Retinopathy of prematurity (ROP) is the leading cause of blindness in premature neonates. ROP is associated with inflammation largely mediated trough interleukin (IL-1), which triggers an initial critical phase of ocular vascular degeneration. ROP is also an excellent model of ischemic retinopathies in general. The formation of new blood vessels after tissue ischemia is not only restricted to local endothelial cell proliferation, but lately shown to involve endothelial progenitor cells (EPCs) capable of forming a neovascular network on their own.

Le projet a pour objet l'étude des conséquences de la pandémie sur la garantie des droits économiques, sociaux et culturels des migrants en situation de mobilité, volontaire ou contrainte, pour des motifs économiques ou des considérations d’ordre humanitaire.
Le contexte d'urgence sanitaire à l'échelle mondiale a conduit de nombreux gouvernements à adopter des mesures d'exception.

This project aims to develop a new deep learning algorithm based on various existing CNN-based algorithms, like SPPNet, Fast RCNN, Faster RCNN, YOLO, RetinaNet, CornerNet, etc. to detect the specific machine, forklift, in an image stream and classify the images by the type of forklift. Meanwhile, the company's existing cloud environment and the speed requested by a real-time system must be considered. The project will resolve a couple of problems.

Many chemical compounds can exist in multiple crystalline forms, which are called polymorphs. Polymorphs have the same composition, but their properties can vary markedly. In many fields, conditions for crystallization are screened exhaustively to generate as many polymorphs as possible, from which the most advantageous form can be selected.

Nowadays, software projects are no longer isolated but drive the business process of many non-IT companies. With the rise of AI applied in many industries, the problem of optimally scheduling tasks and allocating proper resources has significantly increased the challenge because of the diversity of tasks and stakeholders in the project. Due to the large volume and dynamic nature of the required information, manual optimization is typically error-prone and inefficient.

Dirigeants et chercheurs insistent sur l’importance du développement du leadership pour assurer un fonctionnement organisationnel optimal. Les études qui se sont attardées au développement du leadership stipulent que les programmes utilisés à cette fin doivent être développés en se reposant davantage sur la logique de l’utilisation des données probantes en gestion (evidence-based management) pour en assurer l’efficacité.

New point-of-sale (POS) machines help small businesses catalog transactions and inventory by warehousing customer, vendor, product, and sales data. This data, however, is usually warehoused in a data table that is not accessible to modern analytics and management software, such as Lightspeed. To help these businesses take advantage of their data, Enkidoo provides a service to export small business data by building an extract-transform-load (ETL) pipeline to Lightspeed. However, this process can be tedious, due to mismatches in column data and the template.

The enormous amount of data generated can be exploited using state-of-the-art AI algorithms to drive business decisions. However, a significant drawback of existing approaches is that the algorithms require a considerable amount of human effort and energy to prepare and annotate the data. Recent advances in deep learning and AI propose to solve this bottleneck using a paradigm referred to as 'Unsupervised' algorithms.

Ce projet consiste à extraire la nature des accès informatiques des employés d’une organisation et les consolider afin de permettre au gestionnaire de l’employer ou à un auditeur d’avoir une vue d’ensemble des accès informatiques de ses employés.

One of the approaches portfolio managers commonly use to build portfolios, is to rank the underlying assets based on the prediction for the stock returns, as well as other aspects of the portfolio such as the portfolio risk. In this project we aim to apply different deep learning techniques to the problem of stock ranking. The features we want to use to train our models are mainly derived from fundamental company data including quarterly and annual filings of the publicly traded companies.