Generative dynamic texture models (GDTMs) are widely used for dynamic texture (DT) segmentation in the video sequences. GDTMs represent DTs as a set of linear dynamical systems (LDSs). A major limitation of these models concerns the automatic selection of a proper number of DTs. Dirichlet process mixture (DPM) models which have appeared recently as the cornerstone of the non-parametric Bayesian statistics, is an optimistic candidate toward resolving this issue. Under this motivation to resolve the aforementioned drawback, we propose a novel non-parametric fully Bayesian approach for DT segmentation, formulated on the basis of a joint DPM and GDTM construction. This interaction causes the algorithm to overcome the problem of automatic segmentation properly. We derive the Variational Bayesian Expectation-Maximization (VBEM) inference for the proposed model. Moreover, in the E-step of inference, we apply Rauch-Tung-Striebel smoother (RTSS) algorithm on Variational Bayesian LDSs. Ultimately, experiments on different video sequences are performed. Experiment results indicate that the proposed algorithm outperforms the previous methods in efficiency and accuracy noticeably.

We introduce a generative adversarial network (GAN) model to simulate the 3-dimensional Lagrangian motion of particles trapped in the recirculation zone of a buoyancy-opposed flame. The GAN model comprises a stochastic recurrent neural network, serving as a generator, and a convoluted neural network, serving as a discriminator. Adversarial training was performed to the point where the best-trained discriminator failed to distinguish the ground truth from the trajectory produced by the best-trained generator. The model performance was then benchmarked against a statistical analysis performed on both the simulated trajectories and the ground truth, with regard to the accuracy and generalization criteria.

How to understand deep learning systems remains an open problem. In this paper we propose that the answer may lie in the geometrization of deep networks. Geometrization is a bridge to connect physics, geometry, deep network and quantum computation and this may result in a new scheme to reveal the rule of the physical world. By comparing the geometry of image matching and deep networks, we show that geometrization of deep networks can be used to understand existing deep learning systems and it may also help to solve the interpretability problem of deep learning systems.

Our world is changing fast and technological growth is leading to changes in all business sectors. The aim of these changes is to make the process of purchasing products and services more convenient, cheaper and smarter. Regarding the insurance field, the introduction of new technologies to the market is expected to benefit insurance companies, insurance agents, and most of all, the insurees themselves. Several relevant technologies and trends in the field are creating a real revolution in the insurance field. The innovative digitalization we're now witnessing helps insurance companies and agents in the processes of registration, customer service and claim management.

Our world is changing fast and technological growth is leading to changes in all business sectors. The aim of these changes is to make the process of purchasing products and services more convenient, cheaper and smarter. Regarding the insurance field, the introduction of new technologies to the market is expected to benefit insurance companies, insurance agents, and most of all, the insurees themselves. Several relevant technologies and trends in the field are creating a real revolution in the insurance field. The innovative digitalization we're now witnessing helps insurance companies and agents in the processes of registration, customer service and claim management.

Technology has been considered artificial intelligence and might be'smarter' than humans in some areas shortly. We have technology that can do things faster in just a click and technology that can create what we cannot in a few seconds. Sounds good but something is threatening regarding employment. While work may look more straightforward and more accessible, the fear of having no physical employees might be the trend in the years to come, and it should be something alarming for us. However, there will still be jobs that are in need of humans; technology might go the highest level and fill these needs in the future.

Data centres are expanding internationally 3.2.5 Growing maintenance and energy costs 3.2.6 Case study: Australia 3.2.7 Glimpses of the future 4. 5G trends 4.1 5G is an evolutionary process 4.2 5G in the global context 4.2.1 5G statistics and forecasts 4.3 The spectrum issue 4.3.1 5G spectrum developments 4.3.2

Ratul has more than 11 years of experience in technology and advanced analytics, machine learning and AI systems, primarily focused on consumer and SME lending. Ratul worked in leading credit bureau Transunion Cibil in India, where he worked more on automation and machine learning using the power of Big data. Earlier Ratul also worked in Global data science leader SAS in its Research and Development office in Pune, India. Ratul holds a B.Tech from National Institute of Technology, Calicut (India). Analytics India Magazine: How important is Data science & AI within Banking Systems in Malaysia?

This month has seen the launch of the European Commission's AI4EU project, an initiative to create an AI-on-demand platform for Europe and challenge for leadership in this blossoming segment. Having been agreed during December, the AI4EU project already has 79 partners in 21 countries across the bloc, firstly focusing on developing eight industry-driven AI pilots which will demonstrate the value of the AI-on-demand platform as a technological innovation tool. Led by Thales, the group will receive €20 million in funding to begin with. "The European Commission has published its coordinated plan on artificial intelligence, as well as new guidelines on how to deal with the ethical issues relating to AI," said Roberto Viola, the Director General of DG Connect at the European Commission, in a recent blog post. "Both put humans firmly at the centre of this key technology that has the potential to revolutionise all our lives."

Right now we are in the middle of one of the greatest transformations of pedagogical practices in the history of mankind. The Internet is to modern man what printing press was to the Renaissance era. Making information easily accessible is one of the most remarkable implications of the internet since its invention. With time, the ways of conveying information have only changed. And, with youtube, mastery is just a click away.