A partially linearized sigma point filter for latent state estimation in nonlinear time series models

A new technique for the latent state estimation of a wide class of nonlinear time series models is proposed. In particular, we develop a partially linearized sigma point filter in which random samples of possible state values are generated at the prediction step using an exact moment matching algorithm and then a linear programming-based procedure is used in the update step of the state estimation. The effectiveness of the new ¯ltering procedure is assessed via a simulation example that deals with a highly nonlinear, multivariate time series representing an interest rate process

Facilitating economic development through the reform of economic instruction

Economic development in many ways depends on the level of human capital in the national economy, including that of economists. Market reforms in the former Soviet Bloc urged drastic changes in economic curriculum necessary to prepare the next generation of economic leaders. This paper states that the reform of economic instruction in the Former Soviet Union should focus on both learning and action. The incorporation of mathematical methods into the new economic curriculum will occur based on close cooperation among mathematicians and economists. The new economic instruction will have an interdisciplinary character and a multidisciplinary setting. There are several second order organizational changes that need to be made. Bachelor and Master’s Degrees should replace the five-year degree. Changes in the curriculum should include separation of core courses and electives including those from other majors, detail-oriented content of the courses, a decreased number of classes per semester and increased time for each class. Faculty retraining should be coordinated both within and between the universities. Financial incentives should be created to encourage the instructors to participate in retraining, to change the content and method of the instruction, and to work effectively in the classroom.economics, economic instruction, education, reform, transition

An alternative to the breeder's and Lande's equations

The breeder's equation is a cornerstone of quantitative genetics and is widely used in evolutionary modeling. The equation which reads R=h^{2}S relates response to selection R (the mean phenotype of the progeny) to the selection differential S (mean phenotype of selected parents) through a simple proportionality relation. The validity of this relation however relies strongly on the normal (Gaussian) distribution of parent's genotype which is an unobservable quantity and cannot be ascertained. In contrast, we show here that if the fitness (or selection) function is Gaussian, an alternative, exact linear equation in the form of R'=j^{2}S' can be derived, regardless of the parental genotype distribution. Here R' and S' stand for the mean phenotypic lag behind the mean of the fitness function in the offspring and selected populations. To demonstrate this relation, we derive the exact functional relation between the mean phenotype in the selected and the offspring population and deduce all cases that lead to a linear relation between these quantities. These computations, which are confirmed by individual based numerical simulations, generalize naturally to the multivariate Lande's equation \Delta\mathbf{\bar{z}}=GP^{-1}\mathbf{S}

On the Generation of Medical Question-Answer Pairs

Question answering (QA) has achieved promising progress recently. However, answering a question in real-world scenarios like the medical domain is still challenging, due to the requirement of external knowledge and the insufficient quantity of high-quality training data. In the light of these challenges, we study the task of generating medical QA pairs in this paper. With the insight that each medical question can be considered as a sample from the latent distribution of questions given answers, we propose an automated medical QA pair generation framework, consisting of an unsupervised key phrase detector that explores unstructured material for validity, and a generator that involves a multi-pass decoder to integrate structural knowledge for diversity. A series of experiments have been conducted on a real-world dataset collected from the National Medical Licensing Examination of China. Both automatic evaluation and human annotation demonstrate the effectiveness of the proposed method. Further investigation shows that, by incorporating the generated QA pairs for training, significant improvement in terms of accuracy can be achieved for the examination QA system.Comment: AAAI 202