Esteban Granero has some good news, a little light at the end of a long, dark tunnel in Spain, where the coronavirus crisis has left more than 21,000 people dead. "The situation is terrible," says the midfielder, a league title winner with Real Madrid, "but the curve is clearly downward now; we reached the peak on the fourth [of April] and now we're on the way down.

Experts from the private sector and leading research labs in the US and Europe have joined forces to create a toolkit for turning AI ethics principles into practice. The preprint paper, published last week, advocates paying people for finding risks of bias in artificial intelligence (AI) systems – adapting a model used to check the security of new computer systems, in which hackers are paid'bounties' for identifying weaknesses. The paper also proposes better linking independent third-party auditing operations and government policies to foster a market in regulatory systems, and suggests that governments increase funding for researchers in academia to verify performance claims made by industry. The 80-page paper, Toward Trustworthy AI Development: Mechanisms for Supporting Verifiable Claims, has been put together by AI specialists from 30 organisations including Google Brain, Intel, OpenAI, Stanford University and the Leverhulme Centre for the Future of Intelligence. "In order for AI developers to earn trust from system users, customers, civil society, governments, and other stakeholders that they are building AI responsibly, there is a need to move beyond [ethics] principles to a focus on mechanisms for demonstrating responsible behaviour," the executive summary reads.

The health ministry has unveiled a list of more than 10,000 medical clinics accepting new patients for online diagnoses in an effort to curb the spread of the novel coronavirus among doctors and patients. In online meetings with patients, doctors provide recommendations and diagnoses remotely through technology such as smartphones. The method is said to be effective in protecting the medical system from the dangers of increased infections inside health facilities. The ministry said Friday that it will update the list of clinics providing telemedicine for first-time patients as it receives reports from local governments across the country. Amid the coronavirus pandemic, the ministry has modified its stance that the first consultation with each patient should be conducted face-to-face.

A new study has found that using delivery drones in dense urban environments might actually consume more energy than a conventional delivery van. Thomas Kirschstein, an economist at Martin Luther University in Halle-Wittenberg, Germany, developed a simulation to compare how energy efficient different delivery methods would be in a large and crowded city. He compared a delivery drone, electric van, and diesel van as they traveled through a digital recreation of Berlin to see which required the least amount of fossil fuel to complete equivalent delivery routes. The clear winner were electric vans, which consumed more than 50 percent less energy than diesel vans. The biggest surprise, however, came from drones, which turned out to be the most energy hungry of all the delivery methods, consuming as much as 10 times the amount of energy that the electric vans did.

Coronavirus tests should be delivered to people's homes using drones to cut the spread of the deadly infection, a study has suggested. The proposal would see batches tests of tests ferried from centralised test facilities out the the public, allowing authorities to determine who needed to be quarantined. At the same time, removing the need to visit testing facilities would minimise the risk of aiding the disease's spread among the population in the process. They suggest that 36 drones each carrying 100 tests could visit everyone in such a city of population 100,000 inhabitants repeatedly every four days. However, even running tests of individuals every 30 days, they said, 'would flatten the curve quite significantly.' Coronavirus tests should be delivered to people's homes using drones to cut the spread of the deadly infection, a study has suggested The proactive screening of the general population for coronavirus infection -- especially in the case of asymptomatic cases -- has significant potential in helping to curb the spread of COVID-10, but implementing such would have its challenges.

Panic buying has taken hold of many Americans who are stocking up on supplies and leaving shelves empty during the coronavirus. Now, the Pentagon is harnessing the power of artificial intelligence to help predict and address shortages of water, medicines, food, medical supplies and other essentials across the country. The predictive model pulls data from the Census Bureau, Medicare, hospitals and projects how the virus is spreading, along with a number of essential items at retailers to determine where scarcities may occur. Military groups can then use this information to move essential items to specific locations, while retailers can look at the data to predictively restock their shelves. Panic buying has taken hold of many Americans who are stocking up on supplies and leaving shelves empty during the coronavirus.

The neural network method of solving differential equations is used to approximate the electric potential and corresponding electric field in the slit-well microfluidic device. The device's geometry is non-convex, making this a challenging problem to solve using the neural network method. To validate the method, the neural network solutions are compared to a reference solution obtained using the finite element method. Additional metrics are presented that measure how well the neural networks recover important physical invariants that are not explicitly enforced during training: spatial symmetries and conservation of electric flux. Finally, as an application-specific test of validity, neural network electric fields are incorporated into particle simulations. Conveniently, the same loss functional used to train the neural networks also seems to provide a reliable estimator of the networks' true errors, as measured by any of the metrics considered here. In all metrics, deep neural networks significantly outperform shallow neural networks, even when normalized by computational cost. Altogether, the results suggest that the neural network method can reliably produce solutions of acceptable accuracy for use in subsequent physical computations, such as particle simulations.

Subsurface flow problems usually involve some degree of uncertainty. Consequently, uncertainty quantification is commonly necessary for subsurface flow prediction. In this work, we propose a methodology for efficient uncertainty quantification for dynamic subsurface flow with a surrogate constructed by the Theory-guided Neural Network (TgNN). The TgNN here is specially designed for problems with stochastic parameters. In the TgNN, stochastic parameters, time and location comprise the input of the neural network, while the quantity of interest is the output. The neural network is trained with available simulation data, while being simultaneously guided by theory (e.g., the governing equation, boundary conditions, initial conditions, etc.) of the underlying problem. The trained neural network can predict solutions of subsurface flow problems with new stochastic parameters. With the TgNN surrogate, the Monte Carlo (MC) method can be efficiently implemented for uncertainty quantification. The proposed methodology is evaluated with two-dimensional dynamic saturated flow problems in porous medium. Numerical results show that the TgNN based surrogate can significantly improve the efficiency of uncertainty quantification tasks compared with simulation based implementation. Further investigations regarding stochastic fields with smaller correlation length, larger variance, changing boundary values and out-of-distribution variances are performed, and satisfactory results are obtained.

The Hierarchical Vote Collective of Transformation-based Ensembles (HIVE-COTE) is a heterogeneous meta ensemble for time series classification. Since it was first proposed in 2016, the algorithm has undergone some minor changes and there is now a configurable, scalable and easy to use version available in two open source repositories. We present an overview of the latest stable HIVE-COTE, version 1.0, and describe how it differs to the original. We provide a walkthrough guide of how to use the classifier, and conduct extensive experimental evaluation of its predictive performance and resource usage. We compare the performance of HIVE-COTE to three recently proposed algorithms.

The ability to recognize objects despite there being differences in appearance, known as Core Object Recognition, forms a critical part of human perception. While it is understood that the brain accomplishes Core Object Recognition through feedforward, hierarchical computations through the visual stream, the underlying algorithms that allow for invariant representations to form downstream is still not well understood. (DiCarlo et al., 2012) Various computational perceptual models have been built to attempt and tackle the object identification task in an artificial perceptual setting. Artificial Neural Networks, computational graphs consisting of weighted edges and mathematical operations at vertices, are loosely inspired by neural networks in the brain and have proven effective at various visual perceptual tasks, including object characterization and identification. (Pinto et al., 2008) (DiCarlo et al., 2012) For many data analysis tasks, learning representations where each dimension is statistically independent and thus disentangled from the others is useful. If the underlying generative factors of the data are also statistically independent, Bayesian inference of latent variables can form disentangled representations. This thesis constitutes a research project exploring a generalization of the Variational Autoencoder (VAE), $\beta$-VAE, that aims to learn disentangled representations using variational inference. $\beta$-VAE incorporates the hyperparameter $\beta$, and enforces conditional independence of its bottleneck neurons, which is in general not compatible with the statistical independence of latent variables. This text examines this architecture, and provides analytical and numerical arguments, with the goal of demonstrating that this incompatibility leads to a non-monotonic inference performance in $\beta$-VAE with a finite optimal $\beta$.