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diff --git a/docs/img/lhcb-figs/PGun_Lambda_c_mass.png b/docs/img/lhcb-figs/PGun_Lambda_c_mass.png
diff --git a/docs/img/lhcb-figs/Py8_Lambda_c_mass.png b/docs/img/lhcb-figs/Py8_Lambda_c_mass.png
diff --git a/docs/img/lhcb-figs/lamarr-pid-combdll-eff.png b/docs/img/lhcb-figs/lamarr-pid-combdll-eff.png
diff --git a/docs/img/lhcb-figs/lamarr-pid-combdll-hist.png b/docs/img/lhcb-figs/lamarr-pid-combdll-hist.png
diff --git a/docs/img/lhcb-figs/lamarr-trk-efficiency.png b/docs/img/lhcb-figs/lamarr-trk-efficiency.png
diff --git a/docs/img/lhcb-figs/lamarr-trk-resolution.png b/docs/img/lhcb-figs/lamarr-trk-resolution.png
diff --git a/docs/img/qr-code.png b/docs/img/qr-code.png
diff --git a/docs/poster.html b/docs/poster.html
@@ -25,9 +25,7 @@
       <div>
         <h1 property="headline">Lamarr: implementing a flash-simulation paradigm at LHCb</h1>
                           <p>in <strong><em>22nd International Workshop on Advanced Computing and Analysis Techniques in Physics Research</em></strong> (ACAT 2024)</p>
-                  <a href="https://indico.cern.ch/event/1330797"><img alt="indico event" src="https://img.shields.io/badge/indico-event-c89e6c?style=flat&logoColor=white"></a>
-  <a href="https://indico.cern.ch/event/1330797/contributions/5796635"><img alt="indico contribution" src="https://img.shields.io/badge/indico-contribution-087cfc?style=flat&logoColor=white"></a>
-            <address>
+                                <address>
             <span class="medium">
     <a property="author"><strong>M. Mazurek</strong><sup>a</sup> on behalf of the LHCb Simulation Project</a>
   </span>
@@ -149,8 +147,8 @@ <h1 property="headline">Lamarr: implementing a flash-simulation paradigm at LHCb
     </p>
     <hr/>
     <figure>
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-efficiency.svg" alt="Lamarr trk efficiency">
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-resolution.svg" alt="Lamarr trk resolution">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-efficiency.png" alt="Lamarr trk efficiency">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-resolution.png" alt="Lamarr trk resolution">
       <figcaption>
         <small>
           Validation plots for the DNN-based model of the tracking efficiency (left) and the GAN-based model of
@@ -180,8 +178,8 @@ <h1 property="headline">Lamarr: implementing a flash-simulation paradigm at LHCb
     </p>
     <hr/>
     <figure>
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-hist.svg" alt="Lamarr PID histograms">
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-eff.svg" alt="Lamarr PID efficiency">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-hist.png" alt="Lamarr PID histograms">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-eff.png" alt="Lamarr PID efficiency">
       <figcaption>
         <small>
           Validation plots for the proton-kaon separation parameterized with the GAN-based models of the Global PID 
@@ -253,8 +251,8 @@ <h1 property="headline">Lamarr: implementing a flash-simulation paradigm at LHCb
     </p>
     <hr/>
     <figure>
-      <img style="width: 49%" src="img/lhcb-figs/Py8_Lambda_c_mass.svg" alt="Py8 Lambda_c mass">
-      <img style="width: 49%" src="img/lhcb-figs/PGun_Lambda_c_mass.svg" alt="PGun Lambda_c mass">
+      <img style="width: 49%" src="img/lhcb-figs/Py8_Lambda_c_mass.png" alt="Py8 Lambda_c mass">
+      <img style="width: 49%" src="img/lhcb-figs/PGun_Lambda_c_mass.png" alt="PGun Lambda_c mass">
       <figcaption>
         <small>
           Validation plots for the \(\Lambda_c^+\) mass obtained from Pythia8 (left) and particle-gun (right) 
@@ -314,16 +312,19 @@ <h1 property="headline">Lamarr: implementing a flash-simulation paradigm at LHCb
       <ol>
         <li>V. Chekalina <em>et al.</em>, <em>Generative Models for Fast Calorimeter Simulation: the LHCb case</em>, <a href="https://doi.org/10.1051/epjconf/201921402034">EPJ Web Conf. <strong>214</strong> (2019) 02034</a>, <a href="https://arxiv.org/abs/1812.01319">arXiv:1812.01319</a></li>
         <li>A. Maevskiy <em>et al.</em>, <em>Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks</em>, <a href="https://doi.org/10.1088/1742-6596/1525/1/012097">J. Phys. Conf. Ser. <strong>1525</strong> (2020) 012097</a>, <a href="https://arxiv.org/abs/1905.11825">arXiv:1905.11825</a></li>
-        <li>L. Anderlini, <em>Machine Learning for the LHCb Simulation</em>, <a href="https://arxiv.org/abs/2110.07925">arXiv:2110.07925</a></li>
-        <li>L. Anderlini and M. Barbetti, <em>scikinC: a tool for deploying machine learning as binaries</em>, <a href="https://doi.org/10.22323/1.409.0034">PoS <strong>CompTools2021</strong> (2022) 034</a></li>
+                <li>L. Anderlini and M. Barbetti, <em>scikinC: a tool for deploying machine learning as binaries</em>, <a href="https://doi.org/10.22323/1.409.0034">PoS <strong>CompTools2021</strong> (2022) 034</a></li>
         <li>A. Rogachev and F. Ratnikov, <em>GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response</em>, <a href="https://doi.org/10.1088/1742-6596/2438/1/012086">J. Phys. Conf. Ser. <strong>2438</strong> (2023) 012086</a>, <a href="https://arxiv.org/abs/2207.06329">arXiv:2207.06329</a></li>
         <li>L. Anderlini <em>et al.</em>, <em>Lamarr: the ultra-fast simulation option for the LHCb experiment</em>, <a href="https://doi.org/10.22323/1.414.0233">PoS <strong>ICHEP2022</strong> (2023) 233</a></li>
         <li>M. Barbetti, <em>Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss</em>, <a href="https://arxiv.org/abs/2303.11428">arXiv:2303.11428</a></li>
         <li>F. Vaselli <em>et al.</em>, <em>FlashSim prototype: an end-to-end fast simulation using Normalizing Flow</em>, <a href="https://cds.cern.ch/record/2858890">CERN-CMS-NOTE-2023-003</a></li>
         <li>L. Anderlini <em>et al.</em>, <em>The LHCb ultra-fast simulation option, Lamarr: design and validation</em>, <a href="https://arxiv.org/abs/2309.13213">arXiv:2309.13213</a></li>
+        <li>M. Barbetti, <em>The flash-simulation paradigm and its implementation based on Deep Generative Models for the LHCb experiment at CERN</em>, PhD thesis, University of Firenze, 2024</li>
       </ol>
     </small>
-      </article>
+    <figure>
+      <img style="width: 25%" src="img/qr-code.png" alt="QR code">
+    </figure>
+  </article>
     </main>
 
     <footer>

diff --git a/docs/src_poster.jinja2 b/docs/src_poster.jinja2
@@ -21,8 +21,8 @@
 {% endblock %}
 
 {% block badges %}
-  <a href="https://indico.cern.ch/event/1330797"><img alt="indico event" src="https://img.shields.io/badge/indico-event-c89e6c?style=flat&logoColor=white"></a>
-  <a href="https://indico.cern.ch/event/1330797/contributions/5796635"><img alt="indico contribution" src="https://img.shields.io/badge/indico-contribution-087cfc?style=flat&logoColor=white"></a>
+  {# <a href="https://indico.cern.ch/event/1330797"><img alt="indico event" src="https://img.shields.io/badge/indico-event-c89e6c?style=flat&logoColor=white"></a> #}
+  {# <a href="https://indico.cern.ch/event/1330797/contributions/5796635"><img alt="indico contribution" src="https://img.shields.io/badge/indico-contribution-087cfc?style=flat&logoColor=white"></a> #}
   {# <a href="https://indico.cern.ch/event/1330797/contributions/5796635/attachments/xxx/yyy/lamarr_poster_acat2024.pdf"><img alt="poster PDF" src="https://img.shields.io/badge/PDF-poster-EC1C24?style=flat&logo=Adobe%20Acrobat%20Reader&logoColor=white"></a> #}
   {# <a href="https://arxiv.org/abs/2303.11428"><img alt="arXiv preprint" src="https://img.shields.io/badge/arXiv-2303.11428-B31B1B?style=flat&logoColor=white"></a> #}
 {% endblock %}
@@ -186,8 +186,8 @@
     </p>
     <hr/>
     <figure>
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-efficiency.svg" alt="Lamarr trk efficiency">
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-resolution.svg" alt="Lamarr trk resolution">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-efficiency.png" alt="Lamarr trk efficiency">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-trk-resolution.png" alt="Lamarr trk resolution">
       <figcaption>
         <small>
           Validation plots for the DNN-based model of the tracking efficiency (left) and the GAN-based model of
@@ -218,8 +218,8 @@
     </p>
     <hr/>
     <figure>
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-hist.svg" alt="Lamarr PID histograms">
-      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-eff.svg" alt="Lamarr PID efficiency">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-hist.png" alt="Lamarr PID histograms">
+      <img style="width: 49%" src="img/lhcb-figs/lamarr-pid-combdll-eff.png" alt="Lamarr PID efficiency">
       <figcaption>
         <small>
           Validation plots for the proton-kaon separation parameterized with the GAN-based models of the Global PID 
@@ -293,8 +293,8 @@
     </p>
     <hr/>
     <figure>
-      <img style="width: 49%" src="img/lhcb-figs/Py8_Lambda_c_mass.svg" alt="Py8 Lambda_c mass">
-      <img style="width: 49%" src="img/lhcb-figs/PGun_Lambda_c_mass.svg" alt="PGun Lambda_c mass">
+      <img style="width: 49%" src="img/lhcb-figs/Py8_Lambda_c_mass.png" alt="Py8 Lambda_c mass">
+      <img style="width: 49%" src="img/lhcb-figs/PGun_Lambda_c_mass.png" alt="PGun Lambda_c mass">
       <figcaption>
         <small>
           Validation plots for the \(\Lambda_c^+\) mass obtained from Pythia8 (left) and particle-gun (right) 
@@ -363,17 +363,18 @@
       <ol>
         <li>V. Chekalina <em>et al.</em>, <em>Generative Models for Fast Calorimeter Simulation: the LHCb case</em>, <a href="https://doi.org/10.1051/epjconf/201921402034">EPJ Web Conf. <strong>214</strong> (2019) 02034</a>, <a href="https://arxiv.org/abs/1812.01319">arXiv:1812.01319</a></li>
         <li>A. Maevskiy <em>et al.</em>, <em>Fast Data-Driven Simulation of Cherenkov Detectors Using Generative Adversarial Networks</em>, <a href="https://doi.org/10.1088/1742-6596/1525/1/012097">J. Phys. Conf. Ser. <strong>1525</strong> (2020) 012097</a>, <a href="https://arxiv.org/abs/1905.11825">arXiv:1905.11825</a></li>
-        <li>L. Anderlini, <em>Machine Learning for the LHCb Simulation</em>, <a href="https://arxiv.org/abs/2110.07925">arXiv:2110.07925</a></li>
+        {# <li>L. Anderlini, <em>Machine Learning for the LHCb Simulation</em>, <a href="https://arxiv.org/abs/2110.07925">arXiv:2110.07925</a></li> #}
         <li>L. Anderlini and M. Barbetti, <em>scikinC: a tool for deploying machine learning as binaries</em>, <a href="https://doi.org/10.22323/1.409.0034">PoS <strong>CompTools2021</strong> (2022) 034</a></li>
         <li>A. Rogachev and F. Ratnikov, <em>GAN with an Auxiliary Regressor for the Fast Simulation of the Electromagnetic Calorimeter Response</em>, <a href="https://doi.org/10.1088/1742-6596/2438/1/012086">J. Phys. Conf. Ser. <strong>2438</strong> (2023) 012086</a>, <a href="https://arxiv.org/abs/2207.06329">arXiv:2207.06329</a></li>
         <li>L. Anderlini <em>et al.</em>, <em>Lamarr: the ultra-fast simulation option for the LHCb experiment</em>, <a href="https://doi.org/10.22323/1.414.0233">PoS <strong>ICHEP2022</strong> (2023) 233</a></li>
         <li>M. Barbetti, <em>Lamarr: LHCb ultra-fast simulation based on machine learning models deployed within Gauss</em>, <a href="https://arxiv.org/abs/2303.11428">arXiv:2303.11428</a></li>
         <li>F. Vaselli <em>et al.</em>, <em>FlashSim prototype: an end-to-end fast simulation using Normalizing Flow</em>, <a href="https://cds.cern.ch/record/2858890">CERN-CMS-NOTE-2023-003</a></li>
         <li>L. Anderlini <em>et al.</em>, <em>The LHCb ultra-fast simulation option, Lamarr: design and validation</em>, <a href="https://arxiv.org/abs/2309.13213">arXiv:2309.13213</a></li>
+        <li>M. Barbetti, <em>The flash-simulation paradigm and its implementation based on Deep Generative Models for the LHCb experiment at CERN</em>, PhD thesis, University of Firenze, 2024</li>
       </ol>
     </small>
-    {# <figure>
-      <img style="width: 25%" src="img/qr-code.svg" alt="QR code">
-    </figure> #}
+    <figure>
+      <img style="width: 25%" src="img/qr-code.png" alt="QR code">
+    </figure>
   </article>
 {% endblock %}