Housing the skilled migrants in the Helsinki Metropolitan Area : International attractiveness of housing, neighbourhoods and the city region

The aim of this thesis is to examine the skilled migrants’ satisfaction with the Helsinki Metropolitan Area. The examination is executed on three scales: housing, neighbourhoods and the city region. Specific focus is on the built environment and how it meets the needs of the migrants. The empirical data is formed of 25 semi-structured interviews with skilled migrants and additionally 5 expert interviews. Skilled and educated workforce is an increasingly important resource in the new economy, and cities are competing globally for talented workers. With aging population and a need to develop its innovational structure, the Helsinki Metropolitan Area needs migrant workforce. It has been stated that quality of place is a central factor for skilled migrants when choosing where to settle, and from this perspective their satisfaction with the region is significant. In housing, the skilled migrants found the price-quality ratio and the general sizes of apartments inadequate. The housing market is difficult for the migrants to approach, since they often do not speak Finnish and there are prejudices towards foreigners. The general quality of housing was rated well. On the neighbourhood level, the skilled migrants had settled in residential areas which are also preferred by the Finnish skilled workers. While the migrants showed suburban orientation in their settlement patterns, they were not concentrated in the suburban areas which host large shares of traditional immigrant groups. Migrants were usually satisfied with their neighbourhoods; however, part of the suburban dwellers were unsatisfied with the services and social life in their neighbourhoods. Considering the level of the city region, the most challenging feature for the skilled migrants was the social life. The migrants felt that the social environment is homogeneous and difficult to approach. The physical environment was generally rated well, the most appreciated features being public transportation, human scale of the Metropolitan Helsinki, cleanliness, and the urban nature. Urban culture and services were seen good for the city region’s size, but lacking in international comparison.

Middleware for Long-running Applications on Batch Grids. Student Research Symposium poster

Computational grids with multiple batch systems (batch grids) can be powerful infrastructures for executing long-running multicomponent parallel applications. In this paper, we have constructed a middleware framework for executing such long-running applications spanning multiple submissions to the queues on multiple batch systems. We have used our framework for execution of a foremost long-running multi-component application for climate modeling, the Community Climate System Model (CCSM). Our framework coordinates the distribution, execution, migration and restart of the components of CCSM on the multiple queues where the component jobs of the different queues can have different queue waiting and startup times.

Effect of capillarity and heterogeneity in the numerical modelling of multiphase flow of fluids in unsaturated porous medium

The multiphase flow of fluids in the unsaturated porous medium is considered as a three phase flow of water, NAPL, and air simultaneously in the porous medium. The adaptive solution fully implicit modified sequential method is used for the numerical modelling. The effect of capillarity and heterogeneity effect at the interface between the media is studied and it is observed that the interface criteria has to be taken into account for the correct prediction of NAPL migration especially in heterogeneous media. The modified Newton Raphson method is used for the linearization and Hestines and Steifel Conjugate Gradient method is used as the solver.

A mixture model approach for formant tracking and the robustness of student's-t distribution

We address the problem of robust formant tracking in continuous speech in the presence of additive noise. We propose a new approach based on mixture modeling of the formant contours. Our approach consists of two main steps: (i) Computation of a pyknogram based on multiband amplitude-modulation/frequency-modulation (AM/FM) decomposition of the input speech; and (ii) Statistical modeling of the pyknogram using mixture models. We experiment with both Gaussian mixture model (GMM) and Student's-t mixture model (tMM) and show that the latter is robust with respect to handling outliers in the pyknogram data, parameter selection, accuracy, and smoothness of the estimated formant contours. Experimental results on simulated data as well as noisy speech data show that the proposed tMM-based approach is also robust to additive noise. We present performance comparisons with a recently developed adaptive filterbank technique proposed in the literature and the classical Burg's spectral estimator technique, which show that the proposed technique is more robust to noise.

A study on the usefulness of Support Vector Machines for the realtime computational simulation of soft biological organs

Realistic and realtime computational simulation of soft biological organs (e.g., liver, kidney) is necessary when one tries to build a quality surgical simulator that can simulate surgical procedures involving these organs. Since the realistic simulation of these soft biological organs should account for both nonlinear material behavior and large deformation, achieving realistic simulations in realtime using continuum mechanics based numerical techniques necessitates the use of a supercomputer or a high end computer cluster which are costly. Hence there is a need to employ soft computing techniques like Support Vector Machines (SVMs) which can do function approximation, and hence could achieve physically realistic simulations in realtime by making use of just a desktop computer. Present work tries to simulate a pig liver in realtime. Liver is assumed to be homogeneous, isotropic, and hyperelastic. Hyperelastic material constants are taken from the literature. An SVM is employed to achieve realistic simulations in realtime, using just a desktop computer. The code for the SVM is obtained from [1]. The SVM is trained using the dataset generated by performing hyperelastic analyses on the liver geometry, using the commercial finite element software package ANSYS. The methodology followed in the present work closely follows the one followed in [2] except that [2] uses Artificial Neural Networks (ANNs) while the present work uses SVMs to achieve realistic simulations in realtime. Results indicate the speed and accuracy that is obtained by employing the SVM for the targeted realistic and realtime simulation of the liver.

STUDENT'S-t MIXTURE MODEL BASED MULTI-INSTRUMENT RECOGNITION IN POLYPHONIC MUSIC

We address the problem of multi-instrument recognition in polyphonic music signals. Individual instruments are modeled within a stochastic framework using Student's-t Mixture Models (tMMs). We impose a mixture of these instrument models on the polyphonic signal model. No a priori knowledge is assumed about the number of instruments in the polyphony. The mixture weights are estimated in a latent variable framework from the polyphonic data using an Expectation Maximization (EM) algorithm, derived for the proposed approach. The weights are shown to indicate instrument activity. The output of the algorithm is an Instrument Activity Graph (IAG), using which, it is possible to find out the instruments that are active at a given time. An average F-ratio of 0 : 7 5 is obtained for polyphonies containing 2-5 instruments, on a experimental test set of 8 instruments: clarinet, flute, guitar, harp, mandolin, piano, trombone and violin.