Comment on "Topological Nodal-Net Semimetal in a Graphene Network Structure"

Recently, a distinct topological semimetal, nodal-net semimetal, has been identified by Wang et al. through ab initio calculations [Phys. Rev. Lett. 120, 026402 (2018)]. The authors claimed that a new body-centered tetragonal carbon allotrope with I4/mmm symmetry, termed bct-C40, can host this novel state exhibiting boxed-astrisk shaped nodal nets. In this Comment, we demonstrate that bct-C40 is in fact a nodal surface semimetal, the concept of which has been proposed as early as 2016 [Phys. Rev. B 93, 085427 (2016)]

High Resolution Gas Phase Studies Of Inner-shell Excitation And Decay

High resolution {dollar}L\sb{lcub}2,3{rcub}{dollar} x-ray absorption near-edge structure of P, S and Cl compounds are reported and analyzed with the aid of SW-X{dollar}\alpha{dollar} calculations. In the post-edge region, the outgoing channels are dominated by the d channels, and the post-edge features can be understood by the interactions between the d channels and the bonding environment, such as crystal-field splitting, and centrifugal potential. The pre-edge structures are found to be influenced by the d characters mixed into the unoccupied orbitals, which are again determined by the bonding environment.;High resolution photoelectron spectra are reported for the inner valence region of {dollar}N\sb2{dollar} and CO. Vibronic couplings in the extremely complicated 30-40eV binding energy region are clearly resolved for the first time. A preliminary assignment, based on calculations which only considered the electronic excitations, is given.;High resolution photoelectron spectra are reported for the core levels of {dollar}SiH\sb4{dollar} (Si 2p), {dollar}PH\sb3{dollar} (P 2p), {dollar}GeH\sb4{dollar} (Ge 3d), and {dollar}AsH\sb4{dollar} (As 3d). Vibronic couplings are resolved for all these levels, and an analysis of the Franck-Condon factors, aided by ab initio MCSCF and Local Density Functional calculations, reveals the periodic trends for the vibrational structure.;The excitations from the Br 3d level of H Br to the continuum and the unoccupied orbitals, and the subsequent decays, are systematically analyzed. By comparison with a high resolution Br 3d photoelectron spectrum of H Br, the ligand field splitting effects are found to be dominant in the H Br Br 3d photoelectron, the MVV normal Auger, and the 3d photoabsorption spectra. In contrast, ligand field splitting is absent in the high resolution H Br Br 3d resonant Auger spectra. The resonant decay after the Br 3d {dollar}\to \sigma\sp\*{dollar} antibonding transition involves both atomic and molecular decay processes, and the competition between these two processes can be influenced by varying the photon energy and by isotope substitution. The resonant decay after the Br 3d {dollar}\to 5p\pi{dollar} Rydberg transition shows the characteristics of the Auger resonance Raman effect: the resonant Auger peak positions move linearly with the photon energy, and the lifetime broadening does not contribute to the widths of the resonant Auger peaks. Moreover, the ligand-field splitting effects are also absent from the resonant Auger spectra. These results demonstrate the exciting prospect of using resonant Auger spectroscopy for very high resolution studies on core hole decay processes, eliminating both lifetime broadening and ligand field splitting

Traffic Prediction Based on Random Connectivity in Deep Learning with Long Short-Term Memory

Traffic prediction plays an important role in evaluating the performance of telecommunication networks and attracts intense research interests. A significant number of algorithms and models have been put forward to analyse traffic data and make prediction. In the recent big data era, deep learning has been exploited to mine the profound information hidden in the data. In particular, Long Short-Term Memory (LSTM), one kind of Recurrent Neural Network (RNN) schemes, has attracted a lot of attentions due to its capability of processing the long-range dependency embedded in the sequential traffic data. However, LSTM has considerable computational cost, which can not be tolerated in tasks with stringent latency requirement. In this paper, we propose a deep learning model based on LSTM, called Random Connectivity LSTM (RCLSTM). Compared to the conventional LSTM, RCLSTM makes a notable breakthrough in the formation of neural network, which is that the neurons are connected in a stochastic manner rather than full connected. So, the RCLSTM, with certain intrinsic sparsity, have many neural connections absent (distinguished from the full connectivity) and which leads to the reduction of the parameters to be trained and the computational cost. We apply the RCLSTM to predict traffic and validate that the RCLSTM with even 35% neural connectivity still shows a satisfactory performance. When we gradually add training samples, the performance of RCLSTM becomes increasingly closer to the baseline LSTM. Moreover, for the input traffic sequences of enough length, the RCLSTM exhibits even superior prediction accuracy than the baseline LSTM.Comment: 6 pages, 9 figure

Deep Learning with Long Short-Term Memory for Time Series Prediction

Time series prediction can be generalized as a process that extracts useful information from historical records and then determines future values. Learning long-range dependencies that are embedded in time series is often an obstacle for most algorithms, whereas Long Short-Term Memory (LSTM) solutions, as a specific kind of scheme in deep learning, promise to effectively overcome the problem. In this article, we first give a brief introduction to the structure and forward propagation mechanism of the LSTM model. Then, aiming at reducing the considerable computing cost of LSTM, we put forward the Random Connectivity LSTM (RCLSTM) model and test it by predicting traffic and user mobility in telecommunication networks. Compared to LSTM, RCLSTM is formed via stochastic connectivity between neurons, which achieves a significant breakthrough in the architecture formation of neural networks. In this way, the RCLSTM model exhibits a certain level of sparsity, which leads to an appealing decrease in the computational complexity and makes the RCLSTM model become more applicable in latency-stringent application scenarios. In the field of telecommunication networks, the prediction of traffic series and mobility traces could directly benefit from this improvement as we further demonstrate that the prediction accuracy of RCLSTM is comparable to that of the conventional LSTM no matter how we change the number of training samples or the length of input sequences.Comment: 9 pages, 5 figures, 14 reference

Quantifying and predicting ecological and human health risks for binary heavy metal pollution accidents at the watershed scale using Bayesian Networks

The accidental leakage of industrial wastewater containing heavy metals from enterprises poses great risks to resident health, social instability, and ecological safety. During 2005-2018, heavy metal mixed pollution accidents comprised approximately 33% of the major environmental ones in China. A Bayesian Networks-based probabilistic approach is developed to quantitatively predict ecological and human health risks for heavy metal mixed pollution accidents at the watershed scale. To estimate the probability distributions of joint ecological exposure once a heavy metal mixed pollution accident occurs, a Copula-based joint exposure calculation method, comprised of a hydro-dynamic model, emergent heavy metal pollution transport model, and the Copula functions, is embedded. This approach was applied to the risk assessment of acute Cr6+-Hg2+ mixed pollution accidents at 76 electroplating enterprises in 24 risk sub-watersheds of the Dongjiang River downstream watershed. The results indicated that nine sub-watersheds created high ecological risks, while only five created high human health risks. In addition, the ecological and human health risk levels were highest in the tributary (the Xizhijiang River), while the ecological risk was more critical in the river network, and the human health risk was more serious in the mainstream of the Dongjiang River. The quantitative risk assessment provides a substantial support to incident prevention and control, risk management, as well as regulatory decision making for electroplating enterprises. (C) 2020 Elsevier Ltd. All rights reserved.Peer reviewe