IV. Ecosmomics: Independent, UniVersal, Complex Network Systems and a Genetic Code-Script Source

3. Iteracy: A Rosetta Ecosmos Textuality

Yang, Shuo, et al. DNA as a universal chemical substrate for computing and data storage.. Nature Reviews Chemistry.. 8/179, 2024. Shanghai Jiao Tong University, China and Eindhoven University of Technology, the Netherlands including Stephen Mann describe an inherent facility of nature’s helical nucleotides to be capable of further informational abilities and services.

DNA computing and data storage use nucleotide molecules as a computing substrate or a storage medium. In this Review, we explore how DNA can be leveraged with a focus on neural networks and compartmentalized circuits. We discuss emerging approaches to the storage of data in DNA and associated topics such as the writing, reading, retrieval and post-synthesis editing of DNA-encoded data. Finally, we explore the use of DNA for near-memory computing for future information technology and health analysis applications. (Excerpt)

Yildiz, Izzet, et al. From Birdsong to Human Speech Recognition: Bayesian Inference on a Hierarchy of Nonlinear Dynamical Systems. PLoS Computational Biology. 9/9, 2013. Within our Rosetta Cosmos, Max Planck Institute for Human Cognitive and Brain Sciences researchers quantify that evolution has endowed both birds and people with similar modes of coded, informational sound transmission and neural reception. Once again, nature utilizes this same complex organization principles everywhere and everyone.

Our knowledge about the computational mechanisms underlying human learning and recognition of sound sequences, especially speech, is still very limited. One difficulty in deciphering the exact means by which humans recognize speech is that there are scarce experimental findings at a neuronal, microscopic level. Here, we show that our neuronal-computational understanding of speech learning and recognition may be vastly improved by looking at an animal model, i.e., the songbird, which faces the same challenge as humans: to learn and decode complex auditory input, in an online fashion. Motivated by striking similarities between the human and songbird neural recognition systems at the macroscopic level, we assumed that the human brain uses the same computational principles at a microscopic level and translated a birdsong model into a novel human sound learning and recognition model with an emphasis on speech. We show that the resulting Bayesian model with a hierarchy of nonlinear dynamical systems can learn speech samples such as words rapidly and recognize them robustly, even in adverse conditions. (Abstract)

As a model, we employ a novel Bayesian recognition method of dynamical sensory input such as birdsong and speech. The Bayesian approach first requires building of a so-called generative (internal) model, which is then converted to a learning and recognition model. The key advantage of this approach, as opposed to standard models in both human speech recognition and automatic speech recognition, is that the generative model is formulated as hierarchically structured, nonlinear dynamical systems. This means that one can employ generative models specifically tailored to birdsong or speech recognition. (2)

Yose, Joseph, et al. Network Analysis of the Viking Age in Ireland as Portrayed in Cogadh Gaedhel re Gallaibh. arXiv:1707.07526. A millennium later almost to the year, United Kingdom systems scholars Yose and Ralph Kenna, Coventry University, Mairin MacCarron, University of Sheffield, and Padraig MacCarron, Social & Evolutionary Neurscience Research Group, Oxford University parse this classic Irish epic to show how even such classic literature is amenable to, and exemplifies the latest complexity theories.

Cogadh Gaedhel re Gallaibh ("The War of the Gaedhil with the Gaill") is a medieval Irish text, telling how an army under the leadership of Brian Boru challenged Viking invaders and their allies in Ireland, culminating with the Battle of Clontarf in 1014. Brian's victory is widely remembered for breaking Viking power in Ireland, although much modern scholarship disputes traditional perceptions. Here we introduce quantitative measures to the discussions. We present statistical analyses of network data embedded in the text to position its sets of interactions on a spectrum from the domestic to the international. This delivers a picture that lies between antipodal traditional and revisionist extremes. Additionally, we quantitatively compare the network properties of Cogadh Gaedhel re Gallaibh to those of other epic-type narratives and find that, in many ways, they resemble those of the Iliad. (Abstract excerpts)

Yu, Shuiyuan, et al. Zipf’s Law in 50 Languages: Its Structural Pattern, Linguistic Interpretation, and Cognitive Motivation. arXiv:1807.01855. Communication University, Anhui Jianzhu University, and Zhejiang University linguists expand studies of this mathematical regularity that seems to suffuse and arrange all manner of textual scripts. It is here traced to and found in every speech and dialect, which serves to prove its deep natural inherence.

Zipf's law has been found in many human-related fields, including language, where the frequency of a word is persistently found as a power law function of its frequency rank. However, there is much dispute whether it is a universal law or a statistical artifact. To resolve this issue, our study conducted a large scale cross language investigation into Zipf's law. The statistical results show that Zipf's laws in 50 languages all share a 3-segment structural pattern, with each segment demonstrating distinctive linguistic properties. This finding indicates that this deviation is a fundamental and universal feature of word frequency distributions in natural languages, not a statistical error. A computer simulation based on the dual-process theory yields the same structural pattern, suggesting that Zipf's law of natural languages are motivated by common cognitive mechanisms. (Abstract edits)

Zipf's law is an empirical formula using mathematical statistics. It is named after the linguist George Kingsley Zipf (1902-1950), who first proposed it. Zipf's law states that given a large sample of words, the frequency of any word is inversely proportional to its rank in the frequency table. Thus the most frequent word will occur about twice as often as the second most frequent word, three times as often as the third most frequent word, etc. The same relationship occurs in many other rankings, unrelated to language, such as the population ranks of cities in various countries, corporation sizes, income rankings, etc. (Simple English Wikipedia)

Zaharchuk, Holly and Elizabeth Karuza. Multilayer Networks: An Untapped Tool for Understanding Bilingual Neurocognition. Brain and Language. Volume 220, 2021. Penn State linguists perceptively describe how these same connective complexities which distinguish every other natural realm also provide structural topologies across our many languages.

Cross-linguistic similarity is a term so broad and multi-faceted that it is not easily defined. In regard, we consider what makes two languages similar or not increases in complexity when multiple levels of the language hierarchy (e.g., phonology, syntax). How can we account for convergence and divergence at each level of representation, as well as interactions between them? The growing field of network science brings insightful new methodologies to bear on this persistent issue. We summarize current network science approaches to modeling language structure and discuss implications for understanding various linguistic processes. (Abstract)

Zeng, William and Bob Coecke. Quantum Algorithms for Compositional Natural Language Processing. Electronic Proceedings in Theoretical Computer Science. 221, 2016. A paper by Rigetti Computing and Oxford University theorists for the 2016 Workshop on Semantic Spaces at the Intersection of NLP, Physics and Cognitive Science (Google) held in Glasgow, Scotland, also at arXiv:1608.01406. Among a growing number, it considers inherent physical affinities for emergent human conversations.

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