The definition does not include homonuclear molecules which is a pain.
A useless piece of paper (or digital version of it) that you can pay taxes with :)
As opposed to:
- 2020 cryptocurrencies, while governments still don't accept them for taxes, as well as other assets that are also not accepted for taxes (i.e. most assets)
- physical currencies that have intrinsic material value, e.g. gold coins
Mathematics course of the University of Oxford structure Updated 2025-01-10 +Created 1970-01-01
There is a slight variation in temperature of CMB across the sky of the order of 200 microKelvin. It is small to the ~2.7 K average temperature, but it can be measured.
If the initial conditions of the Big Bang and the laws of physics were perfectly symmetric, then we could expect the Universe to just be one perfectly uniform boring soup.
But instead some asymetry made all the fun weird things we see today happen eventually, like galaxies and life.
And the cosmic microwave background serves as a way for us to look back in time to the early conditions of the universe, as it was set in stone as soon as the universe became transparent to this light during recombination.
Or if you want to get poetic, it is the closest we can ever get to listening to the original word of God when he setup the initial conditions of the universe.
The ansiotropies of CMB is the ultimate astronomical compass we will ever have, as it is the thing with the least proper motion.
dumps.wikimedia.org/enwiki/latest/enwiki-latest-category.sql.gz contains a list of categories. It only contains the categories and some counts, but it doesn't contain the subcategories and pages under each category, so it is a bit pointless.
The schema is listed at: www.mediawiki.org/wiki/Manual:Category_table
The SQL first defines the table:followed by a few humongous inserts:which we can see at: en.wikipedia.org/wiki/Category:Computer_storage_devices
CREATE TABLE `category` (
`cat_id` int(10) unsigned NOT NULL AUTO_INCREMENT,
`cat_title` varbinary(255) NOT NULL DEFAULT '',
`cat_pages` int(11) NOT NULL DEFAULT 0,
`cat_subcats` int(11) NOT NULL DEFAULT 0,
`cat_files` int(11) NOT NULL DEFAULT 0,
PRIMARY KEY (`cat_id`),
UNIQUE KEY `cat_title` (`cat_title`),
KEY `cat_pages` (`cat_pages`)
) ENGINE=InnoDB AUTO_INCREMENT=249228235 DEFAULT CHARSET=binary ROW_FORMAT=COMPRESSED;INSERT INTO `category` VALUES (2,'Unprintworthy_redirects',1597224,20,0),(3,'Computer_storage_devices',88,11,0)Se see that en.wikipedia.org/wiki/Category:Computer_storage_devices_by_companyso it contains only categories.
- en.wikipedia.org/wiki/Category:Computer_storage_devices is a subcategory of that category and it appears in that file.
- en.wikipedia.org/wiki/Acronis_Secure_Zone is a page of the category, and it does not appear
We can check this with:and it shows:There doesn't seem to be any interlink between the categories, only page and subcategory counts therefore.
sed -s 's/),/\n/g' enwiki-latest-category.sql | grep Computer_storage_devices(3,'Computer_storage_devices',88,11,0
(521773,'Computer_storage_devices_by_company',6,6,0Kind of works! Notably, has the amazing cycling database offline for you, if you fall within the 6 area downloads. It is worth supporting these people beyond the 6 free downloads however.
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.
There are unlisted articles, also show them or only show them.