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"content": "Quantitative Investment, built on the solid foundation of robust financial theories, is at the center stage in investment industry today. The essence of quantitative investment is the multi-factor model, which explains the relationship between the risk and return of equities. However, the multi-factor model generates enormous quantities of factor data, through which even experienced portfolio managers find it difficult to navigate. This has led to portfolio analysis and factor research being limited by a lack of intuitive visual analytics tools. Previous portfolio visualization systems have mainly focused on the relationship between the portfolio return and stock holdings, which is insufficient for making actionable insights or understanding market trends. In this paper, we present sPortfolio, which, to the best of our knowledge, is the first visualization that attempts to explore the factor investment area. In particular, sPortfolio provides a holistic overview of the factor data and aims to facilitate the analysis at three different levels: a Risk-Factor level, for a general market situation analysis; a Multiple-Portfolio level, for understanding the portfolio strategies; and a Single-Portfolio level, for investigating detailed operations. The system's effectiveness and usability are demonstrated through three case studies. The system has passed its pilot study and is soon to be deployed in industry.",
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"content": "Accurately reconstructing gene regulatory networks (GRNs) from high-throughput gene expression data has been a major challenge in systems biology for decades. Many approaches have been proposed to solve this problem. However, there is still much room for the improvement of GRN inference. Integrating data from different sources is a promising strategy. Epigenetic modifications have a close relationship with gene regulation. Hence, epigenetic data such as histone modification profiles can provide useful information for uncovering regulatory interactions between genes. In this paper, we propose a method to integrate epigenetic data into the inference of GRNs. In particular, a dynamic Bayesian network (DBN) is employed to infer gene regulations from time-series gene expression data. Epigenetic data (histone modification profiles here) are integrated into the prior probability distribution of the Bayesian model. Our method has been validated on both synthetic and real datasets. Experimental results show that the integration of epigenetic data can significantly improve the performance of GRN inference. As more epigenetic datasets become available, our method would be useful for elucidating the gene regulatory mechanisms driving various cellular activities. The source code and testing datasets are available at <uri>https://github.com/Zheng-Lab/MetaGRN/tree/master/histonePrior</uri>.",
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"abstract": "A method for comparing polygons that is a metric, invariant under translation, rotation, and change of scale, reasonably easy to compute, and intuitive is presented. The method is based on the L/sub 2/ distance between the turning functions of the two polygons. It works for both convex and nonconvex polygons and runs in time O(mn log mn), where m is the number of vertices in one polygon and n is the number of vertices in the other. Some examples showing that the method produces answers that are intuitively reasonable are presented.",
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