La, V. P., & Vuong, Q. H. (2019). bayesvl: Visually learning the graphical structure of Bayesian networks and performing MCMC with ‘Stan’. The Comprehensive R Archive Network (CRAN).

The new business opportunities and prospects emerging in Europe within the Common Market and other Western and European countries also raise important ethical challenges. This work comprises a collection of ethical insights to enhance the conduct of business in an evolving Europe.

With the rapid growth of informatics systems’ technology in this modern age, the Internet of Things (IoT) has become more valuable and vital to everyday life in many ways. IoT applications are now more popular than they used to be due to the availability of many gadgets that work as IoT enablers, including smartwatches, smartphones, security cameras, and smart sensors. However, the insecure nature of IoT devices has led to several difficulties, one of which is distributed denial-of-service (DDoS) attacks. IoT (...) systems have several security limitations due to their disreputability characteristics, like dynamic communication between IoT devices. The dynamic communications resulted from the limited resources of these devices, such as their data storage and processing units. Recently, many attempts have been made to develop intelligent models to protect IoT networks against DDoS attacks. The main ongoing research issue is developing a model capable of protecting the network from DDoS attacks that is sensitive to various classes of DDoS and can recognize legitimate traffic to avoid false alarms. Subsequently, this study proposes combining three deep learning algorithms, namely recurrent neural network (RNN), long short-term memory (LSTM)-RNN, and convolutional neural network (CNN), to build a bidirectional CNN-BiLSTM DDoS detection model. The RNN, CNN, LSTM, and CNN-BiLSTM are implemented and tested to determine the most effective model against DDoS attacks that can accurately detect and distinguish DDoS from legitimate traffic. The intrusion detection evaluation dataset (CICIDS2017) is used to provide more realistic detection. The CICIDS2017 dataset includes benign and up-to-date examples of typical attacks, closely matching real-world data of Packet Capture. The four models are tested and assessed using Confusion Metrix against four commonly used criteria: accuracy, precision, recall, and F-measure. The performance of the models is quite effective as they obtain an accuracy rate of around 99.00%, except for the CNN model, which achieves an accuracy of 98.82%. The CNN-BiLSTM achieves the best accuracy of 99.76% and precision of 98.90%. (shrink)

Topics in Cognitive Science, Volume 14, Issue 1, Page 189-208, January 2022.

Attention has been theorized as a system comprising three networks that can be estimated reliably by the attention network test ; the three networks are defined as alerting, orienting, and conflict control. The present study aims to identify the attention networks that are crucial for elite shooting and archery athletes and to examine whether mindfulness training can improve elite athletes' attention networks. We compared the performances in ANT between 62 elite athletes from the Chinese national team (...) of shooting and archery and 49 athletes from a provincial team in China. The results indicate three well-functioned attention networks in both groups, but elite athletes in the national team responded faster overall than athletes in the provincial team. The 62 elite athletes in the national team then received mindfulness training with varied periods ranging from 5 to 8 weeks, after which the ANT was re-administered. After mindfulness training, the elite athletes improved in orienting and conflict control networks compared with their pre-training performances. These results suggest that elite shooting and archery athletes in the national team are more efficient in all three attention networks, which means that they are able to reach the alerting state faster, make better use of environmental information, and suppress interference from distractors more efficiently. Moreover, the orienting and conflict control networks of the elite shooting and archery athletes can be improved by mindfulness training. We conclude that mindfulness practice should be considered as a useful addition to daily training for shooting and archery athletes. (shrink)

Although connectionism is advocated by its proponents as an alternative to the classical computational theory of mind, doubts persist about its _computational_ credentials. Our aim is to dispel these doubts by explaining how connectionist networks compute. We first develop a generic account of computation—no easy task, because computation, like almost every other foundational concept in cognitive science, has resisted canonical definition. We opt for a characterisation that does justice to the explanatory role of computation in cognitive science. Next we (...) examine what might be regarded as the “conventional” account of connectionist computation. We show why this account is inadequate and hence fosters the suspicion that connectionist networks aren’t genuinely computational. Lastly, we turn to the principal task of the paper: the development of a more robust portrait of connectionist computation. The basis of this portrait is an explanation of the representational capacities of connection weights, supported by an analysis of the weight configurations of a series of simulated neural networks. (shrink)

Higher education institutes both foster the advancement of knowledge and address society's socioeconomic and environmental challenges. To fulfil these multiple missions requires significant changes to how the role of a researcher is perceived e.g. a researcher identity that is congruent with the objective of contributing to fundamental knowledge while also engaging with non-academic actors, broadly, and entrepreneurship, in particular. We argue that the early stages of an academic career—namely the PhD training trajectory—and the knowledge networks formed during this period (...) have a major influence on the scientist’s future capacity to develop an appropriate researcher role identity. We draw on knowledge network and identity theories to investigate how the knowledge networks (i.e. business, scientific and career knowledge networks) of PhD students promote changes to, reinforce or conflict with the perception of a researcher role identity. Our longitudinal qualitative network study includes PhD students and their supervisors funded by the H2020 FINESSE project. At the network level, we show that scientific knowledge is distributed equally throughout young academics’ networks but that entrepreneurial (business) and career knowledge tend to be concentrated around certain individuals in these networks. On the PhD student level, we observe different pronunciations of the researcher role identity linked to students’ interactions with their knowledge networks. We distinguish identity conflicts due to misalignment between ego and alters which leads to withdrawal from the network. Our findings have practical implications and suggest that universities and PhD student supervisors should support PhD students to develop a researcher identity which is in line with the individual PhD student’s expectations. (shrink)

Transcranial Photobiomodulation has demonstrated its ability to alter electrophysiological activity in the human brain. However, it is unclear how tPBM modulates brain electroencephalogram networks and is related to human cognition. In this study, we recorded 64-channel EEG from 44 healthy humans before, during, and after 8-min, right-forehead, 1,064-nm tPBM or sham stimulation with an irradiance of 257 mW/cm2. In data processing, a novel methodology by combining group singular value decomposition with the exact low-resolution brain electromagnetic tomography was implemented and (...) performed on the 64-channel noise-free EEG time series. The gSVD+eLORETA algorithm produced 11 gSVD-derived principal components projected in the 2D sensor and 3D source domain/space. These 11 PCs took more than 70% weight of the entire EEG signals and were justified as 11 EEG brain networks. Finally, baseline-normalized power changes of each EEG brain network in each EEG frequency band were quantified during the first 4-min, second 4-min, and post tPBM/sham periods, followed by comparisons of frequency-specific power changes between tPBM and sham conditions. Our results showed that tPBM-induced increases in alpha powers occurred at default mode network, executive control network, frontal parietal network and lateral visual network. Moreover, the ability to decompose EEG signals into individual, independent brain networks facilitated to better visualize significant decreases in gamma power by tPBM. Many similarities were found between the cortical locations of SVD-revealed EEG networks and fMRI-identified resting-state networks. This consistency may shed light on mechanistic associations between tPBM-modulated brain networks and improved cognition outcomes. (shrink)

In a complex network, each edge has different functions on controllability of the whole network. A network may be out of control due to failure or attack of some specific edges. Bridges are a kind of key edges whose removal will disconnect a network and increase connected components. Here, we investigate the effects of removing bridges on controllability of network. Various strategies, including random deletion of edges, deletion based on betweenness centrality, and deletion based on degree of source or target (...) nodes, are used to compare with the effect of removing bridges. It is found that the removing bridges strategy is more efficient on reducing controllability than the other strategies of removing edges for ER networks and scale-free networks. In addition, we also found the controllability robustness under edge attack is related to the average degree of complex networks. Therefore, we propose two optimization strategies based on bridges to improve the controllability robustness of complex networks against attacks. The effectiveness of the proposed strategies is demonstrated by simulation results of some model networks. These results are helpful for people to understand and control spreading processes of epidemic across different paths. (shrink)

In this paper, the stability and stabilization issues for a class of delayed neural networks with time-varying hybrid impulses are investigated. The hybrid effect of two types of impulses including both stabilizing and destabilizing impulses is considered simultaneously in the analysis of systems. To characterize the occurrence features of impulses, the concepts of average impulse interval and average impulse strength are employed. Based on the analysis of stability, a pinning impulsive controller which can ensure the global exponential stability of (...) the studied neural networks is designed by pinning a small fraction of neurons. Finally, two numerical examples are given to illustrate the effectiveness of the proposed control schemes for delayed neural networks with hybrid impulses. (shrink)

A basic deep neural network (DNN) is trained to exhibit a large set of input–output dispositions. While being a good model of the way humans perform some tasks automatically, without deliberative reasoning, more is needed to approach human‐like artificial intelligence. Analysing recent additions brings to light a distinction between two fundamentally different styles of computation: content‐specific and non‐content‐specific computation (as first defined here). For example, deep episodic RL networks draw on both. So does human conceptual reasoning. Combining the two (...) takes advantage of the complementary costs and benefits of each. It also offers a better model of human cognitive competence. (shrink)

This article presents a simple derivation of optimization models for reaction networks leading to a generalized form of the mass-action law, and compares the formal structure of Minimum Information Divergence, Quadratic Programming and Kirchhoff type network models. These optimization models are used in related articles to develop and illustrate the operation of ontology alignment algorithms and to discuss closely connected issues concerning the epistemological and statistical significance of sharp or precise hypotheses in empirical science.

In this paper we compare causal models with reason models in the construction of Bayesian networks for legal evidence. In causal models, arrows in the network are drawn from causes to effects. In a reason model, the arrows are instead drawn towards the evidence, from factum probandum to factum probans. We explore the differences between causal models and reason models and observe several distinct advantages with reason models. Reason models are better aligned with the philosophy of Bayesian inference, as (...) they model reasons for up-dating beliefs. Reason models are better suited for measuring the combined support of the evidence, and a prior probability of guilt that reflects the number of possible perpetrators is accommodated more easily with reason models. (shrink)

Convolutional neural networks (CNNs) are increasingly widely used in psychology and neuroscience to predict how human minds and brains respond to visual images. Typically, CNNs represent these images using thousands of features that are learned through extensive training on image datasets. This raises a question: How many of these features are really needed to model human behavior? Here, we attempt to estimate the number of dimensions in CNN representations that are required to capture human psychological representations in two ways: (...) (1) directly, using human similarity judgments and (2) indirectly, in the context of categorization. In both cases, we find that low-dimensional projections of CNN representations are sufficient to predict human behavior. We show that these low-dimensional representations can be easily interpreted, providing further insight into how people represent visual information. A series of control studies indicate that these findings are not due to the size of the dataset we used and may be due to a high level of redundancy in the features appearing in CNN representations. (shrink)

Following a short discussion of the German philosopher Friedrich A. Kittler’s biographical and intellectual formation, this interview introduces the reader to Kittler’s theoretical efforts to develop our understanding of contemporary culture and society. However, the focus of the interview is on the core concepts of ‘discourse networks’, ‘the military-industrial complex’, and ‘technology’, arguably the three central themes of Kittler’s work to date. As the title of the interview indicates, the idea of ‘cultural mathematics’ is also considered important in this (...) account of Kittler’s particular contribution to social and cultural theory, although this and related notions are critically appraised together with an assessment of their theoretical, social and cultural impact. (shrink)

Viruses and related infectious genetic parasites are the most abundant biological agents on this planet. They invade all cellular organisms, are key agents in the generation of adaptive and innate immune systems, and drive nearly all regulatory processes within living cells.

All the conserved detailed results of evolution stored in DNA must be read, transcribed, and translated via an RNAmediated process. This is required for the development and growth of each individual cell. Thus, all known living organisms fundamentally depend on these RNA-mediated processes. In most cases, they are interconnected with other RNAs and their associated protein complexes and function in a strictly coordinated hierarchy of temporal and spatial steps (i.e., an RNA network). Clearly, all cellular life as we know it (...) could not function without these key agents of DNA replication, namely rRNA, tRNA, and mRNA. Thus, any definition of life that lacks RNA functions and their networks misses an essential requirement for RNA agents that inherently regulate and coordinate (communicate to) cells, tissues, organs, and organisms. The precellular evolution of RNAs occurred at the core of the emergence of cellular life and the question remained of how both precellular and cellular levels are interconnected historically and functionally. RNA-networks andRNA-communication can interconnect these levels.With the reemergence of virology in evolution, it became clear that communicating viruses and subviral infectious genetic parasites are bridging these two levels by invading, integrating, coadapting, exapting, and recombining constituent parts in host genomes for cellular requirements in gene regulation and coordination aims. Therefore, a 21st century understanding of life is of an inherently social process based on communicating RNA networks, in which viruses and cells continuously interact. (shrink)

The predominance of machine learning based techniques in cognitive neuroscience raises a host of philosophical and methodological concerns. Given the messiness of neural activity, modellers must make choices about how to structure their raw data to make inferences about encoded representations. This leads to a set of standard methodological assumptions about when abstraction is appropriate in neuroscientific practice. Yet, when made uncritically these choices threaten to bias conclusions about phenomena drawn from data. Contact between the practices of multivariate pattern analysis (...) and philosophy of science can help to illuminate the conditions under which we can use artificial neural networks to better understand neural mechanisms. This paper considers a specific technique for MVPA called representational similarity analysis. I develop a theoretically-informed account of RSA that draws on early connectionist research and work on idealization in the philosophy of science. By bringing a philosophical account of cognitive modelling in conversation with RSA, this paper clarifies the practices of neuroscientists and provides a generalizable framework for using artificial neural networks to study neural mechanisms in the brain. (shrink)

The article aims to map the contemporary techno-social networks, together with delineation of the algorithmic governmentality, computational unconscious, the epistemic structure of the Eurocentric matrix of power haunted by its own repetition of the constant abyss of horrors, only to search for gestures of resistance. Gestures of resistance, contrary to the false conviction of capitalist realism, can be found everywhere, including in Jordan Peele's Nope (2022). Through a variety of motifs, themes, and cultural and cinematic references, Peele creates a (...) resistance image, i.e., an image that resists the historical trajectory of the violence of the digital colonial matrix of knowledge. In particular with Nope, in which the history of racial violence is disentangled by evoking the relation between the entanglement of capital and epistemic violence embodied in an all-devouring predator UFO. But Nope is also about visualizing silenced histories. Indeed, to strive to capture UFO with the camera is to break away from modernity as a totalizing onto-epistemology and in this register generating a false universal subject of a Man. (shrink)

Why and under what conditions are individuals altruistic to family and friends in their social networks? Evolutionary psychology suggests that such behaviour is primarily the product of adaptations for kin- and reciprocal altruism, dependent on the degree of genetic relatedness and exchange of benefits, respectively. For this reason, individuals are expected to be more altruistic to family members than to friends: whereas family members can be the recipients of kin and reciprocal altruism, friends can be the recipients of reciprocal (...) altruism only. However, there is a question about how the effect of kinship is implemented at the proximate psychological level. One possibility is that kinship contributes to some general measure of relationship quality (such as ‘emotional closeness’), which in turn explains altruism. Another possibility is that the effect of kinship is independent of relationship quality. The present study tests between these two possibilities. Participants (N= 111) completed a self-report questionnaire about their willingness to be altruistic, and their emotional closeness, to 12 family members and friends at different positions in their extended social networks. As expected, altruism was greater for family than friends, and greater for more central layers of the network. Crucially, the results showed that kinship made a significant unique contribution to altruism, even when controlling for the effects of emotional closeness. Thus, participants were more altruistic towards kin than would be expected if altruism was dependent on emotional closeness alone – a phenomenon we label a ‘kinship premium’. These results have implications for the ongoing debate about the extent to which kin relations and friendships are distinct kinds of social relationships, and how to measure the ‘strength of ties’ in social networks. (shrink)

Due to their non-hierarchical structure, socio-technical networks are prone to the occurrence of the problem of many hands. In the present paper an approach is introduced in which people’s opinions on responsibility are empirically traced. The approach is based on the Rawlsian concept of Wide Reflective Equilibrium (WRE) in which people’s considered judgments on a case are reflectively weighed against moral principles and background theories, ideally leading to a state of equilibrium. Application of the method to a hypothetical case (...) with an artificially constructed network showed that it is possible to uncover the relevant data to assess a consensus amongst people in terms of their individual WRE. It appeared that the moral background theories people endorse are not predictive for their actual distribution of responsibilities but that they indicate ways of reasoning and justifying outcomes. Two ways of ascribing responsibilities were discerned, corresponding to two requirements of a desirable responsibility distribution: fairness and completeness. Applying the method triggered learning effects, both with regard to conceptual clarification and moral considerations, and in the sense that it led to some convergence of opinions. It is recommended to apply the method to a real engineering case in order to see whether this approach leads to an overlapping consensus on a responsibility distribution which is justifiable to all and in which no responsibilities are left unfulfilled, therewith trying to contribute to the solution of the problem of many hands. (shrink)

While the rate of Internet use among the older population in China is rapidly increasing, the outcomes associated with Internet use remain largely unexplored. Currently, there are contradictory findings indicating that Internet use is sometimes positively and sometimes negatively associated with older adults’ subjective well-being. Therefore, we examined the associations between different types of Internet use, social networks, and loneliness among Chinese older adults using data from the Chinese Longitudinal Ageing Society Survey. Internet use was classified as interpersonal communication (...) and information acquisition, and social networks were divided into family and friendship ties. The results showed that both interpersonal communication and information acquisition were associated with lower loneliness. Interpersonal communication can increase social networks, and family ties have a mediating effect on the association between Internet use for interpersonal communication and loneliness. Although information acquisition can directly decrease loneliness in older adults, it can also damage existing social networks and further increase loneliness. Family ties act as a suppressor in the association between Internet use for information acquisition and loneliness. Our study further discusses important implications for improving the subjective well-being of older adults in the digital era, based on the empirical findings. (shrink)

As demonstrated by the popularity of social networking sites such as Facebook and Twitter, Internet-based social networks have become an important part of daily life, and many businesses are now involved in such networks either as service providers or as participants. Furthermore, inter-organizational networks are becoming an increasingly common feature of many industries, not only on the Internet. However, despite the growing importance of networks for businesses, there is little theoretical study on the social responsibilities of (...) businesses in such networks, and how these responsibilities are affected by different types of networks. This article explores how social network analysis, which has been developed from studies of social networks of individuals, can be used to shed light on corporate responsibilities in social networks. (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)

Ivancovsky et al. argue that the neurocognitive mechanisms of creativity and curiosity both rely on the interplay among brain networks. Research to date demonstrates that such inter-network dynamics are further complicated by functional fractionation within networks. Investigating how networks subdivide and reconfigure in service of a task offers insights about the precise anatomy that underpins creative and curious behaviour.

Human cognition is said to be systematic: cognitive ability generalizes to structurally related behaviours. The connectionist approach to cognitive theorizing has been strongly criticized for its failure to explain systematicity. Demonstrations of generalization notwithstanding, I show that two widely used networks (feedforward and recurrent) do not support systematicity under the condition of local input/output representations. For a connectionist explanation of systematicity, these results leave two choices, either: (1) develop models capable of systematicity under local input/output representations; or (2) justify (...) the choice of similarity-based (nonlocal) component representations sufficient for systematicity. (shrink)

Driven by privacy-related fears, users of Online Social Networks may start to reduce their network activities. This trend can have a negative impact on network sustainability and its business value. Nevertheless, very little is understood about the privacy-related concerns of users and the impact of those concerns on identity performance. To close this gap, we take a systematic view of user privacy concerns on such platforms. Based on insights from focus groups and an empirical study with 210 subjects, we (...) find that (i) Organizational Threats and (ii) Social Threats stemming from the user environment constitute two underlying dimensions of the construct Privacy Concerns in Online Social Networks . Using a Structural Equation Model, we examine the impact of the identified dimensions of concern on the Amount, Honesty, and Conscious Control of individual self-disclosure on these sites. We find that users tend to reduce the Amount of information disclosed as a response to their concerns regarding Organizational Threats. Additionally, users become more conscious about the information they reveal as a result of Social Threats. Network providers may want to develop specific mechanisms to alleviate identified user concerns and thereby ensure network sustainability. (shrink)

Decades of psychological research have been aimed at modeling how people learn features and categories. The empirical validation of these theories is often based on artificial stimuli with simple representations. Recently, deep neural networks have reached or surpassed human accuracy on tasks such as identifying objects in natural images. These networks learn representations of real‐world stimuli that can potentially be leveraged to capture psychological representations. We find that state‐of‐the‐art object classification networks provide surprisingly accurate predictions of human (...) similarity judgments for natural images, but they fail to capture some of the structure represented by people. We show that a simple transformation that corrects these discrepancies can be obtained through convex optimization. We use the resulting representations to predict the difficulty of learning novel categories of natural images. Our results extend the scope of psychological experiments and computational modeling by enabling tractable use of large natural stimulus sets. (shrink)

In the last decades, research in science mapping has delivered several powerful techniques, based on citation or textual analysis, for charting the intellectual organization of research fields. To map the social network underlying science and scholarship, by contrast, science mapping has mainly relied on one method, co-authorship analysis. This method, however, suffers from well-known limitations related to the practice of authorship. Moreover, it does not perform well on those fields where multi-authored publications are rare. In this study, a new method (...) for mapping the social structure of research fields is advanced, based on the analysis of the acknowledgments of academic publications. We first discuss the standard account of the function of acknowledgments in scholarly communication, then we introduce a new interpretative framework in which the acknowledgments are intended as positioning signals exchanged by researchers. Next, we provide the formal definition of the four acknowledgments-based networks that stand at the core of the method, and we test it on a humanities field, analytic philosophy. Results show that acknowledgement-based networks allow to reconstruct the fine-grained social structure of analytic philosophy from different perspectives. Furthermore, by comparing the citation-based maps of the field with the acknowledgments-based networks, it permits to shed light on the relationship between the intellectual and social layer of analytic philosophy. We conclude by presenting practical limitations of the method and by sketching some further research lines. (shrink)

What do humility, intellectual humility, and open-mindedness mean in the context of inter-group conflict? We spend most of our time with ingroup members, such as family, friends, and colleagues. Yet our biggest disagreements —— about practical, moral, and epistemic matters —— are likely to be with those who do not belong to our ingroup. An attitude of humility towards the former might be difficult to integrate with a corresponding attitude of humility towards the latter, leading to smug tribalism that masquerades (...) as genuine virtue. These potentially conflicting priorities have recently come to the fore because “tribal epistemology” has so thoroughly infected political and social discourse. Most research on these dispositions focuses on individual traits and dyadic peer-disagreement, with little attention to group membership or inter-group conflict. In this chapter, we dilate the social scale to address this pressing philosophical and social problem. (shrink)

In a criminal trial, a judge or jury needs to reason about what happened based on the available evidence, often including statistical evidence. While a probabilistic approach is suitable for analysing the statistical evidence, a judge or jury may be more inclined to use a narrative or argumentative approach when considering the case as a whole. In this paper we propose a combination of two approaches, combining Bayesian networks with scenarios. Whereas a Bayesian network is a popular tool for (...) analysing parts of a case, constructing and understanding a network for an entire case is not straightforward. We propose an explanation method for understanding a Bayesian network in terms of scenarios. This method builds on a previously proposed construction method, which we slightly adapt with the use of scenario schemes for the purpose of explaining. The resulting structure is explained in terms of scenarios, scenario quality and evidential support. A probabilistic interpretation of scenario quality is provided using the concept of scenario schemes. Finally, the method is evaluated by means of a case study. (shrink)

The ability of the Society of Jesus to engage in a broad and enduring tradition of scientific activity is here addressed in terms of its programmatic commitment to the consolidation and extension of the Catholic confession and its mastery of the administrative apparatus necessary to operate long-distance networks. The Society's early move into two major apostolates, one in education and the other in the overseas missions, brought Jesuits into regular contact with the educated elites of Europe and at the (...) same time placed the Society's missionaries in remote parts of the natural world. The modes of organization of travel and communication required by the Society's long-distance networks not only facilitated scientific communication and collaboration within the order, it also provided Jesuits with the resources they needed to engage successfully in 'ministries among the learned'. Evidence of a sustained attempt by Jesuit authors to assume the role of Kulturträger is found in the several genres of scientific publications that dominate the Society's scientific corpus. Thus the Society's early recognition of the "apostolic value" of scientific publications in recruiting friends and allies among Europe's intellectual elites, I argue, allowed a robust interest in natural knowledge to emerge as a legitimate part of the Jesuit vocation. (shrink)

This book explores aesthetic forms of the left to negotiate the political frontiers of post-colonial, post-partition South Asia. Spanning India, Sri Lanka, Pakistan and Bangladesh, the contributors study art, film and literature to illuminate interconnections across regions and countries, and discuss the shifting political contours of the region during the latter half of the 20th century. With a clear focus and conceptualization this volume raises two key questions; how left-wing art generated cultural and social formations, and how aesthetic forms held (...) political value across the region. Reframing political aesthetics within a postcolonial and decolonised framework, it traces the trajectories and nuances the left-wing cultural movement took during decolonization, and focuses on connections and continuities across post-1947 India, Pakistan and Bangladesh. Following the evolution of progressive culture in the 1950s and 60s, networks of leftist filmmakers and theatre activists in postcolonial Pakistan, and the changing fate of left cultural politics in Sri Lanka and India during the 1970s, this book looks to reinvigorate the entangled histories of the left cultural movement in post-partition South Asia. (shrink)

The ability to image the human brain has provided a new perspective for neuropsychologists in their efforts to understand, diagnose, and treat insults to the human brain that might occur as the result of stroke, tumor, traumatic injury, degenerative disease, or errors in development. These new ®ndings are the major theme of this special issue. In our article, we consider brain networks that carry out the functions of attention. We outline several such networks that have been studied in (...) normal and pathological states. These include networks for orienting to sensory stimuli, for maintaining the alert state, and for orchestrating volitional control. (shrink)

According to Mednick’s (1962) theory of individual differences in creativity, creative individuals appear to have a richer and more flexible associative network than less creative individuals. Thus, creative individuals are characterized by “flat” (broader associations) instead of “steep” (few, common associations) associational hierarchies. To study these differences, we implement a novel computational approach to the study of semantic networks, through the analysis of free associations. The core notion of our method is that concepts in the network are related to (...) each other by their association correlations - overlap of similar associative responses (“association clouds”). We began by collecting a large sample of participants who underwent several creativity measurements and used a decision tree approach to divide the sample into low and high creative groups. Next, each group underwent a free association generation paradigm which allowed us to construct and analyze the semantic networks of both groups. Comparison of the semantic memory networks of persons with low creative ability and persons with high creative ability revealed differences between the two networks. The semantic memory network of persons with low creative ability seems to be more rigid, compared to the network of persons with high creative ability, in the sense that it is more spread out and breaks apart into more sub-parts. We discuss how our findings are in accord and extend Mednick’s (1962) theory and the feasibility of using network science paradigms to investigate high level cognition. (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)

The global environmental and social-economic crises of industrialized agriculture have led to the emergence of agroecology as an alternative approach aiming to increase the ecological, social and economic sustainability of agri–food systems. The ‘multi-level perspective’ is now a widely used framework to understand and promote the upscaling of local innovation niches, such as agroecology, to broader scales (e.g., regional, national, international), thus reconfiguring the dominant socio-technical regimes. Additionally, emergent ‘hybrid forums’ can provide a space between niche and regime where niche (...) innovators can become important actors in scaling up and out emergent innovations. In this paper, we examine a university training program (Postgraduate Diploma in Local Agroecological Dynamization at the Universitat Autònoma de Barcelona), to better understand its role as a ‘hybrid forum’. Our analysis focuses especially on how the program, as an example of a hybrid forum, worked to reconfigure practices, concepts, and tools of local development practitioners. We also assess to what extent the program contributed to transitioning local development institutions toward agroecology. An online survey (n = 46) and in-depth interviews (n = 16) were carried out to determine how the training program has impacted the student’s opinions and their respective institutions. The results show that most of the students consider that they have acquired new theoretical frameworks and useful methods to re-framing their local development projects, that new alliances with multi-actor networks have been perceived, and that some internal changes of the local development practices have taken place. We conclude that the training program, as a hybrid forum, is capable of outscaling niche innovations through linkages with different kind of actors both from the niche and the regime. Political changes in the socio-technical landscape level offer an opportunity to amplify the impact of the innovations which are being generated by those multi-actor networks, but with a limited multi-level impact as far as institutional regime-actors not aligned with agroecological transition keep the most of the competencies on agri–food systems. (shrink)