In the rapidly evolving field of quantum computing, silicon spin qubits are emerging as a leading candidate for building scalable, fault-tolerant quantum computers. A new review titled "Single-Electron Spin Qubits in Silicon for Quantum" published May 2 in Intelligent Computing, a Science Partner Journal, highlights the latest advances, challenges and future prospects of silicon spin qubits for quantum computing.

According to a new study, there are tipping points in microbial ecosystems where even small perturbations are enough to cause a collapse. The authors, from the Helmholtz Institute for Functional Marine Biodiversity at the University of Oldenburg, Germany, describe microbial communities as a network based on cross-feeding, the exchange of metabolic by-products between populations. This network of relationships can collapse abruptly if individual populations are lost, they conclude. This mechanism may explain why microbial diversity is difficult to maintain in the laboratory, they write in the journal PNAS.

A new artificial intelligence-based method accurately sorts cancer patients into groups that have similar characteristics before treatment and similar outcomes after treatment, according to a study led by investigators at Weill Cornell Medicine. The new approach has the potential to enable better patient selection in clinical trials and better treatment selection for individual patients.

Point-of-Interest (POI) recommendation is crucial in the recommendation system field. Graph neural networks are used for POI recommendations, but data sparsity affects GNN training. Existing GNN-based methods have two flaws. Firstly, they have coarse granularity for modelling heterogeneity, overlooking complex relationships due to time and space factors. Although some work constructs complex graphs, it may reduce performance by introducing noise. Secondly, they insufficiently consider interaction sparsity issues, with little attention in POI recommendations. To solve these problems, a novel method HestGCL is proposed. It builds a heterogeneous spatio-temporal graph with three node types and three relations to model heterogeneity at a finer granularity. Inspired by self-supervised learning, it uses a cross-view contrastive learning technique, splitting the graph into spatial and temporal views, designing specific graph neural networks, and using node representations for contrastive learning. Experiments on three datasets show that HestGCL outperforms state-of-the-art methods, with relative improvements in Recall@50, and ablation studies prove its effectiveness and robustness.