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At human scale, controlling temperature is a straightforward concept. Turtles sun themselves to keep warm. To cool a pie fresh from the oven, place it on a room-temperature countertop.

At the nanoscale—at distances less than 1/100th the width of the thinnest human hair—controlling temperature is much more difficult. Nanoscale distances are so small that objects easily become thermally coupled: If one object heats up to a certain temperature, so does its neighbor.

When scientists use a as that , there is an additional challenge: Thanks to heat diffusion, materials in the beam path heat up to approximately the same temperature, making it difficult to manipulate the thermal profiles of objects within the beam. Scientists have never been able to use light alone to actively shape and control thermal landscapes at the nanoscale.

Machine learning, introduced 70 years ago, is based on evidence of the dynamics of learning in the brain. Using the speed of modern computers and large datasets, deep learning algorithms have recently produced results comparable to those of human experts in various applicable fields, but with different characteristics that are distant from current knowledge of learning in neuroscience.

Using advanced experiments on neuronal cultures and large scale simulations, a group of scientists at Bar-Ilan University in Israel has demonstrated a new type of ultrafast artificial algorithms—based on the very slow dynamics—which outperform learning rates achieved to date by state-of-the-art learning algorithms.

In an article published today in the journal Scientific Reports, the researchers rebuild the bridge between neuroscience and advanced artificial intelligence algorithms that has been left virtually useless for almost 70 years.

Researchers at the Massachusetts Institute of Technology (MIT) have recently developed a metric that can be used to capture the space of collider events based on the earth mover’s distance (EMD), a measure used to evaluate dissimilarity between two multi-dimensional probability distributions. The metric they proposed, outlined in a paper published in Physical Review Letters, could enable the development of new powerful tools to analyze and visualize collider data, which do not rely on a choice of observables.

“Our research is motivated by a remarkably simple question: When are two similar?” Eric Metodiev, one of the researchers who carried out the study, told Phys.org. “At the Large Hadron Collider (LHC), protons are smashed together at extremely high energies and each collision produces a complex mosaic of particles. Two collider events can look similar, even if they consist of different numbers and types of particles. This is analogous to how two mosaics can look similar, even if they are made up of different numbers and colors of tiles.”

In their study, Metodiev and his colleagues set out to capture the similarity between collider events in a way that is conceptually useful for particle physics. To do this, they employed a strategy that merges ideas related to optimal transport theory, which is often used to develop cutting-edge image recognition tools, with insights from , a construct that describes fundamental particle interactions.

Just five months ago at the RSA conference, the NSA released Ghidra, a piece of open source software for reverse-engineering malware. It was an unusual move for the spy agency, and it’s sticking to its plan for regular updates — including some based on requests from the public.

In the coming months, Ghidra will get support for Android binaries, according to Brian Knighton, a senior researcher for the NSA, and Chris Delikat, a cyber team lead in its Research Directorate, who previewed details of the upcoming release with CyberScoop. Knighton and Delikat are discussing their plans at a session of the Black Hat security conference in Las Vegas Thursday.

Before the Android support arrives, a version 9.1 will include new features intended to save time for users and boost accuracy in reverse-engineering malware — enhancements that will come from features such as processor modules, new support for system calls and the ability to conduct additional editing, known as sleigh editing, in the Eclipse development environment.

The Defense Advanced Research Projects Agency (DARPA) is experimenting with using a swarm of autonomous drones and ground robots to assist with military missions. In a video of a recent test, DARPA showed how its robots analyzed two city blocks to find, surround, and secure a mock city building.

DARPA conducted its test back in June in Georgia, featuring both drones and ground-based robots. The demonstration was part of DARPA’s OFFensive Swarm-Enabled Tactics (OFFSET) program, which is designed to eventually accompany small infantry units as they work in dense urban environments, and could eventually scale up to 250 drones and ground robots. The test back in June was the second of six planned tests, which DARPA says will increase in complexity as they happen over the next couple of years.

Many phenomena of the natural world evidence symmetries in their dynamic evolution which help researchers to better understand a system’s inner mechanism. In quantum physics, however, these symmetries are not always achieved. In laboratory experiments with ultracold lithium atoms, researchers from the Center for Quantum Dynamics at Heidelberg University have proven for the first time the theoretically predicted deviation from classical symmetry. Their results were published in the journal Science.

“In the world of classical , the energy of an ideal gas rises proportionally with the pressure applied. This is a direct consequence of scale symmetry, and the same relation is true in every scale invariant system. In the world of quantum mechanics, however, the interactions between the quantum particles can become so strong that this classical scale symmetry no longer applies,” explains Associate Professor Dr. Tilman Enss from the Institute for Theoretical Physics. His research group collaborated with Professor Dr. Selim Jochim’s group at the Institute for Physics.

In their experiments, the researchers studied the behaviour of an ultracold, superfluid gas of lithium atoms. When the gas is moved out of its equilibrium state, it starts to repeatedly expand and contract in a “breathing” motion. Unlike classical particles, these can bind into pairs and, as a result, the superfluid becomes stiffer the more it is compressed. The group headed by primary authors Dr. Puneet Murthy and Dr. Nicolo Defenu—colleagues of Prof. Jochim and Dr. Enss—observed this deviation from classical scale symmetry and thereby directly verified the quantum nature of this system. The researchers report that this effect gives a better insight into the behaviour of systems with similar properties such as graphene or superconductors, which have no electrical resistance when they are cooled below a certain critical temperature.