Numerical computation of the module of a quadrilateral

The module of a quadrilateral is a positive real number which divides quadrilaterals into conformal equivalence classes. This is an introductory text to the module of a quadrilateral with some historical background and some numerical aspects. This work discusses the following topics: 1. Preliminaries 2. The module of a quadrilateral 3. The Schwarz-Christoffel Mapping 4. Symmetry properties of the module 5. Computational results 6. Other numerical methods Appendices include: Numerical evaluation of the elliptic integrals of the first kind. Matlab programs and scripts and possible topics for future research. Numerical results section covers additive quadrilaterals and the module of a quadrilateral under the movement of one of its vertex.

Integrated semantic processing of complex pictures and spoken sentences - Evidence from event-related potentials

It has been suggested that semantic information processing is modularized according to the input form (e.g., visual, verbal, non-verbal sound). A great deal of research has concentrated on detecting a separate verbal module. Also, it has traditionally been assumed in linguistics that the meaning of a single clause is computed before integration to a wider context. Recent research has called these views into question. The present study explored whether it is reasonable to assume separate verbal and nonverbal semantic systems in the light of the evidence from event-related potentials (ERPs). The study also provided information on whether the context influences processing of a single clause before the local meaning is computed. The focus was on an ERP called N400. Its amplitude is assumed to reflect the effort required to integrate an item to the preceding context. For instance, if a word is anomalous in its context, it will elicit a larger N400. N400 has been observed in experiments using both verbal and nonverbal stimuli. Contents of a single sentence were not hypothesized to influence the N400 amplitude. Only the combined contents of the sentence and the picture were hypothesized to influence the N400. The subjects (n = 17) viewed pictures on a computer screen while hearing sentences through headphones. Their task was to judge the congruency of the picture and the sentence. There were four conditions: 1) the picture and the sentence were congruent and sensible, 2) the sentence and the picture were congruent, but the sentence ended anomalously, 3) the picture and the sentence were incongruent but sensible, 4) the picture and the sentence were incongruent and anomalous. Stimuli from the four conditions were presented in a semi-randomized sequence. Their electroencephalography was simultaneously recorded. ERPs were computed for the four conditions. The amplitude of the N400 effect was largest in the incongruent sentence-picture -pairs. The anomalously ending sentences did not elicit a larger N400 than the sensible sentences. The results suggest that there is no separate verbal semantic system, and that the meaning of a single clause is not processed independent of the context.

Suhteessa kohteeseen ja suhteessa toisiin : Oppiminen ja vuorovaikutus opettajaopiskelijoiden suunnittelutapaamisessa

Objectives. In this research I analyzed the learning process of teacher students in a planning meeting using the expansive learning cycle and types of interaction approaches. In activity theory framework the expansive learning cycle has been applied widely in analyzing learning processes taking several years. However, few studies exist utilizing expansive cycles in analyzing short single meetings. In the activity theory framework talk and interaction have been analyzed using following types of interaction: coordination, cooperation and communication. In these studies single interaction situations have been analyzed, in which the status and power positions of participants has been very different. Interactions of self-directed teams, in which the participants are equal, have been examined very little. I am not aware of any studies, in which both learning actions of the expansive cycle and types of interaction by analyzing the same data would have been utilized. The aim of my study was to describe the process of collaborative innovative learning in a situation where the student group tries to accomplish a broad and ill-defined learning task. I aim to describe, how this planning process proceeds through different phases of learning actions of the expansive cycle. My goal is to understand and describe the transformations in the quality of interaction and transitions which are related to it. Another goal of this study is to specify the possible similarities and differences between expansive learning and types of interactions. Methods. Data of this study consisted of videotaped meetings, which were part of the study module for class teacher degree. The first meeting of the study module was chosen to be the primary research material. Five students were present in the group meeting. Transcription of the conversation was analyzed by classifying the turns of conversation following phases of the expansive cycle. After that the material was categorized again by using types of interaction. Results and conclusions. As a result of this study I was able to trace all the phases of the expansive cycle except one. Also, I was able to identify all interaction types. When I compared the two modes of analysis side by side I was able to find connecting main phases. Thus I was able to identify the interdependence between the two ways of analysis on a higher level, although I was not able to notice correlation on the level of individual phases. Based on this, I conclude that learning of the group was simultaneously specification and formulation of the object at the different phases of expansive learning and transformation of the quality of the interaction while searching for the common object.

Quality of ALICE SSD modules revealed by electrical tests

ALICE (A Large Ion Collider Experiment) is an experiment at CERN (European Organization for Nuclear Research), where a heavy-ion detector is dedicated to exploit the unique physics potential of nucleus-nucleus interactions at LHC (Large Hadron Collider) energies. In a part of that project, 716 so-called type V4 modules were assembles in Detector Laboratory of Helsinki Institute of Physics during the years 2004 - 2006. Altogether over a million detector strips has made this project the most massive particle detector project in the science history of Finland. One ALICE SSD module consists of a double-sided silicon sensor, two hybrids containing 12 HAL25 front end readout chips and some passive components, such has resistors and capacitors. The components are connected together by TAB (Tape Automated Bonding) microcables. The components of the modules were tested in every assembly phase with comparable electrical tests to ensure the reliable functioning of the detectors and to plot the possible problems. The components were accepted or rejected by the limits confirmed by ALICE collaboration. This study is concentrating on the test results of framed chips, hybrids and modules. The total yield of the framed chips is 90.8%, hybrids 96.1% and modules 86.2%. The individual test results have been investigated in the light of the known error sources that appeared during the project. After solving the problems appearing during the learning-curve of the project, the material problems, such as defected chip cables and sensors, seemed to induce the most of the assembly rejections. The problems were typically seen in tests as too many individual channel failures. Instead, the bonding failures rarely caused the rejections of any component. One sensor type among three different sensor manufacturers has proven to have lower quality than the others. The sensors of this manufacturer are very noisy and their depletion voltage are usually outside of the specification given to the manufacturers. Reaching 95% assembling yield during the module production demonstrates that the assembly process has been highly successful.

Methods for Answer Extraction in Textual Question Answering

In this thesis we present and evaluate two pattern matching based methods for answer extraction in textual question answering systems. A textual question answering system is a system that seeks answers to natural language questions from unstructured text. Textual question answering systems are an important research problem because as the amount of natural language text in digital format grows all the time, the need for novel methods for pinpointing important knowledge from the vast textual databases becomes more and more urgent. We concentrate on developing methods for the automatic creation of answer extraction patterns. A new type of extraction pattern is developed also. The pattern matching based approach chosen is interesting because of its language and application independence. The answer extraction methods are developed in the framework of our own question answering system. Publicly available datasets in English are used as training and evaluation data for the methods. The techniques developed are based on the well known methods of sequence alignment and hierarchical clustering. The similarity metric used is based on edit distance. The main conclusions of the research are that answer extraction patterns consisting of the most important words of the question and of the following information extracted from the answer context: plain words, part-of-speech tags, punctuation marks and capitalization patterns, can be used in the answer extraction module of a question answering system. This type of patterns and the two new methods for generating answer extraction patterns provide average results when compared to those produced by other systems using the same dataset. However, most answer extraction methods in the question answering systems tested with the same dataset are both hand crafted and based on a system-specific and fine-grained question classification. The the new methods developed in this thesis require no manual creation of answer extraction patterns. As a source of knowledge, they require a dataset of sample questions and answers, as well as a set of text documents that contain answers to most of the questions. The question classification used in the training data is a standard one and provided already in the publicly available data.

GDNF Receptors: Veterans and Novices

Neurotrophic factors play essential role in the development and functioning of the nervous system and other organs. Glial cell line-Derived Neurotrophic Factor (GDNF) family ligands (GFLs) are of particular interest because they promote the survival of dopaminergic neurons in vitro, in Parkinson s disease animal models and in patients. GDNF is also a potent survival factor for the central motoneurons and thus is considered as a potential lead for the treatment of amyotrophic lateral sclerosis. The survival promoting receptor complex for GFLs consists of a ligand-specific co-receptor, GFRα and a signal transducing module, receptor tyrosine kinase RET. At least GDNF and persephin, a GFL, have established functions outside central nervous system. GDNF is crucial for enteric nervous system and kidney development as well as for spermatogenesis. Persephin controls calcitonin secretion. Communication between cells often occurs in the extracellular matrix (ECM), a meshwork, which is secreted and deposited by the cells and is mainly composed of fibrillar proteins and polymerized sugars. We evaluated the relationship between GFLs and extracellular matrix components and demonstrated that three GFLs - GDNF, neurturin and artemin bind heparan sulfates with nanomolar affinities. The fourth member of the family - persephin binds these polysaccharides thousand times less tightly. GDNF, neurturin and artemin also bind with high affinity to heparan sulfate proteoglycan (HSPG) isolated from the nervous system, syndecan-3. GDNF signals through HSPGs, evoking Src family kinase activation. This signaling induces cell spreading, hippocampal neurite outgrowth in vitro and cellular migration. Specifically, GDNF signaling through syndecan-3 is important for embryonic cortical neuron migration. Syndecan-3-deficient mice, similarly to mice lacking GDNF, have less GABAergic neurons in their cortex, as compared to the wild-type mice. This fact provides indirect evidence that GDNF interaction with syndecan-3 is important for cortical brain development. Noteworthy, in non-neuronal tissues GFLs may signal via other syndecans. We also present the structural model for a GDNF co-receptor, GFRα1. The X-ray structure of the GFRα1 domain 3 was solved with 1.8 Å resolution, revealing a new protein fold. Later we also solved the structure of the truncated GFRα1 in the complex with GDNF and this model was confirmed by site-directed mutagenesis. In summary, our work contributed to the structural characterization of GFRα-based receptor complex and revealed a new receptor for GDNF, neurturin and artemin the HSPG syndecan-3. This information is critically important for the development of GFRα/RET agonists for the treatment of neurodegenerative diseases.

Sensitivity of wetland methane emissions to model assumptions: application and model testing against site observations

Abstract. Methane emissions from natural wetlands and rice paddies constitute a large proportion of atmospheric methane, but the magnitude and year-to-year variation of these methane sources is still unpredictable. Here we describe and evaluate the integration of a methane biogeochemical model (CLM4Me; Riley et al., 2011) into the Community Land Model 4.0 (CLM4CN) in order to better explain spatial and temporal variations in methane emissions. We test new functions for soil pH and redox potential that impact microbial methane production in soils. We also constrain aerenchyma in plants in always-inundated areas in order to better represent wetland vegetation. Satellite inundated fraction is explicitly prescribed in the model because there are large differences between simulated fractional inundation and satellite observations. A rice paddy module is also incorporated into the model, where the fraction of land used for rice production is explicitly prescribed. The model is evaluated at the site level with vegetation cover and water table prescribed from measurements. Explicit site level evaluations of simulated methane emissions are quite different than evaluating the grid cell averaged emissions against available measurements. Using a baseline set of parameter values, our model-estimated average global wetland emissions for the period 1993–2004 were 256 Tg CH4 yr−1, and rice paddy emissions in the year 2000 were 42 Tg CH4 yr−1. Tropical wetlands contributed 201 Tg CH4 yr−1, or 78 % of the global wetland flux. Northern latitude (>50 N) systems contributed 12 Tg CH4 yr−1. We expect this latter number may be an underestimate due to the low high-latitude inundated area captured by satellites and unrealistically low high-latitude productivity and soil carbon predicted by CLM4. Sensitivity analysis showed a large range (150–346 Tg CH4 yr−1) in predicted global methane emissions. The large range was sensitive to: (1) the amount of methane transported through aerenchyma, (2) soil pH (± 100 Tg CH4 yr−1), and (3) redox inhibition (± 45 Tg CH4 yr−1).