ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms

This paper describes ANN-Benchmarks, a tool for evaluating the performance of in-memory approximate nearest neighbor algorithms. It provides a standard interface for measuring the performance and quality achieved by nearest neighbor algorithms on different standard data sets. It supports several different ways of integrating $k$-NN algorithms, and its configuration system automatically tests a range of parameter settings for each algorithm. Algorithms are compared with respect to many different (approximate) quality measures, and adding more is easy and fast; the included plotting front-ends can visualise these as images, $\LaTeX$ plots, and websites with interactive plots. ANN-Benchmarks aims to provide a constantly updated overview of the current state of the art of $k$-NN algorithms. In the short term, this overview allows users to choose the correct $k$-NN algorithm and parameters for their similarity search task; in the longer term, algorithm designers will be able to use this overview to test and refine automatic parameter tuning. The paper gives an overview of the system, evaluates the results of the benchmark, and points out directions for future work. Interestingly, very different approaches to $k$-NN search yield comparable quality-performance trade-offs. The system is available at http://ann-benchmarks.com .Comment: Full version of the SISAP 2017 conference paper. v2: Updated the abstract to avoid arXiv linking to the wrong UR

Coqoon

International audienceUser interfaces for interactive proof assistants have always lagged behind those for mainstream programming languages. Whereas integrated development environments (IDEs) have support for features like project management, version control, dependency analysis and incremental project compilation, " IDE " s for proof assistants typically only operate on files in isolation, relying on external tools to integrate those files into larger projects. In this paper we present Coqoon, an IDE for Coq projects integrated into Eclipse. Coqoon manages proofs as projects rather than isolated source files, and compiles these projects using the Eclipse common build system. Coqoon takes advantage of the latest features of Coq, including asynchronous and parallel processing of proofs, and—when used together with a third-party OCaml extension for Eclipse—can even be used to work on large developments containing Coq plugins

Coqoon An IDE for interactive proof development in Coq

International audienceUser interfaces for interactive proof assistants have always lagged behind those for mainstream programming languages. Whereas integrated development environments—IDEs—have support for features like project management, version control, dependency analysis and in-cremental project compilation, " IDE " s for proof assistants typically only operate on files in isolation, relying on external tools to integrate those files into larger projects. In this paper we present Coqoon, an IDE for Coq developments integrated into Eclipse. Coqoon manages proofs as projects rather than isolated source files, and compiles these projects using the Eclipse common build system. Coqoon takes advantage of the latest features of Coq, including asynchronous and parallel processing of proofs, and—when used together with a third-party OCaml extension for Eclipse—can even be used to work on large developments containing Coq plugins

Proceedings of Patient Reported Outcome Measure’s (PROMs) Conference Oxford 2017: Advances in Patient Reported Outcomes Research

A33-Effects of Out-of-Pocket (OOP) Payments and Financial Distress on Quality of Life (QoL) of People with Parkinson’s (PwP) and their Carer

Big Red: A Development Environment for Bigraphs

We present Big Red, a visual editor for bigraphs and bigraphical reactive systems, based upon Eclipse. The editor integrates with several existing bigraph tools to permit simulation and reachability analysis of bigraphical models. We give a brief introduction to the bigraphs formalism, and show how these concepts manifest within the tool using a small motivating example developed in Big Red. We go on to outline its architecture and implementation, and comment on possible future work