Towards a System Theoretic Approach to Wireless Network Capacity in Finite Time and Space

In asymptotic regimes, both in time and space (network size), the derivation of network capacity results is grossly simplified by brushing aside queueing behavior in non-Jackson networks. This simplifying double-limit model, however, lends itself to conservative numerical results in finite regimes. To properly account for queueing behavior beyond a simple calculus based on average rates, we advocate a system theoretic methodology for the capacity problem in finite time and space regimes. This methodology also accounts for spatial correlations arising in networks with CSMA/CA scheduling and it delivers rigorous closed-form capacity results in terms of probability distributions. Unlike numerous existing asymptotic results, subject to anecdotal practical concerns, our transient one can be used in practical settings: for example, to compute the time scales at which multi-hop routing is more advantageous than single-hop routing

An Emergent Space for Distributed Data with Hidden Internal Order through Manifold Learning

Manifold-learning techniques are routinely used in mining complex spatiotemporal data to extract useful, parsimonious data representations/parametrizations; these are, in turn, useful in nonlinear model identification tasks. We focus here on the case of time series data that can ultimately be modelled as a spatially distributed system (e.g. a partial differential equation, PDE), but where we do not know the space in which this PDE should be formulated. Hence, even the spatial coordinates for the distributed system themselves need to be identified - to emerge from - the data mining process. We will first validate this emergent space reconstruction for time series sampled without space labels in known PDEs; this brings up the issue of observability of physical space from temporal observation data, and the transition from spatially resolved to lumped (order-parameter-based) representations by tuning the scale of the data mining kernels. We will then present actual emergent space discovery illustrations. Our illustrative examples include chimera states (states of coexisting coherent and incoherent dynamics), and chaotic as well as quasiperiodic spatiotemporal dynamics, arising in partial differential equations and/or in heterogeneous networks. We also discuss how data-driven spatial coordinates can be extracted in ways invariant to the nature of the measuring instrument. Such gauge-invariant data mining can go beyond the fusion of heterogeneous observations of the same system, to the possible matching of apparently different systems

Regional innovation networks evolution and firm performance: one or two way causality?

Current research has revealed the existence of a relationship between networks and firm growth (Jarillo, 1989; Huggins, 2000). Nevertheless, network content and specificity and how these networks influence firm economic and financial performance has been little investigated. In addition, the influence of regions in relation to the spatial proximity on inter-firm networks should be an additional dimension taken into account if the determinants of firm performance are to be adequately understood. The most important linkages tend to be characterised by territorial closeness and have relevant effects over firm performance (Oerlemans and Meeus, 2002; Lechner and Dowling, 2003). Since automobile industry can be regarded as a worldwide cluster, where the evolution tendency on constructor’s behalf has been to gradually delegate technological competencies into industry suppliers, the regional networks acquire a renewed importance beyond the recognized benefits of sharing, interaction and reciprocity. Given that networks “do not happen in a virtual space where spatial proximity does not matter” (Lechner and Dowling, 2003: 9), the Portuguese inter-firm cooperation within the automotive industry can be regarded as a possible source of regional advantage for responding to globalisation competitive challenges. Thus, in this paper we explore how firms grow through the use of external linkages and become competitive, using case study material based on a Portuguese inter-firm network of the auto-parts industry (ACECIA) and one of its founding members, Simoldes. Using a set of performance indicators, we concluded that its positive evolution was contemporaneous and last beyond ACECIA´s constitution date. Moreover, evidence of possible leverage effects from the combined collaboration emerged indicating that the relation between networks and firm performance implies a two-way causality association.

Optical communication beyond orbital angular momentum

Mode division multiplexing (MDM) is mooted as a technology to address future bandwidth issues, and has been successfully demonstrated in free space using spatial modes with orbital angular momentum (OAM). To further increase the data transmission rate, more degrees of freedom are required to form a densely packed mode space. Here we move beyond OAM and demonstrate multiplexing and demultiplexing using both the radial and azimuthal degrees of freedom. We achieve this with a holographic approach that allows over 100 modes to be encoded on a single hologram, across a wide wavelength range, in a wavelength independent manner. Our results offer a new tool that will prove useful in realizing higher bit rates for next generation optical networks

Detecting the ultra low dimensionality of real networks

Reducing dimension redundancy to find simplifying patterns in high dimensional datasets and complex networks has become a major endeavor in many scientific fields. However, detecting the dimensionality of their latent space is challenging but necessary to generate efficient embeddings to be used in a multitude of downstream tasks. Here, we propose a method to infer the dimensionality of networks without the need for any a priori spatial embed ding. Due to the ability of hyperbolic geometry to capture the complex con nectivity of real networks, we detect ultra low dimensionality far below values reported using other approaches. We applied our method to real networks from different domains and found unexpected regularities, including: tissue specific biomolecular networks being extremely low dimensional; brain con nectomes being close to the three dimensions of their anatomical embedding; and social networks and the Internet requiring slightly higher dimensionality. Beyond paving the way towards an ultra efficient dimensional reduction, our findings help address fundamental issues that hinge on dimensionality, such as universality in critical behavior.Agencia Estatal de Investigación PID2019-106290GB-C22/AEI/10.13039/501100011033Generalitat de Catalunya 2017SGR106

Spatial Theorizing in Comparative and International Education Research

The authors argue for a critical spatial perspective in comparative and international education. We briefly summarize how time and space have been conceptualized within our field. We then review mainstream social science literature that reflects a metanarrative, which we critique for contributing to false dichotomies between space and place and oversimplified views of the relationship between the global and the local. We present some of the key ideas associated with the “spatial turn,” including a relational understanding and productive capacity of space. In the final part of this article, we analyze the significance of new spatial theorizing for comparative and international education by reviewing examples of both comparative and educational researchers who are engaging with critical spatial theorizing. We argue that a possible way to confront binary thinking about space and place is by shifting attention to the relational conceptions of space, through analyses of networks, connections, and flows.Fil: Larsen, Marianne A.. No especifica;Fil: Beech, Jason. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentin