4,484 research outputs found
Majorana spintronics
We propose a systematic magnetic-flux-free approach to detect, manipulate and
braid Majorana fermions in a semiconductor nanowire-based topological Josephson
junction by utilizing the Majorana spin degree of freedom. We find an intrinsic
-phase difference between spin-triplet pairings enforced by the Majorana
zeros modes (MZMs) at the two ends of a one-dimensional spinful topological
superconductor. This -phase is identified to be a spin-dependent
superconducting phase, referred to as the spin-phase, which we show to be
tunable by controlling spin-orbit coupling strength via electric gates. This
electric controllable spin-phase not only affects the coupling energy between
MZMs but also leads to a fractional Josephson effect in the absence of any
applied magnetic flux, which enables the efficient topological qubit readout.
We thus propose an all-electrically controlled superconductor-semiconductor
hybrid circuit to manipulate MZMs and to detect their non-Abelian braiding
statistics properties. Our work on spin properties of topological Josephson
effects potentially opens up a new thrust for spintronic applications with
Majorana-based semiconductor quantum circuits.Comment: 15 pages, 9 figures, replaced with published versio
Skeleton-Based Human Action Recognition with Global Context-Aware Attention LSTM Networks
Human action recognition in 3D skeleton sequences has attracted a lot of
research attention. Recently, Long Short-Term Memory (LSTM) networks have shown
promising performance in this task due to their strengths in modeling the
dependencies and dynamics in sequential data. As not all skeletal joints are
informative for action recognition, and the irrelevant joints often bring noise
which can degrade the performance, we need to pay more attention to the
informative ones. However, the original LSTM network does not have explicit
attention ability. In this paper, we propose a new class of LSTM network,
Global Context-Aware Attention LSTM (GCA-LSTM), for skeleton based action
recognition. This network is capable of selectively focusing on the informative
joints in each frame of each skeleton sequence by using a global context memory
cell. To further improve the attention capability of our network, we also
introduce a recurrent attention mechanism, with which the attention performance
of the network can be enhanced progressively. Moreover, we propose a stepwise
training scheme in order to train our network effectively. Our approach
achieves state-of-the-art performance on five challenging benchmark datasets
for skeleton based action recognition
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