The coronavirus disease COVID-19 has so far caused about 3,400 deaths, infected about 100.000 people, and is significantly impacting the economy in many countries. We used predictive analytics, a branch of artificial intelligence (AI), to forecast how many confirmed COVID-19 cases and deaths can be expected in the near future. Our method predicts that by March 13, the virus death toll will have climbed to 3,913, and confirmed cases will reach 116,250 worldwide, based on data available up to March 5. To develop contingency plans and hopefully head off the worst effects of the coronavirus, governments need to be able to anticipate the future course of the outbreak. This is where predictive analytics could prove invaluable.

Over the past few years, artificial intelligence has matured into a collection of powerful technologies that are delivering competitive advantage to businesses across industries. Global AI adoption and investment are soaring. By one account, 37 percent of organizations have deployed AI solutions--up 270 percent from four years ago.1 Analysts forecast global AI spending will more than double over the next three years, topping US$79 billion by 2022.2 Get the Deloitte Insights app. Deloitte's State of AI in the Enterprise, 2nd Edition offers a global perspective of AI early adopters, based on surveying 1,900 IT and business executives from seven countries and a variety of industries.3 These adopters are increasing their spending on AI technologies and realizing positive returns. Almost two-thirds (65 percent) report that AI technologies are enabling their organizations to move ahead of the competition.

March is Women's History Month in the US, the UK and Australia, a time to honour women's sometimes underrated contributions to society. According to the US National Women's History Museum, Women's History Month started in 1978 as a local "Women's History Week" celebration in California, with organizers selecting the week to correspond with the March 8 International Women's Day. The US Congress in 1987 passed Public Law 100-9 designating March as the Women's History Month. The past few decades have seen a steady increase in the number of women studying and excelling in the STEM fields. But this is not so in computer science -- the number of women studying or pursuing a career in computer science has been decreasing since around 1990.

Timnit Gebru was wary of being labelled an activist. As a young, black female computer scientist, Gebru – who was born and raised in Addis Ababa, Ethiopia, but now lives in the US – says she'd always been vocal about the lack of women and minorities in the datasets used to train algorithms. She calls them "the undersampled majority", quoting another rising star of the artificial intelligence (AI) world, Joy Buolamwini. But Gebru didn't want her advocacy to affect how she was perceived in her field. "I wanted to be known primarily as a tech researcher. I was very resistant to being pigeonholed as a black woman, doing black woman-y things."

While real-world decisions involve many competing objectives, algorithmic decisions are often evaluated with a single objective function. In this paper, we study algorithmic policies which explicitly trade off between a private objective (such as profit) and a public objective (such as social welfare). We analyze a natural class of policies which trace an empirical Pareto frontier based on learned scores, and focus on how such decisions can be made in noisy or data-limited regimes. Our theoretical results characterize the optimal strategies in this class, bound the Pareto errors due to inaccuracies in the scores, and show an equivalence between optimal strategies and a rich class of fairness-constrained profit-maximizing policies. We then present empirical results in two different contexts --- online content recommendation and sustainable abalone fisheries --- to underscore the applicability of our approach to a wide range of practical decisions. Taken together, these results shed light on inherent trade-offs in using machine learning for decisions that impact social welfare.

Agile satellites are the new generation of Earth observation satellites (EOSs) with stronger attitude maneuvering capability. Since optical remote sensing instruments equipped on satellites cannot see through the cloud, the cloud coverage has a significant influence on the satellite observation missions. We are the first to address multiple agile EOSs scheduling problem under cloud coverage uncertainty where the objective aims to maximize the entire observation profit. The chance constraint programming model is adopted to describe the uncertainty initially, and the observation profit under cloud coverage uncertainty is then calculated via sample approximation method. Subsequently, an improved simulated annealing based heuristic combining a fast insertion strategy is proposed for large-scale observation missions. The experimental results show that the improved simulated annealing heuristic outperforms other algorithms for the multiple AEOSs scheduling problem under cloud coverage uncertainty, which verifies the efficiency and effectiveness of the proposed algorithm.

The vulnerability of Deep Neural Networks (DNNs) to adversarial attacks has led to the development of many defense approaches. Among them, Adversarial Training (AT) is a popular and widely used approach for training adversarially robust models. Mixup Training (MT), a recent popular training algorithm, improves the generalization performance of models by introducing globally linear behavior in between training examples. Although still in its early phase, we observe a shift in trend of exploiting Mixup from perspectives of generalisation to that of adversarial robustness. It has been shown that the Mixup trained models improves the robustness of models but only passively. A recent approach, Mixup Inference (MI), proposes an inference principle for Mixup trained models to counter adversarial examples at inference time by mixing the input with other random clean samples. In this work, we propose a new approach - \textit{VarMixup (Variational Mixup)} - to better sample mixup images by using the latent manifold underlying the data. Our experiments on CIFAR-10, CIFAR-100, SVHN and Tiny-Imagenet demonstrate that \textit{VarMixup} beats state-of-the-art AT techniques without training the model adversarially. Additionally, we also conduct ablations that show that models trained on \textit{VarMixup} samples are also robust to various input corruptions/perturbations, have low calibration error and are transferable.

Computation Tree Logic (CTL) is one of the central formalisms in formal verification. As a specification language, it is used to express a property that the system at hand is expected to satisfy. From both the verification and the system design points of view, some information content of such property might become irrelevant for the system due to various reasons e.g., it might become obsolete by time, or perhaps infeasible due to practical difficulties. Then, the problem arises on how to subtract such piece of information without altering the relevant system behaviour or violating the existing specifications. Moreover, in such a scenario, two crucial notions are informative: the strongest necessary condition (SNC) and the weakest sufficient condition (WSC) of a given property. To address such a scenario in a principled way, we introduce a forgetting-based approach in CTL and show that it can be used to compute SNC and WSC of a property under a given model. We study its theoretical properties and also show that our notion of forgetting satisfies existing essential postulates. Furthermore, we analyse the computational complexity of basic tasks, including various results for the relevant fragment CTLAF.

When learning from streaming data, a change in the data distribution, also known as concept drift, can render a previously-learned model inaccurate and require training a new model. We present an adaptive learning algorithm that extends previous drift-detection-based methods by incorporating drift detection into a broader stable-state/reactive-state process. The advantage of our approach is that we can use aggressive drift detection in the stable state to achieve a high detection rate, but mitigate the false positive rate of standalone drift detection via a reactive state that reacts quickly to true drifts while eliminating most false positives. The algorithm is generic in its base learner and can be applied across a variety of supervised learning problems. Our theoretical analysis shows that the risk of the algorithm is competitive to an algorithm with oracle knowledge of when (abrupt) drifts occur. Experiments on synthetic and real datasets with concept drifts confirm our theoretical analysis.

In high-dimensional data space, semi-supervised feature learning based on Euclidean distance shows instability under a broad set of conditions. Furthermore, the scarcity and high cost of labels prompt us to explore new semi-supervised learning methods with the fewest labels. In this paper, we develop a novel Minor Constraint Disturbances-based Deep Semi-supervised Feature Learning framework (MCD-DSFL) from the perspective of probability distribution for feature representation. There are two fundamental modules in the proposed framework: one is a Minor Constraint Disturbances-based restricted Boltzmann machine with Gaussian visible units (MCDGRBM) for modelling continuous data and the other is a Minor Constraint Disturbances-based restricted Boltzmann machine (MCDRBM) for modelling binary data. The Minor Constraint Disturbances (MCD) consist of less instance-level constraints which are produced by only two randomly selected labels from each class. The Kullback-Leibler (KL) divergences of the MCD are fused into the Contrastive Divergence (CD) learning for training the proposed MCDGRBM and MCDRBM models. Then, the probability distributions of hidden layer features are as similar as possible in the same class and they are as dissimilar as possible in the different classes simultaneously. Despite the weak influence of the MCD for our shallow models (MCDGRBM and MCDRBM), the proposed deep MCD-DSFL framework improves the representation capability significantly under its leverage effect. The semi-supervised strategy based on the KL divergence of the MCD significantly reduces the reliance on the labels and improves the stability of the semi-supervised feature learning in high-dimensional space simultaneously.