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Why is MAGA in meltdown over the Supreme Court birthright ruling? NewsFeed Why is MAGA in meltdown over the Supreme Court birthright ruling? The US Supreme Court has rejected Trump's bid to stop the children of some immigrants obtaining US citizenship at birth and it's sent the MAGA movement into meltdown. The policy was a cornerstone of the US President's anti-immigration platform, as Soraya Lennie explains. Supreme Court's divided ruling on birthright citizenship may be revisited Iran says it couldn't export a'single barrel of oil' during US blockade Mexican fans keep Ecuador's team awake before World Cup showdown

Supreme Court's Divided Ruling on Birthright Citizenship may be revisited NewsFeed Supreme Court's divided ruling on birthright citizenship may be revisited Eric Ham and Adolfo Franco discuss why the Supreme Court's 6-3 ruling on birthright citizenship could signal that the issue may return to the Court in future cases. They point to the justices' differing opinions and the possibility of further constitutional challenges. Why is MAGA in meltdown over the Supreme Court birthright ruling? Iran says it couldn't export a'single barrel of oil' during US blockade Mexican fans keep Ecuador's team awake before World Cup showdown

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Justices Samuel Alito and Clarence Thomas dissented from the Supreme Court's refusal to hear a case they say forces police to create separate rules based on race.

The company says it will'engage constructively' with them. OpenAI is under investigation by a coalition of state attorneys general, according to the Wall Street Journal . On Friday, June 12, the company received a subpoena seeking information and documents related to its activities and impact on users. said it viewed the subpoena sent by New York's attorney general. Based on what the publication saw, the AGs are asking for documentation about the company's advertising, user engagement and retention, as well as its handling of its users' data and health information. They also want to know about the company's activities related to minor and senior users, its deep learning models, its policies and its models' sycophancy.

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Things to Do in L.A. Tap to enable a layout that focuses on the article. O.C. immigration attorneys suspended for filing briefs filled with AI-hallucinated errors The attorneys were fined $2,500 each and suspended from practicing in the U.S. 9th Circuit Court of Appeals for six months. This is read by an automated voice. Please report any issues or inconsistencies here . A pair of Orange County immigration attorneys received temporary suspensions after the court discovered they used generative AI to write briefs that included "multiple nonexistent cases, misattributed quotations, and gross misrepresentations."

A central challenge in dynamic network analysis is to represent temporal evolution in a way that is both geometrically meaningful and statistically identifiable. One approach embeds a sequence of network snapshots as trajectories in a Euclidean space and relates these trajectories to node embeddings. In multilayer and unfolded spectral constructions, however, node embeddings and their underlying latent positions are identifiable only up to general linear transformations. Although this ambiguity preserves edge probabilities, it can distort geometry and invalidate distance based temporal comparisons at both the trajectory and node-levels. We develop Multiscale Euclidean Network Trajectories (MENT), a framework for multiscale temporal trajectories based on second-moment geometry. By imposing an isotropic normalization on the anchor latent positions, we reduce the relevant ambiguity to orthogonal transformations and prevent distortion of the second-moment geometry. In this canonical representation, we define a trace variation distance and mode-wise variation distances along orthogonal directions, and use multidimensional scaling to obtain low-dimensional trajectories of time points at both global and mode-wise levels. The resulting trajectories support interpretation and inference. They admit mode-wise decompositions, support attribution of global and mode-wise temporal changes to nodes, and enable change point detection through 1D trajectories. We prove consistency of the proposed unfolded spectral embedding and of the induced temporal trajectories. Experiments on two synthetic and two real dynamic networks illustrate stable and interpretable recovery of temporal structure and show strong performance against existing change point detection baselines.