Time efficient optimization of instance based problems with application to tone onset detection

A time efficient optimization technique for instance based problems is proposed, where for each parameter setting the target function has to be evaluated on a large set of problem instances. Computational time is reduced by beginning with a performance estimation based on the evaluation of a representative subset of instances. Subsequently, only promising settings are evaluated on the whole data set. As application a comprehensive music onset detection algorithm is introduced where several numerical and categorical algorithm parameters are optimized simultaneously. Here, problem instances are music pieces of a data base. Sequential model based optimization is an appropriate technique to solve this optimization problem. The proposed optimization strategy is compared to the usual model based approach with respect to the goodness measure for tone onset detection. The performance of the proposed method appears to be competitive with the usual one while saving more than 84% of instance evaluation time on average. One other aspect is a comparison of two strategies for handling categorical parameters in Kriging based optimization

Comparison of classical and sequential design of experiments in note onset detection

Design of experiments is an established approach to parameter optimization of industrial processes. In many computer applications however it is usual to optimize the parameters via genetic algorithms. The main idea of this work is to apply design of experiment’s techniques to the optimization of computer processes. The major problem here is finding a compromise between model validity and costs, which increase with the number of experiments. The second relevant problem is choosing an appropriate model, which describes the relationship between parameters and target values. One of the recent approaches here is model combination, which can be used in sequential designs in order to improve automatic prediction of the next trial point. In this paper a musical note onset detection algorithm will be optimized using sequential parameter optimization with model combination. It will be shown that parameter optimization via design of experiments leads to better values of the target variable than usual parameter optimization via grid search or genetic optimization algorithms. Furthermore, the results of this application study reveal, whether the combination of many models brings improvements in finding the optimal parameter setting

Efficient global optimization: Motivation, variations and applications

A popular optimization method of a black box objective function is Efficient Global Optimization (EGO), also known as Sequential Model Based Optimization, SMBO, with kriging and expected improvement. EGO is a sequential design of experiments aiming at gaining as much information as possible from as few experiments as feasible by a skillful choice of the factor settings in a sequential way. In this paper we will introduce the standard procedure and some of its variants. In particular, we will propose some new variants like regression as a modeling alternative to kriging and two simple methods for the handling of categorical variables, and we will discuss focus search for the optimization of the infill criterion. Finally, we will give relevant examples for the application of the method. Moreover, in our group, we implemented all the described methods in the publicly available R package mlrMBO

The Eco-Efficiency Premium Puzzle

There exists a widespread consensus among mainstream academics and investors that socially responsible investing (SRI) leads to inferior, rather than superior, portfolio performance. Using Innovest’s well-established corporate ecoefficiency scores, we provide evidence to the contrary. We compose two equity portfolios that differ in eco-efficiency characteristics and find that our highranked portfolio provided substantially higher average returns compared to its low-ranked counterpart over the period 1995-2003. Using a wide range of performance attribution techniques to address common methodological concerns, we show that this performance differential cannot be explained by differences in market sensitivity, investment style, or industry-specific components. We finally investigate whether this eco-efficiency premium puzzle withstands the inclusion of transaction costs scenarios, and evaluate how excess returns can be earned in a practical setting via a best-in-class stock selection strategy. The results remain significant under all levels of transactions costs, thus suggesting that the incremental benefits of SRI can be substantial

Compound heterozygosity for TNXB genetic variants in a mixed-breed dog with Ehlers-Danlos syndrome.

The Ehlers-Danlos syndromes (EDSs) are a heterogeneous group of inherited connective tissue disorders characterized by skin hyperextensibility, joint hypermobility and tissue fragility. Inherited disorders similar to human EDS have been reported in different mammalian species. In the present study, we investigated a female mixed-breed dog with clinical signs of EDS. Whole-genome sequencing of the affected dog revealed two missense variants in the TNXB gene, encoding the extracellular matrix protein tenascin XB. In humans, TNXB genetic variants cause classical-like EDS or the milder hypermobile EDS. The affected dog was heterozygous at both identified variants. Each variant allele was transmitted from one of the case's parents, consistent with compound heterozygosity. Although one of the variant alleles, XM_003431680.3:c.2012G>A, p.(Ser671Asn), was private to the family of the affected dog and absent from whole-genome sequencing data of 599 control dogs, the second variant allele, XM_003431680.3:c.2900G>A, p.(Gly967Asp), is present at a low frequency in the Chihuahua and Poodle population. Given that TNXB is a functional candidate gene for EDS, we suggest that compound heterozygosity for the identified TNXB variants may have caused the EDS-like phenotype in the affected dog. Chihuahuas and Poodles should be monitored for EDS cases, which might confirm the hypothesized pathogenic effect of the segregating TNXB variant

Industrial Data Science: Developing a Qualification Concept for Machine Learning in Industrial Production

The advent of Industry 4.0 and the availability of large data storage systems lead to an increasing demand for specially educated data-oriented professionals in industrial production. The education of these specialists should combine elements from three fields: Industrial engineering, data analysis and data administration. However, a comprehensive education program incorporating all three elements has not yet been established in Germany. The aim of the acquired research project, titled “Industrial Data Science” is to develop and implement a qualification concept for Machine Learning based on demands coming up in industrial environments. The concept is targeted at two groups: Advanced students from any of the three mentioned fields (Mechanical Engineering, Statistics, Computer Science) and industrial professionals. In the qualification concept the needs of industrial companies are considered. Therefore, a survey was created to inquire the use and potentials of Machine Learning and the requirements for future Data Scientists in industrial production. The evaluation of the survey and the resulting conclusions affecting the qualification concept are presented in this paper

Efficient Global Optimization: Motivation, Variations, and Applications

A popular optimization method of a black box objective function is Efficient Global Optimization (EGO), also known as Sequential Model Based Optimization, SMBO, with kriging and expected improvement. EGO is a sequential design of experiments aiming at gaining as much information as possible from as few experiments as feasible by a skillful choice of the factor settings in a sequential way. In this paper we will introduce the standard procedure and some of its variants. In particular, we will propose some new variants like regression as a modeling alternative to kriging and two simple methods for the handling of categorical variables, and we will discuss focus search for the optimization of the infill criterion. Finally, we will give relevant examples for the application of the method. Moreover, in our group, we implemented all the described methods in the publicly available R package mlrMBO

Practices to support co-design processes: A case-study of co-designing a program for children with parents with a mental health problem in the Austrian region of Tyrol

Forms of collaborative knowledge production, such as community-academic partnerships (CAP), have been increasingly used in health care. However, instructions on how to deliver such processes are lacking. We aim to identify practice ingredients for one element within a CAP, a 6-month co-design process, during which 26 community- and 13 research-partners collaboratively designed an intervention programme for children whose parent have a mental illness. Using 22 published facilitating and hindering factors for CAP as the analytical framework, eight community-partners reflected on the activities which took place during the co-design process. From a qualitative content analysis of the data, we distilled essential practices for each CAP factor. Ten community- and eight research-partners revised the results and co-authored this article. We identified 36 practices across the 22 CAP facilitating or hindering factors. Most practices address more than one factor. Many practices relate to workshop design, facilitation methods, and relationship building. Most practices were identified for facilitating ‘trust among partners’, ‘shared visions, goals and/or missions’, ‘effective/frequent communication’, and ‘well-structured meetings’. Fewer practices were observed for ‘effective conflict resolution’, ‘positive community impact’ and for avoiding ‘excessive funding pressure/control struggles’ and ‘high burden of activities’. Co-designing a programme for mental healthcare is a challenging process that requires skills in process management and communication. We provide practice steps for delivering co-design activities. However, practitioners may have to adapt them to different cultural contexts. Further research is needed to analyse whether co-writing with community-partners results in a better research output and benefits for participants