(logo) Natural Genesis (logo text)
A Sourcebook for the Worldwide Discovery of a Creative Organic Universe
Table of Contents
Introduction
Genesis Vision
Learning Planet
Organic Universe
Earth Life Emerge
Genesis Future
Glossary
Recent Additions
Search
Submit

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

3. Iteracy: A Rosetta Ecosmos Textuality

Ramiro, Christian, et al. Algorithms in the Historical Emergence of Word Senses. Proceedings of the National Academy of Sciences. 115/2323, 2018. We cite this by UC Berkeley, Lehigh University and University of Toronto cognitive and computational psychologists as an example of how our copious textual writings can be yet treated by and seen to express a similar programmic source as many other realms. And by turns cosmic genesis nature takes on a textual essence as a written universal narrative, of which we ordained readers are now invited, meant, to save, continue, and creatively embellish the story.

Human language relies on a finite lexicon to express a potentially infinite set of ideas. A key result of this tension is that words acquire novel senses over time. However, the cognitive processes that underlie the historical emergence of new word senses are poorly understood. Here, we present a computational framework that formalizes competing views of how new senses of a word might emerge by attaching to existing senses of the word. We test the ability of the models to predict the temporal order in which the senses of individual words have emerged, using an historical lexicon of English spanning the past millennium. Our findings suggest that word senses emerge in predictable ways, following an historical path that reflects cognitive efficiency, predominantly through a process of nearest-neighbor chaining. (Abstract)

Ravandi, Babak and Valentina Concu. The Hierarchical Organization of Syntax. arXiv:2112.05783. Northeastern University, Network Science Institute (Albert Barabasi) linguists contribute novel insights into how human language can be appreciated another, complex network adaptive systems as everywhere else. Once again, an independent universality is deeply implied, and our communicative mores, broadly conceived, seem to take on a similar guise to genomes, cerebral cognition and so forth.

Hierarchies are the backbones of complex systems and their analysis allows for a deeper understanding of their structure and how they evolve. We consider languages to be also complex adaptive systems. In one regard, we analyzed the scalar organization of historical syntactic networks from a corpus of German texts from the 11th to 17th centuries. We tracked the emergence of nested, hierarchical structures and mapped them to specific communicative needs. We then hypothesise that the communicative needs of speakers are the organizational force of syntax. We propose that these multiple communicative scales lead to syntax, and are a prerequisite to the Zipf's law. Thus we observe that the objective of language evolution is not only to more efficient information transfer but increases our capacity to communicate more sophisticated abstractions as we advance as a species. (Abstract excerpt)

Reagan, Andrew, et al. The Emotional Arcs of Stories are Dominated by Six Basic Shapes. EPJ Data Science. 5:31, 2016. By way of the latest computational prowess, University of Vermont complex system theorists including Peter Dobbs and Christopher Danforth are able to distill a small set of archetypal storyline themes: rags to riches (rise), tragedy (fall), fall to rise, Icarus (rise-fall), Cinderella (rise-fall-rise) and Oedipus (fall-rise-fall). By virtue of such fascinating findings, this posting has become the most accessed for the online journal. A sample of methods includes self-organizing maps, principle component analysis SVD, and hierarchical clustering. See also Sentiment Analysis Methods for Understanding Large-Scale Texts by this team herein (6:28, 2017). For much more see A. Reagan’s UV doctoral thesis Towards a Science of Human Stories at arXiv:1712.06163.

Advances in computing power, natural language processing, and digitization of text now make it possible to study a culture’s evolution through its texts using a ‘big data’ lens. Our ability to communicate relies in part upon a shared emotional experience, with stories often following distinct emotional trajectories and forming patterns that are meaningful to us. Here, by classifying the emotional arcs for a filtered subset of 1,327 stories from Project Gutenberg’s fiction collection, we find a set of six core emotional arcs which form the essential building blocks of complex emotional trajectories. We strengthen our findings by separately applying matrix decomposition, supervised learning, and unsupervised learning. For each of these six core emotional arcs, we examine the closest characteristic stories in publication today and find that particular emotional arcs enjoy greater success, as measured by downloads. (Abstract)

Sboev, Alexander, et al. The Complex of Neural Networks and Probabilistic Methods for Mathematical Modeling of the Syntactic Structure of a Sentence of Natural Language. Journal of Physics: Conference Series. 681/012011, 2016. A presentation at the International Conference on Computer Simulation in Physics and Beyond 2015 in Moscow by Russian National Research Centre physicists. Akin to the Maths Meets Myths paper by Kenna and Mac Carron at the same meeting, complex system dynamics, as deep learning neural networks, are found to provide an effective way to analyze literary archives. A universal cross-affinity is thus revealed to connect an apparently textual cosmos with our pervasive written cultures.

Schloss, Patrick and Jo Handelsman. The Last Word: Books as a Statistical Metaphor for Microbial Communities. Annual Review of Microbiology. 61/23, 2007. University of Wisconsin biologists offer an intriguing thought exercise by way of an analogous comparison of bacterial colonies with textual libraries.

Microbial communities contain unparalleled complexity, making them difficult to describe and compare. Characterizing this complexity will contribute to understanding the ecological processes that drive microbe-host interactions, bioremediation, and biogeochemistry. Moreover, an estimate of species richness will provide an indication of the completeness of a community profile. Such estimates are difficult, however, because community structure rarely fits a well-defined distribution. We present a model based on the word usage in books to illustrate the power of statistical tools in describing microbial communities and suggesting biological hypotheses. The model also generates data to test these methods when there are insufficient data in the literature. For example, by simulating the word distribution in books, we can predict the number of words that must be read to estimate the size of the vocabulary used to write the book. Combined with other models that have been used to make inaccessible problems tractable, our book model offers a unique approach to the complex problem of describing microbial diversity. (Abstract)

Within the book model, each word in a book represents a 16S rRNA gene sequence. Each distinct word that the author used represents a different OTU (Operational Taxonomic Units, perhaps species) in a sequence collection (species richness). The frequency of each word in the book represents the frequency of OTUs found in a 16S rRNA gene sequence collection (frequency distribution). The combined frequency and vocabulary of words used in a book therefore represents community structure and can be used to make comparisons among different books or communities. (16)

There are numerous other ways that we could use the book model to describe microbial communities. For instance, our analysis has focused on lexical data, such as the raw number of times different words were used. We could also consider content data, which assigns individual words a function, context, and tone. Instead of measuring the context of words, we might be interested in understanding the organization of a community at the gene, operon, genome, and metagenome levels. A common analogy for the human genome is a collection of 23 volumes that tell the story of each of us. Considering that the number of bacteria that live within and on us exceeds our own human cells by a factor of 10 to 100, perhaps it is time to start thinking about the ways in which the other books on the bookshelf of life affect that 23 volume work. (32)

Semple, Stuart, et al. Linguistic Laws in Biology.. Trends in Ecology and Evolution. October, 2021. SS, University of Roehampton, London, Ramon Ferrer-i-Cancho, Quantitative Linguistics, Relational Algorithmics, Learning Research Group, Polytechnic University of Catalunya, Barcelona and Morgan Gustison, Integrative Biology, UT Austin propose that recent advances so to interpret (parse) written script and oral conversation by way of self-organizing complex network theories has reached a maturity so that they can be compared with and integrated into biological phenomena. And we note how well this synthesis accords with our 2020s theme of a natural genesis narrative. A broad array of 118 references are posted in support.

Linguistic laws, the common statistical patterns of human language, have been investigated by quantitative linguists for some decades. Recently, biologists have started to note and study the prevalence of these laws beyond this field to find patterns consistent with linguistic laws across multiple levels of biological organisation from molecular (genomes, genes, and proteins) to organismal (animal behaviour) to ecological (populations and ecosystems). We propose a new conceptual framework for the study of linguistic laws in biology, comprising and integrating distinct levels from description and prediction to novel theories. Adopting this framework will provide critical new insights into the fundamental rules of organisation underpinning natural systems, unifying linguistic laws and core theory in biology. (Abstract)

Seoane, Luis and Ricard Sole. The Morphospace of Language Networks. arXiv:1803.01934. MIT Center for Brains, Minds + Machines (Google) and ICREA-Complex Systems Lab, Universitat Pompeu Fabra, Barcelona polymaths contribute to novel perceptions of nature’s ubiquitous networks as they are found to grace and structure even human linguistic discourse.

Language can be described as a network of interacting objects with different qualitative properties and complexity. These networks include semantic, syntactic, or phonological levels and have been found to provide a new picture of language complexity and its evolution. Most studies of language evolution deal in a way or another with such theoretical contraption and explore the outcome of diverse forms of selection on the communication matrix that somewhat optimizes communication. Here we present a detailed analysis of network properties on a generic model of a communication code, which reveals a rather complex and heterogeneous morphospace of language networks. Additionally, we use curated data of English words to locate and evaluate real languages within this language morphospace. (Abstract excerpts)

Shahzad, Khuram, et al. The Organization of Domains in Proteins Obeys Menzarath-Altmann’s Law of Language. BMC Systems Biology. Online August, 2015. As the realization grows that natural phenomena from universe to us is graced by an inherent textual quality, in this entry University of Illinois, Evolutionary Bioinformatics Laboratory, researchers including Jay Mittenthal and Gustavo Caetano-Anolles find life’s proteomic domains to similarly express this common literacy. Search Ramon Ferrer-i-Cancho, et al about genomes, and Sertac Eroglu on statistical physics for more entries.

Menzerath–Altmann Law (named after Paul Menzerath and Gabriel Altmann), is a linguistic law according to which the increase of a linguistic construct results in a decrease of its constituents, and vice versa. For example, the longer a sentence (measured in terms of the number of clauses) the shorter the clauses (measured in terms of the number of words), or: the longer a word (in syllables or morphs) the shorter the syllables or words in sounds. The law can be explained by the assumption that linguistic segments contain information about its structure (besides the information that needs to be communicated). The assumption that the length of the structure information is independent of the length of the other content of the segment yields the alternative formula that was also successfully empirically tested. Beyond quantitative linguistics, Menzerath's law can be discussed in any multi-level complex systems. (Wikipedia)

Background: The combination of domains in multidomain proteins enhances their function and structure but lengthens the molecules and increases their cost at cellular level. Methods: The dependence of domain length on the number of domains a protein holds was surveyed for a set of 60 proteomes representing free-living organisms from all kingdoms of life. Results: We find that domain length decreases with increasing number of domains in proteins, following the Menzerath-Altmann (MA) law of language. Mathematically, the MA law expresses as a power law relationship that unfolds when molecular persistence P is a function of domain accretion.

The pattern of diminishing returns can therefore be explained as a frustrated interplay between the strategies of economy, flexibility and robustness, matching previously observed trade-offs in the domain makeup of proteomes. Proteomes of Archaea, Fungi and to a lesser degree Plants show the largest push towards molecular economy, each at their own economic stratum. Metazoa achieves maximum flexibility and robustness by harboring compact molecules and complex domain organization, offering a new functional vocabulary for molecular biology. Conclusions: The tendency of parts to decrease their size when systems enlarge is universal for language and music, and now for parts of macromolecules, extending the MA law to natural systems.

Sheng, Long and Chunguang Li. English and Chinese Languages as Weighted Complex Networks. Physica A. 388/2561, 2009. In a comparison of texts, Centre for Nonlinear and Complex Systems, University of Electronic Science and Technology of China, scientists find still another case of West/East, yang/yin, complements. As the quote cites, English works emphasize word elements, while in Chinese scripts, weighted relations prevail. (Also noted in The Complementarity of Civilizations)

The written human language is one of the most important examples of complex systems in nature. Words are the simple elements that combine to form complex structures of this system. If we consider each word as a vertex and their interactions as links between them, then the written human language can be modeled by complex networks. (2561) The above results together indicate that the hub-like words in the English text play a more important role of structural organization than in the Chinese text, while the connections between Chinese characters have larger intensity and density than between English works, which means phrases prevail more in Chinese language than in English. (2569)

Shohamy, Elana and Durk Gorter, eds. Linguistic Landscape. New York: Routledge, 2009. Imaginations of how we live immersed in multimedia textual, hieroglyph, sign-profused communal environments.

Souza, Barbara, et al. Text Characterization Based on Recurrence Networks. arXiv:2201.06665. University of Sao Paulo and Indiana University systems linguists including Diego Amancio and Luciano da Costa, and in accord with his current work in 2022 Syntheses, find further ways that even written documents can be seen to deeply exhibit nature’s universal animate topologies over many general and specific aspects.

Many complex systems reveal intricate characteristics taking place at several scales of time and space. In particular, texts are distinguished by a hierarchical structure that can be studied by multi-scale concepts and methods. Effective approaches can emphasize words with more informational content. Here we advance this work with a focus on mesoscopic representations of networks. We extend this domain to textual narratives wherein recurrent relationships among parts of speech (subject, verb and direct object) form connections among sequential pieces (e.g., paragraphs). (Abstract excerpt)

Stella, Massimo, et al. Multiplex Lexical Networks Reveal Patterns in Early Word Acquisition in Children. arXiv:1609.03207. Stella and Markus Brede, University of Southampton, UK, with Nicole Beckage, University of Colorado find these newly articulated generic multilayer complexities can equally apply to and quantify how youngsters achieve a grammatical vocabulary. See also Mental Lexicon Growth Modelling Reveals the Multiplexity of the English Language by Stella and Brede at 1604.01243. All spatial and temporal phenomena, it lately seems, are becoming graced and informed by a literate multiplex physiology.

Network models of language have provided a way of linking cognitive processes to the structure and connectivity of language. However, one shortcoming of current approaches is focusing on only one type of linguistic relationship at a time, missing the complex multi-relational nature of language. In this work, we overcome this limitation by modelling the mental lexicon of English-speaking toddlers as a multiplex lexical network, i.e. a multi-layered network where N=529 words/nodes are connected according to four types of relationships: (i) free associations, (ii) feature sharing, (iii) co-occurrence, and (iv) phonological similarity. We provide analysis of the topology of the resulting multiplex and then proceed to evaluate single layers as well as the full multiplex structure on their ability to predict empirically observed age of acquisition data of English speaking toddlers. We find that the emerging multiplex network topology is an important proxy of the cognitive processes of acquisition, capable of capturing emergent lexicon structure. (Abstract)

Previous   1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10  Next  [More Pages]