Lifeboat Foundation

RUSSIA plans to hold more than 100 military drill this summer as Vladimir Putin ramps up his nation’s war readiness.

73 years ago, the same scientists who had helped to begin the atomic age set a “doomsday clock” for humanity. It first appeared on the cover of the June 1947 issue of the Bulletin of the Atomic Scientists as a dire warning about the nuclear rivalry between the U.S. and the Soviet Union. At that moment, the Bulletin estimated that we stood at about 7 minutes to midnight, which represented nuclear apocalypse.

The Doomsday Clock wasn’t – and still isn’t – a precise countdown to the end of all things. It’s a metaphor for how dangerous the global situation seems to be at a given moment, in the very well-informed but also subjective opinion of the Bulletin’s board of directors. In June 1947, things looked dire. The U.S. had dropped a pair of atomic bombs on Japan less than two years before; when the Bulletin of the Atomic Scientists first published the Doomsday Clock image, researchers were still studying the aftermath of those bombs. Meanwhile, the Soviet Union was hard at work on its own atomic program, and was just a couple of years away from testing its first atomic bomb in 1949.

Through the Cold War and in the decades since, the clock’s minute hand has moved about two dozen times. In September 1953, it stood at two minutes to midnight, following Russia’s August 1953 hydrogen bomb test – which in turn had followed a U.S. hydrogen bomb test in November 1952. Those tests meant the two feuding superpowers each had much more powerful new weapons with which to destroy each other; the tests also heightened the sense of life-or-death competition that made it more likely that someone would decide to use those terrible new bombs.

A Tesla owner has demonstrated a rather novel way to charge his Model 3. In a recent video, Sean Callaghan of the ItsYeBoi YouTube channel opted to use a series of off-the-shelf solar panel sheets onto a towable trailer to create a mobile charging unit for his all-electric sedan.

Callaghan planned to use only the sun and the solar sheets purchased from e-commerce platform Wish to charge his Model 3. The solar panel sheets would collect energy from the sun and transfer it to a control panel. The control panels were connected to batteries that would hold the energy—the batteries connected to an inverter, which would then charge the Tesla Model 3.

The entire assembly would provide the Model 3 with about 800 watts of energy on a completely sunny day. However, Callaghan shot the video when weather was overcast, so the entire solar panel trailer build only managed to provide around 300 watts throughout the YouTube host’s test.

UNIVERSITY PARK, Pa. — Phone, keys, wallet…ultraviolet light device. Just in case you wanted yet another item to carry around all day, researchers say that portable, handheld COVID-19 killing ultraviolet light devices may be a reality in the future. These gadgets would emit high-intensity ultraviolet light and quickly disinfect targeted areas.

There are two main ways to clean and remove bacteria and viruses from a given surface: chemicals and ultraviolet (UV) radiation exposure. UV radiation between 200 and 300 nanometers can effectively kill a virus and stop it from replicating itself. Obviously, devices emitting UV rays would come in handy these days due to COVID-19, but as of now such devices require an expensive, bulky mercury-containing gas discharge lamp with a short battery life.

The study’s authors, however, believe that much more portable, longer lasting, energy efficient, and environmentally friendly UV light emitting diodes can be developed. The necessary LEDs already exist, but the process has been complicated by the fact that electrode materials must also be transparent.

It’s older than the dinosaurs.

A team of researchers from OpenAI recently published a paper describing GPT-3, a deep-learning model for natural-language with 175 billion parameters, 100x more than the previous version, GPT-2. The model is pre-trained on nearly half a trillion words and achieves state-of-the-art performance on several NLP benchmarks without fine-tuning.

In paper published on arXiv, a team of over 30 co-authors described the model and several experiments. The researchers’ goal was to produce an NLP system that performs well on a variety of tasks with little or no fine-tuning, and previous work had indicated that larger models might be the solution. To test that hypothesis, the team increased the size of their previous model, GPT-2, from 1.5 billion parameters to 175 billion. For training, the team collected several datasets, including the Common Crawl dataset and the English-language Wikipedia. The model was evaluated against several NLP benchmarks, matching state-of-the-art performance on “closed-book” question-answering tasks and setting a new record for the LAMBADA language modeling task.

OpenAI made headlines last year with GPT-2 and their decision not to release the 1.5 billion parameter version of the trained model due to “concerns about malicious applications of the technology.” GPT-2 is one of many large-scale NLP models based on the Transformer architecture. These models are pre-trained on large text corpora, such as the contents Wikipedia, using self-supervised learning. In this scenario, instead of using a dataset containing inputs paired with expected outputs, the model is given a sequence of text with words “masked” and it must learn to predict the masked words based on the surrounding context. After this pre-training, the models are then fine-tuned with a labelled benchmark dataset for a particular NLP task, such as question-answering.

As the pandemic’s economic toll grows around the world, some experts fear it could harm science for decades by putting many thousands of researchers out of work and forcing nations to slash funding as they rebuild societies. Others say the pandemic could highlight the importance of science and spur long-term support, especially for basic research, much as the Second World War did.

Financial crises could spell trouble for science budgets but spending could surge in some countries: part 2 in a series on science after the pandemic.

Skateboarding legend Rodney Mullen teams up with Physics Girl to explain the unusual physics behind skateboard tricks. Filmed with a phantom high speed camera at 1000fps, see Mullen’s tricks like never before.

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For more than six decades, the quest to understand the formation of hot (about 20,000−30,000 K) extreme horizontal branch (EHB) stars in Galactic globular clusters has remained one of the most elusive in stellar evolutionary theory. Here we report on two discoveries that challenge the idea of the stable luminosity of EHB stars. The first mode of EHB variability is periodic and cannot be ascribed to either binary evolution or pulsation. Instead, we attribute it here to the presence of magnetic spots: superficial chemical inhomogeneities whose projected rotation induces the variability. The second mode of EHB variability is aperiodic and manifests itself on timescales of years. In two cases, six-year-long light curves display superflare events that are several million times more energetic than solar analogues. We advocate a scenario in which the two EHB variability phenomena are different manifestations of diffuse, dynamo-generated, weak magnetic fields. Magnetism is therefore a key player driving the formation and evolution of EHB clusters stars and, likewise, operating in the Galactic field counterparts. Our conclusions bridge similar variability/magnetism phenomena in all radiative-enveloped hot-stars: young main-sequence stars, old EHBs and defunct white dwarfs.