Understanding size dependence of phase stability and band gap in CsPbI3 perovskite nanocrystals.

Inorganic halide perovskites CsPbX3 (X = Cl, Br, I) have been widely studied as colloidal quantum dots for their excellent optoelectronic properties. Not only is the long-term stability of these materials improved via nanostructuring, their optical bandgaps are also tunable by the nanocrystal (NC) size. However, theoretical understanding of the impact of the NC size on the phase stability and bandgap is still lacking. In this work, the relative phase stability of CsPbI3 as a function of the crystal size and the chemical potential is investigated by density functional theory. The optically active phases (α- and γ-phase) are found to be thermodynamically stabilized against the yellow δ-phase by reducing the size of the NC below 5.6 nm in a CsI-rich environment. We developed a more accurate quantum confinement model to predict the change in bandgaps at the sub-10 nm regime by including a finite-well effect. These predictions have important implications for synthesizing ever more stable perovskite NCs and bandgap engineering

Linear scaling calculation of maximally-localized Wannier functions with atomic basis set

We have developed a linear scaling algorithm for calculating maximally-localized Wannier functions (MLWFs) using atomic orbital basis. An O(N) ground state calculation is carried out to get the density matrix (DM). Through a projection of the DM onto atomic orbitals and a subsequent O(N) orthogonalization, we obtain initial orthogonal localized orbitals. These orbitals can be maximally localized in linear scaling by simple Jacobi sweeps. Our O(N) method is validated by applying it to water molecule and wurtzite ZnO. The linear scaling behavior of the new method is demonstrated by computing the MLWFs of boron nitride nanotubes.Comment: J. Chem. Phys. in press (2006

A New Ensemble Learning Framework for 3D Biomedical Image Segmentation

3D image segmentation plays an important role in biomedical image analysis. Many 2D and 3D deep learning models have achieved state-of-the-art segmentation performance on 3D biomedical image datasets. Yet, 2D and 3D models have their own strengths and weaknesses, and by unifying them together, one may be able to achieve more accurate results. In this paper, we propose a new ensemble learning framework for 3D biomedical image segmentation that combines the merits of 2D and 3D models. First, we develop a fully convolutional network based meta-learner to learn how to improve the results from 2D and 3D models (base-learners). Then, to minimize over-fitting for our sophisticated meta-learner, we devise a new training method that uses the results of the base-learners as multiple versions of "ground truths". Furthermore, since our new meta-learner training scheme does not depend on manual annotation, it can utilize abundant unlabeled 3D image data to further improve the model. Extensive experiments on two public datasets (the HVSMR 2016 Challenge dataset and the mouse piriform cortex dataset) show that our approach is effective under fully-supervised, semi-supervised, and transductive settings, and attains superior performance over state-of-the-art image segmentation methods.Comment: To appear in AAAI-2019. The first three authors contributed equally to the pape

An all fiber source of frequency entangled photon pairs

We present an all fiber source of frequency entangled photon pairs by using four wave mixing in a Sagnac fiber loop. Special care is taken to suppress the impurity of the frequency entanglement by cooling the fiber and by matching the polarization modes of the photon pairs counter-propagating in the fiber loop. Coincidence detection of signal and idler photons, which are created in pair and in different spatial modes of the fiber loop, shows the quantum interference in the form of spatial beating, while the single counts of the individual signal (idler) photons keep constant. When the production rate of photon pairs is about 0.013 pairs/pulse, the envelope of the quantum interference reveals a visibility of $(95\pm 2)$, which is close to the calculated theoretical limit 97.4%Comment: 11 pages, 6 figures, to appear in Phys. Rev.

A Learning-Based Steganalytic Method against LSB Matching Steganography

This paper considers the detection of spatial domain least significant bit (LSB) matching steganography in gray images. Natural images hold some inherent properties, such as histogram, dependence between neighboring pixels, and dependence among pixels that are not adjacent to each other. These properties are likely to be disturbed by LSB matching. Firstly, histogram will become smoother after LSB matching. Secondly, the two kinds of dependence will be weakened by the message embedding. Accordingly, three features, which are respectively based on image histogram, neighborhood degree histogram and run-length histogram, are extracted at first. Then, support vector machine is utilized to learn and discriminate the difference of features between cover and stego images. Experimental results prove that the proposed method possesses reliable detection ability and outperforms the two previous state-of-the-art methods. Further more, the conclusions are drawn by analyzing the individual performance of three features and their fused feature

Relaxed 2-D Principal Component Analysis by LpL_p Norm for Face Recognition

A relaxed two dimensional principal component analysis (R2DPCA) approach is proposed for face recognition. Different to the 2DPCA, 2DPCA-$L_1$ and G2DPCA, the R2DPCA utilizes the label information (if known) of training samples to calculate a relaxation vector and presents a weight to each subset of training data. A new relaxed scatter matrix is defined and the computed projection axes are able to increase the accuracy of face recognition. The optimal $L_p$-norms are selected in a reasonable range. Numerical experiments on practical face databased indicate that the R2DPCA has high generalization ability and can achieve a higher recognition rate than state-of-the-art methods.Comment: 19 pages, 11 figure

Saving less in China facilitates global CO₂ mitigation

Transforming China’s economic growth pattern from investment-driven to consumption-driven can significantly change global CO₂ emissions. This study is the first to analyse the impacts of changes in China’s saving rates on global CO₂ missions both theoretically and empirically. Here, we show that the increase in the saving rates of Chinese regions has led to increments of global industrial CO₂ emissions by 189 million tonnes (Mt) during 2007–2012. A 15-percentage-point decrease in the saving rate of China can lower global CO₂ emissions by 186 Mt, or 0.7% of global industrial CO₂ emissions. Greener consumption in China can lead to a further 14% reduction in global industrial CO₂ emissions. In particular, decreasing the saving rate of Shandong has the most massive potential for global CO₂ reductions, while that of Inner Mongolia has adverse effects. Removing economic frictions to allow the production system to fit China’s increased consumption can facilitate global CO₂ mitigation