Understanding and Implementation of Case Teaching Method

As a kind of teaching method, case teaching method has its own practical value and scope of application. In practice, it mainly consists of some links such as “knowledge preparation”, “case arrangement” and “provoking guidance”. Knowledge preparation could be implemented through a variety of forms and the teaching process that is prepared for analysis has its unique feature. Cases can be selected from the aspects of “event level”, “fact capacity” and “understanding degree”. And they would endow natural events with the educational significance. Setting questions teachers could stimulate students’ interest of analysis. And during the process of analysis, it is supposed to lead students use topic concept to analyze these questions around the cases

Number of Repetitions in Re-randomization Tests

In covariate-adaptive or response-adaptive randomization, the treatment assignment and outcome can be correlated. Under this situation, re-randomization tests are a straightforward and attractive method to provide valid statistical inference. In this paper, we investigate the number of repetitions in the re-randomization tests. This is motivated by the group sequential design in clinical trials, where the nominal significance bound can be very small at an interim analysis. Accordingly, re-randomization tests lead to a very large number of required repetitions, which may be computationally intractable. To reduce the number of repetitions, we propose an adaptive procedure and compare it with multiple approaches under pre-defined criteria. Monte Carlo simulations are conducted to show the performance of different approaches in a limited sample size. We also suggest strategies to reduce total computation time and provide practical guidance in preparing, executing and reporting before and after data are unblinded at an interim analysis, so one can complete the computation within a reasonable time frame

Circular RNAs:Biogenesis, Mechanism, and Function in Human Cancers

CircRNAs are a class of noncoding RNA species with a circular configuration that is formed by either typical spliceosome-mediated or lariat-type splicing. The expression of circRNAs is usually abnormal in many cancers. Several circRNAs have been demonstrated to play important roles in carcinogenesis. In this review, we will first provide an introduction of circRNAs biogenesis, especially the regulation of circRNA by RNA-binding proteins, then we will focus on the recent findings of circRNA molecular mechanisms and functions in cancer development. Finally, some open questions are also discussed

New progress in hot-spots detection, partial-differential-equation-based model identification and statistical computation

This thesis discusses the new progress in (1) hot-spots detection in spatial-temporal data, (2) partial-differential-equation-based (PDE-based) model identification, and (3) optimization in the Least Absolute Shrinkage and Selection Operator (Lasso) type problem. In this thesis, we have four main works. Chapter 1 and Chapter 2 fall in the first area, i.e., hot-spots detection in spatio-temporal data. Chapter 3 belongs to the second area, i.e., PDE-based model identification. Chapter 4 is for the third area, i.e., optimization in the Lasso-type problem. The detailed description of these four chapters is summarized as follows. In Chapter 1, we aim at detecting hot-spots in multivariate spatio-temporal dataset that are non-stationary over time. To realize this objective, we propose a statistical method to under the framework of tensor decomposition and our method has three steps. First, we fit the observed data into a Smooth Sparse Decomposition Tensor (SSD-Tensor) model that serves as a dimension reduction and de-noising technique: it is an additive model that decomposes the original data into three components: smooth but non-stationary global mean, sparse local anomalies, and random noises. Next, we estimate the model parameters by the penalized framework that includes a combination of Lasso and fused Lasso penalty to address the spatial sparsity and temporal consistency, respectively. Finally, we apply a Cumulative Sum (CUSUM) Control Chart to monitor the model residuals, which allows us to detect when and where the hot-spot event occurs. To demonstrate the usefulness of our proposed SSD-Tensor method, we compare it with several other methods in extensive numerical simulation studies and a real crime rate dataset. The material of this chapter is published in Journal of Applied Statistics in January, 2021 under the title ``Rapid Detection of Hot-spots via Tensor Decomposition with Applications to Crime Rate Data'' with co-authors Hao Yan, Sarah E. Holte and Yajun Mei. In Chapter 2, we improve the methodology in Chapter 1 both statistically and computationally. The statistical improvement is the new methodologies to detect hot-spots with temporal circularity, instead of temporal continuity as in Chapter 1. This helps us handle many bio-surveillance and healthcare applications, where data sources are measured from many spatial locations repeatedly over time, say, daily/weekly/monthly. The computational improvement is the development of a more efficient algorithm. The main tool we use to accelerate the calculation is the tensor decomposition, which is similar to the matrix context where it might be difficult to compute the inverse of a large matrix in general, but it will be straightforward to calculate the inverse of a large block diagonal matrix through the inverse of sub-matrices in the diagonal. The usefulness of the improved methodology is validated through numerical simulations and a real-world dataset in the weekly number of gonorrhea cases from 2006 to 2018 for 50 states in U.S.. The material of this chapter is accepted as a book chapter in Frontiers in Statistical Quality Control 13 in February 2021 under the title ``Rapid Detection of Hot-spot by Tensor Decomposition with Application to Weekly Gonorrhea Data'' with co-authors Hao Yan, Sarah E. Holte, Roxanne P. Kerani and Yajun Mei. In Chapter 3, we propose a two-stage method called Spline Assisted Partial Differential Equation involved Model Identification (SAPDEMI) method to efficiently identify the underlying PDE models from the noisy data. In the first stage -- functional estimation stage -- we employ the cubic spline to estimate the unobservable derivatives, which serve as candidates of the underlying PDE models. The contribution of this stage is that, it is computational efficient because it only requires the computational complexity of the linear polynomial of the sample size, which achieves the lowest possible order of complexity. In the second stage -- model identification stage -- we apply Lasso to identify the underlying PDE model. The contribution of this stage is that, we focus on the model selections, while the existing literature mostly focuses on parameter estimations. Moreover, we develop statistical properties of our method for correct identification, where the main tool we use is the primal-dual witness (PDW) method. Finally, we validate our theory through various numerical examples. In Chapter 4, we focus on developing an algorithm to solve the optimization with a L1 regularization term, namely the Lasso-type problem. The algorithm developed in this chapter can greatly reduce the computational complexity in Chapter 1, Chapter 2 and Chapter 3, where we try to realize sparse identification. The challenge to develop an efficient algorithm for the Lasso-type problem is that the objective function of the Lasso-type problem is not strictly convex when the number of samples is less than the number of features. This special property of the Lasso-problem leads the existing Lasso-type estimator, in general, cannot achieve the optimal rate due to the undesirable behavior of the absolute function at the origin. To overcome the above challenge, we develop a homotopic method, where we use a sequence of surrogate functions to approximate the L1 penalty that is used in the Lasso-type of estimators. The surrogate functions will converge to the L1 penalty in the Lasso estimator. At the same time, each surrogate function is strictly convex, which enables a provable faster numerical rate of convergence. In this chapter, we demonstrate that by meticulously defining the surrogate functions, one can prove a faster numerical convergence rate than any existing methods in computing for the Lasso-type of estimators. Namely, the state-of-the-art algorithms can only guarantee O(1/\epsilon) or O(1/\sqrt{\epsilon}) convergence rates, while we can prove an O([\log(1/\epsilon)]^2) for the newly proposed algorithm. Our numerical simulations show that the new algorithm also performs better empirically.Ph.D

FRP STRENGTHENING OF NON-FLEXURAL REINFORCED CONCRETE MEMBERS BASED ON STRUT-AND-TIE MODELLING

Ph.DDOCTOR OF PHILOSOPH

Adaptation Speed Analysis for Fairness-aware Causal Models

For example, in machine translation tasks, to achieve bidirectional translation between two languages, the source corpus is often used as the target corpus, which involves the training of two models with opposite directions. The question of which one can adapt most quickly to a domain shift is of significant importance in many fields. Specifically, consider an original distribution p that changes due to an unknown intervention, resulting in a modified distribution p*. In aligning p with p*, several factors can affect the adaptation rate, including the causal dependencies between variables in p. In real-life scenarios, however, we have to consider the fairness of the training process, and it is particularly crucial to involve a sensitive variable (bias) present between a cause and an effect variable. To explore this scenario, we examine a simple structural causal model (SCM) with a cause-bias-effect structure, where variable A acts as a sensitive variable between cause (X) and effect (Y). The two models, respectively, exhibit consistent and contrary cause-effect directions in the cause-bias-effect SCM. After conducting unknown interventions on variables within the SCM, we can simulate some kinds of domain shifts for analysis. We then compare the adaptation speeds of two models across four shift scenarios. Additionally, we prove the connection between the adaptation speeds of the two models across all interventions.Comment: CIKM 202

How many keywords do authors assign to research articles – a multi-disciplinary analysis?

Author keywords are one important data source for co-word analysis. The distri-bution of author keywords in papers has not been investigated at the discipline level. We analyzed six research fields from soft science to hard science to reveal the underlying quantitative patterns of author keywords. Normal distribution, Poisson distribution, and Weibull distribution were fitted by applying Maximum Likelihood Estimation. Chi-Square tests and Kolmogorov-Smirnov tests were used to evaluate the goodness of fit. The results show that a large portion of pa-pers have no keyword or only one keyword in all these fields. The author key-word distributions of the six fields are represented. It’s shown that Weibull dis-tribution is the best fitted. This study provides practical implications for keyword selection in co-word analysis

Determinants of cash holdings of Chinese listed companies

"Cash is the king" has always been regarded as the central idea of corporate money management, which indicates that cash holding has an important effect on the companies. Many companies will hold different amount of cash. However, holding too much cash will also have negative impact on the companies. For example, opportunity costs and management costs will increase. The aim of this paper is to analyze the determinants of cash holding . The results have shown that the financial and governance characteristics have a significant impact on cash holdings. The results have shown that the financial characteristics have a significant impact on cash holdings. Debt structure, cash dividend and cash flow are significantly positively correlated with cash holdings; company size, leverage, bank debt and cash substitute are negatively correlated with cash holdings. In terms of the impact of governance factors on cash holdings, it is believed that the share proportion of senior managers ,legal person(lshare), and board size (bsize) are significantly positively correlated with cash holdings. Independent directors are positively related to cash holdings. Outstanding shares is significantly negatively correlated with cash holdings. The share proportion of the largest shareholder (fshare) and state are negatively correlated with cash holdings