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Category: robotics/AI – Page 2,056

A machine learning algorithm can detect signs of anxiety and depression in the speech patterns of young children, potentially providing a fast and easy way of diagnosing conditions that are difficult to spot and often overlooked in young people, according to new research published in the Journal of Biomedical and Health Informatics.

Around one in five suffer from anxiety and depression, collectively known as “internalizing disorders.” But because children under the age of eight can’t reliably articulate their emotional suffering, adults need to be able to infer their mental state, and recognise potential mental health problems. Waiting lists for appointments with psychologists, insurance issues, and failure to recognise the symptoms by parents all contribute to children missing out on vital treatment.

“We need quick, objective tests to catch kids when they are suffering,” says Ellen McGinnis, a at the University of Vermont Medical Center’s Vermont Center for Children, Youth and Families and lead author of the study. “The majority of kids under eight are undiagnosed.”

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Twenty years ago, a cryptographic puzzle was included in the construction of a building on the MIT campus. The structure that houses what is now MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) includes a time capsule designed by the building’s architect, [Frank Gehry]. It contains artifacts related to the history of computing, and was meant to be opened whenever someone solved a cryptographic puzzle, or after 35 years had elapsed.

The puzzle was not expected to be solved early, but [Bernard Fabrot], a developer in Belgium, has managed it using not a supercomputer but a run-of-the-mill Intel i7 processor. The capsule will be opened later in May.

The famous cryptographer, [Ronald Rivest], put together what we now know is a deceptively simple challenge. It involves a successive squaring operation, and since it is inherently sequential there is no possibility of using parallel computing techniques to take any shortcuts. [Fabrot] used the GNU Multiple Precision Arithmetic Library in his code, and took over 3 years of computing time to solve it. Meanwhile another team is using an FPGA and are expecting a solution in months, though have been pipped to the post by the Belgian.

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For most patients, a diagnosis of stage 4 non-small cell lung cancer comes with a dire prognosis. But for patients with specific mutations that cause the disease, there are potentially life-saving therapies.

The problem is that these mutations, known as ALK and EGFR, are not always identified in patients — meaning they never get the treatment.

A new study from the Fred Hutchinson Cancer Research Center in Seattle used machine learning to find these needle-in-a-haystack patients. The idea was to leverage cancer databases to see if patients were being tested for the mutations and receiving these personalized treatments.

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Neuroscience, computer vision collaborate to better understand visual information processing PITTSBURGH—Neuroscientists and computer vision scientists say a new dataset of unprecedented size — comprising brain scans of four volunteers who each viewed 5,000 images — will help researchers better understand how the brain processes images. Researchers at Carnegie Mellon University and Fordham University, reporting today in the journal Scientific Data, said acquiring functional magnetic resonance imaging (fMRI) scans at this scale presented unique challenges. Each volunteer participated in 20 or more hours of MRI scanning, challenging both their perseverance and the experimenters’ ability to coordinate across scanning sessions. The extreme.