Error management in ATLAS TDAQ : an intelligent systems approach

This thesis is concerned with the use of intelligent system techniques (IST) within a large distributed software system, specifically the ATLAS TDAQ system which has been developed and is currently in use at the European Laboratory for Particle Physics(CERN). The overall aim is to investigate and evaluate a range of ITS techniques in order to improve the error management system (EMS) currently used within the TDAQ system via error detection and classification. The thesis work will provide a reference for future research and development of such methods in the TDAQ system. The thesis begins by describing the TDAQ system and the existing EMS, with a focus on the underlying expert system approach, in order to identify areas where improvements can be made using IST techniques. It then discusses measures of evaluating error detection and classification techniques and the factors specific to the TDAQ system. Error conditions are then simulated in a controlled manner using an experimental setup and datasets were gathered from two different sources. Analysis and processing of the datasets using statistical and ITS techniques shows that clusters exists in the data corresponding to the different simulated errors. Different ITS techniques are applied to the gathered datasets in order to realise an error detection model. These techniques include Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and a comparison of the respective advantages and disadvantages is made. The principle conclusions from this work are that IST can be successfully used to detect errors in the ATLAS TDAQ system and thus can provide a tool to improve the overall error management system. It is of particular importance that the IST can be used without having a detailed knowledge of the system, as the ATLAS TDAQ is too complex for a single person to have complete understanding of. The results of this research will benefit researchers developing and evaluating IST techniques in similar large scale distributed systems

Error management in ATLAS TDAQ : an intelligent systems approach

This thesis is concerned with the use of intelligent system techniques (IST) within a large distributed software system, specifically the ATLAS TDAQ system which has been developed and is currently in use at the European Laboratory for Particle Physics(CERN). The overall aim is to investigate and evaluate a range of ITS techniques in order to improve the error management system (EMS) currently used within the TDAQ system via error detection and classification. The thesis work will provide a reference for future research and development of such methods in the TDAQ system. The thesis begins by describing the TDAQ system and the existing EMS, with a focus on the underlying expert system approach, in order to identify areas where improvements can be made using IST techniques. It then discusses measures of evaluating error detection and classification techniques and the factors specific to the TDAQ system. Error conditions are then simulated in a controlled manner using an experimental setup and datasets were gathered from two different sources. Analysis and processing of the datasets using statistical and ITS techniques shows that clusters exists in the data corresponding to the different simulated errors. Different ITS techniques are applied to the gathered datasets in order to realise an error detection model. These techniques include Artificial Neural Networks (ANNs), Support Vector Machines (SVMs) and Cartesian Genetic Programming (CGP) and a comparison of the respective advantages and disadvantages is made. The principle conclusions from this work are that IST can be successfully used to detect errors in the ATLAS TDAQ system and thus can provide a tool to improve the overall error management system. It is of particular importance that the IST can be used without having a detailed knowledge of the system, as the ATLAS TDAQ is too complex for a single person to have complete understanding of. The results of this research will benefit researchers developing and evaluating IST techniques in similar large scale distributed systems.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

The improving outcomes in intermittent exotropia study: outcomes at 2 years after diagnosis in an observational cohort

Background: The purpose of this study was to investigate current patterns of management and outcomes of intermittent distance exotropia [X(T)] in the UK. Methods: This was an observational cohort study which recruited 460 children aged < 12 years with previously untreated X(T). Eligible subjects were enrolled from 26 UK hospital ophthalmology clinics between May 2005 and December 2006. Over a 2-year period of follow-up, clinical data were prospectively recorded at standard intervals from enrolment. Data collected included angle, near stereoacuity, visual acuity, control of X(T) measured with the Newcastle Control Score (NCS), and treatment. The main outcome measures were change in clinical outcomes (angle, stereoacuity, visual acuity and NCS) in treated and untreated X(T), 2 years from enrolment (or, where applicable, 6 months after surgery). Change over time was tested using the chi-square test for categorical, Wilcoxon test for non-parametric and paired-samples t-test for parametric data. Results: At follow-up, data were available for 371 children (81% of the original cohort). Of these: 53% (195) had no treatment; 17% (63) had treatment for reduced visual acuity only (pure refractive error and amblyopia); 13% (50) had non surgical treatment for control (spectacle lenses, occlusion, prisms, exercises) and 17% (63) had surgery. Only 0.5% (2/371) children developed constant exotropia. The surgically treated group was the only group with clinically significant improvements in angle or NCS. However, 8% (5) of those treated surgically required second procedures for overcorrection within 6 months of the initial procedure and at 6-month follow-up 21% (13) were overcorrected. Conclusions: Many children in the UK with X(T) receive active monitoring only. Deterioration to constant exotropia, with or without treatment, is rare. Surgery appears effective in improving angle of X(T) and NCS, but rates of overcorrection are high

Combined exome and whole-genome sequencing identifies mutations in ARMC4 as a cause of primary ciliary dyskinesia with defects in the outer dynein arm

Primary ciliary dyskinesia (PCD) is a rare, genetically heterogeneous ciliopathy disorder affecting cilia and sperm motility. A range of ultrastructural defects of the axoneme underlie the disease, which is characterised by chronic respiratory symptoms and obstructive lung disease, infertility and body axis laterality defects. We applied a next-generation sequencing approach to identify the gene responsible for this phenotype in two consanguineous families

Genomic reconstruction of the SARS-CoV-2 epidemic in England.

The evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus leads to new variants that warrant timely epidemiological characterization. Here we use the dense genomic surveillance data generated by the COVID-19 Genomics UK Consortium to reconstruct the dynamics of 71 different lineages in each of 315 English local authorities between September 2020 and June 2021. This analysis reveals a series of subepidemics that peaked in early autumn 2020, followed by a jump in transmissibility of the B.1.1.7/Alpha lineage. The Alpha variant grew when other lineages declined during the second national lockdown and regionally tiered restrictions between November and December 2020. A third more stringent national lockdown suppressed the Alpha variant and eliminated nearly all other lineages in early 2021. Yet a series of variants (most of which contained the spike E484K mutation) defied these trends and persisted at moderately increasing proportions. However, by accounting for sustained introductions, we found that the transmissibility of these variants is unlikely to have exceeded the transmissibility of the Alpha variant. Finally, B.1.617.2/Delta was repeatedly introduced in England and grew rapidly in early summer 2021, constituting approximately 98% of sampled SARS-CoV-2 genomes on 26 June 2021

Detecting errors in the ATLAS TDAQ system: a neural networks and support vector machines approach

This paper describes how neural networks and support vector machines can be used to detect errors in a large scale distributed system, specifically the ATLAS Trigger and Data AcQuisition (TDAQ) system. By collecting, analysing and preprocessing some of the data available in the system it is possible to recognize and/or predict error situations arising in the system. This can be done without detailed knowledge of the system, nor of the data available. Hence the presented methods could be used in similar system without significant changes. The TDAQ system, and in particular the main components related to this work, is described together with the test setup used. We simulate a number of error situations in the system and simultaneously gather both performance measures and error messages from the system. The data are then preprocessed and neural networks and support vector machines are applied to try to detect the error situations, achieving classification accuracy ranging from 88% to 100% for the neural networks and 90.8% to a 100% for the support vector machines approach

Dynamic error recovery in the ATLAS TDAQ system

This paper describes the new dynamic recovery mechanisms in the ATLAS Trigger and DataAcQuisition (TDAQ) system. The purpose of the new recovery mechanism is to minimize the impact certain errors and failures have on the system. The new recovery mechanisms are capable of analyzing and recovering from a variety of errors, both software and hardware, without stopping the data-gathering operations. An expert system is incorporated to perform the analysis of the errors and to decide what measures are needed. Due to the wide array of sub-systems there is also a need to optimize the way similar errors are handled for the different sub-systems. The main focus of the paper is to consider the design and implementation of the new recovery mechanisms and how expert knowledge is gathered from the different sub-systems and implemented in the recovery procedures

Dynamic error recovery in the ATLAS TDAQ system: using multi-layer perceptron neural networks

In this chapter we will show how intelligent system (IS) approaches can be successfully applied to an existing distributed software system of significant size; comprising 3000 nodes running more than 20000 processes. We will here use several IS engineering techniques in the context of the ATLAS (A Toroidal LHC AppparatuS) TDAQ (Trigger and DataAcQuisition) system currently being developed and used at CERN, Geneva