MIPAS IMK/IAA CFC-11 (CCl3F) and CFC-12 (CCl2F2) measurements: accuracy, precision and long-term stability

Eckert, E. et. al.Profiles of CFC-11 (CCl3F) and CFC-12 (CCl2F2) of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) aboard the European satellite Envisat have been retrieved from versions MIPAS/4.61 to MIPAS/ 4.62 and MIPAS/5.02 to MIPAS/5.06 level-1b data using the scientific level-2 processor run by Karlsruhe Institute of Technology (KIT), Institute of Meteorology and Climate Research (IMK) and Consejo Superior de Investigaciones Científicas (CSIC), Instituto de Astrofísica de Andalucía (IAA). These profiles have been compared to measurements taken by the balloon-borne cryosampler, Mark IV (MkIV) and MIPAS-Balloon (MIPAS-B), the airborne MIPAS-STRatospheric aircraft (MIPAS-STR), the satellite-borne Atmospheric Chemistry Experiment Fourier transform spectrometer (ACE-FTS) and the High Resolution Dynamic Limb Sounder (HIRDLS), as well as the groundbased Halocarbon and other Atmospheric Trace Species (HATS) network for the reduced spectral resolution period (RR: January 2005-April 2012) of MIPAS. ACE-FTS, MkIV and HATS also provide measurements during the high spectral resolution period (full resolution, FR: July 2002-March 2004) and were used to validate MIPAS CFC-11 and CFC-12 products during that time, as well as profiles from the Improved Limb Atmospheric Spectrometer, ILAS-II. In general, we find that MIPAS shows slightly higher values for CFC-11 at the lower end of the profiles (below ∼15 km) and in a comparison of HATS ground-based data and MIPAS measurements at 3 km below the tropopause. Differences range from approximately 10 to 50 pptv (∼5-20 %) during the RR period. In general, differences are slightly smaller for the FR period. An indication of a slight high bias at the lower end of the profile exists for CFC-12 as well, but this bias is far less pronounced than for CFC-11 and is not as obvious in the relative differences between MIPAS and any of the comparison instruments. Differences at the lower end of the profile (below ∼15 km) and in the comparison of HATS and MIPAS measurements taken at 3 km below the tropopause mainly stay within 10-50 pptv (corresponding to ∼2-10%for CFCPublished 12) for the RR and the FR period. Between ∼15 and 30 km, most comparisons agree within 10-20 pptv (10-20 %), apart from ILAS-II, which shows large differences above ∼17 km. Overall, relative differences are usually smaller for CFC-12 than for CFC-11. For both species - CFC-11 and CFC-12 - we find that differences at the lower end of the profile tend to be larger at higher latitudes than in tropical and subtropical regions. In addition, MIPAS profiles have a maximum in their mixing ratio around the tropopause, which is most obvious in tropical mean profiles. Comparisons of the standard deviation in a quiescent atmosphere (polar summer) show that only the CFC-12 FR error budget can fully explain the observed variability, while for the other products (CFC-11 FR and RR and CFC-12 RR) only two-thirds to three-quarters can be explained. Investigations regarding the temporal stability show very small negative drifts in MIPAS CFC-11 measurements. These instrument drifts vary between ∼1 and 3% decade-1. For CFC-12, the drifts are also negative and close to zero up to ∼30 km. Above that altitude, larger drifts of up to ∼50% decade-1 appear which are negative up to ∼35 km and positive, but of a similar magnitude, above. © Author(s) 2016.IMK data analysis was supported by DLR under contract number 50EE0901. MIPAS level 1B data were provided by ESA. We acknowledge support by Deutsche Forschungsgemeinschaft and Open Access Publishing Fund of Karlsruhe Institute of Technology. The Atmospheric Chemistry Experiment (ACE), also known as SCISAT, is a Canadian-led mission mainly supported by the Canadian Space Agency and the Natural Sciences and Engineering Research Council of Canada. Work at the Jet Propulsion Laboratory, California Institute of Technology, was carried out under contract with the National Aeronautics and Space Administration. Data collection and analysis of MIPAS-STR data used here were supported by the EU-project RECONCILE (grant no. 15 226365-FP7-ENV-2008-1) and the BMBF-project ENVIVAL-Life (DLR grant no. 50EE0841).Peer reviewe

Releasing Graph Neural Networks with Differential Privacy Guarantees

With the increasing popularity of graph neural networks (GNNs) in several sensitive applications like healthcare and medicine, concerns have been raised over the privacy aspects of trained GNNs. More notably, GNNs are vulnerable to privacy attacks, such as membership inference attacks, even if only black-box access to the trained model is granted. We propose PrivGNN, a privacy-preserving framework for releasing GNN models in a centralized setting. Assuming an access to a public unlabeled graph, PrivGNN provides a framework to release GNN models trained explicitly on public data along with knowledge obtained from the private data in a privacy preserving manner. PrivGNN combines the knowledge-distillation framework with the two noise mechanisms, random subsampling, and noisy labeling, to ensure rigorous privacy guarantees. We theoretically analyze our approach in the Renyi differential privacy framework. Besides, we show the solid experimental performance of our method compared to several baselines adapted for graph-structured data. Our code is available at https://github.com/iyempissy/privGnn.Comment: Published in TMLR 202

Development and Validation of Targeted Next-Generation Sequencing Panels for Detection of Germline Variants in Inherited Diseases.

Context.-The number of targeted next-generation sequencing (NGS) panels for genetic diseases offered by clinical laboratories is rapidly increasing. Before an NGS-based test is implemented in a clinical laboratory, appropriate validation studies are needed to determine the performance characteristics of the test. Objective.-To provide examples of assay design and validation of targeted NGS gene panels for the detection of germline variants associated with inherited disorders. Data Sources.-The approaches used by 2 clinical laboratories for the development and validation of targeted NGS gene panels are described. Important design and validation considerations are examined. Conclusions.-Clinical laboratories must validate performance specifications of each test prior to implementation. Test design specifications and validation data are provided, outlining important steps in validation of targeted NGS panels by clinical diagnostic laboratories

Private Graph Extraction via Feature Explanations

Privacy and interpretability are two important ingredients for achieving trustworthy machine learning. We study the interplay of these two aspects in graph machine learning through graph reconstruction attacks. The goal of the adversary here is to reconstruct the graph structure of the training data given access to model explanations. Based on the different kinds of auxiliary information available to the adversary, we propose several graph reconstruction attacks. We show that additional knowledge of post-hoc feature explanations substantially increases the success rate of these attacks. Further, we investigate in detail the differences between attack performance with respect to three different classes of explanation methods for graph neural networks: gradient-based, perturbation-based, and surrogate model-based methods. While gradient-based explanations reveal the most in terms of the graph structure, we find that these explanations do not always score high in utility. For the other two classes of explanations, privacy leakage increases with an increase in explanation utility. Finally, we propose a defense based on a randomized response mechanism for releasing the explanations, which substantially reduces the attack success rate. Our code is available at https://github.com/iyempissy/graph-stealing-attacks-with-explanationComment: Accepted at PETS 202