Justin Connolly

Justin Riveagh Connolly (born London, 11 August 1933)[1] is a British composer and teacher.

He was educated at Westminster School, and then briefly studied law at the Middle Temple before deciding on a career in music. He studied with Peter Racine Fricker at the Royal College of Music, graduating with a BMus degree, and then travelled to Yale University in the USA on a Harkness Fellowship in the late 60s, where he studied with Mel Powell. He then briefly taught at Yale before returning to the UK. He taught for many years at the Royal College of Music, later moving to the Royal Academy of Music, retiring from teaching in 1995. He currently lives in Greenwich in the south-east area of greater London.

His music is characterised by an outwardly modernist idiom, although Connolly professes a strong affinity with the music of the nineteenth century. His style is glittering, sometimes pointillist, and is often concerned with the interplay of complex and detailed textures. His music is rigorously crafted and often explores ideas related to philosophy, literature and history.

Works include a brass quintet Cinquepaces, two Sonatinas for piano solo, concertos for viola, organ and piano (the latter the result of a BBC commission premièred in 2004), four vocal cycles setting the poetry of Wallace Stevens, and a number of instrumental chamber pieces. His music is published by Novello & Co (Music Sales).

Highly regarded as a teacher, his former students include the British composer Alwynne Pritchard.

Works
  • Night Thoughts album
gollark: > There is burgeoning interest in designing AI-basedsystems to assist humans in designing computing systems,including tools that automatically generate computer code.The most notable of these comes in the form of the first self-described ‘AI pair programmer’, GitHub Copilot, a languagemodel trained over open-source GitHub code. However, codeoften contains bugs—and so, given the vast quantity of unvettedcode that Copilot has processed, it is certain that the languagemodel will have learned from exploitable, buggy code. Thisraises concerns on the security of Copilot’s code contributions.In this work, we systematically investigate the prevalence andconditions that can cause GitHub Copilot to recommend insecurecode. To perform this analysis we prompt Copilot to generatecode in scenarios relevant to high-risk CWEs (e.g. those fromMITRE’s “Top 25” list). We explore Copilot’s performance onthree distinct code generation axes—examining how it performsgiven diversity of weaknesses, diversity of prompts, and diversityof domains. In total, we produce 89 different scenarios forCopilot to complete, producing 1,692 programs. Of these, wefound approximately 40 % to be vulnerable.Index Terms—Cybersecurity, AI, code generation, CWE
gollark: https://arxiv.org/pdf/2108.09293.pdf
gollark: This is probably below basically everywhere's minimum wage.
gollark: (in general)
gollark: <@!319753218592866315> Your thoughts?

References
  1. Harvard Music Dictionary.


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