Is Evolution an Algorithm? Effects of local entropy in unsupervised learning and protein evolution

L'abstract è presente nell'allegato / the abstract is in the attachmen

Latent Space Data Assimilation by using Deep Learning

Performing Data Assimilation (DA) at a low cost is of prime concern in Earth system modeling, particularly at the time of big data where huge quantities of observations are available. Capitalizing on the ability of Neural Networks techniques for approximating the solution of PDE's, we incorporate Deep Learning (DL) methods into a DA framework. More precisely, we exploit the latent structure provided by autoencoders (AEs) to design an Ensemble Transform Kalman Filter with model error (ETKF-Q) in the latent space. Model dynamics are also propagated within the latent space via a surrogate neural network. This novel ETKF-Q-Latent (thereafter referred to as ETKF-Q-L) algorithm is tested on a tailored instructional version of Lorenz 96 equations, named the augmented Lorenz 96 system: it possesses a latent structure that accurately represents the observed dynamics. Numerical experiments based on this particular system evidence that the ETKF-Q-L approach both reduces the computational cost and provides better accuracy than state of the art algorithms, such as the ETKF-Q.Comment: 15 pages, 7 figures and 3 table

AFRANCI : multi-layer architecture for cognitive agents

Tese de doutoramento. Engenharia Electrotécnica e de Computadores. Faculdade de Engenharia. Universidade do Porto. 201

Memristor Platforms for Pattern Recognition Memristor Theory, Systems and Applications

In the last decade a large scientific community has focused on the study of the memristor. The memristor is thought to be by many the best alternative to CMOS technology, which is gradually showing its flaws. Transistor technology has developed fast both under a research and an industrial point of view, reducing the size of its elements to the nano-scale. It has been possible to generate more and more complex machinery and to communicate with that same machinery thanks to the development of programming languages based on combinations of boolean operands. Alas as shown by Moore’s law, the steep curve of implementation and of development of CMOS is gradually reaching a plateau. It is clear the need of studying new elements that can combine the efficiency of transistors and at the same time increase the complexity of the operations. Memristors can be described as non-linear resistors capable of maintaining memory of the resistance state that they reached. From their first theoretical treatment by Professor Leon O. Chua in 1971, different research groups have devoted their expertise in studying the both the fabrication and the implementation of this new promising technology. In the following thesis a complete study on memristors and memristive elements is presented. The road map that characterizes this study departs from a deep understanding of the physics that govern memristors, focusing on the HP model by Dr. Stanley Williams. Other devices such as phase change memories (PCMs) and memristive biosensors made with Si nano-wires have been studied, developing emulators and equivalent circuitry, in order to describe their complex dynamics. This part sets the first milestone of a pathway that passes trough more complex implementations such as neuromorphic systems and neural networks based on memristors proving their computing efficiency. Finally it will be presented a memristror-based technology, covered by patent, demonstrating its efficacy for clinical applications. The presented system has been designed for detecting and assessing automatically chronic wounds, a syndrome that affects roughly 2% of the world population, through a Cellular Automaton which analyzes and processes digital images of ulcers. Thanks to its precision in measuring the lesions the proposed solution promises not only to increase healing rates, but also to prevent the worsening of the wounds that usually lead to amputation and death

Contributions on distance-based algorithms, multi-classifier construction and pairwise classification

179 p.Aurkezten den ikerketa lan honetan saikapen atazak landu dira, non helburua,sailkapen gainbegiratuaren artearen-egoera aberastea izan den. Sailkapengainbegiratuaren zenbait estrategi analizatu dira, beraien ezaugarri etaahuleziak aztertuz. Beraz, ezaugarri positiboak mantenduz, ahuleziak hobetzekosaiakera egin da. Hau burutu ahal izateko, sailkapen gainbegiratuarenzenbait estrategi konbinatzeaz gain, zenbait bilaketa heuristiko ere erabili dira.Sailkapen gainbegiratuko 3 ikerketa lerro desberdinetan burutu dira ekarpenak.Aurkezten diren lehenengo proposamenak, K-NN algoritmoan zentratzendira, honen zenbait bertsio aurkezten direlarik. Ondoren sailkatzaileen konbinaketarekinerlazionatutako beste lan bat aurkezten da. Eta azkenik, binakakosailkapenaren zenbait estrategi berritzaile proposatzen dira. Ekarpenhauek aldizkari edo konferentzi internazionaletan publikatuak edo bidaliakizan dira.Buruturiko experimentuetan, proposatutako algoritmoak artearen-estatuanaurkituriko zenbait algoritmorekin konparatu dira, emaitza interesgarriak lortuaz.Honetaz gain, emaitza hauetatik ondorio esanguratsuak eskuratzeko asmoz,test estatistikoen erabilera ere burutu da

Time series prediction and forecasting using Deep learning Architectures

Nature brings time series data everyday and everywhere, for example, weather data, physiological signals and biomedical signals, financial and business recordings. Predicting the future observations of a collected sequence of historical observations is called time series forecasting. Forecasts are essential, considering the fact that they guide decisions in many areas of scientific, industrial and economic activity such as in meteorology, telecommunication, finance, sales and stock exchange rates. A massive amount of research has already been carried out by researchers over many years for the development of models to improve the time series forecasting accuracy. The major aim of time series modelling is to scrupulously examine the past observation of time series and to develop an appropriate model which elucidate the inherent behaviour and pattern existing in time series. The behaviour and pattern related to various time series may possess different conventions and infact requires specific countermeasures for modelling. Consequently, retaining the neural networks to predict a set of time series of mysterious domain remains particularly challenging. Time series forecasting remains an arduous problem despite the fact that there is substantial improvement in machine learning approaches. This usually happens due to some factors like, different time series may have different flattering behaviour. In real world time series data, the discriminative patterns residing in the time series are often distorted by random noise and affected by high-frequency perturbations. The major aim of this thesis is to contribute to the study and expansion of time series prediction and multistep ahead forecasting method based on deep learning algorithms. Time series forecasting using deep learning models is still in infancy as compared to other research areas for time series forecasting.Variety of time series data has been considered in this research. We explored several deep learning architectures on the sequential data, such as Deep Belief Networks (DBNs), Stacked AutoEncoders (SAEs), Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs). Moreover, we also proposed two different new methods based on muli-step ahead forecasting for time series data. The comparison with state of the art methods is also exhibited. The research work conducted in this thesis makes theoretical, methodological and empirical contributions to time series prediction and multi-step ahead forecasting by using Deep Learning Architectures