888 resultados para Lejins, Atis: Small states in a turbulent environment
Simple neural networks for the amplification and utilization of small changes in neuron firing rates
Resumo:
I describe physiologically plausible “voter-coincidence” neural networks such that secondary “coincidence” neurons fire on the simultaneous receipt of sufficiently large sets of input pulses from primary sets of neurons. The networks operate such that the firing rate of the secondary, output neurons increases (or decreases) sharply when the mean firing rate of primary neurons increases (or decreases) to a much smaller degree. In certain sensory systems, signals that are generally smaller than the noise levels of individual primary detectors, are manifest in very small increases in the firing rates of sets of afferent neurons. For such systems, this kind of network can act to generate relatively large changes in the firing rate of secondary “coincidence” neurons. These differential amplification systems can be cascaded to generate sharp, “yes–no” spike signals that can direct behavioral responses.
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Two-dimensional insulators with time-reversal symmetry can have two topologically different phases, the quantum spin Hall and the normal phase. The former is revealed by the existence of conducting edge states that are topologically protected. Here we show that the reaction to impurity, in bulk, is radically different in the two phases and can be used as a marker for the topological phase. Within the context of the Kane-Mele model for graphene, we find that strictly normalizable in-gap impurity states only occur in the quantum spin Hall phase and carry a dissipationless current whose chirality is determined by the spin and pseudospin of the residing electron.
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Application of a perpendicular magnetic field to charge neutral graphene is expected to result in a variety of broken symmetry phases, including antiferromagnetic, canted, and ferromagnetic. All these phases open a gap in bulk but have very different edge states and noncollinear spin order, recently confirmed experimentally. Here we provide an integrated description of both edge and bulk for the various magnetic phases of graphene Hall bars making use of a noncollinear mean field Hubbard model. Our calculations show that, at the edges, the three types of magnetic order are either enhanced (zigzag) or suppressed (armchair). Interestingly, we find that preformed local moments in zigzag edges interact with the quantum spin Hall like edge states of the ferromagnetic phase and can induce backscattering.
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This study analyzes the traffic generated on YouTube around television series. We selected a sample of 314 short YouTube videos about 21 Spanish TV series that premiered in 2013 by Spain’s three most popular mainstream television networks (Telecinco, Antena 3, and La1). These videos, which together received more than 24 million views, were classified according to two key variables: the nature (official or nonofficial) of the YouTube channel on which they were located and the exclusivity of their content (already broadcast on TV or Web exclusive). The analysis allows us to characterize the strategies used by TV networks on YouTube and the activity of fans as well as their efforts in the construction of a transmedia narrative universe around TV series.
Resumo:
The edges of graphene and graphene like systems can host localized states with evanescent wave function with properties radically different from those of the Dirac electrons in bulk. This happens in a variety of situations, that are reviewed here. First, zigzag edges host a set of localized non-dispersive state at the Dirac energy. At half filling, it is expected that these states are prone to ferromagnetic instability, causing a very interesting type of edge ferromagnetism. Second, graphene under the influence of external perturbations can host a variety of topological insulating phases, including the conventional quantum Hall effect, the quantum anomalous Hall (QAH) and the quantum spin Hall phase, in all of which phases conduction can only take place through topologically protected edge states. Here we provide an unified vision of the properties of all these edge states, examined under the light of the same one orbital tight-binding model. We consider the combined action of interactions, spin–orbit coupling and magnetic field, which produces a wealth of different physical phenomena. We briefly address what has been actually observed experimentally.
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In this study, a methodology based in a dynamical framework is proposed to incorporate additional sources of information to normalized difference vegetation index (NDVI) time series of agricultural observations for a phenological state estimation application. The proposed implementation is based on the particle filter (PF) scheme that is able to integrate multiple sources of data. Moreover, the dynamics-led design is able to conduct real-time (online) estimations, i.e., without requiring to wait until the end of the campaign. The evaluation of the algorithm is performed by estimating the phenological states over a set of rice fields in Seville (SW, Spain). A Landsat-5/7 NDVI series of images is complemented with two distinct sources of information: SAR images from the TerraSAR-X satellite and air temperature information from a ground-based station. An improvement in the overall estimation accuracy is obtained, especially when the time series of NDVI data is incomplete. Evaluations on the sensitivity to different development intervals and on the mitigation of discontinuities of the time series are also addressed in this work, demonstrating the benefits of this data fusion approach based on the dynamic systems.
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