Studies on Covariance Selection Models : Stepwise Procedure and Local Influence

Analysis of covariance selection models is a useful multivariate method to analyze the covariance structure of a multivariate normal distribution. It is used to reveal cause-and-effect relationships. In the present paper we review the theory and study numerically how the stepwise procedure of covariance selection works in actual data analysis. Then we try to develop a method of influence analysis in covariance selection, and show a numerical example to illustrate the usefulness of the method of influence analysis

Toward Collinearity-Avoidable Localization for Wireless Sensor Network

In accordance with the collinearity problem during computation caused by the beacon nodes used for location estimation which are close to be in the same line or same plane, two solutions are proposed in this paper: the geometric analytical localization algorithm based on positioning units and the localization algorithm based on the multivariate analysis method. The geometric analytical localization algorithm based on positioning units analyzes the topology quality of positioning units used to estimate location and provides quantitative criteria based on that; the localization algorithm based on the multivariate analysis method uses the multivariate analysis method to filter and integrate the beacon nodes coordinate matrixes during the process of location estimation. Both methods can avoid low estimation accuracy and instability caused by multicollinearity

Structure of HIV-1 reverse transcriptase cleaving RNA in an RNA/DNA hybrid

HIV-1 reverse transcriptase (RT) contains both DNA polymerase and RNase H activities to convert the viral genomic RNA to dsDNA in infected host cells. Here we report the 2.65-angstrom resolution structure of HIV-1 RT engaging in cleaving RNA in an RNA/DNA hybrid. A preferred substrate sequence is absolutely required to enable the RNA/DNA hybrid to adopt the distorted conformation needed to interact properly with the RNase H active site in RT. Substituting two nucleotides 4 bp upstream from the cleavage site results in scissile-phosphate displacement by 4 angstrom. We also have determined the structure of HIV-1 RT complexed with an RNase H-resistant polypurine tract sequence, which adopts a rigid structure and is accommodated outside of the nuclease active site. Based on this newly gained structural information and a virtual drug screen, we have identified an inhibitor specific for the viral RNase H but not for its cellular homologs.112Ysciescopu

Two-Dimensional Dirac Fermions Protected by Space-Time Inversion Symmetry in Black Phosphorus

We report the realization of novel symmetry-protected Dirac fermions in a surface-doped two-dimensional (2D) semiconductor, black phosphorus. The widely tunable band gap of black phosphorus by the surface Stark effect is employed to achieve a surprisingly large band inversion up to ~0.6 eV. High-resolution angle-resolved photoemission spectra directly reveal the pair creation of Dirac points and their moving along the axis of the glide-mirror symmetry. Unlike graphene, the Dirac point of black phosphorus is stable, as protected by spacetime inversion symmetry, even in the presence of spin-orbit coupling. Our results establish black phosphorus in the inverted regime as a simple model system of 2D symmetry-protected (topological) Dirac semimetals, offering an unprecedented opportunity for the discovery of 2D Weyl semimetals

Robotic Marine Exploration

ME450 Capstone Design and Manufacturing Experience: Fall 2020Develop a cheap alternative robot design that can map the seafloor accurately.http://deepblue.lib.umich.edu/bitstream/2027.42/164448/1/Robotic_Marine_Exploration.pd

Augmentation Matters: A Simple-yet-Effective Approach to Semi-supervised Semantic Segmentation

Recent studies on semi-supervised semantic segmentation (SSS) have seen fast progress. Despite their promising performance, current state-of-the-art methods tend to increasingly complex designs at the cost of introducing more network components and additional training procedures. Differently, in this work, we follow a standard teacher-student framework and propose AugSeg, a simple and clean approach that focuses mainly on data perturbations to boost the SSS performance. We argue that various data augmentations should be adjusted to better adapt to the semi-supervised scenarios instead of directly applying these techniques from supervised learning. Specifically, we adopt a simplified intensity-based augmentation that selects a random number of data transformations with uniformly sampling distortion strengths from a continuous space. Based on the estimated confidence of the model on different unlabeled samples, we also randomly inject labelled information to augment the unlabeled samples in an adaptive manner. Without bells and whistles, our simple AugSeg can readily achieve new state-of-the-art performance on SSS benchmarks under different partition protocols.Comment: 10 pages, 8 table