Desperate to solve the deadly conundrum of COVID-19, the world is clamoring for fast answers and solutions from a research system not built for haste. The ironic, and perhaps tragic, result: Scientific shortcuts have slowed understanding of the disease and delayed the ability to find out which drugs help, hurt or have no effect at all. As deaths from the coronavirus relentlessly mounted into the hundreds of thousands, tens of thousands of doctors and patients rushed to use drugs before they could be proved safe or effective. "People had an epidemic in front of them and were not prepared to wait," said Dr. Derek Angus, critical care chief at the University of Pittsburgh Medical Center. "We made traditional clinical research look so slow and cumbersome."

The coronavirus crisis has prompted Japan to ease regulations on remote medical treatment, creating an opening for tech companies and offering a glimpse of the future of health care in the world's most rapidly aging society. As coronavirus cases spiked in April, Japan temporarily eased restrictions on remote medical care, allowing doctors to conduct first-time visits online or by telephone and expanding the number of illnesses that can be treated remotely. The changes mark a potential shake-up in one of the world's biggest medical markets, which has lagged countries like Australia, China, and the United States in telemedicine. The reforms could also help the nation grapple with both a skyrocketing health care burden and a lack of doctors in rural areas. Previously doctors were only allowed to treat recurring patients remotely, and for a limited number of diseases.

SARS-CoV-2 virus RNA sequence classification and geographical analysis with convolutional neural networks approach. Abstract Covid-19 infection, which spread to the whole world in December 2019 and is still active, caused more than 250 thousand deaths in the world today. Researches on this subject have been focused on analyzing the genetic structure of the virus, developing vaccines, the course of the disease, and its source. In this study, RNA sequences belonging to the SARS-CoV-2 virus are transformed into gene motifs with two basic image processing algorithms and classified with the convolutional neural network (CNN) models. The CNN models achieved an average of 98% Area Under Curve(AUC) value was achieved in RNA sequences classified as Asia, Europe, America, and Oceania. The resulting artificial neural network model was used for phylogenetic analysis of the variant of the virus isolated in Turkey. The classification results reached were compared with gene alignment values in the GISAID database, where SARS-CoV-2 virus records are kept all over the world. Our experimental results have revealed that now the detection of the geographic distribution of the virus with the CNN models might serve as an efficient method. Keywords: Deep Learning, Bioinformatics, Convolutional neural network, SARS-Cov-2, Pattern Classification Introduction Artificial intelligence practices and particularly deep learning studies are a widely used discipline in many research fields, including medicine and bioinformatics. The CNN models, especially in the field of medical imaging, are very successful in lesions and disease diagnosis. In addition to the success of deep learning methods in the fields of image processing, natural language processing, also has a lot of usage on a time scale with approaches such as Long-Short Term memory. In deep learning practices, low-level features such as DNA sequence, pathology images, and tomography scans can be learned from the data, by largely eliminating the need for engineering applications.

An alternative to current mainstream preprocessing methods is proposed: Value Selection (VS). Unlike the existing methods such as feature selection that removes features and instance selection that eliminates instances, value selection eliminates the values (with respect to each feature) in the dataset with two purposes: reducing the model size and preserving its accuracy. Two probabilistic methods based on information theory's metric are proposed: PVS and P + VS. Extensive experiments on the benchmark datasets with various sizes are elaborated. Those results are compared with the existing preprocessing methods such as feature selection, feature transformation, and instance selection methods. Experiment results show that value selection can achieve the balance between accuracy and model size reduction.

Usage of automated controllers which make decisions on an environment are widespread and are often based on black-box models. We use Knowledge Compilation theory to bring explainability to the controller's decision given the state of the system. For this, we use simulated historical state-action data as input and build a compact and structured representation which relates states with actions. We implement this method in a Traffic Light Control scenario where the controller selects the light cycle by observing the presence (or absence) of vehicles in different regions of the incoming roads.

Smart FinTech has emerged as a new area that synthesizes and transforms AI and finance, and broadly data science, machine learning, economics, etc. Smart FinTech also transforms and drives new economic and financial businesses, services and systems, and plays an increasingly important role in economy, technology and society transformation. This article presents a highly summarized research overview of smart FinTech, including FinTech businesses and challenges, various FinTech-associated data and repositories, FinTech-driven business decision and optimization, areas in smart FinTech, and research methods and techniques for smart FinTech.

The fact that deep neural networks are susceptible to crafted perturbations severely impacts the use of deep learning in certain domains of application. Among many developed defense models against such attacks, adversarial training emerges as the most successful method that consistently resists a wide range of attacks. In this work, based on an observation from a previous study that the representations of a clean data example and its adversarial examples become more divergent in higher layers of a deep neural net, we propose the Adversary Divergence Reduction Network which enforces local/global compactness and the clustering assumption over an intermediate layer of a deep neural network. We conduct comprehensive experiments to understand the isolating behavior of each component (i.e., local/global compactness and the clustering assumption) and compare our proposed model with state-of-the-art adversarial training methods. The experimental results demonstrate that augmenting adversarial training with our proposed components can further improve the robustness of the network, leading to higher unperturbed and adversarial predictive performances.

Node classification in attributed graphs is an important task in multiple practical settings, but it can often be difficult or expensive to obtain labels. Active learning can improve the achieved classification performance for a given budget on the number of queried labels. The best existing methods are based on graph neural networks, but they often perform poorly unless a sizeable validation set of labelled nodes is available in order to choose good hyperparameters. We propose a novel graph-based active learning algorithm for the task of node classification in attributed graphs; our algorithm uses graph cognizant logistic regression, equivalent to a linearized graph convolutional neural network (GCN), for the prediction phase and maximizes the expected error reduction in the query phase. To reduce the delay experienced by a labeller interacting with the system, we derive a preemptive querying system that calculates a new query during the labelling process, and to address the setting where learning starts with almost no labelled data, we also develop a hybrid algorithm that performs adaptive model averaging of label propagation and linearized GCN inference. We conduct experiments on five public benchmark datasets, demonstrating a significant improvement over state-of-the-art approaches and illustrate the practical value of the method by applying it to a private microwave link network dataset.

This article explores the use of machine learning models to build a market generator. The underlying idea is to simulate artificial multi-dimensional financial time series, whose statistical properties are the same as those observed in the financial markets. In particular, these synthetic data must preserve the probability distribution of asset returns, the stochastic dependence between the different assets and the autocorrelation across time. The article proposes then a new approach for estimating the probability distribution of backtest statistics. The final objective is to develop a framework for improving the risk management of quantitative investment strategies, in particular in the space of smart beta, factor investing and alternative risk premia.

In this review paper, we give a comprehensive overview of the large variety of approximation results for neural networks. Approximation rates for classical function spaces as well as benefits of deep neural networks over shallow ones for specifically structured function classes are discussed. While the mainbody of existing results is for general feedforward architectures, we also depict approximation results for convolutional, residual and recurrent neural networks.