Vocational Education and Smart Specialisation Strategies

This report presents the main outcomes of a workshop on Vocational Education and Smart Specialisation held at EIT house on 30 January 2020.JRC.B.3-Territorial Developmen

Flash: Fast and Light Motion Prediction for Autonomous Driving with Bayesian Inverse Planning and Learned Motion Profiles

Motion prediction of road users in traffic scenes is critical for autonomous driving systems that must take safe and robust decisions in complex dynamic environments. We present a novel motion prediction system for autonomous driving. Our system is based on the Bayesian inverse planning framework, which efficiently orchestrates map-based goal extraction, a classical control-based trajectory generator and an ensemble of light-weight neural networks specialised in motion profile prediction. In contrast to many alternative methods, this modularity helps isolate performance factors and better interpret results, without compromising performance. This system addresses multiple aspects of interest, namely multi-modality, motion profile uncertainty and trajectory physical feasibility. We report on several experiments with the popular highway dataset NGSIM, demonstrating state-of-the-art performance in terms of trajectory error. We also perform a detailed analysis of our system's components, along with experiments that stratify the data based on behaviours, such as change lane versus follow lane, to provide insights into the challenges in this domain. Finally, we present a qualitative analysis to show other benefits of our approach, such as the ability to interpret the outputs

Query-based Hard-Image Retrieval for Object Detection at Test Time

There is a longstanding interest in capturing the error behaviour of object detectors by finding images where their performance is likely to be unsatisfactory. In real-world applications such as autonomous driving, it is also crucial to characterise potential failures beyond simple requirements of detection performance. For example, a missed detection of a pedestrian close to an ego vehicle will generally require closer inspection than a missed detection of a car in the distance. The problem of predicting such potential failures at test time has largely been overlooked in the literature and conventional approaches based on detection uncertainty fall short in that they are agnostic to such fine-grained characterisation of errors. In this work, we propose to reformulate the problem of finding "hard" images as a query-based hard image retrieval task, where queries are specific definitions of "hardness", and offer a simple and intuitive method that can solve this task for a large family of queries. Our method is entirely post-hoc, does not require ground-truth annotations, is independent of the choice of a detector, and relies on an efficient Monte Carlo estimation that uses a simple stochastic model in place of the ground-truth. We show experimentally that it can be applied successfully to a wide variety of queries for which it can reliably identify hard images for a given detector without any labelled data. We provide results on ranking and classification tasks using the widely used RetinaNet, Faster-RCNN, Mask-RCNN, and Cascade Mask-RCNN object detectors. The code for this project is available at https://github.com/fiveai/hardest

Substandard Quality of the Antimicrobials Sold in the Street Markets in Haiti

This pilot study was conducted to analyze the quality of the antimicrobials sold in the street markets in Port-au-Prince, Haiti. A total of 258 packs containing antimicrobials were bought in 28 street markets in Port-au-Prince (Haiti). Tablets and contents of capsules included in 196 packs were analyzed using a Raman handheld spectrometer (NanoRAM of BWTEK, Model: BWS456-785) during the first quarter of 2019. Three out of 11 antimicrobials (Amoxicillin, Metronidazole, and Cotrimoxazole) had a high spectral match with an HQI ≥ 90 to the respective authentic medicine for more than 95% of their tablets/capsules. For six antimicrobials (Tetracycline, Erythromycin, Cloxacillin, Azithromycin, Clarithromycin, and the combination Amoxicillin + Clavulanic Acid) none of their tablets/capsules showed a sufficient spectral match with the authentic medicine. This finding indicates that these products sold in the markets did not contain the labeled drug and/or contained a degraded drug. In addition to the fact that prescription antimicrobials can be purchased in street markets, the present field study found that for most of them (including "Watch" antimicrobials according to the AWaRe classification) were substandard, which contributes to the present antimicrobials resistance epidemic