In recent years, with the development of wind energy, the number and scale of wind farms have been developing rapidly. Since offshore wind farms have the advantages of stable wind speed, being clean renewable, non-polluting, and the non-occupation of cultivated land, they have gradually become a new trend in the wind power industry all over the world. The operation and maintenance of offshore wind powe has been developing in the direction of digitization and intelligence. It is of great significance to carry ou research on the monitoring, operation, and maintenance of offshore wind farms, which will be of benefit fo the reduction of the operation and maintenance costs, the improvement of the power generation efficiency improvement of the stability of offshore wind farm systems, and the building of smart offshore wind farms This paper will mainly summarize the monitoring, operation, and maintenance of offshore wind farms, with particular focus on the following points: monitoring of "offshore wind power engineering and biological and environment", the monitoring of power equipment, and the operation and maintenance of smart offshore wind farms. Finally, the future research challenges in relation to the monitoring, operation, and maintenance of smart offshore wind farms are proposed, and the future research directions in this field are explored especially in marine environment monitoring, weather and climate prediction, intelligent monitoring of powe equipment, and digital platforms.

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for traditional deep learning models (e.g. CNNs, RNNs), the emerging Graph Neural Networks (GNNs) are still under exploration, presenting a stark disparity to its broad edge adoptions such as traffic flow forecasting and location-based social recommendation. To bridge this gap, this paper formally studies the cost optimization for distributed GNN processing over a multi-tier heterogeneous edge network. We build a comprehensive modeling framework that can capture a variety of different cost factors, based on which we formulate a cost-efficient graph layout optimization problem that is proved to be NP-hard. Instead of trivially applying traditional data placement wisdom, we theoretically reveal the structural property of quadratic submodularity implicated in GNN's unique computing pattern, which motivates our design of an efficient iterative solution exploiting graph cuts. Rigorous analysis shows that it provides parameterized constant approximation ratio, guaranteed convergence, and exact feasibility. To tackle potential graph topological evolution in GNN processing, we further devise an incremental update strategy and an adaptive scheduling algorithm for lightweight dynamic layout optimization. Evaluations with real-world datasets and various GNN benchmarks demonstrate that our approach achieves superior performance over de facto baselines with more than 95.8% cost eduction in a fast convergence speed.

We present a fully automated framework for building object detectors on satellite imagery without requiring any human annotation or intervention. We achieve this by leveraging the combined power of modern generative models (e.g., StyleGAN) and recent advances in multi-modal learning (e.g., CLIP). While deep generative models effectively encode the key semantics pertinent to a data distribution, this information is not immediately accessible for downstream tasks, such as object detection. In this work, we exploit CLIP's ability to associate image features with text descriptions to identify neurons in the generator network, which are subsequently used to build detectors on-the-fly.

Actionable sentences are terms that, in the most basic sense, imply the necessity of taking a specific action. In Linguistic terms, they are steps to achieve an operation, often through the usage of action verbs. For example, the sentence, `Get your homework finished by tomorrow` qualifies as actionable since it demands a specific action (In this case, finishing homework) to be taken. In contrast, a simple sentence such as, `I like to play the guitar` does not qualify as an actionable phrase since it simply states a personal choice of the person instead of demanding a task to be finished. In this paper, the aim is to explore if Actionables can be extracted from raw text using Linguistic filters designed from scratch. These filters are specially catered to identifying actionable text using Transfer Learning as the lead role. Actionable Detection can be used in detecting emergency tasks during a crisis, Instruction accuracy for First aid and can also be used to make productivity tools like automatic ToDo list generators from conferences. To accomplish this, we use the Enron Email Dataset and apply our Linguistic filters on the cleaned textual data. We then use Transfer Learning with the Universal Sentence Encoder to train a model to classify whether a given string of raw text is actionable or not.

The new 19,200 square meter site brings together three areas of expertise (energy, high-performance computing (HPC) and artificial intelligence) and 1,000 Atos employees who were previously based at Grenoble and the historic site in Echirolles. With a capacity of up to 1,320 people, the new site will be able to accommodate the 250 new hires planned for 2023. With this new campus, Atos reinforces its innovation strategy as the new European R&D center will promote local excellence on a worldwide scale. Funded by the Auvergne-Rhône-Alpes Region, through the European Regional Development Fund (ERDF), and by Grenoble-Alpes Métropole, this center and its 300 employees are mainly focused on Artificial Intelligence. Atos' teams have already partnered with the MIAI@Grenoble Alpes program from the Grenoble Interdisciplinary Institute of Artificial Intelligence (3IA), which has received government support.

British navy personnel planted explosives and blew up the Nord Stream gas pipelines last month, Russia's defence ministry says, a claim London called false and designed to distract from Moscow's military failures in Ukraine. Russia did not give evidence for its allegation that a leading NATO member had sabotaged critical Russian infrastructure amid the worst crisis in relations between the West and Moscow since the depths of the Cold War. The Russian ministry alleged "British specialists" from the same unit that directed Ukrainian drone attacks on ships from the Russian Black Sea fleet in Crimea earlier on Saturday were responsible for the Nord Stream pipeline sabotage. "According to available information, representatives of this unit of the British Navy took part in the planning, provision and implementation of a terrorist attack in the Baltic Sea on September 26 this year – blowing up the Nord Stream 1 and Nord Stream 2 gas pipelines," the ministry said. The United Kingdom denied the accusation.

Sign up to receive the Future Tense newsletter every other Saturday. The U.N.'s COP27 climate summit kicks off on Nov. 6 in Egypt, inviting us, once again, to consider whether we're doing enough, fast enough, to stave off climate chaos and the suffering that will come with it. The scale of change required is head-spinningly drastic, so even unexpectedly rapid expansions in clean energy won't do much to curb malaise and doomsaying. Here in the U.S., the Inflation Reduction Act, the biggest climate investment in the nation's history, has been met, largely, with collective indifference, despite positive buzz about its potential effectiveness. The bill was, predictably, passed without any Republican votes, a grim reminder of the scale of climate denialism.

The Russian army accused Ukraine of a "massive" drone attack on its Black Sea Fleet in Crimea on Saturday, claiming the UK helped in the strike that damaged a ship. Sevastopol in Moscow-annexed Crimea, which has been targeted several times in recent months, serves as the headquarters for the fleet and a logistical hub for operations in Ukraine. The Russian army claimed to have "destroyed" nine aerial drones and seven maritime ones, in an attack early Saturday in the port. Moscow's forces alleged British "specialists", whom they said were based in the southern Ukrainian city of Ochakiv, had helped prepare and train Kyiv to carry out the strike. In a further singling out of the UK -- which Moscow sees as one of the most unfriendly Western countries -- Moscow said the same British unit was involved in explosions of the Nord Stream gas pipeline last month.

Chip Can Transmit All of the Internet's Traffic Every Second Matthew Sparkes New Scientist "A single computer chip has transmitted a record 1.84 petabits of data per second via a fiber-optic cable--enough bandwidth to download 230 million photographs in that time, and more traffic than travels through the entire internet's backbone network per second. It just goes to show that we can go so much further than we are today with internet connections,' said [Asbjørn Arvad Jørgensen]." Physicists Got a Quantum Computer to Work by Blasting It With the Fibonacci Sequence Isaac Schultz Gizmodo "In the recent research, pulsing a laser periodically at the 10 ytterbium qubits kept them in a quantum state--meaning entangled--for 1.5 seconds. But when the researchers pulsed the lasers in the pattern of the Fibonacci sequence, they found that the qubits on the edge of the system remained in a quantum state for about 5.5 seconds, the entire length of the experiment (the qubits could have remained in a quantum state for longer, but the team ended the experiment at the 5.5-second mark)." Technology That Lets Us'Speak' to Our Dead Relatives Has Arrived. Charlotte Jee MIT Technology Review "From what I could glean over a dozen conversations with my virtually deceased parents, this really will make it easier to keep close the people we love.

Interplanetary coronal mass ejections (ICMEs) are one of the main drivers for space weather disturbances. In the past, different approaches have been used to automatically detect events in existing time series resulting from solar wind in situ observations. However, accurate and fast detection still remains a challenge when facing the large amount of data from different instruments. For the automatic detection of ICMEs we propose a pipeline using a method that has recently proven successful in medical image segmentation. Comparing it to an existing method, we find that while achieving similar results, our model outperforms the baseline regarding training time by a factor of approximately 20, thus making it more applicable for other datasets. The method has been tested on in situ data from the Wind spacecraft between 1997 and 2015 with a True Skill Statistic (TSS) of 0.64. Out of the 640 ICMEs, 466 were detected correctly by our algorithm, producing a total of 254 False Positives. Additionally, it produced reasonable results on datasets with fewer features and smaller training sets from Wind, STEREO-A and STEREO-B with True Skill Statistics of 0.56, 0.57 and 0.53, respectively. Our pipeline manages to find the start of an ICME with a mean absolute error (MAE) of around 2 hours and 56 minutes, and the end time with a MAE of 3 hours and 20 minutes. The relatively fast training allows straightforward tuning of hyperparameters and could therefore easily be used to detect other structures and phenomena in solar wind data, such as corotating interaction regions.