Covid-19 patients in the UK are being treated with dexamethasone today after a UK trial of the drug found it could save lives. "The treatment is immediately available and already in use on the NHS," said health minister Matt Hancock. "It is not by any means a cure but it is the best news we have had," Hancock told parliament today. The UK's chief medical officers say it should be used immediately, according to the BBC. A preliminary study found that the steroid, which is already widely prescribed for treating allergies and asthma, reduces the risk of dying from covid-19 by a third for patients on ventilators, and by a fifth for those receiving oxygen. Dexamethasone should only be taken if prescribed by a doctor. Officials in Beijing, China confirmed 31 new coronavirus cases today, bringing the total to 137 in the last six days. The city is again restricting all non-essential travel. Schools, swimming pools and gyms are all closed from today.

Mining is no stranger to digitalisation. The widely held perception of the resources industry is one of workers in mines and not one of machines running almost everything. But technological advances have already resulted in adoption of mechanisation, automation and data-driven production optimisation. Companies such as BHP, Anglo American and Rio Tinto have embraced digitalisation to gain a competitive advantage, mitigate risk and improve performance. They use advanced data analytics, virtual reality and artificial intelligence to reduce costs and increase efficiency in their processes, leading to enhanced ore recovery and less waste, to name a couple of benefits.

When Lauren Dry heard last year that facial recognition cameras were being trialled in the suburb of East Perth, she thought it was a joke. "I just thought to myself: What do you mean facial recognition cameras, that's sci-fi! That doesn't happen in Perth," she told 7.30. "And I looked into it and I was, like, this is real." Ms Dry enjoys a quiet life at home with her young family in Perth's leafy suburbs.

As we are evolving towards this unprecedented time of COVID-19, artificial intelligence proved to be one technology that has encompassed almost all aspects of human lives, whether it be healthcare, banking, shopping as well as running businesses amid this crisis. The impact of this pandemic has showcased the significance of this technology, and therefore companies and government entities are catching up their pace with AI-powered solutions. In fact, according to a PwC's report, it has been noted how this technology can be a game-changer for this era and could contribute up to $15.7 trillion to the global economy by 2030. A lot of this could be attributed to the popular belief that artificial intelligence has the potential to solve some of the complex real-world business problems. However, despite these incredible outcomes that artificial intelligence harnessed over businesses, the pandemic has highlighted some existing problems related to this technology, including explainability, accuracy, and privacy-related risks.

A robot dog from Boston Dynamics is now officially available to purchase. Spot, as the machine has been dubbed, will cost $74,500 (approximately £60,000). The canine droid is only available to customers in the United States at the moment, after they make a $1,000 deposit. It is capable of climbing stairs and crossing rough terrain, with the company sending the mechanical pooch into dangerous environments to carry payloads from place to place or collect data from the site. Users can control spot through its controller, which "easy access" to the robot's body posing, walking gaits, obstacle avoidance, and local navigation. Spot can also be set to follow predefined routes.

The effects of traffic congestion are adverse, primarily including air pollution, commuter stress, and an increase in vehicle operating costs and accidents on the road. In efforts to alleviate these problems in metropolitan cities, logistics companies plan to introduce a new method of everyday commute called air taxis, an Urban Air Mobility (UAM) service. These are electric-powered vehicles that are expected to operate in the forthcoming years by international transportation companies like Airbus, Uber, and Kitty Hawk. Since these flying taxis are emerging mode of transportation, it is necessary to provide recommendations for the initial design, implementation, and operation. This study proposes managerial insights for these upcoming services by analyzing online customer reviews and conducting an internal assessment of helicopter operations. Helicopters are similar to air taxis in regards to their operations, and therefore, customer reviews pertaining to the former can enable us to obtain insights into the strengths and weaknesses of the short-distance aviation service, in general. A four-stage sequential approach is used in this research, wherein the online reviews are mined in Stage 1, analyzed using the bigram and trigram models in Stage 2, 7S internal assessment is conducted for helicopter services in Stage 3, and managerial recommendations for air taxis are proposed in Stage 4. The insights obtained in this paper could assist any air taxi companies in providing better customer service when they venture into the market. Keywords: Air taxi; Emerging technology; Urban Air Mobility (UAM); Helicopter services; Online customer reviews; Text analytics;

Smart devices in the Internet of Things (IoT) paradigm provide a variety of unobtrusive and pervasive means for continuous monitoring of bio-metrics and health information. Furthermore, automated personalization and authentication through such smart systems can enable better user experience and security. In this paper, simultaneous estimation and monitoring of body mass index (BMI) and user identity recognition through a unified machine learning framework using smart beds is explored. To this end, we utilize pressure data collected from textile-based sensor arrays integrated onto a mattress to estimate the BMI values of subjects and classify their identities in different positions by using a deep multitask neural network. First, we filter and extract 14 features from the data and subsequently employ deep neural networks for BMI estimation and subject identification on two different public datasets. Finally, we demonstrate that our proposed solution outperforms prior works and several machine learning benchmarks by a considerable margin, while also estimating users' BMI in a 10-fold cross-validation scheme.

Bayesian optimization (BO) is an efficient method for optimizing expensive black-box functions. In real-world applications, BO often faces a major problem of missing values in inputs. The missing inputs can happen in two cases. First, the historical data for training BO often contain missing values. Second, when performing the function evaluation (e.g. computing alloy strength in a heat treatment process), errors may occur (e.g. a thermostat stops working) leading to an erroneous situation where the function is computed at a random unknown value instead of the suggested value. To deal with this problem, a common approach just simply skips data points where missing values happen. Clearly, this naive method cannot utilize data efficiently and often leads to poor performance. In this paper, we propose a novel BO method to handle missing inputs. We first find a probability distribution of each missing value so that we can impute the missing value by drawing a sample from its distribution. We then develop a new acquisition function based on the well-known Upper Confidence Bound (UCB) acquisition function, which considers the uncertainty of imputed values when suggesting the next point for function evaluation. We conduct comprehensive experiments on both synthetic and real-world applications to show the usefulness of our method.

For instance, Tucker (Tucker, Tensor decomposition methods have recently 1966) and canonical polyadic (CP) (Caroll & Chang, 1970) proven to be efficient for compressing and accelerating tensor decompositions are widely known for compressing neural networks. However, the problem and accelerating convolutional networks (Lebedev of optimal decomposition structure determination et al., 2015; Kim et al., 2016; Kossaifi et al., 2019), and is still not well studied while being quite important. Tensor Train (TT) (Oseledets, 2011) decomposition has Specifically, decomposition ranks present been successfully applied for compressing fully-connected the crucial parameter controlling the compressionaccuracy (FC) (Novikov et al., 2015), convolutional (Garipov et al., tradeoff. In this paper, we introduce 2016), recurrent (Yang et al., 2017; Yu et al., 2017), embedding MARS -- a new efficient method for the automatic (Khrulkov et al., 2019) layers.

Understanding patients' journeys in healthcare system is a fundamental prepositive task for a broad range of AI-based healthcare applications. This task aims to learn an informative representation that can comprehensively encode hidden dependencies among medical events and its inner entities, and then the use of encoding outputs can greatly benefit the downstream application-driven tasks. A patient journey is a sequence of electronic health records (EHRs) over time that is organized at multiple levels: patient, visits and medical codes. The key challenge of patient journey understanding is to design an effective encoding mechanism which can properly tackle the aforementioned multi-level structured patient journey data with temporal sequential visits and a set of medical codes. This paper proposes a novel self-attention mechanism that can simultaneously capture the contextual and temporal relationships hidden in patient journeys. A multi-level self-attention network (MusaNet) is specifically designed to learn the representations of patient journeys that is used to be a long sequence of activities. The MusaNet is trained in end-to-end manner using the training data derived from EHRs. We evaluated the efficacy of our method on two medical application tasks with real-world benchmark datasets. The results have demonstrated the proposed MusaNet produces higher-quality representations than state-of-the-art baseline methods. The source code is available in https://github.com/xueping/MusaNet.