We are pleased to announce a workshop centered on “image and physics” on June 18, 2021, organized by the Center of Mathematical Morphology and Center of Materials, devoted to image analysis, deep learning, physics, and material science. The main objective of this event is to promote exchanges in an informal setting between researchers of MINES ParisTech working on image analysis and deep learning, on the one hand, and on physics and material science in general. You are welcome to send your contributions (title, authors, and abstract) to the organizers before June 4th.
E Podcast vum Lëtzebuerger Journal an Zesummenaarbecht mam Fonds National de la Recherche. De Max Kasel trëfft sech reegelméisseg mat Fuerscher*innen zu Lëtzebuerg a schwätzt mat hinnen iwwert hiert Liewen an hier Passioun fir d’Wëssenschaft. “Mäin Element” weist de Mënsch hannert der Fuerschung.
In the era of Advanced Public Transport Systems, new technologies have been introduced and deployed providing multiple sources of data that can be utilized for demand estimation and analysis. In this study, we investigate the potential of a crowdsourced dataset such as Google Popular Times to quantify the correlation of the demand of a transit line in conjunction with the level of attractivity of its station and the surrounding local businesses to understand to which extent the characteristics of the activities in the area can be associated with the passenger transference at the stops.
The NSF Fellowships are available to promote awareness of mechanistic machine learning and digital twins and knowledge dissemination to all levels in education, including high school, undergraduate, and graduate levels. A portion of the fellowship will be reserved for underrepresented groups to promote equity, diversity, and inclusiveness.
This NSF Fellowship will cover the conference and short course remote registration fees. Only US Citizens and Permanent Residents are eligible for NSF Fellowships.
1). NSF Fellowship for 𝐠𝐫𝐚𝐝𝐮𝐚𝐭𝐞 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 𝐚𝐧𝐝 𝐩𝐨𝐬𝐭𝐝𝐨𝐜 𝐫𝐞𝐬𝐞𝐚𝐫𝐜𝐡𝐞𝐫𝐬 to attend the MMLDT-CSET Conference and Short Course 2.
2). NSF Fellowship for 𝐡𝐢𝐠𝐡-𝐬𝐜𝐡𝐨𝐨𝐥 𝐭𝐞𝐚𝐜𝐡𝐞𝐫𝐬 𝐚𝐧𝐝 𝐚𝐝𝐦𝐢𝐧𝐢𝐬𝐭𝐫𝐚𝐭𝐨𝐫𝐬 to attend the MMLDT-CSET Conference and Short Course 1.
3). NSF Fellowship for 𝐮𝐧𝐝𝐞𝐫𝐠𝐫𝐚𝐝𝐮𝐚𝐭𝐞 𝐬𝐭𝐮𝐝𝐞𝐧𝐭𝐬 to attend the MMLDT-CSET Conference and Short Course 1.
The Conference and Short Courses Early Registration is open until June 30, 2021. The conference will be organized with a hybrid format consisting virtual and on-site sessions in 44 Mini-symposia under 8 Tracks.
On-Site Attendance Includes:
Registration fee covers Ice Breaker Reception on Sunday, Pre-Banquet Reception and Banquet on Tuesday, Coffee Break Refreshments, Panel Discussions, on-site plenary lectures and technical presentations, and remote technical presentations.
In this talk, we consider the problem of estimating a regression function when the distribution of the data belongs to an exponential family. Several interesting problems are under this setting, for example, logit, Poisson, and exponential regressions. We first introduce these three estimation problems and then solve them by means of a new estimation procedure which is called Rho-estimation. Finally, we carry out a simulation study for illustrating and discussing the theoretical properties of rho-estimators as compared to the well-known maximum likelihood ones.
Federated learning (FL) has recently gained much attention as a machine learning setting where multiple clients collaborate in solving a machine learning problem under the coordination of a central server/coordinator. Each client’s raw data is stored locally without being exchanged nor transferred to the central server; instead, the model’s parameters are shared/aggregated and used to achieve the learning objective. Throughout this seminar, we first present the FL concept, the Federated Averaging algorithm, the FL client/server architecture along its challenges/limitations and applications. Second, we demonstrate how we employed FL as a collaborative adversarial strategy to leverage deep learning-assisted attacks against obfuscated (e.g. blurred) face images.
This project focuses on coupling a data-driven model in conjunction with CFD (Computational Fluid Dynamics) to predict the behavior of biomass particles in a fixed bed. This problem (particle-fluid problem) can be solved by two-way coupling CFD and XDEM. Since this methodology is often computationally expensive, two solutions are proposed. Firstly, the neural network (using TensorFlow) is used as a surrogate model to replace XDEM. Afterward, this surrogate model is coupled with the CFD method to solve the particle-fluid problem employing preCICE (Precise Code Interaction Coupling Environment). An alternative approach assumes the behavior of dense particles in the biomass bed similar to that of fluid (of unknown material parameters). The neural network is used to identify the properties of the fluid. Having the properties of the fluid, the CFD method is used solely to solve the mentioned biomass problem.
The uptake of machine learning (ML) approaches in the social and health sciences has been rather slow, among other reasons due to the importance of incorporating domain knowledge into the strategy of data analysis in the social and health sciences. This paper provides a meta-mapping of research questions in the social and health sciences to appropriate ML approaches. We map established distinctions in data science for the purposes of description, prediction, and causal inference to common research goals. Example applications to predict prison violence, assess the prevalence of non-communicable diseases, and explain adverse birth outcomes are presented. The meta-mapping should improve the understanding between computational disciplines and the social and health sciences.
A computational data-driven framework for the development of Reduced Order Models (ROMs) with application to Chemical Vapor Deposition (CVD) reactors is presented. Describing and predicting the behavior of CVD reactors requires complex high-fidelity Computational Fluid Dynamics (CFD) models with millions of unknowns, the solution of which has a significantly high computational cost, making it imperative to develop ROMs. The reduction of the dimension of the problem is based on the combination of the Method of Snapshots (MoS) which is a variant of Proper Orthogonal Decomposition (POD) and properly trained Artificial Neural Networks (ANN). Snapshots are the sequential states of the reactor during the transition from one steady-state to another after a certain disturbance on one of the controlled parameters. These are computed by a simplified, low-fidelity, and time-dependent CFD model without chemical reactions. The aim is to develop a ROM with satisfactory accuracy, based on low-fidelity data obtained at low computational cost. The snapshots are used to find the dominant eigenvectors that span the solution space, as well as to train neural networks to quickly predict the time-dependent coefficients of the ROM. The model prediction is certainly not satisfactory due to the low-fidelity data used in the development of the ROM, but it is sufficient that when fed into a complete and large-scale process model, the solution convergence occurs in significantly less time. In conclusion, a remarkable acceleration of calculations is achieved, that reduces the computational cost of parametric analysis and the general study of the prevailing physical and chemical phenomena of the process.
 P. A. Gkinis, E. D. Koronaki, A. Skouteris, I. G. Aviziotis, A. G. Boudouvis. Chemical Engineering Science 199 (2019) 371-380.
 E. D. Koronaki, P. A. Gkinis, L. Beex, S. P. A. Bordas, C. Theodoropoulos, A. G Boudouvis. Comput. & Chem. Eng. 121 (2019) 148-157.
 R. Spencer, P. A. Gkinis, E. D. Koronaki, D. I. Gerogiorgis, S. P. A. Bordas, A. G. Boudouvis, Comput. & Chem. Eng. 149 (2021) 107289.
In this presentation, we will explain very quickly many applications on which we had the opportunity to work. We will talk about steganalysis, watermark removing, myocardium segmentation after a heart attack, 1d physiological signal compression, and reconstruction, airplane protocol attack detection, autofocus with digital holography. Moreover, we will present 4 collaborations with practical applications: Faurecia, Colruyt and BMW, Firemen.
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