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short-term memory capacity across Time and Language Estimated from Ancient and Modern Literary Texts. Study-Case: New Testament Translations

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Open Journal of Statistics 2023年第3期13卷 379-403页

作者： Emilio Matricciani Dipartimento di Elettronica Informazione e Bioingegneria (DEIB) Politecnico di Milano Milan Italy

We study the short-term memory capacity of ancient readers of the original New Testament written in Greek, of its translations to Latin and to modern languages. To model it, we consider the number of words between any two contiguous interpunctions Ip, because this parameter can model how the human mind memorizes “chunks” of information. Since IP can be calculated for any alphabetical text, we can perform experiments—otherwise impossible— with ancient readers by studying the literary works they used to read. The “experiments” compare the IP of texts of a language/translation to those of another language/translation by measuring the minimum average probability of finding joint readers (those who can read both texts because of similar short-term memory capacity) and by defining an “overlap index”. We also define the population of universal readers, people who can read any New Testament text in any language. Future work is vast, with many research tracks, because alphabetical literatures are very large and allow many experiments, such as comparing authors, translations or even texts written by artificial intelligence tools.

关键词： Alphabetical Languages Artificial Intelligence Writing Greek Latin New Testament Readers Overlap Probability short-term memory capacity Texts Translation Words Interval

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Performance evaluation of new echo state networks based on complex network

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The Journal of China Universities of Posts and Telecommunications 2012年第1期19卷 87-93页

作者： LIU Xiang CUI Hong-yan ZHOU Tian-jun CHEN Jian-ya Key Laboratory of Network System Architecture and Convergence Beijing University of Posts and TelecommtmicationsBeijing 100876China

Recently, echo state networks （ESN） have aroused a lot of interest in their nonlinear dynamic system modeling capabilities. In a classical ESN, its dynamic reservoir （DR） has a sparse and random topology, but the performance of ESN with its DR taking another kind of topology is still unknown. So based on complex network theory, three new ESNs are proposed and investigated in this paper. The small-world topology, scale-free topology and the mixed topology of small-world effect and scale-free feature are considered in these new ESNs. We studied the relationship between DR architecture and prediction capability. In our simulation experiments, we used two widely used time series to test the prediction performance among the new ESNs and classical ESN, and used the independent identically distributed （i.i.d） time series to analyze the short-term memory （STM） capability. We answer the following questions： What are the differences of these ESNs in the prediction performance？ Can the spectral radius of the internal weights matrix be wider？ What is the short-term memory capability？ The experimental results show that the proposed new ESNs have better prediction performance, wider spectral radius and almost the same STM capacity as classical ESN＇s.

关键词： ESN complex network time series prediction short-term memory capacity

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A Statistical Theory of Language Translation Based on Communication Theory

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Open Journal of Statistics 2020年第6期10卷 936-997页

作者： Emilio Matricciani Dipartimento di Elettronica Informazione e Bioingegneria Politecnico di Milano Milan Italy

We propose the first statistical theory of language translation based on communication theory. The theory is based on New Testament translations from Greek to Latin and to other 35 modern languages. In a text translated into another language, all linguistic variables do numerically change. To study the chaotic data that emerge, we model any translation as a complex communication channel affected by “noise”, studied according to Communication Theory applied for the first time to this channel. This theory deals with aspects of languages more complex than those currently considered in machine translations. The input language is the “signal”, the output language is a “replica” of the input language, but largely perturbed by noise, indispensable, however, for conveying the meaning of the input language to its readers. We have defined a noise-to-signal power ratio and found that channels are differently affected by translation noise. Communication channels are also characterized by channel capacity. The translation of novels has more constraints than the New Testament translations. We propose a global readability formula for alphabetical languages, not available for most of them, and conclude with a general theory of language translation which shows that direct and reverse channels are not symmetric. The general theory can also be applied to channels of texts belonging to the

关键词： Channel capacity Communication Theory Greek Latin Linguistic Variables Modern Languages New Testament Noise-to-Signal Power Ratio Readability Index short-term memory capacity Symmetry

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