Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

A statistical shape model of the human second cervical vertebra.

PURPOSE: Statistical shape and appearance models play an important role in reducing the segmentation processing time of a vertebra and in improving results for 3D model development. Here, we describe the different steps in generating a statistical shape model (SSM) of the second cervical vertebra (C2) and provide the shape model for general use by the scientific community. The main difficulties in its construction are the morphological complexity of the C2 and its variability in the population. METHODS: The input dataset is composed of manually segmented anonymized patient computerized tomography (CT) scans. The alignment of the different datasets is done with the procrustes alignment on surface models, and then, the registration is cast as a model-fitting problem using a Gaussian process. A principal component analysis (PCA)-based model is generated which includes the variability of the C2. RESULTS: The SSM was generated using 92 CT scans. The resulting SSM was evaluated for specificity, compactness and generalization ability. The SSM of the C2 is freely available to the scientific community in Slicer (an open source software for image analysis and scientific visualization) with a module created to visualize the SSM using Statismo, a framework for statistical shape modeling. CONCLUSION: The SSM of the vertebra allows the shape variability of the C2 to be represented. Moreover, the SSM will enable semi-automatic segmentation and 3D model generation of the vertebra, which would greatly benefit surgery planning.

Lectio praecursoria: Environmental policy evaluation concepts exemplified in the context of the European Unions strategy for better regulation

Lectio praecursoria 3.2.2006 Tampereen yliopisto

Optical trapping: a review of essential concepts

Optical tweezers are an innovative technique for the non-contact, all-optical manipulation of small material samples, which has extraordinarily expanded and evolved since its inception in the mid-80s of the last century. Nowadays, the potential of optical tweezers has been clearly proven and a wide range of applications both from the physical and biological sciences have solidly emerged, turning the early ideas and techniques into a powerful paradigm for experimentation in the micro- and nanoworld. This review aims at highlighting the fundamental concepts that are essential for a thorough understanding of optical trapping, making emphasis on both its manipulation and measurement capabilities, as well as on the vast array of important biological applications appeared in the last years.

Effects of business-to-business customer orientation, satisfaction and retention on firm profitability

The objective of this master’s thesis was to examine the effect of customer orientation on customer satisfaction and how customer satisfaction and customer retention contribute to firm profitability. Beside customer orientation, also other antecedents of customer satisfaction, i.e. service quality, flexibility, trust and commitment, were investigated as control variables. Literature review revealed several research gaps concerning research of the key concepts. These research calls were also answered. The empirical study focused on one case company, a telecommunication expert. The data for the empirical part was collected with web-based questionnaire from case company’s business customers in January-February 2008. Sample (N=95) produced 59 answers, thus the response rate of the survey was 62,1%. The data was analyzed by using statistical analysis program, SPSS. As a conclusion, the results indicate that customer orientation do not affect customer satisfaction directly, but through service quality, flexibility and trust. Moreover, customer satisfaction has positive impacts on commitment and intentions to stay as a customer in the future, but not on profitability. In the present study, only past purchase behavior, measured with customer database measure, is positively related to firm profitability.

Use of Statistical Methods in Demand Planning

In a very volatile industry of high technology it is of utmost importance to accurately forecast customers’ demand. However, statistical forecasting of sales, especially in heavily competitive electronics product business, has always been a challenging task due to very high variation in demand and very short product life cycles of products. The purpose of this thesis is to validate if statistical methods can be applied to forecasting sales of short life cycle electronics products and provide a feasible framework for implementing statistical forecasting in the environment of the case company. Two different approaches have been developed for forecasting on short and medium term and long term horizons. Both models are based on decomposition models, but differ in interpretation of the model residuals. For long term horizons residuals are assumed to represent white noise, whereas for short and medium term forecasting horizon residuals are modeled using statistical forecasting methods. Implementation of both approaches is performed in Matlab. Modeling results have shown that different markets exhibit different demand patterns and therefore different analytical approaches are appropriate for modeling demand in these markets. Moreover, the outcomes of modeling imply that statistical forecasting can not be handled separately from judgmental forecasting, but should be perceived only as a basis for judgmental forecasting activities. Based on modeling results recommendations for further deployment of statistical methods in sales forecasting of the case company are developed.