Using Neural Network Classifier Support Vector Machine Regression for the prediction of Melting Point of Drug – like compounds

In our study we use a kernel based classification technique, Support Vector Machine Regression for predicting the Melting Point of Drug – like compounds in terms of Topological Descriptors, Topological Charge Indices, Connectivity Indices and 2D Auto Correlations. The Machine Learning model was designed, trained and tested using a dataset of 100 compounds and it was found that an SVMReg model with RBF Kernel could predict the Melting Point with a mean absolute error 15.5854 and Root Mean Squared Error 19.7576

An approximation method using approximate approximations

The aim of this paper is to extend the method of approximate approximations to boundary value problems. This method was introduced by V. Maz'ya in 1991 and has been used until now for the approximation of smooth functions defined on the whole space and for the approximation of volume potentials. In the present paper we develop an approximation procedure for the solution of the interior Dirichlet problem for the Laplace equation in two dimensions using approximate approximations. The procedure is based on potential theoretical considerations in connection with a boundary integral equations method and consists of three approximation steps as follows. In a first step the unknown source density in the potential representation of the solution is replaced by approximate approximations. In a second step the decay behavior of the generating functions is used to gain a suitable approximation for the potential kernel, and in a third step Nyström's method leads to a linear algebraic system for the approximate source density. For every step a convergence analysis is established and corresponding error estimates are given.

Virtual Radio Engine

Software Defined Radio (SDR) hardware platforms use parallel architectures. Current concepts of developing applications (such as WLAN) for these platforms are complex, because developers describe an application with hardware-specifics that are relevant to parallelism such as mapping and scheduling. To reduce this complexity, we have developed a new programming approach for SDR applications, called Virtual Radio Engine (VRE). VRE defines a language for describing applications, and a tool chain that consists of a compiler kernel and other tools (such as a code generator) to generate executables. The thesis presents this concept, as well as describes the language and the compiler kernel that have been developed by the author. The language is hardware-independent, i.e., developers describe tasks and dependencies between them. The compiler kernel performs automatic parallelization, i.e., it is capable of transforming a hardware-independent program into a hardware-specific program by solving hardware-specifics, in particular mapping, scheduling and synchronizations. Thus, VRE simplifies programming tasks as developers do not solve hardware-specifics manually.

Approximate solutions and error estimates for a Stokes boundary value problem

The aim of this paper is the numerical treatment of a boundary value problem for the system of Stokes' equations. For this we extend the method of approximate approximations to boundary value problems. This method was introduced by V. Maz'ya in 1991 and has been used until now for the approximation of smooth functions defined on the whole space and for the approximation of volume potentials. In the present paper we develop an approximation procedure for the solution of the interior Dirichlet problem for the system of Stokes' equations in two dimensions. The procedure is based on potential theoretical considerations in connection with a boundary integral equations method and consists of three approximation steps as follows. In a first step the unknown source density in the potential representation of the solution is replaced by approximate approximations. In a second step the decay behavior of the generating functions is used to gain a suitable approximation for the potential kernel, and in a third step Nyström's method leads to a linear algebraic system for the approximate source density. For every step a convergence analysis is established and corresponding error estimates are given.

Comparing Support Vector Machines with Gaussian Kernels to Radial Basis Function Classifiers

The Support Vector (SV) machine is a novel type of learning machine, based on statistical learning theory, which contains polynomial classifiers, neural networks, and radial basis function (RBF) networks as special cases. In the RBF case, the SV algorithm automatically determines centers, weights and threshold such as to minimize an upper bound on the expected test error. The present study is devoted to an experimental comparison of these machines with a classical approach, where the centers are determined by $k$--means clustering and the weights are found using error backpropagation. We consider three machines, namely a classical RBF machine, an SV machine with Gaussian kernel, and a hybrid system with the centers determined by the SV method and the weights trained by error backpropagation. Our results show that on the US postal service database of handwritten digits, the SV machine achieves the highest test accuracy, followed by the hybrid approach. The SV approach is thus not only theoretically well--founded, but also superior in a practical application.

An Empirical Comparison of SNoW and SVMs for Face Detection

Impressive claims have been made for the performance of the SNoW algorithm on face detection tasks by Yang et. al. [7]. In particular, by looking at both their results and those of Heisele et. al. [3], one could infer that the SNoW system performed substantially better than an SVM-based system, even when the SVM used a polynomial kernel and the SNoW system used a particularly simplistic 'primitive' linear representation. We evaluated the two approaches in a controlled experiment, looking directly at performance on a simple, fixed-sized test set, isolating out 'infrastructure' issues related to detecting faces at various scales in large images. We found that SNoW performed about as well as linear SVMs, and substantially worse than polynomial SVMs.

Notes on PCA, Regularization, Sparsity and Support Vector Machines

We derive a new representation for a function as a linear combination of local correlation kernels at optimal sparse locations and discuss its relation to PCA, regularization, sparsity principles and Support Vector Machines. We first review previous results for the approximation of a function from discrete data (Girosi, 1998) in the context of Vapnik"s feature space and dual representation (Vapnik, 1995). We apply them to show 1) that a standard regularization functional with a stabilizer defined in terms of the correlation function induces a regression function in the span of the feature space of classical Principal Components and 2) that there exist a dual representations of the regression function in terms of a regularization network with a kernel equal to a generalized correlation function. We then describe the main observation of the paper: the dual representation in terms of the correlation function can be sparsified using the Support Vector Machines (Vapnik, 1982) technique and this operation is equivalent to sparsify a large dictionary of basis functions adapted to the task, using a variation of Basis Pursuit De-Noising (Chen, Donoho and Saunders, 1995; see also related work by Donahue and Geiger, 1994; Olshausen and Field, 1995; Lewicki and Sejnowski, 1998). In addition to extending the close relations between regularization, Support Vector Machines and sparsity, our work also illuminates and formalizes the LFA concept of Penev and Atick (1996). We discuss the relation between our results, which are about regression, and the different problem of pattern classification.

An Equivalence Between Sparse Approximation and Support Vector Machines

In the first part of this paper we show a similarity between the principle of Structural Risk Minimization Principle (SRM) (Vapnik, 1982) and the idea of Sparse Approximation, as defined in (Chen, Donoho and Saunders, 1995) and Olshausen and Field (1996). Then we focus on two specific (approximate) implementations of SRM and Sparse Approximation, which have been used to solve the problem of function approximation. For SRM we consider the Support Vector Machine technique proposed by V. Vapnik and his team at AT&T Bell Labs, and for Sparse Approximation we consider a modification of the Basis Pursuit De-Noising algorithm proposed by Chen, Donoho and Saunders (1995). We show that, under certain conditions, these two techniques are equivalent: they give the same solution and they require the solution of the same quadratic programming problem.

Grain size analyses in tin-lead glazes based on 2d-sections

A problem in the archaeometric classification of Catalan Renaissance pottery is the fact, that the clay supply of the pottery workshops was centrally organized by guilds, and therefore usually all potters of a single production centre produced chemically similar ceramics. However, analysing the glazes of the ware usually a large number of inclusions in the glaze is found, which reveal technological differences between single workshops. These inclusions have been used by the potters in order to opacify the transparent glaze and to achieve a white background for further decoration. In order to distinguish different technological preparation procedures of the single workshops, at a Scanning Electron Microscope the chemical composition of those inclusions as well as their size in the two-dimensional cut is recorded. Based on the latter, a frequency distribution of the apparent diameters is estimated for each sample and type of inclusion. Following an approach by S.D. Wicksell (1925), it is principally possible to transform the distributions of the apparent 2D-diameters back to those of the true three-dimensional bodies. The applicability of this approach and its practical problems are examined using different ways of kernel density estimation and Monte-Carlo tests of the methodology. Finally, it is tested in how far the obtained frequency distributions can be used to classify the pottery

Active regions for colour texture segmentation integrating region and boundary information

In this paper a colour texture segmentation method, which unifies region and boundary information, is proposed. The algorithm uses a coarse detection of the perceptual (colour and texture) edges of the image to adequately place and initialise a set of active regions. Colour texture of regions is modelled by the conjunction of non-parametric techniques of kernel density estimation (which allow to estimate the colour behaviour) and classical co-occurrence matrix based texture features. Therefore, region information is defined and accurate boundary information can be extracted to guide the segmentation process. Regions concurrently compete for the image pixels in order to segment the whole image taking both information sources into account. Furthermore, experimental results are shown which prove the performance of the proposed method

Tratamiento de Artritis reumatoide con antagonistas del factor de necrosis tumoral alfa y su asociación con el desarrollo de melanoma cutáneo: revisión sistemática de la literatura y meta-análisis.

Introducción: El tratamiento con antagonistas del factor de necrosis tumoral alfa (anti TNF) ha impactado el pronóstico y la calidad de vida de los pacientes con artritis reumatoide (AR) positivamente, sin embargo, se interroga un incremento en el riesgo de desarrollar melanoma. Objetivo: Conocer la asociación entre el uso de anti TNF y el desarrollo de melanoma maligno en pacientes con AR. Metodología: Se realizó una búsqueda sistemática en MEDLINE, EMBASE, COCHRANE LIBRARY y LILACS para ensayos clínicos, estudios observacionales, revisiones y meta-análisis en pacientes adultos con diagnóstico de AR y manejo con anti TNF (Certolizumab pegol, Adalimumab, Etanercept, Infliximab y Golimumab). Resultados: 37 estudios clínicos cumplieron los criterios de inclusión para el meta-análisis, con una población de 16567 pacientes. El análisis de heterogeneidad no fue significativo (p=1), no se encontró diferencia en el riesgo entre los grupos comparados DR -0.00 (IC 95% -0.001; -0.001). Un análisis adicional de los estudios en los que se reportó al menos 1 caso de melanoma (4222 pacientes) tampoco mostró diferencia en el riesgo DR -0.00 (IC 95% -0.004 ; -0.003). Conclusión: En la evidencia disponible a la fecha no encontramos asociación significativa entre el tratamiento con anti TNF en pacientes con diagnóstico de AR y el desarrollo de melanoma cutáneo.

The crime kuznets curve

We document the existence of a Crime Kuznets Curve in US states since the 1970s. As income levels have risen, crime has followed an inverted U-shaped pattern, first increasing and then dropping. The Crime Kuznets Curve is not explained by income inequality. In fact, we show that during the sample period inequality has risen monotonically with income, ruling out the traditional Kuznets Curve. Our finding is robust to adding a large set of controls that are used in the literature to explain the incidence of crime, as well as to controlling for state and year fixed effects. The Curve is also revealed in nonparametric specifications. The Crime Kuznets Curve exists for property crime and for some categories of violent crime.

Fronts from complex two-dimensional dispersal kernels: theory and application to Reid's paradox

Bimodal dispersal probability distributions with characteristic distances differing by several orders of magnitude have been derived and favorably compared to observations by Nathan [Nature (London) 418, 409 (2002)]. For such bimodal kernels, we show that two-dimensional molecular dynamics computer simulations are unable to yield accurate front speeds. Analytically, the usual continuous-space random walks (CSRWs) are applied to two dimensions. We also introduce discrete-space random walks and use them to check the CSRW results (because of the inefficiency of the numerical simulations). The physical results reported are shown to predict front speeds high enough to possibly explain Reid's paradox of rapid tree migration. We also show that, for a time-ordered evolution equation, fronts are always slower in two dimensions than in one dimension and that this difference is important both for unimodal and for bimodal kernels

Anisotropic dispersion, space competition, and the slowdown of the Neolithic transition

The front speed of the Neolithic (farmer) spread in Europe decreased as it reached Northern latitudes, where the Mesolithic (huntergatherer) population density was higher. Here, we describe a reaction diffusion model with (i) an anisotropic dispersion kernel depending on the Mesolithic population density gradient and (ii) a modified population growth equation. Both effects are related to the space available for the Neolithic population. The model is able to explain the slowdown of the Neolithic front as observed from archaeological data

Estudi de mètodes de classificació borrosa i la seva aplicació a l'agrupació de zones geogràfiques en base a diverses característiques incertes

Aquesta memòria està estructurada en sis capítols amb l'objectiu final de fonamentar i desenvolupar les eines matemàtiques necessàries per a la classificació de conjunts de subconjunts borrosos. El nucli teòric del treball el formen els capítols 3, 4 i 5; els dos primers són dos capítols de caire més general, i l'últim és una aplicació dels anteriors a la classificació dels països de la Unió Europea en funció de determinades característiques borroses. En el capítol 1 s'analitzen les diferents connectives borroses posant una especial atenció en aquells aspectes que en altres capítols tindran una aplicació específica. És per aquest motiu que s'estudien les ordenacions de famílies de t-normes, donada la seva importància en la transitivitat de les relacions borroses. La verificació del principi del terç exclòs és necessària per assegurar que un conjunt significatiu de mesures borroses generalitzades, introduïdes en el capítol 3, siguin reflexives. Estudiem per a quines t-normes es verifica aquesta propietat i introduïm un nou conjunt de t-normes que verifiquen aquest principi. En el capítol 2 es fa un recorregut general per les relacions borroses centrant-nos en l'estudi de la clausura transitiva per a qualsevol t-norma, el càlcul de la qual és en molts casos fonamental per portar a terme el procés de classificació. Al final del capítol s'exposa un procediment pràctic per al càlcul d'una relació borrosa amb l'ajuda d'experts i de sèries estadístiques. El capítol 3 és un monogràfic sobre mesures borroses. El primer objectiu és relacionar les mesures (o distàncies) usualment utilitzades en les aplicacions borroses amb les mesures conjuntistes crisp. Es tracta d'un enfocament diferent del tradicional enfocament geomètric. El principal resultat és la introducció d'una família parametritzada de mesures que verifiquen unes propietats de caràcter conjuntista prou satisfactòries. L'estudi de la verificació del principi del terç exclòs té aquí la seva aplicació sobre la reflexivitat d'aquestes mesures, que són estudiades amb una certa profunditat en alguns casos particulars. El capítol 4 és, d'entrada, un repàs dels principals resultats i mètodes borrosos per a la classificació dels elements d'un mateix conjunt de subconjunts borrosos. És aquí on s'apliquen els resultats sobre les ordenacions de les famílies de t-normes i t-conormes estudiades en el capítol 1. S'introdueix un nou mètode de clusterització, canviant la matriu de la relació borrosa cada vegada que s'obté un nou clúster. Aquest mètode permet homogeneïtzar la metodologia del càlcul de la relació borrosa amb el mètode de clusterització. El capítol 5 tracta sobre l'agrupació d'objectes de diferent naturalesa; és a dir, subconjunts borrosos que pertanyen a diferents conjunts. Aquesta teoria ja ha estat desenvolupada en el cas binari; aquí, el que es presenta és la seva generalització al cas n-ari. Més endavant s'estudien certs aspectes de les projeccions de la relació sobre un cert espai i el recíproc, l'estudi de cilindres de relacions predeterminades. Una aplicació sobre l'agrupació de les comarques gironines en funció de certes variables borroses es presenta al final del capítol. L'últim capítol és eminentment pràctic, ja que s'aplica allò estudiat principalment en els capítols 3 i 4 a la classificació dels països de la Unió Europea en funció de determinades característiques borroses. Per tal de fer previsions per a anys venidors s'han utilitzat sèries temporals i xarxes neuronals. S'han emprat diverses mesures i mètodes de clusterització per tal de poder comparar els diversos dendogrames que resulten del procés de clusterització. Finalment, als annexos es poden consultar les sèries estadístiques utilitzades, la seva extrapolació, els càlculs per a la construcció de les matrius de les relacions borroses, les matrius de mesura i les seves clausures.