Call of Duty is parachuting back into World War II. Special operations forces such as SEAL Team Six grew out of Allied experiments with small squads chosen for specialized missions in World War II. In developing the single-player story campaign, Sledgehammer's creative team worked with historians including Marty Morgan, author of "D-Day: A Photographic History of the Normandy Invasion" who served as technical director on the studio's 2017 game Call of Duty WWII. Video games:Fortnite meets Among Us? New Impostors mode rolling out "We were really inspired by these first special forces operators and they seemed like such interesting characters that we wanted to explore," said David Swenson, creative director of the game's single-player story campaign for development studio Sledgehammer Games. Call of Duty: Vanguard's story is fiction, but "even though we are not beholden to history, we are rooted in history," Swenson said. "It feels realistic and authentic."

New variants have changed the face of the pandemic. What will the virus do next? ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) NEXTSTRAIN; GISAID Edward Holmes does not like making predictions, but last year he hazarded a few. Again and again, people had asked Holmes, an expert on viral evolution at the University of Sydney, how he expected SARS-CoV-2 to change. In May 2020, 5 months into the pandemic, he started to include a slide with his best guesses in his talks. The virus would probably evolve to avoid at least some human immunity, he suggested. But it would likely make people less sick over time, he said, and there would be little change in its infectivity. In short, it sounded like evolution would not play a major role in the pandemic's near future. “A year on I've been proven pretty much wrong on all of it,” Holmes says. Well, not all: SARS-CoV-2 did evolve to better avoid human antibodies. But it has also become a bit more virulent and a lot more infectious, causing more people to fall ill. That has had an enormous influence on the course of the pandemic. The Delta strain circulating now—one of four “variants of concern” identified by the World Health Organization, along with four “variants of interest”—is so radically different from the virus that appeared in Wuhan, China, in late 2019 that many countries have been forced to change their pandemic planning. Governments are scrambling to accelerate vaccination programs while prolonging or even reintroducing mask wearing and other public health measures. As to the goal of reaching herd immunity—vaccinating so many people that the virus simply has nowhere to go—“With the emergence of Delta, I realized that it's just impossible to reach that,” says Müge Çevik, an infectious disease specialist at the University of St. Andrews. Yet the most tumultuous period in SARS-CoV-2's evolution may still be ahead of us, says Aris Katzourakis, an evolutionary biologist at the University of Oxford. There's now enough immunity in the human population to ratchet up an evolutionary competition, pressuring the virus to adapt further. At the same time, much of the world is still overwhelmed with infections, giving the virus plenty of chances to replicate and throw up new mutations. Predicting where those worrisome factors will lead is just as tricky as it was a year and a half ago, however. “We're much better at explaining the past than predicting the future,” says Andrew Read, an evolutionary biologist at Pennsylvania State University, University Park. Evolution, after all, is driven by random mutations, which are impossible to predict. “It's very, very tricky to know what's possible, until it happens,” Read says. “It's not physics. It doesn't happen on a billiard table.” Still, experience with other viruses gives evolutionary biologists some clues about where SARS-CoV-2 may be headed. The courses of past outbreaks show the coronavirus could well become even more infectious than Delta is now, Read says: “I think there's every expectation that this virus will continue to adapt to humans and will get better and better at us.” Far from making people less sick, it could also evolve to become even deadlier, as some previous viruses including the 1918 flu have. And although COVID-19 vaccines have held up well so far, history shows the virus could evolve further to elude their protective effect—although a recent study in another coronavirus suggests that could take many years, which would leave more time to adapt vaccines to the changing threat. Holmes himself uploaded one of the first SARS-CoV-2 genomes to the internet on 10 January 2020. Since then, more than 2 million genomes have been sequenced and published, painting an exquisitely detailed picture of a changing virus. “I don't think we've ever seen that level of precision in watching an evolutionary process,” Holmes says. Making sense of the endless stream of mutations is complicated. Each is just a tiny tweak in the instructions for how to make proteins. Which mutations end up spreading depends on how the viruses carrying those tweaked proteins fare in the real world. The vast majority of mutations give the virus no advantage at all, and identifying the ones that do is difficult. There are obvious candidates, such as mutations that change the part of the spike protein—which sits on the surface of the virus—that binds to human cells. But changes elsewhere in the genome may be just as crucial—yet are harder to interpret. Some genes' functions aren't even clear, let alone what a change in their sequence could mean. The impact of any one change on the virus' fitness also depends on other changes it has already accumulated. That means scientists need real-world data to see which variants appear to be taking off. Only then can they investigate, in cell cultures and animal experiments, what might explain that viral success. The most eye-popping change in SARS-CoV-2 so far has been its improved ability to spread between humans. At some point early in the pandemic, SARS-CoV-2 acquired a mutation called D614G that made it a bit more infectious. That version spread around the world; almost all current viruses are descended from it. Then in late 2020, scientists identified a new variant, now called Alpha, in patients in Kent, U.K., that was about 50% more transmissible. Delta, first seen in India and now conquering the world, is another 40% to 60% more transmissible than Alpha. Read says the pattern is no surprise. “The only way you could not get infectiousness rising would be if the virus popped into humans as perfect at infecting humans as it could be, and the chance of that happening is incredibly small,” he says. But Holmes was startled. “This virus has gone up three notches in effectively a year and that, I think, was the biggest surprise to me,” Holmes says. “I didn't quite appreciate how much further the virus could get.” Bette Korber at Los Alamos National Laboratory and her colleagues first suggested that D614G, the early mutation, was taking over because it made the virus better at spreading. She says skepticism about the virus' ability to evolve was common in the early days of the pandemic, with some researchers saying D614G's apparent advantage might be sheer luck. “There was extraordinary resistance in the scientific community to the idea this virus could evolve as the pandemic grew in seriousness in spring of 2020,” Korber says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) NEXTSTRAIN; GISAID Researchers had never watched a completely novel virus spread so widely and evolve in humans, after all. “We're used to dealing with pathogens that have been in humanity for centuries, and their evolutionary course is set in the context of having been a human pathogen for many, many years,” says Jeremy Farrar, head of the Wellcome Trust. Katzourakis agrees. “This may have affected our priors and conditioned many to think in a particular way,” he says. Another, more practical problem is that real-world advantages for the virus don't always show up in cell culture or animal models. “There is no way anyone would have noticed anything special about Alpha from laboratory data alone,” says Christian Drosten, a virologist at the Charité University Hospital in Berlin. He and others are still figuring out what, at the molecular level, gives Alpha and Delta an edge. Alpha seems to bind more strongly to the human ACE2 receptor, the virus' target on the cell surface, partly because of a mutation in the spike protein called N501Y. It may also be better at countering interferons, molecules that are part of the body's viral immune defenses. Together those changes may lower the amount of virus needed to infect someone—the infectious dose. In Delta, one of the most important changes may be near the furin cleavage site on spike, where a human enzyme cuts the protein, a key step enabling the virus to invade human cells. A mutation called P681R in that region makes cleavage more efficient, which may allow the virus to enter more cells faster and lead to greater numbers of virus particles in an infected person. In July, Chinese researchers posted a preprint showing Delta could lead to virus levels in patient samples 1000 times higher than for previous variants. Evidence is accumulating that infected people not only spread the virus more efficiently, but also faster, allowing the variant to spread even more rapidly. The new variants of SARS-CoV-2 may also cause more severe disease. For example, a study in Scotland found that an infection with Delta was about twice as likely to lead to hospital admission than with Alpha. It wouldn't be the first time a newly emerging disease quickly became more serious. The 1918–19 influenza pandemic also appears to have caused more serious illness as time went on, says Lone Simonsen, an epidemiologist at Roskilde University who studies past pandemics. “Our data from Denmark suggests it was six times deadlier in the second wave.” A popular notion holds that viruses tend to evolve over time to become less dangerous, allowing the host to live longer and spread the virus more widely. But that idea is too simplistic, Holmes says. “The evolution of virulence has proven to be quicksand for evolutionary biologists,” he says. “It's not a simple thing.” Two of the best studied examples of viral evolution are myxoma virus and rabbit hemorrhagic disease virus, which were released in Australia in 1960 and 1996, respectively, to decimate populations of European rabbits that were destroying croplands and wreaking ecological havoc. Myxoma virus initially killed more than 99% of infected rabbits, but then less pathogenic strains evolved, likely because the virus was killing many animals before they had a chance to pass it on. (Rabbits also evolved to be less susceptible.) Rabbit hemorrhagic disease virus, by contrast, got more deadly over time, probably because the virus is spread by blow flies feeding on rabbit carcasses, and quicker death accelerated its spread. Other factors loosen the constraints on deadliness. For example, a virus variant that can outgrow other variants within a host can end up dominating even if it makes the host sicker and reduces the likelihood of transmission. And an assumption about human respiratory diseases may not always hold: that a milder virus—one that doesn't make you crawl into bed, say—might allow an infected person to spread the virus further. In SARS-CoV-2, most transmission happens early on, when the virus is replicating in the upper airways, whereas serious disease, if it develops, comes later, when the virus infects the lower airways. As a result, a variant that makes the host sicker might spread just as fast as before. From the start of the pandemic, researchers have worried about a third type of viral change, perhaps the most unsettling of all: that SARS-CoV-2 might evolve to evade immunity triggered by natural infections or vaccines. Already, several variants have emerged sporting changes in the surface of the spike protein that make it less easily recognized by antibodies. But although news of these variants has caused widespread fear, their impact has so far been limited. Derek Smith, an evolutionary biologist at the University of Cambridge, has worked for decades on visualizing immune evasion in the influenza virus in so-called antigenic maps. The farther apart two variants are on Smith's maps, the less well antibodies against one virus protect against the other. In a recently published preprint, Smith's group, together with David Montefiori's group at Duke University, has applied the approach to mapping the most important variants of SARS-CoV-2 (see graphic, below). The new maps place the Alpha variant very close to the original Wuhan virus, which means antibodies against one still neutralize the other. The Delta variant, however, has drifted farther away, even though it doesn't completely evade immunity. “It's not an immune escape in the way people think of an escape in slightly cartoonish terms,” Katzourakis says. But Delta is slightly more likely to infect fully vaccinated people than previous variants. “It shows the possible beginning of a trajectory and that's what worries me,” Katzourakis says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) DEREK SMITH/UNIVERSITY OF CAMBRIDGE; DAVID MONTEFIORI/DUKE UNIVERSITY Other variants have evolved more antigenic distance from the original virus than Delta. Beta, which first appeared in South Africa, has traveled the farthest on the map, although natural or vaccine-induced immunity still largely protects against it. And Beta's attempts to get away may come at a price, as Delta has outstripped it worldwide. “It's probably the case that when a virus changes to escape immunity, it loses other aspects of its fitness,” Smith says. The map shows that for now, the virus is not moving in any particular direction. If the original Wuhan virus is like a town on Smith's map, the virus has been taking local trains to explore the surrounding area, but it has not traveled to the next city—not yet. Although it's impossible to predict exactly how infectiousness, virulence, and immune evasion will develop in the coming months, some of the factors that will influence the virus' trajectory are clear. One is the immunity that is now rapidly building in the human population. On one hand, immunity reduces the likelihood of people getting infected, and may hamper viral replication even when they are. “That means there will be fewer mutations emerging if we vaccinate more people,” Çevik says. On the other hand, any immune escape variant now has a huge advantage over other variants. In fact, the world is probably at a tipping point, Holmes says: With more than 2 billion people having received at least one vaccine dose and hundreds of millions more having recovered from COVID-19, variants that evade immunity may now have a bigger leg up than those that are more infectious. Something similar appears to have happened when a new H1N1 influenza strain emerged in 2009 and caused a pandemic, says Katia Kölle, an evolutionary biologist at Emory University. A 2015 paper found that changes in the virus in the first 2 years appeared to make the virus more adept at human-to-human transmission, whereas changes after 2011 were mostly to avoid human immunity. It may already be getting harder for SARS-CoV-2 to make big gains in infectiousness. “There are some fundamental limits to exactly how good a virus can get at transmitting and at some point SARS-CoV-2 will hit that plateau,” says Jesse Bloom, an evolutionary biologist at the Fred Hutchinson Cancer Research Center. “I think it's very hard to say if this is already where we are, or is it still going to happen.” Evolutionary virologist Kristian Andersen of Scripps Research guesses the virus still has space to evolve greater transmissibility. “The known limit in the viral universe is measles, which is about three times more transmissible than what we have now with Delta,” he says. ![Figure][1] CREDITS: (GRAPHIC) N. DESAI/ SCIENCE ; (DATA) E. WALL ET AL., THE LANCET , 397:10292, 2331 (2021) The limits of immune escape are equally uncertain. Smith's antigenic maps show the space the virus has explored so far. But can it go much farther? If the variants on the map are like towns, then where are the country's natural boundaries—where does the ocean start? A crucial clue will be where the next few variants appear on the map, Smith says. Beta evolved in one direction away from the original virus and Delta in another. “It's too soon to say this now, but we might be heading for a world where there are two serotypes of this virus that would also both have to be considered in any vaccines,” Drosten says. Immune escape is so worrying because it could force humanity to update its vaccines continually, as happens for flu. Yet the vaccines against many other diseases—measles, polio, and yellow fever, for example—have remained effective for decades without updates, even in the rare cases where immune-evading variants appeared. “There was big alarm around 2000 that maybe we'd need to replace the hepatitis B vaccines,” because an escape variant had popped up, Read says. But the variant has not spread around the world: It is able to infect close contacts of an infected person, but then peters out. The virus apparently faces a trade-off between transmissibility and immune escape. Such trade-offs likely exist for SARS-CoV-2 as well. Some clues about SARS-CoV-2's future path may come from coronaviruses with a much longer history in humans: those that cause common colds. Some are known to reinfect people, but until recently it was unclear whether that's because immunity in recovered people wanes, or because the virus changes its surface to evade immunity. In a study published in April in PLOS Pathogens , Bloom and other researchers compared the ability of human sera taken at different times in the past decades to block virus isolated at the same time or later. They showed that the samples could neutralize strains of a coronavirus named 229E isolated around the same time, but weren't always effective against virus from 10 years or more later. The virus had evidently evolved to evade human immunity, but it had taken 10 years or more. “Immune escape conjures this catastrophic failure of immunity when it is really immune erosion,” Bloom says. “Right now it seems like SARS-CoV-2, at least in terms of antibody escape, is actually behaving a lot like coronavirus 229E.” Others are probing SARS-CoV-2 itself. In a preprint published this month, researchers tinkered with the virus to learn how much it has to change to evade the antibodies generated in vaccine recipients and recovered patients. They found that it took 20 changes to the spike protein to escape current antibody responses almost completely. That means the bar for complete escape is high, says one of the authors, virologist Paul Bieniasz of Rockefeller University. “But it's very difficult to look into a crystal ball and say whether that is going to be easy for the virus to acquire or not,” he says. “It seems plausible that true immune escape is hard,” concludes William Hanage of the Harvard T.H. Chan School of Public Health. “However, the counterargument is that natural selection is a hell of a problem solver and the virus is only beginning to experience real pressure to evade immunity.” And the virus has tricks up its sleeve. Coronaviruses are good at recombining, for instance, which could allow new variants to emerge suddenly by combining the genomes—and the properties—of two different variants. In pigs, recombination of a coronavirus named porcine epidemic diarrhea virus with attenuated vaccine strains of another coronavirus has led to more virulent variants of PEDV. “Given the biology of these viruses, recombination may well factor into the continuing evolution of SARS-CoV-2,” Korber says. Given all that uncertainty, it's worrisome that humanity hasn't done a great job of limiting the spread of SARS-CoV-2, says Eugene Koonin, a researcher at the U.S. National Center for Biotechnology Information. Some dangerous variants may only be possible if the virus hits on a very rare, winning combination of mutations, he says. It might have to replicate an astronomical number of times to get there. “But with all these millions of infected people, it may very well find that combination.” Indeed, Katzourakis adds, the past 20 months are a warning to never underestimate viral evolution. “Many still see Alpha and Delta as being as bad as things are ever going to get,” he says. “It would be wise to consider them as steps on a possible trajectory that may challenge our public health response further.” [1]: pending:yes

The challenges of host-pathogen interactions discovery, and the efforts undertaken to apply the latest machine learning and artificial intelligence.

Significant to economic stability across the world, the current scenario in the aqua-farming industry is far from what it was a decade ago. With fewer changes in people and processes, the growth and evolution of the aqua-farming sector have been steady in the past decade. Although the technological advancements have been limited, yet the onset of IoT has triggered the introduction of AI-based process adoptions and automation. The sector is fairly large and deals with the production, and supply of aquatic animals. Fish, shrimp, oysters, and algae farming are closely associated with the global food industry.

In this era of the Fourth Industrial Revolution, advances in artificial intelligence ("AI") has resulted in AI capable of generating inventions that are novel and inventive. The question becomes whether these AI generated inventions can be protected under the current patent law framework. A recent development in Australian jurisprudence takes a step toward clarifying the applicability of patent law on AI generated inventions. The Federal Court of Australia recently held in Thaler v Commissioner of Patents [2012] FC 879 ("the Thaler case") that an AI system can be named as an inventor in a patent application. After a brief introduction on basic concepts underlying patent law and AI, this article will discuss the Thaler case followed by an analysis on the Malaysian perspective.

Tagging based methods are one of the mainstream methods in relational triple extraction. However, most of them suffer from the class imbalance issue greatly. Here we propose a novel tagging based model that addresses this issue from following two aspects. First, at the model level, we propose a three-step extraction framework that can reduce the total number of samples greatly, which implicitly decreases the severity of the mentioned issue. Second, at the intra-model level, we propose a confidence threshold based cross entropy loss that can directly neglect some samples in the major classes. We evaluate the proposed model on NYT and WebNLG. Extensive experiments show that it can address the mentioned issue effectively and achieves state-of-the-art results on both datasets. The source code of our model is available at: https://github.com/neukg/ConCasRTE.

Knowing the causal structure of a system is of fundamental interest in many areas of science and can aid the design of prediction algorithms that work well under manipulations to the system. The causal structure becomes identifiable from the observational distribution under certain restrictions. To learn the structure from data, score-based methods evaluate different graphs according to the quality of their fits. However, for large nonlinear models, these rely on heuristic optimization approaches with no general guarantees of recovering the true causal structure. In this paper, we consider structure learning of directed trees. We propose a fast and scalable method based on Chu-Liu-Edmonds' algorithm we call causal additive trees (CAT). For the case of Gaussian errors, we prove consistency in an asymptotic regime with a vanishing identifiability gap. We also introduce a method for testing substructure hypotheses with asymptotic family-wise error rate control that is valid post-selection and in unidentified settings. Furthermore, we study the identifiability gap, which quantifies how much better the true causal model fits the observational distribution, and prove that it is lower bounded by local properties of the causal model. Simulation studies demonstrate the favorable performance of CAT compared to competing structure learning methods.

Autonomous spacecraft relative navigation technology has been planned for and applied to many famous space missions. The development of on-board electronics systems has enabled the use of vision-based and LiDAR-based methods to achieve better performances. Meanwhile, deep learning has reached great success in different areas, especially in computer vision, which has also attracted the attention of space researchers. However, spacecraft navigation differs from ground tasks due to high reliability requirements but lack of large datasets. This survey aims to systematically investigate the current deep learning-based autonomous spacecraft relative navigation methods, focusing on concrete orbital applications such as spacecraft rendezvous and landing on small bodies or the Moon. The fundamental characteristics, primary motivations, and contributions of deep learning-based relative navigation algorithms are first summarised from three perspectives of spacecraft rendezvous, asteroid exploration, and terrain navigation. Furthermore, popular visual tracking benchmarks and their respective properties are compared and summarised. Finally, potential applications are discussed, along with expected impediments.

Automated next-best action recommendation for each customer in a sequential, dynamic and interactive context has been widely needed in natural, social and business decision-making. Personalized next-best action recommendation must involve past, current and future customer demographics and circumstances (states) and behaviors, long-range sequential interactions between customers and decision-makers, multi-sequence interactions between states, behaviors and actions, and their reactions to their counterpart's actions. No existing modeling theories and tools, including Markovian decision processes, user and behavior modeling, deep sequential modeling, and personalized sequential recommendation, can quantify such complex decision-making on a personal level. We take a data-driven approach to learn the next-best actions for personalized decision-making by a reinforced coupled recurrent neural network (CRN). CRN represents multiple coupled dynamic sequences of a customer's historical and current states, responses to decision-makers' actions, decision rewards to actions, and learns long-term multi-sequence interactions between parties (customer and decision-maker). Next-best actions are then recommended on each customer at a time point to change their state for an optimal decision-making objective. Our study demonstrates the potential of personalized deep learning of multi-sequence interactions and automated dynamic intervention for personalized decision-making in complex systems.

We investigate ensembling techniques in forecasting and examine their potential for use in nonseasonal time-series similar to those in the early days of the COVID-19 pandemic. Developing improved forecast methods is essential as they provide data-driven decisions to organisations and decision-makers during critical phases. We propose using late data fusion, using a stacked ensemble of two forecasting models and two meta-features that prove their predictive power during a preliminary forecasting stage. The final ensembles include a Prophet and long short term memory (LSTM) neural network as base models. The base models are combined by a multilayer perceptron (MLP), taking into account meta-features that indicate the highest correlation with each base model's forecast accuracy. We further show that the inclusion of meta-features generally improves the ensemble's forecast accuracy across two forecast horizons of seven and fourteen days. This research reinforces previous work and demonstrates the value of combining traditional statistical models with deep learning models to produce more accurate forecast models for time-series from different domains and seasonality.