The B−LB-L Scotogenic Models for Dirac Neutrino Masses

We construct the one-loop and two-loop scotogenic models for Dirac neutrino mass generation in the context of $U(1)_{B-L}$ extensions of standard model. It is indicated that the total number of intermediate fermion singlets is uniquely fixed by anomaly free condition and the new particles may have exotic $B-L$ charges so that the direct SM Yukawa mass term $\bar{\nu}_L\nu_R\overline{\phi^0}$ and the Majorana mass term $(m_N/2)\overline{\nu_R^C}\nu_R$ are naturally forbidden. After the spontaneous breaking of $U(1)_{B-L}$ symmetry, the discrete $Z_{2}$ or $Z_{3}$ symmetry appears as the residual symmetry and give rise to the stability of intermediated fields as DM candidate. Phenomenological aspects of lepton flavor violation, DM, leptogenesis and LHC signatures are discussed.Comment: 18 pages, 16 figure

TIGER: A Tuning-Insensitive Approach for Optimally Estimating Gaussian Graphical Models

We propose a new procedure for estimating high dimensional Gaussian graphical models. Our approach is asymptotically tuning-free and non-asymptotically tuning-insensitive: it requires very few efforts to choose the tuning parameter in finite sample settings. Computationally, our procedure is significantly faster than existing methods due to its tuning-insensitive property. Theoretically, the obtained estimator is simultaneously minimax optimal for precision matrix estimation under different norms. Empirically, we illustrate the advantages of our method using thorough simulated and real examples. The R package bigmatrix implementing the proposed methods is available on the Comprehensive R Archive Network: http://cran.r-project.org/

STAR: A Concise Deep Learning Framework for Citywide Human Mobility Prediction

Human mobility forecasting in a city is of utmost importance to transportation and public safety, but with the process of urbanization and the generation of big data, intensive computing and determination of mobility pattern have become challenging. This study focuses on how to improve the accuracy and efficiency of predicting citywide human mobility via a simpler solution. A spatio-temporal mobility event prediction framework based on a single fully-convolutional residual network (STAR) is proposed. STAR is a highly simple, general and effective method for learning a single tensor representing the mobility event. Residual learning is utilized for training the deep network to derive the detailed result for scenarios of citywide prediction. Extensive benchmark evaluation results on real-world data demonstrate that STAR outperforms state-of-the-art approaches in single- and multi-step prediction while utilizing fewer parameters and achieving higher efficiency.Comment: Accepted by MDM 201

Companion stars of Type Ia supernovae and hypervelocity stars

{Context} Recent investigations of the white dwarf (WD) + He star channel of Type Ia supernovae (SNe Ia) imply that this channel can produce SNe Ia with short delay times. The companion stars in this channel would survive and be potentially identifiable. {Aims} In this Letter, we study the properties of the companion stars of this channel at the moment of SN explosion, which can be verified by future observations. {Methods} According to SN Ia production regions of the WD + He star channel and three formation channels of WD + He star systems, we performed a detailed binary population synthesis study to obtain the properties of the surviving companions. {Results} We obtained the distributions of many properties of the companion stars of this channel at the moment of SN explosion. We find that the surviving companion stars have a high spatial velocity (>400 km/s) after SN explosion, which could be an alternative origin for hypervelocity stars (HVSs), especially for HVSs such as US 708.Comment: 4 pages, 5 figures, accepted for publication in A&A Letter