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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.
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%).
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.
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.
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.
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
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.
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.
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.
Clathrates are candidate materials for thermoelectric applications because of a number of unique properties. The clathrate I phases in the Ba-Ni-Ge ternary system allow controlled variation of the charge carrier concentration by adjusting the Ni content. Depending on the Ni content, the physical properties vary from metal-like to insulator-like and show a transition from p-type to n-type conduction. Here we present first results on the characterization of millimeter-sized single crystals grown by the Bridgman technique. Single crystals with a composition of Ba8Ni3.5Ge42.1h0.4 show metallic behavior (dp/dT > 0) albeit with high resistivity at room temperature [p (300 K) = 1 mOhm cm]. The charge carrier concentration at 300 K, as determined from Hall-effect measurements, is 2.3 e-/unit cell. The dimensionless thermoelectric figure of merit estimated at 680 K is ZT ~ 0.2. Keywords Clathrates - thermoelectric material - intermetallic compound - nickel
We suggest a new method to compute the spectrum and wave functions of excited states. We construct a stochastic basis of Bargmann link states, drawn from a physical probability density distribution and compute transition amplitudes between stochastic basis states. From such transition matrix we extract wave functions and the energy spectrum. We apply this method toU(1)2+1 lattice gauge theory. As a test we compute the energy spectrum, wave functions and thermodynamical functions of the electric Hamiltonian and compare it with analytical results. We find excellent agreement. We observe scaling of energies and wave functions in the variable of time. We also present first results on a small lattice for the full Hamiltonian including the magnetic term.
Central elements of the Bologna declaration have been implemented in a huge variety of curricula in humanities, social sciences, natural sciences and engineering sciences at German universities. Overall the results have been nothing less than disastrous. Surprisingly, this seems to be the perfect time for German universities to talk about introducing a curriculum that is fully compatible with the Bologna declaration for medical education as well. However, German medical education does not have problems the Bologna declaration is intended to solve, such as quality, mobility, internationalization and employability. It is already in the Post-Bologna age.
Meeting Abstract : Deutsche Gesellschaft für Chirurgie. 125. Kongress der Deutschen Gesellschaft für Chirurgie. Berlin, 22.-25.04.2008 Einleitung: Ein wesentliches Ziel der modernen Perforatorlappen vom Unterbauch (DIEP-flap) für die Brustrekonstruktion nach Mammaamputation ist die Schonung der Rektusmuskulatur. Der Funktionserhalt der Muskulatur ist abhängig von der Präparationstechnik. In unserer Studie wird die Interaktion zwischen der Muskel- und Nervendurchtrennung und der postoperativen Muskelfunktion untersucht. Material und Methoden: Unser Patientenkollektiv umfasst 42 Patienten. Im Zeitraum von 6/04 bis 06/07 wurden 44 DIEP-Lappen an unserer Klink nach dem gleichen operativen Standard von unterschiedlichen Operateuren zur Brustrekonstruktion transferiert. Die Standards beinhalten die beidseitige Präparation der Perforatorgefäße des Unterbauches, der SIEA-Gefäße, die Auswahl der 2–4 kräftigsten Perforatoren einer Seite und die schonende Präparation der Rektusmuskulatur und der motorischen Nervenäste.In einer prospektiven monozentrischen Studie haben wir die Rektusmuskulatur präoperativ und 6 Monate postoperativ untersucht. Für die Funktionsanalyse wurde sowohl die Myosonografie der Rektusmuskulatur als auch eine klinischen Untersuchung angewandt. Intraoperativ wurde die Anzahl und Lokalisation der Perforatoren, die Länge der gespreizten Muskulatur, die Länge der durchtrennten Muskulatur und die Anzahl und Lokalisation der durchtrennten intramuskulären Nerven in einer Skizze eingetragen. Die Relation zwischen der intraoperativen Muskel- und Nervenschädigung und der postoperativen Funktion wurde analysiert. Ergebnisse: Bei der Hebung des DIEP – flaps wurden im Durchschnitt 10,8 cm Muskulatur gespreizt, 8,2 cm Muskulatur getunnelt und 2,5 cm Muskulatur durchtrennt. In 41% (18 Pat) wurde 1 motorischer Nervenast durchtrennt, in 27,3% (12 Pat) waren es 2 und in 13,6% (6 Pat) 3 Nervenäste. Bei der klinischen Untersuchung 6 Monate postoperativ hatten 8 Patientinnen noch funktionelle Störungen beim Heben schwerer Gegenstände. Myosonografisch fand sich bei 3 Patientinnen eine Funktionsminderung: 1 vollständiger Funktionsverlust der Muskulatur mit Relaxatio, 2 relevante Minderungen der Kontraktilität Bei keiner Patientin fand sich eine Bauchwandhernie. Bei allen Patientinnen mit einer Beeinträchtigung der Muskulatur waren mind. 2 motorische Nervenäste durchtrennt worden. Schlussfolgerung: Die klinische und myosonografische Funktionsanalyse der Bauchwand ermöglicht die Erstellung von Standards zur verbesserten Operationstechnik. Unsere Ergebnisse zeigen, dass die Durchtrennung von 2 oder mehr motorischen Nervenästen vermieden werden muß. Die Länge der durchtrennten und gespreizten Muskulatur ist dagegen von geringerer Bedeutung.
Einleitung: Die pathologische Stimulierbarkeit von Serum-Calcitonin (CT) im relativ niedrigen Bereich (über 100 bis 300 pg/ml) trennt nicht hinreichend zwischen C-Zell-Hyperplasie (CCH) und C-Zell-(Mikro-)Karzinom (CCC), bei Überwiegen der Fälle mit CCH. Der Schilddrüsenisthmus ist frei von C-Zellen (Lit. mult., eigene Studie). Dies führte zur Methode der ITBL , welche nun an einer größeren prospektiv dokumentierten Serie von Patienten evaluiert wird.
Material und Methoden: 102 Patienten mit präoperativ gering bis mäßig erhöhtem CT (stim.≥100 ≤400 pg/ml) wurden mit der Intention zur ITBL operiert. Bei 30 erfolgte die Komplettierung zur totalen Thyreoidektomie (TTX), davon 27 in gleicher Sitzung, im Fall von Malignität unter Einschluss der systematischen Lymphknotendissektion (LNX). Gründe zur Komplettierung waren Mikrokarzinome (12 medulläre, 7 differenzierte) oder benigne Isthmusknoten (n=11).
Ergebnisse: Bei allen 72 Patienten mit definitiver ITBL (darunter 2 Mikro-CCC, übrige CCH) lag, ebenso wie bei den 30 Patienten mit TTX, das postoperative CT unter der Messgrenze (unter 2 pg/ml), mit einer Ausnahme (3 pg/ml, nicht stimulierbar); maximal stim. CT war bei 5 der 72 Patienten im unteren Normbereich messbar (3 – 4,6 pg/ml), bei den übrigen ebenfalls unter der Messgrenze. Alle 102 Patienten waren "biochemisch geheilt".
Schlussfolgerung: Die ITBL hat sich mit hinreichender Sicherheit als optimale Operationsmethode für Fälle mit CCH erwiesen und ist bzgl. ihrer Radikalität der TTX gleichwertig, unter Belassung eines gesunden Schilddrüsenrestes (Isthmus) von funktioneller Relevanz (2 – 5 g).
Die Virtuelle Fachbibliothek Biologie (www.vifabio.de) bündelt die Recherche nach wissenschaftlich hochwertigen Quellen aus Bibliotheken, Aufsatzbanken und Internet. Zentrales Element von vifabio ist dabei der Virtuelle Katalog: Mit einer Suchanfrage werden mehrere Kataloge zoologisch bzw. ornithologisch relevanter Bibliotheken, Zeitschriftendatenbanken wie Zoological Record (Nationallizenz 1864 bis 2007 für Nutzer in akademischen Einrichtungen), BioLIS und der Aufsatzkatalog OLC, sowie Landesbibliographien und der Internetquellen-Führer von vifabio durchsucht. Verlinkungen zur Elektronischen Zeitschriftenbibliothek Regensburg (EZB), zum Lieferdienst subito sowie zum Karlsruher Virtuellen Katalog (KVK) erleichtern den Zugang zum Volltext oder zum gedruckten Exemplar. Weitere Module von vifabio wie der Internetquellen-Führer bzw. der Datenbank-Führer eröffnen zusätzliche Rechercheoptionen.
Towards correctness of program transformations through unification and critical pair computation
(2010)
Correctness of program transformations in extended lambda-calculi with a contextual semantics is usually based on reasoning about the operational semantics which is a rewrite semantics. A successful approach is the combination of a context lemma with the computation of overlaps between program transformations and the reduction rules, which results in so-called complete sets of diagrams. The method is similar to the computation of critical pairs for the completion of term rewriting systems. We explore cases where the computation of these overlaps can be done in a first order way by variants of critical pair computation that use unification algorithms. As a case study of an application we describe a finitary and decidable unification algorithm for the combination of the equational theory of left-commutativity modelling multi-sets, context variables and many-sorted unification. Sets of equations are restricted to be almost linear, i.e. every variable and context variable occurs at most once, where we allow one exception: variables of a sort without ground terms may occur several times. Every context variable must have an argument-sort in the free part of the signature. We also extend the unification algorithm by the treatment of binding-chains in let- and letrec-environments and by context-classes. This results in a unification algorithm that can be applied to all overlaps of normal-order reductions and transformations in an extended lambda calculus with letrec that we use as a case study.