Clustering and community detection with multiple graphs have typically focused on aligned graphs, where there is a mapping between nodes across the graphs (e.g., multi-view, multi-layer, temporal graphs). However, there are numerous application areas with multiple graphs that are only partially aligned, or even unaligned. These graphs are often drawn from the same population, with communities of potentially different sizes that exhibit similar structure. In this paper, we develop a joint stochastic blockmodel (Joint SBM) to estimate shared communities across sets of heterogeneous non-aligned graphs. We derive an efficient spectral clustering approach to learn the parameters of the joint SBM. We evaluate the model on both synthetic and real-world datasets and show that the joint model is able to exploit cross-graph information to better estimate the communities compared to learning separate SBMs on each individual graph.

This is part 5 of a Guide in 6 parts about Artificial Intelligence. The guide covers some of its basic concepts, history and present applications, possible developments in the future, and also its challenges as opportunities. Reviewing some case studies helps to bring artificial intelligence to life, and to understand how it is used. Here we will review the field of entertainment, where the company Magic Leap has made great strides with the use of artificial intelligence. Magic Leap is a start up company located in the USA.

In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance, due to its stellar performance combined with certain attractive properties, such as learning from less feedback. The multi-armed bandit field is currently flourishing, as novel problem settings and algorithms motivated by various practical applications are being introduced, building on top of the classical bandit problem. This article aims to provide a comprehensive review of top recent developments in multiple real-life applications of the multi-armed bandit. Specifically, we introduce a taxonomy of common MAB-based applications and summarize state-of-art for each of those domains. Furthermore, we identify important current trends and provide new perspectives pertaining to the future of this exciting and fast-growing field.

The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force models (GPLFMs) are hybrid models that combine differential equations representing a physical system with data-driven non-parametric Gaussian process models. In this work, the unknown input forces acting on a structure are modelled as Gaussian processes with some chosen covariance functions which are combined with the mechanistic differential equation representing the structure to construct a GPLFM. The GPLFM is then conveniently formulated as an augmented stochastic state-space model with additional states representing the latent force components, and the joint input and state inference of the resulting model is implemented using Kalman filter. The augmented state-space model of GPLFM is shown as a generalization of the class of input-augmented state-space models, is proven observable, and is robust compared to conventional augmented formulations in terms of numerical stability. The hyperparameters governing the covariance functions are estimated using maximum likelihood optimization based on the observed data, thus overcoming the need for manual tuning of the hyperparameters by trial-and-error. To assess the performance of the proposed GPLFM method, several cases of state and input estimation are demonstrated using numerical simulations on a 10-dof shear building and a 76-storey ASCE benchmark office tower. Results obtained indicate the superior performance of the proposed approach over conventional Kalman filter based approaches.

We present a Deep Neural Network (DNN) model for predicting electrocardiogram (ECG) abnormalities in short-duration 12-lead ECG recordings. The analysis of the digital ECG obtained in a clinical setting can provide a full evaluation of the cardiac electrical activity and have not been studied in an end-to-end machine learning scenario. Using the database of the Telehealth Network of Minas Gerais, under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study, we built a novel dataset with more than 2 million ECG tracings, orders of magnitude larger than those used in previous studies. Moreover, our dataset is more realistic, as it consists of 12-lead ECGs recorded during standard in-clinic exams. Using this data, we trained a residual neural network with 9 convolutional layers to map ECG signals with a duration of 7 to 10 seconds into 6 different classes of ECG abnormalities. High-performance measures were obtained for all ECG abnormalities, with F1 scores above $80\%$ and specificity indexes over $99\%$. We compare the performance with cardiology and emergency resident medical doctors as well as medical students and, considering the F1 score, the DNN matches or outperforms the medical residents and students for all abnormalities. These results indicate that end-to-end automatic ECG analysis based on DNNs, previously used only in a single-lead setup, generalizes well to the 12-lead ECG. This is an important result in that it takes this technology much closer to standard clinical practice.

Spotify has launched a new offer that finally allows you to share your subscription with the most important person in your life. The feature – named "Premium Duo" – allows people to buy a cheaper subscription between two people, letting them sign up with their partner or someone else important in their life. As well as offering a way of getting a Spotify subscription more cheaply, the new deal gives people extra features that aren't usually available on the service. We'll tell you what's true. You can form your own view.

Negotiation is a form of interaction in which a group of agents with conflicting interests and a desire to This paper introduces a new Negotiating Agent for cooperate try to reach a mutually acceptable agreement automated negotiation on continuous domains and on an object of negotiation [2]. The agents without considering a specified deadline. The agent explore solutions according to a predetermined protocol bidding strategy relies on Monte Carlo Tree Search, in order to find an acceptable agreement. Being which is a trendy method since it has been used with widely used in economic domains and with the rise of success on games with high branching factor such as e-commerce applications, the question of automating Go. It uses two opponent modeling techniques for negotiation has gained a lot of interest in the field of its bidding strategy and its utility: Gaussian process artificial intelligence and multi-agent systems.

Multiplex networks have become increasingly more prevalent in many fields, and have emerged as a powerful tool for modeling the complexity of real networks. There is a critical need for developing inference models for multiplex networks that can take into account potential dependencies across different layers, particularly when the aim is community detection. We add to a limited literature by proposing a novel and efficient Bayesian model for community detection in multiplex networks. A key feature of our approach is the ability to model varying communities at different network layers. In contrast, many existing models assume the same communities for all layers. Moreover, our model automatically picks up the necessary number of communities at each layer (as validated by real data examples). This is appealing, since deciding the number of communities is a challenging aspect of community detection, and especially so in the multiplex setting, if one allows the communities to change across layers. Borrowing ideas from hierarchical Bayesian modeling, we use a hierarchical Dirichlet prior to model community labels across layers, allowing dependency in their structure. Given the community labels, a stochastic block model (SBM) is assumed for each layer. We develop an efficient slice sampler for sampling the posterior distribution of the community labels as well as the link probabilities between communities. In doing so, we address some unique challenges posed by coupling the complex likelihood of SBM with the hierarchical nature of the prior on the labels. An extensive empirical validation is performed on simulated and real data, demonstrating the superior performance of the model over single-layer alternatives, as well as the ability to uncover interesting structures in real networks.

The laws will apply only in the EU for now, but it's possible these global companies will apply these laws elsewhere (Microsoft has already applied some EU regulations in other places.) Venezuela used to have an anti-government newspaper, but that was until the government made it impossible for them to get enough paper to print. So Venezuelans have turned to the voice-chat app Zello to spread news, get basic needs, and coordinate aid amid the country's political and economic crisis. Vice President Mike Pence said the US will be sending astronauts to the moon's south pole. Because there's tons of ice Pence says can be turned into life support and even rocket fuel.

For Europe, investment in advanced ICT is a must. With an aging population and a shrinking workforce, Europe needs to tap artificial intelligence (AI), 5G wireless connectivity, quantum computing, and other ICT technologies that could drive the next step change in productivity. To that end, the region can build on a long-standing scientific tradition. Thanks in part to sustained public sector support, Europe is a leading producer of high-quality scientific research. Its scientists excel in aeronautics, transport technologies, and energy and construction, based on the number of widely cited publications.a