Intrinsic Dimension Estimation for non-Euclidean manifolds: from metagenomics to unweighted networks

Within the field of unsupervised manifold learning, Intrinsic Dimension estimators are among the most important analysis tools. The Intrinsic Dimension provides a measure of the dimensionality of the hidden manifold from which data are sampled, even if the manifold is embedded in a space with a much higher number of features. The present Thesis tackles the still unanswered problem of computing the Intrinsic Dimension (ID) of spaces characterised by non-Euclidean metrics. In particular, we focus on datasets where the distances between points are measured by means of Manhattan, Hamming or shortest-path metrics and, thus, can only assume discrete values. This peculiarity has deep consequences on the way datapoints populate the neighbourhoods and on the structure on the manifold. For this reason we develop a general purpose, nearest-neighbours-based ID estimator that has two peculiar features: the capability of selecting explicitly the scale at which the Intrinsic Dimension is computed and a validation procedure to check the reliability of the provided estimate. We thus specialise the estimator to lattice spaces, where the volume is measured by means of the Ehrhart polynomials. After testing the reliability of the estimator on artificial datasets, we apply it to genomics sequences and discover an unexpectedly low ID, suggesting that the evolutive pressure exerts strong restraints on the way the nucleotide basis are allowed to mutate. This same framework is then employed to profile the scaling of the ID of unweighted networks. The diversity of the obtained ID signatures prompted us into using it as a signature to characterise the networks. Concretely, we employ the ID as a summary statistics within an Approximate Bayesian Computation framework in order to pinpoint the parameters of network mechanistic generative models of increasing complexity. We discover that, by targeting the ID of a given network, other typical network properties are also fairly retrieved. As a last methodological development, we improved the ID estimator by adaptively selecting, for each datapoint, the largest neighbourhoods with an approximately constant density. This offers a quantitative criterion to automatically select a meaningful scale at which the ID is computed and, at the same time, allows to enforce the hypothesis of the method, implying more reliable estimates

Testing the "weak form efficient market" hypothesis: an analysis on european and italian equity markets

The purpose of the thesis is testing the EMH in the weak form on Ftse Mib and Stoxx Europe 600 indexes using econometric and statistical tools. a comparison among the methodologies and a critical analysis of the results lead to empirical evidence that both indexes are weak efficent in the examined time frame ( jan. 1999 to feb. 2016)ope

Essays on the nonlinear and nonstochastic nature of stock market data

The nature and structure of stock-market price dynamics is an area of ongoing and rigourous scientific debate. For almost three decades, most emphasis has been given on upholding the concepts of Market Efficiency and rational investment behaviour. Such an approach has favoured the development of numerous linear and nonlinear models mainly of stochastic foundations. Advances in mathematics have shown that nonlinear deterministic processes i.e. "chaos" can produce sequences that appear random to linear statistical techniques. Till recently, investment finance has been a science based on linearity and stochasticity. Hence it is important that studies of Market Efficiency include investigations of chaotic determinism and power laws. As far as chaos is concerned, there are rather mixed or inconclusive research results, prone with controversy. This inconclusiveness is attributed to two things: the nature of stock market time series, which are highly volatile and contaminated with a substantial amount of noise of largely unknown structure, and the lack of appropriate robust statistical testing procedures. In order to overcome such difficulties, within this thesis it is shown empirically and for the first time how one can combine novel techniques from recent chaotic and signal analysis literature, under a univariate time series analysis framework. Three basic methodologies are investigated: Recurrence analysis, Surrogate Data and Wavelet transforms. Recurrence Analysis is used to reveal qualitative and quantitative evidence of nonlinearity and nonstochasticity for a number of stock markets. It is then demonstrated how Surrogate Data, under a statistical hypothesis testing framework, can be simulated to provide similar evidence. Finally, it is shown how wavelet transforms can be applied in order to reveal various salient features of the market data and provide a platform for nonparametric regression and denoising. The results indicate that without the invocation of any parametric model-based assumptions, one can easily deduce that there is more to linearity and stochastic randomness in the data. Moreover, substantial evidence of recurrent patterns and aperiodicities is discovered which can be attributed to chaotic dynamics. These results are therefore very consistent with existing research indicating some types of nonlinear dependence in financial data. Concluding, the value of this thesis lies in its contribution to the overall evidence on Market Efficiency and chaotic determinism in financial markets. The main implication here is that the theory of equilibrium pricing in financial markets may need reconsideration in order to accommodate for the structures revealed

Information Extraction and Modeling from Remote Sensing Images: Application to the Enhancement of Digital Elevation Models

To deal with high complexity data such as remote sensing images presenting metric resolution over large areas, an innovative, fast and robust image processing system is presented. The modeling of increasing level of information is used to extract, represent and link image features to semantic content. The potential of the proposed techniques is demonstrated with an application to enhance and regularize digital elevation models based on information collected from RS images

Sandpile models

This survey is an extended version of lectures given at the Cornell Probability Summer School 2013. The fundamental facts about the Abelian sandpile model on a finite graph and its connections to related models are presented. We discuss exactly computable results via Majumdar and Dhar's method. The main ideas of Priezzhev's computation of the height probabilities in 2D are also presented, including explicit error estimates involved in passing to the limit of the infinite lattice. We also discuss various questions arising on infinite graphs, such as convergence to a sandpile measure, and stabilizability of infinite configurations.Comment: 72 pages - v3 incorporates referee's comments. References closely related to the lectures were added/update

Surface Modifications in Adhesion and Wetting

Advances in surface modification are changing the world. Changing surface properties of bulk materials with nanometer scale coatings enables inventions ranging from the familiar non-stick frying pan to advanced composite aircraft. Nanometer or monolayer coatings used to modify a surface affect the macro-scale properties of a system; for example, composite adhesive joints between the fuselage and internal frame of Boeing\u27s 787 Dreamliner play a vital role in the structural stability of the aircraft. This dissertation focuses on a collection of surface modification techniques that are used in the areas of adhesion and wetting. Adhesive joints are rapidly replacing the familiar bolt and rivet assemblies used by the aerospace and automotive industries. This transition is fueled by the incorporation of composite materials into aircraft and high performance road vehicles. Adhesive joints have several advantages over the traditional rivet, including, significant weight reduction and efficient stress transfer between bonded materials. As fuel costs continue to rise, the weight reduction is accelerating this transition. Traditional surface pretreatments designed to improve the adhesion of polymeric materials to metallic surfaces are extremely toxic. Replacement adhesive technologies must be compatible with the environment without sacrificing adhesive performance. Silane-coupling agents have emerged as ideal surface modifications for improving composite joint strength. As these coatings are generally applied as very thin layers (\u3c50 nm), it is challenging to characterize their material properties for correlation to adhesive performance. We circumvent this problem by estimating the elastic modulus of the silane-based coatings using the buckling instability formed between two materials of a large elastic mismatch. The elastic modulus is found to effectively predict the joint strength of an epoxy/aluminum joint that has been reinforced with silane coupling agents. This buckling technique is extended to investigate the effects of chemical composition on the elastic modulus. Finally, the effect of macro-scale roughness on silane-reinforced joints is investigated within the framework of the unresolved problem of how to best characterize rough surfaces. Initially, the fractal dimension is used to characterize grit-blasted and sanded surfaces. It is found that, contrary to what has been suggested in the literature, the fractal dimension is independent of the roughening mechanism. Instead, the use of an anomalous diffusion coefficient is proposed as a more effective way to characterize a rough surface. Surface modification by preparation of surface energy gradients is then investigated. Materials with gradients in surface energy are useful in the areas of microfluidics, heat transfer and protein adsorption, to name a few. Gradients are prepared by vapor deposition of a reactive silane from a filter paper source. The technique gives control over the size and shape of the gradient. This surface modification is then used to induce droplet motion through repeated stretching and compression of a water drop between two gradient surfaces. This inchworm type motion is studied in detail and offers an alternative method to surface vibration for moving drops in microfluidic devices. The final surface modification considered is the application of a thin layer of rubber to a rigid surface. While this technique has many practical uses, such as easy release coatings in marine environments, it is applied herein to enable spontaneous healing between a rubber surface and a glass cover slip. Study of the diffusion controlled healing of a blister can be made by trapping an air filled blister between a glass cover slip and a rubber film. Through this study we find evidence for an interfacial diffusion process. This mechanism of diffusion is likely to be important in many biological systems

Visual Impact Assessment of Human Interventions on the Landscape: The case of Wind Farms and Solar Power Plants

El incremento de intervenciones humanas sobre el paisaje ha dado paso a una creciente preocupación social por la degradación del mismo y cada vez más, su mantenimiento comienza a plantearse en nuestra sociedad como una prioridad. En las últimas décadas, la investigación sobre el paisaje ha crecido de manera exponencial, impulsada por la necesidad de cumplir con mandatos legales. A pesar de ello, todavía no existe una herramienta universal para la evaluación del impacto visual que pueda ser utilizada por diferentes tipos de usuarios dentro del ámbito de la investigación y la planificación paisajística. Por otra parte, las herramientas desarrolladas son a menudo demasiado complicadas o demasiado específicas para ser útiles en la práctica. El objetivo de este trabajo es por lo tanto, desarrollar herramientas fiables y de fácil aplicación para cuantificar el impacto visual de las intervenciones humanas en el paisaje, por medio de una metodología potencialmente generalizable. Se presta especial atención a los parques eólicos y a las huertas solares. Se presentan tres casos de estudio. Cada investigación tiene como objetivo avanzar desde los resultados de la anterior, como complemento de la metodología, con nuevas herramientas. En el primer estudio, se desarrolla un indicador para medir la magnitud objetiva del impacto visual de los parques eólicos. El indicador combina medidas tangibles de visibilidad, color, fractalidad y continuidad que se puede tomar a partir de fotografías. Se construyen funciones de valor para cada variable y se incorporan al indicador. Este indicador se utilizó para calcular el impacto estético de cinco parques eólicos reales. La comparación estadística de los resultados del indicador con los obtenidos por consulta a una muestra de individuos, muestra que el indicador representa correctamente el orden de impacto según la percepción de la muestra de la población, por lo que es una medida objetiva y adecuada de los efectos visuales de los parquesTorres Sibille, ADC. (2010). Visual Impact Assessment of Human Interventions on the Landscape: The case of Wind Farms and Solar Power Plants [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/9147Palanci

Topology of spatial texture in the acousmatic medium

This research explores the dynamic fabric of experienced space in acousmatic music. The topology of spatial texture is a network of concepts treating music as a flexible, textural space, which deforms, shapes, and transforms in time. A comprehensive terminology is introduced, along with five fixed-media electroacoustic compositions, which exemplify a manifestation of spatial texture in composition and musical thinking. The theory draws from research on the cross-modality of texture perception, philosophical discourse on embodied meaning, physics, psychology of visual art, and discourse on space in acousmatic music. Several different structural perspectives are discussed, which reveal how spatial texture incorporates lower sound-structural levels, materiality, states and processes, motion, global networks and terrains, and relationships between space and time. Emphasis is put on visual and physical connections with spatiality in the acousmatic experience: cogency in spatial structure and dynamics reinforces links among modalities. The concepts and terminology are intended as a contribution to theory in the acousmatic medium, relevant to composition, analysis, and listening. The music represents an aesthetic orientation which emphasises materiality and morphology in texture, transformative processes, spatial design, and spatiotemporal polyvalence

Maurinian Truths : Essays in Honour of Anna-Sofia Maurin on her 50th Birthday

This book is in honour of Professor Anna-Sofia Maurin on her 50th birthday. It consists of eighteen essays on metaphysical issues written by Swedish and international scholars

Engineering Flow and Design

The importance of design in natural and engineered flow systems is undisputed. It is not only essential to life, but also plays a crucial role in our technological world. In Nature, it arises organically, spontaneously, and is the constructal path for systems to persist in time. The generation of the best design is the target of engineered flow systems. Fluid dynamics and thermodynamics have played a crucial role in the search for these flow designs. Analytical, numerical (CFD) and experimental studies played crucial roles in many technological breakthroughs. They provide the frameworks for understanding, simulating and interpreting flow phenomena. The collection of the articles in this issue, along with a complementary and expansive volume devoted to the same subject, reflect and reaffirm the importance and relevance of the study of flow design in natural and man-made flow systems in the twenty-first century