Regional Data Transmission Network

The globalization and development of an information society promptly change shape of the modern world. Cities and especially megacities including Saint-Petersburg are in the center of occuring changes. As a result of these changes the economic activities connected to reception and processing of the information now play very important role in economy of megacities what allows to characterize them as "information". Despite of wide experience in decision of information questions Russia, and in particular Saint-Petersburg, lag behind in development of information systems from the advanced European countries. The given master's thesis is devoted to development of an information system (data transmission network) on the basis of wireless technology in territory of Saint-Petersburg region within the framework of FTOP "Electronic Russia" and RTOP "Electronic Saint-Petersburg" programs. Logically the master's thesis can be divided into 3 parts: 1. The problems, purposes, expected results, terms and implementation of the "Electronic Russia" program. 2. Discussion about wireless data transmission networks (description of technology, substantiation of choice, description of signal's transmission techniques and types of network topology). 3. Fulfillment of the network (organization of central network node, regional centers, access lines, description of used equipment, network's capabilities), financial provision of the project, possible network management models.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Improving the Quality of EEG Data in Patients With Alzheimers Disease Using ICA

Does Independent Component Analysis (ICA) denature EEG signals? We applied ICA to two groups of subjects (mild Alzheimer patients and control subjects). The aim of this study was to examine whether or not the ICA method can reduce both group di®erences and within-subject variability. We found that ICA diminished Leave-One- Out root mean square error (RMSE) of validation (from 0.32 to 0.28), indicative of the reduction of group di®erence. More interestingly, ICA reduced the inter-subject variability within each group (¾ = 2:54 in the ± range before ICA, ¾ = 1:56 after, Bartlett p = 0.046 after Bonfer- roni correction). Additionally, we present a method to limit the impact of human error (' 13:8%, with 75.6% inter-cleaner agreement) during ICA cleaning, and reduce human bias. These ¯ndings suggests the novel usefulness of ICA in clinical EEG in Alzheimer's disease for reduction of subject variability.

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.

Respiratory health effects of fifteen years of improved collective protection in a wheat-processing worker population.

INTRODUCTION: Occupational exposure to grain dust causes respiratory symptoms and pathologies. To decrease these effects, major changes have occurred in the grain processing industry in the last twenty years. However, there are no data on the effects of these changes on workers' respiratory health. OBJECTIVES: The aim of this study was to evaluate the respiratory health of grain workers and farmers involved in different steps of the processing industry of wheat, the most frequently used cereal in Europe, fifteen years after major improvements in collective protective equipment due to mechanisation. MATERIALS AND METHOD: Information on estimated personal exposure to wheat dust was collected from 87 workers exposed to wheat dust and from 62 controls. Lung function (FEV1, FVC, and PEF), exhaled nitrogen monoxide (FENO) and respiratory symptoms were assessed after the period of highest exposure to wheat during the year. Linear regression models were used to explore the associations between exposure indices and respiratory effects. RESULTS: Acute symptoms - cough, sneezing, runny nose, scratchy throat - were significantly more frequent in exposed workers than in controls. Increased mean exposure level, increased cumulative exposure and chronic exposure to more than 6 mg.m (-3) of inhaled wheat dust were significantly associated with decreased spirometric parameters, including FEV1 and PEF (40 ml and 123 ml.s (-1) ), FEV1 and FVC (0.4 ml and 0.5 ml per 100 h.mg.m (-3) ), FEV1 and FVC (20 ml and 20 ml per 100 h at >6 mg.m (-3) ). However, no increase in FENO was associated with increased exposure indices. CONCLUSIONS: The lung functions of wheat-related workers are still affected by their cumulative exposure to wheat dust, despite improvements in the use of collective protective equipment.

Image processing of false identity documents for forensic intelligence

Forensic intelligence has recently gathered increasing attention as a potential expansion of forensic science that may contribute in a wider policing and security context. Whilst the new avenue is certainly promising, relatively few attempts to incorporate models, methods and techniques into practical projects are reported. This work reports a practical application of a generalised and transversal framework for developing forensic intelligence processes referred to here as the Transversal model adapted from previous work. Visual features present in the images of four datasets of false identity documents were systematically profiled and compared using image processing for the detection of a series of modus operandi (M.O.) actions. The nature of these series and their relation to the notion of common source was evaluated with respect to alternative known information and inferences drawn regarding respective crime systems. 439 documents seized by police and border guard authorities across 10 jurisdictions in Switzerland with known and unknown source level links formed the datasets for this study. Training sets were developed based on both known source level data, and visually supported relationships. Performance was evaluated through the use of intra-variability and inter-variability scores drawn from over 48,000 comparisons. The optimised method exhibited significant sensitivity combined with strong specificity and demonstrates its ability to support forensic intelligence efforts.

One, two, or many mechanisms? The brain's processing of complex words

The heated debate over whether there is only a single mechanism or two mechanisms for morphology has diverted valuable research energy away from the more critical questions about the neural computations involved in the comprehension and production of morphologically complex forms. Cognitive neuroscience data implicate many brain areas. All extant models, whether they rely on a connectionist network or espouse two mechanisms, are too underspecified to explain why more than a few brain areas differ in their activity during the processing of regular and irregular forms. No one doubts that the brain treats regular and irregular words differently, but brain data indicate that a simplistic account will not do. It is time for us to search for the critical factors free from theoretical blinders.

Identification of the Emerging Issues in Recycled Fiber Processing

The objectives of this research work “Identification of the Emerging Issues in Recycled Fiber processing” are discovering of emerging research issues and presenting of new approaches to identify promising research themes in recovered paper application and production. The projected approach consists of identifying technological problems often encountered in wastepaper preparation processes and also improving the quality of recovered paper and increasing its proportion in the composition of paper and board. The source of information for the problem retrieval is scientific publications in which waste paper application and production were discussed. The study has exploited several research methods to understand the changes related to utilization of recovered paper. The all assembled data was carefully studied and categorized by applying software called RefViz and CiteSpace. Suggestions were made on the various classes of these problems that need further investigation in order to propose an emerging research trends in recovered paper.

Customer knowledge processing in key account management

The ability of the supplier firm to generate and utilise customer-specific knowledge has attracted increasing attention in the academic literature during the last decade. It has been argued the customer knowledge should treated as a strategic asset the same as any other intangible assets. Yet, at the same time it has been shown that the management of customer-specific knowledge is challenging in practice, and that many firms are better at acquiring customer knowledge than at making use of it. This study examines customer knowledge processing in the context of key account management in large industrial firms. This focus was chosen because key accounts are demanding and complex. It is not unusual for a single key account relationship to constitute a complex web of relationships between the supplier and the key account – thus easily leading to the dispersion of customer-specific knowledge in the supplier firm. Although the importance of customer-specific knowledge generation has been widely acknowledged in the literature, surprisingly little attention has been paid to the processes through which firms generate, disseminate and use such knowledge internally for enhancing the relationships with their major, strategically important key account customers. This thesis consists of two parts. The first part comprises a theoretical overview and draws together the main findings of the study, whereas the second part consists of five complementary empirical research papers based on survey data gathered from large industrial firms in Finland. The findings suggest that the management of customer knowledge generated about and form key accounts is a three-dimensional process consisting of acquisition, dissemination and utilization. It could be concluded from the results that customer-specific knowledge is a strategic asset because the supplier’s customer knowledge processing activities have a positive effect on supplier’s key account performance. Moreover, in examining the determinants of each phase separately the study identifies a number of intra-organisational factors that facilitate the process in supplier firms. The main contribution of the thesis lies in linking the concept of customer knowledge processing to the previous literature on key account management. Moreover, given than this literature is mainly conceptual or case-based, a further contribution is to examine its consequences and determinants based on quantitative empirical data.

Use of data mining and spectral profiles to differentiate condition after harvest of coffee plants

This study aimed at identifying different conditions of coffee plants after harvesting period, using data mining and spectral behavior profiles from Hyperion/EO1 sensor. The Hyperion image, with spatial resolution of 30 m, was acquired in August 28th, 2008, at the end of the coffee harvest season in the studied area. For pre-processing imaging, atmospheric and signal/noise effect corrections were carried out using Flaash and MNF (Minimum Noise Fraction Transform) algorithms, respectively. Spectral behavior profiles (38) of different coffee varieties were generated from 150 Hyperion bands. The spectral behavior profiles were analyzed by Expectation-Maximization (EM) algorithm considering 2; 3; 4 and 5 clusters. T-test with 5% of significance was used to verify the similarity among the wavelength cluster means. The results demonstrated that it is possible to separate five different clusters, which were comprised by different coffee crop conditions making possible to improve future intervention actions.

An Open Source, DDI-based Curation System for Social Science Data

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

Autonomic processing of the cardiovascular reflexes in the nucleus tractus solitarii

The nucleus tractus solitarii (NTS) receives afferent projections from the arterial baroreceptors, carotid chemoreceptors and cardiopulmonary receptors and as a function of this information produces autonomic adjustments in order to maintain arterial blood pressure within a narrow range of variation. The activation of each of these cardiovascular afferents produces a specific autonomic response by the excitation of neuronal projections from the NTS to the ventrolateral areas of the medulla (nucleus ambiguus, caudal and rostral ventrolateral medulla). The neurotransmitters at the NTS level as well as the excitatory amino acid (EAA) receptors involved in the processing of the autonomic responses in the NTS, although extensively studied, remain to be completely elucidated. In the present review we discuss the role of the EAA L-glutamate and its different receptor subtypes in the processing of the cardiovascular reflexes in the NTS. The data presented in this review related to the neurotransmission in the NTS are based on experimental evidence obtained in our laboratory in unanesthetized rats. The two major conclusions of the present review are that a) the excitation of the cardiovagal component by cardiovascular reflex activation (chemo- and Bezold-Jarisch reflexes) or by L-glutamate microinjection into the NTS is mediated by N-methyl-D-aspartate (NMDA) receptors, and b) the sympatho-excitatory component of the chemoreflex and the pressor response to L-glutamate microinjected into the NTS are not affected by an NMDA receptor antagonist, suggesting that the sympatho-excitatory component of these responses is mediated by non-NMDA receptors.

Algorithmic Techniques in Gene Expression Processing. From Imputation to Visualization

The amount of biological data has grown exponentially in recent decades. Modern biotechnologies, such as microarrays and next-generation sequencing, are capable to produce massive amounts of biomedical data in a single experiment. As the amount of the data is rapidly growing there is an urgent need for reliable computational methods for analyzing and visualizing it. This thesis addresses this need by studying how to efficiently and reliably analyze and visualize high-dimensional data, especially that obtained from gene expression microarray experiments. First, we will study the ways to improve the quality of microarray data by replacing (imputing) the missing data entries with the estimated values for these entries. Missing value imputation is a method which is commonly used to make the original incomplete data complete, thus making it easier to be analyzed with statistical and computational methods. Our novel approach was to use curated external biological information as a guide for the missing value imputation. Secondly, we studied the effect of missing value imputation on the downstream data analysis methods like clustering. We compared multiple recent imputation algorithms against 8 publicly available microarray data sets. It was observed that the missing value imputation indeed is a rational way to improve the quality of biological data. The research revealed differences between the clustering results obtained with different imputation methods. On most data sets, the simple and fast k-NN imputation was good enough, but there were also needs for more advanced imputation methods, such as Bayesian Principal Component Algorithm (BPCA). Finally, we studied the visualization of biological network data. Biological interaction networks are examples of the outcome of multiple biological experiments such as using the gene microarray techniques. Such networks are typically very large and highly connected, thus there is a need for fast algorithms for producing visually pleasant layouts. A computationally efficient way to produce layouts of large biological interaction networks was developed. The algorithm uses multilevel optimization within the regular force directed graph layout algorithm.

Post-processing and analysis of tracked hand trajectories

In this thesis, the suitability of different trackers for finger tracking in high-speed videos was studied. Tracked finger trajectories from the videos were post-processed and analysed using various filtering and smoothing methods. Position derivatives of the trajectories, speed and acceleration were extracted for the purposes of hand motion analysis. Overall, two methods, Kernelized Correlation Filters and Spatio-Temporal Context Learning tracking, performed better than the others in the tests. Both achieved high accuracy for the selected high-speed videos and also allowed real-time processing, being able to process over 500 frames per second. In addition, the results showed that different filtering methods can be applied to produce more appropriate velocity and acceleration curves calculated from the tracking data. Local Regression filtering and Unscented Kalman Smoother gave the best results in the tests. Furthermore, the results show that tracking and filtering methods are suitable for high-speed hand-tracking and trajectory-data post-processing.