15,227 research outputs found
Consensus-Based Transfer Linear Support Vector Machines for Decentralized Multi-Task Multi-Agent Learning
Transfer learning has been developed to improve the performances of different
but related tasks in machine learning. However, such processes become less
efficient with the increase of the size of training data and the number of
tasks. Moreover, privacy can be violated as some tasks may contain sensitive
and private data, which are communicated between nodes and tasks. We propose a
consensus-based distributed transfer learning framework, where several tasks
aim to find the best linear support vector machine (SVM) classifiers in a
distributed network. With alternating direction method of multipliers, tasks
can achieve better classification accuracies more efficiently and privately, as
each node and each task train with their own data, and only decision variables
are transferred between different tasks and nodes. Numerical experiments on
MNIST datasets show that the knowledge transferred from the source tasks can be
used to decrease the risks of the target tasks that lack training data or have
unbalanced training labels. We show that the risks of the target tasks in the
nodes without the data of the source tasks can also be reduced using the
information transferred from the nodes who contain the data of the source
tasks. We also show that the target tasks can enter and leave in real-time
without rerunning the whole algorithm
Resonance in the nonadiabatic quantum pumping of the time-dependent Josephson junction
In this work, we investigated the nonadiabatic transport properties of the
one-dimensional time-dependent superconductor-normal metal-superconductor (SNS)
Josephson junction biased by a current source and driven by a
high-frequency-ac-gate-potential applied to the normal-metal layer. BCS
superconductors are considered and treated with the time-dependent
Bogoliubov-de Gennes equation. Using Floquet theory, we compute the
transmission coefficients and the Wigner-Smith delay times as a function of the
incident energy and find that they display resonances when one of the electron
or hole Floquet wavevectors coincides with the bound quasiparticle state within
the superconducting energy gap. The resonance varies with the phase difference
between the two superconductors as a result of the bound quasiparticle level
displacement. The supercurrent flowing through the SNS junction is dramatically
enhanced by the resonances
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