leading baselines achieve only about half the accuracy at the same efficiency
作者暗示当前主流的KV缓存压缩方法在相同效率水平下只能达到约一半的准确率,这表明现有方法存在根本性缺陷。这一尖锐的批评挑战了当前领域内的技术路线,暗示大多数同行可能一直在错误的方向上优化KV压缩。
leading baselines achieve only about half the accuracy at the same efficiency
作者暗示当前主流的KV缓存压缩方法在相同效率水平下只能达到约一半的准确率,这表明现有方法存在根本性缺陷。这一尖锐的批评挑战了当前领域内的技术路线,暗示大多数同行可能一直在错误的方向上优化KV压缩。
For any new environments and databases, you can use just drizzle-kit migrate, and all the migrations together with init will be applied
When you run migrate on a database that already has all the tables from your schema, you need to run it with the drizzle-kit migrate --no-init flag, which will skip the init step. If you run it without this flag and get an error that such tables already exist, drizzle-kit will detect it and suggest you add this flag.
When you introspect the database, you will receive an initial migration without comments. Instead of commenting it out, we will add a flag to journal entity with the init flag, indicating that this migration was generated by introspect action
root@51a758d136a2:~/test/test-project# npx prisma migrate diff --from-empty --to-schema-datamodel prisma/schema.prisma --script > migration.sql root@51a758d136a2:~/test/test-project# cat migration.sql -- CreateTable CREATE TABLE "test" ( "id" SERIAL NOT NULL, "val" INTEGER, CONSTRAINT "test_pkey" PRIMARY KEY ("id") ); root@51a758d136a2:~/test/test-project# mkdir -p prisma/migrations/initial root@51a758d136a2:~/test/test-project# mv migration.sql prisma/migrations/initial/
within one year or so the curve the the line um crosses the non-addicted average Baseline
> for - addiction - abstinence - one year - crosses non-addictive baseline
abstinence from from Coke alcohol and heroin you get um you get an increase in gr matter volume in very similar areas
> for - addiction - abstinence - synaptic growth - in a year, returns to baseline
Deepti Gurdasani. (2022, January 30). Have tried to now visually illustrate an earlier thread I wrote about why prevalence estimates based on comparisons of “any symptom” between infected cases, and matched controls will yield underestimates for long COVID. I’ve done a toy example below here, to show this 🧵 [Tweet]. @dgurdasani1. https://twitter.com/dgurdasani1/status/1487578265187405828
Meaghan Kall. (2021, November 15). There are 2 other impressive conclusions from this study: 1. Comparing vaccine effectiveness of booster vs “fully vaxxed” as the baseline. Booster ADDS 81-85% protection against symptomatic infection ON TOP of what you already had from your primary (2-dose) vaccination https://t.co/5EO7m6GHTZ [Tweet]. @kallmemeg. https://twitter.com/kallmemeg/status/1460207567070769156
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Andrew Wilshere
Andrew Wilshere was working on content at Designlab when he asked me to write an article about the Bauhaus.
I ended up writing something that never got published with Designlab. Instead, it was shared by the Bauhaus Movement to their Facebook followers.
Seow, J., Graham, C., Merrick, B., Acors, S., Steel, K. J. A., Hemmings, O., O’Bryne, A., Kouphou, N., Pickering, S., Galao, R., Betancor, G., Wilson, H. D., Signell, A. W., Winstone, H., Kerridge, C., Temperton, N., Snell, L., Bisnauthsing, K., Moore, A., … Doores, K. (2020). Longitudinal evaluation and decline of antibody responses in SARS-CoV-2 infection. MedRxiv, 2020.07.09.20148429. https://doi.org/10.1101/2020.07.09.20148429
While I wanted to do my best to not judge how I was spending my time during the experiment—to just track it as it is and analyze at the end—I did want to have a baseline to compare my results to. This wasn't a hypothesis of how I spend my time, but more of a vision for how I would like my time to be allocated.
one level is chosen as the “reference”, and its mean behaviour is represented by the intercept. Each column of the resulting matrix represents the difference between the mean of one level and this reference level