Many combinatorial optimization problems map naturally to Ising Hamiltonians, $H({\text{s}}) = - \sum\nolimits_{i,j} {{J_{ij}}} {s_i}{s_j} - \sum\nolimits_i {{h_i}} {s_i}$ , and CMOS ring-oscillator Ising machines solve them in microseconds at milliwatts [1] , [2] . Their key limitation is capacity : the number of spins one solver core can process in a single solve. Because each hardware spin represents one binary Ising variable, capacity directly sets the largest problem solvable in one shot. Our 28 nm five-core COBI chip solves a 45-spin all-to-all subproblem per core in 77.5 µ s, so larger instances require iterative decomposition. This shifts the bottleneck from analog solving to digital orchestration: a CPU-based decomposer needs ∼321 µ s/iter over PCIe, 4× the core solve time, leaving the solver idle 84.9% of the time. We instead co-locate an FPGA decomposer with the chip and derive sizing laws for the required parallelism, achieving 1.93× geomean speedup and > 40× energy reduction vs. an optimized C++ baseline.

Knowledge graph construction is aimed at storing and representing the knowledge of the objective world in a structured form. Existing methods for automatic construction of knowledge graphs have problems such as difficulty in understanding potential semantics and low precision. The emergence of Large Language Models (LLMs) provides an effective way for automatic knowledge graph construction. However, using LLMs as automatic knowledge graph construction engines relies on the embedding of schema layers, which brings challenges to the input length of LLMs. In this paper, we present a framework for Adaptive Construction of Knowledge Graph by leveraging the exceptional generation capabilities of LLMs and the latent relational semantic information of triples, named ACKG-LLM. Our proposed framework divides the knowledge graph construction task into three subtasks within a unified pipeline: triple extraction of open information, additional relational semantic information embedding and knowledge graph normalization based on schema-level embedding. The framework can construct knowledge graphs in different domains, making up for the defects of existing frameworks that need to retrain and fine-tune the internal model. Extensive experiments demonstrate that our proposed ACKG-LLM performs favorably against representative methods on the REBEL and WiKi-NRE datasets. The code is available athttps://github.com/KustTeamWQW/ACKG-LLM

The adaptive immune receptor repertoire (AIRR), consisting of T- and B-cell receptors, is the core component of the immune system. The AIRR sequencing is commonly used in cancer immunotherapy and minimal residual disease (MRD) detection of leukemia and lymphoma. The AIRR is captured by primers and sequenced to yield paired-end (PE) reads. The PE reads could be merged into one sequence by the overlapped region between them. However, the wide range of AIRR data raises the difficulty, so a special tool is required. We developed a software package for IMmune PE reads merger of sequencing data, named IMperm. We used the k-mer-and-vote strategy to pin down the overlapped region rapidly. IMperm could handle all types of PE reads, eliminate adapter contamination and successfully merge low-quality and minor/non-overlapping reads. Compared with existing tools, IMperm performed better in both simulated and sequencing data. Notably, IMperm was well suited to processing the data of MRD detection in leukemia and lymphoma and detected 19 novel MRD clones in 14 patients with leukemia from previously published data. Additionally, IMperm can handle PE reads from other sources, and we demonstrated its effectiveness on two genomic and one cell-free deoxyribonucleic acid datasets. IMperm is implemented in the C programming language and consumes little runtime and memory. It is freely available at https://github.com/zhangwei2015/IMperm.

The recent advance of single-cell copy number variation (CNV) analysis plays an essential role in addressing intratumor heterogeneity, identifying tumor subgroups and restoring tumor-evolving trajectories at single-cell scale. Informative visualization of copy number analysis results boosts productive scientific exploration, validation and sharing. Several single-cell analysis figures have the effectiveness of visualizations for understanding single-cell genomics in published articles and software packages. However, they almost lack real-time interaction, and it is hard to reproduce them. Moreover, existing tools are time-consuming and memory-intensive when they reach large-scale single-cell throughputs. We present an online visualization platform, single-cell Somatic Variant Analysis Suite (scSVAS), for real-time interactive single-cell genomics data visualization. scSVAS is specifically designed for large-scale single-cell genomic analysis that provides an arsenal of unique functionalities. After uploading the specified input files, scSVAS deploys the online interactive visualization automatically. Users may conduct scientific discoveries, share interactive visualizations and download high-quality publication-ready figures. scSVAS provides versatile utilities for managing, investigating, sharing and publishing single-cell CNV profiles. We envision this online platform will expedite the biological understanding of cancer clonal evolution in single-cell resolution. All visualizations are publicly hosted at https://sc.deepomics.org.

Semantic segmentation of aerial images plays an important role in urban area monitoring. But the diversity of buildings makes segmentation a hard task. To detect buildings from aerial images more precisely, this paper proposes a pixel-level segmentation method, named Attention Residual U-net (ARU-net). ARU-net adds two major part into the framework of U-net, i.e. attention path and residual connection, focusing on feature reuse. Attention path utilizes attention mechanism to capture spatial feature details. Residual connection implies the semantic information flow through a 1×1 convolution similar to the residual form. ARU-net can be trained end-to-end. Experiments are conducted to evaluate the effectiveness of the proposed model on the Inria Aerial Image Labeling Dataset. Results indicate that ARU-net outperforms other baselines with an accuracy of 93.84% and intersection over union (IoU) of 60.90%.