Learning the Structure of Deep Sparse Graphical Models

Deep belief networks are a powerful way to model complex probability distributions. However, learning the structure of a belief network, particularly one with hidden units, is difficult. The Indian buffet process has been used as a nonparametric Bayesian prior on the directed structure of a belief network with a single infinitely wide hidden layer. In this paper, we introduce the cascading Indian buffet process (CIBP), which provides a nonparametric prior on the structure of a layered, directed belief network that is unbounded in both depth and width, yet allows tractable inference. We use the CIBP prior with the nonlinear Gaussian belief network so each unit can additionally vary its behavior between discrete and continuous representations. We provide Markov chain Monte Carlo algorithms for inference in these belief networks and explore the structures learned on several image data sets.

Use of dopamine antagonists on induced spawning of Indian major carps

To test the efficacy of the technique of using dopamine-antagonists with pituitary extracts, experiments were conducted in July/August '93, at the CIFE Fresh water Fish Farm, Powerkheda. In all, 35 sets (1 female x 2 male in each set) were tried individually. 19 sets were treated with Domperidone (DOM) and Carp Pituitary Extract (CPE) and they constituted the experimental sets, while the rest 16 were treated with CPE and formed the control sets. The breeding, complete ovulation and hatching was 84.2%, 87.5% and 85.7%, respectively in the experimental sets whereas it was 93.7%, 60% and 72.7%, respectively in case of control sets. Experimental sets yielded 1.20 lakh spawn/kg body wt. (female) as compared to 0.83 lakh spawn kg body wt. (female) received from control sets on average basis. When DOM was used at 50-60% there was 100% breeding success but when increased to 70%, breeding rate fell to 66%. In mass breeding of Catla in the circular hatchery DOM mixed with CPE in 50:50 ratio gave excellent results.

Gaussian etched single transverse mode vertical-cavity surface-emitting laser

A continuous Gaussian profile matched to the fundamental mode was etched onto the aperture of a vertical cavity surface emitting laser (VCSEL). Single Gaussian spot emission was achieved over the entire operating current range.

A new Lagrangian-Eulerian shell-fluid coupling algorithm based on level sets

We propose a computational method for the coupled simulation of a compressible flow interacting with a thin-shell structure undergoing large deformations. An Eulerian finite volume formulation is adopted for the fluid and a Lagrangian formulation based on subdivision finite elements is adopted for the shell response. The coupling between the fluid and the solid response is achieved via a novel approach based on level sets. The basic approach furnishes a general algorithm for coupling Lagrangian shell solvers with Cartesian grid based Eulerian fluid solvers. The efficiency and robustness of the proposed approach is demonstrated with a airbag deployment simulation. It bears emphasis that in the proposed approach the solid and the fluid components as well as their coupled interaction are considered in full detail and modeled with an equivalent level of fidelity without any oversimplifying assumptions or bias towards a particular physical aspect of the problem.

Viscous layer meshes from level sets on cartesian meshes

The long term goal of our work is to enable rapid prototyping design optimization to take place on geometries of arbitrary size in a spirit of a real time computer game. In recent papers we have reported the integration of a Level Set based geometry kernel with an octree-based cut-Cartesian mesh generator, RANS flow solver and post-processing all within a single piece of software - and all implemented in parallel with commodity PC clusters as the target. This work has shown that it is possible to eliminate all serial bottlenecks from the CED Process. This paper reports further progress towards our goal; in particular we report on the generation of viscous layer meshes to bridge the body to the flow across the cut-cells. The Level Set formulation, which underpins the geometry representation, is used as a natural mechanism to allow rapid construction of conformal layer meshes. The guiding principle is to construct the mesh which most closely approximates the body but remains solvable. This apparently novel approach is described and examples given.

Incremental Linear Discriminant Analysis Using Sufficient Spanning Sets and Its Applications

This paper presents an incremental learning solution for Linear Discriminant Analysis (LDA) and its applications to object recognition problems. We apply the sufficient spanning set approximation in three steps i.e. update for the total scatter matrix, between-class scatter matrix and the projected data matrix, which leads an online solution which closely agrees with the batch solution in accuracy while significantly reducing the computational complexity. The algorithm yields an efficient solution to incremental LDA even when the number of classes as well as the set size is large. The incremental LDA method has been also shown useful for semi-supervised online learning. Label propagation is done by integrating the incremental LDA into an EM framework. The method has been demonstrated in the task of merging large datasets which were collected during MPEG standardization for face image retrieval, face authentication using the BANCA dataset, and object categorisation using the Caltech101 dataset. © 2010 Springer Science+Business Media, LLC.

Gaussian Process Regression Networks

We introduce a new regression framework, Gaussian process regression networks (GPRN), which combines the structural properties of Bayesian neural networks with the non-parametric flexibility of Gaussian processes. This model accommodates input dependent signal and noise correlations between multiple response variables, input dependent length-scales and amplitudes, and heavy-tailed predictive distributions. We derive both efficient Markov chain Monte Carlo and variational Bayes inference procedures for this model. We apply GPRN as a multiple output regression and multivariate volatility model, demonstrating substantially improved performance over eight popular multiple output (multi-task) Gaussian process models and three multivariate volatility models on benchmark datasets, including a 1000 dimensional gene expression dataset.

Molecular basis of albinism in the rhesus monkey

Sequence analysis of the tyrosinase (TYR) coding region from one albino rhesus monkey (Macaca mulatta) family revealed that the two monkeys with phenotype similar to human TYR-negative oculocutaneous albinism (OCA) were homozygous for a missense mutation (S184TER) in exon 1 at codon 184. The offspring of one of the albino monkey (''Kangkang'') are all heterozygous for the S184TER mutation, but the S184TER mutation was not observed in 93 control individuals. We conclude that the point mutation is responsible and sufficient to generate the albino rhesus monkey phenotype. The rough age of the S184TER nonsense mutation may be about 0.8 million years using a rate of 0.16% per million years. (C) 2000 Elsevier Science B.V. All rights reserved.

Molecular genetic basis for the rhino mouse from Chinese KM subcolony

Both the rhino mouse and hairless mouse resulted from hairless gene mutation, but they show different phenotypes of skin physiology. The rhino mouse has more similar histological characters to human papular alopecia. Therefore rhino mouse is a good experimental animal model for human papular alopecia. This study reports a hairless mouse named rhino KIZ, arose from KM colony in Kunming Institue of Zoology, by systematic studies on morphology, skin histopathology, gene sequence, pedigree and protein domain analysis. The results demonstrate that a C-to-T transition in exon 11 of hr gene (The mutant gene has been applied for a Chinese patent (patent No. 03135280)) results in the rhino KIZ. The rhino KIZ with clear genetic mechanism will be a useful animal model.