Realization of Polarization Control in High-Order Harmonic Generation

The nature of high-order harmonic generation process limits the harmonics emission to linear polarization. In this paper, we review the recent progress to generate elliptically or circularly polarized high-harmonic EUV pulses. We further demonstrate how complete control of polarization state of isolated high-harmonic pulse can be realized today by noncollinear focusing of two driving pulses with identical ellipticity but counter-rotating helicity. This paper opens a path towards the study of the fastest dynamics--down to attosecond time scales--in circular dichroism of magnetic materials, chiral molecules, and electronic spin motion.Taiwan Ministry of Science and Technology; Academia Sinica; Junta de Castilla y León; Ministerio de Economía y Competitividad; Leonardo Grant for Researchers and Cultural Creators, BBVA Foundation; Ministerio de Ciencia, Innovación y Universidades for a Ramón y Cajal; European Social Fund; Ministerio de Educación, Cultura y Deporte

Multiobjective evolutionary algorithm based on vector angle neighborhood

Selection is a major driving force behind evolution and is a key feature of multiobjective evolutionary algorithms. Selection aims at promoting the survival and reproduction of individuals that are most fitted to a given environment. In the presence of multiple objectives, major challenges faced by this operator come from the need to address both the population convergence and diversity, which are conflicting to a certain extent. This paper proposes a new selection scheme for evolutionary multiobjective optimization. Its distinctive feature is a similarity measure for estimating the population diversity, which is based on the angle between the objective vectors. The smaller the angle, the more similar individuals. The concept of similarity is exploited during the mating by defining the neighborhood and the replacement by determining the most crowded region where the worst individual is identified. The latter is performed on the basis of a convergence measure that plays a major role in guiding the population towards the Pareto optimal front. The proposed algorithm is intended to exploit strengths of decomposition-based approaches in promoting diversity among the population while reducing the user's burden of specifying weight vectors before the search. The proposed approach is validated by computational experiments with state-of-the-art algorithms on problems with different characteristics. The obtained results indicate a highly competitive performance of the proposed approach. Significant advantages are revealed when dealing with problems posing substantial difficulties in keeping diversity, including many-objective problems. The relevance of the suggested similarity and convergence measures are shown. The validity of the approach is also demonstrated on engineering problems.This work was supported by the Portuguese Fundacao para a Ciencia e Tecnologia under grant PEst-C/CTM/LA0025/2013 (Projecto Estrategico - LA 25 - 2013-2014 - Strategic Project - LA 25 - 2013-2014).info:eu-repo/semantics/publishedVersio

Sunyaev-Zel'dovich clusters in millennium gas simulations

Large surveys using the Sunyaev–Zel’dovich (SZ) effect to find clusters of galaxies are now starting to yield large numbers of systems out to high redshift, many of which are new dis- coveries. In order to provide theoretical interpretation for the release of the full SZ cluster samples over the next few years, we have exploited the large-volume Millennium gas cosmo- logical N-body hydrodynamics simulations to study the SZ cluster population at low and high redshift, for three models with varying gas physics. We confirm previous results using smaller samplesthattheintrinsic(spherical)Y500–M500relationhasverylittlescatter(σlog10Y ≃0.04), is insensitive to cluster gas physics and evolves to redshift 1 in accordance with self-similar expectations. Our preheating and feedback models predict scaling relations that are in excel- lent agreement with the recent analysis from combined Planck and XMM–Newton data by the Planck Collaboration. This agreement is largely preserved when r500 and M500 are derived using thehydrostaticmassproxy,YX,500,albeitwithsignificantlyreducedscatter(σlog10Y ≃0.02),a result that is due to the tight correlation between Y500 and YX,500. Interestingly, this assumption also hides any bias in the relation due to dynamical activity. We also assess the importance of projection effects from large-scale structure along the line of sight, by extracting cluster Y500 values from 50 simulated 5 × 5-deg2 sky maps. Once the (model-dependent) mean signal is subtracted from the maps we find that the integrated SZ signal is unbiased with respect to the underlying clusters, although the scatter in the (cylindrical) Y500–M500 relation increases in the preheating case, where a significant amount of energy was injected into the intergalactic medium at high redshift. Finally, we study the hot gas pressure profiles to investigate the origin of the SZ signal and find that the largest contribution comes from radii close to r500 in all cases. The profiles themselves are well described by generalized Navarro, Frenk & White profiles but there is significant cluster-to-cluster scatter. In conclusion, our results support the notion that Y500 is a robust mass proxy for use in cosmological analyses with clusters

Link Prediction in Complex Networks: A Survey

Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labelled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.Comment: 44 pages, 5 figure

Effect Of Sediment Releasing Operation From Reservoir Outlets On The Water Treatment Plant Downstream

With steep slope and strong rainfall intensity, landslide and debris flow could generate huge amount of sediment yield from the watershed, typically during typhoon floods in Taiwan. As such huge amount of sediment moved into a reservoir, serious sedimentation problem reduced storage of reservoir due to decrease of flow velocity and without sufficient sediment releasing bottom outlet. As a result, using flood discharge to release sediment during Typhoon or rain fall duration is very important. This study adopts the data of field observation from outlet structures of the Shihmen reservoir to establish the operational strategies of sediment releasing rule and keep water sustained supply at downstream river intake. Based on the numerical simulation by using 1-D and 2-D models, the relationship of sediment concentration between the Shihmen reservoir and downstream withdraw intake at the YuanShan weir can be established. The sediment concentration reduction ratio is calculated to be 89% from the Shihmen reservoir to the YuanShan weir. This study also focuses on the sediment concentration mitigation influence of tunnel spillway due to the high sediment concentration releasing from bottom outlet. From simulated results, when the extra discharge of tunnel spillway reaches 830m3/s for specific event, the influence of reducing sediment concentration to downstream river by tunnel spillway can maintain water supply of downstream withdraw intake at YuanShan weir. In practice, when using the extra discharge of tunnel spillway to reduce sediment concentration during Typhoon Haitang and Typhoon Wipha, the sediment concentration at YuanShan weir can reduce water turbidity lower than 6000NTU that water treatment plant can deal with

Metaheuristic design of feedforward neural networks: a review of two decades of research

Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models