Simple Entities Research Articles

Isotope evidence for Archean accordion-tectonics in the Superior Province

Archean cratons preserve the oldest continental crust, but their tectonic mode of formation and assembly remain key unknowns. The Superior Province is Earth’s largest Archean craton and comprises Eo-Neoarchean plutonic-gneiss terranes separated by Meso-Neoarchean granite-greenstone terranes and metasedimentary belts. Resolving whether the plutonic-gneiss terranes represent dismembered fragments of a once contiguous proto-craton or a series of unrelated accreted crustal fragments is critical to understanding the early evolution of the Superior Province and Archean tectonics. We present zircon U-Pb-Hf isotopic data from the Tannis and Cedar Lake TTG gneisses, which record the early crustal evolution of the Winnipeg River plutonic-gneiss terrane and are some of the oldest rocks in the western Superior Province. The gneisses yield a large range of zircon εHf(t) signatures (−6 to +4) at igneous formation (ca. 3.3–3.25 Ga) indicating coeval crustal growth and reworking of isotopically depleted and evolved sources. Crustal reworking of Eo-Paleoarchean sources is supported by sub-chondritic ca. 3.5–3.4 Ga zircon xenocrysts in the Cedar Lake gneiss. The early crustal evolution of the central Winnipeg River terrane is similar to the Hudson Bay and Minnesota River Valley terranes, on the northern and southern margins of the Superior Province, respectively. Correlation of the early history amongst the three plutonic-gneiss terranes supports a tectonic model in which a once coherent Eo-Paleoarchean proto-craton disaggregated into three fragments during formation of intervening granite-greenstone terranes in the Mesoarchean. These fragments reaggregated in the Neoarchean. The complex history of the Superior Province highlights that Archean cratons are not simple entities that formed in a single event, but may have experienced times of cratonic breakup and reassembly.

Emergence in complex networks of simple agents

Patterns and processes emerge unbidden in complex systems when many simple entities interact. This overview emphasizes the role of networks in emergence, notably network topology, modules, motifs, critical phase changes, networks of networks and dual-phase evolution. Several driving mechanisms are examined, including percolation, entrainment, and feedback. The account also outlines some of the modelling paradigms and methods used to study emergence, and presents cases to show how emergence occurs, and its implications in economics and other real-world contexts.

Behaviour and the Origin of Organisms.

It is common in origins of life research to view the first stages of life as the passive result of particular environmental conditions. This paper considers the alternative possibility: that the antecedents of life were already actively regulating their environment to maintain the conditions necessary for their own persistence. In support of this proposal, we describe 'viability-based behaviour': a way that simple entities can adaptively regulate their environment in response to their health, and in so doing, increase the likelihood of their survival. Drawing on empirical investigations of simple self-preserving abiological systems, we argue that these viability-based behaviours are simple enough to precede neo-Darwinian evolution. We alsoexplain how their operation can reduce the demanding requirements that mainstream theories place upon the environment(s) in which life emerged.

Drug Resistance: An Incessant Fight against Evolutionary Strategies of Survival

The inherent capacity of all organisms to evolve under selective pressure has created multidrug-resistant microbes and cells that are now threatening decades of progress in human health introduced by the advent of antibiotics. This resistance is developed by all pathogens, from bacteria to cancer cells, through fungi, parasites, or the seemingly simpler entities, i.e., viruses. In this review, we give an overview on this phenomenon, describing the mechanisms by which resistant organisms manage to evade the action of drugs. We also review existing therapies, including some of the most recent. This bibliographic review shows how, despite the encouraging progress that has been achieved in many areas, a permanent effort from scientists is necessary and will always be needed in order to compensate for the continuous microbial response intrinsically linked to the evolutionary process.

Information–energy equivalence and the emergence of self-replicating biological systems

Biological processes are characterized by a decrease in entropy in apparent violation of the second law of thermodynamics. Information stored in genomes help to solve this paradox when interpreted under the relationship between information and energy stated by Brillouin in the 1950’s. However, the origins of living forms from inanimate matter which have no information storage device remains an open question. In this paper, a theoretical approach is developed on this issue. The replication of a simple entity with a binary genome is assumed to require an information-equivalent energy in addition to the standard activation energy. It is found that, in some conditions, a decrease in entropy can be accomplished together with a decrease in Gibbs free energy. An equation of the total energy for the replication of this entity is derived. Three factors are predicted to lower this energy: a small number of states of the coding sequence, a lower temperature, and a high ratio of the reaction on diffusion coefficients. These factors may have favoured the emergence of evolutionary demons—information storage devices that are able to decrease entropy. It is evaluated that some short, single-stranded RNA sequences made only of G and of C may conform to this model. The consequences of this model and its predictions on the origins of life on Earth and on other planets are discussed.

An Ontology-Based and Deep Learning-Driven Method for Extracting Legal Facts from Chinese Legal Texts

The construction of smart courts promotes the in-deep integration of internet, big data, cloud computing and artificial intelligence with judicial trial work, which can both improve trials and ensure judicial justice with more efficiency. High-quality structured legal facts, obtained by extracting information from unstructured legal texts, are the foundation for the construction of smart courts. Based on the strong normative characteristics of Chinese legal text content and structure composition and the strong text feature learning ability of deep learning, this paper proposes an ontology-based and deep learning-driven method for extracting legal facts from Chinese legal texts. The proposed method utilizes rules and patterns generated in the process of knowledge modeling to extract simple entities, and then extracts complex entities hidden in legal text details with deep learning methods. Finally, the extracted entities are mapped into structured legal facts with clear logical relationships by the Chinese Legal Text Ontology. In the information extraction test of judicial datasets composed of Chinese legal texts on theft, the proposed method effectively extracts up to 38 categories of legal facts from legal texts and the number of categories extracted increases significantly. Among them, the rule-based extractor obtains an F1-score of 99.70%, and the deep learning-driven extractor obtains an F1-score of 91.43%. Compared with existing methods, the proposed method has great advantages in extracting the completeness and accuracy of legal facts.

Big History and the Principle of Emergence

Life is a raucous carnival, full of “games” and “rides” whose ongoing interactions continually surprise us. Yet thinkers are too often tempted to treat it as a machine that spits out linear time lines of events, one leading deterministically to another. By its interdisciplinary nature, big history is inclined to treat the world as a carnival; yet the temptation to treat it in the more linear way sometimes prevails. This essay treats one key dynamic that governs life’s carnival—the principle of emergence. Emergence is the process by which a relatively simple entity interacts with its environment to become structurally complex, often in ways that seem impossible to anticipate. In this way, a seed becomes a fruit tree, a small community becomes a vast city, or a shamanic religion in a hunter-gatherer band evolves into a system of belief and practice shared by a billion people. By defining emergence and exploring religion as an extended illustration, this paper makes the case for more fully incorporating the principle of emergence into the study of big history.

Microcosm of Ecologic Taxation in Russia

The article analyzes wordings of the category ‘ecosystem’ as a form of interaction in economics. Drawbacks of the current definitions of the term were identified and the wording of the category ‘ecosystem’, which is the most substantiated for the present research, was presented. The term ‘microcosm of ecologic taxation’ was introduced and defined. Common features and differences, the structure, elements and the internal set-up of the ecosystem and microcosm were described. The author underlined the strategic importance of the ecosystem approach in view of possible evolution of the ecosystem (microcosm) value (principle idea) in conditions of instability, the place of microcosm of ecologic taxation was identified in the economic system of Russia. The key part in microcosm being studied is assigned not to economic entities and administrative agents but to the network of interrelations, tools and control levers integrating them. The forms of its actors’ interaction are shown, i. e. persons united by their involvement in ecologic tax payment (beneficiaries, tax payers and bearers). The idea and definition of the cluster as an association underlying microcosm were given. The cluster was shown as a complicated association of simple entities of economic relations, working in similar social-demographic, climatic and natural-resource conditions. The cluster structure was depicted as an element of the structure of ecologic taxation microcosm. The article substantiated the possibility to use microcosm of ecologic taxation as a management tool aimed at providing ecologic security of the country.

A Farm Management Information System for Semi-Supervised Path Planning and Autonomous Vehicle Control

This paper presents a farm management information system targeting improvements in the ease of use and sustainability of robot farming systems. The system integrates the functionalities of field survey, path planning, monitoring, and controlling agricultural vehicles in real time. Firstly, a Grabcut-based semi-supervised field registration method is proposed for arable field detection from the orthoimage taken by the drone with an RGB camera. It partitions a complex field into simple geometric entities with simple user interaction. The average Mean Intersection over Union is about 0.95 when the field size ranges from 2.74 ha to 5.06 ha. In addition, a desktop software and a web application are developed as the entity of an FMIS. Compared to existing FMISs, this system provides more advanced features in robot farming, while providing simpler user interaction and better results. It allows clients to invoke web services and receive responses independent of programming language and platforms. Moreover, the system is compatible with other services, users, and devices following the open-source access protocol. We have evaluated the system by controlling 5 robot tractors with a 2 Hz communication frequency. The communication protocols will be publicly available to protentional users.

PEMIKIRAN ATEISME RICHARD DAWKINS:

History shows a constant debate between religionists and atheists. Armstrong wrote that any new concept of divinity that emerged in society and rejected established traditions would be labeled as atheist and marginalized. Today, one of the movements that are quite criticized by religious people is New Atheism. He has a great influence in the western world. Richard Dawkins, as the founder, composed a special work entitled God Delusion as a response to the rejection of the supernatural and personal concept of God through Argument from Improbability. He explained that the complexity of the universe is almost impossible to come from a simple entity, namely God, but at the same time, a complex creator also produces infinite regress. Therefore, he took the gradual evolution of the universe as an alternative explanation for the formation of nature. This article will interpret Richard Dawkins’ points of thought in God Delusion, and analyze them using the principles of logic and philosophy. As a result, the author found several weaknesses in the argument from improbability and the lens in viewing religion. First, the assumption of rejecting the argument of resentment is only to deny the supernatural concept of God. Second, the use of statistical probability arguments limited to natural laws does not lead to supernatural concepts. Third, complexity stems primarily from simplicity. Lastly, ignorance of the criteria for the existence of consequences for causes.

Split Responsibility Scheduler for Multi-Connectivity in 5G Cellular Networks

The success of 5G networks relies on well-designed scheduling strategies that can tackle the increasing complexity of emerging mobile networks. Consequently, scheduling solutions are becoming more complicated, requiring high computational power and communication with very low latency. in this context, this work studies how to enhance existing scheduling solutions considering cellular networks with multi-connectivity, where users can have two or more simultaneous connections to different cells. Starting with a literature overview, we identify the main drawbacks of existing scheduling solutions for multi-connectivity scenarios. Then, we propose a novel scheduling concept that simplifies the scheduling task and overcomes several issues of existing scheduling solutions. The proposed solution logically splits the existing scheduling responsibilities across two simpler entities, thus it is named SRS. Furthermore, we propose a scheduling algorithm for practical SRS deployments, which is suitable for current and future multi-connectivity scenarios. Simulations show that the proposed solution has near-optimal performance in 5G multi-connectivity scenarios, when running in centralized and distributed architectures, while outperforming existing solutions by increasing the minimum user rate by 95 percent and the percentage of satisfied users by 50 percent at high system loads.

Multiobjective design of meta-materials exhibiting a targeted non-linear deformation response

This paper presents recent advances in developing a systematic design procedure for nonlinear cellular meta-materials. In previous work by the authors, the Unit Cell Synthesis method was introduced for designing unit-cell-based meta-materials that exhibit targeted nonlinear deformation response. The method is based on a fundamental understanding of simple entities that exhibit geometric nonlinearities under deformation. The geometric parameters associated with these entities can be tuned to achieve the desired nonlinear response. In this work, the original Unit Cell Synthesis method is extended to include a multi-objective optimization step to take into consideration multiple application-specific criteria while optimizing the meta-material design. A case study of designing a meta-material to mimic the nonlinear compressive behavior of an existing elastomer component in a military tracked vehicle is presented to demonstrate the method. Furthermore, to understand the effects of the manufacturing accuracy of design parameters on the performance of the optimized meta-material, a sensitivity analysis is carried out to obtain a feasible range of mechanical behavior for the meta-material and reveal the significance of individual design parameters and their interactions.

A Complex and Evolutive Character: Two Face Aspects of ECM in Tumor Progression.

Tumor microenvironment, including extracellular matrix (ECM) and stromal cells, is a key player during tumor development, from initiation, growth and progression to metastasis. During all of these steps, remodeling of matrix components occurs, changing its biochemical and physical properties. The global and basic cancer ECM model is that tumors are surrounded by activated stromal cells, that remodel physiological ECM to evolve into a stiffer and more crosslinked ECM than in normal conditions, thereby increasing invasive capacities of cancer cells. In this review, we show that this too simple model does not consider the complexity, specificity and heterogeneity of each organ and tumor. First, we describe the general ECM in context of cancer. Then, we go through five invasive and most frequent cancers from different origins (breast, liver, pancreas, colon, and skin), and show that each cancer has its own specific matrix, with different stromal cells, ECM components, biochemical properties and activated signaling pathways. Furthermore, in these five cancers, we describe the dual role of tumor ECM: as a protective barrier against tumor cell proliferation and invasion, and as a major player in tumor progression. Indeed, crosstalk between tumor and stromal cells induce changes in matrix organization by remodeling ECM through invadosome formation in order to degrade it, promoting tumor progression and cell invasion. To sum up, in this review, we highlight the specificities of matrix composition in five cancers and the necessity not to consider the ECM as one general and simple entity, but one complex, dynamic and specific entity for each cancer type and subtype.

Emergence of Simple Characteristics for Heterogeneous Complex Social Agents

Models of interacting social agents often represent agents as very simple entities with a small number of degrees of freedom, as exemplified by binary opinion models for instance. Understanding how such simple individual characteristics may emerge from potentially much more complex agents is thus a natural question. It has been proposed recently in [E. Bertin, P. Jensen, C. R. Phys. 20, 329 (2019)] that some types of interactions among agents with many internal degrees of freedom may lead to a ‘simplification’ of agents, which are then effectively described by a small number of internal degrees of freedom. Here, we generalize the model to account for agent intrinsic heterogeneity. We find two different simplification regimes, one dominated by interactions, where agents become simple and identical as in the homogeneous model, and one where agents remain strongly heterogeneous although effectively with simple characteristics.

A dynamic network analysis of emergent grammar

For languages to survive as complex cultural systems, they need to be learnable. According to traditional approaches, learning is made possible by constraining the degrees of freedom in advance of experience and by the construction of complex structure during development. This article explores a third contributor to complexity: namely, the extent to which syntactic structure can be an emergent property of how simpler entities – words – interact with one another. The authors found that when naturalistic child directed speech was instantiated in a dynamic network, communities formed around words that were more densely connected with other words than they were with the rest of the network. This process is designed to mirror what we know about distributional patterns in natural language: namely, the network communities represented the syntactic hubs of semi-formulaic slot-and-frame patterns, characteristic of early speech. The network itself was blind to grammatical information and its organization reflected (a) the frequency of using a word and (b) the probabilities of transitioning from one word to another. The authors show that grammatical patterns in the input disassociate by community structure in the emergent network. These communities provide coherent hubs which could be a reliable source of syntactic information for the learner. These initial findings are presented here as proof-of-concept in the hope that other researchers will explore the possibilities and limitations of this approach on a larger scale and with more languages. The implications of a dynamic network approach are discussed for the learnability burden and the development of an adult-like grammar.

A scalable fuzzy division model of topological space on GIS

It is difficult to describe topological relationship of complicated geographical phenomena and entity with fuzziness and uncertainty in the real world. Aiming at the above problem, this paper has summarized to the topological relationship of between spatial entities with 4-intersection model and 9-intersection model firstly. On the basis of point-set topology, the formal description model of the topological relationship between fuzzy geographical entities is analysed and gives a detailed and accurate formalization-description and strict math formula. Lastly, this paper proposes a method, which improves and expands topological division to between fuzzy geographical entities. It shows that analysing result, the model can analyse and divide the entity fuzzily from simple entity to complicated entity.

The Apex Set-Up for the Major Transitions in Individuality

Morphological and functional hierarchies occurring in contemporary biological entities are amalgamated via a small number of progressive key-steps termed as Major Transition in Evolution (MTE) that encompass steps of Major Transition in Individuality (MTI). Literature views MTE/MTI in nature as a sequential increase in complexity, and has contributed insights into the emergence of genuine MTI candidates that actually build higher order individuals from simpler entities and into their specific properties. The theory- By considering a novel MTI trajectory termed the ‘MTI continuum’ (independent of the tree of life that contemplates taxonomic correlations), I found no literature consensus for this continuum’s apex. Next, I consider the properties of biological entities termed as ‘superorganism’ (eusocial insects, humans), also considered as highly-developed MTIs. I classify ‘superorganism’ as being on the level of ‘miscellaneous transitions’ that have not yet developed into real MTIs and that do not meet the ‘individual’ physiognomy. Then I assign the emergence of three new MTI diachronic-classes, the colonial-organisms, chimerism and multi-chimerism, suggesting that they represent highly complex MTIs that belong at the apex of the MTI continuum. These novel MTIs are neither fraternal, nor egalitarian, deprived of ‘kinship’ and ‘fairness’ considerations, yet still generate genuine and distinct libertarian entities. I posit that these MTIs embody the qualities of real units of selection.

Cauda Equina Syndrome: A Simple Entity with Lot of Enigma!

Cauda Equina Syndrome: A Simple Entity with Lot of Enigma!

Content and Context: Two-Pronged Bootstrapped Learning for Regex-Formatted Entity Extraction

Regular expressions are an important building block of rule-based information extraction systems. Regexes can encode rules to recognize instances of simple entities which can then feed into the identification of more complex cross-entity relationships. Manually crafting a regex that recognizes all possible instances of an entity is difficult since an entity can manifest in a variety of different forms. Thus, the problem of automatically generalizing manually crafted seed regexes to improve the recall of IE systems has attracted research attention. In this paper, we propose a bootstrapped approach to improve the recall for extraction of regex-formatted entities, with the only source of supervision being the seed regex. Our approach starts from a manually authored high precision seed regex for the entity of interest, and uses the matches of the seed regex and the context around these matches to identify more instances of the entity. These are then used to identify a set of diverse, high recall regexes that are representative of this entity. Through an empirical evaluation over multiple real world document corpora, we illustrate the effectiveness of our approach.

Stability of subsystem solutions in agent-based models

The fact that relatively simple entities, such as particles or neurons, or even ants or bees or humans, give rise to fascinatingly complex behaviour when interacting in large numbers is the hallmark of complex systems science. Agent-based models are frequently employed for modelling and obtaining a predictive understanding of complex systems. Since the sheer number of equations that describe the behaviour of an entire agent-based model often makes it impossible to solve such models exactly, Monte Carlo simulation methods must be used for the analysis. However, unlike pairwise interactions among particles that typically govern solid-state physics systems, interactions among agents that describe systems in biology, sociology or the humanities often involve group interactions, and they also involve a larger number of possible states even for the most simplified description of reality. This begets the question: when can we be certain that an observed simulation outcome of an agent-based model is actually stable and valid in the large system-size limit? The latter is key for the correct determination of phase transitions between different stable solutions, and for the understanding of the underlying microscopic processes that led to these phase transitions. We show that a satisfactory answer can only be obtained by means of a complete stability analysis of subsystem solutions. A subsystem solution can be formed by any subset of all possible agent states. The winner between two subsystem solutions can be determined by the average moving direction of the invasion front that separates them, yet it is crucial that the competing subsystem solutions are characterised by a proper composition and spatiotemporal structure before the competition starts. We use the spatial public goods game with diverse tolerance as an example, but the approach has relevance for a wide variety of agent-based models.