A mysterious large mass of material has been discovered beneath the largest crater in our solar system—the Moon’s South Pole-Aitken basin—and may contain metal from the asteroid that crashed into the Moon and formed the crater, according to a Baylor University study.
“Imagine taking a pile of metal five times larger than the Big Island of Hawaii and burying it underground. That’s roughly how much unexpected mass we detected,” said lead author Peter B. James.
Ph.D., assistant professor of planetary geophysics in Baylor’s College of Arts & Sciences. The crater itself is oval-shaped, as wide as 2,000 kilometers—roughly the distance between Waco, Texas, and Washington, D.C.—and several miles deep. Despite its size, it cannot be seen from Earth because it is on the far side of the Moon.
Chemical engineers at UCLA have been demonstrating what they argue is scientific evidence that bunches of synthetically grown nanowires exhibit behaviors similar to that of memory in a living brain. Whether you believe their claim depends on what you think memory actually is.
Year-round vegetables, minimal resources, climate-resistant—we’ve sung praises about vertical farms many times before. But Singapore’s Sky Greens is something very special.
Sea squirts use stem cells to regenerate their bodies from nothing but fragments of blood vessel, a finding that could help uncover the evolution of regeneration.
Low tech sometimes is really good because when systems can be exploited then basically you see that no tech is sometimes best.
Election Systems & Software has championed electronic voting machines in the US. Now it has had a change of heart about the need for paper records of votes.
Cyber threats: Over half a million electronic machines are used in big US elections. Many produce paper copies of votes that can be used to audit electronic results, but some don’t. That’s a problem because security experts have shown that machines can be hacked.
A group of researchers at Sandia National Laboratories have developed a tool that can cross-train standard convolutional neural networks (CNN) to a spiking neural model that can be used on neuromorphic processors. The researchers claim that the conversion will enable deep learning applications to take advantage of the much better energy efficiency of neuromorphic hardware, which are designed to mimic the way the biological neurons work.
The tool, known as Whetstone, works by adjusting artificial neuron behavior during the training phase to only activate when it reaches an appropriate threshold. As a result, neuron activation become a binary choice – either it spikes or it doesn’t. By doing so, Whetstone converts an artificial neural network into a spiking neural network. The tool does this by using an incremental “sharpening process” (hence Whetstone) through each network layer until the activation becomes discrete.
According to Whetstone researcher Brad Aimone, this discrete activation greatly minimizes communication costs between the layers, and thus energy consumption, but with only minimal loss of accuracy. “We continue to be impressed that without dramatically changing what the networks look like, we can get very close to a standard neural net [in accuracy],” he says. “We’re usually within a percent or so on performance.”
Neuromorphic systems carry out robust and efficient neural computation using hardware implementations that operate in physical time. Typically they are event- or data-driven, they employ low-power, massively parallel hybrid analog/digital VLSI circuits, and they operate using the same physics of computation used by the nervous system. Although there are several forums for presenting research achievements in neuromorphic engineering, none are exclusively dedicated to this increasingly large research community. Either because they are dedicated to single disciplines, such as electrical engineering or computer science, or because they serve research communities which focus on analogous areas (such as biomedical engineering or computational neuroscience), but with fundamentally different goals and objectives. The mission of Neuromorphic Engineering is to provide a publication medium dedicated exclusively and specifically to this field. Topics covered by this publication include: Analog and hybrid analog/digital electronic circuits for implementing neural processes, such as conductances, neurons, synapses, plasticity mechanisms, photoreceptors, cochleae, etc. Neuromorphic circuits and systems for implementing real-time event-based neural processing architectures. Hardware models of neural and sensorimotor processing systems, such as selective attention systems, coordinate transformation systems, auditory and/or visual processing systems, sensory fusion systems, etc. Implementations of neural computational systems found in insects, birds, mammals, etc. Embedded neuromorphic systems, including actuated or robotic platforms which process sensory signals and interact with the environment using event-based sensors and circuits. To ensure high quality and state-of-the-art material, publications should demonstrate experimental results, using physical implementations of neuromorphic systems, and possibly show the links between the artificial system and the neural/biological one they model.
