Efficient reverse engineering of one-qubit filter functions with dynamical invariants

We derive an integral expression for the filter-transfer function of an arbitrary one-qubit gate through the use of dynamical invariant theory and Hamiltonian reverse engineering. We use this result to define a cost functional which can be efficiently optimized to produce one-qubit control pulses that are robust against specified frequency bands of the noise power spectral density. We demonstrate the utility of our result by generating optimal control pulses that are designed to suppress broadband detuning and pulse amplitude noise. We report an order of magnitude improvement in gate fidelity in comparison with known composite pulse sequences. More broadly, we also use the same theoretical framework to prove the robustness of nonadiabatic geometric quantum gates under specific error models and control constraints

Evolutionary Design of the Memory Subsystem

The memory hierarchy has a high impact on the performance and power consumption in the system. Moreover, current embedded systems, included in mobile devices, are specifically designed to run multimedia applications, which are memory intensive. This increases the pressure on the memory subsystem and affects the performance and energy consumption. In this regard, the thermal problems, performance degradation and high energy consumption, can cause irreversible damage to the devices. We address the optimization of the whole memory subsystem with three approaches integrated as a single methodology. Firstly, the thermal impact of register file is analyzed and optimized. Secondly, the cache memory is addressed by optimizing cache configuration according to running applications and improving both performance and power consumption. Finally, we simplify the design and evaluation process of general-purpose and customized dynamic memory manager, in the main memory. To this aim, we apply different evolutionary algorithms in combination with memory simulators and profiling tools. This way, we are able to evaluate the quality of each candidate solution and take advantage of the exploration of solutions given by the optimization algorithm.We also provide an experimental experience where our proposal is assessed using well-known benchmark applications

Middle Ordovician harknessellid brachiopods (Dalmanellidina) from the Mediterranean margin of Gondwana

The family Harknessellidae Bancroft, 1928 (Orthida, Dalmanellidina) was designed to embrace an assemblage of species referred previously to Harknessella Reed, 1917, and included five genera known mainly from the Middle and Upper Ordovician of England. Herein, we suggest reassigning to this family the genus Cacemia Mitchell, 1974, widespread in the middle Darriwilian (upper Middle Ordovician) of the Iberian and Armorican massifs. Since its designation, Cacemia was placed among the dalmanellidin heterorthids, in spite of its strongly mucronate hinge line, which is totally unknown within this Mediterranean family. A new harknessellid has been identified from the upper Darriwilian beds of the Central Iberian Zone (Central Spain): Isabelella fascicostellata Reyes-Abril Villas gen. et sp. nov. It is similar to Horderleyella Bancroft, 1928 for its coarsely fascicostellate radial ornamentation and obtuse cardinal angles, although its convexoplane to convexoconcave profile allows discrimination from the typically dorsibiconvex Horderleyella. A phylogenetic analysis of the family places both Cacemia and Isabelella in basal positions of their clades, which fits with their early stratigraphic record. Based on our study, the family Harknessellidae appears to have originated in the high latitude Mediterranean margins of Gondwana during pre-Darriwilian times, before the detachment of Avalonia from Gondwana. The family reached its highest diversification in Avalonia throughout the Late Ordovician, keeping connections with the Mediterranean and Proto-Andean margins of Gondwana, as well as with the mid-latitude palaeocontinents of Baltica and South China

Middle Ordovician harknessellid brachiopods (Dalmanellidina) from the Mediterranean margin of Gondwana

The family Harknessellidae Bancroft, 1928 (Orthida, Dalmanellidina) was designed to embrace an assemblage of species referred previously to Harknessella Reed, 1917, and included five genera known mainly from the Middle and Upper Ordovician of England. Herein, we suggest reassigning to this family the genus Cacemia Mitchell, 1974, widespread in the middle Darriwilian (upper Middle Ordovician) of the Iberian and Armorican massifs. Since its designation, Cacemia was placed among the dalmanellidin heterorthids, in spite of its strongly mucronate hinge line, which is totally unknown within this Mediterranean family. A new harknessellid has been identified from the upper Darriwilian beds of the Central Iberian Zone (Central Spain): Isabelella fascicostellata Reyes-Abril Villas gen. et sp. nov. It is similar to Horderleyella Bancroft, 1928 for its coarsely fascicostellate radial ornamentation and obtuse cardinal angles, although its convexoplane to convexoconcave profile allows discrimination from the typically dorsibiconvex Horderleyella. A phylogenetic analysis of the family places both Cacemia and Isabelella in basal positions of their clades, which fits with their early stratigraphic record. Based on our study, the family Harknessellidae appears to have originated in the high latitude Mediterranean margins of Gondwana during pre-Darriwilian times, before the detachment of Avalonia from Gondwana. The family reached its highest diversification in Avalonia throughout the Late Ordovician, keeping connections with the Mediterranean and Proto-Andean margins of Gondwana, as well as with the mid-latitude palaeocontinents of Baltica and South China.Peer Reviewe

Particle swarm grammatical evolution for energy demand estimation

[EN] Grammatical Swarm is a search and optimization algorithm that belongs to the more general Grammatical Evolution family, which works with a set of solutions called individuals or particles. It uses the Particle Swarm Optimization algorithm as the search engine in the evolution of solutions. In this paper, we present a Grammatical Swarm algorithm for total energy demand estimation in a country from macroeconomic variables. Each particle in the Grammatical Swarm encodes a different model for energy demand estimation, which will be decoded by a predefined grammar. The parameters of the model are also optimized by the proposed algorithm, in such a way that the model is adjusted to a training set of real energy demand data, selecting the more appropriate variables to appear in the model. We analyze the performance of the Grammatical Swarm evolution in two real problems of one-year ahead energy demand estimation in Spain and France. The proposal is compared with previous approaches with competitive results.Spanish Ministerial Commission of Science and Technology (MICYT), Grant/Award Number: TIN2017-85887-C2-2-P; Ministerio de Ciencia, Innovacion y Universidades, Grant/Award Number: PGC2018-095322-B-C22 and RTI2018-095180-B-I00; Comunidad de Madrid y Fondos Estructurales de la Union Europea, Grant/Award Number: S2018/TCS-4566 and Y2018/NMT-4668; GenObIA-CM, Grant/Award Number: S2017/BMD-3773; Ministerio de Economia, Industria y Competitividad, Grant/Award Number: MTM2017-89664-PMartínez-Rodríguez, D.; Colmenar, JM.; Hidalgo, JI.; Villanueva Micó, RJ.; Salcedo-Sanz, S. (2020). Particle swarm grammatical evolution for energy demand estimation. Energy Science & Engineering. 8(4):1068-1079. https://doi.org/10.1002/ese3.568S1068107984Safarzyńska, K., & van den Bergh, J. C. J. M. (2017). Integrated crisis-energy policy: Macro-evolutionary modelling of technology, finance and energy interactions. Technological Forecasting and Social Change, 114, 119-137. doi:10.1016/j.techfore.2016.07.033Li, F., Song, Z., & Liu, W. (2014). China’s energy consumption under the global economic crisis: Decomposition and sectoral analysis. Energy Policy, 64, 193-202. doi:10.1016/j.enpol.2013.09.014Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., & Del Ser, J. (2015). One-year-ahead energy demand estimation from macroeconomic variables using computational intelligence algorithms. Energy Conversion and Management, 99, 62-71. doi:10.1016/j.enconman.2015.03.109Sánchez-Oro, J., Duarte, A., & Salcedo-Sanz, S. (2016). Robust total energy demand estimation with a hybrid Variable Neighborhood Search – Extreme Learning Machine algorithm. Energy Conversion and Management, 123, 445-452. doi:10.1016/j.enconman.2016.06.050Ceylan, H., & Ozturk, H. K. (2004). Estimating energy demand of Turkey based on economic indicators using genetic algorithm approach. Energy Conversion and Management, 45(15-16), 2525-2537. doi:10.1016/j.enconman.2003.11.010Shaik, S., & Yeboah, O.-A. (2018). Does climate influence energy demand? A regional analysis. Applied Energy, 212, 691-703. doi:10.1016/j.apenergy.2017.11.109United Nations Climate Change Conference.The Paris Agreement. UNTC XXVII 7.d.Suganthi, L., & Samuel, A. A. (2012). Energy models for demand forecasting—A review. Renewable and Sustainable Energy Reviews, 16(2), 1223-1240. doi:10.1016/j.rser.2011.08.014Geem, Z. W., & Roper, W. E. (2009). Energy demand estimation of South Korea using artificial neural network. Energy Policy, 37(10), 4049-4054. doi:10.1016/j.enpol.2009.04.049Ünler, A. (2008). Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025. Energy Policy, 36(6), 1937-1944. doi:10.1016/j.enpol.2008.02.018Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 36, 93-103. doi:10.1016/j.knosys.2012.06.009Kıran, M. S., Özceylan, E., Gündüz, M., & Paksoy, T. (2012). A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey. Energy Conversion and Management, 53(1), 75-83. doi:10.1016/j.enconman.2011.08.004Yu, S., Wei, Y.-M., & Wang, K. (2012). A PSO–GA optimal model to estimate primary energy demand of China. Energy Policy, 42, 329-340. doi:10.1016/j.enpol.2011.11.090Yu, S., Zhu, K., & Zhang, X. (2012). Energy demand projection of China using a path-coefficient analysis and PSO–GA approach. Energy Conversion and Management, 53(1), 142-153. doi:10.1016/j.enconman.2011.08.015Yu, S., & Zhu, K. (2012). A hybrid procedure for energy demand forecasting in China. Energy, 37(1), 396-404. doi:10.1016/j.energy.2011.11.015Geng, Z., Zeng, R., Han, Y., Zhong, Y., & Fu, H. (2019). Energy efficiency evaluation and energy saving based on DEA integrated affinity propagation clustering: Case study of complex petrochemical industries. Energy, 179, 863-875. doi:10.1016/j.energy.2019.05.042Han, Y., Long, C., Geng, Z., Zhu, Q., & Zhong, Y. (2019). A novel DEACM integrating affinity propagation for performance evaluation and energy optimization modeling: Application to complex petrochemical industries. Energy Conversion and Management, 183, 349-359. doi:10.1016/j.enconman.2018.12.120Han, Y., Wu, H., Jia, M., Geng, Z., & Zhong, Y. (2019). Production capacity analysis and energy optimization of complex petrochemical industries using novel extreme learning machine integrating affinity propagation. Energy Conversion and Management, 180, 240-249. doi:10.1016/j.enconman.2018.11.001Colmenar, J. M., Hidalgo, J. I., & Salcedo-Sanz, S. (2018). Automatic generation of models for energy demand estimation using Grammatical Evolution. 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D., & Krause, S. (2010). Swarm intelligence in animals and humans. Trends in Ecology & Evolution, 25(1), 28-34. doi:10.1016/j.tree.2009.06.016Marini, F., & Walczak, B. (2015). Particle swarm optimization (PSO). A tutorial. Chemometrics and Intelligent Laboratory Systems, 149, 153-165. doi:10.1016/j.chemolab.2015.08.020Ling, C. X. (1995). Overfitting and generalization in learning discrete patterns. Neurocomputing, 8(3), 341-347. doi:10.1016/0925-2312(95)00050-

Exploitation of washing products of granitic tin-bearing sands from Conquista (Córdoba)

8 páginas, 10 figuras, 3 tablas, 21 referencias.[EN]: An argillaceous material, high in kaolinite, from washing products of granitic sands of Pedroches (Córdoba) formation, is studied. The following determinations were carried out: mechanical analysis, chemical analysis. X-ray diffraction study, DTA-TG and dilatometric analysis, as well as a ceramic study of pressed and fîred bodies for two hours up to 1.300°C, to obttain the sintering diagrams. Through mineralogical analysis, it is concluded that the raw material is very high in kaolinite with variable contents of quartz, illite, feldspars (potassic), an iron oxides (~5% F2O3) mainly as gel. Through firing up to 1.300°C of presssed bodies, we can observe by the X-ray study the appearance and development of new phases among which mullite stands out, together with great vitreous phase formation. The location and great potential of the deposit, as well as the good ceramic properties of this material, permit their use in the good ceramic properties of this material, permit their use in the floor and tile industry due to its iron oxide content, being possible the processing by pressing, extrusion and slip casting.[ES]: Se estudia un material arcilloso con alto contenido en caolinita, procedente del lavado de arenas graníticas de la formación de Los Pedroches (Córdoba). Se han realizado las siguientes determinaciones: análisis granulométrico, químico y mediante difracción de rayos X, estudio térmico por ATD-TG y dilatométrico, así como estudio de las propiedades cerámicas del material en probetas prensadas y cocidas durante dos horas hasta la temperatura de 1.300°C, obteniéndose los correspondientes diagramas de gresificación. El análisis mineralógico del material crudo lo señala,como altamente caolinítico, con variable contenido eh cuarzo, ilita, feldespatos (potásicos) y óxidos de hierro (~5% F2O3), en estado de gel en su mayor parte. Mediante cocción hasta 1.300°C de las probetas prensadas, el estudio mediante difracción de rayos X permite observar la aparición y desarrollo de nuevas fases, entre las que destaca la mullita, junto con abundante formación de fase vítrea. La situación y gran potencia del yacimiento, así como las buenas propiedades cerámicas de este material natural, permiten su empleo en la industria de pavimentos y revestimientos cerámicos gresificados de pasta roja dado su contenido en óxidos de hierro que preparados adecuadamente pueden modelarse por prensado, extrusión y colaje.Peer reviewe

A Parallel Evolutionary Algorithm to Optimize Dynamic Memory Managers in Embedded Systems

For the last 30 years, several dynamic memory managers (DMMs) have been proposed. Such DMMs include first fit, best fit, segregated fit and buddy systems. Since the performance, memory usage and energy consumption of each DMM differs, software engineers often face difficult choices in selecting the most suitable approach for their applications. This issue has special impact in the field of portable consumer embedded systems, that must execute a limited amount of multimedia applications (e.g., 3D games, video players, signal processing software, etc.), demanding high performance and extensive memory usage at a low energy consumption. Recently, we have developed a novel methodology based on genetic programming to automatically design custom DMMs, optimizing performance, memory usage and energy consumption. However, although this process is automatic and faster than state-of-the-art optimizations, it demands intensive computation, resulting in a time-consuming process. Thus, parallel processing can be very useful to enable to explore more solutions spending the same time, as well as to implement new algorithms. In this paper we present a novel parallel evolutionary algorithm for DMMs optimization in embedded systems, based on the Discrete Event Specification (DEVS) formalism over a Service Oriented Architecture (SOA) framework. Parallelism significantly improves the performance of the sequential exploration algorithm. On the one hand, when the number of generations are the same in both approaches, our parallel optimization framework is able to reach a speed-up of 86.40% when compared with other state-of-the-art approaches. On the other, it improves the global quality (i.e., level of performance, low memory usage and low energy consumption) of the final DMM obtained in a 36.36% with respect to two well-known general-purpose DMMs and two state-of-the-art optimization methodologies

Simulation of High-Performance Memory Allocators

Current general-purpose memory allocators do not provide sufficient speed or flexibility for modern highperformance applications. To optimize metrics like performance, memory usage and energy consumption, software engineers often write custom allocators from scratch, which is a difficult and error-prone process. In this paper, we present a flexible and efficient simulator to study Dynamic Memory Managers (DMMs), a composition of one or more memory allocators. This novel approach allows programmers to simulate custom and general DMMs, which can be composed without incurring any additional runtime overhead or additional programming cost. We show that this infrastructure simplifies DMM construction, mainly because the target application does not need to be compiled every time a new DMM must be evaluated. Within a search procedure, the system designer can choose the "best" allocator by simulation for a particular target application. In our evaluation, we show that our scheme will deliver better performance, less memory usage and less energy consumption than single memory allocator