Pollution Taxes and Location Decision under Free Entry Oligopoly

This paper examines the impact of a pollution tax as a pollution control device on the output and location decisions of undifferentiated oligopolistic firms with free entry. It shows that the optimum output and location of an oligopolistic firm is independent of a change in the pollution tax if the demand function is linear. Furthermore, an increase in the pollution tax will increase (decrease) output and move the plant location toward (away from) the CBD if the demand function is concave (convex). It also shows that a higher pollution tax will increase the pollution damage if the demand function is linear and the location effect dominates the demand effect. These results are significantly different from the conventional results based on the monopolistic location model. It indicates that the demand condition plays an important role in the determination of the impact of a pollution tax on the location decision of an oligopolistic firm and the pollution damage to the CBD residents.

A Quadratically Regularized Functional Canonical Correlation Analysis for Identifying the Global Structure of Pleiotropy with NGS Data

Investigating the pleiotropic effects of genetic variants can increase statistical power, provide important information to achieve deep understanding of the complex genetic structures of disease, and offer powerful tools for designing effective treatments with fewer side effects. However, the current multiple phenotype association analysis paradigm lacks breadth (number of phenotypes and genetic variants jointly analyzed at the same time) and depth (hierarchical structure of phenotype and genotypes). A key issue for high dimensional pleiotropic analysis is to effectively extract informative internal representation and features from high dimensional genotype and phenotype data. To explore multiple levels of representations of genetic variants, learn their internal patterns involved in the disease development, and overcome critical barriers in advancing the development of novel statistical methods and computational algorithms for genetic pleiotropic analysis, we proposed a new framework referred to as a quadratically regularized functional CCA (QRFCCA) for association analysis which combines three approaches: (1) quadratically regularized matrix factorization, (2) functional data analysis and (3) canonical correlation analysis (CCA). Large-scale simulations show that the QRFCCA has a much higher power than that of the nine competing statistics while retaining the appropriate type 1 errors. To further evaluate performance, the QRFCCA and nine other statistics are applied to the whole genome sequencing dataset from the TwinsUK study. We identify a total of 79 genes with rare variants and 67 genes with common variants significantly associated with the 46 traits using QRFCCA. The results show that the QRFCCA substantially outperforms the nine other statistics.Comment: 64 pages including 12 figure

Towards Certain Fixes with Editing Rules and Master Data

A variety of integrity constraints have been studied for data cleaning. While these constraints can detect the presence of errors, they fall short of guiding us to correct the errors. Indeed, data repairing based on these constraints may not find certain fixes that are absolutely correct, and worse, may introduce new errors when repairing the data. We propose a method for finding certain fixes, based on master data, a notion of certain regions , and a class of editing rules . A certain region is a set of attributes that are assured correct by the users. Given a certain region and master data, editing rules tell us what attributes to fix and how to update them. We show how the method can be used in data monitoring and enrichment. We develop techniques for reasoning about editing rules, to decide whether they lead to a unique fix and whether they are able to fix all the attributes in a tuple, relative to master data and a certain region. We also provide an algorithm to identify minimal certain regions, such that a certain fix is warranted by editing rules and master data as long as one of the regions is correct. We experimentally verify the effectiveness and scalability of the algorithm. </jats:p

Epitaxial Cu3Ge Thin Film: Fabrication, Structure, and Property

In this paper, the fabrication and electrical property characterization of epitaxial Cu3Ge thin film are performed. By adjusting deposition parameters, the crystallinity of the as‐grown Cu3Ge thin films is improved, with the formation of twins within it. The average work function of epitaxial Cu3Ge thin film is measured to be ∼4.47 + 0.02 eV, rendering it a desirable mid‐gap gate metal for applications in complementary metal‐oxide semiconductor (CMOS) devices. The present study therefore shows an epitaxial Cu3Ge thin film that is promising for applications

In Situ Transmission Electron Microscopy Studies in Gas/Liquid Environment

Conventional transmission electron microscopy (TEM) typically operates under high vacuum conditions. However, in situ investigation under real-world conditions other than vacuum, such as gaseous or liquidus environment, is essential to obtain practical information for materials including catalysts, fuel cells, biological molecules, lithium ion batteries, etc. Therefore, the ability to study gas/liquid–solid interactions with atomic resolution under ambient conditions in TEM promises new insights into the growth, properties, and functionality of nanomaterials. Different platforms have been developed for in situ TEM observations in ambient environment and can be classified into two categories: open-cell configuration and sealed gas/liquid cell configuration. The sealed cell technique has various advantages over the open-cell approach. This chapter serves as a review of windowed gas/liquid cells for in situ TEM observations

Entity Synonym Discovery via Multipiece Bilateral Context Matching

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SYNONYMNET to determine whether or not two given entities are synonym with each other. Instead of using entities features, SYNONYMNET makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve and 3.19% in terms of Mean Average Precision compared to the best baseline method.Comment: In IJCAI 2020 as a long paper. Code and data are available at https://github.com/czhang99/SynonymNe