The work journey of an NLP specialist is full of challenges. One of the main challenges that all specialists in the field may face is a...

In this article, we share our experience and a few lessons learned dealing with aiortc, a handy python package produced and open sourced by Jeremy Laine [1] that allows establishing a simple python webrtc backend . Webrtc is a widely adopted peer-to-peer media exchange protocol, supported by most browsers and mobile phones, and behind many video conference solutions like GMeet or Facebook Messengers. The aiortc package is not only useful to establish webrtc servers : it also

In the previous articles, we described the problem of Brain Blood Barrier (BBB) permeability and the importance of considering this feature in the drug development process. We also presented the need for constructing ML models that predict substrates and inhibitors of two known BBB influx transporters, OATP1A2 and OATP2B1 . In this article, we describe how we constructed six models that classify drugs into : substrates/non-substrates of BBB transporters inhibitors/non-inhib

In our first article , we have seen that blood-brain barrier (BBB) has a limited permeability for most chemicals and that getting a drug inside the brain, a step key to drug development process, might be tricky. Some drugs can pass through this barrier passively by diffusion and others can only be delivered by specialized proteins named influx transporters. In this second article we are going to get our data ready for Machine Learning models which will predict substrates of

Drug development is a complicated, costly and time-consuming process , where data science and machine learning can bring decisive advantages . For pharmaceutical business, it takes more than ten years and often more than a billion euros to proceed from the first step, target identification, via drug discovery, development and testing, to the final step: drug approval. During this process about 89 % of the candidate molecules fail to become a drug (Kubiniy 2003). Among the

Manufacturing businesses often seek to automate their production processes, but some stages of this automation are still lingering behind...

Today we are going to describe a fun project we built at Preste, using Nvidia Jetson Xavier NX, Deep Learning and Computer Vision. In the...

When it comes to healthy food, many may associate it with boiling, steaming, or baking food, without salt, fat, and - alas - without...

In our first article related to the topic of DeepFakes, we have reminded our viewers on the dangers of these technologies, the various attempts from the industry to tackle the issue (including via a recent Kaggle competition launched by a consortium of major players such as Facebook) and our own idea to explore the topic further, by leveraging segmentation neural networks to not only recognize but also characterize the fakes, through the localization of modified areas. From

Deepfake comes with creative, fun but also more worrying applications. How good are machines at detecting them today ? Let's take a look.

Lately, we have been working at Preste on a project where we needed to build a computer vision solution for real-time processing and tracking of fast-moving objects . We used a 120 frames per second video camera and chose the NVidia Jetson TX2 platform to host our solution. As our algorithm strongly depended on fast tracking/detection capabilities there was a need for an efficient and fast deep neural network. In this post, we would like to share with you how we succeded i

Le Monde s’interroge cette semaine sur l’hypersensibilité des tests COVID-19, via la question à mille euros: « Peut-on vraiment dire (…)...

A few weeks ago we published a blog post about adversarial examples in image classification. Today we share our own research on the topic.

Nowadays, you will hardly find people who have not heard anything about artificial intelligence. Machine Learning (and especially Deep...