Instituto Politécnico Nacional

About: Instituto Politécnico Nacional is a education organization based out in Mexico City, Mexico. It is known for research contribution in the topics: Population & Context (language use). The organization has 43351 authors who have published 63315 publications receiving 938532 citations. The organization is also known as: Instituto Politécnico Nacional & Instituto Politecnico Nacional.

Activation tagging using the En-I maize transposon system in Arabidopsis

Abstract: A method for the generation of stable activation tag inserts was developed in Arabidopsis using the maize (Zea mays) En-I transposon system. The method employs greenhouse selectable marker genes that are useful to efficiently generate large populations of insertions. A population of about 8,300 independent stable activation tag inserts has been produced. Greenhouse-based screens for mutants in a group of plants containing about 2,900 insertions revealed about 31 dominant mutants, suggesting a dominant mutant frequency of about 1%. From the first batch of about 400 stable insertions screened in the greenhouse, four gain-in-function, dominant activation-tagged, morphological mutants were identified. A novel gain-in-function mutant called thread is described, in which the target gene belongs to the same family as the YUCCA flavin-mono-oxygenase that was identified by T-DNA activation tagging. The high frequency of identified gain-in-function mutants in the population suggests that the En-I system described here is an efficient strategy to saturate plant genomes with activation tag inserts. Because only a small number of primary transformants are required to generate an activation tag population, the En-I system appears to be an attractive alternative to study plant species where the present transformation methods have low efficiencies.

Observations of the Large Magellanic Cloud with Fermi

Abstract: Context: The Large Magellanic Cloud (LMC) is to date the only normal external galaxy that has been detected in high-energy gamma rays. High-energy gamma rays trace particle acceleration processes and gamma-ray observations allow the nature and sites of acceleration to be studied. Aims: We characterise the distribution and sources of cosmic rays in the LMC from analysis of gamma-ray observations. Methods: We analyse 11 months of continuous sky-survey observations obtained with the Large Area Telescope aboard the Fermi Gamma-Ray Space Telescope and compare it to tracers of the interstellar medium and models of the gamma-ray sources in the LMC. Results: The LMC is detected at 33 sigma significance. The integrated >100 MeV photon flux of the LMC amounts to (2.6 +/- 0.2) * 10^-7 ph/cm2/s which corresponds to an energy flux of (1.6 +/- 0.1) * 10^-10 erg/cm2/s, with additional systematic uncertainties of ~16%. The analysis reveals the massive star forming region 30 Doradus as a bright source of gamma-ray emission in the LMC in addition to fainter emission regions found in the northern part of the galaxy. The gamma-ray emission from the LMC shows very little correlation with gas density and is rather correlated to tracers of massive star forming regions. The close confinement of gamma-ray emission to star forming regions suggests a relatively short GeV cosmic-ray proton diffusion length. Conclusions: The close correlation between cosmic-ray density and massive star tracers supports the idea that cosmic rays are accelerated in massive star forming regions as a result of the large amounts of kinetic energy that are input by the stellar winds and supernova explosions of massive stars into the interstellar medium.

Online Vehicle Routing With Neural Combinatorial Optimization and Deep Reinforcement Learning

Abstract: Online vehicle routing is an important task of the modern transportation service provider. Contributed by the ever-increasing real-time demand on the transportation system, especially small-parcel last-mile delivery requests, vehicle route generation is becoming more computationally complex than before. The existing routing algorithms are mostly based on mathematical programming, which requires huge computation time in city-size transportation networks. To develop routes with minimal time, in this paper, we propose a novel deep reinforcement learning-based neural combinatorial optimization strategy. Specifically, we transform the online routing problem to a vehicle tour generation problem, and propose a structural graph embedded pointer network to develop these tours iteratively. Furthermore, since constructing supervised training data for the neural network is impractical due to the high computation complexity, we propose a deep reinforcement learning mechanism with an unsupervised auxiliary network to train the model parameters. A multisampling scheme is also devised to further improve the system performance. Since the parameter training process is offline, the proposed strategy can achieve a superior online route generation speed. To assess the proposed strategy, we conduct comprehensive case studies with a real-world transportation network. The simulation results show that the proposed strategy can significantly outperform conventional strategies with limited computation time in both static and dynamic logistic systems. In addition, the influence of control parameters on the system performance is investigated.

Waveguide-coupled single collective excitation of atomic arrays

Abstract: Considerable efforts have been recently devoted to combining ultracold atoms and nanophotonic devices1-4 to obtain not only better scalability and figures of merit than in free-space implementations, but also new paradigms for atom-photon interactions5. Dielectric waveguides offer a promising platform for such integration because they enable tight transverse confinement of the propagating light, strong photon-atom coupling in single-pass configurations and potentially long-range atom-atom interactions mediated by the guided photons. However, the preparation of non-classical quantum states in such atom-waveguide interfaces has not yet been realized. Here, by using arrays of individual caesium atoms trapped along an optical nanofibre6,7, we observe a single collective atomic excitation8,9 coupled to a nanoscale waveguide. The stored collective entangled state can be efficiently read out with an external laser pulse, leading to on-demand emission of a single photon into the guided mode. We characterize the emitted single photon via the suppression of the two-photon component and confirm the single character of the atomic excitation, which can be retrieved with an efficiency of about 25%. Our results demonstrate a capability that is essential for the emerging field of waveguide quantum electrodynamics, with applications to quantum networking, quantum nonlinear optics and quantum many-body physics10,11.