Lifeboat Foundation

In the field of optogenetics, scientists investigate the activity of neurons in the brain using light. A team led by Prof. Dr. Ilka Diester and Dr. David Eriksson from the Optophysiology Laboratory at the University of Freiburg has developed a new method to simultaneously conduct laminar recordings, multifiber stimulations, 3D optogenetic stimulation, connectivity inference, and behavioral quantification on brains. Their results are presented in Nature Communications. “Our work paves the way for large-scale photo-recording and controlled interrogation of fast neural communication in any combination of brain areas,” Diester explains. “This can help us unravel the rapid and multilayered dialogs between neurons that maintain brain function.”

The research group, in collaboration with Dr. Patrick Ruther of the Department of Microsystems Engineering (IMTEK) at the University of Freiburg, is developing a new method for the controlled interrogation and recording of neuronal activity in the brain. To do this, the team is taking advantage of thin, cell-sized optical fibers for minimally invasive optogenetic implantation. “We combine side-emitting fibers with silicon probes to achieve high-quality recordings and ultrafast, multichannel optogenetic control.”

They call the system Fused Fiber Light Emission and eXtracellular Recording, or FFLEXR. In addition to optical fibers that can be attached to any silicon probe, the team uses linear depth-resolved stimulation, a lightweight fiber matrix connector, a flexible multifiber ribbon cable, an optical commutator for efficient multichannel stimulation, a general-purpose patch cable, and an algorithm to manage the photovoltaic response.

Bipartisan hostility toward China means US lawmakers are unlikely to cite Chinese regulations as inspiration. But Beijing’s manoeuvres could perhaps have a subtle effect. In the UK, some lawmakers have called for online companies to shield young people from harmful content in an approach that some have likened to China’s proposals. “These ideas could ripple out,” says Matt Sheehan, a fellow at the Carnegie Endowment for International Peace who researches China’s AI ecosystem. “What’s interesting in China is that they’re going to be able to run experiments at a very large scale on what it actually means to implement these ideas.”

Sweeping rules will cover algorithms that set prices, control search results, recommend videos, and filter content.

When video chatting with colleagues, coworkers, or family, many of us have grown accustomed to using virtual backgrounds and background filters. It has been shown to offer more control over the surroundings, allowing fewer distractions, preserving the privacy of those around us, and even liven up our virtual presentations and get-togethers. However, Background filters don’t always work as expected or perform well for everyone.

Image segmentation is a computer vision process of separating the different components of a photo or video. It has been widely used to improve backdrop blurring, virtual backgrounds, and other augmented reality (AR) effects. Despite advanced algorithms, achieving highly accurate person segmentation seems challenging.

The model used for image segmentation tasks must be incredibly consistent and lag-free. Inefficient algorithms may result in bad experiences for the users. For instance, during a video conference, artifacts generated by erroneous segmentation output might easily confuse persons utilizing virtual background programs. More importantly, segmentation problems may result in unwanted exposure to people’s physical environments when applying backdrop effects.

The Google-backed AI firm taught a reinforcement learning algorithm to control the fiery plasma inside a tokamak nuclear fusion reactor.

We construct quantum algorithms to compute physical observables of nonlinear PDEs with M initial data. Based on an exact mapping between nonlinear and linear PDEs using the level set method, these new quantum algorithms for nonlinear Hamilton-Jacobi and scalar hyperbolic PDEs can be performed with a computational cost that is independent of M, for arbitrary nonlinearity. Depending on the details of the initial data, it can also display up to exponential advantage in both the dimension of the PDE and the error in computing its observables. For general nonlinear PDEs, quantum advantage with respect to M is possible in the large M limit.

Instead of relying on a fixed catalogue of available materials or undergoing trial-and-error attempts to come up with new ones, engineers can turn to algorithms running in supercomputers to design unique materials, based on a “materials genome,” with properties tailored to specific needs. Among the new classes of emerging materials are “transient” electronics and bioelectronics that portend applications and industries comparable to the scale that followed the advent of silicon-based electronics.

In each of the three technological spheres, we find the Cloud increasingly woven into the fabric of innovation. The Cloud itself is, synergistically, evolving and expanding from the advances in new materials and machines, creating a virtuous circle of self-amplifying progress. It is a unique feature of our emerging century that constitutes a catalyst for innovation and productivity, the likes of which the world has never seen.

Researchers have developed a way to identify sources of error in quantum computers through Artificial intelligence and machine learning. Now they can reduce quantum computing errors using custom machine learning algorithms.

Fundamental constants like e and π are ubiquitous in diverse fields of science, including physics, biology, chemistry, geometry, and abstract mathematics. Nevertheless, for centuries new mathematical formulas relating fundamental constants are scarce and are usually discovered sporadically by mathematical intuition or ingenuity.

Our algorithms search for new mathematical formulas. The community can suggest proofs for the conjectures or even propose or develop new algorithms. Any new conjecture, proof, or algorithm suggested will be named after you.

The potential of quantum computers to solve problems that are intractable for classical computers has driven advances in hardware fabrication. In practice, the main challenge in realizing quantum computers is that general, many-particle quantum states are highly sensitive to noise, which inevitably causes errors in quantum algorithms. Some noise sources are inherent to the current materials platforms. de Leon et al. review some of the materials challenges for five platforms for quantum computers and propose directions for their solution.

Science, this issue p. eabb2823.