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    Registration payment:

    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

    image004.jpg

    posted in Joint Structures and Common Foundations of Statistical Physics Information Geometry and Inference for Learning (SP+IG'20)read more
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    Program

    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)

    • Learning with Few Labeled Data - Pratik Chaudhari
    • Sampling and statistical physics via symmetry - Steve Huntsman
    • The Bracket Geometry of Measure-Preserving Flows and Diffusions - Alessandro Barp
    • Exponential Family by Representation Theory - Koichi Tojo
    • Learning Physics from Data - Francisco Chinesta
    • Information Geometry and Integrable Hamiltonian - Jean-Pierre Françoise
    • Information Geometry and Quantum Fields - Kevin Grosvenor
    • Thermodynamic efficiency implies predictive inference- Susanne Still
    • Diffeological Fisher Metric - Hông Vân Lê
    • Deep Learning as Optimal Control - Elena Celledoni
    • Schroedinger's problem, Hamilton-Jacobi-Bellman equations and regularized Mass Transportation - Jean-Claude Zambrini
    • Mechanics of the probability simplex - Luigi Malagò
    • Dirac structures in nonequilibrium thermodynamics - Hiroaki Yoshimura
    • Port Thermodynamic Systems Control - Bernhard Maschke
    • Covariant Momentum Map Thermodynamics - Goffredo Chirco
    • Contact geometry and thermodynamical systems - Manuel de León
    • Computational dynamics of multibody-fluid system in Lie group setting- Zdravko Terze

    Program Schedule

    image007.png

    Mornings will be dedicated to 3 hours courses. Afternoons will be dedicated to long keynotes.

    Poster session will be organized Wednesday morning.

    image009.png

    posted in Joint Structures and Common Foundations of Statistical Physics Information Geometry and Inference for Learning (SP+IG'20)read more
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    Capture du 2020-05-27 09-37-13.png

    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 Senior Scientist/Engineer 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 Head of Data Science and the Chief of Science and Innovation Officer

    Responsibilities:

    1. You will work on content based-image retrieval in medical imaging. You will build efficient search engine services in clinical applications.
    1. You will apply your AI/ML knowledge to develop innovative and robust biomarkers using data coming from medical imaging systems such as MRI and CT scanners and other data sources.
    1. Your work will involve agile 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.
    1. You will work with software development team as well as clinical science team.
    1. 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.
    1. 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.

    Searched profile :

    • Education: PhD in in Mathematics, Computer Science or related fields
    • Main skills and Experience required:
    • Minimum 5 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, R, DL frameworks (i.e. Pytorch, Keras, Tensorflow) and standard packages 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

    Legal

    • Job location: Sophia Antipolis, France
    • Contract: Permanent, Open-Ended
    • Start: As Soon As Possible
    • Offered salary: will depend on candidate’s skills and experience.

    Please apply on our website : https://mediantechnologies.com/job-search/#!careers

    posted in Jobs offers - Call for projects read more
  • Geo-Sci-Info

    Capture du 2020-05-27 09-29-30.png
    In the context of our research and development in artificial intelligence applied to medical imaging, we are looking for: Data Structuring and Clustering 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 Senior Scientist/Engineer 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 the Head of Data Science and the Chief of Science and Innovation Officer.

    • Responsibilities:

    1. You will work on scalable data clustering techniques and develop your know how for knowledge discovery and exploration towards robust biomarkers. You will conduct clustering validity studies

    2. You will work on large scale data mining and feature exraction in medical imaging. You will build and contribute to develop innovative and robust biomarkers for personalized medicine

    3. Your work will involve agile 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.

    1. You will work with software development team as well as clinical science team.
    1. 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.
    1. 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.

    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 machine learning
    • Experience with Medical Imaging, CT/MRI, image signatures, large scale visual information retrieval, features selection
    • Relevant experience with Python, R, DL frameworks (i.e. Pytorch, Keras, Tensorflow) and standard packages as Scikit-learn, Numpy, Scipy, Pandas
    • Data structure inference models, clustering and semi-supervised learning, knowledge discovery and data mapping, saliency map
    • 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

    Legal

    • Job location: Sophia Antipolis, France
    • Contract: Permanent, Open-Ended
    • Start: September 2020
    • Offered salary: will depend on candidate’s skills and experience.

    Please apply on our website : https://mediantechnologies.com/job-search/#!careers

    posted in Jobs offers - Call for projects read more
  • Geo-Sci-Info

    Capture du 2020-05-12 18-26-07.png
    OFFICIAL WEBSITE
    MEMBER RESOURCES
    This website is dedicated to featuring national resources developed by ISO members to support the fight against COVID-19.

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    600_481995012.jpeg

    Alibaba Cloud - Free CT Image Analytics for COVID-19
    Official WEBPAGE
    This technology can assist realizing quantitative analysis, speeding up CT image analytics, avoiding errors caused by fatigue and adjusting treatment plans in time.
    Free for public research institution
    Email Your Submission: Free Computational and AI Platforms to Help Research, Analyze and Combat COVID-19 ("Program") is intended to support public research institutions worldwide for the research analysis and prevention of COVID-19. This technology is subject to availability upon confirmation. You can submit the summary and description of your research project to wanqing.hwq [at] alibaba-inc.com. All submissions will be reviewed for technical feasibility, and eligible applicants will be contacted for more details and the next steps of the process. Once the submission is successful, the applicant will get a certain amount of coupon according to the project, which is valid for 3months. The coupon can be used for all Alibaba cloud products, including HPC, ECS, and GPU, excluding marketplace products and 3rd party products.
    Disclaimer: The technology is not intended to, by itself and without the exercise of professional judgment and clinical evaluation, diagnose any medical condition or disease or conclusively indicate the absence of any disease, including COVID-19 and the technology is not a substitute for diagnosis and treatment by a certified medical professional. The technology has not been thoroughly tested, and are not guaranteed in any way to be accurate, useful, sufficient, satisfactory, available, or otherwise fit for any purpose. To the maximum extent permissible under applicable law, the Solution is provided "AS IS," "WITH ALL FAULTS," and without any warranties or service guarantees.
    Note: The accuracy is calculated using the model trained on about 5000 samples, and is tested based on a sample size of 660 tests with a 1:1:1 ratio of cases of COVID-19 pneumonia, common pneumonia, and other conditions.

    Whole Genome Sequencing Analysis for COVID-19
    Official WEBPAGE
    When facing a severe epidemic such as COVID-19, rapid and accurate virus screening and detection is particularly important for maintaining control. The AI algorithm avoids the high missed detection rate of nearly 40% of PCR, and can accurately detect virus mutations and shorten the duration of genetic analysis of suspected cases from hours to just 30 minutes, greatly reducing the time of virus screening and detection.
    To read disclaimer, click to see Disclaimer in FAQ.

    • Rapid and accurate testing to improve the handling of local outbreaks
    • Core algorithm optimization and comprehensive monitoring of COVID-19 development and changes
    • Quick deployment and easy to use with modular and simplified operation and configuration

    Elastic High Performance Computing Technology
    Official WEBPAGE
    The development of new drugs and vaccines for COVID-19 requires large amounts of data analysis, as well as large-scale literature screening and scientific computing. HPC and AI technology helps scientific research institutions to perform viral gene sequencing, conduct new drug research and development, and shorten the research and development cycle. The Global Health Drug Discovery Institute (GHDDI) has established a scientific data sharing platform and an AI drug screening platform for COVID-19 with the support of this center's open AI computing power. Some research institutions and universities have increased the speed of biological information transmission by 5 times, and reduced the virtual screening time for antiviral drugs from one month to one week.

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    CovidBanner-1.png
    Capture du 2020-04-13 23-01-47.png
    LINK TO WEBSITE

    Covid-19 Interactive Dashboard – MULTIVAC

    OPEN SCIENCE
    Open Source and Open Data

    This app was developed by using open data and open-source libraries. Some of the useful APIs, data sources, cisualizations, analysis and open-source libraries:
    Libraries

    • Streamlit
    • Spark NLP
    • JohnSnowLabs Healthcare
    • Apache Spark
    • Tensorflow
    • Tensorflow Hub
    • Plotly
    • Matplotlib

    APIs

    • COVID-19 Grafana API (repo): JSON API to visualize stats in Grafana
    • COVID-19 GraphQL API (repo)
    • CovidAPI.info (repo): Lightweight, Superfast REST API built to be consumed by dashboards.
    • COVID-19 Ruby Gem

    Data Sources

    Visualizations

    • Covid19 Visualizer (repo): Covid19 Graphical Visualizer
    • EU stats report on COVID-19 (repo): COVID-19 tracker for EU countries
    • CoronaStatistics.live (repo): COVID-19 Global Report
    • COVID-19 World (repo): COVID-19 Global Report
    • COVID-19 Comparator (repo): Coronavirus cases comparator by countries, from chosen date and number of days (PWA)
    • Mobile Friendly COVID-19 Report (repo): Coronavirus daily report in a mobile friendly website (PWA)
    • COVID-19 Daily Report (repo): Coronavirus daily report, updated hourly
    • COVID-19 GLOBAL Report (repo)
    • covid-charts (repo): chart widget with Coronavirus stats for specified country
    • COVID-19 Global Chart (repo): Chart GeoMap with last status by country.
    • COVID-19 Stats (repo): A simple mobile friendly dashboard visualizing the latest stats of the COVID-19 outbreak.
    • Corona.log (repo): A simple COVID-19 data checker per region
    • COVID-19 How Bad Is It (repo): Live graphs with latest news involving Covid-19,
    • COVID-19 Sri Lanka Tracker (repo): Live Updates of COVID-19 Patients in Sri Lanka
    • COVID-19 Countries Trends & Comparison (repo): Country comparison of COVID-19 cases, with per-capita and growth views.
    • felipec covid-19 (repo): Trajectory of confirmed COVID-19 cases per country after 100 in logarithmic scale and growth factor.
    • COVID-19 Global Report (repo): Vue.js app for monitoring the spread of the new coronavirus
    • COVID-19 Regional Relative TimeSeries (repo): Normalized regional comparative timeseries.
    • COVID-19 Country Travel Bans (repo): An interactive map showing countries with travel restrictions and infection counts.
    • COVID-19 Stats and Trends (repo)
    • COVID Reports (repo): Coronavirus trends comparison by country
    • #daysbehinditaly (repo): Number of days various countries are behind Italy in total COVID-19 cases assuming similar case growth rate
    • Covid-19 Project to track the spread of coronavirus (repo): Coronavirus information by country
    • Covid-19 Progress Reports by Country (repo): Coronavirus (Fight against) Progress by country
    • COVID-19-LK (repo): A Sri Lankan COVID-19 Tracker with a map and dark theme <3
    • COVID-19 Mauritius Statistics (repo): A simple page with stats about the current COVID-19 situation in the small island of Mauritius.
    • Flattening the Curve by Country | COVID-19 🦠 (repo): A simple dashboard to showcase flattening of the curve by each country affected with COVID-19 - plotted over time.
    • World map and country comparison timeline: Select multiple countries on the map for a clean comparison of how the number of cases develop.
    • COVID-19 Panel for Digital Signage (repo): Digital Signage-ready and configurable Panel with COVID-19 data.
    • COVID-19 Trends (repo): Simple charts showing COVID-19 trends
    • Covid-19 Race (repo) A basic html5/css/js webapp to compare the cases from a select few countries.
    • COVID-19 India dashboard (repo) - A simple dashboard made with Flask specifically for India with stats of various states and predictions of what's going to happen in the next five days.
    • Open COVID19 Map (repo) Open map visualization with alternative data sources, containment scores, testing rate projection, replay mode
    • I am Covid -19 🦠 (repo) - Visualization of the covid-19 dataset using Nuxtjs(vuejs), Graphql and valuable information about getting through the Covid-19 pandemic.
    • Simple COVID-19 Tracker (repo): Mobile-friendly and minimal page that displays the current total count of coronavirus cases and deaths in a selected region.
    • COVID-19 Report (repo): Coronavirus information by country in a mobile-friendly SPA.
    • Visualizing COVID-19 with D3 (repo): A responsive D3-based data visualization that leverages a Sankey diagram to display the breakdown of the worldwide COVID-19 cases.
    • Coronavirus-meter (repo): Coronavirus meter provides statistics from cases all around the world. View cases from each country up to two months before. Coronavirus cases, deaths, recovered in statistical numbers from all around the world.
    • Telegram COVID-19 Monitoring Telegram alert everyday with the statistics of COVID-19 in each country.
    • Coronavirus Infections (repo): Track Coronavirus infections, deaths, recovers and active cases per country in chart and table.
    • Corona in Charts (repo): Corona graphs for each country with total cases, active cases, recovered and fatalities.
    • COVID-19 Monitoring And Charting(repo): World COVID-19 Tracking, historical data and overview using NodeJS Server
    • COVID-19 Reaction Tracker (repo): Track user reactions across the globe
    • COVID-19 Data Visualization Using R Shiny(repo): Data Summary, Data Visualization, World Map and differnt Analytics plots.
      *Made in r shiny
    • covid19-psvita-data: An app for viewing COVID-19 Data and graphs on a Playstation Vita
    • COVID-19 Timeline: A Flutter app for tracking COVID-19 data
    • COVID-19 in Numbers (repo): Covid-19 stats and charts by country. Made with Blazor.
      Corona-Virus-Numbers: Android and iOS app for visualising COVID-19 graphs developed using Flutter

    Analysis

    • COVID-19 Trends and Growth Rate: A Python implementation of growth rate and other trend analysis
    • Are we dead yet (repo): Live graphs of confirmed, infected and infection rate. Outbreak normalised for comparison.
    • epidemic-simulator (repo): Mathematical model using Macroscopic Rate Equations for simulating the future of the epidemic
    • Coronavirus Cases, Deaths, and Recoveries by Country (repo) - a blog post with charts that update daily
    • COVID-19 Best fit evolution Visualizing the evolution of a best-fit logistic curve over time, showing the difficulty of predicting the number of future cases and deaths
    • PowerBI-driven COVID-2019 Tracking: Power BI Desktop dashboard based on JSON data about COVID-2019 spread

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Capture du 2020-04-13 22-06-29.png

    OFFICIAL WEBPAGE

    The Milken Institute is currently tracking the development of treatments and vaccines for COVID-19 (coronavirus).

    This document contains an aggregation of publicly-available information from validated sources. It is not an endorsement of one approach or treatment over another, but simply a list of all treatments and vaccines currently in development.

    Given the immediacy of the current public health emergency, we believe it is important to make the data accessible to the public in its current form.

    This overview will be updated as new findings come to light. We ask that you check this page on a regular basis.
    Capture du 2020-04-13 22-09-34.png

    image.png

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Capture du 2020-05-12 18-20-36.png
    OFFICIAL WEBSITE COVID-19 imaging datasets at EIBIR
    eibir-logo_275x70.png
    Capture du 2020-05-12 18-17-41.png

    A paper in Arxiv "COVID-CT-Dataset: A CT Scan Dataset about COVID-19"
    Jinyu Zhao (UC San Diego), Yichen Zhang (UC San Diego), Xuehai He (UC San Diego), and Pengtao Xie (UC San Diego, Petuum Inc)

    LINK TO THE PAPER
    CT scans are promising in providing accurate, fast, and cheap screening and testing of COVID-19. In this paper, we build a publicly available COVID-CT dataset, containing 275 CT scans that are positive for COVID-19, to foster the research and development of deep learning methods which predict whether a person is affected with COVID-19 by analyzing his/her CTs. We train a deep convolutional neural network on this dataset and achieve an F1 of 0.85 which is a promising performance but yet to be further improved.

    The data and code are available at:
    https://github.com/UCSD-AI4H/COVID-CT
    eibir-logo_275x70.png
    Data Description
    The COVID-CT-Dataset has 349 CT images containing clinical findings of COVID-19. They are in ./Images-processed/CT_COVID.zip

    Non-COVID CT scans are in ./Images-processed/CT_NonCOVID.zip

    We provide a data split in ./Data-split

    The meta information (e.g., patient ID, DOI, image caption) is in COVID-CT-MetaInfo.xlsx

    The images are collected from COVID19-related papers from medRxiv, bioRxiv, NEJM, JAMA, Lancet, etc. CTs containing COVID-19 abnormalities are selected by reading the figure captions in the papers. All copyrights of the data belong to the authors and publishers of these papers.

    posted in COVID19 open Database ressources and challenges read more
  • Geo-Sci-Info

    Ph.D. and Postdoc positions in Applied Mathematics
    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 Professor Dr. Enrique Zuazua, is looking for outstanding candidates to fill several

    OFFICIAL WEBPAGE

    Ph.D. and Postdoc positions in Applied Mathematics

    Das Aufgabengebiet umfasst u. a.
    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 scientist to contribute to this exiting joint AvH-FAU effort. Possible research projects include but are not limited to:

    • Analysis of Partial Differential Equations (PDE).
    • The interplay between Data Sciences, numerics of PDE and Control Systems.
    • Control of diffusion models arising in Biology and Social Sciences.
    • Modelling and control of multi-agent systems.
    • Hyperbolic models arising in traffic flow and energy transport.
    • Waves in networks and Markov chains.
    • Fractional PDE.
    • Optimal design in Material Sciences.
    • Micro-macro limit processes.
    • The interplay between discrete and continuous modelling in design and control.
    • The emergence of turnpike phenomena in long-time horizons.
    • Inversion and parameter identification.
    • Recommendation systems.
    • Development of new computation tools and software.

    Wünschenswerte Qualifikation
    We look for excellent candidates with expertise in areas of applied mathematics, PDE analysis, control theory, numerical analysis, data sciences and computational mathematics who enjoy interdisciplinary work.

    Bemerkungen
    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@math.fau.de before April 30th
    , 2020.

    Any inquiries about the positions should be sent to Professor Dr. Enrique Zuazua (positions-aa@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 particulary 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]https://en.www.math.fau.de/applied-analysis/

    Bewerbung
    secretary-aa [at]math.fau.de

    posted in Jobs offers - Call for projects read more
  • Geo-Sci-Info

    Project Manager Position to launch the Erlangen Center for Mathematics of Data (ECMD)
    FAU is launching an ambitious project to establish the new ERLANGEN CENTER FOR MATHEMATICS OF DATA (ECMD)
    OFFICIAL WEBPAGE
    Project Manager Position to launch the "Erlangen Center for Mathematics of Data (ECMD)"

    Das Aufgabengebiet umfasst u. a.
    The ECMD is anticipated as a highly visible interdisciplinary research center, an incubator for future collaborative research grants and a hub for the key research priorities of FAU. It will be anchored in the Department of Mathematics, coordinating activities with other groups, Departments and Centers at FAU and in its perimeter, in the broad field of Mathematical and Data Sciences.

    With this goal, we aim to integrate a Project Manager so to contribute to define the strategic plan and launch the start-up of the center under the direction of Professor Dr. Enrique Zuazua (Chair Applied Analysis - Alexander von Humboldt Professorship) in collaboration with FAU partners and other collaborating institutions.

    We search for a full-time Project Manager (part-time contracts could also be considered) with excellent organizational skills, and interest in R&D project management.

    The activities to be developed include:

    • Design of institutional structure and procedures
    • Fund raising and support in applications for third party funding
    • Strategic integration into the FAU innovation agenda
    • Coordination with partner institutions
    • Organization of scientific events
    • Dissemination activities and social media
    • Budget management and monitoring
    • HR management
    • General organization
    • Notwendige Qualifikation
    • Master´s degree. PhD would be an asset
    • Training and experience in R%D project management would be desirable
    • Collaborative and competitive intelligence
    • Professional command of German and English

    Wünschenswerte Qualifikation
    Organizational thinking
    Team player mentality and service-orientation
    Self-organized, responsible and highly proactive working style
    Strong cross-cultural communication skills (written and verbal communication in German and English) to weave, foster and maintain working relationships with different international sectors
    Result orientation and objective fulfilment

    Bemerkungen
    Immediate incorporation into the organization.

    The ECMD will offer a lively research environment, financial support for networking and training, and an intensive supervision within a large and interactive team.

    The appointment is initially limited for a period of two years and can be extended for additional three years, or longer, according to the evolution of the Center.

    In its pursuit of academic excellence, FAU is committed to equality of opportunity and to a proactive and inclusive approach, which supports and encourages all under-represented groups, promotes an inclusive culture and values diverstiy. FAU is a family-friendly employer.

    Please submit your electronic application as a single pdf file* including a motivation letter, vita, diplomas and list of potential reference persons, no later than April 30, 2020 to secretary-aa@math.fau.de (cc: enrique.zuazua@fau.de). Please refer to "*Manager-ECMD" in the reference line of the email.

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  • Geo-Sci-Info

    github-logo.png
    GITHUB LINK - OFFICIAL WEBSITE
    Coronavirus disease 2019 (COVID-19) time series listing confirmed cases, reported deaths and reported recoveries. Data is disaggregated by country (and sometimes subregion). Coronavirus disease (COVID-19) is caused by the Severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2) and has had a worldwide effect. On March 11 2020, the World Health Organization (WHO) declared it a pandemic, pointing to the over 118,000 cases of the coronavirus illness in over 110 countries and territories around the world at the time.

    This dataset includes time series data tracking the number of people affected by COVID-19 worldwide, including:

    confirmed tested cases of Coronavirus infection
    the number of people who have reportedly died while sick with Coronavirus
    the number of people who have reportedly recovered from it
    Data
    Data is in CSV format and updated daily. It is sourced from this upstream repository maintained by the amazing team at Johns Hopkins University Center for Systems Science and Engineering (CSSE) who have been doing a great public service from an early point by collating data from around the world.

    We have cleaned and normalized that data, for example tidying dates and consolidating several files into normalized time series. We have also added some metadata such as column descriptions and data packaged it.

    You can view the data, its structure as well as download it in alternative formats (e.g. JSON) from the DataHub:

    https://datahub.io/core/covid-19

    Sources
    The upstream dataset currently lists the following upstream datasources:

    World Health Organization (WHO): https://www.who.int/
    DXY.cn. Pneumonia. 2020. http://3g.dxy.cn/newh5/view/pneumonia
    BNO News: https://bnonews.com/index.php/2020/02/the-latest-coronavirus-cases/
    National Health Commission of the People’s Republic of China (NHC): http://www.nhc.gov.cn/xcs/yqtb/list_gzbd.shtml
    China CDC (CCDC): http://weekly.chinacdc.cn/news/TrackingtheEpidemic.htm
    Hong Kong Department of Health: https://www.chp.gov.hk/en/features/102465.html
    Macau Government: https://www.ssm.gov.mo/portal/
    Taiwan CDC: https://sites.google.com/cdc.gov.tw/2019ncov/taiwan?authuser=0
    US CDC: https://www.cdc.gov/coronavirus/2019-ncov/index.html
    Government of Canada: https://www.canada.ca/en/public-health/services/diseases/coronavirus.html
    Australia Government Department of Health: https://www.health.gov.au/news/coronavirus-update-at-a-glance
    European Centre for Disease Prevention and Control (ECDC): https://www.ecdc.europa.eu/en/geographical-distribution-2019-ncov-cases
    Ministry of Health Singapore (MOH): https://www.moh.gov.sg/covid-19
    Italy Ministry of Health: http://www.salute.gov.it/nuovocoronavirus
    We will endeavour to provide more detail on how regularly and by which technical means the data is updated. Additional background is available in the CSSE blog, and in the Lancet paper (DOI), which includes this figure:

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    ICTRP_logo_200x200.jpg
    OFFICIAL WEBSITE

    The ICTRP Search Portal
    Important information related to the COVID-19 outbreak!

    Due to heavy traffic generated by the COVID-19 outbreak, the ICTRP Search Portal is not accessible from outside WHO temporarily. Please subscribe to the ICTRP listserv if you wish to be notified when the search portal is working again. Information on how to subscribe can be found on the same page below.

    Click here to download all COVID-19 trials from the ICTRP database
    Click here to download all new/updated records from the ICTRP database

    The ICTRP Search Portal aims to provide a single point of access to information about ongoing and completed clinical trials. It provides a searchable database containing the trial registration data sets made available by data providers around the world meeting criteria for content and quality control.

    Trial registration data sets are available on the ICTRP Search Portal in English only. Some data providers, however, may also store trial registration data sets in other languages. If available, these may be searched by going directly to the web sites of the individual registries. It is possible to search for trials registered in certain Primary Registries in the following languages:

    Chinese - Chinese Clinical Trial Registry (ChiCTR)
    Dutch - The Netherlands National Trial Register (NTR)
    German – German Clinical Trials Register (DRKS)
    Japanese – Japan Primary Registries Network (JPRN)
    Korean – Clinical Research Information Service (CRiS), Republic of Korea
    Persian – Iranian Registry of Clinical Trials (IRCT)
    Portuguese – Brazilian Registry of Clinical Trials (ReBec)
    Spanish – Cuban Public Registry of Clinical Trials(RPCEC) and the Peruvian Clinical Trial Registry (REPEC)

    To facilitate the unambiguous identification of trials, the Search Portal bridges (groups together) multiple records about the same trial.

    The ICTRP Search Portal has been developed to make it easier for users to search for clinical trials. Please contact us if you have any comments or suggestions.
    Subscribe to the ICTRP mailing list for technical updates

    To receive the latest news and events concerning the WHO ICTRP technical updates and notifications. Simply send us an email by clicking the link below.

    Subscribe to the ICTRP Technical updates mailing list

    If you have any difficulties with the link above, please send us an email following these instructions:

    To: listserv [at] who.int
    Subject: subscription: WHO ICTRP Tech updates
    Body: subscribe ictrptech Firstname Lastname (Please replace Firstname and Lastname in this command line with your own.)

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    OFFICIAL WEBSITE

    by Max Roser, Hannah Ritchie and Esteban Ortiz-Ospina
    We thank Bernadeta Dadonaite, Jason Hendry, and Moritz Kraemer for helpful comments and suggestions on earlier versions of this work.Tom Chivers we would like to thank for editorial review and feedback.

    And we would like to thank the many hundreds of you who give us feedback on this work every day.
    Your feedback is what allows us to continuously clarify and improve it. We very much appreciate you taking the time to write.
    Even if we can’t respond to every message we receive, we do read all feedback and take it all into account.

    Note: To inform yourself and understand the risk to the public we recommend to rely on your government body responsible for health and the World Health Organization – their site is here.

    The mission of Our World in Data is to make data and research on the world’s largest problems understandable and accessible.

    Read more about our mission →

    Only on the basis of clearly presented and well-documented data can governments, organizations and individuals hope to respond appropriately to the COVID-19 pandemic. The goal of our work here is to present the best available data and clarify what can – and can not be said – based on this data.

    We list all our visualizations – more than 40 in total – on the pandemic on this page.

    This article covers a developing situation and the Our World in Data team is updating it daily: The last update was made on April 4, 2020 (11:30, London time).
    Summary

    Even the best existing research and data is preliminary and will be revised as the pandemic progresses.
    We reviewed existing global data sources and decided to rely on the global statistics published by the European Center for Disease Prevention and Control (ECDC).

    Deaths
    During a disease outbreak it is the growth rate that deserves attention. We present a table that lists how fast the number of deaths is doubling.
    In interactive charts we present the data on confirmed deaths in all countries in the world.

    Testing
    Without widespread testing for COVID-19 we can neither know how the pandemic is spreading nor appropriately respond to it.

    Cases
    The total number of COVID-19 cases is not known. It is however certain that the total number of COVID-19 cases is higher than the number of known confirmed cases. This is mainly due to limited testing.
    Just as we do for deaths, we focus on the growth rate of confirmed cases. We present a sortable table that lists how fast the number of confirmed cases is doubling.
    In interactive charts we present the data on confirmed cases over time in all countries in the world.

    Mortality
    The case fatality rate (CFR) – the ratio between confirmed deaths and cases – is widely discussed, but during the outbreak of a pandemic with large unknowns it is important to know what can and cannot be said based on currently available statistics.

    We rely on data from the European CDC

    In this document and the many embedded and linked charts we report and visualize the data from the European Center for Disease Prevention and Control (ECDC).

    The European CDC publishes daily statistics on the COVID-19 pandemic. Not just for Europe, but for the entire world.

    The ECDC makes all their data available in a daily updated clean downloadable file. This gets updated daily reflecting data collected up to 6:00 and 10:00 CET. The data made public via the downloadable data file is published at 1pm CET, and is used to produce a page that gets updated daily under the name Situation Update Worldwide.

    We discuss our data sources and why we rely on the data from the ECDC rather than other institutions at the end of this article here.
    Reported new cases on a particular day do not necessarily represent new cases on that day

    The number of confirmed cases or deaths reported by any institution – including the WHO, the ECDC, Johns Hopkins and others – on a given day does not represent the actual number of new cases or deaths on that date. This is because of the long reporting chain that exists between a new case or death, and its inclusion in national or international statistics.

    The steps in this chain are different across countries, but for many countries the reporting chain includes most of the following steps:1

    Doctor or laboratory diagnoses a COVID-19 case based on testing or combination of symptoms and epidemiological probability (such as a close family member testing positive).
    Doctor or laboratory submits report to health department of the city or local district.
    Health department receives the report and records each individual case in the reporting system, including patient information.
    The ministry or another governmental organization brings this data together and publishes the latest figures.
    International data bodies such as the WHO or the ECDC can then collate statistics from hundreds of such national accounts.

    This reporting chain can take several days. This is why the figures reported on any given date do not necessarily reflect the number of new cases or deaths on that specific date.

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    Polymath proposal: clearinghouse for crowdsourcing COVID-19 data and data cleaning requests
    OFFICIAL WEBSITE Terry Tao BLOG

    25 March, 2020 in math.ST, polymath | Tags: Chris Strohmeier, coronavirus

    After some discussion with the applied math research groups here at UCLA (in particular the groups led by Andrea Bertozzi and Deanna Needell), one of the members of these groups, Chris Strohmeier, has produced a proposal for a Polymath project to crowdsource in a single repository (a) a collection of public data sets relating to the COVID-19 pandemic, (b) requests for such data sets, (c) requests for data cleaning of such sets, and (d) submissions of cleaned data sets. (The proposal can be viewed as a PDF, and is also available on Overleaf). As mentioned in the proposal, this database would be slightly different in focus than existing data sets such as the COVID-19 data sets hosted on Kaggle, with a focus on producing high quality cleaned data sets. (Another relevant data set that I am aware of is the SafeGraph aggregated foot traffic data, although this data set, while open, is not quite public as it requires a non-commercial agreement to execute. Feel free to mention further relevant data sets in the comments.)

    This seems like a very interesting and timely proposal to me and I would like to open it up for discussion, for instance by proposing some seed requests for data and data cleaning and to discuss possible platforms that such a repository could be built on. In the spirit of “building the plane while flying it”, one could begin by creating a basic github repository as a prototype and use the comments in this blog post to handle requests, and then migrate to a more high quality platform once it becomes clear what direction this project might move in. (For instance one might eventually move beyond data cleaning to more sophisticated types of data analysis.)

    UPDATE, Mar 25: a prototype page for such a clearinghouse is now up at this wiki page.

    UPDATE, Mar 27: the data cleaning aspect of this project largely duplicates the existing efforts at the United against COVID-19 project, so we are redirecting requests of this type to that project (and specifically to their data discourse page). The polymath proposal will now refocus on crowdsourcing a list of public data sets relating to the COVID-19 pandemic.

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    COVID-19 Open Research Dataset Challenge (CORD-19) An AI challenge with AI2, CZI, MSR, Georgetown, NIH & The White House
    WEBSITE - DATA

    Dataset Description

    In response to the COVID-19 pandemic, the White House and a coalition of leading research groups have prepared the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a resource of over 47,000 scholarly articles, including over 36,000 with full text, about COVID-19, SARS-CoV-2, and related coronaviruses. This freely available dataset is provided to the global research community to apply recent advances in natural language processing and other AI techniques to generate new insights in support of the ongoing fight against this infectious disease. There is a growing urgency for these approaches because of the rapid acceleration in new coronavirus literature, making it difficult for the medical research community to keep up.

    Call to Action

    We are issuing a call to action to the world's artificial intelligence experts to develop text and data mining tools that can help the medical community develop answers to high priority scientific questions. The CORD-19 dataset represents the most extensive machine-readable coronavirus literature collection available for data mining to date. This allows the worldwide AI research community the opportunity to apply text and data mining approaches to find answers to questions within, and connect insights across, this content in support of the ongoing COVID-19 response efforts worldwide. There is a growing urgency for these approaches because of the rapid increase in coronavirus literature, making it difficult for the medical community to keep up.

    A list of our initial key questions can be found under the Tasks section of this dataset. These key scientific questions are drawn from the NASEM’s SCIED (National Academies of Sciences, Engineering, and Medicine’s Standing Committee on Emerging Infectious Diseases and 21st Century Health Threats) research topics and the World Health Organization’s R&D Blueprint for COVID-19.

    Many of these questions are suitable for text mining, and we encourage researchers to develop text mining tools to provide insights on these questions.

    We are maintaining a summary of the community's contributions. For guidance on how to make your contributions useful, we're maintaining a forum thread with the feedback we're getting from the medical and health policy communities.
    Prizes

    Kaggle is sponsoring a $1,000 per task award to the winner whose submission is identified as best meeting the evaluation criteria. The winner may elect to receive this award as a charitable donation to COVID-19 relief/research efforts or as a monetary payment. More details on the prizes and timeline can be found on the discussion post.

    Accessing the Dataset

    We have made this dataset available on Kaggle. Watch out for periodic updates.

    The dataset is also hosted on AI2's Semantic Scholar. And you can search the dataset using AI2's new COVID-19 explorer.

    The licenses for each dataset can be found in the all _ sources _ metadata csv file.

    Acknowledgements

    Capture du 2020-04-05 11-33-51.png

    This dataset was created by the Allen Institute for AI in partnership with the Chan Zuckerberg Initiative, Georgetown University’s Center for Security and Emerging Technology, Microsoft Research, and the National Library of Medicine - National Institutes of Health, in coordination with The White House Office of Science and Technology Policy.

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  • Geo-Sci-Info

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    Through the "2019 Novel Coronavirus Resource" database, you can gain hands-on access to the latest most comprehensive novel coronavirus data and research results. Developed and operated by the China National Center for Bioinformation, Beijing Institute of Genomics, CAS, the database was launched on January 22. It provides a one-stop browse, search and share service for the scientific community.

    To date, the database has included worldwide information on 2,533 non-redundant novel coronavirus genome sequences. At the same time, it has categorized 3,099 international articles related to the novel coronavirus and welcomed more than 83,000 visitors from 155 countries and regions, providing over 4.91 million downloads. About 70% of those visits were from international visitors.

    If you have not checked out the database yet, click the link below!

    WEBSITE - DATA RESSOURCES

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    Capture du 2020-03-09 02-09-50.png
    Appel à Candidatures
    Prix de Thèse Systèmes Complexes 2020

    Depuis 2017, l'Institut des Systèmes Complexes de Paris IdF (ISC-PIF) et ses partenaires décernent annuellement un prix de thèse dont l'objectif est de mettre à l'honneur la recherche dans le domaine des systèmes complexes et de distinguer les travaux de jeunes chercheur·euse.s particulièrement prometteur·euse.s.

    Ce prix est organisé en collaboration avec l'Académie d'Excellence "Systèmes Complexes" de l'Université Côte d'Azur et l’Institut Rhônalpin des Systèmes Complexes (IXXI), avec le soutien du CNRS et de la région Île-de-France.

    Nous vous invitons cordialement à diffuser largement cet appel ou à nominer un.e ou plusieurs candidat.e.s ayant soutenu leurs thèses entre janvier et décembre 2019, dans une école doctorale française.

    Veuillez noter que les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection.

    Pour nominer un.e ou plusieurs candidat.e.s, merci de nous transmettre leurs coordonnées en réponse de ce mail, en les mettant éventuellement en copie.

    Ci dessous, les détails de l'appel à candidature que nous vous invitons cordialement à partager.
    Télécharger le pdf de l'appel

    ÉLÉGIBILITÉ

    Les thèses éligibles auront été soutenues entre janvier et décembre 2019, dans une école doctorale française. Les jeunes docteurs de nationalité étrangère peuvent participer à cette sélection. Les thèses rédigées en anglais sont acceptées par le jury.

    Pour pouvoir concourir à ce prix, les candidat.e.s doivent faire la preuve de la pertinence de leurs travaux par rapport à l'approche systèmes complexes et doivent aborder un ou plusieurs objets et questions propres à la recherche Systèmes Complexes : voir tous les détails du Prix de Thèse 2020 sur notre site.

    CANDIDATURES

    Les candidats et candidates devront remplir leur candidature en ligne avant le 22 mars 2020 à minuit.

    Documents demandés : CV et lettre de motivation, rapport du jury de thèse et pré-rapports des rapporteurs, résumé de la thèse.

    JURY

    Les candidat.e.s sélectionnés seront invités à présenter leur travaux devant un jury interdisciplinaire composé de personnalités reconnues dans le domaine. Les auditions auront lieu à l'ISC-PIF (Paris 13e), le 22 mars 2020.

    Les critères pris en compte par le jury pour sélectionner les lauréat.e.s sont l’importance et l’originalité des contributions dans le domaine des systèmes complexes, la qualité du manuscrit et, éventuellement, l’interdisciplinarité du travail.

    CALENDRIER

    • 22 mars 2020 : Date limite des dépôts de candidatures
    • 15 mai 2020 : Annonce des finalistes
    • 17 juin 2020 : Audition des finalistes et remise des prix
      + d'infos sur le site

    COORDINATEUR.TRICE.S

    • Annick Vignes, CAMS (CNRS EHESS), LISIS.
    • Julien Randon-Furling, SAMM (Univ. Paris 1 Panthéon Sorbonne), Fédération Parisienne de Modélisation Mathématique (CNRS).

    NOUS ÉCRIRE

    contactcom[at]iscpif.fr
    Capture du 2020-03-09 02-07-36.png

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    OFFICIAL WEBSITE - REGISTRATION -

    Announcement (20.3.2020)
    Due to the COVID-19 pandemic, the organizers have been forced to reschedule Entropy2020 to 22-25 September 2020 (22nd reserved for checking/on-site registration). In order to keep track of who is attending and allow us to finalize the conference program, please pre-register here as soon as possible. Please consider that we are planning an alternative long-distance participation (video presentations). For those participants not able to attend in this new date, we kindly ask you to contact us as soon as possible to do the necessary arrangements, which include video presentation, withdraw your abstract and/or cancel your registration.

    Welcome from the Chair
    The concept of entropy emerges initially from the scope of physics, but it is now clear that entropy is deeply related to information theory and the process of inference. Today, entropic techniques have found a broad spectrum of applications in all branches of science.

    The conference will be organized into six sessions, which reflect the inter-disciplinary nature of entropy and its applications:

    • Statistical Physics
    • Information Theory, Probability and Statistics
    • Thermodynamics
    • Quantum Information and Foundations
    • Complex Systems
    • Entropy in Multidisciplinary Applications

    The inter-disciplinary and multi-disciplinary nature of contributions from both theoretical and applied perspectives are welcome, including papers addressing conceptual and methodological developments, as well as new applications of entropy and information theory.

    All accepted abstracts will be published in the proceedings of the conference. Moreover, participants are cordially invited to contribute with a full manuscript to Entropy Special Issue "Entropy: The Scientific Tool of the 21st Century".

    Special Issue Submission deadline: 30 October 2020
    Papers presented at the conference will be granted a 30% discount!

    The conference is sponsored by MDPI, the publisher of the open-access journal Entropy and follows the very successful meeting Entropy 2018: From Physics to Information Sciences and Geometry held in May 2018 in Barcelona, Spain.

    We very much look forward to seeing you at this exciting meeting in Porto.

    Please feel free to download our Conference Poster.

    Conference Secretariat

    • Ms. Yuejiao Hu
    • Ms. Connie Xiong
    • Ms. Stefanie Li
      Email: entropy2020 [at] mdpi.com

    Conference Chairs

    • Professor J. A. Tenreiro Machado, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal, Website

    Conference Committee

    • Professor Walter Lacarbonara, Department of Structural and Geotechnical Engineering , Sapienza University of Rome, Rome, Italy
    • Professor Jose C. Principe, Computational NeuroEngineering Lab, University of Florida, Gainesville, FL, USA
    • Prof. Dr. Gian Paolo Beretta, Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy
    • Professor Geert Verdoolaege, Research Unit Nuclear Fusion, Department of Applied Physics, Ghent University, Ghent, Belgium
    • Prof. Dr. William B. Sherwin, Evolution & Ecology Research Centre, School of Biological Earth and Environmental Science, UNSW Sydney, Sydney, Australia
    • Prof. Dr. Ali Mohammad-Djafari, Former Research Director, CNRS, Paris, France
    • Dr. Ginestra Bianconi, Queen Mary University of London, London, UK

    Invited Speakers

    • Prof. Dr. Kevin H. Knuth, University at Albany (SUNY), Albany, NY, USA
    • Prof. Dr. Miguel Rubi, University of Barcelona, Barcelona, Spain
    • Prof. Dr. Philip Broadbridge, La Trobe University, Melbourne, Australia
    • Prof. Dr. Marcel Ausloos, University of Leicester, Leicester, UK
    • Dr. Remo Garattini, University of Bergamo, Bergamo, Italy
    • Prof. Dr. Nihat Ay, Max Planck Institute for Mathematics in the Sciences, Leipzig, Germany
    • Prof. Dr. Luca Gammaitoni, Università degli Studi di Perugia, Perugia, Italy
    • Prof. Dr. Abraham Marmur, Technion-Israel Institute of Technology, Haifa, Israel
    • Dr. Rosario Lo Franco, Università di Palermo, Italy
    • Prof. Dr. Miguel A. F. Sanjuán, Universidad Rey Juan Carlos, Madrid, Spain

    Conference Organizers

    • Nídia Caetano, Institute of Engineering (ISEP), Department of Chemical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal
    • Carlos Felgueiras, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal
    • Ana Maria Madureira, Instituto Superior de Engenharia do Porto (ISEP), Porto, Portugal
    • Luís M. Afonso, ISRC (Interdisciplinary Studies Research Center) , Instituto Superior de Engenharia do Porto, Porto, Portugal
    • Alexandra M.F. Galhano, Institute of Engineering (ISEP), Department of Electrical Engineering, Polytechnic of Porto (P.Porto), Porto, Portugal

    Sessions

    • SESSION 1. Statistical Physics
      classical, quantum, and relativistic statistical mechanics; kinetics theory; dynamical processes and relaxation phenomena; geometric applications to statistical physics; classical and quantum stochastic processes; combinatorial aspects of statistical physics and quantum field theory; quantum information and entangled states. Statistical physics of complex and disordered systems: biophysics, genomics, ecological and evolutionary systems, climate and earth models (including seismology); traffic flow; nonlinear time series analysis; big data analysis and algorithm problems; networks and graphs; random and fractal systems; pattern formation; collective phenomena in economic and social systems. Show all accepted abstracts (12)
    • SESSION 2. Information Theory, Probability and Statistics
      Shannon entropy; Kullback-Leibler divergence; channel capacity; source coding; channel coding; algorithmic complexity theory; algorithmic information theory; information–theoretic security; information geometry; Bayesian inference; information theoretic learning; deep learning. Show all accepted abstracts (18)
    • SESSION 3. Thermodynamics
      heat transfer; Carnot cycle; heat engine; thermodynamic temperature; entropy generation; Clausius equality/inequality; equilibrium/non-equilibrium thermodynamics; work availability; exergy; laws of thermodynamics. Show all accepted abstracts (26)
    • SESSION 4. Quantum Information and Foundations
      quantum foundations; quantum probability and non-Kolmogorov models; entanglement entropy; Eigenstate thermalization; quantum phase transitions; topological order; black hole information paradox; the holographic principle; quantum coherent transport; quantum coherence in biological phenomena. Show all accepted abstracts (9)
    • SESSION 5. Complex Systems
      complexity; nonlinearity; fractionality; nonlinear dynamics; fractional calculus; chaos; big data; networks; cybernetics; biology. Show all accepted abstracts (18)
    • SESSION 6. Entropy in Multidisciplinary Applications
      signal processing and data analysis; entropy and complexity in biology; geosciences; environment; social network; economy and finance. Show all accepted abstracts (40)

    Sponsor
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