k-SDPP: Fixed-Size Video Summarization via Sequential Determinantal Point Processes

With the explosive growth of video data, video summarization which converts long-time videos to key frame sequences has become an important task in information retrieval and machine learning. Determinantal point processes (DPPs) which are elegant probabilistic models have been successfully applied to video summarization. However, existing DPP-based video summarization methods suffer from poor efficiency of outputting a specified size summary or neglecting inherent sequential nature of videos. In this paper, we propose a new model in the DPP lineage named k-SDPP in vein of sequential determinantal point processes but with fixed user specified size k. Our k-SDPP partitions sampled frames of a video into segments where each segment is with constant number of video frames. Moreover, an efficient branch and bound method (BB) considering sequential nature of the frames is provided to optimally select k frames delegating the summary from the divided segments. Experimental results show that our proposed BB method outperforms not only k-DPP and sequential DPP (seqDPP) but also the partition and Markovian assumption based methods.

A study on the mapping of quantitative trait loci in advanced populations derived from two inbred lines

SummaryIn genetic and biological studies, the F2 population is one of the most popular and commonly used experimental populations mainly because it can be readily produced and its genome structure possesses several niceties that allow for productive investigation. These niceties include the equivalence between the proportion of recombinants and recombination rates, the capability of providing a complete set of three genotypes for every locus and an analytically attractive first-order Markovian property. Recently, there has been growing interest in using the progeny populations from F2 (advanced populations) because their genomes can be managed to meet specific purposes or can be used to enhance investigative studies. These advanced populations include recombinant inbred populations, advanced intercrossed populations, intermated recombinant inbred populations and immortalized F2 populations. Due to an increased number of meiosis cycles, the genomes of these advanced populations no longer possess the Markovian property and are relatively more complicated and different from the F2 genomes. Although issues related to quantitative trait locus (QTL) mapping using advanced populations have been well documented, still these advanced populations are often investigated in a manner similar to the way F2 populations are studied using a first-order Markovian assumption. Therefore, more efforts are needed to address the complexities of these advanced populations in more details. In this article, we attempt to tackle these issues by first modifying current methods developed under this Markovian assumption to propose an ad hoc method (the Markovian method) and explore its possible problems. We then consider the specific genome structures present in the advanced populations without invoking this assumption to propose a more adequate method (the non-Markovian method) for QTL mapping. Further, some QTL mapping properties related to the confounding problems that result from ignoring epistasis and to mapping closely linked QTL are derived and investigated across the different populations. Simulations show that the non-Markovian method outperforms the Markovian method, especially in the advanced populations subject to selfing. The results presented here may give some clues to the use of advanced populations for more powerful and precise QTL mapping.