Savolaisen laulu

Soitinnus: lauluääni, piano.

Siirtolaisen paluu : Immigrant's Return.

Sävelmän alkuperä tuntematon.

Orpopojan valssi

Alkuperäislevy on HMV X2381.

Stjernan

Sanoittajaa ei mainita.

Kalsiumkarbonaatin kemiallisfysikaalinen käsittely ja materiaalin karakterisointi

Saostettua karbonaattia voidaan käyttää useiden eri teollisuuksien tuotteissa. Pääosin saostettua kalsiumkarbonaattia kuitenkin käytetään paperin, maalien, muovien sekä elintarviketuotteiden täyteaineena. Koska monet käyttökohteet vaativat saostetulta kalsiumkarbonaatilta tiettyjä puhtausvaatimuksia, sen koostumuksen tutkiminen on suuren kiinnostuksen kohteena. Työn perimmäisenä tarkoituksena on ollut määrittää saostetun kalsiumkarbonaatin kemiallinen koostumus ja selvittää, vaikuttavatko materiaalin kemiallisfysikaalinen modifiointi sen ominaisuuksiin. Kirjallisuusosassa käsitellään yleisesti kalsiumkarbonaattimateriaaleja, saostetun kalsiumkarbonaatin valmistusmenetelmiä ja vastaavanlaisen materiaalin esikäsittelymenetelmiä. Lisäksi tarkastellaan erilaisia analyysimenetelmiä, joita voidaan käyttää kiinteiden epäorgaanisten tai mineraalinäytteiden kemiallisen koostumuksen sekä fysikaalisten ja kemiallisten reaktioiden määrittämiseen. Kokeellisessa osassa tutkittiin käsittelemättömiä saostettuja kalsiumkarbonaattinäytteiden ominaisuuksia ja kemiallista koostumusta erilaisilla alkuaine-, ioni-/spesies- sekä pyrolyysimittauksilla. Näytteitä modifioitiin lämmityksen ja jauhatuksen avulla. Modifioinnin vaikutusta näytteiden kemiallisiin koostumuksiin tutkittiin vertailemalla tuloksia käsittelemättömien näytteiden antamiin tuloksiin. Tutkimus osoitti, että näytteiden lämpökäsittelyllä ei ollut lähes ollenkaan vaikutusta näytteiden kemialliseen koostumukseen. Toisin osoitti näytteiden jauhatus, joka laski ammoniumin pitoisuutta näytteissä. Laitetekniikkaa käytettäessä kapillaarielektroforeesi, ionikromatografi, ICP-AES ja SEM (FTIR) antoivat luotettavinta tietoa näytteiden kemiallisista koostumuksista. Näytteiden fysikaalisia ja kemiallisia reaktioita voitiin havainnollistaa parhaiten käyttäen STA-QMS -laitetta.

Reading Development During Elementary School Years. Evidence from Eye Movements

The present dissertation examined reading development during elementary school years by means of eye movement tracking. Three different but related issues in this field were assessed. First of all, the development of parafoveal processing skills in reading was investigated. Second, it was assessed whether and to what extent sublexical units such as syllables and morphemes are used in processing Finnish words and whether the use of these sublexical units changes as a function of reading proficiency. Finally, the developmental trend in the speed of visual information extraction during reading was examined. With regard to parafoveal processing skills, it was shown that 2nd graders extract letter identity information approx. 5 characters to the right of fixation, 4th graders approx. 7 characters to the right of fixation, and 6th graders and adults approx. 9 characters to the right of fixation. Furthermore, it was shown that all age groups extract more parafoveal information within compound words than across adjectivenoun pairs of similar length. In compounds, parafoveal word information can be extracted in parallel with foveal word information, if the compound in question is of high frequency. With regard to the use of sublexical units in Finnish word processing, it was shown that less proficient 2nd graders use both syllables and morphemes in the course of lexical access. More proficient 2nd graders as well as older readers seem to process words more holistically. Finally, it was shown that 60 ms is enough for 4th graders and adults to extract visual information from both 4-letter and 8-letter words, whereas 2nd graders clearly needed more than 60 ms to extract all information from 8- letter words for processing to proceed smoothly. The present dissertation demonstrates that Finnish 2nd graders develop their reading skills rapidly and are already at an adult level in some aspects of reading. This is not to say that there are no differences between less proficient (e.g., 2nd graders) and more proficient readers (e.g., adults) but in some respects it seems that the visual system used in extracting information from the text is matured by the 2nd grade. Furthermore, the present dissertation demonstrates that the allocation of attention in reading depends much on textual properties such as word frequency and whether words are spatially unified (as in compounds) or not. This flexibility of the attentional system naturally needs to be captured in word processing models. Finally, individual differences within age groups are quite substantial but it seems that by the end of the 2nd grade practically all Finnish children have reached a reasonable level of reading proficiency.

Handshake ecosystem for Educational Contents between Institutional Repository and OER based Repository

Poster at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Generative Part-Based Gabor Object Detector

Object detection is a fundamental task of computer vision that is utilized as a core part in a number of industrial and scientific applications, for example, in robotics, where objects need to be correctly detected and localized prior to being grasped and manipulated. Existing object detectors vary in (i) the amount of supervision they need for training, (ii) the type of a learning method adopted (generative or discriminative) and (iii) the amount of spatial information used in the object model (model-free, using no spatial information in the object model, or model-based, with the explicit spatial model of an object). Although some existing methods report good performance in the detection of certain objects, the results tend to be application specific and no universal method has been found that clearly outperforms all others in all areas. This work proposes a novel generative part-based object detector. The generative learning procedure of the developed method allows learning from positive examples only. The detector is based on finding semantically meaningful parts of the object (i.e. a part detector) that can provide additional information to object location, for example, pose. The object class model, i.e. the appearance of the object parts and their spatial variance, constellation, is explicitly modelled in a fully probabilistic manner. The appearance is based on bio-inspired complex-valued Gabor features that are transformed to part probabilities by an unsupervised Gaussian Mixture Model (GMM). The proposed novel randomized GMM enables learning from only a few training examples. The probabilistic spatial model of the part configurations is constructed with a mixture of 2D Gaussians. The appearance of the parts of the object is learned in an object canonical space that removes geometric variations from the part appearance model. Robustness to pose variations is achieved by object pose quantization, which is more efficient than previously used scale and orientation shifts in the Gabor feature space. Performance of the resulting generative object detector is characterized by high recall with low precision, i.e. the generative detector produces large number of false positive detections. Thus a discriminative classifier is used to prune false positive candidate detections produced by the generative detector improving its precision while keeping high recall. Using only a small number of positive examples, the developed object detector performs comparably to state-of-the-art discriminative methods.