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

Interregional Redistribution and Budget Institutions under Asymmetric Information

Empirical evidence from the U.S. and the European Union suggests that regions which contribute to interregional redistribution face weaker borrowing constraints than regions which benefit from interregional redistribution. This paper presents an argument in favor of such differentiated budgetary institutions. It develops a two-period model of a federation consisting of two types of regions. The federal government redistributes from one type of regions (contributors) to the other type (recipients). It is shown that a fiscal constitution with lax budget rules for contributors and strict budget rules for recipients solves the self-selection problem the federal government faces in the presence of asymmetric information regarding exogenous characteristics of the regions.asymmetric information, interregional redistribution, borrowing rules

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

Nonlinear Gaussian Filtering : Theory, Algorithms, and Applications

By restricting to Gaussian distributions, the optimal Bayesian filtering problem can be transformed into an algebraically simple form, which allows for computationally efficient algorithms. Three problem settings are discussed in this thesis: (1) filtering with Gaussians only, (2) Gaussian mixture filtering for strong nonlinearities, (3) Gaussian process filtering for purely data-driven scenarios. For each setting, efficient algorithms are derived and applied to real-world problems

Probabilistic Framework for Sensor Management

A probabilistic sensor management framework is introduced, which maximizes the utility of sensor systems with many different sensing modalities by dynamically configuring the sensor system in the most beneficial way. For this purpose, techniques from stochastic control and Bayesian estimation are combined such that long-term effects of possible sensor configurations and stochastic uncertainties resulting from noisy measurements can be incorporated into the sensor management decisions

Proceedings of the 2009 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory

The joint workshop of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB, Karlsruhe, and the Vision and Fusion Laboratory (Institute for Anthropomatics, Karlsruhe Institute of Technology (KIT)), is organized annually since 2005 with the aim to report on the latest research and development findings of the doctoral students of both institutions. This book provides a collection of 16 technical reports on the research results presented on the 2009 workshop

Optimal Design of Intergovernmental Grants under Asymmetric Information

This paper develops a theoretical explanation why it may be optimal for higher-level governments to pay categorical block grants or closed-ended matching grants to local governments. We consider a federation with two types of local governments which differ in the cost of providing public goods. The federal government redistributes between jurisdictions, but cannot observe the type of a jurisdiction. In this asymmetric information setting it is shown that the second-best optimum can be decentralized with the help of categorical block grants and closed-ended matching grants, but not with unconditional block grants or open-ended matching grants.asymmetric information, categorical block grants, closed-ended matching grants