Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Ultradilute quantum liquid of dipolar atoms in a bilayer

We show that ultradilute quantum liquids can be formed with ultracold bosonic dipolar atoms in a bilayer geometry. Contrary to previous realizations of ultradilute liquids, there is no need for stabilizing the system with an additional repulsive short-range potential. The advantage of the proposed system is that dipolar interactions on their own are sufficient for creation of a self-bound state and no additional short-range potential is needed for the stabilization. We perform quantum Monte Carlo simulations and find a rich ground-state phase diagram that contains quantum phase transitions between liquid, solid, atomic gas, and molecular gas phases. The stabilization mechanism of the liquid phase is consistent with the microscopic scenario in which the effective dimer-dimer attraction is balanced by an effective three-dimer repulsion. The equilibrium density of the liquid, which is extremely small, can be controlled by the interlayer distance. From the equation of state, we extract the spinodal density, below which the homogeneous system breaks into droplets. Our results offer a new example of a two-dimensional interacting dipolar liquid in a clean and highly controllable setup.Postprint (author's final draft

Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles

The damaging effects of cyberattacks to an industry like the Cooperative Connected and Automated Mobility (CCAM) can be tremendous. From the least important to the worst ones, one can mention for example the damage in the reputation of vehicle manufacturers, the increased denial of customers to adopt CCAM, the loss of working hours (having direct impact on the European GDP), material damages, increased environmental pollution due e.g., to traffic jams or malicious modifications in sensors’ firmware, and ultimately, the great danger for human lives, either they are drivers, passengers or pedestrians. Connected vehicles will soon become a reality on our roads, bringing along new services and capabilities, but also technical challenges and security threats. To overcome these risks, the CARAMEL project has developed several anti-hacking solutions for the new generation of vehicles. CARAMEL (Artificial Intelligence-based Cybersecurity for Connected and Automated Vehicles), a research project co-funded by the European Union under the Horizon 2020 framework programme, is a project consortium with 15 organizations from 8 European countries together with 3 Korean partners. The project applies a proactive approach based on Artificial Intelligence and Machine Learning techniques to detect and prevent potential cybersecurity threats to autonomous and connected vehicles. This approach has been addressed based on four fundamental pillars, namely: Autonomous Mobility, Connected Mobility, Electromobility, and Remote Control Vehicle. This book presents theory and results from each of these technical directions

Relation between quantum advantage in supervised learning and quantum computational advantage

The widespread use of machine learning has raised the question of quantum supremacy for supervised learning as compared to quantum computational advantage. In fact, a recent work shows that computational and learning advantage are, in general, not equivalent, i.e., the additional information provided by a training set can reduce the hardness of some problems. This paper investigates under which conditions they are found to be equivalent or, at least, highly related. The existence of efficient algorithms to generate training sets emerges as the cornerstone of such conditions. These results are applied to prove that there is a quantum speed-up for some learning tasks based on the prime factorization problem, assuming the classical intractability of this problem

OpenUEBA – A systematic approach to learn behavioural patterns

For years, Security Operations Centers (SOC) have resorted to SIEM and IDS tools as the core defence shield, offering reactive detection capabilities against latent threats. Despite the effectiveness of the tools described above, cybercriminal groups have professionalized themselves by launching very sophisticated campaigns that unfortunately, go unnoticed by current detection tools. In order to revolutionize the current range of security tools, we present our vision and advances in openUEBA; An open-source framework focused on the study of the behaviour of users and entities on the network; Where through state-of-the-art Artificial Intelligence techniques are learn behavioural patterns of those users who later fall into cyber attacks. With the learnt knowledge, the tool calculates the user exposure; in other words, it predicts which users will be victims of latent threats, allowing the analyst to make preventive decisions.Peer ReviewedPostprint (published version

Quantum multiple hypothesis testing based on a sequential discarding scheme

We consider the quantum multiple hypothesis testing problem, focusing on the case of hypothesis represented by pure states. A sequential adaptive algorithm is derived and analyzed first. This strategy exhibits a decay rate in the error probability with respect to the expected value of measurements greater than the optimal decay rate of the fixed-length methods. A more elaborated scheme is developed next, by serially concatenating multiple implementations of the first scheme. In this case each stage considers as a priori hypothesis probability the a posteriori probability of the previous stage. We show that, by means of a fixed number of concatenations, the expected value of measurements to be performed decreases considerably. We also analyze one strategy based on an asymptotically large concatenation of the initial scheme, demonstrating that the expected number of measurements in this case is upper bounded by a constant, even in the case of zero average error probability. A lower bound for the expected number of measurements in the zero error probability setting is also derived.This work was supported in part by the Agencia Estatal de Investigación, Ministerio de Ciencia e Innovación, of the Spanish Government, under Grant RED2018-102668-T and Grant PID2019-104958RB-C41; in part by the Catalan Government under Grant 2017 SGR 578 AGAUR; and in part by the QuantumCAT within the European Regional Development Fund (ERDF) Program of Catalunya under Grant 001-P-001644.Postprint (published version

Characterization of soil mineralogy by FTIR: application to the analysis of mineralogical changes in soils affected by vegetation patches

Aims The objective of this paper was to develop a method based on infrared spectroscopy to compare mineral content in soils and apply it to evaluate soil mineralogical variations in pairs of inter-patch and patch soils in a semi-arid area. Methods Mixtures of several minerals were analyzed by infrared spectroscopy, the second derivative of the spectra was calculated and the spectra normalized respect to calcite or quartz signals (711 cm−1 or 800 cm−1 respectively). The intensities of representative signals of each mineral were related to their concentration in the mixtures. Pairs of patch and inter-patch soils from five different sites were analyzed by this method. Elemental analysis and total lime analysis were performed in some soil pairs. Results Soils were dominated by calcite and quartz, or by montmorillonite and kaolinite. Inter-patch soils were richer in calcite and poorer in quartz or clays than patch soils. Calcite losses in patch soils might be related to soil acidification by CO2 from respiration and/or organic matter. Elemental analysis showed high values of S, Cl, and K in patch soils with respect to inter-patch soils. Conclusions The proposed FTIR method was useful to compare soil mineralogy in specific areas. Fertile spots by accumulation of water, soluble salts and sediments may favor plant growth in semi-arid regions and these plants may increase the fertility of the spot. Changes in soil mineral composition could be used to monitor the biological activity of soil in arid and semi-arid zones.Research funded by the Spanish Ministry of Science and Innovation (projects UNCROACH, CGL2011–30581- C02–01 and GRACCIE Programa Consolider-Ingenio 2010, CSD2007–00067), Spanish Ministry of the Environment, Rural and Marine Areas (Project RECUVES; 077/RN08/04.1) and Generalitat Valenciana (Programa G. Forteza; FPA/2009/029)

Time-dependent contact mechanics

The version of record is available online at: http://dx.doi.org/10.1007/s00605-022-01767-1Contact geometry allows us to describe some thermodynamic and dissipative systems. In this paper we introduce a new geometric structure in order to describe time-dependent contact systems: cocontact manifolds. Within this setting we develop the Hamiltonian and Lagrangian formalisms, both in the regular and singular cases. In the singular case, we present a constraint algorithm aiming to find a submanifold where solutions exist. As a particular case we study contact systems with holonomic time-dependent constraints. Some regular and singular examples are analyzed, along with numerical simulations.We acknowledge fruitful discussions and comments from our colleague Narciso Román-Roy. MdL acknowledges the financial support of the Ministerio de Ciencia e Innovación (Spain), under grants PID2019-106715GB-C2, “Severo Ochoa Programme for Centres of Excellence in R&D” (CEX2019-000904-S) and EIN2020-112107. JG, XG, MCML and XR acknowledge the financial support of the Ministerio de Ciencia, Innovación y Universidades (Spain), project PGC2018-098265-B-C33.Peer ReviewedPostprint (author's final draft

In Situ Electrochemical Oxidation of Cu2S into CuO Nanowires as a Durable and Efficient Electrocatalyst for Oxygen Evolution Reaction

Development of cost-effective oxygen evolution catalysts is of capital importance for the deployment of large-scale energy-storage systems based on metal-air batteries and reversible fuel cells. In this direction, a wide range of materials have been explored, especially under more favorable alkaline conditions, and several metal chalcogenides have particularly demonstrated excellent performances. However, chalcogenides are thermodynamically less stable than the corresponding oxides and hydroxides under oxidizing potentials in alkaline media. Although this instability in some cases has prevented the application of chalcogenides as oxygen evolution catalysts and it has been disregarded in some others, we propose to use it in our favor to produce high-performance oxygen evolution catalysts. We characterize here the in situ chemical, structural, and morphological transformation during the oxygen evolution reaction (OER) in alkaline media of CuS into CuO nanowires, mediating the intermediate formation of Cu(OH). We also test their OER activity and stability under OER operation in alkaline media and compare them with the OER performance of Cu(OH) and CuO nanostructures directly grown on the surface of a copper mesh. We demonstrate here that CuO produced from in situ electrochemical oxidation of CuS displays an extraordinary electrocatalytic performance toward OER, well above that of CuO and Cu(OH) synthesized without this transformation

Biology and population dynamics of by-catch species of the bottom trawl fishery in the western Mediterranean

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