Sångstycken ur Kung Carls jagt Duettino, Kuplett, Duett : musik af F. Pacius; text af Z. Topelius - [Pärmen]

Helsingfors : Wasenius & Co. [1853]

Skörde-maskiner. Bindare och slottermaskiner Buckeye - Buckeye bindare [text]

[Åbo] : [Victor Forselius] c. 1900

The valence band alignment at ultrathin SiO2/Si interfaces

High resolution x-ray photoelectron spectroscopy has been used to determine the valence band alignment at ultrathin SiO2/Si interfaces. In the oxide thickness range 1.6-4.4 nm the constant band-offset values of 4.49 and 4.43 eV have been obtained for the dry SiO2/Si(100) and the wet SiO2/Si(100) interfaces, respectively. The valence band alignment of dry SiO2/Si(111) (4.36 eV) is slightly smaller than the case of the dry SiO2/Si(100) interface.

Business IT Alignment from Business Model to Enterprise Architecture

In this paper, we show how business model modelling can be connected to IT infrastructure, drawing parallels from enterprise architecture models such as ArchiMate. We then show how the proposed visualization based on enterprise architecture, with a strong focus on business model strategy, can help IT alignment, at both the business model and the IT infrastructure level.

Text Classification and Indexing in IP Networks

Internet on elektronisen postin perusrakenne ja ollut tärkeä tiedonlähde akateemisille käyttäjille jo pitkään. Siitä on tullut merkittävä tietolähde kaupallisille yrityksille niiden pyrkiessä pitämään yhteyttä asiakkaisiinsa ja seuraamaan kilpailijoitansa. WWW:n kasvu sekä määrällisesti että sen moninaisuus on luonut kasvavan kysynnän kehittyneille tiedonhallintapalveluille. Tällaisia palveluja ovet ryhmittely ja luokittelu, tiedon löytäminen ja suodattaminen sekä lähteiden käytön personointi ja seuranta. Vaikka WWW:stä saatavan tieteellisen ja kaupallisesti arvokkaan tiedon määrä on huomattavasti kasvanut viime vuosina sen etsiminen ja löytyminen on edelleen tavanomaisen Internet hakukoneen varassa. Tietojen hakuun kohdistuvien kasvavien ja muuttuvien tarpeiden tyydyttämisestä on tullut monimutkainen tehtävä Internet hakukoneille. Luokittelu ja indeksointi ovat merkittävä osa luotettavan ja täsmällisen tiedon etsimisessä ja löytämisessä. Tämä diplomityö esittelee luokittelussa ja indeksoinnissa käytettävät yleisimmät menetelmät ja niitä käyttäviä sovelluksia ja projekteja, joissa tiedon hakuun liittyvät ongelmat on pyritty ratkaisemaan.

Spiraalidynaaminen harjoittelu vaivaisenluun hoidossa

Tutkimuksen tarkoituksena oli selvittää spiraalidynaamisen harjoitteiden tehokkuutta vaivaisenluun yhteydessä esiintyvään kipuun, alaraajojen nivelten liikkuvuuteen sekä isovarpaan toimintoihin ja rakenteeseen sekä alaraajojen nivelten liikkuvuuteen. Tutkimusmenetelmänä käytettiin kokeellista yksittäistapaustutkimusta. Tiedonhankintamenetelmänä käytettiin strukturoituja ja sekamuotoisia kyselylomakkeita sekä alaraajojen nivelien kliinistä tutkimista. Harkinnanvaraisesti valittu tutkimusjoukko (n=6) koostui Helsingin ammattikorkeakoulu Stadian kuntoutusalan Kunto-Stadian asiakkaista. Tutkimus toteutettiin Kunto-Stadian tiloissa maalis - toukokuussa. Seurantamittaus tehtiin 23.-28.8.2007. Tutkimustulokset esitettiin frekvensseinä, ja tilastollisena analyysimenetelmänä käytettiin ristiintaulukointia. Tutkimuksen aikana tutkittavilla vaivaisenluussa esiintyvät kivut hävisivät kokonaan tai helpottuivat huomattavasti. Lonkan ja isovarpaan tyvinivelen nivelissä tapahtui liikelaajuuden lisääntymistä puolella tutkittavista. Jalkaterän lihasvoima ja rakenne vahvistuivat tutkimuksen aikana kaikilla tutkittavilla. Lisäksi alaraajojen linjauksessa tapahtui korjaantumista sekä vaivaisenluukulmat pienenivät lähes kaikilla tutkittavilla. Tutkittavat kokivat harjoitteet hyödyllisiksi ja arkipäivään sulautuviksi. Suurin osa tutkittavista sanoi jatkavansa harjoitteita jokapäiväisessä elämässä tutkimuksen loputtua. Tulosten perusteella spiraalidynaamiset harjoitteet ovat tehokkaita vaivaisenluun hoidossa. Tutkimustuloksia voivat hyödyntää kaikki vaivaisenluita hoitavat mm. jalkaterapeutit, jalkojenhoitajat lääkärit ja fysioterapeutit. Tutkimuksesta saatujen tietojen perusteella voidaan vahvistaa ryhmämuotoisen harjoittelun merkitystä vaivaisenluun hoidossa ja tarjota vaihtoehto leikkauksille.

Publication trends of shared decision making in 15 high impact medical journals: a full-text review with bibliometric analysis.

BACKGROUND: Shared Decision Making (SDM) is increasingly advocated as a model for medical decision making. However, there is still low use of SDM in clinical practice. High impact factor journals might represent an efficient way for its dissemination. We aimed to identify and characterize publication trends of SDM in 15 high impact medical journals. METHODS: We selected the 15 general and internal medicine journals with the highest impact factor publishing original articles, letters and editorials. We retrieved publications from 1996 to 2011 through the full-text search function on each journal website and abstracted bibliometric data. We included publications of any type containing the phrase "shared decision making" or five other variants in their abstract or full text. These were referred to as SDM publications. A polynomial Poisson regression model with logarithmic link function was used to assess the evolution across the period of the number of SDM publications according to publication characteristics. RESULTS: We identified 1285 SDM publications out of 229,179 publications in 15 journals from 1996 to 2011. The absolute number of SDM publications by journal ranged from 2 to 273 over 16 years. SDM publications increased both in absolute and relative numbers per year, from 46 (0.32% relative to all publications from the 15 journals) in 1996 to 165 (1.17%) in 2011. This growth was exponential (P < 0.01). We found fewer research publications (465, 36.2% of all SDM publications) than non-research publications, which included non-systematic reviews, letters, and editorials. The increase of research publications across time was linear. Full-text search retrieved ten times more SDM publications than a similar PubMed search (1285 vs. 119 respectively). CONCLUSION: This review in full-text showed that SDM publications increased exponentially in major medical journals from 1996 to 2011. This growth might reflect an increased dissemination of the SDM concept to the medical community.

New kernel functions and learning methods for text and data mining

Recent advances in machine learning methods enable increasingly the automatic construction of various types of computer assisted methods that have been difficult or laborious to program by human experts. The tasks for which this kind of tools are needed arise in many areas, here especially in the fields of bioinformatics and natural language processing. The machine learning methods may not work satisfactorily if they are not appropriately tailored to the task in question. However, their learning performance can often be improved by taking advantage of deeper insight of the application domain or the learning problem at hand. This thesis considers developing kernel-based learning algorithms incorporating this kind of prior knowledge of the task in question in an advantageous way. Moreover, computationally efficient algorithms for training the learning machines for specific tasks are presented. In the context of kernel-based learning methods, the incorporation of prior knowledge is often done by designing appropriate kernel functions. Another well-known way is to develop cost functions that fit to the task under consideration. For disambiguation tasks in natural language, we develop kernel functions that take account of the positional information and the mutual similarities of words. It is shown that the use of this information significantly improves the disambiguation performance of the learning machine. Further, we design a new cost function that is better suitable for the task of information retrieval and for more general ranking problems than the cost functions designed for regression and classification. We also consider other applications of the kernel-based learning algorithms such as text categorization, and pattern recognition in differential display. We develop computationally efficient algorithms for training the considered learning machines with the proposed kernel functions. We also design a fast cross-validation algorithm for regularized least-squares type of learning algorithm. Further, an efficient version of the regularized least-squares algorithm that can be used together with the new cost function for preference learning and ranking tasks is proposed. In summary, we demonstrate that the incorporation of prior knowledge is possible and beneficial, and novel advanced kernels and cost functions can be used in algorithms efficiently.

The Figure of Radha in Miniature Paintings: From the Pastoral to the Courtly, from Text to Visuality, from Polyphony to Normativity

This article analyses how Radha was depicted in miniature paintings between the 16th and 19th century in North India. Interrogating the link between text and image, contrasting poetry, style and historical settings with the visual representations of this central figure, my reflections focus on the changing nature of Radha. Through various examples from miniature paintings of different periods and schools, this article analyses the way the rich personality of Radha was transposed into images. In order to stress the changes brought to this female figure, I compare her to Krishna, the masculine figure who is always at her side. The main goal of the article is to show the normative power of images on the figure of Radha, with normativity being understood as the simplification, iconisation, aestheticisation and stereotypification of a figure with polysemous references.

A Dependency Parsing Approach to Biomedical Text Mining

Biomedical research is currently facing a new type of challenge: an excess of information, both in terms of raw data from experiments and in the number of scientific publications describing their results. Mirroring the focus on data mining techniques to address the issues of structured data, there has recently been great interest in the development and application of text mining techniques to make more effective use of the knowledge contained in biomedical scientific publications, accessible only in the form of natural human language. This thesis describes research done in the broader scope of projects aiming to develop methods, tools and techniques for text mining tasks in general and for the biomedical domain in particular. The work described here involves more specifically the goal of extracting information from statements concerning relations of biomedical entities, such as protein-protein interactions. The approach taken is one using full parsing—syntactic analysis of the entire structure of sentences—and machine learning, aiming to develop reliable methods that can further be generalized to apply also to other domains. The five papers at the core of this thesis describe research on a number of distinct but related topics in text mining. In the first of these studies, we assessed the applicability of two popular general English parsers to biomedical text mining and, finding their performance limited, identified several specific challenges to accurate parsing of domain text. In a follow-up study focusing on parsing issues related to specialized domain terminology, we evaluated three lexical adaptation methods. We found that the accurate resolution of unknown words can considerably improve parsing performance and introduced a domain-adapted parser that reduced the error rate of theoriginal by 10% while also roughly halving parsing time. To establish the relative merits of parsers that differ in the applied formalisms and the representation given to their syntactic analyses, we have also developed evaluation methodology, considering different approaches to establishing comparable dependency-based evaluation results. We introduced a methodology for creating highly accurate conversions between different parse representations, demonstrating the feasibility of unification of idiverse syntactic schemes under a shared, application-oriented representation. In addition to allowing formalism-neutral evaluation, we argue that such unification can also increase the value of parsers for domain text mining. As a further step in this direction, we analysed the characteristics of publicly available biomedical corpora annotated for protein-protein interactions and created tools for converting them into a shared form, thus contributing also to the unification of text mining resources. The introduced unified corpora allowed us to perform a task-oriented comparative evaluation of biomedical text mining corpora. This evaluation established clear limits on the comparability of results for text mining methods evaluated on different resources, prompting further efforts toward standardization. To support this and other research, we have also designed and annotated BioInfer, the first domain corpus of its size combining annotation of syntax and biomedical entities with a detailed annotation of their relationships. The corpus represents a major design and development effort of the research group, with manual annotation that identifies over 6000 entities, 2500 relationships and 28,000 syntactic dependencies in 1100 sentences. In addition to combining these key annotations for a single set of sentences, BioInfer was also the first domain resource to introduce a representation of entity relations that is supported by ontologies and able to capture complex, structured relationships. Part I of this thesis presents a summary of this research in the broader context of a text mining system, and Part II contains reprints of the five included publications.

Construction of an index of information from clinical practice in Radiology and Imaging Diagnosis based on text mining and thesaurus

Objective To construct a Portuguese language index of information on the practice of diagnostic radiology in order to improve the standardization of the medical language and terminology. Materials and Methods A total of 61,461 definitive reports were collected from the database of the Radiology Information System at Hospital das Clínicas – Faculdade de Medicina de Ribeirão Preto (RIS/HCFMRP) as follows: 30,000 chest x-ray reports; 27,000 mammography reports; and 4,461 thyroid ultrasonography reports. The text mining technique was applied for the selection of terms, and the ANSI/NISO Z39.19-2005 standard was utilized to construct the index based on a thesaurus structure. The system was created in *html. Results The text mining resulted in a set of 358,236 (n = 100%) words. Out of this total, 76,347 (n = 21%) terms were selected to form the index. Such terms refer to anatomical pathology description, imaging techniques, equipment, type of study and some other composite terms. The index system was developed with 78,538 *html web pages. Conclusion The utilization of text mining on a radiological reports database has allowed the construction of a lexical system in Portuguese language consistent with the clinical practice in Radiology.

Text i context. El concurs de cinema amateur «JULIUS» (1987-1996)

El «Julius» és un concurs de cinema amateur que fonamenta la seva singularitat en el fet de partir d’un text literari prefixat del qual els participants han de fer una adaptació audiovisual. En aquest article s’estudia la primera època del concurs: el seu origen, les diverses edicions en què es dugué a terme, les incidències que s’hi produïren..., i el context social i cultural que va contribuir a fer-ne un concurs amb unes característiques úniques que es concretaren en l’anomenat «esperit Julius», sorneguer, llibertari i surrealista.