The seven-arm octopus, Haliphron atlanticus, weighs as much as a person and haunts deep, dark waters from New Zealand to Brazil and British Columbia. So few people have seen this creature alive that researchers must study it in death--typically, as a mound of purplish flesh that washes ashore or turns up in a net. A living seven-arm octopus was scooped up by a Norwegian fishing trawler in 1984, but "when laid on deck the body collapsed," a local zoologist wrote at the time. What remained of the creature, he added, was "sack-shaped, large and flappy." Another turned up in a South Pacific research trawl in the early two-thousands, but the preservation process turned it into a "frozen lump," the giant-squid expert Steve O'Shea wrote.

The event attracted more than 700 attendees and provided insights into how advanced technologies can help Customs administrations facilitate the flow of goods across borders. The publication titled, "The role of advanced technologies in cross-border trade: A customs perspective" provides the current state of play and sheds light on the opportunities and challenges Customs face when deploying these technologies. The publication outlines the key findings of WCO's 2021 Annual Consolidated Survey and its results on Customs' use of advanced technologies such as blockchain, the internet of things, data analytics and artificial intelligence to facilitate trade and enhance safety, security and fair revenue collection. The joint publication highlights the benefits that can result from the adoption of these advanced technologies, such as enhanced transparency of procedures, sharing of information amongst all relevant stakeholders in real time, better risk management, and improved data quality, leading to greater efficiency in Customs processes and procedures. In his remarks, WCO Deputy Secretary General Ricardo Treviño Chapa said, "Technologies will assist implementation of international trade facilitation rules and standards, such as the WCO Revised Kyoto Convention and the WTO Trade Facilitation Agreement. We are therefore delighted to be partnering with the WTO, to ensure that our work in assisting our Members' digital transformation journeys is complementary, that we bring all relevant partners to the same table, and that we avoid duplication."

Tensor PCA is a stylized statistical inference problem introduced by Montanari and Richard to study the computational difficulty of estimating an unknown parameter from higher-order moment tensors. Unlike its matrix counterpart, Tensor PCA exhibits a statistical-computational gap, i.e., a sample size regime where the problem is information-theoretically solvable but conjectured to be computationally hard. This paper derives computational lower bounds on the run-time of memory bounded algorithms for Tensor PCA using communication complexity. These lower bounds specify a trade-off among the number of passes through the data sample, the sample size, and the memory required by any algorithm that successfully solves Tensor PCA. While the lower bounds do not rule out polynomial-time algorithms, they do imply that many commonly-used algorithms, such as gradient descent and power method, must have a higher iteration count when the sample size is not large enough. Similar lower bounds are obtained for Non-Gaussian Component Analysis, a family of statistical estimation problems in which low-order moment tensors carry no information about the unknown parameter. Finally, stronger lower bounds are obtained for an asymmetric variant of Tensor PCA and related statistical estimation problems. These results explain why many estimators for these problems use a memory state that is significantly larger than the effective dimensionality of the parameter of interest.

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world. As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

Santiago Atitlan, Guatemala – On a recent afternoon, the COVID-19 vaccination centre in the heart of the Indigenous Mayan town of Santiago Atitlan was quiet. The health centre had a vaccine supply, but demand was low. The lack of coordination of a Guatemalan government-led campaign to overcome vaccine hesitancy has resulted in the expiration of millions of doses across the country this year, critics have said, as more than half of the population remains unvaccinated. According to Juan Manuel Ramirez, an evangelical preacher in Santiago Atitlan, some community members have taken the vaccine, knowing it helps to protect against severe disease. But others have subscribed to conspiracy theories about its potential dangers.

In a proliferated, multi-channel world, every brand needs to win the heart and mind of the consumer to acquire and retain them. They need to set up a foundation of empathy and connectedness. Artificial intelligence combined with a human-centric approach to marketing might seem like a contrarian model. But the truth is that machine learning, AI and automation are vital for brands today to transform data into empathetic, customer-centric experiences. For marketers, AI-based solutions serve as a scalable and customizable tool capable of understanding the motive behind consumer interactions.

Artificial Intelligence (AI) is much more than just a buzzword nowadays. It powers facial recognition in smartphones and computers, translation between foreign languages, systems which filter spam emails and identify toxic content on social media, and can even detect cancerous tumours. These examples, along with countless other existing and emerging applications of AI, help make people's daily lives easier, especially in the developed world. As of October 2021, 44 countries were reported to have their own national AI strategic plans, showing their willingness to forge ahead in the global AI race. These include emerging economies like China and India, which are leading the way in building national AI plans within the developing world.

FFREE NOW is the multi-service mobility joint venture backed by BMW Group and Daimler AG. Next to ride hailing, FREE NOW also offers micro-mobility services and car sharing. It consists of the services FREE NOW (10 European markets), Βeat (5 Latin American and 1 European market) and hive (e-scooter and e-bikes in 3 European markets). Summed up, those services currently attract 45 million users in 17 markets and more than 150 cities. FREE NOW is therefore the biggest multi-service mobility provider in Europe and the fastest-growing ride-hailer in Latin America. FREE NOW works with various third party providers to offer their customers an even wider range of options to get from A to B. In total, more than 2,200 employees in around 35 offices work for the services of FREE NOW, which is led by CEO Marc Berg.

The size of monkey brains are influenced by social interactions, a new study revealed, finding more friends in a group leads to larger social regions in the brain. A team of researchers from the University of Pennsylvania in Philadelphia, studied the brains, and social interactions of a group of rhesus macaques living on Cayo Santiago, an island off the coast of Puerto Rico. They found that the number of social connections predicted the size of key nodes in parts of the brain responsible for social decision-making and empathy. Though all these findings relate specifically to free-ranging rhesus macaques, they have possible implications for human behavior, in particular to understanding neuro-developmental disorders like autism, according to the team. Researchers determined that, for macaques with more grooming partners, the mid–superior temporal sulcus (STS) and ventral-dysgranular insula grew larger.

Detailed feature investigations and comparisons across climates, continents and time series types can progress our understanding and modelling ability of the Earth's hydroclimate and its dynamics. As a step towards these important directions, we here propose and extensively apply a multifaceted and engineering-friendly methodological framework for the thorough characterization of seasonal hydroclimatic dependence, variability and change at the global scale. We apply this framework using over 13 000 quarterly temperature, precipitation and river flow time series. In these time series, the seasonal hydroclimatic behaviour is represented by 3-month means of earth-observed variables. In our analyses, we also adopt the well-established Koppen-Geiger climate classification system and define continental-scale regions with large or medium density of observational stations. In this context, we provide in parallel seasonal hydroclimatic feature summaries and comparisons in terms of autocorrelation, seasonality, temporal variation, entropy, long-range dependence and trends. We find notable differences to characterize the magnitudes of most of these features across the various Koppen-Geiger climate classes, as well as between several continental-scale geographical regions. We, therefore, deem that the consideration of the comparative summaries could be more beneficial in water resources engineering contexts than the also provided global summaries. Lastly, we apply explainable machine learning to compare the investigated features with respect to how informative they are in explaining and predicting either the main Koppen-Geiger climate or the continental-scale region, with the entropy, long-range dependence and trend features being (roughly) found to be less informative than the remaining ones at the seasonal time scale.