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)

As one of the most effective medical technologies for the infertile patients, in vitro fertilization has been more and more widely developed in recent years. However, prolonged waiting for IVF procedures has become a problem of great concern, since this technology is only mastered by the large general hospitals. To deal with the insufficiency of IVF service capacity, this paper studies an IVF queuing network in an integrated cloud healthcare system, where the two key medical services, that is, egg (...) retrieval and transplantation, are assigned to accomplish in the general hospital, while the routine medical tests are assigned into the community hospital. Based on continuous-time Markov procedure, a dynamic large-scale server scheduling problem in this complicated service network is modeled with consideration of different arrival rates of multiple type of patients and different service capacities of multiple servers that can be defined as doctors of the general hospital. To solve this model, a reinforcement learning algorithm is proposed, where the reward functions are designed for four conflicting subcosts: setup cost, patient waiting cost, penalty cost for unsatisfied patient personal preferences, and medical cost of patient. The experimental results show that the optimal service rule of each server’s queue obtained by the RL method is significantly superior to the traditional service rule. (shrink)

AimResting state functional magnetic resonance imaging was used to analyze changes in functional connectivity within various brain networks and functional network connectivity among various brain regions in patients with high myopia.Methodsrs-fMRI was used to scan 82 patients with HM and 59 healthy control volunteers matched for age, sex, and education level. Fourteen resting state networks were extracted, of which 11 were positive. Then, the FCs and FNCs of RSNs in HM patients were examined by independent component analysis.ResultsCompared with (...) the HC group, FC in visual network 1, dorsal attention network, auditory network 2, visual network 3, and sensorimotor network significantly increased in the HM group. FC in default mode network 1 significantly decreased. Furthermore, some brain regions in default mode network 2, default mode network 3, auditory network 1, executive control network, and significance network increased while others decreased. FNC analysis also showed that the network connection between the default mode network and cerebellar network was enhanced in the HM group.ConclusionCompared with HCs, HM patients showed neural activity dysfunction within and between specific brain networks, particularly in the DMN and CER. Thus, HM patients may have deficits in visual, cognitive, and motor balance functions. (shrink)

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed thesequence encoderis used to learn nearly 75,000 wordform representations through exposure to strings of stress‐marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional (...) slot‐based codes. Then, two large‐scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well‐formed pseudowords as opposed to ill‐formed (scrambled) pseudowords, and by accounting for variance in well‐formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing. (shrink)

Science studies has long been concerned with the theoretical and methodological challenge of mess—the inevitable tendency of technoscientific objects and practices to spill beyond the neat analytic categories we construct for them. Nowhere is this challenge greater than in the messy world of large-scale collaborative science projects, particularly though not exclusively in their start-up phases. This article examines the complicated life and death of the WATERS Network, an ambitious and ultimately abandoned effort at collaborative infrastructure development among hydrologists, (...) engineers, and social scientists studying water. We argue in particular against the “forensic imagination,” a particular style of accounting for failure in the messy world of large-scale network development, and against two common conceptual and empirical pitfalls that it gives rise to: defaults to formalism and defaults to the future. We argue that alternative postforensic approaches to “failures” like the WATERS Network can support forms of learning and accountability better attuned to the complexities of practice and policy in the real world of scientific collaboration and network formation. (shrink)

The forms of words as they appear in text and speech are central to theories and models of lexical processing. Nonetheless, current methods for simulating their learning and representation fail to approach the scale and heterogeneity of real wordform lexicons. A connectionist architecture termed thesequence encoderis used to learn nearly 75,000 wordform representations through exposure to strings of stress‐marked phonemes or letters. First, the mechanisms and efficacy of the sequence encoder are demonstrated and shown to overcome problems with traditional (...) slot‐based codes. Then, two large‐scale simulations are reported that learned to represent lexicons of either phonological or orthographic wordforms. In doing so, the models learned the statistics of their lexicons as shown by better processing of well‐formed pseudowords as opposed to ill‐formed (scrambled) pseudowords, and by accounting for variance in well‐formedness ratings. It is discussed how the sequence encoder may be integrated into broader models of lexical processing. (shrink)

In recent years, a number of research projects have been proposed whose goal is to build large-scale simulations of brain mechanisms at unprecedented levels of biological accuracy. Here it is argued that the roles these simulations are expected to play in neuroscientific research go beyond the “synthetic method” extensively adopted in Artificial Intelligence and biorobotics. In addition we show that, over and above the common goal of simulating brain mechanisms, these projects pursue various modelling ambitions that can be (...) sharply distinguished from one another, and that correspond to conceptually different interpretations of the notion of “biological accuracy”. They include the ambition to reach extremely deep levels in the mechanistic decomposition hierarchy, to simulate networks composed of extremely large numbers of neural units, to build systems able to generate rich behavioural repertoires, to simulate “complex” neuron models, to implement the “best” theories available on brain structure and function. Some questions will be raised concerning the significance of each of these modelling ambitions with respect to the various roles played by simulations in the study of the brain. (shrink)

At the global as well as local scales, some of the geometry of types of neuron arbors—both dendrites and axons—appears to be self-organizing: Their morphogenesis behaves like flowing water, that is, fluid dynamically; waterflow in branching networks in turn acts like a tree composed of cords under tension, that is, vector mechanically. Branch diameters and angles and junction sites conform significantly to this model. The result is that such neuron tree samples globally minimize their total volume—rather than, for example, (...) surface area or branch length. In addition, the arbors perform well at generating the cheapest topology interconnecting their terminals: their large-scale layouts are among the best of all such possible connecting patterns, approaching 5% of optimum. This model also applies comparably to arterial and river networks. S1063-651X 99 16205-6.. (shrink)

The target article is well in accordance with recent theoretical advances considering the complex large-scale brain network organization underlying emotions. Given current limitations of the methods in brain science, however, research is faced with the difficult question as to how it will be possible to elucidate the complex nonlinear interactions, the neurotransmitters involved, and the excitatory or inhibitory nature of neural processes underlying human emotion in such networks. Moreover, while investigating the network properties of neural processes underlying (...) emotions, it is also important to keep in mind that specific brain structures, or specific brain networks, generate specific emotions. Thus, while aiming at elucidating complex large-scale brain networks of emotion, it is important to identify emotional specificity of, or within, these networks. (shrink)

Labels provide a quick and effective solution to obtain people interesting content from large-scale social network information. The current interest label extraction method based on the subgraph stream proves the feasibility of the subgraph stream for user label extraction. However, it is extremely time-consuming for constructing subgraphs. As an effective mathematical method to deal with fuzzy and uncertain information, rough set-based representations for subgraph stream construction are capable of capturing the uncertainties of the social network. Therefore, we propose (...) an effective approach called RS_UNITE_SS, which is suitable for large-scale social network user interest label extraction. Specifically, we first propose the subgraph division algorithm to construct a subgraph stream by incorporating a rough set. Then, the algorithm for user real-time interest label extraction based on upper approximation is proposed by using sequentially characteristics of the subgraph. Empirically, we evaluate RS_UNITE_SS over real-world datasets, and experimental results demonstrate that our proposed approach is more computationally efficient than existing methods while achieving higher precision value and MRR value. (shrink)

As one of the most important routing problems in the complex network within a very-large-scale integration circuit, bus routing has become much more challenging when witnessing the advanced technology node enters the deep nanometer era because all bus bits need to be routed with the same routing topology in the context. In particular, the nonuniform routing track configuration and obstacles bring the largest difficulty for maintaining the same topology for all bus bits. In this paper, we first present (...) a track handling technique to unify the nonuniform routing track configuration with obstacles. Then, we formulate the topology-aware single bus routing as an unsplittable flow problem, which is integrated into a negotiation-based global routing to determine the desired routing regions for each bus. A topology-aware track assignment is also presented to allocate the tracks to each segment of buses under the guidance of the global routing result. Finally, a detailed routing scheme is proposed to connect the segments of each bus. We evaluate our routing result with the benchmark suite of the 2018 CAD Contest. Compared with the top-3 state-of-the-art methods, experimental results show that our proposed algorithm achieves the best overall score regarding specified time limitations. (shrink)

With the improvement of citizens’ risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk (...) media, and big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed， and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China. (shrink)

With the development of distributed networks, the remote controllability of the distributed energy objects and the vulnerability of user-side information security protection measures make distributed energy objects extremely vulnerable to malicious control by attackers. Hence, the large-scale loads may produce abnormal operation performance, such as load casting/dropping synchronously or frequent and synchronous casting and dropping, and hence, it can threaten the security and stable operation of the distribution networks. First, we analyze the security threats faced by (...) industrial controllable load, civil controllable load, and the gains and losses of attacks on the distribution networks. Considering the factors of cyber attacks, we propose a control model and cyber attack model in active distribution networks. And, three types of attacks that the target suffered are defined on the basis of “on” and “off” modes for control. Then, the controllable load was maliciously controlled as the research object, and a suitable scenario is selected. The impact of malicious control of the controllable load on the power supply reliability and power quality of the distribution networks are simulated and analyzed, and risk consequences for different types of attacks are provided. (shrink)

Court judgments contain valuable information on how statutory laws and past court precedents are interpreted and how the interdependence structure among them evolves in the courtroom. Data-mining the evolving structure of such customs and norms that reflect myriad social values from a large-scale court judgment corpus is an essential task from both the academic and industrial perspectives. In this paper, using data from approximately 110,000 court judgments from Japan spanning the period 1998–2018 from the district to the supreme (...) court level, we propose two tasks that grasp such a structure from court judgments and highlight the strengths and weaknesses of major machine learning models. One is a prediction task based on masked language modeling that connects textual information to legal codes and past court precedents. Another is a dynamic link prediction task where we predict the hidden interdependence structure in the law. We make quantitative and qualitative comparisons among major machine learning models to obtain insights for future developments. (shrink)

Low frequency oscillation is an important attribute of human brain activity, and the amplitude of low frequency fluctuation is an effective method to reflect the characteristics of low frequency oscillation, which has been widely used in the treatment of brain diseases and other fields. However, due to the low accuracy of the current analysis methods for low frequency signal extraction of ALFF, we propose the Fourier-based synchrosqueezing transform, which is often used in the field of signal processing to extract the (...) ALFF of the low frequency power spectrum of the whole-time dimension. The low frequency characteristics of the extracted signal are compared with those of FSST and fast Fourier transform through the resting-state data. It is clear that the signal extracted by FSST has more low frequency characteristics, which is significantly different from FFT. (shrink)

A general conceptual framework for large-scale neocortical dynamics based on data from many laboratories is applied to a variety of experimental designs, spatial scales, and brain states. Partly distinct, but interacting local processes (e.g., neural networks) arise from functional segregation. Global processes arise from functional integration and can facilitate (top down) synchronous activity in remote cell groups that function simultaneously at several different spatial scales. Simultaneous local processes may help drive (bottom up) macroscopic global dynamics observed with (...) electroencephalography (EEG) or magnetoencephalography (MEG). A local/global dynamic theory that is consistent with EEG data and the proposed conceptual framework is outlined. This theory is neutral about properties of neural networks embedded in macroscopic fields, but its global component makes several qualitative and semiquantitative predictions about EEG measures of traveling and standing wave phenomena. A more general “metatheory” suggests what large-scale quantitative theories of neocortical dynamics may be like when more accurate treatment of local and nonlinear effects is achieved. The theory describes the dynamics of excitatory and inhibitory synaptic action fields. EEG and MEG provide large-scale estimates of modulation of these synaptic fields around background levels. Brain states are determined by neuromodulatory control parameters. Purely local states are dominated by local feedback gains and rise and decay times of postsynaptic potentials. Dominant local frequencies vary with brain region. Other states are purely global, with moderate to high coherence over large distances. Multiple global mode frequencies arise from a combination of delays in corticocortical axons and neocortical boundary conditions. Global frequencies are identical in all cortical regions, but most states involve dynamic interactions between local networks and the global system. EEG frequencies may involve a “matching” of local resonant frequencies with one or more of the many, closely spaced global frequencies. Key Words: binding problem; cell assemblies; coherence; EEG; limit cycles; neocortical dynamics; pacemakers; phase locking; spatial scale; standing waves; synchronization. Footnotes1 The relationship between the synaptic action fields proposed in the target article and cell assemblies is clarified with Figure R1 (p. 416) of the Response. (This figure was not available to Commentators. (shrink)

Nowadays, the power systems are getting more and more complicated because of the delays introduced by the communication networks. The existence of the delays usually leads to the degradation and/or instability of power system performance. On account of this point, the traditional load frequency control approach for power system sketches a destabilizing impact and an unacceptable system performance. Therefore, this paper proposes a new LFC based on adaptive integral second-order sliding mode control approach for the large-scale power (...) system with communication delays. First, a new linear matrix inequality is derived to ensure the stability of whole power systems using Lyapunov stability theory. Second, an AISOSMC law is designed to ensure the finite time reachability of the system states. To the best of our knowledge, this is the first time the AISOSMC is designed for LFC of the LSPSwCD. In addition, the report of testing results presents that the suggested LFC based on AISOSMC can not only decrease effectively the frequency variation but also make successfully less in mount of power oscillation/fluctuation in tie-line exchange. (shrink)

The Coal Seam Gas extraction industry is developing rapidly within the Surat Basin in southern Queensland, Australia, with licenses already approved for tenements covering more than 24,000 km2. Much of this land is used for a broad range of agricultural purposes and the need for coexistence between the farm and gas industries has been the source of much conflict. Whilst much research has been undertaken into the environmental and economic impacts of CSG, little research has looked into the issues of (...) coexistence between farmers and the CSG industry in the shared space that is a farm business, a home and a resource extraction network. We conducted three workshops with farmers from across a broad region undergoing CSG development to explore farmers’ perceptions of some of the issues arising from large scale land use change. Workshops explored the importance of place identity and landscape aesthetics for farmers, farmers’ acceptance and coping with change, and possible benefits from off-farm income. We found that farmers believed that place identity was not well understood by CSG staff from non-rural backgrounds and that farmers struggled to explain some concerns because of the different way they interpreted their landscape. Furthermore, high staff turnover, and the extensive use of contractors also impacted on communications. These factors were the cause of much frustration and farmers felt that this has led to severe impacts on mental health and wellbeing. Farmers felt that a change in culture within the CSG companies will be required if engagement with farmers is to improve and that efforts to employ local people in these communications was helping this. The workshops also identified a range of issues perceived by farmers arising from increased traffic volumes, impacts to mental health and wellbeing, place identity and loss of water resources for farmers. Finally, it was suggested that scientists and agricultural industry groups will need to work closely with farmers to develop understanding of these emerging issues and to develop solutions that are timely and relevant. (shrink)

Applying machine learning algorithms to automatically infer relationships between concepts from large-scale collections of documents presents a unique opportunity to investigate at scale how human semantic knowledge is organized, how people use it to make fundamental judgments (“How similar are cats and bears?”), and how these judgments depend on the features that describe concepts (e.g., size, furriness). However, efforts to date have exhibited a substantial discrepancy between algorithm predictions and human empirical judgments. Here, we introduce a novel (...) approach to generating embeddings for this purpose motivated by the idea that semantic context plays a critical role in human judgment. We leverage this idea by constraining the topic or domain from which documents used for generating embeddings are drawn (e.g., referring to the natural world vs. transportation apparatus). Specifically, we trained state-of-the-art machine learning algorithms using contextually-constrained text corpora (domain-specific subsets of Wikipedia articles, 50+ million words each) and showed that this procedure greatly improved predictions of empirical similarity judgments and feature ratings of contextually relevant concepts. Furthermore, we describe a novel, computationally tractable method for improving predictions of contextually-unconstrained embedding models based on dimensionality reduction of their internal representation to a small number of contextually relevant semantic features. By improving the correspondence between predictions derived automatically by machine learning methods using vast amounts of data and more limited, but direct empirical measurements of human judgments, our approach may help leverage the availability of online corpora to better understand the structure of human semantic representations and how people make judgments based on those. (shrink)

Cognitive Science, Volume 46, Issue 2, February 2022.

The purpose of crowd counting is to estimate the number of pedestrians in crowd images. Crowd counting or density estimation is an extremely challenging task in computer vision, due to large scale variations and dense scene. Current methods solve these issues by compounding multi-scale Convolutional Neural Network with different receptive fields. In this paper, a novel end-to-end architecture based on Multi-Scale Adversarial Convolutional Neural Network (MSA-CNN) is proposed to generate crowd density and estimate the amount of (...) crowd. Firstly, a multi-scale network is used to extract the globally relevant features in the crowd image, and then fractionally-strided convolutional layers are designed for up-sampling the output to recover the loss of crucial details caused by the earlier max pooling layers. An adversarial loss is directly employed to shrink the estimated value into the realistic subspace to reduce the blurring effect of density estimation. Joint training is performed in an end-to-end fashion using a combination of Adversarial loss and Euclidean loss. The two losses are integrated via a joint training scheme to improve density estimation performance.We conduct some extensive experiments on available datasets to show the significant improvements and supremacy of the proposed approach over the available state-of-the-art approaches. (shrink)

We address the problem of musical variation (identification of different musical sequences as variations) and its implications for mental representations of music. According to reductionist theories, listeners judge the structural importance of musical events while forming mental representations. These judgments may result from the production of reduced memory representations that retain only the musical gist. In a study of improvised music performance, pianists produced variations on melodies. Analyses of the musical events retained across variations provided support for the reductionist account (...) of structural importance. A neural network trained to produce reduced memory representations for the same melodies represented structurally important events more efficiently than others. Agreement among the musicians' improvisations, the network model, and music‐theoretic predictions suggest that perceived constancy across musical variation is a natural result of a reductionist mechanism for producing memory representations. (shrink)

Convolutional neural networks, which automatically learn features from raw data to approximate functions, are being increasingly applied to the end-to-end analysis of electroencephalographic signals, especially for decoding brain states in brain-computer interfaces. Nevertheless, CNNs introduce a large number of trainable parameters, may require long training times, and lack in interpretability of learned features. The aim of this study is to propose a CNN design for P300 decoding with emphasis on its lightweight design while guaranteeing high performance, on the (...) effects of different training strategies, and on the use of post-hoc techniques to explain network decisions. The proposed design, named MS-EEGNet, learned temporal features in two different timescales in an efficient and optimized way, and was validated on three P300 datasets. The CNN was trained using different strategies and was compared with several state-of-the-art algorithms. Furthermore, variants of the baseline MS-EEGNet were analyzed to evaluate the impact of different hyper-parameters on performance. Lastly, saliency maps were used to derive representations of the relevant spatio-temporal features that drove CNN decisions. MS-EEGNet was the lightest CNN compared with the tested SOA CNNs, despite its multiple timescales, and significantly outperformed the SOA algorithms. Post-hoc hyper-parameter analysis confirmed the benefits of the innovative aspects of MS-EEGNet. Furthermore, MS-EEGNet did benefit from transfer learning, especially using a low number of training examples, suggesting that the proposed approach could be used in BCIs to accurately decode the P300 event while reducing calibration times. Representations derived from the saliency maps matched the P300 spatio-temporal distribution, further validating the proposed decoding approach. This study, by specifically addressing the aspects of lightweight design, transfer learning, and interpretability, can contribute to advance the development of deep learning algorithms for P300-based BCIs. (shrink)

People undergo many idiosyncratic experiences throughout their lives that may contribute to individual differences in the size and structure of their knowledge representations. Ultimately, these can have important implications for individuals' cognitive performance. We review evidence that suggests a relationship between individual experiences, the size and structure of semantic representations, as well as individual and age differences in cognitive performance. We conclude that the extent to which experience-dependent changes in semantic representations contribute to individual differences in cognitive aging remains unclear. (...) To help fill this gap, we outline an empirical agenda that utilizes network analysis and involves the concurrent assessment of large-scale semantic networks and cognitive performance in younger and older adults. We present preliminary data to establish the feasibility and limitations of such empirical, network-analytical approaches. (shrink)

Many kinds of complex systems exhibit characteristic patterns of temporal correlations that emerge as the result of functional interactions within a structured network. One such complex system is the brain, composed of numerous neuronal units linked by synaptic connections. The activity of these neuronal units gives rise to dynamic states that are characterized by specific patterns of neuronal activation and co-activation. These patterns, called functional connectivity, are possible neural correlates of perceptual and cognitive processes. Which functional connectivity patterns arise depends (...) on the anatomical structure of the underlying network, which in turn is modified by a broad range of activity-dependent processes. Given this intricate relationship between structure and function, the question of how patterns of anatomical connectivity constrain or determine dynamical patterns is of considerable theoretical importance. The present study develops computational tools to analyze networks in terms of their structure and dynamics. We identify different classes of network, including networks that are characterized by high complexity. These highly complex networks have distinct structural characteristics such as clustered connectivity and short wiring length similar to those of large-scale networks of the cerebral cortex. (shrink)

From one of the most influential scientists of our time, a dazzling exploration of the hidden laws that govern the life cycle of everything from plants and animals to the cities we live in. The former head of the Sante Fe Institute, visionary physicist Geoffrey West is a pioneer in the field of complexity science, the science of emergent systems and networks. The term "complexity" can be misleading, however, because what makes West's discoveries so beautiful is that he has (...) found an underlying simplicity that unites the seemingly complex and diverse phenomena of living systems, including our bodies, our cities and our businesses. Fascinated by issues of aging and mortality, West applied the rigor of a physicist to the biological question of why we live as long as we do and no longer. The result was astonishing, and changed science, creating a new understanding of energy use and metabolism: West found that despite the riotous diversity in the sizes of mammals, they are all, to a large degree, scaled versions of each other. If you know the size of a mammal, you can use scaling laws to learn everything from how much food it eats per day, what its heart-rate is, how long it will take to mature, its lifespan, and so on. Furthermore, the efficiency of the mammal's circulatory systems scales up precisely based on weight: if you compare a mouse, a human and an elephant on a logarithmic graph, you find with every doubling of average weight, a species gets 25% more efficient--and lives 25% longer. This speaks to everything from how long we can expect to live to how many hours of sleep we need. Fundamentally, he has proven, the issue has to do with the fractal geometry of the networks that supply energy and remove waste from the organism's body. (shrink)

Large scale experiments at CERN’s Large Hadron Collider rely heavily on computer simulations, a fact that has recently caught philosophers’ attention. CSs obviously require appropriate modeling, and it is a common assumption among philosophers that the relevant models can be ordered into hierarchical structures. Focusing on LHC’s ATLAS experiment, we will establish three central results here: with some distinct modifications, individual components of ATLAS’ overall simulation infrastructure can be ordered into hierarchical structures. Hence, to a good degree (...) of approximation, hierarchical accounts remain valid at least as descriptive accounts of initial modeling steps. In order to perform the epistemic function Winsberg Model-based reasoning in scientific discovery. Kluwer Academic/plenum Publishers, New York, pp 255–269, 1999) assigns to models in simulation—generate knowledge through a sequence of skillful but non-deductive transformations—ATLAS’ simulation models have to be considered part of a network rather than a hierarchy, in turn making the associated simulation modeling messy rather than motley. Deriving knowledge-claims from this ‘mess’ requires two sources of justification: holistic validation :253–262, 2010; in Carrier M, Nordmann A Science in the context of application. Springer, Berlin, pp 115–130, 2011), and model coherence. As it turns out, the degree of model coherence sets HEP apart from other messy, simulation-intensive disciplines such as climate science, and the reasons for this are to be sought in the historical, empirical and theoretical foundations of the respective discipline. (shrink)

Deliberative democracy started out as an ideal for mass democracy. Lately, however, its large-scale ambitions have mostly been shelved. This article revivifies the ideal of mass deliberative democracy by offering a clear mechanism by which everyone in the community can be included in the same conversation. The trick is to make use of people’s overlapping social communicative networks through which informal deliberative exchanges already occur on an everyday basis. Far from being derailed by threats of polarization, echo (...) chambers, and motivated reasoning, informal networked deliberation can indeed put everyone in touch, directly or indirectly, with everyone else. (shrink)

This article aims to integrate discourse analysis of politically instrumental imagined identity geographies with the relational and territorial geography of the communities of praxis and interpretation that produce them. My case study is the international community of nationalist scientists who classified Europe’s biological races in the 1820s—1940s. I draw on network analysis, relational geography, historical sociology and the historical turn to problematize empirically how spatial patterns of this community’s shifting disciplinary and political coalitions, communication networks and power relations emerged, (...) were structured, persisted, changed, interacted and disappeared. I focus especially on core— periphery relations. I argue that if local historical spatial patterns affect those of later phenomena, geographies like that of European integration should be understood in the context of Europe’s complex historical cultural geography. Unlike discourse deconstruction alone, this complementary relational de-essentialization of geography can identify large-scale, enduring associations of cultural patterns as well as cultural flux and ambiguity. (shrink)

Nowadays, around half of the global population lives in urban areas. This rate is expected to increase up to two-thirds by the year 2050. Most studies analyze urban dynamics in wide geographic ranges, focusing mainly on cities. According to them, the global population is spatially distributed in two extremes: large urban agglomerations and rural deserts. However, this remark is excessively general and imprecise. For this reason, it remains essential to analyze these dynamics at other spatial scales. A close-up look (...) in thinly populated regions shows how urban dynamics are also noticeable. In this paper, we analyze spatiotemporal patterns of population distribution in a predominantly rural area by applying a local-scale approach. These patterns are represented by using spatial networks with nodes representing the human settlements and links showing hierarchies between nodes. This case study is conducted in a small municipality located in northwestern Spain. It is a predominantly rural area with a very particular spatial pattern of population distribution. (shrink)

The authors prove and describe the action of new networking mechanisms of formation of identities which arise in the context of societal transformations of the modern society. The networking mechanisms of identity formation represent a complex of interrelated and interdependent practices in global information and communication space, promoting individual and collective identification, interiorization and reflection. The complex includes the mechanism of network communication, mechanism of reflexive involvement of a person into the public space, mechanism of network topos-structuring and mechanism of (...) public crowdsourcing. The results of the empirical research show that the functionality of the mechanism of network communication for reproducing/positioning traditional identities and projecting new identities resides in its digital nature and a network ethos. The mechanism of reflexive involvement of individuals into the public space enables individual and collective actors to project the independent social worlds requiring the creation of their own virtualized public spaces that are closely linked with the common social space. The mechanism of network toposstructuring and mechanism of public crowdsourcing, forming situation and problem identities, have the high mobilization potential to update the activity of network communities in the form of individuals’ initiatives and large-scale civil movements where new sustained identities form which can also gain the protest nature. The authors come to conclusion that the complex of network mechanisms produces the dynamic matrix of the identity of a modern person allowing to take the opportunities for its development in the contemporary conditions of new social reality formation. At the same time, the complex of networking mechanisms is not stable; its content depends on those institutional practices which determine further conditions, processes and results of formation of identities, requiring their conceptual understanding and empirical research in social sciences. (shrink)

The authors prove and describe the action of new networking mechanisms of formation of identities which arise in the context of societal transformations of the modern society. The networking mechanisms of identity formation represent a complex of interrelated and interdependent practices in global information and communication space, promoting individual and collective identification, interiorization and reflection. The complex includes the mechanism of network communication, mechanism of reflexive involvement of a person into the public space, mechanism of network topos-structuring and mechanism of (...) public crowdsourcing. The results of the empirical research show that the functionality of the mechanism of network communication for reproducing/positioning traditional identities and projecting new identities resides in its digital nature and a network ethos. The mechanism of reflexive involvement of individuals into the public space enables individual and collective actors to project the independent social worlds requiring the creation of their own virtualized public spaces that are closely linked with the common social space. The mechanism of network toposstructuring and mechanism of public crowdsourcing, forming situation and problem identities, have the high mobilization potential to update the activity of network communities in the form of individuals’ initiatives and large-scale civil movements where new sustained identities form which can also gain the protest nature. The authors come to conclusion that the complex of network mechanisms produces the dynamic matrix of the identity of a modern person allowing to take the opportunities for its development in the contemporary conditions of new social reality formation. At the same time, the complex of networking mechanisms is not stable; its content depends on those institutional practices which determine further conditions, processes and results of formation of identities, requiring their conceptual understanding and empirical research in social sciences. (shrink)