This paper outlines connections between Monotone Boolean Functions, LP-Type problems and the Maximum Consensus Problem. The latter refers to a particular type of robust fitting characterisation, popular in Computer Vision (MaxCon). Indeed, this is our main motivation but we believe the results of the study of these connections are more widely applicable to LP-type problems (at least 'thresholded versions', as we describe), and perhaps even more widely. We illustrate, with examples from Computer Vision, how the resulting perspectives suggest new algorithms. Indeed, we focus, in the experimental part, on how the Influence (a property of Boolean Functions that takes on a special form if the function is Monotone) can guide a search for the MaxCon solution.

As humanity struggles to contain the global Covid-19 infection, prophylactic actions are grandly slowed down by the shortage of testing kits. Governments have taken several measures to work around this shortage: the FDA has become more liberal on the approval of Covid-19 tests in the US. In the UK emergency measures allowed to increase the daily number of locally produced test kits to 100,000. China has recently launched a massive test manufacturing program. However, all those efforts are very insufficient and many poor countries are still under threat. A popular method for reducing the number of tests consists in pooling samples, i.e. mixing patient samples and testing the mixed samples once. If all the samples are negative, pooling succeeds at a unitary cost. However, if a single sample is positive, failure does not indicate which patient is infected. This paper describes how to optimally detect infected patients in pools, i.e. using a minimal number of tests to precisely identify them, given the a priori probabilities that each of the patients is healthy. Those probabilities can be estimated using questionnaires, supervised machine learning or clinical examinations. The resulting algorithms, which can be interpreted as informed divide-and-conquer strategies, are non-intuitive and quite surprising. They are patent-free. Co-authors are listed in alphabetical order.

Thanks to the Automatic Identification System (AIS), tracking vessels across the seas provides a powerful means for maritime safety and environmental protection. However, large amounts of streaming AIS positional updates from vessels can hardly contribute additional knowledge about their actual motion patterns. Vessels are generally expected to maintain straight, predictable routes at open sea, except in cases of adverse weather conditions, accidents, traffic restrictions, etc. In [11] a maritime surveillance system was introduced, involving a trajectory detection module that can provide summarized representations of vessel trajectories by consuming AIS positional messages online. The key idea behind the proposed summarization is that keeping only some critical points may be enough to reconstruct with tolerable accuracy the original course of each vessel. Indeed, instead of retaining every incoming position for every vessel or even applying a costly multi-pass trajectory simplification algorithm, this method drops positions along trajectory segments of "normal" motion characteristics. In addition, the retained critical points can be marked with suitable annotations, i.e., indicating stops, turning points, changes in speed, etc. The resulting trajectory synopsis per vessel is derived from those judiciously annotated critical points and can approximately reconstruct its original course.

The latent variables learned by VAEs have seen considerable interest as an unsupervised way of extracting features, which can then be used for downstream tasks. There is a growing interest in the question of whether features learned on one environment will generalize across different environments. We demonstrate here that VAE latent variables often focus on some factors of variation at the expense of others - in this case we refer to the features as ``imbalanced''. Feature imbalance leads to poor generalization when the latent variables are used in an environment where the presence of features changes. Similarly, latent variables trained with imbalanced features induce the VAE to generate less diverse (i.e. biased towards dominant features) samples. To address this, we propose a regularization scheme for VAEs, which we show substantially addresses the feature imbalance problem. We also introduce a simple metric to measure the balance of features in generated images.

Electrocardiogram (ECG) detection and delineation are key steps for numerous tasks in clinical practice, as ECG is the most performed non-invasive test for assessing cardiac condition. State-of-the-art algorithms employ digital signal processing (DSP), which require laborious rule adaptation to new morphologies. In contrast, deep learning (DL) algorithms, especially for classification, are gaining weight in academic and industrial settings. However, the lack of model explainability and small databases hinder their applicability. We demonstrate DL can be successfully applied to low interpretative tasks by embedding ECG detection and delineation onto a segmentation framework. For this purpose, we adapted and validated the most used neural network architecture for image segmentation, the U-Net, to one-dimensional data. The model was trained using PhysioNet's QT database, comprised of 105 ambulatory ECG recordings, for single- and multi-lead scenarios. To alleviate data scarcity, data regularization techniques such as pre-training with low-quality data labels, performing ECG-based data augmentation and applying strong model regularizers to the architecture were attempted. Other variations in the model's capacity (U-Net's depth and width), alongside the application of state-of-the-art additions, were evaluated. These variations were exhaustively validated in a 5-fold cross-validation manner. The best performing configuration reached precisions of 90.12%, 99.14% and 98.25% and recalls of 98.73%, 99.94% and 99.88% for the P, QRS and T waves, respectively, on par with DSP-based approaches. Despite being a data-hungry technique trained on a small dataset, DL-based approaches demonstrate to be a viable alternative to traditional DSP-based ECG processing techniques.

Welcome to the voting for the Audience Choice Demo from HRI 2020. You can see the video and abstract from each demo here, with voting at the bottom. You can also register for the Online HRI 2020 Demo Discussion and Award Presentation on May 21 4:00 PM BST. Abstract: There are many challenges when it comes to deploying robots remotely including lack of situation awareness for the operator, which can lead to decreased trust and lack of adoption. For this demonstration, delegates interact with a social robot who acts as a facilitator and mediator between them and the remote robots running a mission in a realistic simulator. We will demonstrate how such a robot can use spoken interaction and social cues to facilitate teaming between itself, the operator and the remote robots.

Libya's UN-supported government launched a counterattack on Sunday against a strategic military base used by renegade commander Khalifa Haftar to pound the capital Tripoli with rocket fire. The response came after a missile barrage damaged Tripoli's main airport and set fuel tanks and several aircraft ablaze, with at least six civilians killed in surrounding residential areas in the attacks on Saturday. Meanwhile, Turkey - the Government of National Accord's (GNA) main ally defending Tripoli against Haftar's Libyan National Army (LNA) - threatened to step up its attacks against the eastern-based LNA, which has attempted to seize the capital for more than a year. "The forces of war criminal [Haftar] fired more than a hundred rockets and missiles at residential areas in the centre of the capital," the GNA said in a statement on Facebook. The airport was badly damaged and came under renewed rocket fire on Sunday morning, it said.

A man holds a traditional lamp from the balcony of his house as torches and candles illuminate houses and high rise residential buildings as Indians mark the country's fight against the coronavirus pandemic a suburb of New Delhi, India

COVID-19 disease, caused by the SARS-CoV-2 virus, was identified in December 2019 in China and declared a global pandemic by the WHO on 11 March 2020. Artificial Intelligence (AI) is a potentially powerful tool in the fight against the COVID-19 pandemic. AI can, for present purposes, be defined as Machine Learning (ML), Natural Language Processing (NLP), and Computer Vision applications to teach computers to use big data-based models for pattern recognition, explanation, and prediction. These functions can be useful to recognize (diagnose), predict, and explain (treat) COVID-19 infections, and help manage socio-economic impacts. Since the outbreak of the pandemic, there has been a scramble to use and explore AI, and other data analytic tools, for these purposes. In this article, I provide an early review, discussing the actual and potential contribution of AI to the fight against COVID-19, as well as the current constraints on these contributions. It aims to draw quick take-aways from a fast expanding discussion and growing body of work, in order to serve as an input for rapid responses in research, policy and medical analysis. The cost of the pandemic in terms of lives and economic damage will be terrible; at the time of writing, great uncertainty surrounded estimates of just how terrible, and of how successful both non-pharmaceutical and pharmaceutical responses can be. Improving AI, one of the most promising data analytic tools to have been developed over the past decade or so, so as to help reduce these uncertainties, is a worthwhile pursuit.

Chronolife, an artificial intelligence company specialising in digital health, has secured Class IIa medical certification from the European Union for its medical grade smart T-shirt, KeeSense. The multi-sensor wearable device continuously monitors electrocardiography (ECG), thoracic respiration, abdominal respiration, skin temperature, thoracic impedance, and physical activity, enabling healthcare practitioners to remotely track vital clinical data about their patients. The platform's receipt of a CE mark allows KeeSense to be marketed in the European Economic Area as a wearable medical device for healthcare purposes including remote monitoring of patients with chronic diseases and support for diagnostics. The KeeSense T-shirt is designed for round-the-clock use, and is fully reusable and washable, mimicking similar daily use garments. The T-shirt transmits data to its paired smartphone app via Bluetooth, which then sends the data to a server for live or time-delayed analysis by the wearer's healthcare team.