Logical formalisms provide a natural and concise means for specifying and reasoning about preferences. In this paper, we propose lexicographic logic, an extension of classical propositional logic that can express a variety of preferences, most notably lexicographic ones. The proposed logic supports a simple new connective whose semantics can be defined in terms of finite lists of truth values. We demonstrate that, despite the well-known theoretical limitations that pose barriers to the quantitative representation of lexicographic preferences, there exists a subset of the rational numbers over which the proposed new connective can be naturally defined. Lexicographic logic can be used to define in a simple way some well-known preferential operators, like "$A$ and if possible $B$", and "$A$ or failing that $B$". Moreover, many other hierarchical preferential operators can be defined using a systematic approach. We argue that the new logic is an effective formalism for ranking query results according to the satisfaction level of user preferences.

These Visionary companies have a big idea and are well on their way to achieving it, although it isn't always an easy road for any really innovative technology. In the case of Cruise, that meant testing self driving vehicles on the streets of San Francisco, one of the hardest driving environments in the world. Some of our Visionary Awards go to companies who are opening up new market applications for robotics, such as Built Robotics in construction, Dishcraft in food services, Embark in self-driving trucks, Iron Ox in urban agriculture and Zipline in drone delivery. Some are building tools or platforms that the entire robotics industry can benefit from, such as Agility Robotics, Covariant, Formant, RobustAI and Zoox. The companies in our Good Robot Awards also show that'technologies built for us, have to be built by us'.

Fitting regression models with many multivariate responses and covariates can be challenging, but such responses and covariates sometimes have tensor-variate structure. We extend the classical multivariate regression model to exploit such structure in two ways: first, we impose four types of low-rank tensor formats on the regression coefficients. Second, we model the errors using the tensor-variate normal distribution that imposes a Kronecker separable format on the covariance matrix. We obtain maximum likelihood estimators via block-relaxation algorithms and derive their asymptotic distributions. Our regression framework enables us to formulate tensor-variate analysis of variance (TANOVA) methodology. Application of our methodology in a one-way TANOVA layout enables us to identify cerebral regions significantly associated with the interaction of suicide attempters or non-attemptor ideators and positive-, negative- or death-connoting words. A separate application performs three-way TANOVA on the Labeled Faces in the Wild image database to distinguish facial characteristics related to ethnic origin, age group and gender.

Science  has named nine scientific advances as runners-up for the 2020 Breakthrough of the Year. For 5 decades, scientists have struggled to solve one of biology's biggest challenges: predicting the precise 3D shape a string of amino acids will fold into as it becomes a working protein. This year, they achieved that goal, developing an artificial intelligence (AI) program that predicts most protein structures as accurately as laboratory experiments can map them. Because a protein's precise shape determines its biochemical functions, the new program could help researchers uncover mechanisms of disease, develop new drugs, and even create drought-tolerant plants and cheaper biofuels. Researchers traditionally decipher structures using laborious techniques such as x-ray crystallography and cryo–electron microscopy. But detailed molecular maps only exist for about 170,000 of the 200 million known proteins. Computational biologists have dreamed of simply predicting a protein's structure by modeling the amino acid interactions that govern its 3D shape. But because amino acids can interact in so many ways, the number of possible structures for single protein is astronomical. In 1994, structural biologists launched a biennial competition called the Critical Assessment of Protein Structure Prediction (CASP). Entrants are given amino acid sequences for about 100 proteins with as-yet-unknown structures. Some groups try to predict their structures, while others map the same structures in the lab; afterward, their results are compared. Even in CASP's early years, the predictions for small, simple proteins were on par with experimental observations. But predictions for larger, more challenging proteins lagged far behind. Not anymore. This year, an AI program created by researchers at U.K.-based DeepMind tallied a median score of 92.4 on a 100-point scale, where anything above 90 is considered as accurate as an experimentally derived structure. On the most challenging proteins, the AlphaFold program averaged 87, 25 points ahead of its closest competitor. And because contest rules require competitors to reveal enough of their methods for others to make use of them, organizers say it's only a matter of months before other groups match AlphaFold's success. — Robert F. Service Since the revolutionary genome-snipping tool known as CRISPR burst on the scene in 2012, it has given researchers new power to engineer crops and animals, stirred ethical debates, and earned a Nobel Prize—not to mention Science 's Breakthrough of the Year in 2015. Now, CRISPR is again making waves, scoring its first success in the clinic by treating two inherited blood diseases. People with beta-thalassemia have low levels of the oxygen-carrying hemoglobin protein, leading to weakness and exhaustion; those with sickle cell disease make a defective form of the protein, resulting in sickle-shaped red blood cells that block blood vessels and often cause severe pain, organ damage, and strokes. To treat three sickle cell patients, researchers harvested immature blood cells, known as blood stem cells, from each. They then used CRISPR to disable an “off” switch that—in adults—stops production of the fetal form of hemoglobin, which can counter the effects of the sickling mutation. After the patients received chemotherapy to wipe out their diseased blood stem cells, the CRISPR-treated cells were infused back into their bodies. The patients, treated up to 17 months ago, are now making plentiful fetal hemoglobin, and have not experienced the painful attacks that used to strike every few months, the companies CRISPR Therapeutics and Vertex Pharmaceuticals reported in December. One patient, a young mother of three, says the treatment changed her life. The companies also gave the treatment to seven patients who normally receive blood transfusions for beta-thalassemia. They haven't needed transfusions since, the companies reported in the same paper and meeting presentation. With more testing, the new treatment could rival the success of gene therapies that treat the two diseases by adding hemoglobin DNA to stem cells. But like gene therapy, the CRISPR approach requires high-tech medical care and could cost $1 million or more per patient—putting it out of reach for much of Africa, where most people with sickle cell live. — Jocelyn Kaiser More than 40 years ago, the world's leading climate scientists gathered in Woods Hole, Massachusetts, to answer a simple question: How hot would Earth get if humans kept emitting greenhouse gases? Their answer, informed by rudimentary climate models, was broad: If atmospheric carbon dioxide (CO2) doubled from preindustrial levels, the planet would eventually warm between 1.5°C and 4.5°C, a climate sensitivity range encompassing the merely troubling and the catastrophic. Now, they've finally ruled out the mildest scenarios—and the most dire. Narrowing those bounds has taken decades of scientific advancement. Understanding how clouds trap or reflect heat has been a particular challenge. Depending on their thickness, location, and composition, clouds can amplify warming—or suppress it. Now, high-resolution cloud models, supported by satellite evidence, have shown that global warming thins low, light-blocking clouds: Hotter air dries them out and subdues the turbulence that drives their formation. Longer and better temperature records have also helped narrow the range. Studies of Earth's ancient climate, which estimate paleotemperatures and CO2 levels using ice and ocean sediment cores, suggest how greenhouse gases may have driven previous episodes of warming. And modern global warming has now gone on long enough that surface temperatures, 1.1°C hotter than in preindustrial times, can be used to more confidently project trends into the future. This year, these advances enabled 25 scientists affiliated with the World Climate Research Programme to narrow climate sensitivity to a range between 2.6°C and 3.9°C. The study rules out some of the worst-case scenarios—but it all but guarantees warming that will flood coastal cities, escalate extreme heat waves, and displace millions of people. If we're lucky, such clarity might galvanize action. Atmospheric CO2 is already at 420 parts per million—halfway to the doubling point of 560 ppm. Barring more aggressive action on climate change, humanity could reach that threshold by 2060—and lock in the foreseen warming. — Paul Voosen Everyone loves a good mystery. Take fast radio bursts (FRBs)—short, powerful flashes of radio waves from distant galaxies. For 13 years, they tantalized astronomers keen to understand their origins. One running joke said there were more theories explaining what causes FRBs than there were FRBs. (Currently, astronomers know of more than 100.) Now, cosmic sleuths have fingered a likely culprit: magnetars, neutron stars that fizzle and pop with powerful magnetic fields. Because FRBs are so fast, they must come from a small but intense energy source like a magnetar, which are formed when burned-out stars collapse to the size of a city. But although a handful of FRBs had been traced to particular galaxies, no telescope had sharp enough vision to connect them to an individual magnetar at such great distances. Then, in April, an FRB went off in the Milky Way—close enough that astronomers could examine the scene. The Canadian Hydrogen Intensity Mapping Experiment, a pioneering survey telescope in British Columbia responsible for the discovery of many FRBs, narrowed the source to a small area of sky, which was soon confirmed by the U.S. radio array STARE2. Orbiting observatories sensitive to higher frequencies quickly found that a known magnetar in that part of the sky, called SGR 1935+2154, was acting up at the same time, spewing out bursts of x-rays and gamma rays. Although astronomers studying FRBs believe they have finally found their perpetrator, they still don't know exactly how magnetars produce the radio bursts. They could come from close to the magnetar's surface, as magnetic field lines break and reconnect—similar to the Sun's flaring behavior. Or they could come from farther out, as shock waves slam into clouds of charged particles and generate laserlike radio pulses. Stay tuned for a sequel: Crack theorists are on the case. — Daniel Clery More than 40,000 years ago on the Indonesian island of Sulawesi, a prehistoric Pablo Picasso ventured into the depths of a cave and sketched a series of fantastic animal-headed hunters cornering wild hogs and buffaloes. The age of the paintings, pinned down just 1 year ago, makes them the earliest known figurative art made by modern humans. In 2017, when an Indonesian researcher chanced across the scene, the figures alone told him he had found something special. The animals appear to be Sulawesi warty pigs and dwarf buffaloes, both of which still live on the island. But it was the animallike features of the eight hunters, armed with spears or ropes, that captivated archaeologists. Several of the hunters seem to have long muzzles or snouts. One sports a tail. Another's mouth resembles a bird beak. It's possible the artist was depicting the hunters wearing masks or camouflage, the researchers say, but they may also represent mythical animal-human hybrids. Such hybrids appear in other ancient works of art, including a 35,000-year-old ivory figurine of a lion-man found in the German Alps. Parts of the paintings were covered in white, bumpy mineral deposits known as cave popcorn. Uranium in this popcorn decays at a fixed rate, which allowed researchers to date minerals on top of the pigment to about 44,000 years ago. The cave scene must be at least that old—about 4000 years older than any other known figurative rock art, they reported in late December 2019. It decisively unseats Europe as the first place where modern humans are known to have created figurative art. If the figures do depict mythical human-animal hunters, their creators may have already passed an important cognitive milestone: the ability to imagine beings that do not exist. That, the researchers say, forms the roots of most modern—and ancient—religions. — Michael Price Within days of a racially charged confrontation between a white dog owner and a Black birdwatcher in New York City's Central Park in late May, scientists flocked to Twitter to celebrate—and support—Black nature enthusiasts. The #BlackBirdersWeek hashtag was soon followed by others, in disciplines from neuroscience to physics, all aiming to create community among Black scientists on Twitter, Zoom, and other platforms. “We're few and far between, so having us come together as a conglomerate in one virtual space—it really helped,” says Ti'Air Riggins, a biomedical engineering Ph.D. student at Michigan State University who helped organize #BlackInNeuro week. The social media events took place against the backdrop of the anguished response to police killings in the United States, the Black Lives Matter movement, and discussions within science about the need to create a more equitable, welcoming environment for people of color. Through those discussions, many scientists hoped to reach colleagues who had paid little attention to these issues in the past. “People of color across the board are struggling,” says Tanisha Williams, a botanist at Bucknell University who spearheaded #BlackBotanistsWeek. “It's a systemic problem.” Although it's too early to tell whether the events of this year will spur lasting change, many are hopeful. “This year feels different,” says Shirley Malcom, a senior adviser at AAAS (publisher of Science ) who has worked on diversity, equity, and inclusion issues since the 1970s. “All of a sudden, after George Floyd and everything else that came out after that time, you could at least get people's attention,” she says—adding that many scientists now seem more open to the idea that systemic racism is a problem in their community. “I definitely feel like our voices are being heard, and in a different way [than before],” Williams says. “But it's not going to be a quick fix … we have a long road.” — Katie Langin HIV, like all retroviruses, has a nasty feature that allows it to dodge attack: It integrates its genetic material into human chromosomes, creating “reservoirs” where it can hide, undetected by the immune system and invulnerable to antiretroviral drugs. But where it hides may make all the difference. This year, a study of 64 HIV-infected people who have been healthy for years without antiretroviral drugs reveals a link between their unusual success and where the virus has hunkered down in their genomes. Although the new understanding of these “elite controllers” won't lead directly to a cure, it opens up a novel strategy that may routinely allow other infected people to live for decades without treatment. Many studies have examined elite controllers, who make up about 0.5% of the 38 million people living with HIV. But this new work stood apart in size and scope, comparing integrated HIV in the 64 elite controllers with that in 41 HIV-infected people on treatment. HIV does best when it slots itself within genes. When the cell transcribes the genes, the integrated HIV, or “provirus,” can produce new viruses that infect other cells. If it parks in “gene deserts,” portions of chromosomes that rarely transcribe DNA, the provirus sits around like a fully functioning car stuck in a place that doesn't sell gas. The study found that in the elite controllers, 45% of functioning proviruses resided in gene deserts, compared with just 17.8% for the people on treatment. Presumably, immune responses in the elite controllers somehow cleared proviruses from the more dangerous parking spots. Now, the challenge is to figure out interventions that will train the immune systems of the vast majority of people living with HIV to behave similarly. That new insight suggests long-standing, frustrating attempts to cure people by eliminating HIV reservoirs may be too ambitious an approach. Instead, success may depend on shrinking—and then making peace with—these reservoirs, and minding the old real estate dictum of location, location, location. — Jon Cohen Scientists have spent decades searching for materials that conduct electricity without resistance at room temperature. This year they found the first one, a hydrogen- and carbon-containing compound squeezed to a pressure approaching that at the center of Earth. The discovery is setting off a hunt for room temperature superconductors that work at typical surface pressures; such materials could transform technologies and save the vast amounts of energy wasted when electricity moves through wires. Superconductivity got its start in 1911 when physicist Heike Kamerlingh Onnes found that a mercury wire chilled to 4.2°C above absolute zero, or 4.2 K, conducted electrons without the usual heat-producing friction. In 1986, researchers found the same was true of a family of copper oxide ceramics. Because these superconductors worked above 77 K—the temperature of liquid nitrogen—they spawned a new generation of MRI machines and particle accelerator magnets. There were hints that copper oxides might superconduct at room temperature, but they were never verified. Confirmation now comes from high-pressure physics, in which scientists smash flecks of materials between the flattened points of two diamonds at pressures millions of times higher than those at Earth's surface. With such a diamond anvil, researchers in Germany in 2019 compressed a mix of lanthanum and hydrogen to 170 gigapascals (GPa), yielding superconductivity at temperatures up to 250 K, just under the freezing point of water. This year, researchers in the United States topped that result with a hydrogen, carbon, and sulfur compound compressed to 267 GPa. It conducted without resistance to 287 K, the temperature of a chilly room. So far, the new superconductors fall apart when the pressure is released. But the same isn't true of all high-pressure materials: Diamonds born in the crushing depths of Earth, for example, survive after rising to the surface. Now, researchers hope to find a similarly long-lasting gem for their own field. — Robert F. Service Their eyes are beady and their brains are no bigger than a walnut. But two studies published this year suggest birds have startling mental powers. One reveals that part of the avian brain resembles the human neocortex, the source of human intelligence. The other shows that carrion crows are even more aware than researchers had thought—and may be capable of some conscious thought. In humans, the neocortex consists of horizontal layers laced with interconnected columns of nerve cells, which allow for complex thinking. Bird brains, in contrast, were thought to be arranged in simple clusters of nerve cells. By using a technique called 3D polarized light imaging, neuroanatomists took a closer look at the forebrain of homing pigeons and owls and found that nerves there connect both horizontally—like the layers in the neocortex—and vertically, echoing the columns seen in human brains. Another team of scientists probed this part of the brains of carrion crows—well-known for their intelligence—for clues that they are aware of what they see and do. The researchers first trained lab-raised crows to turn their heads when they saw certain sequences of lights flashing on a computer monitor. Electrodes in the crows' brains detected nerve activity between the moment the birds saw the signal and when they moved their heads. The activity developed even when the lights were barely detectable, suggesting it was not simply a response to sensory input, and it was present regardless of whether the birds reacted. The scientists think the neural chatter represents a kind of awareness—a mental representation of what the birds saw. Such “sensory consciousness” is a rudimentary form of the self-awareness that humans experience. Its presence in both birds and mammals suggests to the researchers that some form of consciousness may date back 320 million years, to our last common ancestor. — Elizabeth Pennisi

When the novel coronavirus reached Togo in March, its leaders, like those of many countries, responded with stay-at-home orders to suppress contagion and an economic assistance program to replace lost income. But the way Togo targeted and delivered that aid was in some ways more tech-centric than many larger and richer countries. No one got a paper check in the mail. Instead, Togo's government quickly assembled a system to support its poorest people with mobile cash payments--a technology more established in Africa than in the rich nations supposedly at the forefront of mobile technology. The most recent payments, funded by nonprofit GiveDirectly, were targeted with help from machine learning algorithms, which seek signs of poverty in satellite photos, and cellphone data.

Ranking vertices of multidimensional networks is crucial in many areas of research, including selecting and determining the importance of decisions. Some decisions are significantly more important than others, and their weight categorization is also imortant. This paper defines a completely new method for determining the weight decisions using artificial intelligence for importance ranking of three-dimensional network vertices, improving the existing Ordered Statistics Vertex Extraction and Tracking Algorithm (OSVETA) based on modulation of quantized indices (QIM) and error correction codes. The technique we propose in this paper offers significant improvements the efficiency of determination the importance of network vertices in relation to statistical OSVETA criteria, replacing heuristic methods with methods of precise prediction of modern neural networks. The new artificial intelligence technique enables a significantly better definition of the 3D meshes and a better assessment of their topological features. The new method contributions result in a greater precision in defining stable vertices, significantly reducing the probability of deleting mesh vertices.

High-order clustering aims to identify heterogeneous substructure in multiway dataset that arises commonly in neuroimaging, genomics, and social network studies. The non-convex and discontinuous nature of the problem poses significant challenges in both statistics and computation. In this paper, we propose a tensor block model and the computationally efficient methods, \emph{high-order Lloyd algorithm} (HLloyd) and \emph{high-order spectral clustering} (HSC), for high-order clustering in tensor block model. The convergence of the proposed procedure is established, and we show that our method achieves exact clustering under reasonable assumptions. We also give the complete characterization for the statistical-computational trade-off in high-order clustering based on three different signal-to-noise ratio regimes. Finally, we show the merits of the proposed procedures via extensive experiments on both synthetic and real datasets.

Extreme precipitation events, such as violent rainfall and hail storms, routinely ravage economies and livelihoods around the developing world. Climate change further aggravates this issue. Data-driven deep learning approaches could widen the access to accurate multi-day forecasts, to mitigate against such events. However, there is currently no benchmark dataset dedicated to the study of global precipitation forecasts. In this paper, we introduce \textbf{RainBench}, a new multi-modal benchmark dataset for data-driven precipitation forecasting. It includes simulated satellite data, a selection of relevant meteorological data from the ERA5 reanalysis product, and IMERG precipitation data. We also release \textbf{PyRain}, a library to process large precipitation datasets efficiently. We present an extensive analysis of our novel dataset and establish baseline results for two benchmark medium-range precipitation forecasting tasks. Finally, we discuss existing data-driven weather forecasting methodologies and suggest future research avenues.

Recent advances in Artificial Intelligence (AI) have revived the quest for agents able to acquire an open-ended repertoire of skills. However, although this ability is fundamentally related to the characteristics of human intelligence, research in this field rarely considers the processes that may have guided the emergence of complex cognitive capacities during the evolution of the species. Research in Human Behavioral Ecology (HBE) seeks to understand how the behaviors characterizing human nature can be conceived as adaptive responses to major changes in the structure of our ecological niche. In this paper, we propose a framework highlighting the role of environmental complexity in open-ended skill acquisition, grounded in major hypotheses from HBE and recent contributions in Reinforcement learning (RL). We use this framework to highlight fundamental links between the two disciplines, as well as to identify feedback loops that bootstrap ecological complexity and create promising research directions for AI researchers.

It's been two weeks since Google fired Timnit Gebru, a decision that still seems incomprehensible. Gebru is one of the most highly regarded AI ethics researchers in the world, a pioneer whose work has highlighted the ways tech fails marginalized communities when it comes to facial recognition and more recently large language models. Of course, this incident didn't happen in a vacuum. Case in point: Gebru was fired the same day the National Labor Review Board (NLRB) filed a complaint against Google for illegally spying on employees and the retaliatory firing of employees interested in unionizing. Gebru's dismissal also calls into question issues of corporate influence in research, demonstrates the shortcomings of self-regulation, and highlights the poor treatment of Black people and women in tech in a year when Black Lives Matter sparked the largest protest movement in U.S. history. In an interview with VentureBeat last week, Gebru called the way she was fired disrespectful and described a companywide memo sent by CEO Sundar Pichai as "dehumanizing." To delve further into possible outcomes following Google's AI ethics meltdown, VentureBeat spoke with five experts in the field about Gebru's dismissal and the issues it raises.