Image-Based Coral Reef Classification and Thematic Mapping

This paper presents a novel image classification scheme for benthic coral reef images that can be applied to both single image and composite mosaic datasets. The proposed method can be configured to the characteristics (e.g., the size of the dataset, number of classes, resolution of the samples, color information availability, class types, etc.) of individual datasets. The proposed method uses completed local binary pattern (CLBP), grey level co-occurrence matrix (GLCM), Gabor filter response, and opponent angle and hue channel color histograms as feature descriptors. For classification, either k-nearest neighbor (KNN), neural network (NN), support vector machine (SVM) or probability density weighted mean distance (PDWMD) is used. The combination of features and classifiers that attains the best results is presented together with the guidelines for selection. The accuracy and efficiency of our proposed method are compared with other state-of-the-art techniques using three benthic and three texture datasets. The proposed method achieves the highest overall classification accuracy of any of the tested methods and has moderate execution time. Finally, the proposed classification scheme is applied to a large-scale image mosaic of the Red Sea to create a completely classified thematic map of the reef benthos

The Hydrophobic interaction: modeling hydrophobic interactions and aggregation of non-polar particles in aqueous solutions

Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. L'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Récemment, l?eau liquide a été décrite comme une structure formée d?un réseau aléatoire de liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure à basse température, certaines liaisons hydrogènes sont détruites ce qui est énergétiquement défavorable. Les molécules d?eau s?arrangent alors autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l?eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise les liaisons hydrogènes. Maintenant, la dissolution des particules devient énergétiquement défavorable, et les particules se séparent de l?eau en formant des agrégats qui minimisent leur surface exposée à l?eau. Pourtant, à très haute température, les effets entropiques deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d?eau. En utilisant un modèle basé sur ces changements de structure formée par des liaisons hydrogènes j?ai pu reproduire les phénomènes principaux liés à l?hydrophobicité. J?ai trouvé une région de coexistence de deux phases entre les températures critiques inférieure et supérieure de solubilité, dans laquelle les particules hydrophobes s?agrègent. En dehors de cette région, les particules sont dissoutes dans l?eau. J?ai démontré que l?interaction hydrophobe est décrite par un modèle qui prend uniquement en compte les changements de structure de l?eau liquide en présence d?une particule hydrophobe, plutôt que les interactions directes entre les particules. Encouragée par ces résultats prometteurs, j?ai étudié des solutions aqueuses de particules hydrophobes en présence de co-solvants cosmotropiques et chaotropiques. Ce sont des substances qui stabilisent ou déstabilisent les agrégats de particules hydrophobes. La présence de ces substances peut être incluse dans le modèle en décrivant leur effet sur la structure de l?eau. J?ai pu reproduire la concentration élevée de co-solvants chaotropiques dans le voisinage immédiat de la particule, et l?effet inverse dans le cas de co-solvants cosmotropiques. Ce changement de concentration du co-solvant à proximité de particules hydrophobes est la cause principale de son effet sur la solubilité des particules hydrophobes. J?ai démontré que le modèle adapté prédit correctement les effets implicites des co-solvants sur les interactions de plusieurs corps entre les particules hydrophobes. En outre, j?ai étendu le modèle à la description de particules amphiphiles comme des lipides. J?ai trouvé la formation de différents types de micelles en fonction de la distribution des regions hydrophobes à la surface des particules. L?hydrophobicité reste également un sujet controversé en science des protéines. J?ai défini une nouvelle échelle d?hydrophobicité pour les acides aminés qui forment des protéines, basée sur leurs surfaces exposées à l?eau dans des protéines natives. Cette échelle permet une comparaison meilleure entre les expériences et les résultats théoriques. Ainsi, le modèle développé dans mon travail contribue à mieux comprendre les solutions aqueuses de particules hydrophobes. Je pense que les résultats analytiques et numériques obtenus éclaircissent en partie les processus physiques qui sont à la base de l?interaction hydrophobe.<br/><br/>Despite the importance of water in our daily lives, some of its properties remain unexplained. Indeed, the interactions of water with organic particles are investigated in research groups all over the world, but controversy still surrounds many aspects of their description. In my work I have tried to understand these interactions on a molecular level using both analytical and numerical methods. Recent investigations describe liquid water as random network formed by hydrogen bonds. The insertion of a hydrophobic particle at low temperature breaks some of the hydrogen bonds, which is energetically unfavorable. The water molecules, however, rearrange in a cage-like structure around the solute particle. Even stronger hydrogen bonds are formed between water molecules, and thus the solute particles are soluble. At higher temperatures, this strict ordering is disrupted by thermal movements, and the solution of particles becomes unfavorable. They minimize their exposed surface to water by aggregating. At even higher temperatures, entropy effects become dominant and water and solute particles mix again. Using a model based on these changes in water structure I have reproduced the essential phenomena connected to hydrophobicity. These include an upper and a lower critical solution temperature, which define temperature and density ranges in which aggregation occurs. Outside of this region the solute particles are soluble in water. Because I was able to demonstrate that the simple mixture model contains implicitly many-body interactions between the solute molecules, I feel that the study contributes to an important advance in the qualitative understanding of the hydrophobic effect. I have also studied the aggregation of hydrophobic particles in aqueous solutions in the presence of cosolvents. Here I have demonstrated that the important features of the destabilizing effect of chaotropic cosolvents on hydrophobic aggregates may be described within the same two-state model, with adaptations to focus on the ability of such substances to alter the structure of water. The relevant phenomena include a significant enhancement of the solubility of non-polar solute particles and preferential binding of chaotropic substances to solute molecules. In a similar fashion, I have analyzed the stabilizing effect of kosmotropic cosolvents in these solutions. Including the ability of kosmotropic substances to enhance the structure of liquid water, leads to reduced solubility, larger aggregation regime and the preferential exclusion of the cosolvent from the hydration shell of hydrophobic solute particles. I have further adapted the MLG model to include the solvation of amphiphilic solute particles in water, by allowing different distributions of hydrophobic regions at the molecular surface, I have found aggregation of the amphiphiles, and formation of various types of micelle as a function of the hydrophobicity pattern. I have demonstrated that certain features of micelle formation may be reproduced by the adapted model to describe alterations of water structure near different surface regions of the dissolved amphiphiles. Hydrophobicity remains a controversial quantity also in protein science. Based on the surface exposure of the 20 amino-acids in native proteins I have defined the a new hydrophobicity scale, which may lead to an improvement in the comparison of experimental data with the results from theoretical HP models. Overall, I have shown that the primary features of the hydrophobic interaction in aqueous solutions may be captured within a model which focuses on alterations in water structure around non-polar solute particles. The results obtained within this model may illuminate the processes underlying the hydrophobic interaction.<br/><br/>La vie sur notre planète a commencé dans l'eau et ne pourrait pas exister en son absence : les cellules des animaux et des plantes contiennent jusqu'à 95% d'eau. Malgré son importance dans notre vie de tous les jours, certaines propriétés de l?eau restent inexpliquées. En particulier, l'étude des interactions entre l'eau et les particules organiques occupe des groupes de recherche dans le monde entier et est loin d'être finie. Dans mon travail j'ai essayé de comprendre, au niveau moléculaire, ces interactions importantes pour la vie. J'ai utilisé pour cela un modèle simple de l'eau pour décrire des solutions aqueuses de différentes particules. Bien que l?eau soit généralement un bon solvant, un grand groupe de molécules, appelées molécules hydrophobes (du grecque "hydro"="eau" et "phobia"="peur"), n'est pas facilement soluble dans l'eau. Ces particules hydrophobes essayent d'éviter le contact avec l'eau, et forment donc un agrégat pour minimiser leur surface exposée à l'eau. Cette force entre les particules est appelée interaction hydrophobe, et les mécanismes physiques qui conduisent à ces interactions ne sont pas bien compris à l'heure actuelle. Dans mon étude j'ai décrit l'effet des particules hydrophobes sur l'eau liquide. L'objectif était d'éclaircir le mécanisme de l'interaction hydrophobe qui est fondamentale pour la formation des membranes et le fonctionnement des processus biologiques dans notre corps. Récemment, l'eau liquide a été décrite comme un réseau aléatoire formé par des liaisons hydrogènes. En introduisant une particule hydrophobe dans cette structure, certaines liaisons hydrogènes sont détruites tandis que les molécules d'eau s'arrangent autour de cette particule en formant une cage qui permet de récupérer des liaisons hydrogènes (entre molécules d?eau) encore plus fortes : les particules sont alors solubles dans l'eau. A des températures plus élevées, l?agitation thermique des molécules devient importante et brise la structure de cage autour des particules hydrophobes. Maintenant, la dissolution des particules devient défavorable, et les particules se séparent de l'eau en formant deux phases. A très haute température, les mouvements thermiques dans le système deviennent tellement forts que les particules se mélangent de nouveau avec les molécules d'eau. A l'aide d'un modèle qui décrit le système en termes de restructuration dans l'eau liquide, j'ai réussi à reproduire les phénomènes physiques liés à l?hydrophobicité. J'ai démontré que les interactions hydrophobes entre plusieurs particules peuvent être exprimées dans un modèle qui prend uniquement en compte les liaisons hydrogènes entre les molécules d'eau. Encouragée par ces résultats prometteurs, j'ai inclus dans mon modèle des substances fréquemment utilisées pour stabiliser ou déstabiliser des solutions aqueuses de particules hydrophobes. J'ai réussi à reproduire les effets dûs à la présence de ces substances. De plus, j'ai pu décrire la formation de micelles par des particules amphiphiles comme des lipides dont la surface est partiellement hydrophobe et partiellement hydrophile ("hydro-phile"="aime l'eau"), ainsi que le repliement des protéines dû à l'hydrophobicité, qui garantit le fonctionnement correct des processus biologiques de notre corps. Dans mes études futures je poursuivrai l'étude des solutions aqueuses de différentes particules en utilisant les techniques acquises pendant mon travail de thèse, et en essayant de comprendre les propriétés physiques du liquide le plus important pour notre vie : l'eau.

Combined transcriptomic and proteomic studies reveal non-canonical signaling functions in the jasmonate pathway

Abstract Lipid derived signals mediate many stress and defense responses in multicellular eukaryotes. Among these are the jasmonates, potently active signaling compounds in plants. Jasmonic acid (JA) and 12-oxo-phytodienoic acid (OPDA) are the two best known members of the large jasmonate family. This thesis further investigates their roles as signals using genomic and proteomic approaches. The study is based on a simple genetic model involving two key genes. The first is ALLENE OXIDE SYNTHASE (AOS), encoding the most important enzyme in generating jasmonates. The second is CORONATINE INSENSITIVE 1 (COI1), a gene involved in all currently documented canonical signaling responses. We asked the simple question: do null mutations in AOS and COI1 have analogous effects on the transcriptome ? We found that they do not. If most COI1-dependent genes were also AOS-dependent, the expression of a zinc-finger protein was AOS-dependent but was unaffected by the coi1-1 mutation. We thus supposed that a jasmonate member, most probably OPDA, can alter gene expression partially independently of COI1. Conversely, the expression of at least three genes, one of these is a protein kinase, was shown to be COI1-dependent but did not require a functional AOS protein. We conclude that a non-jasmonate signal might alter gene expression through COIL Proteomic comparison of coi1-1 and aos plants confirmed these observations and highlighted probable protein degradation processes controlled by jasmonates and COI1 in the wounded leaf. This thesis revealed new functions for COI1 and for AOS-generated oxylipins in the jasmonate signaling pathway. Résumé Les signaux dérivés d'acides gras sont des médiateurs de réponses aux stress et de la défense des eucaryotes multicellulaires. Parmi eux, les jasmonates sont de puissants composés de sig¬nalisation chez les plantes. L'acide jasmonique (JA) et l'acide 12-oxo-phytodienoïc (OPDA) sont les deux membres les mieux caractérisés de la grande famille des jasmonates. Cette thèse étudie plus profondément leurs rôles de signalisation en utilisant des approches génomique et protéomique. Cette étude est basée sur un modèle génétique simple n'impliquant que deux gènes. Le premier est PALLENE OXYDE SYNTHASE (AOS) qui encode l'enzyme la plus importante pour la fabrication des jasmonates. Le deuxième est CORONATINE INSENSITIVE 1 (COI1) qui est impliqué dans la totalité des réponses aux jasmonates connues à ce jour. Nous avons posé la question suivante : est-ce que les mutations nulles dans les gènes AOS et COI1 ont des effets analogues sur le transcriptome ? Nous avons trouvé que ce n'était pas le cas. Si la majorité des gènes dépendants de COI1 sont également dépendants d'AOS, l'expression d'un gène codant pour une protéine formée de doigts de zinc n'est pas affectée par la mutation de COI1 tout en étant dépendante d'AOS. Nous avons donc supposé qu'un membre de la famille des jasmonates, probablement OPDA, pouvait modifier l'expression de certains gènes indépendamment de COI1. Inversement, nous avons montré que, tout en étant dépendante de COI1, l'expression d'au moins trois gènes, dont un codant pour une protéine kinase, n'était pas affectée par l'absence d'une protéine AOS fonctionnelle. Nous en avons conclu qu'un signal autre qu'un jasmonate devait modifier l'expression de certains gènes à travers COI1. La comparaison par protéomique de plantes aos et coi1-1 a confirmé ces observations et a mis en évidence un probable processus de dégradation de protéines contrôlé par les jasmonates et COU_ Cette thèse a mis en avant de nouvelles fonctions pour COI1 et pour des oxylipines générées par AOS dans le cadre de la signalisation par les jasmonates.

N=1 SQCD-like theories with N_f massive flavors from AdS/CFT and beta functions

We study new supergravity solutions related to large-N c N=1 supersymmetric gauge field theories with a large number N f of massive flavors. We use a recently proposed framework based on configurations with N c color D5 branes and a distribution of N f flavor D5 branes, governed by a function N f S(r). Although the system admits many solutions, under plausible physical assumptions the relevant solution is uniquely determined for each value of x ≡ N f /N c . In the IR region, the solution smoothly approaches the deformed Maldacena-Núñez solution. In the UV region it approaches a linear dilaton solution. For x < 2 the gauge coupling β g function computed holographically is negative definite, in the UV approaching the NSVZ β function with anomalous dimension γ 0 = −1/2 (approaching − 3/(32π 2)(2N c  − N f )g 3)), and with β g  → −∞ in the IR. For x = 2, β g has a UV fixed point at strong coupling, suggesting the existence of an IR fixed point at a lower value of the coupling. We argue that the solutions with x > 2 describe a"Seiberg dual" picture where N f  − 2N c flips sign.

Evaluation of an eLearning pilot course based on user experiences: corporate case study

Diplomityön tavoitteena oli arvioida sähköisen oppimisen soveltuvuutta kohdeyrityksessä ja selvittää, voidaanko luokkahuonekoulutusta korvata sähköisen oppimisen kursseilla. Tietojärjestelmän raportoinnista tehtiin sähköisen oppimisen kurssi, joka oli koekäytössä. Koekäytön jälkeen tehtiin käyttäjäkysely, kerättiin käyttötietoja kurssista ja tehtiin haastatteluja. Koekäyttäjien kokemuksista tehdyn arvioinnin perusteella sähköinen oppiminen soveltuu käytettäväksi selkeiden asioiden koulutukseen kohdeyrityksessä, mutta se ei voi kokonaan korvata luokkahuonekoulutusta. Luokkahuonekoulutuksessa tulisi keskittyä monimutkaisempiin asioihin ja ongelmanratkaisuun. Positiivisten tulosten perusteella sähköisen oppimisen kehittämistä päätettiin jatkaa yrityksessä. Sähköisen oppimisen kurssin avulla saadaan kustannussäästöjä kohdeyrityksessä, kun käyttäjämäärä on suurempi kuin 66. Jos koko koekäytössä olleen kurssin kohdeyleisö suorittaa kurssin sähköisesti, ovat kustannukset vain noin 15% vastaavista kustannuksista luokkahuoneessa järjestettynä. Lisäksi sähköisen oppimisen tehokkuutta tutkittiin ja koekäytössä olleen kurssin arvioitiin olevan positiivinen työssä kehitetyn Consensus-mallin mukaan.

Architecture for a Session Initiation Protocol Based Presence Client

Mitä on läsnäolo? Tämä työ määrittelee läsnäolon tietyn henkilön, laitteen tai palvelun halukkuudeksi kommunikoida. Nykyään on olemassa lukuisia läsnäolotietoa levittäviä sovelluksia, joista jokainen käyttää erilaista protokollaa tehtävän suorittamiseen. Vasta viime aikoina sovellusten kehittäjät ovat huomanneet tarpeen yhdelle sovellukselle, joka kykenee tukemaan lukuisia läsnäoloprotokollia. Session Initiation Protocol (SIP) voi levittää läsnäolotietoa muiden ominaisuuksiensa lisäksi. Kun muita protokollia käytetään vain reaaliaikaiseen viestintään ja läsnäolotiedon lähetykseen, SIP pystyy moniin muihinkin asioihin. Se on alunperin suunniteltu aloittamaan, muuttamaan ja lopettamaan osapuolien välisiä multimediaistuntoja. Arkkitehtuurin toteutus käyttää kahta Symbian –käyttöjärjestelmän perusominaisuutta: asiakas-palvelin rakennetta ja kontaktitietokantaa. Asiakaspalvelin rakenne erottaa asiakkaan protokollasta tarjoten perustan laajennettavalle usean protokollan arkkitehtuurille ja kontaktitietokanta toimii läsnäolotietojen varastona. Työn tuloksena on Symbianin käyttöjärjestelmässä toimiva läsnäoloasiakas.

The Human Mesenchymal Stromal Cell-Derived Osteocyte Capacity to Modulate Dendritic Cell Functions Is Strictly Dependent on the Culture System.

In vitro differentiation of mesenchymal stromal cells (MSC) into osteocytes (human differentiated osteogenic cells, hDOC) before implantation has been proposed to optimize bone regeneration. However, a deep characterization of the immunological properties of DOC, including their effect on dendritic cell (DC) function, is not available. DOC can be used either as cellular suspension (detached, Det-DOC) or as adherent cells implanted on scaffolds (adherent, Adh-DOC). By mimicking in vitro these two different routes of administration, we show that both Det-DOC and Adh-DOC can modulate DC functions. Specifically, the weak downregulation of CD80 and CD86 caused by Det-DOC on DC surface results in a weak modulation of DC functions, which indeed retain a high capacity to induce T-cell proliferation and to generate CD4(+)CD25(+)Foxp3(+) T cells. Moreover, Det-DOC enhance the DC capacity to differentiate CD4(+)CD161(+)CD196(+) Th17-cells by upregulating IL-6 secretion. Conversely, Adh-DOC strongly suppress DC functions by a profound downregulation of CD80 and CD86 on DC as well as by the inhibition of TGF-β production. In conclusion, we demonstrate that different types of DOC cell preparation may have a different impact on the modulation of the host immune system. This finding may have relevant implications for the design of cell-based tissue-engineering strategies.

A Population-Based Model to Consider the Effect of Seasonal Variation on Serum 25(OH)D and Vitamin D Status.

Background. We elaborated a model that predicts the centiles of the 25(OH)D distribution taking into account seasonal variation. Methods. Data from two Swiss population-based studies were used to generate (CoLaus) and validate (Bus Santé) the model. Serum 25(OH)D was measured by ultra high pressure LC-MS/MS and immunoassay. Linear regression models on square-root transformed 25(OH)D values were used to predict centiles of the 25(OH)D distribution. Distribution functions of the observations from the replication set predicted with the model were inspected to assess replication. Results. Overall, 4,912 and 2,537 Caucasians were included in original and replication sets, respectively. Mean (SD) 25(OH)D, age, BMI, and % of men were 47.5 (22.1) nmol/L, 49.8 (8.5) years, 25.6 (4.1) kg/m(2), and 49.3% in the original study. The best model included gender, BMI, and sin-cos functions of measurement day. Sex- and BMI-specific 25(OH)D centile curves as a function of measurement date were generated. The model estimates any centile of the 25(OH)D distribution for given values of sex, BMI, and date and the quantile corresponding to a 25(OH)D measurement. Conclusions. We generated and validated centile curves of 25(OH)D in the general adult Caucasian population. These curves can help rank vitamin D centile independently of when 25(OH)D is measured.

Classical line shapes based on analytical solutions of bimolecular trajectories in collision induced emission. II. Reactive collisions

The classical theory of collision induced emission (CIE) from pairs of dissimilar rare gas atoms was developed in Paper I [D. Reguera and G. Birnbaum, J. Chem. Phys. 125, 184304 (2006)] from a knowledge of the straight line collision trajectory and the assumption that the magnitude of the dipole could be represented by an exponential function of the inter-nuclear distance. This theory is extended here to deal with other functional forms of the induced dipole as revealed by ab initio calculations. Accurate analytical expression for the CIE can be obtained by least square fitting of the ab initio values of the dipole as a function of inter-atomic separation using a sum of exponentials and then proceeding as in Paper I. However, we also show how the multi-exponential fit can be replaced by a simpler fit using only two analytic functions. Our analysis is applied to the polar molecules HF and HBr. Unlike the rare gas atoms considered previously, these atomic pairs form stable bound diatomic molecules. We show that, interestingly, the spectra of these reactive molecules are characterized by the presence of multiple peaks. We also discuss the CIE arising from half collisions in excited electronic states, which in principle could be probed in photo-dissociation experiments.

The specificity of the familial aggregation of early-onset bipolar disorder: A controlled 10-year follow-up study of offspring of parents with mood disorders.

BACKGROUND: Two major sources of heterogeneity of mood disorders that have been demonstrated in clinical, family and genetic studies are the mood disorder subtype (i.e. bipolar (BPD) and major depressive disorder (MDD)) and age of onset of mood episodes. Using a prospective high-risk study design, our aims were to test the specificity of the parent-child transmission of BPD and MDD and to establish the risk of psychopathology in offspring in function of the age of onset of the parental disorder. METHODS: Clinical information was collected on 208 probands (n=81 with BPD, n=64 with MDD, n=63 medical controls) as well as their 202 spouses and 372 children aged 6-17 years at study entry. Parents and children were directly interviewed every 3 years (mean duration of follow-up=10.6 years). Parental age of onset was dichotomized at age 21. RESULTS: Offspring of parents with early onset BPD entailed a higher risk of BPD HR=7.9(1.8-34.6) and substance use disorders HR=5.0(1.1-21.9) than those with later onset and controls. Depressive disorders were not significantly increased in offspring regardless of parental mood disorder subtype or age of onset. LIMITATIONS: Limited sample size, age of onset in probands was obtained retrospectively, age of onset in co-parents was not adequately documented, and a quarter of the children had no direct interview. CONCLUSIONS: Our results provide support for the independence of familial aggregation of BPD from MDD and the heterogeneity of BPD based on patterns of onset. Future studies should further investigate correlates of early versus later onset BPD.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

A Forwarding Cooperation Protocol for Plain and Cluster-based Ad Hoc Networks

Peer-reviewed

Estimating extreme value cumulative distribution functions using bias-corrected kernel approaches

We propose a new kernel estimation of the cumulative distribution function based on transformation and on bias reducing techniques. We derive the optimal bandwidth that minimises the asymptotic integrated mean squared error. The simulation results show that our proposed kernel estimation improves alternative approaches when the variable has an extreme value distribution with heavy tail and the sample size is small.

Mobile Subscription Acquisition and Retention Costs - An Actvity-Based Model

The objective of this master’s thesis was to develop a model for mobile subscription acquisition cost, SAC, and mobile subscription retention cost, SRC, by applying activity-based cost accounting principles. The thesis was conducted as a case study for a telecommunication company operating on the Finnish telecommunication market. In addition to activity-based cost accounting there were other theories studied and applied in order to establish a theory framework for this thesis. The concepts of acquisition and retention were explored in a broader context with the concepts of customer satisfaction, loyalty and profitability and eventually customer relationship management to understand the background and meaning of the theme of this thesis. The utilization of SAC and SRC information is discussed through the theories of decision making and activity-based management. Also, the present state and future needs of SAC and SRC information usage at the case company as well as the functions of the company were examined by interviewing some members of the company personnel. With the help of these theories and methods it was aimed at finding out both the theory-based and practical factors which affect the structure of the model. During the thesis study it was confirmed that the existing SAC and SRC model of the case company should be used as the basis in developing the activity-based model. As a result the indirect costs of the old model were transformed into activities and the direct costs were continued to be allocated directly to acquisition of new subscriptions and retention of old subscriptions. The refined model will enable managing the subscription acquisition, retention and the related costs better through the activity information. During the interviews it was found out that the SAC and SRC information is also used in performance measurement and operational and strategic planning. SAC and SRC are not fully absorbed costs and it was concluded that the model serves best as a source of indicative cost information. This thesis does not include calculating costs. Instead, the refined model together with both the theory-based and interview findings concerning the utilization of the information produced by the model will serve as a framework for the possible future development aiming at completing the model.

Learning Preferences with Kernel-Based Methods

Learning of preference relations has recently received significant attention in machine learning community. It is closely related to the classification and regression analysis and can be reduced to these tasks. However, preference learning involves prediction of ordering of the data points rather than prediction of a single numerical value as in case of regression or a class label as in case of classification. Therefore, studying preference relations within a separate framework facilitates not only better theoretical understanding of the problem, but also motivates development of the efficient algorithms for the task. Preference learning has many applications in domains such as information retrieval, bioinformatics, natural language processing, etc. For example, algorithms that learn to rank are frequently used in search engines for ordering documents retrieved by the query. Preference learning methods have been also applied to collaborative filtering problems for predicting individual customer choices from the vast amount of user generated feedback. In this thesis we propose several algorithms for learning preference relations. These algorithms stem from well founded and robust class of regularized least-squares methods and have many attractive computational properties. In order to improve the performance of our methods, we introduce several non-linear kernel functions. Thus, contribution of this thesis is twofold: kernel functions for structured data that are used to take advantage of various non-vectorial data representations and the preference learning algorithms that are suitable for different tasks, namely efficient learning of preference relations, learning with large amount of training data, and semi-supervised preference learning. Proposed kernel-based algorithms and kernels are applied to the parse ranking task in natural language processing, document ranking in information retrieval, and remote homology detection in bioinformatics domain. Training of kernel-based ranking algorithms can be infeasible when the size of the training set is large. This problem is addressed by proposing a preference learning algorithm whose computation complexity scales linearly with the number of training data points. We also introduce sparse approximation of the algorithm that can be efficiently trained with large amount of data. For situations when small amount of labeled data but a large amount of unlabeled data is available, we propose a co-regularized preference learning algorithm. To conclude, the methods presented in this thesis address not only the problem of the efficient training of the algorithms but also fast regularization parameter selection, multiple output prediction, and cross-validation. Furthermore, proposed algorithms lead to notably better performance in many preference learning tasks considered.