The U.S. tried permanent daylight saving time--and hated it In 1974, America set its clocks forward for good in the name of energy savings. Between January and September in 1974, President Richard Nixon made daylight saving time permanent for a brief period. Breakthroughs, discoveries, and DIY tips sent every weekday. As fall approaches, so too does the end of daylight savings time (DST). On November 2nd, the hour between 1 a.m. and 2 a.m. will happen twice.

Archaeological predictive modelling estimates where undiscovered sites are likely to occur by combining known locations with environmental, cultural, and geospatial variables. We address this challenge using a deep learning approach but must contend with structural label scarcity inherent to archaeology: positives are rare, and most locations are unlabeled. To address this, we adopt a semi-supervised, positive-unlabeled (PU) learning strategy, implemented as a semantic segmentation model and evaluated on two datasets covering a representative range of archaeological periods. Our approach employs dynamic pseudolabeling, refined with a Conditional Random Field (CRF) implemented via an RNN, increasing label confidence under severe class imbalance. On a geospatial dataset derived from a digital elevation model (DEM), our model performs on par with the state-of-the-art, LAMAP, while achieving higher Dice scores. On raw satellite imagery, assessed end-to-end with stratified k-fold cross-validation, it maintains performance and yields predictive surfaces with improved interpretability. Overall, our results indicate that semi-supervised learning offers a promising approach to identifying undiscovered sites across large, sparsely annotated landscapes.

Drones could be used for NHS-related missions in remote areas, inspecting offshore wind turbines and supplying oil rigs by 2026 as part of a new regulatory regime in the UK. David Willetts, the head of a new government unit helping to deploy new technologies in Britain, said there were obvious situations where drones could be used if the changes go ahead next year. Ministers announced plans this month to allow drones to fly long distances without their operators seeing them. Drones cannot be flown "beyond visual line of sight" under current regulations, making their use for lengthy journeys impossible. In an interview with the Guardian, Lord Willetts, chair of the Regulatory Innovation Office (RIO), said the changes could come as soon as 2026, but that they would apply in "atypical" aviation environments at first, which would mean remote areas and over open water. Referring to the NHS, Willetts said there was potentially a huge market for drone operators.

Paralysis had robbed the two women of their ability to speak. For one, the cause was amyotrophic lateral sclerosis, or ALS, a disease that affects the motor neurons. The other had suffered a stroke in her brain stem. Though they can't enunciate clearly, they remember how to formulate words. Now, after volunteering to receive brain implants, both are able to communicate through a computer at a speed approaching the tempo of normal conversation.

We study the performance of machine learning binary classification techniques in terms of error probabilities. The statistical test is based on the Data-Driven Decision Function (D3F), learned in the training phase, i.e., what is thresholded before the final binary decision is made. Based on large deviations theory, we show that under appropriate conditions the classification error probabilities vanish exponentially, as $\sim \exp\left(-n\,I + o(n) \right)$, where $I$ is the error rate and $n$ is the number of observations available for testing. We also propose two different approximations for the error probability curves, one based on a refined asymptotic formula (often referred to as exact asymptotics), and another one based on the central limit theorem. The theoretical findings are finally tested using the popular MNIST dataset.

Artificial intelligence makes it possible for people who have hindered limb movement or are paralyzed to communicate by text using data interpretation from devices placed at the brain's surface. The fusion of human brainpower and ultra-modern AI technology has allowed a man with paralyzed limbs to communicate using text on a smartphone at nearing speed achieved by his healthy body parts. Researchers from Stanford University have integrated artificial intelligence software with an electronic device, called a brain-computer interface (BCI), rooted in the brain of a man with full-body paralysis. The robust AI software has decoded the BCI information and instantly transforms the man's feelings about handwriting into text on a computer screen. After this integration, the man wrote using this technology more than twice as fast as he could using a former system developed by Stanford researchers, which reported the findings in a 2017 journal.

As he imagines handwriting letters, they appear before him as typed text thanks to a new brain implant. The 65-year-old is "typing" at a speed similar to his peers tapping on a smartphone, using a device that could one day help paralysed people communicate quickly and easily. The research could benefit people suffering spinal cord injuries, strokes or motor neurone disease, said Frank Willett, a research scientist at Stanford University and lead author of the study published Wednesday in the journal Nature. "Imagine if you could only move your eyes up and down but couldn't move anything else -- a device like this could enable you to type your thoughts at speeds that are comparable to that of normal handwriting or typing on a smartphone," he told AFP. Existing devices for those with paralysis rely on eye movement or imagining moving a cursor to point and click on letters. But Willett and his team wondered whether thinking about handwriting letters might be another way for people to express themselves.

For the first time, researchers have deciphered the brain activity associated with trying to write letters by hand. Working with a participant with paralysis who has sensors implanted in his brain, the team used an algorithm to identify letters as he attempted to write them. Then, the system displayed the text on a screen -- in real time. The innovation could, with further development, let people with paralysis rapidly type without using their hands, says study coauthor Krishna Shenoy, a Howard Hughes Medical Institute Investigator at Stanford University who jointly supervised the work with Jaimie Henderson, a Stanford neurosurgeon. By attempting handwriting, the study participant typed 90 characters per minute -- more than double the previous record for typing with such a "brain-computer interface," Shenoy and his colleagues report in the journal Nature on May 12, 2021.

This is an Inside Science story. A man paralyzed below the neck can imagine writing by hand and, with the help of artificial intelligence software, use electronics hooked up to his brain to translate his mental handwriting into words at speeds comparable to typing on a smartphone, a new study finds. By helping convert thoughts into actions, brain-computer interfaces can help people move or speak. Recently, scientists have sought to help people with disabilities communicate by using these mind-machine interfaces to move a cursor on a screen to point and click on letters on a keyboard. The previous speed record for typing with such devices was about 40 characters per minute.

The combination of mental effort and state-of-the-art technology have allowed a man with immobilized limbs to communicate by text at speeds rivaling those achieved by his able-bodied peers texting on a smartphone. Stanford University investigators have coupled artificial-intelligence software with a device, called a brain-computer interface, implanted in the brain of a man with full-body paralysis. The software was able to decode information from the BCI to quickly convert the man's thoughts about handwriting into text on a computer screen. The man was able to write using this approach more than twice as quickly as he could using a previous method developed by the Stanford researchers, who reported those findings in 2017 in the journal eLife. The new findings, published online May 12 in Nature, could spur further advances benefiting hundreds of thousands of Americans, and millions globally, who've lost the use of their upper limbs or their ability to speak due to spinal-cord injuries, strokes or amyotrophic lateral sclerosis, also known as Lou Gehrig's disease, said Jaimie Henderson, MD, professor of neurosurgery.