On Multi-Step Sensor Scheduling via Convex Optimization

Effective sensor scheduling requires the consideration of long-term effects and thus optimization over long time horizons. Determining the optimal sensor schedule, however, is equivalent to solving a binary integer program, which is computationally demanding for long time horizons and many sensors. For linear Gaussian systems, two efficient multi-step sensor scheduling approaches are proposed in this paper. The first approach determines approximate but close to optimal sensor schedules via convex optimization. The second approach combines convex optimization with a \BB search for efficiently determining the optimal sensor schedule.Comment: 6 pages, appeared in the proceedings of the 2nd International Workshop on Cognitive Information Processing (CIP), Elba, Italy, June 201

Experimental research on the development of Ceratium hirundinella O.F.Muller [Translation from: Z.Bot. 14, 337-371, 1922]

The most important aim of this study lay in filling in the great gap in our knowledge of the processes of germination in the Ceratium cyst and the early developmental stages in the standing stock of Ceratium hirundinella. contained rich cysts, we now succeeded extraordinarily well in pursuing the consistent development of Ceratium from the cyst to the completed cell. A series of experiments were carried out on the cysts and the juvenile stages of Ceratium, which showed very interesting results. The author presents in a general descriptive part the normal processes of germination in Ceratium cysts and the development of the juvenile stages in order to show in an experimental part the changes in form of C. hirundinella under the influence of temperature, light and varying salinities

Compatibility of neutron star masses and hyperon coupling constants

It is shown that the modern equations of state for neutron star matter based on microscopic calculations of symmetric and asymmetric nuclear matter are compatible with the lower bound on the maximum neutron-star mass for a certain range of hyperon coupling constants, which are constrained by the binding energies of hyperons in symmetric nuclear matter. The hyperons are included by means of the relativistic Hartree-- or Hartree--Fock approximation. The obtained couplings are also in satisfactory agreement with hypernuclei data in the relativistic Hartree scheme. Within the relativistic Hartree--Fock approximation hypernuclei have not been investigated so far.Comment: 12 pages, 3 figures. Dedicated to Prof. Georg Suessmann on the occasion of his 70th birthday. To be published in Zeitschrift fuer Naturforschung

Symmetric and asymmetric nuclear matter in the relativistic approach at finite temperatures

The properties of hot matter are studied in the frame of the relativistic Brueckner-Hartree-Fock theory. The equations are solved self-consistently in the full Dirac space. For the interaction we used the potentials given by Brockmann and Machleidt. The obtained critical temperatures are smaller than in most of the nonrelativistic investigations. We also calculated the thermodynamic properties of hot matter in the relativistic Hartree--Fock approximation, where the force parameters were adjusted to the outcome of the relativistic Brueckner--Hartree--Fock calculations at zero temperature. Here, one obtains higher critical temperatures, which are comparable with other relativistic calculations in the Hartree scheme.Comment: 8 pages, 9 figures, submitted in a shorter version to Phys. Rev.

Compensating linkage for main rotor control

A compensating linkage for the rotor control system on rotary wing aircraft is described. The main rotor and transmission are isolated from the airframe structure by clastic suspension. The compensating linkage prevents unwanted signal inputs to the rotor control system caused by relative motion of the airframe structure and the main rotor and transmission

Enhancing Decision Tree based Interpretation of Deep Neural Networks through L1-Orthogonal Regularization

One obstacle that so far prevents the introduction of machine learning models primarily in critical areas is the lack of explainability. In this work, a practicable approach of gaining explainability of deep artificial neural networks (NN) using an interpretable surrogate model based on decision trees is presented. Simply fitting a decision tree to a trained NN usually leads to unsatisfactory results in terms of accuracy and fidelity. Using L1-orthogonal regularization during training, however, preserves the accuracy of the NN, while it can be closely approximated by small decision trees. Tests with different data sets confirm that L1-orthogonal regularization yields models of lower complexity and at the same time higher fidelity compared to other regularizers.Comment: 8 pages, 18th IEEE International Conference on Machine Learning and Applications (ICMLA) 201

How sensitive is a neutrino factory to the angle θ13\theta_{13}?

We consider the impact of non-standard interactions of neutrinos (NSI) on the determination of neutrino mixing parameters at a neutrino factory using \pnu{e}\to\pnu{\mu} ``golden channels'' for the measurement of $\theta_{13}$. We show how a small residual NSI leads to a drastic loss in sensitivity in $\theta_{13}$, of up to two orders of magnitude. This can be somewhat overcome if two baselines are combined.Comment: 4 pages, 3 figure