Daily aerosol samples (PM) were collected at a rural station, which is 5 km away from the Turkish-Bulgarian border between April 2006 and March 2008. Aerosol samples were analyzed for elements by ICPMS, ions by IC and black carbon by aethalometer to provide a multi-species aerosol data set, which can represent aerosol population for Northwestern Turkey and Eastern Europe. Average concentration of SO42-, NO3- and NH4+ was 5.8, 2.9 and 2.0 &mu / g m-3, respectively, while total aerosol mass was 66 &mu / g m-3. Seasonal variation of crustal species had maxima in summer, while most of the anthropogenic species had maxima in winter. Rainfall was found as the only local meteorological parameter affecting aerosols concentrations. The dominant sectors of air masses arriving the Northwestern Turkey were northeast in summer and west-northwest in winter. Air masses were classified into five clusters regarding their wind speed and direction. Most species indicated significant differences between clusters. The influence of forest fires in Ukraine and Russian Federation was identified by cluster analysis using soluble K as tracer. Source apportionment of PM was carried out by EPA PMF model and five sources were resolved. Crustal emissions were found to be the major contributor to PM (41%). The second largest source was distant anthropogenic sources with a contribution of 26%. Traffic was also a remarkable source with 16% contribution. Sea salt and stationary combustion sources accounted for 9% and 8% of PM, respectively. Potential source regions of resolved sources were determined by potential source contribution function (PSCF).

This thesis considers regularized block multi-convex optimization, where the feasible set and objective function are generally non-convex but convex in each block of variables. I review some of its interesting examples and propose a generalized block coordinate descent (BCD) method. The generalized BCD uses three different block-update schemes. Based on the property of one block subproblem, one can freely choose one of the three schemes to update the corresponding block of variables. Appropriate choices of block-update schemes can often speed up the algorithm and greatly save computing time. Under certain conditions, I show that any limit point satisfies the Nash equilibrium conditions. Furthermore, I establish its global convergence and estimate its asymptotic convergence rate by assuming a property based on the Kurdyka-{\L}ojasiewicz inequality. As a consequence, this thesis gives a global linear convergence result of cyclic block coordinate descent for strongly convex optimization. The proposed algorithms are adapted for factorizing nonnegative matrices and tensors, as well as completing them from their incomplete observations. The algorithms were tested on synthetic data, hyperspectral data, as well as image sets from the CBCL, ORL and Swimmer databases. Compared to the existing state-of-the-art algorithms, the proposed algorithms demonstrate superior performance in both speed and solution quality.

<p>A numerical high order difference method is developed for solution of the incompressible Navier-Stokes equations. The solution is determined on a staggered curvilinear grid in two dimensions and by a Fourier expansion in the third dimension. The description in curvilinear body-fitted coordinates is obtained by an orthogonal mapping of the equations to a rectangular grid where space derivatives are determined by compact fourth order approximations. The time derivative is discretized with a second order backward difference method in a semi-implicit scheme, where the nonlinear terms are linearly extrapolated with second order accuracy.</p><p>An approximate block factorization technique is used in an iterative scheme to solve the large linear system resulting from the discretization in each time step. The solver algorithm consists of a combination of outer and inner iterations. An outer iteration step involves the solution of two sub-systems, one for prediction of the velocities and one for solution of the pressure. No boundary conditions for the intermediate variables in the splitting are needed and second order time accurate pressure solutions can be obtained.</p><p>The method has experimentally been validated in earlier studies. Here it is validated for flow past a circular cylinder as an example of a physical test case and the fourth order method is shown to be efficient in terms of grid resolution. The method is applied to external flow past a parabolic body and internal flow in an asymmetric diffuser in order to investigate the performance in two different curvilinear geometries and to give directions for future development of the method. It is concluded that the novel formulation of boundary conditions need further investigation.</p><p>A new iterative solution method for prediction of velocities allows for larger time steps due to less restrictive convergence constraints.</p>

Recommendation systems are emerging as an important business application as the demand for personalized services in E-commerce increases. Collaborative filtering techniques are widely used for predicting a user's preference or generating a list of items to be recommended. In this thesis, we develop several new approaches for collaborative filtering based on model combination and kernel smoothing. Specifically, we start with an experimental study that compares a wide variety of CF methods under different conditions. Based on this study, we formulate a combination model similar to boosting but where the combination coefficients are functions rather than constant. In another contribution we formulate and analyze a local variation of matrix factorization. This formulation constructs multiple local matrix factorization models and then combines them into a global model. This formulation is based on the local low-rank assumption, a slightly different but more plausible assumption about the rating matrix. We apply this assumption to both rating prediction and ranking problems, with both empirical validations and theoretical analysis. We contribute with this thesis in four aspects. First, the local approaches we present significantly improve the accuracy of recommendations both in rating prediction and ranking problems. Second, with the more realistic local low-rank assumption, we fundamentally change the underlying assumption for matrix factorization-based recommendation systems. Third, we present highly efficient and scalable algorithms which take advantage of parallelism, suited for recent large scale datasets. Lastly, we provide an open source software implementing the local approaches in this thesis as well as many other recent recommendation algorithms, which can be used both in research and production.

A wide variety of text analysis applications are based on statistical machine learning techniques. The success of those applications is critically affected by how we represent a document. Learning an efficient document representation has two major challenges: sparsity and sequentiality. The sparsity often causes high estimation error, and text's sequential nature, interdependency between words, causes even more complication. This thesis presents novel document representations to overcome the two challenges. First, I employ label characteristics to estimate a compact document representation. Because label attributes implicitly describe the geometry of dense subspace that has substantial impact, I can effectively resolve the sparsity issue while only focusing the compact subspace. Second, while modeling a document as a joint or conditional distribution between words and their sequential information, I can efficiently reflect sequential nature of text in my document representations. Lastly, the thesis is concluded with a document representation that employs both labels and sequential information in a unified formulation. The following four criteria are utilized to evaluate the goodness of representations: how close a representation is to its original data, how strongly a representation can be distinguished from each other, how easy to interpret a representation by a human, and how much computational effort is needed for a representation. While pursuing those good representation criteria, I was able to obtain document representations that are closer to the original data, stronger in discrimination, and easier to be understood than traditional document representations. Efficient computation algorithms make the proposed approaches largely scalable. This thesis examines emotion prediction, temporal emotion analysis, modeling documents with edit histories, locally coherent topic modeling, and text categorization tasks for possible applications.

Ο κυματιδικός μετασχηματισμός αποτελεί το πλέον σύγχρονο μαθηματικό εργαλείο για την ανάλυση σήματος σε βάση συναρτήσεων. Σε σχέση με άλλες παρόμοιες τεχνικές (π.χ. Fourier) παρουσιάζει εμφανή πλεονεκτήματα με κυρίοτερο την τοπικότητα στο χρόνο των συναρτήσεων βάσης. Η δύναμη του κυματιδιακού μετασχηματισμού βρίσκεται στη διακριτή του έκδοση (Discrete Wavelet Transform), που υπολογίζεται με τη βοήθεια διατάξεων FIR φίλτρων ακολουθούμενων από υποδειγματοληψία. Η ταχύτερη και πιο σύγχρονη τεχνική υπολογισμού του DWT ονομάζεται σχήμα lifting και βασίζεται στην παραγοντοποίηση των πινάκων μετασχηματισμού σε γινόμενο αραιών πινάκων. Στο πλαίσιο της εργασίας σχεδιάστηκε και υλοποιήθηκε σε γλώσσα VHDL μία VLSI αρχιτεκτονική ικανή να εκτελεί οποiοδήποτε φίλτρο (ευθύ και αντίστροφο) του DWT τροποποιημένο με τη μέθοδο lifting. Τα φίλτρα είναι αποθηκευμένα σαν μικροπρογράμματα σε μνήμη ελέγχου για ευκολία στη σχεδίαση και δυνατότητα επαναπροσδιορισμού του συστήματος. Το σύστημα εξομοιώθηκε για ορθή λειτουργία κατά την εκτέλεση των φίλτρων του προτύπου JPEG2000, ενώ έγινε και σύνθεση σε FPGA. / The wavelet transform is the most powerful mathematical tool for analysing signals into function bases. Comparing with other such technics (e.g. Fourier transform), wavelets show obvious advantages, with the most important being the spatial locality of the basis functions. The real power of wavelet transform is the Discrete Wavelet Tranfsorm (DWT), which is a filtering operation followed by downsampling. Recently, a new, fast approach for calculating these filter banks has been developed, named the lifting scheme. This method is based on the factorization of the transform matrices into a product of some sparse matrices. Α VLSI architecture that executes wavelet filters (forward and inverse) modified by the lifting scheme is designed and implemented in VHDL code. The filters are considered as microprogramms placed in the system

No description available.

In this thesis we provide three main contributions to blind source separation methods based on NMF. Our first contribution is a group-sparsity inducing penalty specifically tailored for Itakura-Saito NMF. In many music tracks, there are whole intervals where only one source is active at the same time. The group-sparsity penalty we propose allows to blindly indentify these intervals and learn source specific dictionaries. As a consequence, those learned dictionaries can be used to do source separation in other parts of the track were several sources are active. These two tasks of identification and separation are performed simultaneously in one run of group-sparsity Itakura-Saito NMF. Our second contribution is an online algorithm for Itakura-Saito NMF that allows to learn dictionaries on very large audio tracks. Indeed, the memory complexity of a batch implementation NMF grows linearly with the length of the recordings and becomes prohibitive for signals longer than an hour. In contrast, our online algorithm is able to learn NMF on arbitrarily long signals with limited memory usage. Our third contribution deals user informed NMF. In short mixed signals, blind learning becomes very hard and sparsity do not retrieve interpretable dictionaries. Our contribution is very similar in spirit to inpainting. It relies on the empirical fact that, when observing the spectrogram of a mixture signal, an overwhelming proportion of it consists in regions where only one source is active. We describe an extension of NMF to take into account time-frequency localized information on the absence/presence of each source. We also investigate inferring this information with tools from machine learning.

There are several reasons to be interested in conformal field theories in two dimensions. Apart from arising in various physical applications, ranging from statistical mechanics to string theory, conformal field theory is a class of quantum field theories that is interesting on its own. First of all there is a large amount of symmetries. In addition, many of the interesting theories satisfy a finiteness condition, that together with the symmetries allows for a fully non-perturbative treatment, and even for a complete solution in a mathematically rigorous manner. One of the crucial tools which make such a treatment possible is provided by category theory. This thesis contains results relevant for two different classes of conformal field theory. We partly treat rational conformal field theory, but also derive results that aim at a better understanding of logarithmic conformal field theory. For rational conformal field theory, we generalize the proof that the construction of correlators, via three-dimensional topological field theory, satisfies the consistency conditions to oriented world sheets with defect lines. We also derive a classifying algebra for defects. This is a semisimple commutative associative algebra over the complex numbers whose one-dimensional representations are in bijection with the topological defect lines of the theory. Then we relax the semisimplicity condition of rational conformal field theory and consider a larger class of categories, containing non-semisimple ones, that is relevant for logarithmic conformal field theory. We obtain, for any finite-dimensional factorizable ribbon Hopf algebra H, a family of symmetric commutative Frobenius algebras in the category of bimodules over H. For any such Frobenius algebra, which can be constructed as a coend, we associate to any Riemann surface a morphism in the bimodule category. We prove that this morphism is invariant under a projective action of the mapping class group ofthe Riemann surface. This suggests to regard these morphisms as candidates for correlators of bulk fields of a full conformal field theories whose chiral data are described by the category of left-modules over H.

Internet offre une énorme quantité d’informations à portée de main et dans une telle variété de sujets, que tout le monde est en mesure d’accéder à une énorme variété de connaissances. Une telle grande quantité d’information pourrait apporter un saut en avant dans de nombreux domaines (moteurs de recherche, réponses aux questions, tâches NLP liées) si elle est bien utilisée. De cette façon, un enjeu crucial de la communauté d’intelligence artificielle a été de recueillir, d’organiser et de faire un usage intelligent de cette quantité croissante de connaissances disponibles. Heureusement, depuis un certain temps déjà des efforts importants ont été faits dans la collecte et l’organisation des connaissances, et beaucoup d’informations structurées peuvent être trouvées dans des dépôts appelés Bases des Connaissances (BCs). Freebase, Entity Graph Facebook ou Knowledge Graph de Google sont de bons exemples de BCs. Un grand problème des BCs c’est qu’ils sont loin d’êtres complets. Par exemple, dans Freebase seulement environ 30% des gens ont des informations sur leur nationalité. Cette thèse présente plusieurs méthodes pour ajouter de nouveaux liens entre les entités existantes de la BC basée sur l’apprentissage des représentations qui optimisent une fonction d’énergie définie. Ces modèles peuvent également être utilisés pour attribuer des probabilités à triples extraites du Web. On propose également une nouvelle application pour faire usage de cette information structurée pour générer des informations non structurées (spécifiquement des questions en langage naturel). On pense par rapport à ce problème comme un modèle de traduction automatique, où on n’a pas de langage correct comme entrée, mais un langage structuré. Nous adaptons le RNN codeur-décodeur à ces paramètres pour rendre possible cette traduction. / Internet provides a huge amount of information at hand in such a variety of topics, that now everyone is able to access to any kind of knowledge. Such a big quantity of information could bring a leap forward in many areas if used properly. This way, a crucial challenge of the Artificial Intelligence community has been to gather, organize and make intelligent use of this growing amount of available knowledge. Fortunately, important efforts have been made in gathering and organizing knowledge for some time now, and a lot of structured information can be found in repositories called Knowledge Bases (KBs). A main issue with KBs is that they are far from being complete. This thesis proposes several methods to add new links between the existing entities of the KB based on the learning of representations that optimize some defined energy function. We also propose a novel application to make use of this structured information to generate questions in natural language.