New Perspectives on Chinese Manufacturing Industries Using Microdata

This dissertation consists of three essays that study the industrial organization of China's manufacturing sector from an empirical perspective. It uses structural estimation to look into the performance of China's manufacturing sector with a particular emphasis on the steel industry - a key sector in China that produces half of the world's steel. This dissertation also examines the financial constraints that manufacturing firms face. Chapter 1 documents the development of the steel industry in the past two decades. Chapter 2 studies productivity differences in vertically-integrated Chinese steel facilities, using a unique dataset that provides equipment-level information on inputs and output in physical units for each of the three main stages in the steel value chain, i.e., sintering, iron-making and steel making. We find that private integrated facilities are more productive than provincial state-owned facilities, followed by central state-owned facilities. This ranking lines up with our productivity estimates in the two downstream production stages, but central state-owned facilities outperform in sintering, most likely because of their superior access to high-quality raw materials. The productivity differential favoring private facilities declines with the size of integrated facilities, turning negative for facilities larger than the median. We attribute this pattern to differences in the internal configuration of integrated facilities, which reflect the greater constraints confronting expanding private facilities. Increasing returns to scale within each stage of production partially offset these costs, and rationalize the choice of larger facilities. Chapter 3 draws on the Chinese Industrial Survey Data from 1998 to 2007 to examine financing constraints in the manufacturing sector. Building on the Euler Equation approach and applying the dynamic GMM estimation, we find that on average private firms face more obstacles in accessing credit than state-owned enterprises (SOEs). Contrary to the widely accepted view that China's private sector is largely excluded from formal credit allocation, we find that large firms, both state-owned and private, are not credit constrained. Medium and small SOEs are financially constrained, although to an extent less than their private counterparts of similar size. Moreover, the capabilities of firms in accessing external finance differ by economic region and across industries

Exploring the Learning Difficulty of Data Theory and Measure

As learning difficulty is crucial for machine learning (e.g., difficulty-based weighting learning strategies), previous literature has proposed a number of learning difficulty measures. However, no comprehensive investigation for learning difficulty is available to date, resulting in that nearly all existing measures are heuristically defined without a rigorous theoretical foundation. In addition, there is no formal definition of easy and hard samples even though they are crucial in many studies. This study attempts to conduct a pilot theoretical study for learning difficulty of samples. First, a theoretical definition of learning difficulty is proposed on the basis of the bias-variance trade-off theory on generalization error. Theoretical definitions of easy and hard samples are established on the basis of the proposed definition. A practical measure of learning difficulty is given as well inspired by the formal definition. Second, the properties for learning difficulty-based weighting strategies are explored. Subsequently, several classical weighting methods in machine learning can be well explained on account of explored properties. Third, the proposed measure is evaluated to verify its reasonability and superiority in terms of several main difficulty factors. The comparison in these experiments indicates that the proposed measure significantly outperforms the other measures throughout the experiments.Comment: Ou Wu is the corresponding author of this wor

Under-Identification of Structural Models Based on Timing and Information Set Assumptions

We revisit identification based on timing and information set assumptions in structural models, which have been used in the context of production functions, demand equations, and hedonic pricing models (e.g. Olley and Pakes (1996), Blundell and Bond (2000)). First, we demonstrate a general under-identification problem using these assumptions in a simple version of the Blundell-Bond dynamic panel model. In particular, the basic moment conditions can yield multiple discrete solutions: one at the persistence parameter in the main equation and another at the persistence parameter governing the regressor. We then show that the problem can persist in a broader set of models but disappears in models under stronger timing assumptions. We then propose possible solutions in the simple setting by enforcing an assumed sign restriction and conclude by using lessons from our basic identification approach to propose more general practical advice for empirical researchers

OpenPneu: Compact platform for pneumatic actuation with multi-channels

This paper presents a compact system, OpenPneu, to support the pneumatic actuation for multi-chambers on soft robots. Micro-pumps are employed in the system to generate airflow and therefore no extra input as compressed air is needed. Our system conducts modular design to provide good scalability, which has been demonstrated on a prototype with ten air channels. Each air channel of OpenPneu is equipped with both the inflation and the deflation functions to provide a full range pressure supply from positive to negative with a maximal flow rate at 1.7 L/min. High precision closed-loop control of pressures has been built into our system to achieve stable and efficient dynamic performance in actuation. An open-source control interface and API in Python are provided. We also demonstrate the functionality of OpenPneu on three soft robotic systems with up to 10 chambers

Cicatricial Alopecia

Cicatricial alopecia represents a group of disorders sharing a final pathway of destruction followed by replacement with fibrous tissue of the hair follicle unit. Cicatricial alopecia is classified into two categories, namely primary cicatricial alopecia, in which the hair follicle is the sole target of a progressive inflammatory process in a group of diverse skin or systemic diseases, and secondary cicatricial alopecia, referring to the hair follicle destruction as a result of a nonspecific disruption of the dermis. Permanent hair loss may also occur in the late phases of some nonscarring alopecias that are called “biphasic alopecias.” Based on the pathological characteristics, the lesions of primary cicatricial alopecia are divided into lymphocyte-predominant subgroup, neutrophil-predominant subgroup, or mixed subgroup. In principle, the primary goal of the treatment aims to attenuate the progression of the inflammatory and the scarring processes at the earliest phase of the disease. In clinical practice, the lymphocyte-predominant lesions are treated with immunosuppressive agents, whereas the neutrophil-predominant lesions are treated with antimicrobials or dapsone. As the efficacy of medication treatment against the cicatricial alopecia varies significantly, autologous hair transplantation is recommended to patients who have a relatively stable primary or a secondary cicatricial alopecia

Multiattribute Decision Making Based on Entropy under Interval-Valued Intuitionistic Fuzzy Environment

Multiattribute decision making (MADM) is one of the central problems in artificial intelligence, specifically in management fields. In most cases, this problem arises from uncertainty both in the data derived from the decision maker and the actions performed in the environment. Fuzzy set and high-order fuzzy sets were proven to be effective approaches in solving decision-making problems with uncertainty. Therefore, in this paper, we investigate the MADM problem with completely unknown attribute weights in the framework of interval-valued intuitionistic fuzzy (IVIF) set (IVIFS). We first propose a new definition of IVIF entropy and some calculation methods for IVIF entropy. Furthermore, we propose an entropy-based decision-making method to solve IVIF MADM problems with completely unknown attribute weights. Particular emphasis is put on assessing the attribute weights based on IVIF entropy. Instead of the traditional methods, which use divergence among attributes or the probabilistic discrimination of attributes to obtain attribute weights, we utilize the IVIF entropy to assess the attribute weights based on the credibility of the decisionmaking matrix for solving the problem. Finally, a supplier selection example is given to demonstrate the feasibility and validity of the proposed MADM method

Routine Multiplex Mutational Profiling of Melanomas Enables Enrollment in Genotype-Driven Therapeutic Trials

Purpose: Knowledge of tumor mutation status is becoming increasingly important for the treatment of cancer, as mutation-specific inhibitors are being developed for clinical use that target only sub-populations of patients with particular tumor genotypes. Melanoma provides a recent example of this paradigm. We report here development, validation, and implementation of an assay designed to simultaneously detect 43 common somatic point mutations in 6 genes (BRAF, NRAS, KIT, GNAQ, GNA11, and CTNNB1) potentially relevant to existing and emerging targeted therapies specifically in melanoma. Methods: The test utilizes the SNaPshot method (multiplex PCR, multiplex primer extension, and capillary electrophoresis) and can be performed rapidly with high sensitivity (requiring 5–10% mutant allele frequency) and minimal amounts of DNA (10–20 nanograms). The assay was validated using cell lines, fresh-frozen tissue, and formalin-fixed paraffin embedded tissue. Clinical characteristics and the impact on clinical trial enrollment were then assessed for the first 150 melanoma patients whose tumors were genotyped in the Vanderbilt molecular diagnostics lab. Results: Directing this test to a single disease, 90 of 150 (60%) melanomas from sites throughout the body harbored a mutation tested, including 57, 23, 6, 3, and 2 mutations in BRAF, NRAS, GNAQ, KIT, and CTNNB1, respectively. Among BRAF V600 mutations, 79%, 12%, 5%, and 4% were V600E, V600K, V600R, and V600M, respectively. 23 of 54 (43%) patients with mutation harboring metastatic disease were subsequently enrolled in genotype-driven trials. Conclusion: We present development of a simple mutational profiling screen for clinically relevant mutations in melanoma. Adoption of this genetically-informed approach to the treatment of melanoma has already had an impact on clinical trial enrollment and prioritization of therapy for patients with the disease

The emerging regulatory state in China: a study of Shunde's experience

published_or_final_versionPolitics and Social AdministrationMasterMaster of Philosoph