haystack

A needle in a haystack

In an Istanbul neighbourhood of 200,000 people, an OM worker, prompted by God, asks a question to the only person who knows the answer.




haystack

A needle in a haystack

In an Istanbul neighbourhood of 200,000 people, an OM worker, prompted by God, asks a question to the only person who knows the answer.




haystack

“Needle in a haystack” search problem? Check 15 of the very best WordPress themes for 2020

Having too many WordPress themes to choose among is of course better than having too few. But there are times when searching for what you really need is like looking for the proverbial needle in the haystack. If you’ve been looking for a top-of-the-line multipurpose theme you will probably find it right here. We’ve listed […]

The post “Needle in a haystack” search problem? Check 15 of the very best WordPress themes for 2020 appeared first on WebAppers.




haystack

Rare gold specimens unearthed at WA mine compared to 'finding a needle in a haystack'

King Midas has left his mark again on the same Western Australian gold mine which made global headlines last year after producing some of the biggest gold specimens ever seen.




haystack

Eyre Peninsula's Murphy's Haystacks are among the oldest rocks in Australia but they're slowly eroding away

An 'island-rock' formation in South Australian is believed to have formed billions of years ago and while it is eroding, experts say it is not likely to disappear anytime soon.




haystack

Haystacks




haystack

Needles and straw in a haystack: Robust confidence for possibly sparse sequences

Eduard Belitser, Nurzhan Nurushev.

Source: Bernoulli, Volume 26, Number 1, 191--225.

Abstract:
In the general signal$+$noise (allowing non-normal, non-independent observations) model, we construct an empirical Bayes posterior which we then use for uncertainty quantification for the unknown, possibly sparse, signal. We introduce a novel excessive bias restriction (EBR) condition, which gives rise to a new slicing of the entire space that is suitable for uncertainty quantification. Under EBR and some mild exchangeable exponential moment condition on the noise, we establish the local (oracle) optimality of the proposed confidence ball. Without EBR, we propose another confidence ball of full coverage, but its radius contains an additional $sigma n^{1/4}$-term. In passing, we also get the local optimal results for estimation , posterior contraction problems, and the problem of weak recovery of sparsity structure . Adaptive minimax results (also for the estimation and posterior contraction problems) over various sparsity classes follow from our local results.




haystack

[ASAP] Dynamic Tracking Biosensors: Finding Needles in a Haystack

ACS Sensors
DOI: 10.1021/acssensors.0c00083




haystack

Haystacks




haystack

Haystacks