Rededication oc Chapman College Chapel, Orange, California

Rededication of Chapman College Chapel, Orange, California, February, 1978. The wooden-shingled church, constructed in 1909 for the congregation of Trinity Episcopal Church, is located on the northeast corner of East Maple Avenue and North Grand Street. Chapman College (now Chapman University) purchased the church for their chapel when the congregation moved to a new church on Canal Street.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).

Energy Efficient Cache Discovery in Wireless Mobile Ad oc Networks

Data caching is an attractive solution for reducing bandwidth demands and network latency in mobile ad hoc networks. Deploying caches in mobile nodes can reduce the overall traf c considerably. Cache hits eliminate the need to contact the data source frequently, which avoids additional network overhead. In this paper we propose a data discovery and cache management policy for cooperative caching, which reduces the power usage, caching overhead and delay by reducing the number of control messages flooded into the network .A cache discovery process based on position cordinates of neighboring nodes is developed for this .The stimulstion results gives a promising result based on the metrics of the studies.

Prediction of Learning Disabilities in School Age Children using SVM and Decision Tree

This paper highlights the prediction of Learning Disabilities (LD) in school-age children using two classification methods, Support Vector Machine (SVM) and Decision Tree (DT), with an emphasis on applications of data mining. About 10% of children enrolled in school have a learning disability. Learning disability prediction in school age children is a very complicated task because it tends to be identified in elementary school where there is no one sign to be identified. By using any of the two classification methods, SVM and DT, we can easily and accurately predict LD in any child. Also, we can determine the merits and demerits of these two classifiers and the best one can be selected for the use in the relevant field. In this study, Sequential Minimal Optimization (SMO) algorithm is used in performing SVM and J48 algorithm is used in constructing decision trees.

A Comparative study of HMM and SVM in Malayalam Digit Recognition

A primary medium for the human beings to communicate through language is Speech. Automatic Speech Recognition is wide spread today. Recognizing single digits is vital to a number of applications such as voice dialling of telephone numbers, automatic data entry, credit card entry, PIN (personal identification number) entry, entry of access codes for transactions, etc. In this paper we present a comparative study of SVM (Support Vector Machine) and HMM (Hidden Markov Model) to recognize and identify the digits used in Malayalam speech.

Multivariate Density Estimation: An SVM Approach

We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.

Buscar el oc??ano : la formaci??n espiritual como base integradora de nuestra acci??n

Resumen en ingl??s y catal??n. Monogr??fico con el t??tulo: Espiritualidad y acci??n social

La escuela se acerca al oc??ano : ballenas y delfines de nuestros mares, un recurso educativo

Se pretende acercar el medio natural marino al alumnado que por vivir en una isla rodeada de mar dispone de un lugar atractivo para crear situaciones de aprendizaje y de concienciaci??n y cuidado del entorno. No se trata de una simple visita para que el alumnado adquiera comportamientos y valores positivos hacia la conservaci??n del mismo, si no que existe un compendio de tareas educativas relacionadas entre s?? y que gu??an al alumnado en su proceso de aprendizaje antes, durante y despu??s de la salida. Se parte de un dise??o de tarea, desde el aula, donde el alumnado es protagonista, interpreta los datos y obtiene informaci??n necesaria para llevar un trabajo de campo productivo.

Evolutionary tuning of SVM parameter values in multiclass problems

Support vector machines (SVMs) were originally formulated for the solution of binary classification problems. In multiclass problems, a decomposition approach is often employed, in which the multiclass problem is divided into multiple binary subproblems, whose results are combined. Generally, the performance of SVM classifiers is affected by the selection of values for their parameters. This paper investigates the use of genetic algorithms (GAs) to tune the parameters of the binary SVMs in common multiclass decompositions. The developed GA may search for a set of parameter values common to all binary classifiers or for differentiated values for each binary classifier. (C) 2008 Elsevier B.V. All rights reserved.

Substitutional Impurities in PPV Crystals: An Intrinsic Donor-Acceptor System for High V(oc) Photovoltaic Devices

Defects are usually present in organic polymer films and are commonly invoked to explain the low efficiency obtained in organic-based optoelectronic devices. We propose that controlled insertion of substitutional impurities may, on the contrary, tune the optoelectronic properties of the underivatized organic material and, in the case studied here, maximize the efficiency of a solar cell. We investigate a specific oxygen-impurity substitution, the keto-defect -(CH(2)-C=O)- in underivatized crystalline poly(p-phenylenevinylene) (PPV), and its impact on the electronic structure of the bulk film, through a combined classical (force-field) and quantum mechanical (DFT) approach. We find defect states which suggest a spontaneous electron hole separation typical of a donor acceptor interface, optimal for photovoltaic devices. Furthermore, the inclusion of oxygen impurities does not introduce defect states in the gap and thus, contrary to standard donor-acceptor systems, should preserve the intrinsic high open circuit voltage (V(oc)) that may be extracted from PPV-based devices.

Structural effects and hydrogen bonds on N,N `-di(methoxycarbonylsulfenyl)urea, [CH(3)OC(O)SNH](2)CO, studied by experimental and theoretical methods

Pure N,N`-di(methoxycarbonylsulfenyl)urea, [CH(3)OC(O)SNH](2)CO, is quantitatively prepared by the hydrolysis reaction of CH(3)OC(O)SNCO and characterized by (1)H NMR, GC-MS and FTIR spectroscopy techniques. Structural and conformational properties are analyzed using a combined approach with data obtained from X-ray diffraction, vibrational spectra and theoretical calculation methods. The IR and Raman spectra for normal and deuterated species are reported. The crystal structure of [CH(3)OC(O)SNH](2)CO was determined by X-ray diffraction methods. The substance crystallizes in the orthorhombic P2(1)2(1)2 space group with a = 9.524(2), b = 12.003(1), c = 4.481 (1) angstrom, and Z = 2 moieties in the unit cell. The molecule is sited on a twofold crystallographic axis (C(2)) parallel to c and shows the anti-anti conformation (S-N single bonds antiperiplanar with respect to the opposite C-N single bonds in sulfenyl-urea-sic group). Neighboring molecules are arranged in a chain motif that extends along the C(2)-axis and is held by bifurcated NH center dot center dot center dot O center dot center dot center dot HN intermolecular bonds. A local planar symmetry is observed in the crystal for the central -SN(H)C(O)N(H)S- skeleton. Experimental and calculated data allow to trace this structural feature to the occurrence of N-H center dot center dot center dot O=C hydrogen bonding interactions. Calculated vibrational and structural properties are in good agreement with the experimentally determined features. (C) 2008 Elsevier B.V. All rights reserved.

Parameter estimation of one-class SVM on imbalance text classification

Compared with conventional two-class learning schemes, one-class classification simply uses a single class for training purposes. Applying one-class classification to the minorities in an imbalanced data has been shown to achieve better performance than the two-class one. In this paper, in order to make the best use of all the available information during the learning procedure, we propose a general framework which first uses the minority class for training in the one-class classification stage; and then uses both minority and majority class for estimating the generalization performance of the constructed classifier. Based upon this generalization performance measurement, parameter search algorithm selects the best parameter settings for this classifier. Experiments on UCI and Reuters text data show that one-class SVM embedded in this framework achieves much better performance than the standard one-class SVM alone and other learning schemes, such as one-class Naive Bayes, one-class nearest neighbour and neural network.

Microarray data classification using automatic SVM kernel selection

Microarray data classification is one of the most important emerging clinical applications in the medical community. Machine learning algorithms are most frequently used to complete this task. We selected one of the state-of-the-art kernel-based algorithms, the support vector machine (SVM), to classify microarray data. As a large number of kernels are available, a significant research question is what is the best kernel for patient diagnosis based on microarray data classification using SVM? We first suggest three solutions based on data visualization and quantitative measures. Different types of microarray problems then test the proposed solutions. Finally, we found that the rule-based approach is most useful for automatic kernel selection for SVM to classify microarray data.