Polarization and stratification of axionically active plasma in a dyon magnetosphere

The state of a static spherically symmetric relativistic axionically active multi-component plasma in the gravitational, magnetic and electric fields of an axionic dyon is studied in the framework of the Einstein - Maxwell - Boltzmann - axion theory. We assume that the equations of axion electrodynamics, the covariant relativistic kinetic equations, and the equation for the axion field with modified Higgs-type potential are nonlinearly coupled; the gravitational field in the dyon exterior is assumed to be fixed and to be of the Reissner-Nordstr\"om type. We introduce the extended Lorentz force, which acts on the particles in the axionically active plasma, and analyze the consequences of this generalization. The analysis of exact solutions, obtained in the framework of this model for the relativistic Boltzmann electron-ion and electron-positron plasmas, as well as, for degenerated zero-temperature electron gas, shows that the phenomena of polarization and stratification can appear in plasma, attracting attention to the axionic analog of the known Pannekoek-Rosseland effect.Comment: 20 pages, 4 figures, the revised version published in Phys.Rev.

Towards edge robotics: the progress from cloud-based robotic systems to intelligent and context-aware robotic services

Current robotic systems handle a different range of applications such as video surveillance, delivery of goods, cleaning, material handling, assembly, painting, or pick and place services. These systems have been embraced not only by the general population but also by the vertical industries to help them in performing daily activities. Traditionally, the robotic systems have been deployed in standalone robots that were exclusively dedicated to performing a specific task such as cleaning the floor in indoor environments. In recent years, cloud providers started to offer their infrastructures to robotic systems for offloading some of the robot’s functions. This ultimate form of the distributed robotic system was first introduced 10 years ago as cloud robotics and nowadays a lot of robotic solutions are appearing in this form. As a result, standalone robots became software-enhanced objects with increased reconfigurability as well as decreased complexity and cost. Moreover, by offloading the heavy processing from the robot to the cloud, it is easier to share services and information from various robots or agents to achieve better cooperation and coordination. Cloud robotics is suitable for human-scale responsive and delay-tolerant robotic functionalities (e.g., monitoring, predictive maintenance). However, there is a whole set of real-time robotic applications (e.g., remote control, motion planning, autonomous navigation) that can not be executed with cloud robotics solutions, mainly because cloud facilities traditionally reside far away from the robots. While the cloud providers can ensure certain performance in their infrastructure, very little can be ensured in the network between the robots and the cloud, especially in the last hop where wireless radio access networks are involved. Over the last years advances in edge computing, fog computing, 5G NR, network slicing, Network Function Virtualization (NFV), and network orchestration are stimulating the interest of the industrial sector to satisfy the stringent and real-time requirements of their applications. Robotic systems are a key piece in the industrial digital transformation and their benefits are very well studied in the literature. However, designing and implementing a robotic system that integrates all the emerging technologies and meets the connectivity requirements (e.g., latency, reliability) is an ambitious task. This thesis studies the integration of modern Information andCommunication Technologies (ICTs) in robotic systems and proposes some robotic enhancements that tackle the real-time constraints of robotic services. To evaluate the performance of the proposed enhancements, this thesis departs from the design and prototype implementation of an edge native robotic system that embodies the concepts of edge computing, fog computing, orchestration, and virtualization. The proposed edge robotics system serves to represent two exemplary robotic applications. In particular, autonomous navigation of mobile robots and remote-control of robot manipulator where the end-to-end robotic system is distributed between the robots and the edge server. The open-source prototype implementation of the designed edge native robotic system resulted in the creation of two real-world testbeds that are used in this thesis as a baseline scenario for the evaluation of new innovative solutions in robotic systems. After detailing the design and prototype implementation of the end-to-end edge native robotic system, this thesis proposes several enhancements that can be offered to robotic systems by adapting the concept of edge computing via the Multi-Access Edge Computing (MEC) framework. First, it proposes exemplary network context-aware enhancements in which the real-time information about robot connectivity and location can be used to dynamically adapt the end-to-end system behavior to the actual status of the communication (e.g., radio channel). Three different exemplary context-aware enhancements are proposed that aim to optimize the end-to-end edge native robotic system. Later, the thesis studies the capability of the edge native robotic system to offer potential savings by means of computation offloading for robot manipulators in different deployment configurations. Further, the impact of different wireless channels (e.g., 5G, 4G andWi-Fi) to support the data exchange between a robot manipulator and its remote controller are assessed. In the following part of the thesis, the focus is set on how orchestration solutions can support mobile robot systems to make high quality decisions. The application of OKpi as an orchestration algorithm and DLT-based federation are studied to meet the KPIs that autonomously controlledmobile robots have in order to provide uninterrupted connectivity over the radio access network. The elaborated solutions present high compatibility with the designed edge robotics system where the robot driving range is extended without any interruption of the end-to-end edge robotics service. While the DLT-based federation extends the robot driving range by deploying access point extension on top of external domain infrastructure, OKpi selects the most suitable access point and computing resource in the cloud-to-thing continuum in order to fulfill the latency requirements of autonomously controlled mobile robots. To conclude the thesis the focus is set on how robotic systems can improve their performance by leveraging Artificial Intelligence (AI) and Machine Learning (ML) algorithms to generate smart decisions. To do so, the edge native robotic system is presented as a true embodiment of a Cyber-Physical System (CPS) in Industry 4.0, showing the mission of AI in such concept. It presents the key enabling technologies of the edge robotic system such as edge, fog, and 5G, where the physical processes are integrated with computing and network domains. The role of AI in each technology domain is identified by analyzing a set of AI agents at the application and infrastructure level. In the last part of the thesis, the movement prediction is selected to study the feasibility of applying a forecast-based recovery mechanism for real-time remote control of robotic manipulators (FoReCo) that uses ML to infer lost commands caused by interference in the wireless channel. The obtained results are showcasing the its potential in simulation and real-world experimentation.Programa de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Karl Holger.- Secretario: Joerg Widmer.- Vocal: Claudio Cicconett

Learning Generalized Reactive Policies using Deep Neural Networks

We present a new approach to learning for planning, where knowledge acquired while solving a given set of planning problems is used to plan faster in related, but new problem instances. We show that a deep neural network can be used to learn and represent a \emph{generalized reactive policy} (GRP) that maps a problem instance and a state to an action, and that the learned GRPs efficiently solve large classes of challenging problem instances. In contrast to prior efforts in this direction, our approach significantly reduces the dependence of learning on handcrafted domain knowledge or feature selection. Instead, the GRP is trained from scratch using a set of successful execution traces. We show that our approach can also be used to automatically learn a heuristic function that can be used in directed search algorithms. We evaluate our approach using an extensive suite of experiments on two challenging planning problem domains and show that our approach facilitates learning complex decision making policies and powerful heuristic functions with minimal human input. Videos of our results are available at goo.gl/Hpy4e3

Quality of the institutional system and the COVID-19 pandemic: empirical analysis

Меры противодействия пандемии COVID-19, предпринимаемые правительствами, не позволили остановить распространение и снизить опасность заболеваний даже в странах с развитой системой здравоохранения. Пандемия вызывает серьезный кризис с существенными социальными и экономическими последствиями. Исследование выполнено в условиях неполноты и возможной недостоверности исходной информации. Однако полученные результаты позволяют привлечь внимание научного сообщества к аспектам борьбы с пандемией COVID-19 и расширить возможности противодействия вызовам, обусловленным этой и другими возможными пандемиями. Установлена форма и сила взаимосвязи между показателем смертности по причине COVID-19 на 100 тысяч населения, включая здоровое население, (человек) (DEATHS/100K POP.) и такими индикаторами Worldwide Governance Indicators, как «Право голоса и подотчетность» («Voice and Accountability») и «Качество Регулирования» («Regulatory Quality»); между показателем смертности по причине COVID-19 на 100 тысяч населения, включая здоровое население, (человек) (DEATHS/100K POP.) и показателем «Продолжительность жизни в годах при рождении» (2018 год) (Life expectancy at birth, total years); между показателем «Право голоса и подотчетность» («Voice and Accountability») и показателем «Продолжительность жизни в годах при рождении» (2018 год) (Life expectancy at birth, total years); между показателями «Качество Регулирования» («Regulatory Quality») и «Продолжительность жизни в годах при рождении» (2018 год) (Life expectancy at birth, total years). Сделан вывод о том, что уровень смертности от пандемии COVID-19 обусловлен возрастной структурой населения, которая, в свою очередь, обусловлена уровнем качества институциональной системы.Government responses to the COVID-19 pandemic have failed to stop the spread and reduce the risk of the disease, even in countries with developed healthcare systems. The pandemic is causing a serious crisis with significant social and economic consequences. The research was carried out under conditions of incompleteness and possible unreliability of the initial information. However, the obtained results make it possible to draw the attention of the scientific community to the aspects of combating the COVID-19pandemic and to expand the ability to confront the challenges posed by current and other possible pandemics. The strength of the relationship was established between the mortality rate due to COVID-19 per 100 thousand population, including the healthy population, (DEATHS / 100K POP.) and Worldwide Governance Indicators (“Voice and Accountability” and "Regulatory Quality"); between the mortality rate due to COVID-19 per 100 thousand population, including the healthy population, ( (DEATHS / 100K POP.) and the indicator Life expectancy at birth, total years (2018); between the indicator “Voice and Accountability” and the indicator Life expectancy at birth, total years; between the indicator "Regulatory Quality" and the indicator Life expectancy at birth. It is concluded that the mortality rate from the COVID-19 pandemic depends on the age structure of the population, which in turn depends on the level of quality of the institutional system