Ehnasya, 1904 /

Mode of access: Internet.

Bill MacFarland, UM Ice Hockey vs Montreal, 1955

[MacFarland #7 scores ihird goal for hat-trick]

Bill MacFarland, UM Ice Hockey, vs Montreal 1955

[Ensian caption: "Bill MacFarland and Jerry Karpinka ride in right on top of the goal as MacFarland completes a hat trick."]

The blue-nosed witch /

"Blanche, a real witch, though a young one had a nose that glowed a marvelous blue in the dark. One Halloween, cruising on her broom with black cat, Brockett, she found a band of children and joined them on their trick-or-treat forage."

Suit keen renovator: Alternate reality design

Virtual territories and their theme parks are more akin to the physical world of real estate than they might at first appear. The trick in triggering the designer's imagination, is to find a 'nice renovator' (cottage/ house) at a low price, with loads of potential, and by doing it on the cheap to add character, and engage the imagination. Here the designer can construct changes from an imagined space. Vision is more important than how the actual place presents.This work describes a case study involving undergraduate students in the Creative Industries who needed a place to explore, so as to create their own visions and projects. The place had to inspire, trigger engagement, and their imaginations. At the same time it was important that the place did not coerce activity, or distract from the task by confusing tools with task, or architectural navigation with conceptual skills.The solution was an alternate reality.

A COORDENAÇÃO PEDAGÓGICA NO ENSINO MÉDIO: ACOMPANHAR O TRABALHO PEDAGÓGICO OU APAGAR INCÊNDIOS?

O presente estudo faz uma investigação sobre a atribuição de formar professores durante o Horário de Trabalho Pedagógico Coletivo (HTPC), que os professores coordenadores da rede estadual paulista receberam com a implantação do programa Ensino Médio em Rede, no período de 2004 a 2006. O estudo proporciona uma análise da condição de formador desses educadores, a partir das suas trajetórias profissionais, enfocando a construção da sua identidade e procurando encontrar os elementos comuns presentes no percurso de cada um, bem como as dificuldades vivenciadas no desenvolvimento deste trabalho e as estratégias e alternativas adotadas por eles para superá-las. Além das consultas a diversos referenciais teóricos e legais, a pesquisa aconteceu através de trabalho de campo sistematizado a partir de questionários e entrevistas com professores coordenadores. No que se refere à análise das trajetórias profissionais dos professores coordenadores, a undamentação teórica contou com as contribuições de Antônio Nóvoa, Christine Josso e Zeila de Brito Fabri Demartini. Michel de Certeau subsidiou as reflexões sobre os mecanismos usados para burlar a política de formação em massa imposta a todas as escolas de Ensino Médio do Estado de São Paulo.(AU)

Non-zero mean Gaussian process prior wind field models

This report outlines the derivation and application of a non-zero mean, polynomial-exponential covariance function based Gaussian process which forms the prior wind field model used in 'autonomous' disambiguation. It is principally used since the non-zero mean permits the computation of realistic local wind vector prior probabilities which are required when applying the scaled-likelihood trick, as the marginals of the full wind field prior. As the full prior is multi-variate normal, these marginals are very simple to compute.

Generalized KPCA by adaptive rules in feature space

Principal component analysis (PCA) is well recognized in dimensionality reduction, and kernel PCA (KPCA) has also been proposed in statistical data analysis. However, KPCA fails to detect the nonlinear structure of data well when outliers exist. To reduce this problem, this paper presents a novel algorithm, named iterative robust KPCA (IRKPCA). IRKPCA works well in dealing with outliers, and can be carried out in an iterative manner, which makes it suitable to process incremental input data. As in the traditional robust PCA (RPCA), a binary field is employed for characterizing the outlier process, and the optimization problem is formulated as maximizing marginal distribution of a Gibbs distribution. In this paper, this optimization problem is solved by stochastic gradient descent techniques. In IRKPCA, the outlier process is in a high-dimensional feature space, and therefore kernel trick is used. IRKPCA can be regarded as a kernelized version of RPCA and a robust form of kernel Hebbian algorithm. Experimental results on synthetic data demonstrate the effectiveness of IRKPCA. © 2010 Taylor & Francis.