Data and knowledge-driven intelligent investment cognitive reasoning model

The modeling and analysis of information flow from various sources (e.g., analyst reports, news, and social media), and their impact on assets and investment decision- making, have drawn lots of attention. In this paper, we propose a new knowledge inference design framework that provides concrete prescriptions for developing systems capable of supporting knowledge-based investment decision-making. Our framework design incorporates the advantages of both knowledge graphs and symbolic reasoning engines through the concept of a dual system. On the other hand, it overcomes the weaknesses of traditional expert systems, saving time in the knowledge input process, reducing the introduction of errors, and achieving more comprehensive knowledge coverage to obtain better predictive performance. Moreover, our proposed design artifacts are of significant importance in addressing the issues of causality and interpretability in the literature

FINANCIAL IMPLICATIONS OF A NEW FARM POLICY ENVIRONMENT

The 1996 Federal Agriculture Improvement and Reform (FAIR) Act dramatically affects the decision-making environment of farms by introducing provisions for reducing farm income support payments. These program changes are likely to affect not only farm incomes, but also farm capital asset markets. The combined effect of these two financial variables is expected to alter the risk position and the debt repayment capacity on farms. Empirical results of this analysis indicate that the absence of farm income support payments reduces debt repayment capacity and increases the risk position on a representative Louisiana cotton-soybean farm.debt repayment, farm policy, financial leverage, safety-first model, Agricultural and Food Policy, Agricultural Finance,

Investigation of ultrasmall 1 x N AWG for SOI-Based AWG demodulation integration microsystem

Optoelectronic integration technologies based on silicon-on-insulator (SOI) can bring revolutionary change to on-chip arrayed waveguide grating (AWG) demodulation systems. In this study, we present several ultrasmall 1 x N AWGs for an SOI-based AWG demodulation integration microsystem of different scales. The core sizes of the fabricated AWGs are smaller than 400 x 600 μm2. Experimental results match the simulation results, indicating that AWGs have a good transmission spectrum of low crosstalk below -20 dB and low insertion loss below -6.5 dB. The fabricated AWGs can be perfectly applied to improve the integration level and performance of the SOI-based AWG demodulation integration microsystem

Understanding and Improving SAT Solvers via Proof Complexity and Reinforcement Learning

Despite the fact that the Boolean satisfiability (SAT) problem is NP-complete and believed to be intractable, SAT solvers are routinely used by practitioners to solve hard problems in wide variety of fields such as software engineering, formal methods, security, and AI. This gap between theory and practice has motivated an entire line of research whose primary goals are twofold: first, to develop a better theoretical framework aimed at accurately capturing solver behavior and thus prove tighter complexity bounds; and second, to further experimentally verify the soundness of the theory thus developed via rigorous empirical analysis and design theory-inspired techniques to improve solver performance. This interplay between theory and practice is at the heart of the work presented here. More precisely, this thesis contains a collection of results which attempt to resolve the above-described discrepancy between theory and practice. The first two sets of results are centered around the restart problem. Restarts are classes of methods which aim at erasing part of the progress a solver may have made at run time, in order to help solvers escape from the ``bad parts'' of the search space. We provide a detailed theoretical analysis of the power of restarts used in modern Conflict-Driven Clause Learning (CDCL) SAT solvers, where we prove a series of equivalence and separation results for various configurations of solvers with and without restarts. From the intuition developed via this theoretical analysis, we design and implement a machine learning based reset policy, where resets are variants of restarts that erase activity scores in addition to the parts of the solver state erased by restarts. We perform extensive experimental work to show that our reset policy outperforms both baseline and state-of-the-art solvers over a class of cryptographic instances derived from bitcoin mining problems. In a different direction, we propose the concept of hierarchical community structure (HCS) for Boolean formulas. We first theoretically show that formulas with ``good'' HCS parameter values have short CDCL proofs. Then we construct an Empirical Hardness Model using the HCS parameters. These HCS parameters exhibit a robust correlation with solver run time, leading to the development of a classifier capable of accurately distinguishing between easily solvable industrial instances and challenging random/crafted scenarios. We also present scaling studies of formulas with HCS structures to further support of theoretical analysis. In the latter part of the thesis, the focus shifts to satisfaction-driven clause-learning (SDCL) solvers, known to be being exponentially more powerful than CDCL solvers. Despite the theoretical strength of SDCL, it remains a challenge to automate and determinize such solvers. To address this, we again leverage machine learning techniques to strategically decide when to invoke an SDCL subroutine, with the goal of minimizing the associated overhead. The resulting SDCL solver, enhanced with MaxSAT techniques and conflict analysis, outperforms existing solvers on combinatorial benchmarks, particularly demonstrating superior efficacy on Mutilated Chess Board (MCB) problems

Surplus Labor and Mobility in Hebei, China: An Evaluation of the Litu Bu Lixiang Approach.

The most fundamental economic reforms were introduced within the rural areas in China during the 1980s. The resulting greater efficiency in production, and the continued population growth have combined to result in a vast number of surplus laborers in rural areas. While concerned about the rural surplus labor problem, the Chinese government also determined to avoid mass rural-urban migration. In 1984, the Chinese Government promulgated a policy called litu bu lixiang (leave the land, but not the countryside). The basic idea is to absorb surplus labor through the retention of labor in the rural areas by creating non-agricultural opportunities. The purpose of this dissertation is to evaluate the impact of the litu bu lixiang policy. I conducted a field research in two counties--Li and Dingxing County--in Hebei, China in 1992. Multi-scale data--data on individual, household, township, and county levels--were collected. My analysis indicates that the people who shift from agriculture to non-agricultural sectors (litu people) have higher income than the people who still remain in agriculture. I also found that young, male, and more educated people are more likely to leave agriculture to pursue better opportunities. By creating non-agricultural opportunities in local areas, the litu bu lixiang program has successfully retained high-quality labor in rural areas where such workers can continue to contribute in agricultural production. This not only prevents the decline of agricultural production, but also ensures future rural development. For the households in my survey, seeking better economic returns was reported as the major reason for rural-urban migration. My results suggest that better opportunities are not necessarily in the cities; people would prefer to find non-agricultural job in the local areas, because moving to urban areas entails much higher costs, economically and psychologically, while not necessary resulting higher returns. The analysis of xiang level shows that the townships in Li County are more likely to have higher incomes than the townships in Dingxing County. The differences manifested that the local government plays an important role in initializing the litu bu lixiang program

Three Essays in Applied Microeconomics

This dissertation is comprised of three independent chapters. The first chapter studies the effectiveness and consequences of exclusionary school discipline. Exclusionary school discipline techniques, such as out-of-school suspension, are often criticized for their inability to improve students' behavior, their adverse effects on students' achievement outcomes and their disproportionate use on minority students. Using large-scale administrative data on North Carolina public school students, I find that harsher disciplinary rules (measured by higher out-of-school suspension likelihood) significantly deter students from committing first offenses, but that they are less effective (or ineffective) for repeat offenses. I also find that their adverse effects on offending students' achievement outcomes, such as end-of-grade test scores and high school dropout probability, are much smaller than the effects documented in the existing literature. In addition, I find that harsher disciplinary rules could significantly improve the academic achievement of middle school students with no offense record. To carefully address endogeneity and selection issues in a large-scale data context, my preferred identification strategy combines the instrumental variable method and a machine learning cluster method (k-means). These findings suggest that current policy reform of exclusionary school discipline should carefully balance its benefits and costs for different student populations. The second chapter explores the equity in exclusionary school discipline between black and white students and among students from families with different economic backgrounds. The existing literature and popular press report that black students face out-of-school suspension with much greater frequency than white students. Using administrative data on North Carolina public school students over eight academic years, I find that the racial disparity depends importantly on the type of offenses when black and white students are compared within the same school. While black students are more likely to be suspended, for example, for fighting, theft and sexual harassment, white students are more likely to be suspended for insubordination, disrespect toward faculty, or leaving class without permission. I also find that Economically Disadvantaged students are consistently more likely to be suspended out-of-school for different types of offenses, even if the comparison is within schools. The third chapter studies the impacts of social contacts, such as spouses, friends, siblings, parents or children on individual smoking behavior. To identify endogenous social interaction effects, we model an individual and her social contacts' smoking behaviors as a simultaneous move game with complete information. We also allow an individual's smoking behavior to depend on her previous behavior and unobserved heterogeneity. Using unique data from the Framingham Heart Study, which includes complementary social network data, we find statistically significant endogenous social interaction effects of spouses and friends on individual smoking behavior. We also find that endogenous social interaction effects from siblings or parents are not statistically significant after disentangling them from homophily. In addition, we find that the effects of social contacts' cardiovascular disease shocks on individual smoking behavior are not statistically significant.Doctor of Philosoph