Computational Methods for Matrix/Tensor Factorization and Deep Learning Image Denoising

Feature learning is a technique to automatically extract features from raw data. It is widely used in areas such as computer vision, image processing, data mining and natural language processing. In this thesis, we are interested in the computational aspects of feature learning. We focus on rank matrix and tensor factorization and deep neural network models for image denoising. With respect to matrix and tensor factorization, we first present a technique to speed up alternating least squares (ALS) and gradient descent (GD) − two commonly used strategies for tensor factorization. We introduce an efficient, scalable and distributed algorithm that addresses the data explosion problem. Instead of a computationally challenging sub-step of ALS and GD, we implement the algorithm on parallel machines by using only two sparse matrix-vector products. Not only is the algorithm scalable but it is also on average 4 to 10 times faster than competing algorithms on various data sets. Next, we discuss our results of non-negative matrix factorization for hyperspectral image data in the presence of noise. We introduce a spectral total variation regularization and derive four variants of the alternating direction method of multiplier algorithm. While all four methods belong to the same family of algorithms, some perform better than others. Thus, we compare the algorithms using stimulated Raman spectroscopic image will be demonstrated. For deep neural network models, we focus on its application to image denoising. We first demonstrate how an optimal procedure leveraging deep neural networks and convex optimization can combine a given set of denoisers to produce an overall better result. The proposed framework estimates the mean squared error (MSE) of individual denoised outputs using a deep neural network; optimally combines the denoised outputs via convex optimization; and recovers lost details of the combined images using another deep neural network. The framework consistently improves denoising performance for both deterministic denoisers and neural network denoisers. Next, we apply the deep neural network to solve the image reconstruction issues of the Quanta Image Sensor (QIS), which is a single-photon image sensor that oversamples the light field to generate binary measures

DFacTo: Distributed Factorization of Tensors

We present a technique for significantly speeding up Alternating Least Squares (ALS) and Gradient Descent (GD), two widely used algorithms for tensor factorization. By exploiting properties of the Khatri-Rao product, we show how to efficiently address a computationally challenging sub-step of both algorithms. Our algorithm, DFacTo, only requires two sparse matrix-vector products and is easy to parallelize. DFacTo is not only scalable but also on average 4 to 10 times faster than competing algorithms on a variety of datasets. For instance, DFacTo only takes 480 seconds on 4 machines to perform one iteration of the ALS algorithm and 1,143 seconds to perform one iteration of the GD algorithm on a 6.5 million x 2.5 million x 1.5 million dimensional tensor with 1.2 billion non-zero entries.Comment: Under review for NIPS 201

Carnosol induces apoptotic cell death through ROS-dependent inactivation of STAT3 in human melanoma G361 cells

Melanoma is the leading cause of skin cancer deaths, and the poor prognosis of metastatic melanoma has made needs for a novel pharmacological treatment or efficient intervention. Carnosol, a major polyphenolic compound from Rosmarinus officinalis, has a wide range of biological activities including anti-cancer effect. However, the underlying molecular mechanisms of its anti-cancer effect remain poorly understood in malignant human melanoma cells. In the present study, we investigate the apoptotic effect and the underlying anti-cancer mechanisms of carnosol. Our results revealed that carnosol strongly induced apoptosis against human melanoma G361 cells in a dose- and time-dependent manner, and caused dramatical elevation in cellular reactive oxygen species (ROS) level during apoptosis. In mechanistic studies, carnosol treatment decreased protein level of anti-apoptotic B‑cell lymphoma 2 (Bcl-2) and B cell lymphoma-extra large (Bcl-xL), however, increased level of pro-apoptotic Bcl-2-associated X protein (Bax) protein. Moreover, carnosol escalated cellular level of p53, which was accompanied by a decline of mouse double minute 2 homolog (MDM2) level. Also, carnosol inhibited activation of Src and signal transducer and activator of transcription 3 (STAT3), therefore down-regulated STAT3-dependent gene expression, such as D-series cyclin and survivin. These changes by carnosol were attenuated by pre-treatment of N-acetyl cysteine, and abolished progression of carnosol-induced apoptosis. In conclusion, carnosol induced apoptosis in human melanoma G361 cells through ROS generation and inhibition of STAT3-mediated pathway. Our results provide molecular bases of carnosol-induced apoptosis, and suggest a novel candidate for human melanoma treatment.This research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (2018R1D1A1A02050495, J.-S. Choi) and by the Ministry of Science, ICT and Future Planning 2017R1A2B4009831, K.- S. Chun)

17β-Estradiol supplementation changes gut microbiota diversity in intact and colorectal cancer-induced ICR male mice

The composition of the gut microbiota is influenced by sex hormones and colorectal cancer (CRC). Previously, we reported that 17 beta -estradiol (E2) inhibits azoxymethane/dextran sulfate sodium (AOM/DSS)-induced tumorigenesis in male mice. Here, we investigated whether the composition of the gut microbiota is different between male and female, and is regulated by estrogen as a secondary outcome of previous studies. We established four groups of mice based on the sex and estrogen status [ovariectomized (OVX) female and E2-treated male]. Additionally, three groups of males were established by treating them with AOM/DSS, and E2, after subjecting them to AOM/DSS treatment. The mice were sacrificed at 21 weeks old. The composition of the gut microbiota was analyzed using 16S rRNA metagenomics sequencing. We observed a significant increase in the microbial diversity (Chao1 index) in females, males supplemented with E2, and males treated with AOM/DSS/E2 compared with normal males. In normal physiological condition, sex difference and E2 treatment did not affect the ratio of Firmicutes/Bacteroidetes (F/B). However, in AOM/DSS-treated male mice, E2 supplementation showed significantly lower level of the F/B ratio. The ratio of commensal bacteria to opportunistic pathogens was higher in females and E2-treated males compared to normal males and females subjected to OVX. Unexpectedly, this ratio was higher in the AOM/DSS group than that determined in other males and the AOM/DSS/E2 group. Our findings suggest that estrogen alters the gut microbiota in ICR (CrljOri:CD1) mice, particularly AOM/DSS-treated males, by decreasing the F/B ratio and changing Shannon and Simpson index by supply of estrogen. This highlights another possibility that estrogen could cause changes in the gut microbiota, thereby reducing the risk of developing CRC.

Two-Dimensional Dirac Fermions Protected by Space-Time Inversion Symmetry in Black Phosphorus

We report the realization of novel symmetry-protected Dirac fermions in a surface-doped two-dimensional (2D) semiconductor, black phosphorus. The widely tunable band gap of black phosphorus by the surface Stark effect is employed to achieve a surprisingly large band inversion up to ~0.6 eV. High-resolution angle-resolved photoemission spectra directly reveal the pair creation of Dirac points and their moving along the axis of the glide-mirror symmetry. Unlike graphene, the Dirac point of black phosphorus is stable, as protected by spacetime inversion symmetry, even in the presence of spin-orbit coupling. Our results establish black phosphorus in the inverted regime as a simple model system of 2D symmetry-protected (topological) Dirac semimetals, offering an unprecedented opportunity for the discovery of 2D Weyl semimetals