62,434 research outputs found
Learning arbitrary functions with spike-timing dependent plasticity learning rule
A neural network model based on spike-timing-dependent plasticity (STOP) learning rule, where afferent neurons will excite both the target neuron and interneurons that in turn project to the target neuron, is applied to the tasks of learning AND and XOR functions. Without inhibitory plasticity, the network can learn both AND and XOR functions. Introducing inhibitory plasticity can improve the performance of learning XOR function. Maintaining a training pattern set is a method to get feedback of network performance, and will always improve network performance. © 2005 IEEE
Breakdown of adiabatic invariance in spherical tokamaks
Thermal ions in spherical tokamaks have two adiabatic invariants: the
magnetic moment and the longitudinal invariant. For hot ions, variations in
magnetic-field strength over a gyro period can become sufficiently large to
cause breakdown of the adiabatic invariance. The magnetic moment is more
sensitive to perturbations than the longitudinal invariant and there exists an
intermediate regime, super-adiabaticity, where the longitudinal invariant
remains adiabatic, but the magnetic moment does not. The motion of
super-adiabatic ions remains integrable and confinement is thus preserved.
However, above a threshold energy, the longitudinal invariant becomes
non-adiabatic too, and confinement is lost as the motion becomes chaotic. We
predict beam ions in present-day spherical tokamaks to be super-adiabatic but
fusion alphas in proposed burning-plasma spherical tokamaks to be
non-adiabatic.Comment: 6 pages, 8 figure
Similarity-Aware Spectral Sparsification by Edge Filtering
In recent years, spectral graph sparsification techniques that can compute
ultra-sparse graph proxies have been extensively studied for accelerating
various numerical and graph-related applications. Prior nearly-linear-time
spectral sparsification methods first extract low-stretch spanning tree from
the original graph to form the backbone of the sparsifier, and then recover
small portions of spectrally-critical off-tree edges to the spanning tree to
significantly improve the approximation quality. However, it is not clear how
many off-tree edges should be recovered for achieving a desired spectral
similarity level within the sparsifier. Motivated by recent graph signal
processing techniques, this paper proposes a similarity-aware spectral graph
sparsification framework that leverages efficient spectral off-tree edge
embedding and filtering schemes to construct spectral sparsifiers with
guaranteed spectral similarity (relative condition number) level. An iterative
graph densification scheme is introduced to facilitate efficient and effective
filtering of off-tree edges for highly ill-conditioned problems. The proposed
method has been validated using various kinds of graphs obtained from public
domain sparse matrix collections relevant to VLSI CAD, finite element analysis,
as well as social and data networks frequently studied in many machine learning
and data mining applications
Performance evaluation of channel estimation techniques for MIMO-OFDM systems with adaptive sub-carrier allocation
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