The Development of Public Finances and Role of Fiscal Constraints in a Monetary Union: Example eurozone

This thesis is about the development of public debt and deficit in the eurozone, which has been in the center of attention for much of the new millennium. The debt-to-GDP and deficit-to-GDP ratios have changed significantly during the period of the European monetary integration, with sharp increases in the levels since the beginning of the financial crisis. We view the levels both before and after the establishment of the European Central Bank. The subject is complemented by a study of the restrictions on fiscal policy in the eurozone. The thesis begins with a review of the most central agreements in the Economic and Monetary Union, namely the Maastricht Treaty, the Stability and Growth Pact and the Fiscal Compact. We study the instructions and requirements provided by these contracts, with the emphasis being on the debt and deficit values. Furthermore, we view two theories that aim to provide us with information, whether the fiscal restrictions are useful or not. The second and empirical part consists of review on the debt and deficit levels in practice. We take a close look on the values for each of the currency union members. The third and last part summarizes the findings, and analyzes the reasons behind the changes. The result of the thesis is, that even though the levels of public debt and deficit have worsened since the beginning of the financial crisis, tight rules on fiscal policy might not be the best possible solution. Private sector has played a crucial part in the increase of the debt levels, and tight rules have their impact on the long awaited economic growth in the eurozone. It is obvious, though, that some form of fiscal guidelines with scientific ground are needed in order to avoid excessive and harmful debt and deficit levels. The main task is to make these guidelines a more essential part of the fiscal policy in each of the member countries.

The use of future-oriented knowledge in regional innovation processes: Research on knowledge generation, transfer and conversion

The importance of the regional level in research has risen in the last few decades and a vast literature in the fields of, for instance, evolutionary and institutional economics, network theories, innovations and learning systems, as well as sociology, has focused on regional level questions. Recently the policy makers and regional actors have also began to pay increasing attention to the knowledge economy and its needs, in general, and the connectivity and support structures of regional clusters in particular. Nowadays knowledge is generally considered as the most important source of competitive advantage, but even the most specialised forms of knowledge are becoming a short-lived resource for example due to the accelerating pace of technological change. This emphasizes the need of foresight activities in national, regional and organizational levels and the integration of foresight and innovation activities. In regional setting this development sets great challenges especially in those regions having no university and thus usually very limited resources for research activities. Also the research problem of this dissertation is related to the need to better incorporate the information produced by foresight process to facilitate and to be used in regional practice-based innovation processes. This dissertation is a constructive case study the case being Lahti region and a network facilitating innovation policy adopted in that region. Dissertation consists of a summary and five articles and during the research process a construct or a conceptual model for solving this real life problem has been developed. It is also being implemented as part of the network facilitating innovation policy in the Lahti region.

Integrating Financial Perspective to Quality Orientated Management System. Case: ALSTOM Finland Oy

Tutkimuksen päätavoitteena on tutkia taloudellisen näkökulman integroimista laatuajatteluun pohjautuvaan johtamisjärjestelmään esimerkkiyrityksessä. Johtamisjärjestelmän tulee tuottaa tietoa johdon strategiselle päätöksenteolle ja lisäksi täyttää laatujärjestelmän (ISO 9001:2000) asettamat vaatimukset. Tutkimuksen kohteena oleva työkalu on balanced scorecard (tasapainotettu tuloskortti). Työn tarkoituksena on ehdottaa balanced scorecard- talouden tunnuslukuja esimerkkiyritykselle. Tutkimuksen tavoitteisiin päästään empiiristä tutkimusta varten tehdyn teoreettisen viitekehyksen avulla. Empiiristä tutkimustietoa kerätään osallistuvan havainnoinnin, haastattelujen ja keskustelujen avulla. Tutkimusmenetelmänä on laadullinen case -tutkimus. Balanced scorecardin eri näkökulmille ehdotettiin tunnuslukuja empiirisen tutkimuksen pohjalta. Lisäksi talouden näkökulmaa tutkittiin tarkemmin. Tutkimuksen johtopäätöksenä esitettiin, että taloudelliset tunnusluvut mittaavat ensisijaisesti strategiaa eivätkä laatua. Lisäksi huomioitiin, että tuloskorttien tulisi olla koekäytössä ennen bonuspalkkauksen ja balanced scorecardin yhdistämistä.

Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Verification and Implementation of Call Sequence Analysing Algorithm

Työssä arvioidaan ja verifioidaan puheluiden luokitteluun suunniteltu Call Sequence Analysing Algorithm (CSA-algoritmi). Algoritmin tavoitteena on luokitella riittävän samankaltaiset puhelut ryhmiksi tarkempaa vika-analyysia varten. Työssä esitellään eri koneoppimisalgoritmien pääluokitukset ja niiden tyypilliset eroavaisuudet, eri luokitteluprosesseille ominaiset datatyypit, sekä toimintaympäristöt, joissa kyseinen toteutus on suunniteltu toimivaksi. CSA-algoritmille syötetään verkon ylläpitoviesteistä koostuvia viestisarjoja, joiden sisällön perusteella samankaltaiset sarjat ryhmitellään kokonaisuuksiksi. Algoritmin suorituskykyä arvioidaan 94 käsin luokitellun verrokkisarjan avulla. Sarjat on kerätty toimivasta 3G-verkon kontrollerista. Kahta sarjaa vertailemalla sarjaparille muodostetaan keskinäinen tunnusluku: sarjojen samanlaisuutta kuvaava etäisyys. Tässä työssä keskitytään erityisesti Hamming-etäisyyteen. Etäisyyden avulla sarjat koostetaan ryhmiksi. Muuttamalla hyväksyttävää maksimietäisyyttä, jonka perusteella kaksi sarjaa lasketaan kuuluvaksi samaan ryhmään, saadaan aikaiseksi alaryhmiä, joihin kuuluu ainoastaan samankaltaisia sarjoja. Hyväksyttävän etäisyyden kasvaessa, myös virheluokitusten määrä kasvaa. Oikeiden lajittelutulosten vertailukohteena toimii käsin luokiteltu ryhmittely. CSA-algoritmin luokittelutuloksen tarkkuus esitetään prosentuaalisena osuutena tavoiteryhmittelystä maksimietäisyyden funktiona. Työssä osoitetaan, miten etäisyysattribuutiksi valittu Hamming-etäisyys ei sovellu tämän datan luokitteluun. Työn lopussa ehdotetaan menetelmää ja työkalua, joiden avulla useampaa eri lajittelija-algoritmia voidaan testata nopealla kehityssyklillä.