Query: Transfer Learning Based Visual Tracking with Gaussian Processes Regression

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Semi-Supervised Learning Using Gaussian Fields and Harmonic Functions <=SEP=> Active and semi-supervised learning are important techniques when labeled data are scarce. We combine the two under a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The semi-supervised learning problem is then formulated in terms of a Gaussian random field on this graph, the mean of which is characterized in terms of harmonic functions. Active learning is performed on top of the semisupervised learning scheme by greedily selecting queries from the unlabeled data to minimize the estimated expected classification error (risk); in the case of Gaussian fields the risk is efficiently computed using matrix methods. We present experimental results on synthetic data, handwritten digit recognition, and text classification tasks. The active learning scheme requires a much smaller number of queries to achieve high accuracy compared with random query selection. 1. Introduction Semi-supervised learning targets the common situation where labeled data are scarce but unlabeled data are abundant. Under suitable assumptions, it uses unlabeled data to help supervised learning tasks. Various semi-supervised learning methods have been proposed and show promising results; Seeger (2001) gives a survey. These methods typically assume that the labeled data set is given and fixed. In practice, it may make sense to utilize active learning in conjunction with semi-supervised learning. That is, we might allow the learning algorithm to pick a set of unlabeled instances to be labeled by a domain expert, which will then be used as (or to augment) the labeled data set. In other words, if we have to label a few instances for semisupervised learning, it may be attractive to let the learning algorithm tell us which instances to label, rather than selecting them randomly. We will limit the range of query selection to the unlabeled data set, a practice known as poolbased active learning or selective sampling. There has been a great deal of research in active learning. For example, Tong and Koller (2000) select queries to minimize the version space size for support vector machines; Cohn et al. (1996) minimize the variance component of the estimated generalization error; Freund et al. (1997) employ a committee of classifiers, and query a point whenever the committee members disagree. Most of the active learning methods do not take further advantage of the large amount of unlabeled data once the queries are selected. Exceptions include McCallum and Nigam (1998) who use EM with unlabeled data integrated into the active learning, and Muslea et al. (2002) who use a semi-supervised learning method during training. In addition to this body of work from the machine learning community, there is a large literature on the closely related topic of experimental design in statistics; Chaloner and Verdinelli (1995) give a survey of experimental design from a Bayesian perspective. Recently Zhu et al. (2003) introduced a semi-supervised learning framework which is based on Gaussian random fields and harmonic functions. In this paper we demonstrate how this framework allows a combination of active learning and semi-supervised learning. In brief, the framework allows one to efficiently estimate the expected generalization error after querying a point, which leads to a better query selection criterion than naively selecting the point with maximum label ambiguity. Then, once the queries are selected and added to the labeled data set, the classifier can be trained using both the labeled and remaining unlabeled data. We present results on synthetic data, text classificaProceedings of the ICML-2003 Workshop on The Continuum from Labeled to Unlabeled Data, Washington DC, 2003. tion and image classification tasks that indicate the combination of these techniques can result in highly effective learning schemes. 2. Gaussian random fields and harmonic energy minimizing functions We begin by briefly describing the semi-supervised learning framework of Zhu et al. (2003). There are labeled points , and unlabeled points ; usually . We will use , ! to denote the labeled and unlabeled set, and "$#% '&( the total number of points. We assume the labels are binary: *),+.-0/1 243 . We assume a connected graph 56#6 87' 9: is given with nodes 7 corresponding to the " data points, of which the nodes in are labeled with the corresponding ’s. The edges 9 are represented by an "<;=" weight matrix > which is given. For example if ?+A@CB , > can be the radial basis function (RBF): DFEHG #JI KML NPO 2 Q R B STVU E T O G T R W (1) so that nearby points in Euclidean space are assigned large edge weights. Other weightings are possible, of course, and may be more appropriate when is discrete or symbolic. For our purposes the matrix > fully specifies the data manifold structure. We note that a method for learning the scale parameter Q is proposed in (Zhu et al., 2003). The semi-supervised algorithm in this paper is based on a relaxation of the requirement that labels are binary, resulting in a simple and tractable family of algorithms. We allow continuous labels on unlabeled nodes MXZY[7]\^@ . The labels on labeled data are still constrained to be 0 or 1: _` C#a 4)b c_ d+A-e/ 243 for _P#(24 f . We also denote the constraint by g ) U h i . Since we want unlabeled points that are nearby in the graph to have similar labels, we define the energy to be 9j c 1 C# 2 k S E l G D EmG _` O onp R (2) so that low energy corresponds to a slowly varying function over the graph. Define the diagonal matrix q6#sr*_`t*u[ 8r E whose entries r E #wv G DFEmG are the weighted degrees of the nodes in 5 . The combinatorial Laplacian is the "x;y" matrix z{#aq O > . We can rewrite the energy function in matrix form as 9| c 1 #( } z: . We consider the Gaussian random field ~ c 1 C# 2 P I KML< OF 9j c 1 f where  is an “inverse temperature” parameter, and   is the partition function  #s h  i U h i I KpL< OF 9j M r4 . The Gaussian random field differs from the “standard” discrete Markov random field in that the field configurations are over a continuous state space. Moreover, for a Gaussian field the joint probability distribution over unlabeled nodes is Gaussian with covariance matrix  z| 0 . z 0 is the submatrix of z corresponding to unlabeled data. The minimum energy function # arg min h  i U h i 9| c 1 of the Gaussian field is harmonic; that is, it satisfies zA#/ on unlabeled data points ! , and is equal to ) on the labeled data points . The harmonic property means that the value at each unlabeled node is the average of neighboring nodes: b onp C# 2 r G S EG DEmG b _` for n#Z &a24 f &? which is consistent with our prior notion of smoothness with respect to the graph. Because of the maximum principle of harmonic functions (Doyle & Snell, 1984),  is unique. Furthermore,  satisfies /(b onp 2 for n+! when ! is connected and labeled nodes from both classes are present at the boundary (the usual case; otherwise  takes on the extremum 0 or 1). By definition  is the mode of the Gaussian random field, but since the joint distribution is Gaussian,  is also the mean of the field. The harmonic energy minimizing function  can be computed with matrix methods. We partition the Laplacian matrix z into blocks for labeled and unlabeled nodes, z# z m z z e z 0  and let ,#  0   where  # ) , and  denotes the mean values on the unlabeled data points. The solution is given by  # O z  0 z e  (3) It is not hard to show that the Gaussian field, conditioned on labeled data, is a multivariate normal: * ya   fz  0 . To carry out classification with a Gaussian field, we note that the harmonic energy minimizing function  is the mean of the field. Therefore the Bayes classification rule is to label node _ as class 1 in case b c_ dJ/1 H , and to label node _ class 0 otherwise. The harmonic function  has many nice interpretations, of which the random walk view is particularly relevant here. Define the transition matrix # q  > . Consider the random walk on graph 5 by a particle. Starting from an unlabeled node _ , it moves to a node n with probability  EHG after one step. The walk continues until the particle reaches a labeled node. Then  E is the probability that the particle, starting from node _ , reaches a labeled node with label 1. The labeled data are the absorbing boundary for the random walk. More on this semi-supervised learning framework can be found in (Zhu et al., 2003). 3. Active learning We propose to perform active learning with the Gaussian random field model by greedily selecting queries from the unlabeled data to minimize the risk of the harmonic energy minimization function. The risk is the estimated generalization error of the Bayes classifier, and can be efficiently computed with matrix methods. We define the true risk  of the Bayes classifier based on the harmonic function  to be  # S E U S h U l sgn  E # E ~ E g where sgn 8 E is the Bayes decision rule, where (with a slight abuse of notation) sgn  E # 2 if  E ^/  and sgn  E #,/ otherwise. Here ~ E g is the unknown true label distribution at node _ , given the labeled data. Because of this 8 is not computable. In order to proceed, it is necessary to make assumptions. We begin by assuming that we can estimate the unknown distribution ~ c E g d with the mean of the Gaussian field model: ~ c E # 2pg d Z E Intuitively, recalling  E is the probability of reaching 1 in a random walk on the graph, our assumption is that we can approximate the distribution using a biased coin at each node, whose probability of heads is  E . With this assumption we can compute the estimated risk 8 as  8 # S E U sgn  E # / 2 O  E & sgn 8 E #(2  E # S E U  E 2 O  E (4) If we perform active learning and query an unlabeled node , we will receive an answer (0 or 1). Adding this point to the tr

Real-Time Tracking via On-line Boosting <=SEP=> Very recently tracking was approached using classification techniques such as support vector machines. The object to be tracked is discriminated by a classifier from the background. In a similar spirit we propose a novel on-line AdaBoost feature selection algorithm for tracking. The distinct advantage of our method is its capability of on-line training. This allows to adapt the classifier while tracking the object. Therefore appearance changes of the object (e.g. out of plane rotations, illumination changes) are handled quite naturally. Moreover, depending on the background the algorithm selects the most discriminating features for tracking resulting in stable tracking results. By using fast computable features (e.g. Haar-like wavelets, orientation histograms, local binary patterns) the algorithm runs in real-time. We demonstrate the performance of the algorithm on several (publically available) video sequences.

Incremental Learning for Robust Visual Tracking <=SEP=> Visual tracking, in essence, deals with non-stationary image streams that change over time. While most existing algorithms are able to track objects well in controlled environments, they usually fail in the presence of significant variation of the object’s appearance or surrounding illumination. One reason for such failures is that many algorithms employ fixed appearance models of the target. Such models are trained using only appearance data available before tracking begins, which in practice limits the range of appearances that are modeled, and ignores the large volume of information (such as shape changes or specific lighting conditions) that becomes available during tracking. In this paper, we present a tracking method that incrementally learns a low-dimensional subspace representation, efficiently adapting online to changes in the appearance of the target. The model update, based on incremental algorithms for principal component analysis, includes two important features: a method for correctly updating the sample mean, and a forgetting factor to ensure less modeling power is expended fitting older observations. Both of these features contribute measurably to improving overall tracking performance. Numerous experiments demonstrate the effectiveness of the proposed tracking algorithm in indoor and outdoor environments where the target objects undergo large changes in pose, scale, and illumination.

Robust Visual Tracking and Vehicle Classification via Sparse Representation <=SEP=> In this paper, we propose a robust visual tracking method by casting tracking as a sparse approximation problem in a particle filter framework. In this framework, occlusion, noise, and other challenging issues are addressed seamlessly through a set of trivial templates. Specifically, to find the tracking target in a new frame, each target candidate is sparsely represented in the space spanned by target templates and trivial templates. The sparsity is achieved by solving an ℓ1-regularized least-squares problem. Then, the candidate with the smallest projection error is taken as the tracking target. After that, tracking is continued using a Bayesian state inference framework. Two strategies are used to further improve the tracking performance. First, target templates are dynamically updated to capture appearance changes. Second, nonnegativity constraints are enforced to filter out clutter which negatively resembles tracking targets. We test the proposed approach on numerous sequences involving different types of challenges, including occlusion and variations in illumination, scale, and pose. The proposed approach demonstrates excellent performance in comparison with previously proposed trackers. We also extend the method for simultaneous tracking and recognition by introducing a static template set which stores target images from different classes. The recognition result at each frame is propagated to produce the final result for the whole video. The approach is validated on a vehicle tracking and classification task using outdoor infrared video sequences.

Semi-supervised On-Line Boosting for Robust Tracking <=SEP=> Recently, on-line adaptation of binary classifiers for tracking has been investigated. On-line learning allows for simple classifiers since only the current view of the object from its surrounding background needs to be discriminated. However, on-line adaptation faces one key problem: Each update of the tracker may introduce an error which, finally, can lead to tracking failure (drifting). The contribution of this paper is a novel on-line semi-supervised boosting method which significantly alleviates the drifting problem in tracking applications. This allows to limit the drifting problem while still staying adaptive to appearance changes. The main idea is to formulate the update process in a semi-supervised fashion as combined decision of a given prior and an on-line classifier. This comes without any parameter tuning. In the experiments, we demonstrate real-time tracking of our SemiBoost tracker on several challenging test sequences where our tracker outperforms other on-line tracking methods. Helmut Grabner1,2 Christian Leistner1 Horst Bischof1 1 Institute for Computer Graphics and Vision, Graz University of Technology, Austria 2 Computer Vision Laboratory, ETH-Zurich, Switzerland Motivation Robust Tracking Loop search Region actual object position create confidence map update classifier (tracker) evaluate classifier

Predicting program properties from 'big code' <=SEP=> We present a new approach for predicting program properties from massive codebases (aka "Big Code"). Our approach first learns a probabilistic model from existing data and then uses this model to predict properties of new, unseen programs.
 The key idea of our work is to transform the input program into a representation which allows us to phrase the problem of inferring program properties as structured prediction in machine learning. This formulation enables us to leverage powerful probabilistic graphical models such as conditional random fields (CRFs) in order to perform joint prediction of program properties.
 As an example of our approach, we built a scalable prediction engine called JSNice for solving two kinds of problems in the context of JavaScript: predicting (syntactic) names of identifiers and predicting (semantic) type annotations of variables. Experimentally, JSNice predicts correct names for 63% of name identifiers and its type annotation predictions are correct in 81% of the cases. In the first week since its release, JSNice was used by more than 30,000 developers and in only few months has become a popular tool in the JavaScript developer community.
 By formulating the problem of inferring program properties as structured prediction and showing how to perform both learning and inference in this context, our work opens up new possibilities for attacking a wide range of difficult problems in the context of "Big Code" including invariant generation, decompilation, synthesis and others.

Application of Machine Learning Algorithms to an online Recruitment System <=SEP=> In this work, we present a novel approach for evaluating job applicants in online recruitment systems, leveraging machine learning algorithms to solve the candidate ranking problem. An application of our approach is implemented in the form of a prototype system, whose functionality is showcased and evaluated in a real-world recruitment scenario. The proposed system extracts a set of objective criteria from the applicants’ LinkedIn profile, and infers their personality characteristics using linguistic analysis on their blog posts. Our system was found to perform consistently compared to human recruiters; thus, it can be trusted for the automation of applicant ranking and

Kansei Engineering with Online Review Mining for Hotel Service Development <=SEP=> In the Internet era, consumers can comment on their hotel living experience directly through the Internet. Thus, consumers online comments might become a key factor for consumers choosing hotels when planning their tourism itinerary. The large amount of User-Generated Content (UGC) provided by the consumers has also provided chances for the hospitality industry to consumer behavior statistic inferences. This study applies Kansei engineering combined with text mining to develop guidelines for hotel services to help managers meet consumers Kansei needs. Through understanding consumers online comments by text mining, this study identifies the most important customer Kansei and hotel service characteristics, and then analyzes the relationships between them. The finds can provide suggestions for hotel service improvement and development.

Visualizing Time-Dependent Data Using Dynamic t-SNE <=SEP=> Many interesting processes can be represented as time-dependent datasets. We define a time-dependent dataset as a sequence of datasets captured at particular time steps. In such a sequence, each dataset is composed of observations (high-dimensional real vectors), and each observation has a corresponding observation across time steps. Dimensionality reduction provides a scalable alternative to create visualizations (projections) that enable insight into the structure of such datasets. However, applying dimensionality reduction independently for each dataset in a sequence may introduce unnecessary variability in the resulting sequence of projections, which makes tracking the evolution of the data significantly more challenging. We show that this issue affects t-SNE, a widely used dimensionality reduction technique. In this context, we propose dynamic t-SNE, an adaptation of t-SNE that introduces a controllable trade-off between temporal coherence and projection reliability. Our evaluation in two time-dependent datasets shows that dynamic t-SNE eliminates unnecessary temporal variability and encourages smooth changes between projections.

Automatic Chinese Factual Question Generation <=SEP=> Question generation is an emerging research area of artificial intelligence in education. Question authoring tools are important in educational technologies, e.g., intelligent tutoring systems, as well as in dialogue systems. Approaches to generate factual questions, i.e., questions that have concrete answers, mainly make use of the syntactical and semantic information in a declarative sentence, which is then transformed into questions. Recently, some research has been conducted to investigate Chinese factual question generation with some limited success. Reported performance is poor due to unavoidable errors (e.g., sentence parsing, name entity recognition, and rule-based question transformation errors) and the complexity of long Chinese sentences. This article introduces a novel Chinese question generation system based on three stages, sentence simplification, question generation and ranking, to address the challenge of automatically generating factual questions in Chinese. The proposed approach and system have been evaluated on sentences from the New Practical Chinese Reader corpus. Experimental results show that ranking improves more than 20 percentage of questions rated as acceptable by annotators, from 65 percent of all questions to 87 percent of the top ranked 25 percent questions.

The art of scenarios and strategic planning : tools and pitfalls <=SEP=> The term strategy has been misused and even abused. Worse, the word scenario is often confused with strategy to the point that clarification is needed if we are to understand one another. As a prolongation of the work done by the Rand Corporation in the 1960s, strategic planning, management and prospective approaches have been developed to help organizations master change. Over the past 25 years, we have contributed by creating or further developing various methodologies and procedures such as the Mactor and MICMAC methods for use in scenario building. These tools are doubly powerful in that they stimulate the imagination, reduce collective biases, and promote appropriation. One of the main functions of the strategic futures exercise is to eliminate two errors that we usually describe as the “hammer’s risk” and the “nail’s dream.” In other words, we forget what a hammer’s function is when staring at a nail (the nail’s dream) or we know how to use a hammer and imagine that every problem is like a nail (the hammer’s risk). In our case, we strive to give simple tools that may be appropriated. However, these simple tools are inspired by intellectual rigor that enables one to ask the right questions. Of course, these tools do not come with a guarantee. The natural talent, common sense, and intuition of the futurist also count!  2000 Elsevier Science Inc. Introduction Anticipation is not widely practiced by decision makers because when things are going well, they can manage without it, and when things are going badly, it is too late to see beyond the ends of their noses. Fast action is already urgently required! Yet reaction is not an end in itself. Although desirable in the short term, it leads nowhere if not directed towards the firm’s long-term objectives. As Seneca said, “there is no favourable wind for the man who knows not where he is going.” Action becomes meaningless without a goal, and only anticipation points the way to action and gives it both meaning and direction. MICHEL GODET is Professor and holder of the Chaire de prospective industrielle du Conservatoire national des arts et métiers, Paris, France, Director of the Laboratory of Investigation in Prospective and Strategy (LIPS). See: lips@cnam.fr, http://www.cnam.fr/lips/, where there is an on-line working paper Scenarios and Strategies: A Toolbox for Scenario Planning. Address correspondence to Michel Godet, LIPS, CNAM, 2 rue Conte, 75141 Paris, Cedex 03, France. Tel.: 33-01-4027 2530; Fax: 33-01-40 27 27 43; e-mail: lips@cnam.fr. Technological Forecasting and Social Change 65, 3–22 (2000)  2000 Elsevier Science Inc. All rights reserved. 0040-1625/00/$–see front matter 655 Avenue of the Americas, New York, NY 10010 PII S0040-1625(99)00120-1

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A 90–100-GHz 4  $\times$ 4 SiGe BiCMOS Polarimetric Transmit/Receive Phased Array With Simultaneous Receive-Beams Capabilities <=SEP=> This paper presents a 4 × 4 transmit/receive (T/R) SiGe BiCMOS phased-array chip at 90-100 GHz with vertical and horizontal polarization capabilities, 3-bit gain control (9 dB), and 4-bit phase control. The 4 × 4 phased array fits into a 1.6×1.5 mm2 grid, which is required at 94 GHz for wide scan-angle designs. The chip has simultaneous receive (Rx) beam capabilities (V and H) and this is accomplished using dual-nested 16:1 Wilkinson combiners/divider with high isolation. The phase shifter is based on a vector modulator with optimized design between circuit level and electromagnetic simulation and results in 1 dB and gain and phase error, respectively, at 85-110 GHz. The behavior of the vector modulator phase distortion versus input power level is investigated and measured, and design guidelines are given for proper operation in a transmit (Tx) chain. The V and H Rx paths result in a gain of 22 and 25 dB, respectively, a noise figure of 9-9.5 (max. gain), and 11 dB (min. gain) measured without the T/R switch, and an input P1 dB of -31 to -26 dBm over the gain control range. The measured output Psat is ~ -5 dBm per channel, limited by the T/R switch loss. Measurements show ±0.6- and ±0.75-dB variation between the 4 × 4 array elements in the Tx mode (Psat) and Rx mode, respectively, and 40-dB coupling between the different channels on the chip. The chip consumes 1100 mA from a 2-V supply in both the Tx and Rx modes. The design can be scaled to >10 000 elements using polyimide redistribution layers on top of the chip and the application areas are in W-band radars for landing systems.

Invited Paper: Enhanced Architectures, Design Methodologies and CAD Tools for Dynamic Reconfiguration of Xilinx FPGAs <=SEP=> The paper describes architectural enhancements to Xilinx FPGAs that provide better support for the creation of dynamically reconfigurable designs. These are augmented by a new design methodology that uses pre-routed IP cores for communication between static and dynamic modules and permits static designs to route through regions otherwise reserved for dynamic modules. A new CAD tool flow to automate the methodology is also presented. The new tools initially target the Virtex-II, Virtex-II Pro and Virtex-4 families and are derived from Xilinx's commercial CAD tools

Multilabel Sound Event Classification with Neural Networks <=SEP=> TAMPERE UNIVERSITY OF TECHNOLOGY Master‘s Degree Programme in Signal Processing CAKIR, EMRE: Multilabel Sound Event Classification with Neural Networks Master of Science Thesis, 63 pages October 2014 Major: Signal Processing Examiner: Tuomas Virtanen, Heikki Huttunen

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Visual Analytics of Patterns in High-Dimensional Data <=SEP=> Due to the technological progress over the last decades, today’s scientific and commercial applications are capable of generating, storing, and processing, massive amounts of data sets. This influences the type of data generated, which in turn means that with each data entry di erent aspects are combined and stored into one common database. Often the describing attributes are numeric; we name data with more than a handful attributes (dimensions) high-dimensional. Having to make use of these types of data archives provides new challenges to analysis techniques. The work of this thesis centers around the question of finding interesting patterns (meaningful information) in high-dimensional data sets. This task is highly challenging because of the so called curse of dimensionality, expressing that when dimensionality increases the data becomes sparse. This phenomena disturbs standard analysis techniques. Automatic techniques have to deal with the data complexity not only increasing their runtime, but also vitiating their computation functions (like distance functions). Moreover, exploring these data sets visually is hindered by the high number of dimensions that have to be displayed on the two dimensional screen space. This thesis is motivated by the idea that searching for interesting patterns in this kind of data can be done through a mixed approach of automation, visualization, and interaction. The amount of patterns a visualization contains can be measured by so called quality metrics. These automated functions can then filter the high number of highdimensional visualizations and present to the user a pre-filtered good subset for further investigation. We propose quality metrics for scatterplots and parallel coordinates focusing on di erent user tasks like identifying clusters and correlations. We also evaluate these measures with regard to (1) their ability to identify clusters in a variety of real and synthetic datasets; (2) their correlation with human perception of clusters in scatterplots. A thorough discussion of results follows reflecting the impact on directions for future research. As quality metrics were developed for a large number of di erent high-dimensional visualization techniques, we present our reflections on how these methods are related to each other and how the approach can be developed further. For this purpose, we provide an overview of approaches that use quality metrics in high-dimensional data visualization and propose a systematization based on a comprehensive literature review. In high-dimensional data, patterns exist often only in a subset of the dimensions. Subspace clustering techniques aim at finding these subspaces where clusters exist and which might otherwise be hidden if a traditional clustering algorithm is applied. While subspace clustering approaches tackle the sparsity problem in high-dimensional data well, designing e ective visualization to help analyzing the clustering result is not trivial. In addition to the cluster membership information, the relevant sets of dimensions and the overlaps of memberships and dimensions need to also be considered. Although, a number of techniques (for example, scatterplots, heat maps, dendrograms, hierarchical parallel coordinates) exist for visualizing traditional clustering results, little research has been done for visualizing subspace clustering results. Moreover, while extensive research has been carried out with regard to designing subspace clustering algorithms, surprisingly little attention has been paid to the developing of e ective visualization tools analyzing the viii clustering result. Appropriate visualization techniques will not only help in monitoring the clustering process but, with special mining techniques, they could also enable the domain expert to guide and even to steer the subspace clustering process to reveal the patterns of interest. To this goal, we envision a concept that combines subspace clustering algorithms and interactive scalable visual exploration techniques. This work includes the task of comparative visualization and feedback guided computation of alternative clusterings. Zusammenfassung Bedingt durch den technologischen Fortschritt der letzten Jahrzehnte sind heutige kommerzielle Applikationen in der Lage, riesige Datenmengen zu erzeugen, zu speichern und zu verarbeiten. Diese Entwicklung beeinflusst auch die Natur der erzeugten Daten, d.h. dass für jeden Dateneintrag unterschiedliche Aspekte in der gleichen Datenbank gespeichert werden. Oft sind die beschreibenden Attribute numerisch. Datensätze, die mehr als fünf solcher Attribute (Dimensionen) beinhalten, nenne ich hochdimensional. Der wertbringende Gebrauch solcher Datenarchive bringt neue Herausforderungen an Analysetechniken mit sich. Die vorliegende Dissertation bearbeitet die Fragestellung, wie interessante Muster (bedeutende Information) in hochdimensionalen Räumen gefunden werden können. Diese Aufgabenstellung ist durch das Problem des Fluches der Dimensionalität äußerst herausfordernd. Dieses Problem besagt, dass Daten im hochdimensionalen Raum spärlich vorkommen. Herkömmliche Analysetechniken werden dadurch beeinträchtigt. Automatische Methoden müssen die Datenkomplexität nicht nur ihre Laufzeit, sondern auch ihre Berechnungsfunktionen (z.B. Distanzfunktionen) betre end, einbeziehen. Außerdem wird die visuelle Exploration dieser Daten durch die Zweidimensionalität der Darstellungen beeinträchtigt. Diese Dissertation stützt sich auf das Konzept, dass die Suche nach interessanten Mustern in hochdimensionalen Datenmengen mit einem kombinierten Ansatz von automatischen, visuellen und interaktiven Methoden durchgeführt werden kann. Die Ausprägung der Muster einer Visualisierung kann durch sogenannte Qualitätsmaße gemessen werden. Durch diese automatischen Funktionen kann die große Menge an hochdimensionalen Visualisierungen eingegrenzt und dem Benutzer eine ausgewählte Menge zur weiteren Untersuchung zur Verfügung gestellt werden. Ich schlage Qualitätsmaße für Scatterplots und Parallele Koordinaten vor, die sich auf unterschiedliche Aufgaben, wie die Identifikation von Gruppen oder Korrelationen, konzentrieren. Zusätzlich werden diese Techniken bezüglich (1) ihrer Fähigkeit Cluster in unterschiedlichen realen und synthetischen Datensätzen und (2) ihrer Korrelation mit der menschlichen Wahrnehmung untersucht. Der ausführlichen Diskussion dieser Resultate folgen Überlegungen für die zukünftige Forschung. Da viele verschiedene Qualitätsmaße für eine Reihe weiterer hochdimensionaler Visualisierungen entwickelt wurden, werde ich Vorschläge für deren Vernetzung und Weiterentwicklung vorstellen. Hierfür wird eine Übersicht über die verschiedenen Ansätze erstellt, welcher eine Systematisierung zugrunde liegt, die aufgrund einer umfassenden Literaturauswertung zustande kam. Im hochdimensionalen Raum existieren manche Muster nur in verschiedenen Unterräumen des Datenraumes. Subspace Clustering Algorithmen wurden entwickelt, um Unterräume zu finden in denen Cluster existieren, die durch traditionelle Clustering Algorithmen nicht gefunden werden würden. Obwohl diese Algorithmen spärlich mit Daten besetzte, hochdimensionale Räume gut explorieren können, ist das Entwickeln von e ektiven Visualisierungstechniken, um diese Clusteringresultate zu analysieren, nicht trivial. Zusätzlich zu der Clusterzugehörigkeit von Elementen müssen die relevanten Attributmengen eines Clusters und die Objektund Dimensionsüberlappungen von Subspaceclusx tern dargestellt werden. Auch wenn eine Reihe von Techniken für die Visualisierung von traditionellen Clustering Resultaten existiert (z.B. Scatterplots, Heatmaps, Dendrogramme, hierarchische Parallele Koordinaten) gibt es nur wenige Ansätze, um das Resultat von Subspace Clustering Algorithmen zu visualisieren. Außerdem wurden bisher erstaunlich wenige Ansätze vorgestellt, die eine visuelle Analyse der Subspace Clustering Ergebnisse unterstützen können, obwohl im Bereich der Subspace Clustering Algorithmen viel Forschung betrieben wurde. Angemessene Visualisierungstechniken, die von speziellen Methoden zur Extraktion von Informationen unterstützt werden, würden nicht nur die Nachverfolgung der Clustering Ergebnisse ermöglichen, sondern auch Fachleuten dabei helfen, den Subspace Clustering Prozess so zu steuern, dass relevante Muster zum Vorschein kommen. Dieses Ziel vor Augen stelle ich ein Konzept vor, das Subspace Clustering Algorithmen mit interaktiven skalierbaren Visualisierungen kombiniert. Meine Ansätze widmen sich deshalb der Aufgabe der Visualisierung zum Vergleich von alternativen Clustergruppen, die durch Nutzerfeedback gesteuert werden.

3D Printed Reversible Shape Changing Components with Stimuli Responsive Materials. <=SEP=> The creation of reversibly-actuating components that alter their shapes in a controllable manner in response to environmental stimuli is a grand challenge in active materials, structures, and robotics. Here we demonstrate a new reversible shape-changing component design concept enabled by 3D printing two stimuli responsive polymers-shape memory polymers and hydrogels-in prescribed 3D architectures. This approach uses the swelling of a hydrogel as the driving force for the shape change, and the temperature-dependent modulus of a shape memory polymer to regulate the time of such shape change. Controlling the temperature and aqueous environment allows switching between two stable configurations - the structures are relatively stiff and can carry load in each - without any mechanical loading and unloading. Specific shape changing scenarios, e.g., based on bending, or twisting in prescribed directions, are enabled via the controlled interplay between the active materials and the 3D printed architectures. The physical phenomena are complex and nonintuitive, and so to help understand the interplay of geometric, material, and environmental stimuli parameters we develop 3D nonlinear finite element models. Finally, we create several 2D and 3D shape changing components that demonstrate the role of key parameters and illustrate the broad application potential of the proposed approach.

A comparative study of data mining techniques for credit scoring in banking <=SEP=> Credit is becoming one of the most important incomes of banking. Past studies indicate that the credit risk scoring model has been better for Logistic Regression and Neural Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.

A Stroke Order Verification Method for On-Line Handwritten Chinese Characters Based on Tempo-spatial Consistency Analysis <=SEP=> This paper proposes a method to recognize stroke orders of on-line handwritten Chinese characters based on analyzing both spatial and temporal information. A novel control-point-based analysis method is presented for spatial information analysis to match strokes with various shapes and styles. Its computation complexity is much lower than image correlation method and is suitable for applications on mobile devices. For temporal information analysis, Hidden Markov Model is adopted and a proposed rectification method is integrated to find the optimal pair-wise matching result of stroke sequences. Experimental results proved the effectiveness of the proposed method. The verification rate is 99.6% on the test set.

Position and Force Control Based on Mathematical Models of Pneumatic Artificial Muscles Reinforced by Straight Glass Fibers <=SEP=> This paper reports on the position and force control of pneumatic artificial muscles reinforced by straight glass fibers. This type of artificial muscle has a greater contraction ratio and power and a longer lifetime than conventional McKibben types. However, these muscles are highly non-linear; hence, it is difficult to use them in a mechanical system. Furthermore, this actuator has a high compliance characteristic. Though this characteristic is useful for human interactions, the position and force of this actuator cannot be easily controlled. In this paper, a mathematical model of this type of artificial muscle is proposed, and the relationship between design parameters and control specifications is realized. In addition, the position and force based on the mathematical model are determined and applied to artificial muscle linearization.

Efficient set joins on similarity predicates <=SEP=> In this paper we present an efficient, scalable and general algorithm for performing set joins on predicates involving various similarity measures like intersect size, Jaccard-coefficient, cosine similarity, and edit-distance. This expands the existing suite of algorithms for set joins on simpler predicates such as, set containment, equality and non-zero overlap. We start with a basic inverted index based probing method and add a sequence of optimizations that result in one to two orders of magnitude improvement in running time. The algorithm folds in a data partitioning strategy that can work efficiently with an index compressed to fit in any available amount of main memory. The optimizations used in our algorithm generalize to several weighted and unweighted measures of partial word overlap between sets.

Size dependency of nanocrystalline TiO 2 on its optical property and photocatalytic reactivity exemplified by 2-chlorophenol <=SEP=> Anatase TiO2 nanocrystallines (17–29 nm) were successfully synthesized by the metal–organic chemical vapor deposition method (MOCVD). Moderate manipulation of system parameters of MOCVD can control the particle size. The electro-optical and photocatalytic properties of the synthesized TiO2 nanoparticles were studied along with several commercially available ultra-fine TiO2 particles (e.g., 3.8–5.7 nm). The band gap of the TiO2 crystallines was determined using the transformed diffuse reflectance technique according to the Kubelka–Munk theory. Results showed that the band gap of TiO2 monotonically decreased from 3.239 to 3.173 eV when the particle size decreased from 29 to 17 nm and then increased from 3.173 to 3.289 eV as the particle size decreased from 17 to 3.8 nm. The results of band gap change as a function of particle size agreed well with what was predicted by the Brus’ equation, i.e., the effective mass model (EMM). However, results of the photocatalytic oxidation of 2-chlorophenol (2-CP), showed that the smaller the particle size, the faster the degradation rate. This is attributed in part to the combined effect of band gap change relative to the spectrum of the light source and the specific surface area (or particle size) of the photocatalysts. The change of band gap due to particle size represents only a small optical absorption window with respect to the total spectrum of the light source, i.e., from 380 to 400 nm versus>280 nm. Consequently, the gain in optical property of the larger particles was severely compromised by their decrease in specific surface area. Our results clearly indicated the importance of specific surface area in controlling the photocatalytic reactivity of photocatalysts. Results also showed that the secondary particle size grew with time due mainly to particle aggregation. The photocatalytic rate constants decreased exponentially with increase in primary particle size. Primary particle size alone is able to predict the photocatalytic rate as it is closely related to the electro-optical properties of photocatalysts. # 2006 Published by Elsevier B.V.

Structural Insights into the Niemann-Pick C1 (NPC1)-Mediated Cholesterol Transfer and Ebola Infection <=SEP=> Niemann-Pick disease type C (NPC) is associated with mutations in NPC1 and NPC2, whose gene products are key players in the endosomal/lysosomal egress of low-density lipoprotein-derived cholesterol. NPC1 is also the intracellular receptor for Ebola virus (EBOV). Here, we present a 4.4 Å structure of full-length human NPC1 and a low-resolution reconstruction of NPC1 in complex with the cleaved glycoprotein (GPcl) of EBOV, both determined by single-particle electron cryomicroscopy. NPC1 contains 13 transmembrane segments (TMs) and three distinct lumenal domains A (also designated NTD), C, and I. TMs 2-13 exhibit a typical resistance-nodulation-cell division fold, among which TMs 3-7 constitute the sterol-sensing domain conserved in several proteins involved in cholesterol metabolism and signaling. A trimeric EBOV-GPcl binds to one NPC1 monomer through the domain C. Our structural and biochemical characterizations provide an important framework for mechanistic understanding of NPC1-mediated intracellular cholesterol trafficking and Ebola virus infection.

Development of a Danish Language Version of the Manchester Foot Pain and Disability Index: Reproducibility and Construct Validity Testing <=SEP=> Introduction. The Manchester Foot Pain and Disability Index (MFPDI) is a 19-item questionnaire for the assessment of disability caused by foot pain. The aim was to develop a Danish language version of the MFPDI (MFPDI-DK) and evaluate its reproducibility and construct validity. Methods. A Danish version was created, following a forward-backward translation procedure. A sample of 84 adult patients with foot pain was recruited. Participants completed two copies of the MFPDI-DK within a 24- to 48-hour interval, along with the Medical Outcomes Study Short Form 36 (SF-36), and a pain Visual Analog Scale (VAS). Reproducibility was assessed using the intraclass correlation coefficient (ICC) and 95% limits of agreement (Bland-Altman plot). Construct validity was evaluated with Pearson's Rho, using a priori hypothesized correlations with SF-36 subscales and VASmean. Results. The MFPDI-DK showed very good reliability with an ICC of 0.92 (0.88-0.95). The 95% limits of agreement ranged from -6.03 to 6.03 points. Construct validity was supported by moderate to very strong correlations with the SF-36 physical subscales and VASmean. Conclusion. The MFPDI-DK appears to be a valid and reproducible instrument in evaluating foot-pain-related disability in Danish adult patients in cross-sectional samples. Further research is needed to test the responsiveness of the MFPDI-DK.

A retrospective study of SPECT/CT scans using SUV measurement of the normal pelvis with Tc-99m methylene diphosphonate. <=SEP=> OBJECTIVE
To perform quantitative measurement based on the standardized uptake value (SUV) of Tc-99m methylene diphosphonate (MDP) in the normal pelvis using a single-photon emission tomography (SPECT)/computed tomography (CT) scanner.


MATERIAL AND METHODS
This retrospective study was performed on 31 patients with cancer undergoing bone SPECT/CT scans with 99mTc-MDP. SUVmax and SUVmean of the normal pelvis were calculated based on the body weight. SUVmax and SUVmean of the bilateral anterior superior iliac spine, posterior superior iliac spine, facies auricularis ossis ilii, ischial tuberosity, and sacrum were also calculated. Furthermore, the correlation of SUVmax and SUVmean of all parts of pelvis with weight, height, and CT was assessed.


RESULTS
The data for 31 patients (20 women and 11 men; mean age 58.97±9.12 years; age range 37-87 years) were collected. SUVmax and SUVmean changed from 1.65±0.40 to 3.8±1.0 and from 1.15±0.25 to 2.07±0.58, respectively. The coefficient of variation of SUVmax and SUVmean ranged from 0.22 to 0.31. SUVmax and SUVmean had no statistically significant difference between men and women. SUVmax and SUVmean also showed no significant correlation with weight and height. However, part of SUVmax and SUVmean showed a significant correlation with CT. In addition, SUVmax and SUVmean of the bilateral ischial tuberosity showed a significant correlation with CT values.


CONCLUSIONS
Determination of the SUV value of the normal pelvis with 99m Tc-MDP SPECT/CT is feasible and highly reproducible. SUVs of the normal pelvis showed a relatively large variability. As a quantitative imaging biomarker, SUVs might require standardization with adequate reference data for the participant to minimize variability.

Challenges in automotive software engineering <=SEP=> The amount of software in cars grows exponentially. Driving forces of this development are cheaper and more powerful hardware and the demand for innovations by new functions. The rapid increase of software and software based functionality brings various challenges (see [21], [23], [25], [26]) for the automotive industries, for their organization, key competencies, processes, methods, tools, models, product structures, division of work, logistics, maintenance, and long term strategies. From a software engineering perspective, the automotive industry is an ideal and fascinating application domain for advanced techniques. Although the automotive industry may adopt general results and solutions from the software engineering body of knowledge gained in other domains, the specific constraints and domain specific requirements in the automotive industry ask for individual solutions and bring various challenges for automotive software engineering. In cars we find literally all interesting problems and challenging issues of software and systems engineering.

Proofs of Work for Blockchain Protocols <=SEP=> One of the most impactful applications of proofs of work (POW) currently is in the design of blockchain protocols such as Bitcoin. Yet, despite the wide recognition of POWs as the fundamental cryptographic tool in this context, there is no known cryptographic formulation that implies the security of the Bitcoin blockchain protocol. Indeed, all previous works formally arguing the security of the Bitcoin protocol relied on direct proofs in the random oracle model, thus circumventing the di culty of isolating the required properties of the core POW primitive. In this work we ll this gap by providing a formulation of the POW primitive that implies the security of the Bitcoin blockchain protocol in the standard model. Our primitive entails a number of properties that parallel an e cient non-interactive proof system: completeness and fast veri cation, security against malicious provers (termed hardness against tampering and chosen message attacks ) and security for honest provers (termed uniquely successful under chosen key and message attacks ). Interestingly, our formulation is incomparable with previous formulations of POWs that applied the primitive to contexts other than the blockchain. Our result paves the way for proving the security of blockchain protocols in the standard model assuming our primitive can be realized from computational assumptions.

Some Theoretical Considerations in Off-the-Shelf Text Analysis Software <=SEP=> This paper is concerned with theoretical considerations of commercial content analysis software, namely Linguistic Inquiry and Word Count (LIWC), developed by social psychologists at the University of Texas. LIWC is widely cited and forms the basis of many research papers from a range of disciplines. Here, LIWC is taken as an example of a context-independent, word-counting approach to text analysis, and the strengths and potential pitfalls of such a methodology are discussed. It is shown that text analysis software is constrained not only by its functions, but also by its underlying theoretical assumptions. The paper offers recommendations for good practice in software commercialisation and application, stressing the importance of transparency and acknowledgement of biases.

Mutation Spectrum and Phenotypic Features in Noonan Syndrome with PTPN11 Mutations: Definition of Two Novel Mutations. <=SEP=> OBJECTIVES
To evaluate the spectrum of PTPN11 gene mutations in Noonan syndrome patients and to study the genotype-phenotype associations.


METHODS
In this study, twenty Noonan syndrome patients with PTPN11 mutations were included. The patients underwent a detailed clinical and physical evaluation. To identify inherited cases, parents of all mutation positive patients were analyzed.


RESULTS
Thirteen different PTPN11 mutations, two of them being novel, were detected in the study group. These mutations included eleven missense mutations: p.G60A, p.D61N, p.Y62D, p.Y63C, p.E69Q, p.Q79R, p.Y279C,p.N308D, p.N308S, p.M504V, p.Q510R and two novel missense mutations: p.I56V and p.I282M. The frequency of cardiac abnormalities and short stature were found to be 80 % and 80 %, respectively. Mental retardation was not observed in patients having exon 8 mutations. No significant correlations were detected between other phenotypic features and genotypes.


CONCLUSIONS
By identifying genotype-phenotype correlations, this study provides information on phenotypes observed in NS patients with different PTPN11 mutations.