An end-to-end solution for handwritten numeral string recognition is proposed, in which the numeral string is considered as composed of objects automatically detected and recognized by a YoLo-based model. The main contribution of this paper is to avoid heuristic-based methods for string preprocessing and segmentation, the need for task-oriented classifiers, and also the use of specific constraints related to the string length. A robust experimental protocol based on several numeral string datasets, including one composed of historical documents, has shown that the proposed method is a feasible end-to-end solution for numeral string recognition. Besides, it reduces the complexity of the string recognition task considerably since it drops out classical steps, in special preprocessing, segmentation, and a set of classifiers devoted to strings with a specific length.

Medical Triage is of paramount importance to healthcare systems, allowing for the correct orientation of patients and allocation of the necessary resources to treat them adequately. While reliable decision-tree methods exist to triage patients based on their presentation, those trees implicitly require human inference and are not immediately applicable in a fully automated setting. On the other hand, learning triage policies directly from experts may correct for some of the limitations of hard-coded decision-trees. In this work, we present a Deep Reinforcement Learning approach (a variant of DeepQ-Learning) to triage patients using curated clinical vignettes. The dataset, consisting of 1374 clinical vignettes, was created by medical doctors to represent real-life cases. Each vignette is associated with an average of 3.8 expert triage decisions given by medical doctors relying solely on medical history. We show that this approach is on a par with human performance, yielding safe triage decisions in 94% of cases, and matching expert decisions in 85% of cases. The trained agent learns when to stop asking questions, acquires optimized decision policies requiring less evidence than supervised approaches, and adapts to the novelty of a situation by asking for more information. Overall, we demonstrate that a Deep Reinforcement Learning approach can learn effective medical triage policies directly from expert decisions, without requiring expert knowledge engineering. This approach is scalable and can be deployed in healthcare settings or geographical regions with distinct triage specifications, or where trained experts are scarce, to improve decision making in the early stage of care.

The US presidential election campaign is in its final days. Donald Trump is behind in the polls and the pundits are predicting a win for his Democrat challenger, former vice president Joe Biden. He boasts that he will win again. With two weeks to go, his campaign unleashes an offensive in the crucial swing states: adverts, Facebook posts, WhatsApp groups and tweets. They warn of violent crime and civil unrest driven by immigrants and gangs, playing up Trump's endorsement by evangelicals and smearing Biden as a closet atheist. The initiative works and Trump snatches another unlikely victory.

As of March 10, 2020, 467 confirmed cases of COVID-19 have been reported to the Centers for Disease Control and Prevention in the United States. While governments across the globe are working in collaboration with local authorities and health-care providers to track, respond to and prevent the spread of disease caused by the coronavirus, health experts are turning to advanced analytics and artificial intelligence to augment current efforts to prevent further infection. Data and analytics have proved to be useful in combating the spread of disease, and the federal government has access to ample data on the U.S. population's health and travel as well as the migration of both domestic and wild animals -- all of which can be useful in tracking and predicting disease trajectory. Machine learning's ability to consider large amounts of data and offer insights can lead to deeper knowledge about diseases and enable U.S. health and government officials to make better decisions throughout the entire evolution of an outbreak. As the global human population grows and continues to interact with animals, other opportunities for viruses that originate in animals (like COVID-19) could make the jump from to humans and spread.

The study of complex networks is a significant development in modern science, and has enriched the social sciences, biology, physics, and computer science. Models and algorithms for such networks are pervasive in our society, and impact human behavior via social networks, search engines, and recommender systems to name a few. A widely used algorithmic technique for modeling such complex networks is to construct a low-dimensional Euclidean embedding of the vertices of the network, where proximity of vertices is interpreted as the likelihood of an edge. Contrary to the common view, we argue that such graph embeddings do not}capture salient properties of complex networks. The two properties we focus on are low degree and large clustering coefficients, which have been widely established to be empirically true for real-world networks. We mathematically prove that any embedding (that uses dot products to measure similarity) that can successfully create these two properties must have rank nearly linear in the number of vertices. Among other implications, this establishes that popular embedding techniques such as Singular Value Decomposition and node2vec fail to capture significant structural aspects of real-world complex networks. Furthermore, we empirically study a number of different embedding techniques based on dot product, and show that they all fail to capture the triangle structure.

Understanding the loss surface of a neural network is fundamentally important to the understanding of deep learning. This paper presents how piecewise linear activation functions substantially shape the loss surfaces of neural networks. We first prove that {\it the loss surfaces of many neural networks have infinite spurious local minima} which are defined as the local minima with higher empirical risks than the global minima. Our result demonstrates that the networks with piecewise linear activations possess substantial differences to the well-studied linear neural networks. This result holds for any neural network with arbitrary depth and arbitrary piecewise linear activation functions (excluding linear functions) under most loss functions in practice. Essentially, the underlying assumptions are consistent with most practical circumstances where the output layer is narrower than any hidden layer. In addition, the loss surface of a neural network with piecewise linear activations is partitioned into multiple smooth and multilinear cells by nondifferentiable boundaries. The constructed spurious local minima are concentrated in one cell as a valley: they are connected with each other by a continuous path, on which empirical risk is invariant. Further for one-hidden-layer networks, we prove that all local minima in a cell constitute an equivalence class; they are concentrated in a valley; and they are all global minima in the cell.

Dineo Lioma's energy and enthusiasm are infectious, and the way she speaks about things such as artificial intelligence (AI), DNA, enzymes and gene sequencing to aid those in the field of healthcare makes one believe there is hope for our virus-stricken world. Lioma has co-founded two medical technology companies, founded a third and currently runs two of them – and she is not yet 30 years old. She has a master's degree in micro and nanotechnology enterprise with distinction from Cambridge University in the UK. Cambridge offered Lioma the chance to do her PhD there to "work out how to harness solar and mechanical energy", but the engineer, who grew up fiddling with electrical plugs in her family's home in Bloemfontein, Free State, and obtained her BSc in metallurgical and materials engineering with 24 distinctions from Wits University, declined the offer. "I knew I wanted to work in the field of health and to help South Africa progress. There was not a lot going on in micro and nanotechnology here, so I came home. I wanted to give back," she says.

Neanderthals fed regularly on mussels, fish and other omega-3-rich marine life including seals, which likely impacted their cognitive abilities, a new study claims. Archaeological digs along the Portuguese coast reveal the evidence that our cavemen ancestors had as much fondness for seafood as modern humans today. Both Neanderthals and early Homo sapiens tucked into'surf and turf', from molluscs, crabs, fish, waterfowl and dolphins to horse, goat and red deer, as well as pine nuts. The findings are based on ancient remains in the cave of Figueira Brava, Portugal, dating to roughly 106,000-86,000 years ago – when Neanderthals settled in Europe. Figueira Brava is 18.6 miles (30km) south of Lisbon on the slopes of the Serra da Arrábida, a natural park facing south, about a 45-minute drive from Lisbon'Pretty much every potential source of food that existed in the environment they [Neanderthals] exploited and used it,' said Professor João Zilhão, an expert in palaeolithic archaeology at the University of Barcelona.

Chest X-ray is the first imaging technique that plays an important role in the diagnosis of COVID-19 disease. Due to the high availability of large-scale annotated image datasets, great success has been achieved using convolutional neural networks (CNN s) for image recognition and classification. However, due to the limited availability of annotated medical images, the classification of medical images remains the biggest challenge in medical diagnosis. Thanks to transfer learning, an effective mechanism that can provide a promising solution by transferring knowledge from generic object recognition tasks to domain-specific tasks. In this paper, we validate and adopt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images. DeTraC can deal with any irregularities in the image dataset by investigating its class boundaries using a class decomposition mechanism. The experimental results showed the capability of DeTraC in the detection of COVID-19 cases from a comprehensive image dataset collected from several hospitals around the world. High accuracy of 95.12% (with a sensitivity of 97.91%, a specificity of 91.87%, and a precision of 93.36%) was achieved by DeTraC in the detection of COVID-19 X-ray images from normal, and severe acute respiratory syndrome cases.

This paper addresses the difficulty of forecasting multiple financial time series (TS) conjointly using deep neural networks (DNN). We investigate whether DNN-based models could forecast these TS more efficiently by learning their representation directly. To this end, we make use of the dynamic factor graph (DFG) from that we enhance by proposing a novel variable-length attention-based mechanism to render it memory-augmented. Using this mechanism, we propose an unsupervised DNN architecture for multivariate TS forecasting that allows to learn and take advantage of the relationships between these TS. We test our model on two datasets covering 19 years of investment funds activities. Our experimental results show that our proposed approach outperforms significantly typical DNN-based and statistical models at forecasting their 21-day price trajectory.