The power grid frequency is the central observable in power system control, as it measures the balance of electrical supply and demand. A reliable frequency forecast can facilitate rapid control actions and may thus greatly improve power system stability. Here, we develop a weighted-nearest-neighbor (WNN) predictor to investigate how predictable the frequency trajectories are. Our forecasts for up to one hour are more precise than averaged daily profiles and could increase the efficiency of frequency control actions. Furthermore, we gain an increased understanding of the specific properties of different synchronous areas by interpreting the optimal prediction parameters (number of nearest neighbors, the prediction horizon, etc.) in terms of the physical system. Finally, prediction errors indicate the occurrence of exceptional external perturbations. Overall, we provide a diagnostics tool and an accurate predictor of the power grid frequency time series, allowing better understanding of the underlying dynamics.

A primary goal of computer science is to understand which problems can be solved by efficient algorithms. Given the formidable difficulty of proving unconditional computational hardness, stateof-the-art results typically rely on unproven conjectures. While many such results rely only upon the widely-believed conjecture P NP, other results have only been proven under stronger assumptions such as the unique games conjecture [Kho02, Kho05], the exponential time hypothesis [IP01], the learning with errors assumption [Reg09], or the planted clique hypothesis [Jer92, BR13]. It has also been fruitful to conjecture that a specific algorithm (or limited class of algorithms) is optimal for a suitable class of problems. This viewpoint has been particularly prominent in the study of average-case noisy statistical inference problems, where it appears that optimal performance over a large class of problems can be achieved by methods such as the sum-of-squares hierarchy (see [RSS18]), statistical query algorithms [Kea93, BFJ 94], the approximate message passing framework [DMM09, LKZ15], and low-degree polynomials [HS17, HKP 17, Hop18]. It is helpful to have such a conjectured-optimal meta-algorithm because this often admits a systematic analysis of hardness.

This paper proposes a generic approach for crop anomaly detection at the parcel-level based on unsupervised point anomaly detection techniques. The input data is derived from synthetic aperture radar (SAR) and optical images acquired using Sentinel-1 and Sentinel-2 satellites. The proposed strategy consists of four sequential steps: acquisition and preprocessing of optical and SAR images, extraction of optical and SAR indicators, computation of zonal statistics at the parcel-level and point anomaly detection. This paper analyzes different factors that can affect the results of anomaly detection such as the considered features and the anomaly detection algorithm used. The proposed procedure is validated on two crop types in Beauce (France), namely, rapeseed and wheat crops. Two different parcel delineation databases are considered to validate the robustness of the strategy to changes in parcel boundaries.

Recent advances in sensing technologies require the design and development of pattern recognition models capable of processing spatiotemporal data efficiently. In this work, we propose a spatially and temporally aware tensor-based neural network for human pose recognition using three-dimensional skeleton data. Our model employs three novel components. First, an input layer capable of constructing highly discriminative spatiotemporal features. Second, a tensor fusion operation that produces compact yet rich representations of the data, and third, a tensor-based neural network that processes data representations in their original tensor form. Our model is end-to-end trainable and characterized by a small number of trainable parameters making it suitable for problems where the annotated data is limited. Experimental validation of the proposed model indicates that it can achieve state-of-the-art performance. Although in this study, we consider the problem of human pose recognition, our methodology is general enough to be applied to any pattern recognition problem spatiotemporal data from sensor networks.

Countries in the Gulf Cooperation Council are stepping up their use of artificial intelligence tools to halt the spread of the coronavirus pandemic. Governments throughout the GCC -- a group of countries in the Middle East that includes Bahrain, Saudi Arabia, Qatar, Oman, Kuwait and the United Arab Emirates -- have enacted some of world's strictest measures, including suspending passenger flights and imposing curfews on citizens to put brakes on the number of new cases of Covid-19 that currently total over 2 million (2,064,115) globally, according to Johns Hopkins University data. But countries aren't restricting their efforts to simply imploring their residents to stay locked in and shutting down all but the most essential of businesses. They are increasingly deploying sophisticated technology to ensure that movement is limited and social distancing is in place through the use of speed cameras, drones and robots. By applying location-based contact tracing, governments can monitor those who have tested positive for coronavirus, and try to limit their exposure to the population.

Proximie is being deployed across a number of NHS sites, to support the national efforts to fight COVID-19. Proximie uses a combination of machine learning, artificial intelligence and augmented reality, aimed to empower surgeons and clinicians, to virtually and practically interact with each other from anywhere. The platform, which was founded by Dr. Nadine Hachach-Haram FRCS (Plast), BEM, consultant plastic surgeon and head of clinical innovation at Guy's and St. Thomas' NHS Foundation Trust, is being used across a host of NHS sites, as the country battles the pandemic. From enabling self-isolating clinicians to remotely support colleagues on the front line, to virtually connecting MDTs for hand trauma and cancer management, so that every clinician can connect and collaborate off site during COVID-19, the platform is being applied in a number of different ways to support and amplify frontline clinicians. Using augmented reality, healthcare practitioners can remotely interact in a procedure or assessment from start to finish, and mentor a local clinician through a live operation, in a visually and intuitive way.

Artificial intelligence is often associated with prophecies of job destruction. Yet an army of workers in the global south is being pressed into action. In discussions about the locations comprising the key productive nodes of artificial intelligence and other next-generation digital technologies, African workers rarely get a mention. Autonomous vehicles, machine-learning systems, next-generation search engines and recommendations systems--how many of these technologies are'made in Africa'? The answer, actually, is'all of them'.

U.S. President Donald Trump's broadside against the World Health Organization is another blow to international institutions designed to help nations confront global crises -- and may leave countries even less prepared for the next one. Trump's move on Tuesday to suspend WHO funding amid a pandemic that has cost at least 130,000 lives is the latest salvo in a broader struggle between the U.S. and China over global leadership. Both countries are courting other nations and public opinion as they cover up their own shortcomings in the pandemic and position themselves for the post-virus world. China -- widely criticized for missteps early in the outbreak -- has ramped up efforts to send medical supplies to hard-hit nations, even as reports emerged that much of that gear was faulty or expired. The U.S., meanwhile, announced $300 million in aid to countries fighting the virus but rebuffed requests for the most essential gear while receiving donations from the governments of Egypt, Taiwan and Vietnam among others.

Rabat – Moroccan-born professor of computer science at New York University (NYU) Dr. Anasse Bari has designed an artificial intelligence (AI) tool to analyze and curb the evolution of the COVID-19 pandemic. Managing a team of researchers at NYU, Bari helped create and study the efficacy of an AI instrument to predict patients vulnerable to coronavirus and determine the seriousness of COVID-19 infections. "Our goal was to design and deploy a decision-support tool using AI capabilities--mostly predictive analytics--to flag future clinical coronavirus severity," Bari said. "We hope that the tool, when fully developed, will be useful to physicians as they assess which moderately ill patients really need beds and who can safely go home, with hospital resources stretched thin," the computer scientist added, in light of the fact that hospital resources are limited as the COVID-19 outbreak continues. The Moroccan professor holds a bachelor's degree in Computer Engineering from Al Akhawayn University in Ifrane (AUI), and is establishing negotiations between NYU and AUI to use the newly developed technology in tackling the spread of COVID-19 in Morocco.

Manual evaluation of tissue sections using a microscope is a very time-consuming process. The adoption of AI solutions which can automatically recognize and count visual information could help increase the speed and accuracy of image analysis, whilst also freeing up time for pathologists. Technology Networks recently spoke with Dr Gillian Beamer, a pathologist and assistant professor at Tufts University and Thomas Westerling-Bui, Director, Scientific Strategy and Business Development at Aiforia, to learn how the implementation of a cloud-based platform is helping to advance scientific research on Mycobacterium tuberculosis. Anna MacDonald (AM): Can you provide an overview of what your typical daily work involves? What were some of the challenges you faced doing this manually?