remove generated files from repo, update .gitignore to ignore generated files

This commit is contained in:
Pim Nelissen
2026-02-08 18:57:54 +01:00
parent ac6e5ea8cf
commit 1c379a8424
92 changed files with 2 additions and 32893 deletions

2
.gitignore vendored Normal file
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public/
resources/

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<!DOCTYPE html>
<html lang="en">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>Pim Nelissen</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
<meta name="description" content="">
<meta name="keywords" content="">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="404 Page not found">
<meta property="og:url" content="//localhost:1313/404.html">
<meta property="og:site_name" content="Pim Nelissen">
<meta property="og:title" content="404 Page not found">
<meta property="og:locale" content="en">
<meta property="og:type" content="website">
<link rel="canonical" href="//localhost:1313/404.html">
<link rel="preload" href="/fonts/fa-brands-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-solid-900.woff2" as="font" type="font/woff2" crossorigin>
<link rel="stylesheet" href="/css/coder.css" media="screen">
<link rel="stylesheet" href="/css/coder-dark.css" media="screen">
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" sizes="any">
<link rel="icon" type="image/png" href="/images/favicon-32x32.png" sizes="32x32">
<link rel="icon" type="image/png" href="/images/favicon-16x16.png" sizes="16x16">
<link rel="apple-touch-icon" href="/images/apple-touch-icon.png">
<link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png">
<link rel="manifest" href="/site.webmanifest">
<link rel="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
</head>
<body class="preload-transitions colorscheme-auto">
<div class="float-container">
<a id="dark-mode-toggle" class="colorscheme-toggle">
<i class="fa-solid fa-adjust fa-fw" aria-hidden="true"></i>
</a>
</div>
<main class="wrapper">
<nav class="navigation">
<section class="container">
<a class="navigation-title" href="//localhost:1313/">
Pim Nelissen
</a>
<input type="checkbox" id="menu-toggle" />
<label class="menu-button float-right" for="menu-toggle">
<i class="fa-solid fa-bars fa-fw" aria-hidden="true"></i>
</label>
<ul class="navigation-list">
<li class="navigation-item">
<a class="navigation-link " href="/cv/">CV</a>
</li>
<li class="navigation-item">
<a class="navigation-link " href="/projects/">Projects</a>
</li>
<li class="navigation-item">
<a class="navigation-link " href="/contact/">Contact</a>
</li>
</ul>
</section>
</nav>
<div class="content">
<section class="container centered">
<div class="error">
<h1>404</h1>
<h2>Page Not Found</h2>
<p>Sorry, this page does not exist.<br />You can head back to the <a href="//localhost:1313/">homepage</a>.</p>
</div>
</section>
</div>
<footer class="footer">
<section class="container">
©
2023 -
2025
Pim Nelissen
·
Powered by <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
</section>
</footer>
</main>
<script src="/js/coder.js"></script>
</body>
</html>

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<!DOCTYPE html>
<html lang="en">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>Categories · Pim Nelissen</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
<meta name="description" content="">
<meta name="keywords" content="">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Categories">
<meta property="og:url" content="//localhost:1313/categories/">
<meta property="og:site_name" content="Pim Nelissen">
<meta property="og:title" content="Categories">
<meta property="og:locale" content="en">
<meta property="og:type" content="website">
<link rel="canonical" href="//localhost:1313/categories/">
<link rel="preload" href="/fonts/fa-brands-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-solid-900.woff2" as="font" type="font/woff2" crossorigin>
<link rel="stylesheet" href="/css/coder.css" media="screen">
<link rel="stylesheet" href="/css/coder-dark.css" media="screen">
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" sizes="any">
<link rel="icon" type="image/png" href="/images/favicon-32x32.png" sizes="32x32">
<link rel="icon" type="image/png" href="/images/favicon-16x16.png" sizes="16x16">
<link rel="apple-touch-icon" href="/images/apple-touch-icon.png">
<link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png">
<link rel="manifest" href="/site.webmanifest">
<link rel="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
<link rel="alternate" type="application/rss+xml" href="/categories/index.xml" title="Pim Nelissen" />
</head>
<body class="preload-transitions colorscheme-auto">
<div class="float-container">
<a id="dark-mode-toggle" class="colorscheme-toggle">
<i class="fa-solid fa-adjust fa-fw" aria-hidden="true"></i>
</a>
</div>
<main class="wrapper">
<nav class="navigation">
<section class="container">
<a class="navigation-title" href="//localhost:1313/">
Pim Nelissen
</a>
<input type="checkbox" id="menu-toggle" />
<label class="menu-button float-right" for="menu-toggle">
<i class="fa-solid fa-bars fa-fw" aria-hidden="true"></i>
</label>
<ul class="navigation-list">
<li class="navigation-item">
<a class="navigation-link " href="/cv/">CV</a>
</li>
<li class="navigation-item">
<a class="navigation-link " href="/projects/">Projects</a>
</li>
<li class="navigation-item">
<a class="navigation-link " href="/contact/">Contact</a>
</li>
</ul>
</section>
</nav>
<div class="content">
<section class="container taxonomy">
<header>
<h1 class="title">
<a class="title-link" href="//localhost:1313/categories/">Categories</a>
</h1>
</header>
<ul>
</ul>
</section>
</div>
<footer class="footer">
<section class="container">
©
2023 -
2025
Pim Nelissen
·
Powered by <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
</section>
</footer>
</main>
<script src="/js/coder.js"></script>
</body>
</html>

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<?xml version="1.0" encoding="utf-8" standalone="yes"?>
<rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom">
<channel>
<title>Categories on Pim Nelissen</title>
<link>//localhost:1313/categories/</link>
<description>Recent content in Categories on Pim Nelissen</description>
<generator>Hugo</generator>
<language>en</language>
<atom:link href="//localhost:1313/categories/index.xml" rel="self" type="application/rss+xml" />
</channel>
</rss>

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<!DOCTYPE html>
<html lang="en">
<head><script src="/livereload.js?mindelay=10&amp;v=2&amp;port=1313&amp;path=livereload" data-no-instant defer></script>
<title>
Contact · Pim Nelissen
</title>
<meta charset="utf-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<meta name="color-scheme" content="light dark">
<meta name="author" content="Pim Nelissen">
<meta name="description" content="Email: pi0274ne-s@student.lu.se">
<meta name="keywords" content="">
<meta name="twitter:card" content="summary">
<meta name="twitter:title" content="Contact">
<meta name="twitter:description" content="Email: pi0274ne-s@student.lu.se">
<meta property="og:url" content="//localhost:1313/contact/">
<meta property="og:site_name" content="Pim Nelissen">
<meta property="og:title" content="Contact">
<meta property="og:description" content="Email: pi0274ne-s@student.lu.se">
<meta property="og:locale" content="en">
<meta property="og:type" content="article">
<link rel="canonical" href="//localhost:1313/contact/">
<link rel="preload" href="/fonts/fa-brands-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-regular-400.woff2" as="font" type="font/woff2" crossorigin>
<link rel="preload" href="/fonts/fa-solid-900.woff2" as="font" type="font/woff2" crossorigin>
<link rel="stylesheet" href="/css/coder.css" media="screen">
<link rel="stylesheet" href="/css/coder-dark.css" media="screen">
<link rel="icon" type="image/svg+xml" href="/images/favicon.svg" sizes="any">
<link rel="icon" type="image/png" href="/images/favicon-32x32.png" sizes="32x32">
<link rel="icon" type="image/png" href="/images/favicon-16x16.png" sizes="16x16">
<link rel="apple-touch-icon" href="/images/apple-touch-icon.png">
<link rel="apple-touch-icon" sizes="180x180" href="/images/apple-touch-icon.png">
<link rel="manifest" href="/site.webmanifest">
<link rel="mask-icon" href="/images/safari-pinned-tab.svg" color="#5bbad5">
</head>
<body class="preload-transitions colorscheme-auto">
<div class="float-container">
<a id="dark-mode-toggle" class="colorscheme-toggle">
<i class="fa-solid fa-adjust fa-fw" aria-hidden="true"></i>
</a>
</div>
<main class="wrapper">
<nav class="navigation">
<section class="container">
<a class="navigation-title" href="//localhost:1313/">
Pim Nelissen
</a>
<input type="checkbox" id="menu-toggle" />
<label class="menu-button float-right" for="menu-toggle">
<i class="fa-solid fa-bars fa-fw" aria-hidden="true"></i>
</label>
<ul class="navigation-list">
<li class="navigation-item">
<a class="navigation-link " href="/cv/">CV</a>
</li>
<li class="navigation-item">
<a class="navigation-link " href="/projects/">Projects</a>
</li>
<li class="navigation-item">
<a class="navigation-link " href="/contact/">Contact</a>
</li>
</ul>
</section>
</nav>
<div class="content">
<section class="container page">
<article>
<header>
<h1 class="title">
<a class="title-link" href="//localhost:1313/contact/">
Contact
</a>
</h1>
</header>
<p>Email: <a href="mailto:pi0274ne-s@student.lu.se" >pi0274ne-s@student.lu.se</a></p>
</article>
</section>
</div>
<footer class="footer">
<section class="container">
©
2023 -
2025
Pim Nelissen
·
Powered by <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
</section>
</footer>
</main>
<script src="/js/coder.js"></script>
</body>
</html>

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@ -1,835 +0,0 @@
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background-color: #212121; }
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body.colorscheme-dark h2 .title-link,
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body.colorscheme-auto h2 .heading-link,
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body.colorscheme-auto h4 .heading-link,
body.colorscheme-auto h5 .heading-link,
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body.colorscheme-auto h2 .title-link,
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body.colorscheme-auto h4 .title-link,
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body.colorscheme-auto table tr th:first-child {
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body.colorscheme-auto table tr td:last-child,
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body.colorscheme-dark .content .post .tags .tag a:active {
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color: #dadada; }
body.colorscheme-dark .content .list ul li .title:hover, body.colorscheme-dark .content .list ul li .title:focus {
color: #42a5f5; }
body.colorscheme-dark .content .centered .about ul li a {
color: #dadada; }
body.colorscheme-dark .content .centered .about ul li a:hover, body.colorscheme-dark .content .centered .about ul li a:focus {
color: #42a5f5; }
@media (prefers-color-scheme: dark) {
body.colorscheme-auto .content .post .tags .tag {
background-color: #424242; }
body.colorscheme-auto .content .post .tags .tag a {
color: #dadada; }
body.colorscheme-auto .content .post .tags .tag a:active {
color: #dadada; }
body.colorscheme-auto .content .list ul li .title {
color: #dadada; }
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color: #42a5f5; }
body.colorscheme-auto .content .centered .about ul li a {
color: #dadada; }
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border-bottom: 1px solid #212121; }
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body.colorscheme-auto .notice .notice-title {
border-bottom: 1px solid #212121; } }
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body.colorscheme-dark .navigation span {
color: #dadada; }
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color: #42a5f5; }
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body.colorscheme-dark .navigation .navigation-list {
background-color: #212121;
border-top: solid 2px #424242;
border-bottom: solid 2px #424242; } }
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body.colorscheme-dark .navigation .navigation-list .menu-separator {
border-top: 2px solid #dadada; } }
@media only screen and (max-width: 768px) {
body.colorscheme-dark .navigation #menu-toggle:checked + label > i {
color: #424242; } }
body.colorscheme-dark .navigation i {
color: #dadada; }
body.colorscheme-dark .navigation i:hover, body.colorscheme-dark .navigation i:focus {
color: #42a5f5; }
body.colorscheme-dark .navigation .menu-button i:hover, body.colorscheme-dark .navigation .menu-button i:focus {
color: #dadada; }
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body.colorscheme-auto .navigation a,
body.colorscheme-auto .navigation span {
color: #dadada; }
body.colorscheme-auto .navigation a:hover, body.colorscheme-auto .navigation a:focus {
color: #42a5f5; } }
@media only screen and (prefers-color-scheme: dark) and (max-width: 768px) {
body.colorscheme-auto .navigation .navigation-list {
background-color: #212121;
border-top: solid 2px #424242;
border-bottom: solid 2px #424242; } }
@media only screen and (prefers-color-scheme: dark) and (max-width: 768px) {
body.colorscheme-auto .navigation .navigation-list .menu-separator {
border-top: 2px solid #dadada; } }
@media only screen and (prefers-color-scheme: dark) and (max-width: 768px) {
body.colorscheme-auto .navigation #menu-toggle:checked + label > i {
color: #424242; } }
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body.colorscheme-auto .navigation i {
color: #dadada; }
body.colorscheme-auto .navigation i:hover, body.colorscheme-auto .navigation i:focus {
color: #42a5f5; }
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border-color: #4f4f4f; }
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background-color: #212121;
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background-color: #424242;
border-color: #4f4f4f; }
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body.colorscheme-auto .tabs .tab-content {
background-color: #212121;
border-color: #4f4f4f; } }
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body.colorscheme-auto .footer a {
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color: #dadada;
background-color: #424242; }
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color: #42a5f5; }
@media only screen and (max-width: 768px) {
body.colorscheme-dark .float-container a:hover, body.colorscheme-dark .float-container a:focus {
color: #dadada; } }
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body.colorscheme-auto .float-container a {
color: #dadada;
background-color: #424242; }
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@media only screen and (prefers-color-scheme: dark) and (max-width: 768px) {
body.colorscheme-auto .float-container a:hover, body.colorscheme-auto .float-container a:focus {
color: #dadada; } }
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M.Sc. in Computational Science (Physics)
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📅 Sep 2024 Present
Focus on numerical methods for ODEs/PDEs using MATLAB.
B.Sc. in Physics
Lund University
📅 Sep 2020 Jun 2024
Python Deep learning Monte Carlo C# SQL
Thesis: Machine Learning for Statistical Assessment of Radioactive Decay Chains
Topics: Monte Carlo simulation, deep learning, C#, SQL, Python
💼 Professional Experience
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Sales Specialist
Jumbo Supermarkten Oss, Netherlands
📅 2018 2020">
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Lund University
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Focus on numerical methods for ODEs/PDEs using MATLAB. B.Sc. in Physics
Lund University
📅 Sep 2020 Jun 2024
Python Deep learning Monte Carlo C# SQL
Thesis: Machine Learning for Statistical Assessment of Radioactive Decay Chains Topics: Monte Carlo simulation, deep learning, C#, SQL, Python 💼 Professional Experience Link to heading Sales Specialist
Jumbo Supermarkten Oss, Netherlands
📅 2018 2020">
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Lund University
📅 Sep 2024 Present
Focus on numerical methods for ODEs/PDEs using MATLAB. B.Sc. in Physics
Lund University
📅 Sep 2020 Jun 2024
Python Deep learning Monte Carlo C# SQL
Thesis: Machine Learning for Statistical Assessment of Radioactive Decay Chains Topics: Monte Carlo simulation, deep learning, C#, SQL, Python 💼 Professional Experience Link to heading Sales Specialist
Jumbo Supermarkten Oss, Netherlands
📅 2018 2020">
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<p><strong>M.Sc. in Computational Science (Physics)</strong><br>
<em>Lund University</em><br>
📅 <em>Sep 2024 Present</em></p>
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<li>Focus on numerical methods for ODEs/PDEs using MATLAB.</li>
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<p><strong>B.Sc. in Physics</strong><br>
<em>Lund University</em><br>
📅 <em>Sep 2020 Jun 2024</em></p>
<p><code>Python</code> <code>Deep learning</code> <code>Monte Carlo</code> <code>C#</code> <code>SQL</code></p>
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<li>Thesis: <em>Machine Learning for Statistical Assessment of Radioactive Decay Chains</em></li>
<li>Topics: Monte Carlo simulation, deep learning, C#, SQL, Python</li>
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<p><strong>Sales Specialist</strong><br>
<em>Jumbo Supermarkten Oss, Netherlands</em><br>
📅 <em>2018 2020</em></p>
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<li>Managed product presentation and customer experience.</li>
<li>Occasionally led a team of 35 employees.</li>
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<p><strong>Sales Assistant</strong><br>
<em>Jumbo Supermarkten Oss, Netherlands</em><br>
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<p><strong>Bachelors Thesis</strong><br>
<strong>P. Nelissen</strong>, <em>Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains</em>, Lund University, 2024.<br>
📄 <a href="http://lup.lub.lu.se/student-papers/record/9168893" class="external-link" target="_blank" rel="noopener">Read Online</a></p>
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<p>I enjoy both in-depth technical challenges and broader scientific learning. Im an avid reader, enjoy the outdoors, and thrive in environments that balance curiosity, creativity, and rigor.</p>
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M.Sc. in Computational Science (Physics)
Lund University, Sweden 🇸🇪
Numerical ODE/PDE solvers MATLAB Monte Carlo Subatomic Physics
2020 - 2024
B.Sc. in Physics
Lund University, Sweden 🇸🇪
Python Deep learning Monte Carlo C# SQL
Bachelor thesis: &#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;, Lund University Publications, 2024. Open Access
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Numerical ODE/PDE solvers MATLAB Monte Carlo Subatomic Physics
2020 - 2024 B.Sc. in Physics Lund University, Sweden 🇸🇪
Python Deep learning Monte Carlo C# SQL
Bachelor thesis: &#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;, Lund University Publications, 2024. Open Access 💼 Professional Experience Link to heading 2018 - 2020 Sales Specialist Jumbo Supermarkten, Netherlands 🇳🇱">
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Numerical ODE/PDE solvers MATLAB Monte Carlo Subatomic Physics
2020 - 2024 B.Sc. in Physics Lund University, Sweden 🇸🇪
Python Deep learning Monte Carlo C# SQL
Bachelor thesis: &#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;, Lund University Publications, 2024. Open Access 💼 Professional Experience Link to heading 2018 - 2020 Sales Specialist Jumbo Supermarkten, Netherlands 🇳🇱">
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<li class="timeline-content">Bachelor thesis: <i>"Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains"</i>, Lund University Publications, 2024. <a href="http://lup.lub.lu.se/student-papers/record/9168893" target="_blank">Open Access</a></li>
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<h4>Sales Specialist</h4>
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<p><i class="fas fa-lightbulb"></i> <code>GNU/Linux</code> <code>Docker</code> <code>Nextcloud</code></p>
<li class="timeline-content">Maintained Linux infrastructure for a digital rights non-profit.</li>
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<p><i class="fas fa-lightbulb"></i> <code>Teaching</code> <code>Organising</code> <code>Nextcloud</code></p>
<li class="timeline-content">I organised and taught a digital skills course to uplift senior citizens in the community.</li>
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<h1>Pim Nelissen</h1>
<h2>M.Sc. Candidate in Computational Science/Physics<br>B.Sc. Physics<br></h2>
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<title>Random forest models for the prediction of biome types and climate variables</title>
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<pubDate>Thu, 14 Nov 2024 00:00:00 +0000</pubDate>
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<description>&lt;div class=&#34;notice tip&#34;&gt;&#xA; &lt;div class=&#34;notice-title&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-lightbulb&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;Tip&#xA; &lt;/div&gt;&#xA; &lt;div class=&#34;notice-content&#34;&gt;The report is available &lt;a href=&#34;//localhost:1313/files/random-forests-biomes.pdf&#34; &gt;here&lt;/a&gt;.&lt;/div&gt;&#xA;&lt;/div&gt;&#xA;&#xA;&lt;h1 id=&#34;reflection&#34;&gt;&#xA; Reflection&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#reflection&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This project was a big learning moment when it comes selecting training and testing datasets appropriately in statistical learning. The model can only ever be as good as the data we use. It was one of the first times working with geographical, grid-based data, which was also interesting. Since all of the data worked with was directly fed from a model, it&amp;rsquo;s also important to know the limits of one&amp;rsquo;s original model which provided the data. Sometimes, the problem may not be our classifier or regression model, but simply that we did not have enough, or the right, information to properly distinguish data in the first place.&lt;/p&gt;</description>
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<title>Machine learning applied to radioactive decay data (Bachelor thesis)</title>
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<description>&lt;div class=&#34;notice tip&#34;&gt;&#xA; &lt;div class=&#34;notice-title&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-lightbulb&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;Tip&#xA; &lt;/div&gt;&#xA; &lt;div class=&#34;notice-content&#34;&gt;The paper is available for &lt;a href=&#34;http://lup.lub.lu.se/student-papers/record/9168893&#34; class=&#34;external-link&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;open access&lt;/a&gt;.&lt;/div&gt;&#xA;&lt;/div&gt;&#xA;&#xA;&lt;div class=&#34;notice tip&#34;&gt;&#xA; &lt;div class=&#34;notice-title&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-lightbulb&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;Tip&#xA; &lt;/div&gt;&#xA; &lt;div class=&#34;notice-content&#34;&gt;The code I wrote for the decay chain simulations is openly available on my &lt;a href=&#34;https://git.pimnel.com/pim/shn-decay-chains&#34; class=&#34;external-link&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Git instance&lt;/a&gt;.&lt;/div&gt;&#xA;&lt;/div&gt;&#xA;&#xA;&lt;h1 id=&#34;reflection&#34;&gt;&#xA; Reflection&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#reflection&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.&lt;/p&gt;</description>
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<description>&lt;head&gt;&#xA; &lt;link rel=&#34;stylesheet&#34; href=&#34;//localhost:1313/css/timeline.css&#34;&gt;&#xA;&lt;/head&gt;&#xA;&lt;h2 id=&#34;-education&#34;&gt;&#xA; 🎓 Education&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#-education&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;timeline&#34;&gt;&#xA; &lt;div class=&#34;timeline-item&#34;&gt;&#xA; &lt;div class=&#34;dot&#34;&gt;&lt;/div&gt;&#xA; &lt;div class=&#34;timeline-date&#34;&gt;2024 - &lt;i&gt;now&lt;/i&gt;&lt;/div&gt;&#xA; &lt;div class=&#34;timeline-content&#34;&gt;&#xA; &lt;h4&gt;M.Sc. in Computational Science (Physics)&lt;/h4&gt;&#xA; &lt;p&gt;&lt;i class=&#34;fas fa-university&#34;&gt;&lt;/i&gt; Lund University, Sweden 🇸🇪&lt;/p&gt;&#xA; &lt;p&gt;&lt;i class=&#34;fas fa-lightbulb&#34;&gt;&lt;/i&gt; &lt;code&gt;Numerical ODE/PDE solvers&lt;/code&gt; &lt;code&gt;MATLAB&lt;/code&gt; &lt;code&gt;Monte Carlo&lt;/code&gt; &lt;code&gt;Subatomic Physics&lt;/code&gt;&lt;/p&gt;&#xA; &lt;/div&gt;&#xA; &lt;/div&gt;&#xA; &lt;div class=&#34;timeline-item&#34;&gt;&#xA; &lt;div class=&#34;dot&#34;&gt;&lt;/div&gt;&#xA; &lt;div class=&#34;timeline-date&#34;&gt;2020 - 2024&lt;/div&gt;&#xA; &lt;div class=&#34;timeline-content&#34;&gt;&#xA; &lt;h4&gt;B.Sc. in Physics&lt;/h4&gt;&#xA; &lt;p&gt;&lt;i class=&#34;fas fa-university&#34;&gt;&lt;/i&gt; Lund University, Sweden 🇸🇪&lt;/p&gt;&#xA; &lt;p&gt;&lt;i class=&#34;fas fa-lightbulb&#34;&gt;&lt;/i&gt; &lt;code&gt;Python&lt;/code&gt; &lt;code&gt;Deep learning&lt;/code&gt; &lt;code&gt;Monte Carlo&lt;/code&gt; &lt;code&gt;C#&lt;/code&gt; &lt;code&gt;SQL&lt;/code&gt;&lt;/p&gt;&#xA; &lt;li class=&#34;timeline-content&#34;&gt;Bachelor thesis: &lt;i&gt;&#34;Scrutinizing the Schmidt Test and Exploring the Use of Machine Learning for Statistical Assessment of Radioactive Decay Chains&#34;&lt;/i&gt;, Lund University Publications, 2024. &lt;a href=&#34;http://lup.lub.lu.se/student-papers/record/9168893&#34; target=&#34;_blank&#34;&gt;Open Access&lt;/a&gt;&lt;/li&gt;&#xA; &lt;/div&gt;&#xA; &lt;/div&gt;&#xA;&lt;/div&gt;&#xA;&lt;hr&gt;&#xA;&lt;h2 id=&#34;-professional-experience&#34;&gt;&#xA; 💼 Professional Experience&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#-professional-experience&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;div class=&#34;timeline&#34;&gt;&#xA; &lt;div class=&#34;timeline-item&#34;&gt;&#xA; &lt;div class=&#34;dot&#34;&gt;&lt;/div&gt;&#xA; &lt;div class=&#34;timeline-date&#34;&gt;2018 - 2020&lt;/div&gt;&#xA; &lt;div class=&#34;timeline-content&#34;&gt;&#xA; &lt;h4&gt;Sales Specialist&lt;/h4&gt;&#xA; &lt;p&gt;&lt;i class=&#34;fas fa-briefcase&#34;&gt;&lt;/i&gt; Jumbo Supermarkten, Netherlands 🇳🇱&lt;/p&gt;</description>
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<description>&lt;h2 id=&#34;core-competencies&#34;&gt;&#xA; Core Competencies&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#core-competencies&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h2&gt;&#xA;&lt;p&gt;From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My &lt;a href=&#34;http://lup.lub.lu.se/student-papers/record/9168893&#34; class=&#34;external-link&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;bachelor degree thesis&lt;/a&gt; is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.&lt;/p&gt;</description>
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<p>This project was a big learning moment when it comes selecting training and testing datasets appropriately in statistical learning. The model can only ever be as good as the data we use. It was one of the first times working with geographical, grid-based data, which was also interesting. Since all of the data worked with was directly fed from a model, it&rsquo;s also important to know the limits of one&rsquo;s original model which provided the data. Sometimes, the problem may not be our classifier or regression model, but simply that we did not have enough, or the right, information to properly distinguish data in the first place.</p>
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<p>In this project, I developed some random forest models to predict biome classes (both binary and multi-class) as well as two continuous climate variables: vegetation carbon pool (VegC) and net primary productivity (NPP). To adress class imbalances, I used SMOTE up-sampling, which notably improved recall for underrepresented classes. By grid-search cross-validation I tried to tune the models better. Performance was evaluated using accuracy, precision, recall, and F₁ score for classifiers, and RMSE for regressors. Finally, I analyzed feature importance to better understand which climate variables, such as seasonal precipitation or extreme temperatures, were driving the model predictions.</p>
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<p>The binary biome classifier achieved up to 85.7% accuracy after SMOTE resampling, effectively distinguishing between temperate deciduous and mixed forests, with winter temperature and autumn precipitation emerging as key predictors. The multi-class classifier reached a weighted F₁ score of around 0.65, although it struggled to separate closely related biomes, reflecting the continuity between the biomes proves challenging for ML to solve. The regression models performed well overall, but revealed spatial biases around coastal and desert areas, suggesting the need to account for additional local processes like soil variability or ocean influences.</p>
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<p>This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.</p>
<p>On an intellectual level, it was a challenge in the beginning to adjust to the academic writing style of papers. Information is more dense and sometimes not explained in detail, referring the reader to some other paper. This trail of research papers that one can go down, even in a small topic as the one I was researching, is truly eye-opening to me. The amount of information that is out there is unimaginable to me. The project also introduced me to new applications of statistics and tested some old statistical knowledge, such as that on exponential distributions, statistical moments like variance and higher order ones. On top of that, I enjoyed learning about Monte Carlo simulations. I had always figured this term to sound complicated, but to learn that it is a rather easy methodology which has such strong use cases in various real-world applications was very interesting to learn. While not the main focus of my thesis, I also enjoyed researching the experimental background, of the process of superheavy synthesis and how detection is done.</p>
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<p>Experimental nuclear structure data coming from superheavy nuclei synthesis experiments often consists of correlated alpha decay chains. In the absence of neutron detectors - which would fully characterize the exit channel after the fusion-evaporation reaction - the sequence of decay energies and half-lives are the &lsquo;fingerprint&rsquo; of the exit channel itself. Experimental data in this region is sparse, and its interpretation can be liable to error or confirmation bias. A so-called &ldquo;Schmidt test&rdquo; is a method for determining the congruence of correlation times for a set of measurements of one decay step. Its outcome is not always entirely conclusive, however. This study evaluates the congruence derived from the Schmidt test using Monte Carlo simulated data with various level of contamination from incongruent data. Furthermore, the study also includes the evaluation of congruence of data stemming from single decays and multi-step decay chains. A multi-layer perceptron was trained on extracted features from simulated decay chain sets with one step. The Schmidt test performs well with larger decay sets and when the half-life of the contaminating species is longer than the original species by a factor 5 or 10. However, the test performs poorly in low counting statistics, where few recorded decay times are available. The newly proposed machine learning model outperforms the Schmidt test in certain high statistics scenarios, but also fails when few decay times are available. Its performance is also poor when the half-life of the contaminant is shorter than the original half-life. The learning behaviour of the model is analysed, showing significant contributions from higher statistical moments in training. Future work involves including chain correlations across multiple steps, alpha decay energies, as well as the potential use of alternative machine learning models.</p>
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<p>One of the key results of the thesis was the heatmap in Figure 1, showing how the amount of contamination in a decay time data set (that is, number of decay times from a species other than the one we are interested in) affects the Schmidt test and its conclusions. This was tested for various factors of contaminant half lives. The result is that the Schmidt test seems rather unsensitive to any contaminants with a half life on the same order of magnitude as the species we seek to study. This was further motivation to look beyond the Schmidt test, which solely relies on variance of the dataset, to see if other statistical methods (e.g. machine learning) could be used.</p>
<figure><img src="/images/st_factors.png"
alt="Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination."><figcaption>
<p>Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination.</p>
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<p>This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.</p>
<p>On an intellectual level, it was a challenge in the beginning to adjust to the academic writing style of papers. Information is more dense and sometimes not explained in detail, referring the reader to some other paper. This trail of research papers that one can go down, even in a small topic as the one I was researching, is truly eye-opening to me. The amount of information that is out there is unimaginable to me. The project also introduced me to new applications of statistics and tested some old statistical knowledge, such as that on exponential distributions, statistical moments like variance and higher order ones. On top of that, I enjoyed learning about Monte Carlo simulations. I had always figured this term to sound complicated, but to learn that it is a rather easy methodology which has such strong use cases in various real-world applications was very interesting to learn. While not the main focus of my thesis, I also enjoyed researching the experimental background, of the process of superheavy synthesis and how detection is done.</p>
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<p>Experimental nuclear structure data coming from superheavy nuclei synthesis experiments often consists of correlated alpha decay chains. In the absence of neutron detectors - which would fully characterize the exit channel after the fusion-evaporation reaction - the sequence of decay energies and half-lives are the &lsquo;fingerprint&rsquo; of the exit channel itself. Experimental data in this region is sparse, and its interpretation can be liable to error or confirmation bias. A so-called &ldquo;Schmidt test&rdquo; is a method for determining the congruence of correlation times for a set of measurements of one decay step. Its outcome is not always entirely conclusive, however. This study evaluates the congruence derived from the Schmidt test using Monte Carlo simulated data with various level of contamination from incongruent data. Furthermore, the study also includes the evaluation of congruence of data stemming from single decays and multi-step decay chains. A multi-layer perceptron was trained on extracted features from simulated decay chain sets with one step. The Schmidt test performs well with larger decay sets and when the half-life of the contaminating species is longer than the original species by a factor 5 or 10. However, the test performs poorly in low counting statistics, where few recorded decay times are available. The newly proposed machine learning model outperforms the Schmidt test in certain high statistics scenarios, but also fails when few decay times are available. Its performance is also poor when the half-life of the contaminant is shorter than the original half-life. The learning behaviour of the model is analysed, showing significant contributions from higher statistical moments in training. Future work involves including chain correlations across multiple steps, alpha decay energies, as well as the potential use of alternative machine learning models.</p>
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<p><img src="st_factors.png" alt="Schmidt Test heatmaps"></p>
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<meta name="twitter:description" content="Tip The paper is available for open access. Tip The code I wrote for the decay chain simulations is openly available on my Git instance. Reflection Link to heading This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.">
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<meta property="og:description" content="Tip The paper is available for open access. Tip The code I wrote for the decay chain simulations is openly available on my Git instance. Reflection Link to heading This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.">
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Machine learning applied to radioactive decay data (Bachelor thesis)
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<div class="notice-content">The paper is available for <a href="http://lup.lub.lu.se/student-papers/record/9168893" class="external-link" target="_blank" rel="noopener">open access</a>.</div>
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<p>This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.</p>
<p>On an intellectual level, it was a challenge in the beginning to adjust to the academic writing style of papers. Information is more dense and sometimes not explained in detail, referring the reader to some other paper. This trail of research papers that one can go down, even in a small topic as the one I was researching, is truly eye-opening to me. The amount of information that is out there is unimaginable to me. The project also introduced me to new applications of statistics and tested some old statistical knowledge, such as that on exponential distributions, statistical moments like variance and higher order ones. On top of that, I enjoyed learning about Monte Carlo simulations. I had always figured this term to sound complicated, but to learn that it is a rather easy methodology which has such strong use cases in various real-world applications was very interesting to learn. While not the main focus of my thesis, I also enjoyed researching the experimental background, of the process of superheavy synthesis and how detection is done.</p>
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<p>Experimental nuclear structure data coming from superheavy nuclei synthesis experiments often consists of correlated alpha decay chains. In the absence of neutron detectors - which would fully characterize the exit channel after the fusion-evaporation reaction - the sequence of decay energies and half-lives are the &lsquo;fingerprint&rsquo; of the exit channel itself. Experimental data in this region is sparse, and its interpretation can be liable to error or confirmation bias. A so-called &ldquo;Schmidt test&rdquo; is a method for determining the congruence of correlation times for a set of measurements of one decay step. Its outcome is not always entirely conclusive, however. This study evaluates the congruence derived from the Schmidt test using Monte Carlo simulated data with various level of contamination from incongruent data. Furthermore, the study also includes the evaluation of congruence of data stemming from single decays and multi-step decay chains. A multi-layer perceptron was trained on extracted features from simulated decay chain sets with one step. The Schmidt test performs well with larger decay sets and when the half-life of the contaminating species is longer than the original species by a factor 5 or 10. However, the test performs poorly in low counting statistics, where few recorded decay times are available. The newly proposed machine learning model outperforms the Schmidt test in certain high statistics scenarios, but also fails when few decay times are available. Its performance is also poor when the half-life of the contaminant is shorter than the original half-life. The learning behaviour of the model is analysed, showing significant contributions from higher statistical moments in training. Future work involves including chain correlations across multiple steps, alpha decay energies, as well as the potential use of alternative machine learning models.</p>
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<p>One of the key results of the thesis was the heatmap in Figure 1, showing how the amount of contamination in a decay time data set (that is, number of decay times from a species other than the one we are interested in) affects the Schmidt test and its conclusions. This was tested for various factors of contaminant half lives. The result is that the Schmidt test seems rather unsensitive to any contaminants with a half life on the same order of magnitude as the species we seek to study. This was further motivation to look beyond the Schmidt test, which solely relies on variance of the dataset, to see if other statistical methods (e.g. machine learning) could be used.</p>
<figure><img src="/images/st_factors.png"
alt="Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination."><figcaption>
<p>Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination.</p>
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<description>&lt;div class=&#34;notice tip&#34;&gt;&#xA; &lt;div class=&#34;notice-title&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-lightbulb&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;Tip&#xA; &lt;/div&gt;&#xA; &lt;div class=&#34;notice-content&#34;&gt;The paper is available for &lt;a href=&#34;http://lup.lub.lu.se/student-papers/record/9168893&#34; class=&#34;external-link&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;open access&lt;/a&gt;.&lt;/div&gt;&#xA;&lt;/div&gt;&#xA;&#xA;&lt;div class=&#34;notice tip&#34;&gt;&#xA; &lt;div class=&#34;notice-title&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-lightbulb&#34; aria-hidden=&#34;true&#34;&gt;&lt;/i&gt;Tip&#xA; &lt;/div&gt;&#xA; &lt;div class=&#34;notice-content&#34;&gt;The code I wrote for the decay chain simulations is openly available on my &lt;a href=&#34;https://git.pimnel.com/pim/shn-decay-chains&#34; class=&#34;external-link&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Git instance&lt;/a&gt;.&lt;/div&gt;&#xA;&lt;/div&gt;&#xA;&#xA;&lt;h1 id=&#34;reflection&#34;&gt;&#xA; Reflection&#xA; &lt;a class=&#34;heading-link&#34; href=&#34;#reflection&#34;&gt;&#xA; &lt;i class=&#34;fa-solid fa-link&#34; aria-hidden=&#34;true&#34; title=&#34;Link to heading&#34;&gt;&lt;/i&gt;&#xA; &lt;span class=&#34;sr-only&#34;&gt;Link to heading&lt;/span&gt;&#xA; &lt;/a&gt;&#xA;&lt;/h1&gt;&#xA;&lt;p&gt;This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.&lt;/p&gt;</description>
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<pubDate>Tue, 15 Jun 2021 00:00:00 +0000</pubDate>
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Solar forcing and its effect on seasonal climate variability in the Northern Hemisphere · Pim Nelissen
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Solar forcing and its effect on seasonal climate variability in the Northern Hemisphere
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<p>This 6 month journey has been one with many challenges, but also one of a lot of personal growth. Life was not always easy during this semester, which had caused unfortunate delays in the project. As my supervisor assured me, this is how it always goes in research. Plans continuously change, timetables need adjusting and ambitious ideas put on hold. This project then certainly made me improve my planning skills. Dealing with setbacks was also more common in this rather independent project, compared to any coursework before.</p>
<p>On an intellectual level, it was a challenge in the beginning to adjust to the academic writing style of papers. Information is more dense and sometimes not explained in detail, referring the reader to some other paper. This trail of research papers that one can go down, even in a small topic as the one I was researching, is truly eye-opening to me. The amount of information that is out there is unimaginable to me. The project also introduced me to new applications of statistics and tested some old statistical knowledge, such as that on exponential distributions, statistical moments like variance and higher order ones. On top of that, I enjoyed learning about Monte Carlo simulations. I had always figured this term to sound complicated, but to learn that it is a rather easy methodology which has such strong use cases in various real-world applications was very interesting to learn. While not the main focus of my thesis, I also enjoyed researching the experimental background, of the process of superheavy synthesis and how detection is done.</p>
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Official abstract
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<p>Experimental nuclear structure data coming from superheavy nuclei synthesis experiments often consists of correlated alpha decay chains. In the absence of neutron detectors - which would fully characterize the exit channel after the fusion-evaporation reaction - the sequence of decay energies and half-lives are the &lsquo;fingerprint&rsquo; of the exit channel itself. Experimental data in this region is sparse, and its interpretation can be liable to error or confirmation bias. A so-called &ldquo;Schmidt test&rdquo; is a method for determining the congruence of correlation times for a set of measurements of one decay step. Its outcome is not always entirely conclusive, however. This study evaluates the congruence derived from the Schmidt test using Monte Carlo simulated data with various level of contamination from incongruent data. Furthermore, the study also includes the evaluation of congruence of data stemming from single decays and multi-step decay chains. A multi-layer perceptron was trained on extracted features from simulated decay chain sets with one step. The Schmidt test performs well with larger decay sets and when the half-life of the contaminating species is longer than the original species by a factor 5 or 10. However, the test performs poorly in low counting statistics, where few recorded decay times are available. The newly proposed machine learning model outperforms the Schmidt test in certain high statistics scenarios, but also fails when few decay times are available. Its performance is also poor when the half-life of the contaminant is shorter than the original half-life. The learning behaviour of the model is analysed, showing significant contributions from higher statistical moments in training. Future work involves including chain correlations across multiple steps, alpha decay energies, as well as the potential use of alternative machine learning models.</p>
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Results
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<p>One of the key results of the thesis was the heatmap in Figure 1, showing how the amount of contamination in a decay time data set (that is, number of decay times from a species other than the one we are interested in) affects the Schmidt test and its conclusions. This was tested for various factors of contaminant half lives. The result is that the Schmidt test seems rather unsensitive to any contaminants with a half life on the same order of magnitude as the species we seek to study. This was further motivation to look beyond the Schmidt test, which solely relies on variance of the dataset, to see if other statistical methods (e.g. machine learning) could be used.</p>
<figure><img src="/images/st_factors.png"
alt="Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination."><figcaption>
<p>Figure 1: Heatmaps for the Schmidt test congruence for various contaminant half-lives. Each pixel is a unique combination of set size N and C contaminated decay times, where the value of the pixel represents the percentage of i=100 simulated decay chain sets with j=1 step for which the measure σ(Θ,exp) falls within the confidence interval, indicating likely congruence. The longer the contaminant half life is compared to the original, the more sets are deemed incongruent by the Schmidt test. Additionally, it can be seen that when nearly all of the decay times in the data set are from the contaminant species, the data becomes congruent again. The number of sets deemed congruent is somewhat symmetrical around the the line of 50% contamination.</p>
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From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My bachelor degree thesis is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.">
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Core Competencies
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<p>From my background in physics and computational science, I have a strong foundation in numerical methods and Monte Carlo simulations for solving physical problems. I am particularly interested by nuclear physics. My <a href="http://lup.lub.lu.se/student-papers/record/9168893" class="external-link" target="_blank" rel="noopener">bachelor degree thesis</a> is about applying machine learning on radioactive decay data. I am proficient in Python and have some exposure to C#, MATLAB, and SQL. I am very comfortable with GNU/Linux and Git workflows.</p>
<p>On the note of Git, I have some projects available publicly on my self-hosted <a href="https://git.pimnel.com/pim/" class="external-link" target="_blank" rel="noopener">Git instance</a>.</p>
<p><code>Python</code> <code>MATLAB</code> <code>TensorFlow</code> <code>C#</code> <code>SQL</code> <code>Git</code> <code>GNU/Linux</code></p>
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🧪 Attended Workshops
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<p>I like to attend (online) workshops in order to broaden my knowledge or refresh some old skills. Below is a timeline of selected workshops which I have followed in the past.</p>
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<div class="timeline-date">Jun 2025</div>
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<h4>Intermediate Bash and Linux</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.hpc2n.umu.se/" target="_blank">HPC2N, Umeå University</a>, Sweden 🇸🇪</p>
<p><code>grep</code> <code>Sed and AWK</code> <code>Bash scripting</code></p>
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<div class="timeline-date">Mar 2025</div>
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<h4>Bayesian Statistical Learning</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.fz-juelich.de/en/ias/jsc" target="_blank">Jülich Supercomputing Centre</a>, Germany 🇩🇪</p>
<p><code>MCMC</code> <code>PyMC3</code> <code>Supercomputer usage</code></p>
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<div class="timeline-date">Mar 2025</div>
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<h4>Programming with Fortran</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.sc.lrz.de/" target="_blank">Leibniz Supercomputing Centre</a>, Germany 🇩🇪</p>
<p><code>Arrays</code> <code>Subroutines</code> <code>Derived Types</code> <code>I/O</code> <code>Performance considerations</code></p>
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<div class="timeline-date">Feb 2025</div>
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<h4>Code & Collaborate: The FAIRytale of Software Development</h4>
<p><i class="fas fa-graduation-cap"></i> <a href="https://www.cecam.org/" target="_blank">EPFL CECAM</a> & <a href="https://biont-training.eu/" target="_blank">BioNT</a>, Switzerland 🇨🇭</p>
<p><code>Git (CLI)</code> <code>Continuous Integration</code> <code>Code Documentation</code></p>
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<description>Recent content in Tags on Pim Nelissen</description>
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<?xml version="1.0" encoding="utf-8" standalone="yes"?>
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<!DOCTYPE html>
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Pim Nelissen
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Byggd med <a href="https://gohugo.io/" target="_blank" rel="noopener">Hugo</a> & <a href="https://github.com/luizdepra/hugo-coder/" target="_blank" rel="noopener">Coder</a>.
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<?xml version="1.0" encoding="utf-8" standalone="yes"?>
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<title>Tags on Pim Nelissen</title>
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color: #ffa198;
background-color: #490202; }
body.colorscheme-dark .chroma .ge {
font-style: italic; }
body.colorscheme-dark .chroma .gr {
color: #ffa198; }
body.colorscheme-dark .chroma .gh {
color: #79c0ff;
font-weight: bold; }
body.colorscheme-dark .chroma .gi {
color: #56d364;
background-color: #0f5323; }
body.colorscheme-dark .chroma .go {
color: #8b949e; }
body.colorscheme-dark .chroma .gp {
color: #8b949e; }
body.colorscheme-dark .chroma .gs {
font-weight: bold; }
body.colorscheme-dark .chroma .gu {
color: #79c0ff; }
body.colorscheme-dark .chroma .gt {
color: #ff7b72; }
body.colorscheme-dark .chroma .gl {
text-decoration: underline; }
body.colorscheme-dark .chroma .w {
color: #6e7681; }
@media (prefers-color-scheme: dark) {
body.colorscheme-auto {
/* Background */
/* PreWrapper */
/* Other */
/* Error */
/* CodeLine */
/* LineLink */
/* LineTableTD */
/* LineTable */
/* LineHighlight */
/* LineNumbersTable */
/* LineNumbers */
/* Line */
/* Keyword */
/* KeywordConstant */
/* KeywordDeclaration */
/* KeywordNamespace */
/* KeywordPseudo */
/* KeywordReserved */
/* KeywordType */
/* Name */
/* NameAttribute */
/* NameBuiltin */
/* NameBuiltinPseudo */
/* NameClass */
/* NameConstant */
/* NameDecorator */
/* NameEntity */
/* NameException */
/* NameFunction */
/* NameFunctionMagic */
/* NameLabel */
/* NameNamespace */
/* NameOther */
/* NameProperty */
/* NameTag */
/* NameVariable */
/* NameVariableClass */
/* NameVariableGlobal */
/* NameVariableInstance */
/* NameVariableMagic */
/* Literal */
/* LiteralDate */
/* LiteralString */
/* LiteralStringAffix */
/* LiteralStringBacktick */
/* LiteralStringChar */
/* LiteralStringDelimiter */
/* LiteralStringDoc */
/* LiteralStringDouble */
/* LiteralStringEscape */
/* LiteralStringHeredoc */
/* LiteralStringInterpol */
/* LiteralStringOther */
/* LiteralStringRegex */
/* LiteralStringSingle */
/* LiteralStringSymbol */
/* LiteralNumber */
/* LiteralNumberBin */
/* LiteralNumberFloat */
/* LiteralNumberHex */
/* LiteralNumberInteger */
/* LiteralNumberIntegerLong */
/* LiteralNumberOct */
/* Operator */
/* OperatorWord */
/* Punctuation */
/* Comment */
/* CommentHashbang */
/* CommentMultiline */
/* CommentSingle */
/* CommentSpecial */
/* CommentPreproc */
/* CommentPreprocFile */
/* Generic */
/* GenericDeleted */
/* GenericEmph */
/* GenericError */
/* GenericHeading */
/* GenericInserted */
/* GenericOutput */
/* GenericPrompt */
/* GenericStrong */
/* GenericSubheading */
/* GenericTraceback */
/* GenericUnderline */
/* TextWhitespace */ }
body.colorscheme-auto .bg {
color: #c9d1d9;
background-color: #0d1117; }
body.colorscheme-auto .chroma {
color: #c9d1d9;
background-color: #0d1117; }
body.colorscheme-auto .chroma .err {
color: #f85149; }
body.colorscheme-auto .chroma .lnlinks {
outline: none;
text-decoration: none;
color: inherit; }
body.colorscheme-auto .chroma .lntd {
vertical-align: top;
padding: 0;
margin: 0;
border: 0; }
body.colorscheme-auto .chroma .lntable {
border-spacing: 0;
padding: 0;
margin: 0;
border: 0; }
body.colorscheme-auto .chroma .hl {
background-color: #ffffcc; }
body.colorscheme-auto .chroma .lnt {
white-space: pre;
user-select: none;
margin-right: 0.4em;
padding: 0 0.4em 0 0.4em;
color: #64686c; }
body.colorscheme-auto .chroma .ln {
white-space: pre;
user-select: none;
margin-right: 0.4em;
padding: 0 0.4em 0 0.4em;
color: #6e7681; }
body.colorscheme-auto .chroma .line {
display: flex; }
body.colorscheme-auto .chroma .k {
color: #ff7b72; }
body.colorscheme-auto .chroma .kc {
color: #79c0ff; }
body.colorscheme-auto .chroma .kd {
color: #ff7b72; }
body.colorscheme-auto .chroma .kn {
color: #ff7b72; }
body.colorscheme-auto .chroma .kp {
color: #79c0ff; }
body.colorscheme-auto .chroma .kr {
color: #ff7b72; }
body.colorscheme-auto .chroma .kt {
color: #ff7b72; }
body.colorscheme-auto .chroma .nc {
color: #f0883e;
font-weight: bold; }
body.colorscheme-auto .chroma .no {
color: #79c0ff;
font-weight: bold; }
body.colorscheme-auto .chroma .nd {
color: #d2a8ff;
font-weight: bold; }
body.colorscheme-auto .chroma .ni {
color: #ffa657; }
body.colorscheme-auto .chroma .ne {
color: #f0883e;
font-weight: bold; }
body.colorscheme-auto .chroma .nf {
color: #d2a8ff;
font-weight: bold; }
body.colorscheme-auto .chroma .nl {
color: #79c0ff;
font-weight: bold; }
body.colorscheme-auto .chroma .nn {
color: #ff7b72; }
body.colorscheme-auto .chroma .py {
color: #79c0ff; }
body.colorscheme-auto .chroma .nt {
color: #7ee787; }
body.colorscheme-auto .chroma .nv {
color: #79c0ff; }
body.colorscheme-auto .chroma .l {
color: #a5d6ff; }
body.colorscheme-auto .chroma .ld {
color: #79c0ff; }
body.colorscheme-auto .chroma .s {
color: #a5d6ff; }
body.colorscheme-auto .chroma .sa {
color: #79c0ff; }
body.colorscheme-auto .chroma .sb {
color: #a5d6ff; }
body.colorscheme-auto .chroma .sc {
color: #a5d6ff; }
body.colorscheme-auto .chroma .dl {
color: #79c0ff; }
body.colorscheme-auto .chroma .sd {
color: #a5d6ff; }
body.colorscheme-auto .chroma .s2 {
color: #a5d6ff; }
body.colorscheme-auto .chroma .se {
color: #79c0ff; }
body.colorscheme-auto .chroma .sh {
color: #79c0ff; }
body.colorscheme-auto .chroma .si {
color: #a5d6ff; }
body.colorscheme-auto .chroma .sx {
color: #a5d6ff; }
body.colorscheme-auto .chroma .sr {
color: #79c0ff; }
body.colorscheme-auto .chroma .s1 {
color: #a5d6ff; }
body.colorscheme-auto .chroma .ss {
color: #a5d6ff; }
body.colorscheme-auto .chroma .m {
color: #a5d6ff; }
body.colorscheme-auto .chroma .mb {
color: #a5d6ff; }
body.colorscheme-auto .chroma .mf {
color: #a5d6ff; }
body.colorscheme-auto .chroma .mh {
color: #a5d6ff; }
body.colorscheme-auto .chroma .mi {
color: #a5d6ff; }
body.colorscheme-auto .chroma .il {
color: #a5d6ff; }
body.colorscheme-auto .chroma .mo {
color: #a5d6ff; }
body.colorscheme-auto .chroma .o {
color: #ff7b72;
font-weight: bold; }
body.colorscheme-auto .chroma .ow {
color: #ff7b72;
font-weight: bold; }
body.colorscheme-auto .chroma .c {
color: #8b949e;
font-style: italic; }
body.colorscheme-auto .chroma .ch {
color: #8b949e;
font-style: italic; }
body.colorscheme-auto .chroma .cm {
color: #8b949e;
font-style: italic; }
body.colorscheme-auto .chroma .c1 {
color: #8b949e;
font-style: italic; }
body.colorscheme-auto .chroma .cs {
color: #8b949e;
font-weight: bold;
font-style: italic; }
body.colorscheme-auto .chroma .cp {
color: #8b949e;
font-weight: bold;
font-style: italic; }
body.colorscheme-auto .chroma .cpf {
color: #8b949e;
font-weight: bold;
font-style: italic; }
body.colorscheme-auto .chroma .gd {
color: #ffa198;
background-color: #490202; }
body.colorscheme-auto .chroma .ge {
font-style: italic; }
body.colorscheme-auto .chroma .gr {
color: #ffa198; }
body.colorscheme-auto .chroma .gh {
color: #79c0ff;
font-weight: bold; }
body.colorscheme-auto .chroma .gi {
color: #56d364;
background-color: #0f5323; }
body.colorscheme-auto .chroma .go {
color: #8b949e; }
body.colorscheme-auto .chroma .gp {
color: #8b949e; }
body.colorscheme-auto .chroma .gs {
font-weight: bold; }
body.colorscheme-auto .chroma .gu {
color: #79c0ff; }
body.colorscheme-auto .chroma .gt {
color: #ff7b72; }
body.colorscheme-auto .chroma .gl {
text-decoration: underline; }
body.colorscheme-auto .chroma .w {
color: #6e7681; } }
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