1987–88 Cuban National Series

Vegueros, from Pinar del Río Province, repeated as champions in the playoffs of the 27th Cuban National Series. La Habana and Santiago de Cuba possessed stellar regular seasons in which they won 80% of their games. However, Vegueros, with an all-star lineup including Luis Giraldo Casanova and Omar Linares, prevailed in the playoffs.

Standings

Team	W	L	Pct.	GB
La Habana	39	9	.800	-
Vegueros	35	13	.729	4
Industriales	34	14	.708	5
Henequeneros	26	22	.541	13
Metropolitanos	22	26	.458	17
Cienfuegos	22	26	.458	17
Forestales	18	30	.375	21
Isla de la Juventud	10	38	.208	29
Citricultores	10	38	.208	29

Team	W	L	Pct.	GB
Santiago de Cuba	40	8	.833	-
Camagüey	29	19	.604	11
Granma	29	19	.604	11
Villa Clara	25	23	.520	15
Las Tunas	22	26	.458	18
Ciego de Ávila	20	28	.416	20
Sancti Spíritus	20	28	.416	20
Holguín	17	31	.354	23
Guantánamo	14	34	.291	26

Playoffs

Team	W	L	Pct.	GB
Vegueros	5	1	.833	-
Santiago de Cuba	3	3	.500	2
La Habana	3	3	.500	2
Camagüey	1	5	.167	4

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

gollark: https://arxiv.org/pdf/2108.09293.pdf

gollark: This is probably below basically everywhere's minimum wage.

References

"Las 40 primeras Series Nacionales". Granma (in Spanish). Archived from the original on December 27, 2014.
"Cronología: 1988" (in Spanish). Archived from the original on February 26, 2005. Retrieved 2006-10-11. (Note - text is printed in a white font on a white background, depending on browser used.)

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