Learning, conditionals, causation

This dissertation is on conditionals and causation. In particular, we (i) propose a method of how an agent learns conditional information, and (ii) analyse causation in terms of a new type of conditional. Our starting point is Ramsey's (1929/1990) test: accept a conditional when you can infer its consequent upon supposing its antecedent. Inspired by this test, Stalnaker (1968) developed a semantics of conditionals. In Ch. 2, we define and apply our new method of learning conditional information. It says, roughly, that you learn conditional information by updating on the corresponding Stalnaker conditional. By generalising Lewis's (1976) updating rule to Jeffrey imaging, our learning method becomes applicable to both certain and uncertain conditional information. The method generates the correct predictions for all of Douven's (2012) benchmark examples and Van Fraassen's (1981) Judy Benjamin Problem. In Ch. 3, we prefix Ramsey's test by suspending judgment on antecedent and consequent. Unlike the Ramsey Test semantics by Stalnaker (1968) and Gärdenfors (1978), our strengthened semantics requires the antecedent to be inferentially relevant for the consequent. We exploit this asymmetric relation of relevance in a semantic analysis of the natural language conjunction 'because'. In Ch. 4, we devise an analysis of actual causation in terms of production, where production is understood along the lines of our strengthened Ramsey Test. Our analysis solves the problems of overdetermination, conjunctive scenarios, early and late preemption, switches, double prevention, and spurious causation -- a set of problems that still challenges counterfactual accounts of actual causation in the tradition of Lewis (1973c). In Ch. 5, we translate our analysis of actual causation into Halpern and Pearl's (2005) framework of causal models. As a result, our analysis is considerably simplified on the cost of losing its reductiveness. The upshot is twofold: (i) Jeffrey imaging on Stalnaker conditionals emerges as an alternative to Bayesian accounts of learning conditional information; (ii) the analyses of causation in terms of our strengthened Ramsey Test conditional prove to be worthy rivals to contemporary counterfactual accounts of causation

Proper motions for HST observations in three off-axis bulge fields

Aims. This is the second in a series of papers that attempt to unveil the kinematic structure of the Galactic bulge through studying radial velocities and proper motions. We report here ~15000 new proper motions for three low foreground-extinction off-axis fields of the Galactic bulge. Methods. Proper motions were derived from a combination of Hubble Space Telescope Wide Field Planetary Camera 2 (WFPC2) and Advanced Camera for Surveys (ACS) images taken 8 and 9 years apart, and ACS observations taken 9 and 10 years apart, and they reach accuracies better than 0.9 mas/yr for more than ~10000 objects with magnitudes F814W < 24. Results. The proper motion distributions in these fields are similar to those of Galactic minor axis bulge fields. We observe the rotation of main sequence stars below the turn-off within the Galactic bulge, as in the minor axis fields. Conclusions. Our stellar proper motions measurements show a significant bulge rotation for fields as far from the galactic plane as b=-8.Comment: 14 pages, 14 figures, published in Astronomy & Astrophysic

Learning, conditionals, causation

This dissertation is on conditionals and causation. In particular, we (i) propose a method of how an agent learns conditional information, and (ii) analyse causation in terms of a new type of conditional. Our starting point is Ramsey's (1929/1990) test: accept a conditional when you can infer its consequent upon supposing its antecedent. Inspired by this test, Stalnaker (1968) developed a semantics of conditionals. In Ch. 2, we define and apply our new method of learning conditional information. It says, roughly, that you learn conditional information by updating on the corresponding Stalnaker conditional. By generalising Lewis's (1976) updating rule to Jeffrey imaging, our learning method becomes applicable to both certain and uncertain conditional information. The method generates the correct predictions for all of Douven's (2012) benchmark examples and Van Fraassen's (1981) Judy Benjamin Problem. In Ch. 3, we prefix Ramsey's test by suspending judgment on antecedent and consequent. Unlike the Ramsey Test semantics by Stalnaker (1968) and Gärdenfors (1978), our strengthened semantics requires the antecedent to be inferentially relevant for the consequent. We exploit this asymmetric relation of relevance in a semantic analysis of the natural language conjunction 'because'. In Ch. 4, we devise an analysis of actual causation in terms of production, where production is understood along the lines of our strengthened Ramsey Test. Our analysis solves the problems of overdetermination, conjunctive scenarios, early and late preemption, switches, double prevention, and spurious causation -- a set of problems that still challenges counterfactual accounts of actual causation in the tradition of Lewis (1973c). In Ch. 5, we translate our analysis of actual causation into Halpern and Pearl's (2005) framework of causal models. As a result, our analysis is considerably simplified on the cost of losing its reductiveness. The upshot is twofold: (i) Jeffrey imaging on Stalnaker conditionals emerges as an alternative to Bayesian accounts of learning conditional information; (ii) the analyses of causation in terms of our strengthened Ramsey Test conditional prove to be worthy rivals to contemporary counterfactual accounts of causation

Operation of Modular Smart Grid Applications Interacting through a Distributed Middleware

IoT-functionality can broaden the scope of distribution system automation in terms of functionality and communication. However, it also poses risks regarding resource consumption and security. This article presents a field approved IoT-enabled smart grid middleware, which allows for flexible deployment and management of applications within smart grid operation. In the first part of the work, the resource consumption of the middleware is analyzed and current memory bottlenecks are identified. The bottlenecks can be resolved by introducing a new entity that allows to dynamically load multiple applications within one JVM. The performance was experimentally tested and the results suggest that its application can significantly reduce the applications' memory footprint on the physical device. The second part of the study identifies and discusses potential security threats, with a focus on attacks stemming from malicious software applications within the framework. In order to prevent such attacks a proxy based prevention mechanism is developed and demonstrated

Detection and Selection of Behavioral Patterns Using Theme: A Concrete Example in Grassroots Soccer

Observational methodology provides a rigorous yet flexible framework for capturing behaviors over time to allow for the performance of subsequent diachronic analyses of the data captured. Theme is a specialized software program that detects hidden temporal behavioral patterns (T-patterns) within data sets. It is increasingly being used to analyze performance in soccer and other sports. The aim of this study was to show how to select and interpret T-patterns generated by the application of three “quantitative” sort options in Theme and three “qualitative” filters established by the researchers. These will be used to investigate whether 7-a-side (F7) or 8-a-side (F8) soccer is best suited to the learning and skills development needs of 7- and 8-year-old male soccer players. The information contained in the T-patterns generated allowed us to characterize patterns of play in children in this age group. For both formats, we detected technical-tactical behaviors showing that children of this age have difficulty with first-touch actions and controlling the ball after a throw-in. We also found that ball control followed by a pass or a shot at the goal are common in the central corridor of the pitch. Further, depth of play is achieved by ball control, followed by dribbling and a pass or shot. In F8, we saw that depth of play was achieved through ball control, followed by dribbling and passing of one or more opponents leading to a pass or shot. However, in F7, we saw that players succeeded in advancing from their goal area to the rival goal area through a sequence of actions.Secretaria de Estado de Investigacion, Desarrollo e Innovacion del Ministerio de Economia y Competitividad DEP2015-66069-P Avances metodologicos y tecnologicos en el estudio observacional del comportamiento deportivo (Secretaria de Estado de Investigacion, Desarrollo e Innovacion del Ministerio de Economia y Competitividad) PSI2015-71947-REDT University of La RiojaPeer Reviewe