A Model of Layered Architectures

Architectural styles and patterns play an important role in software engineering. One of the most known ones is the layered architecture style. However, this style is usually only stated informally, which may cause problems such as ambiguity, wrong conclusions, and difficulty when checking the conformance of a system to the style. We address these problems by providing a formal, denotational semantics of the layered architecture style. Mainly, we present a sufficiently abstract and rigorous description of layered architectures. Loosely speaking, a layered architecture consists of a hierarchy of layers, in which services communicate via ports. A layer is modeled as a relation between used and provided services, and layer composition is defined by means of relational composition. Furthermore, we provide a formal definition for the notions of syntactic and semantic dependency between the layers. We show that these dependencies are not comparable in general. Moreover, we identify sufficient conditions under which, in an intuitive sense which we make precise in our treatment, the semantic dependency implies, is implied by, or even coincides with the reflexive-transitive closure of the syntactic dependency. Our results provide a technology-independent characterization of the layered architecture style, which may be used by software architects to ensure that a system is indeed built according to that style.Comment: In Proceedings FESCA 2015, arXiv:1503.0437

Transport-Based Neural Style Transfer for Smoke Simulations

Artistically controlling fluids has always been a challenging task. Optimization techniques rely on approximating simulation states towards target velocity or density field configurations, which are often handcrafted by artists to indirectly control smoke dynamics. Patch synthesis techniques transfer image textures or simulation features to a target flow field. However, these are either limited to adding structural patterns or augmenting coarse flows with turbulent structures, and hence cannot capture the full spectrum of different styles and semantically complex structures. In this paper, we propose the first Transport-based Neural Style Transfer (TNST) algorithm for volumetric smoke data. Our method is able to transfer features from natural images to smoke simulations, enabling general content-aware manipulations ranging from simple patterns to intricate motifs. The proposed algorithm is physically inspired, since it computes the density transport from a source input smoke to a desired target configuration. Our transport-based approach allows direct control over the divergence of the stylization velocity field by optimizing incompressible and irrotational potentials that transport smoke towards stylization. Temporal consistency is ensured by transporting and aligning subsequent stylized velocities, and 3D reconstructions are computed by seamlessly merging stylizations from different camera viewpoints.Comment: ACM Transaction on Graphics (SIGGRAPH ASIA 2019), additional materials: http://www.byungsoo.me/project/neural-flow-styl

Cross-Platform Presentation of Interactive Volumetric Imagery

Volume data is useful across many disciplines, not just medicine. Thus, it is very important that researchers have a simple and lightweight method of sharing and reproducing such volumetric data. In this paper, we explore some of the challenges associated with volume rendering, both from a classical sense and from the context of Web3D technologies. We describe and evaluate the pro- posed X3D Volume Rendering Component and its associated styles for their suitability in the visualization of several types of image data. Additionally, we examine the ability for a minimal X3D node set to capture provenance and semantic information from outside ontologies in metadata and integrate it with the scene graph