10,015 research outputs found
Adsorption, Segregation and Magnetization of a Single Mn Adatom on the GaAs (110) Surface
Density functional calculations with a large unit cell have been conducted to
investigate adsorption, segregation and magnetization of Mn monomer on
GaAs(110). The Mn adatom is rather mobile along the trench on GaAs(110), with
an energy barrier of 0.56 eV. The energy barrier for segregation across the
trenches is nevertheless very high, 1.67 eV. The plots of density of states
display a wide gap in the majority spin channel, but show plenty of
metal-induced gap states in the minority spin channel. The Mn atoms might be
invisibl in scanning tunneling microscope (STM) images taken with small biases,
due to the directional p-d hybridization. For example, one will more likely see
two bright spots on Mn/GaAs(110), despite the fact that there is only one Mn
adatom in the system
Strong energy enhancement in a laser-driven plasma-based accelerator through stochastic friction
Conventionally, friction is understood as an efficient dissipation mechanism
depleting a physical system of energy as an unavoidable feature of any
realistic device involving moving parts, e.g., in mechanical brakes. In this
work, we demonstrate that this intuitive picture loses validity in nonlinear
quantum electrodynamics, exemplified in a scenario where spatially random
friction counter-intuitively results in a highly directional energy flow. This
peculiar behavior is caused by radiation friction, i.e., the energy loss of an
accelerated charge due to the emission of radiation. We demonstrate
analytically and numerically how radiation friction can enhance the performance
of a specific class of laser-driven particle accelerators. We find the
unexpected directional energy boost to be due to the particles' energy being
reduced through friction whence the driving laser can accelerate them more
efficiently. In a quantitative case we find the energy of the laser-accelerated
particles to be enhanced by orders of magnitude.Comment: 14 pages, 3 figure
Imbalanced Deep Learning by Minority Class Incremental Rectification
Model learning from class imbalanced training data is a long-standing and
significant challenge for machine learning. In particular, existing deep
learning methods consider mostly either class balanced data or moderately
imbalanced data in model training, and ignore the challenge of learning from
significantly imbalanced training data. To address this problem, we formulate a
class imbalanced deep learning model based on batch-wise incremental minority
(sparsely sampled) class rectification by hard sample mining in majority
(frequently sampled) classes during model training. This model is designed to
minimise the dominant effect of majority classes by discovering sparsely
sampled boundaries of minority classes in an iterative batch-wise learning
process. To that end, we introduce a Class Rectification Loss (CRL) function
that can be deployed readily in deep network architectures. Extensive
experimental evaluations are conducted on three imbalanced person attribute
benchmark datasets (CelebA, X-Domain, DeepFashion) and one balanced object
category benchmark dataset (CIFAR-100). These experimental results demonstrate
the performance advantages and model scalability of the proposed batch-wise
incremental minority class rectification model over the existing
state-of-the-art models for addressing the problem of imbalanced data learning.Comment: Accepted for IEEE Trans. Pattern Analysis and Machine Intelligenc
Superconductivity and Phase Diagram in (LiFe)OHFeSeS
A series of (LiFe)OHFeSeS (0 x 1)
samples were successfully synthesized via hydrothermal reaction method and the
phase diagram is established. Magnetic susceptibility suggests that an
antiferromagnetism arising from (LiFe)OH layers coexists with
superconductivity, and the antiferromagnetic transition temperature nearly
remains constant for various S doping levels. In addition, the lattice
parameters of the both a and c axes decrease and the superconducting transition
temperature T is gradually suppressed with the substitution of S for Se,
and eventually superconductivity vanishes at = 0.90. The decrease of T
could be attributed to the effect of chemical pressure induced by the smaller
ionic size of S relative to that of Se, being consistent with the effect of
hydrostatic pressure on (LiFe)OHFeSe. But the detailed
investigation on the relationships between and the crystallographic
facts suggests a very different dependence of on anion height from
the Fe2 layer or -Fe2- angle from those in FeAs-based superconductors.Comment: 6 pages, 6 figure
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