CHINA´S TERMS OF TRADE IN MANIFACTURES 1993-2000

Recent years have witnessed the rapid growth of China’s imports and exports of manufactures, as well as critical changes in its terms of trade. This study compares trends in China’s price indices for exports and imports between 1993 and 2000. It also examines the terms of trade for China’s manufactures with respect to (i) different partner countries and country groups, including all developed countries, all developing countries, the European Union, the United States, Japan, the four first-tier East Asian NIEs, the ASEAN Four (Indonesia, Malaysia, the Philippines and Thailand) and other developing countries, and (ii) different product groups, including total exports and imports as well as various categories of manufactured products. The study attempts to explore and assess the factors that shaped the trends, and, based on the resulting conclusions, to make recommendations for developing countries seeking to improve their terms of trade for manufactures.

Statistical Inference on Trimmed Means, Lorenz Curves, and Partial Area Under ROC Curves by Empirical Likelihood Method

Traditionally the inference on trimmed means, Lorenz Curves, and partial AUC (pAUC) under ROC curves have been done based on the asymptotic normality of the statistics. Based on the theory of empirical likelihood, in this dissertation we developed novel methods to do statistical inferences on trimmed means, Lorenz curves, and pAUC. A common characteristic among trimmed means, Lorenz curves, and pAUC is that their inferences are not based on the whole set of samples. Qin and Tsao (2002), Qin et al. (2013), and Qin et al. (2011) recently published their re- searches on the inferences of trimmed means, Lorenz curves, and pAUC based on empirical likelihood method, where they treated the cutting points in the samples fixed at the sample quantiles. They concluded that the limiting distributions of the empirical likelihood tests had scaled chi-square distributions under the null hypotheses. In our novel empirical likelihood methods, we treat the cutting points as the nuisance parameter(s). We conduct the inferences on trimmed means, Lorenz Curves, and pAUC in two steps. First, we make inferences on the parameter interested ( trimmed means, Lorenz curves, or pAUC) and the nuisance parameter(s) (the cutting point(s) in the samples) simultaneously. Then we profile out the nuisance parameter(s) from the test statistics. Under the null hypotheses, the limiting distributions of our empirical likelihood methods are chi-square. We innovate a computational algorithm ’ELseesaw’ to accomplish our empirical likelihood method for the inference on pAUC. Eventually, we contribute a R package to implement our empirical likelihood inferences on trimmed means, Lorenz curves, and pAUC. The R package we have developed can be downloaded free-of-charge on the internet at http://www.ms.uky.edu/~mai/EmpLik.html

Legal Study on the Climate Change-Induced Migrants in China

While climate change is a natural phenomenon, it has also caused a series of social problems for human society. One of the most serious repercussions of climate change is the impact on population movements. As the effects of climate change grow exponentially, the number of climate change-induced migrants will also increase. Climate change-induced migrants are individuals who spontaneously or forcibly migrate temporarily or permanently from their hometowns to other regions under the influence of climate policies or climate-related projects. Climate change, either suddenly or gradually, negatively affected these migrants’ living conditions, making it impossible to survive where they were located. China’s climate is complex and its ecological environment is fragile, making it very vulnerable to the adverse impacts of climate change. Since 2012, China has suffered from frequent extreme weather conditions that have taken a heavy toll on agriculture and people’s lives. Because China has not paid adequate attention to climate change-induced migrants, migrants must deal with many legal barriers both when they leave their home and when they resettle. The main legal barriers are the obstacles encountered during the shift from urban to rural environments (or vice versa), the acquisition of interests in land, and the religious conflicts between migrants and local residents. China is currently struggling with how to support these migrants and remove the legal obstacles. At the same time, China struggles to better understand how to reduce overall migration caused by climate change. This paper will focus on China’s recent efforts to remove legal obstacles for climate change-induced migrants. This paper consists of an introduction and three chapters. The introduction describes the definition of climate change-induced migrants, China’s climate, what causes the migrations, and the main regions where these migrations occur. The three chapters introduce two cases of climate change-induced migrants, analyze the main legal dilemmas they are faced with, and propose some legal countermeasures to remove these obstacles

Feature Selection Methods for Uplift Modeling

Uplift modeling is a predictive modeling technique that estimates the user-level incremental effect of a treatment using machine learning models. It is often used for targeting promotions and advertisements, as well as for the personalization of product offerings. In these applications, there are often hundreds of features available to build such models. Keeping all the features in a model can be costly and inefficient. Feature selection is an essential step in the modeling process for multiple reasons: improving the estimation accuracy by eliminating irrelevant features, accelerating model training and prediction speed, reducing the monitoring and maintenance workload for feature data pipeline, and providing better model interpretation and diagnostics capability. However, feature selection methods for uplift modeling have been rarely discussed in the literature. Although there are various feature selection methods for standard machine learning models, we will demonstrate that those methods are sub-optimal for solving the feature selection problem for uplift modeling. To address this problem, we introduce a set of feature selection methods designed specifically for uplift modeling, including both filter methods and embedded methods. To evaluate the effectiveness of the proposed feature selection methods, we use different uplift models and measure the accuracy of each model with a different number of selected features. We use both synthetic and real data to conduct these experiments. We also implemented the proposed filter methods in an open source Python package (CausalML)

Current Research Advance on Echinococcosis

Echinococcosis is caused by infection with larva (metacestode) of the tapeworms of the genus Echinococcus. Within genus Echinococcus, two species are known as public health concern worldwide: Echinococcus guanulosus causing cystic echinococcosis (CE) and Echinococcus multilocularis causing alveolar echinococcosis (AE). The co-evaluation due to the interaction between parasites and their hosts has been well known to be able to allow tolerating to maintain parasitism as long as possible. With many research advanced findings, scientists have been much interested in using either those molecules from parasites producing due to invading and surviving or those cytokines from hosts responding due to defenses to carry out immunotherapeutic practice that is not only against parasitic infection but also for cancer or other immunological related disorders. Taken advance of knowledge on Echinococcus genome research outcomes, recent attentions regarding the discoveries of targeting antiparasitic drug and/or vaccine were extensively discussed in this review

Research on the reaction of furil with ammonium acetate

The direct reaction of furil with ammonium acetate in refluxing glacial acetic acid under the absence of appropriate aldehydes was systematically studied. The principal product with furan rings and imidazole ring 2,4,5-tri(furan-2-yl)-1H-imidazole (I) was obtained in moderate yield, and two new byproducts containing furan rings were successfully purified by C18 reversed phase column. All compounds were characterized by elemental analysis, MS, IR, 1H and 13C NMR spectroscopy. The structure of I was further confirmed by the 13C-1H COSY spectroscopy. The putative reaction mechanism via stable 1,2-di(furan-2-yl)ethane-1,2-diimine, furan-2-yl-(2,4,5-tri-furan-2-yl-2H-imidazol-2-yl)-methanone and intermediate 5 traced by GC-MS was proposed

Improving Pseudo Labels for Open-Vocabulary Object Detection

Recent studies show promising performance in open-vocabulary object detection (OVD) using pseudo labels (PLs) from pretrained vision and language models (VLMs). However, PLs generated by VLMs are extremely noisy due to the gap between the pretraining objective of VLMs and OVD, which blocks further advances on PLs. In this paper, we aim to reduce the noise in PLs and propose a method called online Self-training And a Split-and-fusion head for OVD (SAS-Det). First, the self-training finetunes VLMs to generate high quality PLs while prevents forgetting the knowledge learned in the pretraining. Second, a split-and-fusion (SAF) head is designed to remove the noise in localization of PLs, which is usually ignored in existing methods. It also fuses complementary knowledge learned from both precise ground truth and noisy pseudo labels to boost the performance. Extensive experiments demonstrate SAS-Det is both efficient and effective. Our pseudo labeling is 3 times faster than prior methods. SAS-Det outperforms prior state-of-the-art models of the same scale by a clear margin and achieves 37.4 AP$_{50}$ and 27.3 AP$_r$ on novel categories of the COCO and LVIS benchmarks, respectively.Comment: 20 pages, 8 figure