If Not Here, There. Explaining Machine Learning Models for Fault Localization in Optical Networks

Machine Learning (ML) is being widely investigated to automate safety-critical tasks in optical-network management. However, in some cases, decisions taken by ML models are hard to interpret, motivate and trust, and this lack of explainability complicates ML adoption in network management. The rising field of Explainable Artificial Intelligence (XAI) tries to uncover the reasoning behind the decision-making of complex ML models, offering end-users a stronger sense of trust towards ML-Automated decisions. In this paper we showcase an application of XAI, focusing on fault localization, and analyze the reasoning of the ML model, trained on real Optical Signal-To-Noise Ratio measurements, in two scenarios. In the first scenario we use measurements from a single monitor at the receiver, while in the second we also use measurements from multiple monitors along the path. With XAI, we show that additional monitors allow network operators to better understand model's behavior, making ML model more trustable and, hence, more practically adoptable

Low-Margin Optical-Network Design with Multiple Physical-Layer Parameter Uncertainties

Analytical QoT models require safety margins to account for uncertain knowledge of input parameters. We propose and evaluate a design procedure that gradually decreases these margins in presence of multiple physical-layer uncertainties, by leveraging monitoring data to build a ML-based QoT regressor

Federated-Learning-Assisted Failure-Cause Identification in Microwave Networks

Machine Learning (ML) adoption for automated failure management is becoming pervasive in today's communication networks. However, ML-based failure management typically requires that monitoring data is exchanged between network devices, where data is collected, and centralized locations, e.g., servers in data centers, where data is processed. ML algorithms in this centralized location are then trained to learn mappings between collected data and desired outputs, e.g., whether a failure exists, its cause, location, etc. This paradigm poses several challenges to network operators in terms of privacy as well as in terms of computational and communication resource usage, as a massive amount of sensible failure data is transmitted over the network. To overcome such limitations, Federated Learning (FL) can be adopted, which consists of training multiple distributed ML models at multiple decentralized locations (called 'clients') using a limited amount of locally-collected data, and of sharing these trained models to a centralized location (called 'server'), where these models are aggregated and shared again with clients. FL reduces data exchange between clients and a server and improves algorithms' performance thanks to sharing knowledge among different domains (i.e., clients), leveraging different sources of local information in a collaborative environment. In this paper, we focus on applying FL to perform failure-cause identification in microwave networks. The problem is modeled as a multi-class ML classification problem with six pre-defined failure causes. Specifically, using real failure data from an operational microwave network composed of more than 10000 microwave links, we emulate a multi-operator scenario in which one operator has partial knowledge of failure causes during the training phase. Thanks to knowledge sharing, numerical results show that FL achieves up to 72% precision in identifying an unknown particular class concerning traditional ML (non- FL) approaches where training is performed without knowledge sharing

Statin‐induced myopathy: Translational studies from preclinical to clinical evidence

Statins are the most prescribed and effective drugs to treat cardiovascular diseases (CVD). Nevertheless, these drugs can be responsible for skeletal muscle toxicity which leads to reduced compliance. The discontinuation of therapy increases the incidence of CVD. Thus, it is essential to assess the risk. In fact, many studies have been performed at preclinical and clinical level to investigate pathophysiological mechanisms and clinical implications of statin myotoxicity. Consequently, new toxicological aspects and new biomarkers have arisen. Indeed, these drugs may affect gene transcription and ion transport and contribute to muscle function impairment. Identifying a marker of toxicity is important to prevent or to cure statin induced myopathy while assuring the right therapy for hypercholesterolemia and counteracting CVD. In this review we focused on the mechanisms of muscle damage discovered in preclinical and clinical studies and high-lighted the pathological situations in which statin therapy should be avoided. In this context, preventive or substitutive therapies should also be evaluated

Minimizing equipment and energy cost in mixed 10G and 100G/200G filterless horseshoe networks with hierarchical OTN boards

Emerging 5G services are changing the way operators manage and optimize their optical metro networks, and the transmission technology and network design process must be tailored to the specific conditions in this segment of the network. Ensuring cost-efficient and energy-efficient network design requires novel approaches that optimize across all network layers. Therefore, to moderate the growth of operators’ expenses, in this paper, we investigate low-cost and energy-efficient cross-layer deployment of hierarchical optical transport network (OTN) boards minimizing equipment and energy consumption cost in mixed 10G and 100G/200G filterless metro networks. We propose an integer linear programming (ILP) model and a genetic algorithm (GA) approach that decide: (i) the node structure by deploying various stacked OTN boards (performing traffic-grooming at the electrical layer) and (ii) lightpath establishment considering coherent and non-coherent transmission technologies. Simulative results on real filterless horseshoe networks with real traffic matrices show that our proposed approaches achieve up to 50% cost savings compared to real-world benchmark deployments

Availability Evaluation of Service Function Chains Under Different Protection Schemes

Network Function Virtualization (NFV) calls for a new resource management approach where virtualized network functions (VNFs) replace traditional network hardware appliances. Thanks to NFV, operators are given a much greater flexibility, as these VNFs can be deployed as virtual nodes and chained together to form Service Function Chains (SFCs). An SFC represents a set of dedicated virtualized resources deployed to provide a certain service to the consumer. One of its most important performance requirements is availability. In this paper, the availability achieved by SFCs is evaluated analytically, by modelling several protection schemes and given different availability values for the network components. The cost of each protection scheme, based on its network resource consumption, is also taken into account. Extensive numerical results are reported, considering various SFC characteristics, such as availability requirements, number of NFV nodes and availability values of network components. The lowest-cost protection strategy, in terms of number of occupied network components, which meets availability requirement, is identified. Our analysis demonstrates that, in most cases, resource-greedy protection schemes, such as end-to-end protection, can be replaced by less aggressive schemes, even when availability requirements are in the order of five or six nines, depending on the number of elements in the service function chain

Electronic polymers in lipid membranes

Electrical interfaces between biological cells and man-made electrical devices exist in many forms, but it remains a challenge to bridge the different mechanical and chemical environments of electronic conductors (metals, semiconductors) and biosystems. Here we demonstrate soft electrical interfaces, by integrating the metallic polymer PEDOT-S into lipid membranes. By preparing complexes between alkyl-ammonium salts and PEDOT-S we were able to integrate PEDOT-S into both liposomes and in lipid bilayers on solid surfaces. This is a step towards efficient electronic conduction within lipid membranes. We also demonstrate that the PEDOT-S@alkyl-ammonium:lipid hybrid structures created in this work affect ion channels in the membrane of Xenopus oocytes, which shows the possibility to access and control cell membrane structures with conductive polyelectrolytes

[Assessment of pulmonary function in a follow-up of premature infants: our experience].

Respiratory diseases are a major cause of morbidity in neonates, especially preterm infants; a long term complication of prematurity such as bronchopulmonary dysplasia (BPD) is particularly relevant today. The exact role of the Pulmonary Function Test (PFT) in this area is not yet well defined; the PFT in newborns and infants - in contrast to what happens in uncooperative children and adults - are routinely used only in a few centers. The assessment of pulmonary function in newborns and infants, however, is nowadays possible with the same reliability that in cooperative patients with the possibility to extend the assessment of polmonary function from bench to bed. The assessment of pulmonary function must be carried out with non invasive and safe methods, at the bedside, with the possibility of continuous monitoring and providing adequate calculation and management of data. The ability to assess lung function helps to define the mechanisms of respiratory failure, improving the treatment and its effects and is therefore a useful tool in the follow-up of newborn and infant with pulmonary disease