A simple Hebbian/anti-Hebbian network learns the sparse, independent components of natural images

Slightly modified versions of an early Hebbian/anti-Hebbian neural network are shown to be capable of extracting the sparse, independent linear components of a prefiltered natural image set. An explanation for this capability in terms of a coupling between two hypothetical networks is presented. The simple networks presented here provide alternative, biologically plausible mechanisms for sparse, factorial coding in early primate vision.     

Raman scattering of InAs/AlAs quantum dot superlattices grown on (001) and (311)B GaAs surfaces

ABSTRACT: We present a comparative analysis of Raman scattering by acoustic and optical phonons in InAs/AlAs quantum dot superlattices grown on (001) and (311)B GaAs surfaces. Doublets of folded longitudinal acoustic phonons up to the fifth order were observed in the Raman spectra of (001)- and (311)B-oriented quantum dot superlattices measured in the polarized scattering geometries. The energy positions of the folded acoustic phonons are well described by the elastic continuum model. Besides the acoustic phonons, the spectra display features related to confined transverse and longitudinal optical as well as interface phonons in quantum dots and spacer layers. Their frequency positions are discussed in terms of phonon confinement, elastic stress, and atomic intermixing.     

A Stochastic Kinematic Model of Class Averaging in Single-Particle Electron Microscopy

Single-particle electron microscopy is an experimental technique that is used to determine the 3D structure of biological macromolecules and the complexes that they form. In general, image processing techniques and reconstruction algorithms are applied to micrographs, which are two-dimensional (2D) images taken by electron microscopes. Each of these planar images can be thought of as a projection of the macromolecular structure of interest from an a priori unknown direction. A class is defined as a collection of projection images with a high degree of similarity, presumably resulting from taking projections along similar directions. In practice, micrographs are very noisy and those in each class are aligned and averaged in order to reduce the background noise. Errors in the alignment process are inevitable due to noise in the electron micrographs. This error results in blurry averaged images. In this paper, we investigate how blurring parameters are related to the properties of the background noise in the case when the alignment is achieved by matching the mass centers and the principal axes of the experimental images. We observe that the background noise in micrographs can be treated as Gaussian. Using the mean and variance of the background Gaussian noise, we derive equations for the mean and variance of translational and rotational misalignments in the class averaging process. This defines a Gaussian probability density on the Euclidean motion group of the plane. Our formulation is validated by convolving the derived blurring function representing the stochasticity of the image alignments with the underlying noiseless projection and comparing with the original blurry image.     

Treatment of parking lot stormwater using a StormTreat system

The effectiveness of a StormTreat system in treating stormwater from a commercial parking lot in Connecticut was evaluated. Flow-weighted composite samples were collected from StormTreat inflow and outflow during a 2-yr study. Bypass flow was not monitored. The StormTreat significantly (P < 0.05) reduced total suspended solids, total phosphorus, total Kjeldahl-N, total zinc, total copper, and fecal coliform bacteria on a concentration basis. The StormTreat system retained 49% total suspended solids, 74% total phosphorus, 44% total Kjeldahl-N, 45% total zinc, 29% total copper, 2% total lead on a mass basis, and 99% fecal coliform on a concentration basis. Treatment efficiency was not associated with storm size, chamber stage, discharge rate, or hydraulic retention time (r < 0.355). The system retained ammonia-N more efficiently during the summer than during the winter (P < 0.01) and retained total zinc less efficiently during the summer than during the winter (P < 0.05). Season did not significantly (P > 0.05) affect the treatment of other monitored water quality variables. The StormTreat system reduced the concentrations of stormwater pollutants commonly found in parking lot runoff.