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<a href="https://keunhong.com" target="_blank">Keunhong Park</a><sup>1</sup>,</span>
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<a href="https://utkarshsinha.com" target="_blank">Utkarsh Sinha</a><sup>2</sup>,</span>
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<a href="https://jonbarron.info" target="_blank">Jonathan T. Barron</a><sup>2</sup>,
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<a href="http://sofienbouaziz.com" target="_blank">Sofien Bouaziz</a><sup>2</sup>,
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<a href="https://www.danbgoldman.com" target="_blank">Dan B Goldman</a><sup>2</sup>,
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<a href="https://homes.cs.washington.edu/~seitz/" target="_blank">Steven M. Seitz</a><sup>1,2</sup>,
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<a href="http://www.ricardomartinbrualla.com" target="_blank">Ricardo Martin-Brualla</a><sup>2</sup>
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<span class="author-block"><sup>2</sup>Google Research</span>
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<span class="dnerf">Nerfies</span> turns selfie videos from your phone into
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Our approach augments neural radiance fields
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(NeRF) by optimizing an
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additional continuous volumetric deformation field that warps each observed point into a
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canonical 5D NeRF.
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We observe that these NeRF-like deformation fields are prone to local minima, and
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propose a coarse-to-fine optimization method for coordinate-based models that allows for
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more robust optimization.
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By adapting principles from geometry processing and physical simulation to NeRF-like
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models, we propose an elastic regularization of the deformation field that further
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improves robustness.
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We show that <span class="dnerf">Nerfies</span> can turn casually captured selfie
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photos/videos into deformable NeRF
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models that allow for photorealistic renderings of the subject from arbitrary
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viewpoints, which we dub <i>"nerfies"</i>. We evaluate our method by collecting data
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using a
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rig with two mobile phones that take time-synchronized photos, yielding train/validation
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images of the same pose at different viewpoints. We show that our method faithfully
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reconstructs non-rigidly deforming scenes and reproduces unseen views with high
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fidelity.
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<h3 class="title is-4">Interpolating states</h3>
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We can also animate the scene by interpolating the deformation latent codes of two input
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<a href="https://www.albertpumarola.com/research/D-NeRF/index.html" target="_blank">D-NeRF</a> and <a href="https://gvv.mpi-inf.mpg.de/projects/nonrigid_nerf/" target="_blank">NR-NeRF</a>
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both use deformation fields to model non-rigid scenes.
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Some works model videos with a NeRF by directly modulating the density, such as <a href="https://video-nerf.github.io/" target="_blank">Video-NeRF</a>, <a href="https://www.cs.cornell.edu/~zl548/NSFF/" target="_blank">NSFF</a>, and <a href="https://neural-3d-video.github.io/" target="_blank">DyNeRF</a>
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There are probably many more by the time you are reading this. Check out <a href="https://dellaert.github.io/NeRF/" target="_blank">Frank Dellart's survey on recent NeRF papers</a>, and <a href="https://github.com/yenchenlin/awesome-NeRF" target="_blank">Yen-Chen Lin's curated list of NeRF papers</a>.
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<h2 class="title">BibTeX</h2>
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<pre><code>@article{park2021nerfies,
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author = {Park, Keunhong and Sinha, Utkarsh and Barron, Jonathan T. and Bouaziz, Sofien and Goldman, Dan B and Seitz, Steven M. and Martin-Brualla, Ricardo},
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title = {Nerfies: Deformable Neural Radiance Fields},
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journal = {ICCV},
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year = {2021},
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}</code></pre>
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This website is licensed under a <a rel="license"
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This means you are free to borrow the <a target="_blank"
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<meta charset="utf-8">
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<meta name="description" content="DeepSeek: Advancing Open-Source Language Models">
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<meta name="keywords" content="DeepSeek, LLM, AI">
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<meta name="viewport" content="width=device-width, initial-scale=1">
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<title>DeepSeek: Advancing Open-Source Language Models</title>
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<link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel="stylesheet">
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<div class="container is-max-desktop">
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<div class="columns is-centered">
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<div class="column has-text-centered">
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<h1 class="title is-1 publication-title">DeepSeek: Advancing Open-Source Language Models</h1>
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<div class="is-size-5 publication-authors">
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A collection of groundbreaking research papers in AI and language models
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</div>
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</div>
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</div>
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</section>
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<section class="section">
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<div class="container is-max-desktop">
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<!-- Abstract. -->
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<div class="columns is-centered has-text-centered">
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<div class="column is-four-fifths">
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<h2 class="title is-3">Overview</h2>
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<div class="content has-text-justified">
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<p>
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DeepSeek has released a series of significant papers detailing advancements in large language models (LLMs).
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Each paper represents a step forward in making AI more capable, efficient, and accessible.
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</p>
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</div>
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<!--/ Abstract. -->
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<!-- Paper Collection -->
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<div class="columns is-centered has-text-centered">
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<div class="column is-four-fifths">
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<h2 class="title is-3">Research Papers</h2>
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<!-- Paper 1 -->
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeekLLM: Scaling Open-Source Language Models with Longer-termism</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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<div class="is-size-5 publication-authors">
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Released: November 29, 2023
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</div>
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</div>
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<div class="content has-text-justified">
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<p>This foundational paper explores scaling laws and the trade-offs between data and model size,
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establishing the groundwork for subsequent models.</p>
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</div>
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</div>
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<!-- Paper 2 -->
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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<div class="is-size-5 publication-authors">
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Released: May 2024
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</div>
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</div>
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<div class="content has-text-justified">
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<p>Introduces a Mixture-of-Experts (MoE) architecture, enhancing performance while reducing
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training costs by 42%.</p>
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</div>
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</div>
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<!-- Additional papers following same structure -->
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeek-V3 Technical Report</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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<div class="is-size-5 publication-authors">
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Released: December 2024
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</div>
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</div>
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<div class="content has-text-justified">
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<p>Discusses the scaling of sparse MoE networks to 671 billion parameters.</p>
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</div>
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</div>
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeek-R1: Incentivizing Reasoning Capability in LLMs</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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<div class="is-size-5 publication-authors">
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Released: January 20, 2025
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</div>
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</div>
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<div class="content has-text-justified">
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<p>Enhances reasoning capabilities through large-scale reinforcement learning.</p>
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</div>
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</div>
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeekMath: Pushing the Limits of Mathematical Reasoning</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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<div class="is-size-5 publication-authors">
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Released: April 2024
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</div>
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</div>
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<div class="content has-text-justified">
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<p>Presents methods to improve mathematical reasoning in LLMs.</p>
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</div>
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</div>
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeek-Prover: Advancing Theorem Proving in LLMs</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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</div>
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<div class="content has-text-justified">
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<p>Focuses on enhancing theorem proving capabilities using synthetic data for training.</p>
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</div>
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</div>
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeek-Coder-V2: Breaking the Barrier of Closed-Source Models</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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</div>
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<div class="content has-text-justified">
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<p>Details advancements in code-related tasks with emphasis on open-source methodologies.</p>
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</div>
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</div>
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<div class="publication-block">
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<div class="publication-header">
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<h3 class="title is-4">DeepSeekMoE: Advancing Mixture-of-Experts Architecture</h3>
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<span class="tag is-primary is-medium">Deep Dive Coming Soon</span>
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</div>
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<div class="content has-text-justified">
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<p>Discusses the integration and benefits of the Mixture-of-Experts approach.</p>
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</div>
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<footer class="footer">
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<div class="container">
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<div class="content has-text-centered">
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<a class="icon-link" href="https://github.com/deepseek-ai" target="_blank" class="external-link">
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<i class="fab fa-github"></i>
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<div class="content">
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This website is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative
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Commons Attribution-ShareAlike 4.0 International License</a>.
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