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Learning distance to subspace for the nearest subspace methods in high-dimensional data classification
The nearest subspace methods (NSM) are a category of classification methods widely applied to classify high-dimensional data. In this paper, we propose to improve the classification performance of NSM through learning tailored distance metrics from samples to class subspaces. The learned distance metric is termed as ‘learned distance to subspace’ (LD2S). Using LD2S in the classification rule of NSM can make the samples closer to their correct class subspaces while farther away from their wrong class subspaces. In this way, the classification task becomes easier and the classification performance of NSM can be improved. The superior classification performance of using LD2S for NSM is demonstrated on three real-world high-dimensional spectral datasets
Scaling Between Periodic Anderson and Kondo Lattice Models
Continuous-Time Quantum Monte Carlo (CT-QMC) method combined with Dynamical
Mean Field Theory (DMFT) is used to calculate both Periodic Anderson Model
(PAM) and Kondo Lattice Model (KLM). Different parameter sets of both models
are connected by the Schrieffer-Wolff transformation. For degeneracy N=2, a
special particle-hole symmetric case of PAM at half filling which always fixes
one electron per impurity site is compared with the results of the KLM. We find
a good mapping between PAM and KLM in the limit of large on-site Hubbard
interaction U for different properties like self-energy, quasiparticle residue
and susceptibility. This allows us to extract quasiparticle mass
renormalizations for the f electrons directly from KLM. The method is further
applied to higher degenerate case and to realsitic heavy fermion system CeRhIn5
in which the estimate of the Sommerfeld coefficient is proven to be close to
the experimental value
Single Image Super-Resolution Using Multi-Scale Convolutional Neural Network
Methods based on convolutional neural network (CNN) have demonstrated
tremendous improvements on single image super-resolution. However, the previous
methods mainly restore images from one single area in the low resolution (LR)
input, which limits the flexibility of models to infer various scales of
details for high resolution (HR) output. Moreover, most of them train a
specific model for each up-scale factor. In this paper, we propose a
multi-scale super resolution (MSSR) network. Our network consists of
multi-scale paths to make the HR inference, which can learn to synthesize
features from different scales. This property helps reconstruct various kinds
of regions in HR images. In addition, only one single model is needed for
multiple up-scale factors, which is more efficient without loss of restoration
quality. Experiments on four public datasets demonstrate that the proposed
method achieved state-of-the-art performance with fast speed
A Dual Digital Signal Processor VME Board For Instrumentation And Control Applications
A Dual Digital Signal Processing VME Board was developed for the Continuous
Electron Beam Accelerator Facility (CEBAF) Beam Current Monitor (BCM) system at
Jefferson Lab. It is a versatile general-purpose digital signal processing
board using an open architecture, which allows for adaptation to various
applications. The base design uses two independent Texas Instrument (TI)
TMS320C6711, which are 900 MFLOPS floating-point digital signal processors
(DSP). Applications that require a fixed point DSP can be implemented by
replacing the baseline DSP with the pin-for-pin compatible TMS320C6211. The
design can be manufactured with a reduced chip set without redesigning the
printed circuit board. For example it can be implemented as a single-channel
DSP with no analog I/O.Comment: 3 PDF page
Critical domain-wall dynamics of model B
With Monte Carlo methods, we simulate the critical domain-wall dynamics of
model B, taking the two-dimensional Ising model as an example. In the
macroscopic short-time regime, a dynamic scaling form is revealed. Due to the
existence of the quasi-random walkers, the magnetization shows intrinsic
dependence on the lattice size . A new exponent which governs the
-dependence of the magnetization is measured to be .Comment: 10pages, 4 figure
Stationary and dynamical properties of a zero range process on scale-free networks
We study the condensation phenomenon in a zero range process on scale-free
networks. We show that the stationary state property depends only on the degree
distribution of underlying networks. The model displays a stationary state
phase transition between a condensed phase and an uncondensed phase, and the
phase diagram is obtained analytically. As for the dynamical property, we find
that the relaxation dynamics depends on the global structure of underlying
networks. The relaxation time follows the power law with the
network size in the condensed phase. The dynamic exponent is found to
take a different value depending on whether underlying networks have a tree
structure or not.Comment: 9 pages, 6 eps figures, accepted version in PR
Hamiltonian equation of motion and depinning phase transition in two-dimensional magnets
Based on the Hamiltonian equation of motion of the theory with
quenched disorder, we investigate the depinning phase transition of the
domain-wall motion in two-dimensional magnets. With the short-time dynamic
approach, we numerically determine the transition field, and the static and
dynamic critical exponents. The results show that the fundamental Hamiltonian
equation of motion belongs to a universality class very different from those
effective equations of motion.Comment: 6 pages, 7 figures, have been accept by EP
Spatial structures in a simple model of population dynamics for parasite-host interactions
Spatial patterning can be crucially important for understanding the behavior
of interacting populations. Here we investigate a simple model of parasite and
host populations in which parasites are random walkers that must come into
contact with a host in order to reproduce. We focus on the spatial arrangement
of parasites around a single host, and we derive using analytics and numerical
simulations the necessary conditions placed on the parasite fecundity and
lifetime for the populations long-term survival. We also show that the parasite
population can be pushed to extinction by a large drift velocity, but,
counterintuitively, a small drift velocity generally increases the parasite
population.Comment: 6 pages, 6 figure
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