to interesting investigation in the future.
72

The application of spontaneous parametric downconversion to develop tools for validating photonic quantum information technologies

Thomas, Peter James January 2010 (has links)
This portfolio of work contributes to the remit of the National Physical Laboratory (NPL) to develop the underpinning expertise and tools for validating nascent and future optical quantum technologies based on the discrete and quantum properties of photons. This requirement overlaps with the requirement to provide validation for devices operating in the photon-counting regime. A common theme running through the portfolio is photon pairs generated through spontaneous parametric downconversion (SPDC). A Hong-Ou-Mandel (HOM) interferometer sourced with visible wavelength photon pairs from an SPDC process in beta-barium borate (BBO) was designed, built and characterised. The visibility of the HOM interference is dependent on the indistinguishability of the interfering photons, but is also influenced by imperfections of the interferometer; therefore an investigation was carried out to quantify the effects of the interferometer imperfections on the measured visibility so that the true photon indistinguishability could be measured with a quantified uncertainty. A bright source of correlated pair photons in the telecoms band based upon a pump enhanced SPDC process in periodically-poled potassium titanyl phosphate (PPKTP) was designed, built and characterised. From the characterisation measurements the source brightness was estimated to be 6.2×10⁴ pairs/ s/ mw pump. The photon pairs were further characterised through their incorporation as a source in a HOM interference experiment. The developed correlated photon pair source was at the heart of a novel scheme for the generation of polarisation entangled photon pairs, for which the design, build and characterisation work is presented. The source was demonstrated to produce two of the four maximally entangled Bell states with quantum interference visibilities of around 0.95. The generated states were also shown to break a form of Bell's inequality by around six standard deviations. The polarisation entangled photon pair source was originally built at the University of St Andrews and was later transferred to the NPL where it will extend NPL's capabilities to this key spectral region. Finally a study was carried out to investigate the possibility of a wavelength tuneable device for the absolute measurement of single photon detector quantum efficiencies based upon an established SPDC technique.
73

Numerical Study of the Fractional Quantum Hall Effect: a Few-Body Perspective

Bin Yan (6622667) 15 May 2019 (has links)
<div><div><div><p>When confined to a finite, two-dimensional area and exposed to a strong magnetic field, electrons exhibit a complicated, highly correlated quantum behavior known as the quantum Hall effect. This dissertation consists of finite size numerical investigations of this effect. One line of study develops treatment of the fractional quantum Hall effect using the hyperspherical method, in conjunction with applications to the few-body quantum Hall systems, e.g., highly-controlled atomic systems. Another line of research fully utilizes the developed numerical techniques to study on the platform of finite size fractional quantum Hall states the bulk-edge correspondence principle, which is universal for phases in topological orders. It has been demonstrated that the eigenstates associated with the entanglement spectrum reveal more information about the ground state than the spectrum alone.</p></div></div></div>
74

Metric Learning via Linear Embeddings for Human Motion Recognition

Kong, ByoungDoo 18 December 2020 (has links)
We consider the application of Few-Shot Learning (FSL) and dimensionality reduction to the problem of human motion recognition (HMR). The structure of human motion has unique characteristics such as its dynamic and high-dimensional nature. Recent research on human motion recognition uses deep neural networks with multiple layers. Most importantly, large datasets will need to be collected to use such networks to analyze human motion. This process is both time-consuming and expensive since a large motion capture database must be collected and labeled. Despite significant progress having been made in human motion recognition, state-of-the-art algorithms still misclassify actions because of characteristics such as the difficulty in obtaining large-scale leveled human motion datasets. To address these limitations, we use metric-based FSL methods that use small-size data in conjunction with dimensionality reduction. We also propose a modified dimensionality reduction scheme based on the preservation of secants tailored to arbitrary useful distances, such as the geodesic distance learned by ISOMAP. We provide multiple experimental results that demonstrate improvements in human motion classification.
75

On Transfer Learning Techniques for Machine Learning

Debasmit Das (8314707) 30 April 2020 (has links)
<pre><pre><p> </p><p>Recent progress in machine learning has been mainly due to the availability of large amounts of annotated data used for training complex models with deep architectures. Annotating this training data becomes burdensome and creates a major bottleneck in maintaining machine-learning databases. Moreover, these trained models fail to generalize to new categories or new varieties of the same categories. This is because new categories or new varieties have data distribution different from the training data distribution. To tackle these problems, this thesis proposes to develop a family of transfer-learning techniques that can deal with different training (source) and testing (target) distributions with the assumption that the availability of annotated data is limited in the testing domain. This is done by using the auxiliary data-abundant source domain from which useful knowledge is transferred that can be applied to data-scarce target domain. This transferable knowledge serves as a prior that biases target-domain predictions and prevents the target-domain model from overfitting. Specifically, we explore structural priors that encode relational knowledge between different data entities, which provides more informative bias than traditional priors. The choice of the structural prior depends on the information availability and the similarity between the two domains. Depending on the domain similarity and the information availability, we divide the transfer learning problem into four major categories and propose different structural priors to solve each of these sub-problems.</p><p> </p><p>This thesis first focuses on the unsupervised-domain-adaptation problem, where we propose to minimize domain discrepancy by transforming labeled source-domain data to be close to unlabeled target-domain data. For this problem, the categories remain the same across the two domains and hence we assume that the structural relationship between the source-domain samples is carried over to the target domain. Thus, graph or hyper-graph is constructed as the structural prior from both domains and a graph/hyper-graph matching formulation is used to transform samples in the source domain to be closer to samples in the target domain. An efficient optimization scheme is then proposed to tackle the time and memory inefficiencies associated with the matching problem. The few-shot learning problem is studied next, where we propose to transfer knowledge from source-domain categories containing abundantly labeled data to novel categories in the target domain that contains only few labeled data. The knowledge transfer biases the novel category predictions and prevents the model from overfitting. The knowledge is encoded using a neural-network-based prior that transforms a data sample to its corresponding class prototype. This neural network is trained from the source-domain data and applied to the target-domain data, where it transforms the few-shot samples to the novel-class prototypes for better recognition performance. The few-shot learning problem is then extended to the situation, where we do not have access to the source-domain data but only have access to the source-domain class prototypes. In this limited information setting, parametric neural-network-based priors would overfit to the source-class prototypes and hence we seek a non-parametric-based prior using manifolds. A piecewise linear manifold is used as a structural prior to fit the source-domain-class prototypes. This structure is extended to the target domain, where the novel-class prototypes are found by projecting the few-shot samples onto the manifold. Finally, the zero-shot learning problem is addressed, which is an extreme case of the few-shot learning problem where we do not have any labeled data in the target domain. However, we have high-level information for both the source and target domain categories in the form of semantic descriptors. We learn the relation between the sample space and the semantic space, using a regularized neural network so that classification of the novel categories can be carried out in a common representation space. This same neural network is then used in the target domain to relate the two spaces. In case we want to generate data for the novel categories in the target domain, we can use a constrained generative adversarial network instead of a traditional neural network. Thus, we use structural priors like graphs, neural networks and manifolds to relate various data entities like samples, prototypes and semantics for these different transfer learning sub-problems. We explore additional post-processing steps like pseudo-labeling, domain adaptation and calibration and enforce algorithmic and architectural constraints to further improve recognition performance. Experimental results on standard transfer learning image recognition datasets produced competitive results with respect to previous work. Further experimentation and analyses of these methods provided better understanding of machine learning as well.</p><p> </p></pre></pre>
76

Generace fázově stabilních ultrakrátkých pulzů ve střední infračervené oblasti / Generation of carrier-envelope-stable few-cycle pulses in the mid-infrared spectral region

Peterka, Pavel January 2020 (has links)
In this thesis we present the realization of a source of 1.5-cycle carrier-envelope phase stable laser pulses in the mid-infrared spectral region. We used ytterbium laser system generating 1 µm pulses as a pump of setup, where the beam is split into several parts and interact in nonlinear optical media. 2 µJ pulses with duration 18 fs at 50 kHz repe- tition rate are produced. By spectral broadening in crystal GGG, 9,9 fs pulses can be achieved. The mid-IR pulses was characterized by third harmonics generation frequency resolved optical gating in the interferometric configuration. Fourier filtering of the mea- sured interferogram allows for the complete reconstruction of amplitude and phase of the ultrashort pulses generated by our setup. The pulses will in future serve for experimental investigation of ultrafast strong-field phenomena in solids. 1
77

A Systematic Literature Review on Meta Learning for Predictive Maintenance in Industry 4.0

Fisenkci, Ahmet January 2022 (has links)
Recent refinements in Industry 4.0 and Machine Learning demonstrate the positive effects of using deep learning models for intelligent maintenance. The primary benefit of Deep Learning (DL) is its capability to extract attributes and make fast, accurate, and automated predictions without supervision. However, DL requires high computational power, significant data preprocessing, and vast amounts of data to make accurate predictions for intelligent maintenance. Given the considerable obstacles, meta-learning has been developed as a novel way to overcome these challenges. As a learning technique, meta-learning aims to quickly acquire knowledge of new tasks using theminimal available data by learning through meta-knowledge. There has been less research in the area of using meta-learning for Predictive Maintenance (PdM) and we considered it necessary to conduct this review to understand the applicability of meta-learning’s capabilities and functions to PdM since the outcomes of this technique seem to be rather promising. The review started with the development of a methodology and four research questions: (1) What is the taxonomy of meta-learning for PdM?, (2) What are the current state-of-the-art methodologies? (3) Which datasets are available for meta-learning in PdM?, and (4) What are the open issues, challenges, and opportunities of meta-learning in PdM?. To answer the first and second questions, a new taxonomy was proposed and meta-learnings role in predictive maintenance was identified from selected 55 papers. To answer the third question, we determined which types of datasets and their characteristics exist for this domain. Finally, the challenges, open issues, and opportunities of meta-learning in predictive maintenance were examined to answer the final question. The results of the research questions provided suggestions for future research topics.
78

Mechanical characterization of two-dimensional heterostructures by a blister test

Calis, Metehan 24 May 2023 (has links)
As the family of two−dimensional(2D) materials has grown, two−dimensional heterostructure devices have emerged as great alternatives to replace conventional electronic materials and enable new functionality such as flexible and bendable electronics. The fabrication and performance of these devices depend critically on the understanding and ability to manipulate the mechanical interplay between the stacked materials. In this dissertation, we investigate adhesive interactions and determine the shear modulus of heterostructure devices made from Molybdenum Disulfide (MoS2). MoS2 has been attracting attention recently due to its semiconductor nature (having a direct band gap of 1.9 eV) along with its exceptional mechanical strength and flexibility. As the first step of our research, we suspended MoS2 flakes grown through chemical vapor deposition (CVD) over substrates made of metal (gold, titanium, chromium), semiconductor (germanium, silicon), insulator (silicon oxide), and semi-metal (graphite). Then, by creating pressure differences across the membrane, we forced MoS2 to bulge upward until we observe separation from the surface of the substrates. We demonstrated that MoS2 on graphite has the highest work of separation within the tested surface materials. Furthermore, we measured considerable adhesion hysteresis between the work of separation and the work of adhesion. We proposed that surface roughness and chemical interactions play a role in surface adhesion and separation of 2D materials. These experiments are critical to guiding the future design of electrical and mechanical devices based on 2D materials. Next, we measured the effective shear modulus of MoS2/few−layer graphene (FLG) heterostructures by employing a blister test. Again, by introducing a pressure differential across the suspended MoS2 membrane over the FLG substrate, the MoS2/FLG heterostructure peeled off from the silicon oxide surface once the critical pressure is exceeded. Incorporating a modified free energy model and Hencky’s axisymmetric membrane solution, we determine the average effective shear modulus of the heterostructure. This is the first experimental measurement of the shear modulus of heterostructure devices using a blister test and this platform can be extended to determine the shear modulus of other 2D heterostructures as well. / 2024-05-24T00:00:00Z
79

Advances in the Application of the Similarity Renormalization Group to Strongly Interacting Systems

Wendt, Kyle Andrew 17 December 2013 (has links)
No description available.
80

Testing the Low Energy Theorem for Spinless “Proton-Neutron” Bremsstrahlung

Pidopryhora, Yurii 04 August 2003 (has links)
No description available.

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