Deep Scattering: Rendering Atmospheric Clouds with Radiance-Predicting Neural Networks

We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with high accuracy while using few coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds interactively. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, also for high-quality production of animated content.Comment: ACM Transactions on Graphics (Proceedings of SIGGRAPH Asia 2017

Herausforderung Sexuelle Bildung im Sachunterricht. Theoretische, empirische und praktische Perspektiven

Soll, darf oder muss das Thema Sexualität Gegenstand von Unterricht in der Grundschule sein? Die Autor*innen zeigen nicht nur die Herausforderungen auf, die sich im Zusammenhang mit dem Themenfeld ergeben. Anhand eines speziell entwickelten Tandemseminars beschreiben sie auch erste Ideen zur Professionalisierung angehender Sachunterrichtslehrkräfte im Kontext Sexueller Bildung/Prävention sexualisierter Gewalt. (DIPF/Orig.)This article summarizes the presentations and discussions of the symposium “Sexual Education [Sexuelle Bildung] in and for the Future”, which took place during the 31st annual conference of the GDSU and focuses on sexual education as an educational concept/topic that is relatively neglected and challenging in primary education and subject didactics. The article first summarizes the status quo of the didactic discourse on sexual education in primary schools and General Studies [Sachunterricht] and identifies desiderata and perspectives for empirical research on the topic. Based on a recent qualitative interview study (Coers 2019), requirements for teacher education regarding sexual education are then empirically substantiated. Finally, possibilities and challenges in the professionalization of teachers are presented using the example of a seminar on sexual education and prevention of sexualized violence. (DIPF/Orig.

Real-Time Neural Appearance Models

We present a complete system for real-time rendering of scenes with complex appearance previously reserved for offline use. This is achieved with a combination of algorithmic and system level innovations. Our appearance model utilizes learned hierarchical textures that are interpreted using neural decoders, which produce reflectance values and importance-sampled directions. To best utilize the modeling capacity of the decoders, we equip the decoders with two graphics priors. The first prior -- transformation of directions into learned shading frames -- facilitates accurate reconstruction of mesoscale effects. The second prior -- a microfacet sampling distribution -- allows the neural decoder to perform importance sampling efficiently. The resulting appearance model supports anisotropic sampling and level-of-detail rendering, and allows baking deeply layered material graphs into a compact unified neural representation. By exposing hardware accelerated tensor operations to ray tracing shaders, we show that it is possible to inline and execute the neural decoders efficiently inside a real-time path tracer. We analyze scalability with increasing number of neural materials and propose to improve performance using code optimized for coherent and divergent execution. Our neural material shaders can be over an order of magnitude faster than non-neural layered materials. This opens up the door for using film-quality visuals in real-time applications such as games and live previews.Comment: Equal contribution by the first six authors. Order determined by a rock-paper-scissors tournament. Project page: https://research.nvidia.com/labs/rtr/neural_appearance_models

Search for heavy resonances decaying into a vector boson and a Higgs boson in final states with charged leptons, neutrinos, and b quarks

Peer reviewe

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments

Event generators for high-energy physics experiments

We provide an overview of the status of Monte-Carlo event generators for high-energy particle physics. Guided by the experimental needs and requirements, we highlight areas of active development, and opportunities for future improvements. Particular emphasis is given to physics models and algorithms that are employed across a variety of experiments. These common themes in event generator development lead to a more comprehensive understanding of physics at the highest energies and intensities, and allow models to be tested against a wealth of data that have been accumulated over the past decades. A cohesive approach to event generator development will allow these models to be further improved and systematic uncertainties to be reduced, directly contributing to future experimental success. Event generators are part of a much larger ecosystem of computational tools. They typically involve a number of unknown model parameters that must be tuned to experimental data, while maintaining the integrity of the underlying physics models. Making both these data, and the analyses with which they have been obtained accessible to future users is an essential aspect of open science and data preservation. It ensures the consistency of physics models across a variety of experiments

Eine Big Data-Anwendung zum Identifizieren von Aktivitätsmustern und Sicherheitsproblemen

Diese Bachelorarbeit beschäftigt sich im Big Data Kontext mit der Analyse, Realisierung und Bereitstellung einer Infrastruktur. Auf die Infrastruktur setzen Anwendungen auf, die unterschiedliche Arten der Benutzung aufzeigen. Für die Datenakquisition werden ARP-Pakete in einem Netzwerk aufgezeichnet, die auf Grundlage verschiedener Szenarien analysiert und in einem Dashboard ausgewertet werden. Anhand einer Fallstudie wird die realisierte Architektur getestet und Ergebnisse erläutert.In context of big data this Bachelor-Thesis describes the analysis, realization and deployment of an infrastructure. A variety of applications are attached to the infrastructure to demonstrate different usage scenarios. For data collection purposes ARP-packets are recorded in a network, analysed and illustrated in a dashboard. The realized architecture is tested bases on a case study and the results are evaluated

Sexuelle Bildung in der (Grund-)Schule? – Reflexionen zu themenbezogenen Diskursen und Forschungen

Kapitel aus: Urban, M., Wienholz, S., & Khamis, C. (Eds.). (2022). Sexuelle Bildung für das Lehramt – Zur Notwendigkeit der Professionalisierung. Psychosozial-Verlag.https://www.psychosozial-verlag.de/catalog/product_info.php/cPath/20000/products_id/7825peerReviewedpublishedVersio

Dimension and Margin Bounds for Reflection-invariant Kernels ∗

A kernel over the Boolean domain is said to be reflection-invariant, if its value does not change when we flip the same bit in both arguments. (Many popular kernels have this property.) We study the geometric margins that can be achieved when we represent a specific Boolean function f by a classifier that employs a reflectioninvariant kernel. It turns out ‖ ˆ f‖ ∞ is an upper bound on the average margin. Furthermore, ‖ ˆ f‖−1 ∞ is a lower bound on the smallest dimension of a feature space associated with a reflection-invariant kernel that allows for a correct representation of f. This is, to the best of our knowledge, the first paper that exhibits margin and dimension bounds for specific functions (as opposed to function families). Several generalizations are considered as well. The main mathematical results are presented in a setting with arbitrary finite domains and a quite general notion of invariance.