Context-aware Distance Measures for Dynamic Networks

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

Implementation, Characterization and Application of Path Changing Switch based Arbiter PUF on FPGA as a lightweight Security Primitive for IoT

Secure authentication of any Internet-of-Things (IoT) device becomes the utmost necessity due to the lack of specifically designed IoT standards and intrinsic vulnerabilities with limited resources and heterogeneous technologies. Despite the suitability of arbiter physically unclonable function (APUF) among other PUF variants for the IoT applications, implementing it on field-programmable gate arrays (FPGAs) is challenging. This work presents the complete characterization of the path changing switch (PCS) 1 based APUF on two different families of FPGA, like Spartan-3E (90 nm CMOS) and Artix-7 (28 nm CMOS). A comprehensive study of the existing tuning concept for programmable delay logic (PDL) based APUF implemented on FPGA is presented, leading to establishment of its practical infeasibility. We investigate the entropy, randomness properties of the PCS based APUF suitable for practical applications, and the effect of temperature variation signifying the adequate tolerance against environmental variation. The XOR composition of PCS based APUF is introduced to boost performance and security. The robustness of the PCS based APUF against machine learning based modeling attack is evaluated, showing similar characteristics as the conventional APUF. Experimental results validate the efficacy of PCS based APUF with a little hardware footprint removing the paucity of lightweight security primitive for IoT.

A Survey on Generative Adversarial Networks: Variants, Applications, and Training

The Generative Models have gained considerable attention in unsupervised learning via a new and practical framework called Generative Adversarial Networks (GAN) due to their outstanding data generation capability. Many GAN models have been proposed, and several practical applications have emerged in various domains of computer vision and machine learning. Despite GANs excellent success, there are still obstacles to stable training. The problems are Nash equilibrium, internal covariate shift, mode collapse, vanishing gradient, and lack of proper evaluation metrics. Therefore, stable training is a crucial issue in different applications for the success of GANs. Herein, we survey several training solutions proposed by different researchers to stabilize GAN training. We discuss (I) the original GAN model and its modified versions, (II) a detailed analysis of various GAN applications in different domains, and (III) a detailed study about the various GAN training obstacles as well as training solutions. Finally, we reveal several issues as well as research outlines to the topic.

ParTBC: Faster Estimation of Top- k Betweenness Centrality Vertices on GPU

Betweenness centrality (BC) is a popular centrality measure, based on shortest paths, used to quantify the importance of vertices in networks. It is used in a wide array of applications including social network analysis, community detection, clustering, biological network analysis, and several others. The state-of-the-art Brandes’ algorithm for computing BC has time complexities of and for unweighted and weighted graphs, respectively. Brandes’ algorithm has been successfully parallelized on multicore and manycore platforms. However, the computation of vertex BC continues to be time-consuming for large real-world graphs. Often, in practical applications, it suffices to identify the most important vertices in a network; that is, those having the highest BC values. Such applications demand only the top vertices in the network as per their BC values but do not demand their actual BC values. In such scenarios, not only is computing the BC of all the vertices unnecessary but also exact BC values need not be computed. In this work, we attempt to marry controlled approximations with parallelization to estimate the k -highest BC vertices faster, without having to compute the exact BC scores of the vertices. We present a host of techniques to determine the top- k vertices faster , with a small inaccuracy, by computing approximate BC scores of the vertices. Aiding our techniques is a novel vertex-renumbering scheme to make the graph layout more structured , which results in faster execution of parallel Brandes’ algorithm on GPU. Our experimental results, on a suite of real-world and synthetic graphs, show that our best performing technique computes the top- k vertices with an average speedup of 2.5× compared to the exact parallel Brandes’ algorithm on GPU, with an error of less than 6%. Our techniques also exhibit high precision and recall, both in excess of 94%.

Computation of local permeability in granular soils using spatial time domain reflectometer

This letter proposes semi-analytical methods to obtain the local permeability for granular soils based on indirect measurements of the local porosity profile in a large coaxial cell permeameter using spatial time domain reflectometry. The porosity profile is used to obtain the local permeability using the modified Kozeny-Carman and Katz-Thompson equations, which incorporated an effective particle diameter that accounted for particle migration within the permeameter. The profiles of the local permeability obtained from the proposed methods are compared with experimentally obtained permeability distributions using pressure measurements and flow rate. The permeabilities obtained with the proposed methods are comparable with the experimentally obtained permeabilities and are within one order of magnitude deviation, which is an acceptable range for practical applications.

Self-Healing of SiC-Al2O3-B4C Ceramic Composites at Low Temperatures

Self-healing ceramics have been researched at high temperatures, but few have been considered at lower temperatures. In this study, SiC-Al2O3-B4C ceramic composite was compacted by spark plasma sintering (SPS). A Vickers indentation was introduced, and the cracks were healed between 600 °C and 800 °C in air. Cracks could be healed completely in air above 700 °C. The ceramic composite had the best healing performance at 700 °C for 30 min, recovering flexural strength of up to 94.2% of the original. Good crack-healing ability would make this composite highly useful as it could heal defects and flaws autonomously in practical applications. The healing mechanism was also proposed to be the result of the oxidation of B4C.

Alginate Hydrogel Microtubes for Salivary Gland Cell Organization and Cavitation

Understanding the different regulatory functions of epithelial and mesenchymal cell types in salivary gland development and cellular organization is essential for proper organoid formation and salivary gland tissue regeneration. Here, we demonstrate a biocompatible platform using pre-formed alginate hydrogel microtubes to facilitate direct epithelial–mesenchymal cell interaction for 3D salivary gland cell organization, which allows for monitoring cellular organization while providing a protective barrier from cell-cluster loss during medium changes. Using mouse salivary gland ductal epithelial SIMS cells as the epithelial model cell type and NIH 3T3 fibroblasts or primary E16 salivary mesenchyme cells as the stromal model cell types, self-organization from epithelial–mesenchymal interaction was examined. We observed that epithelial and mesenchymal cells undergo aggregation on day 1, cavitation by day 4, and generation of an EpCAM-expressing epithelial cell layer as early as day 7 of the co-culture in hydrogel microtubes, demonstrating the utility of hydrogel microtubes to facilitate heterotypic cell–cell interactions to form cavitated organoids. Thus, pre-formed alginate microtubes are a promising co-culture method for further understanding epithelial and mesenchymal interaction during tissue morphogenesis and for future practical applications in regenerative medicine.

Tuning of frictional properties in torsional contact by means of disk grading

The contact of two surfaces in relative rotating motion occurs in many practical applications, from mechanical devices to human joints, displaying an intriguing interplay of effects at the onset of sliding due to the axisymmetric stress distribution. Theoretical and numerical models have been developed for some typical configurations, but work remains to be done to understand how to modify the emergent friction properties in this configuration. In this paper, we extend the two-dimensional (2D) spring-block model to investigate friction between surfaces in torsional contact. We investigate how the model describes the behavior of an elastic surface slowly rotating over a rigid substrate, comparing results with analytical calculations based on energy conservation. We show that an appropriate grading of the tribological properties of the surface can be used to avoid a non-uniform transition to sliding due to the axisymmetric configuration.