A tutorial on the EM algorithm for Bayesian networks: application to self-diagnosis of GPON-FTTH networks

International audienceNetwork behavior modelling is a central issue for model-based approaches of self-diagnosis of telecommunication networks. There are two methods to build such models. The model can be built from expert knowledge acquired from network standards and/or the model can be learnt from data generated by network components by data mining algorithms. In a recent work, we proposed a model of architecture and fault propagation for the GPON-FTTH (Gigabit Passive Optical Network-Fiber To The Home) access network. This model is based on a Bayesian network which encodes expert knowledge. This includes dependencies that encode fault propagation and conditional probability distributions that encode the strength of those dependencies. In this paper we consider the problem of automatically tuning the above mentioned probability distributions. This is a parameter estimation problem under missing data conditions that we solve with the Expectation Maximization (EM) algorithm. Conditional probability distributions are learnt from the tremendous amount of alarms generated by an operating GPON-FTTH network during two months in 2015. Self-diagnosis is carried out to analyze the root cause of alarms. The performance of the diagnosis is evaluated with respect to an expert system based on deterministic decision rules currently used by the Internet Access Provider to diagnose network problems

LILAC pilot study : effects of metformin on mTOR activation and HIV reservoir persistence during antiretroviral therapy

Background: Chronic inflammation and residual HIV transcription persist in people living with HIV (PLWH) receiving antiretroviral therapy (ART), thus increasing the risk of developing non-AIDS co-morbidities. The mechanistic target of rapamycin (mTOR) is a key regulator of cellular metabolism and HIV transcription, and therefore represents an interesting novel therapeutic target. Methods: The LILAC pilot clinical trial, performed on non-diabetic ART-treated PLWH with CD4+ /CD8+ T-cell ratios <0.8, evaluated the effects of metformin (12 weeks oral administration; 500-850 mg twice daily), an indirect mTOR inhibitor, on the dynamics of immunological/virological markers and changes in mTOR activation/phosphorylation in blood collected at Baseline, Week 12, and 12 weeks after metformin discontinuation (Week 24) and sigmoid colon biopsies (SCB) collected at Baseline and Week 12. Findings: CD4+ T-cell counts, CD4+ /CD8+ T-cell ratios, plasma markers of inflammation/gut damage, as well as levels of cell-associated integrated HIV-DNA and HIV-RNA, and transcriptionally-inducible HIV reservoirs, underwent minor variations in the blood in response to metformin. The highest levels of mTOR activation/ phosphorylation were observed in SCB at Baseline. Consistently, metformin significantly decreased CD4+ Tcell infiltration in the colon, as well as mTOR activation/phosphorylation, especially in CD4+ T-cells expressing the Th17 marker CCR6. Also, metformin decreased the HIV-RNA/HIV-DNA ratios, a surrogate marker of viral transcription, in colon-infiltrating CD4+ T-cells of 8/13 participants

Statistical ecology comes of age

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.Peer reviewe

Recommendations for the design of therapeutic trials for neonatal seizures

Although seizures have a higher incidence in neonates than any other age group and are associated with significant mortality and neurodevelopmental disability, treatment is largely guided by physician preference and tradition, due to a lack of data from welldesigned clinical trials. There is increasing interest in conducting trials of novel drugs to treat neonatal seizures, but the unique characteristics of this disorder and patient population require special consideration with regard to trial design. The Critical Path Institute formed a global working group of experts and key stakeholders from academia, the pharmaceutical industry, regulatory agencies, neonatal nurse associations, and patient advocacy groups to develop consensus recommendations for design of clinical trials to treat neonatal seizures. The broad expertise and perspectives of this group were invaluable in developing recommendations addressing: (1) use of neonate-specific adaptive trial designs, (2) inclusion/exclusion criteria, (3) stratification and randomization, (4) statistical analysis, (5) safety monitoring, and (6) definitions of important outcomes. The guidelines are based on available literature and expert consensus, pharmacokinetic analyses, ethical considerations, and parental concerns. These recommendations will ultimately facilitate development of a Master Protocol and design of efficient and successful drug trials to improve the treatment and outcome for this highly vulnerable population

Deep Infinite Mixture Models for Fault Discovery in GPON-FTTH Networks

International audienceFault diagnosis in telecommunication networks requires extensive expert knowledge and is key to efficient network operations and high service availability. Specifically, discovering and identifying new faults occurring in the network is a challenging task. Some dominant methods in industry are based on expert systems or Bayesian networks. Both of these methods require considerable expert knowledge and time resources to construct and maintain the diagnosis system. In this paper, we propose a data driven approach for the clustering and identification of new faults, based on existing knowledge, using neural networks and infinite mixture models. In our approach deep infinite mixture models are capable of extracting interesting features from labeled data, which are then leveraged in the clustering process to identify new relevant faults in unlabeled data. We apply our method to real operational data from Fiber-to-the Home services based on Gigabit-capable Passive Optical Networks. We show that our approach can be trained end-to-end, and allows to identify and interpret new faults. INDEX TERMS Network fault diagnosis, deep learning, infinite mixture models, variational inference

Status Reporting versus Non Status Reporting Dynamic Bandwidth Allocation

International audienceOptical access technologies allow service providers to propose high bandwidth services in both directions (upstream and downstream) and thus to develop values added services. Passive Optical Network (PON) technologies offer, in the upstream, a controlled access to a shared media. Dedicated control mechanisms to the upstream channel have been proposed by ITU-T for Gigabit-capable Passive Optical Network (G-PON) and 10 Gigabit-capable Passive Optical Network (XG-PON1). Such mechanisms rely on Dynamic Bandwidth Allocation (DBA) procedures that manage bandwidth allocation according to customers requirements. This paper focuses on comparing two such DBA algorithms: Status Reporting (SR) in which the customer explicitly reports its backlog, and Non Status Reporting (NSR) in which the Optical Line Termination (OLT) infers customers' requirements by assessing how previously allocated resources were used. We develop several models in order to illustrate the respective performances of SR and NSR

Application of probabilistic modeling and machine learning to the diagnosis of FTTH GPON networks

International audienceThis paper presents insights on the promises of probabilistic modeling and machine learning for fault diagnosis in optical access networks. A Bayesian inference engine, called Probabilistic tool for GPON-FTTH Access Network self-DiAgnosis (PANDA), is applied to fault diagnosis of Gigabit capable Passive Optical Networks (GPON). PANDA approach has been assessed on real diagnosis data, showing very satisfactory alignment with an operational rule-based expert system. Furthermore, it provides diagnosis conclusions for all tested cases, even if some monitoring data is missing or incomplete. Finally, an expectation maximization algorithm allows to finely tune the probabilistic model

A highly adaptable probabilistic model for self-diagnosis of GPON-FTTH access network

International audienceModel-based approaches for self-diagnosing of telecommunication networks develop reasonings based on formal and explicit representation of network structure and network behavior. Network behavior modeling is a central issue for these methods. In a recent work, we have proposed a model of architecture and fault propagation of the FTTH (Fiber To The Home) access networks based on GPON (Gigabit capable Passive Optical Network). This model is based on a Bayesian network which encodes expert knowledge about the transport network and the connection network of subscribers. In this paper we extend this model by designing a model of the distribution network which fits to the various engineering techniques of the GPON-FTTH network. We carried out self-diagnosis of an operating GPON-FTTH network based on these two models. The performance of self-diagnostic of the new model is evaluated with respect to the previous model of the GPON-FTTH network