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We propose a method that can generate soft segments, i.e. layers that represent the semantically meaningful regions as well as the soft transitions between them, automatically by fusing high-level and low-level image features in a single graph structure. The semantic soft segments, visualized by assigning each segment a solid color, can be used as masks for targeted image editing tasks, or selected layers can be used for compositing after layer color estimation.

Abstract

Accurate representation of soft transitions between image regions is essential for high-quality image editing and compositing. Current techniques for generating such representations depend heavily on interaction by a skilled visual artist, as creating such accurate object selections is a tedious task. In this work, we introduce semantic soft segments, a set of layers that correspond to semantically meaningful regions in an image with accurate soft transitions between different objects. We approach this problem from a spectral segmentation angle and propose a graph structure that embeds texture and color features from the image as well as higher-level semantic information generated by a neural network. The soft segments are generated via eigendecomposition of the carefully constructed Laplacian matrix fully automatically. We demonstrate that otherwise complex image editing tasks can be done with little effort using semantic soft segments.

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At the cellular level, aging and cancer are two sides of the same coin. The mechanism that limits a cell’s lifespan can be slowed down, but that can turn them cancerous, as they divide unchecked. Now, scientists at EPFL have found a way to manipulate that mechanism to effectively turn off cancer’s immortality, letting it die slowly and naturally.

Every time a cell divides, it consults the blueprints contained in the chromosomes, but some genetic information is lost with every division. To protect the important bits, the tips of the chromosomes are covered with repeating sequences of “junk” DNA known as telomeres. Eventually even they erode away, leaving the cell vulnerable to damage – which we recognize as wrinkles, grey hairs, decreased metabolism, and higher chances of disease. In that way, telomere length is inextricably linked to aging.

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Found in turmeric, curcumin has previously been shown to have anti-inflammatory and antioxidant properties in lab studies. It also has been suggested as a possible reason that senior citizens in India, where curcumin is a dietary staple, have a lower prevalence of Alzheimer’s disease and better cognitive performance.

“Exactly how curcumin exerts its effects is not certain, but it may be due to its ability to reduce brain inflammation, which has been linked to both Alzheimer’s disease and major depression,” said Dr. Gary Small, director of geriatric psychiatry at UCLA’s Longevity Center and of the geriatric psychiatry division at the Semel Institute for Neuroscience and Human Behavior at UCLA, and the study’s first author.

The double-blind, placebo-controlled study involved 40 adults between the ages of 50 and 90 years who had mild memory complaints. Participants were randomly assigned to receive either a placebo or 90 milligrams of curcumin twice daily for 18 months.

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Insect-sized flying robots could help with time-consuming tasks like surveying crop growth on large farms or sniffing out gas leaks. These robots soar by fluttering tiny wings because they are too small to use propellers, like those seen on their larger drone cousins. Small size is advantageous: These robots are cheap to make and can easily slip into tight places that are inaccessible to big drones.

But current flying robo-insects are still tethered to the ground. The electronics they need to power and control their wings are too heavy for these miniature robots to carry.

Now, engineers at the University of Washington have for the first time cut the cord and added a brain, allowing their RoboFly to take its first independent flaps. This might be one small flap for a robot, but it’s one giant leap for robot-kind. The team will present its findings May 23 at the International Conference on Robotics and Automation in Brisbane, Australia.

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He now officially owns them! #CyborgsRule

Port Richey, Fla. (WOFL FOX 35) — Johnny Metheny sits at an electric piano in his Port Richey home self-teaching himself the song Amazing Grace. Johnny’s never played before, but he’s determined to master the song. He plays through fairly well with his right hand.

“That side I got down pretty good,” said Metheny.

Then he raises his left hand to the keyboard, the hand he’s only known for a few short months, and that he never thought he’d be raising. The world’s first fully mind controlled artificial arm.

Continue reading “Florida man tests world’s first fully mind-controlled artificial arm” | >

There’s always a lot of talk about how AI will steal all our jobs and how machines will bring about the collapse of employment as we know it. It’s certainly hard to blame people for worrying with all the negative press around the issue.

But the reality is that AI is completely dependent on humans, and it appears as if it will stay that way for the foreseeable future. In fact, as AI grows as an industry and machine learning becomes more widely used, this will actually create a whole host of new jobs for people.

Let’s take a look at some of the roles humans currently play in the AI industry and the kind of jobs that will continue to be important in the future.

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The technical skills of programmer John Carmack helped create the 3D world of Doom, the first-person shooter that took over the world 25 years ago. But it was level designers like John Romero and American McGee that made the game fun to play. Level designers that, today, might find their jobs threatened by the ever-growing capabilities of artificial intelligence.

One of the many reasons Doom became so incredibly popular was that id Software made tools available that let anyone create their own levels for the game, resulting in thousands of free ways to add to its replay value. First-person 3D games and their level design have advanced by leaps and bounds since the original Doom’s release, but the sheer volume of user-created content made it the ideal game for training an AI to create its own levels.

Researchers at the Politecnico di Milano university in Italy created a generative adversarial network for the task, which essentially uses two artificially intelligent algorithms working against each other to optimise the overall results. One algorithm was fed thousands of Doom levels which it analysed for criteria like overall size, enemy placement, and the number of rooms. It then used what it learned to generate its own original Doom levels.

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