70,623 research outputs found
Japanese Modernism And Cine-Text : Fragments And Flows At Empire\u27s Edge In Kitagawa Fuyuhiko And Yokomitsu Riichi
This article notes that Kitagawa Fuyuhiko\u27s writings from the 1920s and 1930s, together with the contemporaneous works of prose author Yokomitsu Riichi, are strongly marked by the confluence of the literary and the cinematic. Kitagawa and Yokomitsu\u27s engagement with film was not limited to a fascination with the precision, objectivity, or mobility of the “camera eye.” Rather, it extended to the entire ability of the cinematic apparatus to capture the temporality of objects in motion, and of the ability of the filmmaker to organize segments of space into a new synthetic whole. The article explores this confluence through a brief examination of four instances of “cine-text”: Kitagawa\u27 poetry collection War, Yokomitsu\u27 novel Shanghai, the concept of literary formalism Yokomitsu proposed around the year 1930, and the theory of the “prose film” that Kitagawa unveiled in the following decade
The ReaxFF reactive force-field : development, applications and future directions
The reactive force-field (ReaxFF) interatomic potential is a powerful computational tool for exploring, developing and optimizing material properties. Methods based on the principles of quantum mechanics (QM), while offering valuable theoretical guidance at the electronic level, are often too computationally intense for simulations that consider the full dynamic evolution of a system. Alternatively, empirical interatomic potentials that are based on classical principles require significantly fewer computational resources, which enables simulations to better describe dynamic processes over longer timeframes and on larger scales. Such methods, however, typically require a predefined connectivity between atoms, precluding simulations that involve reactive events. The ReaxFF method was developed to help bridge this gap. Approaching the gap from the classical side, ReaxFF casts the empirical interatomic potential within a bond-order formalism, thus implicitly describing chemical bonding without expensive QM calculations. This article provides an overview of the development, application, and future directions of the ReaxFF method
Maximum Entropy, Word-Frequency, Chinese Characters, and Multiple Meanings
The word-frequency distribution of a text written by an author is well
accounted for by a maximum entropy distribution, the RGF (random group
formation)-prediction. The RGF-distribution is completely determined by the a
priori values of the total number of words in the text (M), the number of
distinct words (N) and the number of repetitions of the most common word
(k_max). It is here shown that this maximum entropy prediction also describes a
text written in Chinese characters. In particular it is shown that although the
same Chinese text written in words and Chinese characters have quite
differently shaped distributions, they are nevertheless both well predicted by
their respective three a priori characteristic values. It is pointed out that
this is analogous to the change in the shape of the distribution when
translating a given text to another language. Another consequence of the
RGF-prediction is that taking a part of a long text will change the input
parameters (M, N, k_max) and consequently also the shape of the frequency
distribution. This is explicitly confirmed for texts written in Chinese
characters. Since the RGF-prediction has no system-specific information beyond
the three a priori values (M, N, k_max), any specific language characteristic
has to be sought in systematic deviations from the RGF-prediction and the
measured frequencies. One such systematic deviation is identified and, through
a statistical information theoretical argument and an extended RGF-model, it is
proposed that this deviation is caused by multiple meanings of Chinese
characters. The effect is stronger for Chinese characters than for Chinese
words. The relation between Zipf's law, the Simon-model for texts and the
present results are discussed.Comment: 15 pages, 10 figures, 2 table
Personalized Fuzzy Text Search Using Interest Prediction and Word Vectorization
In this paper we study the personalized text search problem. The keyword
based search method in conventional algorithms has a low efficiency in
understanding users' intention since the semantic meaning, user profile, user
interests are not always considered. Firstly, we propose a novel text search
algorithm using a inverse filtering mechanism that is very efficient for label
based item search. Secondly, we adopt the Bayesian network to implement the
user interest prediction for an improved personalized search. According to user
input, it searches the related items using keyword information, predicted user
interest. Thirdly, the word vectorization is used to discover potential targets
according to the semantic meaning. Experimental results show that the proposed
search engine has an improved efficiency and accuracy and it can operate on
embedded devices with very limited computational resources
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