POPULATION GENETICS AND DESICCATION STRESS OF PORPHYRA UMBILICALIS KÜTZING IN THE GULF OF MAINE

The red alga Porphyra umbilicalis Kützing is an ecologically and economically important marine macroalga in the Northern Atlantic. Porphyra umbilicalis has a broad distribution within the North Atlantic. In the Northeast Atlantic, it is dioecious and reproduces both sexually and asexually, while in the Northwest Atlantic only asexual reproduction has been observed. As a high intertidal alga, P. umbilicalis regularly experiences desiccation and rehydration cycles with the tidal cycles, so it has high tolerance towards various abiotic stresses. The present work attempts to understand the population structure in asexual populations of P. umbilicalis in the Gulf of Maine by applying putative single nucleotide polymorphisms (SNP) markers developed from transcriptome data (Chapter 1). In order to understand the desiccation tolerance of P. umbilicalis, the contents of putative compatible solutes were measured and the genes involved in the synthesis of these solutes were analyzed in response to desiccation and rehydration treatments (Chapter 2). In addition, a comparative transcriptomic analysis was performed using P. umbilicalis under fresh, dehydrated, desiccated and rehydrated conditions in order to gain insights into the mechanisms of its desiccation tolerance in responses to water loss and water gain (Chapter 3). My work represents the first attempt to develop a suitable bioinformatic pipeline for RNA-seq to detect SNP markers for the red alga P. umbilicalis and to apply these SNP markers for population analysis. The compatible solutes study verifies the occurrences of nanomolar concentrations of trehalose in P. umbilicalis for the first time and identifies additional genes, possibly encoding trehalose phosphate synthases. The transcriptome study suggested distinct molecular responses may occur during dehydration and desiccation and confirmed that the rehydration-induced responses play an important role in the mechanisms of desiccation tolerance in P. umbilicalis

Market implied funding liquidity and asset prices

PhD ThesisThis study examines a market-wide liquidity measure based on systematic deviations from Put-Call parity in US equity option markets. We show that this implied funding liquidity measure significantly predicts future excess market returns and explains cross-sectional variations of stock returns. We provide evidence that investing in stocks with the largest exposure to the innovations in implied funding liquidity and shorting stocks with the smallest generate significant returns of about 7.3% per annum. We also observe that implied funding liquidity significantly predicts future changes in a number of macroeconomic variables over a horizon of six months. This result indicates that the funding liquidity measure obtained from the option markets provides forward-looking information about developments in the economy. Furthermore, we also examine the relationship between implied funding liquidity and the cross section of excess returns arising from the carry trades, which are strategies for investing in high interest rate currencies while borrowing in low interest rate currencies. We show that this implied funding liquidity is significantly associated with high interest rate currencies. We also consider the assetpricing implications of the funding liquidity for other asset classes such as hedge funds

Approximate Multiplication of Sparse Matrices with Limited Space

Approximate matrix multiplication with limited space has received ever-increasing attention due to the emergence of large-scale applications. Recently, based on a popular matrix sketching algorithm---frequent directions, previous work has introduced co-occuring directions (COD) to reduce the approximation error for this problem. Although it enjoys the space complexity of $O((m_x+m_y)\ell)$ for two input matrices $X\in\mathbb{R}^{m_x\times n}$ and $Y\in\mathbb{R}^{m_y\times n}$ where $\ell$ is the sketch size, its time complexity is $O\left(n(m_x+m_y+\ell)\ell\right)$, which is still very high for large input matrices. In this paper, we propose to reduce the time complexity by exploiting the sparsity of the input matrices. The key idea is to employ an approximate singular value decomposition (SVD) method which can utilize the sparsity, to reduce the number of QR decompositions required by COD. In this way, we develop sparse co-occuring directions, which reduces the time complexity to \widetilde{O}\left((\nnz(X)+\nnz(Y))\ell+n\ell^2\right) in expectation while keeps the same space complexity as $O((m_x+m_y)\ell)$, where \nnz(X) denotes the number of non-zero entries in $X$. Theoretical analysis reveals that the approximation error of our algorithm is almost the same as that of COD. Furthermore, we empirically verify the efficiency and effectiveness of our algorithm

Image dehazing based on partitioning reconstruction and entropy-based alternating fast-weighted guided filters

A robust image dehazing algorithm based on the first-order scattering of the image degradation model is proposed. In this work, there are three contributions toward image dehazing: (i) a robust method for assessing the global irradiance from the most hazy-opaque regions of the imagery is proposed; (ii) more detailed depth information of the scene can be recovered through the enhancement of the transmission map using scene partitions and entropy-based alternating fast-weighted guided filters; and (iii) crucial model parameters are extracted from in-scene information. This paper briefly outlines the principle of the proposed technique and compares the dehazed results with four other dehazing algorithms using a variety of different types of imageries. The dehazed images have been assessed through a quality figure-of-merit, and experiments have shown that the proposed algorithm effectively removes haze and has achieved a much better quality of dehazed images than all other state-of-the-art dehazing methods employed in this work

Improved Projection-free Online Continuous Submodular Maximization

We investigate the problem of online learning with monotone and continuous DR-submodular reward functions, which has received great attention recently. To efficiently handle this problem, especially in the case with complicated decision sets, previous studies have proposed an efficient projection-free algorithm called Mono-Frank-Wolfe (Mono-FW) using $O(T)$ gradient evaluations and linear optimization steps in total. However, it only attains a $(1-1/e)$-regret bound of $O(T^{4/5})$. In this paper, we propose an improved projection-free algorithm, namely POBGA, which reduces the regret bound to $O(T^{3/4})$ while keeping the same computational complexity as Mono-FW. Instead of modifying Mono-FW, our key idea is to make a novel combination of a projection-based algorithm called online boosting gradient ascent, an infeasible projection technique, and a blocking technique. Furthermore, we consider the decentralized setting and develop a variant of POBGA, which not only reduces the current best regret bound of efficient projection-free algorithms for this setting from $O(T^{4/5})$ to $O(T^{3/4})$, but also reduces the total communication complexity from $O(T)$ to $O(\sqrt{T})$