Comparative study on making loans to large companies and SME's in China

Thesis (S.M.)--Massachusetts Institute of Technology, Sloan School of Management, 2013.Cataloged from PDF version of thesis.Includes bibliographical references (p. 41-45).The SME financing problem in China has been widely acknowledged during the recent financial crisis. The SMEs compose 99% of all registered enterprises and employ more than 60% of labors in China. They contributed significantly to the economic vitality, as well as to ensuring stable employment and sustainable development of China. However, the SMEs have been short of external funding because they are considered to be riskier than large companies which maintained closer relationships to the governments and state-own-commercial-banks, which are the main financial source under the current banking system in China. Are the SMEs riskier than the large companies in terms of loan issuance? The thesis explores the issue by discussing the current banking structure in China and explaining the reasons from political, economic, social and technological perspectives. Comparing the financial indicators including ROE (return on equity), ROA (return on assets), NPM (net profit margin), ICR (interest coverage ratio), CR (current ratio) and revenue growth of selected SMEs and large companies before and after they received the commercial loans, I find that SMEs did not necessarily demonstrate worse performance than the large companies. Contrarily, the SMEs could more effectively utilize their loans to realize better business outcomes than the large companies did. To further improve the financing issue of SMEs, the thesis will raise recommendations from the perspectives of the government regulatory bodies and the local commercial banks.by Michael Jiaming Tian.S.M

SIMBA: scalable inversion in optical tomography using deep denoising priors

Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.https://arxiv.org/abs/1911.13241First author draf

Efficient Generation of Intense Broadband Terahertz Pulses from Quartz

The intense terahertz (THz) pulses facilitate the observation of various nonlinear optical effects and manipulation of material properties. In this work, we report a convenient approach that can produce strong broadband terahertz pulses with center frequency tunable between 2-4 THz. The coherent THz light source with pulse energy of 1.2 microjoule can be generated from a low-cost crystalline quartz pumped by an ultrashort tilted wave-front pulse. Thanks to the wide transparent spectral window and high damage threshold, our theoretical analysis and experiment show that the optical rectification in quartz is as efficient as that in LiNbO3, but covers much broader spectral range. This work not only provides the light source that is urgently needed for nonlinear THz spectroscopy beyond 1 THz, but offers an alternative route in the selection of nonlinear optical crystals for optical frequency conversion

A Comprehensive Comparison of Projections in Omnidirectional Super-Resolution

Super-Resolution (SR) has gained increasing research attention over the past few years. With the development of Deep Neural Networks (DNNs), many super-resolution methods based on DNNs have been proposed. Although most of these methods are aimed at ordinary frames, there are few works on super-resolution of omnidirectional frames. In these works, omnidirectional frames are projected from the 3D sphere to a 2D plane by Equi-Rectangular Projection (ERP). Although ERP has been widely used for projection, it has severe projection distortion near poles. Current DNN-based SR methods use 2D convolution modules, which is more suitable for the regular grid. In this paper, we find that different projection methods have great impact on the performance of DNNs. To study this problem, a comprehensive comparison of projections in omnidirectional super-resolution is conducted. We compare the SR results of different projection methods. Experimental results show that Equi-Angular cube map projection (EAC), which has minimal distortion, achieves the best result in terms of WS-PSNR compared with other projections. Code and data will be released.Comment: Accepted to ICASSP202

CABM: Content-Aware Bit Mapping for Single Image Super-Resolution Network with Large Input

With the development of high-definition display devices, the practical scenario of Super-Resolution (SR) usually needs to super-resolve large input like 2K to higher resolution (4K/8K). To reduce the computational and memory cost, current methods first split the large input into local patches and then merge the SR patches into the output. These methods adaptively allocate a subnet for each patch. Quantization is a very important technique for network acceleration and has been used to design the subnets. Current methods train an MLP bit selector to determine the propoer bit for each layer. However, they uniformly sample subnets for training, making simple subnets overfitted and complicated subnets underfitted. Therefore, the trained bit selector fails to determine the optimal bit. Apart from this, the introduced bit selector brings additional cost to each layer of the SR network. In this paper, we propose a novel method named Content-Aware Bit Mapping (CABM), which can remove the bit selector without any performance loss. CABM also learns a bit selector for each layer during training. After training, we analyze the relation between the edge information of an input patch and the bit of each layer. We observe that the edge information can be an effective metric for the selected bit. Therefore, we design a strategy to build an Edge-to-Bit lookup table that maps the edge score of a patch to the bit of each layer during inference. The bit configuration of SR network can be determined by the lookup tables of all layers. Our strategy can find better bit configuration, resulting in more efficient mixed precision networks. We conduct detailed experiments to demonstrate the generalization ability of our method. The code will be released.Comment: Accepted to CVPR202

Probing Interface of Perovskite Oxide Using Surface-specific Terahertz Spectroscopy

The surface/interface species in perovskite oxides play an essential role in many novel emergent physical phenomena and chemical processes. With low eigen-energy in the terahertz region, such species at buried interfaces remain poorly understood due to the lack of feasible experimental techniques. Here, we show that vibrational resonances and two-dimensional electron gas at the interface can be characterized using surface-specific nonlinear spectroscopy in the terahertz range. This technique uses intra-pulse difference frequency mixing (DFM) process, which is allowed only at surface/interface of a medium with inversion symmetry. Sub-monolayer sensitivity can be achieved using the state-of-the-art detection scheme for the terahertz emission from surface/interface. As a demonstration, Drude-like nonlinear response from the two-dimensional electron gas emerging at LaAlO3/SrTiO3 or Al2O3/ SrTiO3 interface was successfully observed. Meanwhile, the interfacial vibrational spectrum of the ferroelectric soft mode of SrTiO3 at 2.8 THz was also obtained that was polarized by the surface field in the interfacial region. The corresponding surface/interface potential, which is a key parameter for SrTiO3-based interface superconductivity and photocatalysis, can now be determined optically via quantitative analysis on the polarized phonon spectrum. The interfacial species with resonant frequencies in the THz region revealed by our method provide more insights into the understanding of physical properties of complex oxides.Comment: arXiv admin note: substantial text overlap with arXiv:2207.1461

Single-pixel imaging based on deep learning

Single-pixel imaging can collect images at the wavelengths outside the reach of conventional focal plane array detectors. However, the limited image quality and lengthy computational times for iterative reconstruction still impede the practical application of single-pixel imaging. Recently, deep learning has been introduced into single-pixel imaging, which has attracted a lot of attention due to its exceptional reconstruction quality, fast reconstruction speed, and the potential to complete advanced sensing tasks without reconstructing images. Here, this advance is discussed and some opinions are offered. Firstly, based on the fundamental principles of single-pixel imaging and deep learning, the principles and algorithms of single-pixel imaging based on deep learning are described and analyzed. Subsequently, the implementation technologies of single-pixel imaging based on deep learning are reviewed. They are divided into super-resolution single-pixel imaging, single-pixel imaging through scattering media, photon-level single-pixel imaging, optical encryption based on single-pixel imaging, color single-pixel imaging, and image-free sensing according to diverse application fields. Finally, major challenges and corresponding feasible approaches are discussed, as well as more possible applications in the future