Some Extensions of the K-Means Algorithm for Image Segmentation and Pattern Classification

In this paper we present some extensions to the k-means algorithm for vector quantization that permit its efficient use in image segmentation and pattern classification tasks. It is shown that by introducing state variables that correspond to certain statistics of the dynamic behavior of the algorithm, it is possible to find the representative centers fo the lower dimensional maniforlds that define the boundaries between classes, for clouds of multi-dimensional, mult-class data; this permits one, for example, to find class boundaries directly from sparse data (e.g., in image segmentation tasks) or to efficiently place centers for pattern classification (e.g., with local Gaussian classifiers). The same state variables can be used to define algorithms for determining adaptively the optimal number of centers for clouds of data with space-varying density. Some examples of the applicatin of these extensions are also given.

Pattern Motion Perception: Feature Tracking or Integration of Component Motions?

A key question regarding primate visual motion perception is whether the motion of 2D patterns is recovered by tracking distinctive localizable features [Lorenceau and Gorea, 1989; Rubin and Hochstein, 1992] or by integrating ambiguous local motion estimates [Adelson and Movshon, 1982; Wilson and Kim, 1992]. For a two-grating plaid pattern, this translates to either tracking the grating intersections or to appropriately combining the motion estimates for each grating. Since both component and feature information are simultaneously available in any plaid pattern made of contrast defined gratings, it is unclear how to determine which of the two schemes is actually used to recover the plaid"s motion. To address this problem, we have designed a plaid pattern made with subjective, rather than contrast defined, gratings. The distinguishing characteristic of such a plaid pattern is that it contains no contrast defined intersections that may be tracked. We find that notwithstanding the absence of such features, observers can accurately recover the pattern velocity. Additionally we show that the hypothesis of tracking "illusory features" to estimate pattern motion does not stand up to experimental test. These results present direct evidence in support of the idea that calls for the integration of component motions over the one that mandates tracking localized features to recover 2D pattern motion. The localized features, we suggest, are used primarily as providers of grouping information - which component motion signals to integrate and which not to.

Learning and Example Selection for Object and Pattern Detection

This thesis presents a learning based approach for detecting classes of objects and patterns with variable image appearance but highly predictable image boundaries. It consists of two parts. In part one, we introduce our object and pattern detection approach using a concrete human face detection example. The approach first builds a distribution-based model of the target pattern class in an appropriate feature space to describe the target's variable image appearance. It then learns from examples a similarity measure for matching new patterns against the distribution-based target model. The approach makes few assumptions about the target pattern class and should therefore be fairly general, as long as the target class has predictable image boundaries. Because our object and pattern detection approach is very much learning-based, how well a system eventually performs depends heavily on the quality of training examples it receives. The second part of this thesis looks at how one can select high quality examples for function approximation learning tasks. We propose an {em active learning} formulation for function approximation, and show for three specific approximation function classes, that the active example selection strategy learns its target with fewer data samples than random sampling. We then simplify the original active learning formulation, and show how it leads to a tractable example selection paradigm, suitable for use in many object and pattern detection problems.

Some Aspects of Pattern Recognition by Computer

A computer may gather a lot of information from its environment in an optical or graphical manner. A scene, as seen for instance from a TV camera or a picture, can be transformed into a symbolic description of points and lines or surfaces. This thesis describes several programs, written in the language CONVERT, for the analysis of such descriptions in order to recognize, differentiate and identify desired objects or classes of objects in the scene. Examples are given in each case. Although the recognition might be in terms of projections of 2-dim and 3-dim objects, we do not deal with stereoscopic information. One of our programs (Polybrick) identifies parallelepipeds in a scene which may contain partially hidden bodies and non-parallelepipedic objects. The program TD works mainly with 2-dimensional figures, although under certain conditions successfully identifies 3-dim objects. Overlapping objects are identified when they are transparent. A third program, DT, works with 3-dim and 2-dim objects, and does not identify objects which are not completely seen. Important restrictions and suppositions are: (a) the input is assumed perfect (noiseless), and in a symbolic format; (b) no perspective deformation is considered. A portion of this thesis is devoted to the study of models (symbolic representations) of the objects we want to identify; different schemes, some of them already in use, are discussed. Focusing our attention on the more general problem of identification of general objects when they substantially overlap, we propose some schemes for their recognition, and also analyze some problems that are met.

Characteristics of elution profile in radial chromatography under linear conditions

Based on the mass balance equations of solute transfer in the radial chromatographic column, the theoretical expression to describe the column efficiency and shape of elution profile is obtained under linear isotherm case. Moreover, the tendency for the variation of column efficiency and symmetry of peak profile is systematically discussed. The results showed that in radial chromatography the relationship between the column efficiency and volumetric flow rate is similar with that relationship in axial chromatography; relatively high column efficiency still can be obtained under high flow rate in radial chromatography. Accompanying the increase of retention factor of solutes and injection time, the column efficiency decreases monotonously. The effect of column diameter and column length on the column efficiency interfere with each other. It is more advantageous to increase the column efficiency by applying columns with larger column diameter and shorter column length. According to the discussion of the effect of diffusion on the column efficiency, radial chromatography is proved to be suitable for the separation of samples with relatively high diffusion coefficient, which predicts its obvious advantage in the preparative separation of samples such as proteins and DNA.

Changes in innovation styles - comprehensive study of the changes in innovation styles to identify the causes and effects of different influencing factors and capabilities to create a general innovation pattern.

An investigation in innovation management and entrepreneurial management is conducted in this thesis. The aim of the research is to explore changes of innovation styles in the transformation process from a start-up company to a more mature phase of business, to predict in a second step future sustainability and the probability of success. As businesses grow in revenue, corporate size and functional complexity, various triggers, supporters and drivers affect innovation and company's success. In a comprehensive study more than 200 innovative and technology driven companies have been examined and compared to identify patterns in different performance levels. All of them have been founded under the same formal requirements of the Munich Business Plan Competition -a research approach which allowed a unique snapshot that only long-term studies would be able to provide. The general objective was to identify the correlation between different factors, as well as different dimensions, to incremental and radical innovations realised. The 12 hypothesis were formed to prove have been derived from a comprehensive literature review. The relevant academic and practitioner literature on entrepreneurial, innovation, and knowledge management as well as social network theory revealed that the concept of innovation has evolved significantly over the last decade. A review of over 15 innovation models/frameworks contributed to understand what innovation in context means and what the dimensions are. It appears that the complex theories of innovation can be described by the increasing extent of social ingredients in the explanation of innovativeness. Originally based on tangible forms of capital, and on the necessity of pull and technology push, innovation management is today integrated in a larger system. Therefore, two research instruments have been developed to explore the changes in innovations styles. The Innovation Management Audits (IMA Start-up and IMA Mature) provided statements related to product/service development, innovativeness in various typologies, resources for innovations, innovation capabilities in conjunction to knowledge and management, social networks as well as the measurement of outcomes to generate high-quality data for further exploration. In obtaining results the mature companies have been clustered in the performance level low, average and high, while the start-up companies have been kept as one cluster. Firstly, the analysis exposed that knowledge, the process of acquiring knowledge, interorganisational networks and resources for innovations are the most important driving factors for innovation and success. Secondly, the actual change of the innovation style provides new insights about the importance of focusing on sustaining success and innovation ii 16 key areas. Thirdly, a detailed overview of triggers, supporters and drivers for innovation and success for each dimension support decision makers in putting their company in the right direction. Fourthly, a critical review of contemporary strategic management in conjunction to the findings provides recommendation of how to apply well-known management tools. Last but not least, the Munich cluster is analysed providing an estimation of the success probability of the different performance cluster and start-up companies. For the analysis of the probability of success of the newly developed as well as statistically and qualitative validated ICP Model (Innovativeness, Capabilities & Potential) has been developed and applied. While the model was primarily developed to evaluate the probability of success of companies; it has equal application in the situation to measure innovativeness to identify the impact of various strategic initiatives within small or large enterprises. The main findings of the model are that competitor, and customer orientation and acquiring knowledge important for incremental and radical innovation. Formal and interorganisation networks are important to foster innovation but informal networks appear to be detrimental to innovation. The testing of the ICP model h the long term is recommended as one subject of further research. Another is to investigate some of the more intangible aspects of innovation management such as attitude and motivation of mangers. IV

Advanced interatrial block: A well-defined electrocardiographic pattern with clinical arrhythmological implications

3.050 JCR (2013) Q2, 44/125 Cardiac & cardiovascular systems

Muscle function and body composition profile in adolescents withrestrictive anorexia nervosa: Does resistance training help?

The aim of this study was to analyze the effects of short-term resistance training on the body composition profile and muscle function in a group of Anorexia Nervosa restricting type (AN-R) patients. The sample consisted of AN-R female adolescents (12.8 ± 0.6 years) allocated into the control and intervention groups (n¼18 each). Body composition and relative strength were assessed at baseline, after 8 weeks and 4 weeks following the intervention. Body mass index (BMI) increased throughout the study (p = 0.011). Significant skeletal muscle mass (SMM) gains were found in the intervention group (p = 0.045, d = 0.6) that correlated to the change in BMI (r = 0.51, p < 0.031). Meanwhile, fat mass (FM) gains were significant in the control group (p = 0.047, d = 0.6) and correlated (r > 0.60) with change in BMI in both the groups. Significant relative strength increases (p < 0.001) were found in the intervention group and were sustained over time.

Qualitative evaluation of KA24 (Knowledge Access 24)

Durbin, J. & Urquhart, C. (2003). Qualitative evaluation of KA24 (Knowledge Access 24). Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: Knowledge Access 24 (NHS)

Boundary layer growth and advection of heat over snow and soil patches: Modelling and parametrization

Essery, RLH, RJ Granger and JW Pomeroy, 2006. Boundary layer growth and advection of heat over snow and soil patches: Modelling and parametrization. Hydrological Processes, 20, 953 - 967.