La complexité des données contenues dans les grandes bases de données a augmenté considérablement. Par conséquent, des opérations plus élaborées que les requêtes traditionnelles sont indispensable pour extraire toutes les informations requises de la base de données. L'intérêt de la communauté de base de données a particulièrement augmenté dans les recherches basées sur la similarité. Deux sortes de recherche de similarité bien connues sont la requête par intervalle (Rq) et par k-plus proches voisins (kNNq). Ces deux techniques, comme les requêtes traditionnelles, peuvent être accélérées par des structures d'indexation des Systèmes de Gestion de Base de Données (SGBDs).Une autre façon d'accélérer les requêtes est d'exécuter le procédé d'optimisation des requêtes. Dans ce procédé les données métriques sont recueillies et utilisées afin d'ajuster les paramètres des algorithmes de recherche lors de chaque exécution de la requête. Cependant, bien que l'intégration de la recherche de similarités dans le SGBD ait commencé à être étudiée en profondeur récemment, le procédé d'optimisation des requêtes a été développé et utilisé pour répondre à des requêtes traditionnelles. L'exécution des requêtes de similarité a tendance à présenter un coût informatique plus important que l'exécution des requêtes traditionnelles et ce même en utilisant des structures d'indexation efficaces. Deux stratégies peuvent être appliquées pour accélérer l'execution de quelques requêtes, et peuvent également être employées pour répondre aux requêtes de similarité. La première stratégie est la réécriture de requêtes basées sur les propriétés algébriques et les fonctions de coût. La deuxième stratégie est l'utilisation des facteurs externes de la requête, tels que la sémantique attendue par les usagers, pour réduire le nombre des résultats potentiels. Cette thèse vise à contribuer au développement des techniques afin d'améliorer le procédé d'optimisation des requêtes de similarité, tout en exploitant les propriétés algébriques et les restrictions sémantiques pour affiner les requêtes. / The complexity of data stored in large databases has increased at very fast paces. Hence, operations more elaborated than traditional queries are essential in order to extract all required information from the database. Therefore, the interest of the database community in similarity search has increased significantly. Two of the well-known types of similarity search are the Range (Rq) and the k-Nearest Neighbor (kNNq) queries, which, as any of the traditional ones, can be sped up by indexing structures of the Database Management System (DBMS). Another way of speeding up queries is to perform query optimization. In this process, metrics about data are collected and employed to adjust the parameters of the search algorithms in each query execution. However, although the integration of similarity search into DBMS has begun to be deeply studied more recently, the query optimization has been developed and employed just to answer traditional queries.The execution of similarity queries, even using efficient indexing structures, tends to present higher computational cost than the execution of traditional ones. Two strategies can be applied to speed up the execution of any query, and thus they are worth to employ to answer also similarity queries. The first strategy is query rewriting based on algebraic properties and cost functions. The second technique is when external query factors are applied, such as employing the semantic expected by the user, to prune the answer space. This thesis aims at contributing to the development of novel techniques to improve the similarity-based query optimization processing, exploiting both algebraic properties and semantic restrictions as query refinements. / A complexidade dos dados armazenados em grandes bases de dados tem aumentadosempre, criando a necessidade de novas operaoes de consulta. Uma classe de operações de crescente interesse são as consultas por similaridade, das quais as mais conhecidas sãoas consultas por abrangência (Rq) e por k-vizinhos mais próximos (kNNq). Qualquerconsulta é agilizada pelas estruturas de indexaçãodos Sistemas de Gerenciamento deBases de Dados (SGBDs). Outro modo de agilizar as operações de busca é a manutençãode métricas sobre os dados, que são utilizadas para ajustar parâmetros dos algoritmos debusca em cada consulta, num processo conhecido como otimização de consultas. Comoas buscas por similaridade começaram a ser estudadas seriamente para integração emSGBDs muito mais recentemente do que as buscas tradicionais, a otimização de consultas,por enquanto, é um recurso que tem sido utilizado para responder apenas a consultastradicionais.Mesmo utilizando as melhores estruturas existentes, a execução de consultas por similaridadetende a ser mais custosa do que as operações tradicionais. Assim, duas estratégiaspodem ser utilizadas para agilizar a execução de qualquer consulta e, assim, podem serempregadas também para responder às consultas por similaridade. A primeira estratégiaé a reescrita de consultas baseada em propriedades algébricas e em funções de custo. Asegunda técnica faz uso de fatores externos à consulta, tais como a semântica esperadapelo usuário, para restringir o espaço das respostas. Esta tese pretende contribuir parao desenvolvimento de técnicas que melhorem o processo de otimização de consultas porsimilaridade, explorando propriedades algébricas e restrições semânticas como refinamentode consultas

This thesis makes an original contribution to knowledge in the fi eld of data objects' comparison where the objects are described by attributes of fuzzy or heterogeneous (numeric and symbolic) data types. Many real world database systems and applications require information management components that provide support for managing such imperfect and heterogeneous data objects. For example, with new online information made available from various sources, in semi-structured, structured or unstructured representations, new information usage and search algorithms must consider where such data collections may contain objects/records with di fferent types of data: fuzzy, numerical and categorical for the same attributes. New approaches of similarity have been presented in this research to support such data comparison. A generalisation of both geometric and set theoretical similarity models has enabled propose new similarity measures presented in this thesis, to handle the vagueness (fuzzy data type) within data objects. A framework of new and unif ied similarity measures for comparing heterogeneous objects described by numerical, categorical and fuzzy attributes has also been introduced. Examples are used to illustrate, compare and discuss the applications and e fficiency of the proposed approaches to heterogeneous data comparison. / Libyan Embassy

Measuring semantic similarity is a task needed in many Natural Language Processing (NLP) applications. For example, in Machine Translation evaluation, semantic similarity is used to assess the quality of the machine translation output by measuring the degree of equivalence between a reference translation and the machine translation output. The problem of semantic similarity (Corley and Mihalcea, 2005) is de ned as measuring and recognising semantic relations between two texts. Semantic similarity covers di erent types of semantic relations, mainly bidirectional and directional. This thesis proposes new methods to address the limitations of existing work on both types of semantic relations. Recognising Textual Entailment (RTE) is a directional relation where a text T entails the hypothesis H (entailment pair) if the meaning of H can be inferred from the meaning of T (Dagan and Glickman, 2005; Dagan et al., 2013). Most of the RTE methods rely on machine learning algorithms. de Marne e et al. (2006) propose a multi-stage architecture where a rst stage determines an alignment between the T-H pairs to be followed by an entailment decision stage. A limitation of such approaches is that instead of recognising a non-entailment, an alignment that ts an optimisation criterion will be returned, but the alignment by itself is a poor predictor for iii non-entailment. We propose an RTE method following a multi-stage architecture, where both stages are based on semantic representations. Furthermore, instead of using simple similarity metrics to predict the entailment decision, we use a Markov Logic Network (MLN). The MLN is based on rich relational features extracted from the output of the predicate-argument alignment structures between T-H pairs. This MLN learns to reward pairs with similar predicates and similar arguments, and penalise pairs otherwise. The proposed methods show promising results. A source of errors was found to be the alignment step, which has low coverage. However, we show that when an alignment is found, the relational features improve the nal entailment decision. The task of Semantic Textual Similarity (STS) (Agirre et al., 2012) is de- ned as measuring the degree of bidirectional semantic equivalence between a pair of texts. The STS evaluation campaigns use datasets that consist of pairs of texts from NLP tasks such as Paraphrasing and Machine Translation evaluation. Methods for STS are commonly based on computing similarity metrics between the pair of sentences, where the similarity scores are used as features to train regression algorithms. Existing methods for STS achieve high performances over certain tasks, but poor results over others, particularly on unknown (surprise) tasks. Our solution to alleviate this unbalanced performances is to model STS in the context of Multi-task Learning using Gaussian Processes (MTL-GP) ( Alvarez et al., 2012) and state-of-the-art iv STS features ( Sari c et al., 2012). We show that the MTL-GP outperforms previous work on the same datasets.

Many technological hurdles remain before a viable commercial magnetic fusion energy reactor can be constructed, including the development of plasma-facing components with long lifetimes that can survive the harsh environment inside a reactor. One such component, the divertor, which maintains the purity of the plasma by removing fusion byproducts from the reactor, must be able to accommodate very large incident heat fluxes of at least 10 MW/m^2 during normal operation. Modular helium-cooled tungsten divertors are one of the leading divertor designs for future commercial fusion reactors, and a number of different candidates have been proposed including the modular He-cooled divertor concept with pin array (HEMP), the modular He-cooled divertor concept with multiple-jet-cooling (HEMJ), and the helium-cooled flat plate (HCFP). These three designs typically operate with helium coolant inlet temperatures of 600 °C and inlet pressures of 10 MPa. Performing experiments at these conditions to evaluate the thermal performance of each design is both challenging and expensive. An alternative, more economical approach for evaluating different designs exploits dynamic similarity. Here, geometrically similar mockups of a single divertor module are tested using coolants at lower temperatures and pressures. Dynamically similar experiments were performed on an HEMP-like divertor with helium and argon at inlet temperatures close to room temperature, inlet pressures below 1.4 MPa, and incident heat fluxes up to 2 MW/m^2. The results are used to predict the maximum heat flux that the divertor can accommodate, and the pumping power as a fraction of incident thermal power, for a given maximum tungsten temperature. A new nondimensional parameter, the thermal conductivity ratio, is introduced in the Nusselt number correlations which accounts for variations in the amount of conduction heat transfer through the walls of the divertor module. Numerical simulations of the HCFP divertor are performed to investigate how the thermal conductivity ratio affects predictions for the maximum heat flux obtained in previous studies. Finally, a helium loop is constructed and used to perform dynamically similar experiments on an HEMJ module at inlet temperatures as high as 300 °C, inlet pressures of 10 MPa, and incident heat fluxes as great as 4.9 MW/m^2. The correlations generated from this work can be used in system codes to determine optimal designs and operating conditions for a variety of fusion reactor designs.

<p>Upplevd likhet med målpersonen har ansetts vara viktig för empati och viss forskning har visat att empatin ökar med upplevd etnisk sam-hörighet. Denna studies primära syfte var att experimentellt undersöka om svenska och icke-svenska deltagare kände olika mycket empati beroende på målpersonens etnicitet samt upplevd likhet med mål-personen. Majoriteten av de 160 deltagarna rekryterades från Mälar-dalens högskola, varav 102 var svenska och 84 var kvinnliga. Resultatet visade två signifikanta disordinala interaktioner där svenska deltagare kände signifikant mer empati och upplevd likhet med en svensk än icke-svensk målperson, medan icke-svenska inte visade signifikant mer empati eller upplevd likhet med en icke-svensk än svensk målperson. Ingen signifikant skillnad i empati fanns mellan äldre och yngre deltagare. Män uppvisade signifikant lägre empati än kvinnor och inget av könen väckte mer empati. Orsaken till de disordinala interaktionerna diskuterades i termer av social kategorisering. Vidare forskning med en annan definition av begreppet etnicitet föreslogs.</p>

This paper extends recent research studying biases in venture capitalist's decision-making. We contribute to this literature by analyzing biases arising due to similarity between a venture capitalist and members of a venture team. We summarize the psychological foundations of such similarity effects and derive a set of hypotheses regarding the impact of similarity on the assessement of team quality. Using data from a conjoint experiment with 51 respondents, we find that venture capitalists tend to favor teams that are similar to themselves w.r.t. the type of training and professional experience. Our results have important implications for academics and practitioners alike. (authors' abstract)

Description Logics (DLs) are a family of logic-based knowledge representation languages used to describe the knowledge of an application domain and reason about it in formally well-defined way. They allow users to describe the important notions and classes of the knowledge domain as concepts, which formalize the necessary and sufficient conditions for individual objects to belong to that concept. A variety of different DLs exist, differing in the set of properties one can use to express concepts, the so-called concept constructors, as well as the set of axioms available to describe the relations between concepts or individuals. However, all classical DLs have in common that they can only express exact knowledge, and correspondingly only allow exact inferences. Either we can infer that some individual belongs to a concept, or we can't, there is no in-between. In practice though, knowledge is rarely exact. Many definitions have their exceptions or are vaguely formulated in the first place, and people might not only be interested in exact answers, but also in alternatives that are "close enough". This thesis is aimed at tackling how to express that something "close enough", and how to integrate this notion into the formalism of Description Logics. To this end, we will use the notion of similarity and dissimilarity measures as a way to quantify how close exactly two concepts are. We will look at how useful measures can be defined in the context of DLs, and how they can be incorporated into the formal framework in order to generalize it. In particular, we will look closer at two applications of thus measures to DLs: Relaxed instance queries will incorporate a similarity measure in order to not just give the exact answer to some query, but all answers that are reasonably similar. Prototypical definitions on the other hand use a measure of dissimilarity or distance between concepts in order to allow the definitions of and reasoning with concepts that capture not just those individuals that satisfy exactly the stated properties, but also those that are "close enough".

The investigation examined the effects of formal and semantic intralist stimulus similarity (ISS) on transfer of stimulus differentiations in the A-B, A-C paradigm.

Would you like a stranger more who shifts his/her attitudes to more closely align with yours? How would you feel if he/she aligned with everyone as opposed to just you? In Experiment 1, participants discussed with a partner disagreed upon social issues and received false feedback about whether the partner engaged in attitude alignment (shifted his/her attitude toward the participant’s attitude) following discussion. Participants also received false feedback about proportion of similarity (25%, 50%, or 75%) to the partner. Participants reported greater attraction toward partners who engaged in attitude alignment and who were more similar. However, similarity only predicted attraction in the absence of attitude alignment. Additionally, partner attitude alignment led to participant attitude alignment, and perceived reasoning ability marginally mediated the attitude alignment-attraction relationship. Similar to Experiment 1, participants in Experiment 2 received attitude alignment feedback, but they also received feedback about whether the partner engaged in attitude alignment with no others besides the participant (selective) or with many others besides the participant (unselective). Participants reported greater attraction toward partners who engaged in attitude alignment with them regardless of the partners’ attitude alignment with others. Perceived reasoning ability again mediated the attitude alignment-attraction relationship, and appeared to be more important in explaining this relation than cognitive evaluation or inferred attraction. Finally, participants reported greater trust and respect for partners who engaged in attitude alignment, but were no more willing to help those partners. This work extends our understanding of attitude alignment and its potential to affect interpersonal relationships, and it considers the influence of judgments about individuals outside of the dyad (i.e., alignment with others relative to alignment with the self).

Learning and representing semantics is one of the most important tasks that significantly contribute to some growing areas, as successful stories in the recent survey of Turney and Pantel (2010). In this thesis, we present an in- novative (and first) framework for creating a multimodal distributional semantic model from state of the art text-and image-based semantic models. We evaluate this multimodal semantic model on simulating similarity judgements, concept clustering and the newly introduced BLESS benchmark. We also propose an effective algorithm, namely Parameter Estimation, to integrate text- and image- based features in order to have a robust multimodal system. By experiments, we show that our technique is very promising. Across all experiments, our best multimodal model claims the first position. By relatively comparing with other text-based models, we are justified to affirm that our model can stay in the top line with other state of the art models. We explore various types of visual features including SIFT and other color SIFT channels in order to have prelim- inary insights about how computer-vision techniques should be applied in the natural language processing domain. Importantly, in this thesis, we show evi- dences that adding visual features (as the perceptual information coming from...