Study of macroscopic and microscopic properties of liposomes produced using microfluidic methods

For the last decades, lipid vesicles or liposomes, vesicles formed by a bilayer of amphiphilic lipids, have been used as a toy model for studying the cell membrane and for applications in cosmetics and drug delivery. Traditional methods for producing liposomes face some problems such as the heterogeneity in size and composition of the liposomes produced. A few years ago, a novel method that produces liposomes with homogeneous size and composition was developed. This novel method is based on the use of water in oil in water ultra-thin double emulsions, with lipids dissolved in the oil phase, as templates for the liposome production. These ultra-thin double emulsions are produced using glass capillary microfluidic devices. This new method for producing liposomes seems very promising, but since the liposomes are formed by the oil phase evaporation of the double emulsions, the doubt that some residual oil in the bilayer may alter the properties of the liposomes appears. In this work different phenomena and properties of liposomes that have been studied for the ones produced using conventional methods are studied for liposomes produced using microfluidic methods. The microfluidic apprOutgoin

Towards machine learning applied to time series based network traffic forecasting

This TFG will explore some specific use cases of the application of Machine Learning techniques to Software-Define Networks, in particular to overlay protocols such as LISP, VXLAN, etc.The aim of this project is to implement a network traffic forecasting model using time series and improve its performance with machine learning techniques, offering a better prediction based in outlier correction. This is a project developed in the Computer Architecture Department (DAC) at the Universitat Politècnica de Catalunya (UPC). Time Series modeling methodology is able to shape a trend and take care of any existing outlier, however it does not cover outlier impact on forecasting. In order to achieve more precision and better confidence intervals, the model combines outlier detection methodology and Artificial Neural Networks to quantify and predict outliers. A study is realized over external data to find out if there is an improvement and its effect on the predictions. Machine learning techniques as Artificial Neural Networks has proven to be an improvement of the current methodology to realize forecasting using Time Series modeling. Future work will be oriented to create an improved standard of this system focused on generalize the model.El objetivo de este proyecto es implementar un modelo de previsión de tráfico de red utilizando series temporales y mejorar su rendimiento con técnicas de aprendizaje automático, generando una mejor predicción basada en la corrección de valores atípicos. Se trata de un proyecto desarrollado en el Departamento de Arquitectura de Computadores (DAC) de la Universidad Politécnica de Cataluña (UPC). La metodología de modelado de series temporales es capaz de predecir una tendencia y hacerse cargo de cualquier valor atípico ya existente, sin embargo, no cubre el impacto de estos sobre la predicción. Con el fin de lograr una mayor precisión y mejores intervalos de confianza, el modelo combina la metodología de detección de valores atípicos y redes neuronales artificiales para cuantificar y predecir los atípicos. Un estudio se realiza sobre datos externos para averiguar si hay una mejora y su efecto sobre las predicciones. Las técnicas de aprendizaje automático, como redes neuronales artificiales, han demostrado ser una mejora de la metodología actual para realizar la predicción utilizando modelos de series de tiempo. El trabajo futuro se orientará para crear un mejor nivel de este sistema se centró en generalizar el modelo.L'objectiu d'aquest projecte és implementar un model de previsió de tràfic de xarxa utilitzant sèries temporals i millorar el seu rendiment amb tècniques d'aprenentatge automàtic, generant una millor predicció basada en la correcció de valors atípics. Es tracta d'un projecte desenvolupat al Departament d'Arquitectura de Computadors (DAC) de la Universitat Politècnica de Catalunya (UPC). La metodologia de modelatge de sèries temporals és capaç de predir una tendència i fer-se càrrec de qualsevol valor atípic ja existent, però, no cobreix l'impacte d'aquests sobre la predicció. Per tal d'aconseguir una major precisió i millors intervals de confiança, el model combina la metodologia de detecció de valors atípics i xarxes neuronals artificials per quantificar i predir els atípics. Un estudi es realitza sobre dades externes per esbrinar si hi ha una millora i el seu efecte sobre les prediccions. Les tècniques d'aprenentatge automàtic, com xarxes neuronals artificials, han demostrat ser una millora de la metodologia actual per a fer predicció utilitzant models de sèries de temps. El treball futur s'orientarà per crear un millor nivell d'aquest sistema es va centrar en generalitzar el model

Locally linear approximation for Kernel methods : the Railway Kernel

In this paper we present a new kernel, the Railway Kernel, that works properly for general (nonlinear) classification problems, with the interesting property that acts locally as a linear kernel. In this way, we avoid potential problems due to the use of a general purpose kernel, like the RBF kernel, as the high dimension of the induced feature space. As a consequence, following our methodology the number of support vectors is much lower and, therefore, the generalization capability of the proposed kernel is higher than the obtained using RBF kernels. Experimental work is shown to support the theoretical issues

Schroeder and Whiting on Knowledge and Defeat

Daniel Whiting has argued, in this journal, that Mark Schroeder’s analysis of knowledge in terms of subjectively and objectively sufficient reasons for belief makes wrong predictions in fake barn cases. Schroeder has replied that this problem may be avoided if one adopts a suitable account of perceptual reasons. I argue that Schroeder’s reply fails to deal with the general worry underlying Whiting’s purported counterexample, because one can construct analogous potential counterexamples that do not involve perceptual reasons at all. Nevertheless, I claim that it is possible to overcome Whiting’s objection, by showing that it rests on an inadequate characterization of how defeat works in the examples in question

Representing functional data in reproducing Kernel Hilbert Spaces with applications to clustering and classification

Functional data are difficult to manage for many traditional statistical techniques given their very high (or intrinsically infinite) dimensionality. The reason is that functional data are essentially functions and most algorithms are designed to work with (low) finite-dimensional vectors. Within this context we propose techniques to obtain finitedimensional representations of functional data. The key idea is to consider each functional curve as a point in a general function space and then project these points onto a Reproducing Kernel Hilbert Space with the aid of Regularization theory. In this work we describe the projection method, analyze its theoretical properties and propose a model selection procedure to select appropriate Reproducing Kernel Hilbert spaces to project the functional data.Functional data, Reproducing, Kernel Hilbert Spaces, Regularization theory

A robust partial least squares method with applications

Partial least squares regression (PLS) is a linear regression technique developed to relate many regressors to one or several response variables. Robust methods are introduced to reduce or remove the effect of outlying data points. In this paper we show that if the sample covariance matrix is properly robustified further robustification of the linear regression steps of the PLS algorithm becomes unnecessary. The robust estimate of the covariance matrix is computed by searching for outliers in univariate projections of the data on a combination of random directions (Stahel-Donoho) and specific directions obtained by maximizing and minimizing the kurtosis coefficient of the projected data, as proposed by Peña and Prieto (2006). It is shown that this procedure is fast to apply and provides better results than other procedures proposed in the literature. Its performance is illustrated by Monte Carlo and by an example, where the algorithm is able to show features of the data which were undetected by previous methods