Imaging and writing magnetic domains in the non-collinear antiferromagnet Mn3Sn

Non-collinear antiferromagnets are revealing many unexpected phenomena and they became crucial for the field of antiferromagnetic spintronics. To visualize and prepare a well-defined domain structure is of key importance. The spatial magnetic contrast, however, remains extraordinarily difficult to be observed experimentally. Here, we demonstrate a magnetic imaging technique based on a laser induced local thermal gradient combined with detection of the anomalous Nernst effect. We employ this method in one the most actively studied representatives of this class of materials—Mn3Sn. We demonstrate that the observed contrast is of magnetic origin. We further show an algorithm to prepare a well-defined domain pattern at room temperature based on heat assisted recording principle. Our study opens up a prospect to study spintronics phenomena in non-collinear antiferromagnets with spatial resolution

Band structure of CuMnAs probed by optical and photoemission spectroscopy

5 pages, 5 figures + Supplementary InformationTetragonal phase of CuMnAs progressively appears as one of the key materials for antiferromagnetic spintronics due to efficient current-induced spin-orbit torques whose existence can be directly inferred from crystal symmetry. Theoretical understanding of spintronic phenomena in this material, however, relies on the detailed knowledge of electronic structure (band structure and corresponding wave functions) which has so far been tested only to a limited extent. We show that AC permittivity (obtained from ellipsometry) and UV photoelectron spectra agree with density functional calculations. Together with the x-ray diffraction and precession electron diffraction tomography, our analysis confirms recent theoretical claim [Phys.Rev.B 96, 094406 (2017)] that copper atoms occupy lattice positions in the basal plane of the tetragonal unit cell.We acknowledge support from National Grid Infrastructure MetaCentrum provided under the programme “Projects of Large Research, Development, and Innovations Infrastructures” (CESNET LM2015042); Grant Agency of the Czech Republic under Grant No. 15-13436S; CEDAMNF (CZ.02.1.01/0.0/0.0/15_003/0000358) of the Czech ministry of education (MŠMT) as well as its LM2015087 and LNSMLNSpin grants; Cariplo Foundation, Grant No. 2013-0726 (MAGISTER); Spanish MINECO under MAT2015-67593-P project and the ‘Severo Ochoa’ Programme (SEV-2015-0496); EU FET Open RIA Grant No. 766566; Engineering and Physical Sciences Research Council Grant No. EP/P019749/1. P.W. acknowledges support from the Royal Society through a University Research Fellowship.Peer reviewe

Relational Descriptive Analysis of Gene Expression Data

Abstract. This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to help find description of groups of genes differentialy expressed in specific cancers. The descriptions are represented by means of relational features

Tuning Spin Hall Conductivity in GeTe by Ferroelectric Polarization

Controlling charge-spin current conversion by electric fields is crucial in spintronic devices, which can be realized in diatom ferroelectric semiconductor GeTe where it is established that ferroelectricity can change the spin texture. We demonstrated that the spin Hall conductivity (SHC) can be further tuned by ferroelectricity based on the density functional theory calculations. The spin texture variation driven by the electric fields was elucidated from the symmetry point of view, highlighting the interlocked spin and orbital degrees of freedom. We observed that the origin of SHC can be attributed to the Rashba effect and the intrinsic spin-orbit coupling. The magnitude of one component of SHC {\sigma}_xy^z can reach as large as 100 {\hbar}/e/({\Omega}cm) in the vicinity of the band edge, which is promising for engineering spintronic devices. Our work on tunable spin transport properties via the ferroelectric polarization brings novel assets into the field of spintronics.Comment: 6 figure

Gene Expression Mining Guided by Background Knowledge

This chapter points out the role of genomic background knowledge in gene expression data mining. The authors demonstrate its application in several tasks such as relational descriptive analysis, constraint-based knowledge discovery, feature selection and construction or quantitative association rule mining. The chapter also accentuates diversity of background knowledge. In genomics, it can be stored in formats such as free texts, ontologies, pathways, links among biological entities, and many others. The authors hope that understanding of automated integration of heterogeneous data sources helps researchers to reach compact and transparent as well as biologically valid and plausible results of their gene-expression data analysis