The Effects of Reforming the Chinese Dual-Track Price System

We formulate a microeconomic model of the dual-track price system for Households and use it to analyze 'transitional policy' reforms, which we characterize as a rise in plan-track price and a reduction in the plan-track quantity. Each of these reforms has a negative effect on market price, but a positive effect on the weighted average price (CPI). When households are homogeneous, transitional policy reform reduces welfare (if profits are not fully distributed). Under fairly mild assumptions, if households are heterogeneous and resale of goods can occur, transitional policy reform creates losers (state employees) as well as winners (non-state employees).

Dirichlet process probit misclassification mixture model for misclassified binary data

Mislabelling or misclassification in binary data refers to incorrectly labelled responses and could arise due to problems in the labelling process or imperfect evidence for labelling. The latent misclassification process could take a variety of forms depending on how it relates to the true labels as well as the associated covariates of each response. Modelling under misclas- sification is challenging because of the inherent identifiability issues and ignoring misclassi- fication could lead to inaccurate inferences. Statistical methods addressing misclassification have appeared in the literature in a variety of contexts, sometimes using di↵erent terminology, and often focusing on a particular application. In this thesis, we first cast existing statistical methods under a unified framework and later propose a new flexible Bayesian mixture model for modelling misclassified binary data - the Dirichlet process probit misclassification mix- ture model. The main idea is to assume a Dirichlet process mixture model over the covariate space and misclassification probabilities. This naturally partitions observations into clusters where di↵erent clusters can possess di↵erent misclassification probabilities. The clustering uses both covariates and observed responses and covariates are approximated using a Dirich- let mixture of multivariate Gaussians. The incorporation of cluster-specific misclassification probabilities takes into consideration of the misclassification in the observed responses. An e cient Gibbs-like algorithm is available based on the truncated approximation of Dirichlet process and the stick-breaking construction. This thesis is motivated by the pervasiveness of label noise in a wide variety of applica- tions, coupled with the lack of unified statistical exposition and comparison of all available methods. The structure of the thesis as follows. Chapter 1 introduces the problem of label misclassification and reviews existing methods for modelling misclassification in binary data. Chapter 2 discusses the basic of Bayesian nonparametrics, Dirichlet process, Dirichlet pro- cess mixture models, and posterior inference procedures for Dirichlet process mixture models, which are essential components of the Dirichlet process probit misclassification mixtures that we propose later. Chapter 3 describes our proposed model for modelling mislabelled binary data. Chapter 4 presents experimental studies on our proposed model using a real dataset. Section 5 wraps up the discussion on the topic and include final remarks such as possible model extension

Panel: Teaching To Increase Diversity and Equity in STEM

TIDES (Teaching to Increase Diversity and Equity in STEM) is a three-year initiative to transform colleges and universities by changing what STEM faculty, especially CS instructors, are doing in the classroom to encourage the success of their students, particularly those that have been traditionally underrepresented in computer science. Each of the twenty projects selected proposed new interdisciplinary curricula and adopted culturally sensitive pedagogies, with an eye towards departmental and institutional change. The four panelists will each speak about their TIDES projects, which all involved educating faculty about cultural competency. Three of the panelists infused introductory CS courses with applications from other disciplines, while one of the projects taught computational skills in natural science courses

Panel: Teaching To Increase Diversity and Equity in STEM

TIDES (Teaching to Increase Diversity and Equity in STEM) is a three-year initiative to transform colleges and universities by changing what STEM faculty, especially CS instructors, are doing in the classroom to encourage the success of their students, particularly those that have been traditionally underrepresented in computer science. Each of the twenty projects selected proposed new interdisciplinary curricula and adopted culturally sensitive pedagogies, with an eye towards departmental and institutional change. The four panelists will each speak about their TIDES projects, which all involved educating faculty about cultural competency. Three of the panelists infused introductory CS courses with applications from other disciplines, while one of the projects taught computational skills in natural science courses

Brain MRI-based 3D Convolutional Neural Networks for Classification of Schizophrenia and Controls

Convolutional Neural Network (CNN) has been successfully applied on classification of both natural images and medical images but not yet been applied to differentiating patients with schizophrenia from healthy controls. Given the subtle, mixed, and sparsely distributed brain atrophy patterns of schizophrenia, the capability of automatic feature learning makes CNN a powerful tool for classifying schizophrenia from controls as it removes the subjectivity in selecting relevant spatial features. To examine the feasibility of applying CNN to classification of schizophrenia and controls based on structural Magnetic Resonance Imaging (MRI), we built 3D CNN models with different architectures and compared their performance with a handcrafted feature-based machine learning approach. Support vector machine (SVM) was used as classifier and Voxel-based Morphometry (VBM) was used as feature for handcrafted feature-based machine learning. 3D CNN models with sequential architecture, inception module and residual module were trained from scratch. CNN models achieved higher cross-validation accuracy than handcrafted feature-based machine learning. Moreover, testing on an independent dataset, 3D CNN models greatly outperformed handcrafted feature-based machine learning. This study underscored the potential of CNN for identifying patients with schizophrenia using 3D brain MR images and paved the way for imaging-based individual-level diagnosis and prognosis in psychiatric disorders.Comment: 4 PAGE

Decomposing 3D Neuroimaging into 2+1D Processing for Schizophrenia Recognition

Deep learning has been successfully applied to recognizing both natural images and medical images. However, there remains a gap in recognizing 3D neuroimaging data, especially for psychiatric diseases such as schizophrenia and depression that have no visible alteration in specific slices. In this study, we propose to process the 3D data by a 2+1D framework so that we can exploit the powerful deep 2D Convolutional Neural Network (CNN) networks pre-trained on the huge ImageNet dataset for 3D neuroimaging recognition. Specifically, 3D volumes of Magnetic Resonance Imaging (MRI) metrics (grey matter, white matter, and cerebrospinal fluid) are decomposed to 2D slices according to neighboring voxel positions and inputted to 2D CNN models pre-trained on the ImageNet to extract feature maps from three views (axial, coronal, and sagittal). Global pooling is applied to remove redundant information as the activation patterns are sparsely distributed over feature maps. Channel-wise and slice-wise convolutions are proposed to aggregate the contextual information in the third view dimension unprocessed by the 2D CNN model. Multi-metric and multi-view information are fused for final prediction. Our approach outperforms handcrafted feature-based machine learning, deep feature approach with a support vector machine (SVM) classifier and 3D CNN models trained from scratch with better cross-validation results on publicly available Northwestern University Schizophrenia Dataset and the results are replicated on another independent dataset

Analysis of dynamic stability for wind turbine blade under fluid-structure interaction

Aiming at improving vibration performance of 1.5 MW wind turbine blades, the theoretical model and the calculation process of vibration problems under geometric nonlinearity and unidirectional fluid-structure interaction (UFSI) were presented. The dynamic stability analysis on a 1.5 MW wind turbine blade was carried out. Both the maximum brandish displacement and the maximum Mises stress increase nonlinearly with the increase of wind speed. The influences of turbulent effect, wind shear effect and their joint effect on displacement and stress increase sequentially. Furthermore, the stability critical curves are calculated and analyzed. As a result, the stability region is established where the wind turbine blade can run safely