The Distribution and Importance of Arthropod Pests and Weeds of Agriculture and Forestry Plantations in Southern China

Crop Production/Industries,

Review and key of East Palaearctic species of the genus Podismopsis Zubovsky (Orthoptera: Acridoidea) with description of a new species from China

In this paper, a list and key of all East Palaearctic Podismopsis species are presented. In addition, Podismopsis squamopennis sp. n. is described from the Small Northern Lake of Heilongjiang province in China. It mostly resembles P. gynaemorpha Ikonnov

Genus Gelastorhinus Brunner-Wattenwyl (Orthoptera: Acridoidea) in China with description of a new species

A new species of the genus Gelastorhinus is described from Liaoning, China. The new species closely resembles Gelastorhinus filatus (Walker, 1870). In addition, an identification key for the Chinese species of Gelastorhinus is presented

Product-based Neural Networks for User Response Prediction

Predicting user responses, such as clicks and conversions, is of great importance and has found its usage in many Web applications including recommender systems, web search and online advertising. The data in those applications is mostly categorical and contains multiple fields; a typical representation is to transform it into a high-dimensional sparse binary feature representation via one-hot encoding. Facing with the extreme sparsity, traditional models may limit their capacity of mining shallow patterns from the data, i.e. low-order feature combinations. Deep models like deep neural networks, on the other hand, cannot be directly applied for the high-dimensional input because of the huge feature space. In this paper, we propose a Product-based Neural Networks (PNN) with an embedding layer to learn a distributed representation of the categorical data, a product layer to capture interactive patterns between inter-field categories, and further fully connected layers to explore high-order feature interactions. Our experimental results on two large-scale real-world ad click datasets demonstrate that PNNs consistently outperform the state-of-the-art models on various metrics.Comment: 6 pages, 5 figures, ICDM201

Mathematical Modeling and Intelligent Algorithm for Multirobot Path Planning

10.1155/2017/1465158Mathematical Problems in Engineering2017146515

GeoUDF: Surface Reconstruction from 3D Point Clouds via Geometry-guided Distance Representation

We present a learning-based method, namely GeoUDF,to tackle the long-standing and challenging problem of reconstructing a discrete surface from a sparse point cloud.To be specific, we propose a geometry-guided learning method for UDF and its gradient estimation that explicitly formulates the unsigned distance of a query point as the learnable affine averaging of its distances to the tangent planes of neighboring points on the surface. Besides,we model the local geometric structure of the input point clouds by explicitly learning a quadratic polynomial for each point. This not only facilitates upsampling the input sparse point cloud but also naturally induces unoriented normal, which further augments UDF estimation. Finally, to extract triangle meshes from the predicted UDF we propose a customized edge-based marching cube module. We conduct extensive experiments and ablation studies to demonstrate the significant advantages of our method over state-of-the-art methods in terms of reconstruction accuracy, efficiency, and generality. The source code is publicly available at https://github.com/rsy6318/GeoUDF

Noise bridges dynamical correlation and topology in coupled oscillator networks

We study the relationship between dynamical properties and interaction patterns in complex oscillator networks in the presence of noise. A striking finding is that noise leads to a general, one-to-one correspondence between the dynamical correlation and the connections among oscillators for a variety of node dynamics and network structures. The universal finding enables an accurate prediction of the full network topology based solely on measuring the dynamical correlation. The power of the method for network inference is demonstrated by the high success rate in identifying links for distinct dynamics on both model and real-life networks. The method can have potential applications in various fields due to its generality, high accuracy and efficiency.Comment: 2 figures, 2 tables. Accepted by Physical Review Letter