doi | A Rails app for minting Digital Object Identifiers
kandi X-RAY | doi Summary
kandi X-RAY | doi Summary
Mint == About - Mint is a Rails app for minting Digital Object Identifiers (DOIs). How does it work? - The only input required by the user is a Pure ID. Using this, Mint retrieves metadata from Pure and prepares metadata for the target DOI Registration Agent. DOIs and URLs (to which the DOIs resolve) are generated automatically for Pure portal and together with the metadata are used to mint a DOI with the target DOI Registration Agent. DOI minting transactions are stored in a database. DOIs can be reserved for deferred minting. A full-text search facility is available for minted DOIs when the database adapter is PostgreSQL. Supported DOI registration agents - ![DataCite logo] /app/assets/images/datacite-logo.png). Mint currently works with DataCite which provides DOIs for datasets and theses (doctoral and master’s). DataCite’s metadata schema version 4.0 is supported. Ruby version - 2.1. Database creation - Hosted databases such as Postgres will need to be created, together with a database user.
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Lines of Code : 161License : Non-SPDX (Apache License 2.0)
Copydef atrous_conv2d_transpose(value,
filters,
output_shape,
rate,
padding,
name=None):
"""The transpose of `at def conv1d_transpose(
input, # pylint: disable=redefined-builtin
filters,
output_shape,
strides,
padding="SAME",
data_format="NWC",
dilations=None,
name=None):
"""The transpose of `conv1d`.
This operation is some def conv_transpose(input, # pylint: disable=redefined-builtin
filters,
output_shape,
strides,
padding="SAME",
data_format=None,
dilatio Community Discussions
Trending Discussions on doi
QUESTION
I am running a multivariate model (4 response variables) with two random effects using MCMCglmm(). I am currently using a inverse Wishart prior.
ANSWER
Answered 2021-Jun-12 at 01:25This is a two-part question:
- what priors should I use for a multivariate random effect where the likelihood is concentrated at small values? (I am assuming that this is the reason you are looking for an alternative to the default inverse Wishart priors) [more suitable for CrossValidated]
- which of these are available in
MCMCglmm, and how do I implement them there? [good for Stack Overflow]
The general trick is to decompose the covariance matrix into a multivariate component (the correlation matrix or inverse correlation matrix or something) and a vector of scaling parameters for the standard deviations (or inverse standard deviations); Lemoine suggests U(0,100) for the scaling priors, which I think is bad (why flat? I can't get to the precise page of Gelman and Hill 2007 where they discuss which distribution to use for scaling priors ... but I would be a little surprised if they actually recommended a uniform distribution on the variance scale ...)
update having actually looked at your code (!): I think you're doing the right thing, except that nu=0.002 seems really extreme; see end for that discussion.
This is basically what MCMCglmm does, but it uses a different (IMO better) choice for the scaling priors. It sounds scary:
These priors are all from the non-central scaled F-distribution, which implies the prior for the standard deviation is a non-central folded scaled t-distribution (Gelman, 2006).
but it boils down to choosing four parameters, only two of which you really have to think about.
V: the prior mean variance (or the prior mean covariance matrix, if you have a multivariate random effect term). According to the course notes, "without loss of generality V can be set to one" (or in the case of a multivariate model, to an identity matrix)alpha.mu: we almost always want this to be zero (or as in your example, a vector of zeros); that way the prior for the standard deviation will be a Student t distribution. (There may be a use case foralpha.mu != 0, but I've never run across it.)alpha.V: withVset to 1 (or an identity matrix), this is the prior mean of the covariance matrix. A diagonal matrix with a reasonable scale for your problem is a good choicenu: the shape parameter; asnu→ ∞ we get a half-Normal prior for the standard deviations, withnu=1 we get a Cauchy distribution. Smaller values have fatter tails (less conservative/allowing broader samples, but also giving more danger of weird sampling behaviour in the tails).
For the univariate case Hadfield says the t prior with V=1 is
QUESTION
I have some high dimensional repeated measures data, and i am interested in fitting random forest model to investigate the suitability and predictive utility of such models. Specifically i am trying to implement the methods in the LongituRF package. The methods behind this package are detailed here :
Conveniently the authors provide some useful data generating functions for testing. So we have
...ANSWER
Answered 2021-Apr-09 at 14:46When the function DataLongGenerator() creates Z, it's a random uniform data in a matrix. The actual coding is
QUESTION
i am trying to develop a quarkus app that will run as a function and will be triggered by a timer.
my function.json looks like this
...ANSWER
Answered 2021-Jun-08 at 08:29You get a NPE because Quarkus is not loaded properly so the CDI container didn't wired up the dependencies.
Quarkus only supports running Azure fonctions via its HTTP layer, it didn't support running arbitrary method like you setup.
You can have a look at the following guide for Quarkus Azure fonction support: https://quarkus.io/guides/azure-functions-http
You can propose an extension proposal to support this kind of Azure function via a new extension proposal on the Quarkus github repository: https://github.com/quarkusio/quarkus/issues/new?assignees=&labels=kind%2Fextension-proposal&template=extension_proposal.md&title=
QUESTION
I want to parse xml using xpath 2.0 or 3.0 expressions. I would like to use the most updated version for XPath, so I download Saxon jars. This is my code:
...ANSWER
Answered 2021-Jun-03 at 23:40XdmNode.getExternalNode() will only return a result if the XDM node is a wrapper/view of an external node such as a DOM node. A node built using the Saxon DocumentBuilder is a native XDM node, not a view of an external DOM node. If you want to use DOM with Saxon you can - just build the DOM node externally and wrap it using DocumentBuilder.wrap(domNode). But note that Saxon is 5 to 10 times slower when processing DOM nodes than when using its native XDM tree model.
QUESTION
I'm currently struggeling with my BibLaTeX file. I wanna separate the bibtex entries which are connected by the last name of the author (as you can see with the first and second entry). Also i wanna turn the (Hrsg.) Tag like the rest of the author information in bold.
below you can find a mre where the magic happens.
regards and stay healthy!
...ANSWER
Answered 2021-Jun-05 at 19:18You already know how to make the author names bold from biblatex: customizing bibliography entry - the same technique can be used for the editorstrg:
QUESTION
I need to prepare queries that are made of characters strings (DOI, Digital Object Identifier) stored in a data frame. All strings associated with the same case have to be joined to produce one query.
The df looks like this:
Case DOI 1 1212313/dfsjk23 1 322332/jdkdsa12 2 21323/xsw.w3 2 311331313/q1231 2 1212121/1231312The output should be a data frame looking like this:
Case Query 1 DO=(1212313/dfsjk23 OR 322332/jdkdsa12) 2 DO=(21323/xsw.w3 OR 311331313/q1231 OR 1212121/1231312)The prefix ("DO="), suffix (")") and "OR" are not critical, I can add them later, but how to aggregate character strings based on a case number?
...ANSWER
Answered 2021-Jun-02 at 17:37We can use glue with str_c to collapse the 'DOI' column after grouping by 'Case'
QUESTION
I would like to remove the DOI from the bibliographic references in my markdown script. Is there a way I can do this?
Here is my markdown file:
...ANSWER
Answered 2021-May-29 at 10:56I am assuming that you want to have this done on the fly while knitting the PDF.
The way the references are rendered is controlled by the applied citation styles.
So, one way would be to change the citation style and in the YAML header to a style that does not include the DOI (note that for the PDF output you would need to add the natbib line).
QUESTION
from my knowledge, Power Spectral Density (PSD) should stay relatively constant with the total time sampled (or aka. N-points sampled), however I have having trouble obtaining this result.
As I know from Discrete Fourier Transform (DFT), the amplitude normalization is 1/N. (e.g Amplitude Spectrum = DFT/N). However, from various sources, the PSD is defined as (DFT * DFT-conjugate / N).
How can this be possible? It is true that the Amplitude Spectrum has a 1/N normalization constant, then shouldn't the PSD have a 1/N^2 normalization constant (since DFT is proportional to N and so is its conjugate).
More specifically, I am trying to calcuated the PSD of a continuous electric field wave using the Eq. 9 of this paper. However I can't make sense of it's constants infront of the DFT since the factors of N's cancel out leaving behind only the summation of the window function squared. I tested this result and found that the PSD does not stay relatively constant with sampling size.
In summary, I have having troubles since my PSD varies with the amount of total time of the signal sampled. Any help would be great, thanks!
...ANSWER
Answered 2021-May-27 at 03:29I've found the PSD of a time-series does increase linearly with the number of points sampled, N, however, an appropriately FITTED function (or some sort of averaging) allows the PSD to remain constant with N. One would then take the PSD at a point on this fitted function.
This is a direct result of conserving the area of a curve, AKA Plancherel's theorem.
QUESTION
I have a 73 million row dataset, and I need to filter out rows that match any of a few conditions. I have been doing this with Boolean indexing, but it's taking a really long time (~30mins) and I want to know if I can make it faster (e.g. fancy indexing, np.where, np.compress?)
My code:
...ANSWER
Answered 2021-May-18 at 11:13If you have this many rows, I imagine it will be faster to first remove the records one step at a time. Regex is typically slow so you could use that as a last step with a much smaller data frame.
For example:
QUESTION
I'm currently struggeling with my BibLaTeX file. I wanna turn these two infos into bold.
I'm using the template below and cannot find the right place to insert a textbf or a mkbibbold property and even don't know if this is the right property in this use case. Every attempt is failing and / or crashing my whole project.
Here is a mre (Thanks to @samcarter_is_at_topanswers.xyz) The %%%%% area is where the magic happens..
regards and stay healthy!
...ANSWER
Answered 2021-May-18 at 12:57As a quick hack, you could redefine:
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