Lifeboat Foundation: Safeguarding Humanity
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Increased demand for super tiny electronic sensors coming from healthcare, environmental services and the Internet of Things is prompting a search for equally tiny ways to power these sensors. A review of the state of ultracompact supercapacitors, or “micro-supercapacitors,” concludes there is still a lot of research to be done before these devices can deliver on their promise.

The review appeared in the journal Nano Research Energy.

The explosion of demand in recent years for miniaturized , such as health monitors, environmental sensors and wireless communications technologies has in turn driven demand for components for those devices that have ever smaller size and weight, with lower energy consumption, and all of this at cheaper prices.

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Early last year, our research team from the Visual Computing Group introduced Swin Transformer, a Transformer-based general-purpose computer vision architecture that for the first time beat convolutional neural networks on the important vision benchmark of COCO object detection and did so by a large margin. Convolutional neural networks (CNNs) have long been the architecture of choice for classifying images and detecting objects within them, among other key computer vision tasks. Swin Transformer offers an alternative. Leveraging the Transformer architecture’s adaptive computing capability, Swin can achieve higher accuracy. More importantly, Swin Transformer provides an opportunity to unify the architectures in computer vision and natural language processing (NLP), where the Transformer has been the dominant architecture for years and has benefited the field because of its ability to be scaled up.

So far, Swin Transformer has shown early signs of its potential as a strong backbone architecture for a variety of computer vision problems, powering the top entries of many important vision benchmarks such as COCO object detection, ADE20K semantic segmentation, and CelebA-HQ image generation. It has also been well-received by the computer vision research community, garnering the Marr Prize for best paper at the 2021 International Conference on Computer Vision (ICCV). Together with works such as CSWin, Focal Transformer, and CvT, also from teams within Microsoft, Swin is helping to demonstrate the Transformer architecture as a viable option for many vision challenges. However, we believe there’s much work ahead, and we’re on an adventurous journey to explore the full potential of Swin Transformer.

In the past few years, one of the most important discoveries in the field of NLP has been that scaling up model capacity can continually push the state of the art for various NLP tasks, and the larger the model, the better its ability to adapt to new tasks with very little or no training data. Can the same be achieved in computer vision, and if so, how?

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Astronomers build new telescopes and peer at the night sky to see what they might find. Janelia Group Leader Abraham Beyene takes a similar approach when looking at the cells that make up the human brain.

Beyene and his team design and synthesize new types of highly sensitive biosensors they use to peer at to see what they can learn.

“You have this new tool that now helps us make the kinds of measurements that we’ve never been able to make before, and we go into the lab and deploy this technology and we see what happens,” Beyene says. “What you see is that some really interesting phenomena begin to emerge that you haven’t even begun to think about.”

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Scientists have created “synthetic” mouse embryos from stem cells without a dad’s sperm or a mom’s egg or womb.

The lab-created embryos mirror a natural mouse embryo up to 8 ½ days after fertilization, containing the same structures, including one like a beating heart.

In the near term, researchers hope to use these so-called embryoids to better understand early stages of development and study mechanisms behind disease without the need for as many lab animals. The feat could also lay the foundation for creating synthetic human embryos for research in the future.

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After a tantalizing year-and-a-half wait since the Mars Perseverance Rover touched down on our nearest planetary neighbor, new data is arriving—and bringing with it a few surprises.

The rover, which is about the size of car and carries seven , has been probing Mars’ 30-mile-wide Jezero crater, once the site of a lake and an ideal spot to search for evidence of ancient life and information about the planet’s geological and climatic past.

In a paper published today in the journal Science Advances, a research team led by UCLA and the University of Oslo reveals that beneath the crater’s floor, observed by the rover’s ground-penetrating radar instrument, are unexpectedly inclined. The slopes, thicknesses and shapes of the inclined sections suggest they were either formed by slowly cooling lava or deposited as sediments in the former lake.

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Physicists at the Max Planck Institute of Quantum Optics have managed to entangle more than a dozen photons efficiently and in a defined way. They are thus creating a basis for a new type of quantum computer. Their study is published in Nature.

The phenomena of the quantum world, which often seem bizarre from the perspective of the common everyday world, have long since found their way into technology. For example, entanglement: a quantum-physical connection between particles that links them in a strange way over arbitrarily long distances. It can be used, for example, in a quantum computer—a computing machine that, unlike a conventional computer, can perform numerous mathematical operations simultaneously. However, in order to use a quantum computer profitably, a large number of entangled particles must work together. They are the for calculations, so-called qubits.

“Photons, the particles of light, are particularly well suited for this because they are robust by nature and easy to manipulate,” says Philip Thomas, a doctoral student at the Max Planck Institute of Quantum Optics (MPQ) in Garching near Munich. Together with colleagues from the Quantum Dynamics Division led by Prof. Gerhard Rempe, he has now succeeded in taking an important step towards making usable for technological applications such as quantum computing: For the first time, the team generated up to 14 entangled photons in a defined way and with high efficiency.

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An algorithm developed by researchers from Helmholtz Munich, the Technical University of Munich (TUM) and its University Hospital rechts der Isar, the University Hospital Bonn (UKB) and the University of Bonn is able to learn independently across different medical institutions. The key feature is that it is self-learning, meaning it does not require extensive, time-consuming findings or markings by radiologists in the MRI images.

This federated was trained on more than 1,500 MRI scans of healthy study participants from four institutions while maintaining data privacy. The algorithm then was used to analyze more than 500 patient MRI scans to detect diseases such as multiple sclerosis, vascular disease, and various forms of brain tumors that the algorithm had never seen before. This opens up new possibilities for developing efficient AI-based federated algorithms that learn autonomously while protecting privacy. The study has now been published in the journal Nature Machine Intelligence.

Health care is currently being revolutionized by artificial intelligence. With precise AI solutions, doctors can be supported in diagnosis. However, such algorithms require a considerable amount of data and the associated radiological specialist findings for training. The creation of such a large, central database, however, places special demands on . Additionally, the creation of the findings and annotations, for example the marking of tumors in an MRI image, is very time-consuming.

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