189,898 research outputs found
The 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 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
charges so that the direct SM Yukawa mass term
and the Majorana mass term
are naturally forbidden. After the spontaneous
breaking of symmetry, the discrete or 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
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