Sampling on networks: estimating spectral centrality measures and their impact in evaluating other relevant network measures

We perform an extensive analysis of how sampling impacts the estimate of several relevant network measures. In particular, we focus on how a sampling strategy optimized to recover a particular spectral centrality measure impacts other topological quantities. Our goal is on one hand to extend the analysis of the behavior of TCEC [Ruggeri2019], a theoretically-grounded sampling method for eigenvector centrality estimation. On the other hand, to demonstrate more broadly how sampling can impact the estimation of relevant network properties like centrality measures different than the one aimed at optimizing, community structure and node attribute distribution. Finally, we adapt the theoretical framework behind TCEC for the case of PageRank centrality and propose a sampling algorithm aimed at optimizing its estimation. We show that, while the theoretical derivation can be suitably adapted to cover this case, the resulting algorithm suffers of a high computational complexity that requires further approximations compared to the eigenvector centrality case.Comment: 8 pages, 5 figure

integrated sensing system for upper limbs in neurologic rehabilitation

Abstract Wearable sensing devices for monitoring physiological parameters have proved their benefits in reducing the recovery time of mobility and in restoring the neuro-cognitive processes underlying the movement of the body. This is particularly evident in neurological patients from trauma or degenerative diseases. This kind of devices are generally wired sensors fixed on flexible supports, with complicated configuration and calibration. The work presented here has the goal to provide the design and implementation of a training system for rehabilitation including seven types of sensors, dedicated areas for data transmission in wireless mode, power management and signal multiplexing

A framework to generate hypergraphs with community structure

In recent years hypergraphs have emerged as a powerful tool to study systems with multi-body interactions which cannot be trivially reduced to pairs. While highly structured methods to generate synthetic data have proved fundamental for the standardized evaluation of algorithms and the statistical study of real-world networked data, these are scarcely available in the context of hypergraphs. Here we propose a flexible and efficient framework for the generation of hypergraphs with many nodes and large hyperedges, which allows specifying general community structures and tune different local statistics. We illustrate how to use our model to sample synthetic data with desired features (assortative or disassortative communities, mixed or hard community assignments, etc.), analyze community detection algorithms, and generate hypergraphs structurally similar to real-world data. Overcoming previous limitations on the generation of synthetic hypergraphs, our work constitutes a substantial advancement in the statistical modeling of higher-order systems.Comment: 18 pages, 8 figures, revised versio

The 3D printing of a polymeric electrochemical cell body and its characterisation

An undivided flow cell was designed and constructed using additive manufacturing technology and its mass transport characteristics were evaluated using the reduction of ferricyanide, hexacyanoferrate (III) ions at a nickel surface. The dimensionless mass transfer correlation Sh = aRebScdLee was obtained using the convective-diffusion limiting current observed in linear sweep voltammetry; this correlation compared closely with that reported in the literature from traditionally machined plane parallel rectangular flow channel reactors. The ability of 3D printer technology, aided by computational graphics, to rapidly and conveniently design, manufacture and re-design the geometrical characteristics of the flow cell ishighlighted