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Kamil MuzykaYes you are!

Nicholas MacDonald “Newton. Pfft.”

Omuterema Akhahenda shared a link.

In the early days of the COVID pandemic, Cuba decided it was going to make its own vaccine – even though vaccine development historically takes years, even decades, to bear fruit.

Why did the Communist island nation decide to go it alone?

On November 16, during its online Quantum Summit, IBM announced that it had successfully completed initial development of the 127-qubit (quantum bit) Eagle quantum computer. Last year, IBM’s Hummingbird quantum computer handled 65 qubits, and, the year before that, the company’s Falcon quantum computer was handling calculations using 27 qubits. So the company has been steadily increasing the number of qubits that its quantum machines can handle, roughly doubling the number of operational qubits in its quantum machines on an annual basis. However, the Eagle quantum computer is the last member of IBM’s Quantum System One family. Designs have reached the limit of the cryogenic refrigerator used to cool the Josephson Junctions that hold the qubits, so IBM has had to work with Bluefors Cryogenics to develop a new, larger cryogenic platform for bigger machines.

If you don’t understand qubits or how quantum computers work, join the club. Nothing in the binary word of today’s digital computers prepares you to understand quantum computing, although there are some superficial similarities. For example, quantum computers store data in qubits just as digital computers store data in bits. However, a bit can store only a “1” or a “0.” Each qubit stores both a “1” and a “0” at the same time in a state of superposition. Consequently, information density is much higher for qubit storage.

Further, qubits can be entangled, a phenomenon that Albert Einstein once described as “spooky action at a distance.” Quantum entanglement, a property of the quantum world, was once the stuff of science fiction. However, it’s quite real and an important part of quantum computing.

Despite the arrests and wider ransomware crackdowns in Russia, the Trickbot group has not exactly gone into hiding. Toward the end of last year, the group boosted its operations, says Limor Kessem, an executive security advisor at IBM Security. “They’re trying to infect as many people as possible by contracting out the infection,” she says. Since the start of 2022, the IBM security team has seen Trickbot increase its efforts to evade security protections and conceal its activity. The FBI also formally linked the use of the Diavol ransomware to Trickbot at the beginning of the year. “Trickbot doesn’t seem to be targeting very specifically; I think what they have is numerous affiliates working with them, and whoever brings the most money is welcome to stay,” Limor says.

Holden too says he has seen evidence that Trickbot is ramping up its operations. “Last year they invested more than $20 million into their infrastructure and growth of their organization,” he explains, citing internal messages he has seen. This money, he says, is being spent on everything Trickbot does. “Staffing, technology, communications, development, extortion” are all getting extra investment, he says. The move points to a future where—after the takedown of REvil—the Trickbot group may become the primary Russia-linked cybercrime gang. “You expand in the hope of getting that money back in spades,” Holden says. “It’s not like they are planning to close the shop. It’s not like they are planning to downsize or run and hide.”

Denoising an image is a classical problem that researchers are trying to solve for decades. In earlier times, researchers used filters to reduce the noise in the images. They used to work fairly well for images with a reasonable level of noise. However, applying those filters would add a blur to the image. And if the image is too noisy, then the resultant image would be so blurry that most of the critical details in the image are lost.

There has to be a better way to solve this problem. As a result, I have implemented several deep learning architectures that far surpass the traditional denoising filters. In this blog, I will explain my approach step-by-step as a case study, starting from the problem formulation to implementing the state-of-the-art deep learning models, and then finally see the results.

Japanese researcher Sagawa Masato has won this year’s Queen Elizabeth Prize for Engineering for developing the world’s “strongest” permanent magnet.

The winner of the sixth edition of the British prize was announced online on Tuesday. It had been held every other year since 2013, but became an annual event, starting this year, to keep up with the pace of scientific and technological advances.

Sagawa invented the neodymium-iron-boron magnet, which is said to be the world’s most powerful permanent magnet. The breakthrough led to the development of small and high-performance motors. This has enabled higher-performance products in various fields, such as wind power, electric vehicles and home electrical appliances.