State Observation and Diagnosis of Discrete-Event Systems Described by Stochastic Automata

The problems ofstate observation and diagnosis are solved for discrete–eventsystems, which are described by stochastic automata. As manysystems are not observable in the sense that it is possible toreconstruct the state unambiguously, the observation problemis set up as the problem of determining the smallest possibleset of states that are compatible with the measured input andoutput sequences. The diagnostic problem is shown to be, in principle,an observation problem. Conditions for the observability anddiagnosability of stochastic automata are presented. The resultsare illustrated by examples.     

Learning with "relevance": using a third factor to stabilize Hebbian learning

It is a well-known fact that Hebbian learning is inherently unstable because of its self-amplifying terms: the more a synapse grows, the stronger the postsynaptic activity, and therefore the faster the synaptic growth. This unwanted weight growth is driven by the autocorrelation term of Hebbian learning where the same synapse drives its own growth. On the other hand, the cross-correlation term performs actual learning where different inputs are correlated with each other. Consequently, we would like to minimize the autocorrelation and maximize the cross-correlation. Here we show that we can achieve this with a third factor that switches on learning when the autocorrelation is minimal or zero and the cross-correlation is maximal. The biological counterpart of such a third factor is a neuromodulator that switches on learning at a certain moment in time. We show in a behavioral experiment that our three-factor learning clearly outperforms classical Hebbian learning.     

On the analysis of stochastic divide and conquer algorithms

This paper develops general tools for the analysis of stochastic divide and conquer algorithms. We concentrate on the average performance and the distribution of the running time of the algorithm. As a special example we analyse the average performance and the running time distribution of the (2k + 1)-median version of Quicksort. Online publication October 13, 2000.     

The metabolism of fenazox in tomato plants

Results from investigations on the metabolism of 14C-Fenazox show that in 6-8 weeks old tomato-plants (sort "Harzfeuer") the agent undergoes a biotransformation. After chromatographic separation the structure of the biotransformation products was elucidated by comparison to authentic test substances, by derivatization as well as by means of physico-chemical methods. With that, the following metabolites were identified: non-transformed Fenazox, o-hydroxyazobenzene, o-hydroxyaniline, and p-hydroxyaniline. Probably, the agent is first transformed enzymatically or non-enzymatically into o-hydroxyazobenzene, then follows a reductive cleavage into o-hydroxyaniline and aniline. In its turn, the latter is transformed into o-hydroxy- and p-hydroxyaniline. Experiments in the field of transformation kinetics indicated that the concentration of the applied Fenazox rapidly decreases and that the first transformation product o-hydroxyazobenzene in the beginning increases and then again decreases in its concentration. With increasing application time it comes to an increase of the non-extractable residues.     

Magnetization and torque measurements on rotating niobium cylinders and ellipsoids

The magnetization and torque of niobium samples of different shapes rotating in a magnetic field are studied with emphasis on the behavior near the upper critical field H _c2. The observed rotational magnetization is free of hysteresis and is found to represent a good approximation to the thermodynamic equilibrium magnetization of the samples if pinning is not too strong and the frequency of rotation is sufficiently low. The viscous contribution to the torque exhibits a step at H _c2whose dependence on sample geometry is in good agreement with predictions derived under the simple assumption of a homogeneous arrangement of parallel straight vortices in the rotating sample.     

Computing turning points of curves implicitly defined by nonlinear equations depending on a parameter

Let the space curveL be defined implicitly by the (n, n+1) nonlinear systemH(u)=0. A new direct Newton-like method for computing turning points ofL is described that requires per step only the evaluation of one Jacobian and 5 function values ofH. Moreover, a linear system of dimensionn+1 with 4 different right hand sides has to be solved per step. Under suitable conditions the method is shown to converge locally withQ-order two if a certain discretization stepsize is appropriately chosen. Two numerical examples confirm the theoretical results.     

The influence of the extraction voltage on the energetic electron population of an electron cyclotron resonance ion source plasma

An influence of the extraction voltage on the high energy slope of bremsstrahlung radiation spectra has been reported in ECRIS experiments, which is not well understood so far. In order to provide more detailed data on this effect, we have measured bremsstrahlung radiation spectra accompanying especially the evolution of highly charge ions (i.e., by monitoring the Ar(14+) charge state) as the extraction voltage is changed from 0 to 20 kV, in dedicated experiments at the Frankfurt 14 GHz-ECRIS.     

Exploring US Business Cycles with Bivariate Loops Using Penalized Spline Regression

The phrase business cycle is usually used for short term fluctuations in macroeconomic time series. In this paper we focus on the estimation of business cycles in a bivariate manner by fitting two series simultaneously. The underlying model is thereby nonparametric in that no functional form is prespecified but smoothness of the functions are assumed. The functions are then estimated using penalized spline estimation. The bivariate approach will allow to compare business cycles, check and compare phase lengths and visualize this in forms of loops in a bivariate way. Moreover, the focus is on separation of long and short phase fluctuation, where only the latter is the classical business cycle while the first is better known as Friedman or Goodwin cycle, respectively. Again, we use nonparametric models and fit the functional shape with penalized splines. For the separation of long and short phase components we employ an Akaike criterion.