Graph layout for applications in compiler construction

We address graph visualization from the viewpoint of compiler construction. Most data structures in compilers are large, dense graphs such as annotated control flow graph, syntax trees, dependency graphs. Our main focus is the animation and interactive exploration of these graphs. Fast layout heuristics and powerful browsing methods are needed. We give a survey of layout heuristics for general directed and undirected graphs and present the browsing facilities that help to manage large structured graph

Animation in relational information visualization

In order to be able to navigate in the world without memorizing each detail, the human brain builds a mental map of its environment. The mental map is a distorted and abstracted representation of the real environment. Unimportant areas tend to be collapsed to a single entity while important landmarks are overemphasized. When working with visualizations of data we build a mental map of the data which is closely linked to the particular visualization. If the visualization changes significantly due to changes in the data or the way it is presented we loose the mental map and have to rebuild it from scratch. The purpose of the research underlying this thesis was to investigate and devise methods to create smooth transformations between visualizations of relational data which help users in maintaining or quickly updating their mental map

Animation in relational information visualization

In order to be able to navigate in the world without memorizing each detail, the human brain builds a mental map of its environment. The mental map is a distorted and abstracted representation of the real environment. Unimportant areas tend to be collapsed to a single entity while important landmarks are overemphasized. When working with visualizations of data we build a mental map of the data which is closely linked to the particular visualization. If the visualization changes significantly due to changes in the data or the way it is presented we loose the mental map and have to rebuild it from scratch. The purpose of the research underlying this thesis was to investigate and devise methods to create smooth transformations between visualizations of relational data which help users in maintaining or quickly updating their mental map

Real time moving scene holographic camera system

A holographic motion picture camera system producing resolution of front surface detail is described. The system utilizes a beam of coherent light and means for dividing the beam into a reference beam for direct transmission to a conventional movie camera and two reflection signal beams for transmission to the movie camera by reflection from the front side of a moving scene. The system is arranged so that critical parts of the system are positioned on the foci of a pair of interrelated, mathematically derived ellipses. The camera has the theoretical capability of producing motion picture holograms of projectiles moving at speeds as high as 900,000 cm/sec (about 21,450 mph)

Graph layout for applications in compiler construction

We address graph visualization from the viewpoint of compiler construction. Most data structures in compilers are large, dense graphs such as annotated control flow graph, syntax trees, dependency graphs. Our main focus is the animation and interactive exploration of these graphs. Fast layout heuristics and powerful browsing methods are needed. We give a survey of layout heuristics for general directed and undirected graphs and present the browsing facilities that help to manage large structured graph

Unmasking Clever Hans Predictors and Assessing What Machines Really Learn

Current learning machines have successfully solved hard application problems, reaching high accuracy and displaying seemingly "intelligent" behavior. Here we apply recent techniques for explaining decisions of state-of-the-art learning machines and analyze various tasks from computer vision and arcade games. This showcases a spectrum of problem-solving behaviors ranging from naive and short-sighted, to well-informed and strategic. We observe that standard performance evaluation metrics can be oblivious to distinguishing these diverse problem solving behaviors. Furthermore, we propose our semi-automated Spectral Relevance Analysis that provides a practically effective way of characterizing and validating the behavior of nonlinear learning machines. This helps to assess whether a learned model indeed delivers reliably for the problem that it was conceived for. Furthermore, our work intends to add a voice of caution to the ongoing excitement about machine intelligence and pledges to evaluate and judge some of these recent successes in a more nuanced manner.Comment: Accepted for publication in Nature Communication

Extreme Spatial Dispersion in Nonlocally-Resonant Elastic Metamaterials

To date, the vast majority of architected materials have leveraged two physical principles to control wave behavior, namely Bragg interference and local resonances. Here, we describe a third path: structures that accommodate a finite number of delocalized zero-energy modes, leading to anomalous dispersion cones that nucleate from extreme spatial dispersion at 0 Hz. We explain how to design such zero-energy modes in the context of elasticity and show that many of the landmark wave properties of metamaterials can also be induced at an extremely subwavelength scale by the associated anomalous cones, without suffering from the same bandwidth limitations. We then validate our theory through a combination of simulations and experiments. Finally, we present an inverse design method to produce anomalous cones at desired locations in momentum space