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Spin-orbit interactions are important in determining nuclear structure. They lead to a shift in the energy levels in the nuclear shell model, which could explain the sequence of magic numbers in nuclei. Also in nucleon-nucleon scattering, the large nucleon polarization observed perpendicular to the plane of scattering needs to be explained by adding the spin-orbit interactions in the potential. Their effects change the equation of state and other properties of nuclear matter. Therefore, the simulation of spin-orbit interactions is necessary in nuclear matter.
The auxiliary field diffusion Monte Carlo is an effective and accurate method for calculating the ground state and low-lying exited states in nuclei and nuclear matter. It has successfully employed the Argonne v6' two-body potential to calculate the equation of state in nuclear matter, and has been applied to light nuclei with reasonable agreement with experimental results. However, the spin-orbit interactions were not included in the previous simulations, because the isospin-dependent spin-orbit potential is difficult in the quantum Monte Carlo method. This work develops a new method using extra auxiliary fields to break up the interactions between nucleons, so that the spin-orbit interaction with isospin can be included in the Hamiltonian, and ground-state energy and other properties can be obtained.
The auxiliary field diffusion Monte Carlo is an effective and accurate method for calculating the ground state and low-lying exited states in nuclei and nuclear matter. It has successfully employed the Argonne v6' two-body potential to calculate the equation of state in nuclear matter, and has been applied to light nuclei with reasonable agreement with experimental results. However, the spin-orbit interactions were not included in the previous simulations, because the isospin-dependent spin-orbit potential is difficult in the quantum Monte Carlo method. This work develops a new method using extra auxiliary fields to break up the interactions between nucleons, so that the spin-orbit interaction with isospin can be included in the Hamiltonian, and ground-state energy and other properties can be obtained.
ContributorsZhang, Jie (Author) / Schmidt, Kevin E (Thesis advisor) / Alarcon, Ricardo (Committee member) / Lebed, Richard (Committee member) / Shumway, John (Committee member) / Arizona State University (Publisher)
Created2014
Description中国大陆证券市场上的Ａ、Ｂ股市场，是世界独特的分割市场，其中，双重上市公司A、B股（以下简称AB股），同股同权，但B股相对A股价格长期折价，被称为“Ｂ股难题”（B Share Puzzle）, 这是国际资本市场上的一个热点问题，此相关问题研究也一直延续。本文尝试研究中国政府出台的对股市长期发展进行调节的政策与B股折价之间的关系，通过对AB股发展历史的回顾，梳理出二个对AB股长期发展干预和调节的政策，即2001年2月中国政府允许中国大陆居民投资B股（简称政策一）和2005年4月29日开始的中国证券市场股权分置改革（简称政策二），并在此基础上，运用计量统计方法实证分析，研究发现中国政府出台的对股市长期发展进行调节的政策一、政策二与B股折价率有显著相关性，同时政策的干预和调节是分别有针对性进行的，使得B股折价率变化在政策影响下，通过A股价格或者B股价格的显著变化而实现。另外发现，B股平均折价率具有波动聚集特性，有小幅波动和均值回归特点，具有可预测性。
ContributorsLiu, Li (Author) / Li, Hongmin (Thesis advisor) / Zhang, Jie (Thesis advisor) / Chen, Hui (Committee member) / Arizona State University (Publisher)
Created2023
Description中资地产美元债近年来蓬勃发展，潜力巨大，成为市场的新亮点。截至2021年末，地产债是仅次于金融债的第二大中资离岸债品种。但是，目前关于中资地产美元债的实证研究几乎是空白。本文选取2017年初至2021年末发行的所有中资地产美元债为样本，通过多元线性回归的方法，构建中资地产美元债一级市场发行定价模型，深入分析中资地产美元债发行信用利差的影响因素。基于实践，本文创新性地选择房企基本面、美联储货币政策、房地产调控强度的代理变量，用实证方法来考察各界关注且影响市场发展的关键问题，包括中资地产美元债定价影响因素和作用机制等。研究发现，（1）发行人土地储备规模与中资地产美元债发行利差显著负相关；（2）人民币兑美元汇率与中资地产美元债发行利差显著正相关；（3）房地产调控政策强度与中资地产美元债发行利差显著正相关；（4）对于高收益板块发行人，净负债率指标对发行利差影响并不显著，但调控政策影响显著增强。基于此，本文就发行人合理控制融资成本和推动中资美元债市场创新监管提出相关建议。
ContributorsCao, Ziyan (Author) / Shen, Wei (Thesis advisor) / Wu, Fei (Thesis advisor) / Zhang, Jie (Committee member) / Arizona State University (Publisher)
Created2023
Description
Recently, a well-designed and well-trained neural network can yield state-of-the-art results across many domains, including data mining, computer vision, and medical image analysis. But progress has been limited for tasks where labels are difficult or impossible to obtain. This reliance on exhaustive labeling is a critical limitation in the rapid deployment of neural networks. Besides, the current research scales poorly to a large number of unseen concepts and is passively spoon-fed with data and supervision.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
To overcome the above data scarcity and generalization issues, in my dissertation, I first propose two unsupervised conventional machine learning algorithms, hyperbolic stochastic coding, and multi-resemble multi-target low-rank coding, to solve the incomplete data and missing label problem. I further introduce a deep multi-domain adaptation network to leverage the power of deep learning by transferring the rich knowledge from a large-amount labeled source dataset. I also invent a novel time-sequence dynamically hierarchical network that adaptively simplifies the network to cope with the scarce data.
To learn a large number of unseen concepts, lifelong machine learning enjoys many advantages, including abstracting knowledge from prior learning and using the experience to help future learning, regardless of how much data is currently available. Incorporating this capability and making it versatile, I propose deep multi-task weight consolidation to accumulate knowledge continuously and significantly reduce data requirements in a variety of domains. Inspired by the recent breakthroughs in automatically learning suitable neural network architectures (AutoML), I develop a nonexpansive AutoML framework to train an online model without the abundance of labeled data. This work automatically expands the network to increase model capability when necessary, then compresses the model to maintain the model efficiency.
In my current ongoing work, I propose an alternative method of supervised learning that does not require direct labels. This could utilize various supervision from an image/object as a target value for supervising the target tasks without labels, and it turns out to be surprisingly effective. The proposed method only requires few-shot labeled data to train, and can self-supervised learn the information it needs and generalize to datasets not seen during training.
ContributorsZhang, Jie (Author) / Wang, Yalin (Thesis advisor) / Liu, Huan (Committee member) / Stonnington, Cynthia (Committee member) / Liang, Jianming (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2020
Description随着经济和社会的进步，企业不仅要以盈利为目标，也为利益相关者和生态环境负责并承担相应的社会责任。社会公众也日渐对企业社会责任问题加以重视，伴随着社会责任这一理念的深入，监管部门制定并出台了一系列与企业社会责任信息披露有关的政策和法规，用以规范和引导企业社会责任信息的披露工作。本文以有效市场理论、信息不对称理论和利益相关者理论为基础，将2010-2018年香港证券交易所上市公司为作为研究对象，运用实证研究的方法，将企业社会责任融入股票崩盘风险的研究视角。本文结合理论演绎和实证检验的方法，突破已有文献以收益框架为研究视角的限制，从金融资本市场的角度出发研究企业社会责任的崩盘效应，系统的探索了企业社会责任影响股票崩盘风险的效应及其影响因素。研究结果显示，对比未披露企业社会责任的公司而言，披露企业社会责任相关信息的公司，未来股价崩盘风险越小。基于香港股市主要以机构投资者为主，进一步考察了社会责任信息披露和机构投资者对股价未来崩盘风险的交互作用，研究发现在机构持股比例越低的公司中，企业社会责任信息披露对未来崩盘效应的抑制作用越明显。此外，本文以独立董事占董事会人员比例作为企业治理因素，探索了社会责任信息披露和董事会独立性对股价崩盘风险的交互作用，研究发现企业董事独立性越强，社会责任信息披露对股票崩盘风险的抑制作用更为显著。最后，相对于非国有企业而言，国有企业性质削弱了企业社会责任信息披露对未来崩盘效应的抑制作用。
ContributorsHe, Jie (Author) / Zhu, David, H. (Thesis advisor) / Zhang, Jie (Thesis advisor) / Hu, Jie (Committee member) / Arizona State University (Publisher)
Created2021