62 research outputs found

    Action diffusion and lifetimes of quasistationary states in the Hamiltonian Mean Field model

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    Out-of-equilibrium quasistationary states (QSSs) are one of the signatures of a broken ergodicity in long-range interacting systems. For the widely studied Hamiltonian Mean-Field model, the lifetime of some QSSs has been shown to diverge with the number N of degrees of freedom with a puzzling N^1.7 scaling law, contradicting the otherwise widespread N scaling law. It is shown here that this peculiar scaling arises from the locality properties of the dynamics captured through the computation of the diffusion coefficient in terms of the action variable. The use of a mean first passage time approach proves to be successful in explaining the non-trivial scaling at stake here, and sheds some light on another case, where lifetimes diverging as e^N above some critical energy have been reported

    Spin-Glass Model Governs Laser Multiple Filamentation

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    We show that multiple filamentation patterns in high-power laser beams, can be described by means of two statistical physics concepts, namely self-similarity of the patterns over two nested scales, and nearest-neighbor interactions of classical rotators. The resulting lattice spin model perfectly reproduces the evolution of intense laser pulses as simulated by the Non-Linear Schr\"odinger Equation, shedding a new light on multiple filamentation. As a side benefit, this approach drastically reduces the computing time by two orders of magnitude as compared to the standard simulation methods of laser filamentation.Comment: 8 pages, 4 figure

    Laser filamentation as a new phase transition universality class

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    We show that the onset of laser multiple filamentation can be described as a critical phenomenon that we characterize both experimentally and numerically by measuring a set of seven critical exponents. This phase transition deviates from any existing universality class, and offers a unique perspective of conducting two-dimensional experiments of statistical physics at a human scale.Comment: 8 pages, 9 figure

    Reversibility of laser filamentation

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    We investigate the reversibility of laser filamentation, a self-sustained, non-linear propagation regime including dissipation and time-retarded effects. We show that even losses related to ionization marginally affect the possibility of reverse propagating ultrashort pulses back to the initial conditions, although they make it prone to finite-distance blow-up susceptible to prevent backward propagation.Comment: 12 pages, 3 figure

    Non-linear Synthesis of Complex Laser Waveforms at Remote Distances

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    Strong deformation of ultrashort laser pulse shapes is unavoidable when delivering high intensities at remote distances due to non-linear effects taking place while propagating. Relying on the reversibility of laser filamentation, we propose to explicitly design laser pulse shapes so that propagation serves as a non-linear field synthesizer at a remote target location. Such an approach allows, for instance, coherent control of molecules at a remote distance, in the context of standoff detection of pathogens or explosives.Comment: 17 pages, 6 figure

    Geographical Analysis of Sanitation Workers in the Urban District of Kut

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    Cleaning is one of the services provided by the state and its ad hoc institutions. Every developed country is clean due to the good planning and attention given to the staff of all municipalities and service institutions. Beside, the attention paid to this sector is considered one of the windows to maintain the states’ aesthetics. If janitors stop disposing of all the discarded wastes, the result will be having big piles of waste, affecting accordingly the health and safety of its citizens and the aesthetics of the areas. The janitors and those responsible for them are those that the state must take care of because of their importance to the state in general and to the province in particular. The importance of the research comes from the fact that the janitors are an integral part of the urban population of kut district. They further have a positive role in the removal of waste within the district, and because they are a neglected segment, the researcher has decided to address them in the present research because of the  importance of the topic. The study has thus adopted the general approach in geography, i.e., the descriptive analytical approach to show detailed data on the sanitation workers. The study has mainly concluded that most of the janitors are of illiterate level of education and the lowest level of education is a preparatory school or above. Moreover, their income is of a low level, and so they live in slums, and some of them have no housing owned by them

    Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT

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    [EN] The purpose of a Network Intrusion Detection System is to detect intrusive, malicious activities or policy violations in a host or host's network. In current networks, such systems are becoming more important as the number and variety of attacks increase along with the volume and sensitiveness of the information exchanged. This is of particular interest to Internet of Things networks, where an intrusion detection system will be critical as its economic importance continues to grow, making it the focus of future intrusion attacks. In this work, we propose a new network intrusion detection method that is appropriate for an Internet of Things network. The proposed method is based on a conditional variational autoencoder with a specific architecture that integrates the intrusion labels inside the decoder layers. The proposed method is less complex than other unsupervised methods based on a variational autoencoder and it provides better classification results than other familiar classifiers. More important, the method can perform feature reconstruction, that is, it is able to recover missing features from incomplete training datasets. We demonstrate that the reconstruction accuracy is very high, even for categorical features with a high number of distinct values. This work is unique in the network intrusion detection field, presenting the first application of a conditional variational autoencoder and providing the first algorithm to perform feature recovery.This work has been partially funded by the Ministerio de Economia y Competitividad del Gobierno de Espana and the Fondo de Desarrollo Regional (FEDER) within the project "Inteligencia distribuida para el control y adaptacion de redes dinamicas definidas por software, Ref: TIN2014-57991-C3-2-P", and the Project "Distribucion inteligente de servicios multimedia utilizando redes cognitivas adaptativas definidas por software", Ref: TIN2014-57991-C3-1-P, in the Programa Estatal de Fomento de la Investigacion Cientifica y Tecnica de Excelencia, Subprograma Estatal de Generacion de Conocimiento.Lopez-Martin, M.; Carro, B.; Sánchez-Esguevillas, A.; Lloret, J. (2017). Conditional Variational Autoencoder for Prediction and Feature Recovery Applied to Intrusion Detection in IoT. Sensors. 17(9):1-17. https://doi.org/10.3390/s17091967S11717
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