Annonce April 2019

annonce@lrde.epita.fr

3 participants
4 discussions

New publication at TUG 2019

by Didier Verna

Hello, I'm happy to announce the acceptance of a new publication for TUG 2019 (the TeX users group conference) to be held in Palo Alto, CA, USA, next August. See below for the title and abstract: Quickref: a Stress Test for Texinfo Quickref is a global documentation project for the Common Lisp ecosystem. It creates reference manuals automatically by introspecting libraries and generating a corresponding documentation in Texinfo format. The Texinfo files may subsequently be converted into PDF or HTML. Quickref is non-intrusive: software developers do not have anything to do to get their libraries documented by the system. Quickref may be used to create a local website documenting your current, partial, working environment, but it is also able to document the whole Common Lisp ecosystem at once. The result is a website containing almost two thousand reference manuals. Quickref provides a Docker image for an easy recreation of this website, but a public version is also available and kept up to date on quickref.common-lisp.net. Quickref constitutes an enormous (and successful) stress test for Texinfo, and not only because of the number of files generated and processed. The Texinfo file sizes range from 7K to 15M (make it double for the generated HTML ones). The number of lines of Texinfo code in those files extend from 364 to 285,020, the indexes may contain between 14 and 44500 entries, and the processing times vary from .3s to 1m 38s per file. In this paper, we give an overview of the design and architecture of the system, describe the challenges and difficulties in generating valid Texinfo code automatically, and put some emphasis on the currently remaining problems and deficiencies. -- Resistance is futile. You will be jazzimilated. Lisp, Jazz, Aïkido: http://www.didierverna.info

5 years

Quickref 2.0 is released

by Didier Verna

I'm happy to announce the release of Quickref 2.0, codename "Be Quick or Be Dead". Quickref is a global documentation project for Common Lisp libraries, currently offering 1720 reference manuals on the official website: http://quickref.common-lisp.net. Quickref can also be run locally, either from the provided Docker image, or directly from the Lisp REPL. This release is appropriately named after its most important new feature: a support for parallelism which has been presented last week at ELS 2019, the 12th European Lisp Symposium. In addition to that, we also have a new, author based, index to the provided manuals, and a continuous integration infrastructure for keeping track of new Quicklisp (or Quickref) releases more easily. Thanks to Antoine Hacquard, Antoine Martin, and Erik Huelsmann for their contribution to this version! -- Resistance is futile. You will be jazzimilated. Lisp, Jazz, Aïkido: http://www.didierverna.info

5 years

[Seminaire-LRDE] Mercredi 10 avril 2019: Guillaume Charpiat (TAU-team, INRIA Saclay / LRI - Université Paris-Sud)

by Edwin Carlinet

Chers collègues, La prochaine session du séminaire Performance et Généricité du LRDE (Laboratoire de Recherche et Développement de l'EPITA) aura lieu le Mercredi 10 avril 2019 (11h -- 12h), Amphi 4. Vous trouverez sur le site du séminaire [1] les prochaines séances, les résumés, captations vidéos et planches des exposés précédents [2], le détail de cette séance [3] ainsi que le plan d'accès [4]. [1] http://seminaire.lrde.epita.fr [2] http://seminaire.lrde.epita.fr/Archives [3] http://seminaire.lrde.epita.fr/2019-04-10 [4] http://www.lrde.epita.fr/wiki/Contact Au programme du Mercredi 10 avril 2019 : * 11h -- 12h: Deep Learning for Satellite Imagery: Semantic Segmentation, Non-Rigid Alignment, and Self-Denoising -- Guillaume Charpiat (TAU-team, INRIA Saclay / LRI - Université Paris-Sud) https://www.lri.fr/~gcharpia/ Neural networks have been producing impressive results in computer vision these last years, in image classification or segmentation in particular. To be transferred to remote sensing, this tool needs adaptation to its specifics: large images, many small objects per image, keeping high-resolution output, unreliable ground truth (usually mis-registered). We will review the work done in our group for remote sensing semantic segmentation, explaining the evolution of our neural net architecture design to face these challenges, and finally training a network to register binary cadaster maps to RGB images while detecting new buildings if any, in a multi-scale approach. We will show in particular that it is possible to train on noisy datasets, and to make predictions at an accuracy much better than the variance of the original noise. To explain this phenomenon, we build theoretical tools to express input similarity from the neural network point of view, and use them to quantify data redundancy and associated expected denoising effects. If time permits, we might also present work on hurricane track forecast from reanalysis data (2-3D coverage of the Earth's surface with temperature/pressure/etc. fields) using deep learning. -- After a PhD thesis at ENS on shape statistics for image segmentation, and a year in Bernhard Schölkopf's team at MPI Tübingen on kernel methods for medical imaging, Guillaume Charpiat joined INRIA Sophia-Antipolis to work on computer vision, and later INRIA Saclay to work on machine learning. Lately, he has been focusing on deep learning, with in particular remote sensing imagery as an application field. L'entrée du séminaire est libre. Merci de bien vouloir diffuser cette information le plus largement possible. N'hésitez pas à nous faire parvenir vos suggestions d'orateurs. -- Edwin Carlinet Laboratoire R&D de l'EPITA (LRDE) _______________________________________________ Seminaire mailing list Seminaire(a)lrde.epita.fr https://lists.lrde.epita.fr/listinfo/seminaire

5 years

New journal publication: Model checking with generalized Rabin and Fin-less automata

by Alexandre Duret-Lutz

I'm happy to announce that the following journal article has been published in the International Journal on Software Tools for Technology Transfer Model checking with generalized Rabin and Fin-less automata Vincent Bloemen (1), Alexandre Duret-Lutz (2), Jaco van de Pol (3) (1) University of Twente, Enschede, The Netherlands (2) LRDE, EPITA, Kremlin-Bicêtre, France (3) University of Aarhus, Aarhus, Denmark https://www.lrde.epita.fr/wiki/Publications/bloemen.19.sttt Abstract: In the automata theoretic approach to explicit state LTL model checking, the synchronized product of the model and an automaton that represents the negated formula is checked for emptiness. In practice, a (transition-based generalized) Büchi automaton (TGBA) is used for this procedure. This paper investigates whether using a more general form of acceptance, namely a transition-based generalized Rabin automaton (TGRA), improves the model checking procedure. TGRAs can have significantly fewer states than TGBAshowever the corresponding emptiness checking procedure is more involved. With recent advances in probabilistic model checking and LTL to TGRA translators, it is only natural to ask whether checking a TGRA directly is more advantageous in practice. We designed a multi-core TGRA checking algorithm and performed experiments on a subset of the models and formulas from the 2015 Model Checking Contest and generated LTL formulas for models from the BEEM database. While we found little to no improvement by checking TGRAs directlywe show how various aspects of a TGRA's structure influences the model checking performance. In this paper, we also introduce a Fin-less acceptance condition, which is a disjunction of TGBAs. We show how to convert TGRAs into automata with Fin-less acceptance and show how a TGBA emptiness procedure can be extended to check Fin-less automata. -- Alexandre Duret-Lutz

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Annonce April 2019