MOTIVATION: Low-complexity (LC) DNA sequences are compositionally repetitive sequences that are often associated with spurious homologous matches and variant calling artifacts. While algorithms for identifying LC sequences exist, they either lack concise mathematical definition of complexity or are inefficient with long or variable context windows. RESULTS: Longdust is a new algorithm that efficiently identifies long LC sequences including centromeric satellite and tandem repeats with moderately long motifs. It defines string complexity by statistically modeling the k-mer count distribution with the parameters: the k-mer length, the context window size and a threshold on complexity. Longdust exhibits high performance on real data and high consistency with existing methods. AVAILABILITY AND IMPLEMENTATION: https://github.com/lh3/longdust.

Chips with tens of billions of transistors have become today's norm. These designs are straining our electronic design automation tools throughout the design process, requiring ever more computational resources. In many tools, parallelisation has improved both latency and throughput for the designer's benefit. However, tools largely remain restricted to a single machine and in the case of RTL simulation, we believe that this leaves much potential performance on the table. We introduce Metro-MPI to improve RTL simulation for modern 10 billion transistor-scale chips. Metro-MPI exploits the natural boundaries present in chip designs to partition RTL simulations and leverage High Performance Computing (HPC) techniques to extract parallelism. For chip designs that scale in size by exploiting latency-insensitive interfaces like networks-on-chip and AXI, Metro-MPI offers a new paradigm for RTL simulation scalability. Our implementation of Metro-MPI in Open-Piton+Ariane delivers 2.7 MIPS of RTL simulation throughput for the first time on a design with more than 10 billion transistors and 1,024 Linux-capable cores, opening new avenues for distributed RTL simulation of emerging system-on-chip designs. Compared to sequential and multithreaded RTL simulations of smaller designs, Metro-MPI achieves up to$135.98\times$and$9.29\times$speedups. Similarly, for a representative regression run, Metro-Mpireduces energy consumption by up to$2.53\times$and$2.91\times$.

The spatial layout of scene images is essential to recognizing them. Without considering spatial layout information, the deep convolutional neural networks could not achieve satisfied performance on scene recognition. In this paper a novel network architecture, namely the randomized spatial pooling (RS-Pooling) layer, is proposed to incorporate the spatial layout information into the deep model. By partitioning the feature maps via randomized patterns, the RS-Pooling layer offers the probability to handle various image layouts. Moreover, a maxout objective function is adopted to adaptively choose the optimal partition pattern to characterize the image layout. The experimental results on the scene recognition benchmarks demonstrate the effectiveness of the proposed RS-Pooling architecture.

We discuss two problems from the relatively new field of computational geometry to illustrate two advanced notions in algorithm design and analysis.One problem deals with the triangulation of convex polygons while the other delves into minimum enclosing rectangles Our work continues with the objective set forth in an earlier paper of a similar title.