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In this paper, we propose a novel algorithm for energy-efficient low-latency dynamic mobile edge computing (MEC), in the context of beyond 5G networks endowed with reconfigurable intelligent surfaces (RISs). We consider a scenario where new computing requests are continuously generated by a set of devices and are handled through a dynamic queueing system. Building on stochastic optimization tools, we devise a dynamic learning algorithm that jointly optimizes the allocation of radio resources (i.e., power, transmission rates, sleep mode and duty cycle), computation resources (i.e., CPU cycles), and RIS reflectivity parameters (i.e., phase shifts), while guaranteeing a target performance in terms of average end-to-end delay. The proposed strategy enables dynamic control of the system, performing a low-complexity optimization on a per-slot basis while dealing with time-varying radio channels and task arrivals, whose statistics are unknown. The presence and optimization of RISs helps boosting the performance of dynamic MEC, thanks to the capability to shape and adapt the wireless propagation environment. Numerical results assess the performance in terms of service delay, learning, and adaptation capabilities of the proposed strategy for RIS-empowered MEC. [ABSTRACT FROM AUTHOR]

The goal of this paper is to propose novel strategies for adaptive learning of signals defined over graphs, which are observed over a (randomly) time-varying subset of vertices. We recast two classical adaptive algorithms in the graph signal processing framework, namely the least mean squares (LMS) and the recursive least squares (RLS) adaptive estimation strategies. For both methods, a detailed mean-square analysis illustrates the effect of random sampling on the adaptive reconstruction capability and the steady-state performance. Then, several probabilistic sampling strategies are proposed to design the sampling probability at each node in the graph, with the aim of optimizing the tradeoff between steady-state performance, graph sampling rate, and convergence rate of the adaptive algorithms. Finally, a distributed RLS strategy is derived and shown to be convergent to its centralized counterpart. Numerical simulations carried out over both synthetic and real data illustrate the good performance of the proposed sampling and recovery strategies for (distributed) adaptive learning of signals defined over graphs. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose distributed strategies for adaptive learning of signals defined over graphs. Assuming the graph signal to be bandlimited, the method enables distributed reconstruction, with guaranteed performance in terms of mean-square error, and tracking from a limited number of sampled observations taken from a subset of vertices. A detailed mean-square analysis is carried out and illustrates the role played by the sampling strategy on the performance of the proposed method. Finally, some useful strategies for distributed selection of the sampling set are provided. Several numerical results validate our theoretical findings, and illustrate the performance of the proposed method for distributed adaptive learning of signals defined over graphs. [ABSTRACT FROM PUBLISHER]

The aim of this paper is to develop a general framework for training neural networks (NNs) in a distributed environment, where training data is partitioned over a set of agents that communicate with each other through a sparse, possibly time-varying, connectivity pattern. In such distributed scenario, the training problem can be formulated as the (regularized) optimization of a non-convex social cost function, given by the sum of local (non-convex) costs, where each agent contributes with a single error term defined with respect to its local dataset. To devise a flexible and efficient solution, we customize a recently proposed framework for non-convex optimization over networks, which hinges on a (primal) convexification–decomposition technique to handle non-convexity, and a dynamic consensus procedure to diffuse information among the agents. Several typical choices for the training criterion (e.g., squared loss, cross entropy, etc.) and regularization (e.g., ℓ 2 norm, sparsity inducing penalties, etc.) are included in the framework and explored along the paper. Convergence to a stationary solution of the social non-convex problem is guaranteed under mild assumptions. Additionally, we show a principled way allowing each agent to exploit a possible multi-core architecture (e.g., a local cloud) in order to parallelize its local optimization step, resulting in strategies that are both distributed (across the agents) and parallel (inside each agent) in nature. A comprehensive set of experimental results validate the proposed approach. [ABSTRACT FROM AUTHOR]

The aim of this paper is to propose diffusion strategies for distributed estimation over adaptive networks, assuming the presence of spatially correlated measurements distributed according to a Gaussian Markov random field (GMRF) model. The proposed methods incorporate prior information about the statistical dependency among observations, while at the same time processing data in real time and in a fully decentralized manner. A detailed mean-square analysis is carried out in order to prove stability and evaluate the steady-state performance of the proposed strategies. Finally, we also illustrate how the proposed techniques can be easily extended in order to incorporate thresholding operators for sparsity recovery applications. Numerical results show the potential advantages of using such techniques for distributed learning in adaptive networks deployed over GMRF. [ABSTRACT FROM PUBLISHER]

This paper proposes a distributed resource assignment strategy for cognitive networks mimicking a swarm foraging mechanism, assuming that the communication among the cognitive nodes is impaired by random link failures and quantization noise. Using results from stochastic approximation theory, we propose a swarm mechanism that converges almost surely to a final allocation even in the presence of imperfect communication scenarios. The theoretical findings are corroborated by numerical results showing that the only effect of the random link failures is to decrease the convergence rate of the algorithm. We propose then a fast swarming approach, robust to random disturbances, that adapts its behavior with respect to the interference power perceived by every node, thus increasing the speed of convergence and improving the resource allocation capabilities. [ABSTRACT FROM PUBLISHER]

The goal of this paper is to propose a bio-inspired radio access mechanism for cognitive networks mimicking the behavior of a flock of birds swarming in search for food in a cohesive fashion without colliding with each other. The equivalence between swarming and radio resource allocation is established by modeling the interference distribution in the resource domain, e.g., frequency and time, as the spatial distribution of food, while the position of the single bird represents the radio resource chosen by each radio node. The swarming mechanism is enforced by letting every node allocate its resources (power/bits) in the time-frequency regions where the interference is minimum (the food density is maximum), avoiding collisions with other nodes (birds), yet limiting the spread in the time-frequency domain (i.e., maintaining the swarm cohesion). The solution is given as the distributed minimization of a functional, borrowed from social foraging swarming models, containing the average interference plus repulsion and attraction terms that help to avoid conflicts and maintain cohesiveness, respectively. [ABSTRACT FROM AUTHOR]

Epithelial ovarian cancer (EOC) is the most frequent cause of death among women with gynecologic malignant tumors. Primary debulking surgery (PDS) with maximal surgical effort to reach completeness of cytoreduction, followed by chemotherapy, has become the standard of care; moreover, some experiences have shown that a comprehensive treatment approach of surgery combined with hyperthermic intraperitoneal chemotherapy (HIPEC) could improve the prognosis of ovarian cancer. We carried out a retrospective analysis of all consecutive sixty-six patients diagnosed with primary advanced or recurrent ovarian cancer who underwent debulking surgery plus HIPEC in a single center between September 2005 and October 2020. For 33 patients with primary EOC, with a median follow-up period of 70 months, the median overall survival was 56 months (range: 48.1–96.9); and the median disease-free survival (DFS) was 13 months (range: 19.9–53.7). In the recurrent population, the median follow-up period was 78 months, the median overall survival (OS) was 82 months (range: 48.1–96.9), and the median DFS was 17 months (range: 19.7–53.0). In our study, we have found that CRS plus HIPEC is feasible, with very low rates of major complications and good results in terms of overall survival. [ABSTRACT FROM AUTHOR]

Background: Adulteration of diesel fuel by the addition of vegetable oil is a problem that touches several countries around the world and bypasses the complexity of the specifications regarding the automotive diesel fuel distinguishing between customs, fiscal, and commercial–environmental specifications. At an international level, the adoption of the same analysis methods is important for the harmonization processes and the fluidity of the market. In this context, we assist to the diffusion of the same fraud touching several countries or continents since the limit of the same methods are common to many specifications. For several European countries, the revenue lost as a result of this adulteration consists of billions of euros per year. This enormous amount of illicit money feeds organized criminal networks with huge social and environmental damages. Objective: This work presents a GC method to quantify vegetable oils in the range of 0.2–7% (w/w) in adulterated diesel fuel, intended for use as motor fuel, with or without extraneous heavy mineral oil. Methods: Our study was realized on 100 fraudulent samples collected by the Italian fiscal police in regular oil stations and by the Italian fiscal police and customs officers as a consequence of controls on trucks transporting, in suspicious circumstances, “oil” often declared “antistick and anticorrosion oil” or “protective oil.” Conclusions and Highlights: High reliability of results, external validity, ease of replication, simple instrumentation, and sample preparation make this method well suited for a new “smart” protocol for diesel fuels analysis for customs, fiscal, and antifraud purposes. [ABSTRACT FROM AUTHOR]

Reconfigurable intelligent surfaces (RISs) constitute the key enabler for programmable electromagnetic propagation environments and are lately being considered as a candidate physical-layer technology for the demanding connectivity, reliability, localisation, and sustainability requirements of next-generation wireless networks. In this paper, we first present the deployment scenarios for RIS-enabled smart wireless environments that have been recently designed within the ongoing European Union Horizon 2020 RISE-6G project, as well as a network architecture integrating RISs with existing standardised interfaces. We identify various RIS deployment strategies and sketch the core architectural requirements in terms of RIS control and signalling, depending on the RIS hardware architectures and respective capabilities. Furthermore, we introduce and discuss, with the aid of simulations and reflect array measurements, two novel metrics that emerge in the context of RIS-empowered wireless systems: the RIS bandwidth of influence and the RIS area of influence. Their extensive investigation corroborates the need for careful deployment and planning of the RIS technology in future wireless networks. [ABSTRACT FROM AUTHOR]

The semi-supervised support vector machine (S 3 VM) is a well-known algorithm for performing semi-supervised inference under the large margin principle. In this paper, we are interested in the problem of training a S 3 VM when the labeled and unlabeled samples are distributed over a network of interconnected agents. In particular, the aim is to design a distributed training protocol over networks, where communication is restricted only to neighboring agents and no coordinating authority is present. Using a standard relaxation of the original S 3 VM, we formulate the training problem as the distributed minimization of a non-convex social cost function. To find a (stationary) solution in a distributed manner, we employ two different strategies: (i) a distributed gradient descent algorithm; (ii) a recently developed framework for In-Network Nonconvex Optimization (NEXT), which is based on successive convexifications of the original problem, interleaved by state diffusion steps. Our experimental results show that the proposed distributed algorithms have comparable performance with respect to a centralized implementation, while highlighting the pros and cons of the proposed solutions. To the date, this is the first work that paves the way toward the broad field of distributed semi-supervised learning over networks. [ABSTRACT FROM AUTHOR]

We have used Kepler photometry to characterize variability in four radio-loud active galactic nuclei (AGN; three quasars and one object tentatively identified as a Seyfert 1.5 galaxy) on timescales from minutes to months, comparable to the light crossing time of the accretion disk around the central supermassive black hole or the base of the relativistic jet. Kepler’s almost continuous observations provide much better temporal coverage than is possible from ground-based observations. We report the first such data analyzed for quasars. We have constructed power spectral densities using eight Kepler quarters of long-cadence (30-minute) data for three AGN, six quarters for one AGN and two quarters of short-cadence (1-minute) data for all four AGN. On timescales longer than about 0.2–0.6 days, we find red noise with mean power-law slopes ranging from −1.8 to −1.2, consistent with the variability originating in turbulence either behind a shock or within an accretion disk. Each AGN has a range of red noise slopes which vary slightly by month and quarter of observation. No quasi-periodic oscillations of astrophysical origin were detected.We detected flares of several days long when brightness increased by 3%–7% in two objects. No flares on timescales of minutes to hours were detected. Our observations imply that the duty cycle for enhanced activity in these radio-loud AGN is small. These well-sampled AGN light curves provide an impetus to develop more detailed models of turbulence in jets and instabilities in accretion disks. [ABSTRACT FROM AUTHOR]