Drawing Exact Samples: Rejection Sampling, Density Fusion and Constrained Disaggregation

Sampling is an important topic in the area of computational statistics. Being able to draw samples from a designated distribution allows one to numerically compute various statistics without the need to solve for solutions analytically. A popular branch of the sampling method generates samples by evolving a stationary Markov chain that admits the target distribution as its stationary distribution. The problem, however, is that one does not have a universal criterion to assess whether the chain is stationary. On the other hand, exact simulation methods, being the focus of this thesis, always produce samples that precisely follow the target distribution. We first begin with the path-space rejection sampling for the exact simulation of diffusion bridges and show how this rejection scheme can be further set up into an exact simulation method for sampling product densities. We provide guidance on how to tune the algorithm parameters in order to attain a near-optimal performance and introduce the construction of an importance sampler/particle filter based on the same theoretical result for better efficiency. Finally, we show a variant of the sampler that deals with linear constraints which render most of the target distributions intractable. Two application studies are conducted in the end to demonstrate the effectiveness of the algorithm

Feeling Right at Home: Hometown CEOs and Firm Innovation

Extending the theories of social and place identity, we predict that CEO hometown identity has a positive and significant influence on firm innovation. Our empirical evidence, from publicly traded firms in China during 2002–2016, suggests that a firm whose CEO's hometown is in the same province or city as the firm's headquarters tends to invest more in R&D and generate more patent applications. Our results are robust to the firm fixed effects and we use difference-in-differences analysis and instrument variable regressions to mitigate endogeneity concerns. CEOs' hometown identity still has a strong and positive impact on innovation after we control for measures of social capital of CEOs. We identify the mechanisms behind the positive relation between firm innovation and CEO hometown identity: hometown CEOs enjoy more support from the board of directors, they are more willing to take risks, and they are more likely to have long-term visions

Effect of Cooling Process on Microstructure and Properties of Low Carbon Bainite Steel

This article used Mn-Mo-Cr-B low-carbon bainitic steel as the experimental material. The continuous cooling transformation  curve of the steel during continuous cooling was determined using a Gleeble-1500D thermal simulation test machine, and a corresponding phase transformation model for bainitic steel during continuous cooling was established. The influence of different cooling rates and final cooling temperatures on the microstructure and mechanical properties of the steel was investigated. Employing metallography, SEM, and EBSD techniques, the microstructure, crystallographic orientation, and grain boundary angle distribution of the low-carbon bainitic steel were explored, and their relationship with the steel's strength and toughness was studied. The research findings reveal that varying cooling rates and final cooling temperatures impact the phase transformation process and microstructure of the steel, consequently affecting its mechanical properties indirectly. With increasing cooling rate, the diffusion and fineness of martensite increase, and the quantity of lath bainite grows while the laths become finer. Elevated final cooling temperatures lead to larger martensitic-austenitic (MA) islands and reduced lath bainite quantity, causing the laths to become wider. Through analysis of the substructure of bainitic steel, it was determined that the bainite organization in the tested steel comprises primary austenite grains, lath packet, and lath block in succession. Lath packets are composed of lath blocks with different orientations, where lath size predominantly controls strength. Finer lath size corresponds to higher strength, and the influence of substructure on toughness is comparatively minor

Privacy Guarantees in Posterior Sampling under Contamination

In recent years, differential privacy has been adopted by tech-companies and governmental agencies as the standard for measuring privacy in algorithms. We study the level of differential privacy in Bayesian posterior sampling setups. As opposed to the common privatization approach of injecting Laplace/Gaussian noise into the output, Huber's contamination model is considered, where we replace at random the data points with samples from a heavy-tailed distribution. We derived bounds for the differential privacy level $(\epsilon,\delta)$ for our approach while lifting the common restriction on assuming bounded observation and parameter space seen in the existing literature. We further consider the effect of sample size on privacy level and the convergence rate of $(\epsilon,\delta)$ to zero. Asymptotically, the contamination approach is fully private at no cost of information loss. We also provide some examples depicting inference models that our setup is applicable to with a theoretical estimation of convergence rate.Comment: 52 pages, 0 figure

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Gendered STEM Workforce in the United Kingdom:The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Gendered STEM Workforce in the United Kingdom: The Role of Gender Bias in Job Advertising

Evidence submitted to the ‘Diversity in STEM’ Inquiry, Science and Technology Committee, House of Commons, UK Parliamen

Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving

With widening deployments of natural language processing (NLP) in daily life, inherited social biases from NLP models have become more severe and problematic. Previous studies have shown that word embeddings trained on human-generated corpora have strong gender biases that can produce discriminative results in downstream tasks. Previous debiasing methods focus mainly on modeling bias and only implicitly consider semantic information while completely overlooking the complex underlying causal structure among bias and semantic components. To address these issues, we propose a novel methodology that leverages a causal inference framework to effectively remove gender bias. The proposed method allows us to construct and analyze the complex causal mechanisms facilitating gender information flow while retaining oracle semantic information within word embeddings. Our comprehensive experiments show that the proposed method achieves state-of-the-art results in gender-debiasing tasks. In addition, our methods yield better performance in word similarity evaluation and various extrinsic downstream NLP tasks

Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation

Despite progress towards gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications

Balancing Gender Bias in Job Advertisements With Text-Level Bias Mitigation

Despite progress toward gender equality in the labor market over the past few decades, gender segregation in labor force composition and labor market outcomes persists. Evidence has shown that job advertisements may express gender preferences, which may selectively attract potential job candidates to apply for a given post and thus reinforce gendered labor force composition and outcomes. Removing gender-explicit words from job advertisements does not fully solve the problem as certain implicit traits are more closely associated with men, such as ambitiousness, while others are more closely associated with women, such as considerateness. However, it is not always possible to find neutral alternatives for these traits, making it hard to search for candidates with desired characteristics without entailing gender discrimination. Existing algorithms mainly focus on the detection of the presence of gender biases in job advertisements without providing a solution to how the text should be (re)worded. To address this problem, we propose an algorithm that evaluates gender bias in the input text and provides guidance on how the text should be debiased by offering alternative wording that is closely related to the original input. Our proposed method promises broad application in the human resources process, ranging from the development of job advertisements to algorithm-assisted screening of job applications