A house price index defined in the potential outcomes framework

Current methods for constructing house price indices are based on comparisons of sale prices of residential properties sold two or more times and on regression of the sale prices on the attributes of the properties and of their locations. The two methods have well recognised deficiencies, selection bias and model assumptions, respectively. We introduce a new method based on propensity score matching. The average house prices for two periods are compared by selecting pairs of properties, one sold in each period, that are as similar on a set of available attributes (covariates) as is feasible to arrange. The uncertainty associated with such matching is addressed by multiple imputation, framing the problem as involving missing values. The method is applied to aregister of transactions ofresidential properties in New Zealand and compared with the established alternatives.

Corporate responsibility in the postnational constellation : a multiple-case study

In my thesis I present the findings of a multiple-case study on the CSR approach of three multinational companies, applying Basu and Palazzo's (2008) CSR-character as a process model of sensemaking, Suchman's (1995) framework on legitimation strategies, and Habermas (1996) concept of deliberative democracy. The theoretical framework is based on the assumption of a postnational constellation (Habermas, 2001) which sends multinational companies onto a process of sensemaking (Weick, 1995) with regards to their responsibilities in a globalizing world. The major reason is that mainstream CSR-concepts are based on the assumption of a liberal market economy embedded in a nation state that do not fit the changing conditions for legitimation of corporate behavior in a globalizing world. For the purpose of this study, I primarily looked at two research questions: (i) How can the CSR approach of a multinational corporation be systematized empirically? (ii) What is the impact of the changing conditions in the postnational constellation on the CSR approach of the studied multinational corporations? For the analysis, I adopted a holistic approach (Patton, 1980), combining elements of a deductive and inductive theory building methodology (Eisenhardt, 1989b; Eisenhardt & Graebner, 2007; Glaser & Strauss, 1967; Van de Ven, 1992) and rigorous qualitative data analysis. Primary data was collected through 90 semi-structured interviews in two rounds with executives and managers in three multinational companies and their respective stakeholders. Raw data originating from interview tapes, field notes, and contact sheets was processed, stored, and managed using the software program QSR NVIVO 7. In the analysis, I applied qualitative methods to strengthen the interpretative part as well as quantitative methods to identify dominating dimensions and patterns. I found three different coping behaviors that provide insights into the corporate mindset. The results suggest that multinational corporations increasingly turn towards relational approaches of CSR to achieve moral legitimacy in formalized dialogical exchanges with their stakeholders since legitimacy can no longer be derived only from a national framework. I also looked at the degree to which they have reacted to the postnational constellation by the assumption of former state duties and the underlying reasoning. The findings indicate that CSR approaches become increasingly comprehensive through integrating political strategies that reflect the growing (self-) perception of multinational companies as political actors. Based on the results, I developed a model which relates the different dimensions of corporate responsibility to the discussion on deliberative democracy, global governance and social innovation to provide guidance for multinational companies in a postnational world. With my thesis, I contribute to management research by (i) delivering a comprehensive critique of the mainstream CSR-literature and (ii) filling the gap of thorough qualitative research on CSR in a globalizing world using the CSR-character as an empirical device, and (iii) to organizational studies by further advancing a deliberative view of the firm proposed by Scherer and Palazzo (2008).

Active deformation fields: dense deformation field estimation for atlas-based segmentation using the active contour framework.

This paper presents a new and original variational framework for atlas-based segmentation. The proposed framework integrates both the active contour framework, and the dense deformation fields of optical flow framework. This framework is quite general and encompasses many of the state-of-the-art atlas-based segmentation methods. It also allows to perform the registration of atlas and target images based on only selected structures of interest. The versatility and potentiality of the proposed framework are demonstrated by presenting three diverse applications: In the first application, we show how the proposed framework can be used to simulate the growth of inconsistent structures like a tumor in an atlas. In the second application, we estimate the position of nonvisible brain structures based on the surrounding structures and validate the results by comparing with other methods. In the final application, we present the segmentation of lymph nodes in the Head and Neck CT images, and demonstrate how multiple registration forces can be used in this framework in an hierarchical manner.

The M-Coffee web server: a meta-method for computing multiple sequence alignments by combining alternative alignment methods.

The M-Coffee server is a web server that makes it possible to compute multiple sequence alignments (MSAs) by running several MSA methods and combining their output into one single model. This allows the user to simultaneously run all his methods of choice without having to arbitrarily choose one of them. The MSA is delivered along with a local estimation of its consistency with the individual MSAs it was derived from. The computation of the consensus multiple alignment is carried out using a special mode of the T-Coffee package [Notredame, Higgins and Heringa (T-Coffee: a novel method for fast and accurate multiple sequence alignment. J. Mol. Biol. 2000; 302: 205-217); Wallace, O'Sullivan, Higgins and Notredame (M-Coffee: combining multiple sequence alignment methods with T-Coffee. Nucleic Acids Res. 2006; 34: 1692-1699)] Given a set of sequences (DNA or proteins) in FASTA format, M-Coffee delivers a multiple alignment in the most common formats. M-Coffee is a freeware open source package distributed under a GPL license and it is available either as a standalone package or as a web service from www.tcoffee.org.

CBS: an open platform that integrates predictive methods and epigenetics information to characterize conserved regulatory features in multiple Drosophila genomes.

Background: Information about the composition of regulatory regions is of great value for designing experiments to functionally characterize gene expression. The multiplicity of available applications to predict transcription factor binding sites in a particular locus contrasts with the substantial computational expertise that is demanded to manipulate them, which may constitute a potential barrier for the experimental community. Results: CBS (Conserved regulatory Binding Sites, http://compfly.bio.ub.es/CBS) is a public platform of evolutionarily conserved binding sites and enhancers predicted in multiple Drosophila genomes that is furnished with published chromatin signatures associated to transcriptionally active regions and other experimental sources of information. The rapid access to this novel body of knowledge through a user-friendly web interface enables non-expert users to identify the binding sequences available for any particular gene, transcription factor, or genome region. Conclusions: The CBS platform is a powerful resource that provides tools for data mining individual sequences and groups of co-expressed genes with epigenomics information to conduct regulatory screenings in Drosophila.

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.

Research framework for the connection between environmental collaboration and firm performance

This thesis focuses on collaborative activities with regard to environmental issues both within the firm and outside the firm with the key suppliers and customers, i.e. internal and external environmental collaboration. Integrating environmental thinking into supply chain management has received increasing interest in recent years. The relational view and the natural resource-based-view together suggest that environmental capabilities can be built jointly with supply chain partners and used to gain sustained competitive advantage. Several studies have been undertaken to analyse the connection between environmental activities and firm performance but most studies have taken only economic performance into account. This study pays attention also to two other dimensions of firm performance, intra-firm supply chain performance and environmental performance, and aims at presenting the linkages between them and environmental collaboration. This thesis creates a research framework for the connections between environmental collaboration and firm performance and suggests approaches to analyse these. In order to find out the key concepts and their relationship, an extensive literature review is conducted. The research framework proposes a positive connection between internal and external environmental collaboration and all three dimensions of firm performance. In addition, environmental performance and intra-firm supply chain performance are expected to contribute positively to economic performance. Hence, firms are suggested to benefit from environmental collaboration both within the firm and outside the firm. Empirical testing of the developed research framework is out of the scope of this study. However, this thesis proposes using a mixed methods research approach, including survey research and multiple case studies. Finland State of Logistics 2012 survey commissioned by the Finnish Ministry of Transport and Communications and conducted by Turku School of Economics is used as an example of data for the quantitative phase. The applicability of these two methods is discussed at a general level and with regard to analysing the research framework developed in the thesis. Future research will aim at the development of the research framework and the methods in order to confirm the connection between environmental collaboration and firm performance.

Magnetic Resocance Imaging Methods in the Follow-up of Multiple Sclerosis and in Farby Disease

Multiple sclerosis (MS) is a chronic immune-mediated inflammatory disorder of the central nervous system. MS is the most common disabling central nervous system (CNS) disease of young adults in the Western world. In Finland, the prevalence of MS ranges between 1/1000 and 2/1000 in different areas. Fabry disease (FD) is a rare hereditary metabolic disease due to mutation in a single gene coding α-galactosidase A (alpha-gal A) enzyme. It leads to multi-organ pathology, including cerebrovascular disease. Currently there are 44 patients with diagnosed FD in Finland. Magnetic resonance imaging (MRI) is commonly used in the diagnostics and follow-up of these diseases. The disease activity can be demonstrated by occurrence of new or Gadolinium (Gd)-enhancing lesions in routine studies. Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are advanced MR sequences which can reveal pathologies in brain regions which appear normal on conventional MR images in several CNS diseases. The main focus in this study was to reveal whether whole brain apparent diffusion coefficient (ADC) analysis can be used to demonstrate MS disease activity. MS patients were investigated before and after delivery and before and after initiation of diseasemodifying treatment (DMT). In FD, DTI was used to reveal possible microstructural alterations at early timepoints when excessive signs of cerebrovascular disease are not yet visible in conventional MR sequences. Our clinical and MRI findings at 1.5T indicated that post-partum activation of the disease is an early and common phenomenon amongst mothers with MS. MRI seems to be a more sensitive method for assessing MS disease activity than the recording of relapses. However, whole brain ADC histogram analysis is of limited value in the follow-up of inflammatory conditions in a pregnancy-related setting because the pregnancy-related physiological effects on ADC overwhelm the alterations in ADC associated with MS pathology in brain tissue areas which appear normal on conventional MRI sequences. DTI reveals signs of microstructural damage in brain white matter of FD patients before excessive white matter lesion load can be observed on conventional MR scans. DTI could offer a valuable tool for monitoring the possible effects of enzyme replacement therapy in FD.

Neuroethologic differences in sleep deprivation induced by the single- and multiple-platform methods

It has been proposed that the multiple-platform method (MP) for desynchronized sleep (DS) deprivation eliminates the stress induced by social isolation and by the restriction of locomotion in the single-platform (SP) method. MP, however, induces a higher increase in plasma corticosterone and ACTH levels than SP. Since deprivation is of heuristic value to identify the functional role of this state of sleep, the objective of the present study was to determine the behavioral differences exhibited by rats during sleep deprivation induced by these two methods. All behavioral patterns exhibited by a group of 7 albino male Wistar rats submitted to 4 days of sleep deprivation by the MP method (15 platforms, spaced 150 mm apart) and by 7 other rats submitted to sleep deprivation by the SP method were recorded in order to elaborate an ethogram. The behavioral patterns were quantitated in 10 replications by naive observers using other groups of 7 rats each submitted to the same deprivation schedule. Each quantification session lasted 35 min and the behavioral patterns presented by each rat over a period of 5 min were counted. The results obtained were: a) rats submitted to the MP method changed platforms at a mean rate of 2.62 ± 1.17 platforms h-1 animal-1; b) the number of episodes of noninteractive waking patterns for the MP animals was significantly higher than that for SP animals (1077 vs 768); c) additional episodes of waking patterns (26.9 ± 18.9 episodes/session) were promoted by social interaction in MP animals; d) the cumulative number of sleep episodes observed in the MP test (311) was significantly lower (chi-square test, 1 d.f., P<0.05) than that observed in the SP test (534); e) rats submitted to the MP test did not show the well-known increase in ambulatory activity observed after the end of the SP test; f) comparison of 6 MP and 6 SP rats showed a significantly shorter latency to the onset of DS in MP rats (7.8 ± 4.3 and 29.0 ± 25.0 min, respectively; Student t-test, P<0.05). We conclude that the social interaction occurring in the MP test generates additional stress since it increases the time of forced wakefulness and reduces the time of rest promoted by synchronized sleep.

Individual and social determinants of multiple chronic disease behavioural risk factors in Canadian children and adolescents

Contexte: Les facteurs de risque comportementaux, notamment l’inactivité physique, le comportement sédentaire, le tabagisme, la consommation d’alcool et le surpoids sont les principales causes modifiables de maladies chroniques telles que le cancer, les maladies cardiovasculaires et le diabète. Ces facteurs de risque se manifestent également de façon concomitante chez l’individu et entraînent des risques accrus de morbidité et de mortalité. Bien que les facteurs de risque comportementaux aient été largement étudiés, la distribution, les patrons d’agrégation et les déterminants de multiples facteurs de risque comportementaux sont peu connus, surtout chez les enfants et les adolescents. Objectifs: Cette thèse vise 1) à décrire la prévalence et les patrons d’agrégation de multiples facteurs de risque comportementaux des maladies chroniques chez les enfants et adolescents canadiens; 2) à explorer les corrélats individuels, sociaux et scolaires de multiples facteurs de risque comportementaux chez les enfants et adolescents canadiens; et 3) à évaluer, selon le modèle conceptuel de l’étude, l’influence longitudinale d’un ensemble de variables distales (c’est-à-dire des variables situées à une distance intermédiaire des comportements à risque) de type individuel (estime de soi, sentiment de réussite), social (relations sociales, comportements des parents/pairs) et scolaire (engagement collectif à la réussite, compréhension des règles), ainsi que de variables ultimes (c’est-à-dire des variables situées à une distance éloignée des comportements à risque) de type individuel (traits de personnalité, caractéristiques démographiques), social (caractéristiques socio-économiques des parents) et scolaire (type d’école, environnement favorable, climat disciplinaire) sur le taux d’occurrence de multiples facteurs de risque comportementaux chez les enfants et adolescents canadiens. Méthodes: Des données transversales (n = 4724) à partir du cycle 4 (2000-2001) de l’Enquête longitudinale nationale sur les enfants et les jeunes (ELNEJ) ont été utilisées pour décrire la prévalence et les patrons d’agrégation de multiples facteurs de risque comportementaux chez les jeunes canadiens âgés de 10-17 ans. L’agrégation des facteurs de risque a été examinée en utilisant une méthode du ratio de cas observés sur les cas attendus. La régression logistique ordinale a été utilisée pour explorer les corrélats de multiples facteurs de risque comportementaux dans un échantillon transversal (n = 1747) de jeunes canadiens âgés de 10-15 ans du cycle 4 (2000-2001) de l’ELNEJ. Des données prospectives (n = 1135) à partir des cycle 4 (2000-2001), cycle 5 (2002-2003) et cycle 6 (2004-2005) de l’ELNEJ ont été utilisées pour évaluer l’influence longitudinale des variables distales et ultimes (tel que décrit ci-haut dans les objectifs) sur le taux d’occurrence de multiples facteurs de risque comportementaux chez les jeunes canadiens âgés de 10-15 ans; cette analyse a été effectuée à l’aide des modèles de Poisson longitudinaux. Résultats: Soixante-cinq pour cent des jeunes canadiens ont rapporté avoir deux ou plus de facteurs de risque comportementaux, comparativement à seulement 10% des jeunes avec aucun facteur de risque. Les facteurs de risque comportementaux se sont agrégés en de multiples combinaisons. Plus précisément, l’occurrence simultanée des cinq facteurs de risque était 120% plus élevée chez les garçons (ratio observé/attendu (O/E) = 2.20, intervalle de confiance (IC) 95%: 1.31-3.09) et 94% plus élevée chez les filles (ratio O/E = 1.94, IC 95%: 1.24-2.64) qu’attendu. L’âge (rapport de cotes (RC) = 1.95, IC 95%: 1.21-3.13), ayant un parent fumeur (RC = 1.49, IC 95%: 1.09-2.03), ayant rapporté que la majorité/tous de ses pairs consommaient du tabac (RC = 7.31, IC 95%: 4.00-13.35) ou buvaient de l’alcool (RC = 3.77, IC 95%: 2.18-6.53), et vivant dans une famille monoparentale (RC = 1.94, IC 95%: 1.31-2.88) ont été positivement associés aux multiples comportements à risque. Les jeunes ayant une forte estime de soi (RC = 0.92, IC 95%: 0.85-0.99) ainsi que les jeunes dont un des parents avait un niveau d’éducation postsecondaire (RC = 0.58, IC 95%: 0.41-0.82) étaient moins susceptibles d’avoir de multiples facteurs de risque comportementaux. Enfin, les variables de type social distal (tabagisme des parents et des pairs, consommation d’alcool par les pairs) (Log du rapport de vraisemblance (LLR) = 187.86, degrés de liberté = 8, P < 0,001) et individuel distal (estime de soi) (LLR = 76.94, degrés de liberté = 4, P < 0,001) ont significativement influencé le taux d’occurrence de multiples facteurs de risque comportementaux. Les variables de type individuel ultime (âge, sexe, anxiété) et social ultime (niveau d’éducation du parent, revenu du ménage, structure de la famille) ont eu une influence moins prononcée sur le taux de cooccurrence des facteurs de risque comportementaux chez les jeunes. Conclusion: Les résultats suggèrent que les interventions de santé publique devraient principalement cibler les déterminants de type individuel distal (tel que l’estime de soi) ainsi que social distal (tels que le tabagisme des parents et des pairs et la consommation d’alcool par les pairs) pour prévenir et/ou réduire l’occurrence de multiples facteurs de risque comportementaux chez les enfants et les adolescents. Cependant, puisque les variables de type distal (telles que les caractéristiques psychosociales des jeunes et comportements des parents/pairs) peuvent être influencées par des variables de type ultime (telles que les caractéristiques démographiques et socioéconomiques), les programmes et politiques de prévention devraient également viser à améliorer les conditions socioéconomiques des jeunes, particulièrement celles des enfants et des adolescents des familles les plus démunies.

A unified framework for the comprehension of software's time dimension

Les logiciels sont de plus en plus complexes et leur développement est souvent fait par des équipes dispersées et changeantes. Par ailleurs, de nos jours, la majorité des logiciels sont recyclés au lieu d’être développés à partir de zéro. La tâche de compréhension, inhérente aux tâches de maintenance, consiste à analyser plusieurs dimensions du logiciel en parallèle. La dimension temps intervient à deux niveaux dans le logiciel : il change durant son évolution et durant son exécution. Ces changements prennent un sens particulier quand ils sont analysés avec d’autres dimensions du logiciel. L’analyse de données multidimensionnelles est un problème difficile à résoudre. Cependant, certaines méthodes permettent de contourner cette difficulté. Ainsi, les approches semi-automatiques, comme la visualisation du logiciel, permettent à l’usager d’intervenir durant l’analyse pour explorer et guider la recherche d’informations. Dans une première étape de la thèse, nous appliquons des techniques de visualisation pour mieux comprendre la dynamique des logiciels pendant l’évolution et l’exécution. Les changements dans le temps sont représentés par des heat maps. Ainsi, nous utilisons la même représentation graphique pour visualiser les changements pendant l’évolution et ceux pendant l’exécution. Une autre catégorie d’approches, qui permettent de comprendre certains aspects dynamiques du logiciel, concerne l’utilisation d’heuristiques. Dans une seconde étape de la thèse, nous nous intéressons à l’identification des phases pendant l’évolution ou pendant l’exécution en utilisant la même approche. Dans ce contexte, la prémisse est qu’il existe une cohérence inhérente dans les évènements, qui permet d’isoler des sous-ensembles comme des phases. Cette hypothèse de cohérence est ensuite définie spécifiquement pour les évènements de changements de code (évolution) ou de changements d’état (exécution). L’objectif de la thèse est d’étudier l’unification de ces deux dimensions du temps que sont l’évolution et l’exécution. Ceci s’inscrit dans notre volonté de rapprocher les deux domaines de recherche qui s’intéressent à une même catégorie de problèmes, mais selon deux perspectives différentes.

Evaluation of agri-environmental and forestry schemes with multiple objectives

The evaluation of EU policy in the area of rural land use management often encounters problems of multiple and poorly articulated objectives. Agri-environmental policy has a range of aims, including natural resource protection, biodiversity conservation and the protection and enhancement of landscape quality. Forestry policy, in addition to production and environmental objectives, increasingly has social aims, including enhancement of human health and wellbeing, lifelong learning, and the cultural and amenity value of the landscape. Many of these aims are intangible, making them hard to define and quantify. This article describes two approaches for dealing with such situations, both of which rely on substantial participation by stakeholders. The first is the Agri-Environment Footprint Index, a form of multi-criteria participatory approach. The other, applied here to forestry, has been the development of ‘multi-purpose’ approaches to evaluation, which respond to the diverse needs of stakeholders through the use of mixed methods and a broad suite of indicators, selected through a participatory process. Each makes use of case studies and involves stakeholders in the evaluation process, thereby enhancing their commitment to the programmes and increasing their sustainability. Both also demonstrate more ‘holistic’ approaches to evaluation than the formal methods prescribed in the EU Common Monitoring and Evaluation Framework.

An iterative contractive framework for probe methods: LASSO

We present a new iterative approach called Line Adaptation for the Singular Sources Objective (LASSO) to object or shape reconstruction based on the singular sources method (or probe method) for the reconstruction of scatterers from the far-field pattern of scattered acoustic or electromagnetic waves. The scheme is based on the construction of an indicator function given by the scattered field for incident point sources in its source point from the given far-field patterns for plane waves. The indicator function is then used to drive the contraction of a surface which surrounds the unknown scatterers. A stopping criterion for those parts of the surfaces that touch the unknown scatterers is formulated. A splitting approach for the contracting surfaces is formulated, such that scatterers consisting of several separate components can be reconstructed. Convergence of the scheme is shown, and its feasibility is demonstrated using a numerical study with several examples.