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Things to Do in L.A. Tap to enable a layout that focuses on the article. Self-driving vehicles charge at the Waymo station in Santa Monica. This is read by an automated voice. Please report any issues or inconsistencies here . Waymo is taking the city of Santa Monica to court after the city ordered the company to cease charging its autonomous vehicles at two facilities overnight, claiming the lights and beeping at the lots were a nuisance to residents.

The search for primordial gravitational waves is a central goal of cosmic microwave background (CMB) surveys. Isolating the characteristic $B$-mode polarization signal sourced by primordial gravitational waves is challenging for several reasons: the amplitude of the signal is inherently small; astrophysical foregrounds produce $B$-mode polarization contaminating the signal; and secondary $B$-mode polarization fluctuations are produced via the conversion of $E$ modes. Current and future low-noise, multi-frequency observations enable sufficient precision to address the first two of these challenges such that secondary $B$ modes will become the bottleneck for improved constraints on the amplitude of primordial gravitational waves. The dominant source of secondary $B$-mode polarization is gravitational lensing by large scale structure. Various strategies have been developed to estimate the lensing deflection and to reverse its effects the CMB, thus reducing confusion from lensing $B$ modes in the search for primordial gravitational waves. However, a few complications remain. First, there may be additional sources of secondary $B$-mode polarization, for example from patchy reionization or from cosmic polarization rotation. Second, the statistics of delensed CMB maps can become complicated and non-Gaussian, especially when advanced lensing reconstruction techniques are applied. We previously demonstrated how a deep learning network, ResUNet-CMB, can provide nearly optimal simultaneous estimates of multiple sources of secondary $B$-mode polarization. In this paper, we show how deep learning can be applied to estimate and remove multiple sources of secondary $B$-mode polarization, and we further show how this technique can be used in a likelihood analysis to produce nearly optimal, unbiased estimates of the amplitude of primordial gravitational waves.

Explainable machine learning aims to strike a balance between prediction accuracy and model transparency, particularly in settings where black-box predictive models, such as deep neural networks or kernel-based methods, achieve strong empirical performance but remain difficult to interpret. This work introduces a mixture of generalized additive models (GAMs) in which random Fourier feature (RFF) representations are leveraged to uncover locally adaptive structure in the data. In the proposed method, an RFF-based embedding is first learned and then compressed via principal component analysis. The resulting low-dimensional representations are used to perform soft clustering of the data through a Gaussian mixture model. These cluster assignments are then applied to construct a mixture-of-GAMs framework, where each local GAM captures nonlinear effects through interpretable univariate smooth functions. Numerical experiments on real-world regression benchmarks, including the California Housing, NASA Airfoil Self-Noise, and Bike Sharing datasets, demonstrate improved predictive performance relative to classical interpretable models. Overall, this construction provides a principled approach for integrating representation learning with transparent statistical modeling.

Effective feature selection is essential for high-dimensional data analysis and machine learning. Unsupervised feature selection (UFS) aims to simultaneously cluster data and identify the most discriminative features. Most existing UFS methods linearly project features into a pseudo-label space for clustering, but they suffer from two critical limitations: (1) an oversimplified linear mapping that fails to capture complex feature relationships, and (2) an assumption of uniform cluster distributions, ignoring outliers prevalent in real-world data. To address these issues, we propose the Robust Autoencoder-based Unsupervised Feature Selection (RAEUFS) model, which leverages a deep autoencoder to learn nonlinear feature representations while inherently improving robustness to outliers. We further develop an efficient optimization algorithm for RAEUFS. Extensive experiments demonstrate that our method outperforms state-of-the-art UFS approaches in both clean and outlier-contaminated data settings.

Conformal prediction (CP) provides distribution-free, finite-sample coverage guarantees but critically relies on exchangeability, a condition often violated under distribution shift. We study the robustness of split conformal prediction under adversarial perturbations at test time, focusing on both coverage validity and the resulting prediction set size. Our theoretical analysis characterizes how the strength of adversarial perturbations during calibration affects coverage guarantees under adversarial test conditions. We further examine the impact of adversarial training at the model-training stage. Extensive experiments support our theory: (i) Prediction coverage varies monotonically with the calibration-time attack strength, enabling the use of nonzero calibration-time attack to predictably control coverage under adversarial tests; (ii) target coverage can hold over a range of test-time attacks: with a suitable calibration attack, coverage stays within any chosen tolerance band across a contiguous set of perturbation levels; and (iii) adversarial training at the training stage produces tighter prediction sets that retain high informativeness.

Novo Nordisk's semaglutide will soon be available in a daily pill Americans can take for weight loss. The US Food and Drug Administration today approved a pill version of the blockbuster anti-obesity drug Wegovy. Made by Novo Nordisk, the pill is taken once a day. The company's original version of Wegovy is a weekly injection. Both drugs contain the same active ingredient, semaglutide.

Vince Zampella, who co-created the widely-popular video game Call of Duty, has died in a single-vehicle Ferrari crash in California, aged 55. Zampella's death was confirmed by Electronic Arts, which owns Respawn Entertainment, a game studio he co-founded. This is an unimaginable loss, and our hearts are with Vince's family, his loved ones, and all those touched by his work, a spokesperson for Electronic Arts told the BBC. Officials said the person on the vehicle's passenger seat was ejected while the driver remained trapped. It is unclear if Zampella was driving the car.

The latest Epstein Files release appears to contain hundreds of photographs along with court records and other materials. Over the weekend, the Justice Department released three new data sets comprising files related to Jeffrey Epstein . The DOJ had previously released nearly 4,000 documents prior to the Friday midnight deadline required by the Epstein Files Transparency Act . As with Friday's release, the new tranche appears to contain hundreds of photographs, along with various court records pertaining to Epstein. There are around 1,200 pages in all, including images WIRED is currently going through the materials and will update with more detail.

Russia has intensified its strikes on the southern Ukrainian region of Odesa, causing widespread power cuts and threatening the region's maritime infrastructure. Ukrainian Deputy Prime Minister Oleksiy Kuleba said Moscow was carrying out systematic attacks on the region. Last week, he warned that the focus of the war may have shifted towards Odesa. President Volodymyr Zelensky said the repeated attacks were an attempt by Moscow to block Ukraine's access to maritime logistics. Earlier in December, Russian President Vladimir Putin threatened to sever Ukraine's access to the sea as retaliation for drone attacks on tankers of Russia's shadow fleet in the Black Sea.