On the Design of LQR Kernels for Efficient Controller Learning

Finding optimal feedback controllers for nonlinear dynamic systems from data is hard. Recently, Bayesian optimization (BO) has been proposed as a powerful framework for direct controller tuning from experimental trials. For selecting the next query point and finding the global optimum, BO relies on a probabilistic description of the latent objective function, typically a Gaussian process (GP). As is shown herein, GPs with a common kernel choice can, however, lead to poor learning outcomes on standard quadratic control problems. For a first-order system, we construct two kernels that specifically leverage the structure of the well-known Linear Quadratic Regulator (LQR), yet retain the flexibility of Bayesian nonparametric learning. Simulations of uncertain linear and nonlinear systems demonstrate that the LQR kernels yield superior learning performance.Comment: 8 pages, 5 figures, to appear in 56th IEEE Conference on Decision and Control (CDC 2017

Automatic LQR Tuning Based on Gaussian Process Global Optimization

This paper proposes an automatic controller tuning framework based on linear optimal control combined with Bayesian optimization. With this framework, an initial set of controller gains is automatically improved according to a pre-defined performance objective evaluated from experimental data. The underlying Bayesian optimization algorithm is Entropy Search, which represents the latent objective as a Gaussian process and constructs an explicit belief over the location of the objective minimum. This is used to maximize the information gain from each experimental evaluation. Thus, this framework shall yield improved controllers with fewer evaluations compared to alternative approaches. A seven-degree-of-freedom robot arm balancing an inverted pole is used as the experimental demonstrator. Results of a two- and four-dimensional tuning problems highlight the method's potential for automatic controller tuning on robotic platforms.Comment: 8 pages, 5 figures, to appear in IEEE 2016 International Conference on Robotics and Automation. Video demonstration of the experiments available at https://am.is.tuebingen.mpg.de/publications/marco_icra_201

On Probation. An Experimental Analysis

Does probation pay a double dividend? Society saves the cost of incarceration, and convicts preserve their liberty. But does probation also reduce the risk of recidivism? In a meta-study we show that the field evidence is inconclusive. Moreover it struggles with an identification problem: those put on probation are less likely to recidivate in the first place. We therefore complement the field evidence by a lab experiment that isolates the definitional feature of probation: the first sanction is conditional on being sanctioned again during the probation period. We find that probationers contribute less to a joint project; punishment cost is higher; efficiency is lower; inequity is higher. While experimental subjects are on probation, they increase their contributions to a joint project. However, once the probation period expires, they reduce their contributions. While in the aggregate these two effects almost cancel out, critically those not punished themselves do trust the institution less if punishment does not become effective immediately.probation, recidivism, public goods, punishment, experimental economics

Can the Balanced Scorecard Help in Designing Conference Calls? The Effect of Balanced Information Composition on the Cost of Capital

Most recent studies on conference calls focus on the costs for firms that can arise from the calls' open nature. We study the benefits of conference calls and hypothesize that firms could use the balanced scorecard concept as a framework for presenting the information (i.e. balanced information composition) in conference calls to lower the cost of capital. Our results show a negative association between a more balanced information composition in conference calls and a firm's cost of capital. Additional tests substantiate that the effect of such a balanced information composition on the cost of capital is driven by a reduction in information asymmetry. Overall, the findings suggest that firms can benefit from the balanced scorecard concept by using it as a framework for preparing their conference calls

Protein dynamics in the reductive activation of a B12-containing enzyme

B12-dependent proteins are involved in methyl transfer reactions ranging from the biosynthesis of methionine in humans to the formation of acetyl-CoA in anaerobic bacteria. During their catalytic cycle, they undergo large conformational changes to interact with various proteins. Recently, the crystal structure of the B12-containing corrinoid iron–sulfur protein (CoFeSP) in complex with its reductive activator (RACo) was determined, providing a first glimpse of how energy is transduced in the ATP-dependent reductive activation of corrinoid-containing methyltransferases. The thermodynamically uphill electron transfer from RACo to CoFeSP is accompanied by large movements of the cofactor-binding domains of CoFeSP. To refine the structure-based mechanism, we analyzed the conformational change of the B12-binding domain of CoFeSP by pulsed electron–electron double resonance and Förster resonance energy transfer spectroscopy. We show that the site-specific labels on the flexible B12-binding domain and the small subunit of CoFeSP move within 11 Å in the RACo:CoFeSP complex, consistent with the recent crystal structures. By analyzing the transient kinetics of formation and dissociation of the RACo:CoFeSP complex, we determined values of 0.75 μM–1 s–1 and 0.33 s–1 for rate constants kon and koff, respectively. Our results indicate that the large movement observed in crystals also occurs in solution and that neither the formation of the protein encounter complex nor the large movement of the B12-binding domain is rate-limiting for the ATP-dependent reductive activation of CoFeSP by RACo

Environmental management control systems:Exploring the economic motivation behind their implementation

Environmental management control systems (EMCSs) effectively integrate environmental objectives into corporate decision-making, yet implementation costs may discourage their adoption. To understand firms’ economic motivation for implementing EMCSs, we theorize that internal and external factors drive both their economic performance and the decision to implement EMCSs. We argue that the environmental costs induced by firms’ pollution intensity drive the economic benefits of EMCSs as well as their implementation. Additionally, we suggest that this relationship depends on society's environmental awareness. By introducing an archival measure of EMCS implementation, we test these hypotheses on a longitudinal dataset of European and US firms. Our results support the argument that environmental costs drive EMCSs’ economic benefits and implementation. We also find that environmental awareness in societies influences the impact of environmental costs. Our study highlights the importance of environmental awareness in society for aligning environmental and economic goals and thus to increase corporate environmentalism.</p

A new type of quantum speed meter interferometer: measuring speed to search for intermediate mass black holes

The recent discovery of gravitational waves (GW) by LIGO has impressively launched the novel field of gravitational astronomy and it allowed us to glimpse at exciting objects we could so far only speculate about. Further sensitivity improvements at the low frequency end of the detection band of future GW observatories rely on quantum non-demolition (QND) methods to suppress fundamental quantum fluctuations of the light fields used to readout the GW signal. Here we invent a novel concept of how to turn a conventional Michelson interferometer into a QND speed meter interferometer with coherently suppressed quantum back-action noise by using two orthogonal polarisations of light and an optical circulator to couple them. We carry out a detailed analysis of how imperfections and optical loss influence the achievable sensitivity and find that the configuration proposed here would significantly enhance the low frequency sensitivity and increase the observable event rate of binary black hole coalescences in the range of $10^2-10^3 M_\odot$ by a factor of up to $\sim300$.Comment: 8 pages, 5 figures. Modified figures and text in v

On smoothness of Black Saturns

We prove smoothness of the domain of outer communications (d.o.c.) of the Black Saturn solutions of Elvang and Figueras. We show that the metric on the d.o.c. extends smoothly across two disjoint event horizons with topology R x S^3 and R x S^1 x S^2. We establish stable causality of the d.o.c. when the Komar angular momentum of the spherical component of the horizon vanishes, and present numerical evidence for stable causality in general.Comment: 47 pages, 5 figure

People’s reactions to decisions by human vs. algorithmic decision-makers: the role of explanations and type of selection tests

Research suggests that people prefer human over algorithmic decision-makers at work. Most of these studies, however, use hypothetical scenarios and it is unclear whether such results replicate in more realistic contexts. We conducted two between-subjects studies (N=270; N=183) in which the decision-maker (human vs. algorithmic, Study 1 and 2), explanations regarding the decision- process (yes vs. no, Study 1 and 2), and the type of selection test (requiring human vs. mechanical skills for evaluation, Study 2) were manipulated. While Study 1 was based on a hypothetical scenario, participants in pre-registered Study 2 volunteered to participate in a qualifying session for an attractively remunerated product test, thus competing for real incentives. In both studies, participants in the human condition reported higher levels of trust and acceptance. Providing explanations also positively influenced trust, acceptance, and perceived transparency in Study 1, while it did not exert any effect in Study 2. Type of the selection test affected fairness ratings, with higher ratings for tests requiring human vs. mechanical skills for evaluation. Results show that algorithmic decision-making in personnel selection can negatively impact trust and acceptance both in studies with hypothetical scenarios as well as studies with real incentives