At least seven people were killed in Russian strikes across Ukraine overnight, including five in the central city of Dnipro, where officials said an apartment building was hit. Ukrainian President Volodymyr Zelensky said the latest attack lasted practically all night, while rescue workers were still searching for survivors under rubble in Dnipro on Saturday morning. British jets were scrambled from Romania during the heavy attack when Russian drones were detected near the border, though the UK Ministry of Defence rejected a report it had shot some down. Meanwhile, Ukraine carried out some of its longest-distance drone strikes deep inside Russian territory. In Yekaterinburg, almost 1,000 miles (1,600km) from Ukraine's border, the governor said six people were injured when a building was struck - while in nearby Chelyabinsk, a local leader said drones targeting an industrial facility were shot down.

With A.I., Anyone Can Be an Influencer TikTok and Instagram made it easy to monetize the physical self. Now the social-media-savvy can use A.I. to play with their identity, or overhaul it entirely. A few months ago, a forty-five-year-old homemaker living in Georgia, whom I'll call Robin, started playing around with an A.I. image generator. Growing up, Robin had loved reading; she dabbled in writing, too, but after her first child was born, the habit faded. A.I. offered something different--a kind of world-building that allowed her to project herself into places and situations she'd never inhabited.

Graph Neural Networks (GNNs) have achieved remarkable performance in the task of semi-supervised node classification. However, most existing GNN models require sufficient labeled data for effective network training. Their performance can be seriously degraded when labels are extremely limited. To address this issue, we propose a new framework termed Contrastive Graph Poisson Networks (CGPN) for node classification under extremely limited labeled data. Specifically, our CGPN derives from variational inference; integrates a newly designed Graph Poisson Network (GPN) to effectively propagate the limited labels to the entire graph and a normal GNN, such as Graph Attention Network, that flexibly guides the propagation of GPN; applies a contrastive objective to further exploit the supervision information from the learning process of GPN and GNN models. Essentially, our CGPN can enhance the learning performance of GNNs under extremely limited labels by contrastively propagating the limited labels to the entire graph. We conducted extensive experiments on different types of datasets to demonstrate the superiority of CGPN.

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