Professor Chen Ping Yang's early significant contributions to mathematical physics

In the 60's Professor Chen Ping Yang with Professor Chen Ning Yang published several seminal papers on the study of Bethe's hypothesis for various problems of physics. The works on the lattice gas model, critical behaviour in liquid-gas transition, the one-dimensional (1D) Heisenberg spin chain, and the thermodynamics of 1D delta-function interacting bosons are significantly important and influential in the fields of mathematical physics and statistical mechanics. In particular, the work on the 1D Heisenberg spin chain led to subsequent developments in many problems using Bethe's hypothesis. The method which Yang and Yang proposed to treat the thermodynamics of the 1D system of bosons with a delta-function interaction leads to significant applications in a wide range of problems in quantum statistical mechanics. The Yang and Yang thermodynamics has found beautiful experimental verifications in recent years.Comment: 5 pages + 3 figure

The Volatility Spillover Effects and Optimal Hedging Strategy in the Corn Market

This article examines the volatility spillovers from energy market to corn market. Using a volatility spillover model from the finance literature, we found significant spillovers from energy market to corn cash and futures markets, and the spillover effects are time-varying. The business cycle proxied by crude oil prices is shown to affect the magnitude of spillover effects over time. Based on the strong informational linkage between energy market and corn market, a cross hedge strategy is proposed and its performance studied. The simulation outcomes show that compared to alternative strategies of no hedge, constant hedge, and GARCH hedge, the cross hedge does not yield superior risk-reduction performance.Volatility Spillover, GARCH, Optimal Hedge Ratio, Energy Price, Corn Price, Risk and Uncertainty,

Farm Capital Structure Choice under Credit Constraint: Theory and Application

This study proposed a theoretical framework for analyzing farm capital structure choice. The theoretical model recognizes that the costs of debt are endogenously determined which in turn reflect the degree of credit constraint faced by individual borrowers. Based on the proposed model, we derived the impacts of different determinants on capital structure choice analytically. The theoretical inferences are further tested with empirical data. Methodologically, we proposed a fixed-effect quantile regression procedure to estimate the impacts of determinants at different ranges of leverage. The effects of determinants are discussed in the empirical application.Capital Structure, Cost of Debt, Credit Constraint, Quantile Regression, Agricultural Finance,

Supporting Cellulosic Ethanol Biomass Production and its Impact on Land Use Conversion

One of the problems facing the cellulosic ethanol industry is the cellulose material supply. The U.S. forestlands have considerable potential to become one of the main sources of biomass to meet the 2022 renewable fuel target. Focusing on the land exiting the Conservation Reserve Program (CRP), the article finds that few landowners are willing to convert their land to forestland after the CRP contract is expired. Our econometric estimates show the choice decision is responsive to net returns of land use alternatives, especially cropland. Two policy initiatives are suggested to provide direct incentives for land use change. The nested logit estimates are used to simulate landowners‘ responses to policy mechanism. The results show that subsidies can substantially increase forestland, although a spillover effect exists.Cellulosic Ethanol, Biomass, Land Use, the CRP, Forestland, Environmental Economics and Policy,

Modeling Agricultural Risk, Risk Preferences and Perceptions

Risk and Uncertainty,

Matrix of Polynomials Model based Polynomial Dictionary Learning Method for Acoustic Impulse Response Modeling

We study the problem of dictionary learning for signals that can be represented as polynomials or polynomial matrices, such as convolutive signals with time delays or acoustic impulse responses. Recently, we developed a method for polynomial dictionary learning based on the fact that a polynomial matrix can be expressed as a polynomial with matrix coefficients, where the coefficient of the polynomial at each time lag is a scalar matrix. However, a polynomial matrix can be also equally represented as a matrix with polynomial elements. In this paper, we develop an alternative method for learning a polynomial dictionary and a sparse representation method for polynomial signal reconstruction based on this model. The proposed methods can be used directly to operate on the polynomial matrix without having to access its coefficients matrices. We demonstrate the performance of the proposed method for acoustic impulse response modeling.Comment: 5 pages, 2 figure

Hierarchical Attention Network for Visually-aware Food Recommendation

Food recommender systems play an important role in assisting users to identify the desired food to eat. Deciding what food to eat is a complex and multi-faceted process, which is influenced by many factors such as the ingredients, appearance of the recipe, the user's personal preference on food, and various contexts like what had been eaten in the past meals. In this work, we formulate the food recommendation problem as predicting user preference on recipes based on three key factors that determine a user's choice on food, namely, 1) the user's (and other users') history; 2) the ingredients of a recipe; and 3) the descriptive image of a recipe. To address this challenging problem, we develop a dedicated neural network based solution Hierarchical Attention based Food Recommendation (HAFR) which is capable of: 1) capturing the collaborative filtering effect like what similar users tend to eat; 2) inferring a user's preference at the ingredient level; and 3) learning user preference from the recipe's visual images. To evaluate our proposed method, we construct a large-scale dataset consisting of millions of ratings from AllRecipes.com. Extensive experiments show that our method outperforms several competing recommender solutions like Factorization Machine and Visual Bayesian Personalized Ranking with an average improvement of 12%, offering promising results in predicting user preference for food. Codes and dataset will be released upon acceptance