Supersymmetric AdS_6 Solutions of Type IIB Supergravity

We study the general requirement for supersymmetric AdS$_6$ solutions in type IIB supergravity. We employ the Killing spinor technique and study the differential and algebraic relations among various Killing spinor bilinears to find the canonical form of the solutions. Our result agrees precisely with the work of Apruzzi et. al. \cite{Apruzzi:2014qva} which used the pure spinor technique. We also obtained the four-dimensional theory through the dimensional reduction of type IIB supergravity on AdS$_6$. This effective action is essentially a nonlinear sigma model with five scalar fields parametrizing $\textrm{SL}(3,\mathbb{R})/\textrm{SO}(2,1)$, modified by a scalar potential and coupled to Einstein gravity in Euclidean signature. We argue that the scalar potential can be explained by a subgroup CSO(1,1,1) $\subset\textrm{SL}(3,\mathbb{R})$ in a way analogous to gauged supergravity.Comment: v2: 24 pages, misprints corrected, published in EPJ

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Effect of time-varying flow-shear on the nonlinear stability of the boundary of magnetized toroidal plasmas

We propose a phenomenological yet general model in a form of extended complex Ginzburg-Landau equation to understand edge-localized modes (ELMs), a class of quasi-periodic fluid instabilities in the boundary of toroidal magnetized high-temperature plasmas. The model reproduces key dynamical features of the ELMs (except the final explosive relaxation stage) observed in the high-confinement state plasmas on the Korea Superconducting Tokamak Advanced Research: quasi-steady states characterized by field-aligned filamentary eigenmodes, transitions between different quasi-steady eigenmodes, and rapid transition to non-modal filamentary structure prior to the relaxation. It is found that the inclusion of time-varying perpendicular sheared flow is crucial for reproducing all of the observed dynamical features

Biopsychological traits of Sasang typology based on Sasang personality questionnaire and body mass index

BACKGROUND: The purpose of present study was to examine biological and psychological characteristics of people according to the Sasang typology, which is popular in Korea. We evaluated the Sasang Personality Questionnaire (SPQ) as a measure of temperament, and Body Mass Index (BMI) as a measure of the somatic properties of each Sasang type. METHODS: Subjects were 2506 (877 males, 1629 females) outpatients between the ages of 20 through 70 who requested traditional medical assessment and treatment in Korea. The structural validity of the SPQ was examined and its correlation with BMI was analyzed. The SPQ and BMI measures of each Sasang type across age and gender were presented and their differences were analyzed with Analysis of Variance. RESULTS: Confirmatory factor analysis and path analysis identified an acceptable three-factor structure of the SPQ measuring differences in individual’s behavior, emotion, and cognition. SPQ scores (29.71 ± 1.00, 28.29 ± 0.19 and 26.14 ± 0.22) and BMI scores (22.92 ± 0.09, 25.56 ± 0.10 and 21.44 ± 0.10) were significantly (p < 0.001) different among So-Yang, Tae-Eum and So-Eum Sasang types, respectively. CONCLUSIONS: The results showed that the SPQ and BMI is a reliable measure for quantifying the biopsychological characteristics of each types, and useful for guiding personalized and type-specific treatment with medical herbs and acupuncture

Protein Tyrosine signaling and its potential therapeutic implications in carcinogenesis

Protein tyrosine phosphorylation is a crucial signaling mechanism that plays a role in epithelial carcinogenesis. Protein tyrosine kinases (PTKs) control various cellular processes including growth, differentiation, metabolism, and motility by activating major signaling pathways including STAT3, AKT, and MAPK. Genetic mutation of PTKs and/or prolonged activation of PTKs and their downstream pathways can lead to the development of epithelial cancer. Therefore, PTKs became an attractive target for cancer prevention. PTK inhibitors are continuously being developed, and they are currently used for the treatment of cancers that show a high expression of PTKs. Protein tyrosine phosphatases (PTPs), the homeostatic counterpart of PTKs, negatively regulate the rate and duration of phosphotyrosine signaling. PTPs initially were considered to be only housekeeping enzymes with low specificity. However, recent studies have demonstrated that PTPs can function as either tumor suppressors or tumor promoters, depending on their target substrates. Together, both PTK and PTP signal transduction pathways are potential therapeutic targets for cancer prevention and treatment

RaPlace: Place Recognition for Imaging Radar using Radon Transform and Mutable Threshold

Due to the robustness in sensing, radar has been highlighted, overcoming harsh weather conditions such as fog and heavy snow. In this paper, we present a novel radar-only place recognition that measures the similarity score by utilizing Radon-transformed sinogram images and cross-correlation in frequency domain. Doing so achieves rigid transform invariance during place recognition, while ignoring the effects of radar multipath and ring noises. In addition, we compute the radar similarity distance using mutable threshold to mitigate variability of the similarity score, and reduce the time complexity of processing a copious radar data with hierarchical retrieval. We demonstrate the matching performance for both intra-session loop-closure detection and global place recognition using a publicly available imaging radar datasets. We verify reliable performance compared to existing stable radar place recognition method. Furthermore, codes for the proposed imaging radar place recognition is released for community

Variational bayes inference of Ising models and their applications

Ising models originated in statistical physics have been widely used in modeling spatial data and computer vision problems. However, statistical inference of this model and its application to many practical fields remain challenging due to intractable nature of the normalizing constant in the likelihood. This dissertation consists of two main themes, (1) parameter estimation of Ising model and (2) structured variable selection based on the Ising model using variational Bayes (VB). In Chapter 1, we review the background, research questions and development of Ising model, variational Bayes, and other statistical concepts. An Ising model basically deal with a binary random vector in which each component is dependent on its neighbors. There exist various versions of Ising model depending on parameterization and neighboring structure. In Chapter 2, with two-parameter Ising model, we describe a novel procedure for the parameter estimation based on VB which is computationally efficient and accurate compared to existing methods. Traditional pseudo maximum likelihood estimate (PMLE) can provide accurate results only for smaller number of neighbors. A Bayesian approach based on Markov chain Monte Carlo (MCMC) performs better even with a large number of neighbors. Computational costs of MCMC, however, are quite expensive in terms of time. Accordingly, we propose a VB method with two variational families, mean-field (MF) Gaussian family and bivariate normal (BN) family. Extensive simulation studies validate the efficacy of the families. Using our VB methods, computing times are remarkably decreased without deterioration in performance accuracy, or in some scenarios we get much more accurate output. In addition, we demonstrates theoretical properties of the proposed VB method under MF family. The main theoretical contribution of our work lies in establishing the consistency of the variational posterior for the Ising model with the true likelihood replaced by the pseudolikelihood. Under certain conditions, we first derive the rates at which the true posterior based on the pseudo-likelihood concentrates around the \uce\ue6n- shrinking neighborhoods of the true parameters. With a suitable bound on the Kullback-Leibler distance between the true and the variational posterior, we next establish the rate of contraction for the variational posterior and demonstrate that the variational posterior also concentrates around \uce\ue6n-shrinking neighborhoods of the true parameter. In Chapter 3, we propose a Bayesian variable selection technique for a regression setup in which the regression coefficients hold structural dependency. We employ spike and slab priors on the regression coefficients as follows: (i) In order to capture the intrinsic structure, we first consider Ising prior on latent binary variables. If a latent variable takes one, the corresponding regression coefficient is active, otherwise, it is inactive. (ii) Employing spike and slab prior, we put Gaussian priors (slab) on the active coefficients and inactive coefficients will be zeros with probability one (spike).Thesis (Ph. D.)--Michigan State University. Statistics, 2022Includes bibliographical references (pages 94-99

PU-EdgeFormer: Edge Transformer for Dense Prediction in Point Cloud Upsampling

Despite the recent development of deep learning-based point cloud upsampling, most MLP-based point cloud upsampling methods have limitations in that it is difficult to train the local and global structure of the point cloud at the same time. To solve this problem, we present a combined graph convolution and transformer for point cloud upsampling, denoted by PU-EdgeFormer. The proposed method constructs EdgeFormer unit that consists of graph convolution and multi-head self-attention modules. We employ graph convolution using EdgeConv, which learns the local geometry and global structure of point cloud better than existing point-to-feature method. Through in-depth experiments, we confirmed that the proposed method has better point cloud upsampling performance than the existing state-of-the-art method in both subjective and objective aspects. The code is available at https://github.com/dohoon2045/PU-EdgeFormer.Comment: Accepted to ICASSP 202

Spatial Analytics with Hospitality Big Data: Examining the Impact of Locational Determinants on Customer Satisfaction in the U.S. Hotel Market

Although hotel location has been recognized as one of the important factors affecting hotel selection and guest satisfaction, relatively few studies have examined guest satisfaction with hotel location and its locational determinants at a macro level. This study aims to identify the locational determinants of hotel guest satisfaction through big data spatial analytics via a case study of 5,302 hotels in 151 cities in the U.S. Based on the framework of hotel location satisfaction, we classified all location-related factors into three categories: accessibility to points of interest, transport convenience, and surrounding environment. Our findings indicated that hotel property’s proximity to city area, landmark, park, shopping center, and highway as well as, attraction-driven tourism industry specialization, and hotel industry agglomeration were significant determinants. Furthermore, the impacts of these factors were spatially heterogeneous. These findings can provide geographical insights that are critical for developing a customer service experience and satisfaction model