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We present a hierarchy of polynomial time lattice basis reduction algorithms that stretch from Lenstra, Lenstra, Lovász reduction to Korkine–Zolotareff reduction. Let λ(L) be the length of a shortest nonzero element of a lattice L. We present an algorithm which for k∈N finds a nonzero lattice vector b so that |b|2⩽(6k2)nkλ(L)2. This algorithm uses O(n2(kk+o(k))+n2)log B) arithmetic operations on O(n log B)-bit integers. This holds provided that the given basis vectors b1,…,bn∈Zn are integral and have the length bound B. This algorithm successively applies Korkine–Zolotareff reduction to blocks of length k of the lattice basis. We also improve Kannan's algorithm for Korkine-Zolotareff reduction.
Performance and storage requirements of topology-conserving maps for robot manipulator control
(1989)
A new programming paradigm for the control of a robot manipulator by learning the mapping between the Cartesian space and the joint space (inverse Kinematic) is discussed. It is based on a Neural Network model of optimal mapping between two high-dimensional spaces by Kohonen. This paper describes the approach and presents the optimal mapping, based on the principle of maximal information gain. It is shown that Kohonens mapping in the 2-dimensional case is optimal in this sense. Furthermore, the principal control error made by the learned mapping is evaluated for the example of the commonly used PUMA robot, the trade-off between storage resources and positional error is discussed and an optimal position encoding resolution is proposed.
It is well known that artificial neural nets can be used as approximators of any continous functions to any desired degree. Nevertheless, for a given application and a given network architecture the non-trivial task rests to determine the necessary number of neurons and the necessary accuracy (number of bits) per weight for a satisfactory operation. In this paper the problem is treated by an information theoretic approach. The values for the weights and thresholds in the approximator network are determined analytically. Furthermore, the accuracy of the weights and the number of neurons are seen as general system parameters which determine the the maximal output information (i.e. the approximation error) by the absolute amount and the relative distribution of information contained in the network. A new principle of optimal information distribution is proposed and the conditions for the optimal system parameters are derived. For the simple, instructive example of a linear approximation of a non-linear, quadratic function, the principle of optimal information distribution gives the the optimal system parameters, i.e. the number of neurons and the different resolutions of the variables.
It is well known that artificial neural nets can be used as approximators of any continuous functions to any desired degree and therefore be used e.g. in high - speed, real-time process control. Nevertheless, for a given application and a given network architecture the non-trivial task remains to determine the necessary number of neurons and the necessary accuracy (number of bits) per weight for a satisfactory operation which are critical issues in VLSI and computer implementations of nontrivial tasks. In this paper the accuracy of the weights and the number of neurons are seen as general system parameters which determine the maximal approximation error by the absolute amount and the relative distribution of information contained in the network. We define as the error-bounded network descriptional complexity the minimal number of bits for a class of approximation networks which show a certain approximation error and achieve the conditions for this goal by the new principle of optimal information distribution. For two examples, a simple linear approximation of a non-linear, quadratic function and a non-linear approximation of the inverse kinematic transformation used in robot manipulator control, the principle of optimal information distribution gives the the optimal number of neurons and the resolutions of the variables, i.e. the minimal amount of storage for the neural net. Keywords: Kolmogorov complexity, e-Entropy, rate-distortion theory, approximation networks, information distribution, weight resolutions, Kohonen mapping, robot control.
One of the most interesting domains of feedforward networks is the processing of sensor signals. There do exist some networks which extract most of the information by implementing the maximum entropy principle for Gaussian sources. This is done by transforming input patterns to the base of eigenvectors of the input autocorrelation matrix with the biggest eigenvalues. The basic building block of these networks is the linear neuron, learning with the Oja learning rule. Nevertheless, some researchers in pattern recognition theory claim that for pattern recognition and classification clustering transformations are needed which reduce the intra-class entropy. This leads to stable, reliable features and is implemented for Gaussian sources by a linear transformation using the eigenvectors with the smallest eigenvalues. In another paper (Brause 1992) it is shown that the basic building block for such a transformation can be implemented by a linear neuron using an Anti-Hebb rule and restricted weights. This paper shows the analog VLSI design for such a building block, using standard modules of multiplication and addition. The most tedious problem in this VLSI-application is the design of an analog vector normalization circuitry. It can be shown that the standard approaches of weight summation will not give the convergence to the eigenvectors for a proper feature transformation. To avoid this problem, our design differs significantly from the standard approaches by computing the real Euclidean norm. Keywords: minimum entropy, principal component analysis, VLSI, neural networks, surface approximation, cluster transformation, weight normalization circuit.
We present a framework for the self-organized formation of high level learning by a statistical preprocessing of features. The paper focuses first on the formation of the features in the context of layers of feature processing units as a kind of resource-restricted associative multiresolution learning We clame that such an architecture must reach maturity by basic statistical proportions, optimizing the information processing capabilities of each layer. The final symbolic output is learned by pure association of features of different levels and kind of sensorial input. Finally, we also show that common error-correction learning for motor skills can be accomplished also by non-specific associative learning. Keywords: feedforward network layers, maximal information gain, restricted Hebbian learning, cellular neural nets, evolutionary associative learning
After a short introduction into traditional image transform coding, multirate systems and multiscale signal coding the paper focuses on the subject of image encoding by a neural network. Taking also noise into account a network model is proposed which not only learns the optimal localized basis functions for the transform but also learns to implement a whitening filter by multi-resolution encoding. A simulation showing the multi-resolution capabilitys concludes the contribution.
The paper focuses on the division of the sensor field into subsets of sensor events and proposes the linear transformation with the smallest achievable error for reproduction: the transform coding approach using the principal component analysis (PCA). For the implementation of the PCA, this paper introduces a new symmetrical, lateral inhibited neural network model, proposes an objective function for it and deduces the corresponding learning rules. The necessary conditions for the learning rate and the inhibition parameter for balancing the crosscorrelations vs. the autocorrelations are computed. The simulation reveals that an increasing inhibition can speed up the convergence process in the beginning slightly. In the remaining paper, the application of the network in picture encoding is discussed. Here, the use of non-completely connected networks for the self-organized formation of templates in cellular neural networks is shown. It turns out that the self-organizing Kohonen map is just the non-linear, first order approximation of a general self-organizing scheme. Hereby, the classical transform picture coding is changed to a parallel, local model of linear transformation by locally changing sets of self-organized eigenvector projections with overlapping input receptive fields. This approach favors an effective, cheap implementation of sensor encoding directly on the sensor chip. Keywords: Transform coding, Principal component analysis, Lateral inhibited network, Cellular neural network, Kohonen map, Self-organized eigenvector jets.
This paper describes the use of a radial basis function (RBF) neural network. It approximates the process parameters for the extrusion of a rubber profile used in tyre production. After introducing the problem, we describe the RBF net algorithm and the modeling of the industrial problem. The algorithm shows good results even using only a few training samples. It turns out that the „curse of dimensions“ plays an important role in the model. The paper concludes by a discussion of possible systematic error influences and improvements.
In this paper we regard first the situation where parallel channels are disturbed by noise. With the goal of maximal information conservation we deduce the conditions for a transform which "immunizes" the channels against noise influence before the signals are used in later operations. It shows up that the signals have to be decorrelated and normalized by the filter which corresponds for the case of one channel to the classical result of Shannon. Additional simulations for image encoding and decoding show that this constitutes an efficient approach for noise suppression. Furthermore, by a corresponding objective function we deduce the stochastic and deterministic learning rules for a neural network that implements the data orthonormalization. In comparison with other already existing normalization networks our network shows approximately the same in the stochastic case but, by its generic deduction ensures the convergence and enables the use as independent building block in other contexts, e.g. whitening for independent component analysis. Keywords: information conservation, whitening filter, data orthonormalization network, image encoding, noise suppression.
This paper describes the problems and an adaptive solution for process control in rubber industry. We show that the human and economical benefits of an adaptive solution for the approximation of process parameters are very attractive. The modeling of the industrial problem is done by the means of artificial neural networks. For the example of the extrusion of a rubber profile in tire production our method shows good results even using only a few training samples.
The encoding of images by semantic entities is still an unresolved task. This paper proposes the encoding of images by only a few important components or image primitives. Classically, this can be done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the signal processing and neural network community. Using this as pattern primitives we aim for source patterns with the highest occurrence probability or highest information. For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that the Independent Principal Components (IPC) in contrast to the Principal Independent Components (PIC) implement the classical demand of Shannon’s rate distortion theory.
This paper proposes a new approach for the encoding of images by only a few important components. Classically, this is done by the Principal Component Analysis (PCA). Recently, the Independent Component Analysis (ICA) has found strong interest in the neural network community. Applied to images, we aim for the most important source patterns with the highest occurrence probability or highest information called principal independent components (PIC). For the example of a synthetic image composed by characters this idea selects the salient ones. For natural images it does not lead to an acceptable reproduction error since no a-priori probabilities can be computed. Combining the traditional principal component criteria of PCA with the independence property of ICA we obtain a better encoding. It turns out that this definition of PIC implements the classical demand of Shannon’s rate distortion theory.
Im Zeitraum 1. 11. 1993 bis 30. 3. 1997 wurden 1149 allgemeinchirurgische Intensivpatienten prospektiv erfaßt, von denen 114 die Kriterien des septischen Schocks erfüllten. Die Letalität der Patienten mit einem septischen Schock betrug 47,3%. Nach Training eines neuronalen Netzes mit 91 (von insgesamt n = 114) Patienten ergab die Testung bei den verbleibenden 23 Patienten bei der Berücksichtigung von Parameterveränderungen vom 1. auf den 2. Tag des septischen Schocks folgendes Ergebnis: Alle 10 verstorbenen Patienten wurden korrekt als nicht überlebend vorhergesagt, von den 13 Überlebenden wurden 12 korrekt als überlebend vorhergesagt (Sensitivität 100%; Spezifität 92,3%).
Diese Arbeit plädiert für eine rationale Behandlung von Patientendaten und untersucht dazu die Analyse der Daten mit Hilfe neuronale Netze etwas näher. Erfolgreiche Beispielanwendungen zeigen, daß die menschlichen Diagnosefähigkeiten deutlich schlechter sind als neuronale Diagnosesysteme. Für das Beispiel der neueren Architektur mit RBF-Netzen wird die Funktionalität näher erläutert und gezeigt, wie menschliche und neuronale Expertise miteinander gekoppelt werden kann. Der Ausblick deutet Anwendungen und Praxisproblematik derartiger Systeme an.
This paper describes the use of a Radial Basis Function (RBF) neural network in the approximation of process parameters for the extrusion of a rubber profile in tyre production. After introducing the rubber industry problem, the RBF network model and the RBF net learning algorithm are developed, which uses a growing number of RBF units to compensate the approximation error up to the desired error limit. Its performance is shown for simple analytic examples. Then the paper describes the modelling of the industrial problem. Simulations show good results, even when using only a few training samples. The paper is concluded by a discussion of possible systematic error influences, improvements and potential generalisation benefits. Keywords: Adaptive process control; Parameter estimation; RBF-nets; Rubber extrusion
The prevention of credit card fraud is an important application for prediction techniques. One major obstacle for using neural network training techniques is the high necessary diagnostic quality: Since only one financial transaction of a thousand is invalid no prediction success less than 99.9% is acceptable. Due to these credit card transaction proportions complete new concepts had to be developed and tested on real credit card data. This paper shows how advanced data mining techniques and neural network algorithm can be combined successfully to obtain a high fraud coverage combined with a low false alarm rate.
In contrast to the symbolic approach, neural networks seldom are designed to explain what they have learned. This is a major obstacle for its use in everyday life. With the appearance of neuro-fuzzy systems which use vague, human-like categories the situation has changed. Based on the well-known mechanisms of learning for RBF networks, a special neuro-fuzzy interface is proposed in this paper. It is especially useful in medical applications, using the notation and habits of physicians and other medically trained people. As an example, a liver disease diagnosis system is presented.
In its first part, this contribution reviews shortly the application of neural network methods to medical problems and characterizes its advantages and problems in the context of the medical background. Successful application examples show that human diagnostic capabilities are significantly worse than the neural diagnostic systems. Then, paradigm of neural networks is shortly introduced and the main problems of medical data base and the basic approaches for training and testing a network by medical data are described. Additionally, the problem of interfacing the network and its result is given and the neuro-fuzzy approach is presented. Finally, as case study of neural rule based diagnosis septic shock diagnosis is described, on one hand by a growing neural network and on the other hand by a rule based system. Keywords: Statistical Classification, Adaptive Prediction, Neural Networks, Neurofuzzy, Medical Systems
In diesem Bericht wurde das in [Pae02] eingeführte Verfahren "GenDurchschnitt" auf die symbolischen Daten zweier Datenbanken septischer Schock-Patienten angewendet. Es wurden jeweils Generalisierungsregeln generiert, die neben einer robusten Klassifikation der Patienten in die Klassen "überlebt" und "verstorben" auch eine Interpretation der Daten ermöglichten. Ein Vergleich mit den aktuellen Verfahren A-priori und FP-Baum haben die gute Verwendbarkeit des Algorithmus belegt. Die Heuristiken führten zu Laufzeitverbesserungen. Insbesondere die Möglichkeit, die Wichtigkeit von Variablen pro Klasse zu berechnen, führte zu einer Variablenreduktion im Eingaberaum und zu der Identifikation wichtiger Items. Einige Regelbeispiele wurden für jeden Datensatz genannt. Die Frühzeitigkeit von Regeln lieferte für die beiden Datenbanken ein unterschiedliches Ergebnis: Bei den ASK-Daten treten die Regeln für die Klasse "verstorben" früher als die der Klasse "überlebt" auf; bei den MEDAN-Klinikdaten ist es umgekehrt. Eine Erklärung hierfür könnte sein, dass es sich im Vergleich zu den MEDAN-Klinikdaten bei den ASK-Daten um ein Patientenkollektiv mit einer anderen, speziellen Patientencharakteristik handelt. Anhand der Ähnlichkeit der Regeln konnten für den Anwender eine überschaubare Anzahl zuverlässiger Regeln ausgegeben werden, die möglichst unähnlich zueinander sind und somit für einen Arzt in ihrer Gesamtheit interessant sind. Assoziationsregeln und FP-Baum-Regeln erzeugen zwar kürzere Regeln, die aber zu zahlreich und nicht hinreichend sind (vgl. [Pae02, Abschnitt 4]). Zusätzlich zu der Analyse der symbolischen Daten ist auch die Analyse der metrischen MEDAN-Klinikdaten der septischen Schock-Patienten interessant. Ebenfalls ist eine Kombination der Analysen der metrischen und symbolischen Daten sinnvoll. Solche Analysen wurden ebenfalls durchgeführt; die Ergebnisse dieser Analysen werden an anderer Stelle präsentiert werden. Weitere Anwendungen der Generalisierungsregeln sind denkbar. Auch eine Verbesserung des theoretischen Fundaments (vgl. [Pae02]) erscheint sinnvoll, da erst das Zusammenspiel theoretischer und praktischer Anstrengungen zum Ziel führt.
The early prediction of mortality is one of the unresolved tasks in intensive care medicine. This contribution models medical symptoms as observations cased by transitions between hidden markov states. Learning the underlying state transition probabilities results in a prediction probability success of about 91%. The results are discussed and put in relation to the model used. Finally, the rationales for using the model are reflected: Are there states in the septic shock data?
In intensive care units physicians are aware of a high lethality rate of septic shock patients. In this contribution we present typical problems and results of a retrospective, data driven analysis based on two neural network methods applied on the data of two clinical studies. Our approach includes necessary steps of data mining, i.e. building up a data base, cleaning and preprocessing the data and finally choosing an adequate analysis for the medical patient data. We chose two architectures based on supervised neural networks. The patient data is classified into two classes (survived and deceased) by a diagnosis based either on the black-box approach of a growing RBF network and otherwise on a second network which can be used to explain its diagnosis by human-understandable diagnostic rules. The advantages and drawbacks of these classification methods for an early warning system are discussed.
A novel method for identifying the nature of QCD transitions in heavy-ion collision experiments is introduced. PointNet based Deep Learning (DL) models are developed to classify the equation of state (EoS) that drives the hydrodynamic evolution of the system created in Au-Au collisions at 10 AGeV. The DL models were trained and evaluated in different hypothetical experimental situations. A decreased performance is observed when more realistic experimental effects (acceptance cuts and decreased resolutions) are taken into account. It is shown that the performance can be improved by combining multiple events to make predictions. The PointNet based models trained on the reconstructed tracks of charged particles from the CBM detector simulation discriminate a crossover transition from a first order phase transition with an accuracy of up to 99.8%. The models were subjected to several tests to evaluate the dependence of its performance on the centrality of the collisions and physical parameters of fluid dynamic simulations. The models are shown to work in a broad range of centralities (b=0–7 fm). However, the performance is found to improve for central collisions (b=0–3 fm). There is a drop in the performance when the model parameters lead to reduced duration of the fluid dynamic evolution or when less fraction of the medium undergoes the transition. These effects are due to the limitations of the underlying physics and the DL models are shown to be superior in its discrimination performance in comparison to conventional mean observables.
The Internet as the biggest human library ever assembled keeps on growing. Although all kinds of information carriers (e.g. audio/video/hybrid file formats) are available, text based documents dominate. It is estimated that about 80% of all information worldwide stored electronically exists in (or can be converted into) text form. More and more, all kinds of documents are generated by means of a text processing system and are therefore available electronically. Nowadays, many printed journals are also published online and may even discontinue to appear in print form tomorrow. This development has many convincing advantages: the documents are both available faster (cf. prepress services) and cheaper, they can be searched more easily, the physical storage only needs a fraction of the space previously necessary and the medium will not age. For most people, fast and easy access is the most interesting feature of the new age; computer-aided search for specific documents or Web pages becomes the basic tool for information-oriented work. But this tool has problems. The current keyword based search machines available on the Internet are not really appropriate for such a task; either there are (way) too many documents matching the specified keywords are presented or none at all. The problem lies in the fact that it is often very difficult to choose appropriate terms describing the desired topic in the first place. This contribution discusses the current state-of-the-art techniques in content-based searching (along with common visualization/browsing approaches) and proposes a particular adaptive solution for intuitive Internet document navigation, which not only enables the user to provide full texts instead of manually selected keywords (if available), but also allows him/her to explore the whole database.
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence - ICTAI 2003
In bioinformatics, biochemical signal pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically obtaining the most appropriate model and learning its parameters is extremely interesting. One of the most often used approaches for model selection is to choose the least complex model which “fits the needs”. For noisy measurements, the model which has the smallest mean squared error of the observed data results in a model which fits too accurately to the data – it is overfitting. Such a model will perform good on the training data, but worse on unknown data. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated. Keywords: biochemical pathways, differential equations, septic shock, parameter estimation, overfitting, minimum description length.
Data driven automatic model selection and parameter adaptation – a case study for septic shock
(2004)
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. This paper propose as model selection criterion the least complex description of the observed data by the model, the minimum description length. For the small, but important example of inflammation modeling the performance of the approach is evaluated.
In bioinformatics, biochemical pathways can be modeled by many differential equations. It is still an open problem how to fit the huge amount of parameters of the equations to the available data. Here, the approach of systematically learning the parameters is necessary. In this paper, for the small, important example of inflammation modeling a network is constructed and different learning algorithms are proposed. It turned out that due to the nonlinear dynamics evolutionary approaches are necessary to fit the parameters for sparse, given data. Keywords: model parameter adaption, septic shock. coupled differential equations, genetic algorithm.
Since the description of sepsis by Schottmüller in 1914, the amount on knowledge available on sepsis and its underlying pathophysiology has substantially increased. Epidemiologic examinations of abdominal septic shock patients show the potential for high risk posed by and the extensive therapy situation in the intensive care unit (ICU) (5). Unfortunately, until now it has not been possible to significantly reduce the mortality rate of septic shock, which is as high as 50-60% worldwide, although PROWESS' results (1) are encouraging. This paper summarizes the main results of the MEDAN project and their medical impacts. Several aspects are already published, see the references. The heterogeneity of patient groups and the variations in therapy strategies is seen as one of the main problems for sepsis trials. In the MEDAN multi-center study of 71 intensive care units in Germany, a group of 382 patients made up exclusively of abdominal septic shock patients who met the consensus criteria for septic shock (3) was analysed. For use within scores or stand-alone experiments variables are often studied as isolated variables, not as a multidimensional whole, e.g. a recent study takes a look at the role thrombocytes play (15). To avoid this limitation, our study compares several established scores (SOFA, APACHE II, SAPS II, MODS) by a multi-dimensional neuronal network analysis. For outcome prediction the data of 382 patients was analysed by using most of the commonly documented vital parameters and doses of medicine (metric variables). Data was collected in German hospitals from 1998 to 2001. The 382 handwritten patient records were transferred to an electronic database giving the amount of 2.5 million data entries. The metric data contained in the database is composed of daily measurements and doses of medicine. We used range and plausibility checks to allow no faulty data in the electronic database. 187 of the 382 patients are deceased (49 %).
Succinctness is a natural measure for comparing the strength of different logics. Intuitively, a logic L_1 is more succinct than another logic L_2 if all properties that can be expressed in L_2 can be expressed in L_1 by formulas of (approximately) the same size, but some properties can be expressed in L_1 by (significantly) smaller formulas.
We study the succinctness of logics on linear orders. Our first theorem is concerned with the finite variable fragments of first-order logic. We prove that:
(i) Up to a polynomial factor, the 2- and the 3-variable fragments of first-order logic on linear orders have the same succinctness. (ii) The 4-variable fragment is exponentially more succinct than the 3-variable fragment. Our second main result compares the succinctness of first-order logic on linear orders with that of monadic second-order logic. We prove that the fragment of monadic second-order logic that has the same expressiveness as first-order logic on linear orders is non-elementarily more succinct than first-order logic.
At present, there are no quantitative, objective methods for diagnosing the Parkinson disease. Existing methods of quantitative analysis by myograms suffer by inaccuracy and patient strain; electronic tablet analysis is limited to the visible drawing, not including the writing forces and hand movements. In our paper we show how handwriting analysis can be obtained by a new electronic pen and new features of the recorded signals. This gives good results for diagnostics. Keywords: Parkinson diagnosis, electronic pen, automatic handwriting analysis
We present a higher-order call-by-need lambda calculus enriched with constructors, case-expressions, recursive letrec-expressions, a seq-operator for sequential evaluation and a non-deterministic operator amb that is locally bottom-avoiding. We use a small-step operational semantics in form of a single-step rewriting system that defines a (nondeterministic) normal order reduction. This strategy can be made fair by adding resources for bookkeeping. As equational theory we use contextual equivalence, i.e. terms are equal if plugged into any program context their termination behaviour is the same, where we use a combination of may- as well as must-convergence, which is appropriate for non-deterministic computations. We show that we can drop the fairness condition for equational reasoning, since the valid equations w.r.t. normal order reduction are the same as for fair normal order reduction. We evolve different proof tools for proving correctness of program transformations, in particular, a context lemma for may- as well as mustconvergence is proved, which restricts the number of contexts that need to be examined for proving contextual equivalence. In combination with so-called complete sets of commuting and forking diagrams we show that all the deterministic reduction rules and also some additional transformations preserve contextual equivalence.We also prove a standardisation theorem for fair normal order reduction. The structure of the ordering <=c a is also analysed: Ω is not a least element, and <=c already implies contextual equivalence w.r.t. may-convergence.
Various static analyses of functional programming languages that permit infinite data structures make use of set constants like Top, Inf, and Bot, denoting all terms, all lists not eventually ending in Nil, and all non-terminating programs, respectively. We use a set language that permits union, constructors and recursive definition of set constants with a greatest fixpoint semantics in the set of all, also infinite, computable trees, where all term constructors are non-strict. This internal report proves decidability, in particular DEXPTIME-completeness, of inclusion of co-inductively defined sets by using algorithms and results from tree automata and set constraints, and contains detailed proofs. The test for set inclusion is required by certain strictness analysis algorithms in lazy functional programming languages and could also be the basis for further set-based analyses.
In this contribution we present algorithms for model checking of analog circuits enabling the specification of time constraints. Furthermore, a methodology for defining time-based specifications is introduced. An already known method for model checking of integrated analog circuits has been extended to take into account time constraints. The method will be presented using three industrial circuits. The results of model checking will be compared to verification by simulation.
Raytracing und Szenegraphen
(2006)
Raytracing ist ein bekanntes Verfahren zur Erzeugung fotorealistischer Bilder. Globale Beleuchtungseffekte einer 3D-Szene werden durch das Raytracing-Verfahren physikalisch korrekt dargestellt. Erst aktuelle Forschungsarbeiten erm¨oglichen es, das sehr rechenintensive Verfahren bei interaktiven Bildraten in Echtzeit zu berechnen.
Komplexe 3D-Szenen, wie sie beispielsweise in 3D-Spielen oder Simulationen vorkommen, können durch einen Szenengraphen modelliert und animiert werden. Damit die Rendering-Ergebnisse eines Szenengraphen n¨aher an einem realen Bild liegen, ist es erforderlich das Raytracing-Verfahren in einen Szenengraphen einzugliedern.
In dieser Arbeit werden die Möglichkeiten zur Integration eines Echtzeit-Raytracers in eine Szenengraph-API untersucht. Ziel dieser Diplomarbeit ist die Darstellung dynamischer Szenen bei interaktiven Bildraten unter Verwendung des Raytracing-Verfahrens auf einem herk¨ommlichen PC. Zun¨achst m¨ussen bestehende Open Source Szenengraph-APIs und aktuelle Echtzeit-Raytracer auf ihre Eignung zur Integration hin überprüft werden.
Bei der Verarbeitung dynamischer Szenen spielt die verwendete Beschleunigungsdatenstruktur des Raytracers eine entscheidende Rolle. Da eine komplette Neuerstellung der Datenstruktur in jedem Bild zuviel Zeit in Anspruch nimmt, ist eine schnelle und kostengünstige Aktualisierung erforderlich. Die in [LAM01] vorgestellte Lösung, eine Hüllkörperhierarchie (BVH) als Beschleunigungsdatenstruktur zu verwenden, fügt sich sehr gut in das Konzept eines Szenengraphen ein. Dadurch wird eine einfache Aktualisierung ermöglicht.
Um das Ziel dieser Arbeit zu erreichen, ist es notwendig, die Parallelisierbarkeit des Raytracing-Verfahrens auszunutzen. Purcell zeigt in [Pur04], dass Grafikprozessoren (GPUs) neben ihrer eigentlichen Aufgabe auch für allgemeine, parallele Berechnungen wie das Raytracing verwendet werden können.
Die in bisherigen Arbeiten über GPU-basiertes Raytracing entwickelten Systeme können dynamische Szenen nicht bei interaktiven Bildraten darstellen. Aus diesem Grund wird in dieser Diplomarbeit ein neues System konzipiert und implementiert, das den in [TS05] entwickelten Raytracer erweitert und in die Open Source Szenengraph-API OGRE 3D integriert.
Das implementierte System ermöglicht die Darstellung statischer und dynamischer Szenen unter Verwendung einer Consumer-Grafikkarte bei interaktiven Bildraten. Durch seine Erweiterbarkeit bildet das System das Grundger¨ust für ein Realtime-High-Quality-Rendering-System.
Attraction and commercial success of web sites depend heavily on the additional values visitors may find. Here, individual, automatically obtained and maintained user profiles are the key for user satisfaction. This contribution shows for the example of a cooking information site how user profiles might be obtained using category information provided by cooking recipes. It is shown that metrical distance functions and standard clustering procedures lead to erroneous results. Instead, we propose a new mutual information based clustering approach and outline its implications for the example of user profiling.
This paper proves several generic variants of context lemmas and thus contributes to improving the tools to develop observational semantics that is based on a reduction semantics for a language. The context lemmas are provided for may- as well as two variants of mustconvergence and a wide class of extended lambda calculi, which satisfy certain abstract conditions. The calculi must have a form of node sharing, e.g. plain beta reduction is not permitted. There are two variants, weakly sharing calculi, where the beta-reduction is only permitted for arguments that are variables, and strongly sharing calculi, which roughly correspond to call-by-need calculi, where beta-reduction is completely replaced by a sharing variant. The calculi must obey three abstract assumptions, which are in general easily recognizable given the syntax and the reduction rules. The generic context lemmas have as instances several context lemmas already proved in the literature for specific lambda calculi with sharing. The scope of the generic context lemmas comprises not only call-by-need calculi, but also call-by-value calculi with a form of built-in sharing. Investigations in other, new variants of extended lambda-calculi with sharing, where the language or the reduction rules and/or strategy varies, will be simplified by our result, since specific context lemmas are immediately derivable from the generic context lemma, provided our abstract conditions are met.
We develop a proof method to show that in a (deterministic) lambda calculus with letrec and equipped with contextual equivalence the call-by-name and the call-by-need evaluation are equivalent, and also that the unrestricted copy-operation is correct. Given a let-binding x = t, the copy-operation replaces an occurrence of the variable x by the expression t, regardless of the form of t. This gives an answer to unresolved problems in several papers, it adds a strong method to the tool set for reasoning about contextual equivalence in higher-order calculi with letrec, and it enables a class of transformations that can be used as optimizations. The method can be used in different kind of lambda calculi with cyclic sharing. Probably it can also be used in non-deterministic lambda calculi if the variable x is “deterministic”, i.e., has no interference with non-deterministic executions. The main technical idea is to use a restricted variant of the infinitary lambda-calculus, whose objects are the expressions that are unrolled w.r.t. let, to define the infinite developments as a reduction calculus on the infinite trees and showing a standardization theorem.
The goal of this report is to prove correctness of a considerable subset of transformations w.r.t. contextual equivalence in an extended lambda-calculus LS with case, constructors, seq, let, and choice, with a simple set of reduction rules; and to argue that an approximation calculus LA is equivalent to LS w.r.t. the contextual preorder, which enables the proof tool of simulation. Unfortunately, a direct proof appears to be impossible.
The correctness proof is by defining another calculus L comprising the complex variants of copy, case-reduction and seq-reductions that use variable-binding chains. This complex calculus has well-behaved diagrams and allows a proof of correctness of transformations, and that the simple calculus LS, the calculus L, and the calculus LA all have an equivalent contextual preorder.
In dieser Diplomarbeit wird ein Echtzeit-Verfahren vorgestellt, um einen wassergefüllten Ballon zu simulieren. Grundlage des Verfahrens ist ein Feder-Masse-Dämpfer–System, das zusammen mit Methoden zur Erhaltung des Innenvolumens sowie einer topologieerhaltenden Datenstruktur kombiniert wurde. Die Masse des Wassers wird dabei auf Massepartikel an der Oberfläche des Gummiballons aufgeteilt, an denen die Wirkung der physikalischen Kräfte Gravitation, Innendruck und elastische Zugkraft der Oberfläche ausgewertet wird. Dies erfolgt durch iterative Anwendung eines Simulationsschrittes, bei dem die auf die Massepartikel wirkenden Beschleunigungen ermittelt und in eine Bewegung übertragen wird. Bei der Umsetzung in C++ wurde das Verfahren mit Hilfe des Echtzeit-3D-Szenengraphen OGRE (Object-oriented Graphics Rendering Engine) implementiert.
Mögliche Einsatzgebiete sind interaktive Simulationsumgebungen oder andere Echtzeit-Anwendungen in den Bereichen Multimedia und Unterhaltung sowie Nicht-Echtzeit-Verfahren zur Bildgenerierung und physikalischen Simulation.
Die Gotik ist eine Kunstepoche des Mittelalters, die eine große Anzahl beeindruckender Bauten hervorgebracht hat. Hierbei sind besonders die monumentalen Sakralbauten dieser Zeit mit ihren eindrucksvollen Fenstern hervorzuheben. Rosetten gehören zu den größten Fenstern in gotischen Kathedralen. Sie sind kreisförmige Fenster, die vor allem durch ihre komplexe Aufbauweise auffallen. Das Grundbauelement der Rosetten ist das sog. Maßwerk - ein Steinwerk das als dekoratives Muster für Fenster und Wände in gotischen Bauten eingesetzt wird. Charakteristisch für diese Muster sind sich wiederholende, verschieden große geometrische Formen, was an die Eigenschaft der Selbstähnlichkeit bei Fraktalen erinnert.
Die manuelle Modellierung gotischer Fenster ist auf Grund deren komplexen Aufbaus sehr aufwendig. Eine prozedurale Generierung hingegen automatisiert den Modellierungsprozess weitgehend und verringert damit den Zeit- und Modellierungsaufwand per Hand.
In der vorliegenden Arbeit wurde eine prozedurale Methode beschrieben, die die aufwendige manuelle Modellierung der Rosetten bzw. des Maßwerks zum größten Teil ersetzt. Diese Methode basiert auf der Analyse der fraktalen Struktur des Maßwerks und nutzt dabei dessen Selbstähnlichkeit aus, um Rosetten automatisch zu generieren. Mit der in dieser Arbeit entwickelten Implementierung ist es mögliche, eine große Vielfalt gotischer Rosetten zu beschreiben und mit Hilfe der 3D-Grafik-Engine OGRE graphisch darzustellen.
In der Computergraphik werden immer wieder verschiedenste Objekte des realen Lebens modelliert. Dabei werden oft die Regeln ihres Aufbaus ausgenutzt, um diese Modelle automatisch zu erzeugen.
Gotische Architektur bietet daf¨ur gute Voraussetzungen. Auf Grund ihres hohen Grades an selbst¨ahnlichen Strukturen besteht die Möglichkeit, solche Regeln aus ihrem Aufbau abzuleiten. Wie bei vielen gotischen Elementen lassen sich auch bei den Pfeilern, die die Basis jedes gotischen Gewölbes bilden, solche Strukturen in ihrem teilweise komplexen Grundriss finden.
Die vorliegende Diplomarbeit stellt eine Methode vor, mit der die Grundrisse verschiedener gotischer Pfeiler beschrieben werden können. Die in dieser Arbeit entwickelte Querschnittsbeschreibung, wird die Darstellung der Grundrisse möglichst vieler verschiedener Pfeiler erlauben und automatisch erzeugbar sein. Der Aufbau der Beschreibung erm¨oglicht die Generierung eines 3D-Modells.
Um dies zu erreichen, wird zunächst eine Analyse der Querschnitte verschiedener gotischer Pfeiler vorgenommen. Mit den in der Analyse gewonnenen Informationen wird formal eine Querschnittsbeschreibung entwickelt, die die oben beschriebenen Anforderungen erf¨ullt. Die automatische Erzeugung erfolgt über ein parametrisches L-System. Aus der Beschreibung des Querschnitts wird schließlich das 3D-Modell erzeugt.
Die Implementierung erfolgt komplett in C++. Für die Erzeugung des 3D-Modells wird der Open Source Szenengraph Ogre3D verwendet, der die notwendige 3D-Grafik-Funktionalit¨at zur Verfügung stellt.
Mit der realisierten Anwendung ist es m¨oglich, mit wenigen Eingaben ein Modell eines komplexen gotischen Pfeilers zu erstellen.
In dieser Arbeit wurde ausgehend von aktuellen Matchmaking Systemen ein 3D Lobbysystem geschaffen. Dabei wurde speziell auf ein intuitives Matchmaking und eine einfache Bedienung wertgelegt, um dieses nicht nur für Core Gamer, sondern auch für Casual Gamer interessant zu machen. Zudem versteht sich dieses Lobbysystem nicht als endgültig, sondern mehr als ein flexibles leicht anpassbares System. Daher ist sie besonders einfach für zukünftige Spiele anpassbar: Sämtliche Szenen, Avatare, Animationen, Einstellungen und GUI Dialoge lassen sich ohne Änderung des Quelltextes nur über Scripte, XML Tabellen und Datenbanken sehr leicht modifizieren. Um ein so komplexes Projekt in kurzer Zeit umzusetzen, war es nicht möglich ohne vorhandene Bibliotheken auszukommen. Aus diesem Grund wurden neben Nebula 2 als 3D Engine, das Mangalore Game Framework, sowie für die Netzwerktechnik die Rakknet Multiplayer Network Engine bei der Implementation des Lobbysystems verwendet. Wie die Tests zeigen befindet sich das entwickelte System in einem einsatzfähigen Zustand. So können sich gleichzeitig in der Lobby bis zu 200 Spieler aufhalten und das Matchmaking durchführen, ohne mit Lags oder Timeouts vom Server rechnen zu müssen. Lediglich die Framerate der einzelnen Clients kann bei sehr vielen eingeloggten Nutzern unter 20 FPS fallen. Je nach der erwarteten Anzahl von Spielern sollte hier ggf. auf Avatare mit weniger Polygonen zurückgegriffen werden.
Manipulierte Bilder werden zu einem immer gröÿeren Problem in der aktuellen Berichterstattung und sie verursachen in vielen Fällen Empörung unter den Lesern.
In dieser Diplomarbeit werden verschiedene Ansätze aus der aktuellen Forschung aufgezeigt, die zur Erkennung von manipulierten digitalen Bildern benutzt werden können. Hierbei liegt der Schwerpunkt besonders auf verschiedenen statistischen Ansätzen von Farid, Johnson und Popescu. Ein Abriss über die wichtigsten inhaltsbasierten Algorithmen wird ebenfalls gegeben.
Weiterhin wird für die Algorithmen, die im Hinblick auf technische Realisierbarkeit, Laufzeit und ein breites Spektrum von möglichen Szenarien vielversprechend wirken, eine Automatisierung entwickelt, die die Analyse ohne weitere Benutzereingaben durchführt. Das Augenmerk liegt hier besonders darauf, dass die zu analysierenden Bilder möglichst wenige Vorraussetzungen erfüllen müssen, damit es eine Möglichkeit der korrekten Erkennung gibt.
Diese Automatisierungen werden implementiert, wenn möglich verbessert und auf einer Menge von Bildern getestet. Enthalten sind sowohl zufallsgenerierte Bilder, als auch aus geometrischen Formen synthetisierte und natürliche Bilder. Die Erkennung der auf die Bilder angewandten Fälschungstechniken beschäftigt sich vor allem mit Duplikationen, Einfügen und Interpolation von Bereichen.
Der Test dieser Implementierung konzentriert sich auf die absolute Effektivität und Effiienz gegen die gegebene Testmenge, betrachtet jedoch auch die spezifischen Vor- und Nachteile der ursprünglichen Algorithmen und der entwickelten Verbesserung. Ihre Ergebnisse, die sie auf den Testbildern erbringen, legen die Grundlage für eine Beurteilung der Algorithmen bezüglich Laufzeit und Effiienz.
Aufbauend auf diesen Analysen wird eine Bewertung der Algotihmen vorgenommen, die auch einen Ausblick auf mögliche Szenarien in der digitalen Bildbearbeitung und der Erkennung von Fälschungen für die nächsten Jahre geben soll.