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 (Li0.8_{0.8}Fe0.2_{0.2})OHFeSe1−x_{1-x}Sx_x

A series of (Li$_{0.8}$Fe$_{0.2}$)OHFeSe$_{1-x}$S$_x$ (0 $\leq$ x $\leq$ 1) samples were successfully synthesized via hydrothermal reaction method and the phase diagram is established. Magnetic susceptibility suggests that an antiferromagnetism arising from (Li$_{0.8}$Fe$_{0.2}$)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$_c$ is gradually suppressed with the substitution of S for Se, and eventually superconductivity vanishes at $x$ = 0.90. The decrease of T$_c$ 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 (Li$_{0.8}$Fe$_{0.2}$)OHFeSe. But the detailed investigation on the relationships between $T_{\rm c}$ and the crystallographic facts suggests a very different dependence of $T_{\rm c}$ on anion height from the Fe2 layer or $Ch$-Fe2-$Ch$ angle from those in FeAs-based superconductors.Comment: 6 pages, 6 figure