Day: May 6, 2019

360 Video: Go on a Mission With Zipline’s Delivery Drones

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Immerse yourself in the action as Zipline catapults its drones into the Rwandan sky With 360 video, IEEE Spectrum takes you behind the scenes with one of the world’s first drone-delivery companies. Zipline, based in California, is using drones to deliver blood to hospitals throughout Rwanda. At an operations center in Muhanga, you’ll watch as Zipline technicians assemble the modular drones, fill their cargo holds, and launch them via catapult. You’ll see a package float down from the sky above a rural hospital, and you’ll get a closeup look at Zipline’s ingenious method for capturing returning drones. You can follow the action in a 360-degree video in three ways: 1) Watch on your computer, using your mouse to click and drag on the video; 2) watch on your phone, moving the phone around to change your view; or 3) watch on a VR headset for the full immersive experience. For more about Continue reading 360 Video: Go on a Mission With Zipline’s Delivery Drones→

These Robotic Objects Are Designed to Be Stabbed and Beaten to Help You Feel Better

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Cathartic objects help users physically express strong emotional states At a human-computer interaction conference this week in Glasgow, U.K., Carnegie Mellon University researcher Michal Luria is presenting a paper on “Challenges of Designing HCI for Negative Emotions.” The discussion includes a case study involving what Luria calls “cathartic objects”: robotic contraptions that you can beat, stab, smash, and swear at to help yourself feel better.

Merging cell datasets, panorama style

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A new algorithm developed by MIT researchers takes cues from panoramic photography to merge massive, diverse cell datasets into a single source that can be used for medical and biological studies. Single-cell datasets profile the gene expressions of human cells — such as a neurons, muscles, and immune cells — to gain insight into human health and treating disease. Datasets are produced by a range of labs and technologies, and contain extremely diverse cell types. Combining these datasets into a single data pool could open up new research possibilities, but that’s difficult to do effectively and efficiently. Traditional methods tend to cluster cells together based on nonbiological patterns — such as by lab or technologies used — or accidentally merge dissimilar cells that appear the same. Methods that correct these mistakes don’t scale well to large datasets, and require all merged datasets share at least one common cell type. In a Continue reading Merging cell datasets, panorama style→