The Art of Madness

The nineteenth century was not an entirely kind time for the female artist. Coming of age as the 1800’s bridged into its latter half, literary artists Elizabeth Stuart Phelps Ward, Charlotte Perkins Gilman, and Kate Chopin were all well aware of their uncharitable culture. Equipped with firm feminist bents and creative visions, each of three women produced a seminal work – The Story of Avis, “The Yellow Wallpaper,” and The Awakening, respectively – taking that atmosphere to task. In these stories, each of the three women produces a female protagonist who struggles for having been born simultaneously an artist and a woman. The writers pit their women’s desires against the restrictive latitude of their time and show how such conditions drive women to madness, as a result of which they are forced to either escape into the blind mind of insanity or deal daily with their pain and inescapable societal condemnation. In an age where “hysteria” was a frequent hit in the vernacular, Phelps, Gilman and Chopin use art and literature as mediums to show that, indeed, there is a method behind the madness.

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.

Phase interpretation for polarization-dependent near-field images of high-density gratings

It has been described that the near-field images of a high-density grating at the half self-imaging distance could be different for TE and TM polarization states. We propose that the phases of the diffraction orders play an important role in such polarization dependence. The view is verified through the coincidence of the numerical result of finite-difference time-domain method and the reconstructed results from the rigorous coupled-wave analysis. Field distributions of TE and TM polarizations are given numerically for a grating with period d = 2.3 lambda, which are verified through experiments with the scanning near-field optical microscopy technique. The concept of phase interpretation not only explains the polarization dependence at the half self-imaging distance of gratings with a physical view, but also, it could be widely used to describe the near-field diffraction of a variety of periodic diffractive optical elements whose feature size comparable to the wavelength. (C) 2008 Elsevier B.V. All rights reserved.

Transbordamentos: elementos para uma cartografia dos campos cegos nos jogos da arte

O trabalho explora a dinâmica dos jogos plásticos nas artes e das operações de suas obras enquanto máquinas de visão, dispositivos através dos quais determinados aprendizados se desenvolvem enquanto promovem certos processos de invisibilização. Entre negociações dos modos de ver se instaura a instituição do estatuto coletivo do visível. Fatores sociais, tecnológicos, econômicos e políticos se imbricam nessas negociações, implicando na expansão dos processos de produção e reprodução de imagens que culminam na constituição dos campos cegos na cultura visual, áreas abarrotadas de visibilidades ofuscantes. A arte, ciente de ser elemento ativo na constituição dos limites da visão, responde a esse panorama com ações de transbordamento e alargamento desses limites. Adota os campos cegos e suas dinâmicas internas como base para práticas reflexivas. Direciona-se para a identificação de um estatuto do invisível como parte da constituição do próprio estatuto do visível na proposição de experiências sensíveis a partir de diálogos com a invisibilidade. Algumas dessas experiências são analisadas nesta dissertação que reconhece nelas elementos para um mapear dos campos cegos e para uma discussão ampliada da visão