The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization

This paper focuses on an examination of an applicability of Recurrent Neural Network models for detecting anomalous behavior of the CERN superconducting magnets. In order to conduct the experiments, the authors designed and implemented an adaptive signal quantization algorithm and a custom GRU-based detector and developed a method for the detector parameters selection. Three different datasets were used for testing the detector. Two artificially generated datasets were used to assess the raw performance of the system whereas the 231 MB dataset composed of the signals acquired from HiLumi magnets was intended for real-life experiments and model training. Several different setups of the developed anomaly detection system were evaluated and compared with state-of-the-art OC-SVM reference model operating on the same data. The OC-SVM model was equipped with a rich set of feature extractors accounting for a range of the input signal properties. It was determined in the course of the experiments that the detector, along with its supporting design methodology, reaches F1 equal or very close to 1 for almost all test sets. Due to the profile of the data, the best_length setup of the detector turned out to perform the best among all five tested configuration schemes of the detection system. The quantization parameters have the biggest impact on the overall performance of the detector with the best values of input/output grid equal to 16 and 8, respectively. The proposed solution of the detection significantly outperformed OC-SVM-based detector in most of the cases, with much more stable performance across all the datasets.Comment: Related to arXiv:1702.0083

Real-Time Sensor Networks and Systems for the Industrial IoT

The Industrial Internet of Things (Industrial IoT—IIoT) has emerged as the core construct behind the various cyber-physical systems constituting a principal dimension of the fourth Industrial Revolution. While initially born as the concept behind specific industrial applications of generic IoT technologies, for the optimization of operational efficiency in automation and control, it quickly enabled the achievement of the total convergence of Operational (OT) and Information Technologies (IT). The IIoT has now surpassed the traditional borders of automation and control functions in the process and manufacturing industry, shifting towards a wider domain of functions and industries, embraced under the dominant global initiatives and architectural frameworks of Industry 4.0 (or Industrie 4.0) in Germany, Industrial Internet in the US, Society 5.0 in Japan, and Made-in-China 2025 in China. As real-time embedded systems are quickly achieving ubiquity in everyday life and in industrial environments, and many processes already depend on real-time cyber-physical systems and embedded sensors, the integration of IoT with cognitive computing and real-time data exchange is essential for real-time analytics and realization of digital twins in smart environments and services under the various frameworks’ provisions. In this context, real-time sensor networks and systems for the Industrial IoT encompass multiple technologies and raise significant design, optimization, integration and exploitation challenges. The ten articles in this Special Issue describe advances in real-time sensor networks and systems that are significant enablers of the Industrial IoT paradigm. In the relevant landscape, the domain of wireless networking technologies is centrally positioned, as expected

Fiducial cross-section measurements of the production of a prompt photon in association with a top-quark pair at s=13\sqrt{s}=13 TeV with the ATLAS detector at the LHC

The cross sections for top-quark pair production in association with a photon are measured in a fiducial volume with the ATLAS detector at a centre-of-mass energy of 13 TeV. Results are presented using proton-proton collision data collected by the LHC during 2015 and 2016, amounting to a total of 36.1 fb$^{-1}$. This also presents the first $t\bar{t}\gamma$ cross-section measurements performed in the dilepton channels. Exactly one photon is required to have $p_{\text{T}} > 20$ GeV and be isolated based on track and calorimeter information. At least two (four) jets are required in the dilepton (single-lepton) channels, with at least one jet originating from a $b$-quark. Two separate neural network algorithms are used to help reduce the impact backgrounds play in the final measurements. The Prompt Photon Tagger is trained on information from energy deposits in the calorimeters to distinguish prompt photons from hadronic fake photons. The output of this neural network is fed into the Event-level Discriminator that uses event information to classify signal from the sum of all backgrounds. A maximum likelihood fit is performed on the output of the Event-level Discriminator to determine the fiducial cross section of the signal process. The fiducial cross section for the single-lepton and dilepton channel are measured to be $521 \pm 9 \text{(stat.)} \pm 41 \text{(sys.)}$ fb and $69 \pm 3 \text{(stat.)} \pm 4\text{(sys.)}$ fb, respectively. In total, eight cross-section measurements are performed and all agree with theoretical next-to-leading-order predictions.Comment: PhD dissertatio

Online learning of physics during a pandemic: A report from an academic experience in Italy

The arrival of the Sars-Cov II has opened a new window on teaching physics in academia. Frontal lectures have left space for online teaching, teachers have been faced with a new way of spreading knowledge, adapting contents and modalities of their courses. Students have faced up with a new way of learning physics, which relies on free access to materials and their informatics knowledge. We decided to investigate how online didactics has influenced students’ assessments, motivation, and satisfaction in learning physics during the pandemic in 2020. The research has involved bachelor (n = 53) and master (n = 27) students of the Physics Department at the University of Cagliari (N = 80, 47 male; 33 female). The MANOVA supported significant mean differences about gender and university level with higher values for girls and master students in almost all variables investigated. The path analysis showed that student-student, student-teacher interaction, and the organization of the courses significantly influenced satisfaction and motivation in learning physics. The results of this study can be used to improve the standards of teaching in physics at the University of Cagliar