1.55 µm AlGaInAs/InP sampled grating laser diodes for mode-locking at THz frequencies

We report mode locking in lasers integrated with semiconductor optical amplifiers, using either conventional or phase shifted sampled grating distributed Bragg reflectors(DBRs). For a conventional sampled grating with a continuous grating coupling coefficient of ~80 cm-1, mode-locking was observed at a fundamental frequency of 628 GHz and second harmonic of 1.20 THz. The peak output power was up to 142 mW. In the phase shifted sampled grating design, the grating is present along the entire length of the reflector with π-phase shift steps within each sampled section. The effective coupling coefficient is therefore increased substantially. Although the continuous grating coupling coefficient for the phase shifted gratings was reduced to ~23 cm-1 because of a different fabrication technology, the lasers demonstrated mode locking at fundamental repetition frequencies of 620 GHz and 1 THz, with a much lower level of amplified spontaneous emission seen in the output spectra than from conventional sampled grating devices. Although high pulse reproducibility and controllability over a wide operation range was seen for both types of grating, the π-phase-shifted gratings already demonstrate fundamental mode-locking to 1 THz. The integrated semiconductor optical amplifier makes sampled grating DBR lasers ideal pump sources for generating THz signals through photomixing

The "amplitude" parameter of Gamma-Ray Bursts and its implications for GRB classification

Traditionally gamma-ray bursts (GRBs) are classified in the $T_{90}$-hardness ratio two-dimensional plane into long/soft and short/hard GRBs. In this paper, we suggest to add the "amplitude" of GRB prompt emission as the third dimension as a complementary criterion to classify GRBs, especially those of short durations. We define three new parameters $f$, $f_{\rm eff}$ and $f_{\rm eff,z}$ as ratios between the measured/simulated peak flux of a GRB/pseudo-GRB and the flux background, and discuss the applications of these parameters to GRB classification. We systematically derive these parameters to find that most short GRBs are likely not "tip-of-iceberg" of long GRBs. However, one needs to be cautious if a short GRB has a relatively small $f$ (e.g. $f<1.5$), since the chance for an intrinsically long GRB to appear as a "disguised" short GRB is higher. Based on avaialble data, we quantify the probability of a disguised short GRB below a certain $f$ value is as $P (<f)\sim 0.78^{+0.71}_{-0.4} f^{-4.33\pm 1.84}$. By progressively "moving" a long GRB to higher redshifts through simulations, we also find that most long GRBs would show up as rest-frame short GRBs above a certain redshift.Comment: 11 pages, 14 figures. Accepted by MNRA

Deep fusion of multi-channel neurophysiological signal for emotion recognition and monitoring

How to fuse multi-channel neurophysiological signals for emotion recognition is emerging as a hot research topic in community of Computational Psychophysiology. Nevertheless, prior feature engineering based approaches require extracting various domain knowledge related features at a high time cost. Moreover, traditional fusion method cannot fully utilise correlation information between different channels and frequency components. In this paper, we design a hybrid deep learning model, in which the 'Convolutional Neural Network (CNN)' is utilised for extracting task-related features, as well as mining inter-channel and inter-frequency correlation, besides, the 'Recurrent Neural Network (RNN)' is concatenated for integrating contextual information from the frame cube sequence. Experiments are carried out in a trial-level emotion recognition task, on the DEAP benchmarking dataset. Experimental results demonstrate that the proposed framework outperforms the classical methods, with regard to both of the emotional dimensions of Valence and Arousal

The electromagnetic and gravitational-wave radiations of X-ray transient CDF-S XT2

Binary neutron star (NS) mergers may result in remnants of supra-massive or even stable NS, which have been supported indirectly by observed X-ray plateau of some gamma-ray bursts (GRBs) afterglow. Recently, Xue et al. (2019) discovered a X-ray transient CDF-S XT2 that is powered by a magnetar from merger of double NS via X-ray plateau and following stepper phase. However, the decay slope after the plateau emission is a little bit larger than the theoretical value of spin-down in electromagnetic (EM) dominated by losing its rotation energy. In this paper, we assume that the feature of X-ray emission is caused by a supra-massive magnetar central engine for surviving thousands of seconds to collapse black hole. Within this scenario, we present the comparisons of the X-ray plateau luminosity, break time, and the parameters of magnetar between CDF-S XT2 and other short GRBs with internal plateau samples. By adopting the collapse time to constrain the equation of state (EOS), we find that three EOSs (GM1, DD2, and DDME2) are consistent with the observational data. On the other hand, if the most released rotation energy of magnetar is dominated by GW radiation, we also constrain the upper limit of ellipticity of NS for given EOS, and it is range in $[0.32-1.3]\times 10^{-3}$. Its GW signal can not be detected by aLIGO or even for more sensitive Einstein Telescope in the future.Comment: 13 pages, 5 figures,1 table. Accepted for publication by Research in Astronomy and Astrophysic

Testing the Weak-Form Market Efficiency Hypothesis for Canadian and Chinese Stock

The main empirical test methods for Weak-form efficiency market hypothesis can be divided into two categories: one is to test the randomness of stock prices; the other is to test the invalidity of technical analysis, which testing the predictability of earnings. This study mainly focused on the first category.To examining the hypothesis whether Canadian and Chinese stock markets are efficient in the weak form, two types of test are conducted. They are parametric and non-parametric tests. For Non-parametric test, we implement the Runs test and Kolmogrov–Smirnov goodness of fit test. For parametric test, autocorrelation (LBQ test), variance ratio and ARMA model have been chosen. The empirical analysis in this study uses daily closing prices of indices from Shanghai Stock Exchange (SSE) and Toronto Stock Exchange (TSX). To avoiding the biases of choosing testing period, we implemented the same tests among different sample periods for each market.The overall testing results are mixed from sample period to sample period for both markets. In general, for the early testing period, almost all testing techniques generate unfavoured results against the weak-form efficient market hypothesis (EMH) for both TSX and SSE. Several testing results based on more recent sample periods align with the assumption under the EMH, but it is still early to claim that either the Canadian or the Chinese stock market hasbecome the weak form efficient. More comprehensive testing results and analysis can be found under section 5 and 6

Maximum size of C≤kC_{\leq k}-free strong digraphs with out-degree at least two

Let $\mathscr{H}$ be a family of digraphs. A digraph $D$ is \emph{$\mathscr{H}$-free} if it contains no isomorphic copy of any member of $\mathscr{H}$. For $k\geq2$, we set $C_{\leq k}=\{C_{2}, C_{3},\ldots,C_{k}\}$, where $C_{\ell}$ is a directed cycle of length $\ell\in\{2,3,\ldots,k\}$. Let $D_{n}^{k}(\xi,\zeta)$ denote the family of \emph{${C}_{\le k}$-free} strong digraphs on $n$ vertices with every vertex having out-degree at least $\xi$ and in-degree at least $\zeta$, where both $\xi$ and $\zeta$ are positive integers. Let $\varphi_{n}^{k}(\xi,\zeta)=\max\{|A(D)|:\;D\in D_{n}^{k}(\xi,\zeta)\}$ and $\Phi_{n}^{k}(\xi,\zeta)=\{D\in D_{n}^{k}(\xi,\zeta): |A(D)|=\varphi_{n}^{k}(\xi,\zeta)\}$. Bermond et al.\;(1980) verified that $\varphi_{n}^{k}(1,1)=\binom{n-k+2}{2}+k-2$. Chen and Chang\;(2021) showed that $\binom{n-1}{2}-2\leq\varphi_{n}^{3}(2,1)\leq\binom{n-1}{2}$. This upper bound was further improved to $\binom{n-1}{2}-1$ by Chen and Chang\;(DAM, 2022), furthermore, they also gave the exact values of $\varphi_{n}^{3}(2,1)$ for $n\in \{7,8,9\}$. In this paper, we continue to determine the exact values of $\varphi_{n}^{3}(2,1)$ for $n\ge 10$, i.e., $\varphi_{n}^{3}(2,1)=\binom{n-1}{2}-2$ for $n\geq10$.Comment: 21 page