Post-Keynesian alternative policies to curb macroeconomic imbalances in the Euro area

In this paper we outline alternative post-Keynesian policy recommendations addressing the problems of differential inflation, divergence in competitiveness and associated current account imbalances within the Euro area. We provide a basic framework in order to systematically address the related issues making use of Anthony P. Thirlwall's (1979, 2002) model of a 'balance-of-payments-constrained growth rate' (BPCGR). Based on this framework, we outline the required stance for alternative economic policies and then we discuss the implications for alternative monetary, wage/incomes and fiscal policies in the Euro area as a whole, as well as the consequences for structural and regional policies in the Euro area periphery, in particular

Batch Reinforcement Learning on the Industrial Benchmark: First Experiences

The Particle Swarm Optimization Policy (PSO-P) has been recently introduced and proven to produce remarkable results on interacting with academic reinforcement learning benchmarks in an off-policy, batch-based setting. To further investigate the properties and feasibility on real-world applications, this paper investigates PSO-P on the so-called Industrial Benchmark (IB), a novel reinforcement learning (RL) benchmark that aims at being realistic by including a variety of aspects found in industrial applications, like continuous state and action spaces, a high dimensional, partially observable state space, delayed effects, and complex stochasticity. The experimental results of PSO-P on IB are compared to results of closed-form control policies derived from the model-based Recurrent Control Neural Network (RCNN) and the model-free Neural Fitted Q-Iteration (NFQ). Experiments show that PSO-P is not only of interest for academic benchmarks, but also for real-world industrial applications, since it also yielded the best performing policy in our IB setting. Compared to other well established RL techniques, PSO-P produced outstanding results in performance and robustness, requiring only a relatively low amount of effort in finding adequate parameters or making complex design decisions

Implementing Scaled-Agile Frameworks at Non-Digital Born Companies - A Multiple Case Study

For traditional enterprises to harness the advantages of organizational agility, scaled-agile frameworks seem to be more appropriate to adopt agile practices at large scale. However, the adoption of agile practices often creates trade-offs between the implementation of an ideal theoretical framework and company-specific necessities. While extant research has covered the implications and challenges when adopting agile structures, our research focuses on the how and why of such trade-offs using Socio-Technical Systems Theory. Drawing on the results of an exploratory multiple case study, we reveal that companies either choose a top-down or bottom-up approach for implementation. While the first often is triggered by the need to increase customer centricity, the latter is mostly triggered by the need to increase the number of releases. Moreover, we found that the selected implementation approach has significant impact on the key design parameters for and the content of the implementation of scaled-agile frameworks

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

A Benchmark Environment Motivated by Industrial Control Problems

In the research area of reinforcement learning (RL), frequently novel and promising methods are developed and introduced to the RL community. However, although many researchers are keen to apply their methods on real-world problems, implementing such methods in real industry environments often is a frustrating and tedious process. Generally, academic research groups have only limited access to real industrial data and applications. For this reason, new methods are usually developed, evaluated and compared by using artificial software benchmarks. On one hand, these benchmarks are designed to provide interpretable RL training scenarios and detailed insight into the learning process of the method on hand. On the other hand, they usually do not share much similarity with industrial real-world applications. For this reason we used our industry experience to design a benchmark which bridges the gap between freely available, documented, and motivated artificial benchmarks and properties of real industrial problems. The resulting industrial benchmark (IB) has been made publicly available to the RL community by publishing its Java and Python code, including an OpenAI Gym wrapper, on Github. In this paper we motivate and describe in detail the IB's dynamics and identify prototypic experimental settings that capture common situations in real-world industry control problems

Recent developments in the characterization of superconducting films by microwaves

We describe and analyze selected surface impedance data recently obtained by different groups on cuprate, ruthenate and diboride superconducting films on metallic and dielectric substrates for fundamental studies and microwave applications. The discussion includes a first review of microwave data on MgB2, the weak-link behaviour of RABiTS-type YBa2Cu3O7-d tapes, and the observation of a strong anomalous power-dependence of the microwave losses in MgO at low temperatures. We demonstrate how microwave measurements can be used to investigate electronic, magnetic, and dielectric dissipation and relaxation in the films and substrates. The impact of such studies reaches from the extraction of microscopic information to the engineering of materials and further on to applications in power systems and communication technology.Comment: Invited contribution to EUCAS2001, accepted for publication in Physica C in its present for

SUrvival Control Chart EStimation Software in R: the success package

Monitoring the quality of statistical processes has been of great importance, mostly in industrial applications. Control charts are widely used for this purpose, but often lack the possibility to monitor survival outcomes. Recently, inspecting survival outcomes has become of interest, especially in medical settings where outcomes often depend on risk factors of patients. For this reason many new survival control charts have been devised and existing ones have been extended to incorporate survival outcomes. The R package success allows users to construct risk-adjusted control charts for survival data. Functions to determine control chart parameters are included, which can be used even without expert knowledge on the subject of control charts. The package allows to create static as well as interactive charts, which are built using ggplot2 (Wickham 2016) and plotly (Sievert 2020).Comment: 29 pages, 10 figures, guide for the R package success, see https://cran.r-project.org/package=succes