Density Estimation Using Nonparametric Bayesian Methods

In modern data analysis, nonparametric Bayesian methods have become increasingly popular. These methods can solve many important statistical inference problems, such as density estimation, regression and survival analysis. In this thesis, We utilize several nonparametric Bayesian methods for density estimation. In particular, we use mixtures of Dirichlet processes (MDP) and mixtures of Polya trees (MPT) priors to perform Bayesian density estimation based on simulated data. The target density is a mixture of normal distributions, which makes the estimation problem non-trivial. The performance of these methods with frequentist nonparametric kernel density estimators is assessed according to a mean-square error criterion. For the cases we consider, the nonparametric Bayesian methods outperform their frequentist counterpart

A estratégia do investimento externo direto chinês no Brasil: motivações e desafios

O advento do Neoliberalismo modificou e reordenou as relações econômicas entre os países. Surgem novos players no mercado global com a ascensão das economias emergentes. A China é um dos países que vem ganhando espaço no cenário global através da sua política de incentivo a exportação. Todavia, o investimento externo das empresas chinesas vem ganhando força nos últimos anos e pouca atenção tem sido dada a essa nova posição da China como investidora mundial. Assim, o objetivo do trabalho é identificar as razões que motivam o investimento chinês no mundo e, principalmente no Brasil, os desafios enfrentados por empresas chinesas aqui instaladas e o papel exercido pelo governo chinês na internacionalização de suas empresas. Para isso, foi feito, inicialmente, um levantamento bibliográfico sobre as motivações e vantagens das empresas investirem no exterior e quais são as características do investimento chinês. Em seguida, foi feita uma avaliação realizada por meio de quatro entrevistas com pessoas que estão nesta ponte entre Brasil e China. Concluiu-se que a principal motivação da entrada de investimento chinês no Brasil até no ano de 2010 foi a garantia de commodities para suprir o seu mercado doméstico e, já no ano de 2011, o investimento é voltado para o setor industrial. Os desafios identificados na internacionalização de empresas chinesas no Brasil foram de ordem cultural e institucional. O papel do governo chinês foi fundamental na internacionalização das empresas chinesas. Uma das conclusões, é que o Brasil, pode sim, ganhar com o investimento direto chinês por meio de política de coordenação no recebimento destes

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources.This work was supported by the European Union’s Horizon 2020 research and innovation programme under grant agreements No. 955606 (DEEP-SEA) and No. 777778 (MATHROCKS). Furthermore, the research leading of this study has received funding from the Ministerio de Educación y Ciencia (Spain) under Project TED2021-131882B-C42.Peer ReviewedPostprint (published version

Electromagnetic imaging and deep learning for transition to renewable energies: a technology review

Electromagnetic imaging is a technique that has been employed and perfected to investigate the Earth subsurface over the past three decades. Besides the traditional geophysical surveys (e.g., hydrocarbon exploration, geological mapping), several new applications have appeared (e.g., characterization of geothermal energy reservoirs, capture and storage of carbon dioxide, water prospecting, and monitoring of hazardous-waste deposits). The development of new numerical schemes, algorithms, and easy access to supercomputers have supported innovation throughout the geo-electromagnetic community. In particular, deep learning solutions have taken electromagnetic imaging technology to a different level. These emerging deep learning tools have significantly contributed to data processing for enhanced electromagnetic imaging of the Earth. Herein, we review innovative electromagnetic imaging technologies and deep learning solutions and their role in better understanding useful resources for the energy transition path. To better understand this landscape, we describe the physics behind electromagnetic imaging, current trends in its numerical modeling, development of computational tools (traditional approaches and emerging deep learning schemes), and discuss some key applications for the energy transition. We focus on the need to explore all the alternatives of technologies and expertise transfer to propel the energy landscape forward. We hope this review may be useful for the entire geo-electromagnetic community and inspire and drive the further development of innovative electromagnetic imaging technologies to power a safer future based on energy sources

Expression and Clinical Relevance of uPA and ET-1 in Non-small Cell Lung Cancer

Background and objective uPA and ET-1 proteins have been reported to be up-regulated in some of human cancers. The aim of this study is to investigate the alteration and clinical relevance of uPA and ET-1 protein levels in non-small cell lung cancer (NSCLC). Methods Expressions of uPA and ET-1 protein were detected in 155 cases of NSCLC with tissue microarrays and immunohistochemistry (TMA-IHC) technique. The correlations between the alteration of the two proteins and clinicopathological parameters were analyzed. Results Negative/weak, moderate and high expression of uPA were observed in 12.3%, 64.4% and 23.3% of squamous cell carcinomas, in 12.2%, 53.7% and 34.1% of adenocarcinomas, and in 12.3%, 58.7% and 29.0% of all cases. ET-1 presented negative/weak, moderate and high expression in 2.7%, 42.5% and 54.8% of squamous cell carcinomas, in 11.0%, 30.5% and 58.5% of adenocarcinomas, and in 7.1%, 36.1% and 56.8% of all cases. Simultaneously high expression of uPA and ET-1 were found in adenocarcinomas without lymph node metastasis (P=0.017). Adenocarcinoma patients with high expression of uPA or with high expression of both ET-1 and uPA had the longer survival time (P=0.007 and 0.016). Conclusion Detection of uPA and ET-1 protein levels might contribute to the prognosis evaluation of NSCLC

A multi-target prediction model for dam seepage field

Prediction of dam behavior based on monitoring data is important for dam safety and emergency management. It is crucial to analyze and predict the seepage field. Different from the mechanism-based physical models, machine learning models predict directly from data with high accuracy. However, current prediction models are generally based on environmental variables and single measurement point time series. Sometimes point-by-point modeling is used to obtain multi-point prediction values. In order to improve the prediction accuracy and efficiency of the seepage field, a novel multi-target prediction model (MPM) is proposed in which two deep learning methods are integrated into one frame. The MPM model can capture causal temporal features between environmental variables and target values, as well as latent correlation features between different measurement points at each moment. The features of these two parts are put into fully connected layers to establish the mapping relationship between the comprehensive feature vector and the multi-target outputs. Finally, the model is trained for prediction in the framework of a feed-forward neural network using standard back propagation. The MPM model can not only describe the variation pattern of measurement values with the change of load and time, but also reflect the spatial distribution relationship of measurement values. The effectiveness and accuracy of the MPM model are verified by two cases. The proposed MPM model is commonly applicable in prediction of other types of physical fields in dam safety besides the seepage field

Fault prognostic based on AR-LSSVR for electrolytic capacitor

U radu se opisuje metoda predviđanja greške na osnovu Autoregressive - Support Vector Regression Metode (AR-LSSVR) za elektrolitički kondenzator. Budući da je elektrolitički kondenzator jeftin, a velik, uveliko se primjenjuje u elektroničkim krugovima. Najprije se daje osnovni model i algoritam za predviđanje greške za AR, LSSVM i AR-LSSVR. Model AR-LSSVR kombinira prednosti algoritma LSSVR-a i modela AR te ih dopunjuje kako bi se povećala točnost predviđanja. Daje se dijagram toka predviđanja pojave greške na temelju AR-LSSVR. Konačno se AR-LSSVR model primjenjuje na Buck strujni krug. Rezultati pokazuju da je predviđanje greške elektrolitičkog kondenzatora bolje primjenom modela AR-LSSVR.This paper puts forward a method of fault prognostic based on Autoregressive - Support Vector Regression Method (AR-LSSVR) for electrolytic capacitor. Because the electrolytic capacitor is low in cost and large in volume, it is widely used in power electronic circuits. Firstly it introduces the basic model and the fault prognostic algorithm of the AR, LSSVM and AR-LSSVR. The AR-LSSVR prediction model combines the prediction algorithm advantage of the LSSVR and the AR model and complements the two to enhance prediction accuracy. It introduces the flow chart of fault trend prediction based on AR-LSSVR. Finally, the AR-LSSVR model is applied to the Buck circuit. The results indicate that the AR-LSSVR model performs better in trend prediction of electrolytic capacitor

Сильвестр Гогоцький: матеріали до життєпису

Вводячи до наукового обігу низку архівних джерел, автор реконструює віхи життя і творчості С. Гогоцького – видатного вітчизняного філософа ХІХ ст

Landau Quantization of Massless Dirac Fermions in Topological Insulator

The recent theoretical prediction and experimental realization of topological insulators (TI) has generated intense interest in this new state of quantum matter. The surface states of a three-dimensional (3D) TI such as Bi_2Te_3, Bi_2Se_3 and Sb_2Te_3 consist of a single massless Dirac cones. Crossing of the two surface state branches with opposite spins in the materials is fully protected by the time reversal (TR) symmetry at the Dirac points, which cannot be destroyed by any TR invariant perturbation. Recent advances in thin-film growth have permitted this unique two-dimensional electron system (2DES) to be probed by scanning tunneling microscopy (STM) and spectroscopy (STS). The intriguing TR symmetry protected topological states were revealed in STM experiments where the backscattering induced by non-magnetic impurities was forbidden. Here we report the Landau quantization of the topological surface states in Bi_2Se_3 in magnetic field by using STM/STS. The direct observation of the discrete Landau levels (LLs) strongly supports the 2D nature of the topological states and gives direct proof of the nondegenerate structure of LLs in TI. We demonstrate the linear dispersion of the massless Dirac fermions by the square-root dependence of LLs on magnetic field. The formation of LLs implies the high mobility of the 2DES, which has been predicted to lead to topological magneto-electric effect of the TI.Comment: 15 pages, 4 figure