Towards quantitative acousto-optic imaging in tissue

We have investigated the possibilities and limitations of the application of ultrasound modulated coherent light to obtain quantitative information of local absorbers in light-scattering objects, among which tissue. For all objects studied, the combined use of microsecond ultrasound and light pulses enabled us to construct a 3D map of local absorbers with a spatial resolution of ∼2 mm. Moreover, in relatively homogeneous model systems, mimicking a blood vessel embedded in tissue, the use of a calibration procedure allowed for a determination of the local absorbance. Speckle decorrelation times for real tissue containing blood vessels, in which appreciable motion of scatterers can exist, were found to be smaller than 1ms. These relatively short times present a major challenge for acousto-optics to be applied in living tissue systems

Towards quantitative tissue absorption imaging by combining photoacoustics and acousto-optics

We propose a strategy for quantitative photoacoustic mapping of chromophore concentrations that can be performed purely experimentally. We exploit the possibility of acousto-optic modulation using focused ultrasound, and the principle that photons follow trajectories through a turbid medium in two directions with equal probability. A theory is presented that expresses the local absorption coefficient inside a medium in terms of noninvasively measured quantities and experimental parameters. Proof of the validity of the theory is given with Monte Carlo simulations.Comment: 14 pages, 5 figure

Implementing a neural machine translation engine for mobile devices: the Lingvanex use case

In this paper, we present the challenge entailed by implementing a mobile version of a neural machine translation system, where the goal is to maximise translation quality while minimising model size. We explain the whole process of implementing the translation engine on an English–Spanish example and we describe all the difficulties found and the solutions implemented. The main techniques used in this work are data selection by means of Infrequent n-gram Recovery, appending a special word at the end of each sentence, and generating additional samples without the final punctuation marks. The last two techniques were devised with the purpose of achieving a translation model that generates sentences without the final full stop, or other punctuation marks. Also, in this work, the Infrequent n-gram Recovery was used for the first time to create a new corpus, and not enlarge the in-domain dataset. Finally, we get a small size model with quality good enough to serve for daily use.Work partially supported by MINECO under grant DI-15-08169 and by Sciling under its R+D programme