Stability of Weyl points in magnetic half-metallic Heusler compounds

We employ {\it ab-initio} fully-relativistic electronic structure calculations to study the stability of the Weyl points in the momentum space within the class of the half-metallic ferromagnetic full Heusler materials, by focusing on Co$_2$TiAl as a well-established prototype compound. Here we show that both the number of the Weyl points together with their $k$-space coordinates can be controlled by the orientation of the magnetization. This alternative degree of freedom, which is absent in other topological materials (e.g. in Weyl semimetals), introduces novel functionalities, specific for the class of half-metallic ferromagnets. Of special interest are Weyl points which are preserved irrespectively of any arbitrary rotation of the magnetization axis

Ferrimagnetism of the magnetoelectric compound Cu2_2OSeO3_3 probed by 77^{77}Se NMR

We present a thorough $^{77}$Se NMR study of a single crystal of the magnetoelectric compound Cu$_2$OSeO$_3$. The temperature dependence of the local electronic moments extracted from the NMR data is fully consistent with a magnetic phase transition from the high-T paramagnetic phase to a low-T ferrimagnetic state with 3/4 of the Cu$^{2+}$ ions aligned parallel and 1/4 aligned antiparallel to the applied field of 14.09 T. The transition to this 3up-1down magnetic state is not accompanied by any splitting of the NMR lines or any abrupt modification in their broadening, hence there is no observable reduction of the crystalline symmetry from its high-T cubic \textit{P}2$_1$3 space group. These results are in agreement with high resolution x-ray diffraction and magnetization data on powder samples reported previously by Bos {\it et al.} [Phys. Rev. B, {\bf 78}, 094416 (2008)]. We also develop a mean field theory description of the problem based on a microscopic spin Hamiltonian with one antiferromagnetic ($J_\text{afm}\simeq 68$ K) and one ferromagnetic ($J_\text{fm}\simeq -50$ K) nearest-neighbor exchange interaction

Combinatorial interpretation of Haldane-Wu fractional exclusion statistics

Assuming that the maximal allowed number of identical particles in state is an integer parameter, q, we derive the statistical weight and analyze the associated equation which defines the statistical distribution. The derived distribution covers Fermi-Dirac and Bose-Einstein ones in the particular cases q = 1 and q -> infinity (n_i/q -> 1), respectively. We show that the derived statistical weight provides a natural combinatorial interpretation of Haldane-Wu fractional exclusion statistics, and present exact solutions of the distribution equation.Comment: 8 pages, 2 eps-figure

Noise of Kondo dot with ac gate: Floquet-Green's function and Noncrossing Approximation Approach

The transport properties of an ac-driving quantum dot in the Kondo regime are studied by the Floquet-Green's function method with slave-boson infinite-$U$ noncrossing approximation. Our results show that the Kondo peak of the local density of states is robust against weak ac gate modulation. Significant suppression of the Kondo peak can be observed when the ac gate field becomes strong. The photon-assisted noise of Kondo resonance as a function of dc voltage does not show singularities which are expected for noninteracting resonant quantum dot. These findings suggest that one may make use of the photon-assisted noise measurement to tell apart whether the resonant transport is via noninteracting resonance or strongly-correlated Kondo resonance

Maximum a Posteriori Adaptation of Network Parameters in Deep Models

We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance. Given an abundance of DNN parameters but with only a limited amount of data, the effectiveness of the adapted DNN model can often be compromised. We formulate maximum a posteriori (MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an augmented linear hidden networks connected to the output tied states, or senones, and compare it to feature space MAP linear regression previously proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street Journal task demonstrate the feasibility of the proposed framework. In supervised adaptation, the proposed MAP adaptation approach provides more than 10% relative error reduction and consistently outperforms the conventional transformation based methods. Furthermore, we present an initial attempt to generate hierarchical priors to improve adaptation efficiency and effectiveness with limited adaptation data by exploiting similarities among senones

Novel method for refinement of retained austenite in micro/nano-structured bainitic steels

A comparative study was conducted to assess the effects of two different heat treatments on the amount and morphology of the retained austenite in a micro/nano-structured bainitic steel. The heat treatments used in this work were two-stage bainitic transformation and bainitic-partitioning transformation. Both methods resulted in the generation of a multi-phase microstructure containing nanoscale bainitic ferrite, and/or fresh martensitic phases and much finer retained austenite. Both heat treatments were verified to be effective in refining the retained austenite in micro/nano-structured bainite and increasing the hardness. However, the bainitic transformation followed by partitioning cycle was proved to be a more viable approach than the two-stage bainitic transformation due to much shorter processing time, i.e. ∼2 h compared to ∼4 day, respectively