Molecular Phylogeny of Chinese Thuidiaceae with emphasis on Thuidium and Pelekium

We present molecular phylogenetic investigation of Thuidiaceae, especially on Thudium and Pelekium. Three chloroplast sequences (trnL-F, rps4, and atpB-rbcL) and one nuclear sequence (ITS) were analyzed. Data partitions were analyzed separately and in combination by employing MP (maximum parsimony) and Bayesian methods. The influence of data conflict in combined analyses was further explored by two methods: the incongruence length difference (ILD) test and the partition addition bootstrap alteration approach (PABA). Based on the results, ITS 1& 2 had crucial effect in phylogenetic reconstruction in this study, and more chloroplast sequences should be combinated into the analyses since their stability for reconstructing within genus of pleurocarpous mosses. We supported that Helodiaceae including Actinothuidium, Bryochenea, and Helodium still attributed to Thuidiaceae, and the monophyletic Thuidiaceae s. lat. should also include several genera (or species) from Leskeaceae such as Haplocladium and Leskea. In the Thuidiaceae, Thuidium and Pelekium were resolved as two monophyletic groups separately. The results from molecular phylogeny were supported by the crucial morphological characters in Thuidiaceae s. lat., Thuidium and Pelekium.Comment: 20 pages, 4 tables, 3 figure

Charge Transfer and Functionalization of Monolayer InSe by Physisorption of Small Molecules for Gas Sensing

First-principles calculations are performed to investigate the effects of the adsorption of gas molecules (CO, NO, NO2, H2S, N2, H2O, O2, NH3 and H2) on the electronic properties of atomically thin indium selenium (InSe). Our study shows that the lone-pair states of Se are located at the top of the valence band of InSe and close to the Fermi energy level, implying its high sensitivity to external adsorbates. Among these gas molecules, H2 and H2S are strong donors, NO, NO2, H2O and NH3 are effective acceptors, while CO and N2 exhibit negligible charge transfer. The O2 molecule has very limited oxidizing ability and a relatively weak interaction with InSe which is comparable to the N2 adsorption. A clear band gap narrowing is found for the H2S, NO2, and NH3 adsorbed systems whereas a Fermi level shifting to the conduction band is observed upon a moderate uptake of H2 molecules. Our analysis suggests several interesting applications of InSe: 1) Due to the different interaction behaviors with these external molecules, InSe can be used for gas sensing applications; 2) By monitoring the adsorption/desorption behavior of these gas molecules, the population of hole states in InSe due to photon stimulation or defect production can be quantitatively estimated; and 3) It is promising for novel electronic and optoelectronic applications since the adsorption-induced in-gap states and strong charge transfer are able to change the content and polarity of charged carriers and lead to different optical properties

Polarity Reversed Robust Carrier Mobility in Monolayer MoS2 Nanoribbons

Using first-principles calculations and deformation potential theory, we investigate the intrinsic carrier mobility ({\mu}) of monolayer MoS2 sheet and nanoribbons. In contrast to the dramatic three orders of magnitude of deterioration of {\mu} in graphene upon forming nanoribbons, the magnitude of {\mu} in armchair MoS2 nanoribbons is comparable to that in monolayer MoS2 sheet, albeit oscillating with width. Surprisingly, a room-temperature transport polarity reversal is observed with {\mu} of hole (h) and electron (e) being 200.52 (h) and 72.16 (e) cm2V-1s-1 in sheet, and 49.72 (h) and 190.89 (e) cm2V-1s-1 in 4 nm-wide nanoribbon. The robust magnitudes of {\mu} and polarity reversal are attributable to the different characteristics of edge states inherent in MoS2 nanoribbons. Our study suggests that width-reduction together with edge engineering provide a promising route for improving the transport properties of MoS2 nanostructures

Optical Transient Object Classification in Wide Field Small Aperture Telescopes with Neural Networks

Wide field small aperture telescopes are working horses for fast sky surveying. Transient discovery is one of their main tasks. Classification of candidate transient images between real sources and artifacts with high accuracy is an important step for transient discovery. In this paper, we propose two transient classification methods based on neural networks. The first method uses the convolutional neural network without pooling layers to classify transient images with low sampling rate. The second method assumes transient images as one dimensional signals and is based on recurrent neural networks with long short term memory and leaky ReLu activation function in each detection layer. Testing with real observation data, we find that although these two methods can both achieve more than 94% classification accuracy, they have different classification properties for different targets. Based on this result, we propose to use the ensemble learning method to further increase the classification accuracy to more than 97%.Comment: 13 pages, 10 figures. Accepted by AJ and all the code can be downloaded from aojp.lamost.org. Comments welcom

Predicting the Popularity of Online Videos via Deep Neural Networks

Predicting the popularity of online videos is important for video streaming content providers. This is a challenging problem because of the following two reasons. First, the problem is both "wide" and "deep". That is, it not only depends on a wide range of features, but also be highly non-linear and complex. Second, multiple competitors may be involved. In this paper, we propose a general prediction model using the multi-task learning (MTL) module and the relation network (RN) module, where MTL can reduce over-fitting and RN can model the relations of multiple competitors. Experimental results show that our proposed approach significantly increases the accuracy on predicting the total view counts of TV series with RN and MTL modules

Comment on "Quantum Key Distribution with Classical Bob"

In this comment, we present a frequency-shift attack on "quantum key distribution with classical Bob". This practical attack should also be considered in other two-way quantum key distribution protocols.Comment: In this comment, we present a frequency-shift attack on "quantum key distribution with classical Bob". This practical attack should also be considered in other two-way quantum key distribution protocol

Quantum thermal transport in stanene

By way of the non-equilibrium Green's function simulations and analytical expressions, the quantum thermal conductance of stanene is studied. We find that, due to the existence of Dirac fermion in stanene, the ratio of electron thermal conductance and electric conductance becomes a chemical-potential-dependent quantity, violating the Wiedemann-Franz law. This finding is applicable to any two-dimensional (2D) materials that possess massless Dirac fermions. In strong contrast to the negligible electronic contribution in graphene, surprisingly, the electrons and phonons in stanene carry a comparable heat current. The unusual behaviours in stanene widen our knowledge of quantum thermal transport in 2D materials

Model Slicing for Supporting Complex Analytics with Elastic Inference Cost and Resource Constraints

Deep learning models have been used to support analytics beyond simple aggregation, where deeper and wider models have been shown to yield great results. These models consume a huge amount of memory and computational operations. However, most of the large-scale industrial applications are often computational budget constrained. In practice, the peak workload of inference service could be 10x higher than the average cases, with the presence of unpredictable extreme cases. Lots of computational resources could be wasted during off-peak hours and the system may crash when the workload exceeds system capacity. How to support deep learning services with dynamic workload cost-efficiently remains a challenging problem. In this paper, we address the challenge with a general and novel training scheme called model slicing, which enables deep learning models to provide predictions within the prescribed computational resource budget dynamically. Model slicing could be viewed as an elastic computation solution without requiring more computational resources. Succinctly, each layer in the model is divided into groups of contiguous block of basic components (i.e. neurons in dense layers and channels in convolutional layers), and then partially ordered relation is introduced to these groups by enforcing that groups participated in each forward pass always starts from the first group to the dynamically-determined rightmost group. Trained by dynamically indexing the rightmost group with a single parameter slice rate, the network is engendered to build up group-wise and residual representation. Then during inference, a sub-model with fewer groups can be readily deployed for efficiency whose computation is roughly quadratic to the width controlled by the slice rate. Extensive experiments show that models trained with model slicing can effectively support on-demand workload with elastic inference cost.Comment: 14 pages, 8 figures. arXiv admin note: text overlap with arXiv:1706.02093 by other author

Scale-dependent CMB power asymmetry from primordial speed of sound and a generalized δ\deltaN formalism

We explore a plausible mechanism that the hemispherical power asymmetry in the CMB is produced by the spatial variation of the primordial sound speed parameter. We suggest that in a generalized approach of the $\delta N$ formalism the local e-folding number may depend on some other primordial parameters besides the initial values of inflaton. Here the $\delta N$ formalism is extended by considering the effects of a spatially varying sound speed parameter caused by a super-Hubble perturbation of a light field. Using this generalized $\delta N$ formalism, we systematically calculate the asymmetric primordial spectrum in the model of multi-speed inflation by taking into account the constraints of primordial non-Gaussianities. We further discuss specific model constraints, and the corresponding asymmetry amplitudes are found to be scale-dependent, which can accommodate current observations of the power asymmetry at different length scales.Comment: 14 pages, 2 figures, several references added, version published in JCA

Direction dependent thermal conductivity of monolayer phosphorene: parameterization of Stillinger-Weber potential and molecular dynamics study

A Stillinger-Weber interatomic potential is parameterized for phosphorene. It well reproduces the crystal structure, cohesive energy and phonon dispersion predicted by first-principles calculations. The thermal conductivity of phosphorene is further explored by equilibrium molecular dynamics simulations adopting the optimal set of potential parameters. At room temperature, the intrinsic thermal conductivities along zigzag and armchair directions are about 152.7 and 33.0 W/mK, respectively, with a large anisotropy ratio of five. The remarkably directional dependence of thermal conductivity in phosphorene, consistent with previous reports, is mainly due to the strong anisotropy of phonon group velocities, and weak anisotropy of phonon lifetimes as revealed by lattice dynamics calculations. Moreover, the effective phonon mean free paths at zigzag and armchair directions are about 141.4 and 43.4nm, respectively.Comment: accepted by J. Appl. Phy