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FIRST ANNOUNCEMENT Workshop "Geometry and Computer Science - GnCS 2017", Pescara, Italy, February 8-10, 2017 http://www.sci.unich.it/gncs2017 Geometry and computer science interact with each other in a very profitable way (see below). In this workshop, each talk will describe a challenge in geometry or computer science and explain how the other science helps addressing it. *If your expertise area is geometry OR computer science, join this workshop* to explore new interactions between two sciences that are only at a first sight apart! A (limited) financial support is available for young researchers. Speakers: Daniele Angella (Università degli Studi di Firenze, Italy) Davide Ferrario (Università degli Studi di Milano-Bicocca, Italy) Éric Gourgoulhon (Observatoire de Paris, France) Marco Maggesi (Università degli Studi di Firenze, Italy) Luigi Malagò (Romanian Institute of Science and Technology, Romania), to be confirmed David Monniaux (CNRS & Université Grenoble Alpes, France) Claudio Sacerdoti Coen (Università di Bologna, Italy) Laurent Théry (INRIA, France) Enea Zaffanella (Università degli Studi di Parma, Italy) Scientific Committee: Gianluca Amato (Università degli Studi di Chieti-Pescara, Italy) Giovanni Bazzoni (Philipps-Universität Marburg, Germany) Marco Maggesi (Università degli Studi di Firenze, Italy) Maurizio Parton (Università degli Studi di Chieti-Pescara, Italy) Francesca Scozzari (Università degli Studi di Chieti-Pescara, Italy) Register at http://www.sci.unich.it/gncs2017/#registration No registration fee is required. ------------------------------------------------------- CONTEXT OF THE WORKSHOP The workshop "Geometry and Computer Science" aims to deepen the connection between research in mathematics and research in computer science. Computer science supports research in mathematics. The simplest case is utilization of computer algebra systems like SageMath, Mathematica, Maple, that enables execution of huge amounts of symbolic computations. A more sophisticated approach is using software tools to generate and explore conjectures [1]. This can be done in several ways: assisted theorem proving, proof certification, automatic theorem proving. Assisted proof means helping a mathematician to refine an existing line of proof [2,3]. Proof certification means providing internal confidence of an existing, complete proof [3] or of complex computations [4]. Automatic proof means exploring empirically the mathematical problem, to support intuition [5]. Beyond this, computational geometry has traditionally seen a massive utilization of algorithms to address geometrical problems [6]. On the other hand, geometry supports research in computer science. Abstract interpretation, for instance, is a field where geometrical objects in the configuration space of the variables of a program are used to prove that the program is correct [7,8]. Recently, the seminal work of Voevodsky in the homotopy type theory and the univalent foundation of mathematics [9] showed a deep connection between homotopy theory (geometry), logic and theory of types (computer science). Topics like neural networks and deep learning benefit from Riemannian optimization techniques and differential geometry [10]. [1] Hales - Gonthier - Harrison - Wiedijk. A special issue on formal proof. Notices Amer. Math. Soc. 55(11), 2008. [2] Hales. A proof of the Kepler conjecture. Annals of Mathematics 162(3), 2005. [3] Ciolli - Gentili - Maggesi. A certified proof of the cartan fixed point theorems. Journal of Automated Reasoning 47(3), 2011. [4] Théry. Certified version of Buchberger’s algorithm. Automated Deduction - CADE-15, 1998. [5] Fuchs - Théry. A Formalization of Grassmann-Cayley Algebra in COQ and Its Application to Theorem Proving in Projective Geometry. Automated Deduction in Geometry: 8th International Workshop, 2011. [6] Boucetta - Morvan. Differential Geometry and Topology, Discrete and Computational Geometry. IOS press, 2005. [7] Rodríguez-Carbonell - Kapur. Automatic generation of polynomial invariants of bounded degree using abstract interpretation. Science of Computer Programming, 64(1), 2007. [8] Cousot - Halbwachs. Automatic discovery of linear restraints among variables of a program. http://www.di.ens.fr/~cousot/COUSOTpapers/POPL78.shtml, 1978. [9] Univalent Foundations of Mathematics. Homotopy Type Theory. https://homotopytypetheory.org/book, 2013. [10] Luigi Malagò. Research Project "Riemannian Optimization Methods for Deep Learning". http://rist.ro/en/details/news/postdoc-positions-in-machine-learning-optimization-deep-learning-and-information-geometry.html, 2016. ------------------------------------------------------- =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=- This message was sent to you via the Geometry List, which announces conferences in geometry and closely related areas to almost 1500 mathematicians worldwide. At http://listserv.utk.edu/archives/geometry.html there are many functions available, including checking the archives since November 2005, changing your e-mail address or preferences, and joining/leaving the list. If you have problems that cannot be resolved at this website, send a message to [log in to unmask] Before sending an announcement, please carefully read the following. 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