Not a quantum computing pure-play, they also do sensing.
This game is quite detailed: www.youtube.com/watch?v=w4Jmqp8a_bU
Given a bunch of points in dimensions, PCA maps those points to a new dimensional space with .
is a hyperparameter, and are common choices when doing dataset exploration, as they can be easily visualized on a planar plot.
The mapping is done by projecting all points to a dimensional hyperplane. PCA is an algorithm for choosing this hyperplane and the coordinate system within this hyperplane.
The hyperplane choice is done as follows:
- the hyperplane will have origin at the mean point
- the first axis is picked along the direction of greatest variance, i.e. where points are the most spread out.Intuitively, if we pick an axis of small variation, that would be bad, because all the points are very close to one another on that axis, so it doesn't contain as much information that helps us differentiate the points.
- then we pick a second axis, orthogonal to the first one, and on the direction of second largest variance
- and so on until orthogonal axes are taken
www.sartorius.com/en/knowledge/science-snippets/what-is-principal-component-analysis-pca-and-how-it-is-used-507186 provides an OK-ish example with a concrete context. In there, each point is a country, and the input data is the consumption of different kinds of foods per year, e.g.:so in this example, we would have input points in 4D.
- flour
- dry codfish
- olive oil
- sausage
The question is then: we want to be able to identify the country by what they eat.
Suppose that every country consumes the same amount of flour every year. Then, that number doesn't tell us much about which country each point represents (has the least variance), and the first PCA axes would basically never point anywhere near that direction.
Another cool thing is that PCA seems to automatically account for linear dependencies in the data, so it skips selecting highly correlated axes multiple times. For example, suppose that dry codfish and olive oil consumption are very high in Portugal and Spain, but very low in Germany and Poland. Therefore, the variation is very high in those two parameters, and contains a lot of information.
However, suppose that dry codfish consumption is also directly proportional to olive oil consumption. Because of this, it would be kind of wasteful if we selected:since the information about codfish already tells us the olive oil. PCA apparently recognizes this, and instead picks the first axis at a 45 degree angle to both dry codfish and olive oil, and then moves on to something else for the second axis.
- dry codfish as the first axis
- olive oil as the second axis
We can see that much like the rest of machine learning, PCA can be seen as a form of compression.
The most powerful GUI file manager ever?? Infinite configurability??
Ciro Santilli wasted some time on it before he gave up on file managers altogether.
Smaller files, scalable image size, and editability. Why would you use anything else for programmatically generated images?!?!
The schema is listed at: www.mediawiki.org/wiki/Manual:Categorylinks_table
On the SQL:
CREATE TABLE `categorylinks` (
`cl_from` int(8) unsigned NOT NULL DEFAULT 0,
`cl_to` varbinary(255) NOT NULL DEFAULT '',
`cl_sortkey` varbinary(230) NOT NULL DEFAULT '',
`cl_timestamp` timestamp NOT NULL DEFAULT current_timestamp() ON UPDATE current_timestamp(),
`cl_sortkey_prefix` varbinary(255) NOT NULL DEFAULT '',
`cl_collation` varbinary(32) NOT NULL DEFAULT '',
`cl_type` enum('page','subcat','file') NOT NULL DEFAULT 'page',
PRIMARY KEY (`cl_from`,`cl_to`),
KEY `cl_timestamp` (`cl_to`,`cl_timestamp`),
KEY `cl_sortkey` (`cl_to`,`cl_type`,`cl_sortkey`,`cl_from`),
KEY `cl_collation_ext` (`cl_collation`,`cl_to`,`cl_type`,`cl_from`)
) ENGINE=InnoDB DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;TODO what is
cl_from? We've tried:page_id: nope, there is notpage_idof 3
cl_to appears to always be a category string name.The format appears to be described at: www.mediawiki.org/wiki/Manual:Categorylinks_table
A sample INSERT entry is:
(3,'Computer_storage_devices',88,11,0)XPath kind of died with the rise of CSS selectors around the beginnning of the 2010's. But that is a shame. XPath is a good standard, and was generally more powerful than CSS selectors for many many years.
Photon hits excited electron, makes that electron go down, and generates a new identical photon in the process, with the exact same:This is the basis of lasers.
- frequency
- polarization
- direction
Bibliography:
This dude actually managed to convince a brain-dead British court that he was Satoshi and force a takedown of the Bitcoin whitepaper from bitcoin.org/bitcoin.pdf where it had been for many years prior: coinmarketcap.com/academy/article/bitcoin-org-ordered-to-take-down-bitcoin-whitepaper-because-of-copyright-infringement The page was updated to simply display the following Satoshi quote:
It takes advantage of the nature of information being easy to spread but hard to stifle. - Satoshi Nakamoto
The mere thought that Satoshi would attempt to copyright takedown the Bitcoin whitepaper, and not be able to back his identidy with any cryptographic keys, makes one shrivel to the bones.
Also, kids, this is why you put a fucking license on everything you release to the public, and especially when doing so anonymously!!! A quick CC BY-SA on that paper would have prevented all this bullshit.
The existence of this outrageous fraudster has had two good effects on the world however it must be said:
- the release of Adam Back and Martti Malmi early email history with Satoshi: www.forbes.com/sites/digital-assets/2024/02/23/new-emails-reveal-staggering-clues-to-the-mystery-of-bitcoin-creator-satoshi-nakamoto
- the memes: Craig Steven Wright memes
Timeline:
- 2015-12-08 Wired article claims he may be Satoshi: www.wired.com/2015/12/bitcoins-creator-satoshi-nakamoto-is-probably-this-unknown-australian-genius/. A few days later, evidence of foul play emerged, and on 2019-04-30 Wired retracted the article altogether
- 2016-05-02 publicly claims he is Satoshi www.timesofisrael.com/australian-entrepreneur-craig-wright-says-he-created-bitcoin/
- 2024-05-20 British judge James Mellor fisting the fuck out of Craig: www.reuters.com/technology/self-proclaimed-bitcoin-inventor-lied-repeatedly-support-claim-says-uk-judge-2024-05-20/
An Australian computer scientist who claimed he invented bitcoin lied "extensively and repeatedly" and forged documents "on a grand scale" to support his false claim, a judge at London's High Court ruled on Monday.
Dr Wright presents himself as an extremely clever person. However, in my judgment, he is not nearly as clever as he thinks he is.
Social media:
Interesting
- www.reddit.com/r/Bitcoin/comments/4i7k9a/strange_edits_on_craig_wrights_wikipedia_page/ "Strange edits on Craig Wright's Wikipedia page made two days before the revelation, from an IP address in Barbados (possibly made by Craig himself?)"
Who needs a hackable general purpose computer, when you can buy a completely locked down computer that only runs useless programs for which you have to pay thousands of dollars to develop for, cannot run a large percentage of major titles from competitor hardware due to business deals (see also) and will inevitably reach planned obsolescence in 4 years?
By company:
Served as both input, output and storage system in the eary days!
Using Punch Cards by Bubbles Whiting (2016)
Source. Interview at the The Centre for Computing History.These appear to be benchmarks that don't involve running anything concretely, just compiling and likely then counting gates:
Immediately follows e. Coli K-12 MG1655 gene thrA,
Part of E. Coli K-12 MG1655 operon thrLABC.
Nick Leeson and the Fall of the House of Barings by Adam Curtis (1996) Updated 2025-01-10 +Created 1970-01-01
Output:With our understanding of the discrete Fourier transform we see clearly that:
sin(t)
fft
real 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 10
rfft
real 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 0 0 0 0 0 0 0
sin(t) + sin(4t)
fft
real 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 -10 0 0 0 0 0 0 0 0 0 0 0 10 0 0 10
rfft
real 0 0 0 0 0 0 0 0 0 0 0
imag 0 -10 0 0 -10 0 0 0 0 0 0- the signal is being decomposed into sinusoidal components
- because we are doing the Discrete Fourier transform of a real signal, for the
fft, so there is redundancy in the. We also understand thatrfftsimply cuts off and only keeps half of the coefficients
Asked:
On Ubuntu 23.10, under Settings > Displays, Mirror works, but "Join" does not allow to Apply with message:Works on X Window System, it is a Wayland bug.
Changes Cannot be Applied. This could be due to hardware limitations.
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