Microscopic origin of light emission in Al_yGa_{1-y}N/GaN superlattice: Band profile and active site

We present first-principles calculations of AlGaN/GaN superlattice, clarifying the microscopic origin of the light emission and revealing the effect of local polarization within the quantum well. Profile of energy band and distributions of electrons and holes demonstrate the existence of a main active site in the well responsible for the main band-edge light emission. This site appears at the position where the local polarization becomes zero. With charge injection, the calculated optical spectra show that the broadening of the band gap at the active site leads to the blueshift of emission wavelength

Strategic Asset Seeking and Innovation Performance: The Role of Innovation Capabilities and Host Country Institutions

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Origins of the Isospin Violation of Dark Matter Interactions

Light dark matter (DM) with a large DM-nucleon spin-independent cross section and furthermore proper isospin violation (ISV) $f_n/f_p\approx-0.7$ may provide a way to understand the confusing DM direct detection results. Combing with the stringent astrophysical and collider constraints, we systematically investigate the origin of ISV first via general operator analyses and further via specifying three kinds of (single) mediators: A light $Z'$ from chiral $U(1)_X$, an approximate spectator Higgs doublet (It can explain the $W+jj$ anomaly simultaneously) and color triplets. In addition, although $Z'$ from an exotic $U(1)_X$ mixing with $U(1)_Y$ generating $f_n=0$, we can combine it with the conventional Higgs to achieve proper ISV. As a concrete example, we propose the $U(1)_X$ model where the $U(1)_X$ charged light sneutrino is the inelastic DM, which dominantly annihilates to light dark states such as $Z'$ with sub-GeV mass. This model can address the recent GoGeNT annual modulation consistent with other DM direct detection results and free of exclusions.Comment: References added and English greatly improve

Massive and Red Objects predicted by a semianalytical model of galaxy formation

We study whether hierarchical galaxy formation in a concordance $\Lambda$CDM universe can produce enough massive and red galaxies compared to the observations. We implement a semi-analytical model in which the central black holes gain their mass during major mergers of galaxies and the energy feedback from active galaxy nuclei (AGN) suppresses the gas cooling in their host halos. The energy feedback from AGN acts effectively only in massive galaxies when supermassive black holes have been formed in the central bulges. Compared with previous models without black hole formation, our model predicts more massive and luminous galaxies at high redshift, agreeing with the observations of K20 up to $z\sim 3$. Also the predicted stellar mass density from massive galaxies agrees with the observations of GDDS. Because of the energy feedback from AGN, the formation of new stars is stopped in massive galaxies with the termination of gas cooling and these galaxies soon become red with color $R-K>$5 (Vega magnitude), comparable to the Extremely Red Objects (EROs) observed at redshift $z\sim$1-2. Still the predicted number density of very EROs is lower than observed at $z\sim 2$, and it may be related to inadequate descriptions of dust extinction, star formation history and AGN feedback in those luminous galaxies.Comment: Accepted for Publication in ApJ, added reference

Is the Number of Giant Arcs in LCDM Consistent With Observations?

We use high-resolution N-body simulations to study the galaxy-cluster cross-sections and the abundance of giant arcs in the $\Lambda$CDM model. Clusters are selected from the simulations using the friends-of-friends method, and their cross-sections for forming giant arcs are analyzed. The background sources are assumed to follow a uniform ellipticity distribution from 0 to 0.5 and to have an area identical to a circular source with diameter 1\arcsec. We find that the optical depth scales as the source redshift approximately as \tau_{1''} = 2.25 \times 10^{-6}/[1+(\zs/3.14)^{-3.42}] (0.6<\zs<7). The amplitude is about 50% higher for an effective source diameter of 0.5\arcsec. The optimal lens redshift for giant arcs with the length-to-width ratio ($L/W$) larger than 10 increases from 0.3 for \zs=1, to 0.5 for \zs=2, and to 0.7-0.8 for \zs>3. The optical depth is sensitive to the source redshift, in qualitative agreement with Wambsganss et al. (2004). However, our overall optical depth appears to be only $\sim$ 10% to 70% of those from previous studies. The differences can be mostly explained by different power spectrum normalizations ($\sigma_8$) used and different ways of determining the $L/W$ ratio. Finite source size and ellipticity have modest effects on the optical depth. We also found that the number of highly magnified (with magnification $|\mu|>10$) and ``undistorted'' images (with $L/W<3$) is comparable to the number of giant arcs with $|\mu|>10$ and $L/W>10$. We conclude that our predicted rate of giant arcs may be lower than the observed rate, although the precise `discrepancy' is still unclear due to uncertainties both in theory and observations.Comment: Revised version after the referee's reports (32 pages,13figures). The paper has been significantly revised with many additions. The new version includes more detailed comparisons with previous studies, including the effects of source size and ellipticity. New discussions about the redshift distribution of lensing clusters and the width of giant arcs have been adde

Soft filter pruning for accelerating deep convolutional neural networks

© 2018 International Joint Conferences on Artificial Intelligence. All right reserved. This paper proposed a Soft Filter Pruning (SFP) method to accelerate the inference procedure of deep Convolutional Neural Networks (CNNs). Specifically, the proposed SFP enables the pruned filters to be updated when training the model after pruning. SFP has two advantages over previous works: (1) Larger model capacity. Updating previously pruned filters provides our approach with larger optimization space than fixing the filters to zero. Therefore, the network trained by our method has a larger model capacity to learn from the training data. (2) Less dependence on the pre-trained model. Large capacity enables SFP to train from scratch and prune the model simultaneously. In contrast, previous filter pruning methods should be conducted on the basis of the pre-trained model to guarantee their performance. Empirically, SFP from scratch outperforms the previous filter pruning methods. Moreover, our approach has been demonstrated effective for many advanced CNN architectures. Notably, on ILSCRC-2012, SFP reduces more than 42% FLOPs on ResNet-101 with even 0.2% top-5 accuracy improvement, which has advanced the state-of-the-art