Nicole Muskatewitz

Nicole Muskatewitz (born August 6, 1994) is a German female curler.[2]

Nicole Muskatewitz
 
Born (1994-08-06) August 6, 1994
Team
Curling clubBaden Hills Golf & Curling Club[1],
SC Riessersee
Garmisch-Partenkirchen, Germany
Career
Member Association Germany
World Championship
appearances
2 (2013, 2014)
European Championship
appearances
2 (2012, 2013)
Other appearancesWinter Universiade: 1 (2017),
European Junior Challenge: 2 (2012, 2013),
Winter Youth Olympics: 2012 (mixed, mixed doubles)

At the national level, she is a two-time German women's champion (2013, 2014).

At the international level, she is a 2012 Winter Youth Olympics mixed doubles curling champion alongside Swiss curler Michael Brunner.

Teams

Women's

Season Skip Third Second Lead Alternate Coach Events
2011–12 Aylin LutzFrederike MannerNicole MuskatewitzClaudia BeerLisa-Marie RitterSina FreyEJCC 2012 (9th)
2012–13 Aylin LutzFrederike MannerNicole MuskatewitzClaudia BeerMaike BeerSina FreyEJCC 2013
Andrea SchöppImogen Oona LehmannStella HeißCorinna ScholzNicole MuskatewitzRainer Schöpp (EuCC, WCC), Martin Beiser (EuCC)ECC 2012 (7th)
GWCC 2013 [3]
WCC 2013 (11th)
2013–14 Imogen Oona LehmannCorinna ScholzNicole MuskatewitzStella HeißClaudia Beer (WCC)Holger Höhne (WCC)GWCC 2014 [4]
WCC 2014 (8th)
2014–15 Imogen Oona LehmannCorinna ScholzStella HeißNicole MuskatewitzGWCC 2015 [5]
2015–16 Maike BeerSina FreyNicole MuskatewitzCarola SinzGWCC 2016 [6]
2016–17 Maike BeerClaudia BeerEmira AbbesNicole MuskatewitzSven GoldemannWUG 2017 (8th)

Mixed

Season Skip Third Second Lead Coach Events
2011–12 Daniel RothballerFrederike MannerKevin LehmannNicole MuskatewitzHolger HöhneWYOG 2012 (15th)

Mixed doubles

Season Male Female Coach Events
2011–12 Michael BrunnerNicole MuskatewitzBrigitte Brunner[7]WYOG 2012
gollark: As if that's possible.
gollark: Fearsome.
gollark: I might have to release apioforms from the beecloud.
gollark: It must comfort you to think so.
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

References


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