Sparse multivariate Gaussian mixture regression

Fitting a multivariate Gaussian mixture to data represents an attractive, as well as challenging problem, in especial when sparsity in the solution is demanded. Achieving this objective requires the concurrent update of all parameters (weight, centers, and precisions) of all multivariate Gaussian functions during the learning process. Such is the focus of this paper, which presents a novel method founded on the minimization of the error of the generalized logarithmic utility function (GLUF). This choice, which allows us to move smoothly from the mean square error (MSE) criterion to the one based on the logarithmic error, yields an optimization problem that resembles a locally convex problem and can be solved with a quasi-Newton method. The GLUF framework also facilitates the comparative study between both extremes, concluding that the classical MSE optimization is not the most adequate for the task. The performance of the proposed novel technique is demonstrated on simulated as well as realistic scenarios

Enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Modern advances in technology have led to more complex manufacturing processes whose success centres on the ability to control these processes with a very high level of accuracy. Plant complexity inevitably leads to poor models that exhibit a high degree of parametric or functional uncertainty. The situation becomes even more complex if the plant to be controlled is characterised by a multivalued function or even if it exhibits a number of modes of behaviour during its operation. Since an intelligent controller is expected to operate and guarantee the best performance where complexity and uncertainty coexist and interact, control engineers and theorists have recently developed new control techniques under the framework of intelligent control to enhance the performance of the controller for more complex and uncertain plants. These techniques are based on incorporating model uncertainty. The newly developed control algorithms for incorporating model uncertainty are proven to give more accurate control results under uncertain conditions. In this paper, we survey some approaches that appear to be promising for enhancing the performance of intelligent control systems in the face of higher levels of complexity and uncertainty

Machine Learning Algorithms for Provisioning Cloud/Edge Applications

Mención Internacional en el título de doctorReinforcement Learning (RL), in which an agent is trained to make the most favourable decisions in the long run, is an established technique in artificial intelligence. Its popularity has increased in the recent past, largely due to the development of deep neural networks spawning deep reinforcement learning algorithms such as Deep Q-Learning. The latter have been used to solve previously insurmountable problems, such as playing the famed game of “Go” that previous algorithms could not. Many such problems suffer the curse of dimensionality, in which the sheer number of possible states is so overwhelming that it is impractical to explore every possible option. While these recent techniques have been successful, they may not be strictly necessary or practical for some applications such as cloud provisioning. In these situations, the action space is not as vast and workload data required to train such systems is not as widely shared, as it is considered commercialy sensitive by the Application Service Provider (ASP). Given that provisioning decisions evolve over time in sympathy to incident workloads, they fit into the sequential decision process problem that legacy RL was designed to solve. However because of the high correlation of time series data, states are not independent of each other and the legacy Markov Decision Processes (MDPs) have to be cleverly adapted to create robust provisioning algorithms. As the first contribution of this thesis, we exploit the knowledge of both the application and configuration to create an adaptive provisioning system leveraging stationary Markov distributions. We then develop algorithms that, with neither application nor configuration knowledge, solve the underlying Markov Decision Process (MDP) to create provisioning systems. Our Q-Learning algorithms factor in the correlation between states and the consequent transitions between them to create provisioning systems that do not only adapt to workloads, but can also exploit similarities between them, thereby reducing the retraining overhead. Our algorithms also exhibit convergence in fewer learning steps given that we restructure the state and action spaces to avoid the curse of dimensionality without the need for the function approximation approach taken by deep Q-Learning systems. A crucial use-case of future networks will be the support of low-latency applications involving highly mobile users. With these in mind, the European Telecommunications Standards Institute (ETSI) has proposed the Multi-access Edge Computing (MEC) architecture, in which computing capabilities can be located close to the network edge, where the data is generated. Provisioning for such applications therefore entails migrating them to the most suitable location on the network edge as the users move. In this thesis, we also tackle this type of provisioning by considering vehicle platooning or Cooperative Adaptive Cruise Control (CACC) on the edge. We show that our Q-Learning algorithm can be adapted to minimize the number of migrations required to effectively run such an application on MEC hosts, which may also be subject to traffic from other competing applications.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Antonio Fernández Anta.- Secretario: Diego Perino.- Vocal: Ilenia Tinnirell

VII Workshop on Computational Data Analysis and Numerical Methods

Dear participants, colleagues and friends, it is a great honour and a privilege to give you all a warmest welcome to the VII Workshop on Computational Data Analysis and Numerical Methods (VII WCDANM), which is organized by the Polytechnic Institute of Tomar (located in the center of Portugal in the beautiful city of Tomar), with the support of some Portuguese research centers, hoping that the final result may exceed the expectations of the participants, sponsors and organizers. Due to the worldwide pandemic caused by the COVID-19 virus, for the first time, this meeting will be transmitted through videoconference (webminar). Nevertheless, the important contributions of Adélia Sequeira (University of Lisbon, Portugal), Sílvia Barbeiro (University of Coimbra, Portugal), Malay Banerjee (Indian Instituto of Techonology Kampur, India) and Indranhil Ghosh (University of North Carolina at Wilmington, USA) as Plenary Speakers, the high scientific level of oral and poster presentations and an active audience will certainly contribute to the success of the meeting. Part of the accepted papers (theoretical and applied) by the VII WCDANM involve big data, data mining, data science and machine learning, in different areas of research, some giving emphasis to coronavirus. A very special thanks to this small, yet important, scientific community, since this event could not be possible without any of these essential and complementary parts. This year, there is also the possibility to attend a course on ŞModeling Partial Least Squares Structural Equations (PLS-SEM) using SmartPLSŤ given by Christian M. Ringle ((TUHH) Hamburg University of Technology, Germany), who kindly and readily accepted our invitation and to whom we are very grateful. A special acknowledgment is also due to the Members of the Executive, Scientific and Organizing Committees. In particular, Anuj Mubayi (Arizona StateUniversity,USA),MilanStehlík(Johannes KeplerUniversity,Austria), AnaNata,IsabelPitacasandManuelaFernandes(hostsfromthePolytechnic Institute of Tomar, Portugal), A. Manuela Gonçalves (University of Minho, Portugal), Teresa Oliveira (Aberta University) and Fernando Carapau (University of Évora, Portugal) have been relentless in search for a balanced, broad and interesting program, having achieved an excellent result. For the third consecutive year, the Journal of Applied Statistics (Taylor & Francis) and Neural Computing and Applications (Springer) are also associated to the event, being extremely important in the dissemination of the scientific results achieved at the meeting. Given the above, it is a pleasure to be ”together” with all of you in this web conference, hoping it may provide an intellectual stimulus and an opportunity for the scientific community to jointly work and disseminate scientific research, namely presenting approaches that may contribute to the solution of the pandemic we are experiencing in the expectation that the present might be past in the near future

Radio Communications

In the last decades the restless evolution of information and communication technologies (ICT) brought to a deep transformation of our habits. The growth of the Internet and the advances in hardware and software implementations modified our way to communicate and to share information. In this book, an overview of the major issues faced today by researchers in the field of radio communications is given through 35 high quality chapters written by specialists working in universities and research centers all over the world. Various aspects will be deeply discussed: channel modeling, beamforming, multiple antennas, cooperative networks, opportunistic scheduling, advanced admission control, handover management, systems performance assessment, routing issues in mobility conditions, localization, web security. Advanced techniques for the radio resource management will be discussed both in single and multiple radio technologies; either in infrastructure, mesh or ad hoc networks

Enabling Technology in Optical Fiber Communications: From Device, System to Networking

This book explores the enabling technology in optical fiber communications. It focuses on the state-of-the-art advances from fundamental theories, devices, and subsystems to networking applications as well as future perspectives of optical fiber communications. The topics cover include integrated photonics, fiber optics, fiber and free-space optical communications, and optical networking

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app.United States Department of Energy (DOE) DE-AC02-05CH11231government of Japan (Ministry of Education, Culture, Sports, Science and Technology)Istituto Nazionale di Fisica Nucleare (INFN)Physical Society of Japan (JPS)European Laboratory for Particle Physics (CERN)United States Department of Energy (DOE

Review of Particle Physics

The Review summarizes much of particle physics and cosmology. Using data from previous editions, plus 2,143 new measurements from 709 papers, we list, evaluate, and average measured properties of gauge bosons and the recently discovered Higgs boson, leptons, quarks, mesons, and baryons. We summarize searches for hypothetical particles such as supersymmetric particles, heavy bosons, axions, dark photons, etc. Particle properties and search limits are listed in Summary Tables. We give numerous tables, figures, formulae, and reviews of topics such as Higgs Boson Physics, Supersymmetry, Grand Unified Theories, Neutrino Mixing, Dark Energy, Dark Matter, Cosmology, Particle Detectors, Colliders, Probability and Statistics. Among the 120 reviews are many that are new or heavily revised, including a new review on Machine Learning, and one on Spectroscopy of Light Meson Resonances. The Review is divided into two volumes. Volume 1 includes the Summary Tables and 97 review articles. Volume 2 consists of the Particle Listings and contains also 23 reviews that address specific aspects of the data presented in the Listings. The complete Review (both volumes) is published online on the website of the Particle Data Group (pdg.lbl.gov) and in a journal. Volume 1 is available in print as the PDG Book. A Particle Physics Booklet with the Summary Tables and essential tables, figures, and equations from selected review articles is available in print, as a web version optimized for use on phones, and as an Android app