Since 2002, Median Technologies has been expanding the boundaries of the identification, interpretation, analysis and reporting of imaging data in the medical world. Our core activity is to develop advanced imaging software solutions and platforms for clinical drug development in oncology, diagnostic support, and cancer patient care. Our software solutions improve the management of cancer patients by helping to better identify pathologies, develop and select patient-specific therapies (precision medicine).
The company employs a highly-qualified team and leverages its scientific, technical, medical, and regulatory expertise to develop innovative medical imaging analysis software based on Artificial Intelligence, cloud computing and big data. We are driven by our core values** that are essential to us. These values define who we are, what we do, the way we do it, and what we, as Median, aspire to**:
• Leading innovation with purpose
• Committing to quality in all we do
• Supporting our customers in achieving their goals
• Always remembering to put the patient first
Today, we are a team of 170+ people spread worldwide in the US, Europe and China. Our company is growing in a fulfilling international and multicultural environment.
In the context of our research and development in artificial intelligence applied to medical imaging, we are looking for: Research Scientist (Ph.D.) "Data Science and Deep Learning applied to MRI", M / F
Integrated into a multidisciplinary research and development team within the iBiopsy® project, you are a scientist in the research and development of innovative medical imaging solutions using machine learning and other AI methods.
Medical imaging is one of the fastest growing fields in machine learning. We are looking for an enthusiastic, dynamic, and organized Data Scientist with strong ML experience, excellent communication skills who will thrive at the heart of technological innovation.
Presentation of activities and main tasks linked to the job
Position under the supervision of Artificial Intelligence and Data Science Director
Responsibilities:
You will apply your AI/ML/Deep Learning knowledge to develop innovative and robust biomarkers using data coming from medical imaging systems such as MRI and CT scanners and any other relevant data sources.
Your work will involve research and development of innovative machine learning algorithms> Being part of our front-end innovation organization, you will actively scout, keep track of, evaluate, and leverage disruptive technologies, as well as the emergence of new industrial, academic, and technological trends.
You will work in collaboration with iBiopsy’s software development team as well as clinical science team.
In addition, you will transfer technology, and share insights and best practices across innovation teams. You will generate intellectual property for the company. You will be expected to author peer reviewed papers, present results at industry/scientific conferences.
We expect you to build breakthrough AI-enabled imaging solutions relying on cloud computing; applying supervised and unsupervised Machine Learning techniques to create value from the imaging and clinical data databases generated by our medical research and pharmaceutical industry partners. These AI enabled systems and services go beyond image analysis to transform medical practice and drug development.
Searched profile
Education: PhD in in Mathematics, Computer Science or related fields
Main skills and Experience required:
• Minimum 3 years of relevant work experience in (deep) machine learning
• Experience with Medical Imaging (MRI required, CT is an asset), image signatures, large scale visual information extraction, features selection
• Relevant experience with Python, DL frameworks (i.e. Pytorch) and standard packages such as Scikit-learn, Numpy, Scipy, Pandas
• Experience desired in Semi-Supervised Learning, Self-supervised Learning, Reinforcement Learning, Adversarial methods.
• Extraction of multimodal feature extraction
• Author on related research publication / conferences
• Solid experience with opensource technologies to accelerate innovation
Required knowledge:
• In-depth technical knowledge of AI, deep learning and computer vision
• Strong fundamental knowledge of statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques and dimensionality reduction
Additional skills:
• Strong interpersonal, communication and presentation skills as well as ability to work in global team and organize work
• Fluent in written and oral English
Legal
o Job location: Sophia Antipolis, France
o Contract: Permanent, Open-Ended
o Start: As Soon As Possible
o Offered salary: will depend on candidate’s skills and experience.
Benefits offered by the company
o Fulfilling working and living environment
o Meal vouchers
o Canteen
o Health Plan
Why working with us ?
o Join an international, multicultural and fast-growing company
o Be at the heart of innovation
Depuis 2002, Median Technologies repousse les limites de l'identification, de l'interprétation, de l'analyse et de la communication des données d'imagerie pour le monde médical. Le cœur de notre activité est le développement de logiciels et de plateformes innovants d’imagerie pour les essais cliniques en oncologie, l’aide au diagnostic et le suivi des patients atteints de cancers ; ces logiciels ont pour but d’améliorer la prise en charge des patients souffrant de cancers en aidant à l’identification des pathologies, à la mise au point et à la sélection de thérapies adaptées aux patients (médecine de précision).
Notre activité est à la convergence de plusieurs disciplines telles que la médecine, l’imagerie médicale et les technologies de l’information. Nos collaborateurs possèdent des expertises scientifiques (intelligence artificielle, sciences des données), techniques (logiciel, cloud computing), médicales, réglementaires, et de business development toutes utilisées dans le développement et la mise sur le marché de nos applications et des services qui leur sont associés.** Dans notre travail quotidien, nous sommes guidés par quatre valeurs, toutes fondamentales pour nous :**
• Donner du sens à l’innovation technologique
• Aider nos clients à atteindre leurs objectifs
• Mettre la qualité au cœur de notre savoir-être et de notre savoir-faire
• Amélioration de la qualité des soins patient
Aujourd’hui, nous sommes 170+ personnes réparties dans le monde entier aux États-Unis, en Europe et en Chine. Nous travaillons dans un contexte international et multiculturel particulièrement attractif et épanouissant.
Dans le cadre de notre recherche et développement en Intelligence Artificielle appliquée à l’imagerie médicale, nous recherchons : un Docteur (PhD) “Science des données et Apprentissage Profond appliqué à l’IRM", H/F
Intégré dans une équipe de recherche et développement multidisciplinaire au sein du projet iBiopsy®, vous êtes un scientifique spécialiste de l’IA (Machine Learning, Réseaux Profonds et Science des données) pour la recherche et le développement de solutions innovantes d’imagerie médicale.
L’imagerie médicale est l’un des domaines les plus dynamiques du Machine Learning. Nous recherchons un Scientifique expérimenté passionné, dynamique, et organisé avec une forte expérience en Machine Learning appliqué en imagerie médicale, possédant d’excellentes compétences en communication pour s’épanouir au cœur de l’innovation technologique.
Présentation des activités et tâches principales associées au poste
o Poste sous la supervision du directeur de l’Intelligence Artificielle et de la Science des données
o Responsabilités :
Vous utiliserez vos connaissances en Intelligence Artificielle (Machine Learning et Deep Learning) pour développer des biomarqueurs solides et innovants, sur la base de données provenant de systèmes d’imagerie médicale tels que IRM et CT scanners en vous aidant de toutes sources de données pertinentes.
Votre travail impliquera la recherche et le développement d’algorithmes d’apprentissage novateurs. Etant au cœur de l'innovation de notre organisation, vous participerez activement à l’exploration, la veille technologique, l'évaluation et l'exploitation de technologies innovantes, ainsi que l’émergence de nouvelles tendances industrielles, académiques et technologiques.
Vous travaillerez en collaboration avec l’équipe de développement logiciel ainsi que l’équipe de science clinique d’iBiopsy.
En outre, vous transmettrez vos connaissances technologiques et partagerez idées et bonnes pratiques entre les équipes. Vous générerez de la propriété intellectuelle pour l'entreprise. Vous rédigerez des articles scientifiques et présenterez des résultats lors de conférences industrielles/scientifiques.
Nous attendons de vous la participation au développement de solutions d’imagerie innovantes, basées sur l’intelligence artificielle et s’appuyant sur de l’informatique dématérialisé; l’application de techniques supervisées et non supervisées de Machine Learning pour créer de la valeur depuis des bases de données d’images et de données cliniques générées par nos partenaires en recherche médicale et de l’industrie pharmaceutique. Ces systèmes et services basés sur l’intelligence artificielle iront au-delà de l’analyse d’image pour transformer la pratique médicale et le développement de médicaments.
Profil sollicité
o Formation : Doctorat en Mathématiques, Science Informatique, ou domaines équivalents.
o Principales compétences et expériences requises :
• Minimum 3 ans d’expérience pertinente en (Deep) Machine Learning.
• Expérience appliquées aux images médicales (IRM essentiel, CT optionnel), signatures d’image, Extraction d’informations visuelles à grande échelle, techniques de sélection.
• Compréhension des différentes séquences utilisées en IRM abdominales et en connaitre les spécificités
• Expérience pertinente en Python, DL frameworks (i.e. Pytorch) et packages standard comme Scikit-learn, Numpy, Scipy, Pandas
• Expérience souhaité en Semi-Supervised Learning, Self-supervised Learning, Reinforcement Learning, Adversarial methods.
• Extraction de caractéristiques multimodales.
• Auteur sur des recherches associées (publications/conférences).
• Solide expérience en technologies OpenSource pour accélérer l’innovation
o Connaissances requises :
• Connaissance technique approfondie en IA, Deep Learning et en Vision par ordinateur
• Solides connaissances fondamentales en traitement de données statistiques, techniques de régression, réseaux de neurones, arbres de décision, classification, reconnaissance de formes, théorie des probabilités, systèmes stochastiques, inférence bayésienne, techniques statistiques et réduction de la dimensionnalité.
o Compétences additionnelles :
• Fortes aptitudes relationnelles, de communication et de présentation, ainsi que la capacité à travailler en équipe et d’organisation de son travail
• Maîtrise de l’anglais oral et écrit
Eléments du contrat
o Poste basé à : Sophia-Antipolis, France
o Type de contrat : CDI
o Date de début du contrat : au plus tôt
o Rémunération : à négocier selon profil
**Avantages offerts par la société **
o Tickets restaurant
o Restaurant d’entreprise
o Mutuelle d’entreprise
o Cadre épanouissant
Pourquoi nous rejoindre ?
o Rejoignez une société internationale, multiculturelle et en pleine croissance
o Soyez au cœur de l’innovation
avec un début d'embauche possible à partir d'Octobre 2021.
Les thématiques recherchées pour les candidats couvrent les disciplines:
En bref, le programme DesCartes développe une IA hybride, mêlant Apprentissage, Connaissance et Raisonnement, qui ait de bonnes propriétés (besoin de moins de ressources et de données, sécurité, robustesse, équité, respect de la vie privée, éthique), et démontrée sur des applications industrielles de la ville intelligente (énergie digitale, monitoring de structures, contrôle aérien). Le programme rassemble 80 chercheurs permanents (moitié en France, moitié à Singapour), avec le support de grands groupes industriels (Thales SG, Edf SG, ESI group , CETIM Matcor, ARIA ...).
La recherche aura lieu principalement à Singapour, dans les locaux de CNRS@CREATE, avec un salaire compétitif et un financement généreux pour les missions.
Plus d'information sont disponibles sur cette page temporaire: https://perso.crans.org/genest/DesCartes.html
Pour candidater en IHM (ou avoir plus de précisions sur les sujets abordés), veuillez vous adresser à C. Jouffrais - Christophe.Jouffrais [at]cnrs.fr
Pour candidater dans les autres domaines, veuillez faire passer vos dossiers à Blaise Genest - blaise.genest@irisa.fr
Dossier en anglais avec:
Cordialement,
C. Jouffrais
"Energy and entropy concepts to characterize and understand brain activity"
Organizers: Alain Destexhe, Jennifer Goldman, Mavi Sanchez-Vives, Pier Stanislao Paolucci, Chiara DeLuca, Cristiano Capone, Trang-Anh Nghiem
Abstract: Brain circuits in vivo or in vitro display a wide variety of activity states, and they may also respond to external inputs differently in each state. The goal of this workshop is to evaluate and compare tools to understand both the activity state, and its responsiveness, based on energy and entropy concepts commonly used in physics. We will review different ways of defining relevant measures of energy and entropy, and how they apply to analyze brain activity, including brain states and cognitive tasks, and what useful information can be extracted to better understand the system. We will also evaluate the opportunity of writing a collaborative paper that reviews the different techniques and their usefulness.
List of confirmed speakers:
Updated program:
Day 1 - May 5
Session 1: Theoretical aspects of energy and entropy I
Session 2: Applications to neural data I
Session 3: Theoretical aspects of energy and entropy II
DAY 2 - May 6
Session 4: Theoretical aspects of energy and entropy III
Session 5: Applications to neural data II
Session 6: Theoretical aspects of energy and entropy IV
]]>Event details of Topological Data Analysis and Information Theory (online)
Date: Tuesday 29th June 2021 and monday 5th July 2021
Time: 14:00 -17:30
Organised by Fernando Nobrega Santos , Rick Quax
High-order interactions are interactions that go beyond a sequence of pairwise interactions. Multiple approaches exist that aim to detect and quantify high-order interactions that are qualitatively different. Two of the most prominent approaches are topological data analysis (TDA) and information theory (IT). Central questions addressed in this lecture series are: what do these two approaches have in common? How can they complement each other? And what could they bring to application domains, especially in neuroscience?
Programme
Tuesday 29 June 2021
Monday 5 July 2021
Each lecture will be 50 min, followed by Q&A. To participate, register below.
Tuesday 29 June 2021
Monday 5 July 2021
ORIGINAL WEBPAGE - OFFICIAL WEBSITE
Topos theory can be regarded as a unifying subject within Mathematics; in the words of Grothendieck, who invented the concept of topos, “It is the theme of toposes which is this “bed”, or this “deep river”, in which come to be married geometry and algebra, topology and arithmetic, mathematical logic and categorytheory, the world of the continuous and that of the “discontinuous” or “discrete” structures. It is what I have conceived of most broad, to perceive with finesse, by the same language rich of geometric resonances, an “essence” which is common to situations most distant from each other”.
The event "Toposes online" represents the third edition of the main international conference on topos theory, following the previous ones "Topos à l’IHES" and "Toposes in Como".
The format of the event is the same as that of the other two editions: it will consist of a three-day school, offering introductory courses for the benefit of students and mathematicians who are not already familiar with topos theory, followed by a three-day congress featuring both invited and contributed presentations on new theoretical advances in the subject as well as applications of toposes in different fields such as algebra, topology, number theory, algebraic geometry, logic, homotopy theory, functional analysis, and computer science.
The main aim of this conference series is to celebrate the unifying power and interdisciplinary applications of toposes and encourage further developments in this spirit, by promoting exchanges amongst researchers in different branches of mathematics who use toposes in their work and by introducing a new generation of scholars to the subject.
Because of the pandemic, this edition of the conference will take place entirely online. The participants may take advantage of the associated forum to discuss with each other (please register to it if you wish to post messages).
School lecturers
Invited speakers
Scientific and Organizing Committee
Sponsors
We gratefully acknowledge IHES and the University of Insubria for their support; in particular, the videos of "Toposes online" will be made available through the IHES YouTube channel.
Programme
Mini-courses:
Invited talks:
Contributed talks:
Francesco Genovese (joint work with Julia Ramos González): “A derived Gabriel-Popescu Theorem for T-structures via derived injectives” VIDEO
Matthias Hutzler: “Gluing classifying toposes” VIDEO
Company presentation
Since 2002, Median Technologies has been expanding the boundaries of the identification, interpretation, analysis and reporting of imaging data in the medical world. Our core activity is to develop advanced imaging software solutions and platforms for clinical drug development in oncology, diagnostic support, and cancer patient care. Our software solutions improve the management of cancer patients by helping to better identify pathologies, develop and select patient-specific therapies (precision medicine).
The company employs a highly-qualified team and leverages its scientific, technical, medical, and regulatory expertise to develop innovative medical imaging analysis software based on Artificial Intelligence, cloud computing and big data. We are driven by our core values that are essential to us. These values define who we are, what we do, the way we do it, and what we, as Median, aspire to:
• Leading innovation with purpose
• Committing to quality in all we do
• Supporting our customers in achieving their goals
• Always remembering to put the patient first
Today, we are a team of 130+ people. Most of us are based at our HQ, in Sophia Antipolis (French Riviera) and we have a subsidiary in the US and another one in China. Our company is growing in a fulfilling international and multicultural environment.
Job description
In the context of our research and development in artificial intelligence applied to medical imaging, we are looking for: Data Science and Machine Learning Research Scientist M/F
Integrated into a multidisciplinary research and development team within the iBiopsy® project, you are a scientist in the research and development of innovative medical imaging solutions using machine learning and other AI methods.
Medical imaging is one of the fastest growing fields in machine learning. We are looking for an enthusiastic, dynamic, and organized Data Scientist with strong ML experience, excellent communication skills who will thrive at the heart of technological innovation.
Assignments
o Position under the supervision of Head of Data Science
o Responsibilities:
You will apply your AI/ML/Deep Learning knowledge to develop innovative and robust biomarkers using data coming from medical imaging systems such as MRI and CT scanners and other data sources.
Your work will involve research and development of novel machine learning algorithms and systems. Being part of our front-end innovation organization, you will actively scout, keep track of, evaluate, and leverage disruptive technologies, and emerging industrial, academic and technological trends.
You will work closely with iBiopsy’s software development team as well as clinical science team.
In addition, you will transfer technology, and share insights and best practices across innovation teams. You will generate intellectual property for the company. You will be expected to author peer reviewed papers, present results at industry/scientific conferences.
We look at you to building breakthrough AI-enabled imaging solutions leveraging cloud computing and apply supervised and unsupervised Machine Learning techniques to create value from the imaging and clinical data repositories generated by our medical research and pharmaceutical industry partners. These AI enabled systems and services go beyond image analysis to transform medical practice and drug development.
Profile required
o Education: PhD in in Mathematics, Computer Science or related fields
o Main skills and Experience required:
• Minimum 3 years of relevant work experience in (deep) machine learning
• Experience with Medical Imaging, CT/MRI, image signatures, large scale visual information retrieval, features selection
• Relevant experience with Python, DL frameworks (i.e. Pytorch) and standard packages such as Scikit-learn, Numpy, Scipy, Pandas
• Semi-Supervised Learning, Self-supervised Learning, Reinforcement Learning, Adversarial methods.
• Multimodal feature extraction
• Author on related research publication / conferences
• Strong experience with opensource technologies to accelerate innovation
Knowledge:
• In depth technical knowledge of AI, deep learning and computer vision
• Strong fundamental knowledge of statistical data processing, regression techniques, neural networks, decision trees, clustering, pattern recognition, probability theory, stochastic systems, Bayesian inference, statistical techniques and dimensionality reduction
Additional qualities:
• Strong interpersonal, communication and presentation skills as well as ability to work in global team
• Fluent in written and oral English
Le CNRS et the University of Tokyo financeront des bourses doctorales en sciences humaines et sociales, intelligence artificielle, science quantique, changement climatique et biologie moléculaire et cellulaire. Date limite des candidatures le 22 avril 2021.
Pour candidater : https://international.cnrs.fr/wp-content/uploads/2021/02/Guidelines-PhD-Joint-program-CNRS-UTokyo-1.pdf
]]>The RTG Computational Cognition aims at reintegrating Cognitive Science and Artificial Intelligence. PhD students of the RTG will be educated in both subjects in order to combine the findings of these fields and thus to get a better understanding of human and machine intelligence. Research fields involved in the RTG are Neuroinformatics, NeuroBioPsychology, Bio-Inspired Computer Vision, Knowledge-Based Systems, Cognitive Natural Language Processing & Communication, Cognitive Modeling, Artificial Intelligence, Psycho/Neurolinguistics, Computational Linguistics and Cognitive Computing.
The RTG focuses on the integration of two research fields. Further information on the RTG is available at www.comco.uni-osnabrueck.de. Detailed information on the core areas of the offered PhD projects can be obtained from the spokesmen of the RTG, Prof. Dr. Gordon Pipa (gpipa[at]uni-osnabrueck.de) and Prof. Dr. Peter König (pkoenig[at]uni-osnabrueck.de).
The RTG is incorporated into the Cognitive Science PhD program founded in 2002. PhD students of the RTG will take advantage of an interdisciplinary environment, which nevertheless focuses on a common research topic and offers a broad range of methodological synergies between the projects.
Required Qualifications:
Applicants are expected to have an academic degree (Master/Diploma), experience in at least one of the domains listed above, proven experience in interdisciplinary work as well as a good command of the English language.
Osnabrück University is committed to helping working/studying parents balance their family and working lives.
Osnabrück University seeks to guarantee equality of opportunity for women and men and strives to correct any gender imbalance in its schools and departments.
If two candidates are equally qualified, preference will be given to the candidate with disability status.
Applications with the usual documentation should be submitted by e-mail in a single PDF file to the director of the Institute of Cognitive Science, Prof. Dr. Gunther Heidemann (gheidema[at]uni-osnabrueck.de) with a cc to office[at]ikw.uni-osnabrueck.de no later than April 19, 2021.
To inquire additional information on for example specific research projects you can contact the coordinator Gabriela Pipa (gapipa[at]uos.de).
--
Professor and Chair of the Neuroinformatics Department
Dr. rer. nat. Gordon Pipa
Institute of Cognitive Science, Room 50/218
University of Osnabrueck
Wachsbleiche 27, 49090 Osnabrück, Germany
tel. +49 (0) 541-969-2277
fax (private). +49 (0) 5405- 500 80 98
home office. +49 (0) 5405- 500 90 95
e-mail: gpipa[at]uos.de
webpage: http://www.ni.uos.de
research gate: https://www.researchgate.net/profile/Gordon_Pipa/?ev=prf_act
linkedin: https://de.linkedin.com/in/gordon-pipa-47771539
Personal Assistent and Secretary of the Neuroinformatic lab:
Anna Jungeilges
Tel. +49 (0)541 969-2390
Fax +49 (0)541 969-2246
Email: anna.jungeilges[at]uni-osnabrueck.de
visit us on
http://www.facebook.com/CognitiveScienceOsnabruck
https://twitter.com/#!/CogSciUOS
Co-chairs of the session:
This session will focus on the advances of information theory, probability and statistics in Algebraic Topology (see [1-56] bellow). The field is currently knowing an impressive development, both on the side of the categorical, homotopical, or topos foundations of probability theorie and statistics, and of the information functions characterisation in cohomology and homotopy theory.
Bliographicical references: (to be completed)
[1] Cencov, N.N. Statistical Decision Rules and Optimal Inference. Translations of Mathematical Monographs. 1982.
[2] Ay, N. and Jost, J. and Lê, H.V. and Schwachhöfer, L. Information geometry and sufficient statistics. Probability Theory and Related Fields 2015 PDF
[3] Cathelineau, J. Sur l’homologie de sl2 a coefficients dans l’action adjointe, Math. Scand., 63, 51-86, 1988. PDF
[4] Kontsevitch, M. The 1+1/2 logarithm. Unpublished note, Reproduced in Elbaz-Vincent & Gangl, 2002, 1995 PDF
[5] Elbaz-Vincent, P., Gangl, H. On poly(ana)logs I., Compositio Mathematica, 130(2), 161-214. 2002. PDF
[6] Tomasic, I., Independence, measure and pseudofinite fields. Selecta Mathematica, 12 271-306. Archiv. 2006.
[7] Connes, A., Consani, C., Characteristic 1, entropy and the absolute point. preprint arXiv:0911.3537v1. 2009.
[8] Marcolli, M. & Thorngren, R. Thermodynamic Semirings, arXiv 10.4171 / JNCG/159, Vol. abs/1108.2874, 2011.
[9] Abramsky, S., Brandenburger, A., The Sheaf-theoretic structure of non-locality and contextuality, New Journal of Physics, 13 (2011). PDF
[10] Gromov, M. In a Search for a Structure, Part 1: On Entropy, unpublished manuscript, 2013. PDF
[11] McMullen, C.T., Entropy and the clique polynomial, 2013. PDF
[12] Marcolli, M. & Tedeschi, R. Entropy algebras and Birkhoff factorization, arXiv, Vol. abs/1108.2874, 2014.
[13] Doering, A., Isham, C.J., Classical and Quantum Probabilities as Truth Values, arXiv:1102.2213, 2011 PDF
[14] Baez, J.; Fritz, T. & Leinster, T. A Characterization of Entropy in Terms of Information Loss Entropy, 13, 1945-1957, 2011. PDF
[15] Baez J. C.; Fritz, T. A Bayesian characterization of relative entropy. Theory and Applications of Categories, Vol. 29, No. 16, p. 422-456. 2014. PDF
[16] Drummond-Cole, G.-C., Park., J.-S., Terrila, J., Homotopy probability theory I. J. Homotopy Relat. Struct. November 2013. PDF
[17] Drummond-Cole, G.-C., Park., J.-S., Terrila, J., Homotopy probability theory II. J. Homotopy Relat. Struct. April 2014. PDF
[18] Burgos Gil J.I., Philippon P., Sombra M., Arithmetic geometry of toric varieties. Metrics, measures and heights, Astérisque 360. 2014 . PDF
[19] Gromov, M. Symmetry, probability, entropy. Entropy 2015. PDF
[20] Gromov, M. Morse Spectra, Homology Measures, Spaces of Cycles and Parametric Packing Problems, april 2015. PDF
[21] Park., J.-S., Homotopy theory of probability spaces I: classical independence and homotopy Lie algebras. Archiv . 2015
[22] Baudot P., Bennequin D. The homological nature of entropy. Entropy, 17, 1-66; 2015. PDF
[23] Elbaz-Vincent, P., Gangl, H., Finite polylogarithms, their multiple analogues and the Shannon entropy. (2015) Vol. 9389 Lecture Notes in Computer Science. 277-285, Archiv.
[24] M. Marcolli, Information algebras and their applications. International Conference on Geometric Science of Information (2015), 271-276
[25] Abramsky S., Barbosa R.S., Lal K.K.R., Mansfield, S., Contextuality, Cohomology and Paradox. 2015. arXiv:1502.03097
[26] M. Nguiffo Boyom, Foliations-Webs-Hessian Geometry-Information Geometry-Entropy and Cohomology. Entropy 18(12): 433 (2016) PDF
[27] M. Nguiffo Boyom, A. Zeglaoui, Amari Functors and Dynamics in Gauge Structures. GSI 2017: 170-178
[28] G.-C. Drummond-Cole, Terila, Homotopy probability theory on a Riemannian manifold and the Euler equation , New York Journal of Mathematics, Volume 23 (2017) 1065-1085. PDF
[29] P. Forré,, JM. Mooij. Constraint-based Causal Discovery for Non-Linear Structural Causal Models with Cycles and Latent Confounders. In A. Globerson, & R. Silva (Eds.) (2018), pp. 269-278)
[30] T. Fritz and P. Perrone, Bimonoidal Structure of Probability Monads. Proceedings of MFPS 34, ENTCS, (2018). PDF
[31] Jae-Suk Park, Homotopical Computations in Quantum Fields Theory, (2018) arXiv:1810.09100 PDF
[32] G.C. Drummond-Cole, An operadic approach to operator-valued free cumulants. Higher Structures (2018) 2, 42–56. PDF
[33] G.C. Drummond-Cole, A non-crossing word cooperad for free homotopy probability theory. MATRIX Book (2018) Series 1, 77–99. PDF
[34] T. Fritz and P. Perrone, A Probability Monad as the Colimit of Spaces of Finite Samples, Theory and Applications of Categories 34, 2019. PDF.
[35] M. Esfahanian, A new quantum probability theory, quantum information functor and quantum gravity. (2019) PDF
[36] T. Leinster, Entropy modulo a prime, (2019) arXiv:1903.06961 PDF
[37] T. Leinster, E. Roff, The maximum entropy of a metric space, (2019) arXiv:1908.11184 PDF
[38] T. Maniero, Homological Tools for the Quantum Mechanic. arXiv 2019, arXiv:1901.02011. PDF
[39] M. Marcolli, Motivic information, Bollettino dell'Unione Matematica Italiana (2019) 12 (1-2), 19-41
[40] J.P. Vigneaux, Information theory with finite vector spaces, in IEEE Transactions on Information Theory, vol. 65, no. 9, pp. 5674-5687, Sept. (2019)
[41] Baudot P., Tapia M., Bennequin, D. , Goaillard J.M., Topological Information Data Analysis. (2019), Entropy, 21(9), 869
[42] Baudot P., The Poincaré-Shannon Machine: Statistical Physics and Machine Learning aspects of Information Cohomology. (2019), Entropy , 21(9),
[43] G. Sergeant-Perthuis, Bayesian/Graphoid intersection property for factorisation models, (2019), arXiv:1903.06026
[44] J.P. Vigneaux, Topology of Statistical Systems: A Cohomological Approach to Information Theory, PhD Thesis (2019).
[45] Forré, P., & Mooij, J. M. (2019). Causal Calculus in the Presence of Cycles, Latent Confounders and Selection Bias. In A. Globerson, & R. Silva (Eds.), Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence: UAI 2019, (2019)
[46] Y. Manin, M. Marcolli Homotopy Theoretic and Categorical Models of Neural Information Networks. arXiv (2020) preprint arXiv:2006.15136
[47] T. Leinster The categorical origins of Lebesgue integration (2020) arXiv:2011.00412 PDF
[48] T. Fritz, T. Gonda, P. Perrone, E. Fjeldgren Rischel, Representable Markov Categories and Comparison of Statistical Experiments in Categorical Probability. (2020) arXiv:2010.07416 PDF
[49] T. Fritz, E. Fjeldgren Rischel, Infinite products and zero-one laws in categorical probability (2020) arXiv:1912.02769 PDF
[50] T. Fritz, A synthetic approach to Markov kernels, conditional independence and theorems on sufficient statistics (2020) arXiv:1908.07021 PDF
[51] T. Fritz and P. Perrone, Stochastic Order on Metric Spaces and the Ordered Kantorovich Monad, Advances in Mathematics 366, 2020. PDF
[52] T. Fritz and P. Perrone, Monads, partial evaluations, and rewriting. Proceedings of MFPS 36, ENTCS, 2020. PDF.
[53] D. Bennequin. G. Sergeant-Perthuis, O. Peltre, and J.P. Vigneaux, Extra-fine sheaves and interaction decompositions, (2020) arXiv:2009.12646
[54] J.P. Vigneaux, Information structures and their cohomology, in Theory and Applications of Categories, Vol. 35, (2020), No. 38, pp 1476-1529.
[55] O. Peltre, Message-Passing Algorithms and Homology, PhD Thesis (2020), arXiv:2009.11631
[56] G. Sergeant-Perthuis, Interaction decomposition for presheafs, (2020) arXiv:2008.09029
[57] K. Hess, Topological adventures in neuroscience, in the Proceedings of the 2018 Abel Symposium: Topological Data Analysis, Springer Verlag, (2020).
[58] C. Curto, N. Youngs. Neural ring homomorphisms and maps between neural codes. Submitted. arXiv.org preprint.
[59] N.C. Combe, Y, Manin, F-manifolds and geometry of information, arXiv:2004.08808v.2, (2020) Bull. London MS.
[60] Abramsky, S. , Classical logic, classical probability, and quantum mechanics 2020 arXiv:2010.13326
chairs of the sessions
Topics
This session will focus on the advances on Algebraic Topology and Geometrical methods in neurosciences (see [1-105] bellow, among many others). The field is currently knowing an impressive development coming both:
_ from theoretical neuroscience and machine learning fields, like Graph Neural Networks [30-42], Bayesian geometrical inference [27-29], Message Passing, probability and cohomology [92-95], Information Topology [53-54,62-66,96-105] or Networks [83-85,90-91], higher order n-body statistical interactions [67,74,94-95,99,101]
_ from topological data analysis applications to real neural recordings, ranging from subcellular [43,51] genetic or omic expressions [81,101], spiking dynamic and neural coding [1-25,45-47,50-52,79], to cortical areas fMRI, EEG [26,67-72,76-80,84-89], linguistic [54-61] and consciousness [48,53,102].
Bibliographical references: (to be completed)
Carina Curto, Nora Youngs and Vladimir Itskov and colleagues:
[1] C. Curto, N. Youngs. Neural ring homomorphisms and maps between neural codes. Submitted. arXiv.org preprint.
[2] C. Curto, J. Geneson, K. Morrison. Fixed points of competitive threshold-linear networks. Neural Computation, in press, 2019. arXiv.org preprint.
[3] C. Curto, A. Veliz-Cuba, N. Youngs. Analysis of combinatorial neural codes: an algebraic approach. Book chapter in Algebraic and Combinatorial Computational Biology. R. Robeva, M. Macaulay (Eds), 2018.
[4] C. Curto, V. Itskov. Combinatorial neural codes. Handbook of Discrete and Combinatorial Mathematics, Second Edition, edited by Kenneth H. Rosen, CRC Press, 2018. pdf
[5] C. Curto, E. Gross, J. Jeffries, K. Morrison, M. Omar, Z. Rosen, A. Shiu, N. Youngs. What makes a neural code convex? SIAM J. Appl. Algebra Geometry, vol. 1, pp. 222-238, 2017. pdf, SIAGA link, and arXiv.org preprint
[6] C. Curto. What can topology tells us about the neural code? Bulletin of the AMS, vol. 54, no. 1, pp. 63-78, 2017. pdf, Bulletin link.
[7] C. Curto, K. Morrison. Pattern completion in symmetric threshold-linear networks. Neural Computation, Vol 28, pp. 2825-2852, 2016. pdf, arXiv.org preprint.
[8] C. Giusti, E. Pastalkova, C. Curto, V. Itskov. Clique topology reveals intrinsic geometric structure in neural correlations. PNAS, vol. 112, no. 44, pp. 13455-13460, 2015. pdf, PNAS link.
[9] C. Curto, A. Degeratu, V. Itskov. Encoding binary neural codes in networks of threshold-linear neurons. Neural Computation, Vol 25, pp. 2858-2903, 2013. pdf, arXiv.org preprint.
[10] K. Morrison, C. Curto. Predicting neural network dynamics via graphical analysis. Book chapter in Algebraic and Combinatorial Computational Biology. R. Robeva, M. Macaulay (Eds), 2018. arXiv.org preprint,
[11] C. Curto, V. Itskov, A. Veliz-Cuba, N. Youngs. The neural ring: an algebraic tool for analyzing the intrinsic structure of neural codes. Bulletin of Mathematical Biology, Volume 75, Issue 9, pp. 1571-1611, 2013. arXiv.org preprint.
[12] C. Curto, V. Itskov, K. Morrison, Z. Roth, J.L. Walker. Combinatorial neural codes from a mathematical coding theory perspective. Neural Computation, Vol 25(7):1891-1925, 2013. arXiv.org preprint.
[13] C. Curto, A. Degeratu, V. Itskov. Flexible memory networks. Bulletin of Mathematical Biology, Vol 74(3):590-614, 2012. arXiv.org preprint.
[14] V. Itskov, C. Curto, E. Pastalkova, G. Buzsaki. Cell assembly sequences arising from spike threshold adaptation keep track of time in the hippocampus. Journal of Neuroscience, Vol. 31(8):2828-2834, 2011.
[15] K.D. Harris, P. Bartho, et al.. How do neurons work together? Lessons from auditory cortex. Hearing Research, Vol. 271(1-2), 2011, pp. 37-53.
[16] P. Bartho, C. Curto, A. Luczak, S. Marguet, K.D. Harris. Population coding of tone stimuli in auditory cortex: dynamic rate vector analysis. European Journal of Neuroscience, Vol. 30(9), 2009, pp. 1767-1778.
[17] C. Curto, V. Itskov. Cell groups reveal structure of stimulus space. PLoS Computational Biology, Vol. 4(10): e1000205, 2008.
[18] E. Gross , N. K. Obatake , N. Youngs, Neural ideals and stimulus space visualization, Adv. Appl.Math., 95 (2018), pp. 65–95.
[19] C. Giusti, V. Itskov. A no-go theorem for one-layer feedforward networks. Neural Computation, 26 (11):2527-2540, 2014.
[20] V. Itskov, L.F. Abbott. Capacity of a Perceptron for Sparse Discrimination . Phys. Rev. Lett. 101(1), 2008.
[21] V. Itskov, E. Pastalkova, K. Mizuseki, G. Buzsaki, K.D. Harris. Theta-mediated dynamics of spatial information in hippocampus. Journal of Neuroscience, 28(23), 2008.
[22] V. Itskov, C. Curto, K.D. Harris. Valuations for spike train prediction. Neural Computation, 20(3), 644-667, 2008.
[23] E. Pastalkova, V. Itskov , A. Amarasingham , G. Buzsaki. Internally Generated Cell Assembly Sequences in the Rat Hippocampus. Science 321(5894):1322 - 1327, 2008.
[24] V. Itskov, A. Kunin, Z. Rosen. Hyperplane neural codes and the polar complex. To appear in the Abel Symposia proceedings, Vol. 15, 2019.
Alexander Ruys de Perez and colleagues:
[25] A. Ruys de Perez, L.F. Matusevich, A. Shiu, Neural codes and the factor complex, Advances in Applied Mathematics 114 (2020).
Sunghyon Kyeong and colleagues:
[26] Sunghyon Kyeong, Seonjeong Park, Keun-Ah Cheon, Jae-Jin Kim, Dong-Ho Song, and Eunjoo Kim, A New Approach to Investigate the Association between Brain Functional Connectivity and Disease Characteristics of Attention-Deficit/Hyperactivity Disorder: Topological Neuroimaging Data Analysis, PLOS ONE, 10 (9): e0137296, DOI: 10.1371/journal.pone.0137296 (2015)
Jonathan Pillow and colleagues:
[27] Aoi MC & Pillow JW (2017). Scalable Bayesian inference for high-dimensional neural receptive fields. bioRxiv 212217; doi: https://doi.org/10.1101/212217
[28] Aoi MC, Mante V, & Pillow JW. (2020). Prefrontal cortex exhibits multi-dimensional dynamic encoding during decision-making. Nat Neurosci.
[29] Calhoun AJ, Pillow JW, & Murthy M. (2019). Unsupervised identification of the internal states that shape natural behavior. Nature Neuroscience 22:2040-20149.
[30] Dong X, Thanou D, Toni L, et al., 2020, Graph Signal Processing for Machine Learning: A Review and New Perspectives, Ieee Signal Processing Magazine, Vol:37, ISSN:1053-5888, Pages:117-127
Michael Bronstein, Federico Monti, Giorgos Bouritsas and colleagues:
[31] G. Bouritsas, F. Frasca, S Zafeiriou, MM Bronstein, Improving graph neural network expressivity via subgraph isomorphism counting. arXiv (2020) preprint arXiv:2006.09252
[32] M. Bronstein , G. Pennycook, L. Buonomano, T.D. Cannon, Belief in fake news, responsiveness to cognitive conflict, and analytic reasoning engagement, Thinking and Reasoning (2020), ISSN: 1354-6783
[33] X. Dong, D. Thanou, L. Toni, M. Bronstein, P. Frossard, Graph Signal Processing for Machine Learning: A Review and New Perspectives, IEEE Signal Processing Magazine (2020), Vol: 37, Pages: 117-127, ISSN: 1053-5888
[34] Y. Wang, Y. Sun, Z. Liu, S.E. Sarma, M. Bronstein, J.M. Solomon, Dynamic Graph CNN for Learning on Point Clouds, ACM Transactions on graphics (2020), Vol: 38, ISSN: 0730-0301
[35] M. Bronstein, J. Everaert, A. Castro, J. Joormann, T. D. Cannon, Pathways to paranoia: Analytic thinking and belief flexibility., Behav Res Ther (2019), Vol: 113, Pages: 18-24
[36] G. Bouritsas, S. Bokhnyak, S. Ploumpis, M. Bronstein, S. Zafeiriou, Neural 3D Morphable Models: Spiral Convolutional Networks for 3D Shape Representation Learning and Generation, (2019) IEEE/CVF ICCV 2019, 7212
[37] O. Litany, A. Bronstein, M. Bronstein, A. Makadia et al., Deformable Shape Completion with Graph Convolutional Autoencoders (2018), Pages: 1886-1895, ISSN: 1063-6919
[38] R. Levie, F. Monti, X. Bresson X, M. Bronstein, CayleyNets: Graph Convolutional Neural Networks with Complex Rational Spectral Filters, IEEE Transactions on Signal Processing (2018), Vol: 67, Pages: 97-109, ISSN: 1053-587X
[39] F. Monti, K. Otness, M. Bronstein, Motifnet: a motif-based graph convolutional network for directed graphs (2018), Pages: 225-228
[40] F. Monti, M. Bronstein, X. Bresson, Geometric matrix completion with recurrent multi-graph neural networks, Neural Information Processing Systems (2017), Pages: 3700-3710, ISSN: 1049-5258
[41] F. Monti F, D. Boscaini, J. Masci, E. Rodola, J. Svoboda, M. Bronstein, Geometric deep learning on graphs and manifolds using mixture model CNNs, (2017) IEEE Conference on Computer Vision and Pattern Recognition, p: 3-3
[42] M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst et al., Geometric Deep Learning Going beyond Euclidean data, IEEE Signal Processing Magazine (2017), Vol: 34, Pages: 18-42, ISSN: 1053-5888
Kathryn Hess and colleagues:
[43] L. Kanari, H. Dictus, W. Van Geit, A. Chalimourda, B. Coste, J. Shillcock, K. Hess, and H. Markram, Computational synthesis of cortical dendritic morphologies, bioRvix (2020) 10.1101/2020.04.15.040410, submitted.
[44] G. Tauzin, U. Lupo, L. Tunstall, J. Burella Prez, M. Caorsi, A. Medina-Mardones, A, Dassatti, and K. Hess, giotto-tda: a topological data analysis toolkit for machine learning and data exploration, arXiv:2004.02551
[45] E. Mullier, J. Vohryzek, A. Griffa, Y. Alemàn-Gómez, C. Hacker, K. Hess, and P. Hagmann, Functional brain dynamics are shaped by connectome n-simplicial organization, (2020) submitted.
[46] M. Fournier, M. Scolamiero, etal., Topology predicts long-term functional outcome in early psychosis, Molecular Psychiatry (2020). https://doi.org/10.1038/s41380-020-0826-1.
[47] K. Hess, Topological adventures in neuroscience, in the Proceedings of the 2018 Abel Symposium: Topological Data Analysis, Springer Verlag, (2020).
[48] A. Doerig, A. Schurger, K. Hess, and M. H. Herzog, The unfolding argument: why IIT and other causal structure theories of consciousness are empirically untestable, Consciousness and Cognition 72 (2019) 49-59.
[49] L. Kanari, S. Ramaswamy, et al., Objective classification of neocortical pyramidal cells, Cerebral Cortex (2019) bhy339, https://doi.org/10.1093/cercor/bhy339.
[50] J.-B. Bardin, G. Spreemann, K. Hess, Topological exploration of artificial neuronal network dynamics, Network Neuroscience (2019) https://doi.org/10.1162/netn_a_00080.
[51] L. Kanari, P. Dłotko, M. Scolamiero, R. Levi, J. C. Shillcock, K. Hess, and H. Markram, A topological representation of branching morphologies, Neuroinformatics (2017) doi: 10.1007/s12021-017-9341-1.
[52] M. W. Reimann, M. Nolte,et al., Cliques of neurons bound into cavities provide a missing link between structure and function, Front. Comput. Neurosci., 12 June (2017), doi: 10.3389/fncom.2017.00048.
Mathilde Marcoli, Yuri Manin, and colleagues:
[53] Y. Manin, M. Marcolli Homotopy Theoretic and Categorical Models of Neural Information Networks. arXiv (2020) preprint arXiv:2006.15136
[54] M. Marcolli, Lumen Naturae: Visions of the Abstract in Art and Mathematics, MIT Press (2020)
[55] A. Port, T. Karidi, M. Marcolli, Topological Analysis of Syntactic Structures (2019) arXiv preprint arXiv:1903.05181
[56] M. Marcolli, Motivic information, Bollettino dell'Unione Matematica Italiana (2019) 12 (1-2), 19-41
[57] A. Port, I. Gheorghita, D. Guth, J.M. Clark, C. Liang, S. Dasu, M. Marcolli, Persistent topology of syntax, Mathematics in Computer Science (2018) 12 (1), 33-50 20
[58] K. Shu, S. Aziz, VL Huynh, D Warrick, M Marcolli, Syntactic phylogenetic trees, Foundations of Mathematics and Physics One Century After Hilbert (2018), 417-441
[59] K. Shu, A. Ortegaray, R Berwick, M Marcolli Phylogenetics of Indo-European language families via an algebro-geometric analysis of their syntactic structures. arXiv (2018) preprint arXiv:1712.01719
[60] K. Shu, M. Marcolli, Syntactic structures and code parameters Mathematics in Computer Science (2018) 11 (1), 79-90
[61] K Siva, J Tao, M Marcolli. Syntactic Parameters and Spin Glass Models of Language Change Linguist. Anal (2017) 41 (3-4), 559-608
[62] M. Marcolli, N. Tedeschi, Entropy algebras and Birkhoff factorization. Journal of Geometry and Physics (2015) 97, 243-265
[63] M. Marcolli, Information algebras and their applications. International Conference on Geometric Science of Information (2015), 271-276
[64] K. Siva, J. Tao, M. Marcolli Spin glass models of syntax and language evolution, arXiv preprint (2015) arXiv:1508.00504
[65] Y. Manin, M. Marcolli, Kolmogorov complexity and the asymptotic bound for error-correcting codes Journal of Differential Geometry (2014) 97 (1), 91-108
[66] M. Marcolli, R. Thorngren, Thermodynamic semirings, ArXiv preprint (2011) arXiv:1108.2874
Bosa Tadić and colleagues:
[67] M. Andjelkovic, B. Tadic, R. Melnik, The topology of higher-order complexes associated with brain-function hubs in human connectomes , available on arxiv.org/abs/2006.10357, published in Scientific Reports 10:17320 (2020)
[68] B. Tadic, M. Andjelkovic, M. Suvakov, G.J. Rodgers, Magnetisation Processes in Geometrically Frustrated Spin Networks with Self-Assembled Cliques, Entropy 22(3), 336 (2020)
[69] B. Tadic, M. Andjelkovic, R. Melnik, Functional Geometry of Human Connectomes published in ScientificReports Nature:ScientificReports 9:12060 (2019) previous version: Functional Geometry of Human Connectome and Robustness of Gender Differences, arXiv preprint arXiv:1904.03399 April 6, 2019
[70] B. Tadic, M. Andjelkovic, M. Suvakov, Origin of hyperbolicity in brain-to-brain coordination networks, FRONTIERS in PHYSICS vol.6, ARTICLE{10.3389/fphy.2018.00007}, (2018) OA
[71] B. Tadic, M. Andjelkovic, Algebraic topology of multi-brain graphs: Methods to study the social impact and other factors onto functional brain connections, in Proceedings of BELBI (2016)
[72] B. Tadic, M. Andjelkovic, B.M. Boskoska, Z. Levnajic, Algebraic Topology of Multi-Brain Connectivity Networks Reveals Dissimilarity in Functional Patterns during Spoken Communications, PLOS ONE Vol 11(11), e0166787 (2016)
[73] M. Mitrovic and B. Tadic, Search for Weighted Subgraphs on Complex Networks with MLM, Lecture Notes in Computer Science, Vol. 5102 pp. 551-558 (2008)
Giovanni Petri, Francesco Vaccarino and collaborators:
[74] F. Battiston, G. Cencetti, et al., Networks beyond pairwise interactions: structure and dynamics, Physics Reports (2020), arXiv:2006.01764
[75] M. Guerra, A. De Gregorio, U. Fugacci, G. Petri, F. Vaccarino, Homological scaffold via minimal homology bases. arXiv (2020) preprint arXiv:2004.11606
[76] J. Billings, R. Tivadar, M.M. Murray, B. Franceschiello, G. Petri, Topological Features of Electroencephalography are Reference-Invariant, bioRxiv 2020
[77] J. Billings, M. Saggar, S. Keilholz, G. Petri, Topological Segmentation of Time-Varying Functional Connectivity Highlights the Role of Preferred Cortical Circuits, bioRxiv 2020
[78] E. Ibáñez-Marcelo, L. Campioni, et al., Topology highlights mesoscopic functional equivalence between imagery and perception: The case of hypnotizability. NeuroImage (2019) 200, 437-449
[79] P. Expert, L.D. Lord, M.L. Kringelbach, G. Petri. Topological neuroscience. Network Neuroscience (2019) 3 (3), 653-655
[80] C. Geniesse, O. Sporns, G. Petri, M. Saggar, Generating dynamical neuroimaging spatiotemporal representations (DyNeuSR) using topological data analysis. Network Neuroscience (2019) 3 (3), 763-778
[81] A. Patania, P. Selvaggi, M. Veronese, O. Dipasquale, P. Expert, G. Petri, Topological gene expression networks recapitulate brain anatomy and function. Network Neuroscience (2019) 3 (3), 744-762
[82] E. Ibáñez‐Marcelo, L. Campioni, D.et al.. Spectral and topological analyses of the cortical representation of the head position: Does hypnotizability matter? Brain and behavior (2018) 9 (6), e01277
[83] G. Petri, A. Barrat, Simplicial activity driven model, Physical review letters 121 (22), 228301
[84] A. Phinyomark, E. Ibanez-Marcelo, G. Petri. Resting-state fmri functional connectivity: Big data preprocessing pipelines and topological data analysis. IEEE Transactions on Big Data (2017) 3 (4), 415-428
[85] G. Petri, S. Musslick, B. Dey, K. Ozcimder, D. Turner, N.K. Ahmed, T. Willke. Topological limits to parallel processing capability of network architectures. arXiv preprint (2017) arXiv:1708.03263
[86] K. Ozcimder, B. Dey, S. Musslick, G. Petri, N.K. Ahmed, T.L. Willke, J.D. Cohen, A Formal Approach to Modeling the Cost of Cognitive Control, arXiv preprint (2017) arXiv:1706.00085
[87] L.D. Lord, P. Expert, et al. , Insights into brain architectures from the homological scaffolds of functional connectivity networks, Frontiers in systems neuroscience (2016) 10, 85
[88] J. Binchi, E. Merelli, M. Rucco, G. Petri, F. Vaccarino. jHoles: A Tool for Understanding Biological Complex Networks via Clique Weight Rank Persistent Homology. Electron. Notes Theor. Comput. Sci. (2014) 306, 5-18
[89] G. Petri, P. Expert, F. Turkheimer, R. Carhart-Harris, D. Nutt, P.J. Hellyer et al., Homological scaffolds of brain functional networks. Journal of The Royal Society Interface (2014) 11 (101), 20140873
[90] G. Petri, M. Scolamiero, I. Donato, F. Vaccarino, Topological strata of weighted complex networks. PloS one (2013) 8 (6), e66506
[91] G. Petri, M. Scolamiero, I. Donato, ., Networks and cycles: a persistent homology approach to complex networks Proceedings of the european conference on complex systems (2013), 93-99
Daniel Bennequin, Juan-Pablo Vigneaux, Olivier Peltre, Pierre Baudot and colleagues:
[92] D. Bennequin. G. Sergeant-Perthuis, O. Peltre, and J.P. Vigneaux, Extra-fine sheaves and interaction decompositions, (2020) arXiv:2009.12646
[93] O. Peltre, Message-Passing Algorithms and Homology, PhD Thesis (2020), arXiv:2009.11631
[94] G. Sergeant-Perthuis, Interaction decomposition for presheafs, (2020) arXiv:2008.09029
[95] G. Sergeant-Perthuis, Bayesian/Graphoid intersection property for factorisation models, (2019), arXiv:1903.06026
[96] J.P. Vigneaux, Topology of Statistical Systems: A Cohomological Approach to Information Theory, PhD Thesis (2019).
[97] J.P. Vigneaux, Information structures and their cohomology, in Theory and Applications of Categories, Vol. 35, (2020), No. 38, pp 1476-1529.
[98] J.P. Vigneaux, Information theory with finite vector spaces, in IEEE Transactions on Information Theory, vol. 65, no. 9, pp. 5674-5687, Sept. (2019)
[99] Baudot P., Tapia M., Bennequin, D. , Goaillard J.M., Topological Information Data Analysis. (2019), Entropy, 21(9), 869
[100] Baudot P., The Poincaré-Shannon Machine: Statistical Physics and Machine Learning aspects of Information Cohomology. (2019), Entropy , 21(9),
[101] Tapia M., Baudot P., et al. Neurotransmitter identity and electrophysiological phenotype are genetically coupled in midbrain dopaminergic neurons. Scientific Reports. (2018). BioArXiv168740
[102] Baudot P., Elements of qualitative cognition: an Information Topology Perspective. Physics of Life Reviews. (2019) Arxiv. arXiv:1807.04520
[103] Baudot P., Bennequin D., The homological nature of entropy. Entropy, (2015), 17, 1-66; doi:10.3390
[104] D. Bennequin. Remarks on Invariance in the Primary Visual Systems of Mammals, pages 243–333. Neuromathematics of Vision Part of the series Lecture Notes in Morphogenesis Springer, 2014.
[105] Baudot P., Bennequin D., Information Topology I and II. Random models in Neuroscience (2012)
Title: Exploring Quantum Statistics for Machine Learning
Abstract: Quantum mechanics represents a rather bizarre theory of statistics that is very different from the ordinary classical statistics that we are used to. In this talk I will explore if there are ways that we can leverage this theory in developing new machine learning tools: can we design better neural networks by thinking about entangled variables? Can we come up with better samplers by viewing them as observations in a quantum system? Can we generalize probability distributions? We hope to develop better algorithms that can be simulated efficiently on classical computers, but we will naturally also consider the possibility of much faster implementations on future quantum computers. Finally, I hope to discuss the role of symmetries in quantum theories.
Reference:
Roberto Bondesan, Max Welling, Quantum Deformed Neural Networks, arXiv:2010.11189v1 [quant-ph], 21st October 2020 ; https://arxiv.org/abs/2010.11189
Jean PETITOT
Directeur d'Études, Centre d'Analyse et de Mathématiques, Sociales, École des Hautes Études, Paris.
Born in 1944, Jean Petitot is an applied mathematician interested in dynamical modeling in neurocognitive sciences. He is the former director of the CREA (Applied Epistemology Research Center) at the Ecole Polytechnique.
Philisopher of science http://jeanpetitot.com
Title : The primary visual cortex as a Cartan engine
Abstract: Cortical visual neurons detect very local geometric cues as retinal positions, local contrasts, local orientations of boundaries, etc. One of the main theoretical problem of low level vision is to understand how these local cues can be integrated so as to generate the global geometry of the images perceived, with all the well-known phenomena studied since Gestalt theory. It is an empirical evidence that the visual brain is able to perform a lot of routines belonging to differential geometry. But how such routines can be neurally implemented ? Neurons are « point-like » processors and it seems impossible to do differential geometry with them. Since the 1990s, methods of "in vivo optical imaging based on activity-dependent intrinsic signals" have made possible to visualize the extremely special connectivity of the primary visual areas, their “functional architectures.” What we called « Neurogeometry » is based on the discovery that these functional architectures implement structures such as the contact structure and the sub-Riemannian geometry of jet spaces of plane curves. For reasons of principle, it is the geometrical reformulation of differential calculus from Pfaff to Lie, Darboux, Frobenius, Cartan and Goursat which turns out to be suitable for neurogeometry.
References:
Yvette Kosmann-Schwarzbach
Professeur des universités honoraire ; former student of the Ecole normale supérieure Sèvres, 1960-1964; aggregation of mathematics, 1963; CNRS research associate, 1964-1969; doctorate in science, Lie derivatives of spinors, University of Paris, 1970 under supervision of André Lichnerowicz; lecturer then professor at the University of Lille (1970-1976 and 1982-1993), at Brooklyn College, New York (1979-1982), at the École polytechnique (1993-2006)
Title: Structures of Poisson Geometry: old and new
Abstract: How did the brackets that Siméon-Denis Poisson introduce in 1809 evolve into the Poisson geometry of the 1970's? What are Poisson groups and, more generally, Poisson groupoids? In what sense does Dirac geometry generalize Poisson geometry and why is it relevant for applications? I shall sketch the definition of these structures and try to answer these questions.
References
Michel Broniatowski
Sorbonne Université, Paris
Title: Some insights on statistical divergences and choice of models
Abstract: Divergences between probability laws or more generally between measures define inferential criteria, or risk functions. Their estimation makes it possible to deal with the questions of model choice and statistical inference, in connection with the regularity of the models considered; depending on the nature of these models (parametric or semi-parametric), the nature of the criteria and their estimation methods vary. Representations of these divergences as large deviation rates for specific empirical measures allow their estimation in nonparametric or semi parametric models, by making use of information theory results (Sanov's theorem and Gibbs principles), by Monte Carlo methods. The question of the choice of divergence is wide open; an approach linking nonparametric Bayesian statistics and MAP estimators provides elements of understanding of the specificities of the various divergences in the Ali-Silvey-Csiszar-Arimoto class in relation to the specific choices of the prior distributions.
References:
Maurice de Gosson
Professor, Senior Researcher at the University of Vienna https://homepage.univie.ac.at/maurice.de.gosson
Faculty of Mathematics, NuHAG group
Title: Gaussian states from a symplectic geometry point of view
Abstract: Gaussian states play an ubiquitous role in quantum information theory and in quantum optics because they are easy to manufacture in the laboratory, and have in addition important extremality properties. Of particular interest are their separability properties. Even if major advances have been made in their study in recent years, the topic is still largely open. In this talk we will discuss separability questions for Gaussian states from a rigorous point of view using symplectic geometry, and present some new results and properties.
References:
Giuseppe LONGO
Centre Cavaillès, CNRS & Ens Paris and School of Medicine, Tufts University, Boston http://www.di.ens.fr/users/longo/
Title: Use and abuse of "digital information" in life sciences, is Geometry of Information a way out?
Abstract: Since WWII, the war of coding, and the understanding of the structure of the DNA (1953), the latter has been considered as the digital encoding of the Aristotelian Homunculus. Till now DNA is viewed as the "information carrier" of ontogenesis, the main or unique player and pilot of phylogenesis. This heavily affected our understanding of life and reinforced a mechanistic view of organisms and ecosystems, a component of our disruptive attitude towards ecosystemic dynamics. A different insight into DNA as a major constraint to morphogenetic processes brings in a possible "geometry of information" for biology, yet to be invented. One of the challenges is in the need to move from a classical analysis of morphogenesis, in physical terms, to a "heterogenesis" more proper to the historicity of biology.
References
Welcome to “Geometric Science of Information” 202 Conference
On behalf of both the organizing and the scientific committees, it is our great pleasure to welcome all delegates, representatives and participants from around the world to the fifth International SEE conference on “Geometric Science of Information” (GSI’21), sheduled in July 2021.
GSI’21 benefits from scientific sponsor and financial sponsors.
The 3-day conference is also organized in the frame of the relations set up between SEE and scientific institutions or academic laboratories such as Ecole Polytechnique, Ecole des Mines ParisTech, INRIA, CentraleSupélec, Institut Mathématique de Bordeaux, Sony Computer Science Laboratories.
The GSI conference cycle has been initiated by the Brillouin Seminar Team as soon as 2009. The GSI’21 event has been motivated in the continuity of first initiatives launched in 2013 (https://www.see.asso.fr/gsi2013) at Mines PatisTech, consolidated in 2015 (https://www.see.asso.fr/gsi2015) at Ecole Polytechnique and opened to new communities in 2017 (https://www.see.asso.fr/gsi2017) at Mines ParisTech and 2019 (https://www.see.asso.fr/gsi2019) at ENAC Toulouse. We mention that in 2011, we organized an indo-french workshop on “Matrix Information Geometry” that yielded an edited book in 2013, and in 2017, collaborate to CIRM seminar in Luminy TGSI’17 “Topoplogical & Geometrical Structures of Information” (http://forum.cs-dc.org/category/94/tgsi2017). Last GSI’19 Proceedings have been edited by SPRINGER in Lecture Notes (https://www.springer.com/gp/book/9783030269791).
GSI satellites event have been organized in 2019 and 2020 as, FGSI’19 “Foundation of Geometric Science of Information” in Montpellier and Les Houches Seminar SPIGL’20 “Joint Structures and Common Foundations of Statistical Physics, Information Geometry and Inference for Learning” .
The technical program of GSI’21 covers all the main topics and highlights in the domain of “Geometric Science of Information” including Information Geometry Manifolds of structured data/information and their advanced applications. This proceedings consists solely of original research papers that have been carefully peer-reviewed by two or three experts before, and revised before acceptance.
Historical background
As for the GSI’13, GSI’15, GSI’17, and GSI’19 GSI'21 addresses inter-relations between different mathematical domains like shape spaces (geometric statistics on manifolds and Lie groups, deformations in shape space, ...), probability/optimization & algorithms on manifolds (structured matrix manifold, structured data/Information, ...), relational and discrete metric spaces (graph metrics, distance geometry, relational analysis,...), computational and Hessian information geometry, geometric structures in thermodynamics and statistical physics, algebraic/infinite dimensional/Banach information manifolds, divergence geometry, tensor-valued morphology, optimal transport theory, manifold & topology learning, ... and applications like geometries of audio-processing, inverse problems and signal/image processing. GSI’21 topics were enriched with contributions from Lie Group Machine Learning, Harmonic Analysis on Lie Groups, Geometric Deep Learning, Geometry of Hamiltonian Monte Carlo, Geometric & (Poly)Symplectic Integrators, Contact Geometry & Hamiltonian Control, Geometric and structure preserving discretizations, Probability Density Estimation & Sampling in High Dimension, Geometry of Graphs and Networks and Geometry in Neuroscience & Cognitive Sciences.
At the turn of the century, new and fruitful interactions were discovered between several branches of science: Information Science (information theory, digital communications, statistical signal processing,), Mathematics (group theory, geometry and topology, probability, statistics, sheaves theory,...) and Physics (geometric mechanics, thermodynamics, statistical physics, quantum mechanics, ...). GSI conference cycle is a tentative to discover joint mathematical structures to all these disciplines by elaboration of a “General Theory of Information” embracing physics science, information science, and cognitive science in a global scheme.
Frank Nielsen, co-chair : Ecole Polytechnique, Palaiseau, France, Sony Computer Science Laboratories, Tokyo, Japan
Frédéric Barbaresco, co-chair: President of SEE ISIC Club (Ingénierie des Systèmes d'Information et de Communications),
Representative of KTD PCC (Key Technology Domain / Processing, Computing & Cognition ) Board, THALES LAND & AIR SYSTEMS, France
As for GSI’13, GSI’15, GSI’17 and GSI’19, the objective of this SEE GSI’21 conference, hosted in PARIS, is to bring together pure/applied mathematicians and engineers, with common interest for Geometric tools and their applications for Information analysis.
It emphasizes an active participation of young researchers to discuss emerging areas of collaborative research on “Geometric Science of Information and their Applications”.
Current and ongoing uses of Information Geometry Manifolds in applied mathematics are the following: Advanced Signal/Image/Video Processing, Complex Data Modeling and Analysis, Information Ranking and Retrieval, Coding, Cognitive Systems, Optimal Control, Statistics on Manifolds, Topology/Machine/Deep Learning, Artificial Intelligence, Speech/sound recognition, natural language treatment, Big Data Analytics, Learning for Robotics, etc., which are substantially relevant for industry.
The Conference will be therefore held in areas of topics of mutual interest with the aim to:
• Provide an overview on the most recent state-of-the-art
• Exchange mathematical information/knowledge/expertise in the area
• Identify research areas/applications for future collaboration
Provisional topics of interests:
A special session will deal with:
Advanced information on article submission and publication
As for previous editions, GSI’21 Proceedings will be published in SPRINGER LNCS. See GSI’19 Proceedings
8 pages SPRINGER LNCS format is required for Initial paper submission.
A detailed call for contributions will be published shortly.
CALL FOR PAPERS
Ph.D. and Postdoc positions in Applied Mathematics
Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
Application deadline: January 10th, 2021
The Group
The Chair of Applied Analysis – Alexander von Humboldt Professorship at the Department of Mathematics of the Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), in Erlangen (Germany), led by Prof. Dr. Enrique Zuazua, is looking for outstanding candidates to fill several
Ph.D. and Postdoctoral positions
In the broad area of Applied Mathematics, the Chair develops and applies methods of Analysis, Computational Mathematics and Data Sciences to model, understand and control the dynamics of various phenomena arising in the interphase of Mathematics with Engineering, Physics, Biology and Social Sciences.
We welcome applications by young and highly motivated scientists to contribute to this exciting joint AvH-FAU effort. Possible research projects include but are not limited to:
We look for excellent candidates with expertise in the areas of applied mathematics, PDE analysis, control theory, numerical analysis, data sciences and computational mathematics who enjoy interdisciplinary work.
The Chair contributes to the development of a new Center of Research at FAU, in the broad area of “Mathematics of Data”, conceived as a highly visible interdisciplinary research site, an incubator for future collaborative research grants and a turntable for the key research priorities of FAU. The recruited candidates will have the added opportunity to participate in this challenging endeavour.
How to apply
Applications, including cover/motivation letter, curriculum vitae, list of publications, statement of research and two or three names of experts for reference should be submitted via e-mail as a single pdf file to secretary-aa[at]math.fau.de before January 10th, 2012.
Any inquiries about the positions should be sent to Prof. Enrique Zuazua (positions-aa[at]math.fau.de). Applications will be accepted until the positions are filled.
FAU is a member of “The Family in Higher Education Institutions” best practice club and also aims to increase the number of women in scientific positions. Female candidates are therefore particularly encouraged to apply. In case of equal qualifications, candidates with disabilities will take precedence.
For more detailed information about the Chair, please visit Chair of Applied Analysis – Alexander von Humboldt Professorship
]]>We offer a position for a doctoral candidate (75% / TV-L E13, initially ad interim until 30.06.2022) at FAU Erlangen-Nürnberg to work on the dynamics, control and of partial differential equations motivated by gas network dynamics.
Applicants should have a qualified degree in mathematics or a related field with fundamental knowledge in mathematical modelling and partial differential equations. Proficiency in oral and written English is required.
The candidate will be part of the research team of the SFB/Transregio TRR 154 (www.trr154.fau.de) having access to a integrative research environment such as a specific graduate program, regular research meetings and specific funding for guests and conference attendances. Once recruited, the chosen PhD candidate will perform research in a highly relevant research area in applied mathematics that is centered around the “turnaround in energy policy”, in particular in the context of gas networks. The main aim of TRR 154 is to provide certified novel answers to mathematical challenges arising in this context, based on mathematical modeling, game theory, simulation, and optimization.
The advertised position will contribute to subproject C08 (project leaders: Falk Hante (Humboldt-Universität zu Berlin) and Enrique Zuazua (FAU Erlangen-Nürnberg) which is focusing on hyperbolic and parabolic dynamics on networks and random batch methods for control. The candidate will integrate the Chair of Applied Analysis – Alexander von Humboldt Professorship led by E. Zuazua at FAU-Erlangen and, beyond the close collaboration within TRR 154, will also be linked to activities in the perimeter of FAU, such as the Energy Campus Nürnberg (www.encn.de/markt) with strong expertise regarding the mathematical modeling of energy markets (Electricity, Gas, Hydrogen) and regarding the development of sustainable mobility concepts.
Our research group offers a lively research environment, financial support for attending conferences, and an intensive supervision within a large and interactive team. Salary corresponds to the German pay scale (75% / TV-L E13). In order to increase the proportion of female staff members, applications from female scientists are particularly encouraged. Preference will be given to disabled persons with the same qualification.
For further information about the position, please contact Professor Enrique Zuazua (enrique.zuazua@fau.de) or Professor Falk Hante (falk.hante@hu-berlin.de). Please submit your electronic application as a single pdf file including the standard materials (vita and diplomas) to secretary-aa@math.fau.de. Please refer to “PHD position TRR154” in the reference line of the email. The deadline for all application materials is 30 October 2020.
Here you will find the offer as pdf:
https://www.caa-avh.nat.fau.eu/files/2020/10/application-trr-154-c08.pdf
Prof. Zuazua: Chair of Applied Analysis – Alexander von Humboldt Professorship
Prof. Hante: Lehrstuhl für Angewandte Mathematik mit Schwerpunkt Optimierung komplexer Systeme
Find all the docs and tutorials of the version 0.2.3 in the read the docs website:
N.B.: This is still an alpha release! Please send me your feedback: I will polish the user interface, implement Hausdorff divergences, add support for meshes, images, volumes and clean the documentation over the summer of 2020.
The GeomLoss library provides efficient GPU implementations for:
Kernel norms (also known as Maximum Mean Discrepancies).
Hausdorff divergences, which are positive definite generalizations of the ICP loss, analogous to log-likelihoods of Gaussian Mixture Models.
Unbiased Sinkhorn divergences, which are cheap yet positive definite approximations of Optimal Transport (Wasserstein) costs.
These loss functions, defined between positive measures, are available through the custom PyTorch layers SamplesLoss, ImagesLoss and VolumesLoss which allow you to work with weighted point clouds (of any dimension), density maps and volumetric segmentation masks. Geometric losses come with three backends each:
A simple tensorized implementation, for small problems (< 5,000 samples).
A reference online implementation, with a linear (instead of quadratic) memory footprint, that can be used for finely sampled measures.
A very fast multiscale code, which uses an octree-like structure for large-scale problems in dimension <= 3.
GeomLoss is a simple interface for cutting-edge Optimal Transport algorithms. It provides:
Note, however, that SamplesLoss does not implement the Fast Multipole or Fast Gauss transforms. If you are aware of a well-packaged implementation of these algorithms on the GPU, please contact me!
The divergences implemented here are all symmetric, positive definite and therefore suitable for measure-fitting applications. For positive input measures 𝛼 and 𝛽, our Loss
functions are such that
Loss(𝛼,𝛽) = Loss(𝛽,𝛼),
0 = Loss(𝛼,𝛼) ⩽ Loss(𝛼,𝛽),
0 = Loss(𝛼,𝛽) ⟺ 𝛼=𝛽.
GeomLoss can be used in a wide variety of settings, from shape analysis (LDDMM, optimal transport…) to machine learning (kernel methods, GANs…) and image processing. Details and examples are provided below:
GeomLoss is licensed under the MIT license.
Author and Contributors
Feel free to contact us for any bug report or feature request:
Related projects
You may be interested by:
The KeOps library, which provides efficient CUDA routines for point cloud processing, with full PyTorch support.
Rémi Flamary and Nicolas Courty’s Python Optimal Transport library, which provides a reference implementation of OT-related methods for small problems.
Bernhard Schmitzer’s Optimal Transport toolbox, which provides a reference multiscale solver for the OT problem, on the CPU.
1-2 Fully funded (4yrs) PhD position on AI/machine learning with the Department of Computer Science, UiT The Arctic University of Norway.
Application Link - https://www.jobbnorge.no/en/available-jobs/job/192788/1-2-phd-fellows-in-computer-science-artificial-intelligence-for-virtual-staining-of-label-free-cell-and-tissue-images
Deadline - 18th October 2020
Location- Tromsø, Norway
Qualification:
These positions require a Master’s degree or equivalent in Computer Science, or Mathematics and Computing. In addition, the candidates must have:
Experience of working with computer vision and deep learning toolkits on at least one of the following platforms – Python, C/C++, MATLAB, Keras, PyTorch, Tensor Flow
Demonstration of programming proficiency in at least two of the following platforms: Python, C/C++, MATLAB, OpenCV, etc.
Postgraduate coursework or master thesis strongly related to at least four of the following topics:
Requirement:
Your application must include:
Cover letter explaining your motivation and research interests
CV - summarizing education, positions and academic work
Diplomas and transcripts from completed Bachelor’s and Master’s degrees
Documentation of English proficiency
1-3 references with contact details
Master thesis, and any other academic works
Documentation has to be in English or a Scandinavian language. We only accept applications through Jobbnorge.
Remuneration -
approx. 48,000 Euro per annum (Remuneration of the PhD position is in State salary scale code 1017. A compulsory contribution of 2% to the Norwegian Public Service Pension Fund will be deducted.)
Description
VirtualStain is a project funded under thematic call for strategic funding by UiT The Arctic University of Norway. It involves developing AI solutions for segmenting, identity allocation, and modeling of the processes of sub-cellular structures such as mitochondria in cells and cellular structures in tissues using label-free images and videos of cells and tissues. Interpreting life processes and label-free images of cells and tissues is a daunting task. The PhD students will work on the following problem:
Images of unlabeled samples appear as gray scale images devoid of color, texture, and edges. Therefore, they lack features conventionally used in deep models for identification of individual structures. New suitably designed and trained intelligence models have to be developed specific to the chosen label-free imaging technology. If conventional AI approaches such as deep learning and generative networks are used, large training dataset with correlated image sets of labeled and label-free images are needed, which is a significant challenge. There is a need of new out-of-box AI solutions that derive and improve intelligence, as new data becomes available.
Project page - https://en.uit.no/project/virtualstain
]]>INTRODUCTION LECTURES
Introduction and presentation of the conferences by Frederic Barbaresco. VIDEO
Presentation of Geometric Sciences of Information and GSI 2021 by Frederic Barbaresco. VIDEO
LECTURES (90 min)
1. Langevin Dynamics
2. Computational Information Geometry:
2.1. Information Manifold modeled with Orlicz Spaces : Giovanni Pistone . VIDEO
2.2. Recent contributions to Distances and Information Geometry: a computational viewpoint : Frank Nielsen . VIDEO - SLIDES
3. Non-Equilibrium Thermodynamic Geometry
4. Geometric Mechanics
4.1. Galilean Mechanics and Thermodynamics of continua : Géry de Saxcé. VIDEO - SLIDES
4.2. Souriau-Casimir Lie Groups Thermodynamics and Machine Learning : Frederic Barbaresco. VIDEO - SLIDES
5. "Structure des Systèmes Dynamiques" (SSD) Jean-Marie Souriau’s book 50th Birthday Wikipedia page
5.1. Souriau Familly and "structure of motion": Jean-Marie Souriau, Michel Souriau, Paul Souriau and Etienne Souriau : Frederic Barbaresco . VIDEO - SLIDES
5.2. SSD Jean-Marie Souriau’s book 50th birthday: Géry de Saxcé SLIDES
KEYNOTES (60 min)
Learning Physics from Data : Francisco Chinesta . VIDEO VIDEO - SLIDES
Information Geometry and Integrable Systems : Jean-Pierre Françoise. VIDEO VIDEO - SLIDES
Learning with Few Labeled Data : Pratik Chaudhari . VIDEO - SLIDES
Information Geometry and Quantum Fields : Kevin Grosvenor SLIDES
Port Thermodynamic Systems Control : Bernhard Maschke . VIDEO - SLIDES
Dirac Structures in Nonequilibrium Thermodynamics : Hiroaki Yoshimura . VIDEO - SLIDES
Thermodynamic efficiency implies predictive inference : Susanne Still . VIDEO - SLIDES
Computational dynamics of reduced coupled multibody-fluid system in Lie group setting : Zdravko Terze . VIDEO - SLIDES
Exponential Family by Representation Theory : Koichi Tojo . VIDEO - SLIDES
Deep Learning as Optimal Control Problems and Structure Preserving Deep Learning : Elena Celledoni . VIDEO - SLIDES
Contact geometry and thermodynamical systems : Manuel de León. VIDEO - SLIDES
Mechanics of the probability simplex : Luigi Malagò. VIDEO - SLIDES
Covariant Momentum Map Thermodynamics : Goffredo Chirco. VIDEO - SLIDES
Sampling and statistical physics via symmetry : Steve Huntsman. VIDEO - SLIDES
Geometry of Measure-preserving Flows and Hamiltonian Monte Carlo : Alessandro Barp. VIDEO - SLIDES
Schroedinger's problem, Hamilton-Jacobi-Bellman equations and regularized Mass Transportation : Jean-Claude Zambrini. VIDEO - SLIDES
POSTERS
PDF of posters:
Registration fees for Summer Week is 450 euros, including catering (bedroom and 3 meals a dayon 5 days) and all accommodation on site: https://www.houches-school-physics.com/practical-information/facilities/ https://www.houches-school-physics.com/practical-information/your-stay/
Registration will be paid at Les Houches reception desk at your arrival by credit card (or VAD payment of your lab).
Any registration canceled less than two weeks before the arrival date will be due.
Arrival/Departure:
The arrival is Sunday July 26th starting from 3:00 pm. On the day of arrival, only the evening meal is planned. On Sunday, the secretariat is open from 6:00 pm to 7:30 pm. Summer Week will be closed Friday July 31st at 4 pm.
Access to Les Houches:
https://www.houches-school-physics.com/practical-information/access/
Ecole de Physique des Houches, 149 Chemin de la Côte, F-74310 Les Houches, France Les Houches is a village located in Chamonix valley, in the French Alps. Established in 1951, the Physics School is situated at 1150 m above sea level in natural surroundings, with breathtaking views on the Mont-Blanc mountain range.
https://houches-school-physics.com
Excursion:
Wednesday afternoon is free. Excursion could be organized to
· The Mer de Glace (Sea of Ice): It is the largest glacier in France, 7 km long and 200m deep and is one of the biggest attractions in the Chamonix Valley: https://www.chamonix.net/english/leisure/sightseeing/mer-de-glace
· L’Aiguille du midi: From its height of 3,777m, the Aiguille du Midi and its laid-out terraces offer a 360° view of all the French, Swiss and Italian Alps. A lift brings you to the summit terrace at 3,842m, where you will have a clear view of Mont Blanc: https://www.chamonix.com/aiguille-du-midi-step-into-the-void,80,en.html
]]>See attached Poster, Scientific Program and Poster Program.
8 Lectures (90 min)
Langevin Dynamics: Old and News (x 2) – Eric Moulines
Computational Information Geometry
On statistical distances and information geometry for ML – Frank Nielsen
Information Manifold modeled with Orlicz Spaces – Giovanni Pistone
Non-Equilibrium Thermodynamic Geometry
A variational perspective of closed and open systems - François Gay-Balmaz
A Homogeneous Symplectic Approach - Arjan van der Schaft
Geometric Mechanics
Gallilean Mechanics & Thermodynamics of Continua - Géry de Saxcé
Souriau-Casimir Lie Groups Thermodynamics & Machine Learning – Frédéric Barbaresco
17 Keynotes (60 min)
Program Schedule
Mornings will be dedicated to 3 hours courses. Afternoons will be dedicated to long keynotes.
Poster session will be organized Wednesday morning.
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