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Confirmatory Bayesian Online Change Point Detection in the Covariance Structure of Gaussian Processes

In the analysis of sequential data, the detection of abrupt changes is important in predicting future events. In this paper, we propose statistical hypothesis tests for detecting covariance structure changes in locally smooth time series modeled by Gaussian Processes (GPs). We provide theoretically justified thresholds for the tests, and use them to improve Bayesian Online Change Point etection (BOCPD) by confirming statistically signifi-cant changes and non-changes. Our Confirmatory BOCPD (CBOCPD) algorithm finds multiple structural breaks in GPs even when hyperparameters are not tuned precisely. We also provide conditions under which CBOCPD provides the lower prediction error compared to BOCPD. Experimental results on synthetic and real-world datasets show that our proposed algorithm outperforms existing methods for the prediction of nonstationarity in terms of both regression error and log likelihood

Generative Design of Electromagnetic Structures Through Bayesian Learning

We propose a novel Bayesian learning algorithm, Bayesian clique learning (BCL), for searching the optimal electromagnetic ( EM) design parameter by using the structural property of EM simulation data set. Our method constructs a new topological structure called statistical clique that encodes EM information, which reduces our search space by cutting down unnecessary data. Our BCL then search optimum design parameters by exploiting embedded cliques in the data. Our BCL allows us to reuse learning parameters from the trained EM data set to the new EM data set with little modifications. We classify our data in three ranges and run our learning to find range specific parameters. Our learning algorithm is scalable, and works on any general EM structure for automated design. We have given a bound for the computational complexity of our method and discuss the tradeoff of the complexity with the uncertainty. We compare the computational complexity of two different EM structures that has weakly linear negative correlated data sets

Cardiovascular manifestation of end-stage liver disease and perioperative echocardiography for liver transplantation: anesthesiologist’s view

Liver transplantation (LT) is the curative therapy for decompensated cirrhosis. However, anesthesiologists can find it challenging to manage patients undergoing LT due to the underlying pathologic conditions of patients with end-stage liver disease and the high invasiveness of the procedure, which is frequently accompanied by massive blood loss. Echocardiography is a non-invasive or semi-invasive imaging tool that provides real-time information about the structural and functional status of the heart and is considered to be able to improve outcomes by enabling accurate and detailed assessments. This article reviews the pathophysiologic changes of the heart accompanied by cirrhosis that mainly affect hemodynamics. We also present a comparative review of the diagnostic criteria for cirrhotic cardiomyopathy published by the World Congress of Gastroenterology in 2005 and the Cirrhotic Cardiomyopathy Consortium in 2019. This article discusses the conditions that could affect hemodynamic stability and postoperative outcomes, such as coronary artery disease, left ventricular outflow tract obstruction, portopulmonary hypertension, hepatopulmonary syndrome, pericardial effusion, cardiac tamponade, patent foramen ovale, and ascites. Finally, we cover a number of intraoperative factors that should be considered, including intraoperative blood loss, rapid reaccumulation of ascites, manipulation of the inferior vena cava, post-reperfusion syndrome, and adverse effects of excessive fluid infusion and transfusion. This article aimed to summarize the cardiovascular manifestations of cirrhosis that can affect hemodynamics and can be evaluated using perioperative echocardiography. We hope that this article will provide information about the hemodynamic characteristics of LT recipients and stimulate more active use of perioperative echocardiography

A ¹H HR-MAS NMR-Based Metabolomic Study for Metabolic Characterization of Rice Grain from Various <i>Oryza sativa</i> L. Cultivars

Rice grain metabolites are important for better understanding of the plant physiology of various rice cultivars and thus for developing rice cultivars aimed at providing diverse processed products. However, the variation of global metabolites in rice grains has rarely been explored. Here, we report the identification of intra- or intercellular metabolites in rice (<i>Oryza sativa</i> L.) grain powder using a ¹H high-resolution magic angle spinning (HR-MAS) NMR-based metabolomic approach. Compared with nonwaxy rice cultivars, marked accumulation of lipid metabolites such as fatty acids, phospholipids, and glycerophosphocholine in the grains of waxy rice cultivars demonstrated the distinct metabolic regulation and adaptation of each cultivar for effective growth during future germination, which may be reflected by high levels of glutamate, aspartate, asparagine, alanine, and sucrose. Therefore, this study provides important insights into the metabolic variations of diverse rice cultivars and their associations with environmental conditions and genetic backgrounds, with the aim of facilitating efficient development and the improvement of rice grain quality through inbreeding with genetic or chemical modification and mutation