We present a novel approach for adaptive, differentiable parameterization of large-scale random fields. If the approach is coupled with any gradient-based optimization algorithm, it can be applied to a variety of optimization problems, including history matching. The developed technique is based on principal component analysis (PCA) but modifies a purely data-driven basis of principal components considering objective function behavior. To define an efficient encoding, Gradient-Sensitive PCA uses an objective function gradient with respect to model parameters. We propose computationally efficient implementations of the technique, and two of them are based on stationary perturbation theory (SPT). Optimality, correctness, and low computational costs of the new encoding approach are tested, verified, and discussed. Three algorithms for optimal parameter decomposition are presented and applied to an objective of 2D synthetic history matching. The results demonstrate improvements in encoding quality regarding objective function minimization and distributional patterns of the desired field. Possible applications and extensions are proposed.

Respiratory complications due to coronavirus disease COVID-19 have claimed tens of thousands of lives in 2020. Many cases of COVID-19 escalate from Severe Acute Respiratory Syndrome (SARS-CoV-2) to viral pneumonia to acute respiratory distress syndrome (ARDS) to death. Extracorporeal membranous oxygenation (ECMO) is a life-sustaining oxygenation and ventilation therapy that may be used for patients with severe ARDS when mechanical ventilation is insufficient to sustain life. While early planning and surgical cannulation for ECMO can increase survival, clinicians report the lack of a risk score hinders these efforts. In this work, we leverage machine learning techniques to develop the PEER score, used to highlight critically ill patients with viral or unspecified pneumonia at high risk of mortality or decompensation in a subpopulation eligible for ECMO. The PEER score is validated on two large, publicly available critical care databases and predicts mortality at least as well as other existing risk scores. Stratifying our cohorts into low-risk and high-risk groups, we find that the high-risk group also has a higher proportion of decompensation indicators such as vasopressor and ventilator use. Finally, the PEER score is provided in the form of a nomogram for direct calculation of patient risk, and can be used to highlight at-risk patients among critical care patients eligible for ECMO.

The important peaks related to the physical properties of a lithium ion rechargeable battery were extracted from the measured X ray diffraction spectrum by a convolutional neural network based on the Attention mechanism. Among the deep features, the lattice constant of the cathodic active material was selected as a cell voltage predictor, and the crystallographic behavior of the active anodic and cathodic materials revealed the rate property during the charge discharge states. The machine learning automatically selected the significant peaks from the experimental spectrum. Applying the Attention mechanism with appropriate objective variables in multi task trained models, one can selectively visualize the correlations between interesting physical properties. As the deep features are automatically defined, this approach can adapt to the conditions of various physical experiments.

Graph neural networks (GNNs) have found application Performing the reasoning is achieved via neural execution, for learning in the space of algorithms. in a similar fashion to Veličković et al. (2020). GNNs have However, the algorithms chosen by existing research been both empirically (Veličković et al., 2020) and theoretically (sorting, Breadth-First search, shortest path (Xu et al., 2020) shown to be applicable to algorithmic finding, etc.) usually align perfectly with a standard tasks on graphs, strongly generalising on inputs of sizes GNN architecture. This report describes much larger than trained on. However, these algorithms how neural execution is applied to a complex algorithm, rely on a locally contained and fixed dataflow which aligns such as finding maximum bipartite matching perfectly with a standard GNN architecture, making them by reducing it to a flow problem and using easy to model with GNNs (c.f.

Hoy traemos a este espacio esta conferencia titulada "Mind Blowing Tech in Learning: AI, VR, and AR featuring" del Prof. Donald Clark, del Center for Online Innovation in Learning y que nos presentan así: Artificial intelligence (AI) is now the most potent force in IT and will shape learning technology, allowing us to escape from the 30 year paradigm of flat, linear e-learning. During this COIL Fischer Speaker Series presentation, Professor Donald Clark debunks some myths about AI and provide real examples of AI used now in content creation, feedback, assessment and spaced practice. In addition he will talk about virtual reality (VR) & augmented reality (AR) as reviving'learning by doing' and their power to democratize experience. Donald Clark is an EdTech entrepreneur and was CEO and one of the original founders of Epic Group plc, which established itself as the leading company in the UK online learning market, floated on the Stock Market in 1996 and sold in 2005, now CEO of Wildfire Ltd. he also invests in, and advises, EdTech companies. Describing himself as'free from the tyranny of employment', he is a board member of Cogbooks, LearningPool, WildFire and Deputy Chair of Brighton Dome & Arts Festival as well as a Visiting Professor at The University of Derby and Fellow of the Royal Society of Arts (FRSA).

During cluster analysis domain experts and visual analysis are frequently relied on to identify the optimal clustering structure. This process tends to be adhoc, subjective and difficult to reproduce. This work shows how competency questions can be used to formalise expert knowledge and application requirements for context specific evaluation of a clustering application in the residential energy consumption sector. While cluster analysis is an established unsupervised machine learning technique, identifying the optimal set of clusters for a specific application requires extensive experimentation and domain knowledge. Cluster compactness and distinctness are two important attributes that characterise a good cluster set (Sarle et al., 1990) and different metrics, such as the Mean Index Adequacy (MIA), Davies-Bouldin Index (DBI) and the Silhouette Index have been proposed to measure cluster compactness and distinctness.

Recurrent neural networks (RNNs) are more suitable for learning non-linear dependencies in dynamical systems from observed time series data. In practice all the external variables driving such systems are not known a priori, especially in economical forecasting. A class of RNNs called Error Correction Neural Networks (ECNNs) was designed to compensate for missing input variables. It does this by feeding back in the current step the error made in the previous step. The ECNN is implemented in Python by the computation of the appropriate gradients and it is tested on stock market predictions. As expected it out performed the simple RNN and LSTM and other hybrid models which involve a de-noising pre-processing step. The intuition for the latter is that de-noising may lead to loss of information.

You, a person who's currently on the English-speaking internet in The Year of The Pandemic, have definitely seen public service information about Covid-19. You've probably been unable to escape seeing quite a lot of it, both online and offline, from handwashing posters to social distancing tape to instructional videos for face covering. But if we want to avoid a pandemic spreading to all the humans in the world, this information also has to reach all the humans of the world--and that means translating Covid PSAs into as many languages as possible, in ways that are accurate and culturally appropriate. It's easy to overlook how important language is for health if you're on the English-speaking internet, where "is this headache actually something to worry about?" is only a quick Wikipedia article or WebMD search away. For over half of the world's population, people can't expect to Google their symptoms, nor even necessarily get a pamphlet from their doctor explaining their diagnosis, because it's not available in a language they can understand.

Malaysia 5.0 outlines a problem-solving approach to society's challenges and problems through the deployment and implementation of Fourth Industrial Revolution (IR4.0) The term "Society 5.0" describes the next stage of the evolution of societal communities, following the hunting society (Society 1.0), agricultural society (Society 2.0), industrial society (Society 3.0), and information society (Society 4.0). The key differentiation of Society 5.0 (the digital age) from Society 4.0 (the information age) is the convergence of the virtual world with the physical world. Covid-19 has accelerated the migration of society from physical infrastructures onto digital infrastructures, but Society 5.0 holds the promise to bring these back together through the use of IR4.0 technologies such as artificial intelligence (AI), internet of things (IoT), blockchain and digital assets (FinTech). A national IR4.0 policy is needed to create a new narrative for Malaysia as an innovation economy that can compete in a disruptive technology world, serve as a springboard into Asean, bridge Asia, the Middle East and Africa, as well as connect with the 1.8 billion Muslims worldwide.

Singapore park-goers have been reminded of their social distancing obligations by Boston Dynamics' yellow "dog". The robot hound is equipped with numerous cameras and sensors, which it can use to detect transgressors and broadcast pre-recorded warnings. The authorities have reassured locals it is not a quadruped data-collection device. In Milton Keynes a recently expanded fleet of six-wheeled robots has been delivering food and small supermarket shopping consignments to hungry residents. The town's large network of cycle paths makes it ideally suited to the knee-high machines, which trundle along at a top speed of 4mph.