BackgroundIn phylogenetics, we often seek to reconcile gene trees with species trees within the framework of an evolutionary model. While the most popular models for eukaryotic species allow for only gene duplication and gene loss or only multispecies coalescence, recent work has combined these phenomena through a reconciliation structure, the labeled coalescent tree (LCT), that simultaneously describes the duplication-loss and coalescent history of a gene family. However, the LCT makes the simplifying assumption that only one individual is sampled per species whereas, with advances in gene sequencing, we now have access to multiple samples per species.ResultsWe demonstrate that with these additional samples, there exist gene tree topologies that are impossible to reconcile with any species tree. In particular, the multiple samples enforce new constraints on the placement of duplications within a valid reconciliation. To model these constraints, we extend the LCT to a new structure, the partially labeled coalescent tree (PLCT) and demonstrate how to use the PLCT to evaluate the feasibility of a gene tree topology. We apply our algorithm to two clades of apes and flies to characterize possible sources of infeasibility.ConclusionGoing forward, we believe that this model represents a first step towards understanding reconciliations in duplication-loss-coalescence models with multiple samples per species.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-017-1701-1) contains supplementary material, which is available to authorized users.

No abstract

No abstract

Robotics competitions at the high school level attract a large number of students across the world. However, there is little emphasis on leveraging robotics to get middle school students excited about pursuing STEM education. In this paper, we describe a new program that targets middle school students in a local, four-week setting at the Massachusetts Institute of Technology (MIT). It aims to excite students by teaching the very basics of computer vision and robotics. The students program mini car-like robots, equipped with state-of-theart computers, to navigate autonomously in a mock race track. We describe the hardware and software infrastructure that enables the program, the details of our curriculum, and the results of a short assessment. In addition, we describe four short programs, as well as a session where we teach high school teachers how to teach similar courses at their schools to their own students. The self-assessment indicates that the students feel more confident in programming and robotics after leaving the program, which we hope will enable them to pursue STEM education and robotics initiatives at school.

We propose an approach to data memory prefetching which augments the standard prefetch buffer with selection criteria based on performance and usage pattern of a given instruction. This approach is built on top of a pattern matching based prefetcher, specifically one which can choose between a stream, a stride, or a stream followed by a stride. We track the most recently called instructions to make a decision on the quantity of data to prefetch next. The decision is based on the frequency with which these instructions are called and the hit/miss rate of the prefetcher. In our approach, we separate the amount of data to prefetch into three categories: a high degree, a standard degree and a low degree. We ran tests on different values for the high prefetch degree, standard prefetch degree and low prefetch degree to determine that the most optimal combination was 1, 4, 8 lines respectively. The 2 dimensional selection criteria improved the performance of the prefetcher by up to 9.5% over the first data prefetching championship winner. Unfortunately performance also fell by as much as 14%, but remained similar on average across all of the benchmarks we tested.