New basal Odonatoptera (Insecta) from the lower Carboniferous (Serpukhovian) of Argentina

Nuevos Odonatoptera basales del Serpukhoviano superior (325-324 Ma) son descriptos de la localidad Guandacol 1, Quebrada de las Libélulas, Formación Guandacol, provincia de La Rioja, centro oeste de la Argentina. Otras dos especies conocidas del Serpukhoviano, Eugeropteron lunatum Riek, 1983 y Geropteron arcuatum Riek, 1983, de Cuestita de La Herradura, Formación Malanzán, provincia de La Rioja, son discutidas. Varios taxones de orden superior nuevos son nominados para incluir estas especies, resultando en una nueva clasificación: 1 Superorden Odonatoptera, 1.1 Eugeroptera ord. nov., 1.1.1 Eugeropteridae, 1.1.1.1 Eugeropteron, 1.1.1.1.1 Eugeropteron lunatum, 1.1.1.1.2 Tupacsala niunamenos gen. nov. et sp. nov., 1.2 Palaeodonatoptera taxon nov., 1.2.1 Kukaloptera ord. nov., 1.2.1.1 Kirchneralidae fam. nov., 1.2.1.1.1 Kirchnerala treintamil gen. nov. et sp. nov., 1.2.2 Plesiodonatoptera taxon nov., 1.2.2.1 Argentinoptera ord. nov., 1.2.2.1.1 Argentinalidae fam. nov., 1.2.2.1.1.1 Argentinala cristinae gen. nov. et sp. nov., 1.2.2.2 Apodonatoptera taxon nov., 1.2.2.2.1 Orden Geroptera, 1.2.2.2.1.1 Geropteridae fam. nov., 1.2.2.2.1.1.1 Geropteron, 1.2.2.2.1.1.1.1 Geropteron arcuatum, 1.2.2.2.2 Neodonatoptera.Three new basal species of Odonatoptera from the upper Serpukhovian (325-324 Ma) of Guandacol 1 locality, Quebrada de las Libélulas, Guandacol Formation, La Rioja province, central West Argentina, are described. Two known species also from the Serpukhovian, Eugeropteron lunatum Riek, 1983 and Geropteron arcuatum Riek, 1983, from Cuestita de La Herradura, Malanzán Formation, La Rioja province, are discussed. Several higher taxa are nominated to include these species, resulting in a new classification: 1 Superorder Odonatoptera, 1.1 Eugeroptera ord. nov., 1.1.1 Eugeropteridae, 1.1.1.1 Eugeropteron, 1.1.1.1.1 Eugeropteron lunatum, 1.1.1.1.2 Tupacsala niunamenos gen. nov. et sp. nov., 1.2 Palaeodonatoptera taxon nov., 1.2.1 Kukaloptera ord. nov., 1.2.1.1 Kirchneralidae fam. nov., 1.2.1.1.1 Kirchnerala treintamil gen. nov. et sp. nov., 1.2.2 Plesiodonatoptera taxon nov., 1.2.2.1 Argentinoptera ord. nov., 1.2.2.1.1 Argentinalidae fam. nov., 1.2.2.1.1.1 Argentinala cristinae gen. nov. et sp. nov., 1.2.2.2 Apodonatoptera taxon nov., 1.2.2.2.1 Order Geroptera, 1.2.2.2.1.1 Geropteridae fam. nov., 1.2.2.2.1.1.1 Geropteron, 1.2.2.2.1.1.1.1 Geropteron arcuatum, 1.2.2.2.2 Neodonatoptera.Fil: Petrulevicius, Julian Fernando. Universidad Nacional de La Plata. Facultad de Ciencias Naturales y Museo; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Gutierrez, Pedro Raul. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Parque Centenario. Museo Argentino de Ciencias Naturales "Bernardino Rivadavia"; Argentin

The \mu-Calculus Alternation Hierarchy Collapses over Structures with Restricted Connectivity

It is known that the alternation hierarchy of least and greatest fixpoint operators in the mu-calculus is strict. However, the strictness of the alternation hierarchy does not necessarily carry over when considering restricted classes of structures. A prominent instance is the class of infinite words over which the alternation-free fragment is already as expressive as the full mu-calculus. Our current understanding of when and why the mu-calculus alternation hierarchy is not strict is limited. This paper makes progress in answering these questions by showing that the alternation hierarchy of the mu-calculus collapses to the alternation-free fragment over some classes of structures, including infinite nested words and finite graphs with feedback vertex sets of a bounded size. Common to these classes is that the connectivity between the components in a structure from such a class is restricted in the sense that the removal of certain vertices from the structure's graph decomposes it into graphs in which all paths are of finite length. Our collapse results are obtained in an automata-theoretic setting. They subsume, generalize, and strengthen several prior results on the expressivity of the mu-calculus over restricted classes of structures.Comment: In Proceedings GandALF 2012, arXiv:1210.202

Breve memoria acerca del origen conservación y límites del Obispado de Astorga

Copia digital. Valladolid : Junta de Castilla y León. Consejería de Cultura y Turismo, 2010-201

Parallelization of a Six Degree of Freedom Entry Vehicle Trajectory Simulation Using OpenMP and OpenACC

The art and science of writing parallelized software, using methods such as Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC), is dominated by computer scientists. Engineers and non-computer scientists looking to apply these techniques to their project applications face a steep learning curve, especially when looking to adapt their original single threaded software to run multi-threaded on graphics processing units (GPUs). There are significant changes in mindset that must occur; such as how to manage memory, the organization of instructions, and the use of if statements (also known as branching). The purpose of this work is twofold: 1) to demonstrate the applicability of parallelized coding methodologies, OpenMP and OpenACC, to tasks outside of the typical large scale matrix mathematics; and 2) to discuss, from an engineers perspective, the lessons learned from parallelizing software using these computer science techniques. This work applies OpenMP, on both multi-core central processing units (CPUs) and Intel Xeon Phi 7210, and OpenACC on GPUs. These parallelization techniques are used to tackle the simulation of thousands of entry vehicle trajectories through the integration of six degree of freedom (DoF) equations of motion (EoM). The forces and moments acting on the entry vehicle, and used by the EoM, are estimated using multiple models of varying levels of complexity. Several benchmark comparisons are made on the execution of six DoF trajectory simulation: single thread Intel Xeon E5-2670 CPU, multi-thread CPU using OpenMP, multi-thread Xeon Phi 7210 using OpenMP, and multi-thread NVIDIA Tesla K40 GPU using OpenACC. These benchmarks are run on the Pleiades Supercomputer Cluster at the National Aeronautics and Space Administration (NASA) Ames Research Center (ARC), and a Xeon Phi 7210 node at NASA Langley Research Center (LaRC)

Advanced UAV Trajectory Generation: Planning and Guidance

As technology and legislation move forward (JAA & Eurocontrol, 2004) remotely controlled, semi-autonomous or autonomous Unmanned Aerial Systems (UAS) will play a significant role in providing services and enhancing safety and security of the military and civilian community at large (e.g. surveillance and monitoring) (Coifman et al., 2004). The potential market for UAVs is, however, much bigger than just surveillance. UAVs are ideal for risk assessment and neutralization in dangerous areas such as war zones and regions stricken by disaster, including volcanic eruptions, wildfires, floods, and even terrorist acts. As they become more autonomous, UAVs will take on additional roles, such as air-to-air combat and even planetary science exploration (Held et al., 2005). As the operational capabilities of UAVs are developed there is a perceived need for a significant increase in their level of autonomy, performance, reliability and integration with a controlled airspace full of manned vehicles (military and civilian). As a consequence researchers working with advanced UAVs have moved their focus from system modeling and low-level control to mission planning, supervision and collision avoidance, going from vehicle constraints to mission constraints (Barrientos et al., 2006). This mission-based approach is most useful for commercial applications where the vehicle must accomplish tasks with a high level of performance and maneuverability. These tasks require flexible and powerful trajectory-generation and guidance capabilities, features lacking in many of the current commercial UAS. For this reason, the purpose of this work is to extend the capabilities of commercially available autopilots for UAVs. Civil systems typically use basic trajectory-generation algorithms, capable only of linear waypoint navigation (Rysdyk, 2003), with a minimum or non-existent control over the trajectory. These systems are highly constrained when maneuverability is a mission requirement. On the other hand, military researchers have developed algorithms for high-performance 3D path planning and obstacle avoidance (Price, 2006), but these are highly proprietary technologies that operate with different mission constraints (target acquisition, threat avoidance and situational awareness) so they cannot be used in civil scenarios

Incentive Engineering for Concurrent Games

We consider the problem of incentivising desirable behaviours in multi-agent systems by way of taxation schemes. Our study employs the concurrent games model: in this model, each agent is primarily motivated to seek the satisfaction of a goal, expressed as a Linear Temporal Logic (LTL) formula; secondarily, agents seek to minimise costs, where costs are imposed based on the actions taken by agents in different states of the game. In this setting, we consider an external principal who can influence agents' preferences by imposing taxes (additional costs) on the actions chosen by agents in different states. The principal imposes taxation schemes to motivate agents to choose a course of action that will lead to the satisfaction of their goal, also expressed as an LTL formula. However, taxation schemes are limited in their ability to influence agents' preferences: an agent will always prefer to satisfy its goal rather than otherwise, no matter what the costs. The fundamental question that we study is whether the principal can impose a taxation scheme such that, in the resulting game, the principal's goal is satisfied in at least one or all runs of the game that could arise by agents choosing to follow game-theoretic equilibrium strategies. We consider two different types of taxation schemes: in a static scheme, the same tax is imposed on a state-action profile pair in all circumstances, while in a dynamic scheme, the principal can choose to vary taxes depending on the circumstances. We investigate the main game-theoretic properties of this model as well as the computational complexity of the relevant decision problems.Comment: In Proceedings TARK 2023, arXiv:2307.0400