The edge cloud: A holistic view of communication, computation and caching

The evolution of communication networks shows a clear shift of focus from just improving the communications aspects to enabling new important services, from Industry 4.0 to automated driving, virtual/augmented reality, Internet of Things (IoT), and so on. This trend is evident in the roadmap planned for the deployment of the fifth generation (5G) communication networks. This ambitious goal requires a paradigm shift towards a vision that looks at communication, computation and caching (3C) resources as three components of a single holistic system. The further step is to bring these 3C resources closer to the mobile user, at the edge of the network, to enable very low latency and high reliability services. The scope of this chapter is to show that signal processing techniques can play a key role in this new vision. In particular, we motivate the joint optimization of 3C resources. Then we show how graph-based representations can play a key role in building effective learning methods and devising innovative resource allocation techniques.Comment: to appear in the book "Cooperative and Graph Signal Pocessing: Principles and Applications", P. Djuric and C. Richard Eds., Academic Press, Elsevier, 201

Graph-based learning under perturbations via total least-squares

Graphs are pervasive in different fields unveiling complex relationships between data. Two major graph-based learning tasks are topology identification and inference of signals over graphs. Among the possible models to explain data interdependencies, structural equation models (SEMs) accommodate a gamut of applications involving topology identification. Obtaining conventional SEMs though requires measurements across nodes. On the other hand, typical signal inference approaches “blindly trust” a given nominal topology. In practice however, signal or topology perturbations may be present in both tasks, due to model mismatch, outliers, outages or adversarial behavior. To cope with such perturbations, this work introduces a regularized total least-squares (TLS) approach and iterative algorithms with convergence guarantees to solve both tasks. Further generalizations are also considered relying on structured and/or weighted TLS when extra prior information on the perturbation is available. Analyses with simulated and real data corroborate the effectiveness of the novel TLS-based approaches

A seasonal periodicity in relapses of multiple sclerosis? A single-center, population-based, preliminary study conducted in Bologna, Italy

Fabrizio Salvi, Ilaria Bartolomei, Angelo Lorusso, and Elena Barbarossa are with the Department of Neuroscience, Multiple Sclerosis Center, Bellaria Hospital, Bologna, Italy -- Michael H. Smolensky is with the Department of Biomedical Engineering, the University of Texas at Austin, USA -- Ann Maria Malagoni, Paolo Zamboni, and Roberto Manfredini are with the Vascular Diseases Center, University of Ferrara, Italy -- Roberto Manfredini is with the Department of Internal Medicine, Hospital of the Delta, Azienda Unità Sanitaria Locale, Ferrara, Italy and the Department of Clinical and Experimental Medicine, Clinica Medica and Vascular Diseases Center, University of Ferrara, ItalyBackground: Temporal, i.e., 24-hour, weekly, and seasonal patterns in the occurrence of acute cardiovascular and cerebrovascular events are well documented; however, little is known about temporal, especially seasonal, variation in multiple sclerosis (MS) and its relapses. This study investigated, by means of a validated chronobiological method, whether severe relapses of MS, ones requiring medical specialty consultation, display seasonal differences, and whether they are linked with seasonal differences in local meteorological variables. Results: We considered 96 consecutive patients with severe MS relapse (29 men, 67 women, mean age 38.5 ± 8.8 years), referred to the Multiple Sclerosis Center, Bellaria Hospital, Bologna, Italy, between January 1, 2007 and December 31, 2008. Overall, we analyzed 164 relapses (56 in men, 108 in women; 115 in patients aged < 40 years, 49 in patients ≥40 years). Relapses were more frequent in May and June (12.2% each) and the least frequent in September (3.7%). Chronobiological analysis showed a biphasic pattern (major peak in May-June, secondary peak in November-December, p = 0.030). Analysis of monthly mean meteorological data showed a significant seasonal pattern in ambient temperature (peak in July, p < 0.001), relative humidity (peak in January, p < 0.001), and wind speed (peak in June, p = 0.011). Conclusions: In this Italian setting we found a biphasic pattern, peaks in spring and autumn, in severe MS relapses requiring medical consultation by doctors of the MS specialty center apparently unrelated to meteorological variables. Confirmations of the findings on larger multi-center populations residing in different climatic conditions are needed to further explore the potential seasonality of MS relapses and associated environmental triggers.Biomedical [email protected]

Graph signal processing in the presence of topology uncertainties

The goal of this paper is to expand graph signal processing tools to deal with cases where the graph topology is not perfectly known. Assuming that the uncertainty affects only a limited number of edges, we make use of small perturbation analysis to derive closed form expressions instrumental to formulate signal processing algorithms that are resilient to imperfect knowledge of the graph topology. Then, we formulate a Bayesian approach to estimate the presence/absence of uncertain edges based only on the observed data and on the statistics of the data. Finally, we exploit our perturbation analysis to analyze clustering and semi-supervised learning algorithms. Numerical tests confirm the benefits of our perturbation-aware methods

Overbooking radio and computation resources in mmW-mobile edge computing to reduce vulnerability to channel intermittency

One of the key features of 5G roadmap is mobile edge computing (MEC), as an effective way to bring information technology (IT) services close to the mobile user. Moving computation and caching resources at the edge of the access network enables low latency and high reliability services, as required in many of the verticals associated to 5G, such as Industry 4.0 or automated driving. Merging MEC with millimeter wave (mmW) communications provides a further thrust to enable low latency and high reliability services, thanks to the high data rate of mmW links and the ability to handle interference through massive beamforming. However, mmW links are prone to blocking events, which could limit the effectiveness of the mmW-MEC deployment. To overcome blocking events and robustify mmW-MEC, in this paper we propose and analyze the performance of two strategies to overcome the effect of blocking: i) overbooking of computation and communication resources, based on the statistics of blocking events, and ii) adopting multi-link communications

Enabling effective mobile edge computing using millimeterwave links

Mobile Edge Computing (MEC) plays a key role in the 5G roadmap, as a way to bring information technology (IT) services closer to the mobile users by empowering radio access points with additional functionalities, like caching or computation offloading capabilities. At the physical layer, some of the key technologies enabling very low latency mobile services are dense deployment of radio access points, massive MIMO and millimeter-wave (mmW) communications for radio access as well as for radio fronthaul/backhaul. In this paper, we merge computation offloading techniques for mobile edge computing with mmW communications and show how the joint optimization of computation/communication resources is crucial to design an energy efficient mobile edge computing system. In particular, we tackle the intermittency of mmW links by considering multi-link communications and show how to take advantage of preliminary estimation of blocking probabilities

Deepening the Whole Transcriptomics of Bovine Liver Cells Exposed to AFB1: A Spotlight on Toll-like Receptor 2

Aflatoxin B1 (AFB1) is a food contaminant metabolized mostly in the liver and leading to hepatic damage. Livestock species are differently susceptible to AFB1, but the underlying mechanisms of toxicity have not yet been fully investigated, especially in ruminants. Thus, the aim of the present study was to better characterize the molecular mechanism by which AFB1 exerts hepatotoxicity in cattle. The bovine fetal hepatocyte cell line (BFH12) was exposed for 48 h to three different AFB1 concentrations (0.9 µM, 1.8 µM and 3.6 µM). Whole-transcriptomic changes were measured by RNA-seq analysis, showing significant differences in the expression of genes mainly involved in inflammatory response, oxidative stress, drug metabolism, apoptosis and cancer. As a confirmatory step, post-translational investigations on genes of interest were implemented. Cell death associated with necrosis rather than apoptosis events was noted. As far as the toxicity mechanism is concerned, a molecular pathway linking inflammatory response and oxidative stress was postulated. Toll-Like Receptor 2 (TLR2) activation, consequent to AFB1 exposure, triggers an intracellular signaling cascade involving a kinase (p38β MAPK), which in turn allows the nuclear translocation of the activator protein-1 (AP-1) and NF-κB, finally leading to the release of pro-inflammatory cytokines. Furthermore, a p38β MAPK negative role in cytoprotective genes regulation was postulated. Overall, our investigations improved the actual knowledge on the molecular effects of this worldwide relevant natural toxin in cattle

Relationship between PROP phenotype and gustin gene and <i>TAS2R38</i> polymorphisms.

<p>PROP threshold (A) and bitterness intensity ratings (3.2 mM) (B) of individuals with genotypes AA, AG and GG of gustin (CA6) polymorphism <i>rs2274333</i> (A/G), and of individuals with genotypes PAV/PAV, PAV/AVI and AVI/AVI of <i>TAS2R38</i>. All values are mean (± SEM). n=63. Different letters indicate significant difference (<i>p</i>≤0.0471; Newman-Keuls test subsequent to main effects ANOVA).</p

Relationship between density of fungiform papillae and gustin gene and <i>TAS2R38</i> polymorphisms.

<p>Mean values ± SEM of density of fungiform papillae (No. /cm²) on the anterior part of the tongue of individuals with genotypes AA, AG and GG of gustin (CA6) polymorphism rs2274333 (A/G) (upper graph) and of individuals with genotypes PAV/PAV, PAV/AVI and AVI/AVI of <i>TAS2R38</i> (lower graph). n=63. Different letters indicate significant difference (<i>p</i>≤0.0379; Newman-Keuls test subsequent to main effects ANOVA). Examples of the 6-mm-diameter stained area of the tongue tip where measures were taken are shown to the right of the graphs.</p

Effect of gustin gene polymorphism <i>rs2274333</i> (A/G) <i>in vitro</i> experiments.

<p>A, Number of cells, expressed as percentage of control, after treatments with saliva of subjects with genotype AA (n=12) or with saliva of subjects with genotype GG (n=12); different letters indicate significant difference (<i>p</i>=0.0135; Student’s <i>t</i> test). B, Fluorescence emission at a wavelength of 590 nm obtained from cells treated for 72 h with saliva of subjects with genotype AA, genotype GG and control; n=12; different letters indicate significant differences (<i>p</i>≤0.00023; Newman-Keuls test subsequent to one-way ANOVA). C, Fluorescence emission at a wavelength of 590 nm obtained from cells treated for 72 h with the two iso-forms of isolated gustin (gustin Ser90 or gustin Gly90) + Zn, control + Zn, or control; n=33; different letters indicate significant differences (<i>p</i>≤0.00067; Newman-Keuls test subsequent to one-way ANOVA).</p