The High-Level Expert Group on Artificial Intelligence ("AI HLEG"), an independent expert group set up by the European Commission in June 2018 as part of its AI strategy, has published its final Ethics Guidelines for Trustworthy Artificial Intelligence ("AI") (the "Guidelines"). These Guidelines form part of a wider focus by the Commission on AI, with President-elect of the European Commission, Ursula von der Leyen commenting most recently on July 16, in her proposed political guidelines, that: "In my first 100 days in office, I will put forward legislation for a coordinated European approach on the human and ethical implications of Artificial Intelligence…". The AI HLEG appreciates that AI has the potential to benefit a wide range of sectors and has a wide variety of uses. However, it also acknowledges that the use of AI also brings new challenges and raises various legal and ethical questions. It is with this in mind that the Guidelines have been developed: with a view to providing a framework to achieve and operationalize Trustworthy AI.

In his brilliant, provocative 1966 essay, The Prospects of Recording, Glenn Gould proposed elevating – pardon the pun – elevator music from pernicious drone to enriching ear training. In his view, the ubiquitous presence of background sound could subversively train listeners to be sensitive to the building blocks, structural forms and hidden meanings of music, turning the art form into the universal language of the emotions that it was destined to be. In a not-unrelated development, Gould had somewhat recently traded the concert hall for the recording studio, an act echoed by The Beatles' release in 1967 of Sgt. Peppers' Lonely Hearts Club Band, an album conceived and produced in a multi-track recording studio and never meant to be played in concert. And while Gould's dream of a transformative elevator music never quite panned out, it is clear that from the 1940s through the '60s -- from Les Paul and Mary Ford's pioneering use of overdubs in How High the Moon, to the birth of rock and roll with Chuck Berry's "Maybellene" in 1955, and on to Schaeffer, Stockhausen, Gould, The Beatles and many more -- a totally new art form, enabled by magnetic tape recording and processing, was born.

Prominent thinkers in the fields of artificial intelligence say that predictive policing tools are not only'useless,' but may be helping to drive mass incarceration. In a letter published earlier this month the experts, from MIT, Harvard, Princeton, NYU, UC Berkeley and Columbia spoke out on the topic in an unprecedented showing of skepticism toward the technology. 'When it comes to predicting violence, risk assessments offer more magical thinking than helpful forecasting,' wrote AI experts Chelsea Barabas, Karthik Dinakar and Colin Doyle in a New York Times op-ed. Both police and judges have relied on algorithms to predict crime and recidivism. But, experts warn it could have major consequences.

In this paper, a method for malfunctioning smart meter detection, based on Long Short-Term Memory (LSTM) and Temporal Phase Convolutional Neural Network (TPCNN), is proposed originally. This method is very useful for some developing countries where smart meters have not been popularized but in high demand. In addition, it is a new topic that people try to increase the service life span of smart meters to prevent unnecessary waste by detecting malfunctioning meters. We are the first people complete a combination of malfunctioning meters detection and prediction model based on deep learning methods. To the best our knowledge, our approach is the first method that achieves the malfunctioning meter detection of specific residential areas with their residents' data in practice. The procedure proposed creatively in this paper mainly consists of four components: data collecting and cleaning, prediction about electricity consumption based on LSTM, sliding window detection, and single user classification based on CNN. To make better classifying of malfunctioned user meters, we combine recurrence plots as image-input and combine them with sequence-input, which is the first work that applies one and two dimensions as two paths CNN's input for sequence data classification. Finally, many classical methods are compared with the method proposed in this paper. After comparison with classical methods, Elastic Net and Gradient Boosting Regression, the result shows that our method has higher accuracy. The average area under the Receiver Operating Characteristic (ROC) curve is 0.80 and the standard deviation is 0.04. The average area under the Precision-Recall Curve (PRC) is 0.84.

Deep models, while being extremely versatile and accurate, are vulnerable to adversarial attacks: slight perturbations that are imperceptible to humans can completely flip the prediction of deep models. Many attack and defense mechanisms have been proposed, although a satisfying solution still largely remains elusive. In this work, we give strong evidence that during training, deep models maximize the minimum margin in order to achieve high accuracy, but at the same time decrease the \emph{average} margin hence hurting robustness. Our empirical results highlight an intrinsic trade-off between accuracy and robustness for current deep model training. To further address this issue, we propose a new regularizer to explicitly promote average margin, and we verify through extensive experiments that it does lead to better robustness. Our regularized objective remains Fisher-consistent, hence asymptotically can still recover the Bayes optimal classifier.

Research on neural networks has gained significant momentum over the past few years. A plethora of neural networks is currently being trained on available data in research as well as in industry. Because training is a resource-intensive process and training data cannot always be made available to everyone, there has been a recent trend to attempt to re-use already-trained neural networks. As such, neural networks themselves have become research data. In this paper, we present the Neural Network Ontology, an ontology to make neural networks findable, accessible, interoperable and reusable as suggested by the well-established FAIR guiding principles for scientific data management and stewardship. We created the new FAIRnets Dataset that comprises about 2,000 neural networks openly accessible on the internet and uses the Neural Network Ontology to semantically annotate and represent the neural networks. For each of the neural networks in the FAIRnets Dataset, the relevant properties according to the Neural Network Ontology such as the description and the architecture are stored. Ultimately, the FAIRnets Dataset can be queried with a set of desired properties and responds with a set of neural networks that have these properties. We provide the service FAIRnets Search which is implemented on top of a SPARQL endpoint and allows for querying, searching and finding trained neural networks annotated with the Neural Network Ontology. The service is demonstrated by a browser-based frontend to the SPARQL endpoint.

Deep generative models are rapidly gaining traction in medical imaging. Nonetheless, most generative architectures struggle to capture the underlying probability distributions of volumetric data, exhibit convergence problems, and offer no robust indices of model uncertainty. By comparison, the autoregressive generative model PixelCNN can be extended to volumetric data with relative ease, it readily attempts to learn the true underlying probability distribution and it still admits a Bayesian reformulation that provides a principled framework for reasoning about model uncertainty. Our contributions in this paper are two fold: first, we extend PixelCNN to work with volumetric brain magnetic resonance imaging data. Second, we show that reformulating this model to approximate a deep Gaussian process yields a measure of uncertainty that improves the performance of semi-supervised learning, in particular classification performance in settings where the proportion of labelled data is low. We quantify this improvement across classification, regression, and semantic segmentation tasks, training and testing on clinical magnetic resonance brain imaging data comprising T1-weighted and diffusion-weighted sequences.

Indeed, it was the recommended choice in the seminal paper of Milligan and Cooper (1986), where five criteria were examined regarding the task of comparison of hierarchical clustering algorithms across different hierarchy levels. Their recommendation is based on the fact that, for the null case data (i.e., for a synthetic sample with randomly assigned class labels, showing no significant cluster structure), the ARI was the only index that produced a flat response curve across hierarchy levels, with mean values close to zero, hence indicating that the agreement between the randomly assigned labels and the algorithm solution was due to chance. Another popular measure for clustering validation, not included in Milligan and Cooper's study, is the misclassification error distance (MED). Its first appearance in the literature dates back at least to R egnier (1965), where it was introduced as a distance between partitions of a finite set, and it was called transfer distance. It is also referred to as partition distance (Gusfield, 2002) or maximum matching distance (Rossi, 2015).

--Image reconstruction from undersampled k - space data has been playing an important role for fast MRI. Recently, deep learning has demonstrated tremendous success in various fields and has also shown potential to significantly speed up MRI reconstruction with reduced measurements. This article gives an overview of deep learning -based image reconstruction methods for MRI. Three types of deep learning -based approaches are reviewed, the data - driven, model - driven and integrated approaches. T he main structure of each network in three approaches is explained and the analysis of common parts of reviewed networks and differences in - between are highlighted. Based on the review, a number of signal processing issues are discussed for maximizing the potential of deep reconstruction for fast MRI. The discussion may facilitate further development of "optimal" network and performance analysis from a theoretical point of view. I. INTRODUCTION Since its inception in the early 70's, magnetic resonance imaging (MRI) has revolutionized radiology and medicine. However, MRI is known to be a slow imaging modality and many techniques have been devel oped to reconstruct the desired image from undersampled measured data to improve the imaging speed [1]. During the past decades, compressed sensing (CS) has become an important strategy for fast MR imaging based on the sparsity prior. However, the iterative solution procedure takes a relatively long time to achieve a high -quality reconstruction, and the selection of the regularization parameter is empirical.

Next-generation cosmic microwave background (CMB) experiments will have lower noise and therefore increased sensitivity, enabling improved constraints on fundamental physics parameters such as the sum of neutrino masses and the tensor-to-scalar ratio r. Achieving competitive constraints on these parameters requires high signal-to-noise extraction of the projected gravitational potential from the CMB maps. Standard methods for reconstructing the lensing potential employ the quadratic estimator (QE). However, the QE performs suboptimally at the low noise levels expected in upcoming experiments. Other methods, like maximum likelihood estimators (MLE), are under active development. In this work, we demonstrate reconstruction of the CMB lensing potential with deep convolutional neural networks (CNN) - ie, a ResUNet. The network is trained and tested on simulated data, and otherwise has no physical parametrization related to the physical processes of the CMB and gravitational lensing. We show that, over a wide range of angular scales, ResUNets recover the input gravitational potential with a higher signal-to-noise ratio than the QE method, reaching levels comparable to analytic approximations of MLE methods. We demonstrate that the network outputs quantifiably different lensing maps when given input CMB maps generated with different cosmologies. We also show we can use the reconstructed lensing map for cosmological parameter estimation. This application of CNN provides a few innovations at the intersection of cosmology and machine learning. First, while training and regressing on images, we predict a continuous-variable field rather than discrete classes. Second, we are able to establish uncertainty measures for the network output that are analogous to standard methods. We expect this approach to excel in capturing hard-to-model non-Gaussian astrophysical foreground and noise contributions.