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

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

Is Alzheimer\u27s Associated Amyloid Beta an Innate Immune Protein

There is now abundant evidence that chronic inflammation in the brain is central to the pathogenesis of Alzheimer\u27s disease (AD) and that this is precipitated through accumulation of amyloid beta (Aβ) peptides. In this review, we first outline this evidence and how specific receptors on microglia and monocyte/macrophages determine whether extracellular Aβ peptides can be cleared through non‐inflammatory phagocytosis or instead result in pro‐inflammatory cytokine generation. Most efforts of treatment for AD so far have focused on reduction of Aβ levels (in particular neurotoxic oligomers of Aβ1‐42) in the brain. Recent findings suggest an alternative approach in which pro‐inflammatory responses to Aβ peptides are targeted to reduce injury. Most recently evidence has come to light that Aβ peptides resemble anti‐microbial peptides which are part of the innate defense system against infection. Such peptides act both by directly inactivating pathogens, but also by modulating responses of innate immune cells, including phagocytes. Indeed, Aβ peptides, particularly Aβ1‐42, do inhibit a range of potential pathogens, including bacteria, fungi, and viruses. Coupling this with evidence linking chronic viral, bacteria, or fungal infection to AD suggests that production of Aβ peptides in the brain is part of an innate immune response which might normally be beneficial, but which leads to harm when it is chronic or uncontrolled. This suggests that discovery of the original possibly infectious (or other inflammatory) stimulus for Aβ production would allow early intervention to prevent development of AD

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

Ratchet Effect in Surface Electromigration: Smoothing Surfaces by an ac Field

We demonstrate that for surfaces that have a nonzero Schwoebel barrier the application of an ac field parallel to the surface induces a net electro- migration current that points in the descending step direction. The magnitude of the current is calculated analytically and compared with Monte Carlo simulations. Since a downhill current smoothes the surface, our results imply that the application of ac fields can aid the smoothing process during annealing and can slow or eliminate the Schwoebel-barrier-induced mound formation during growth.Comment: 4 pages, LaTeX, 4 ps 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

From quantum cellular automata to quantum lattice gases

A natural architecture for nanoscale quantum computation is that of a quantum cellular automaton. Motivated by this observation, in this paper we begin an investigation of exactly unitary cellular automata. After proving that there can be no nontrivial, homogeneous, local, unitary, scalar cellular automaton in one dimension, we weaken the homogeneity condition and show that there are nontrivial, exactly unitary, partitioning cellular automata. We find a one parameter family of evolution rules which are best interpreted as those for a one particle quantum automaton. This model is naturally reformulated as a two component cellular automaton which we demonstrate to limit to the Dirac equation. We describe two generalizations of this automaton, the second of which, to multiple interacting particles, is the correct definition of a quantum lattice gas.Comment: 22 pages, plain TeX, 9 PostScript figures included with epsf.tex (ignore the under/overfull \vbox error messages); minor typographical corrections and journal reference adde