GhostVLAD for set-based face recognition

The objective of this paper is to learn a compact representation of image sets for template-based face recognition. We make the following contributions: first, we propose a network architecture which aggregates and embeds the face descriptors produced by deep convolutional neural networks into a compact fixed-length representation. This compact representation requires minimal memory storage and enables efficient similarity computation. Second, we propose a novel GhostVLAD layer that includes {\em ghost clusters}, that do not contribute to the aggregation. We show that a quality weighting on the input faces emerges automatically such that informative images contribute more than those with low quality, and that the ghost clusters enhance the network's ability to deal with poor quality images. Third, we explore how input feature dimension, number of clusters and different training techniques affect the recognition performance. Given this analysis, we train a network that far exceeds the state-of-the-art on the IJB-B face recognition dataset. This is currently one of the most challenging public benchmarks, and we surpass the state-of-the-art on both the identification and verification protocols.Comment: Accepted by ACCV 201

A new comparison between solid-state thermionics and thermoelectrics

It is shown that equations for electrical current in solid-state thermionic and thermoelectric devices converge for devices with a width equal to the mean free path of electrons, yielding a common expression for intensive electronic efficiency in the two types of devices. This result is used to demonstrate that the materials parameters for thermionic and thermoelectric devices are equal, rather than differing by a multiplicative factor as previously thought

Seed, Expand and Constrain: Three Principles for Weakly-Supervised Image Segmentation

We introduce a new loss function for the weakly-supervised training of semantic image segmentation models based on three guiding principles: to seed with weak localization cues, to expand objects based on the information about which classes can occur in an image, and to constrain the segmentations to coincide with object boundaries. We show experimentally that training a deep convolutional neural network using the proposed loss function leads to substantially better segmentations than previous state-of-the-art methods on the challenging PASCAL VOC 2012 dataset. We furthermore give insight into the working mechanism of our method by a detailed experimental study that illustrates how the segmentation quality is affected by each term of the proposed loss function as well as their combinations.Comment: ECCV 201

Improving Whole Slide Segmentation Through Visual Context - A Systematic Study

While challenging, the dense segmentation of histology images is a necessary first step to assess changes in tissue architecture and cellular morphology. Although specific convolutional neural network architectures have been applied with great success to the problem, few effectively incorporate visual context information from multiple scales. With this paper, we present a systematic comparison of different architectures to assess how including multi-scale information affects segmentation performance. A publicly available breast cancer and a locally collected prostate cancer datasets are being utilised for this study. The results support our hypothesis that visual context and scale play a crucial role in histology image classification problems

Effects of goshajinkigan (Niu-Che-Sen-Qi-Wan) for resiniferatoxin-sensitive afferents on detrusor overactivity induced by acetic acid in conscious rats

Electronic version of an article published as [AMERICAN JOURNAL OF CHINESE MEDICINE, 34, 2, 2006, 285-293] [doi:10.1142/S0192415X06003837] © [copyright World Scientific Publishing Company] [http://www.worldscinet.com/ajcm/ajcm.shtml]ArticleAmerican Journal of Chinese Medicine. 34(2): 285-293 (2006)journal articl

Reciprocal space mapping of magnetic order in thick epitaxial MnSi films

We report grazing incidence small angle neutron scattering (GISANS) and complementary off-specular neutron reflectometry (OSR) of the magnetic order in a single-crystalline epitaxial MnSi film on Si(111) in the thick film limit. Providing a means of direct reciprocal space mapping, GISANS and OSR reveal a magnetic modulation perpendicular to the films under magnetic fields parallel and perpendicular to the film, where additional polarized neutron reflectometry (PNR) and magnetization measurements are in excellent agreement with the literature. Regardless of field orientation, our data does not suggest the presence of more complex spin textures, notably the formation of skyrmions. This observation establishes a distinct difference with bulk samples of MnSi of similar thickness under perpendicular field, in which a skyrmion lattice dominates the phase diagram. Extended x-ray absorption fine structure measurements suggest that small shifts of the Si positions within the unstrained unit cell control the magnetic state, representing the main difference between the films and thin bulk samples

Improved quark mass density- dependent model with quark and non-linear scalar field coupling

The improved quark mass density- dependent model which includes the coupling between the quarks and a non-linear scalar field is presented. Numerical analysis of solutions of the model is performed over a wide range of parameters. The wave functions of ground state and the lowest one-particle excited states with even and odd parity are given. The root-mean squared radius, the magnetic moment and the ratio between the axial-vector and the vector beta-decay coupling constants of the nucleon are calculated. We found that the present model is successful to describe the properties of nucleon.Comment: 7pages, 6 figure

Linear Algebraic Calculation of Green's function for Large-Scale Electronic Structure Theory

A linear algebraic method named the shifted conjugate-orthogonal-conjugate-gradient method is introduced for large-scale electronic structure calculation. The method gives an iterative solver algorithm of the Green's function and the density matrix without calculating eigenstates.The problem is reduced to independent linear equations at many energy points and the calculation is actually carried out only for a single energy point. The method is robust against the round-off error and the calculation can reach the machine accuracy. With the observation of residual vectors, the accuracy can be controlled, microscopically, independently for each element of the Green's function, and dynamically, at each step in dynamical simulations. The method is applied to both semiconductor and metal.Comment: 10 pages, 9 figures. To appear in Phys. Rev. B. A PDF file with better graphics is available at http://fujimac.t.u-tokyo.ac.jp/lses

Hall resistance in the hopping regime, a "Hall Insulator"?

The Hall conductivity and resistivity of strongly localized electrons at low temperatures and at small magnetic fields are obtained. It is found that the results depend on whether the conductivity or the resistivity tensors are averaged to obtain the macroscopic Hall resistivity. In the second case the Hall resistivity always {\it diverges} exponentially as the temperature tends to zero. But when the Hall resistivity is derived from the averaged conductivity, the resulting temperature dependence is sensitive to the disorder configuration. Then the Hall resistivity may approach a constant value as $T\to 0$. This is the Hall insulating behavior. It is argued that for strictly dc conditions, the transport quantity that should be averaged is the resistivity.Comment: Late

Quantum-Classical Transition of Photon-Carnot Engine Induced by Quantum Decoherence

We study the physical implementation of the Photon Carnot engine (PCE) based on the cavity QED system [M. Scully et al, Science, \textbf{299}, 862 (2003)]. Here, we analyze two decoherence mechanisms for the more practical systems of PCE, the dissipation of photon field and the pure dephasing of the input atoms. As a result we find that (I) the PCE can work well to some extent even in the existence of the cavity loss (photon dissipation); and (II) the short-time atomic dephasing, which can destroy the PCE, is a fatal problem to be overcome.Comment: 6 pages, 3 figure