Regularized logistic regression and multi-objective variable selection for classifying MEG data

This paper addresses the question of maximizing classifier accuracy for classifying task-related mental activity from Magnetoencelophalography (MEG) data. We propose the use of different sources of information and introduce an automatic channel selection procedure. To determine an informative set of channels, our approach combines a variety of machine learning algorithms: feature subset selection methods, classifiers based on regularized logistic regression, information fusion, and multiobjective optimization based on probabilistic modeling of the search space. The experimental results show that our proposal is able to improve classification accuracy compared to approaches whose classifiers use only one type of MEG information or for which the set of channels is fixed a priori.

Desarrollo de aplicaciones basadas en Linux Embedded en una arquitectura basada en Cyclone V SoC (System on Chip) de Altera

We attempt to integrate and start up the set of necessary tools to deploy the design cycle of embedded systems based on Embedded Linux on a "Cyclone V SoC" made by Altera. First, we will analyze the available tools for designing the hardware system of the SoCkit development kit, made by Arrow, which has a "Cyclone V SoC" system (based on a "ARM Cortex-A9 MP Core" architecture). When designing the SoCkit board hardware, we will create a new peripheral to integrate it into the hardware system, so it can be used as any other existent resource of the SoCkit board previously configured. Next, we will analyze the tools to generate an Embedded Linux distribution adapted to the SoCkit board. In order to generate the Linux distribution we will use, on the one hand, a software package from Yocto recommended by Altera; on the other hand, the programs and tools of Altera, Embedded Development Suite. We will integrate all the components needed to build the Embedded Linux distribution, creating a complete and functional system which can be used for developing software applications. Finally, we will study the programs for developing and debugging applications in C or C++ language that will be executed in this hardware platform, then we will program a Linux application as an example to illustrate the use of SoCkit board resources. RESUMEN Se pretende integrar y poner en funcionamiento el conjunto de herramientas necesarias para desplegar el ciclo de diseño de sistemas embebidos basados en "Embedded Linux" sobre una "Cyclone V SoC" de Altera. En primer lugar, se analizarán las diversas herramientas disponibles para diseñar el sistema hardware de la tarjeta de desarrollo SoCkit, fabricada por Arrow, que dispone de un sistema "Cyclone V SoC" (basado en una arquitectura "ARM Cortex A9 MP Core"). En el diseño hardware de la SoCkit se creará un periférico propio y se integrará en el sistema, pudiendo ser utilizado como cualquier otro recurso de la tarjeta ya existente y configurado. A continuación, también se analizarán las herramientas para generar una distribución de "Embedded Linux" adaptado a la placa SoCkit. Para generar la distribución de Linux se utilizará, por una parte, un paquete software de Yocto recomendado por Altera y, por otra parte, las propias herramientas y programas de Altera. Se integrarán todos los componentes necesarios para construir la distribución Linux, creando un sistema completo y funcional que se pueda utilizar para el desarrollo de aplicaciones software. Por último, se estudiarán las herramientas para el diseño y depuración de aplicaciones en lenguaje C ó C++ que se ejecutarán en esta plataforma hardware. Se pretende desarrollar una aplicación de ejemplo para ilustrar el uso de los recursos más utilizados de la SoCkit.

Classifying GABAergic interneurons with semi-supervised projected model-based clustering

Objectives: A recently introduced pragmatic scheme promises to be a useful catalog of interneuron names.We sought to automatically classify digitally reconstructed interneuronal morphologies according tothis scheme. Simultaneously, we sought to discover possible subtypes of these types that might emergeduring automatic classification (clustering). We also investigated which morphometric properties weremost relevant for this classification.Materials and methods: A set of 118 digitally reconstructed interneuronal morphologies classified into thecommon basket (CB), horse-tail (HT), large basket (LB), and Martinotti (MA) interneuron types by 42 of theworld?s leading neuroscientists, quantified by five simple morphometric properties of the axon and fourof the dendrites. We labeled each neuron with the type most commonly assigned to it by the experts. Wethen removed this class information for each type separately, and applied semi-supervised clustering tothose cells (keeping the others? cluster membership fixed), to assess separation from other types and lookfor the formation of new groups (subtypes). We performed this same experiment unlabeling the cells oftwo types at a time, and of half the cells of a single type at a time. The clustering model is a finite mixtureof Gaussians which we adapted for the estimation of local (per-cluster) feature relevance. We performedthe described experiments on three different subsets of the data, formed according to how many expertsagreed on type membership: at least 18 experts (the full data set), at least 21 (73 neurons), and at least26 (47 neurons).Results: Interneurons with more reliable type labels were classified more accurately. We classified HTcells with 100% accuracy, MA cells with 73% accuracy, and CB and LB cells with 56% and 58% accuracy,respectively. We identified three subtypes of the MA type, one subtype of CB and LB types each, andno subtypes of HT (it was a single, homogeneous type). We got maximum (adapted) Silhouette widthand ARI values of 1, 0.83, 0.79, and 0.42, when unlabeling the HT, CB, LB, and MA types, respectively,confirming the quality of the formed cluster solutions. The subtypes identified when unlabeling a singletype also emerged when unlabeling two types at a time, confirming their validity. Axonal morphometricproperties were more relevant that dendritic ones, with the axonal polar histogram length in the [pi, 2pi) angle interval being particularly useful.Conclusions: The applied semi-supervised clustering method can accurately discriminate among CB, HT, LB, and MA interneuron types while discovering potential subtypes, and is therefore useful for neuronal classification. The discovery of potential subtypes suggests that some of these types are more heteroge-neous that previously thought. Finally, axonal variables seem to be more relevant than dendritic ones fordistinguishing among the CB, HT, LB, and MA interneuron types.

Classifying image analysis techniques from their output

In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

Glossary of paper terms and instructions to exporters for guidance in properly listing and classifying exports of paper and paper products on shippers' export declarations.

Mode of access: Internet.

Cataloging and classifying audio-visual materials; a guide to the preparation of films, filmstrips, slides, and records for use in the Materials Center of the School of Library Training and Service, Florida State University.

Mode of access: Internet.

Engineering aspects of Cyclone Tracy, Darwin, Australia, 1974 /

Includes bibliographical references.

Project cyclone: solution of a flutter problem. U.S. Navy Bureau of Aeronautics contract no as 54-545-C.

Mode of access: Internet.

Methods of classifying the lactic-acid bacteria /

"September 17, 1912."

Nonnuclear industrial hazardous waste : classifying for hazard management.

S/N 052-003-00859-9

Classifying client goals in community-based ABI rehabilitation: a taxonomy for profiling service delivery and conceptualizing outcomes

Purpose: To develop, confirm and trial a framework for analysing the content of goals set within community-based rehabilitation. This framework (taxonomy) is proposed as a tool to assist in service evaluation and outcome exploration. Method: Qualitative thematic analysis and categorization of 1765 rehabilitation goal statements in a four phase process of synthesis, refinement, verification and application. Results: A taxonomy of goal content was developed comprising 21 categories within five domains, utilizing 125 descriptors. The taxonomy demonstrated good inter-rater consistency and was able to discriminate between similar but related data sets comprising goal statements. Conclusion: Structured analysis of the content of goal setting (particularly in community rehabilitation) utilizing a framework such as the proposed taxonomy has considerable potential as a 'window' into service delivery to broaden the parameters of existing service evaluation and to more clearly link outcome exploration to intervention.