Experimentelle Untersuchungen zu Spielerleben und Risikobereitschaft bei Videorennspielen

Risikoverherrlichende Videorennspiele haben in den vergangenen Jahren vermehrt das Interesse der Forschung geweckt. In derartigen Videospielen werden die Folgen einer riskanten Fahrweise nicht nur verharmlost, sondern Spieler werden sogar für ihre Risikobereitschaft belohnt. Bei bekannten und viel verkauften Spieltiteln wie Need for Speed – Hot Pursuit schlüpfen die Spieler in die Rolle eines rücksichtslosen Rasers und versuchen sich auf graphisch realistisch dargestellten Rennstrecken mit gefährlichen und halsbrecherischen Manövern gegen ihre Kontrahenten durchzusetzen. Wie bei gewalthaltigen Videospielen liegt die Vermutung nahe, dass der häufige Konsum risikoverherrlichender Videorennspiele mit negativen Konsequenzen wie beispielsweise einer erhöhten Risikobereitschaft im Straßenverkehr einhergeht. Aufbauend auf den Erkenntnissen der vorliegenden experimentellen Studien in diesem Bereich wurden zwei Untersuchungen durchgeführt. In der ersten Untersuchung mit 272 Versuchsteilnehmern sollte überprüft werden, ob sich Auswirkungen des Rennspielkonsums auch nach einem dreitätigen Treatment nachweisen lassen. Die Probanden spielten an drei aufeinander folgenden Tagen für jeweils 20 Minuten entweder verschiedene klassische Rennsimulationen (Kontrollgruppe) oder unterschiedliche risikoverherrlichende Videorennspiele (Experimentalgruppe) und bearbeiteten direkt im Anschluss an das Treatment verschiedene Aufgaben zur Erfassung ihrer Risikobereitschaft. Die statistische Auswertung ergab keine signifikanten Gruppenunterschiede. Die zweite Untersuchung diente dazu, die möglichen Auswirkungen einer sozialen Wettbewerbssituation bei diesem Spielgenre zu überprüfen. Die insgesamt 75 Probanden wurden einer der vier realisierten Versuchsbedingungen zugewiesen und spielten für die Zeit von 20 Minuten allein oder gegen einen Konföderierten entweder eine Tennissimulation (Kontrollgruppe) oder ein risikoverherrlichendes Videorennspiel (Experimentalgruppe). Die Auswertung zeigte, dass die Probanden das Treatment abhängig von Spielinhalt und Spielsetting sehr unterschiedlich erlebten. Bedeutsame Unterschiede hinsichtlich der Risikobereitschaft stellten sich hingegen nicht ein. Mögliche Gründe für die durchweg nichtsignifikanten Ergebnisse in Bezug auf die Risikobereitschaft werden eingehend diskutiert. Aus Sicht des Autors sprechen die gewonnenen Befunde nicht zwingend gegen Medieneffekte bei risikoverherrlichenden Videorennspielen, sie verdeutlichen jedoch, dass bestehende Modellvorstellungen zur Medienwirkung überarbeitet und im deutschsprachigen Raum geeignete Testinstrumente zur Erfassung der Risikobereitschaft hervorgebracht und erprobt werden müssen

Point to the Hidden: Exposing Speech Audio Splicing via Signal Pointer Nets

Verifying the integrity of voice recording evidence for criminal investigations is an integral part of an audio forensic analyst's work. Here, one focus is on detecting deletion or insertion operations, so called audio splicing. While this is a rather easy approach to alter spoken statements, careful editing can yield quite convincing results. For difficult cases or big amounts of data, automated tools can support in detecting potential editing locations. To this end, several analytical and deep learning methods have been proposed by now. Still, few address unconstrained splicing scenarios as expected in practice. With SigPointer, we propose a pointer network framework for continuous input that uncovers splice locations naturally and more efficiently than existing works. Extensive experiments on forensically challenging data like strongly compressed and noisy signals quantify the benefit of the pointer mechanism with performance increases between about 6 to 10 percentage points.Comment: accepted at Interspeech 202

Investigation of inverse design of multilayer thin-films with conditional invertible Neural Networks

The task of designing optical multilayer thin-films regarding a given target is currently solved using gradient-based optimization in conjunction with methods that can introduce additional thin-film layers. Recently, Deep Learning and Reinforcement Learning have been been introduced to the task of designing thin-films with great success, however a trained network is usually only able to become proficient for a single target and must be retrained if the optical targets are varied. In this work, we apply conditional Invertible Neural Networks (cINN) to inversely designing multilayer thin-films given an optical target. Since the cINN learns the energy landscape of all thin-film configurations within the training dataset, we show that cINNs can generate a stochastic ensemble of proposals for thin-film configurations that that are reasonably close to the desired target depending only on random variables. By refining the proposed configurations further by a local optimization, we show that the generated thin-films reach the target with significantly greater precision than comparable state-of-the art approaches. Furthermore, we tested the generative capabilities on samples which are outside the training data distribution and found that the cINN was able to predict thin-films for out-of-distribution targets, too. The results suggest that in order to improve the generative design of thin-films, it is instructive to use established and new machine learning methods in conjunction in order to obtain the most favorable results

TMM-Fast: A Transfer Matrix Computation Package for Multilayer Thin-Film Optimization

Achieving the desired optical response from a multilayer thin-film structure over a broad range of wavelengths and angles of incidence can be challenging. An advanced thin-film structure can consist of multiple materials with different thicknesses and numerous layers. Design and optimization of complex thin-film structures with multiple variables is a computationally heavy problem that is still under active research. To enable fast and easy experimentation with new optimization techniques, we propose the Python package TMM-Fast which enables parallelized computation of reflection and transmission of light at different angles of incidence and wavelengths through the multilayer thin-film. By decreasing computational time, generating datasets for machine learning becomes feasible and evolutionary optimization can be used effectively. Additionally, the sub-package TMM-Torch allows to directly compute analytical gradients for local optimization by using PyTorch Autograd functionality. Finally, an OpenAi Gym environment is presented which allows the user to train reinforcement learning agents on the problem of finding multilayer thin-film configurations.Comment: Technical note, 8 pages, introduction to Python package TMM-Fast, Repository: https://github.com/MLResearchAtOSRAM/tmm_fast

Parameterized Reinforcement Learning for Optical System Optimization

Designing a multi-layer optical system with designated optical characteristics is an inverse design problem in which the resulting design is determined by several discrete and continuous parameters. In particular, we consider three design parameters to describe a multi-layer stack: Each layer's dielectric material and thickness as well as the total number of layers. Such a combination of both, discrete and continuous parameters is a challenging optimization problem that often requires a computationally expensive search for an optimal system design. Hence, most methods merely determine the optimal thicknesses of the system's layers. To incorporate layer material and the total number of layers as well, we propose a method that considers the stacking of consecutive layers as parameterized actions in a Markov decision process. We propose an exponentially transformed reward signal that eases policy optimization and adapt a recent variant of Q-learning for inverse design optimization. We demonstrate that our method outperforms human experts and a naive reinforcement learning algorithm concerning the achieved optical characteristics. Moreover, the learned Q-values contain information about the optical properties of multi-layer optical systems, thereby allowing physical interpretation or what-if analysis.Comment: Presented as a poster at the workshop on machine learning for engineering modeling, simulation and design @ NeurIPS 202

Playing Ping Pong with Light: Directional Emission of White Light

Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light

Directional emission of white light via selective amplification of photon recycling and Bayesian optimization of multi-layer thin films

Over the last decades, light-emitting diodes (LED) have replaced common light bulbs in almost every application, from flashlights in smartphones to automotive headlights. Illuminating nightly streets requires LEDs to emit a light spectrum that is perceived as pure white by the human eye. The power associated with such a white light spectrum is not only distributed over the contributing wavelengths but also over the angles of vision. For many applications, the usable light rays are required to exit the LED in forward direction, namely under small angles to the perpendicular. In this work, we demonstrate that a specifically designed multi-layer thin film on top of a white LED increases the power of pure white light emitted in forward direction. Therefore, the deduced multi-objective optimization problem is reformulated via a real-valued physics-guided objective function that represents the hierarchical structure of our engineering problem. Variants of Bayesian optimization are employed to maximize this non-deterministic objective function based on ray tracing simulations. Eventually, the investigation of optical properties of suitable multi-layer thin films allowed to identify the mechanism behind the increased directionality of white light: angle and wavelength selective filtering causes the multi-layer thin film to play ping pong with rays of light

Market-Driven Innovation

A new method for starting the iterative innovation process from the market side based on a sociological trend has been developed. It eliminates the traditional difference between the innovators and the sociological group that carries this trend, which can only be achieved by combining real-world innovation with innovation education. The method for market need discovery is presented as a step-by-step process with detailed reasoning, followed by a real-world example that details the outcomes at every step along the way. The example concludes with a detailed description of the outcome after the first innovation iteration cycle. The richness of the resulting concept demonstrates that an innovation process can be successfully started from the market side via the proposed method

Left atrial appendage volume is an independent predictor of atrial arrhythmia recurrence following cryoballoon pulmonary vein isolation in persistent atrial fibrillation

PurposePulmonary vein isolation (PVI) is the cornerstone of atrial fibrillation (AF) ablation in persistent AF (persAF), and cryoballoon PVI emerged as an initial ablation strategy. Symptomatic atrial arrhythmia recurrence following successful PVI in persAF is observed more frequently than in paroxysmal AF. Predictors for arrhythmia recurrence following cryoballoon PVI for persAF are not well described, and the role of left atrial appendage (LAA) anatomy is uncertain.MethodsPatients with symptomatic persAF and pre-procedural cardiac computed tomography angiography (CCTA) images undergoing initial second-generation cryoballoon (CBG2) were enrolled. Left atrial (LA), pulmonary vein (PV) and LAA anatomical data were assessed. Clinical outcome and predictors for atrial arrhythmia recurrence were evaluated by univariate and multivariate regression analysis.ResultsFrom May 2012 to September 2016, 488 consecutive persAF patients underwent CBG2-PVI. CCTA with sufficient quality for measurements was available in 196 (60.4%) patients. Mean age was 65.7 ± 9.5 years. Freedom from arrhythmia was 58.2% after a median follow-up of 19 (13; 29) months. No major complications occurred. Independent predictors for arrhythmia recurrence were LAA volume (HR 1.082; 95% CI, 1.032 to 1.134; p = 0.001) and mitral regurgitation ≥ grade 2 (HR, 2.49; 95% CI 1.207 to 5.126; p = 0.013). LA volumes ≥110.35 ml [sensitivity: 0.81, specificity: 0.40, area under the curve (AUC) = 0.62] and LAA volumes ≥9.75 ml (sensitivity: 0.56, specificity 0.70, AUC = 0.64) were associated with recurrence. LAA-morphology, classified as chicken-wing (21.9%), windsock (52.6%), cactus (10.2%) and cauliflower (15.3%), did not predict outcome (log-rank, p = 0.832).ConclusionLAA volume and mitral regurgitation were independent predictors for arrhythmia recurrence following cryoballoon ablation in persAF. LA volume was less predictive and correlated with LAA volume. LAA morphology did not predict the clinical outcome. To improve outcomes in persAF ablation, further studies should focus on treatment strategies for persAF patients with large LAA and mitral regurgitation