Topological superconductivity in three-dimensional centrosymmetric MoTe2_2

One key challenge in the field of topological superconductivity (Tsc) has been the rareness of material realization. This is true not only for the first-order Tsc featuring Majorana surface modes, but also the higher-order Tsc, which host Majorana hinge and corner modes. In this work, we first propose a general material-searching "recipe" for higher-order Tsc with Majorana corner modes. This recipe is obtained from a set of symmetry indicators that can diagnose Majorana boundary patterns in centrosymmetric superconductors. We derive these indicators by establishing the bulk-boundary correspondence through a real- and momentum-space basis matching procedure. Following this recipe, we propose that centrosymmetric MoTe$_2$, which was found superconducting experimentally, is a higher-order Tsc. By performing ab initio calculation and mean-field analysis for a realistic 44-band tight-binding model, we find that centrosymmetric MoTe$_2$ could host Majorana corner and hinge modes. Our recipe and proposed material candidate could provide general guiding principle for material searching and designing of higher-order Tsc and mobilize experimental effort in higher-order Tsc.Comment: 5+3 pages, 3+2 figures. v2: changes in affiliation and acknowledgemen

A Two-stage Architecture for Stock Price Forecasting by Integrating Self-Organizing Map and Support Vector Regression

Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model

Floating Point Arithmetic Protocols for Constructing Secure Data Analysis Application

AbstractA large variety of data mining and machine learning techniques are applied to a wide range of applications today. There- fore, there is a real need to develop technologies that allows data analysis while preserving the confidentiality of the data. Secure multi-party computation (SMC) protocols allows participants to cooperate on various computations while retaining the privacy of their own input data, which is an ideal solution to this issue. Although there is a number of frameworks developed in SMC to meet this challenge, but they are either tailored to perform only on specific tasks or provide very limited precision. In this paper, we have developed protocols for floating point arithmetic based on secure scalar product protocols, which is re- quired in many real world applications. Our protocols follow most of the IEEE-754 standard, supporting the four fundamental arithmetic operations, namely addition, subtraction, multiplication, and division. We will demonstrate the practicality of these protocols through performing various statistical calculations that is widely used in most data analysis tasks. Our experiments show the performance of our framework is both practical and promising

Protein subcellular localization prediction based on compartment-specific features and structure conservation

BACKGROUND: Protein subcellular localization is crucial for genome annotation, protein function prediction, and drug discovery. Determination of subcellular localization using experimental approaches is time-consuming; thus, computational approaches become highly desirable. Extensive studies of localization prediction have led to the development of several methods including composition-based and homology-based methods. However, their performance might be significantly degraded if homologous sequences are not detected. Moreover, methods that integrate various features could suffer from the problem of low coverage in high-throughput proteomic analyses due to the lack of information to characterize unknown proteins. RESULTS: We propose a hybrid prediction method for Gram-negative bacteria that combines a one-versus-one support vector machines (SVM) model and a structural homology approach. The SVM model comprises a number of binary classifiers, in which biological features derived from Gram-negative bacteria translocation pathways are incorporated. In the structural homology approach, we employ secondary structure alignment for structural similarity comparison and assign the known localization of the top-ranked protein as the predicted localization of a query protein. The hybrid method achieves overall accuracy of 93.7% and 93.2% using ten-fold cross-validation on the benchmark data sets. In the assessment of the evaluation data sets, our method also attains accurate prediction accuracy of 84.0%, especially when testing on sequences with a low level of homology to the training data. A three-way data split procedure is also incorporated to prevent overestimation of the predictive performance. In addition, we show that the prediction accuracy should be approximately 85% for non-redundant data sets of sequence identity less than 30%. CONCLUSION: Our results demonstrate that biological features derived from Gram-negative bacteria translocation pathways yield a significant improvement. The biological features are interpretable and can be applied in advanced analyses and experimental designs. Moreover, the overall accuracy of combining the structural homology approach is further improved, which suggests that structural conservation could be a useful indicator for inferring localization in addition to sequence homology. The proposed method can be used in large-scale analyses of proteomes

Applying a Kano quality model for intelligent green building design strategies in Taiwan

Intelligent green building (IGB) industry has received considerable global recognition due to the rapid development of advanced technology, intelligent materials, innovative products, and services in recent years. Although various cross-domain experiments and practices with respect to IGB projects are ready for operation, the notion and benefits of IGB are still ambiguous and debatable. The purpose of this study is to apply a Kano quality model and a customer satisfaction matrix to evaluate professional designers’ and general users’ satisfaction, preferences, and acceptability of IGB design strategies. The study result reveals that the proposed approach could be a useful tool to explore similarities and discrepancies of strategy preferences between designers and users, and these findings could effectively decrease the communication gap for future IGB design

Face recognition using nonparametric-weighted Fisherfaces

This study presents an appearance-based face recognition scheme called the nonparametric-weighted Fisherfaces (NW-Fisherfaces). Pixels in a facial image are considered as coordinates in a high-dimensional space and are transformed into a face subspace for analysis by using nonparametric-weighted feature extraction (NWFE). According to previous studies of hyperspectral image classification, NWFE is a powerful tool for extracting hyperspectral image features. The Fisherfaces method maximizes the ratio of between-class scatter to that of within-class scatter. In this study, the proposed NW-Fisherfaces weighted the between-class scatter to emphasize the boundary structure of the transformed face subspace and, therefore, enhances the separability for different persons' face. The proposed NW-Fisherfaces was compared with Orthogonal Laplacianfaces, Eigenfaces, Fisherfaces, direct linear discriminant analysis, and null space linear discriminant analysis methods for tests on five facial databases. Experimental results showed that the proposed approach outperforms other feature extraction methods for most databases. © 2012 Li et al

Sedimentological characteristics and seafloor failure offshore SW Taiwan

In this study, analysis results reveal two main deposition zones are located at the flank of upper Gaoping Submarine Canyon and Lower Fangliao Basin offshore SW Taiwan. The non-event related sediments deposited in past 150 years (i.e., 632 Mt km-2) was delivered and transported from Gaoping River by suspension process with tides and coastal currents and gradually spread out offshore. Meanwhile, the total mass of accumulation sediments is 1922 Mt km-2, accounting for 40% Gaoping Riverâs sediment load and suggesting that the deposition process is mainly controlled by natural hazards. Sedimentation rates in much of the study area, except in the main deposition zones, are less than 0.5 cm yr-1 (5 m kyr-1). Compared with the studies at the instability seafloor caused by high sedimentation rates (~30 m kyr-1), the offshore southwestern Taiwan is relatively stable. In this study, we also discovered a series of sediment waves located on the upper continental slope between Gaoping and Fangliao Submarine Canyons, which is related to the creeping process on seafloor. In summary, our results reveal the fluid activities, existence of weak layers and earthquake triggering are potential factors which might induced seafloor failures offshore southwestern Taiwan