var sms = '<div id="sms_bulletin">  <div id="sms_header">  <div class="do_not_print" id="arrow_left"><a href="?next=-18"><img src="//www2.math.ethz.ch/smsjs/images/arrow_left.png"/></a></div><div class="do_not_print" id="arrow_right"><a href="?next=-16"><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. 13</span>      <span style="float: right; text-align:right; width: 250px;">25.8 - 29.8.2025</span>      <span style="text-align: center; display: block; width: 250px; margin: auto; position: relative;">Summer Break 2025</span>    </div>  </div>  <div></div>  <div id="sms_content">    <div class="day">      <div class="day_title">        Monday, August 25      </div>      <div class="day_content">        <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.unil.ch/dsa/en/home/menuinst/seminaires--evenements/research-seminars.html">Talks in Actuarial Mathematics </a></div><br /><div class="event_speaker"> Julia Eisenberg (Technical University of Vienna)</div><div class="event_title">Transforming public pensions <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />Pay-as-you-go (PAYG) pension systems are particularly vulnerable to demographic risks.This raises serious concerns about the long-term sustainability of public pension systems that rely on a PAYG financing model, where current contributions fund current pension expenditures.In this talk, we explore two potential approaches to reforming such systems.The first approach assumes that any deficit in the PAYG scheme is immediately covered by the state. In return, individuals have the opportunity to invest into a prespecified fund and repay the "debt" to the state from the return on investment.We compare the outcomes of this approach with those of the traditional direct contribution model and identify the conditions under which the proposed scheme would be advantageous.The second scenario introduces a mixed pension framework (a mixture of PAYG and funding) tailored for economies with a declining working-age population.In this setup, we analyse the consequences of guaranteeing a zero return on the investments made in the funding component. Although this mixed system does not provide a hedge against demographic risks, it serves to delay the need for immediate pension reforms.Based on joint works with Jennifer Alonso-Garcia, Carmen Boado-Penas and Ralf KornSeminar organized by Prof. Hansjoerg Albrecher</div><div class="event_time">11:00 &bull; EPF Lausanne, Extranef 110</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Tuesday, August 26      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_ge"><a class="external" href="https://unige.ch">Université de Genève</a></div><div class="event_type"><a class="external" href="https://www.unige.ch/math/annonces/groupes-geometrie">Groupes et Géométrie </a></div><br /><div class="event_speaker"> Leon Pernak (Université de la Sarre)</div><div class="event_title">Quadratic equations in wreath products of abelian groups <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />One of the strongest results that one can hope for when studying decidability questions in groups is the decidability of equations - is there an algorithm that, if we feed it a group equation, tells us if the equation has or does not have a solution in a given group? I will discuss this problem in the setting of wreath products of abelian groups. In particular, I will explain how to prove that the problem is decidable for quadratic equations, using techniques and intuitions inspired by commutative algebra. This is joint work with Ruiwen Dong and Jan-Philipp Wächter.</div><div class="event_time">10:30 &bull; Université de Genève, Conseil Général 7-9, Room 1-05</div>        </div>        <div class="day_content_item normal"><div class="event_location uni_ge"><a class="external" href="https://unige.ch">Université de Genève</a></div><div class="event_type"><a class="external" href="https://www.unige.ch/math/annonces/groupes-geometrie">Groupes et Géométrie </a></div><br /><div class="event_speaker"> Lukas Vandeputte (Université de Louvain)</div><div class="event_title">Conjugacy separability and the Conjugacy problem <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />One of the classical motivations for conjugacy separability, is that conjugacy separable groups which are finitely presented have solvable conjugacy problem. In this talk we study the interplay between these concepts in more detail.</div><div class="event_time">11:20 &bull; Université de Genève, Conseil Général 7-9, Room 1-05</div>        </div>        <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://stat.ethz.ch/events/research_seminar">Research Seminar in Statistics</a></div><br /><div class="event_speaker"> Hao Chen (University of California, Davis, USA)</div><div class="event_title">Change-point detection for modern complex data <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />Change-point analysis is thriving in this big data era, addressing problems that arise across many fields where massive data sequences are collected to study complex phenomena over time. It plays a crucial role in processing these data by segmenting long sequences into homogeneous parts for subsequent studies. Observations could be high-dimensional or not lie in the Euclidean space, such as network data, which are challenging to characterize using parametric models. We utilize the inter-point information of the observations and propose a series of nonparametric methods to address the issue. In particular, we take into account a pattern caused by the curse of dimensionality so that the proposed methods can accommodate a broad range of alternatives. Additionally, we work out ways to analytically approximate the p-values of the test statistics, enabling rapid type I error control. The methods are applied to Neuropixels data in the analysis of thousands of neurons’ activities.</div><div class="event_time">15:00 &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, August 27      </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">        Thursday, August 28      </div>      <div class="day_content">        <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://theory.epfl.ch/seminar/">Theory and Combinatorics Seminar</a></div><br /><div class="event_speaker"> Anup Rao (UW Seattle)</div><div class="event_title">XOR lemmas for communication complexity <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br /> If a function is hard to compute, is it even harder to compute it many times? In this talk I will discuss this question in the context of communication complexity. I will talk about two recent results:1. If f is a Boolean function with communication complexity c, then computing the n-fold xor of f evaluated on n inputs has communication complexity  n (Omega(sqrt(c)) - 1)).2. If f is a Boolean function with randomized communication complexity c, then computing the n-fold xor of f evaluated on n inputs with advantage exp(-n/100) has randomized communication complexity Omega( c sqrt(n)/ log(cn)).Both results are joint work with Siddharth Iyer. The first result uses an additional observation of Guangxu Yang.</div><div class="event_time">11:00 &bull; EPF Lausanne, CM 1 517</div>        </div>        <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/sfs/news-and-events/data-science-seminar">ETH-FDS seminar </a></div><br /><div class="event_speaker"> John Duchi  (Stanford University)</div><div class="event_title">On labels in supervised learning problems <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />When we teach statistics and machine learning, we typically imagineproblems in which we wish to predict some target Y from data X, or tobuild understanding of the relationship between these two variables, orto test some predicted effect of intervening between them. We fit modelsbased on samples of these pairs. Yet we rarely investigate preciselywhere our labeled data comes from, referring instead to labels (Y) insupervised learning problems as "gold-standard" feedback, or somethingsimilar. Yet these labels are constructed via sophisticated pipelines,aggregating expert (or non-expert) feedback, combining observations insophisticated ways, and we do not model these choices in our statisticallearning pipelines. In this talk, I will discuss some work we have beendoing to try to open up this bigger picture of statistics, providingsome food for thought about how we might move beyond our standardstatistical analyses.</div><div class="event_time">15:15 &bull; ETH Zentrum, R&auml;mistrasse 101, Z&uuml;rich, Building HG, Room D 1.2</div>        </div>      </div>    </div>    <div class="day">      <div class="day_title">        Friday, August 29      </div>      <div class="day_content">        <div class="day_content_item normal"><div class="event_location uni_ba"><a class="external" href="https://unibas.ch">Universität Basel</a></div><div class="event_type"><a class="external" href="https://dmi.unibas.ch/de/forschung/mathematik/seminar-in-numerical-analysis">Seminar in Numerical Analysis</a></div><br /><div class="event_speaker"> Dinh Dung (Vietnam National University)</div><div class="event_title"><a class="external" href="https://dmi.unibas.ch/de/news-events/detail/seminar-in-numerical-analysis-dinh-dung-vietnam-national-university/">Sampling recovery in Bochner spaces and applications to parametric PDEs</a> <span class="abstract-button do_not_print">abstract</span></div><div class="abstract-text" style="display: none"><strong>Abstract:</strong><br />We prove convergence rates of linear sampling recovery of functions in abstract Bochner spaces satisfying weighted summability of their generalized polynomial chaos expansion coefficients. The underlying algorithm is a function-valued extension of the least squares method widely used and thoroughly studied in scalar-valued function recovery. We apply our theory to  collocation approximation of solutions to parametric elliptic or parabolic PDEs with log-normal random inputs and to relevant approximation of infinite dimensional holomorphic functions. The application allows us to significantly improve known results in Computational Uncertainty Quantification for these problems. Our results are also applicable for parametric PDEs with affine inputs, where they match the known rates. For further information about the seminar, please visit this webpage .</div><div class="event_time">11:00 &bull; Universität Basel, Room 05.001, Spiegelgasse 5, 4051 Basel</div>        </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>';