A map of mouse development from conception to birth tracks 12.4 million cells at single-cell resolution as they mature into organs and other tissues.
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New U.S. vehicle emission standards for 2027–2032
The Biden administration has today announced new vehicle emission standards for 2027–2032, as it pushes for wider adoption of hybrid and electric vehicles.
Tesla Model 3 at a charging station in Delaware, United States. Credit: K.A
Automobile emissions in the United States could be slashed by half within the next eight years, thanks to new standards finalised and published today by the Environmental Protection Agency (EPA). The ambitious set of rules – not only aimed at reducing air pollution, but also cutting fuel costs – are to be phased in between 2027 and 2032.
Pair accused of stealing battery manufacturing secrets from Tesla and starting their own company
Two men are accused of starting a business in China using battery manufacturing technology pilfered from Tesla and trying to sell the proprietary information, federal prosecutors in New York said Tuesday.
Klaus Pflugbeil, 58, a Canadian citizen who lives in Ningbo, China, was arrested Tuesday morning on Long Island, where he thought he was going to meet with businessmen to negotiate a sale price for the information, federal authorities said. Instead, the businessmen were undercover federal agents.
The other man named in the criminal complaint is Yilong Shao, 47, also of Ningbo. He remains at large. They are charged with conspiracy to transmit trade secrets, which carries up to 10 years in prison if convicted.
Privacy in the AI era: How do we protect our personal information?
The AI boom, including the advent of large language models (LLMs) and their associated chatbots, poses new challenges for privacy. Is our personal information part of a model’s training data? Are our prompts being shared with law enforcement? Will chatbots connect diverse threads from our online lives and output them to anyone?
Introducing Floorlocator, a system that enhances indoor navigation
Indoor positioning is transforming with applications demanding precise location tracking. Traditional methods, including fingerprinting and sensor-based techniques, though widely used, face significant drawbacks, such as the need for extensive training data, poor scalability, and reliance on additional sensor information. Recent advancements have sought to leverage deep learning, yet issues such as low scalability and high computational costs remain unaddressed.