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<!DOCTYPE HTML>
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<title>Research Center for Logic, Optimization and Security (LOS)</title>
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<b> LOS </b>
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<h1>LOS Seminar</h1>
<p>Logic, Optimization & Security</p>
<p>DEPARTMENT of COMPUTER SCIENCE</p>
<p>FACULTY of MATHEMATICS and COMPUTER SCIENCE</p>
<p> UNIVERSITY of BUCHAREST </p>
</section>
<section id="LOS-seminar" class="wrapper special">
<div class="inner">
<header style="text-align:left;">
<h3>Organizers: <a href="https://cs.unibuc.ro/~pirofti/">Paul Irofti</a>,
<a href="https://cs.unibuc.ro/~lleustean">Laurențiu Leuștean</a> and
<a href="https://cs.unibuc.ro/~apatrascu/">Andrei Pătraşcu</a> </h3>
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<article>
<p>The LOS Seminar is the working seminar of the LOS research center.
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<article>
<p>All seminars, except where otherwise indicated, will be Tuesdays between 14:00 and 15.50.
The seminars are online. <!--hybrid, both remotely and in the Google Hall of the
<a href="https://fmi.unibuc.ro">Faculty of Mathematics and Computer Science</a>,
University of Bucharest.-->
</p>
</article>
<article>
<p>To receive announcements about the seminar, please send an email to
<a href="mailto:los@fmi.unibuc.ro">los@fmi.unibuc.ro</a>.
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<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, October 19, 2021</h3>
</header>
<p style="text-align:left;">
<a href="https://cs.unibuc.ro/~abaltoiu/">Andra Băltoiu</a> (LOS)
<br>
Dictionary Learning Heuristics Control Through Adaptive Strategies and Applications to Anomaly Detection
<br><br>
Abstract: Dictionary learning has two model-dependent heuristics: sparsity level and dictionary size.
Both are highly dependent on the signals and there are few applications in which an informed,
accurate choice is possible. Particular problems, such as classification, entail further parameters
that interfere with learning.
We present several adaptive strategies that are aimed at finding the optimal values for these
heuristics or at avoiding the choice altogether.
We also discuss the implications on anomaly detection, which we treat as a special case of classification.
</p>
</article>
<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, November 2, 2021</h3>
</header>
<p style="text-align:left;">
<a href="https://cs.unibuc.ro/~pirofti/">Paul Irofti</a> (LOS)
<br>
Anomaly Detection ABC
<br><br>
Abstract: We briefly revisit and discuss the standard methods for anomaly detection, such as
One Class SVM, SVDD, Isolation Forrest, and Gaussian Mixtures. For each method we analyze the
optimization problem, its anomaly detection applications, and the improvements gained by introducing
non-linear transformations to the inputs via kernel methods (connecting the anomaly detection field to Hilbert spaces).
The papers of interest can be found in the ABC directory <a href="https://tinyurl.com/FMILearnAD">here</a>.
</p>
</article>
<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, November 9, 2021</h3>
</header>
<p style="text-align:left;">
<a href="https://cs.unibuc.ro/~apatrascu/">Andrei Pătraşcu</a> (LOS)
<br>
Inexact Proximal Point Algorithm for Convex Optimization under Holderian Growth
<br><br>
Abstract: Proximal Point Algorithm (PPA) gained a long-lasting attraction
for both abstract operator theory and numerical optimization communities. Even in modern applications,
researchers still use proximal minimization theory to design scalable algorithms that
overcome nonsmoothness. Remarkable works established tight relations between the convergence behaviour of PPA and the regularity
of the objective function. In this talk we present nonasymptotic iteration complexity of
exact and inexact PPA to minimize convex functions under Holderian growth. In particular, we recover well-known results on PPA: finite
convergence for sharp minima and linear convergence for quadratic growth, even under presence
of inexactness. However, without taking into account the concrete computational effort paid for
computing each PPA iteration, any iteration complexity remains abstract and purely informative.
Therefore, based on an inner (proximal) gradient/subgradient method subroutine that computes inexact
PPA iteration, we secondly present computational complexity bounds of a restarted inexact
PPA, that are available when no information on the growth of the objective function is known.
</p>
</article>
<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, November 16, 2021</h3>
</header>
<p style="text-align:left;">
<a href="https://cs.unibuc.ro/~rbalucea/">Ruxandra Bălucea</a> (LOS)
<br>
Low-level Attacks and Software Mitigations on RISC-V
<br><br>
Abstract:
RISC-V is a modern open-source architecture developed in 2013 at the University of Berkeley.
It maintains the old features of the classic RISC (Reduced Instruction Set Computer) architectures,
but tries to remove the vulnerabilities discovered in the past years. In this context, the
main goal of RISC-V is to evolve without compromise, improving the performance of the chips
only if this is done completely safely. In this context, we introduce some of the most popular
attacks like Buffer Overflow, ROP and we present some variants of Spectre, a famous family of
attacks reproduced on a speculative RISC-V core named BOOM. We also present a software mitigation
for one of these variants and other future directions.
</p>
</article>
<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, November 23, 2021</h3>
</header>
<p style="text-align:left;">
<a href="https://cs.unibuc.ro/~hcheval/">Horațiu Cheval</a> (LOS)
<br>
Quadratic rates of asymptotic regularity for the Tikhonov-Mann iteration
<br><br>
Abstract:
We compute quadratic rates of asymptotic regularity for the Tikhonov-Mann iteration in W-hyperbolic spaces.
This iteration is an extension to a nonlinear setting of the modified Mann iteration defined recently by Bot,
Csetnek and Meier in Hilbert spaces. We obtain these quantitative results by applying methods from proof mining. <br>
This is joint work with Laurențiu Leuștean.
</p>
</article>
<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, December 7, 2021</h3>
</header>
<p style="text-align:left;">
Mihai Adamescu (University of Bucharest)
<br>
Interdisciplinarity and transdisciplinary
<br><br>
Abstract: Ecology, sustainability science, drones, sensors, HPC, HCI, ioT, complexity science, digital
humanities, mathematics, and sociology. What do all of them have in common? How can we
answer even more complex questions that society has? How can we address some of these
questions? Why is our thinking so fragmented? How can we find common ground?<br>
A short presentation of the <a href="https://rcses.unibuc.ro">Research Center in Systems Ecology
and Sustainability</a>, the Research
Infrastructures (<a href="https://elter-ri.eu">eLTER</a>, <a href="https://www.lifewatch.eu">LifeWatch</a>,
<a href="https://www.icos-cp.eu">ICOS</a>, <a href="https://www.bioscaneurope.org">BIOSCAN</a>)
that we are promoting, and the need to cooperate, including possible cooperation themes.
</p>
</article>
<article>
<header style="text-align:left;">
<h3 id="content">Tuesday, December 14, 2021</h3>
</header>
<p style="text-align:left;">
<a href="http://alin.stefanescu.eu">Alin Ştefănescu</a> (University of Bucharest)
<br>
River: fuzz testing using AI for binaries and IoT
<br><br>
Abstract: TBA
</p>
</article>
</div>
</section>
<section id="past seminars" class="wrapper">
<div class="inner"><header class="align-left"><h3>Past seminars</h3></header>
<ul>
<li> <a href="seminar-LOS-2021.html">LOS Seminar in 2020/2021</a> </li>
</ul>
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