about : Entropica Labs
Entropica Labs is a small Singaporean based start-up that carries out research in Quantum Computing. We worked with BMW in order to see whether Quantum Computing could provide benefits for industrial applications they were interested in. For example we tackled the ride hailing (taxi passenger assignment) problem, where we try to assign the most efficiently possible the taxis such that the passenger waiting time is minimized. Thus, multiple solvers were implemented in order to compare the results on small test instances and larger datasets taken from the New York taxi dataset. The Quantum and Quantum inspired solvers where not successful because of the exceedingly large problem sizes and the requirement to obtain a “good” solution within a short execution time. However a new family of custom classical heuristics were successfully developed and were able to outperform the other solvers over a wide range of scenarios as seen in the figure below. This was done while maintaining the short execution time of some of the simple solvers such as the Hungarian (Munkres) algorithm, which was used when the problem could be cast into a simple assignment problem (SAP).
Caption: Illustrating the quality of the solutions found with various solvers. The quality is measured by comparing the waiting time obtained by the global solver and dividing it by the estimated waiting time obtained with a greedy assignment policy. Therefore the lower the ratio the better the global solver. These tests were carried out over a wide range of scenarios. The P/T ratio per GOTW is the number of passenger requests per taxi during a global optimization window (GOTW). It thus illustrates how well the solvers perform during rush hour or low demand periods.
I really enjoyed working at Entropica Labs in a very international atmosphere where despite the limitations of Covid-19, I still managed to get an insight into Asian culture and enjoy great food. Listing in on the decision making process of a small company was totally new and certainly informative. Overall the internship and living abroad was certainly an enriching experience for me.
About RUAG, Suisse
RUAG Aerodynamics is a department of RUAG Switzerland AG located in Emmen and specialized in wind tunnel testing. The facility disposes of two wind tunnels used in the domains of aerospace and automotive predominantly. Other competencies of the department are among others the design of high precision balances and computational fluid dynamics
The main task for this internship was to optimize an existing wind tunnel using CFD analysis and comparison with theoretical models and design guidelines for such facilities. To do so, a Lattice-Boltzmann Solver was used. After exploring various possibilities of improvements, a convergence towards an improved configuration was obtained. This improvement enables to increase the test section velocity significantly using a similar electrical power input.
Figure 1: Velocity cut of the wind tunnel test section using a Large-Eddy simulation software
The remaining part of this internship comprised various tasks such as the simulation of a high speed train for validation purposes using OpenFoamR and the design of a mechanical system to improve the accuracy of automotive wind tunnel measurements. Finally the simulation of one of the wind tunnels of the facility in order to reproduce precisely different characteristics observed experimentally was undertaken.
Figure 2: Vortices and detachment point visualisation on the ICE3 high speed train
Jean Ventura – Implementation and analysis of the Once-for-All algorithm
About : Sony, Allemagne
During this internship, a procedure for Neural Architecture Search using networks subsampling (called Once-for-All) was explored on the network MobileNetV2 using the dataset CIFAR10. The internship took place at Sony’s Artificial Intelligence Laboratory in Stuttgart, the laboratory is mainly focusing on Artificial Intelligence along with Speech and Sound Recognition and Detection.
Using the Once-for-All training procedure, we were able to cut the computational and memory resources by more than a third without significantly decreasing the accuracy of the subnetwork. While still a preliminary experiment, the results are promising and could be a great technique to reduce the training cost when multiple devices with various resources constraints are used. Indeed, sampling a subnetwork satisfying the constraints and having a good accuracy would eliminate the need to retrain from scratch.