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  1. In this paper, we develop a drift homotopy implicit particle filter method. The methodology of our approach is to adopt the concept of drift homotopy in the resampling procedure of the particle filter method for solving the nonlinear filtering problem, and we introduce an implicit particle filter method to improve the efficiency of the drift homotopy resampling procedure. Numerical experiments are carried out to demonstrate the effectiveness and efficiency of our drift homotopy implicit particle filter.

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  2. A central focus of evolutionary biology is inferring the historical relationships among species and using this context to learn about how evolution has shaped diverse organisms. These historical relationships are represented by phylogenetic trees, and the methods used to infer these trees have been an active area of research for several decades. Despite this attention, phylogenetic workflows have changed little, even though extraordinary advances have occurred in the scale and pace at which genomic data have been collected in the past 20 years. Modern phylogenomic datasets have also raised fascinating new questions. Why do different parts of a genome often support different relationships among species? How are these different signals distributed across chromosomes? We developed a new computational framework, CloudForest, to tackle such questions. CloudForest is flexible, efficient, and tightly integrates a diverse set of tools. Here, we briefly describe the architecture of CloudForest, including the advantages it provides, and use it to investigate the distribution of phylogenetic signal along the entire X chromosome of 24 cat (Felidae) species. 
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