Erratum to “Atmospheric effects on extensive air showers observed with the surface detector of the Pierre Auger observatory” [Astroparticle Physics 32(2) (2009), 89-99]

The affiliations were incorrectly published in the original version of this article for the following authors D. Allard, J.A. Bellido, R.M. Kieckhafer, L. Nellen, R. Pelayo, I. Rodriguez-Cabo, B.E. Smith, D. Veberic, L. Wiencke, D. Zavrtanik and M. Zavrtanik which has been corrected now.Este documento es una errata de "Atmospheric effects on extensive air showers observed with the surface detector of the Pierre Auger Observatory" (ver "Documentos relacionados").La lista completa de autores puede verse en el archivo asociado.Instituto de Física La Plat

Measurement of Aerosols at the Pierre Auger Observatory

The air fluorescence detectors (FDs) of the Pierre Auger Observatory are vital for the determination of the air shower energy scale. To compensate for variations in atmospheric conditions that affect the energy measurement, the Observatory operates an array of monitoring instruments to record hourly atmospheric conditions across the detector site, an area exceeding 3,000 square km. This paper presents results from four instruments used to characterize the aerosol component of the atmosphere: the Central Laser Facility (CLF), which provides the FDs with calibrated laser shots; the scanning backscatter lidars, which operate at three FD sites; the Aerosol Phase Function monitors (APFs), which measure the aerosol scattering cross section at two FD locations; and the Horizontal Attenuation Monitor (HAM), which measures the wavelength dependence of aerosol attenuation.Comment: Contribution to the 30th International Cosmic Ray Conference, Merida Mexico, July 2007; 4 pages, 4 figure

Predictive value of preoperative albumin-bilirubin score and other risk factors for short-term outcomes after open pancreatoduodenectomy

Background: Pancreatoduodenectomy represents a complex procedure involving extensive organ resection and multiple alimentary reconstructions. It is still associated with high morbidity, even in high-volume centres. Prediction tools including preoperative patient-related factors to preoperatively identify patients at high risk for postoperative complications could enable tailored perioperative management and improve patient outcomes. Aim: To evaluate the clinical significance of preoperative albumin-bilirubin score and other risk factors in relation to short-term postoperative outcomes in patients after open pancreatoduodenectomy. Methods: This retrospective study included all patients who underwent open pancreatic head resection (pylorus-preserving pancreatoduodenectomy or Whipple resection) for various pathologies during a five-year period (2017-2021) in a tertiary care setting at University Medical Centre Ljubljana, Slovenia and Cattinara Hospital, Trieste, Italy. Short-term postoperative outcomes, namely, postoperative complications, postoperative pancreatic fistula, reoperation, and mortality, were evaluated in association with albumin-bilirubin score and other risk factors. Multiple logistic regression models were built to identify risk factors associated with these short-term postoperative outcomes. Results: Data from 347 patients were collected. Postoperative complications, major postoperative complications, postoperative pancreatic fistula, reoperation, and mortality were observed in 52.7%, 22.2%, 23.9%, 21.3%, and 5.2% of patients, respectively. There was no statistically significant association between the albumin-bilirubin score and any of these short-term postoperative complications based on univariate analysis. When controlling for other predictor variables in a logistic regression model, soft pancreatic texture was statistically significantly associated with postoperative complications [odds ratio (OR): 2.09; 95% confidence interval (95%CI): 1.19-3.67]; male gender (OR: 2.12; 95%CI: 1.15-3.93), soft pancreatic texture (OR: 3.06; 95%CI: 1.56-5.97), and blood loss (OR: 1.07; 95%CI: 1.00-1.14) were statistically significantly associated with major postoperative complications; soft pancreatic texture was statistically significantly associated with the development of postoperative pancreatic fistula (OR: 5.11; 95%CI: 2.38-10.95); male gender (OR: 1.97; 95%CI: 1.01-3.83), soft pancreatic texture (OR: 2.95; 95%CI: 1.42-6.11), blood loss (OR: 1.08; 95%CI: 1.01-1.16), and resection due to duodenal carcinoma (OR: 6.58; 95%CI: 1.20-36.15) were statistically significantly associated with reoperation. Conclusion: The albumin-bilirubin score failed to predict short-term postoperative outcomes in patients undergoing pancreatoduodenectomy. However, other risk factors seem to influence postoperative outcomes, including male sex, soft pancreatic texture, blood loss, and resection due to duodenal carcinoma

Simulation of radiation-induced defects

Mainly due to their outstanding performance the position sensitive silicon detectors are widely used in the tracking systems of High Energy Physics experiments such as the ALICE, ATLAS, CMS and LHCb at LHC, the world's largest particle physics accelerator at CERN, Geneva. The foreseen upgrade of the LHC to its high luminosity (HL) phase (HL-LHC scheduled for 2023), will enable the use of maximal physics potential of the facility. After 10 years of operation the expected fluence will expose the tracking systems at HL-LHC to a radiation environment that is beyond the capacity of the present system design. Thus, for the required upgrade of the all-silicon central trackers extensive measurements and simulation studies for silicon sensors of different designs and materials with sufficient radiation tolerance have been initiated within the RD50 Collaboration. Supplementing measurements, simulations are in vital role for e.g. device structure optimization or predicting the electric fields and trapping in the silicon sensors. The main objective of the device simulations in the RD50 Collaboration is to develop an approach to model and predict the performance of the irradiated silicon detectors using professional software. The first successfully developed quantitative models for radiation damage, based on two effective midgap levels, are able to reproduce the experimentally observed detector characteristics like leakage current, full depletion voltage and charge collection efficiency (CCE). Recent implementations of additional traps at the SiO$_2$/Si interface or close to it have expanded the scope of the experimentally agreeing simulations to such surface properties as the interstrip resistance and capacitance, and the position dependency of CCE for strip sensors irradiated up to $\sim$$1.5\times10^{15}$ n$_{\textrm{eq}}\textrm{cm}^{-2}$.Comment: 13 pages, 11 figures, 6 tables, 24th International Workshop on Vertex Detectors, 1-5 June 2015, Santa Fe, New Mexico, US

Exploring Plain ViT Reconstruction for Multi-class Unsupervised Anomaly Detection

This work studies the recently proposed challenging and practical Multi-class Unsupervised Anomaly Detection (MUAD) task, which only requires normal images for training while simultaneously testing both normal/anomaly images for multiple classes. Existing reconstruction-based methods typically adopt pyramid networks as encoders/decoders to obtain multi-resolution features, accompanied by elaborate sub-modules with heavier handcraft engineering designs for more precise localization. In contrast, a plain Vision Transformer (ViT) with simple architecture has been shown effective in multiple domains, which is simpler, more effective, and elegant. Following this spirit, this paper explores plain ViT architecture for MUAD. Specifically, we abstract a Meta-AD concept by inducing current reconstruction-based methods. Then, we instantiate a novel and elegant plain ViT-based symmetric ViTAD structure, effectively designed step by step from three macro and four micro perspectives. In addition, this paper reveals several interesting findings for further exploration. Finally, we propose a comprehensive and fair evaluation benchmark on eight metrics for the MUAD task. Based on a naive training recipe, ViTAD achieves state-of-the-art (SoTA) results and efficiency on the MVTec AD and VisA datasets without bells and whistles, obtaining 85.4 mAD that surpasses SoTA UniAD by +3.0, and only requiring 1.1 hours and 2.3G GPU memory to complete model training by a single V100 GPU. Source code, models, and more results are available at https://zhangzjn.github.io/projects/ViTAD

A Comprehensive Augmentation Framework for Anomaly Detection

Data augmentation methods are commonly integrated into the training of anomaly detection models. Previous approaches have primarily focused on replicating real-world anomalies or enhancing diversity, without considering that the standard of anomaly varies across different classes, potentially leading to a biased training distribution.This paper analyzes crucial traits of simulated anomalies that contribute to the training of reconstructive networks and condenses them into several methods, thus creating a comprehensive framework by selectively utilizing appropriate combinations.Furthermore, we integrate this framework with a reconstruction-based approach and concurrently propose a split training strategy that alleviates the issue of overfitting while avoiding introducing interference to the reconstruction process. The evaluations conducted on the MVTec anomaly detection dataset demonstrate that our method outperforms the previous state-of-the-art approach, particularly in terms of object classes. To evaluate generalizability, we generate a simulated dataset comprising anomalies with diverse characteristics since the original test samples only include specific types of anomalies and may lead to biased evaluations. Experimental results demonstrate that our approach exhibits promising potential for generalizing effectively to various unforeseen anomalies encountered in real-world scenarios

Measurement of the Depth of Maximum of Extensive Air Showers above 10^18 eV

We describe the measurement of the depth of maximum, Xmax, of the longitudinal development of air showers induced by cosmic rays. Almost four thousand events above 10^18 eV observed by the fluorescence detector of the Pierre Auger Observatory in coincidence with at least one surface detector station are selected for the analysis. The average shower maximum was found to evolve with energy at a rate of (106 +35/-21) g/cm^2/decade below 10^(18.24 +/- 0.05) eV and (24 +/- 3) g/cm^2/decade above this energy. The measured shower-to-shower fluctuations decrease from about 55 to 26 g/cm^2. The interpretation of these results in terms of the cosmic ray mass composition is briefly discussed.Comment: Accepted for publication by PR

Exploring the Relationship between Samples and Masks for Robust Defect Localization

Defect detection aims to detect and localize regions out of the normal distribution.Previous approaches model normality and compare it with the input to identify defective regions, potentially limiting their generalizability.This paper proposes a one-stage framework that detects defective patterns directly without the modeling process.This ability is adopted through the joint efforts of three parties: a generative adversarial network (GAN), a newly proposed scaled pattern loss, and a dynamic masked cycle-consistent auxiliary network. Explicit information that could indicate the position of defects is intentionally excluded to avoid learning any direct mapping.Experimental results on the texture class of the challenging MVTec AD dataset show that the proposed method is 2.9% higher than the SOTA methods in F1-Score, while substantially outperforming SOTA methods in generalizability

DSR -- A dual subspace re-projection network for surface anomaly detection

The state-of-the-art in discriminative unsupervised surface anomaly detection relies on external datasets for synthesizing anomaly-augmented training images. Such approaches are prone to failure on near-in-distribution anomalies since these are difficult to be synthesized realistically due to their similarity to anomaly-free regions. We propose an architecture based on quantized feature space representation with dual decoders, DSR, that avoids the image-level anomaly synthesis requirement. Without making any assumptions about the visual properties of anomalies, DSR generates the anomalies at the feature level by sampling the learned quantized feature space, which allows a controlled generation of near-in-distribution anomalies. DSR achieves state-of-the-art results on the KSDD2 and MVTec anomaly detection datasets. The experiments on the challenging real-world KSDD2 dataset show that DSR significantly outperforms other unsupervised surface anomaly detection methods, improving the previous top-performing methods by 10% AP in anomaly detection and 35% AP in anomaly localization.Comment: Accepted at ECCV202