MOTIVATION: The k-mer frequency in whole genome sequences provides researchers with an insightful perspective on genomic complexity, comparative genomics, metagenomics and phylogeny. The current k-mer counting tools are typically slow, and they require large memory and hard disk for assembled genome analysis. RESULTS: We propose a novel and ultra-fast k-mer counting algorithm, KCOSS, to fulfill k-mer counting mainly for assembled genomes with segmented Bloom filter, lock-free queue, lock-free thread pool and cuckoo hash table. We optimize running time and memory consumption by recycling memory blocks, merging multiple consecutive first-occurrence k-mers into C-read, and writing a set of C-reads to disk asynchronously. KCOSS was comparatively tested with Jellyfish2, CHTKC and KMC3 on seven assembled genomes and three sequencing datasets in running time, memory consumption, and hard disk occupation. The experimental results show that KCOSS counts k-mer with less memory and disk while having a shorter running time on assembled genomes. KCOSS can be used to calculate the k-mer frequency not only for assembled genomes but also for sequencing data. AVAILABILITYAND IMPLEMENTATION: The KCOSS software is implemented in C++. It is freely available on GitHub: https://github.com/kcoss-2021/KCOSS. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Abstract The target of Emotion modeling is to establish an system that can perceive, recognize, and express emotions with concurrency which humanity have by proper mathematical models. A big challenge in Emotion modeling is to establish the complex personal system of an intelligent agent or machine in a quasi‐physical and quasi‐sociological way, so that it rationally responds to emotions and behaviors by different internal and external stimuli with concurrency. For this purpose, an emotion classification model is presented for the audio‐visual external stimuli based on an improved long short‐term memory network. Then, based on Gross's emotional regulation theory, a hidden Markov model is constructed to imitate the process framework of human emotional cognition and personalized emotional regulation and expression, so as to realize the machine's intuitive and reasonable response to stimuli with concurrency. In this article, a framework of machine personalized artificial emotion simulation is preliminarily constructed, which provides a new model for human–computer interaction, in order to provide a reference solution for machine emotion understanding and expression.