Locating influential nodes via dynamics-sensitive centrality

With great theoretical and practical significance, locating influential nodes of complex networks is a promising issues. In this paper, we propose a dynamics-sensitive (DS) centrality that integrates topological features and dynamical properties. The DS centrality can be directly applied in locating influential spreaders. According to the empirical results on four real networks for both susceptible-infected-recovered (SIR) and susceptible-infected (SI) spreading models, the DS centrality is much more accurate than degree, $k$-shell index and eigenvector centrality.Comment: 6 pages, 1 table and 2 figure

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

We introduce an extremely computation-efficient CNN architecture named ShuffleNet, which is designed specially for mobile devices with very limited computing power (e.g., 10-150 MFLOPs). The new architecture utilizes two new operations, pointwise group convolution and channel shuffle, to greatly reduce computation cost while maintaining accuracy. Experiments on ImageNet classification and MS COCO object detection demonstrate the superior performance of ShuffleNet over other structures, e.g. lower top-1 error (absolute 7.8%) than recent MobileNet on ImageNet classification task, under the computation budget of 40 MFLOPs. On an ARM-based mobile device, ShuffleNet achieves ~13x actual speedup over AlexNet while maintaining comparable accuracy

Deep Metric Learning with Angular Loss

The modern image search system requires semantic understanding of image, and a key yet under-addressed problem is to learn a good metric for measuring the similarity between images. While deep metric learning has yielded impressive performance gains by extracting high level abstractions from image data, a proper objective loss function becomes the central issue to boost the performance. In this paper, we propose a novel angular loss, which takes angle relationship into account, for learning better similarity metric. Whereas previous metric learning methods focus on optimizing the similarity (contrastive loss) or relative similarity (triplet loss) of image pairs, our proposed method aims at constraining the angle at the negative point of triplet triangles. Several favorable properties are observed when compared with conventional methods. First, scale invariance is introduced, improving the robustness of objective against feature variance. Second, a third-order geometric constraint is inherently imposed, capturing additional local structure of triplet triangles than contrastive loss or triplet loss. Third, better convergence has been demonstrated by experiments on three publicly available datasets.Comment: International Conference on Computer Vision 201

A Non-Intrusive Pressure Sensor by Detecting Multiple Longitudinal Waves

Pressure vessels are widely used in industrial fields, and some of them are safety-critical components in the system - for example, those which contain flammable or explosive material. Therefore, the pressure of these vessels becomes one of the critical measurements for operational management. In the paper, we introduce a new approach to the design of non-intrusive pressure sensors, based on ultrasonic waves. The model of this sensor is built based upon the travel-time change of the critically refracted longitudinal wave (LCR wave) and the reflected longitudinal waves with the pressure. To evaluate the model, experiments are carried out to compare the proposed model with other existing models. The results show that the proposed model can improve the accuracy compared to models based on a single wave

Spontaneous edge-defect formation and defect-induced conductance suppression in graphene nanoribbons

We present a first-principles study of the migration and recombination of edge defects (carbon adatom and/or vacancy) and their influence on electrical conductance in zigzag graphene nanoribbons (ZGNRs). It is found that at room temperature, the adatom is quite mobile while the vacancy is almost immobile along the edge of ZGNRs. The recombination of an adatom-vacancy pair leads to a pentagon-heptagon ring defect structure having a lower energy than the perfect edge, implying that such an edge-defect can be formed spontaneously. This edge defect can suppresses the conductance of ZGNRs drastically, which provides some useful hints for understanding the observed semiconducting behavior of the fabricated narrow GNRs.Comment: 6 pages, 4 figures, to appear in PR

From Dust To Planetesimal: The Snowball Phase ?

The standard model of planet formation considers an initial phase in which planetesimals form from a dust disk, followed by a phase of mutual planetesimal-planetesimal collisions, leading eventually to the formation of planetary embryos. However, there is a potential transition phase (which we call the "snowball phase"), between the formation of the first planetesimals and the onset of mutual collisions amongst them, which has often been either ignored or underestimated in previous studies. In this snowball phase, isolated planetesimals move on Keplerian orbits and grow solely via the direct accretion of sub-cm sized dust entrained with the gas in the protoplanetary disk. Using a simplified model in which planetesimals are progressively produced from the dust, we consider the expected sizes to which the planetesimals can grow before mutual collisions commence and derive the dependence of this size on a number of critical parameters, including the degree of disk turbulence, the planetesimal size at birth and the rate of planetesimal creation. For systems in which turbulence is weak and the planetesimals are created at a low rate and with relatively small birth size, we show that the snowball growth phase can be very important, allowing planetesimals to grow by a factor of 10^6 in mass before mutual collisions take over. In such cases, the snowball growth phase can be the dominant mode to transfer mass from the dust to planetesimals. Moreover, such growth can take place within the typical lifetime of a protoplanetary gas disk. A noteworthy result is that ... ...(see the paper). For the specific case of close binaries such as Alpha Centauri ... ... (see the paper). From a more general perspective, these preliminary results suggest that an efficient snowball growth phase provides a large amount of "room at the bottom" for theories of planet formation.Comment: Accepted for publication in the Astrophysical Journal. 15 pages, 4 figures, 1 tabl