var sms = '<div id="sms_bulletin">  <div id="sms_header">  <div class="do_not_print" id="arrow_left"><a href="?next=-34"><img src="//www2.math.ethz.ch/smsjs/images/arrow_left.png"/></a></div><div class="do_not_print" id="arrow_right"><a href="?next=-32"><img src="//www2.math.ethz.ch/smsjs/images/arrow_right.png"/></a></div>    <div class="sms_title">      <span style="float: left;">SMS</span>      <span>SWISS MATHEMATICAL SOCIETY</span>      <span style="float: right;">SMG</span>    </div>    <div class="sms_info">      <span style="float: left; width: 250px;">Bulletin no. 14</span>      <span style="float: right; text-align:right; width: 250px;">2.9 - 6.9.2024</span>      <span style="text-align: center; display: block; width: 250px; margin: auto; position: relative;">Summer Break 2024</span>    </div>  </div>  <div></div>  <div id="sms_content">    <div class="day">      <div class="day_title">        Monday, September 2      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Tuesday, September 3      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_zh"><a class="external" href="https://ethz.ch">ETH Z&uuml;rich</a></div><div class="event_type"><a class="external" href="https://math.ethz.ch/s/talks-in-financial-and-insurance-mathematics">Talks in Financial and Insurance Mathematics</a></div><br /><div class="event_speaker">Prof. Dr. Sotirios Sabanis (University of Edinburgh)</div><div class="event_title">Stochastic optimization and diffusion-based generative models <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />Diffusion processes have become one of the most significant mathematical frameworks in machine learning (ML), exerting considerable influence across various subfields, including generative modelling, sampling techniques, and nonconvex optimization. The exceptional empirical success of diffusion-based approaches has prompted a growing interest in exploring their mathematical foundations and has driven the development of novel diffusion-based algorithms. In the context of finance, one could argue that applications of diffusion-based generative models can benefit tasks such as scenario generation for risk management purposes and generation of approximate solutions to complex pricing models, such as those used for path-dependent options or exotic derivatives. We will review some recent progress in diffusion-based stochastic optimizers and diffusion-based/score-based generative models.</div><div class="event_time">13:30 &bull; ETH Zentrum, R&auml;mistrasse 101, Z&uuml;rich, Building HG, Room G 19.1</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Wednesday, September 4      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_sg"><a class="external" href="https://www.unisg.ch">Universit&auml;t St. Gallen</a></div><div class="event_type"><a class="external" href="https://www.math.uzh.ch/aa/?events">Neuchatel - St.Gallen - Zurich Seminar in Coding Theory and Cryptography</a></div><br /><div class="event_speaker">Prof. Dr. Margreta Kuijper (The University of Melbourne)</div><div class="event_title">Linear System Detection and Correction of Adversarial Attacks <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />This talk looks at linear systems under attack from an adversary. Such cyber attacks cause malfunctioning and damage and there is an increasing need for automated responses as part of the resilience of the system. I will explore several fundamental ideas around linear systems under attack, such as the notion of &quot;system security index&quot; as an analogon to the notion of &quot;minimum distance&quot; in error control coding. Perpetuating this coding theorist approach I will make a link with the behavioral system theory approach and show how this combination of approaches can be used for the purpose of attack detection and automated attack correction.</div><div class="event_time">11:00 &bull; Uni St. Gallen, 64-110</div>        </div>        <div class="day_content_item normal"><div class="event_location uni_la"><a class="external" href="https://www.epfl.ch">EPF Lausanne</a></div><div class="event_type"><a class="external" href="https://www.epfl.ch/labs/mathicse/">Computational Science and Engineering </a></div><br /><div class="event_speaker"> Yoshihito  Kazashi  (University of Strathclyde Glasgow)</div><div class="event_title">Multigrid Monte Carlo Revisited: Theory and Bayesian Inference <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />In this talk, I revisit the multigrid Monte Carlo (MGMC) method proposed by Goodman and Sokal [Goodman and Sokal, (1989) Multigrid Monte Carlo method. Conceptual foundations], a random sampler analogue of deterministic multigrid solvers. MGMC accelerates random samplers, such as Gibbs samplers, by drawing on insights from numerical analysis. The primary focus of this talk is to provide theoretical support. We discuss a grid-size-independent convergence theory for MGMC, applicable to general Gaussian random variables. This theory demonstrates that the first two moments, which fully characterize the Gaussian distribution, converge exponentially to their target values at a uniform rate. Additionally, we examine the exponential decay of autocorrelations in the generated samples. Furthermore, we extend the application of the MGMC method to address the important scenario of sampling posterior Gaussian distributions conditioned on noisy data. This is joint work with Eike Müller (Bath, UK) and Robert Scheichl (Heidelberg, Germany)</div><div class="event_time">16:15 &bull; EPF Lausanne, CM 1 517</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Thursday, September 5      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Friday, September 6      </div>      <div class="day_content">        <div class="day_content_item no_event">        No events scheduled today!        </div>      </div>    </div>  </div>  <div id="sms_footer">    address: Alessandra Naldi Windler, Departement Mathematik, HG G 51.4, ETH Zurich, Raemistrasse 101, CH-8092 Zurich<br />    email: <a href="mailto:bulletin@math.ch">bulletin@math.ch</a> | phone: +41 44 632 3684 | url: <a href="https://math.ch">https://math.ch</a>    <br /><br /><img src="//www2.math.ethz.ch/smsjs/sms_logo.gif" width="150" alt="SMS/SMG Logo" title="SMS/SMG Logo" />  </div></div>';