8,932 research outputs found
Anisotropic flow of Pb+Pb = 5.02 TeV from A Multi-Phase Transport Model
Anisotropic flow is an important observable in the study of the Quark-Gluon
Plasma that is expected to be formed in heavy-ion collisions. With a multiphase
transport (AMPT) model we investigate the elliptic(\emph{v}_{2}),
triangular(\emph{v}_{3}), and quadrangular(\emph{v}_{4}) flow of charged
particles in Pb+Pb collisions at = 5.02 TeV. Then We
compare our flow results with the published ALICE flow results. We found our
AMPT simulated results are consistent with ALICE experimental data
Quantum Size Effect on Dielectric Properties of Ultrathin Metallic Film: A First-Principles Study of Al(111)
Quantum manifestations of various properties of metallic thin films by
quantum size effect (QSE) have been studied intensively. Here, using
first-principles calculations, we show quantum manifestation in dielectric
properties of Al(111) ultrathin films. The QSE on the dielectric function is
revealed, which arises from size dependent contributions from both intraband
and interband electronic transitions. More importantly, the in-plane interband
transitions in the films thinner than 15 monolayers are found to be smaller
than the bulk counterpart in the energy range from 1.5~eV to 2.5~eV. This
indicates less energy loss with plasmonic material of Al in the form of
ultrathin film. Our findings may shed light on searching for low-loss plasmonic
materials via quantum size effect
Heavy quark correlations and the effective volume for quarkonia production
Using the Boltzmann transport approach, we study the effective volume of a
correlated heavy quark pair in a partonic medium based on their collision rate.
We find that the effective volume is finite and depends sensitively on the
momentum of the heavy quark and the temperature of the medium. Generally, it
increases linearly with time at the very beginning and the increase then
becomes slower due to multiple scattering, and finally it increases linearly
with respect to . We further find that the colliding heavy quark pair
has an effective temperature similar to that of the medium even though their
initial transverse momentum spectra are far from thermal equilibrium.Comment: 7 pages, 7 figure
Effects of Li doping on H-diffusion in MgH: a first-principles study
The effects of Li doping in MgH on H-diffusion process are investigated,
using first-principles calculations. We have identified two key effects: (1)
The concentration of H vacancy in the charge state (V) can
increase by several orders of magnitude upon Li doping, which significantly
increases the vacancy mediated H diffusion rate. It is caused by the preferred
charge states of substitutional Li in the state (Li) and of
interstitial Li in the state (Li), which indirectly reduce the
formation energy of V by up to 0.39 eV depending on the position of
Fermi energy. (2) The interaction between V and Li is
found to be attractive with a binding energy of 0.55 eV, which immobilizes the
V next to Li at high Li doping concentration. As a result,
the competition between these two effects leads to large enhancement of H
diffusion at low Li doping concentration due to the increased H-vacancy
concentration, but only limited enhancement at high Li concentration due to the
immobilization of H vacancies by too many Li
Giant Rashba-Spin Splitting of Bi(111) Bilayer on Large Band Gap InSe
Experimentally it is still challenging to epitaxially grow Bi(111) bilayer
(BL) on conventional semiconductor substrate. Here, we propose a substrate of
InSe(0001) with van der Waals like cleavage and large band gap
of 1.2~eV. We have investigated the electronic structure of BL on one
quintuple-layer (QL) InSe(0001) using density functional theory
calculation. It is found that the intermediate hybridization between BL and one
QL InSe(0001) results in the formation of bands with giant
Rashba spin splitting in the large band gap of the substrate. Furthermore the
Rashba parameter can be increased significantly by tensile strain of
substrate. Our findings provide a good candidate substrate for BL growth to
experimentally realize spin splitting Rashba states with insignificant effect
of spin degenerate states from the substrate
sd2 Graphene: Kagome Band in Hexagonal lattice
Graphene, made of sp2 hybridized carbon, is characterized with a Dirac band,
representative of its underlying 2D hexagonal lattice. Fundamental
understanding of graphene has recently spurred a surge of searching for 2D
topological quantum phases in solid-state materials. Here, we propose a new
form of 2D material, consisting of sd2 hybridized transition metal atoms in
hexagonal lattice, called sd2 graphene. The sd2 graphene is characterized with
bond-centered electronic hopping, which transforms the apparent atomic
hexagonal lattice into the physics of kagome lattice that may exhibit a wide
range of topological quantum phases. Based on first-principles calculations,
room temperature quantum anomalous Hall states with an energy gap of 0.1 eV are
demonstrated for one such lattice made of W, which can be epitaxially grown on
a semiconductor surface of 1/3 monolayer Cl-covered Si(111), with high
thermodynamic and kinetic stability.Comment: Phys. Rev. Lett.(2014), In press. It includes main text and 5
figures. Supplemental material is available upon reques
Implicit Distortion and Fertility Models for Attention-based Encoder-Decoder NMT Model
Neural machine translation has shown very promising results lately. Most NMT
models follow the encoder-decoder framework. To make encoder-decoder models
more flexible, attention mechanism was introduced to machine translation and
also other tasks like speech recognition and image captioning. We observe that
the quality of translation by attention-based encoder-decoder can be
significantly damaged when the alignment is incorrect. We attribute these
problems to the lack of distortion and fertility models. Aiming to resolve
these problems, we propose new variations of attention-based encoder-decoder
and compare them with other models on machine translation. Our proposed method
achieved an improvement of 2 BLEU points over the original attention-based
encoder-decoder.Comment: 11 pages, updated detail
A Simple Pooling-Based Design for Real-Time Salient Object Detection
We solve the problem of salient object detection by investigating how to
expand the role of pooling in convolutional neural networks. Based on the
U-shape architecture, we first build a global guidance module (GGM) upon the
bottom-up pathway, aiming at providing layers at different feature levels the
location information of potential salient objects. We further design a feature
aggregation module (FAM) to make the coarse-level semantic information well
fused with the fine-level features from the top-down pathway. By adding FAMs
after the fusion operations in the top-down pathway, coarse-level features from
the GGM can be seamlessly merged with features at various scales. These two
pooling-based modules allow the high-level semantic features to be
progressively refined, yielding detail enriched saliency maps. Experiment
results show that our proposed approach can more accurately locate the salient
objects with sharpened details and hence substantially improve the performance
compared to the previous state-of-the-arts. Our approach is fast as well and
can run at a speed of more than 30 FPS when processing a
image. Code can be found at http://mmcheng.net/poolnet/.Comment: Accepted by CVPR201
Thermodynamics of the - transition in cerium studied by an LDA + Gutzwiller method
The - transition in cerium has been studied in both zero and
finite temperature by Gutzwiller density functional theory. We find that the
first order transition between and phases persists to the
zero temperature with negative pressure. By further including the entropy
contributed by both electronic quasi-particles and lattice vibration, we obtain
the total free energy at given volume and temperature, from which we obtain the
- transition from the first principle calculation. We also
computed the phase diagram and pressure versus volume isotherms of cerium at
finite temperature and pressure, finding excellent agreement with the
experiments. Our calculation indicate that both the electronic entropy and
lattice vibration entropy plays important role in the -
transition.Comment: 5 pages, 4 figure
Learning Pixel-wise Labeling from the Internet without Human Interaction
Deep learning stands at the forefront in many computer vision tasks. However,
deep neural networks are usually data-hungry and require a huge amount of
well-annotated training samples. Collecting sufficient annotated data is very
expensive in many applications, especially for pixel-level prediction tasks
such as semantic segmentation. To solve this fundamental issue, we consider a
new challenging vision task, Internetly supervised semantic segmentation, which
only uses Internet data with noisy image-level supervision of corresponding
query keywords for segmentation model training. We address this task by
proposing the following solution. A class-specific attention model unifying
multiscale forward and backward convolutional features is proposed to provide
initial segmentation "ground truth". The model trained with such noisy
annotations is then improved by an online fine-tuning procedure. It achieves
state-of-the-art performance under the weakly-supervised setting on PASCAL
VOC2012 dataset. The proposed framework also paves a new way towards learning
from the Internet without human interaction and could serve as a strong
baseline therein. Code and data will be released upon the paper acceptance
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