Measurement of gas flow through porous structures using thz spectroscopy

Comparing THz transmission through a sample with the transmission through free-space allows one to calculate the THz absorbance of a sample. Previous studies have focused on using THz absorbance to measure the diffusion of liquid through materials. In this study, the ability of THz spectroscopy to measure the flow of gas in a porous material, packaging foam, is investigated. Specifically, Terahertz spectroscopy from 0.5 to 0.7 THz is used to measure the THz absorption of 1, 1 -difluoroethane. Several experiments are performed to test the ability of THz spectroscopy to measure the flow of gas in a porous material: (1) The gas cell is empty (no foam) during the filling and purging of the gas cell. (2) Two layers of foam are put in the gas cell. The gas outlet at the bottom of the gas cell is left open. (3) Two layers of foam are placed in the gas cell. In this condition, the gas outlet at the bottom of the cell is left only partially open. The data shows that THz spectroscopy can be used to measure the flow of gas in opaque porous materials. The longer term goal of thesis research is to eventually use the THz properties of the gas to measure its diffusion through different porous products and correlate the gas diffusion with the material’s structure

CAPITAL ADEQUACY OF HEDGE FUNDS: A VALUE-AT-RISK APPROACH

This paper studies the risk profile and capital adequacy of hedge funds by extending the sample period used in the research of Gupta and Liang (2005). We apply a VaR-based approach to evaluate over 6,000 hedge funds from the Lipper Tass Academic Hedge Fund Database, including live funds and graveyard funds, and find that only a small percentage of them are undercapitalized as of September 2014. By conducting a cross-sectional regression of fund capitalization on various characteristics of hedge funds, we reach a conclusion that whether a hedge fund is adequately capitalized is related to its age and investment style. Standard deviation and leverage ratio often underestimate the market risk hedge funds face, whereas VaR-based measures successfully capture both static and dynamic risk profile of hedge funds

Does Credit Supply Drive the LBO Market?

I examine how supply of credit affects investment and capital structure decisions by studying the leveraged buyout (LBO) market. I employ the structural changes in credit markets that led to the explosion in collateralized debt obligations (CDOs) to identify shocks in credit supply. Using instruments that are not likely affected by credit demand in the LBO market, I show that the easy credit from the CDO market encouraged banks to arrange more loans to finance LBOs, leading to the recent LBO boom. This structured lending supported by CDOs led to cheaper credit, looser covenants, and more aggressive use of bank loans in financing LBOs. However, in sharp contrast to the LBO boom in the late 1980s, this easy credit did not lead to riskier LBO deals. My findings point to the effects of disintermediation of banks as they switched from an originate-and-hold to an originate-and-distribute model

Depth-wise Decomposition for Accelerating Separable Convolutions in Efficient Convolutional Neural Networks

Very deep convolutional neural networks (CNNs) have been firmly established as the primary methods for many computer vision tasks. However, most state-of-the-art CNNs are large, which results in high inference latency. Recently, depth-wise separable convolution has been proposed for image recognition tasks on computationally limited platforms such as robotics and self-driving cars. Though it is much faster than its counterpart, regular convolution, accuracy is sacrificed. In this paper, we propose a novel decomposition approach based on SVD, namely depth-wise decomposition, for expanding regular convolutions into depthwise separable convolutions while maintaining high accuracy. We show our approach can be further generalized to the multi-channel and multi-layer cases, based on Generalized Singular Value Decomposition (GSVD) [59]. We conduct thorough experiments with the latest ShuffleNet V2 model [47] on both random synthesized dataset and a large-scale image recognition dataset: ImageNet [10]. Our approach outperforms channel decomposition [73] on all datasets. More importantly, our approach improves the Top-1 accuracy of ShuffleNet V2 by ~2%.Comment: CVPR 2019 workshop, Efficient Deep Learning for Computer Visio