1,721,142 research outputs found
The stock of human capital in the ltalian regions
No full textThe purpose of this note is to document how we constructed annual series for average educational attainment in the 20 Italian administrative regions throughout the 1970-1998 period. These annual data are disaggregated by gender and by eight sectors (Agriculture, Energy, Manufacturing, Construction, Trade, Transport and Communication, Finance and Insurance, Others). Some salient features of the data suggest that they provide useful insights in the relationships between human capital and growth in Italy
Cross-Task and Cross-Lightpath Failure Detection and Localization in Optical Networks Using Transfer Learning
Full text linkPractical deployments of Machine-Learning(ML)-based solutions for failure management in optical networks often suffer from limited data availability, due to, especially, scarcity of labelled data describing different failure scenarios. Transfer Learning (TL) is regarded as a promising direction in cases of data scarcity, thanks to its ability to transfer knowledge from a Source Domain (SD) (e.g. SD could be a digital twin or a laboratory testbed) to a Target Domain (TD) (e.g., the infield network). In this paper, we focus on cross-lightpath and cross-task application of TL for failure localization and failure detection in optical networks. We found that, depending on the number of retrained parameters in the ML model, cross-lightpath TL for failure localization provides satisfactory accuracy (higher than 90%, in some cases) with limited amounts of TD data, and is also convenient in terms of TD retraining duration with respect to cases where TL is not used. Moreover, we found that cross-task failure detection/localization reaches up to 12% or 25% improvement in TD accuracy when considering failure localization and detection as TD task, respectively
Reducing probes for quality of transmission estimation in optical networks with active learning
Full text linkEstimating the quality of transmission (QoT) of a lightpath before its establishment is a critical procedure for efficient design and management of optical networks. Recently, supervised machine learning (ML) techniques for QoT estimation have been proposed as an effective alternative to well-established, yet approximated, analytic models that often require the introduction of conservative margins to compensate for model inaccuracies and uncertainties. Unfortunately, to ensure high estimation accuracy, the training set (i.e., the set of historical field data, or samples, required to train these supervised ML algorithms) must be very large, while in real network deployments, the number of monitored/monitorable lightpaths is limited by several practical considerations. This is especially true for lightpaths with an above-threshold bit error rate (BER) (i.e., malfunctioning or wrongly dimensioned lightpaths), which are infrequently observed during network operation. Samples with above-threshold BERs can be acquired by deploying probe lightpaths, but at the cost of increased operational expenditures and wastage of spectral resources. In this paper, we propose to use active learning to reduce the number of probes needed for ML-based QoT estimation. We build an estimation model based on Gaussian processes, which allows iterative identification of those QoT instances that minimize estimation uncertainty. Numerical results using synthetically generated datasets show that, by using the proposed active learning approach, we can achieve the same performance of standard offline supervised ML methods, but with a remarkable reduction (at least 5% and up to 75%) in the number of training samples
Cost-Efficient Resource Sharing in Ethernet-based 5G Mobile Fronthaul Networks
No full textIn 5G, Mobile Fronthaul (MF) is referred to as the connection between Remote Radio Head (RRH) and BaseBand processing Unit (BBU) pool. To save cost while satisfying its rising bandwidth demand, we propose a MF Resource-Sharing (MFRS) scheme that allows to share (1) the capacity of the Ethernet network among MF traffic and background traffic and (2) the Baseband Processing Functions (BPFs) among various RRHs. To estimate the resource savings achievable with MFRS, we formulate a routing and BPF placement problem. The goal is to minimize cost in terms of required BPFs under the constraints of latency and network capacity. Simulation results show significant improvement in terms of number of supported RRHs and required BPFs compared to two baseline schemes
Machine Learning methods for Quality-of-Transmission estimation : Chapter Seven
No full textMachine Learning (ML) is becoming an integral part of Quality-of-Transmission (QoT) estimation frameworks in optical networks. Application of ML is motivated by the increase in design and management complexity deriving by the emergence of new technologies such as elastic optical networking and coherent transmission. This chapter provides an overview of the application of ML-based methods for QoT estimation in optical networks. We start by introducing classical estimation approaches based on classification and regression, then we cover more recent methodologies, such as active learning and transfer learning. Additionally, we provide a discussion on the integration of ML-based QoT estimation within optimization tools for resource allocation. Finally, illustrative numerical results on the application of ML for QoT estimation conclude the chapter
Routing and Spectrum Assignment Integrating Machine-Learning-Based QoT Estimation in Elastic Optical Networks
Full text linkMachine Learning (ML) is under intense investigation in optical networks as it promises to lead to automation of a variety of management tasks, as amplifier gain equalization, fault recognition, Quality of Transmission (QoT) estimation, and many others. Though several studies focus on each of these specific tasks, the integration of ML-based estimations inside Routing and Spectrum Assignment (RSA) is still largely unexplored.This paper moves towards such integration. We develop a framework that leverages the probabilistic outputs of a ML-based QoT estimator to define the reach constraints in an Integer Linear Programming (ILP) formulation for RSA in an elastic optical network. In this integrated procedure, the RSA problem is solved iteratively by updating the reach constraints based on the outcome of a QoT estimator, to exclude lightpaths with unacceptable QoT. In our numerical evaluation, the proposed integrated method achieves savings in spectrum occupation up to 30% (around 20% on average) compared to traditional ILP-based RSA approaches with reach constraints based on margined analytical models
Using Active Learning to Decrease Probes for QoT Estimation in Optical Networks
No full textWe use active learning to reduce the number of probes needed for machine-learning-based QoT estimation. When building an estimation model based on Gaussian processes, only QoT instances that minimize estimation uncertainty are iteratively requested
Reliable Provisioning for Dynamic Content Requests in Optical Metro Networks
Full text linkWe investigate new methods for reliable provisioning of dynamic content requests in optical metro networks. Our methods leverage content replication across multiple edge datacenters and multipath routing. (C) 2021 The Author(s
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
- …
