Air pollution in cities can be an acute problem leading to damaging effects on people, animals, plants and property. It is an important topic which is getting increased attention as the human population of cities continues to increase. This year it was the subject the 2018 KDD Cup, the annual data mining and knowledge discovery competition organized by ACM SIGKDD. The burning of fossil fuels for transport and home heating is a major contributor to air pollution in urban environments, creating the pollutant nitrogen dioxide (NO2). This is a secondary pollutant produced by the oxidation of NO. It is a major contributor to respiratory problems. In the European Union, the Cleaner Air For Europe (CAFÉ) Directive 2008/50/EC established an hourly limit of 200 μg/m3 and an annual mean limit of 40 μg/m3 in respect of NO2.

G'day, I'm Lee Hopkins, and this is bcr vidcast edition 108. Three things to consider today: AI and the pharmaceutical industry; will AI kill off too many jobs? Pharmaceutical companies are adopting an'Us WITH Them' approach to AI. 'Augmented intelligence' is the catchphrase that Pamela Spence uses; Pamela is EY's global sector leader for life sciences. Data is being analysed by algorithms, then suggestions are made to human decision-makers. AI is not making decisions by itself.

This article is the fifth in a series about how business leaders can become better prepared for managing the AI disruption. AI is great at cognitive thinking but terrible at ethical thinking. So bad at ethical judgment, in fact, that questions of ethics are likely to remain one of the most challenging aspects of developing large-scale commercial applications of AI. The ethical and moral implications of AI can impact business, society, or both at the same time. Consider the Google employees who resigned -- and the thousands who co-signed a letter to their CEO -- in protest of Pentagon-funded projects.

China has been making massive investments to create a huge pool of AI experts. The Chinese government wants to overtake the United States and be the global leader in the field by 2030. Artificial Intelligence or AI, simply described as the machine intelligence, has come to apply itself in several different sectors across countries in recent years, including healthcare, finance, education and security. But it has also increasingly become inserted into wider geopolitical conversations about the capabilities of major powers, including the United States and China. Within that aspect of the ongoing conversation, in terms of market share within the industry, the leadersin the field include the United States, with around 40 percent of the global market by some accounts, with countries like China, Israel, Germany, Canada and Russia fast catching up.

Despite the successful application of machine learning (ML) in a wide range of domains, adaptability---the very property that makes machine learning desirable---can be exploited by adversaries to contaminate training and evade classification. In this paper, we investigate the feasibility of applying a specific class of machine learning algorithms, namely, reinforcement learning (RL) algorithms, for autonomous cyber defence in software-defined networking (SDN). In particular, we focus on how an RL agent reacts towards different forms of causative attacks that poison its training process, including indiscriminate and targeted, white-box and black-box attacks. In addition, we also study the impact of the attack timing, and explore potential countermeasures such as adversarial training.

In scientific inference problems, the underlying statistical modeling assumptions have a crucial impact on the end results. There exist, however, only a few automatic means for validating these fundamental modelling assumptions. The contribution in this paper is a general criterion to evaluate the consistency of a set of statistical models with respect to observed data. This is achieved by automatically gauging the models' ability to generate data that is similar to the observed data. Importantly, the criterion follows from the model class itself and is therefore directly applicable to a broad range of inference problems with varying data types. The proposed data consistency criterion is illustrated and evaluated using three synthetic and two real data sets.

Deep neuroevolution, that is evolutionary policy search methods based on deep neural networks, have recently emerged as a competitor to deep reinforcement learning algorithms due to their better parallelization capabilities. However, these methods still suffer from a far worse sample efficiency. In this paper we investigate whether a mechanism known as "importance mixing" can significantly improve their sample efficiency. We provide a didactic presentation of importance mixing and we explain how it can be extended to reuse more samples. Then, from an empirical comparison based on a simple benchmark, we show that, though it actually provides better sample efficiency, it is still far from the sample efficiency of deep reinforcement learning, though it is more stable.

This paper describes the Pressure Ulcers Online Website, which is a first step solution towards a new and innovative platform for helping people to detect, understand and manage pressure ulcers. It outlines the reasons why the project has been developed and provides a central point of contact for pressure ulcer analysis and ongoing research. Using state-of-the-art technologies in convolutional neural networks and transfer learning along with end-to-end web technologies, this platform allows pressure ulcers to be analysed and findings to be reported. As the system evolves through collaborative partnerships, future versions will provide decision support functions to describe the complex characteristics of pressure ulcers along with information on wound care across multiple user boundaries. This project is therefore intended to raise awareness and support for people suffering with or providing care for pressure ulcers.

We analyze the uncertainties in the minimum norm solution of full-rank regression problems, arising from Gaussian linear models, computed by randomized (row-wise sampling and, more generally, sketching) algorithms. From a deterministic perspective, our structural perturbation bounds imply that least squares problems are less sensitive to multiplicative perturbations than to additive perturbations. From a probabilistic perspective, our expressions for the total expectation and variance with regard to both model- and algorithm-induced uncertainties, are exact, hold for general sketching matrices, and make no assumptions on the rank of the sketched matrix. The relative differences between the total bias and variance on the one hand, and the model bias and variance on the other hand, are governed by two factors: (i) the expected rank deficiency of the sketched matrix, and (ii) the expected difference between projectors associated with the original and the sketched problems. A simple example, based on uniform sampling with replacement, illustrates the statistical quantities.

Hence, it is seldom the case where a single oracle labels an entire collection. Furthermore, the Web, through its social nature, also exploits the wisdom of crowds to annotate large collections of documents and images. By categorizing texts, tagging images or rating products and places, Web users are generating large volumes of labeled content. However, when learning supervised models from crowds, the quality of labels can vary significantly due to task subjectivity and differences in annotator reliability (or bias) [9], [10]. If we consider a sentiment analysis task, it becomes clear that the subjectiveness of the exercise is prone to generate considerably distinct labels from different annotators. Similarly, online product reviews are known to vary considerably depending on the personal biases and volatility of the reviewer's opinions. It is therefore essential to account for these issues when learning from this increasingly common type of data. Hence, the interest of researchers on building models that take the reliabilities of different annotators into consideration and mitigate the effect of their biases has spiked during the last few years (e.g.