bluuebunny/crossref_metadata_2025 huggingface dataset:
The Effects of Clear Speech on American English Listener Perception of Spanish-Accented and Native English Speech
Authors: Behrman Alison , Ferguson Sarah Hargus , Flom Peter
Published: November 2020

Purpose

The purpose of this study was to explore (a) the effect of clear speech on intelligibility, ease of understanding, and accentedness in Spanish-accented and native English speakers of American English and (b) the extent to which accentedness predicts intelligibility and ease of understanding.

Method

Ten adult talkers, five native English speakers and five native Spanish speakers, read aloud 28 short sentences in habitual speech and clear speech. One hundred monolingual English listeners transcribed what they heard and rated ease of understanding using a 10-cm visual analog scale while hearing the sentences in six-talker babble. Then, accentedness (using a visual analog scale) was judged in quiet. Intelligibility was calculated as the number of words transcribed correctly for each sentence. Ten listeners with significant exposure to Spanish-accented speech rated each talker. Each listener heard only one talker, rating half the sentences in clear speech and half in habitual speech, so that no listener heard a sentence more than once for the intelligibility and ease of understanding assessments.

Results

Clear speech improved intelligibility and ease of understanding for both language groups, with greater improvement in nonnative talkers. Clear speech did not alter accentedness in nonnative talkers but did increase accentedness in native talkers. Accentedness did not strongly predict intelligibility but did predict ease of understanding, although the relationship was nonlinear.

Conclusions

Listeners who are experienced in hearing Spanish-accented speech derive significant benefits from clear speech. The difference in the predictability of accentedness for intelligibility and ease of understanding contributes further evidence to the difference in underlying cognitive processes for these two variables.
The RR Lyrae projected density distribution from the Galactic centre to the halo
Authors: Navarro María Gabriela , Minniti Dante , Capuzzo-Dolcetta Roberto , Alonso-García Javier , Contreras Ramos Rodrigo , Majaess Daniel , Ripepi Vincenzo
Published: November 2020

The projected density distribution of type ab RR Lyrae (RRab) stars was characterised from the innermost regions of the Milky Way to the halo, with the aim of placing constraints on the Galaxy’s evolution. The compiled sample (NRRab = 64 850) stems from fundamental mode RR Lyrae variables identified by the VVV, OGLE, and Gaia surveys. The distribution is well fitted by three power laws over three radial intervals. In the innermost region (R < 2.2°) the distribution follows ΣRRab[1] ∝ R−0.94 ± 0.051, while in the external region the distribution adheres to ΣRRab[2] ∝ R−1.50 ± 0.019 for 2.2° < R <  8.0° and ΣRRab[3] ∝ R−2.43 ± 0.043 for 8.0° < R < 30.0°. Conversely, the cumulative distribution of red clump (RC) giants exhibits a more concentrated distribution in the mean, but in the central R < 2.2° the RRab population is more peaked, whereas globular clusters (GCs) follow a density power law (ΣGCs ∝ R−1.59 ± 0.060 for R < 30.0°) similar to that of RRab stars, especially when considering a more metal-poor subsample ([Fe/H] < −1.1 dex). The main conclusion emerging from the analysis is that the RRab distribution favours the star cluster infall and merger scenario for creating an important fraction (> 18%) of the central Galactic region. The radii containing half of the populations (half populations radii) are RH RRab = 6.8° (0.99 kpc), RH RC = 4.2° (0.61 kpc), and RH GCs = 11.9° (1.75 kpc) for the RRab stars, RC giants, and GCs, respectively. Finally, merely ∼1% of the stars have been actually discovered in the innermost region (R <  35 pc) out of the expected (based on our considerations) total number of RRab therein: N ∼ 1562. That deficit will be substantially ameliorated with future space missions like the Nancy Grace Roman Space Telescope (formerly WFIRST).
Model selection for fish growth patterns based on a Bayesian approach: A case study of five freshwater fish species
Authors: Zhang Kui , Zhang Jun , Li Jiajun , Liao Baochao
Published: November 2020

Selecting an appropriate growth pattern for individual fish is a meaningful but complex topic in fishery research. We model four growth functions − the commonly used von Bertalanffy growth model (VBGM), and the Gompertz growth model (GGM), Schnute–Richards growth model (SRGM), and generalized VBGM (G-VBGM) − to examine possible growth patterns. Mean total length-at-age fish datasets for five commercial fish species (yellow perchPerca flavescens, walleyeSander vitreus, northern pikeEsox lucius, largemouth bassMicropterus salmoidesand lake herringCoregonus artedi) from North American freshwater ecosystems, were analyzed. Using a Markov chain Monte Carlo (MCMC) algorithm, we structured four models combining informative priors of model parameters. It is the first time that deviance information criterion (DIC) and leave-one-out cross-validation (LOOCV) were combined to select the best growth model. During the model-selection process, the smooth LOOCV error successfully followed the trend of the LOOCV error, although there were difference in the curve shapes. Values of scale reduction factor (SRF) for all four models indicated convergence, ranging 1.02–1.06, below the 1.2 threshold. The GGM was selected forC. artedi, and the G-VBGM for the other four species. Our approach provided a robust process in model-selection uncertainty analysis, with the G-VBGM having the best prediction ability among our datasets.
A novel ANN-based method for simultaneous estimation of thermophysical properties using experimental photothermal data
Authors: Reoyo-Prats Reine , Grieu Stéphane , Faugeroux Olivier , Claudet Bernard
Published: November 2020

In this paper, a novel artificial neural network (ANN) based method dedicated to simultaneously estimating thermal conductivity and thermal diffusivity of CSP (concentrating solar power) plant receiver materials is presented. By monitoring the evolution of these two correlated thermophysical properties during aging cycles, CSP plants' cost efficiency could be maintained. The proposed method is based on the processing of experimental photothermal data using classification and estimation networks. All the networks are feedforward ANN trained with supervised learning algorithms. A pseudo random binary signal (PRBS) is used as excitation and the impact on performance of both the photothermal response length, which is used as model input, and the number of training examples has been evaluated. Of course, the networks' topology has been optimized, allowing the generalization ability to be controlled. Despite the lack of data, the results are promising. Mean relative errors are between 8% and 20%, and the main levers for improvement are identified. In this paper, the study deals with a large range of materials (polymers and metallic alloys).
CHEP 2019: Preface to the Proceedings
Authors: Doglioni Caterina , Jackson Paul , Kamleh Waseem , Kim Doris Y. , Silvestris Lucia , Stewart Graeme A.
Published: November 2020

The 24th International Conference on Computing in High Energy and Nuclear Physics (CHEP) took place at the Adelaide Convention Centre, Adelaide, South Australia from 4–8 November 2019. 525 registered participants took part in the conference, where there were plenary sessions as well as a wide ranging set of ten parallel tracks across all areas of work in the field and allied sciences. The conference hosted 34 plenary presentations, 370 oral presentations in parallel sessions and 131 posters.
Space-point calibration of the ALICE TPC with track residuals
Authors: Schmidt Marten Ole
Published: November 2020

In the LHC Run 3, starting in 2021, the upgraded Time Projection Chamber (TPC) of the ALICE experiment will record minimum bias Pb–Pb collisions in a continuous readout mode at an interaction rate up to 50 kHz. This corresponds to typically 4-5 overlapping collisions during the electron drift time in the detector. Despite careful tuning of the new quadruple GEM-based readout chambers, which fulfill the design requirement of an ion backflow below 1%, these conditions will lead to space-charge distortions of several centimeters that fluctuate in time. They will be corrected via a calibration procedure that uses the information of the Inner Tracking System (ITS), which is located inside, and the Transition Radiation Detector (TRD) and Time-Of-Flight system (TOF), located around the TPC, respectively. By using such a procedure the intrinsic track resolution of the TPC of a few hundred micrometers can be restored.

The required online tracking algorithm for the TRD, which is based on a Kalman filter, is presented. The procedure matches extrapolated ITS-TPC tracks to TRD space-points utilizing GPUs. Subsequently these global tracks are refitted neglecting the TPC information. The residuals of the TPC clusters to the interpolation of the refitted tracks are used to create a map of spacecharge distortions. Regular updates of the map compensate for changes in the TPC conditions. The map is applied in the final reconstruction of the data. First performance results of the tracking algorithm will be shown.
Upgrade of the KEDR detector DAQ system
Authors: Maximov Dmitriy , Talyshev Alexey , Ruban Alexander , Kozyrev Alexey
Published: November 2020

The KEDR experiment is ongoing at the VEPP-4M e+e− collider at Budker INP in Novosibirsk. The collider center of mass energy range covers a wide spectrum from 2 to 11 GeV. Most of the up-to-date statistics were taken at the lower end of the energy range around the charmonia region. Planned activities at greater energies up to the bottomonia would lead to a significant increase of event recording rates and accelerator backgrounds, thus stressing the existing DAQ and trigger systems beyond their limits. The described DAQ upgrade plan includes: the redesign of the trigger electronics using modern components to improve the trigger decision time; the development of new readout processors using ethernet connections; new software for collecting events and electronics management; high level of parallelization of data transfers and events processing; improved reliability based on readout computing cluster with redundancy. The upgraded DAQ system is going to be very flexible and could be considered as a concept prototype of the projected BINP Super Charm-Tau Factory.
Feasibility tests of RoCE v2 for LHCb event building
Authors: Krawczyk Rafał Dominik , Colombo Tommaso , Neufeld Niko , Pisani Flavio , Valat Sébastien
Published: November 2020

This paper evaluates the utilization of Remote Direct Memory Access (RDMA) over Converged Ethernet (RoCE) for the Run 3 LHCb event building at CERN. The acquisition system of the detector will collect partial data from approximately 1000 separate detector streams. The total estimated throughput equals 32 Terabits per second. Full events will be assembled for subsequent processing and data selection in the filtering farm of the online trigger. High-throughput transmissions with up to 90% links utilization will be an essential feature of the system. The data exchange mechanism must support zero-copy transmissions. In this work, the RoCE high-throughput kernel bypass Ethernet protocol is benchmarked as a potential alternative to InfiniBand. A RoCE-based event building network is presented and two implementations are considered. The former variant combined shallow-buffered and deep-buffered switches with enabled flow control. In the latter setup, only deep-buffered devices are used, where operation relied on their memory throughput and capacity. Feasibility tests were conducted with selected Ethernet switches. Memory bandwidth utilization was investigated, in comparison with InfiniBand. Relevant utilization and interoperability issues of RoCE flow control are detailed with lessons learned along the road.
Network simulation of a 40 MHz event building system for the LHCb experiment
Authors: Pisani Flavio , Colombo Tommaso , Neufeld Niko , Marconi Umberto , Krawczyk Rafal , Galli Domenico , Schwemmer Rainer , Durante Paolo
Published: November 2020

The LHCb experiment will be upgraded in 2021, and a new triggerless readout system will be implemented. In the upgraded system, both Event Building (EB) and event selection will be performed in software for every collision produced in every bunch-crossing of the LHC. In order to transport the full data rate of 32 Tb/s, we will use state of the art off-the-shelf network technologies, e.g. InfiniBand EDR. The full event building system will require around 500 nodes interconnected together via a non-blocking topology. Because of the size of the system it is challenging to test at production scale, before the actual procurement. Therefore, we resort to network simulations as a powerful tool for finding the optimal configuration. We developed an accurate low-level description of an InfiniBand based network with event building like traffic. We will present a full-scale simulation of a possible implementation of the LHCb EB network.
ATLAS Operational Monitoring Data Archival and Visualization
Authors: Soloviev Igor , Avolio Giuseppe , Kazymov Andrei , Vasile Matei
Published: November 2020

The Information Service (IS) is an integral part of the Trigger and Data Acquisition (TDAQ) system of the ATLAS experiment at the Large Hadron Collider (LHC) at CERN. The IS allows online publication of operational monitoring data, and it is used by all sub-systems and sub-detectors of the experiment to constantly monitor their hardware and software components including more than 25000 applications running on more than 3000 computers. The Persistent Back-End for the ATLAS Information System (PBEAST) service stores all raw operational monitoring data for the lifetime of the experiment and provides programming and graphical interfaces to access them including Grafana dashboards and notebooks based on the CERN SWAN platform. During the ATLAS data taking sessions (for the full LHC Run 2 period) PBEAST acquired data at an average information update rate of 200 kHz and stored 20 TB of highly compacted and compressed data per year. This paper reports how over six years PBEAST became an essential piece of the experiment operations including details of the challenging requirements, the failures and successes of the various attempted implementations, the new types of monitoring data and the results of the time-series database technology evaluations for the improvements towards LHC Run 3.
Recording and reconstructing 10 billion unbiased b hadron decays in CMS
Authors: Bainbridge Robert
Published: November 2020

The CMS experiment has recorded a high-purity sample of 10 billion unbiased b hadron decays. The CMS trigger and data acquisition systems were configured to deliver a custom data stream at an average throughput of 2 GB s−1, which was “parked” prior to reconstruction. The data stream was defined by level-1 and high level trigger algorithms that operated at peak trigger rates in excess of 50 and 5 kHz, respectively. New algorithms have been developed to reconstruct and identify electrons with high efficiency at transverse momenta as low as 0.5 GeV. The trigger strategy and electron reconstruction performance were validated with pilot processing campaigns. The accumulation and reconstruction of this data set, now complete, were delivered without significant impact on the core physics programme of CMS. This unprecedented sample provides a unique opportunity for physics analyses in the flavour sector and beyond.
The ALICE O2data quality control system
Authors: Konopka Piotr , von Haller Barthélémy
Published: November 2020

The ALICE Experiment at CERN LHC (Large Hadron Collider) is undertaking a major upgrade during LHC Long Shutdown 2 in 2019–2021. The raw data input from the ALICE detectors will then increase a hundredfold, up to 3.5 TB/s. In order to cope with such a large amount of data, a new online-offline computing system, called O2, will be deployed.

One of the key software components of the O2system will be the data Quality Control (QC) that replaces the existing online Data Quality Monitoring and offline Quality Assurance. It involves the gathering, the analysis by user-defined algorithms and the visualization of monitored data, in both the synchronous and asynchronous parts of the O2system.

This paper presents the architecture and design, as well as the latest and upcoming features, of the ALICE O2QC. The results of the extensive benchmarks which have been carried out for each component of the system are later summarized. Finally, the adoption of this tool amongst the ALICE Collaboration and the measures taken to develop, in synergy with their respective teams, efficient monitoring modules for the detectors, are discussed.
DAQExpert the service to increase CMS data-taking efficiency
Authors: Badaro Gilbert , Behrens Ulf , Branson James , Brummer Philipp , Cittolin Sergio , Da Silva-Gomes Diego , Darlea Georgiana-Lavinia , Deldicque Christian , Dobson Marc , Doualot Nicolas , Fulcher Jonathan Richard , Gigi Dominique , Gladki Maciej , Glege Frank , Golubovic Dejan , Gomez-Ceballos Guillelmo , Hegeman Jeroen , James Thomas Owen , Li Wei , Mecionis Audrius , Meijers Frans , Meschi Emilio , Mommsen Remigius K. , Mor Keyshav , Morovic Srecko , Orsini Luciano , Papakrivopoulos Ioannis , Paus Christoph , Petrucci Andrea , Pieri Marco , Rabady Dinyar , Raychino Kolyo , Racz Attila , Rodriguez-Garcia Alvaro , Sakulin Hannes , Schwick Christoph , Simelevicius Dainius , Soursos Panagiotis , Stahl Andre , Stankevicius Mantas , Suthakar Uthayanath , Vazquez-Velez Cristina , Zahid Awais , Zejdl Petr
Published: November 2020

The Data Acquisition (DAQ) system of the Compact Muon Solenoid (CMS) experiment at the LHC is a complex system responsible for the data readout, event building and recording of accepted events. Its proper functioning plays a critical role in the data-taking efficiency of the CMS experiment. In order to ensure high availability and recover promptly in the event of hardware or software failure of the subsystems, an expert system, the DAQ Expert, has been developed. It aims at improving the data taking efficiency, reducing the human error in the operations and minimising the on-call expert demand. Introduced in the beginning of 2017, it assists the shift crew and the system experts in recovering from operational faults, streamlining the post mortem analysis and, at the end of Run 2, triggering fully automatic recovery without human intervention. DAQ Expert analyses the real-time monitoring data originating from the DAQ components and the high-level trigger updated every few seconds. It pinpoints data flow problems, and recovers them automatically or after given operator approval. We analyse the CMS downtime in the 2018 run focusing on what was improved with the introduction of automated recovery; present challenges and design of encoding the expert knowledge into automated recovery jobs. Furthermore, we demonstrate the web-based, ReactJS interfaces that ensure an effective cooperation between the human operators in the control room and the automated recovery system. We report on the operational experience with automated recovery.
Track Reconstruction with PANDA at FAIR
Authors: Papenbrock Michael , Ikegami Andersson Walter , Regina Jenny
Published: November 2020

The future PANDA experiment at FAIR aims to investigate a wide range of physics processes in antiproton-proton collisions at rates of up to 20 MHz, while employing a purely software-based event filter. To educate the trigger decisions, a full event reconstruction has to be carried out in real time. This challenge is amplified when considering tracks from particles with long lifetimes and displaced decay vertices, which add to the complexity of the reconstruction algorithms. Here, we present modifications to a cellular automatonbased track finder taking detector time-stamps into account in addition to spatial information, as well as several pattern recognition methods for longitudinal track reconstrucion with PANDA’s Straw Tube Tracker.
Geant4 electromagnetic physics progress
Authors: Ivanchenko Vladimir , Bagulya Alexander , Bakr Samer , Bandieramonte Marilena , Bernard Denis , Bordage Marie-Claude , Burkhardt Helmut , Dondero Paolo , Grichine Vladimir , Guatelli Susanna , Hřivnáčová Ivana , Incerti Sebastien , Kadri Omrane , Konstantinov Dmitri , Kyriakou Ioanna , Maire Michel , Mantero Alfonso , RamosMendez Jose , Novak Mihaly , Pandola Luciano , Sakata Dousatsu , Sawkey Daren , Semeniouk Igor , Shin Wook Geun , Tran Ngoc Hoang , Urban Laszlo
Published: November 2020

The Geant4 electromagnetic (EM) physics sub-packages are a component of LHC experiment simulations. During long shutdown 2 for LHC, these packages are under intensive development and we report progress of EM physics in Geant4 versions 10.5 and 10.6, which includes faster computation, more accurate EM models, and extensions to the validation suite. New approaches are developed to simulate radiation damage for silicon vertex detectors and for configuration of multiple scattering per detector region. Improvements in user interfaces developed for low-energy and the Geant4-DNA project are used also for LHC simulation optimisation.
ROOT I/O compression improvements for HEP analysis
Authors: Shadura Oksana , Bockelman Brian Paul , Canal Philippe , Piparo Danilo , Zhang Zhe
Published: November 2020

We overview recent changes in the ROOT I/O system, enhancing it by improving its performance and interaction with other data analysis ecosystems. Both the newly introduced compression algorithms, the much faster bulk I/O data path, and a few additional techniques have the potential to significantly improve experiment’s software performance.

The need for efficient lossless data compression has grown significantly as the amount of HEP data collected, transmitted, and stored has dramatically increased over the last couple of years. While compression reduces storage space and, potentially, I/O bandwidth usage, it should not be applied blindly, because there are significant trade-offs between the increased CPU cost for reading and writing files and the reduces storage space.
BESIII Drift Chamber Tracking with Machine Learning
Authors: Zhang Yao , Yuan Ye , Ma Qiumei
Published: November 2020

The tracking efficiency and the quality for the drift chamber of the BESIII experiment is essential to the physics analysis. The tracking efficiency of the drift chamber of BESIII is high for the high momentum tracks but still have room to improve for the low momentum tracks, especially for the tracks with multiple turn. A novel way to use a convolutional network called U-Net network is represented to solve the identification of the first turn’s hits for the multiple-turn tracks.
Provision and use of GPU resources for distributed workloads via the Grid
Authors: Traynor Daniel , Froy Terry
Published: November 2020

The Queen Mary University of London WLCG Tier-2 Grid site has been providing GPU resources on the Grid since 2016. GPUs are an important modern tool to assist in data analysis. They have historically been used to accelerate computationally expensive but parallelisable workloads using frameworks such as OpenCL and CUDA. However, more recently their power in accelerating machine learning, using libraries such as TensorFlow and Coffee, has come to the fore and the demand for GPU resources has increased. Significant effort is being spent in high energy physics to investigate and use machine learning to enhance the analysis of data. GPUs may also provide part of the solution to the compute challenge of the High Luminosity LHC. The motivation for providing GPU resources via the Grid is presented. The installation and configuration of the SLURM batch system together with Compute Elements (CREAM and ARC) for use with GPUs is shown. Real world use cases are presented and the success and issues discovered are discussed.
Automatic log analysis with NLP for the CMS workflow handling
Authors: Layer Lukas , Abercrombie Daniel Robert , Bakhshiansohi Hamed , Adelman-McCarthy Jennifer , Agarwal Sharad , Hernandez Andres Vargas , Si Weinan , Vlimant Jean-Roch
Published: November 2020

The central Monte-Carlo production of the CMS experiment utilizes the WLCG infrastructure and manages daily thousands of tasks, each up to thousands of jobs. The distributed computing system is bound to sustain a certain rate of failures of various types, which are currently handled by computing operators a posteriori. Within the context of computing operations, and operation intelligence, we propose a Machine Learning technique to learn from the operators with a view to reduce the operational workload and delays. This work is in continuation of CMS work on operation intelligence to try and reach accurate predictions with Machine Learning. We present an approach to consider the log files of the workflows as regular text to leverage modern techniques from Natural Language Processing (NLP). In general, log files contain a substantial amount of text that is not human language. Therefore, different log parsing approaches are studied in order to map the log files’ words to high dimensional vectors. These vectors are then exploited as feature space to train a model that predicts the action that the operator has to take. This approach has the advantage that the information of the log files is extracted automatically and the format of the logs can be arbitrary. In this work the performance of the log file analysis with NLP is presented and compared to previous approaches.
Grid Information Systems: Past, Present and Future
Authors: Field Laurence , Schulz Markus W. , Alandes Pradillo Maria
Published: November 2020

Grid information systems enable the discovery of resources in a grid computing infrastructure and provide further information about their structure and state. The original concepts for a grid information system were defined over 20 years ago and the GLUE 2.0 information model specification was published 10 years ago. This contribution describes the current status and highlights the changes over the years. It provides an overview of today’s usage from analysing the system logs and compares this with results from over a decade ago. A critical analysis of the system is provided with lessons learned and some perspectives for the future.
CERN’s Identity and Access Management: A journey to Open Source
Authors: Aguado Corman Asier , Fernández Rodríguez Daniel , Georgiou Maria V. , Rische Julien , Schuszter Ioan Cristian , Short Hannah , Tedesco Paolo
Published: November 2020

Until recently, CERN had been considered eligible for academic pricing of Microsoft products. Now, along with many other research institutes, CERN has been disqualified from this educational programme and faces a 20 fold increase in license costs. CERN’s current Authentication and Authorization Infrastructure, dating from 2008, comprises multiple Microsoft services from the web Single-Sign-On to the Accounts Database. Replacing these core components is an opportunity to rebuild the CERN infrastructure using the latest technologies and concepts and to respond to evolving requirements of the community. It is also the appropriate moment to consider the alignment of CERN’s and the Worldwide LHC Computing Grid’s approaches to identity management, to create a more consistent environment for operators, developers and users. 2019 saw the launch of an Alpha version of CERN’s next generation Authentication and Authorization Infrastructure, focusing on free and open source products and responding to the limitations experienced by the current system. We describe the new solution and focus on key changes.
DUNE Production processing and workflow management software evaluation
Authors: Herner Kenneth
Published: November 2020

The Deep Underground Neutrino Experiment (DUNE) will be the world’s foremost neutrino detector when it begins taking data in the mid-2020s. Two prototype detectors, collectively known as ProtoDUNE, have begun taking data at CERN and have accumulated over 3 PB of raw and reconstructed data since September 2018. Particle interaction within liquid argon time projection chambers are challenging to reconstruct, and the collaboration has set up a dedicated Production Processing group to perform centralized reconstruction of the large ProtoDUNE datasets as well as to generate large-scale Monte Carlo simulation. Part of the production infrastructure includes workflow management software and monitoring tools that are necessary to efficiently submit and monitor the large and diverse set of jobs needed to meet the experiment’s goals. We will give a brief overview of DUNE and ProtoDUNE, describe the various types of jobs within the Production Processing group’s purview, and discuss the software and workflow management strategies are currently in place to meet existing demand. We will conclude with a description of our requirements in a workflow management software solution and our planned evaluation process.
Monitoring distributed computing beyond the traditional time-series histogram
Authors: Doidge M S , Love P. A. , Thornton J
Published: November 2020

In this work we describe a novel approach to monitor the operation of distributed computing services. Current monitoring tools are dominated by the use of time-series histograms showing the evolution of various metrics. These can quickly overwhelm or confuse the viewer due to the large number of similar looking graphs. We propose a supplementary approach through the sonification of real-time data streamed directly from a variety of distributed computing services. The real-time nature of this method allows operations staff to quickly detect problems and identify that a problem is still ongoing, avoiding the case of investigating an issue a-priori when it may already have been resolved. In this paper we present details of the system architecture and provide a recipe for deployment suitable for both site and experiment teams.
Erasure Coding for production in the EOS Open Storage system
Authors: Peters Andreas-Joachim , Simon Michal Kamil , Sindrilaru Elvin Alin
Published: November 2020

The storage group of CERN IT operates more than 20 individual EOS[1] storage services with a raw data storage volume of more than 340 PB. Storage space is a major cost factor in HEP computing and the planned future LHC Run 3 and 4 increase storage space demands by at least an order of magnitude.

A cost effective storage model providing durability is Erasure Coding (EC) [2]. The decommissioning of CERN’s remote computer center (Wigner/Budapest) allows a reconsideration of the currently configured dual-replica strategy where EOS provides one replica in each computer center.

EOS allows one to configure EC on a per file bases and exposes four different redundancy levels with single, dual, triple and fourfold parity to select different quality of service and variable costs.

This paper will highlight tests which have been performed to migrate files on a production instance from dual-replica to various EC profiles. It will discuss performance and operational impact, and highlight various policy scenarios to select the best file layout with respect to IO patterns, file age and file size.

We will conclude with the current status and future optimizations, an evaluation of cost savings and discuss an erasure encoded EOS setup as a possible tape storage replacement.
The eXtreme-DataCloud project solutions for data management services in distributed e-infrastructures
Authors: Cesini Daniele , Donvito Giacinto , Costantini Alessandro , Aguilar Gomez Fernando , Duma Doina Cristina , Fuhrmann Patrick , Dutka Lukasz , Viljolen Matthew , Battaglia Serena , Poireau Vincent , Dell’Agnello Luca , Keeble Oliver , Lemrani Rachid , Ohmann Christian , Marco de Lucas Jesus
Published: November 2020

The eXtreme DataCloud (XDC) project is aimed at developing data management services capable to cope with very large data resources allowing the future e-infrastructures to address the needs of the next generation extreme scale scientific experiments. Started in November 2017, XDC is combining the expertise of 8 large European research organisations. The project aims at developing scalable technologies for federating storage resources and managing data in highly distributed computing environments. The project is use case driven with a multidisciplinary approach, addressing requirements from research communities belonging to a wide range of scientific domains: Life Science, Biodiversity, Clinical Research, Astrophysics, High Energy Physics and Photon Science, that represent an indicator in terms of data management needs in Europe and worldwide. The use cases proposed by the different user communities are addressed integrating different data management services ready to manage an increasing volume of data. Different scalability and performance tests have been defined to show that the XDC services can be harmonized in different contexts and complex frameworks like the European Open Science Cloud. The use cases have been used to measure the success of the project and to prove that the developments fulfil the defined needs and satisfy the final users. The present contribution describes the results carried out from the adoption of the XDC solutions and provides a complete overview of the project achievements.
CERN Tape Archive: production status, migration from CASTOR and new features
Authors: Cano Eric , Bahyl Vladimír , Caffy Cédric , Cancio Germán , Davis Michael , Kotlyar Viktor , Leduc Julien , Lin Tao , Murray Steven
Published: November 2020

During 2019 and 2020, the CERN tape archive (CTA) will receive new data from LHC experiments and import existing data from CASTOR, which will be phased out for LHC experiments before Run 3.

This contribution will present the statuses of CTA as a service and of its integration with EOS and FTS and the data flow chains of LHC experiments.

The latest enhancements and additions to the software as well as the development outlook will be presented. With the development of the repack function, a necessary behind-the-scenes feature, CTA can now take over custodial data and handle media migration, compaction and failures. Further metadata handling optimisations allowed doubling the maximum file rate performance to 200Hz per queue.

New retrieve scheduling options are being developed at the request of experiments, with optional FIFO behaviour to ensure better control of the timing for datasets retrieve, and fair share support for competing activities within the same VO.

Support for multiple backend databases (Oracle, PostgreSQL, MySQL) have been developed at CERN and contributed by external institutes.

This contribution will also report on the challenges of and solutions for migrating data from the decades old CASTOR to CTA. The practical example of the preparation for the migration of ATLAS data will be presented.
FTS improvements for LHC Run-3 and beyond
Authors: Karavakis Edward , Manzi Andrea , Arsuaga Rios Maria , Keeble Oliver , Garcia Cabot Carles , Simon Michal , Patrascoiu Mihai , Angelogiannopoulos Aris
Published: November 2020

The File Transfer Service (FTS) developed at CERN and in production since 2014, has become a fundamental component for the LHC experiments and is tightly integrated with experiment frameworks. Starting from the beginning of 2018 with the participation to the European Commission funded project eXtreme Data Cloud (XDC) and the activities carried out in the context of the WLCG DOMA TPC and QoS working groups, a series of new developments and improvements have been planned and performed taking also into account the requirements from the experiments in preparation for the LHC Run-3. This paper provides a detailed overview of these developments; more specifically, the integration with OpenID Connect (OIDC), the QoS integration, the service scalability enhancements, the support for XRootD and HTTP Third Party Copy (TPC) transfers along with the integration with the new CERN Tape Archive (CTA) system.
Jiskefet, a bookkeeping application for ALICE
Authors: Teitsma Marten , Barosso Vasco Chibante , Boeschoten Pascal , Hendriks Patrick
Published: November 2020

A new bookkeeping system called Jiskefet is being developed for A Large Ion Collider Experiment (ALICE) during Long Shutdown 2, to be in production until the end of LHC Run 4 (2029). Jiskefet unifies two functionalities: a) gathering, storing and presenting metadata associated with the operations of the ALICE experiment and b) tracking the asynchronous processing of the physics data. It will replace the existing ALICE Electronic Logbook and AliMonitor, allowing for a technology refresh and the inclusion of new features based on the experience collected during Run 1 and Run 2. The front end leverages web technologies much in use nowadays such as TypeScript and NodeJS and is adaptive to various clients such as tablets, mobile devices and other screens. The back end includes an OpenAPI specification based REST API and a relational database. This paper will describe the organization of the work done by various student teams who work on Jiskefet in sequential and parallel semesters and how continuity is guaranteed by using guidelines on coding, documentation and development. It will also describe the current status of the development, the initial experience in detector stand-alone commissioning setups and the future plans.
Smart Caching at CMS: applying AI to XCache edge services
Authors: Spiga Daniele , Ciangottini Diego , Tracolli Mirco , Tedeschi Tommaso , Cesini Daniele , Boccali Tommaso , Poggioni Valentina , Baioletti Marco , Kuznetsov Valentin Y.
Published: November 2020

The projected Storage and Compute needs for the HL-LHC will be a factor up to 10 above what can be achieved by the evolution of current technology within a flat budget. The WLCG community is studying possible technical solutions to evolve the current computing in order to cope with the requirements; one of the main focus is resource optimization, with the ultimate aim of improving performance and efficiency, as well as simplifying and reducing operation costs. As of today the storage consolidation based on a Data Lake model is considered a good candidate for addressing HL-LHC data access challenges. The Data Lake model under evaluation can be seen as a logical system that hosts a distributed working set of analysis data. Compute power can be “close” to the lake, but also remote and thus completely external. In this context we expect data caching to play a central role as a technical solution to reduce the impact of latency and reduce network load. A geographically distributed caching layer will be functional to many satellite computing centers that might appear and disappear dynamically. In this talk we propose a system of caches, distributed at national level, describing both deployment and results of the studies made to measure the impact on the CPU efficiency. In this contribution, we also present the early results on novel caching strategy beyond the standard XRootD approach whose results will be a baseline for an AI-based smart caching system.
Development of the JUNO Conditions Data Management System
Authors: Huang Xingtao
Published: November 2020

The Jiangmen Underground Neutrino Observatory (JUNO) experiment is designed to determine the neutrino mass hierarchy and precisely measure oscillation parameters with an unprecedented energy resolution of 3% at 1 MeV. It is composed of a 20 kton liquid scintillator central detector equipped with 18000 20-inch PMTs and 25000 3-inch PMTs, a water pool with 2000 20-inch PMTs, and a top tracker. Conditions data, coming from calibration and detector monitoring, are heterogeneous, different type of conditions data has different write rates, data format and data volume. JUNO conditions data management system (JCDMS) is developed to homogeneously treat all these heterogeneous conditions data in order to provide easy management and convenient access with both Restful API and web interfaces, support good scalability and maintenance for long time running. The paper describes the status and development of JCDMS including the data model, workflows, interfaces, data caching and performance of the system.
An Information Aggregation and Analytics System for ATLAS Frontier
Authors: Formica Andrea , Ozturk Nurcan , Si Amer Millissa , Bahilo Julio Lozano , Gallas Elizabeth J , Vukotic Ilija
Published: November 2020

ATLAS event processing requires access to centralized database systems where information about calibrations, detector status and data-taking conditions are stored. This processing is done on more than 150 computing sites on a world-wide computing grid which are able to access the database using the Squid-Frontier system. Some processing workflows have been found which overload the Frontier system due to the Conditions data model currently in use, specifically because some of the Conditions data requests have been found to have a low caching efficiency. The underlying cause is that non-identical requests as far as the caching are actually retrieving a much smaller number of unique payloads. While ATLAS is undertaking an adiabatic transition during the LHC Long Shutdown 2 and Run 3 from the current COOL Conditions data model to a new data model called CREST for Run 4, it is important to identify the problematic Conditions queries with low caching efficiency and work with the detector subsystems to improve the storage of such data within the current data model. For this purpose ATLAS put together an information aggregation and analytics system. The system is based on aggregated data from the Squid-Frontier logs using the Elasticsearch technology. This paper§ describes the components of this analytics system from the server based on Flask/Celery application to the user interface and how we use Spark SQL functionalities to filter data for making plots, storing the caching efficiency results into a Elasticsearch database and finally deploying the package via a Docker container.
Bringing heterogeneity to the CMS software framework
Authors: Bocci Andrea , Dagenhart David , Innocente Vincenzo , Jones Christopher , Kortelainen Matti , Pantaleo Felice , Rovere Marco
Published: November 2020

The advent of computing resources with co-processors, for example Graphics Processing Units (GPU) or Field-Programmable Gate Arrays (FPGA), for use cases like the CMS High-Level Trigger (HLT) or data processing at leadership-class supercomputers imposes challenges for the current data processing frameworks. These challenges include developing a model for algorithms to offload their computations on the co-processors as well as keeping the traditional CPU busy doing other work. The CMS data processing framework, CMSSW, implements multithreading using the Intel Threading Building Blocks (TBB) library, that utilizes tasks as concurrent units of work. In this paper we will discuss a generic mechanism to interact effectively with non-CPU resources that has been implemented in CMSSW. In addition, configuring such a heterogeneous system is challenging. In CMSSW an application is configured with a configuration file written in the Python language. The algorithm types are part of the configuration. The challenge therefore is to unify the CPU and co-processor settings while allowing their implementations to be separate. We will explain how we solved these challenges while minimizing the necessary changes to the CMSSW framework. We will also discuss on a concrete example how algorithms would offload work to NVIDIA GPUs using directly the CUDA API.
PODIO: recent developments in the Plain Old Data EDM toolkit
Authors: Gaede Frank , Hegner Benedikt , Stewart Graeme A.
Published: November 2020

PODIO is a C++ toolkit for the creation of event data models (EDMs) with a fast and efficient I/O layer. It employs plain-old-data (POD) data structures wherever possible, while avoiding deep object-hierarchies and virtual inheritance. A lightweight layer of handle classes provides the necessary highlevel interface for the physicist. PODIO creates all EDM code from simple instructive YAML files, describing the actual EDM entities. Since its original development PODIO has been very actively used for Future Circular Collider (FCC) studies. In its original version, the underlying I/O was entirely based on the automatic streaming code generated with ROOT dictionaries. Recently two additional I/O implementations have been added. One is based on HDF5 and the other uses SIO, a simple binary I/O library provided by LCIO. We briefly introduce the main features of PODIO and then report on recent developments with a focus on performance comparisons between the available I/O implementations. We conclude with presenting recent activities on porting the well-established LCIO EDM to PODIO and the recent EDM4hep project.
Enhancements in Functionality of the Interactive Visual Explorer for ATLAS Computing Metadata
Authors: Grigorieva M.A. , Alekseev A.A. , Artamonov A.A. , Galkin T.P. , Grin D.V. , Korchuganova T.A. , Padolski S.V. , Titov M.A. , Klimentov A.A.
Published: November 2020

The development of the Interactive Visual Explorer (InVEx), a visual analytics tool for the computing metadata of the ATLAS experiment at LHC, includes research of various approaches for data handling both on server and client sides. InVEx is implemented as a web-based application which aims at the enhancing of analytical and visualization capabilities of the existing monitoring tools and facilitates the process of data analysis with the interactivity and human supervision. The current work is focused on the architecture enhancements of the InVEx application. First, we will describe the user-manageable data preparation stage for cluster analysis. Then, the Level-of-Detail approach for the interactive visual analysis will be presented. It starts with the low detailing, when all data records are grouped (by clustering algorithms or by categories) and aggregated. We provide users with means to look deeply into this data, incrementally increasing the level of detail. Finally, we demonstrate the development of data storage backend for InVEx, which is adapted for the Level-of-Detail method to keep all stages of data derivation sequence.
Lessons Learned from the Assessment of Software Defect Prediction on WLCG Software: A Study with Unlabelled Datasets and Machine Learning Techniques
Authors: Ronchieri Elisabetta , Canaparo Marco , Belgiovine Mauro , Salomoni Davide , Martelli Barbara
Published: November 2020

Software defect prediction is an activity that aims at narrowing down the most likely defect-prone software modules and helping developers and testers to prioritize inspection and testing. This activity can be addressed by using Machine Learning techniques applied to software metrics datasets that are usually unlabelled, i.e. they lack modules classification in terms of defectiveness. To overcome this limitation, in addition to the usual data pre-processing operations to manage mission values and/or to remove inconsistencies, researches have to adopt an approach to label their unlabelled software datasets. The extraction of defectiveness data to label all the instances of the datasets is an extremely time and effort consuming operation. In literature, many studies have introduced approaches to build a defect prediction models on unlabelled datasets.

In this paper, we describe the analysis of new unlabelled datasets from WLCG software, coming from HEP-related experiments and middleware, by using Machine Learning techniques. We have experimented new approaches to label the various modules due to the heterogeneity of software metrics distribution. We discuss a number of lessons learned from conducting these activities, what has worked, what has not and how our research can be improved.