A Worldwide Production Grid Service Built on EGEE and OSG Infrastructures â Lessons Learnt and Long-term Requirements

Using the Grid Infrastructures provided by EGEE, OSG and others, a worldwide production service has been built that provides the computing and storage needs for the 4 main physics collaborations at CERN's Large Hadron Collider (LHC). The large number of users, their geographical distribution and the very high service availability requirements make this experience of Grid usage worth studying for the sake of a solid and scalable future operation. This service must cater for the needs of thousands of physicists in hundreds of institutes in tens of countries. A 24x7 service with availability of up to 99% is required with major service responsibilities at each of some ten "Tier1" and of the order of one hundred "Tier2" sites. Such a service - which has been operating for some 2 years and will be required for at least an additional decade - has required significant manpower and resource investments from all concerned and is considered a major achievement in the field of Grid computing. We describe the main lessons learned in offering a production service across heterogeneous Grids as well as the requirements for long-term operation and sustainability

Mesangial cells are key contributors to the fibrotic damage seen in the lupus nephritis glomerulus.

Background: Lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus patients. Mesangial cells (MCs) comprise a third of the glomerular cells and are key contributors to fibrotic changes within the kidney. This project aims to identify the roles of MCs in an in vitro model of LN. Methods: Conditionally immortalised MCs were treated with pro-inflammatory cytokines or with patient sera in an in vitro model of LN and assessed for their roles in inflammation and fibrosis. Results: MCs were shown to produce pro-inflammatory cytokines in response to a model of the inflammatory environment in LN. Further the cells expressed increased levels of mRNA for extracellular matrix (ECM) proteins (COL1A1, COL1A2, COL4A1 and LAMB1), matrix metalloproteinase enzymes (MMP9) and tissue inhibitors of matrix metalloproteinases (TIMP1). Treatment of MCs with serum from patients with active LN was able to induce a similar, albeit milder phenotype. Treatment of MCs with cytokines or patient sera was able to induce secretion of TGF-β1, a known inducer of fibrotic changes. Inhibition of TGF-β1 actions through SB-431542 (an activin A receptor type II-like kinase (ALK5) inhibitor) was able to reduce these responses suggesting that the release of TGF-β1 plays a role in these changes. Conclusions: MCs contribute to the inflammatory environment in LN by producing cytokines involved in leukocyte recruitment, activation and maturation. Further the cells remodel the ECM via protein deposition and enzymatic degradation. This occurs through the actions of TGF-β1 on its receptor, ALK5. This may represent a potential therapeutic target for treatment of LN-associated fibrosis

Models of Corporate and Bank Default and Credit Migration

This thesis presents three studies on credit risk modelling. The first study compares the real default probabilities produced by three main structural models of default, Merton model, Longstaff and Schwartz model and Leland and Toft model, to the observed real default probabilities reported by Moody's for the BBB, BB and B rated bonds. We find that none of the models can accurately predict the default probabilities in all these cases. Merton as well as Leland and Toft models underpredict default probabilities. Longstaff and Schwartz model although it produces in some cases Expected Default Frequencies (EDFs) that are close to the observed ones, it tends to overestimate the default probabilities of riskier bonds as well as the default probabilities of bonds with the same rating but higher equity volatility. We also find that structural models tend to underestimate the default probabilities in early years. The second study examines whether information from equity markets, as summarized in the distance to default measure derived from a Merton-Moody's KMV (MKMV) model, provides useful additional information over accounting variables for predicting changes in bank credit ratings. Using a dataset of 98 equity listed banks from 1997 to 2004, we find that di~tance to default measure I has additional explanatory power for modeling current ratings, or predicting credit rating changes over a 6-month or l2-month horizon, but only for the smaller sized banks. We find no evidence that changes in distance-to-default have additional explanatory power for predicting rating categories, regardless of the size ofthe bank. The third study compares two proprietary models, Moody's KMV (MKMV) and BARRA models that use information from the equity and debt market respectively for the estimation of market implied ratings that can be updated continuously. We compare the empirical performance ofthese models in terms of their ability to predict in a timely fashion changes in credit quality by employing a sample of 4594 bonds issued by 447 firms from US for a period of3 years. We find that I?-either model provides a close mapping to observed ratings. Both however are useful for prediction of credit transitions

TooManyEyes: Super-recogniser directed identification of target individuals on CCTV

For the current research, a ‘Spot the Face in a Crowd Test’ (SFCT) comprising six video clips depicting target-actors and multiple bystanders was loaded on TooManyEyes, a bespoke multi-media platform adapted here for the human-directed identification of individuals in CCTV footage. To test the utility of TooManyEyes, police ‘super-recognisers’ (SRs) who may possess exceptional face recognition ability, and police controls attempted to identify the target-actors from the SFCT. As expected, SRs correctly identified more target-actors; with higher confidence than controls. As such, the TooManyEyes system provides a useful platform for uploading tests for selecting police or security staff for CCTV review deploymen

Neural Sign Reenactor: Deep Photorealistic Sign Language Retargeting

In this paper, we introduce a neural rendering pipeline for transferring the facial expressions, head pose, and body movements of one person in a source video to another in a target video. We apply our method to the challenging case of Sign Language videos: given a source video of a sign language user, we can faithfully transfer the performed manual (e.g., handshape, palm orientation, movement, location) and non-manual (e.g., eye gaze, facial expressions, mouth patterns, head, and body movements) signs to a target video in a photo-realistic manner. Our method can be used for Sign Language Anonymization, Sign Language Production (synthesis module), as well as for reenacting other types of full body activities (dancing, acting performance, exercising, etc.). We conduct detailed qualitative and quantitative evaluations and comparisons, which demonstrate the particularly promising and realistic results that we obtain and the advantages of our method over existing approaches.Comment: Accepted at AI4CC Workshop at CVPR 202

Experimental investigation of solubility trapping in 3D printed micromodels

Understanding interfacial mass transfer during dissolution of gas in a liquid is vital for optimising large-scale carbon capture and storage operations. While the dissolution of CO2 bubbles in reservoir brine is a crucial mechanism towards safe CO2 storage, it is a process that occurs at the pore-scale and is not yet fully understood. Direct numerical simulation (DNS) models describing this type of dissolution exist and have been validated with semi-analytical models on simple cases like a rising bubble in a liquid column. However, DNS models have not been experimentally validated for more complicated scenarios such as dissolution of trapped CO2 bubbles in pore geometries where there are few experimental datasets. In this work we present an experimental and numerical study of trapping and dissolution of CO2 bubbles in 3D printed micromodel geometries. We use 3D printing technology to generate three different geometries, a single cavity geometry, a triple cavity geometry and a multiple channel geometry. In order to investigate the repeatability of the trapping and dissolution experimental results, each geometry is printed three times and three identical experiments are performed for each geometry. The experiments are performed at low capillary number representative of flow during CO2 storage applications. DNS simulations are then performed and compared with the experimental results. Our results show experimental reproducibility and consistency in terms of CO2 trapping and the CO2 dissolution process. At such low capillary number, our numerical simulator cannot model the process accurately due to parasitic currents and the strong time step constraints associated with capillary waves. However, we show that, for the single and triple cavity geometry

The human glomerular endothelial cells are potent pro-inflammatory contributors in an in vitro model of lupus nephritis

Juvenile-onset lupus nephritis (LN) affects up to 80% of juvenile-onset systemic lupus erythematosus patients (JSLE). As the exact role of human renal glomerular endothelial cells (GEnCs) in LN has not been fully elucidated, the aim of this study was to investigate their involvement in LN. Conditionally immortalised human GEnCs (ciGEnCs) were treated with pro-inflammatory cytokines known to be involved in LN pathogenesis and also with LPS. Secretion and surface expression of pro-inflammatory proteins was quantified via ELISA and flow cytometry. NF-κΒ and STAT-1 activation was investigated via immunofluorescence. Serum samples from JSLE patients and from healthy controls were used to treat ciGEnCs to determine via qRT-PCR potential changes in the mRNA levels of pro-inflammatory genes. Our results identified TNF-α, IL-1β, IL-13, IFN-γ and LPS as robust in vitro stimuli of ciGEnCs. Each of them led to significantly increased production of different pro-inflammatory proteins, including; IL-6, IL-10, MCP-1, sVCAM-1, MIP-1α, IP-10, GM-CSF, M-CSF, TNF-α, IFN-γ, VCAM-1, ICAM-1, PD-L1 and ICOS-L. TNF-α and IL-1β were shown to activate NF-κB, whilst IFN-γ activated STAT-1. JSLE patient serum promoted IL-6 and IL-1β mRNA expression. In conclusion, our in vitro model provides evidence that human GEnCs play a pivotal role in LN-associated inflammatory process

NELIOTA: The wide-field, high-cadence lunar monitoring system at the prime focus of the Kryoneri telescope

We present the technical specifications and first results of the ESA-funded, lunar monitoring project "NELIOTA" (NEO Lunar Impacts and Optical TrAnsients) at the National Observatory of Athens, which aims to determine the size-frequency distribution of small Near-Earth Objects (NEOs) via detection of impact flashes on the surface of the Moon. For the purposes of this project a twin camera instrument was specially designed and installed at the 1.2 m Kryoneri telescope utilizing the fast-frame capabilities of scientific Complementary Metal-Oxide Semiconductor detectors (sCMOS). The system provides a wide field-of-view (17.0' $\times$ 14.4') and simultaneous observations in two photometric bands (R and I), reaching limiting magnitudes of 18.7 mag in 10 sec in both bands at a 2.5 signal-to-noise level. This makes it a unique instrument that can be used for the detection of NEO impacts on the Moon, as well as for any astronomy projects that demand high-cadence multicolor observations. The wide field-of-view ensures that a large portion of the Moon is observed, while the simultaneous, high-cadence, monitoring in two photometric bands makes possible, for the first time, the determination of the temperatures of the impacts on the Moon's surface and the validation of the impact flashes from a single site. Considering the varying background level on the Moon's surface we demonstrate that the NELIOTA system can detect NEO impact flashes at a 2.5 signal-to-noise level of ~12.4 mag in the I-band and R-band for observations made at low lunar phases ~0.1. We report 31 NEO impact flashes detected during the first year of the NELIOTA campaign. The faintest flash was at 11.24 mag in the R-band (about two magnitudes fainter than ever observed before) at lunar phase 0.32. Our observations suggest a detection rate of $1.96 \times 10^{-7}$ events $km^{-2} h^{-1}$.Comment: Accepted for publication in A&