In this paper we propose a highly efficient and very accurate method for estimating the propagation pathloss from a point x to all points y on the 2D plane. Our method, termed RadioUNet, is a deep neural network. For applications such as user-cell site association and device-to-device (D2D) link scheduling, an accurate knowledge of the pathloss function for all pairs of locations is very important. Commonly used statistical models approximate the pathloss as a decaying function of the distance between the points. However, in realistic propagation environments characterized by the presence of buildings, street canyons, and objects at different heights, such radial-symmetric functions yield very misleading results. In this paper we show that properly designed and trained deep neural networks are able to learn how to estimate the pathloss function, given an urban environment, very accurately and extremely quickly. Our proposed method generates pathloss estimations that are very close to estimations given by physical simulation, but much faster. Moreover, experimental results show that our method significantly outperforms previously proposed methods based on radial basis function interpolation and tensor completion.

Infosys has opened a digital innovation center in Dusseldorf, Germany, to use the local talent and shrink the IT skills gaps in Europe. The new innovation center will help Infosys to work closely with its clients in the region and support their digital transformation journey. The center will focus on cloud-based services, 5G, Artificial Intelligence, Machine Learning, Internet of Things, notes announcement. Infosys revealed that the innovation center would serve as a link between the businesses and some of the leading educational establishments in Germany to reduce the skills gap in the region. Executive Opinion Chief Operating Officer, Infosys, Pravin Rao, said, "This investment in Germany builds on Infosys' long-standing commitment to Europe, our investment in developing a highly-skilled workforce, and our focus on achieving breakthrough innovation for our clients. Dusseldorf is at the vanguard of technological innovation, with a highly skilled labor supply, productivity, social, legal, and regulatory credentials. The new center, along with our strategic academic partnerships, will help us build the next generation of technology talent."

In this paper, we investigate the effect of machine learning based anonymization on anomalous subgroup preservation. In particular, we train a binary classifier to discover the most anomalous subgroup in a dataset by maximizing the bias between the group's predicted odds ratio from the model and observed odds ratio from the data. We then perform anonymization using a variational autoencoder (VAE) to synthesize an entirely new dataset that would ideally be drawn from the distribution of the original data. We repeat the anomalous subgroup discovery task on the new data and compare it to what was identified pre-anonymization. We evaluated our approach using publicly available datasets from the financial industry. Our evaluation confirmed that the approach was able to produce synthetic datasets that preserved a high level of subgroup differentiation as identified initially in the original dataset. Such a distinction was maintained while having distinctly different records between the synthetic and original dataset. Finally, we packed the above end to end process into what we call Utility Guaranteed Deep Privacy (UGDP) system. UGDP can be easily extended to onboard alternative generative approaches such as GANs to synthesize tabular data.

Autonomous Vehicles (AVs) are increasingly embraced around the world to advance smart mobility and more broadly, smart, and sustainable cities. Algorithms form the basis of decision-making in AVs, allowing them to perform driving tasks autonomously, efficiently, and more safely than human drivers and offering various economic, social, and environmental benefits. However, algorithmic decision-making in AVs can also introduce new issues that create new safety risks and perpetuate discrimination. We identify bias, ethics, and perverse incentives as key ethical issues in the AV algorithms' decision-making that can create new safety risks and discriminatory outcomes. Technical issues in the AVs' perception, decision-making and control algorithms, limitations of existing AV testing and verification methods, and cybersecurity vulnerabilities can also undermine the performance of the AV system. This article investigates the ethical and technical concerns surrounding algorithmic decision-making in AVs by exploring how driving decisions can perpetuate discrimination and create new safety risks for the public. We discuss steps taken to address these issues, highlight the existing research gaps and the need to mitigate these issues through the design of AV's algorithms and of policies and regulations to fully realise AVs' benefits for smart and sustainable cities.

UiPath CEO Daniel Dines, seen here with technologist Kai-Fu Lee at a May tech event in Paris, ... [ ] recently approved layoffs to more than 10% of the startup's workforce. Investors looking for fresh signs the party is over for high-valued tech companies got more ammunition on Wednesday as robotic process automation company UiPath confirmed layoffs of several hundred employees just months after raising money at a $7 billion valuation. UiPath, a Bucharest, Romania-founded business now based in New York, said the layoffs affected between 300 and 400 employees, or about 11% of its workforce. The company said its chief financial officer is stepping down at year's end in what it says is an unrelated move. News of the layoffs and departure were first reported by the New York Business Journal.

We describe the submission of the Quo Vadis team to the Traffic4cast competition, which was organized as part of the NeurIPS 2019 series of challenges. Our system consists of a temporal regression module, implemented as $1\times1$ 2d convolutions, augmented with spatio-temporal biases. We have found that using biases is a straightforward and efficient way to include seasonal patterns and to improve the performance of the temporal regression model. Our implementation obtains a mean squared error of $9.47\times 10^{-3}$ on the test data, placing us on the eight place team-wise. We also present our attempts at incorporating spatial correlations into the model; however, contrary to our expectations, adding this type of auxiliary information did not benefit the main system. Our code is available at https://github.com/danoneata/traffic4cast.

Anomaly detection in networks often boils down to identifying an underlying graph structure on which the abnormal occurrence rests on. Financial fraud schemes are one such example, where more or less intricate schemes are employed in order to elude transaction security protocols. We investigate the problem of learning graph structure representations using adaptations of dictionary learning aimed at encoding connectivity patterns. In particular, we adapt dictionary learning strategies to the specificity of network topologies and propose new methods that impose Laplacian structure on the dictionaries themselves. In one adaption we focus on classifying topologies by working directly on the graph Laplacian and cast the learning problem to accommodate its 2D structure. We tackle the same problem by learning dictionaries which consist of vectorized atomic Laplacians, and provide a block coordinate descent scheme to solve the new dictionary learning formulation. Imposing Lapla-cian structure on the dictionaries is also proposed in an adaptation of the Single Block Orthogonal learning method. Results on synthetic graph datasets comprising different graph topologies confirm the potential of dictionaries to directly represent graph structure information. Keywords: anomaly detection, dictionary learning, graph Laplacian classification, money laundering. 1 Introduction The benefits of global inter-connectivity and the general increase of the quality of life led to the democratization of the general population's access to banking resources such as accounts, cards and cash machines.

Financial fraud detection represents the challenge of finding anomalies in networks of financial transactions. In general, the anomaly detection (AD) is the problem of distinguishing between normal data samples with well defined patterns or signatures and those that do not conform to the expected profiles. The fraudulent behaviour in money laundering may manifest itself through unusual patterns in financial transaction networks. In such networks, nodes represents customers and the edges are transactions: a directed edge between two nodes illustrates that there is a money transfer in the respective direction, where the weight on the edge is the transferred amount. In this paper we present a survey on the fundamental anomaly detection techniques and then present briefly the relevant literature in connection with fraud detection context.

Despite the significant advances in recent years, Generative Adversarial Networks (GANs) are still notoriously hard to train. In this paper, we propose three novel curriculum learning strategies for training GANs. All strategies are first based on ranking the training images by their difficulty scores, which are estimated by a state-of-the-art image difficulty predictor. Our first strategy is to divide images into gradually more difficult batches. Our second strategy introduces a novel curriculum loss function for the discriminator that takes into account the difficulty scores of the real images. Our third strategy is based on sampling from an evolving distribution, which favors the easier images during the initial training stages and gradually converges to a uniform distribution, in which samples are equally likely, regardless of difficulty. We compare our curriculum learning strategies with the classic training procedure on two tasks: image generation and image translation. Our experiments indicate that all strategies provide faster convergence and superior results. For example, our best curriculum learning strategy applied on spectrally normalized GANs (SNGANs) fooled human annotators in thinking that generated CIFAR-like images are real in 25.0% of the presented cases, while the SNGANs trained using the classic procedure fooled the annotators in only 18.4% cases. Similarly, in image translation, the human annotators preferred the images produced by the Cycle-consistent GAN (CycleGAN) trained using curriculum learning in 40.5% cases and those produced by CycleGAN based on classic training in only 19.8% cases, 39.7% cases being labeled as ties.

Benefits claimants could soon be using robots to claim cash as the Government speeds up the development of an AI system by working with a billionaire tech boss who declared recently: 'I want a bot for every person'. The Department for Work and Pensions has employed more than 1,000 new IT staff and created an £8million-a-year'intelligent automation garage' to develop up to 100 new robots to help support Britain's welfare system - used by 7million people each year. The UK government is working with New York-based UiPath, co-founded by billionaire Daniel Dines, whose £7billion company is viewed as a future Google of robotics and Artificial Intelligence. Mr Dines' software is already used by Walmart, Toyota and many banks and now will help the DWP develop systems to check benefits claims with tech giants IBM, Tata Consultancy and Capgemini also involved. Developers believe a'virtual workforce' could handle simpler welfare cases and payments faster and with fewer mistakes than today - while more complicated cases would still be dealt with by human staff.