Lifeboat Foundation

The world is one step closer to having a totally secure internet and an answer to the growing threat of cyber-attacks, thanks to a team of international scientists who have created a unique prototype that could transform how we communicate online.

The invention led by the University of Bristol, revealed today in the journal Science Advances, has the potential to serve millions of users, is understood to be the largest-ever quantum network of its kind, and could be used to secure people’s online communication, particularly in these internet-led times accelerated by the COVID-19 pandemic.

Human limitations are part of the reason we conjured up meta-humans with DNA that enables them to do things we could never dream of doing, such as fly, turn invisible, and regenerate.

The West African lungfish sounds like a creature spawned from science fiction. It can regrow its tail and fins if hungry jaws snap a part of it off, much like a salamander. Its incredible regeneration abilities indicate that these particular traits came from a common vertebrate ancestor — and humans are also vertebrates. Now evolutionary biologist Igor Schneider and his research team are trying to understand the mechanism behind this almost paranormal power, and how it could apply to a human.

“Hyperdegeneracy” could be used in quantum computing.

Microscopic oil droplets held aloft with optical tweezers can contain more than 200 resonant optical modes of similar energies, creating “hyperdegeneracy” for the first time. That is the claim of researchers in Israel, Spain and the US, who say that their breakthrough could ultimately find application in high-speed optical communications, sensing, quantum data processing and even the creation of dynamic optical circuits.

When optical materials with a high refractive index are formed into certain symmetrical shapes — such as rings, cylinders or spheres —light can be repeatedly reflected around the inside of the material, much in the same way that sound waves pass around the inside edge of St Paul’s Cathedral’s famous “whispering gallery”. The circulating light undergoes constructive interference, forming discrete resonant modes – or so-called degenerate states – with similar energies.

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Composite fermions are exotic quasi-particles found in interacting 2-D fermion systems at relatively large perpendicular magnetic fields. These quasi-particles, which are composed of an electron and two magnetic flux quanta, have often been used to describe a physical phenomenon known as the fractional quantum Hall effect.

Researchers at Princeton University and Pennsylvania State University recently used composite fermions to test a theory introduced by physicist Felix Bloch almost a century ago, suggesting that at very low densities, a paramagnetic Fermi “sea” of electrons should spontaneously transition to a fully magnetized state, which is now referred to as Bloch ferromagnetism. Their paper, published in Nature Physics, provides evidence of an abrupt transition to full magnetization that is closely aligned with the state theorized by Bloch.

“Composite fermions are truly remarkable,” Mansour Shayegan, professor of Electrical Engineering at Princeton University and one of the researchers who carried out the study, told Phys.org. “They are born of interaction and magnetic flux, and yet they map such a complex system to a simple collection of quasi-particles that to a large degree behave as non-interacting and also behave as if they don’t feel the large magnetic field. One of their most interesting properties is their spin polarization.”

The removal of one gene renders poxviruses—a lethal family of viral infections that are known to spread from animals to humans—harmless, a new study in the journal Science Advances reports.

During this ground-breaking study, scientists from the Spanish National Research Council and the University of Surrey investigated the immune response of cells to poxviruses. Poxviruses, such as cowpox and monkeypox, can spread to humans from infected animals, causing skin lesions, fever, swollen lymph nodes and even death.

Viruses contain genetic material which helps them outsmart host cells, enabling replication and the spread of the infection. Cells in the body are comprised of molecules that sense the presence of viruses, sometimes via the recognition of their genetic material, and alert the immune system of an upcoming infection. Poxviruses, unlike other viruses, are highly unusual in that they have large DNA genomes that are replicated exclusively in the cell cytosol, an area of the cell full of sensors. How poxviruses manage to stay undetectable has remained unknown.

Artificial intelligence (AI) experts at the University of Massachusetts Amherst and the Baylor College of Medicine report that they have successfully addressed what they call a “major, long-standing obstacle to increasing AI capabilities” by drawing inspiration from a human brain memory mechanism known as “replay.”

First author and postdoctoral researcher Gido van de Ven and principal investigator Andreas Tolias at Baylor, with Hava Siegelmann at UMass Amherst, write in Nature Communications that they have developed a new method to protect—” surprisingly efficiently”— deep neural networks from “catastrophic forgetting;” upon learning new lessons, the networks forget what they had learned before.

Siegelmann and colleagues point out that deep neural networks are the main drivers behind recent AI advances, but progress is held back by this forgetting.

Artificial intelligence researchers at North Carolina State University have improved the performance of deep neural networks by combining feature normalization and feature attention modules into a single module that they call attentive normalization (AN). The hybrid module improves the accuracy of the system significantly, while using negligible extra computational power.

“Feature normalization is a crucial element of training deep neural networks, and feature attention is equally important for helping networks highlight which features learned from raw data are most important for accomplishing a given task,” says Tianfu Wu, corresponding author of a paper on the work and an assistant professor of electrical and computer engineering at NC State. “But they have mostly been treated separately. We found that combining them made them more efficient and effective.”

To test their AN module, the researchers plugged it into four of the most widely used neural network architectures: ResNets, DenseNets, MobileNetsV2 and AOGNets. They then tested the networks against two industry standard benchmarks: the ImageNet-1000 classification benchmark and the MS-COCO 2017 object detection and instance segmentation benchmark.

There are a variety of complementary observations that could be used in the search for life in extraterrestrial settings. At the molecular scale, patterns in the distribution of organics could provide powerful evidence of a biotic component. In order to observe these molecular biosignatures during spaceflight missions, it is necessary to perform separation science in situ. Microchip electrophoresis (ME) is ideally suited for this task. Although this technique is readily miniaturized and numerous instruments have been developed over the last 3 decades, to date, all lack the automation capabilities needed for future missions of exploration. We have developed a portable, automated, battery-powered, and remotely operated ME instrument coupled to laser-induced fluorescence detection. This system contains all the necessary hardware and software interfaces for end-to-end functionality. Here, we report the first application of the system for amino acid analysis coupled to an extraction unit in order to demonstrate automated sample-to-data operation. The system was remotely operated aboard a rover during a simulated Mars mission in the Atacama Desert, Chile. This is the first demonstration of a fully automated ME analysis of soil samples relevant to planetary exploration. This validation is a critical milestone in the advancement of this technology for future implementation on a spaceflight mission.

We tackle the crucial challenge of fusing different modalities of features for multimodal sentiment analysis. Mainly based on neural networks, existing approaches largely model multimodal interactions in an implicit and hard-to-understand manner. We address this limitation with inspirations from quantum theory, which contains principled methods for modeling complicated interactions and correlations. In our quantum-inspired framework, the word interaction within a single modality and the interaction across modalities are formulated with superposition and entanglement respectively at different stages. The complex-valued neural network implementation of the framework achieves comparable results to state-of-the-art systems on two benchmarking video sentiment analysis datasets. In the meantime, we produce the unimodal and bimodal sentiment directly from the model to interpret the entangled decision.