Object Discovery via Cohesion Measurement

Color and intensity are two important components in an image. Usually, groups of image pixels, which are similar in color or intensity, are an informative representation for an object. They are therefore particularly suitable for computer vision tasks, such as saliency detection and object proposal generation. However, image pixels, which share a similar real-world color, may be quite different since colors are often distorted by intensity. In this paper, we reinvestigate the affinity matrices originally used in image segmentation methods based on spectral clustering. A new affinity matrix, which is robust to color distortions, is formulated for object discovery. Moreover, a Cohesion Measurement (CM) for object regions is also derived based on the formulated affinity matrix. Based on the new Cohesion Measurement, a novel object discovery method is proposed to discover objects latent in an image by utilizing the eigenvectors of the affinity matrix. Then we apply the proposed method to both saliency detection and object proposal generation. Experimental results on several evaluation benchmarks demonstrate that the proposed CM based method has achieved promising performance for these two tasks.Comment: 14 pages, 14 figure

Gene regulation in plant herbivory defense: deffect of insect mechanical wounding and chemical oral secretion factors

To study the different roles of mechanical wounding and chemical elicitors caused by insect feeding on plant in regulating plant responses, gene regulation of Arabidopsis thaliana leaves after insect (Plutella xylostella) feeding and sole mechanical wounding (MecWorm) was investigated by whole genome microarray analysis of wounded and systemic leaves. Genes could be assigned to four different types of gene regulation induction: Induced only by (i) chemical or (ii) mechanical factors, induced by mechanical wounding but (iii) changed significantly or (iv) suppressed by chemical factors. Analysis of the gene related pathways showed that after insect feeding, damaged leaves are the direct battle field against insect attack. Plants react by reducing photosynthesis and cell reproduction and shift their major activities in the damaged leaves to defense. Systemic leaves conduct resources production for plant recovery and defense with powerful signaling and communication. In plant-insect interaction gene regulation, mechanical wounding is the major trigger, while chemical factors are fine tuners for a more efficient and biotic stress focused defense machinery. To mimic insect feeding more precisely, both mechanically and chemically, ‘SpitWorm’ was developed based on MecWorm. For the proof of concept delivery speed and dilution of oral secretion of Spodoptera littorals was optimized. By comparing regulation of JA responsive genes and induced volatile emissions of lima bean (Phaseolus lunatus) leaves damaged by S. littoralis, MecWorm, and SpitWorm, it could be shown that SpitWorm is able to induce a volatile bouquet almost identical to herbivory induction, qualitatively and quantitatively and induced a gene regulation pattern identical to S. littoralis feeding

ASIAM-HGNN: Automatic Selection and Interpretable Aggregation of Meta-Path Instances for Heterogeneous Graph Neural Network

In heterogeneous information network (HIN)-based applications, the existing methods usually use Heterogeneous Graph Neural Networks (HGNN) to handle some complex tasks. However, these methods still have some shortcomings: 1) they manually pre-select some meta-paths and thus some important ones are missing, while the missing ones still contains the information and features of the node in the entire graph structure; and 2) they have no high interpretability since they do not consider the logical sequences in an HIN. In order to deal with them, we propose ASIAM-HGNN: a heterogeneous graph neural network combined with the automatic selection and interpretable aggregation of meta-path instances. Our model can automatically filter important meta paths for each node, while preserving the logical sequence between nodes, so as to solve the problems existing in other models. A group of experiments are conducted on real-world datasets, and the results demonstrate that the models learned by our method have a better performance in most of task scenarios

A computational study of positive streamers interacting with dielectrics

We use numerical simulations to study the dynamics of surface discharges, which are common in high-voltage engineering. We simulate positive streamer discharges that propagate towards a dielectric surface, attach to it, and then propagate over the surface. The simulations are performed in air with a two-dimensional plasma fluid model, in which a flat dielectric is placed between two plate electrodes. Electrostatic attraction is the main mechanism that causes streamers to grow towards the dielectric. Due to the net charge in the streamer head, the dielectric gets polarized, and the electric field between the streamer and the dielectric is increased. Compared to streamers in bulk gas, surface streamers have a smaller radius, a higher electric field, a higher electron density, and higher propagation velocity. A higher applied voltage leads to faster inception and faster propagation of the surface discharge. A higher dielectric permittivity leads to more rapid attachment of the streamer to the surface and a thinner surface streamer. Secondary emission coefficients are shown to play a modest role, which is due to relatively strong photoionization in air. In the simulations, a high electric field is present between the positive streamers and the dielectric surface. We show that the magnitude and decay of this field are affected by the positive ion mobility.Comment: 13 pages, 18 figures, 47 reference

Mathematical Problems in Petri Nets Theory and Applications

(Editorial