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

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

Ultrasound tissue perfusion imaging

Enhanced blood perfusion in a tissue mass is an indication of neo-vascularity and potential malignancy. Ultrasonic pulsed Doppler imaging is a safe and economical modality for noninvasive monitoring of blood flow. However, weak blood echoes make it difficult to detect perfusion using standard methods without the expense of contrast enhancement. Additionally, imaging requires high sensitivity to slow, disorganized blood-flow patterns while simultaneously rejecting clutter and noise. An approach to address these challenges involves arranging acquisition data in a multi-dimensional structure to facilitate the characterization and separation of independent scattering sources. The resulting data array involves a linear combination of spatial, slow-time (kHz-order sampling), and frame-time (Hz-order sampling) coordinates. Applying an eigenfilter that exploits higher-order singular value decomposition (HOSVD) can technically transform the array and reduce the dimensions to yield power estimates for blood flow and perfusion that are well isolated from tissue clutter. Studies using microcirculation-mimicking simulations and phantoms enable the optimization of the filtering algorithm to maximize estimation efficiency. These techniques are applied to murine models of ischemia and melanoma at 24 MHz to form perfusion images. The results show enhancements of tissue perfusion maps, which help researchers access lesions without contrast enhancement. In a study aimed at peripheral artery disease (PAD), the enhanced sensitivity and specificity of ultrasonic-pulsed-Doppler imaging enable differentiation of perfusion between healthy and ischemic states. In addition, the use of the new ultrasound imaging coupled with other imaging modalities helps to illuminate the complex mechanism that mediates neovascularization in response to vascular occlusion. Consequently, these techniques have the potential to increase the effectiveness of existing medical imaging technologies in safe, cost-effective ways that promote sustainable medicine

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

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

A numerically efficient output-only system-identification framework for stochastically forced self-sustained oscillators

Self-sustained oscillations are ubiquitous in nature and engineering. In this paper, we propose a novel output-only system-identification framework for identifying the system parameters of a self-sustained oscillator affected by Gaussian white noise. A Langevin model that characterizes the self-sustained oscillator is postulated, and the corresponding Fokker--Planck equation is derived from stochastic averaging. From the drift and diffusion terms of the Fokker--Planck equation, unknown parameters of the system are identified. We develop a numerically efficient algorithm for enhancing the accuracy of parameter identification. In particular, a modified Levenberg--Marquardt optimization algorithm tailored to output-only system identification is introduced. The proposed framework is demonstrated on both numerical and experimental oscillators with varying system parameters that develop into self-sustained oscillations. The results show that the computational cost required for performing the system identification is dramatically reduced by using the proposed framework. Also, system parameters that were difficult to be extracted with the existing method could be efficiently computed with the system identification method developed in this study. Pertaining to the robustness and computational efficiency of the presented framework, this study can contribute to an accurate and fast diagnosis of dynamical systems under stochastic forcing.Comment: 17 pages, 10 figure

Protein Local Tertiary Structure Prediction by Super Granule Support Vector Machines with Chou-Fasman Parameter

Prediction of a protein's tertiary structure from its sequence information alone is considered a major task in modern computational biology.  In order to closer the gap between protein sequences to its tertiary structures, we discuss the correlation between protein sequence and local tertiary structure information in this paper.  The strategy we used in this work is predict small portions (local) of protein tertiary structure with high confidence from conserved protein sequences, which are called “protein sequence motifs”. 799 protein sequence motifs that transcend protein family boundaries were obtained from our previous work.  The prediction accuracy generated from the best group of protein sequence motifs always keep higher than 90% while more than 8% of the independent testing data segments are predicted. Since the most meaningful result published in latest publication is merely 70.02% accuracy under the coverage of 4.45%, the research results achieved in this paper are obviously outperformed. Besides, we also set up a stricter evaluation to our prediction to further understand the relation between protein sequence motifs and tertiary structure predictions.  The results suggest that the hidden sequence-to-structure relationship can be uncovered using the Super Granule SVM Model with the Chou-Fasman Parameter.  With the high local tertiary structure prediction accuracy provided in this article, the hidden relation between protein primary sequences and their 3D structure are uncovered considerably