In this work we describe a new deep learning approach for automatic sleep staging, and carry out its validation by addressing its generalization capabilities on a wide range of sleep staging databases. Prediction capabilities are evaluated in the context of independent local and external generalization scenarios. Effectively, by comparing both procedures it is possible to better extrapolate the expected performance of the method on the general reference task of sleep staging, regardless of data from a specific database. In addition, we examine the suitability of a novel approach based on the use of an ensemble of individual local models and evaluate its impact on the resulting inter-database generalization performance. Validation results show good general performance, as compared to the expected levels of human expert agreement, as well as state-of-the-art automatic sleep staging approaches. Keywords Sleep Staging, Deep Learning, Inter-database Generalization, Classification ensemble 1. INTRODUCTION Sleep staging is one of the most important tasks during the clinical examination of polysomnographic sleep recordings (PSGs). A PSG records the relevant biomedical signals of a patient in the context of Sleep Medicine studies, representing the basic tool for the diagnosis of many sleep disorders. Sleep staging characterizes the patient's sleep macrostructure leading to the so-called hypnogram. The hypnogram plays also a fundamental role for the interpretation of several other biosignal activities of interest, such as the evaluation of the respiratory function, or the identification of different body and limb movement [1] [2]. Each epoch can be classified into five possible states (wakefulness, stages N1, N2, N3, and R) according to the observed signal pattern activity in the reference PSG interval. Specifically, for sleep staging, neurophysiological activity of interest involves monitoring of different traces of electroencephalographic (EEG), electromyographic (EMG) and electrooculographic (EOG) activity [1]. A typical PSG examination comprises 8 up to 24 hours of continuous signal recording, and its analysis is usually carried out manually by an expert clinician. The scoring process is consequently expensive and highly demanding, due to the involved clinician's time, and the complexity of the analysis itself. Moreover, the demand for PSG investigations is growing in relation with the general public awareness, motivated by clinical findings over the last years uncovering the negative impact that sleep disorders exert over health. This represents a challenge for the already congested sleep centers, with steadily increasing waiting lists.

Each year there are nearly 57 million deaths around the world, with over 2.7 million in the United States. Timely, accurate and complete death reporting is critical in public health, as institutions and government agencies rely on death reports to analyze vital statistics and to formulate responses to communicable diseases. Inaccurate death reporting may result in potential misdirection of public health policies. Determining the causes of death is, nevertheless, challenging even for experienced physicians. To facilitate physicians in accurately reporting causes of death, we present an advanced AI approach to determine a chronically ordered sequence of clinical conditions that lead to death, based on decedent's last hospital admission discharge record. The sequence of clinical codes on the death report is named as causal chain of death, coded in the tenth revision of International Statistical Classification of Diseases (ICD-10); the priority-ordered clinical conditions on the discharge record are coded in ICD-9. We identify three challenges in proposing the causal chain of death: two versions of coding system in clinical codes, medical domain knowledge conflict, and data interoperability. To overcome the first challenge in this sequence-to-sequence problem, we apply neural machine translation models to generate target sequence. We evaluate the quality of generated sequences with the BLEU (BiLingual Evaluation Understudy) score and achieve 16.44 out of 100. To address the second challenge, we incorporate expert-verified medical domain knowledge as constraint in generating output sequence to exclude infeasible causal chains. Lastly, we demonstrate the usability of our work in a Fast Healthcare Interoperability Resources (FHIR) interface to address the third challenge.

In this paper, we study the problem of obtaining a control policy that can mimic and then outperform expert demonstrations in Markov decision processes where the reward function is unknown to the learning agent. One main relevant approach is the inverse reinforcement learning (IRL), which mainly focuses on inferring a reward function from expert demonstrations. The obtained control policy by IRL and the associated algorithms, however, can hardly outperform expert demonstrations. To overcome this limitation, we propose a novel method that enables the learning agent to outperform the demonstrator via a new concurrent reward and action policy learning approach. In particular, we first propose a new stereo utility definition that aims to address the bias in the interpretation of expert demonstrations. We then propose a loss function for the learning agent to learn reward and action policies concurrently such that the learning agent can outperform expert demonstrations. The performance of the proposed method is first demonstrated in OpenAI environments. Further efforts are conducted to experimentally validate the proposed method via an indoor drone flight scenario.

Neural networks have excelled at regression and classification problems when the input space consists of scalar variables. As a result of this proficiency, several popular packages have been developed that allow users to easily fit these kinds of models. However, the methodology has excluded the use of functional covariates and to date, there exists no software that allows users to build deep learning models with this generalized input space. To the best of our knowledge, the functional neural network (FuncNN) library is the first such package in any programming language; the library has been developed for R and is built on top of the keras architecture. Throughout this paper, several functions are introduced that provide users an avenue to easily build models, generate predictions, and run cross-validations. A summary of the underlying methodology is also presented. The ultimate contribution is a package that provides a set of general modelling and diagnostic tools for data problems in which there exist both functional and scalar covariates.

Recently, a hybrid Deep Neural Network (DNN) algorithm, TreNet was proposed for predicting trends in time series data. While TreNet was shown to have superior performance for trend prediction to other DNN and traditional ML approaches, the validation method used did not take into account the sequential nature of time series data sets and did not deal with model update. In this research we replicated the TreNet experiments on the same data sets using a walk-forward validation method and tested our optimal model over multiple independent runs to evaluate model stability. We compared the performance of the hybrid TreNet algorithm, on four data sets to vanilla DNN algorithms that take in point data, and also to traditional ML algorithms. We found that in general TreNet still performs better than the vanilla DNN models, but not on all data sets as reported in the original TreNet study. This study highlights the importance of using an appropriate validation method and evaluating model stability for evaluating and developing machine learning models for trend prediction in time series data.

We present a new approach for synthesizing training data given only a single example of each class. Rather than learn over a large but fixed dataset of examples, we generate our entire training set using only the synthetic examples provided. The goal is to learn a classifier that generalizes to a non-synthetic domain without pretraining or fine-tuning on any real world data. We evaluate our approach by training neural networks for two standard benchmarks for real-world image classification: on the GTSRB traffic sign recognition benchmark, we achieve 96% test accuracy using only one clean example of each sign on a blank background; on the MNIST handwritten digit benchmark, we achieve 90% test accuracy using a single example of each digit taken from a computer font. Our performance is competitive with state-of-the-art results from the few-shot learning and domain transfer literature, while using significantly less data.

The original example of a deepfake (by reddit user /u/deepfake) swapped the face of an actress onto the body of a porn performer in a video – which was, of course, completely unethical, although not initially illegal. Other deepfakes have changed what famous people were saying, or the language they were speaking. Deepfakes extend the idea of video (or movie) compositing, which has been done for decades. Significant video skills, time, and equipment go into video compositing; video deepfakes require much less skill, time (assuming you have GPUs), and equipment, although they are often unconvincing to careful observers. Originally, deepfakes relied on autoencoders, a type of unsupervised neural network, and many still do.

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Next year, a squad of souped-up Dallara race cars will reach speeds of up to 200 miles per hour as they zoom around the legendary Indianapolis Motor Speedway to discover whether a computer could be the next Mario Andretti. The planned Indy Autonomous Challenge--taking place in October 2021 in Indianapolis--is intended for 31 university computer science and engineering teams to push the limits of current self-driving car technology. There will be no human racers sitting inside the cramped cockpits of the Dallara IL-15 race cars. Instead, onboard computer systems will take their place, outfitted with deep-learning software enabling the vehicles to drive themselves. In order to win, a team's autonomous car must be able to complete 20 laps--which equates to a little less than 50 miles in distance--and cross the finish line first in 25 minutes or less.

Digital transformation is one of the top priorities for industrial companies. The largest players are already moving in this direction, for many years continuously working to improve production efficiency and launching large-scale optimisation programs. They're called advanced analytics or digital innovation, and at their core, the technology could be summarised under artificial intelligence. In all cases, the efforts to utilise AI models or data analytics systems are part of a bigger digital transformation effort of the progressing companies. In an industrial context, such strategies for cost-saving and process optimisation often start from pilot projects, or top management directives for digital change guide them. In general, changes in processes or investments in capital-intensive and competitive industries require large sums of money. Traditional capital expenditures usually stretch over a long period, so a current financial standing may not allow for a complete physical overhaul of the plants or facilities. These high costs lead to the search for cheaper alternatives.