Recent dielectric constant theory and its relation to problems of electrical insulation

There has been indicated the importance of a knowledge of the chemical and capillary structures of insulating materials if electrical energy losses in them are to be explained.It has been demonstrated that there are available, first, methods by which the structures of their simpler molecules may be determined, second, methods by which the sizes and shapes of their aggregates may be approximated, and third, methods by which it may be expected that something may be learned concerning the size, form and arrangement of their capillaries. In certain cases these methods have been given in some detail, while in others they have only been mentioned. Their nature and the importance of their solution make them deserving of every attention the chemist and physicist can devote to them.     

城市的意义

一、城市与城市化城市是什么？刘易斯·芒福德在《城市文化》中对城市的功能做了这样的阐释：“城市,就是人类社会权力和历史文化所形成的一种最大限度的汇聚体。在城市这种地方．人类社会散发出来的一条条互不相同的光束,     

Wave refraction at a moving dielectric layer

The interaction between a moving dielectric sheet and the radiation field to which it is exposed is analyzed. The reflection and transmission factors are obtained, and the relevant resonance patterns are exhibited.     

Topic analysis using a finite mixture model

Addressed here is the issue of ‘topic analysis’ which is used to determine a text’s topic structure, a representation indicating what topics are included in a text and how those topics change within the text. Topic analysis consists of two main tasks: topic identification and text segmentation. While topic analysis would be extremely useful in a variety of text processing applications, no previous study has so far sufficiently addressed it. A statistical learning approach to the issue is proposed in this paper. More specifically, topics here are represented by means of word clusters, and a finite mixture model, referred to as a stochastic topic model (STM), is employed to represent a word distribution within a text. In topic analysis, a given text is segmented by detecting significant differences between STMs, and topics are identified by means of estimation of STMs. Experimental results indicate that the proposed method significantly outperforms methods that combine existing techniques.