Computer Science

Researchers propose the use of quantum cascade lasers to achieve private free-space communications

Free-space optical communication, the communication between two devices at a distance using light to carry information, is a highly promising system for achieving high-speed communication. This system of communication is known to be immune to electromagnetic interference (EMI), a disturbance generated by external sources that affects electrical circuits and can disrupt radio signals.
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Classic magic trick may enable quantum computing

Quantum computing could solve problems that are difficult for traditional computer systems. It may seem like magic. One step toward achieving quantum computing even resembles a magician's trick: levitation. A new project at the U.S. Department of Energy's Thomas Jefferson National Accelerator Facility will attempt this trick by levitating a microscopic particle in a superconducting radiofrequency (SRF) cavity to observe quantum phenomena.
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'Flashed' nanodiamonds are just a phase, research team finds

Diamond may be just a phase carbon goes through when exposed to a flash of heat, but that makes it far easier to obtain. The Rice University lab of chemist James Tour is now able to "evolve" carbon through phases that include valuable nanodiamond by tightly controlling the flash Joule heating process they developed 18 months ago.
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Researchers prepare radiation resistance of Er-doped silica glass and optical fiber

Rare earth-doped active fibers are crucial in space-based applications, such as space laser communication, laser radar, and space waste disposal. However, the space radiation environment can lead to a sharp increase in the optical loss of rare earth-doped active fibers, and a sharp decrease in the output laser slope efficiency or gain performance. Therefore, it is very important to improve the radiation-resistance property of rare earth-doped silica fiber.
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In the future, the pictures we take could move. Here’s how

A new deep learning method can animate moving material, such as waterfalls, smoke, or clouds, from a single photo. The technique predicts how things were moving when a photo was taken, and then uses that information to create the animation. The method also doesn’t require any user input or extra...
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MIT’s New AI Carpet Can ‘See’ Your Health, Poses, Falls Without Cameras

Image via MIT Computer Science & Artificial Intelligence Lab. Researchers at MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL) have created a new tactile sensing carpet, which can gauge human poses without the use of cameras. This technology could be a big step towards creating improved personalized healthcare, smart homes, and gaming gadgets.

Information dynamics in neuromorphic nanowire networks

Neuromorphic systems comprised of self-assembled nanowires exhibit a range of neural-like dynamics arising from the interplay of their synapse-like electrical junctions and their complex network topology. Additionally, various information processing tasks have been demonstrated with neuromorphic nanowire networks. Here, we investigate the dynamics of how these unique systems process information through information-theoretic metrics. In particular, Transfer Entropy (TE) and Active Information Storage (AIS) are employed to investigate dynamical information flow and short-term memory in nanowire networks. In addition to finding that the topologically central parts of networks contribute the most to the information flow, our results also reveal TE and AIS are maximized when the networks transitions from a quiescent to an active state. The performance of neuromorphic networks in memory and learning tasks is demonstrated to be dependent on their internal dynamical states as well as topological structure. Optimal performance is found when these networks are pre-initialised to the transition state where TE and AIS are maximal. Furthermore, an optimal range of information processing resources (i.e. connectivity density) is identified for performance. Overall, our results demonstrate information dynamics is a valuable tool to study and benchmark neuromorphic systems.

Quantum entangled fractional topology and curvatures

Topological spaces have numerous applications for quantum matter with protected chiral edge modes related to an integer-valued Chern number, which also characterizes the global response of a spin-1/2 particle to a magnetic field. Such spin-1/2 models can also describe topological Bloch bands in lattice Hamiltonians. Here we introduce interactions in a system of spin-1/2s to reveal a class of topological states with rational-valued Chern numbers for each spin providing a geometrical and physical interpretation related to curvatures and quantum entanglement. We study a driving protocol in time to reveal the stability of the fractional topological numbers towards various forms of interactions in the adiabatic limit. We elucidate a correspondence of a one-half topological spin response in bilayer semimetals on a honeycomb lattice with a nodal ring at one Dirac point and a robust π Berry phase at the other Dirac point.

Rainbow Archimedean spiral emission from optical fibres

We demonstrate a new practical approach for generating multicolour spiral-shaped beams. It makes use of a standard silica optical fibre, combined with a tilted input laser beam. The resulting breaking of the fibre axial symmetry leads to the propagation of a helical beam. The associated output far-field has a spiral shape, independently of the input laser power value. Whereas, with a high-power near-infrared femtosecond laser, a visible supercontinuum spiral emission is generated. With appropriate control of the input laser coupling conditions, the colours of the spiral spatially self-organize in a rainbow distribution. Our method is independent of the laser source wavelength and polarization. Therefore, standard optical fibres may be used for generating spiral beams in many applications, ranging from communications to optical tweezers and quantum optics.

Machine learning enhances non-verbal communication in online classrooms

Researchers in the Center for Research on Entertainment and Learning (CREL) at the University of California San Diego have developed a system to analyze and track eye movements to enhance teaching in tomorrow's virtual classrooms—and perhaps future virtual concert halls. UC San Diego music and computer science professor Shlomo Dubnov,...

Dr. Patrick Marcinek Analyzes Cells with a Flow Cytometer (image)

Leibniz-Institut für Lebensmittel-Systembiologie an der TU München. Disclaimer: AAAS and EurekAlert! are not responsible for the accuracy of news releases posted to EurekAlert! by contributing institutions or for the use of any information through the EurekAlert system.

Polynomial Interpolation

Imagine that you are interested in tracking the temperature throughout the day in your vegetable garden. You measure the temperature exactly every hour, and you end up with the following 24 temperature measurements:. You can plot this data on a graph using any tool of your choice to obtain a...

Why are augmented reality apps designed mainly for youngsters?

Augmented reality (AR) is poised to revolutionize the way people complete essential everyday tasks, yet older adults—who have much to gain from the technology—will be excluded from using it unless more thought goes into designing software that makes sense to them. The danger of older adults falling through the gaps...

Speeding up machine learning for particle physics

Machine learning is everywhere. For example, it's how Spotify gives you suggestions of what to listen to next or how Siri answers your questions. And it's used in particle physics too, from theoretical calculations to data analysis. Now a team including researchers from CERN and Google has come up with a new method to speed up deep neural networks—a form of machine-learning algorithms—for selecting proton–proton collisions at the Large Hadron Collider (LHC) for further analysis. The technique, described in a paper just published in Nature Machine Intelligence, could also be used beyond particle physics.

scGCN is a graph convolutional networks algorithm for knowledge transfer in single cell omics

Single-cell omics is the fastest-growing type of genomics data in the literature and public genomics repositories. Leveraging the growing repository of labeled datasets and transferring labels from existing datasets to newly generated datasets will empower the exploration of single-cell omics data. However, the current label transfer methods have limited performance, largely due to the intrinsic heterogeneity among cell populations and extrinsic differences between datasets. Here, we present a robust graph artificial intelligence model, single-cell Graph Convolutional Network (scGCN), to achieve effective knowledge transfer across disparate datasets. Through benchmarking with other label transfer methods on a total of 30 single cell omics datasets, scGCN consistently demonstrates superior accuracy on leveraging cells from different tissues, platforms, and species, as well as cells profiled at different molecular layers. scGCN is implemented as an integrated workflow as a python software, which is available at

Teaching Digital Literacy in Homeschool

You may often feel that the endless recommendations for improving digital literacy are irrelevant to you, especially when they also talk about children, but behind this cumbersome phrase hides a thousand small daily skills at which we don’t all excel. Let’s figure out what digital literacy is and how to teach it to children.