The Necker cube and the productive class of related stimuli involving multiple depth interpretations driven by corner-like line junctions are often taken to be ambiguous. This idea is normally taken to be as little in need of defense as the claim that the Necker cube gives rise to multiple distinct percepts. In the philosophy of language, it is taken to be a substantive question whether a stimulus that affords multiple interpretations is a case of ambiguity. If we take into account (...) what have been identified as hallmark features of ambiguity and look at the empirical record, it appears that the Necker cube and related stimuli are not ambiguous. I argue that this raises problems for extant models of multistable perception in cognitive neuroscience insofar as they are purported to apply to these stimuli. Helpfully, similar considerations also yield reasons to suggest that the relevant models are well motivated for other instances of multistable perception. However, a different breed of model seems to be required for the Necker cube and related stimuli. I end with a sketch how one may go about designing such a model relying on oscillatory patters in neural firing. I suggest that distinctions normally confined to the philosophy of language are important for the study of perception, a perspective with a growing number of adherents. (shrink)

We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...) have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high‐dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small‐world statistics and power‐law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks. (shrink)

We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...) have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high‐dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small‐world statistics and power‐law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks. (shrink)

We present statistical analyses of the large‐scale structure of 3 types of semantic networks: word associations, WordNet, and Roget's Thesaurus. We show that they have a small‐world structure, characterized by sparse connectivity, short average path lengths between words, and strong local clustering. In addition, the distributions of the number of connections follow power laws that indicate a scale‐free pattern of connectivity, with most nodes having relatively few connections joined together through a small number of hubs with many connections. These regularities (...) have also been found in certain other complex natural networks, such as the World Wide Web, but they are not consistent with many conventional models of semantic organization, based on inheritance hierarchies, arbitrarily structured networks, or high‐dimensional vector spaces. We propose that these structures reflect the mechanisms by which semantic networks grow. We describe a simple model for semantic growth, in which each new word or concept is connected to an existing network by differentiating the connectivity pattern of an existing node. This model generates appropriate small‐world statistics and power‐law connectivity distributions, and it also suggests one possible mechanistic basis for the effects of learning history variables (age of acquisition, usage frequency) on behavioral performance in semantic processing tasks. (shrink)

This paper highlights alternative responsible business models with an outspoken and profound dedication to contribute to a sustainable development. Themarket of alternatives and their networking activities to make the alternative market grow will be discussed in terms of programs and anti-programs. Another issue brought up is their ability to stick to the responsible knitting, as they grow and develops in a capitalistic market surrounding.

Over the last two decades, network-focused approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While the network approach continues to grow very rapidly, some of its conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. Consequently, the central focus of this theme issue will (...) be on the definition, motivation and application of key concepts in biological network science, such as levels, hierarchies, and explanatory directionality. A unification will be achieved by formulating norms for delimiting the distinctively network-topological class of explanations that connect general as well as very specific biological research questions. The impact of this theme issue is broad and encompassing, as it is highly interdisciplinary and opens a uniquely normative perspective on the foundational aspects of network-based explanations and modelling. This theme issue is intended to become a landmark publication for practical research as well as funding policy decisions, by unifying network approaches in biological sciences in terms of fundamental concepts and informing the public understanding of the network science. (shrink)

Coherence and correspondence are classical contenders as theories of truth. In this paper we examine them instead as interacting factors in the dynamics of belief across epistemic networks. We construct an agent-based model of network contact in which agents are characterized not in terms of single beliefs but in terms of internal belief suites. Individuals update elements of their belief suites on input from other agents in order both to maximize internal belief coherence and to incorporate ‘trickled in’ elements (...) of truth as correspondence. Results, though often intuitive, prove more complex than in simpler models (Hegselmann & Krause 2002, 2006; Grim, Singer, Reade & Fisher 2015). The optimistic finding is that pressures toward internal coherence can exploit and expand on small elements of truth as correspondence is introduced into epistemic networks. Less optimistic results show that pressures for coherence can also work directly against the incorporation of truth, particularly when coherence is established first and new data is introduced later. (shrink)

This paper presents the results of a Catalan project in which an academic institution acted as a practitioner to promote corporate social responsibility (CSR) in small and medium-sized enterprises (SMEs). The project involved the establishment of a working network with intermediate organisations and the creation of specific tools for the purpose. The paper is set up as a case study, emphasising inclusion, representativity and legitimacy as key elements for the successful construction of a network to promote CSR in (...) SMEs. It underlines the assumptions behind the functioning of this network and the learning findings from this public–private initiative. Presented from a public policy perspective, the paper emphasises the need for coordination in terms of the growing number of initiatives fostering CSR in SMEs. It presents a brief account of the material results, focusing on the process of creating a consensus within the network. It opens up a path for future research, exploring how network management and leadership can be seen as key issues when talking about corporate social responsibility (CSR) promotion in SMEs. (shrink)

This paper presents the results of a Catalan project in which an academic institution acted as a practitioner to promote corporate social responsibility (CSR) in small and medium-sized enterprises (SMEs). The project involved the establishment of a working network with intermediate organisations and the creation of specific tools for the purpose. The paper is set up as a case study, emphasising inclusion, representativity and legitimacy as key elements for the successful construction of a network to promote CSR in (...) SMEs. It underlines the assumptions behind the functioning of this network and the learning findings from this public–private initiative. Presented from a public policy perspective, the paper emphasises the need for coordination in terms of the growing number of initiatives fostering CSR in SMEs. It presents a brief account of the material results, focusing on the process of creating a consensus within the network. It opens up a path for future research, exploring how network management and leadership can be seen as key issues when talking about corporate social responsibility (CSR) promotion in SMEs. (shrink)

The comorbidity of chronic pain and opioid addiction is a serious problem that has been growing with the practice of prescribing opioids for chronic pain. Neuroimaging research has shown that chronic pain and opioid dependence both affect brain structure and function, but this is the first study to evaluate the neurophysiological alterations in patients with comorbid chronic pain and addiction. Eighteen participants with chronic low back pain and opioid addiction were compared with eighteen age- and sex-matched healthy individuals in (...) a pain-induction fMRI task. Unified structural equation modeling (SEM) with Lagrange multiplier (LM) testing yielded a network model of pain processing for patient and control groups based on 19 a priori defined regions. Tests of differences between groups on specific regression parameters were determined on a path-by-path basis using z-tests corrected for the number of comparisons. Patients with the chronic pain and addiction comorbidity had increased connection strengths; many of these connections were interhemispheric and spanned regions involved in sensory, affective, and cognitive processes. The affected regions included those that are commonly altered in chronic pain or addiction alone, indicating that this comorbidity manifests with neurological symptoms of both disorders. Understanding the neural mechanisms involved in the comorbidity is crucial to finding a comprehensive treatment, rather than treating the symptoms individually. (shrink)

There is growing interest in the role of new urban agriculture models to increase local food production capacity in cities of the Global North. Urban rooftop greenhouses and hydroponics are examples of such models receiving increasing attention as a technological approach to year-round local food production in cities. Yet, little research has addressed the unintended consequences of new modes of urban farming and food distribution, such as increased competition with existing peri-urban and rural farmers. We examine how (...) small-scale farmers perceive and have responded to a recently established rooftop greenhouse and online marketplace enterprise in Montréal, Canada. Drawing on interviews with key informants and small-scale farmers, we find that peri-urban and rural producers have been affected in three key ways that represent tensions, adaptations, and synergies arising from this new urban agriculture and food distribution enterprise. First, many farmers are concerned about increased competition and value conflation with the ideals of community supported agriculture and organic farming. Second, some farmers have adapted by developing novel marketing strategies and working with local bridge organizations to collectively market their produce to urban consumers. Third, a few farmers have decided to wholesale their produce to this new enterprise, allowing them to specialize production and avoid marketing their produce directly to urban consumers. Our study suggests that the emergence of a new form of alternative food network in Montréal has created both positive and negative disruptions for existing small-scale producers. Advocates for the expansion of new urban food production and distribution models should therefore give greater consideration to the effects on other actors in the local food system. (shrink)

Buildings energy consumption is growing gradually and put away around 40% of total energy use. Predicting heating and cooling loads of a building in the initial phase of the design to find out optimal solutions amongst different designs is very important, as ell as in the operating phase after the building has been finished for efficient energy. In this study, an artificial neural network model was designed and developed for predicting heating and cooling loads of a building based (...) on a dataset for building energy performance. The main factors for input variables are: relative compactness, roof area, overall height, surface area, glazing are a, wall area, glazing area distribution of a building, orientation, and the output variables: heating and cooling loads of the building. The dataset used for training are the data published in the literature for various 768 residential buildings. The model was trained and validated, most important factors affecting heating load and cooling load are identified, and the accuracy for the validation was 99.60%. (shrink)

This conceptual article suggests a reexamination of current governance structures, specifically those of unitary boards after the financial crisis of 2008.We suggest that the existing governance structures are based on an outdated paradigm of business, rooted in economics. We propose an alternative paradigm, a more humanistic paradigm, which allows conceiving alternative, network-oriented governance structures. As hierarchical firms grow larger and more complex, the risk of failure increases from biases, errors, and missing data in communication and control systems. These problems (...) are exacerbated by information overload on senior managers, directors, and their respective regulators. In contrast to traditional corporate governance, network governance introduces a division of power via multiple boards, checks and balances, and active stakeholder engagement. We argue that those features could have prevented the stresses and failures of financial firms in 2008, since they were anticipated by both individuals within firms and external commentators. However, those exposed to risks possessed insufficient influence in either governing and/or regulating firms to take corrective action. (shrink)

Recent research in philosophy of science has shown that scientists rely on a plurality of strategies to develop successful explanations of different types of phenomena. In the case of biology, most of these strategies go far beyond the traditional and reductionistic models of scientific explanation that have proven so successful in the fundamental sciences. Concretely, in the last two decades, philosophers of science have discovered the existence of at least two different types of scientific explanation at work in the (...) biological sciences, namely: mechanistic and structural explanations. Despite the growing evidence about the radically different nature of these two types of explanation, no inquiry has been conducted to date to determine the ontological reasons that might underlie these differences, nor the way in which these types of explanations can be systematically related with each other. Here, we aim to cover this gap by connecting this plurality of research strategies with the existence of emergent levels of reality. We argue that the existence of these different—and apparently incompatible—explanatory strategies to account for biological phenomena derives from the existence of “ontological jumps” in nature, which generate different regimes of causation that in turn demand the development of different explanatory frameworks. We identify two of these strategies—mechanistic modelling and network modelling—and connect them to the existence of two ontological regimes of causation. Finally, we relate them with each other in a systematic way. In this vein, our paper provides an ontological justification for the plurality of explanatory strategies that we see in the life sciences. (shrink)

The usage of new and progressive technologies brings with it new types of security threats and security incidents. Their number is constantly growing.The current trend is to move from reactive to proactive activities. For this reason, the organization should be aware of the current security situation, including the forecasting of the future state. The main goal of organizations, especially their security operation centres, is to handle events, identify potential security incidents, and effectively forecast the network security situation awareness (...) (NSSA). In this paper, we focus on increasing the efficiency of utilization of this part of cybersecurity. The paper’s main aim is to compare selected statistical models and models based on neural networks to find out which models are more suitable for NSSA forecasting. Based on the analysis provided in this paper, neural network methods prove a more accurate alternative than classical statistical prediction models in NSSA forecasting. In addition, the paper analyses the selection criteria and suitability of time series, which do not only reflect information about the total number of security events but represent a category of security event (e.g. recon scanning), port or protocol. (shrink)

As a criterion of a good firm, a lucrative and growing business has been said to be important. Recently, however, high profitability and high growth potential are insufficient for the criteria, because social influences exerted by recent firms have been extremely significant. In this paper, high social relationship is added to the list of the criteria. Empirical corporate social performance versus corporate financial performance (CSP–CFP) relationship studies that consider social relationship are very limited in Japan, and there are no (...) definite conclusions for the studies in the world, because of scant data and the inappropriate methods, especially for supporting linear hypothesis which these studies are based on. In this paper, the CSP–CFP relationship is analyzed by an artificial neural networks model, which can deal with a non-linear relationship, using 10-year follow-up survey data. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse fields of biology, including neuroscience, ecology, molecular biology and genetics. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. This challenge particularly concerns the question of whether a generalized account of explanatory, organisational and descriptive levels of networks can be applied universally across biological sciences. To this end, this highly interdisciplinary theme issue focuses on the definition, (...) motivation and application of key concepts in biological network science, such as explanatory power of distinctively network explanations, network levels, and network hierarchies. (shrink)

Through structural analysis of 8-year tag networks from online knowledge-sharing platforms, this study finds that, with the scale of tag networks growing quickly, the growth trend of number edges indicates that tag network follows densification law. The clustering coefficient and the average shortest path of the network show that the rapid growth of network size does not bring about the compartmentalization of the knowledge network, and the degree distribution of tag networks shows a truncated power-law (...) distribution. According to the structural characteristics of the tag network, this study proposes a tag network model based on the BA model. Based on the preference attachment, the triadic closure mechanism is employed to construct the edges between the old nodes, which revises the limitation that the BA model only connects edges between old and new nodes. The results show that the simulation model matches the actual tag network structure well. The generation mechanism of the tag network model provides a reference for understanding the knowledge construction process of the online knowledge-sharing platform to a certain extent. (shrink)

A growing number of doctors in the United States are joining online professional networks that cater exclusively to licensed physicians. The most popular are Sermo, with more than 135,000 members, and Doximity, with more than 100,000. Both companies claim to offer a valuable service by enabling doctors to “connect” in a secure online environment. But their business models raise ethical concerns. The sites generate revenue by selling access to their large networks of physician-users to clients that include global (...) pharmaceutical companies, market research and consulting firms, and hedge funds and other investors. In exchange for a fee, these clients are offered a variety of tools to monitor, analyze, and solicit physicians’ opinions. In Sermo's case, clients are also offered opportunities to conduct “awareness campaigns” on the site that are aimed at influencing physician sentiment about specific drugs and medical devices. In effect, these online networks have created an even more efficient means for the pharmaceutical industry to track physician sentiment, disseminate messages, and cultivate key opinion leaders. This paper argues that the dual nature of these sites (a) undermines their integrity and transparency as forums for the exchange of medical opinion and (b) presents an ethical conflict for the doctors who use them. (shrink)

There is a vast literature within philosophy of mind that focuses on artificial intelligence, but hardly mentions methodological questions. There is also a growing body of work in philosophy of science about modeling methodology that hardly mentions examples from cognitive science. Here these discussions are connected. Insights developed in the philosophy of science literature about the importance of idealization provide a way of understanding the neural implausibility of connectionist networks. Insights from neurocognitive science illuminate how relevant similarities between (...) class='Hi'>models and targets are picked out, how modeling inferences are justified, and the metaphysical status of models. (shrink)

Most growth models for complex networks consider networks comprising a single connected block or island, which contains all the nodes in the network. However, it has been demonstrated that some large complex networks have more than one island, with an island size distribution obeying a power-law function Is~s-α. This paper introduces a growth model that considers the emergence of islands as the network grows. The proposed model addresses the following two features: the probability that a new island (...) is generated decreases as the network grows and new islands are created with a constant probability at any stage of the growth. In the first case, the model produces an island size distribution that decays as a power-law Is~s-α with a fixed exponent α=1 and in-degree distribution that decays as a power-law Qi~i-γ with γ=2. When the second case is considered, the model describes island size and in-degree distributions that decay as a power-law with 2<α<∞ and 2<γ<∞, respectively. (shrink)

Over the last decades, network-based approaches have become highly popular in diverse areas of biology. While these approaches continue to grow very rapidly, some of their conceptual and methodological aspects still require a programmatic foundation. In order to unify and systematize network approaches across biological sciences, this theme issue brings together scientists working in many diverse areas of biological sciences as well as philosophers working on foundational issues of network explanations and modelling, who together aim to develop (...) universally applicable norms of network explanations, as well as systematize network concepts, such as levels and hierarchies. (shrink)

A socio-cognitive approach to trust can help us envisage a notion of networked trust for multi-agent systems (MAS) based on different interacting agents. In this framework, the issue is to evaluate whether or not a socio-cognitive analysis of trust can apply to the interactions between human and autonomous agents. Two main arguments support two alternative hypothesis; one suggests that only reliance applies to artificial agents, because predictability of agents’ digital interaction is viewed as an absolute value and human relation is (...) judged to be a necessary requirement for trust. The other suggests that trust may apply to autonomous agents because predictability of agents’ interaction is viewed only as a relative value since the digital normativity that grows out of the communication process between interacting agents in MAS has always deal with some unpredictable outcomes (_reduction of uncertainty_). Furthermore, human touch is not judged to be a necessary requirement for trust. In this perspective, a diverse notion of trust is elaborated, as trust is no longer conceived only as a relation between interacting agents but, rather, as a relation between cognitive states of control and lack of control (_double bind_). (shrink)

To answer the question in the title, we take as an example the model for the regulation of agonistic antagonistic couples (MRAAC). It is a model that associates 4 non-linear differential equations and allows to simulate balance, imbalance between two state variables, and control, if necessary, by two control variables of the same nature as the state variables: this control is defined as a bilateral strategy (bipolar therapy in the medical field). The super model for the regulation of agonism antagonistic (...) couples (SMRAAC), that may be also considered as an agonistic antagonistic network (AAN), associates several elementary MRAAC's, but the interconnection structure falls equally under the category of the agonistic antagonism (AA). First we indicate what may be the results of the interconnection, favourable or not. In the last case, a control can be performed, by acting upon a sole couple of the SMRAAC, even if other couples were imbalanced (with two AA control variables only). Simulation examples are given. We bring also a new result with the SMRAAC, i.e. the appearance of strange auractors with this model. The general models belonging to the AA phylum should put on a growing importance in the future, because they could be the sole models enable to control some complex systems by avoiding the perverse effects of the unilateral strategies. (shrink)

Microtubules form a highly dynamic filament network in all eukaryotic cells. Individual microtubules grow by tubulin dimer subunit addition and frequently switch between phases of growth and shortening. These unique dynamics are powered by GTP hydrolysis and drive microtubule network remodeling, which is central to eukaryotic cell biology and morphogenesis. Yet, our knowledge of the molecular events at growing microtubule ends remains incomplete. Here, recent ultrastructural, biochemical and cell biological data are integrated to develop a realistic model (...) of growing microtubule ends comprised of structurally distinct but biochemically overlapping zones. Proteins that recognize microtubule lattice conformations associated with specific tubulin guanosine nucleotide states may independently control major structural transitions at growing microtubule ends. A model is proposed in which tubulin dimer addition and subsequent closure of the MT wall are optimized in cells to achieve rapid physiological microtubule growth. (shrink)

The subject matter of this book is the mental lexicon, that is, the way in which the form and meaning of words is stored by speakers of specific languages. This book attempts to narrow the gap between the results of experimental neurology and the concerns of theoretical linguistics in the area of lexical semantics. The prime goal as regards linguistic theory is to show how matters of lexical organization can be analysed and discussed within a neurologically informed framework that is (...) both adaptable and constrained. It combines the perspectives of distributed network modelling and linguistic semantics, and draws upon the accruing evidence from neuroimaging studies as regards the cortical regions involved. It engages with a number of controversial current issues in both disciplines. The book is intended as a tool for linguists interested in psychological adequacy and the latest advances in cognitive science. It provides a principled means of distinguishing those semantic features required by a mental lexicon that have a direct bearing on grammar from those that do not. It will appeal to researchers in neurolinguistics and lexical semantics."--p. 4 of cover. (shrink)

In recent years, due to the growing demand for computational resources, particularly in cloud computing systems, the data centers’ energy consumption is continually increasing, which directly causes price rise and reductions of resources’ productivity. Although many energy-aware approaches attempt to minimize the consumption of energy, they cannot minimize the violation of service-level agreements at the same time. In this paper, we propose a method using a granular neural network, which is used to model data processing. This method identifies (...) the physical hosts’ workloads before the overflow and can improve energy consumption while also reducing violation of service-level agreements. Unlike the other techniques that use a single criterion, namely, worked on the basis of the history of using the processor, we simultaneously use all the productivity rates criteria, that is, processor productivity rates, main memory, and bandwidth. Extensive real-world simulations using the CloudSim simulator show the high efficiency of the proposed algorithm. (shrink)

Systems biology provides alternatives to the strategies to developing mechanistic explanations traditionally pursued in cell and molecular biology and much discussed in accounts of mechanistic explanation. Rather than starting by identifying a mechanism for a given phenomenon and decomposing it, systems biologists often start by developing cell-wide networks of detected connections between proteins or genes and construe clusters of highly interactive components as potential mechanisms. Using inference strategies such as ‘guilt-by-association’, researchers advance hypotheses about functions performed of these mechanisms. I (...) examine several examples of research on budding yeast, first on what are taken to be enduring networks and subsequently on networks that change as cells perform different activities or respond to different external conditions. (shrink)

In this paper, we explore the conceptual problems arising when using network analysis in person- centered care (PCC) in psychiatry. Personalized network models are potentially helpful tools for PCC, but we argue that using them in psychiatric practice raises boundary problems, i.e., problems in demarcating what should and should not be included in the model, which may limit their ability to provide clinically-relevant knowledge. Models can have explanatory and representational boundaries, among others. We argue that we (...) can make more explicit what kind of questions personalized network models can address in PCC, given their representational and explanatory boundaries, using perspectival reasoning. (shrink)

The scale of know­ledge is growing rapidly in the big data environment, and traditional know­ledge organization and services have faced the dilemma of semantic inaccuracy and untimeliness. From a know­ledge fusion perspective-combining the precise semantic superiority of traditional ontology with the large-scale graph processing power and the predicate attribute expression ability of property graph-this paper presents an ontology and property graph fusion framework (OPGFF). The fusion process is divided into content layer fusion and constraint layer fusion. The result of (...) the fusion, that is, the know­ledge representation model is called know­ledge big graph. In addition, this paper applies the know­ledge big graph model to the ownership network in the China’s financial field and builds a financial ownership know­ledge big graph. Furthermore, this paper designs and implements six consistency inference algorithms for finding contradictory data and filling in missing data in the financial ownership know­ledge big graph, five of which are completely domain agnostic. The correctness and validity of the algorithms have been experimentally verified with actual data. The fusion OPGFF framework and the implementation method of the know­ledge big graph could provide technical reference for big data know­ledge organization and services. (shrink)

In recent years, AI research has become more and more computationally demanding. In natural language processing, this tendency is reflected in the emergence of large language models like GPT-3. These powerful neural network-based models can be used for a range of NLP tasks and their language generation capacities have become so sophisticated that it can be very difficult to distinguish their outputs from human language. LLMs have raised concerns over their demonstrable biases, heavy environmental footprints, and future (...) social ramifications. In December 2020, critical research on LLMs led Google to fire Timnit Gebru, co-lead of the company’s AI Ethics team, which sparked a major public controversy around LLMs and the growing corporate influence over AI research. This article explores the role LLMs play in the political economy of AI as infrastructural components for AI research and development. Retracing the technical developments that have led to the emergence of LLMs, we point out how they are intertwined with the business model of big tech companies and further shift power relations in their favour. This becomes visible through the Transformer, which is the underlying architecture of most LLMs today and started the race for ever bigger models when it was introduced by Google in 2017. Using the example of GPT-3, we shed light on recent corporate efforts to commodify LLMs through paid API access and exclusive licensing, raising questions around monopolization and dependency in a field that is increasingly divided by access to large-scale computing power. (shrink)

Cluster growth models are utilized for a wide range of scientific and engineering applications, including modeling epidemics and the dynamics of liquid propagation in porous media. Invasion percolation is a stochastic branching process in which a network of sites is getting occupied that leads to the formation of clusters. The occupation of sites is governed by their resistance distribution; the invasion annexes the sites with the least resistance. An iterative cluster growth model is considered for computing the expected (...) size and perimeter of the growing cluster. A necessary ingredient of the model is the description of the mean perimeter as the function of the cluster size. We propose such a relationship for the site square lattice. The proposed model exhibits the expected phase transition of percolation models, i.e., it diverges at the percolation threshold p c. We describe an application for the porosimetry percolation model. The calculations of the cluster growth model compare well with simulation results. (shrink)

The purpose of this study is to train an artificial neural network model for predicting student failure in the academic leveling course of the Escuela Politécnica Nacional of Ecuador, based on academic and socioeconomic information. For this, 1308 higher education students participated, 69.0% of whom failed the academic leveling course; besides, 93.7% of the students self-identified as mestizo, 83.9% came from the province of Pichincha, and 92.4% belonged to general population. As a first approximation, a neural network model (...) was trained with twelve variables containing students’ academic and socioeconomic information. Then, a dimensionality reduction process was performed from which a new neural network was modeled. This dimension reduced model was trained with the variables application score, vulnerability index, regime, gender, and population segment, which were the five variables that explained more than 80% of the first model. The classification accuracy of the dimension reduced model was 0.745, while precision and recall were 0.883 and 0.778, respectively. The area under ROC curve was 0.791. This model could be used as a guide to lead intervention policies so that the failure rate in the academic leveling course would decrease. (shrink)

Network analysis is increasingly used to discover and represent the organization of complex systems. Focusing on examples from neuroscience in particular, I argue that whether network models explain, how they explain, and how much they explain cannot be answered for network models generally but must be answered by specifying an explanandum, by addressing how the model is applied to the system, and by specifying which kinds of relations count as explanatory.

The objective of the study is to re-examine the Verba, Nie, and Kim 's path-breaking analysis of political participation and political equality, under the inclusion of a social network model in Japan. In particular, the present research investigates how and why we find the extremely low correlations between one's socio-economic resource level and political participation in Japan, the evidence unsatisfactorily explained by the VNK analysis. Building on the social network model and employing the first wave of the Asian (...) Barometer survey conducted in 2003, this research presents a more comprehensive model of political participation. The study finds three major kinds of causes for the weak associations between SERLpsychological involvement linkage. From the viewpoint of the social network model, it is clear that the weak SERL–participation linkage is derived from the equalizing impact of group-based processes, yet uniquely Japanese style of network involvement. (shrink)

Localist networks represent information in a very simple and straightforward way. However, localist modelling of complex behaviours ultimately entails the use of intricate “hand-designed” connectionist structures. It is, in fact, mainly these two aspects of localist network models that I believe have turned many researchers off them (perhaps wrongly so).

Cook (1995) criticizes the work of Jacobs and Kosslyn (1994) on spatial relations, shape representations, and receptive fields in neural network models on the grounds that first‐order correlations between input and output unit activities can explain the results. We reply briefly to Cook's arguments here (and in Kosslyn, Chabris, Marsolek, Jacobs & Koenig, 1995) and discuss how new simulations can confirm the importance of receptive field size as a crucial variable in the encoding of categorical and coordinate spatial (...) relations and the corresponding shape representations; such simulations would testify to the computational distinction between the different types of representations. (shrink)

This chapter explains how artificial neural networks may be used as models for reasoning, conditionals, and conditional logic. It starts with the historical overlap between neural network research and logic, it discusses connectionism as a paradigm in cognitive science that opposes the traditional paradigm of symbolic computationalism, it mentions some recent accounts of how logic and neural networks may be combined, and it ends with a couple of open questions concerning the future of this area of research.

In this article, the author proposes a dynamic, interdisciplinary, network conception of expertise that differs from conventional static, linear conceptions. Using a range of graphic images, the author propose specific visualizations of this network conception of expertise. First, he discusses attempts to pin expertise down in a definition. Then he considers the network of notions from which expertise emerges. The author briefly describes representative nodes in the network, such as experience and excellence. He concludes with the (...) view that there is no need to compromise the many existing conceptions of expertise by forcing them into a false common ground. Instead, he shows that existing accounts of expertise can be better understood by viewing them as connected parts of a complex network. (shrink)

As a result of the development of new technologies such as satellite communication, digitalization, and multimedia computer networks, new media such as blogs, online magazines, and wireless network media have sparked a lot of interest. This study uses 3D clothing display technologies to improve the customer experience of online clothing marketing, aid in the improvement of online clothing marketing efficacy, and extensively discuss the digital clothing anthropometric model. Furthermore, this study employs the convex hull approach and NURBS fitting technology (...) to address concerns with digital clothing anthropometric measurement and provides a practical solution for measuring straight line length, circumference length, and curve length. Furthermore, this research incorporates 3D intelligent technology to develop an online fitting system as well as an intelligent display system for garments. The simulation experiment investigation revealed that the smart clothing 3D display system proposed in this work has a substantial impact on growing online e-commerce clothing marketing as well as improving clothing marketing effectiveness. (shrink)

Deep neural networks (DNNs) have had extraordinary successes in classifying photographic images of objects and are often described as the best models of biological vision. This conclusion is largely based on three sets of findings: (1) DNNs are more accurate than any other model in classifying images taken from various datasets, (2) DNNs do the best job in predicting the pattern of human errors in classifying objects taken from various behavioral datasets, and (3) DNNs do the best job in (...) predicting brain signals in response to images taken from various brain datasets (e.g., single cell responses or fMRI data). However, these behavioral and brain datasets do not test hypotheses regarding what features are contributing to good predictions and we show that the predictions may be mediated by DNNs that share little overlap with biological vision. More problematically, we show that DNNs account for almost no results from psychological research. This contradicts the common claim that DNNs are good, let alone the best, models of human object recognition. We argue that theorists interested in developing biologically plausible models of human vision need to direct their attention to explaining psychological findings. More generally, theorists need to build models that explain the results of experiments that manipulate independent variables designed to test hypotheses rather than compete on making the best predictions. We conclude by briefly summarizing various promising modeling approaches that focus on psychological data. (shrink)

Cramer et al.'s account of comorbidity comes with a substantive philosophical view concerning the nature of psychological disorders. Although the network account is responsive to problems with extant approaches, it faces several practical and conceptual challenges of its own, especially in cases where the individual differences in network structures require the analysis of intra-individual time-series data.

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The (...) naming module associates category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)

In lieu of an abstract, here is a brief excerpt of the content:From Children's Perspectives:A Model of Aesthetic Processing in TheatreJeanne Klein (bio)Since the children's theatre movement began, producers have sought to create artistic theatre experiences that best correspond to the adult-constructed aesthetic "needs" of young audiences by categorizing common differences according to age groups. For decades, directors simply chose plays on the basis of dramatic genres (e.g., fairy tales), as defined by children's presupposed interests or "tastes," by subscribing to (...) Winifred Ward's broad descriptions of the "imaginative period" (ages six to nine), the "heroic period" (ages nine to twelve), and the "romantic period" (over age thirteen).1 Years later, Moses Goldberg elaborated upon these generalized divisions while cautioning that "no individual fits exactly into any set of categories."2 In his position paper on aesthetic development, he argued for individual access to aesthetic techniques, processes, and products for all ages, paralleled by four stages of cognitive capabilities, based on exposure to (1) arena-style participation theatre (for ages five to eight), which emphasizes story enactments; (2) a wide range of proscenium-style theatrical conventions; (3) relevant play content that directly relates characters' problems to spectators' lives and growing self-awareness; and (4) social issue plays that pose ethical dilemmas as cultural reflections.3 Meanwhile, Jed Davis compiled Age Group Profiles, organized by cognitive, spatial, emotional, and moral/ethical development, from his review of Piagetian literature; and Johnny Saldaña summarized stages of young interpretations of theatre from his seven-year longitudinal study.4Suffice it to say, the field of developmental psychology has come a long way since Piaget formulated his four constructivist stages. Since the mid-1950s, the cognitive revolution has spurred more integrated theories of information processing and depth-of-processing models that explain and predict how people construct schematic frameworks of knowledge and [End Page 40] assimilate and accommodate new information as a function of perception, attention, emotion, comprehension, memory, and social and moral behaviors. Although masculinist metaphors of hierarchical "staircases" and linear networks of associative learning still dominate computational models, feminist perspectives, which engender social cognitive, co-relational negotiations of narratives in situated contexts, have gained more widespread acceptance—by including theatre as an apt metaphor for development.5 Over the past twenty years or more, cognitive psychologists have finally discovered what dramatic theorists have known about narrative structures since Aristotle's Poetics: Powerful stories contain universal themes about the human condition through characters' conflicting actions. The most successful dramas are those that allow spectators to enter into a protagonist's psychological consciousness or "inner" vision in order to empathize with her "outer" reality.6While several developmental models of aesthetic understanding, experience, and appreciation exist in the realms of visual art and music education, few examples have been proposed in regard to theatre, particularly for child audiences.7 Instead, producers of Theatre for Young Audiences (TYA) tend to romanticize its social and moral "effects" upon youth by believing passionately that viewing one remarkable performance has the potential (the operative word) to "cause" positive, social changes in children's minds, hearts, and behaviors, given the presumed "power of human imagination."8 This obsession with progressive change foregrounds adults' desires to control human destiny by predicting "causes and effects" located in children's responses to live performances. However, this "folk theory" fails to explain how young imaginations actually function aesthetically from the contextual evidence of reception studies with child audiences. Just as feminists have argued for a distinctive "feminist aesthetics," based on women's alternative ways of knowing,9 I argue that children gaze upon theatre in differential ways by including age as a variable factor. "Children's aesthetics," their perceived interpretations of theatre, differ from that of adults' theorized postulations, based in part on their shorter life experiences with this medium. To this end, over the course of my career, I have sought to understand theatre from children's perspectives by focusing my research in the social cognitive theories and social science methods of child development, primarily through the vast literature of television and mass media studies conducted with young people over the past forty years. In particular, medium-specific studies that parse theatrical forms from dramatic content... (shrink)

Studying drug relationships can provide deeper information for the construction and maintenance of biomedical databases and provide more important references for disease treatment and drug development. The research model has expanded from the previous focus on a certain drug to the systematic analysis of the pharmaceutical network formed between drugs. Network model is suitable for the study of the nonlinear relationship of the pharmaceutical relationship by modeling the data learning. Association rule mining is used to find the potential (...) correlations between the various sets of massive data. Therefore, based on the network model, this research proposed an algorithm for drug interaction under improved association rules, which achieved accurate analysis and decision-making of drug relationship. Meanwhile, this research applied the established association rule algorithm to discuss the relationship between Chinese medicine and mental illness medicine and conducted the algorithm research and simulation analysis of the association relationship. The results showed the association rule algorithm based on the network model constructed was better than other association algorithms. It had reliability and superiority in decision-making in improving the drug-drug relationship. It also promoted the rational use of medicines and played a guiding role in pharmaceutical research. This provides scientific research personnel with research basis and research ideas for disease-related diagnosis. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The (...) naming module associates category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)