Atomically thin mononitrides SiN and GeN: new two-dimensional semiconducting materials

Low-dimensional Si-based semiconductors are unique materials that can both match well with the Si-based electronics and satisfy the demand of miniaturization in modern industry. Owing to the lack of such materials, many researchers put their efforts into this field. In this work, employing a swarm structure search method and density functional theory, we theoretically predict two-dimensional atomically thin mononitrides SiN and GeN, both of which present semiconducting nature. Furthermore study shows that SiN and GeN behave as indirect band gap semiconductors with the gap of 1.75 and 1.20 eV, respectively. The ab initio molecular dynamics calculation tells that both two mononitrides can exist stably even at extremely high temperature of 2000 K. Notably, electron mobilities are evaluated as 0.888x$10^3$ $cm^2V^{-1}s^{-1}$ and 0.413x$10^3$ $cm^2V^{-1}s^{-1}$ for SiN and GeN, respectively. The present work expands the family of low-dimensional Si-based semiconductors.Comment: arXiv admin note: text overlap with arXiv:1703.0389

Autonomous learning for face recognition in the wild via ambient wireless cues

Facial recognition is a key enabling component for emerging Internet of Things (IoT) services such as smart homes or responsive offices. Through the use of deep neural networks, facial recognition has achieved excellent performance. However, this is only possibly when trained with hundreds of images of each user in different viewing and lighting conditions. Clearly, this level of effort in enrolment and labelling is impossible for wide-spread deployment and adoption. Inspired by the fact that most people carry smart wireless devices with them, e.g. smartphones, we propose to use this wireless identifier as a supervisory label. This allows us to curate a dataset of facial images that are unique to a certain domain e.g. a set of people in a particular office. This custom corpus can then be used to finetune existing pre-trained models e.g. FaceNet. However, due to the vagaries of wireless propagation in buildings, the supervisory labels are noisy and weak. We propose a novel technique, AutoTune, which learns and refines the association between a face and wireless identifier over time, by increasing the inter-cluster separation and minimizing the intra-cluster distance. Through extensive experiments with multiple users on two sites, we demonstrate the ability of AutoTune to design an environment-specific, continually evolving facial recognition system with entirely no user effort

Improving Efficiency for CUDA-based Volume Rendering by Combining Segmentation and Modified Sampling Strategies

The objective of this paper is to present a speed-up method to improve the rendering speed of ray casting at the same time obtaining high-quality images. Ray casting is the most commonly used volume rendering algorithm, and suitable for parallel processing. In order to improve the efficiency of parallel processing, the latest platform-Compute Unified Device Architecture (CUDA) is used. The speed-up method uses improved workload allocation and sampling strategies according to CUDA features. To implement this method, the optimal number of segments of each ray is dynamically selected based on the change of the corresponding visual angle, and each segment is processed by a distinct thread processor. In addition, for each segment, we apply different sampling quantity and density according to the distance weight. Rendering speed results show that our method achieves an average 70% improvement in terms of speed, and even 145% increase in some special cases, compared to conventional ray casting on Graphics Processing Unit (GPU). Speed-up ratio shows that this method can effectively improve the factors that influence efficiency of rendering. Excellent rendering performance makes this method contribute to real-time 3-D reconstruction

Network‐based feature selection reveals substructures of gene modules responding to salt stress in rice

Rice, an important food resource, is highly sensitive to salt stress, which is directly related to food security. Although many studies have identified physiological mechanisms that confer tolerance to the osmotic effects of salinity, the link between rice genotype and salt tolerance is not very clear yet. Association of gene co‐expression network and rice phenotypic data under stress has penitential to identify stress‐responsive genes, but there is no standard method to associate stress phenotype with gene co‐expression network. A novel method for integration of gene co‐expression network and stress phenotype data was developed to conduct a system analysis to link genotype to phenotype. We applied a LASSO‐based method to the gene co‐expression network of rice with salt stress to discover key genes and their interactions for salt tolerance‐related phenotypes. Submodules in gene modules identified from the co‐expression network were selected by the LASSO regression, which establishes a linear relationship between gene expression profiles and physiological responses, that is, sodium/potassium condenses under salt stress. Genes in these submodules have functions related to ion transport, osmotic adjustment, and oxidative tolerance. We argued that these genes in submodules are biologically meaningful and useful for studies on rice salt tolerance. This method can be applied to other studies to efficiently and reliably integrate co‐expression network and phenotypic data

Characterization of the transcriptional divergence between the subspecies of cultivated rice (Oryza sativa)

Background: Cultivated rice consists of two subspecies, Indica and Japonica, that exhibit well-characterized differences at the morphological and genetic levels. However, the differences between these subspecies at the transcriptome level remains largely unexamined. Here, we provide a comprehensive characterization of transcriptome divergence and cis-regulatory variation within rice using transcriptome data from 91 accessions from a rice diversity panel (RDP1). Results: The transcriptomes of the two subspecies of rice are highly divergent. Japonica have significantly lower expression and genetic diversity relative to Indica, which is likely a consequence of a population bottleneck during Japonica domestication. We leveraged high-density genotypic data and transcript levels to identify cis-regulatory variants that may explain the genetic divergence between the subspecies. We identified significantly more eQTL that were specific to the Indica subspecies compared to Japonica, suggesting that the observed differences in expression and genetic variability also extends to cis-regulatory variation. Conclusions: Using RNA sequencing data for 91diverse rice accessions and high-density genotypic data, we show that the two species are highly divergent with respect to gene expression levels, as well as the genetic regulation of expression. The data generated by this study provide, to date, the largest collection of genome-wide transcriptional levels for rice, and provides a community resource to accelerate functional genomic studies in rice