Samsung Galaxy Unpacked 2026 is Feb. 25 Valve's Steam Machine: Everything we know Tesla stops using'Autopilot' to promote its EVs in California The company has avoided a 30-day suspension by making the change. Tesla has stopped using the term "Autopilot" to sell its cars in California, thereby avoiding a 30-day sales and manufacturing ban in the state. If you'll recall, a California administrative law judge ruled in December that the automaker misled consumers by using the terms "Autopilot" and "Full Self-Driving." The judge recommended the suspension, but the California DMV gave Tesla 60 days to remove any untrue and misleading language in its marketing materials. In its announcement, the DMV said Tesla has taken corrective action and has stopped using Autopilot for marketing.

A key part of the SpaceX-xAI deal's rationale is to move datacentres - the central nervous system of AI tools - into space. A key part of the SpaceX-xAI deal's rationale is to move datacentres - the central nervous system of AI tools - into space. Why has Elon Musk merged his rocket company with his AI startup? SpaceX's acquisition of xAI creates business worth $1.25tn but whether premise behind deal will work is questioned The acquisition of xAI by SpaceX is a typical Elon Musk deal: big numbers backed by big ambition. As well as extending "the light of consciousness to the stars", as Musk described it, the transaction creates a business worth $1.25tn (£920bn) by combining Musk's rocket company with his artificial intelligence startup.

California regulators are threatening to suspend Tesla's licence to sell its electric cars in the state early next year unless the car maker tones down its marketing tactics for its self-driving features after a judge concluded that the Elon Musk-led company has been misleading consumers about the technology's capabilities. The potential 30-day blackout of Tesla's sales in California in the United States is the primary punishment being recommended to the state's Department of Motor Vehicles in a decision released late on Tuesday. After presiding over five days of hearings held in Oakland, California, in July, Cox also recommended suspending Tesla's licence to manufacture cars at its plant in Fremont, California. But California regulators will not impose that part of the judge's proposed penalty. Tesla will have a 90-day window to make changes that more clearly convey the limits of its self-driving technology to avoid having its California sales licence suspended.

Early detection and characterization of coronavirus disease (COVID-19), caused by SARS-CoV-2, remain critical for effective clinical response and public-health planning. The global availability of large-scale viral sequence data presents significant opportunities for computational analysis; however, existing approaches face notable limitations. Phylogenetic tree-based methods are computationally intensive and do not scale efficiently to today's multi-million-sequence datasets. Similarly, current embedding-based techniques often rely on aligned sequences or exhibit suboptimal predictive performance and high runtime costs, creating barriers to practical large-scale analysis. In this study, we focus on the most prevalent SARS-CoV-2 lineages associated with the spike protein region and introduce a scalable embedding method that leverages hashing to generate compact, low-dimensional representations of spike sequences. These embeddings are subsequently used to train a variety of machine learning models for supervised lineage classification. We conduct an extensive evaluation comparing our approach with multiple baseline and state-of-the-art biological sequence embedding methods across diverse metrics. Our results demonstrate that the proposed embeddings offer substantial improvements in efficiency, achieving up to 86.4\% classification accuracy while reducing embedding generation time by as much as 99.81\%. This highlights the method's potential as a fast, effective, and scalable solution for large-scale viral sequence analysis.

Previously, image interpretation in radiology relied heavily on manual methods. However, manual classification of brain tumor medical images is time-consuming and labor-intensive. Even with shallow convolutional neural network models, the accuracy is not ideal. To improve the efficiency and accuracy of brain tumor image classification, this paper proposes a brain tumor classification model based on an improved ResNet34 network. This model uses the ResNet34 residual network as the backbone network and incorporates multi-scale feature extraction. It uses a multi-scale input module as the first layer of the ResNet34 network and an Inception v2 module as the residual downsampling layer. Furthermore, a channel attention mechanism module assigns different weights to different channels of the image from a channel domain perspective, obtaining more important feature information. The results after a five-fold crossover experiment show that the average classification accuracy of the improved network model is approximately 98.8%, which is not only 1% higher than ResNet34, but also only 80% of the number of parameters of the original model. Therefore, the improved network model not only improves accuracy but also reduces clutter, achieving a classification effect with fewer parameters and higher accuracy.

Achieving high-fidelity, compact RGBD imaging presents a dual challenge: conventional compact optics struggle with RGB sharpness across the entire depth-of-field, while software-only Monocular Depth Estimation (MDE) is an ill-posed problem reliant on unreliable semantic priors. While deep optics with elements like DOEs can encode depth, they introduce trade-offs in fabrication complexity and chromatic aberrations, compromising simplicity. To address this, we first introduce a novel bio-inspired all-spherical monocentric lens, around which we build the Bionic Monocentric Imaging (BMI) framework, a holistic co-design. This optical design naturally encodes depth into its depth-varying Point Spread Functions (PSFs) without requiring complex diffractive or freeform elements. We establish a rigorous physically-based forward model to generate a synthetic dataset by precisely simulating the optical degradation process. This simulation pipeline is co-designed with a dual-head, multi-scale reconstruction network that employs a shared encoder to jointly recover a high-fidelity All-in-Focus (AiF) image and a precise depth map from a single coded capture. Extensive experiments validate the state-of-the-art performance of the proposed framework. In depth estimation, the method attains an Abs Rel of 0.026 and an RMSE of 0.130, markedly outperforming leading software-only approaches and other deep optics systems. For image restoration, the system achieves an SSIM of 0.960 and a perceptual LPIPS score of 0.082, thereby confirming a superior balance between image fidelity and depth accuracy. This study illustrates that the integration of bio-inspired, fully spherical optics with a joint reconstruction algorithm constitutes an effective strategy for addressing the intrinsic challenges in high-performance compact RGBD imaging. Source code will be publicly available at https://github.com/ZongxiYu-ZJU/BMI.

Tesla vehicles line a parking area at the company's factory in Fremont, California. Tesla vehicles line a parking area at the company's factory in Fremont, California. Carmaker exceeded Wall Street's expectations with more than $26bn in revenue, but saw a 37% drop in profits Despite record vehicle sales, Tesla saw a precipitous drop in profit in its most recent quarter. A rush to buy electric vehicles before a US tax credit for them disappears had boosted Tesla's flagging sales, leading to the automaker exceeding some of Wall Street's projections in its most recent financial quarter. Yet the company failed to meet earnings expectations and its stock fell in after-hours trading.

Astronomical time-series analysis faces a critical limitation: the scarcity of labeled observational data. We present a pre-training approach that leverages simulations, significantly reducing the need for labeled examples from real observations. Our models, trained on simulated data from multiple astronomical surveys (ZTF and LSST), learn generalizable representations that transfer effectively to downstream tasks. Using classifier-based architectures enhanced with contrastive and adversarial objectives, we create domain-agnostic models that demonstrate substantial performance improvements over baseline methods in classification, redshift estimation, and anomaly detection when fine-tuned with minimal real data. Remarkably, our models exhibit effective zero-shot transfer capabilities, achieving comparable performance on future telescope (LSST) simulations when trained solely on existing telescope (ZTF) data. Furthermore, they generalize to very different astronomical phenomena (namely variable stars from NASA's \textit{Kepler} telescope) despite being trained on transient events, demonstrating cross-domain capabilities. Our approach provides a practical solution for building general models when labeled data is scarce, but domain knowledge can be encoded in simulations.

The faithful transfer of contextually-embedded meaning continues to challenge contemporary machine translation (MT), particularly in the rendering of culture-bound terms--expressions or concepts rooted in specific languages or cultures, resisting direct linguistic transfer. Existing computational approaches to explicitating these terms have focused exclusively on in-text solutions, overlooking paratextual apparatus in the footnotes and endnotes employed by professional translators. In this paper, we formalize Genette's (1987) theory of paratexts from literary and translation studies to introduce the task of paratextual explicitation for MT. We construct a dataset of 560 expert-aligned paratexts from four English translations of the classical Chinese short story collection Liaozhai and evaluate LLMs with and without reasoning traces on choice and content of explicitation. Experiments across intrinsic prompting and agentic retrieval methods establish the difficulty of this task, with human evaluation showing that LLM-generated paratexts improve audience comprehension, though remain considerably less effective than translator-authored ones. Beyond model performance, statistical analysis reveals that even professional translators vary widely in their use of paratexts, suggesting that cultural mediation is inherently open-ended rather than prescriptive. Our findings demonstrate the potential of paratextual explicitation in advancing MT beyond linguistic equivalence, with promising extensions to monolingual explanation and personalized adaptation.

IEEE TRANSACTIONS ON ANTENNAS AND PROP AGA TION 1 A Deep Learning Framework for Two-Dimensional, Multi-Frequency Propagation Factor Estimation Sarah E. Wessinger, Member, IEEE, Leslie N. Smith, Jacob Gull, Jonathan Gehman, Zachary Beever, and Andrew J. Kammerer Abstract --Accurately estimating propagation factor over multiple frequencies within the marine atmospheric boundary layer is crucial for the effective deployment of radar technologies. Traditional parabolic equation simulations, while effective, can be computationally expensive and time-intensive, limiting their practical application. This communication explores a novel approach using deep neural networks to estimate the pattern propagation factor, a critical parameter for characterizing environmental impacts on signal propagation. Image-to-image translation generators designed to ingest modified refractivity data and generate predictions of pattern propagation factors over the same domain were developed. Findings demonstrate that deep neural networks can be trained to analyze multiple frequencies and reasonably predict the pattern propagation factor, offering an alternative to traditional methods.