105,474 research outputs found
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 . 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
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