Uncertainty-Aware QoT Forecasting in Optical Networks with Bayesian Recurrent Neural Networks

We consider the problem of forecasting the Quality-of-Transmission (QoT) of deployed lightpaths in a Wavelength Division Multiplexing (WDM) optical network. QoT forecasting plays a determinant role in network management and planning, as it allows network operators to proactively plan maintenance or detect anomalies in a lightpath. To this end, we leverage Bayesian Recurrent Neural Networks for learning uncertainty-aware probabilistic QoT forecasts, i.e., for modelling a probability distribution of the QoT over a time horizon. We evaluate our proposed approach on the open-source Microsoft Wide Area Network (WAN) optical backbone dataset. Our illustrative numerical results show that our approach not only outperforms state-of-the-art models from literature, but also predicts intervals providing near-optimal empirical coverage. As such, we demonstrate that uncertainty-aware probabilistic modelling enables the application of QoT forecasting in risk-sensitive application scenarios

Hollow-Core-Fiber Placement in Latency-Constrained Metro Networks with edgeDCs

We investigate the optimal placement of Hollow-Core Fibers (HCF) in latency-constrained metro networks with edgeDCs, performing physical-layer validation. Upgrading 24% of links to HCF reduces edgeDCs number by 29% compared to a network without HCFs

Poster: Continual Network Learning

We make a case for in-network Continual Learning as a solution for seamless adaptation to evolving network conditions without forgetting past experiences. We propose implementing Active Learning-based selective data filtering in the data plane, allowing for data-efficient continual updates. We explore relevant challenges and propose future research directions

Explainable Artificial Intelligence in communication networks: A use case for failure identification in microwave networks

Artificial Intelligence (AI) has demonstrated superhuman capabilities in solving a significant number of tasks, leading to widespread industrial adoption. For in-field network-management application, AI-based solutions, however, have often risen skepticism among practitioners as their internal reasoning is not exposed and their decisions cannot be easily explained, preventing humans from trusting and even understanding them. To address this shortcoming, a new area in AI, called Explainable AI (XAI), is attracting the attention of both academic and industrial researchers. XAI is concerned with explaining and interpreting the internal reasoning and the outcome of AI-based models to achieve more trustable and practical deployment. In this work, we investigate the application of XAI for network management, focusing on the problem of automated failure-cause identification in microwave networks. We first introduce the concept of XAI, highlighting its advantages in the context of network management, and we discuss in detail the concept behind Shapley Additive Explanations (SHAP), the XAI framework considered in our analysis. Then, we propose a framework for a XAI-assisted ML-based automated failure-cause identification in microwave networks, spanning model's development and deployment phases. For the development phase, we show how to exploit SHAP for feature selection and how to leverage SHAP to inspect misclassified instances during model's development process, and how to describe model's global behavior based on SHAP's global explanations. For the deployment phase, we propose a framework based on predictions uncertainty to detect possibly wrong predictions that will be inspected through XAI

Obtaining Phytoplankton Diversity from Ocean Color: A Scientific Roadmap for Future Development

To improve our understanding of the role of phytoplankton for marine ecosystems and global biogeochemical cycles, information on the global distribution of major phytoplankton groups is essential. Although algorithms have been developed to assess phytoplankton diversity from space for over two decades, so far the application of these data sets has been limited. This scientific roadmap identifies user needs, summarizes the current state of the art, and pinpoints major gaps in long-term objectives to deliver space-derived phytoplankton diversity data that meets the user requirements. These major gaps in using ocean color to estimate phytoplankton community structure were identified as: (a) the mismatch between satellite, in situ and model data on phytoplankton composition, (b) the lack of quantitative uncertainty estimates provided with satellite data, (c) the spectral limitation of current sensors to enable the full exploitation of backscattered sunlight, and (d) the very limited applicability of satellite algorithms determining phytoplankton composition for regional, especially coastal or inland, waters. Recommendation for actions include but are not limited to: (i) an increased communication and round-robin exercises among and within the related expert groups, (ii) the launching of higher spectrally and spatially resolved sensors, (iii) the development of algorithms that exploit hyperspectral information, and of (iv) techniques to merge and synergistically use the various streams of continuous information on phytoplankton diversity from various satellite sensors' and in situ data to ensure long-term monitoring of phytoplankton composition

Tunability experiments at the FERMI@Elettra free-electron laser

FERMI@Elettra is a free electron-laser (FEL)-based user facility that, after two years of commissioning, started preliminary users' dedicated runs in 2011. At variance with other FEL user facilities, FERMI@Elettra has been designed to deliver improved spectral stability and longitudinal coherence. The adopted scheme, which uses an external laser to initiate the FEL process, has been demonstrated to be capable of generating FEL pulses close to the Fourier transform limit. We report on the first instance of FEL wavelength tuning, both in a narrow and in a large spectral range (fine- and coarse-tuning). We also report on two different experiments that have been performed exploiting such FEL tuning. We used fine-tuning to scan across the 1s–4p resonance in He atoms, at ≈23.74 eV (52.2 nm), detecting both UV–visible fluorescence (4p–2s, 400 nm) and EUV fluorescence (4p–1s, 52.2 nm). We used coarse-tuning to scan the M4,5 absorption edge of Ge (∼29.5 eV) in the wavelength region 30–60 nm, measured in transmission geometry with a thermopile positioned on the rear side of a Ge thin foil

Measurement of the muon decay spectrum with the ICARUS liquid Argon TPC

Examples are given which prove the ICARUS detector quality through relevant physics measurements. We study the muon decay energy spectrum from a sample of stopping muon events acquired during the test run of the ICARUS T600 detector. This detector allows the spatial reconstruction of the events with fine granularity, hence, the precise measurement of the range and dE/dx of the muon with high sampling rate. This information is used to compute the calibration factors needed for the full calorimetric reconstruction of the events. The Michel rho parameter is then measured by comparison of the experimental and Monte Carlo simulated muon decay spectra, obtaining rho = 0.72 +/- 0.06(stat.) +/- 0.08(syst.). The energy resolution for electrons below ~50 MeV is finally extracted from the simulated sample, obtaining (Emeas-Emc)/Emc = 11%/sqrt(E[MeV]) + 2%.Comment: 16 pages, 8 figures, LaTex, A4. Some text and 1 figure added. Final version as accepted for publication in The European Physical Journal

Sentinel node biopsy in early vulvar cancer

Lymph node pathologic status is the most important prognostic factor in vulvar cancer; however, complete inguinofemoral node dissection is associated with significant morbidity. Lymphoscintigraphy associated with gamma-probe guided surgery reliably detects sentinel nodes in melanoma and breast cancer patients. This study evaluates the feasibility of the surgical identification of sentinel groin nodes using lymphoscintigraphy and a gamma-detecting probe in patients with early vulvar cancer. Technetium-99m-labelled colloid human albumin was administered perilesionally in 37 patients with invasive epidermoid vulvar cancer (T1–T2) and lymphoscintigraphy performed the day before surgery. An intraoperative gamma-detecting probe was used to identify sentinel nodes during surgery. A complete inguinofemoral node dissection was then performed. Sentinel nodes were submitted separately to pathologic evaluation. A total of 55 groins were dissected in 37 patients. Localization of the SN was successful in all cases. Eight cases had positive nodes: in all the sentinel node as positive; the sentinel node was the only positive node in five cases. Twenty-nine patients showed negative sentinel nodes: all of them were negative for lymph node metastases. Lymphoscintigraphy and sentinel-node biopsy under gamma-detecting probe guidance proved to be an easy and reliable method for the detection of sentinel node in early vulvar cancer. This technique may represent a true advance in the direction of less aggressive treatments in patients with vulvar cancer. © 2000 Cancer Research Campaig