1,975 research outputs found
Brightness as an Augmentation Technique for Image Classification
Augmentation techniques are crucial for accurately training convolution neural networks (CNNs). Therefore, these techniques have become the preprocessing methods. However, not every augmentation technique can be beneficial, especially those that change the image’s underlying structure, such as color augmentation techniques. In this study, the effect of eight brightness scales was investigated in the task of classifying a large histopathology dataset. Four state-of-the-art CNNs were used to assess each scale’s performance. The use of brightness was not beneficial in all the experiments. Among the different brightness scales, the [0.75–1.00] scale, which closely resembles the original brightness of the images, resulted in the best performance. The use of geometric augmentation yielded better performance than any brightness scale. Moreover, the results indicate that training the CNN without applying any augmentation techniques led to better results than considering brightness augmentation. Therefore, experimental results support the hypothesis that brightness augmentation techniques are not beneficial for image classification using deep-learning models and do not yield any performance gain. Furthermore, brightness augmentation techniques can significantly degrade the model’s performance when they are applied with extreme values
Cluster Dynamics for Randomly Frustrated Systems with Finite Connectivity
In simulations of some infinite range spin glass systems with finite
connectivity, it is found that for any resonable computational time, the
saturatedenergy per spin that is achieved by a cluster algorithm is lowered in
comparison to that achieved by Metropolis dynamics.The gap between the average
energies obtained from these two dynamics is robust with respect to variations
of the annealing schedule. For some probability distribution of the
interactions the ground state energy is calculated analytically within the
replica symmetry assumptionand is found to be saturated by a cluster algorithm.Comment: Revtex, 4 pages with 3 figure
Magic Islands and Barriers to Attachment: A Si/Si(111)7x7 Growth Model
Surface reconstructions can drastically modify growth kinetics during initial
stages of epitaxial growth as well as during the process of surface
equilibration after termination of growth. We investigate the effect of
activation barriers hindering attachment of material to existing islands on the
density and size distribution of islands in a model of homoepitaxial growth on
Si(111)7x7 reconstructed surface. An unusual distribution of island sizes
peaked around "magic" sizes and a steep dependence of the island density on the
growth rate are observed. "Magic" islands (of a different shape as compared to
those obtained during growth) are observed also during surface equilibration.Comment: 4 pages including 5 figures, REVTeX, submitted to Physical Review
Mean Field Behavior of Cluster Dynamics
The dynamic behavior of cluster algorithms is analyzed in the classical mean
field limit. Rigorous analytical results below establish that the dynamic
exponent has the value for the Swendsen-Wang algorithm and
for the Wolff algorithm.
An efficient Monte Carlo implementation is introduced, adapted for using
these algorithms for fully connected graphs. Extensive simulations both above
and below demonstrate scaling and evaluate the finite-size scaling
function by means of a rather impressive collapse of the data.Comment: Revtex, 9 pages with 7 figure
Impurity-induced diffusion bias in epitaxial growth
We introduce two models for the action of impurities in epitaxial growth. In
the first, the interaction between the diffusing adatoms and the impurities is
``barrier''-like and, in the second, it is ``trap''-like. For the barrier
model, we find a symmetry breaking effect that leads to an overall down-hill
current. As expected, such a current produces Edwards-Wilkinson scaling. For
the trap model, no symmetry breaking occurs and the scaling behavior appears to
be of the conserved-KPZ type.Comment: 5 pages(with the 5 figures), latex, revtex3.0, epsf, rotate, multico
General Framework for phase synchronization through localized sets
We present an approach which enables to identify phase synchronization in
coupled chaotic oscillators without having to explicitly measure the phase. We
show that if one defines a typical event in one oscillator and then observes
another one whenever this event occurs, these observations give rise to a
localized set. Our result provides a general and easy way to identify PS, which
can also be used to oscillators that possess multiple time scales. We
illustrate our approach in networks of chemically coupled neurons. We show that
clusters of phase synchronous neurons may emerge before the onset of phase
synchronization in the whole network, producing a suitable environment for
information exchanging. Furthermore, we show the relation between the localized
sets and the amount of information that coupled chaotic oscillator can
exchange
Coiling Instability of Multilamellar Membrane Tubes with Anchored Polymers
We study experimentally a coiling instability of cylindrical multilamellar
stacks of phospholipid membranes, induced by polymers with hydrophobic anchors
grafted along their hydrophilic backbone. Our system is unique in that coils
form in the absence of both twist and adhesion. We interpret our experimental
results in terms of a model in which local membrane curvature and polymer
concentration are coupled. The model predicts the occurrence of maximally tight
coils above a threshold polymer occupancy. A proper comparison between the
model and experiment involved imaging of projections from simulated coiled
tubes with maximal curvature and complicated torsions.Comment: 11 pages + 7 GIF figures + 10 JPEG figure
Wetting layer thickness and early evolution of epitaxially strained thin films
We propose a physical model which explains the existence of finite thickness
wetting layers in epitaxially strained films. The finite wetting layer is shown
to be stable due to the variation of the non-linear elastic free energy with
film thickness. We show that anisotropic surface tension gives rise to a
metastable enlarged wetting layer. The perturbation amplitude needed to
destabilize this wetting layer decreases with increasing lattice mismatch. We
observe the development of faceted islands in unstable films.Comment: 4 pages, 3 eps figure
Chaotic oscillations in a map-based model of neural activity
We propose a discrete time dynamical system (a map) as phenomenological model
of excitable and spiking-bursting neurons. The model is a discontinuous
two-dimensional map. We find condition under which this map has an invariant
region on the phase plane, containing chaotic attractor. This attractor creates
chaotic spiking-bursting oscillations of the model. We also show various
regimes of other neural activities (subthreshold oscillations, phasic spiking
etc.) derived from the proposed model
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