Analysis of 2600 tumors could help match cancer patients to targeted treatments.
Mumbai: Prime Minister Narendra Modi’s call for a nine-minute blackout at 9 pm on April 5 has raised concerns for power grid managers as they are gearing up for ensuring grid stability during the period.
State-run Power System Operation Corporation (POSOCO), which is responsible for integrated operation of the grid, is working towards ensuring there is no pressure on the grid due to the possible grid collapse and resultant blackout throughout the country.
The Central Electricity Regulatory Authority (CERA) necessitates permissible range of the frequency band of 49.95−50.05 Hz for normal running of grid and if there is any discrepancy in the same with sudden increase or decrease in power flow, it might result into grid collapse.
The Royal Society is to create a network of disease modelling groups amid academic concern about the nation’s reliance on a single group of epidemiologists at Imperial College London whose predictions have dominated government policy, including the current lockdown.
It is to bring in modelling experts from fields as diverse as banking, astrophysics and the Met Office to build new mathematical representations of how the coronavirus epidemic is likely to spread across the UK — and how the lockdown can be ended.
The first public signs of academic tensions over Imperial’s domination of the debate came when Sunetra Gupta, professor of theoretical epidemiology at Oxford University, published a paper suggesting that some of Imperial’s key assumptions could be wrong.
Despite the huge contributions of deep learning to the field of artificial intelligence, there’s something very wrong with it: It requires huge amounts of data. This is one thing that both the pioneers and critics of deep learning agree on. In fact, deep learning didn’t emerge as the leading AI technique until a few years ago because of the limited availability of useful data and the shortage of computing power to process that data.
Reducing the data-dependency of deep learning is currently among the top priorities of AI researchers.
In his keynote speech at the AAAI conference, computer scientist Yann LeCun discussed the limits of current deep learning techniques and presented the blueprint for “self-supervised learning,” his roadmap to solve deep learning’s data problem. LeCun is one of the godfathers of deep learning and the inventor of convolutional neural networks (CNN), one of the key elements that have spurred a revolution in artificial intelligence in the past decade.
A migrant worker in India dies after walking 200 km on the way back to his home [1].
Rural itinerant workers in China are being blocked from cities, kicked out of apartments and rejected by companies [2].
“Poverty will kill us before the virus” — Rajneesh, a migrant worker, walking 247Km on foot to his home [3].
Swapping out spent uranium rods requires hundreds of technicians—challenging right now.
He is remembered for his ‘transformational’ and ‘immeasurable’ contributions to scientific research.
I made a video on the possible treatments for COVID-19 and how it targets different components of SARS-CoV-2, the virus that causes it 😃
You can watch it here: https://youtu.be/DaXG3Qd8soo And let me know if you have questionsor suggestions 😃.
Accelerating electrons to such high energies in a laboratory setting, however, is challenging: typically, the more energetic the electrons, the bigger the particle accelerator. For instance, to discover the Higgs boson—the recently observed “God particle,” responsible for mass in the universe—scientists at the CERN laboratory in Switzerland used a particle accelerator nearly 17 miles long.
But what if there was a way to scale down particle accelerators, producing high-energy electrons in a fraction of the distance?