Two-loop triangle integrals with 4 scales for the HZVHZV vertex

We calculate analytically the two-loop triangle integrals entering the $\mathcal{O}(\alpha\alpha_s)$ corrections to the $HZV$ vertex with $V=Z^*,\gamma^*$ using the method of differential equations. Our result provides a prototype to study the analytic properties of multi-loop multi-scale Feynman integrals, and also allows fast numeric evaluation for phenomenological studies. We apply our results to the leptonic decay of the Higgs boson and to $ZH$ production at electron-positron colliders. Besides the top quark loop, we include also the bottom quark loop contributions, whose evaluation takes a lot of time using purely numeric methods, but is very efficient with our analytic results.Comment: 5 pages, 4 figure

Self-Assembly of Glycine on Cu (001): the Tales of Polarity and Temperature

Glycine on Cu(001) is used as an example to illustrate the critical role of molecular polarity and finite temperature effect in self-assembly of biomolecules at a metal surface. A unified picture for glycine self-assembly on Cu(001) is derived based on full polarity compensation considerations, implemented as a generic rule. Temperature plays a non-trivial role: the ground-state structure at 0 K is absent at room temperature, where intermolecular hydrogen bonding overweighs competing molecule-substrate interactions. The unique p(2X4) structure from the rule is proved as the most stable one by ab initio molecular dynamics at room temperature, and its STM images and anisotropic free-electron-like dispersion are in excellent agreement with experiments. Moreover, the rich self-assembling patterns including the heterochiral and homochiral phases, and their interrelationships are entirely governed by the same mechanism.Comment: 6 pages, 5 figure

Backward Compton Scattering and QED with Noncommutative Plane in the Strong Uniform Magnetic Field

In the strong uniform magnetic field, the noncommutative plane (NCP) caused by the lowest Landau level (LLL) effect, and QED with NCP (QED-NCP) are studied. Being similar to the condensed matter theory of quantum Hall effect, an effective filling factor $f(B)$ is introduced to character the possibility that the electrons stay on the LLL. The analytic and numerical results of the differential cross section for the process of backward Compton scattering in the accelerator with unpolarized or polarized initial photons are calculated. The existing data of BL38B2 in Spring-8 have been analyzed roughly and compared with the numerical predictions primitively. We propose a precise measurement of the differential cross sections of backward Compton scattering in a strong perpendicular magnetic field, which may lead to reveal the effects of QED-NCP.Comment: 13 pages, 5 figure

Backward Compton Scattering in Strong Uniform Magnetic Field

In strong uniform magnetic field, the vacuum Non-Commutative Plane (NCP) caused by the lowest Landau level(LLL) effect and the QED with NCP (QED-NCP) are studied. Being similar to the theory of Quantum Hall effect, an effective filling factor $f(B)$ is introduced to character the possibility that the electrons stays on LLL. The backward Compton scattering amplitudes of QED-NCP are derived, and the differential cross sections for the process with polarized initial electrons and photons are calculated. The existing Spring-8's data has been analyzed primitively and some hints for QED-NCP effects are shown. We propose to precisely measure the differential cross sections of the backward Compton scattering in perpendicular magnetic field experimentally, which may lead to reveal the effects of QED-NCP. PACS number: 12.20.Ds; 11.10.Nx; 71.70.Di; 73.43.Fj.Comment: 13 pages, 8 figure

Bag-of-Words as Target for Neural Machine Translation

A sentence can be translated into more than one correct sentences. However, most of the existing neural machine translation models only use one of the correct translations as the targets, and the other correct sentences are punished as the incorrect sentences in the training stage. Since most of the correct translations for one sentence share the similar bag-of-words, it is possible to distinguish the correct translations from the incorrect ones by the bag-of-words. In this paper, we propose an approach that uses both the sentences and the bag-of-words as targets in the training stage, in order to encourage the model to generate the potentially correct sentences that are not appeared in the training set. We evaluate our model on a Chinese-English translation dataset, and experiments show our model outperforms the strong baselines by the BLEU score of 4.55.Comment: accepted by ACL 201

L1/2L_{1/2} Regularization: Convergence of Iterative Half Thresholding Algorithm

In recent studies on sparse modeling, the nonconvex regularization approaches (particularly, $L_{q}$ regularization with $q\in(0,1)$) have been demonstrated to possess capability of gaining much benefit in sparsity-inducing and efficiency. As compared with the convex regularization approaches (say, $L_{1}$ regularization), however, the convergence issue of the corresponding algorithms are more difficult to tackle. In this paper, we deal with this difficult issue for a specific but typical nonconvex regularization scheme, the $L_{1/2}$ regularization, which has been successfully used to many applications. More specifically, we study the convergence of the iterative \textit{half} thresholding algorithm (the \textit{half} algorithm for short), one of the most efficient and important algorithms for solution to the $L_{1/2}$ regularization. As the main result, we show that under certain conditions, the \textit{half} algorithm converges to a local minimizer of the $L_{1/2}$ regularization, with an eventually linear convergence rate. The established result provides a theoretical guarantee for a wide range of applications of the \textit{half} algorithm. We provide also a set of simulations to support the correctness of theoretical assertions and compare the time efficiency of the \textit{half} algorithm with other known typical algorithms for $L_{1/2}$ regularization like the iteratively reweighted least squares (IRLS) algorithm and the iteratively reweighted $l_{1}$ minimization (IRL1) algorithm.Comment: 12 pages, 5 figure

Reusing Wireless Power Transfer for Backscatter-assisted Cooperation in WPCN

This paper studies a novel user cooperation method in a wireless powered communication network (WPCN), where a pair of closely located devices first harvest wireless energy from an energy node (EN) and then use the harvested energy to transmit information to an access point (AP). In particular, we consider the two energy-harvesting users exchanging their messages and then transmitting cooperatively to the AP using space-time block codes. Interestingly, we exploit the short distance between the two users and allow the information exchange to be achieved by energy-conserving backscatter technique. Meanwhile the considered backscatter-assisted method can effectively reuse wireless power transfer for simultaneous information exchange during the energy harvesting phase. Specifically, we maximize the common throughput through optimizing the time allocation on energy and information transmission. Simulation results show that the proposed user cooperation scheme can effectively improve the throughput fairness compared to some representative benchmark methods.Comment: The paper has been accepted for publication in MLICOM 201

Accidental degeneracy and topological phase transitions in two-dimensional core-shell dielectric photonic crystals

A simple core-shell two-dimensional photonic crystal is studied where the triangle lattice symmetry and $C_{6v}$ rotation symmetry leads to rich physics in the study of accidental degeneracy's in photonic bands. We systematically evaluate different types of accidental nodal points, depending on the dispersions around them and their topological properties, when the geometry and permittivity are continuously changed. These accidental nodal points can be the critical states lying between a topological phase and a normal phase and are thus important for the study of topological photonic states. In time-reversal systems, this leads to the photonic quantum spin Hall insulator where the spin is defined upon the orbital angular momentum for transverse-magnetic polarization. We study the topological phase transition as well as the properties of the edge and bulk states and their application potentials in optics

Rough extreme learning machine: a new classification method based on uncertainty measure

Extreme learning machine (ELM) is a new single hidden layer feedback neural network. The weights of the input layer and the biases of neurons in hidden layer are randomly generated, the weights of the output layer can be analytically determined. ELM has been achieved good results for a large number of classification tasks. In this paper, a new extreme learning machine called rough extreme learning machine (RELM) was proposed. RELM uses rough set to divide data into upper approximation set and lower approximation set, and the two approximation sets are utilized to train upper approximation neurons and lower approximation neurons. In addition, an attribute reduction is executed in this algorithm to remove redundant attributes. The experimental results showed, comparing with the comparison algorithms, RELM can get a better accuracy and repeatability in most cases, RELM can not only maintain the advantages of fast speed, but also effectively cope with the classification task for high-dimensional data.Comment: 23 page

Multi-Label Robust Factorization Autoencoder and its Application in Predicting Drug-Drug Interactions

Drug-drug interactions (DDIs) are a major cause of preventable hospitalizations and deaths. Predicting the occurrence of DDIs helps drug safety professionals allocate investigative resources and take appropriate regulatory action promptly. Traditional DDI prediction methods predict DDIs based on the similarity between drugs. Recently, researchers revealed that predictive performance can be improved by better modeling the interactions between drug pairs with bilinear forms. However, the shallow models leveraging bilinear forms suffer from limitations on capturing complicated nonlinear interactions between drug pairs. To this end, we propose Multi-Label Robust Factorization Autoencoder (abbreviated to MuLFA) for DDI prediction, which learns a representation of interactions between drug pairs and has the capability of characterizing complicated nonlinear interactions more precisely. Moreover, a novel loss called CuXCov is designed to effectively learn the parameters of MuLFA. Furthermore, the decoder is able to generate high-risk chemical structures of drug pairs for specific DDIs, assisting pharmacists to better understand the relationship between drug chemistry and DDI. Experimental results on real-world datasets demonstrate that MuLFA consistently outperforms state-of-the-art methods; particularly, it increases 21:3% predictive performance compared to the best baseline for top 50 frequent DDIs.We also illustrate various case studies to demonstrate the efficacy of the chemical structures generated by MuLFA in DDI diagnosis