The lifelong control problem of an off-grid microgrid is composed of two tasks, namely estimation of the condition of the microgrid devices and operational planning accounting for the uncertainties by forecasting the future consumption and the renewable production. The main challenge for the effective control arises from the various changes that take place over time. In this paper, we present an open-source reinforcement framework for the modeling of an off-grid microgrid for rural electrification. The lifelong control problem of an isolated microgrid is formulated as a Markov Decision Process (MDP). We categorize the set of changes that can occur in progressive and abrupt changes. We propose a novel model based reinforcement learning algorithm that is able to address both types of changes. In particular the proposed algorithm demonstrates generalisation properties, transfer capabilities and better robustness in case of fast-changing system dynamics. The proposed algorithm is compared against a rule-based policy and a model predictive controller with look-ahead. The results show that the trained agent is able to outperform both benchmarks in the lifelong setting where the system dynamics are changing over time.

This dissertation uses supervised and unsupervised data mining techniques to analyse office floor plans in an attempt to gain a better understanding of their geometry-to-function relationship. This question was deemed relevant after a background review of the state-of-the-art in automated floor-plan generation tools showed that such tools have been prototyped since the 1960s, but their search space is ill-informed because there are few formalisms to describe spatial affordance. To show and evaluate the relationship of geometry and use, data from visual graph analysis were used to train three supervised learners and compare these to a baseline accuracy established with a ZeroR classifier. This showed that for the office dataset examined, visual mean depth and integration are most tightly linked to usage and that the supervised learning algorithm J48 can correctly predict class performance on unseen examples to up to 79.5%. The thesis also includes an evaluation of the layout case studies with unsupervised learners, which showed that use could not be immediately reverse-engineered based solemnly on the VGA information to achieve a strong cluster-to-class evaluation.

Alternating Direction Method of Multipliers (ADMM) is a popular algorithm for distributed learning, where a network of nodes collaboratively solve a regularized empirical risk minimization by iterative local computation associated with distributed data and iterate exchanges. When the training data is sensitive, the exchanged iterates will cause serious privacy concern. In this paper, we aim to propose a new differentially private distributed ADMM algorithm with improved accuracy for a wide range of convex learning problems. In our proposed algorithm, we adopt the approximation of the objective function in the local computation to introduce calibrated noise into iterate updates robustly, and allow multiple primal variable updates per node in each iteration. Our theoretical results demonstrate that our approach can obtain higher utility by such multiple approximate updates, and achieve the error bounds asymptotic to the state-of-art ones for differentially private empirical risk minimization.

Another 3 million US citizens filed for unemployment benefits last week, bringing the total to 36.5 million since mid-March, about 22 per cent of the US workforce. The total number of people who have lost their jobs is likely to be an underestimate because many states still have a backlog of claims to get through. Brazil has become a hotspot for coronavirus infections as the country confirmed a record 11,385 daily coronavirus cases and 749 more deaths yesterday. The total number of confirmed cases is now more than 190,000, the sixth highest in the world. Doctors in the country say a lack of adequate testing means the true number of cases could be ten times higher. A coronavirus antibody test developed by Swiss pharmaceutical company Roche has been approved for use by Public Health England. UK health minister Edward Argar said the test "appears to be extremely reliable". Unlike other forms of testing, antibody tests detect whether someone has been previously infected with the ...

A pharmacological effect of a drug on cells, organs and systems refers to the specific biochemical interaction produced by a drug substance, which is called its mechanism of action. Drug repositioning (or drug repurposing) is a fundamental problem for the identification of new opportunities for the use of already approved or failed drugs. In this paper, we present a method based on a multi-relation unsupervised graph embedding model that learns latent representations for drugs and diseases so that the distance between these representations reveals repositioning opportunities. Once representations for drugs and diseases are obtained we learn the likelihood of new links (that is, new indications) between drugs and diseases. Known drug indications are used for learning a model that predicts potential indications. Compared with existing unsupervised graph embedding methods our method shows superior prediction performance in terms of area under the ROC curve, and we present examples of repositioning opportunities found on recent biomedical literature that were also predicted by our method.

Online Social Networks have become an important medium for communication among people who suffer from mental disorders to share moments of hardship and to seek support. Here we analyze how Reddit discussions can help improve the health conditions of its users. Using emotional tone of user publications as a proxy for his emotional state, we uncover relationships between state changes and interactions he has in a given community. We observe that authors of negative posts often write more positive comments after engaging in discussions. Second, we build models based on state-of-the-art embedding techniques and RNNs to predict shifts in emotional tone. We show that it is possible to predict with good accuracy the reaction of users of mental disorder online communities to the interactions experienced in these platforms. Our models could assist in interventions promoted by health care professionals to provide support to people suffering from mental health illnesses.

The brain electrical activity presents several short events during sleep that can be observed as distinctive micro-structures in the electroencephalogram (EEG), such as sleep spindles and K-complexes. These events have been associated with biological processes and neurological disorders, making them a research topic in sleep medicine. However, manual detection limits their study because it is time-consuming and affected by significant inter-expert variability, motivating automatic approaches. We propose a deep learning approach based on convolutional and recurrent neural networks for sleep EEG event detection called Recurrent Event Detector (RED). RED uses one of two input representations: a) the time-domain EEG signal, or b) a complex spectrogram of the signal obtained with the Continuous Wavelet Transform (CWT). Unlike previous approaches, a fixed time window is avoided and temporal context is integrated to better emulate the visual criteria of experts. When evaluated on the MASS dataset, our detectors outperform the state of the art in both sleep spindle and K-complex detection with a mean F1-score of at least 80.9% and 82.6%, respectively. Although the CWT-domain model obtained a similar performance than its time-domain counterpart, the former allows in principle a more interpretable input representation due to the use of a spectrogram. The proposed approach is event-agnostic and can be used directly to detect other types of sleep events.

The training dynamics of hidden layers in deep learning are poorly understood in theory. Recently, the Information Plane (IP) was proposed to analyze them, which is based on the information-theoretic concept of mutual information (MI). The Information Bottleneck (IB) theory predicts that layers maximize relevant information and compress irrelevant information. Due to the limitations in MI estimation from samples, there is an ongoing debate about the properties of the IP for the supervised learning case. In this work, we derive a theoretical convergence for the IP of autoencoders. The theory predicts that ideal autoencoders with a large bottleneck layer size do not compress input information, whereas a small size causes compression only in the encoder layers. For the experiments, we use a Gram-matrix based MI estimator recently proposed in the literature. We propose a new rule to adjust its parameters that compensates scale and dimensionality effects. Using our proposed rule, we obtain experimental IPs closer to the theory. Our theoretical IP for autoencoders could be used as a benchmark to validate new methods to estimate MI in neural networks. In this way, experimental limitations could be recognized and corrected, helping with the ongoing debate on the supervised learning case.

Uber will require its drivers to wear face masks as journeys start out of coronavirus lockdowns – and will use new technology to confirm that they are complying. "Our new technology will verify if the driver is wearing a mask by asking them to take a selfie. After we verify the driver is covering their face, we'll let the rider know via an in-app message" the company said in a blog post. Unlike it's other facial recognition software the "Real-Time ID Check", which the company says "protects riders from unverified drivers, and also prevents fraud by ensuring drivers' accounts are not compromised", this technology only identifies the mask rather than the driver's face or other biometric information. Drivers in the United States, Canada, India and most of Europe and Latin America, will be affected by the change, which will start 18 May.

UK GDP fell by 2 per cent in the first quarter of 2020, the most rapid contraction of the UK's economy since the 2008 financial crisis. Rishi Sunak, the chancellor of the exchequer, said, "It is now very likely that the UK economy will face a significant recession this year, and we're already in the middle of that as we speak." The Bank of England predicts that the UK economy could shrink by as much as 14 per cent in 2020. In England some people who aren't able to work from home returned to work today, as part of the government's recent easing of certain restrictions. Despite the government urging people to avoid public transport if they could, some commuters said buses and trains were too crowded to practice social distancing. It could be as long as "four or five years" before covid-19 is under control and the pandemic could "potentially get worse", according to the World Health Organization's chief scientist Soumya Swaminathan. Speaking at an FT conference, she said a vaccine "seems ...