DAO as Rhizome: Reimagining Rules, Governance and Organisations

Mille Plateaux, You Tarzan: a musicology of (an Anthropology of (an Anthropology of A Thousand Plateaus)) I John Rahn Introduction: About TP IN THE BEGINNING, or ostensibly, or literally, itwas erotic. A Thou sand Plateaus ("TP") evolved from the Anti-Oedipus, also by Gilles Deleuze and Felix Guattari ("D&G"), who were writing in 1968, responding to themini-revolution in the streets of Paris which catalyzed explosive growth in French thinking, both on the right (Lacan, Girard) and on the left. It is a left-wing theory against patriarchy, and by exten sion, even against psychic and bodily integration, pro-wschizoanalysis" (Guattari's metier) and in favor of the Body Without Organs. 82 PerspectivesofNew Music Eat roots raw. The notion of the rhizome is everywhere: an underground tubercular system or mat of roots, a non-hierarchical network, is the ideal and paradigm. The chapters inTP may be read in any order. The order inwhich they are numbered and printed cross-cuts the temporal order of the dates each chapter bears (e.g., "November 28, 1947: How Do You Make Yourself a Body Without Organs?"). TP preaches and instantiates a rigorous devotion to the ideal ofmultiplicity, nonhierarchy, transformation, and escape from boundaries at every moment. TP is concerned with subverting amindset oriented around an identity which is unchanging essence, but equally subversive of the patriarchal move towards transcendence. This has political implications ?as it does in the ultra-right and centrist philosopher Plato, who originally set the terms of debate. Given a choice, though, between one or many Platos, D&G would pick a pack of Platos. How does the program of TP, folded (as well as expressed) atmany levels into itswriting and dissed "organization," avoid conflict with its anthropological structuralism? Like the work of Claude Levi-Strauss (e.g., theRaw and theCooked, La Pensee Sauvage), TP proceeds byway of paired and opposed terms: Rhizomes vs. hierarchical trees; Territor ialization vs. Deterritorialization. TP has a quasi-spatial dimensionality as metaphor (immanent n-dimensional "planes of consistency" vs. the n + 1 overview which conceals a motion towards transcendence within itself); Striated vs. Smooth space; Monadology and Nomadology; and so on. However, polarities or pairs are not themselves rhizomatic, presenting another possible conflict.We will explore this issue later on. The poetry of the language of TP is part of itsmessage: things, people, bodies, concepts ooze, slide around, morph into each other, and generally engage in a kind of climax-free erotic play. The rhetoric is strong and persuasive, as well as being pervasively sensuous. It exhorts, preaches, orders us around (more than a hint of S&M), all for our enjoyment. TP is a brilliant and inspiring book that has been very influential, partly because these philosophers are practicing on us and on themselves. Bateson's Ideas In order to analyze the world of the TP in a way that connects with music, we begin with the thinker D&G credit with the concept of "plateau" in their sense, the anthropologist Gregory Bateson, whose work influences TP rhizomatically. According toD&G, Bateson Milles Plateaux, You Tarzan 83 uses the term "plateau" to designate something very special: a con tinuous, self-vibrating region of intensities whose development avoids any orientation toward a culminating point or external end. Bateson cites Balinese culture as an example. . . . "Some sort of continuing plateau of intensity is substituted for [sexual] climax," war, or a culmination point. It is a regrettable characteristic of the Western mind to relate expressions and actions to exterior or tran scendent ends, instead of evaluating them on a plane of consistency on the basis of their intrinsicvalue.1 The reference inTP is to Bateson's 1949 essay, "Bali: The Value System of a Steady State."2 In it,Bateson is concerned to refine his theoretical concepts of "ethos" and "schismogenesis" for the understanding of cul tures and societies, by studying a counterexample, Bali, which does not fitwell into his previous generalizations. Already in the 1930s, Bateson was thinking in terms amounting to a kind of "systems theory" or "cybernetics" of culture, pre-dating even the von Neumann game theory and "information theory" that Bateson adapts and adopts to some extent once they in turnwere elaborated in the 1940s. In a 1935 article called "Culture...

Background: The worldwide discussion focused on investigating the impact of institutional constraints, management capabilities, and both structural and strategic constraints on performance outcomes of small, medium, and micro enterprises (SMMEs).Objective: This study aims to enhance and widen the analysis by investigating how institutional, strategic, and structural constraints, along with management capabilities, affect the performance measures of Black-owned SMMEs, intending to provide a more thorough and expansive analysisDesign/methodology/approach: A quantitative analysis approach was employed, using a structural equation model to examine the interconnections among various factors, including financial, informational, and human capabilities in relation to management capabilities, as well as financial and organisational development in relation to performance. A total of 544 small businesses owned by Black individuals in South Africa participated in the study.Results: The results demonstrate a negative correlation between institutional constraints and business performance (H1), along with a similar negative relationship between strategic and structural constraints and performance (H2). In contrast, the anticipated positive link between management competencies and performance (H3) was not confirmed. These findings highlight the necessity for proactive measures to transform regulatory settings and improve organisational structures.Conclusion: This study tackles a crucial void in the existing literature by exploring the connections among organisational limitations, operational and strategic challenges, management skills, and entrepreneurial success. There is an urgent need for collaborative initiatives among policymakers, business development agencies, and stakeholders to promote entrepreneurship, improve management skills, and bolster organisational frameworks. Through these interventions, Black-owned SMMEs will thrive, generate employment, inspire innovation, and aid in overall economic and social progress.Originality/value: The present research is the initial exploration into the detrimental impact of institutional, strategic, and structural limitations, along with management capacities, on the entrepreneurial performance of SMMEs within South African contexts. Keywords: black-owned smmes, business performance, institutional constraints, management competencies, structural barriers

The cerebellum is increasingly recognized as a key contributor to language and cognitive processing, but its dynamic network alterations in poststroke aphasia remain poorly understood. This study investigated dynamic cerebellar networks in patients with poststroke aphasia using resting-state functional MRI. We examined intracerebellar and cerebellar-cortical dynamic functional connectivity and quantified their temporal properties and graph-theoretical topology. Seventy-seven right-handed patients with poststroke aphasia and 79 healthy controls underwent 3T resting-state functional MRI. Dynamic cerebellar functional networks were constructed using the Seitzman-27 cerebellar atlas. A sliding window approach (30 TR window, 1 TR step) was applied, followed by K-means clustering to identify distinct connectivity states. Graph-theoretical analyses were performed to quantify state-specific network topology. The variability of dynamic functional connectivity between the cerebellar and cortical regions was calculated. Partial correlation analyses were performed to examine the relationships among dynamic network measures, lesion volume, and language and cognitive function. Two cerebellar dynamic functional connectivity states were identified in poststroke aphasia: a predominant segregated state (78.93%) with widespread reductions in connectivity and decreased clustering coefficient (d = -1.29), characteristic path length (d = -0.62), and local efficiency (d = -1.11) but higher global efficiency (d = 1.06) and a less frequent integrated state (21.07%) with enhanced connectivity and a higher clustering coefficient (d = 0.57) and characteristic path length (d = 0.70) and diminished global efficiency (d = -1.25) and small-worldness (d = -0.92) and small-world index (d = -0.89). Poststroke aphasia showed reduced variability of dynamic functional connectivity between the cerebellar and cortical regions involved in language and cognition (Gaussian random field correction, voxel-level P < .001, cluster-level P < .05). Lesion volume negatively correlated with Aphasia Quotient, repetition, memory, executive function, and attention (P < .005). State-specific network metrics and variability measures were associated with language and cognitive performance independent of lesion volume. Patients with poststroke aphasia exhibited a segregated cerebellar state with reduced intracerebellar connectivity and efficiency and an integrated state with enhanced connectivity and small-world properties, together with reduced variability in cerebellar-cortical connections to language- and cognition-related regions. These state-specific network alterations were linked to distinct behavioral domains independent of lesion volume, highlighting a dissociation between structural constraints and dynamic, lesion-independent plasticity.

PurposeThis study explores the dynamics of collaborative networks (CNs) in navigating rapid technological advancements, global interconnectivity and unprecedented disruptions. It examines how CNs enhance resilience and develop antifragility, advancing the understanding of adaptive organizational strategies in volatile environments.Design/methodology/approachThe research employs a mixed-methods approach, integrating qualitative insights from interviews with European industry experts and quantitative data from surveys of CN members in the information technology and software development sectors. Thematic analysis of qualitative data, alongside advanced statistical techniques such as correlation analysis and structural equation modeling, identifies key patterns and relationships among variables influencing CN adaptability.FindingsThe study demonstrates that CN resilience and antifragility are primarily driven by innovation, robust governance, trust, reciprocity and diversity of thought. Antifragility – a novel concept in CN research – relies on decentralized governance structures and the ability to leverage disruptions as opportunities for growth. Innovation emerges as a crucial enabler, fostering adaptability, creative problem-solving and long-term sustainability. The findings underscore actionable strategies, including fostering a culture of innovation, promoting trust-based collaboration and implementing decentralized governance to enhance CN performance.Originality/valueThis study makes a significant contribution by introducing and operationalizing the concept of antifragility within CNs, extending existing resilience frameworks. It highlights the interconnected roles of governance, trust and innovation, challenging traditional resilience-centric approaches and offering a forward-thinking perspective. Practical recommendations equip CN leaders and policymakers with actionable tools to navigate complexity, enabling organizations to thrive amid uncertainty and disruption. The originality lies not in the individual concepts of resilience, antifragility or innovation, but in their systematic integration within the specific context of collaborative networks, revealing how their convergence enables CNs to evolve through disruption rather than merely recover.

Most existing works on network analysis mainly focus on only the existence of relations between entities. However, in trying to understand a real network, we naturally use not only the existence of relations but also information on the kind of relations, the attributes in the nodes, and the changes in time. In addition, we can observe some of the measures that are obtained as a result of the whole network structure. In order to extract some meaningful structural changes and integrity constraints from a dynamical network constructed from survey data, we are proposing a novel data mining framework in this paper that includes the above information that has not been used in previous studies. In the proposed framework, we start by detecting the change point in the dynamic network according to the change in the characteristic quantity. Then, by using the detected points, a dynamic network will be divided into two groups. In other words, we associate the class information to each network in a dynamic network. Finally, meaningful structural changes and integrity constraints can be obtained by applying inductive logic programming to a dynamic network and the related background knowledge represented in the first order logic. In experiments using real world data, we succeeded in obtaining meaningful results. Thus, we confirmed the usefulness of the proposed framework.

The brain is organized as a complex network architecture, which can be mapped into structural (SC) and functional connectivity (FC) by advanced neuroimaging techniques. Achievements in brain network research have revealed that modularity is a universal trait in brain networks and may be vital for cognitive segregation and integration. Large-scale brain network modeling is a promising computational approach to combine neuroimaging data with generative rules for brain dynamics. Recently, it has been proposed that chimera states, a type of dynamics referring to the coexistence of coherent and incoherent participants, have traits in common with cognitive functions like segregated and integrated brain processing. Previous studies have reported the existence of chimera-like dynamics in large-scale brain network models, whereas they did not account for the relationship between chimera-like dynamics and corresponding functional modular organizations of the brain network. By specifying qualitatively different network dynamics in an anatomically-constrained brain network model, we compare the different modular organizations of FC unfolded by network dynamics. Our simulations reveal that chimera-like dynamics support a meaningful pattern of functional modular organization, which promotes a diversity of node roles with a distributed pattern of functional cartography. The distinct node roles in modular FC are also found to occur with a spatial preference in specific brain regions, and, to some extent, reflect the underlying structure constraints. Our results support the view that chimera-like dynamics is a functionally meaningful scenario that may play a fundamental role in the segregation and integration of brain functioning.

Convolution Neural Networks (CNNs), despite being one of the most successful image classification methods, are not robust to most geometric transformations (rotation, isotropic scaling) because of their structural constraints. Recently, scale steerable filters have been proposed to allow scale invariance in CNNs. Although these filters enhance the network performance in scaled image classification tasks, they cannot maintain the scale information across the network. In this paper, this problem is addressed. First, a CNN is built with the usage of scale steerable filters. Then, a scale equivariat network is acquired by adding a feature map to each layer so that the scale-related features are retained across the network. At last, by defining the cost function as the cross entropy, this solution is evaluated and the model parameters are updated. The results show that it improves the perfromance about 2% over other comparable methods of scale equivariance and scale invariance, when run on the FMNIST-scale dataset.

Ensembles of suspended spinning particles in liquids form a distinct category of active matter systems known as chiral fluids. Recent experimental instances of dense chiral fluids have comprised of spinning colloidal magnets powered by an external rotating magnetic field. These particles interact through both magnetic and hydrodynamic forces, organizing collectively into circulating clusters characterized by unidirectional edge flows. Here, we externally drive the collective behavior of spinning colloids by leveraging diffusiophoretic interactions among the geometrically anisotropic particles. We show that these nanoscale interfacial flows lead to the formation of bound states between spinning colloids that are stabilized through near-field hydrodynamic and chemical interactions. At a collective level, we demonstrate that added diffusiophoretic interactions cause a loss in structural cohesion of the circulating clusters and promote expansion, while preserving global cluster inter-connectivity. The expanded cluster state is characterized by the formation of a dynamic interconnected network promoted by axi-asymmetric interactions around particles with attractive dipolar interactions dominating along the direction of the magnetic moment. This process is observed to be entirely reversible, offering external control over the emergent dynamics in dense chiral fluids, paving the way for new self-organization routes in chiral fluids and broader forms of active matter.

The article attempts a philosophical and cultural-theoretical interpretation of the deep transformations of organizational culture in the context of the contemporary digital turn. The focus of the research is on the phenomenon of the implementation of Agile philosophy in Russian IT companies, which is considered not merely as a managerial innovation but as a large-scale sociocultural event that reveals the conflict between different discursive formations and archetypal matrices. The methodological synthesis of Michel Foucault's genealogical analysis of power, Carl Gustav Jung's archetypal theory, and Gilles Deleuze and F&amp;#233;lix Guattari's rhizomatic model makes it possible to reconstruct the process of hybridization, which gives rise to the phenomenon of "controlled self-organization" – a stable form of corporate culture in which the language games of flexibility and autonomy paradoxically serve the purposes of reproducing and perfecting control mechanisms. Based on empirical material (case studies of SberTech, Rostelecom, Yandex, Mail.ru Group, Kaspersky, and others), the author demonstrates how, under the influence of the global Agile discourse, what occurs is not a simple replacement but a complex semiotic recoding of entrenched cultural archetypes ("Soviet Paternalist", "Engineer-God", "Survivor"), thereby generating new hybrid subjectivities ("Translator", "Architect", "Communicator"). It is ultimately concluded that the organizational culture of the Russian IT sector represents a rhizomatic symbiosis, where the interaction of archetypes forms a non-hierarchical network, and Agile acts as a plane for the constant redefinition of relations between power, knowledge, and collective identity.

Traffic flow prediction plays a crucial role in intelligent transportation systems as it enables effective control and management of urban traffic. However, existing methods that based on Graph Convolutional Networks (GCNs) primarily utilize local neighborhood information for message passing, resulting in limited perception of global structures. Additionally, it is also a challenge to extract spatial-temporal similarity features due to the constraints of graph structures. To address these issues, we propose a novel traffic flow prediction model based on Dynamic Spatial-Temporal Similarity Pyramid Network (DSTSPYN). Our model employs a spatial-temporal pyramid architecture, which dynamically adjusts the weights of central, edge, and global spatial-temporal features using an enhanced attention mechanism. Furthermore, it captures dynamic temporal dependencies at different scales through pyramid gated convolution. Meanwhile, the spatial similarity features of different time steps can be extracted through the spatial-temporal global similarity (STGS) module. We evaluate our model on four public transportation datasets and demonstrate that the DSTSPYN model outperforms several baseline methods in terms of prediction accuracy. It effectively captures the dynamic spatial-temporal correlations of the road network and edge node features, making it well-suited for long-term traffic flow prediction.

This paper investigates the behaviour of small and medium sized enterprises (SMEs) within the heritage tourism supply chain (HTSC), in two emerging heritage regions. SMEs are conceptualised as implementers, working within the constraints of government level tourism structures and the heritage tourism supply chain. The research employs a case study approach, focusing on two emerging regions in Northern Ireland. In-depth interviews were carried out with small business owners and community associations operating within the regions. The research identifies SME dissatisfaction with the supply chain and the processes in place for the delivery of the tourism product. To overcome the perceived inadequacies of the heritage tourism supply chain SMEs engage in entrepreneurial behaviour by attempting to deliver specific products and services to meet the need of tourists. The challenge for tourism organisations is how they can integrate the entrepreneurial, innovative activities of SMEs into the heritage tourism system.

This paper addresses the problem of learning Bayesian network structures from data based on score functions that are decomposable. It describes properties that strongly reduce the time and memory costs of many known methods without losing global optimality guarantees. These properties are derived for different score criteria such as Minimum Description Length (or Bayesian Information Criterion), Akaike Information Criterion and Bayesian Dirichlet Criterion. Then a branch-and-bound algorithm is presented that integrates structural constraints with data in a way to guarantee global optimality. As an example, structural constraints are used to map the problem of structure learning in Dynamic Bayesian networks into a corresponding augmented Bayesian network. Finally, we show empirically the benefits of using the properties with state-of-the-art methods and with the new algorithm, which is able to handle larger data sets than before.

On-line Optimization for Fault Tolerant Flight Control

Dynamic Bayesian Network (DBN) is a mainstream approach to modeling various biological networks including the gene regulatory network (GRN). For such DBN models that consist only of inter-timeslice arcs, most current methods for learning it employ either a score and search approach or Markov chain Monte Carlo (MCMC) simulation, both of which ignore the structural constraints of DBN models. These structural constraints were first applied to translate the structure learning problem into discovering associations among variables, and then a new method was presented to obtain inter-timeslice arcs. This method was based on maximal information coefficient (MIC). Experiment results showed that the proposed MIC-based method outperformed MI-based, MCMC, and K2 algorithm methods on the quality of learned structure.

Regulatory networks are able to process complex signals and respond appropriately to the cellular context. Thus, an increasing effort by systems biology researchers is being focused on understanding which interactions are responsible for a given functional response. When translated into specific mathematical models, however, it has been repeatedly shown that this mapping between topology and function is not one-to-one, even for the simplest networks. Moreover, dynamical behavior may play an important role which is necessary to integrate in the general picture. We propose a unified theoretical/statistical approach to characterize the structure-function relationship in molecular networks when temporal features of both input signal and output response are important. The theory allows fast computation of network responses in terms of interaction strengths irrespective of molecular details, while statistical analysis identifies constraints between structural and dynamical features and network function. Investigating different feedback and feedforward loop architectures, we find that processing of temporal signals is strongly correlated to certain combinations of structural and dynamical characteristics, rather than to individual interactions. Our analysis offers new insight into the structure-function relationship in network motifs, quantifying how much the tuning of specific interactions affects network outcome, identifying key structural parameters for a given response and relating dynamics to network topology and function. This kind of analyses can be especially useful for synthetic biology approaches, where promoter libraries with a range of inputs and outputs can be engineered, and one has to choose the correct component needed to produce the desired network function.