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X-WR-CALNAME:GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
X-ORIGINAL-URL:https://www.greyc.fr
X-WR-CALDESC:évènements pour GREYC UMR CNRS 6072 - Groupe de Recherche en Informatique, Image, et Instrumentation de Caen
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TZID:Europe/Paris
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TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20260329T010000
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DTSTART:20261025T010000
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260106T104500
DTEND;TZID=Europe/Paris:20260106T114500
DTSTAMP:20260430T053457
CREATED:20251219T084449Z
LAST-MODIFIED:20251219T085309Z
UID:12009-1767696300-1767699900@www.greyc.fr
SUMMARY:Séminaire Algorithmique : « Post-Quantum Public-Key Pseudorandom Correlation Functions for Oblivious Transfer »\, Mahshid Riahinia (ENS Paris et IRIF\, Univ. Paris Cité)
DESCRIPTION:Public-Key Pseudorandom Correlation Functions (PK-PCF) are functions that generate pseudorandom correlated strings. These correlations can then be used to speed up secure computation protocols. Recent works have made significant progress building PK-PCFs using group-based assumptions\, however\, these assumptions do not hold up against quantum attackers. Much less is known about PK-PCFs in the post-quantum regime. In this talk\, I will introduce an efficient lattice-based PK-PCF for oblivious transfer (OT) correlations. At the heart of our result lie several technical contributions that might be of independent interest. In particular\, we introduce the first efficient lattice-based constrained pseudorandom functions for low-degree polynomials\, from a new but natural “secret-power” variant of ring learning with errors (ring-LWE) assumption.
URL:https://www.greyc.fr/event/seminaire-algorithmique-post-quantum-public-key-pseudorandom-correlation-functions-for-oblivious-transfer-mahshid-riahinia-ens-paris-et-irif-univ-paris-cite/
LOCATION:Sciences 3- S3 351
CATEGORIES:Amacc,General,News,Séminaire Algo
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BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260120T104500
DTEND;TZID=Europe/Paris:20260120T114500
DTSTAMP:20260430T053457
CREATED:20251219T085001Z
LAST-MODIFIED:20260107T104620Z
UID:12013-1768905900-1768909500@www.greyc.fr
SUMMARY:Séminaire Algorithmique : « Eunoia: A Framework for SMT Proof Calculi »\, Hans-Jörg Schurr (Univ. of Iowa\, USA)
DESCRIPTION:Satisfiability Modulo Theories (SMT) solvers combine decision procedures for various theories into a homogeneous automated reasoning framework. They are an indispensable tools to solve formal verification and decision problems. To validate the SMT solver’s responses we can use proof certificates. However\, a major challenge is that different theories and decision procedures use different proof calculi. \nWe will discuss the novel Eunoia language which allows SMT developers to define proof rules precisely. The syntax of Eunoia resembles the standard SMT-LIB format. Proofs using Eunoia-defined rules can be checked using the dedicated Ethos proof checker. We specified all proof calculus of the industrial strength SMT solver cvc5 in Eunoia. We will also discuss ongoing efforts to provide a formal and mechanized type system for Eunoia\, and future directions for proof certificates for SMT solvers.
URL:https://www.greyc.fr/event/seminaire-algorithmique-hans-jorg-schurr-univ-of-iowa-usa/
LOCATION:Sciences 3- S3 351
CATEGORIES:Amacc,General,News,Séminaire Algo
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20260127T104500
DTEND;TZID=Europe/Paris:20260127T114500
DTSTAMP:20260430T053457
CREATED:20251219T085126Z
LAST-MODIFIED:20260130T101130Z
UID:12015-1769510700-1769514300@www.greyc.fr
SUMMARY:Séminaire Algorithmique : « Data-driven design of logic-based models of biological processes »\, Samuel Pastva (Masaryk Univ. Brno\, Tchéquie)
DESCRIPTION:Modern cell biology generates a treasure trove of experimental data\, allowing us to measure many biochemical processes in individual cells with single-molecule resolution. However\, applying this data to generate explainable predictions is often challenging due to the high number of entities\, interactions\, and environmental factors in question. Systems biology utilizes logic-based explainable models (e.g.\, Boolean networks) to address this challenge. Historically\, many such models were designed manually by domain experts; however\, this approach is not scalable for the modern era\, and new data-driven approaches are needed. \nIn this talk\, I will provide a state-of-the-art overview of methods for developing and analyzing logic-based models that integrate these large\, cutting-edge genomic datasets. First\, we will cover the formalization of biological observations into logical constraints. Specifically\, which formal assumptions can be extracted from biological data and what the limitations of our current measurement techniques are. Then\, I will present methods based on automated reasoning (SAT/SMT/ASP) and symbolic data structures (binary decision diagrams) that allow us to learn formally verified model candidates from these observations. Finally\, because complex systems rarely have a single “best” logic-based model\, we will conclude by discussing how to analyze and refine large ensembles of logic-based models (so-called partially specified Boolean models) that capture the plausible behaviors of a biological system.
URL:https://www.greyc.fr/event/seminaire-algorithmique-samuel-pastva-masaryk-univ-brno-tchequie/
LOCATION:Sciences 3- S3 351
CATEGORIES:Amacc,General,News,Séminaire Algo
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