1,895 research outputs found
Semiclassical theory of spin-orbit torques in disordered multiband electron systems
We study spin-orbit torques (SOT) in non-degenerate multiband electron
systems in the weak disorder limit. In order to have better physical
transparency a semiclassical Boltzmann approach equivalent to the Kubo
diagrammatic approach in the non-crossing approximation is formulated. This
semiclassical framework accounts for the interband- coherence effects induced
by both the electric field and static impurity scattering. Using the
two-dimensional Rashba ferromagnet as a model system, we show that the
antidamping-like SOT arising from disorder-induced interband-coherence effects
is very sensitive to the spin structure of disorder and may have the same sign
as the intrinsic SOT in the presence of spin-dependent disorder. While the
cancellation of this SOT and the intrinsic one occurs only in the case of
spin-independent short-range disorder.Comment: 10 pages, 2 figures, accepted by Physical Review
Collaborative Inference of Coexisting Information Diffusions
Recently, \textit{diffusion history inference} has become an emerging
research topic due to its great benefits for various applications, whose
purpose is to reconstruct the missing histories of information diffusion traces
according to incomplete observations. The existing methods, however, often
focus only on single information diffusion trace, while in a real-world social
network, there often coexist multiple information diffusions over the same
network. In this paper, we propose a novel approach called Collaborative
Inference Model (CIM) for the problem of the inference of coexisting
information diffusions. By exploiting the synergism between the coexisting
information diffusions, CIM holistically models multiple information diffusions
as a sparse 4th-order tensor called Coexisting Diffusions Tensor (CDT) without
any prior assumption of diffusion models, and collaboratively infers the
histories of the coexisting information diffusions via a low-rank approximation
of CDT with a fusion of heterogeneous constraints generated from additional
data sources. To improve the efficiency, we further propose an optimal
algorithm called Time Window based Parallel Decomposition Algorithm (TWPDA),
which can speed up the inference without compromise on the accuracy by
utilizing the temporal locality of information diffusions. The extensive
experiments conducted on real world datasets and synthetic datasets verify the
effectiveness and efficiency of CIM and TWPDA
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