Synchronization in networks of networks: the onset of coherent collective behavior in systems of interacting populations of heterogeneous oscillators

The onset of synchronization in networks of networks is investigated. Specifically, we consider networks of interacting phase oscillators in which the set of oscillators is composed of several distinct populations. The oscillators in a given population are heterogeneous in that their natural frequencies are drawn from a given distribution, and each population has its own such distribution. The coupling among the oscillators is global, however, we permit the coupling strengths between the members of different populations to be separately specified. We determine the critical condition for the onset of coherent collective behavior, and develop the illustrative case in which the oscillator frequencies are drawn from a set of (possibly different) Cauchy-Lorentz distributions. One motivation is drawn from neurobiology, in which the collective dynamics of several interacting populations of oscillators (such as excitatory and inhibitory neurons and glia) are of interest.Comment: The original was replaced with a version that has been accepted to Phys. Rev. E. The new version has the same content, but the title, abstract, and the introductory text have been revise

Image super-resolution with dense-sampling residual channel-spatial attention networks for multi-temporal remote sensing image classification

Image super-resolution (SR) techniques can benefit a wide range of applications in the remote sensing (RS) community, including image classification. This issue is particularly relevant for image classification on time series data, considering RS datasets that feature long temporal coverage generally have a limited spatial resolution. Recent advances in deep learning brought new opportunities for enhancing the spatial resolution of historic RS data. Numerous convolutional neural network (CNN)-based methods showed superior performance in terms of developing efficient end-to-end SR models for natural images. However, such models were rarely exploited for promoting image classification based on multispectral RS data. This paper proposes a novel CNNbased framework to enhance the spatial resolution of time series multispectral RS images. Thereby, the proposed SR model employs Residual Channel Attention Networks (RCAN) as a backbone structure, whereas based on this structure the proposed models uniquely integrate tailored channel-spatial attention and dense-sampling mechanisms for performance improvement. Subsequently, state-of-the-art CNN-based classifiers are incorporated to produce classification maps based on the enhanced time series data. The experiments proved that the proposed SR model can enable unambiguously better performance compared to RCAN and other (deep learning-based) SR techniques, especially in a domain adaptation context, i.e., leveraging Sentinel-2 images for generating SR Landsat images. Furthermore, the experimental results confirmed that the enhanced multi-temporal RS images can bring substantial improvement on fine-grained multi-temporal land use classification

Multitemporal Relearning with Convolutional LSTM Models for Land Use Classification

In this article, we present a novel hybrid framework, which integrates spatial–temporal semantic segmentation with postclassification relearning, for multitemporal land use and land cover (LULC) classification based on very high resolution (VHR) satellite imagery. To efficiently obtain optimal multitemporal LULC classification maps, the hybrid framework utilizes a spatial–temporal semantic segmentation model to harness temporal dependency for extracting high-level spatial–temporal features. In addition, the principle of postclassification relearning is adopted to efficiently optimize model output. Thereby, the initial outcome of a semantic segmentation model is provided to a subsequent model via an extended input space to guide the learning of discriminative feature representations in an end-to-end fashion. Last, object-based voting is coupled with postclassification relearning for coping with the high intraclass and low interclass variances. The framework was tested with two different postclassification relearning strategies (i.e., pixel-based relearning and object-based relearning) and three convolutional neural network models, i.e., UNet, a simple Convolutional LSTM, and a UNet Convolutional-LSTM. The experiments were conducted on two datasets with LULC labels that contain rich semantic information and variant building morphologic features (e.g., informal settlements). Each dataset contains four time steps from WorldView-2 and Quickbird imagery. The experimental results unambiguously underline that the proposed framework is efficient in terms of classifying complex LULC maps with multitemporal VHR images

Using deep neural networks for predictive modelling of informal settlements in the context of flood risk

Abstract Global climate change has substantially increased the risks of cities being adversely affected by natural hazards such as floods. Among the inhabitants of cities at risk, residents dwelling in informal settlements are the most vulnerable group. To identify the future exposure of informal settlements, we adopt a data-driven model from the machine learning domain to anticipate the growth patterns of formal and informal settlements in flood-prone areas. The potential emergence of informal settlements in Shenzhen, China, is predicted by the proposed method. Then, through an analysis of the flood susceptibility of the predicted informal settlement areas, the emerging vulnerability of Shenzhen towards flooding is revealed.</jats:p

Exact Results for the Kuramoto Model with a Bimodal Frequency Distribution

We analyze a large system of globally coupled phase oscillators whose natural frequencies are bimodally distributed. The dynamics of this system has been the subject of long-standing interest. In 1984 Kuramoto proposed several conjectures about its behavior; ten years later, Crawford obtained the first analytical results by means of a local center manifold calculation. Nevertheless, many questions have remained open, especially about the possibility of global bifurcations. Here we derive the system's complete stability diagram for the special case where the bimodal distribution consists of two equally weighted Lorentzians. Using an ansatz recently discovered by Ott and Antonsen, we show that in this case the infinite-dimensional problem reduces exactly to a flow in four dimensions. Depending on the parameters and initial conditions, the long-term dynamics evolves to one of three states: incoherence, where all the oscillators are desynchronized; partial synchrony, where a macroscopic group of phase-locked oscillators coexists with a sea of desynchronized ones; and a standing wave state, where two counter-rotating groups of phase-locked oscillators emerge. Analytical results are presented for the bifurcation boundaries between these states. Similar results are also obtained for the case in which the bimodal distribution is given by the sum of two Gaussians.Comment: 28 pages, 7 figures; submitted to Phys. Rev. E Added comment

Pharmacokinetics of Py-Im Polyamides Depend on Architecture: Cyclic versus Linear

The pharmacokinetic properties of three pyrrole-imidazole (Py-Im) polyamides of similar size and Py-Im content but different shape were studied in the mouse. Remarkably, hairpin and cyclic oligomers programmed for the same DNA sequence 5′-WGGWWW-3′ displayed distinct pharmacokinetic properties. Furthermore, the hairpin 1 and cycle 2 exhibited vastly different animal toxicities. These data provide a foundation for design of DNA binding Py-Im polyamides to be tested in vivo

Acupuncture for frozen-thawed embryo transfer cycles: A double-blind randomized controlled trial

The role of acupuncture on the pregnancy rate has not been evaluated in frozen-thawed embryo transfer (FET) cycles. This randomized double-blind study aimed to determine whether acupuncture performed on the day of FET improves clinical outcomes. On the day of FET, 226 patients were randomly allocated to either real or placebo acupuncture according to a computergenerated randomization list in sealed opaque envelopes. They received a session of real or placebo acupuncture for 25 min on site immediately after FET. The anxiety level and serum cortisol concentration were evaluated before and after real and placebo acupuncture. There were no significant differences in rates of overall pregnancy, clinical pregnancy, ongoing pregnancy, live birth and implantation in the placebo acupuncture group, when compared with the real acupuncture group. The anxiety level and serum cortisol concentration were similar for both groups. Only the placebo acupuncture group had significantly higher ongoing pregnancy (P = 0.022) and implantation rates (P = 0.038) than those who declined to join the study and received no acupuncture. In conclusion, comparable pregnancy and live birth rates of FET treatment were found in patients who had one session of real or placebo acupuncture after FET. © 2010, Reproductive Healthcare Ltd. Published by Elsevier Ltd. All rights reserved.postprin

Spin Seebeck insulator

Thermoelectric generation is an essential function of future energy-saving technologies. However, this generation has been an exclusive feature of electric conductors, a situation which inflicts a heavy toll on its application; a conduction electron often becomes a nuisance in thermal design of devices. Here we report electric-voltage generation from heat flowing in an insulator. We reveal that, despite the absence of conduction electrons, a magnetic insulator LaY2Fe5O12 converts a heat flow into spin voltage. Attached Pt films transform this spin voltage into electric voltage by the inverse spin Hall effect. The experimental results require us to introduce thermally activated interface spin exchange between LaY2Fe5O12 and Pt. Our findings extend the range of potential materials for thermoelectric applications and provide a crucial piece of information for understanding the physics of the spin Seebeck effect.Comment: 19 pages, 5 figures (including supplementary information