A Framework for Program Development Based on Schematic Proof

Often, calculi for manipulating and reasoning about programs can be recast as calculi for synthesizing programs. The difference involves often only a slight shift of perspective: admitting metavariables into proofs. We propose that such calculi should be implemented in logical frameworks that support this kind of proof construction and that such an implementation can unify program verification and synthesis. Our proposal is illustrated with a worked example developed in Paulson's Isabelle system. We also give examples of existent calculi that are closely related to the methodology we are proposing and others that can be profitably recast using our approach

Sketchnote Components, Design Space Dimensions, and Strategies for Effective Visual Note Taking

Sketchnoting is a form of visual note taking where people listen to, synthesize, and visualize ideas from a talk or other event using a combination of pictures, diagrams, and text. Little is known about the design space of this kind of visual note taking. With an eye towards informing the implementation of digital equivalents of sketchnoting, inking, and note taking, we introduce a classification of sketchnote styles and techniques, with a qualitative analysis of 103 sketchnotes, and situated in context with six semi-structured follow up interviews. Our findings distill core sketchnote components (content, layout, structuring elements, and visual styling) and dimensions of the sketchnote design space, classifying levels of conciseness, illustration, structure, personification, cohesion, and craftsmanship. We unpack strategies to address particular note taking challenges, for example dealing with constraints of live drawings, and discuss relevance for future digital inking tools, such as recomposition, styling, and design suggestions

Semantic Photo Manipulation with a Generative Image Prior

Despite the recent success of GANs in synthesizing images conditioned on inputs such as a user sketch, text, or semantic labels, manipulating the high-level attributes of an existing natural photograph with GANs is challenging for two reasons. First, it is hard for GANs to precisely reproduce an input image. Second, after manipulation, the newly synthesized pixels often do not fit the original image. In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image. Our method can accurately reconstruct the input image and synthesize new content, consistent with the appearance of the input image. We demonstrate our interactive system on several semantic image editing tasks, including synthesizing new objects consistent with background, removing unwanted objects, and changing the appearance of an object. Quantitative and qualitative comparisons against several existing methods demonstrate the effectiveness of our method.Comment: SIGGRAPH 201