Coaches` subjective talent criteria in top-level soccer

Introduction: In professional soccer, talent selection relies on the subjective judgment of scouts and coaches. To date, little is known about coaches´ “eye for talent” (Christensen, 2009, p. 379) and the nature of the subjective criteria they use to identify those players with the greatest potential to achieve peak performance in adulthood (Williams & Reilly, 2000). Drawing on a constructivist approach (Kelly, 1991), this study explores coaches´ subjective talent criteria. It is assumed that coaches are able to verbalise and specify their talent criteria, and that these are related to their talent selection decisions based on instinct. Methods: Participants and generation of data. Five national youth soccer coaches (Mage = 55.6; SD = 5.03) were investigated at three appointments: (1) talent selection decision based on instinct, (2) semi-structured inductive interview to elicit each coaches´ talent criteria in detail, (3) communicative validation and evaluation of the players by each coach using the repertory grid technique (Fromm, 2004). Data Analysis: Interviews were transcribed and summarized with regard to each specified talent criterion. Each talent criterion was categorized using a bottom-up-approach (meaning categorization, Kvale, 1996). The repertory grid data was analysed using descriptive statistics and correlation analysis. Results and Discussion: For each coach, six to nine talent criteria were elicited and specified. The subjective talent criteria include aspects of personality, cognitive perceptual skills, motor abilities, development, technique, social environment and physical constitution, which shows that the coaches use a multi-dimensional concept of talent. However, more than half of all criteria describe personality characteristics, in particular achievement motivation, volition and self-confidence. In contrast to Morris (2000), this result shows that coaches have a differentiated view of the personality characteristics required to achieve peak performance. As an indication of criterion validity, moderate to high correlations (.57 ≤ r ≤ .81) are found between the evaluations of the players according to the coaches´ talent criteria and their talent selection decision. The study shows that coaches are able to specify their subject talent criteria and that those criteria are strongly related to their instinctive selection decisions. References: Christensen, M. K. (2009). "An Eye for Talent": Talent Identification and the "Practical Sense" of Top-Level Soccer Coaches. Sociology of Sport Journal, 26, 365–382. Fromm, M. (2004). Introduction to the Repertory Grid Interview. Münster: Waxmann. Kelly, G. A. (1991). The Psychology of Personal Constructs: Volume One: Theory and personality. London: Routledge. Kvale, S. (1996). InterViews: An introduction to Qualitative Research Interviewing. Thousand Oaks: Sage. Morris, T. (2000). Psychological characteristics and talent identification in soccer. Journal of Sports Sciences, 18, 715–726. Williams, A. M., & Reilly, T. (2000). Talent identification and development in soccer. Journal of Sports Sciences, 18, 657–667.

Optimization of Schwinger pair production in colliding laser pulses

Recent studies of Schwinger pair production have demonstrated that the asymptotic particle spectrum is extremely sensitive to the applied field profile. We extend the idea of the dynamically assisted Schwinger effect from single pulse profiles to more realistic field configurations to be generated in an all-optical experiment searching for pair creation. We use the quantum kinetic approach to study the particle production and employ a multi-start method, combined with optimal control theory, to determine a set of parameters for which the particle yield in the forward direction in momentum space is maximized. We argue that this strategy can be used to enhance the signal of pair production on a given detector in an experimental setup.

Service level agreements-driven management of distributed applications in cloud computing environments

Advancements in cloud computing have enabled the proliferation of distributed applications, which require management and control of multiple services. However, without an efficient mechanism for scaling services in response to changing workload conditions, such as number of connected users, application performance might suffer, leading to violations of Service Level Agreements (SLA) and possible inefficient use of hardware resources. Combining dynamic application requirements with the increased use of virtualised computing resources creates a challenging resource Management context for application and cloud-infrastructure owners. In such complex environments, business entities use SLAs as a means for specifying quantitative and qualitative requirements of services. There are several challenges in running distributed enterprise applications in cloud environments, ranging from the instantiation of service VMs in the correct order using an adequate quantity of computing resources, to adapting the number of running services in response to varying external loads, such as number of users. The application owner is interested in finding the optimum amount of computing and network resources to use for ensuring that the performance requirements of all her/his applications are met. She/he is also interested in appropriately scaling the distributed services so that application performance guarantees are maintained even under dynamic workload conditions. Similarly, the infrastructure Providers are interested in optimally provisioning the virtual resources onto the available physical infrastructure so that her/his operational costs are minimized, while maximizing the performance of tenants’ applications. Motivated by the complexities associated with the management and scaling of distributed applications, while satisfying multiple objectives (related to both consumers and providers of cloud resources), this thesis proposes a cloud resource management platform able to dynamically provision and coordinate the various lifecycle actions on both virtual and physical cloud resources using semantically enriched SLAs. The system focuses on dynamic sizing (scaling) of virtual infrastructures composed of virtual machines (VM) bounded application services. We describe several algorithms for adapting the number of VMs allocated to the distributed application in response to changing workload conditions, based on SLA-defined performance guarantees. We also present a framework for dynamic composition of scaling rules for distributed service, which used benchmark-generated application Monitoring traces. We show how these scaling rules can be combined and included into semantic SLAs for controlling allocation of services. We also provide a detailed description of the multi-objective infrastructure resource allocation problem and various approaches to satisfying this problem. We present a resource management system based on a genetic algorithm, which performs allocation of virtual resources, while considering the optimization of multiple criteria. We prove that our approach significantly outperforms reactive VM-scaling algorithms as well as heuristic-based VM-allocation approaches.

SOMS: SurrOgate MultiStart algorithm for use with nonlinear programming for global optimization

SOMS is a general surrogate-based multistart algorithm, which is used in combination with any local optimizer to find global optima for computationally expensive functions with multiple local minima. SOMS differs from previous multistart methods in that a surrogate approximation is used by the multistart algorithm to help reduce the number of function evaluations necessary to identify the most promising points from which to start each nonlinear programming local search. SOMS’s numerical results are compared with four well-known methods, namely, Multi-Level Single Linkage (MLSL), MATLAB’s MultiStart, MATLAB’s GlobalSearch, and GLOBAL. In addition, we propose a class of wavy test functions that mimic the wavy nature of objective functions arising in many black-box simulations. Extensive comparisons of algorithms on the wavy testfunctions and on earlier standard global-optimization test functions are done for a total of 19 different test problems. The numerical results indicate that SOMS performs favorably in comparison to alternative methods and does especially well on wavy functions when the number of function evaluations allowed is limited.

Global Optimization with Sparse and Local Gaussian Process Models

We present a novel surrogate model-based global optimization framework allowing a large number of function evaluations. The method, called SpLEGO, is based on a multi-scale expected improvement (EI) framework relying on both sparse and local Gaussian process (GP) models. First, a bi-objective approach relying on a global sparse GP model is used to determine potential next sampling regions. Local GP models are then constructed within each selected region. The method subsequently employs the standard expected improvement criterion to deal with the exploration-exploitation trade-off within selected local models, leading to a decision on where to perform the next function evaluation(s). The potential of our approach is demonstrated using the so-called Sparse Pseudo-input GP as a global model. The algorithm is tested on four benchmark problems, whose number of starting points ranges from 102 to 104. Our results show that SpLEGO is effective and capable of solving problems with large number of starting points, and it even provides significant advantages when compared with state-of-the-art EI algorithms.