Authorities said the driver was using "an automated driving assistance system" in a Model 3. A woman died after a Tesla driver, who was reportedly using an automated driving assistance system crashed into a house in Katy, Texas, according to local authorities. The Harris County Sheriff's Office said that the driver, who was identified as Michael Butler, was in a Tesla Model 3 with the driving assistance system engaged and hit the house at 1907 Blooming Park Lane on Friday night. The police reported that the Model 3 failed to drive in a single lane, left the roadway and struck the residence at a high rate of speed. The crash involved a woman, Martha Avila, who was inside the house. She was transported to a local hospital where she was pronounced dead due to injuries she sustained from the crash, police said.

Sparse recovery is among the most well-studied problems in learning theory and high-dimensional statistics. In this work, we investigate the statistical and computational landscapes of sparse recovery with $\ell_\infty$ error guarantees. This variant of the problem is motivated by \emph{variable selection} tasks, where the goal is to estimate the support of a $k$-sparse signal in $\mathbb{R}^d$. Our main contribution is a provable separation between the \emph{oblivious} (``for each'') and \emph{adaptive} (``for all'') models of $\ell_\infty$ sparse recovery. We show that under an oblivious model, the optimal $\ell_\infty$ error is attainable in near-linear time with $\approx k\log d$ samples, whereas in an adaptive model, $\gtrsim k^2$ samples are necessary for any algorithm to achieve this bound. This establishes a surprising contrast with the standard $\ell_2$ setting, where $\approx k \log d$ samples suffice even for adaptive sparse recovery. We conclude with a preliminary examination of a \emph{partially-adaptive} model, where we show nontrivial variable selection guarantees are possible with $\approx k\log d$ measurements.

Tesla just objectively decreased the value of the Model 3 and Model Y. On Thursday, the company said it's paywalling its lane-centering feature, Autosteer, for new purchases of the two EVs in the US and Canada. This was previously a standard feature. Lane centering is now part of the Full Self-Driving Supervised (FSD) package, which costs $99 per month. Speculating on why Tesla would do this doesn't require much imagination. Remember the pay package Tesla shareholders approved for Musk in November?

We consider training and testing on mixture distributions with different training and test proportions. We show that in many settings, and in some sense generically, distribution shift can be beneficial, and test performance can improve due to mismatched training proportions, even if the components are unrelated and with no transfer between components. In a variety of scenarios, we identify the optimal training proportions and the extent to which such distribution shift can be beneficial. We show how the same analysis applies also to a compositional setting with differing distribution of component "skills'' at training and test.

High-energy materials (HEMs) are critical for propulsion and defense domains, yet their discovery remains constrained by experimental data and restricted access to testing facilities. This work presents a novel approach toward high-energy molecules by combining Long Short-Term Memory (LSTM) networks for molecular generation and Attentive Graph Neural Networks (GNN) for property predictions. We propose a transformative embedding space construction strategy that integrates fixed SHA-256 embeddings with partially trainable representations. Unlike conventional regularization techniques, this changes the representational basis itself, reshaping the molecular input space before learning begins. Without recourse to pretraining, the generator achieves 67.5% validity and 37.5% novelty. The generated library exhibits a mean Tanimoto coefficient of 0.214 relative to training set signifying the ability of framework to generate a diverse chemical space. We identified 37 new super explosives higher than 9 km/s predicted detonation velocity.

The electric vehicle tax credit is gone, and Tesla's new, more affordable models don't quite close the gap. For nearly two decades, CEO Elon Musk has promised Tesla would make a more affordable electric vehicle, to, as he put it in 2006, "help expedite the move from a mine-and-burn hydrocarbon economy towards a solar electric economy." On Tuesday, Tesla announced a new Model Y and Model 3 Standard, versions of its popular compact SUV and sedan stripped of a few higher-end touches and features to bring the price down to $39,990 and $36,990, respectively. They're both about $5,000 cheaper than the Premium variants, which goes a ways--but not all the way--toward recouping the $7,500 tax credit canceled by the GOP-led Congress this past summer . The price point also puts Tesla's newest models firmly in the "more affordable" EV camp.

Tesla unveiled more affordable versions of its best-selling Model Y SUV and its Model 3 sedan at $39,990 and $36,990, respectively, as the electric vehicle (EV) manufacturer seeks to reverse falling sales and waning market share amid rising competition. The EV maker announced its new models on Tuesday. Late last year, Musk said the vehicle would be priced below the "key threshold" of $30,000, including US EV tax credits. In the United States, prices effectively rose by $7,500 at the end of last month, when the EV tax credit ended. That helped goose quarterly sales to a record, but expectations are that they will slow down for the rest of the year, unless the affordable car comes to the rescue.

Modern socio-economic systems are undergoing deep integration with artificial intelligence technologies. This paper constructs a heterogeneous agent-based modeling framework that incorporates both human workers and autonomous AI agents, to study the impact of AI collaboration under resource constraints on aggregate social output. We build five progressively extended models: Model 1 serves as the baseline of pure human collaboration; Model 2 introduces AI as collaborators; Model 3 incorporates network effects among agents; Model 4 treats agents as independent producers; and Model 5 integrates both network effects and independent agent production. Through theoretical derivation and simulation analysis, we find that the introduction of AI agents can significantly increase aggregate social output. When considering network effects among agents, this increase exhibits nonlinear growth far exceeding the simple sum of individual contributions. Under the same resource inputs, treating agents as independent producers provides higher long-term growth potential; introducing network effects further demonstrates strong characteristics of increasing returns to scale.

We present a hybrid surrogate model for electric vehicle parameter estimation and power consumption. We combine our novel architecture Spectral Parameter Operator built on a Fourier Neural Operator backbone for global context and a differentiable physics module in the forward pass. From speed and acceleration alone, it outputs time-varying motor and regenerative braking efficiencies, as well as aerodynamic drag, rolling resistance, effective mass, and auxiliary power. These parameters drive a physics-embedded estimate of battery power, eliminating any separate physics-residual loss. The modular design lets representations converge to physically meaningful parameters that reflect the current state and condition of the vehicle. We evaluate on real-world logs from a Tesla Model 3, Tesla Model S, and the Kia EV9. The surrogate achieves a mean absolute error of 0.2kW (about 1% of average traction power at highway speeds) for Tesla vehicles and about 0.8kW on the Kia EV9. The framework is interpretable, and it generalizes well to unseen conditions, and sampling rates, making it practical for path optimization, eco-routing, on-board diagnostics, and prognostics health management.

It has been dubbed'Britain's most confusing junction', thanks to its complex system of mini–roundabouts. But while many drivers struggle to navigate their way around Swindon's Magic Roundabout, the junction proved to be light work for a self–driving car. To put its Full Self Driving (FSD) mode to the test, Tesla sent a Model 3 through the complex intersection. Footage shows the car expertly navigating the roundabout – not just once, but three times – as cars continuously join from seemingly every direction. Fans have flocked to X to discuss the feat, with one calling it'superb'.