The government will set guidelines by the end of March next year for preventing technology leaks from universities that conduct research with foreign firms, sources close to the matter said Wednesday. The move comes as the United States and China grow cautious about advanced technologies such as artificial intelligence being converted for military use. While Japan already regulates the disclosure of sensitive technologies and products by the nation's state organizations and companies to overseas firms under a foreign exchange and foreign trade law, university laboratories have been managing infrequent arrangements on their own, leading some experts to voice concerns about the risk of information leaks. The envisioned guidelines would require universities and other research institutions to set regulations on joint projects involving foreign entities. They will be based on the comprehensive innovation strategy adopted by the Cabinet in 2018 aimed at promoting university research on AI, biotechnology and other leading technologies.

Hitachi Ltd. said Wednesday it has reached a deal to buy U.S. assembly robot-maker JR Automation Technologies LLC for $1.43 billion (¥160 billion) to strengthen its factory automation business in the American market. Hitachi said it agreed Tuesday to buy the manufacturer from private equity firm Crestview Partners, which holds a 93 percent stake in the company. The Japanese manufacturer will acquire all shares in the Michigan-based technology provider by the end of this year. JR Automation Technologies was founded in 1980 and has strengths in automated manufacturing and technology solutions. The company has about 2,000 employees at 23 manufacturing facilities in North America, Europe and Asia, it said.

Facial recognition technology is being deployed in airports, security cameras and in our phones. Now, Tokyo is using facial recognition in an unexpected way - to serve up targeted advertisements to taxi passengers as they're ferried to their destination, based on their age and gender. The unsettling practice was discovered by Google privacy engineer Rosa Golijan, who posted a photo of a tablet she encountered when hopping into a taxi in Japan. Facial recognition technology is being deployed in airports, security cameras and in our phones. Now, Japan is using the tech to serve up targeted ads to passengers in taxis.

Dreams of dishing laundry duty to an in-house robot just got a little less hopeful after the company behind the automated assistant, Laundroid, filed for bankruptcy - effectively putting its bot to bed. According to Engadget, the company Seven Dreamers, which has worked to bring Laundroid to market since 2014, still owes 200 creditors about $20 million after its bankruptcy filing in Japan on April 23. In its Consumer Electronic Show (CES) debut in 2017, Seven Dreamers offered up what they purported would be an all-in-one laundry-folding and sorting machine that was able to take laundry, fold it, and in some cases organize it by color or owner and then deliver the final product to its wielder. The days of laundry folding robots got a little less hopeful after the makers of folding and sorting assistant Laundroid filed for bankruptcy. Users were meant to load the machine with clean dry clothes while a robot arm inside folds and sorts them.

When Seven Dreamers Laboratories Inc. unveiled its prototype laundry-folding robot in 2015, it generated a buzz, with people saying they couldn't wait to buy one if it ever went to market. But the AI-based tidying device dubbed Laundroid is apparently coming to an end before its commercial debut, as the Tokyo-based developer filed for bankruptcy Tuesday with the Tokyo District Court, citing insufficient funds to continue operations. A spokesperson for Seven Dreamers, a contest-winning startup that had received over ¥10 billion in funding, said development of robot is over for now. According to Teikoku Databank Ltd., a credit research company, Seven Dreamers Laboratories had accumulated ¥2.2 billion in debt as it struggled to ship the robot and invested heavily in research and development. After postponing its initial sales goal in fiscal 2017, it had to push back its goal for fiscal 2018, too.

Tesla plans to have a fleet of robotic taxis roaming the streets without drivers next year, Elon Musk has said. The claim is just the latest in a series of exciting pronouncements from the chief executive, who has repeatedly missed his own targets. But he has bet a considerable part of his business on the technology underpinning it. As well as allowing for the robot taxis that will drive themselves around the streets, Mr Musk says that by next year there will be a million Tesla cars on the streets that have full autonomous technology and are able to drive themselves. We'll tell you what's true.

Two years ago, Nike unveiled what has been called the fastest shoe on the planet. It proved that claim last year, when it carried Eliud Kipchoge across the finish line at the Berlin Marathon, more quickly than ever before, not just breaking the world record but shaving a minute and 18 seconds off it all in one go. That shoe, the elite version of Nike's Zoom Vaporfly 4% Flyknit, even became controversial because it seemed to be just so good. The 4 per cent in its name referred to the extra efficiency boost it gave to its wearer – that in turn led to concern that the shoe was making its runners too fast, to an extent that almost seemed unfair. Now, Nike says it has made yet another step forward.

Five years ago, Paul Johnson wouldn't have thought predicting earthquakes would ever be possible. "I can't say we will, but I'm much more hopeful we're going to make a lot of progress within decades," the Los Alamos National Laboratory seismologist says. "I'm more hopeful now than I've ever been." The main reason for that new hope is a technology Johnson started looking into about four years ago: machine learning. Many of the sounds and small movements along tectonic fault lines where earthquakes occur have long been thought to be meaningless.

Mfine, an AI health care startup headquartered in Bangalore, today announced that it has raised $17.2 million in a series B funding round led by Japan-based venture group SBI Investment, with participation from SBI Ven Capital, Beenext, Stellaris Venture Partners, and Prime Venture Partners. This comes after a $4.2 million series A round in May 2018 and brings Mfine's total raised to $24 million, according to Crunchbase. CEO and cofounder Prasad Kompalli says the funds will be used to acquire new customers and expand service across India. "We believe that India will leapfrog the methods of health care delivery that were adopted in the developed nations, and mobile will be at the center of this disruption. The current funding is an endorsement to Mfine's unique model of working with reputed and accredited hospitals and using technology to make quality health care accessible to millions of people," he said.

Background and Purpose: A current algorithm to obtain a synthetic myelin volume fraction map (SyMVF) from rapid simultaneous relaxometry imaging (RSRI) has a potential problem, that it does not incorporate information from surrounding pixels. The purpose of this study was to develop a method that utilizes a convolutional neural network (CNN) to overcome this problem. Methods: RSRI and magnetization transfer images from 20 healthy volunteers were included. A CNN was trained to reconstruct RSRI-related metric maps into a myelin volume-related index (generated myelin volume index: GenMVI) map using the myelin volume index map calculated from magnetization transfer images (MTMVI) as reference. The SyMVF and GenMVI maps were statistically compared by testing how well they correlated with the MTMVI map. The correlations were evaluated based on: (i) averaged values obtained from 164 atlas-based ROIs, and (ii) pixel-based comparison for ROIs defined in four different tissue types (cortical and subcortical gray matter, white matter, and whole brain). Results: For atlas-based ROIs, the overall correlation with the MTMVI map was higher for the GenMVI map than for the SyMVF map. In the pixel-based comparison, correlation with the MTMVI map was stronger for the GenMVI map than for the SyMVF map, and the difference in the distribution for the volunteers was significant (Wilcoxon sign-rank test, P<.001) in all tissue types. Conclusion: The proposed method is useful, as it can incorporate more specific information about local tissue properties than the existing method.