STUDIES ON HOUSING MARKET DYNAMICS AND COINTEGRATION ANALYSIS WITH LATENT FACTORS

This dissertation consists of two essays on housing market dynamics and cointegration analysis with latent factors. The theme of this dissertation is housing market dynamics, with the first essay an application of advanced panel time series models to the studies of housing market dynamics, and the second essay a theoretic derivation of an econometric tool on cointegration analysis with latent factors that can be applied to the housing market analysis. This first essay develops a parsimonious dynamic model to study the impact of a common demand shock in the housing market on housing prices and construction activities across a set of locations with heterogeneous supply side conditions. Embedded within the model is a lead-lag structure that allows one to identify from where shocks propagate while allowing for and yielding estimates of cross-sectional differences in housing supply elasticities. The findings indicate that local supply conditions may matter more than distance when modeling spatiotemporal dynamics in the housing market. The second essay considers estimating and testing cointegration between an integrated series of interest and a vector of possibly cointegrated nonstationary latent factors. The fully modified least squares (FM-OLS) estimation is adopted to the estimation of the cointegration relation of interest. The asymptotic properties of the FM-OLS estimators are derived, and the residual-based cointegration tests are shown to work as usual even with latent factors. Based on the estimated cointegration relation, it is demonstrated that an error correction term added to the traditional diffusion index forecast model improves forecasting accuracy

Automatic Context Pattern Generation for Entity Set Expansion

Entity Set Expansion (ESE) is a valuable task that aims to find entities of the target semantic class described by given seed entities. Various NLP and IR downstream applications have benefited from ESE due to its ability to discover knowledge. Although existing bootstrapping methods have achieved great progress, most of them still rely on manually pre-defined context patterns. A non-negligible shortcoming of the pre-defined context patterns is that they cannot be flexibly generalized to all kinds of semantic classes, and we call this phenomenon as "semantic sensitivity". To address this problem, we devise a context pattern generation module that utilizes autoregressive language models (e.g., GPT-2) to automatically generate high-quality context patterns for entities. In addition, we propose the GAPA, a novel ESE framework that leverages the aforementioned GenerAted PAtterns to expand target entities. Extensive experiments and detailed analyses on three widely used datasets demonstrate the effectiveness of our method. All the codes of our experiments will be available for reproducibility.Comment: This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Wip1-dependent modulation of macrophage migration and phagocytosis

Macrophage accumulation within the vascular wall is a hallmark of atherosclerosis. Controlling macrophage conversion into foam cells remains a major challenge for treatment of atherosclerotic diseases. Here, we show that Wip1, a member of the PP2C family of Ser/Thr protein phosphatases, modulates macrophage migration and phagocytosis associated with atherosclerotic plaque formation. Wip1 deficiency increases migratory and phagocytic activities of the macrophage under stress conditions. Enhanced migration of Wip1-/- macrophages is mediated by Rac1-GTPase and PI3K/AKT signalling pathways. Elevated phagocytic ability of Wip1-/- macrophages is linked to CD36 plasma membrane recruitment that is regulated by AMPK activity. Our study identifies Wip1 as an intrinsic negative regulator of macrophage chemotaxis. We propose that Wip1-dependent control of macrophage function may provide avenues for preventing or eliminating plaque formation in atherosclerosis

Finishing the euchromatic sequence of the human genome

The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead

ExARN: self-attending RNN for target speaker extraction

Target speaker extraction is to extract the target speaker, specified by enrollment utterance, in an environment with other competing speakers. Therefore, the task needs to solve two problems, speaker identification and separation, at the same time. In this paper, we combine self-attention and Recurrent Neural Networks (RNN). Further, we exploit various ways to combining different auxiliary information with mixed representations. Experimental results show that our proposed model achieves excellent performance on the task of target speaker extraction.Comment: The overall quality of the article is not good enoug