Lifeboat Foundation

Icon’s new three-bedroom home with uniquely curved walls proves that 3D printing can create welcoming and warm houses for the average homeowner.

Black holes are among the most compelling mysteries of the universe. Nothing, not even light, can escape a black hole. And at the center of nearly every galaxy there is a supermassive black hole that’s millions to billions of times more massive than the sun. Understanding black holes, and how they become supermassive, could shed light on the evolution of the universe.

Three physicists at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory have recently developed a model to explain the formation of supermassive black holes, as well as the nature of another phenomenon: dark matter. In a paper published in Physical Review Letters, theoretical physicists Hooman Davoudiasl, Peter Denton, and Julia Gehrlein describe a cosmological phase transition that facilitated the formation of supermassive black holes in a dark sector of the universe.

A cosmological phase transition is akin to a more familiar type of phase transition: bringing water to a boil. When water reaches the exact right temperature, it erupts into bubbles and vapor. Imagine that process taking place with a primordial state of matter. Then, shift the process in reverse so it has a cooling effect and magnify it to the scale of the universe.

A relic from a billion-year-old collision.

A relic from the billion-year-old collision that birthed the Abell 3,667 galaxy cluster was captured in fresh, stunning detail.

Summary: The perceptual accuracy of visual information and its subjective interpretation use separate neural mechanisms that can be manipulated independently of each other.

Source: University of Bologna

A research group from the University of Bologna discovered the first causal evidence of the double dissociation between what we see and what we believe we see: these two different mechanisms derive from the frequency and amplitude of alpha oscillations.

UK-based AI chipmaker Graphcore has announced a project called The Good Computer. This will be capable of handling neural network models with 500 trillion parameters – large enough to enable what the company calls ‘ultra-intelligence’.

Designed to train students to practice responsible technology development, the Social and Ethical Responsibilities of Computing (SERC) publishes a collection of original pedagogical materials developed for instructional use on MIT OpenCourseWare.

Insulation and leak-proofing might not make neighbors jealous, but they can dramatically bring down a homeowner’s utility bills.

Canada’s major oil sands producers are working together on a first-of-its-kind net-zero initiative that could help ensure long-term, secure supplies of affordable and responsible energy for North America.

Together, Canadian Natural, Cenovus Energy, ConocoPhillips Canada, Imperial, MEG Energy, and Suncor Energy have formed the Oil Sands Pathways to Net-Zero Alliance.

The goal of the Alliance is to achieve a phased reduction in emissions from oil sands operations, reaching net-zero by 2050, working in collaboration with Canadian federal and provincial governments.

A team from the Max Planck Institute for Intelligent Systems in Germany have developed a novel thumb-shaped touch sensor capable of resolving the force of a contact, as well as its direction, over the whole surface of the structure. Intended for dexterous manipulation systems, the system is constructed from easily sourced components, so should scale up to a larger assemblies without breaking the bank. The first step is to place a soft and compliant outer skin over a rigid metallic skeleton, which is then illuminated internally using structured light techniques. From there, machine learning can be used to estimate the shear and normal force components of the contact with the skin, over the entire surface, by observing how the internal envelope distorts the structured illumination.

The novelty here is the way they combine both photometric stereo processing with other structured light techniques, using only a single camera. The camera image is fed straight into a pre-trained machine learning system (details on this part of the system are unfortunately a bit scarce) which directly outputs an estimate of the contact shape and force distribution, with spatial accuracy reported good to less than 1 mm and force resolution down to 30 millinewtons. By directly estimating normal and shear force components the direction of the contact could be resolved to 5 degrees. The system is so sensitive that it can reportedly detect its own posture by observing the deformation of the skin due its own weight alone!

Continue reading “A Soft Thumb-Sized Vision-Based Touch Sensor” »