SUMMARY: We present RabbitSketch, a highly optimized library of sketching algorithms such as MinHash, OrderMinHash, and HyperLogLog that can exploit the power of modern multi-core CPUs. It provides significant speedups compared to existing implementations, ranging from 2.30× to 49.55×, as well as flexible and easy-to-use interfaces for both Python and C++. As a result, the similarity analysis of 455GB genomic data can be completed in only 5 minutes using RabbitSketch with merely 20 lines of Python code. As a case study, we enhanced RabbitTClust by integrating RabbitSketch's Kssd algorithm, resulting in a 1.54× speedup with no loss in accuracy. AVAILABILITY AND IMPLEMENTATION: RabbitSketch is available at https://github.com/RabbitBio/RabbitSketch with an archived version at Zenodo: https://doi.org/10.5281/zenodo.14903962. Detailed API documentation is available at https://rabbitsketch.readthedocs.io/en/latest.

Trimming is an essential step in sequencing data processing. However, many existing trimming tools, such as Trimmomatic and Ktrim, are limited by suboptimal implementations and fail to fully leverage the computational power of modern multi-core platforms. To address this, we introduce RabbitTrim, a highly optimized and versatile trimming tool that fully supports the functionalities of Trimmomatic and Ktrim. RabbitTrim's performance is enhanced through efficient I/O strategies, parallel (de)compression engines, block-based memory pools, bitwise operations, and vectorization techniques. Compared to Trimmomatic, RabbitTrim (in trimmomatic mode) achieves speedups ranging from 1.8x to 6.0x for plain FASTQ files and 3.7x to 14.0x for gzip-compressed FASTQ files on a 48-core Intel server. Similarly, compared to Ktrim, RabbitTrim (in ktrim mode) achieves speedups ranging from 1.5x to 2.5x for plain FASTQ files and 2.7x to 5.6x for gzip-compressed FASTQ files on the same server. Moreover, RabbitTrim is able to process 101 GB gzip-compressed sequencing data in only 5 minutes while Trimmomatic requires at least 21 minutes.

With the continuous advancement of sequencing technology, the scale of biological data has rapidly increased. BAM format, widely used for storing aligned sequence data, is very popular due to its ease of use and good compression ratio. However, existing BAM-format file I/O libraries often fail to fully leverage the computational power of modern multi-core platforms, resulting in low CPU utilization. To address this, we introduce RabbitBAM, a fast BAM-format file I/O library. RabbitBAM employs pre-parsing and parallel parsing techniques to eliminate parsing bottlenecks and improve parallel efficiency. Additionally, we optimize multi-threaded data handling through the use of dedicated lock-free queues and memory pools. RabbitBAM achieves 2.1-3.3x speedups on next-generation sequencing data and 1-2.2x speedups on third-generation sequencing data compared to state-of-the-art SAMtools (HTSlib). We also present two case studies (BAM file quality control and sorting) using RabbitBAM, demonstrating 1.4-2.4x speedups compared to other implementations.

When been threatened by hijacking or suicide-bypilots, the airliner may either crash itself or be shot down due to the potential of the suicide attack. There exist some solutions that allow air traffic controllers or federal agents to take over pilots' authority in the emergency. Though rarely, an air traffic controller may abuse this privilege to mishandle airliners that leads to catastrophic events. In this paper, to mitigate such risks, we propose a multisecret value access control framework based on new designed and existing cryptographic techniques such as XOR-based secret sharing schemes (SSSs). It not only satisfies the efficiency requirement but also assures that each nation owns a unique secret value. We further develop and implement the XOR-based SSS on Linux system. Both experimental results and performance evaluation demonstrate that our solution is not only efficient and bust also secure by design for the multinational air traffic management.

Network virtualization is widely considered to be one of the main paradigms to solve the Internet ossification problem. One of the most challenging works in this paradigm will be the efficient use of the substrate resources, which is known as virtual network embedding (VNE) problem. The VNE problem is known to be an NP-hard problem which needs some heuristic and approximate algorithms to be solved. In this paper, the VNE is decomposed into two stages: virtual node and virtual link mapping. In the node mapping stage, the breadth first search algorithm is employed to construct loop-free tree for each substrate node, then we explored different hops sufficient capacity of each SN node via the loop-free tree, and analyze the best value of hops that contributes to gain better performance for the algorithm. And the link mapping stage, it can be seen as multi-constraint routing, which is known to be an NP-hard problem. The paths algebra framework was adopted to address this problem, for its convergence for multi-constraint routing problem and a large solution space that the algorithm can gain an enhanced optimization performance.