Integration of NEMO into an existing particle physics environment through virtualization

With the ever-growing amount of data collected with the experiments at the Large Hadron Collider (LHC) (Evans et al., 2008), the need for computing resources that can handle the analysis of this data is also rapidly increasing. This increase will even be amplified after upgrading to the High Luminosity LHC (Apollinari et al., 2017). High-Performance Computing (HPC) and other cluster computing resources provided by universities can be useful supplements to the resources dedicated to the experiment as part of the Worldwide LHC Computing Grid (WLCG) (Eck et al., 2005) for data analysis and production of simulated event samples. Computing resources in the WLCG are structured in four layers – so-called Tiers. The first layer comprises two Tier-0 computing centres located at CERN in Geneva, Switzerland and at the Wigner Research Centre for Physics in Budapest, Hungary. The second layer consists of thirteen Tier-1 centres, followed by 160 Tier-2 sites, which are typically universities and other scientific institutes. The final layer are Tier-3 sites which are directly used by local users. The University of Freiburg is operating a combined Tier-2/Tier-3, the ATLAS-BFG (Backofen et al., 2006). The shared HPC cluster »NEMO« at the University of Freiburg has been made available to local ATLAS (Aad et al., 2008) users through the provisioning of virtual machines incorporating the ATLAS software environment analogously to the bare metal system at the Tier-3. In addition to the provisioning of the virtual environment, the on-demand integration of these resources into the Tier-3 scheduler in a dynamic way is described. In order to provide the external NEMO resources to the user in a transparent way, an intermediate layer connecting the two batch systems is put into place. This resource scheduler monitors requirements on the user-facing system and requests resources on the backend-system

Dynamic Virtualized Deployment of Particle Physics Environments on a High Performance Computing Cluster

The NEMO High Performance Computing Cluster at the University of Freiburg has been made available to researchers of the ATLAS and CMS experiments. Users access the cluster from external machines connected to the World-wide LHC Computing Grid (WLCG). This paper describes how the full software environment of the WLCG is provided in a virtual machine image. The interplay between the schedulers for NEMO and for the external clusters is coordinated through the ROCED service. A cloud computing infrastructure is deployed at NEMO to orchestrate the simultaneous usage by bare metal and virtualized jobs. Through the setup, resources are provided to users in a transparent, automatized, and on-demand way. The performance of the virtualized environment has been evaluated for particle physics applications

Deep learning based classification of dynamic processes in time-resolved X-ray tomographic microscopy

Time-resolved X-ray tomographic microscopy is an invaluable technique to investigate dynamic processes in 3D for extended time periods. Because of the limited signal-to-noise ratio caused by the short exposure times and sparse angular sampling frequency, obtaining quantitative information through post-processing remains challenging and requires intensive manual labor. This severely limits the accessible experimental parameter space and so, prevents fully exploiting the capabilities of the dedicated time-resolved X-ray tomographic stations. Though automatic approaches, often exploiting iterative reconstruction methods, are currently being developed, the required computational costs typically remain high. Here, we propose a highly efficient reconstruction and classification pipeline (SIRT-FBP-MS-D-DIFF) that combines an algebraic filter approximation and machine learning to significantly reduce the computational time. The dynamic features are reconstructed by standard filtered back-projection with an algebraic filter to approximate iterative reconstruction quality in a computationally efficient manner. The raw reconstructions are post-processed with a trained convolutional neural network to extract the dynamic features from the low signal-to-noise ratio reconstructions in a fully automatic manner. The capabilities of the proposed pipeline are demonstrated on three different dynamic fuel cell datasets, one exploited for training and two for testing without network retraining. The proposed approach enables automatic processing of several hundreds of datasets in a single day on a single GPU node readily available at most institutions, so extending the possibilities in future dynamic X-ray tomographic investigations

Effect of a Dual-Strain Probiotic on Necrotizing Enterocolitis in Neonates with Ductal-Dependent Congenital Heart Disease: A Retrospective Cohort Study

Background: Newborns with ductal-dependent congenital heart disease (CHD) are at increased risk for developing necrotizing enterocolitis (NEC). Objectives: To investigate whether the use of dual-strain probiotics is beneficial for prevention of NEC in CHD patients, as demonstrated for premature infants. Study Design: Single-center retrospective cohort study of newborns with ductal-dependent CHD before and after implementation of oral dual-strain probiotics containing Bifidobacterium infantis and Lactobacillus acidophilus, on each day of exposure to prostaglandin E1 (PGE1). Results: Birth weight, gestational age, and distribution of heart defects were similar in both cohorts. NEC occurred in 6 of 247 (2.4%) patients without probiotics, and in 3 of 242 (1.2%) patients who received probiotics (p = 0.504). NEC-related mortality (0.4 vs. 0.4%, p = 1.000) and overall mortality (11.0 vs. 8.7%, p = 0.448) were likewise not different. PGE1 exposure was 1,788 and 2,455 days, respectively. In subgroup analysis of 152 infants with aortic arch malformations, such as coarctation of the aorta and interrupted aortic arch, we observed a significant reduction of NEC frequency (5.6 vs. 0.0%, p = 0.048). Conclusions: This is the first study to investigate the effect of a dual-strain probiotic on NEC in CHD patients. Infants with aortic arch malformations appear to benefit from dual-strain probiotics. Due to the scarcity of concurrence of ductal-dependent CHD and NEC, a clinical trial on probiotics to decrease risk of NEC in infants with ductal-dependent CHD would require several thousand infants

Integration of a heterogeneous compute resource in the ATLAS workflow

With the ever-growing amount of data collected with the experiments at the Large Hadron Collider (LHC), the need for computing resources that can handle the analysis of this data is also rapidly increasing. This increase will even be amplified after upgrading to the High Luminosity LHC [1]. High-Performance Computing (HPC) and other cluster computing resources provided by universities can be useful supplements to the resources dedicated to the experiment as part of the Worldwide LHC Computing Grid (WLCG) for data analysis and production of simulated event samples. Freiburg is operating a combined Tier2/Tier3, the ATLAS-BFG [2]. The shared HPC cluster "NEMO" at the University of Freiburg has been made available to local ATLAS [3] users through the provisioning of virtual machines incorporating the ATLAS software environment analogously to the bare metal system of the local ATLAS Tier2/Tier3 centre. In addition to the provisioning of the virtual environment, the on-demand integration of these resources into the Tier3 scheduler in a dynamic way is described. In order to provide the external NEMO resources to the user in a transparent way, an intermediate layer connecting the two batch systems is put into place. This resource scheduler monitors requirements on the user-facing system and requests resources on the backend-system

The Impact of Prematurity on Morbidity and Mortality in Newborns with Dextro-transposition of the Great Arteries

Prematurity is a risk factor for adverse outcomes after arterial switch operation in newborns with D-TGA (D-TGA). In this study, we sought to investigate the impact of prematurity on postnatal and perioperative clinical management, morbidity, and mortality during hospitalization in neonates with simple and complex D-TGA who received arterial switch operation (ASO). Monocentric retrospective analysis of 100 newborns with D-TGA. Thirteen infants (13.0%) were born premature. Preterm infants required significantly more frequent mechanical ventilation in the delivery room (69.2% vs. 34.5%, p = 0.030) and during the preoperative course (76.9% vs. 37.9%, p = 0.014). Need for inotropic support (30.8% vs. 8.0%, p = 0.035) and red blood cell transfusions (46.2% vs. 10.3%, p = 0.004) was likewise increased. Preoperative mortality (23.1% vs 0.0%, p = 0.002) was significantly increased in preterm infants, with necrotizing enterocolitis as cause of death in two of three infants. In contrast, mortality during and after surgery did not differ significantly between the two groups. Cardiopulmonary bypass times were similar in both groups (median 275 vs. 263 min, p = 0.322). After ASO, arterial lactate (34.5 vs. 21.5 mg/dL, p = 0.007), duration of mechanical ventilation (median 175 vs. 106 h, p = 0.038), and venous thrombosis (40.0% vs. 4.7%, p = 0.004) were increased in preterm, as compared to term infants. Gestational age (adjusted unit odds ratio 0.383, 95% confidence interval 0.179-0.821, p = 0.014) was independently associated with mortality. Prematurity is associated with increased perioperative morbidity and increased preoperative mortality in D-TGA patients

Fast ionic diffusion in Li2S investigated by quasielastic neutron scattering

The Li diffusion in Li2S at high temperatures was investigated by quasielastic neutron scattering techniques on a single crystal of 7Li2S. It is shown by using polarized neutrons that the scattering is almost purely incoherent. The data were analysed in terms of an extended Chudley-Elliott jump diffusion model. The Li diffusion process takes place by hopping between regular tetrahedral and interstitial octahedral sites. The mean residence times on the regular Li sites were estimated to be 17.3 ps at T=1173 K, 6.7 ps at 1273 K and 4.3 ps at 1363 K

Effects of Gas Diffusion Layer Substrates on PEFC Water Management: Part II. In Situ Liquid Water Removal via Evaporation

Desaturation of polymer electrolyte fuel cells (PEFCs) is a critical operation step for providing cell cold-start performance by minimizing residual water in the gas diffusion layers (GDLs), flow field (FF) channels, catalyst layers and membrane after cell shutdown. In this work, transient liquid water removal processes in the FF channels and GDLs are visualized and quantified by subsecond in situ X-ray tomographic microscopy (XTM), and correlated to high frequency resistance (HFR) measurements of the cell. Time-resolved desaturation profiles are analyzed for three commercially available GDLs with representative substrate dimensions. The influence of different substrates on the GDL desaturation behavior is investigated with a cluster connectivity analysis and saturation-dependent effective diffusivities are determined by numerical simulations. Characteristic drying phases are identified for the HFR curves and confirmed with XTM imaging results, providing fundamental understanding of the desaturation dynamics in the PEFCs and enabling the optimization of GDL substrates and gas purge protocols accordingly.ISSN:0013-4651ISSN:1945-711

Optimal Image Denoising for In Situ X-ray Tomographic Microscopy of Liquid Water in Gas Diffusion Layers of Polymer Electrolyte Fuel Cells

Improvements in synchrotron based operando X-ray tomographic microscopy (XTM) of polymer electrolyte fuel cells (PEFCs) have paved the way for 4D imaging studies of the water distribution in the gas diffusion layer (GDL). In order to capture the full water dynamics in 4D, a decrease of the scan time towards 0.1 s is aspired, posing significant challenges in image processing for quantitative water detection. In this work, ex situ and in situ X-ray tomographic microscopy experiments were conducted to study the influence of imaging parameters and image denoising settings on image quality and water detectability in the GDL. The image quality is quantified for scan times between 50 ms and 12.8 s at the TOMCAT beamline of the Swiss Light Source. Denoising strategies for a broad range of image qualities were identified, which enable high in situ water detectability rate of 96% at a scan time of 1.6 s and 88% at subsecond scan time as short as 0.4 s. The presented methodology can be transferred to other PEFC or similar XTM imaging setups and image processing pipelines to verify their water detection capabilities.ISSN:0013-4651ISSN:1945-711