A model and an algebra for semi-structured and full-text queries

The need for a convergence between semi-structured data management and Information Retrieval techniques is manifest to the scientific community. In order to fulfil this growing request, W3C has recently proposed XQuery Full Text, an IR-oriented extension of XQuery. However, the issue of query optimization requires the study of important properties like query equivalence and containment; to this aim, a formal representation of document and queries is needed. The goal of this thesis is to establish such formal background. We define a data model for XML documents and propose an algebra able to represent most of XQuery Full-Text expressions. We show how an XQuery Full-Text expression can be translated into an algebraic expression and how an algebraic expression can be optimized.

Kernel Methods for Tree Structured Data

Machine learning comprises a series of techniques for automatic extraction of meaningful information from large collections of noisy data. In many real world applications, data is naturally represented in structured form. Since traditional methods in machine learning deal with vectorial information, they require an a priori form of preprocessing. Among all the learning techniques for dealing with structured data, kernel methods are recognized to have a strong theoretical background and to be effective approaches. They do not require an explicit vectorial representation of the data in terms of features, but rely on a measure of similarity between any pair of objects of a domain, the kernel function. Designing fast and good kernel functions is a challenging problem. In the case of tree structured data two issues become relevant: kernel for trees should not be sparse and should be fast to compute. The sparsity problem arises when, given a dataset and a kernel function, most structures of the dataset are completely dissimilar to one another. In those cases the classifier has too few information for making correct predictions on unseen data. In fact, it tends to produce a discriminating function behaving as the nearest neighbour rule. Sparsity is likely to arise for some standard tree kernel functions, such as the subtree and subset tree kernel, when they are applied to datasets with node labels belonging to a large domain. A second drawback of using tree kernels is the time complexity required both in learning and classification phases. Such a complexity can sometimes prevents the kernel application in scenarios involving large amount of data. This thesis proposes three contributions for resolving the above issues of kernel for trees. A first contribution aims at creating kernel functions which adapt to the statistical properties of the dataset, thus reducing its sparsity with respect to traditional tree kernel functions. Specifically, we propose to encode the input trees by an algorithm able to project the data onto a lower dimensional space with the property that similar structures are mapped similarly. By building kernel functions on the lower dimensional representation, we are able to perform inexact matchings between different inputs in the original space. A second contribution is the proposal of a novel kernel function based on the convolution kernel framework. Convolution kernel measures the similarity of two objects in terms of the similarities of their subparts. Most convolution kernels are based on counting the number of shared substructures, partially discarding information about their position in the original structure. The kernel function we propose is, instead, especially focused on this aspect. A third contribution is devoted at reducing the computational burden related to the calculation of a kernel function between a tree and a forest of trees, which is a typical operation in the classification phase and, for some algorithms, also in the learning phase. We propose a general methodology applicable to convolution kernels. Moreover, we show an instantiation of our technique when kernels such as the subtree and subset tree kernels are employed. In those cases, Direct Acyclic Graphs can be used to compactly represent shared substructures in different trees, thus reducing the computational burden and storage requirements.

Electrosynthesis and characterization of structured catalysts

In this research work the optimization of the electrochemical system of LDHs as catalytic precursors on FeCrAlY foams was carried out. Preliminary sintheses were performed on flat surfaces in order to easily characterize the deposited material. From the study of pH evolution vs time at different cathodic potentials applied to a Pt electrode, the theoretical best working conditions for the synthesis of single hydroxides and LDH compounds was achieved. In order to define the optimal potential for the synthesis of a particular LDH compound, the collected data were compared with the interval of precipitation determined by titration with NaOH. However, the characterization of the deposited material on Pt surfaces did not confirm the deposition of a pure and homogeneous LDH phase during the synthesis. Instead a sequential deposition linked to the pH of precipitation of the involved elements is observed. The same behavior was observed during the synthesis of the RhMgAl LDH on FeCrAlY foam as catalytic precursor. Several parameters were considered in order to optimize the synthesis.. The development of electrochemical cells with different feature, such as the counter electrode dimensions or the contact between the foam and the potentiostat, had been carried out in order to obtain a better coating of the foam. The influence of the initial pH of the electrolyte solution, of the applied potential, of the composition of the electrolytic solution were investigated in order to improve a better coating of the catalyst support. Catalytic tests were performed after the calcination of the deposited foam for the CPO and SR reactions, showing an improve of performances along with optimization of the precursors synthesis conditions.

Development of newly conceived biomimetic nano-structured biomaterials as scaffolds for bone and osteochondral regeneration

The present research thesis was focused on the development of new biomaterials and devices for application in regenerative medicine, particularly in the repair/regeneration of bone and osteochondral regions affected by degenerative diseases such as Osteoarthritis and Osteoporosis or serious traumas. More specifically, the work was focused on the synthesis and physico-chemical-morphological characterization of: i) a new superparamagnetic apatite phase; ii) new biomimetic superparamagnetic bone and osteochondral scaffolds; iii) new bioactive bone cements for regenerative vertebroplasty. The new bio-devices were designed to exhibit high biomimicry with hard human tissues and with functionality promoting faster tissue repair and improved texturing. In particular, recent trends in tissue regeneration indicate magnetism as a new tool to stimulate cells towards tissue formation and organization; in this perspective a new superparamagnetic apatite was synthesized by doping apatite lattice with di-and trivalent iron ions during synthesis. This finding was the pin to synthesize newly conceived superparamagnetic bone and osteochondral scaffolds by reproducing in laboratory the biological processes yielding the formation of new bone, i.e. the self-assembly/organization of collagen fibrils and heterogeneous nucleation of nanosized, ionically substituted apatite mimicking the mineral part of bone. The new scaffolds can be magnetically switched on/off and function as workstations guiding fast tissue regeneration by minimally invasive and more efficient approaches. Moreover, in the view of specific treatments for patients affected by osteoporosis or traumas involving vertebrae weakening or fracture, the present work was also dedicated to the development of new self-setting injectable pastes based on strontium-substituted calcium phosphates, able to harden in vivo and transform into strontium-substituted hydroxyapatite. The addition of strontium may provide an anti-osteoporotic effect, aiding to restore the physiologic bone turnover. The ceramic-based paste was also added with bio-polymers, able to be progressively resorbed thus creating additional porosity in the cement body that favour cell colonization and osseointegration.