The Iray Light Transport Simulation and Rendering System

While ray tracing has become increasingly common and path tracing is well understood by now, a major challenge lies in crafting an easy-to-use and efficient system implementing these technologies. Following a purely physically-based paradigm while still allowing for artistic workflows, the Iray light transport simulation and rendering system allows for rendering complex scenes by the push of a button and thus makes accurate light transport simulation widely available. In this document we discuss the challenges and implementation choices that follow from our primary design decisions, demonstrating that such a rendering system can be made a practical, scalable, and efficient real-world application that has been adopted by various companies across many fields and is in use by many industry professionals today

Declarative modeling based on knowledge

Les nouvelles technologies de l'image 3D permettent la création de mondes virtuels et des créatures qui les peuplent avec un tel niveau de détails, que pour les effets spéciaux de cinéma, il est difficile de distinguer les éléments sont générés par ordinateur. Cependant, cette technologie est dans les mains habiles de designers, artistes et programmeurs, pour lesquels il faut des semaines à plusieurs années pour se former aux outils et obtenir ces résultats. La Modélisation Déclarative est une méthode qui permet de créer des modèles en fournissant les propriétés donnant la description des composants du modèle. Appliquée à l’infographie, la modélisation déclarative est utilisée pour générer le monde virtuel, en déterminant le contexte nécessaire à l'animation et à la conception de la scène, en calculant la position de chaque objet relativement aux relations spatiales, et en générant le rendu de la scène, utilisé par une système d'animation et de visualisation. Ce mémoire présente les travaux de recherche consacrés à l'utilisation de la modélisation déclarative pour créer des environnements virtuels, en tirant partie des connaissances sur le contexte de la scène. Les connaissances sont utilisées afin de faciliter la tâche de description, en automatisant ce qui peut être déduit, comme les usages et les fonctionnalités habituelles. Elles sont également fondamentales pour que le résultat produit corresponde le mieux possible à ce qui est attendu par le concepteur à partir de la description fournie. Les connaissances sont enfin nécessaires pour faciliter la transition entre le modèle de données et l'architecture qui aura la charge d'animer et de faire évoluer la scène.Modern technology has allowed the creation and presentation or VirtualWorlds and creatures with such a high level of detail, that when used in movies, sometimes it is difficult to tell which elements arecomputer-generated and which not. Also, video-games had reached a level close to photographicrealism. However, such technology is at the hands of skillful designer, artists, and programmers, for whom ittakes from weeks to years to complete these results.Declarative modeling is a method which allows to create models specifying just a few properties for the model’s components. Applied to VW creation, declarative modeling can be used to construct theVW, establishing the layout for the objects, generating the necessary context to provide animation and scene design, and generate the outputs used by a visualization/animation system.This document present a research devoted to explore the use of declarative modeling to create VirtualEnvironments, using knowledge exploitation to support the process and ease the transition from the data model to an underlaying architecture which take the task of animating and evolving the scene

A planning approach to the automated synthesis of template-based process models

The design-time specification of flexible processes can be time-consuming and error-prone, due to the high number of tasks involved and their context-dependent nature. Such processes frequently suffer from potential interference among their constituents, since resources are usually shared by the process participants and it is difficult to foresee all the potential tasks interactions in advance. Concurrent tasks may not be independent from each other (e.g., they could operate on the same data at the same time), resulting in incorrect outcomes. To tackle these issues, we propose an approach for the automated synthesis of a library of template-based process models that achieve goals in dynamic and partially specified environments. The approach is based on a declarative problem definition and partial-order planning algorithms for template generation. The resulting templates guarantee sound concurrency in the execution of their activities and are reusable in a variety of partially specified contextual environments. As running example, a disaster response scenario is given. The approach is backed by a formal model and has been tested in experiment

A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community

In recent years, deep learning (DL), a re-branding of neural networks (NNs), has risen to the top in numerous areas, namely computer vision (CV), speech recognition, natural language processing, etc. Whereas remote sensing (RS) possesses a number of unique challenges, primarily related to sensors and applications, inevitably RS draws from many of the same theories as CV; e.g., statistics, fusion, and machine learning, to name a few. This means that the RS community should be aware of, if not at the leading edge of, of advancements like DL. Herein, we provide the most comprehensive survey of state-of-the-art RS DL research. We also review recent new developments in the DL field that can be used in DL for RS. Namely, we focus on theories, tools and challenges for the RS community. Specifically, we focus on unsolved challenges and opportunities as it relates to (i) inadequate data sets, (ii) human-understandable solutions for modelling physical phenomena, (iii) Big Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and learning algorithms for spectral, spatial and temporal data, (vi) transfer learning, (vii) an improved theoretical understanding of DL systems, (viii) high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote Sensin

Consciosusness in Cognitive Architectures. A Principled Analysis of RCS, Soar and ACT-R

This report analyses the aplicability of the principles of consciousness developed in the ASys project to three of the most relevant cognitive architectures. This is done in relation to their aplicability to build integrated control systems and studying their support for general mechanisms of real-time consciousness.\ud To analyse these architectures the ASys Framework is employed. This is a conceptual framework based on an extension for cognitive autonomous systems of the General Systems Theory (GST).\ud A general qualitative evaluation criteria for cognitive architectures is established based upon: a) requirements for a cognitive architecture, b) the theoretical framework based on the GST and c) core design principles for integrated cognitive conscious control systems