Three essays on regional and development economics

My dissertation comprises three chapters. The first chapter examines the impacts of the U.S. shale boom on local patents. The second chapter assesses how more competitive political competitions in Sub-Saharan African countries and positive birth-year rainfall shocks affect child mortality rates. The third chapter explores the effects of access and adoption of broadband on self-employment and work-from-home.The first chapter examines the impacts of the U.S. shale boom on local patenting at a commuting zone level. I expect that the shale boom will negatively affect patents because shale development may crowd out labor and capital investments in other non-energy industries. My findings show that a one standard deviation increase in non-vertical drilling well density decreases patent intensity by 3.6% of the mean. Areas with higher drilling densities have lower levels of patented innovation compared to their counterfactuals. This study contributes to the existing literature related to the "natural resource curse." I provide new evidence based on local patenting, which is an important indicator for regional innovation and long-term economic growth.In the second chapter, I empirically test three hypotheses that affect child mortality based on the rural sample in Sub-Saharan African countries. In the first hypothesis, I assess the effects of more competitive presidential elections on child mortality. In the second hypothesis, I investigate the impacts of birth year rainfall shocks on child mortality. In the third hypothesis, I argue the effects of political competition can be heterogeneous due to different environment conditions. So I interact the presidential election variable with the rainfall variable to examine the heterogeneous effects when there are good rainfall shocks during a more competitive presidential election period. The results show that both competitive elections and positive rainfall shocks reduce child mortality. Their interaction indicates positive rainfall shocks may be less effective to reduce child mortality during a more competitive election time period.In the third chapter, using the American Community Survey and the Federal Communications Commission data, I examine how broadband affects self-employment and work-from-home for married women. Based on different sources of internet variables, I investigate the impacts of internet from both the adoption and access to broadband. I find that adoption and access to high-speed broadband have significantly positive impacts on self-employment and work-from-home. This study contributes to the existing literature that examines how Information and Communications Technology affects the labor market

Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation and Koos Grade Prediction based on Semi-Supervised Contrastive Learning

Domain adaptation has been widely adopted to transfer styles across multi-vendors and multi-centers, as well as to complement the missing modalities. In this challenge, we proposed an unsupervised domain adaptation framework for cross-modality vestibular schwannoma (VS) and cochlea segmentation and Koos grade prediction. We learn the shared representation from both ceT1 and hrT2 images and recover another modality from the latent representation, and we also utilize proxy tasks of VS segmentation and brain parcellation to restrict the consistency of image structures in domain adaptation. After generating missing modalities, the nnU-Net model is utilized for VS and cochlea segmentation, while a semi-supervised contrastive learning pre-train approach is employed to improve the model performance for Koos grade prediction. On CrossMoDA validation phase Leaderboard, our method received rank 4 in task1 with a mean Dice score of 0.8394 and rank 2 in task2 with Macro-Average Mean Square Error of 0.3941. Our code is available at https://github.com/fiy2W/cmda2022.superpolymerization

Spin Coherence and Spin Relaxation in Hybrid Organic-Inorganic Lead and Mixed Lead-Tin Perovskites

Metal halide perovskites make up a promising class of materials for semiconductor spintronics. Here we report a systematic investigation of coherent spin precession, spin dephasing and spin relaxation of electrons and holes in two hybrid organic-inorganic perovskites MA0.3FA0.7PbI3 and MA0.3FA0.7Pb0.5Sn0.5I3 using time-resolved Faraday rotation spectroscopy. With applied in-plane magnetic fields, we observe robust Larmor spin precession of electrons and holes that persists for hundreds of picoseconds. The spin dephasing and relaxation processes are likely to be sensitive to the defect levels. Temperature-dependent measurements give further insights into the spin relaxation channels. The extracted electron Land\'e g-factors (3.75 and 4.36) are the biggest among the reported values in inorganic or hybrid perovskites. Both the electron and hole g-factors shift dramatically with temperature, which we propose to originate from thermal lattice vibration effects on the band structure. These results lay the foundation for further design and use of lead- and tin-based perovskites for spintronic applications

DisAsymNet: Disentanglement of Asymmetrical Abnormality on Bilateral Mammograms using Self-adversarial Learning

Asymmetry is a crucial characteristic of bilateral mammograms (Bi-MG) when abnormalities are developing. It is widely utilized by radiologists for diagnosis. The question of 'what the symmetrical Bi-MG would look like when the asymmetrical abnormalities have been removed ?' has not yet received strong attention in the development of algorithms on mammograms. Addressing this question could provide valuable insights into mammographic anatomy and aid in diagnostic interpretation. Hence, we propose a novel framework, DisAsymNet, which utilizes asymmetrical abnormality transformer guided self-adversarial learning for disentangling abnormalities and symmetric Bi-MG. At the same time, our proposed method is partially guided by randomly synthesized abnormalities. We conduct experiments on three public and one in-house dataset, and demonstrate that our method outperforms existing methods in abnormality classification, segmentation, and localization tasks. Additionally, reconstructed normal mammograms can provide insights toward better interpretable visual cues for clinical diagnosis. The code will be accessible to the public

GSMorph: Gradient Surgery for cine-MRI Cardiac Deformable Registration

Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the deformation field. Therefore, they inevitably require tuning the hyperparameter for optimal registration performance. Tuning the hyperparameters is highly computationally expensive and introduces undesired dependencies on domain knowledge. In this study, we construct a registration model based on the gradient surgery mechanism, named GSMorph, to achieve a hyperparameter-free balance on multiple losses. In GSMorph, we reformulate the optimization procedure by projecting the gradient of similarity loss orthogonally to the plane associated with the smoothness constraint, rather than additionally introducing a hyperparameter to balance these two competing terms. Furthermore, our method is model-agnostic and can be merged into any deep registration network without introducing extra parameters or slowing down inference. In this study, We compared our method with state-of-the-art (SOTA) deformable registration approaches over two publicly available cardiac MRI datasets. GSMorph proves superior to five SOTA learning-based registration models and two conventional registration techniques, SyN and Demons, on both registration accuracy and smoothness.Comment: Accepted at MICCAI 202

Revealing unusual bandgap shifts with temperature and bandgap renormalization effect in phase-stabilized metal halide perovskites

Hybrid organic-inorganic metal halide perovskites are emerging materials in photovoltaics, whose bandgap is one of the most crucial parameters governing their light harvesting performance. Here we present temperature and photocarrier density dependence of the bandgap in two phase-stabilized perovskite thin films (MA0.3FA0.7PbI3 and MA0.3FA0.7Pb0.5Sn0.5I3) using photoluminescence and absorption spectroscopy. Contrasting bandgap shifts with temperature are observed between the two perovskites. By utilizing X-ray diffraction and in situ high pressure photoluminescence spectroscopy, we show that the thermal expansion plays only a minor role on the large bandgap blueshift due to the enhanced structural stability in our samples. Our first-principles calculations further demonstrate the significant impact of thermally induced lattice distortions on the bandgap widening and reveal that the anomalous trends are caused by the competition between the static and dynamic distortions. Additionally, both the bandgap renormalization and band filling effects are directly observed for the first time in fluence-dependent photoluminescence measurements and are employed to estimate the exciton effective mass. Our results provide new insights into the basic understanding of thermal and charge-accumulation effects on the band structure of hybrid perovskites

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https://learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods

Industry Fluctuations and College Major Choices: Evidence from an Energy Boom and Bust

This paper examines how college students in the United States altered their college majors during the energy boom and bust of the 1970s and 1980s. We focus on petroleum engineering and geology, two majors closely related to the energy industry. We find strong evidence that the energy boom increased the prevalence of these two energy-related majors and the energy bust lowered the prevalence of these majors. Effects are particularly strong for young people born in energy intensive states. Thus, college major decisions responded to industry fluctuations with important location-specific effects consistent with frictions to migration and information flows.JEL Classification: I20, J20, J60, R10This is a manuscript of an article published as Han, Luyi, and John V. Winters. "Industry fluctuations and college major choices: Evidence from an energy boom and bust." Economics of Education Review 77 (2020): 101996. doi:10.1016/j.econedurev.2020.101996. Posted with permission.This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License