Live Programming Environment for Deep Learning with Instant and Editable Neural Network Visualization

Artificial intelligence (AI) such as deep learning has achieved significant success in a variety of application domains. Several visualization techniques have been proposed for understanding the overall behavior of the neural network defined by deep learning code. However, they show visualization only after the code or network definition is written and it remains complicated and unfriendly for newbies to build deep neural network models on a code editor. In this paper, to help user better understand the behavior of networks, we augment a code editor with instant and editable visualization of network model, inspired by live programming which provides continuous feedback to the programmer

Programmatic and Direct Manipulation, Together at Last

Direct manipulation interfaces and programmatic systems have distinct and complementary strengths. The former provide intuitive, immediate visual feedback and enable rapid prototyping, whereas the latter enable complex, reusable abstractions. Unfortunately, existing systems typically force users into just one of these two interaction modes. We present a system called Sketch-n-Sketch that integrates programmatic and direct manipulation for the particular domain of Scalable Vector Graphics (SVG). In Sketch-n-Sketch, the user writes a program to generate an output SVG canvas. Then the user may directly manipulate the canvas while the system immediately infers a program update in order to match the changes to the output, a workflow we call live synchronization. To achieve this, we propose (i) a technique called trace-based program synthesis that takes program execution history into account in order to constrain the search space and (ii) heuristics for dealing with ambiguities. Based on our experience with examples spanning 2,000 lines of code and from the results of a preliminary user study, we believe that Sketch-n-Sketch provides a novel workflow that can augment traditional programming systems. Our approach may serve as the basis for live synchronization in other application domains, as well as a starting point for yet more ambitious ways of combining programmatic and direct manipulation.Comment: PLDI 2016 Paper + Supplementary Appendice

Yield prediction by analysis of multispectral scanner data

A preliminary model describing the growth and grain yield of wheat was developed. The modeled growth characteristics of the wheat crop were used to compute wheat canopy reflectance using a model of vegetation canopy reflectance. The modeled reflectance characteristics were compared with the corresponding growth characteristics and grain yield in order to infer their relationships. It appears that periodic wheat canopy reflectance characteristics potentially derivable from earth satellites will be useful in forecasting wheat grain yield