Proposing Ties in a Dense Hypergraph of Academics

Nearly all personal relationships exhibit a multiplexity where people relate to one another in many different ways. Using a set of faculty CVs from multiple research institutions, we mined a hypergraph of researchers connected by co-occurring named entities (people, places and organizations). This results in an edge-sparse, link-dense structure with weighted connections that accurately encodes faculty department structure. We introduce a novel model that generates dyadic proposals of how well two nodes should be connected based on both the mass and distributional similarity of links through shared neighbors. Similar link prediction tasks have been primarily explored in unipartite settings, but for hypergraphs where hyper-edges out-number nodes 25-to-1, accounting for link similarity is crucial. Our model is tested by using its proposals to recover link strengths from four systematically lesioned versions of the graph. The model is also compared to other link prediction methods in a static setting. Our results show the model is able to recover a majority of link mass in various settings and that it out-performs other link prediction methods. Overall, the results support the descriptive fidelity of our text-mined, named entity hypergraph of multi-faceted relationships and underscore the importance of link similarity in analyzing link-dense multiplexitous relationships

Advancements in latent space network modelling

The ubiquity of relational data has motivated an extensive literature on network analysis, and over the last two decades the latent space approach has become a popular network modelling framework. In this approach, the nodes of a network are represented in a low-dimensional latent space and the probability of interactions occurring are modelled as a function of the associated latent coordinates. This thesis focuses on computational and modelling aspects of the latent space approach, and we present two main contributions. First, we consider estimation of temporally evolving latent space networks in which interactions among a fixed population are observed through time. The latent coordinates of each node evolve other time and this presents a natural setting for the application of sequential monte carlo (SMC) methods. This facilitates online inference which allows estimation for dynamic networks in which the number of observations in time is large. Since the performance of SMC methods degrades as the dimension of the latent state space increases, we explore the high-dimensional SMC literature to allow estimation of networks with a larger number of nodes. Second, we develop a latent space model for network data in which the interactions occur between sets of the population and, as a motivating example, we consider a coauthorship network in which it is typical for more than two authors to contribute to an article. This type of data can be represented as a hypergraph, and we extend the latent space framework to this setting. Modelling the nodes in a latent space provides a convenient visualisation of the data and allows properties to be imposed on the hypergraph relationships. We develop a parsimonious model with a computationally convenient likelihood. Furthermore, we theoretically consider the properties of the degree distribution of our model and further explore its properties via simulation

Latent Space Representations of Hypergraphs

The increasing prevalence of relational data describing interactions among a target population has motivated a wide literature on statistical network analysis. In many applications, interactions may involve more than two members of the population and this data is more appropriately represented by a hypergraph. In this paper we present a model for hypergraph data which extends the latent space distance model of Hoff et al. (2002) and, by drawing a connection to constructs from computational topology, we develop a model whose likelihood is inexpensive to compute. We obtain posterior samples via an MCMC scheme and we rely on Bookstein coordinates to remove the identifiability issues associated with the latent representation. We demonstrate that the latent space construction imposes desirable properties on the hypergraphs generated in our framework and provides a convenient visualisation of the data. Furthermore, through simulation, we investigate the flexibility of our model and consider estimating predictive distributions. Finally, we explore the application of our model to a real world co-occurrence dataset

Processes and diagrams: an integrated and multidisciplinary approach for the education of quantum information science

The background to this thesis is the παιδέια , the education. To educate is a dialecti- cal process that moves from an abstract line of thought, through scientifically designed techniques, into concrete action; and vice versa. We believe that educating today means enabling teachers first and their students second, to be able to read and interpret the complexity of phenomena, to teach them a model for observing this complexity, describing it, analyzing it and, finally, making it their own. In this thesis, we attempt to make sense of these needs by describing an integrated and multidisciplinary pathway, whose diagram- matic language pushes towards the search for a universal approach to science. An initial educational contribution is thus made to the understanding of the dialectic between disciplines: theoretical physics, experimental physics, computer science, mathe- matics and mathematical logic are presented in their mutual influence, in an attempt to clarify the informational viewpoint on modern physics. The search for this dialectic for educational purposes is, in our opinion, the most significant contribution of the present work. To address this issue, we sought to build a community of practice on the topics of the second quantum revolution. Guided by the Model of Educational Reconstruction (MER), we built a first course for teacher professional development that would enable teachers to be introduced to quantum computation and quantum communication. The emergence and development of quantum technologies provides the impetus for a deep conceptual change: “a paradigm shift from quantum theory as a theory of microscopic matter to quantum theory as a framework for technological applications and information processing”. This shift is supported, theoretically, by the informational interpretation of the postulates of quantum mechanics: preparation, transformation and measurement are reinterpreted com- putationally as the encoding, processing and decoding of information; and vice versa. In this interpretation, what changes between classical and quantum theory? From a logical point of view, the transition from bit to qubit, from a physical point of view, the laws of composition of systems. We therefore present monoidal categories as a natural theoretical framework for the description of physical systems and processes for quantum and non- quantum computation and communication, demonstrating how this language is suitable for an integrated and multidisciplinary approach. The cultural impact of the proposal, the fruitful interaction between researchers in physics education and those in the area of theoretical research, and the passion of some teachers made it possible to start a collaboration to build an educational sequence for students. The result of this collaboration is a teaching leaning sequence on quantum technologies for students, led by the MER and based on inquiry-based learning and the modelling- based teaching. Supported by these methodological frameworks, we produced lessons and worksheets all along the way that had the dual task of supporting teachers’ work and students’ learning. They also made it possible to experimentally verify the positive and critical effects of the proposal. The instructional materials constructed, the data analysis and the constant monitoring with the teachers involved, determined the development of a second course for teacher professional development, inspired by the first, based entirely on research. We hope that this attempt at integrated and multidisciplinary approach for the education of quantum information science, based on the concept of compositionality and the diagrammatic model, can be increased and provide inspiration for future educational paths in other disciplines as well

Enhancing the Prediction of Missing Targeted Items from the Transactions of Frequent, Known Users

The ability for individual grocery retailers to have a single view of its customers across all of their grocery purchases remains elusive, and is considered the “holy grail” of grocery retailing. This has become increasingly important in recent years, especially in the UK, where competition has intensified, shopping habits and demographics have changed, and price sensitivity has increased. Whilst numerous studies have been conducted on understanding independent items that are frequently bought together, there has been little research conducted on using this knowledge of frequent itemsets to support decision making for targeted promotions. Indeed, having an effective targeted promotions approach may be seen as an outcome of the “holy grail”, as it will allow retailers to promote the right item, to the right customer, using the right incentives to drive up revenue, profitability, and customer share, whilst minimising costs. Given this, the key and original contribution of this study is the development of the market target (mt) model, the clustering approach, and the computer-based algorithm to enhance targeted promotions. Tests conducted on large scale consumer panel data, with over 32000 customers and 51 million individual scanned items per year, show that the mt model and the clustering approach successfully identifies both the best items, and customers to target. Further, the algorithm segregates customers into differing categories of loyalty, in this case it is four, to enable retailers to offer customised incentives schemes to each group, thereby enhancing customer engagement, whilst preventing unnecessary revenue erosion. The proposed model is compared with both a recently published approach, and the cross-sectional shopping patterns of the customers on the consumer scanner panel. Tests show that the proposed approach outperforms the other approach in that it significantly reduces the probability of having “false negatives” and “false positives” in the target customer set. Tests also show that the customer segmentation approach is effective, in that customers who are classed as highly loyal to a grocery retailer, are indeed loyal, whilst those that are classified as “switchers” do indeed have low levels of loyalty to the selected grocery retailer. Applying the mt model to other fields has not only been novel but yielded success. School attendance is improved with the aid of the mt model being applied to attendance data. In this regard, an action research study, involving the proposed mt model and approach, conducted at a local UK primary school, has resulted in the school now meeting the required attendance targets set by the government, and it has halved its persistent absenteeism for the first time in four years. In medicine, the mt model is seen as a useful tool that could rapidly uncover associations that may lead to new research hypotheses, whilst in crime prevention, the mt value may be used as an effective, tangible, efficiency metric that will lead to enhanced crime prevention outcomes, and support stronger community engagement. Future work includes the development of a software program for improving school attendance that will be offered to all schools, while further progress will be made on demonstrating the effectiveness of the mt value as a tangible crime prevention metric

Social Network Extraction and Exploration of Historic Correspondences

Historic correspondences, in the form of letters, provide a scenario in which historic figures and events are reflected and thus play a ubiquitous role in the study of history. Confronted with the digitization of thousands of historic letters and motivated by the potentially valuable insights into history and intuitive quantitative relations between historic persons, researchers have recently focused on the network analysis of historic correspondences. However, most related research constructs the correspondence networks only based on the sender-recipient relation with the objective of visualization. Very few of them have proceeded beyond the above stage to exploit the detailed modeling of correspondence networks, let alone to develop novel concepts and algorithms derived from network analysis or formal approaches to the data uncertainty issue in historic correspondence. In the context of this dissertation, we develop a comprehensive correspondence network model, which integrates the personal, temporal, geographical, and topic information extracted from letter metadata and letter content into a hypergraph structure. Based on our correspondence network model, we analyze three types of person-person relations (sender-recipient, co-sender, and co-recipient) and two types of person-topic relations (author-topic and sender-recipient-topic) statically and dynamically. We develop multiple measurements, such as local and global reciprocity for quantifying reciprocal behavior in weighted networks, and the topic participation score for quantifying interests or the focus of individuals or real-life communities. We investigate the rising and the fading trends of topics in order to find correlations among persons, topics, and historic events. Furthermore, we develop a novel probabilistic framework for refinement of uncertain person names, geographical location names, and temporal expressions in the metadata of historic letters. We conduct extensive experiments using letter collections to validate and evaluate the proposed models and measurements in this dissertation. A thorough discussion of experimental results shows the effectiveness, applicability and advantages of our developed models and approaches

INTER-ORGANIZATIONAL NETWORKS FOR INNOVATIONS AND SUSTAINABILITY

The present Thesis is structured as a collection of three essays linked by one core idea: contributing to research knowledge on inter-organizational network dynamics in the context of innovation and the promotion of sustainability. In this Thesis, the author takes a systemic perspective and analyses the interactions between diverse groups of stakeholders, aiming to identify and interpret the logic underlying the formation of inter-organizational partnerships to promote innovation and sustainability. The dynamics of inter-organizational networks are influenced by several internal and external factors, such as strategic cooperation with stakeholders, structural changes (such as an R&I policy change), and exogenous shocks (such as COVID-19). The present work’s value is developing research inputs and providing empirical ground and methodological support for innovation management framed by inter-organizational networks and mission-oriented public policy evolution. The present work is divided into three main chapters, and their abstracts are presented below. Finally, the Thesis ends with conclusions that summarize the outputs of the empirical works. CHAPTER 1 An appropriate starting point to comprehend the inter-organizational networks for sustainability is to deepen the research knowledge on stakeholders’ role in sustainable innovation and disentangle the antecedents, management, and potential sustainable innovation outcomes. Using the Scopus database, we collected papers that represent works carried out in the field of sustainable innovation and stakeholders’ involvement in organizational practices for these innovations. Based on the data process selection method, we carry out a literature review of the 59 selected papers. This literature review aims to describe the sustainable innovation phenomena and offer a comprehensive overview of the knowledge produced on the theme to practitioners and policymakers So, this chapter presents an interpretative framework of extant literature and discuss the following questions related to the inter-organizational resource-management of sustainable innovation: (a) with whom to work; (b) when to work; (c) how to work together; (d) what challenges should organizations learn to face. Theoretical and practical business implications of the proposed framework are discussed. CHAPTER 2 This chapter aims to analyze the inter-organizational R&I collaboration network dynamics at a mesoscopic level as a consequence of an external environment change. In particular, the study’s empirical setting is the policy change that occurred when passing from the EU 7th Framework program (FP7) to the HORIZON 2020 program (H2020). This change’s effect on the patterns of evolution of the inter-organizational networks between financed actors is stressed. In such R&I context, inter-organizational networks play a particularly critical role as innovation catalysts. Using a dataset of more than 22,228 unique projects in FP7 and 22,153 in H2020, we constructed two collaboration networks. We apply network analysis as a research instrument to identify and measure the fundamental structural properties of networks. At the mesoscopic level, the resulting communities for both networks have been analyzed and compared. Results show that under a policy change, the Horizon 2020 network becomes more assortative than the FP7 network. Preferential attachment (reach-club phenomenon) between leading R&I institutions is demonstrated within the system. The network is supported by the sporadic participation of (many) new actors. Also, the work outcomes demonstrate three different architectures of inter-organizational connections that can define network dynamics: (i) persistent stability or knowledge concentration, (ii) expansion of clusters or knowledge spread, and (iii) merging effect or knowledge aggregation. With these results, we contribute to organizational and network theories by detecting and identifying structural patterns for innovation links in such a complex system as the EU framework program stressing the policy’s impact on them as a dynamics booster. CHAPTER 3 The last chapter examines the impact of an exogenous shock on an inter-organizational R&I network. We concentrate on healthcare public-private partnerships and investigate the history dependencies within them and how an exogenous shock such as COVID-19 fosters an evolution of the complex R&I network. In total, data of 2087 funded projects (FP7, HORIZON 2020, and Innovative Medicines Initiative) are involved in this study to understand the evolution process(es) these types of networks manifest under emergency conditions. The results demonstrate that the present crisis’s urgency shifts the healthcare sector to test new working paths. Two opposite behaviors of the actors in these networks are observable: (i) highly innovative partnerships and (ii) strong lock-in effects. Additionally, we state that non-EU countries demonstrated strong cooperation and co-creation openness under this exogenous shock. Furthermore, the urgency conditions in COVID-19 push policymakers to demonstrate vital flexibility and adaptability of the EU R&I call to the societal needs. Finally, it is possible to underline that network analysis is a powerful research tool for developing new knowledge regarding R&I cooperation evolution under external factors. Accordingly, this work provides a theoretical and an empirical framework for managing the inter-organizational innovation network based on a dynamic complex system theory perspective (Simon 1996; Sawyer, 2005). In particular, it is possible to mention the newly developed insight capable of describing the network’s dynamics through the meso and micro levels of analysis.The present Thesis is structured as a collection of three essays linked by one core idea: contributing to research knowledge on inter-organizational network dynamics in the context of innovation and the promotion of sustainability. In this Thesis, the author takes a systemic perspective and analyses the interactions between diverse groups of stakeholders, aiming to identify and interpret the logic underlying the formation of inter-organizational partnerships to promote innovation and sustainability. The dynamics of inter-organizational networks are influenced by several internal and external factors, such as strategic cooperation with stakeholders, structural changes (such as an R&I policy change), and exogenous shocks (such as COVID-19). The present work’s value is developing research inputs and providing empirical ground and methodological support for innovation management framed by inter-organizational networks and mission-oriented public policy evolution. The present work is divided into three main chapters, and their abstracts are presented below. Finally, the Thesis ends with conclusions that summarize the outputs of the empirical works. CHAPTER 1 An appropriate starting point to comprehend the inter-organizational networks for sustainability is to deepen the research knowledge on stakeholders’ role in sustainable innovation and disentangle the antecedents, management, and potential sustainable innovation outcomes. Using the Scopus database, we collected papers that represent works carried out in the field of sustainable innovation and stakeholders’ involvement in organizational practices for these innovations. Based on the data process selection method, we carry out a literature review of the 59 selected papers. This literature review aims to describe the sustainable innovation phenomena and offer a comprehensive overview of the knowledge produced on the theme to practitioners and policymakers So, this chapter presents an interpretative framework of extant literature and discuss the following questions related to the inter-organizational resource-management of sustainable innovation: (a) with whom to work; (b) when to work; (c) how to work together; (d) what challenges should organizations learn to face. Theoretical and practical business implications of the proposed framework are discussed. CHAPTER 2 This chapter aims to analyze the inter-organizational R&I collaboration network dynamics at a mesoscopic level as a consequence of an external environment change. In particular, the study’s empirical setting is the policy change that occurred when passing from the EU 7th Framework program (FP7) to the HORIZON 2020 program (H2020). This change’s effect on the patterns of evolution of the inter-organizational networks between financed actors is stressed. In such R&I context, inter-organizational networks play a particularly critical role as innovation catalysts. Using a dataset of more than 22,228 unique projects in FP7 and 22,153 in H2020, we constructed two collaboration networks. We apply network analysis as a research instrument to identify and measure the fundamental structural properties of networks. At the mesoscopic level, the resulting communities for both networks have been analyzed and compared. Results show that under a policy change, the Horizon 2020 network becomes more assortative than the FP7 network. Preferential attachment (reach-club phenomenon) between leading R&I institutions is demonstrated within the system. The network is supported by the sporadic participation of (many) new actors. Also, the work outcomes demonstrate three different architectures of inter-organizational connections that can define network dynamics: (i) persistent stability or knowledge concentration, (ii) expansion of clusters or knowledge spread, and (iii) merging effect or knowledge aggregation. With these results, we contribute to organizational and network theories by detecting and identifying structural patterns for innovation links in such a complex system as the EU framework program stressing the policy’s impact on them as a dynamics booster. CHAPTER 3 The last chapter examines the impact of an exogenous shock on an inter-organizational R&I network. We concentrate on healthcare public-private partnerships and investigate the history dependencies within them and how an exogenous shock such as COVID-19 fosters an evolution of the complex R&I network. In total, data of 2087 funded projects (FP7, HORIZON 2020, and Innovative Medicines Initiative) are involved in this study to understand the evolution process(es) these types of networks manifest under emergency conditions. The results demonstrate that the present crisis’s urgency shifts the healthcare sector to test new working paths. Two opposite behaviors of the actors in these networks are observable: (i) highly innovative partnerships and (ii) strong lock-in effects. Additionally, we state that non-EU countries demonstrated strong cooperation and co-creation openness under this exogenous shock. Furthermore, the urgency conditions in COVID-19 push policymakers to demonstrate vital flexibility and adaptability of the EU R&I call to the societal needs. Finally, it is possible to underline that network analysis is a powerful research tool for developing new knowledge regarding R&I cooperation evolution under external factors. Accordingly, this work provides a theoretical and an empirical framework for managing the inter-organizational innovation network based on a dynamic complex system theory perspective (Simon 1996; Sawyer, 2005). In particular, it is possible to mention the newly developed insight capable of describing the network’s dynamics through the meso and micro levels of analysis

A feature-based approach to the Computer-Aided Design of sculptured products

Computer-Aided Design systems offer considerable potential for improving design process efficiency. To reduce the 'ease of use' barrier hindering full realisation of this potential amongst general mechanical engineering industries, many commercial systems are adopting a Feature-Based Design (FBD) metaphor. Typically the user is allowed to define and manipulate the design model using interface elements that introduce and control parametric geometry clusters, with engineering meaning, representing specific product features (such as threaded holes, slots, pockets and bosses). Sculptured products, such as golf club heads, shoe lasts, crockery and sanitary ware, are poorly supported by current FBD systems and previous research, because their complex shapes cannot be accurately defined using the geometrically primitive feature sets implemented. Where sculptured surface regions are allowed for, the system interface, data model and functionality are little different from that already provided in many commercial surface modelling systems, and so offer very little improvement in ease of use, quality or efficiency. This thesis presents research to propose and develop an FBD methodology and system suitable for sculptured products. [Continues.

An Initial Framework Assessing the Safety of Complex Systems

Trabajo presentado en la Conference on Complex Systems, celebrada online del 7 al 11 de diciembre de 2020.Atmospheric blocking events, that is large-scale nearly stationary atmospheric pressure patterns, are often associated with extreme weather in the mid-latitudes, such as heat waves and cold spells which have significant consequences on ecosystems, human health and economy. The high impact of blocking events has motivated numerous studies. However, there is not yet a comprehensive theory explaining their onset, maintenance and decay and their numerical prediction remains a challenge. In recent years, a number of studies have successfully employed complex network descriptions of fluid transport to characterize dynamical patterns in geophysical flows. The aim of the current work is to investigate the potential of so called Lagrangian flow networks for the detection and perhaps forecasting of atmospheric blocking events. The network is constructed by associating nodes to regions of the atmosphere and establishing links based on the flux of material between these nodes during a given time interval. One can then use effective tools and metrics developed in the context of graph theory to explore the atmospheric flow properties. In particular, Ser-Giacomi et al. [1] showed how optimal paths in a Lagrangian flow network highlight distinctive circulation patterns associated with atmospheric blocking events. We extend these results by studying the behavior of selected network measures (such as degree, entropy and harmonic closeness centrality)at the onset of and during blocking situations, demonstrating their ability to trace the spatio-temporal characteristics of these events.This research was conducted as part of the CAFE (Climate Advanced Forecasting of sub-seasonal Extremes) Innovative Training Network which has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 813844

Aesthetic Animism: Digital Poetry as Ontological Probe

This thesis is about the poetic edge of language and technology. It inter-relates both computational creation and poetic reception by analysing typographic animation softwares and meditating (speculatively) on a future malleable language that possesses the quality of being (and is implicitly perceived as) alive. As such it is a composite document: a philosophical and practice-based exploration of how computers are transforming literature, an ontological meditation on life and language, and a contribution to software studies. Digital poetry introduces animation, dimensionality and metadata into literary discourse. This necessitates new terminology; an acronym for Textual Audio-Visual Interactivity is proposed: Tavit. Tavits (malleable digital text) are tactile and responsive in ways that emulate living entities. They can possess dimensionality, memory, flocking, kinematics, surface reflectivity, collision detection, and responsiveness to touch, etc…. Life-like tactile tavits involve information that is not only semantic or syntactic, but also audible, imagistic and interactive. Reading mediated language-art requires an expanded set of critical, practical and discourse tools, and an awareness of the historical continuum that anticipates this expansion. The ontological and temporal design implications of tavits are supported with case-studies of two commercial typographic-animation softwares and one custom software (Mr Softie created at OBX Labs, Concordia) used during a research-creation process