1,937 research outputs found
Continuous Elastic Phase Transitions in Pure and Disordered Crystals
We review the theory of second--order (ferro--)elastic phase transitions,
where the order parameter consists of a certain linear combination of strain
tensor components, and the accompanying soft mode is an acoustic phonon. In
three--dimensional crystals, the softening can occur in one-- or
two--dimensional soft sectors. The ensuing anisotropy reduces the effect of
fluctuations, rendering the critical behaviour of these systems classical for a
one--dimensional soft sector, and classical with logarithmic corrections in
case of a two--dimensional soft sector. The dynamical critical exponent is , and as a consequence the sound velocity vanishes as , while the phonon damping coefficient is essentially
temperature--independent. Disorder may lead to a variety of precursor effects
and modified critical behaviour. Defects that locally soften the crystal may
induce the phenomenon of local order parameter condensation. When the
correlation length of the pure system exceeds the average defect separation
, a disorder--induced phase transition to a state with
non--zero average order parameter can occur at a temperature
well above the transition temperature of the pure crystal. Near
, the order--parameter curve, susceptibility, and specific heat appear
rounded. For the spatial inhomogeneity induces a static
central peak with finite width in the scattering cross section, accompanied
by a dynamical component that is confined to the very vicinity of the
disorder--induced phase transition.Comment: 26 pages, Latex (rs.sty now IS included), 11 figures can be obtained
from U.C. T\"auber ([email protected]); will appear in Phil. Trans. Roy.
Soc. Lond. A (October 1996
Carabid and Staphylinid beetles from agricultural land in the lower Fraser Valley, British Columbia
Pit-traps were emptied every two or three days for two seasons in crop, fallow, and grass plots to determine the species and population density of Carabidae and Staphylinidae associated with agricultural land, and their relationship with brassica crops. Half of the plots were enclosed by plastic barriers and the beetles were trapped to extinction: half were not enclosed. Thirty-three carabid and 16 staphylinid species were captured. The dominant species was the small, generalized. European carabid predator, <i>Bembidion lampros</i>, which had a population on crop and fallow land of about 29000/hectare. It was almost absent in grass. Other numerous carabids were <i>Harpalus aeneus</i>, <i>Calathus fuscipes</i>, and <i>Clivina fossor</i>, all introduced European spp., with populations of almost 2000, 5600, and llOOO/hectare respectively. The first and third of these were scarce in grassland but the second was abundant. In plots of Brussels sprouts <i>Aleochara bilineata</i>, a staphylinid, was effectively parasitic on root maggots, and averaged more than 6000/hectare. Soil cores taken in October centred on a Brussels sprouts plant averaged 26.4 <i>Hylemya puparia</i> per core of which 44% were parasitized by <i>A. bilineata</i>
Fully Point-wise Convolutional Neural Network for Modeling Statistical Regularities in Natural Images
Modeling statistical regularity plays an essential role in ill-posed image
processing problems. Recently, deep learning based methods have been presented
to implicitly learn statistical representation of pixel distributions in
natural images and leverage it as a constraint to facilitate subsequent tasks,
such as color constancy and image dehazing. However, the existing CNN
architecture is prone to variability and diversity of pixel intensity within
and between local regions, which may result in inaccurate statistical
representation. To address this problem, this paper presents a novel fully
point-wise CNN architecture for modeling statistical regularities in natural
images. Specifically, we propose to randomly shuffle the pixels in the origin
images and leverage the shuffled image as input to make CNN more concerned with
the statistical properties. Moreover, since the pixels in the shuffled image
are independent identically distributed, we can replace all the large
convolution kernels in CNN with point-wise () convolution kernels while
maintaining the representation ability. Experimental results on two
applications: color constancy and image dehazing, demonstrate the superiority
of our proposed network over the existing architectures, i.e., using
1/101/100 network parameters and computational cost while achieving
comparable performance.Comment: 9 pages, 7 figures. To appear in ACM MM 201
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