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Handling of manual refresh for publications list (#56)
* made the link on the news page to the actual publication item * cosmetics + force doi in getcomputo-pub.fsx, fix badge alignment in publications.ejs * updated publications metadata to include DOI * Enhance publication display: update DOI handling, improve layout, and add source links * Refactor BibTeX handling and update mock papers - Renamed `getAbstract` to `getBibTeX` for clarity and updated its implementation to return the BibTeX entry. - Introduced a new function `getAbstract` to extract the abstract from a BibTeX entry. - Added `getBibTeXFromDict` to retrieve BibTeX from a dictionary containing repository objects. - Updated `getAbstractFromDict` to utilize the new BibTeX handling functions. - Enhanced the `publications.ejs` template to generate BibTeX entries directly from the item data. * Add lightbox filter to author guidelines for enhanced image display * Enhance GitHub Actions workflow with forced run option and update publication handling * Remove unnecessary callout formatting from README.md [skip ci] * Comment out QUARTO_PROJECT_RENDER_ALL exit check in getcomputo-pub.fsx * Update environment variable for GitHub token to API_GITHUB_TOKEN because GITHUB_* is forbidden for repo secrets [skip ci] * Add API_GITHUB_TOKEN environment variable for publication refresh step * Update publications from getcomputo-pub.fsx [skip ci] * remove lightbox extension as it is included in quarto 1.7 --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
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‎.github/workflows/build.yml‎

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branches:
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- master
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workflow_dispatch:
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inputs:
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force:
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description: 'Set to "true" to mark this run as forced when manually triggered'
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required: false
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default: 'false'
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jobs:
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build:
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# Skip this job on push events when the head commit message contains [skip ci]
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if: ${{ github.event_name != 'push' || !contains(github.event.head_commit.message, '[skip ci]') }}
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permissions:
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contents: write
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runs-on: ubuntu-latest
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steps:
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- name: Checkout code
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uses: actions/checkout@v3
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with:
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fetch-depth: 0
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persist-credentials: true
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- name: Setup Quarto
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uses: quarto-dev/quarto-actions/setup@v2
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- name: refresh publications and commit changes
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if: ${{ github.event_name == 'workflow_dispatch' || github.event.inputs.force == 'true' }}
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env:
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API_GITHUB_TOKEN: ${{ secrets.API_GITHUB_TOKEN }}
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run: |
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dotnet fsi getcomputo-pub.fsx
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git config user.name "github-actions[bot]"
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git config user.email "github-actions[bot]@users.noreply.github.com"
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# Stage the generated files (ignore errors if files missing)
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git add site/published.yml site/pipeline.yml site/mock-papers.yml || true
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# Only commit if there are staged changes
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if git diff --staged --quiet; then
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echo "No publication changes to commit"
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else
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git commit -m "Update publications from getcomputo-pub.fsx [skip ci]"
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# push to the branch that triggered the workflow
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git push origin HEAD:${{ github.ref_name }}
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fi
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- name: Build site
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uses: quarto-dev/quarto-actions/render@v2
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- name: Upload artifact

‎README.md‎

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Our website has been built with [Quarto](https://quarto.org), an open-source scientific and technical publishing system. The first thing you need to compile the website is therefore to install Quarto, which can be done by downloading the corresponing installer here: <https://quarto.org/docs/get-started/>.
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::: {.callout-note}
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## Positron
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If you are using the new [Positron IDE](https://positron.posit.co), quarto is already bundled with it. You can simply type `which quarto` within the built-in terminal in Positron and add the returned path to your `PATH`.
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:::
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### Microsoft DotNet SDK
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‎_quarto.yml‎

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project:
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type: website
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output-dir: _site
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# pre-render: dotnet fsi getcomputo-pub.fsx
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website:
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title: COMPUTO
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site-url: https://computo.sfds.asso.fr/

‎getcomputo-pub.fsx‎

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open System.Threading.Tasks
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// exit if QUARTO_PROJECT_RENDER_ALL is set in the environment
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if System.Environment.GetEnvironmentVariable("QUARTO_PROJECT_RENDER_ALL") = null then
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printfn "QUARTO_PROJECT_RENDER_ALL is not set, exiting."
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exit 0
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// if System.Environment.GetEnvironmentVariable("QUARTO_PROJECT_RENDER_ALL") = null then
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// printfn "QUARTO_PROJECT_RENDER_ALL is not set, exiting."
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// exit 0
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// Load environment variables from .env file
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Env.Load(".env-secret")
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let client =
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let client = new GitHubClient(new ProductHeaderValue("computo"))
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// Using environment variable for token is a good security practice
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match System.Environment.GetEnvironmentVariable("GITHUB_TOKEN") with
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match System.Environment.GetEnvironmentVariable("API_GITHUB_TOKEN") with
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| null
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| "" -> client // No authentication
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| token ->

‎site/mock-papers.yml‎

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- abstract': >-
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- abstract'@: >-
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We present a new technique called “t-SNE” that visualizes
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high-dimensional data by giving each datapoint a location in a two
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or three-dimensional map. The technique is a variation of Stochastic
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Neighbor Embedding {[}@hinton:stochastic{]} that is much easier to
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optimize, and produces significantly better visualizations by
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reducing the tendency to crowd points together in the center of the
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map. t-SNE is better than existing techniques at creating a single
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map that reveals structure at many different scales. This is
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particularly important for high-dimensional data that lie on several
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different, but related, low-dimensional manifolds, such as images of
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objects from multiple classes seen from multiple viewpoints. For
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visualizing the structure of very large data sets, we show how t-SNE
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can use random walks on neighborhood graphs to allow the implicit
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structure of all the data to influence the way in which a subset of
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the data is displayed. We illustrate the performance of t-SNE on a
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wide variety of data sets and compare it with many other
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non-parametric visualization techniques, including Sammon mapping,
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Isomap, and Locally Linear Embedding. The visualization produced by
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t-SNE are significantly better than those produced by other
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techniques on almost all of the data sets.
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authors@: Laurens van der Maaten and Geoffrey Hinton
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bibtex@: >+
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@article{van_der_maaten2008,
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author = {van der Maaten, Laurens and Hinton, Geoffrey},
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publisher = {French Statistical Society},
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title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
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journal = {Computo},
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date = {2008-08-11},
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doi = {10.57750/xxxxxx},
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issn = {2824-7795},
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langid = {en},
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abstract = {We present a new technique called “t-SNE” that visualizes
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high-dimensional data by giving each datapoint a location in a two
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or three-dimensional map. The technique is a variation of Stochastic
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Neighbor Embedding {[}@hinton:stochastic{]} that is much easier to
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optimize, and produces significantly better visualizations by
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reducing the tendency to crowd points together in the center of the
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map. t-SNE is better than existing techniques at creating a single
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map that reveals structure at many different scales. This is
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particularly important for high-dimensional data that lie on several
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different, but related, low-dimensional manifolds, such as images of
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objects from multiple classes seen from multiple viewpoints. For
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visualizing the structure of very large data sets, we show how t-SNE
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can use random walks on neighborhood graphs to allow the implicit
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structure of all the data to influence the way in which a subset of
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the data is displayed. We illustrate the performance of t-SNE on a
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wide variety of data sets and compare it with many other
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non-parametric visualization techniques, including Sammon mapping,
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Isomap, and Locally Linear Embedding. The visualization produced by
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t-SNE are significantly better than those produced by other
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techniques on almost all of the data sets.}
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}
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date@: 2008-08-11
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description@: >
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This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
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doi@: 10.57750/xxxxxx
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draft@: false
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journal@: Computo
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pdf@: ''
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repo@: published-paper-tsne
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title@: Visualizing Data using t-SNE (mock contributon)
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url@: ''
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year@: 2008
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abstract': >-
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We present a new technique called “t-SNE” that visualizes
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high-dimensional data by giving each datapoint a location in a two
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or three-dimensional map. The technique is a variation of Stochastic
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bibtex: >+
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@article{van_der_maaten2008,
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author = {van der Maaten, Laurens and Hinton, Geoffrey},
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publisher = {Société Française de Statistique},
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title = {Visualizing {Data} Using {t-SNE:} A Practical Computo Example
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(Mock)},
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publisher = {French Statistical Society},
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title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
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journal = {Computo},
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date = {2008-08-11},
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url = {https://computo.sfds.asso.fr/published-paper-tsne},
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doi = {10.57750/xxxxxx},
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issn = {2824-7795},
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langid = {en},
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abstract = {We present a new technique called “t-SNE” that visualizes
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date: 2008-08-11
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description: >
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This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
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doi: ''
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doi: 10.57750/xxxxxx
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draft: false
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journal: Computo
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pdf: ''
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repo: published-paper-tsne
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title: Visualizing Data using t-SNE (mock contributon)
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url: https://computo-journal.org/published-paper-tsne
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url: ''
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year: 2008
67-
- abstract': >-
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- abstract'@: >-
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We present a new technique called “t-SNE” that visualizes
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high-dimensional data by giving each datapoint a location in a two
134+
or three-dimensional map. The technique is a variation of Stochastic
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Neighbor Embeddi{[}@hinton:stochastic{]} that is much easier to
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optimize, and produces significantly better visualizations by
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reducing the tendency to crowd points together in the center of the
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map. t-SNE is better than existing techniques at creating a single
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map that reveals structure at many different scales. This is
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particularly important for high-dimensional data that lie on several
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different, but related, low-dimensional manifolds, such as images of
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objects from multiple classes seen from multiple viewpoints. For
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visualizing the structure of very large data sets, we show how t-SNE
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can use random walks on neighborhood graphs to allow the implicit
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structure of all the data to influence the way in which a subset of
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the data is displayed. We illustrate the performance of t-SNE on a
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wide variety of data sets and compare it with many other
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non-parametric visualization techniques, including Sammon mapping,
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Isomap, and Locally Linear Embedding. The visualization produced by
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t-SNE are significantly better than those produced by other
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techniques on almost all of the data sets.
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authors@: Laurens van der Maaten and Geoffrey Hinton
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bibtex@: >+
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@article{van_der_maaten2008,
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author = {van der Maaten, Laurens and Hinton, Geoffrey},
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publisher = {French Statistical Society},
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title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
158+
journal = {Computo},
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date = {2008-08-11},
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doi = {10.57750/xxxxxx},
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issn = {2824-7795},
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langid = {en},
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abstract = {We present a new technique called “t-SNE” that visualizes
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high-dimensional data by giving each datapoint a location in a two
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or three-dimensional map. The technique is a variation of Stochastic
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Neighbor Embeddi{[}@hinton:stochastic{]} that is much easier to
167+
optimize, and produces significantly better visualizations by
168+
reducing the tendency to crowd points together in the center of the
169+
map. t-SNE is better than existing techniques at creating a single
170+
map that reveals structure at many different scales. This is
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particularly important for high-dimensional data that lie on several
172+
different, but related, low-dimensional manifolds, such as images of
173+
objects from multiple classes seen from multiple viewpoints. For
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visualizing the structure of very large data sets, we show how t-SNE
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can use random walks on neighborhood graphs to allow the implicit
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structure of all the data to influence the way in which a subset of
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the data is displayed. We illustrate the performance of t-SNE on a
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wide variety of data sets and compare it with many other
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non-parametric visualization techniques, including Sammon mapping,
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Isomap, and Locally Linear Embedding. The visualization produced by
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t-SNE are significantly better than those produced by other
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techniques on almost all of the data sets.}
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}
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date@: 2008-08-11
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description@: >
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This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
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doi@: 10.57750/xxxxxx
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draft@: false
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journal@: Computo
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pdf@: ''
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repo@: published-paper-tsne-R
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title@: Visualizing Data using t-SNE (mock contributon)
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url@: ''
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year@: 2008
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abstract': >-
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We present a new technique called “t-SNE” that visualizes
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high-dimensional data by giving each datapoint a location in a two
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or three-dimensional map. The technique is a variation of Stochastic
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@article{van_der_maaten2008,
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author = {van der Maaten, Laurens and Hinton, Geoffrey},
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publisher = {French Statistical Society},
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title = {Visualizing {Data} Using {t-SNE:} A Practical {Computo}
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Example (Mock)},
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title = {Visualizing {Data} Using {t-SNE} (Mock Contributon)},
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journal = {Computo},
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date = {2008-08-11},
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url = {https://computo-journal.org/published-paper-tsne-R},
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doi = {10.57750/xxxxxx},
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issn = {2824-7795},
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langid = {en},
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abstract = {We present a new technique called “t-SNE” that visualizes
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date: 2008-08-11
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description: >
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This page is a reworking of the original t-SNE article using the Computo template. It aims to help authors submitting to the journal by using some advanced formatting features. We warmly thank the authors of t-SNE and the editor of JMLR for allowing us to use their work to illustrate the Computo spirit.
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doi: ''
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doi: 10.57750/xxxxxx
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draft: false
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journal: Computo
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pdf: ''
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repo: published-paper-tsne-R
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title: Visualizing Data using t-SNE (mock contributon)
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url: https://computo-journal.org/published-paper-tsne-R
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url: ''
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year: 2008

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