Avian brood parasitism — a growing research area in behavioral ecology (part Ⅱ)

正We are pleased to publish the second special issue on avian brood parasitism and to be responsible guest editors for the two special issues of Chinese Birds (Vol. 3, No. 4, 2012 and Vol. 4, No. 1, 2013), entitled "Avian Brood Parasitism - A Growing Research Area in Behavioral Ecology". The first issue was published in December 2012. The goal of the two special issues is to publish accumulated knowledge and some of the recent developments

RESEARCH ON THE COMPLICATED DYNAMICAL BEHAVIORS OF NONLINEAR ECOSYSTEMS(Ⅱ)

This paper is a further study of reference [1]. In this paper, we mainly discuss the complicated dynamical behaviors resulting from a simple one-dimensional model of nonlinear ecosystems: fixed point motion, periodic motion and chaotic motion etc., and briefly discuss the universality of the complicated dynamical behaviors, which can be described by the first and the second M. Feigenbaun. constants. At last, we discuss the 'one-side lowering phenomenon' due to near unstabilization when the nonlinear ecosystem approaches bifurcation points from unbifurcation side. It is of important theoretical and practical meanings both in the development and utilization of ecological resources ar.d in the design and management of artifilial ecosystems.

Image Semantic Segmentation Approach for Studying Human Behavior on Image Data

Image semantic segmentation is an essential technique for studying human behavior through image data.This paper proposes an image semantic segmentation method for human behavior research.Firstly,an end-to-end convolutional neural network architecture is proposed,which consists of a depth-separable jump-connected fully convolutional network and a conditional random field network;then jump-connected convolution is used to classify each pixel in the image,and an image semantic segmentation method based on convolu-tional neural network is proposed;and then a conditional random field network is used to improve the effect of image segmentation of hu-man behavior and a linear modeling and nonlinear modeling method based on the semantic segmentation of conditional random field im-age is proposed.Finally,using the proposed image segmentation network,the input entrepreneurial image data is semantically segmented to obtain the contour features of the person;and the segmentation of the images in the medical field.The experimental results show that the image semantic segmentation method is effective.It is a new way to use image data to study human behavior and can be extended to other research areas.

Exo-atmospheric target discrimination using probabilistic neural network

Exo-atmospheric targets are especially difficult to distinguish using currently available techniques, because all target parts follow the same spatial trajectory. The feasibility of distinguishing multiple type compo- nents of exo-atmospheric targets is demonstrated by applying the probabilistic neural network. Differences in thermM behavior and time-varying signals of space-objects are analyzed during the selection of features used as inputs of the neural network. A novel multi-colorimetric technology is introduced to measure precisely the temporal evolutional characteristics of temperature and emissivity-area products. To test the effectiveness of the recognition algorithm, the results obtained from a set of synthetic multispectral data set are presented and discussed. These results indicate that the discrimination algorithm can obtain a remarkable success rate.

Temporal self-modifying characteristics of pulse propagation in multiphoton absorbers

In this Letter, temporal self-modifying behavior of amplitude modulation pulse propagation characteristics in multiphoton absorbers is presented by solving the underlying theoretical model coupling the propagation equa- tion with the rate equations. The characteristics of the output temporal shapes are of primary concern and are discussed in detail. Amplitude modulation suppressing effects of multiphoton absorbers are numerically dem- onstrated; they have not been reported previously, to our knowledge. By taking a time resolved absorption coefficients, the corresponding physical mechanism is explicitly interpreted.

Visual perception of textiles using surface and display samples

In this study, 15 parameters representing textile properties are evaluated by the psychophysical method of categorical judgment with samples presented either in a light booth or on a display to investigate human visual perception based on the textural features of textiles. Visual perceptions of textiles can be expressed by the properties of regularity, smoothness, and warmth, which respectively explain the geometric placement rules of primitive elements, depth information, and emotion of textiles. Textiles with large primitive elements have high regularity and coarseness, whereas textiles with subtle primitive elements exhibit randomness and smoothness. Texture analysis results of the surface samples coincide well with those of display samples.