Monte Carlo simulation of cooling induced by parametric resonance

We demonstrate that the parametric resonance in a magnetic quadrupole trap can be exploited to cool atoms by using Bird's method. In our programme the parametric resonance was realized by anisotropically modulating the trap potential. The modulation frequency dependences of temperature and fraction of the trapped atoms are explored. Furthermore, the temperature after the modulation as functions of the modulation amplitude and the mean elastic collision time are also studied. These results are valuable for the experiment of parametric resonance in a quadrupole trap.

Advanced Monte Carlo Simulation and Machine Learning for Frequency Domain Optical Coherence Tomography

Optical Coherence Tomography(OCT) is a popular, rapidly growing imaging technique with an increasing number of bio-medical applications due to its noninvasive nature. However, there are three major challenges in understanding and improving an OCT system: (1) Obtaining an OCT image is not easy. It either takes a real medical experiment or requires days of computer simulation. Without much data, it is difficult to study the physical processes underlying OCT imaging of different objects simply because there aren't many imaged objects. (2) Interpretation of an OCT image is also hard. This challenge is more profound than it appears. For instance, it would require a trained expert to tell from an OCT image of human skin whether there is a lesion or not. This is expensive in its own right, but even the expert cannot be sure about the exact size of the lesion or the width of the various skin layers. The take-away message is that analyzing an OCT image even from a high level would usually require a trained expert, and pixel-level interpretation is simply unrealistic. The reason is simple: we have OCT images but not their underlying ground-truth structure, so there is nothing to learn from. (3) The imaging depth of OCT is very limited (millimeter or sub-millimeter on human tissues). While OCT utilizes infrared light for illumination to stay noninvasive, the downside of this is that photons at such long wavelengths can only penetrate a limited depth into the tissue before getting back-scattered. To image a particular region of a tissue, photons first need to reach that region. As a result, OCT signals from deeper regions of the tissue are both weak (since few photons reached there) and distorted (due to multiple scatterings of the contributing photons). This fact alone makes OCT images very hard to interpret.

This thesis addresses the above challenges by successfully developing an advanced Monte Carlo simulation platform which is 10000 times faster than the state-of-the-art simulator in the literature, bringing down the simulation time from 360 hours to a single minute. This powerful simulation tool not only enables us to efficiently generate as many OCT images of objects with arbitrary structure and shape as we want on a common desktop computer, but it also provides us the underlying ground-truth of the simulated images at the same time because we dictate them at the beginning of the simulation. This is one of the key contributions of this thesis. What allows us to build such a powerful simulation tool includes a thorough understanding of the signal formation process, clever implementation of the importance sampling/photon splitting procedure, efficient use of a voxel-based mesh system in determining photon-mesh interception, and a parallel computation of different A-scans that consist a full OCT image, among other programming and mathematical tricks, which will be explained in detail later in the thesis.

Next we aim at the inverse problem: given an OCT image, predict/reconstruct its ground-truth structure on a pixel level. By solving this problem we would be able to interpret an OCT image completely and precisely without the help from a trained expert. It turns out that we can do much better. For simple structures we are able to reconstruct the ground-truth of an OCT image more than 98% correctly, and for more complicated structures (e.g., a multi-layered brain structure) we are looking at 93%. We achieved this through extensive uses of Machine Learning. The success of the Monte Carlo simulation already puts us in a great position by providing us with a great deal of data (effectively unlimited), in the form of (image, truth) pairs. Through a transformation of the high-dimensional response variable, we convert the learning task into a multi-output multi-class classification problem and a multi-output regression problem. We then build a hierarchy architecture of machine learning models (committee of experts) and train different parts of the architecture with specifically designed data sets. In prediction, an unseen OCT image first goes through a classification model to determine its structure (e.g., the number and the types of layers present in the image); then the image is handed to a regression model that is trained specifically for that particular structure to predict the length of the different layers and by doing so reconstruct the ground-truth of the image. We also demonstrate that ideas from Deep Learning can be useful to further improve the performance.

It is worth pointing out that solving the inverse problem automatically improves the imaging depth, since previously the lower half of an OCT image (i.e., greater depth) can be hardly seen but now becomes fully resolved. Interestingly, although OCT signals consisting the lower half of the image are weak, messy, and uninterpretable to human eyes, they still carry enough information which when fed into a well-trained machine learning model spits out precisely the true structure of the object being imaged. This is just another case where Artificial Intelligence (AI) outperforms human. To the best knowledge of the author, this thesis is not only a success but also the first attempt to reconstruct an OCT image at a pixel level. To even give a try on this kind of task, it would require fully annotated OCT images and a lot of them (hundreds or even thousands). This is clearly impossible without a powerful simulation tool like the one developed in this thesis.

Penaeus indicus H. Milne Edwards, 1837: resumo das conclusões dos cruzeiros de pesca exploratória entre 1979 e 1983 no banco de Sofala em Moçambique

From 1979 to 1983, several surveys were carried out with research and fishing vessels at Sofala Bank in Mozambique. Their main objective was the assessment of shallow water prawn stocks, as this resource is of great economic importance for the country. A summary of the conclusions of these surveys regarding the species Penaeus indicus is presented. During the rainy season the species occurs closer to the shore than during the dry season. Estimates of biomass are very variable. The spawning peak seems to occur at the beginning of the rainy season (September-October). The spawning areas are located very close to the shore in the northern part of Sofala Bank and South of 17 degree 10'S in the 15-25 m depth interval.

Leptina e grelina na regulação do comportamento alimentar da tartaruga Eretmochelys imbricata (Linnaeus 1766)

Durante a temporada de nidação, fêmeas de tartarugas marinhas costumam reduzir ou cessar por completo a ingestão de alimentos. Este fato sugere que o armazenamento de energia e nutrientes para a reprodução ocorra durante o período que antecede a migração para os sítios reprodutivos, enquanto estes animais ainda se encontram nas áreas de alimentação. Do ponto de vista fisiológico, tartarugas em atividade reprodutiva são capazes de permanecer longos períodos em jejum. Fatores neuroendócrinos vêm sendo recentemente apontados como os mais relevantes para a manutenção da homeostase energética de todos os vertebrados; entre eles, a leptina (hormônio anorexígeno) e a grelina (peptídeo orexígeno). Com o objetivo de compreender o mecanismo de fome e saciedade nas tartarugas marinhas, investigamos os níveis séricos destes hormônios e de outros indicadores nutricionais em fêmeas de Eretmochelys imbricata desovando no litoral do Rio Grande do Norte, Brasil. Foram coletadas amostras de sangue de 41 tartarugas durante as temporadas reprodutivas de 2010/2011 e 2011/2012. Os níveis séricos de leptina diminuíram significativamente ao longo do período de nidação, de modo a explicar a busca por alimentos ao término da temporada. Ao mesmo tempo, registramos uma tendência crescente nos níveis séricos de grelina, fator este que também justifica a remigração para as áreas de alimentação no fim do período. Não foram observadas tendências lineares para alguns dos parâmetros avaliados, entre eles: hematócrito, alanina aminotransferase (ALT), aspartato aminotransferase (AST), fosfatase alcalina (FA), gama glutamil transferase (GGT), lipoproteínas de baixa densidade (LDL) e lipoproteínas de alta densidade (HDL). É possível que a maior parte dos indicadores nutricionais tenha apresentado redução gradativa devido ao estresse fisiológico decorrente da vitelogênese e de repetidas oviposições. No entanto, é valido ressaltar que o quadro de restrição calórica por tempo prolongado é o principal responsável pelas alterações em índice de massa corpórea e padrões bioquímicos nestes animais.