Converting normal insulators into topological insulators via tuning orbital levels

Tuning the spin-orbit coupling strength via foreign element doping and/or modifying bonding strength via strain engineering are the major routes to convert normal insulators to topological insulators. We here propose an alternative strategy to realize topological phase transition by tuning the orbital level. Following this strategy, our first-principles calculations demonstrate that a topological phase transition in some cubic perovskite-type compounds CsGeBr$_3$ and CsSnBr$_3$ could be facilitated by carbon substitutional doping. Such unique topological phase transition predominantly results from the lower orbital energy of the carbon dopant, which can pull down the conduction bands and even induce band inversion. Beyond conventional approaches, our finding of tuning the orbital level may greatly expand the range of topologically nontrivial materials

Phonon-assisted tunneling in asymmetric resonant tunneling structures

Based on the dielectric continuum model, we calculated the phonon assisted tunneling (PAT) current of general double barrier resonant tunneling structures (DBRTSs) including both symmetric and antisymmetric ones. The results indicate that the four higher frequency interface phonon modes (especially the one which peaks at either interface of the emitter barrier) dominate the PAT processes, which increase the valley current and decrease the PVR of the DBRTSs. We show that an asymmetric structure can lead to improved performance.Comment: 1 paper and 5 figure

Phonon-Mediated High-Temperature Superconductivity in Few-Hydrogen Metal-Bonded Perovskite Al4H\rm {Al_4H} up to 54 K under Ambient Pressure

Multi-hydrogen lanthanum hydrides have shown the highest critical temperature $T_c$ at 250-260 K under 170-200 GPa. However, such high pressure is a great challenge for sample preparation and practical application. To address this challenge, we propose a novel design strategy for high-$T_c$ superconductors by constructing new few-hydrogen metal-bonded perovskite hydrides at ambient pressure, such as $\rm {Al_4H}$, with better ductility than the well-known multi-hydrogen, cuprate and iron-based superconductors. Based on the Migdal-Eliashberg theory, we predict that the structurally stable $\rm {Al_4H}$ has a favorable high $T_c$ up to 54 K under atmospheric pressure, similar to SmOFeAs.Comment: 6 pages, 4 figure

Causality-based Neural Network Repair

Neural networks have had discernible achievements in a wide range of applications. The wide-spread adoption also raises the concern of their dependability and reliability. Similar to traditional decision-making programs, neural networks can have defects that need to be repaired. The defects may cause unsafe behaviors, raise security concerns or unjust societal impacts. In this work, we address the problem of repairing a neural network for desirable properties such as fairness and the absence of backdoor. The goal is to construct a neural network that satisfies the property by (minimally) adjusting the given neural network's parameters (i.e., weights). Specifically, we propose CARE (\textbf{CA}usality-based \textbf{RE}pair), a causality-based neural network repair technique that 1) performs causality-based fault localization to identify the `guilty' neurons and 2) optimizes the parameters of the identified neurons to reduce the misbehavior. We have empirically evaluated CARE on various tasks such as backdoor removal, neural network repair for fairness and safety properties. Our experiment results show that CARE is able to repair all neural networks efficiently and effectively. For fairness repair tasks, CARE successfully improves fairness by $61.91\%$ on average. For backdoor removal tasks, CARE reduces the attack success rate from over $98\%$ to less than $1\%$. For safety property repair tasks, CARE reduces the property violation rate to less than $1\%$. Results also show that thanks to the causality-based fault localization, CARE's repair focuses on the misbehavior and preserves the accuracy of the neural networks

Bis(2-amino-3H-benzothia­zolium) bis­(7-oxabicyclo­[2.2.1]heptane-2,3-dicarboxyl­ato)cobaltate(II) hexa­hydrate

In the crystal structure of the title salt, (C7H7N2S)2[Co(C8H8O5)2]·6H2O, the heterocyclic N atom of the 2-amino­benzothia­zole mol­ecule is protonated. The CoII atom is situated on an inversion centre and exhibits a slightly distorted octa­hedral CoO6 coordination defined by the bridging O atoms of the bicyclo­heptane unit and four carboxyl­ate O atoms of two symmetry-related and fully deprotonated ligands. The crystal packing is stabilized by N—H⋯O hydrogen bonds between the cations and anions and by O—H⋯O hydrogen bonds including the crystal water mol­ecules

New Inexact Line Search Method for Unconstrained Optimization

We propose a new inexact line search rule and analyze the global convergence and convergence rate of related descent methods. The new line search rule is similar to the Armijo line-search rule and contains it as a special case. We can choose a larger stepsize in each line-search procedure and maintain the global convergence of related line-search methods. This idea can make us design new line-search methods in some wider sense. In some special cases, the new descent method can reduce to the Barzilai and Borewein method. Numerical results show that the new line-search methods are efficient for solving unconstrained optimization problems.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/45195/1/10957_2005_Article_6553.pd