Previously, synchronization of spin-torque oscillator ͑STO͒ has usually been analyzed by phase oscillator model. Here we show that STO is more precisely described as a perturbed heteroclinic cycle where the frequency is sensitive to the distance between the cycle and a saddle. In the presence of external signal or coupling, the frequency changes mainly due to the modification on this distance rather than on the phase directly. Multiple attractors coexist and synchronization depends sensitively on the initial conditions when a STO is driven by an external ac. We reveal that these properties underlay the mechanisms why synchronization region in two serially connected STOs is quite small and why time delay can enhance synchronization. When more STOs are added, the parameter region for a globally attracting synchronization state may disappear. Our analysis suggests that initial conditions have to be controlled or different designs have to be fabricated in order to obtain robust synchronization of a large number of STOs for the purpose of applications.

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Skeleton-based action recognition has attracted considerable attention since the skeleton data is more robust to the dynamic circumstances and complicated backgrounds than other modalities. Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. However, conventional GCNs are feedforward networks for which it is impossible for the shallower layers to access semantic information in the high-level layers. In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN). This is the first work that introduces a feedback mechanism into GCNs for action recognition. Compared with conventional GCNs, FGCN has the following advantages:(1) A multi-stage temporal sampling strategy is designed to extract spatial-temporal features for action recognition in a coarse to fine process; (2) A Feedback Graph Convolutional Block (FGCB) is proposed to introduce dense feedback connections into the GCNs. It transmits the high-level semantic features to the shallower layers and conveys temporal information stage by stage to model video level spatial-temporal features for action recognition; (3) The FGCN model provides predictions on-the-fly. In the early stages, its predictions are relatively coarse. These coarse predictions are treated as priors to guide the feature learning in later stages, to obtain more accurate predictions. Extensive experiments on three datasets, NTU-RGB+D, NTU-RGB+D120 and Northwestern-UCLA, demonstrate that the proposed FGCN is effective for action recognition. It achieves the state-of-the-art performance on all three datasets.

Nitrate-nitrogen (NO-N) concentrations threaten water supplies and contribute to impairments of surface water resources. In this study, we analyzed concentration trends at 60 ambient river monitoring sites in Iowa for the years 1998 to 2012 to assess the presence of linear trends in the NO-N concentration data using a time-series method that accounted for temporal correlation and combined the trend information from individual sites into an assessment of the state-wide rate of change in river NO-N concentrations. Forty-six of the sites had sufficient records for trend analysis. Study results indicated that 37 out of 46 sites (80%) did not have statistically significant trends over the monitoring period ( > 0.1). Six monitoring sites in western Iowa had statistically significant increasing trends ( < 0.05), and three additional sites located in western and southern Iowa showed nominally significant increasing trends ( < 0.1). The rate of statistically significant increases ranged from 0.15 to 0.33 mg L yr. Aggregated across the state, the overall trend of NO-N concentrations in Iowa rivers is increasing, with an average and median rate of 0.05 and 0.03 mg L yr, respectively. Increasing concentration is likely associated with increasing trends in fertilizer sales and animal production, but better tracking is needed to establish a definitive relation. Reducing NO-N concentrations using conservation practices is a major focus of the recently proposed Iowa Nutrient Reduction Strategy, and our study provides an important milestone preceding implementation of the strategy.

Optocoupler is fabricated by applying two separated devices as its two important composition units, the photoemitter (input) and photosensor (output), and then combining them together. It is difficult to realize precise pixel-to-pixel alignment when the active area becomes smaller and smaller, which is a limitation in the microelectronics applications. Based on a double-side Indium Tin Oxide (ITO) substrate, a highly integratable organic optocoupler (OOC) was developed. It can reduce the limitations mentioned above of traditional optocouplers. The current transfer ratio can be enhanced to the maximum 1.87% by changing the light-emitting area. And the isolation voltage can also be improved to over 3000 V by the patterning lithography treatment to ITO substrate. This sort of OOC has a promising application in the micromation of electronic devices and can be also used in low-voltage-control circuits.