As fishermen have noticed this behaviour, they have used both natural and man-made floating objects, or drifting Fish Aggregating Devices (dFADs), as a tool for finding and catching tropical tunas. The use of dFADs in tuna purse-seine fisheries has gradually increased since the 1980s to the present time, where vessels using dFADs now contribute to 36% of the world's total tropical tuna catch (Davies et al., 2014; Wain et al., 2021; ISSF, 2021). These widespread changes have highlighted the need to better understand the potential ecological effects of dFADs on tuna ecology and the marine environment, in order to ensure adequate management of fish stocks and dFAD usage. Indeed, both the dynamics of how and why tuna associate to dFADs are still poorly understood. Regarding the reasons behind tuna aggregation to dFADs, a number of hypotheses have been suggested (Fréon and Dagorn, 2000; Dempster and Taquet, 2004; Castro et al., 2002). Of these, two have gained traction: the "meeting-point" hypothesis, which considers that dFADs facilitate the encounter between individuals or schools, thus constituting larger schools that could benefit survival rates (Castro et al., 2002); and the "indicator-log" hypothesis, by which tunas may be safeguarding the survival of their eggs, larvae and juvenile stages by using drifting objects as indicators of areas where plankton and food is readily available (Hall et al., 1992). This scenario has led some authors to postulate that man-made dFADs could have detrimental effects on tuna populations by creating a so-called "ecological trap" which would lead tuna to remain associated to dFADs even as these drift into areas that could negatively affect the tuna's behaviour and biology (Marsac et al., 2000; Hallier and Gaertner, 2008). To the best of our knowledge, there is yet no sufficient evidence to either confirm or reject this hypothesis (see Dagorn et al. (2012) and references therein). Given the concerns around the widespread use of dFADs in tuna fisheries today, it is not surprising that a considerable amount of research has been devoted to characterizing the dynamics at play when tunas aggregate to dFADs.

The standard problem setting in cooperative multi-agent settings is self-play (SP), where the goal is to train a team of agents that works well together. However, optimal SP policies commonly contain arbitrary conventions ("handshakes") and are not compatible with other, independently trained agents or humans. This latter desiderata was recently formalized by Hu et al. 2020 as the zero-shot coordination (ZSC) setting and partially addressed with their Other-Play (OP) algorithm, which showed improved ZSC and human-AI performance in the card game Hanabi. OP assumes access to the symmetries of the environment and prevents agents from breaking these in a mutually incompatible way during training. However, as the authors point out, discovering symmetries for a given environment is a computationally hard problem. Instead, we show that through a simple adaption of k-level reasoning (KLR) Costa Gomes et al. 2006, synchronously training all levels, we can obtain competitive ZSC and ad-hoc teamplay performance in Hanabi, including when paired with a human-like proxy bot. We also introduce a new method, synchronous-k-level reasoning with a best response (SyKLRBR), which further improves performance on our synchronous KLR by co-training a best response.

Demystifying the delay propagation mechanisms among multiple airports is fundamental to precise and interpretable delay prediction, which is crucial during decision-making for all aviation industry stakeholders. The principal challenge lies in effectively leveraging the spatiotemporal dependencies and exogenous factors related to the delay propagation. However, previous works only consider limited spatiotemporal patterns with few factors. To promote more comprehensive propagation modeling for delay prediction, we propose SpatioTemporal Propagation Network (STPN), a space-time separable graph convolutional network, which is novel in spatiotemporal dependency capturing. From the aspect of spatial relation modeling, we propose a multi-graph convolution model considering both geographic proximity and airline schedule. From the aspect of temporal dependency capturing, we propose a multi-head self-attentional mechanism that can be learned end-to-end and explicitly reason multiple kinds of temporal dependency of delay time series. We show that the joint spatial and temporal learning models yield a sum of the Kronecker product, which factors the spatiotemporal dependence into the sum of several spatial and temporal adjacency matrices. By this means, STPN allows cross-talk of spatial and temporal factors for modeling delay propagation. Furthermore, a squeeze and excitation module is added to each layer of STPN to boost meaningful spatiotemporal features. To this end, we apply STPN to multi-step ahead arrival and departure delay prediction in large-scale airport networks. To validate the effectiveness of our model, we experiment with two real-world delay datasets, including U.S and China flight delays; and we show that STPN outperforms state-of-the-art methods. In addition, counterfactuals produced by STPN show that it learns explainable delay propagation patterns.

Few-shot sound event detection is the task of detecting sound events, despite having only a few labelled examples of the class of interest. This framework is particularly useful in bioacoustics, where often there is a need to annotate very long recordings but the expert annotator time is limited. This paper presents an overview of the second edition of the few-shot bioacoustic sound event detection task included in the DCASE 2022 challenge. A detailed description of the task objectives, dataset, and baselines is presented, together with the main results obtained and characteristics of the submitted systems. This task received submissions from 15 different teams from which 13 scored higher than the baselines. The highest F-score was of 60% on the evaluation set, which leads to a huge improvement over last year's edition. Highly-performing methods made use of prototypical networks, transductive learning, and addressed the variable length of events from all target classes. Furthermore, by analysing results on each of the subsets we can identify the main difficulties that the systems face, and conclude that few-show bioacoustic sound event detection remains an open challenge.

New research that harnessed the power of artificial intelligence has identified the specific crater on Mars that ejected the ancient Black Beauty meteorite. The researchers named the Mars crater after the Australian city of Karratha, which is home to one of the oldest terrestrial rocks. The discovery offers never-known details about the Martian meteorite NWA 7034, nicknamed'Black Beauty,' which was found in Africa in 2011, according to researchers. 'For the first time, we know the geological context of the only brecciated Martian sample available on Earth,' says Dr. Anthony Lagain. 'For the first time, we know the geological context of the only brecciated Martian sample available on Earth, 10 years before the NASA's Mars Sample Return mission is set to send back samples collected by the Perseverance rover currently exploring the Jezero crater,' lead author Dr. Anthony Lagain, from Curtin University's Space Science and Technology Center in the School of Earth and Planetary Sciences, says in a statement.

Meta has announced that it has built and open-sourced'No Language Left Behind' NLLB-200, a single Artificial Intelligence (AI) model that is the first to translate across 200 different languages, including 55 African languages with state-of-the-art results. Meta is using the modelling techniques and learnings from the project to improve and extend translations on Facebook, Instagram, and Wikipedia. In an effort to develop high-quality machine translation capabilities for most of the world's low-resource languages, this single AI model was designed with a focus on African languages. They are challenging from a machine translation perspective. AI models require lots and lots of data to help them learn, and there's not a lot of human-translated training data for these languages.

The graph represents a network of 758 Twitter users whose tweets in the requested range contained "neo4j", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Tuesday, 12 July 2022 at 05:30 UTC. The requested start date was Tuesday, 12 July 2022 at 00:01 UTC and the maximum number of days (going backward) was 14. The maximum number of tweets collected was 7,500. The tweets in the network were tweeted over the 13-day, 22-hour, 7-minute period from Tuesday, 28 June 2022 at 00:51 UTC to Monday, 11 July 2022 at 22:59 UTC.

Digital technology will play a significant role in achieving sustainable human development worldwide. In 2015, United Nations Member States set 17 goals, The Sustainable Development Goals (SDGs), to provide a road map for the achievement of Earth’s peace and human prosperity by 2030. SDG 3 as one of the goals which is aimed at ensuring healthy lives and promoting well-being for all at all ages, will greatly benefit from the implementation of digital technology. With over a billion people, Africa can be better positioned to surmount its health challenges -especially regarding maternal and child health, infectious and non-communicable disease- using digital technology including artificial intelligence.Artificial intelligence (AI) is defined as the automation of activities associated with human thinking such as decision-making, problem-solving and learning.1AI was first used in medicine in the 1970s when medical expert systems – based on Bayesian statistics and decision theory – diagnosed and recommended treatments for glaucoma and infectious disease.2 Progress in Bayesian networks, artificial neural networks, and hybrid intelligent systems in the late 1990s has scaled up bioinformatics research; thereby expanding uptake of Medical Artificial Intelligence (MAI).7 Global investment in MAI is projected to hit about $6.6 Billion by 2021 as it is anticipated that AI implementations in healthcare can help save $150 Billion in costs by 2026.8At present, a more meaningful applicat...

TOKYO, July 12, 2022 (GLOBE NEWSWIRE) -- The Global Artificial Intelligence Robots Market size was valued at USD 6,214 Million in 2021 and is expected to reach USD 66,662 Million by 2030 growing at a CAGR of 30.5% during the forecast period from 2022 to 2030. Human-robot interaction is becoming more common as robots make everyone's lives easier and more comfortable, and as a result, the market for AI robots is expanding. AI, or machine intelligence in robotic systems, is the implementation of AI technology into robots to allow them to perform repetitive tasks more efficiently without human intervention. AI also enables robots to communicate with other autonomous systems. For entrepreneurs, robotic systems will prove to be more effective and cost-effective labor.

"What do you think the rate of Covid-19 is for us?" This is the question that many Black people living in Berlin asked me at the beginning of March 2020. The answer: We don't know. Unlike other countries, notably the United States and the United Kingdom, the German government does not record racial identity information in official documents and statistics. Due to the country's history with the Holocaust, calling Rasse (race) by its name has long been contested.