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Combinatorial sketching for finite programs
TLDR
SKETCH is a language for finite programs with linguistic support for sketching and its combinatorial synthesizer is complete for the class of finite programs, guaranteed to complete any sketch in theory, and in practice has scaled to realistic programming problems.
Sketching stencils
TLDR
This paper develops a sketching synthesizer that works for stencil computations, a large class of programs that, unlike circuits, have unbounded inputs and outputs, as well as an unbounded number of computations.
Using deformations for browsing volumetric data
TLDR
This work considers various deformation strategies and presents a number of interaction techniques based on different metaphors, which are valuable for showing portions of the data spatially situated in context with surrounding data.
Probabilistic Data Management for Pervasive Computing: The Data Furnace Project
TLDR
The Data Furnace project at Intel Research and UC-Berkeley aims to build a probabilistic data management infrastructure for pervasive computing environments that handles the uncertain nature of such data as a first-class citizen through a principled framework grounded in probabilism models and inference techniques.
Probabilistic Complex Event Triggering
TLDR
The key goal of pcet is to build an in-frastructure that can automatically infer andreason about the probabilities of triggered events, using a principled probabilistic model for the underlying sensor data.
Sketching with Partial Programs
TLDR
SKETCH is developed, a language for finite programs with linguistic support for sketching and a synthesizer that is complete for the class of finite programs: it is guaranteed to complete any sketch in theory, and in practice has scaled to complex real-world programming problems.
Extending the Applicability of Sketching
TLDR
Four extensions to the SKETCH programming system developed at UC Berkeley improve the performance of the system, add new functionality, and address scalability concerns for real-world algorithms.
Symbolic Proof Generation for Resizing Sketches Gilad Arnold and
We propose an approach for resizing finite implementations generated by the SKETCH synthesis framework. Our solution generates a formal proof of equivalence between size-parameterized versions of a