This is the most detailed picture of a human cell ever made 🧪
https://www.instagram.com/reel/DX1CGIZMKJs/?igsh=NTc4MTIwNjQ2YQ
Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy
Perfection Because It Doesn’t Exist 🎐
完美因为完美不存在 🎐
📷 Nikon F4E
🎞️ Kentmere 400
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Wise
Two Brunettes & A Gay
Indulge in an hour of witty banter and lively discussions with these cheeky 30-somethings, along with special guests from the entertainment industry and beyond...
Great Australian Pods Podcast Directory: https://www.greataustralianpods.com/two-br…
A physical tool bar 😉
From the 2024 XOXO talk by @… about the artist Wes Cook.
https://xoxofest.com/2024/videos/cabel-sasser/
An Information-Theoretic Analysis of Threshold Group Testing
Remco van der Hofstad, Noela M\"uller, Connor Riddlesden
https://arxiv.org/abs/2606.11353 https://arxiv.org/pdf/2606.11353 https://arxiv.org/html/2606.11353
arXiv:2606.11353v1 Announce Type: new
Abstract: We study the Threshold Group Testing (TGT) problem in the noiseless and non-adaptive setting, where the objective is to exactly recover a sparse binary vector from pooled tests, using as few tests as possible. In TGT, each test applied to a subset of items returns a positive outcome if the number of 1's (defective items) in that subset meets or exceeds a specified threshold, and has a negative outcome otherwise. We investigate how the complexity of TGT compares to that of Classical Group Testing (CGT), corresponding to the special case of the threshold equal to one, and analyse the impact of increasing the threshold on the required number of tests.
Our main contribution is the derivation of a sharp information-theoretic phase transition at $c_{\mathrm{inf}}^{\mathrm{TGT}}k\log(n/k)$ (non-adaptive) tests for TGT within the constant-column test design. The threshold constant $c_{\mathrm{inf}}^{\mathrm{TGT}}$ is expressed as a function of the prevalence of defectives and the threshold value. Our upper bound is derived under an analytic assumption, and we verify that this assumption is satisfied for a threshold value of 2.
The value of $c_{\mathrm{inf}}^{\mathrm{TGT}}$ reveals that TGT on the constant-column design has the same information-theoretic behaviour as CGT in the low-prevalence regime. Yet, strikingly, at higher prevalences, the threshold leads to a significant reduction in the number of tests.
On the other hand, we provide evidence that when the asymptotic proportion of defective items is positive, TGT actually becomes strictly harder than CGT (excluding trivial reductions).
toXiv_bot_toot
from my link log —
The design and implementation of the Berkeley Internet Name Domain (BIND) servers.
https://www2.eecs.berkeley.edu/Pubs/TechRpts/1984/5962.html
saved 2020-08-27
Facebook is a hub for illegal wildlife trade, and that’s by design, report says https://news.mongabay.com/2026/05/facebook-is-a-hub-for-illegal-wildlife-trade-and-thats-by-design-report-says/
Same Place 🐲
同一個地方 🐲
📷 Nikon F4E
🎞️ Kentmere 400
If you like my work, Support by buying me a coffee or a roll of film from
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Wise
City Silhouettes V🏙️
城市轮廓线 V 🏙️
📷 Pentax 6x7
🎞️ Kentmere 400 (6x7)
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