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 $L$. A new exponent which governs the $L$-dependence of the magnetization is measured to be $\sigma=0.243(8)$.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 $\tau \sim L^z$ with the network size $L$ in the condensed phase. The dynamic exponent $z$ 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 $\phi^4$ 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