Razer isn’t afraid to float some interesting product ideas around CES each year. Over the past few years, the gaming hardware company has offered up such concepts as Project Christine, a modular desktop PC, and Project Fiona, a Windows 8 gaming tablet.
This year is no exception, though 2018’s moonshot seems a little more practical. Project Linda actually takes an idea that’s been previously developed — pairing a smartphone with a shell of a laptop to serve essentially as a dock — by companies big (from Motorola back in 2011 to HP last year) and small (crowdfunded campaigns like the Superbook and the Mirabook), though it gives it the flair that Razer is known for.
Like HP’s Elite x3, Project Linda has more style than just a laptop shell. For instance, the aluminum-clad chassis features a 13.3-inch “Quad HD” (2560 x 1440) display compared to the Elite x3’s 12.5-inch 1,920×1,080 screen. It would also come with 200GB of built-in storage to supplement smartphone storage, which other phone docks usually don’t include.
Something else that other docks don’t provide that Project Linda does is a docking area carved out of the space where a touchpad typically goes. That’s because it’s specifically designed to work with the recently released Razer Phone, the company’s high-end Android smartphone that can either serve as a touchpad or an auxiliary screen when connected to the dock.
The dock has the ability to charge the Razer Phone while it’s connected, and the keyboard has Android-specific keys for loading apps and navigating the OS. As you might expect, Razer is touting Project Linda’s ability to enhance the Android gaming experience with the larger playing screen and the ability to use a mouse to control games, though the result probably wouldn’t be as immersive as the company’s more powerful and Windows-based Razer Blade family of gaming laptops.
As with its other projects, Razer is seeking community feedback on Project Linda before it decides whether to bring the product to actual fruition. So while there’s obviously no pricing or release date for the docking system, this concept may have a better chance of coming to market as it supports an existing device in the Razer Phone and probably won’t be extravagantly expensive since it doesn’t have an expensive processor and graphics card inside. Stay tuned and we’ll report if Project Linda ever sees the light of day.
Johnson & Johnson’s single-shot COVID-19 vaccine is effective and has a “favorable safety profile,” according to scientists at the Food and Drug Administration.
The endorsement comes out of a review released by the regulatory agency Wednesday. The FDA has been looking over data on Johnson & Johnson’s vaccine since February 4, when the company applied for Emergency Use Authorization. The agency’s green light is a positive sign ahead of this Friday, February 26, when the FDA will convene an advisory committee to make a recommendation on whether the FDA should grant the EUA. The FDA isn’t obligated to follow the committee’s recommendation, but it usually does.
If Johnson & Johnson’s vaccine is granted an EUA, it will become the third COVID-19 vaccine available for use in the US. The other two vaccines are both two-dose, mRNA-based vaccines, one made by Pfizer and its German partner BioNTech and the other from Moderna, which developed its vaccine in collaboration with researchers at the US National Institutes of Health.
According to data from a Phase III clinical trial involving more than 44,000 participants, Johnson & Johnson’s vaccine is less effective than the two mRNA vaccines, which were both around 95 percent effective at preventing symptomatic COVID-19. Johnson & Johnson’s vaccine was found to be 66 percent effective overall at preventing moderate to severe COVID-19. However, efficacy differed based on the trial’s location sites, with efficacy found to be 72 percent in the United States, 66 percent in Latin America, and 57 percent in South Africa. The differences may be partly explained by the circulation of variants in Latin America and South Africa, which have been found to reduce the efficacy of vaccines.
But overall, Johnson & Johnson’s vaccine was 85 percent effective against severe COVID-19. Even in South Africa, the vaccine was 82 percent effective against severe and critical COVID-19, according to the FDA’s review.
After the shot, six vaccinated participants and 42 participants who received the placebo were hospitalized. When researchers looked out 28 days after vaccination, zero vaccinated participants were hospitalized, compared with 16 in the placebo group. There were seven deaths in the trial, but all were in the placebo.
Though the efficacy numbers are lower than the mRNA vaccines, experts spotlight the high efficacy against severe disease and death—the most critical functions of any vaccine. Moreover, Johnson & Johnson’s vaccine has clear logistical advantages over the other vaccines. It is only one shot, rather than two, and it also doesn’t require freezer temperatures during shipping.
In terms of side effects, the FDA found that the vaccine has a favorable safety profile, with no specific safety concerns and the most common effects being mild to moderate pain at the injection site, headache, fatigue, and myalgia.
The fate of the vaccine now moves to the FDA advisory committee, which will dive deeper into all the data. If the FDA grants the EUA, Johnson & Johnson’s executive said in congressional testimony this week that the company would provide 4 million doses after the EUA, with a total of 20 million ready by the end of March and a total of 100 million by the end of June.
Early on in D-Wave’s history, the company made bold claims about its quantum annealer outperforming algorithms run on traditional CPUs. Those claims turned out to be premature, as improvements to these algorithms pulled the traditional hardware back in front. Since then, the company has been far more circumspect about its performance claims, even as it brought out newer generations of hardware.
But in the run-up to the latest hardware, the company apparently became a bit more interested in performance again. And it recently got together with Google scientists to demonstrate a significant boost in performance compared to a classical algorithm, with the gap growing as the problem became complex—although the company’s scientists were very upfront about the prospects of finding a way to boost classical hardware further. Still, there are a lot of caveats even beyond that, so it’s worth taking a detailed look at what the company did.
Magnets, how do they flip?
D-Wave’s system is based on a large collection of quantum devices that are connected to some of their neighbors. Each device can have its state set separately, and the devices are then given the chance to influence their neighbors as the system moves through different states and individual devices change their behavior. These transitions are the equivalent of performing operations. And because of the quantum nature of these devices, the hardware seems to be able to “tunnel” to new states, even if the only route between them involves high-energy states that are impossible to reach.
In the end, if the system is operated properly, the final state of the devices can be read out as an answer to the calculation performed by the operations. And because of the quantum effects, it can potentially provide solutions that a classical computer might find difficult to reach.
Validating that idea, however, has proven challenging, as noted above. Where the system has done best is in modeling quantum systems that look a lot like the quantum annealing hardware itself. And that’s what the D-Wave/Google team has done here. The problem can be described as an array of quantum magnets, with the orientation of each magnet influencing that of its neighbors. The system is in the lowest energy state when all of a magnet’s neighbors have the opposite orientation. Depending on the precise configuration of the array, however, that might not be possible to satisfy.
Now, imagine that you start the system in a configuration where the magnets aren’t in a stable state—there are too many cases where neighboring magnets have the same orientation. Magnets will start flipping to get there, but in the process, they may cause their neighbors to flip. The whole thing may work through a variety of intermediate configurations to make its way toward stability. Because of the quantum nature of the device’s components, the progression through different states may involve some steps that are, to our non-quantum brains, difficult to understand.
Quantum Monte Carlo
This system is interesting for a couple of reasons: it’s an approachable way to examine complicated quantum behaviors, and other interesting problems can be mapped onto its behavior. So researchers have figured out how to look at its behavior using computer algorithms. The one the research team says shows the highest performance is what’s called Path-Integral Monte Carlo. “Path-integral” simply indicates that there are multiple valid paths between a starting state and a low-energy state, and the software looks at a subset of them, since there are so many. “Monte Carlo” is an indication that the paths it does sample are chosen randomly.
But the D-Wave system looks a lot like an array of quantum magnets, so it’s possible to configure it so that it behaves a lot like what is being modeled. There’s a chance that configuring the D-Wave machine properly can get it to very efficiently recapitulate the behavior of the system being modeled.
This is what the team tried for the paper, but it found out there was a little problem. With the traditional computing algorithm, it’s easy to essentially stop the system and look at how it’s evolving. With the D-Wave system, things moved so quickly that it ended up carrying on to the final state before it could be sampled. Instead, the researchers had to arrange some fairly tortured configurations to slow the D-Wave hardware down long enough to have a look at what was going on.
The performance measurement the team cared about isn’t the final state; instead, it’s trying to figure out how quickly a given configuration of magnets will take to reach a stable, equilibrium state.
For generating this measure, the researchers found that the D-Wave hardware could outperform the x86 CPU they were using (a hyperthreading Xeon with 26 cores). And the advantage grew larger as the research team increased the complexity of the magnets’ arrangement, reaching up to 3 million times faster. And while the entire D-Wave system didn’t behave as a single quantum object, there were quantum interactions that were larger than the smallest groups of magnets in the D-Wave hardware (linked groups of four).
To start with, the gap in performance is between a single Xeon and a chip that requires a cabinet-sized cooling system with some pretty hefty energy use. Should the classical computer algorithm scale with additional processors, it should be relatively simple to put this on a cluster and take a big chunk out of D-Wave’s speed advantage. But Ars’ own Chris Lee notes that even on the simpler problems, the Xeon (which has 26 cores) was already struggling with any increase in complexity. This might be a sign that there are only limited gains we can expect from throwing more processors at the issue.
That said, D-Wave was also not operating at its full advantage. While it recently introduced a new generation of processors, the work was done on an experimental processor that was part of the development of the new generation. This had the same hardware layout—same number and connections among the quantum devices—as the previous generation of hardware. But it was made with a new manufacturing process that lowered the noise in the system and was put into full use in the latest generation of chips.
In addition, the new generation more than doubles the quantum devices on the chip and boosts the connectivity among them. These advances should allow the system to model larger and more complicated magnet arrays, expanding D-Wave’s advantage back.
Finally, the team behind the work emphasizes that there may be ways to optimize the performance of the classical algorithm as well, saying, “Our study does not constitute a demonstration of superiority over all possible classical methods.” How this all shakes out will undoubtedly come with additional work, so we may not have an update on where performance stands for a couple of years.
Still, it’s interesting that D-Wave has become so interested in performance again. The company recently announced that it had adapted its control software so that a specific type of operation (a quadratic unconstrained binary optimization) could be both used by a D-Wave machine and sent to the Qiskit software package that would allow it to run on IBM’s quantum computers. This makes sense for the company’s user base; a large percentage of the base is made up of companies that are simply trying to make sure they’re ready for any disruptive computing technologies, so they are looking at all the quantum hardware on the market. But in the press release announcing the data, the company says this “opens the door to performance comparisons.”
China has officially approved the development of a super heavy lift rocket, named the Long March 9, or CZ-9 vehicle. The decision was revealed on Wednesday by Chinese state television.
In a snippet from an interview with CCTV, the deputy director of the China National Space Agency, Wu Yanhua, said the main purpose of the new rocket is for any “crewed lunar landing or crewed Mars landing missions” the country may undertake.
According to Chinese officials, the country will target the year 2030 for a debut launch. This is consistent with previous timeline estimates. The rocket is planned to have a lift capacity of 140 metric tons, with the capability of sending 50 or more tons into lunar orbit. It would be an immense vehicle, with a 10-meter diameter core, and 5-meter side boosters. China would also like to eventually make the rocket, or at least part of it, reusable.
The prospects for the long-rumored CZ-9 super heavy lifter came under some doubt last year, when China said it was also considering development of a triple-core rocket that would have a similar appearance to two US rockets, SpaceX’s Falcon Heavy and United Launch Alliance’s Delta IV Heavy. For now, at least, it appears that China is pressing ahead with both, with a 2025 launch target for the triple-core design.
The target date of 2030 for CZ-9 might seem distant for rocket development, but it would be relatively speedy compared to NASA’s design and construction of the Space Launch System rocket, which began a decade ago. The initial configuration of the SLS rocket will have a lift capacity of 70 to 85 tons and may launch in 2022. Only after two additional major updates—likely to cost about $20 billion and not to be completed before 2030—will NASA’s rocket have a lift capacity of about 130 tons.
China’s announcement offers a powerful statement of its intent to press ahead with human deep space exploration. The country has already talked in generalities about establishing a Moon base in about a decade, but this appears to be the first time the country is also expressing an interest in sending humans to Mars as a follow-up mission.