Things to Do in L.A. Tap to enable a layout that focuses on the article. Inside LAUSD's alleged $22-million money-laundering scheme, 'the largest' in district history This is read by an automated voice. Please report any issues or inconsistencies here . Los Angeles Unified is seeking to recover $22 million from a contractor after alleging that a former district manager steered lucrative IT contracts to the company in exchange for kickbacks. Peng and Sampath have denied wrongdoing.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia and Ukraine have resumed air attacks after a United States-brokered three-day truce expired, with President Volodymyr Zelenskyy saying more than 200 drones were used to attack Ukraine overnight. Russian aerial attacks across Ukraine's Dnipropetrovsk region on Tuesday morning killed at least one person and injured four others, according to regional administration chief Oleksandr Ganzha. Russia also carried out attacks on the regions of Kharkiv, Zhytomyr, Sumy and Chernihiv, according to authorities. More than 200 long-range drones were used in the wave of attacks, Zelenskyy said. "Russia itself chose to end the partial silence that had lasted for several days," he said in a post on X. Russia's military, meanwhile, said its defences downed 27 Ukrainian drones over the regions of Belgorod, Voronezh and Rostov.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The group had reportedly been starving on the front line after up to 17 days without food deliveries and months without rotation. The fighters were holed up on the left, eastern bank of the Oskil River in the southeastern Donetsk region after Russian bombs destroyed the bridges connecting them to their brigade on the right bank. "They weren't listened to on the radio, or perhaps no one wanted to listen to them. My husband shouted and begged, saying there was no food and water," Silchuk wrote. She did not respond to Al Jazeera's request for an interview.

Donald Trump is heading to China this week, and if his guest list is any clue, he wants to discuss technology with Xi Jinping. Donald Trump is heading to China this week, and if his guest list is any clue, he wants to discuss technology with Xi Jinping. Donald Trump is heading to China this week. If his guest list is any clue, he wants to discuss technology with Xi Jinping, though perhaps after the war in Iran. On Monday, news broke that outgoing Apple CEO, Tim Cook, as well as SpaceX and Tesla CEO, Elon Musk, would join the US president.

Science fiction has long imagined a world where our brains interact with machines to restore and augment our abilities -- think of the neural implants that connected to Geordi La Forge's visor in Star Trek or allowed Alex Murphy to be reborn as cyborg law enforcer in RoboCop. In the real world, researchers have been working for decades on so-called brain-computer interfaces to help people who suffer from paralysis, blindness, hearing loss, and more, regain function. Some individuals have used these devices to control a computer cursor with their minds; others have managed to move a robotic arm or transcribe some of their thoughts into text. The technology is still nascent and the number of people who have received implants is only in the hundreds. Just a few companies have received regulatory approval to progress beyond clinical trials to commercial use -- and even that's for limited applications.

OpenAI has just launched Daybreak, a cybersecurity initiative that's clearly the company's competitor to Anthropic's Project Glasswing . If you'll recall, Glasswing uses Anthropic's unreleased AI model, Claude Mythos Preview, to provide its clients' cyber defense needs. It's been promising, so far: Mozilla revealed in April that Mythos helped it find and patch 271 vulnerabilities in the latest release of the Firefox browser. OpenAI says Daybreak uses its various AI models, including its specialized security agent Codex. In its announcement, the company explained that Daybreak is built around the premise that cyber defense should be built into software from the start and not just revolve around finding and fixing vulnerabilities.

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.

We study the problem of learning generative models for discrete sequences in a continuous embedding space. Whereas prior approaches typically operate in Euclidean space or on the probability simplex, we instead work on the sphere $\mathbb S^{d-1}$. There the von Mises-Fisher (vMF) distribution induces a natural noise process and admits a closed-form conditional score. The conditional velocity is in general intractable. Exploiting the radial symmetry of the vMF density we reduce the continuity equation on $\mathbb S^{d-1}$ to a scalar ODE in the cosine similarity, whose unique bounded solution determines the velocity. The marginal velocity and marginal score on $(\mathbb S^{d-1})^L$ both decompose into posterior-weighted tangent sums that differ only by per-token scalar weights. This gives access to both ODE and predictor-corrector (PC) sampling. The posterior is the only learned object, trained by a cross-entropy loss. Experiments compare the vMF path against geodesic and Euclidean alternatives. The combination of vMF and PC sampling significantly improves results on Sudoku and language modeling.

Gaussian process (GP) marginal likelihood scores and kernel conditional independence tests are theoretically appealing for nonlinear causal discovery but computationally prohibitive at scale. We present three complementary RFF-based methods forming a practical toolkit for score-based, constraint-based, and hybrid causal discovery. The Fourier Feature Marginal Likelihood (FFML) score approximates the exact GP marginal likelihood by replacing the $n x n$ kernel Gram matrix with a finite-dimensional feature representation, reducing cost to $O(nm^2 + m^3)$ while retaining the probabilistic interpretation and automatic complexity penalty of the exact score. FFML extends to mixed (continuous and discrete) parent sets via a product-kernel construction, with a Kronecker path for small discrete parent sets and a Hadamard-product path otherwise. The Tetrad Random Fourier Feature (TRFF) score is a complementary BIC-style alternative using penalized Student-t regression with random Fourier features. TRFF offers robustness to heavy-tailed noise and faster runtime than FFML. Empirically, TRFF and FFML exhibit a complementary precision-recall profile: TRFF achieves higher precision while FFML achieves better recall and lower SHD overall. The Fourier Feature Conditional Independence (FFCI) test is a fast nonparametric CI test for mixed data, using ridge residualization in feature space and a Frobenius-norm cross-covariance statistic approximated as a weighted sum of chi-squared variables. Empirically, BOSS+FFML achieves the lowest SHD on nonlinear data, while BOSS+TRFF offers the highest precision. When run through PC-Max, FFCI and RCIT exhibit complementary precision-recall profiles: RCIT is more precise while FFCI achieves better recall and substantially lower SHD, at approximately twice the runtime.

Post-deployment monitoring of healthcare AI requires statistically valid, label-efficient methods, but gold-standard labels from clinician chart review are expensive. Prediction-powered inference (PPI) and active statistical inference (ASI) reduce label cost by combining a small labeled sample with abundant model predictions, but both are restricted to a single predictor, a poor fit for modern clinical pipelines that have multiple predictors of differing cost and accuracy available at inference time. We propose Active Multiple-Prediction-Powered Inference (AM-PPI), which routes each instance to a cost-appropriate predictor subset, samples gold-standard labels in proportion to the chosen subset's residual uncertainty, and reweights predictions to minimize estimator variance, all under a single deployment-time budget. AM-PPI generalizes ASI to leverage multiple predictors and extends Multiple-PPI from global per-predictor allocation to per-instance adaptive routing. We derive closed-form Karush-Kuhn-Tucker (KKT) conditions for all three decisions and prove, via biconvexity and strong duality, that the resulting fixed point is a global optimum despite the joint problem being non-jointly-convex. We establish asymptotic normality with valid coverage, minimum-variance unbiasedness within the linear-prediction augmented inverse propensity weighted (AIPW) class, and a closed-form criterion identifying when multiple predictors help. On synthetic data and three healthcare monitoring tasks, AM-PPI produces 10 to 40 percent narrower confidence intervals (CIs) than single-predictor ASI in the budget regime where routing matters, and matches the better baseline elsewhere.