Impact of adversarial examples on deep learning models for biomedical image segmentation

Deep learning models, which are increasingly being used in the field of medical image analysis, come with a major security risk, namely, their vulnerability to adversarial examples. Adversarial examples are carefully crafted samples that force machine learning models to make mistakes during testing time. These malicious samples have been shown to be highly effective in misguiding classification tasks. However, research on the influence of adversarial examples on segmentation is significantly lacking. Given that a large portion of medical imaging problems are effectively segmentation problems, we analyze the impact of adversarial examples on deep learning-based image segmentation models. Specifically, we expose the vulnerability of these models to adversarial examples by proposing the Adaptive Segmentation Mask Attack (ASMA). This novel algorithm makes it possible to craft targeted adversarial examples that come with (1) high intersection-over-union rates between the target adversarial mask and the prediction and (2) with perturbation that is, for the most part, invisible to the bare eye. We lay out experimental and visual evidence by showing results obtained for the ISIC skin lesion segmentation challenge and the problem of glaucoma optic disc segmentation. An implementation of this algorithm and additional examples can be found at https://github.com/utkuozbulak/adaptive-segmentation-mask-attack

Measurements of greenhouse gases and related tracers at Bialystok tall tower station in Poland

Quasi-continuous, in-situ measurements of atmospheric CO2, O2/N2, CH4, CO, N2O, and SF6 have been performed since August 2005 at the tall tower station near Bialystok, in Eastern Poland, from five heights up to 300 m. Besides the in-situ measurements, flask samples are filled approximately weekly and measured at Max-Planck Institute for Biogeochemistry for the same species and, in addition, for H2, Ar/N2 and the stable isotopes 13C and 18O in CO2. The in-situ measurement system was built based on commercially available analysers: a LiCor 7000 for CO2, a Sable Systems "Oxzilla" FC-2 for O2, and an Agilent 6890 gas chromatograph for CH4, CO, N2O and SF6. The system was optimized to run continuously with very little maintenance and to fulfill the precision requirements of the CHIOTTO project. The O2/N2 measurements in particular required special attention in terms of technical setup and quality assurance. The evaluation of the performance after more than three years of operation gave overall satisfactory results, proving that this setup is suitable for long term remote operation with little maintenance. The precision achieved for all species is within or close to the project requirements. The comparison between the in-situ and flask sample results, used to verify the accuracy of the in-situ measurements, showed no significant difference for CO2, O2/N2, CH4 and N2O, and a very small difference for SF6. The same comparison however revealed a statistically significant difference for CO, of about 6.5 ppb, for which the cause could not be fully explained. From more than three years of data, the main features at Bialystok have been characterized in terms of variability, trends, and seasonal and diurnal variations. CO2 and O2/N2 show large short term variability, and large diurnal signals during the warm seasons, which attenuate with the increase of sampling height. The trends calculated from this dataset, over the period August 2005 to December 2008, are 2.02±0.46 ppm/year for CO2 and -23.2±2.5 per meg/year for O2/N2. CH4, CO and N2O show also higher variability at the lower sampling levels, which in the case of CO is strongly seasonal. Diurnal variations in CH4, CO and N2O mole fractions can be observed during the warm season, due to the periodicity of vertical mixing combined with the diurnal cycle of anthropogenic emissions. We calculated increase rates of 10.1±4.4 ppb/year for CH4, (-8.3)±5.3 ppb/year for CO and 0.67±0.08 ppb/year for N2O. SF6 shows only few events, and generally no vertical gradients, which suggests that there are no significant local sources. A weak SF6 seasonal cycle has been detected, which most probably is due to the seasonality of atmospheric circulation. SF6 increased during the time of our measurement at an average rate of 0.29±0.01 ppt/year

In-situ measurements of oxygen, carbon monoxide and greenhouse gases from Ochsenkopf tall tower in Germany

We present 2.5 years (from June 2006 to December 2008) of in-situ measurements of CO2, O2, CH4, CO, N2O and SF6 mixing ratios sampled from 23, 90 and 163m above ground on the Ochsenkopf tower in the Fichtelgebirge range, Germany (50?0104900 N, 11?4803000 E, 1022ma.s.l.). In addition to the in-situ measurements, flask samples are taken at Ochsenkopf at approximately weekly intervals and are subsequently analysed for the mixing ratios of the same species, as well as H2, and the stable isotopes, ?13C, ?18O in CO2. The in-situ measurements of CO2 and O2 from 23m show substantial diurnal variations that are modulated by biospheric fluxes, combustion of fossil fuels, and by diurnal changes in the planetary boundary layer height. Measurements from 163m exhibit only very weak diurnal variability, as this height (1185ma.s.l.) is generally above the nocturnal boundary layer. CH4, CO, N2O and SF6 show little diurnal variation even at 23m owing to the absence of any significant diurnal change in the fluxes and the absence of any strong local sources or sinks. From the in-situ record, the seasonal cycles of the gas species have been characterized and the multi-annual trends determined. Because the record is short, the calculation of the trend is sensitive to inter-annual variations in the amplitudes of the seasonal cycles. However, for CH4 a significant change in the growth-rate was detected for 2006.5–2008.5 as compared with the global mean from 1999 to 2006 and is consistent with other recent observations of a renewed increasing global growth rate in CH4 since the beginning of 2007

Continuous measurements of greenhouse gases and atmospheric oxygen at the Namib Desert atmospheric observatory

A new coastal background site has been established for observations of greenhouse gases (GHGs) in the central Namib Desert at Gobabeb, Namibia. The location of the site was chosen to provide observations for a data-poor region in the global sampling network for GHGs. Semi-automated continuous measurements of carbon dioxide, methane, nitrous oxide, carbon monoxide, atmospheric oxygen, and basic meteorology are made at a height of 21 m a.g.l., 50 km from the coast at the northern border of the Namib Sand Sea. Atmospheric oxygen is measured with a differential fuel cell analyzer (DFCA). Carbon dioxide and methane are measured with an early-model cavity ring-down spectrometer (CRDS); nitrous oxide and carbon monoxide are measured with an off-axis integrated cavity output spectrometer (OA-ICOS). Instrument-specific water corrections are employed for both the CRDS and OA-ICOS instruments in lieu of drying. The performance and measurement uncertainties are discussed in detail. As the station is located in a remote desert environment, there are some particular challenges, namely fine dust, high diurnal temperature variability, and minimal infrastructure. The gas handling system and calibration scheme were tailored to best fit the conditions of the site. The CRDS and DFCA provide data of acceptable quality when base requirements for operation are met, specifically adequate temperature control in the laboratory and regular supply of electricity. In the case of the OA-ICOS instrument, performance is significantly improved through the implementation of a drift correction through frequent measurements of a reference cylinder

Global modelling of the total OH reactivity: investigations on the “missing” OH sink and its atmospheric implications

The hydroxyl radical (OH) plays a crucial role in the chemistry of the atmosphere as it initiates the removal of most trace gases. A number of field campaigns have observed the presence of a “missing” OH sink in a variety of regions across the planet. A comparison of direct measurements of the OH loss frequency, also known as total OH reactivity (kOH), with the sum of individual known OH sinks (obtained via the simultaneous detection of species such as volatile organic compounds and nitrogen oxides) indicates that, in some cases, up to 80 % of kOH is unaccounted for. In this work, the UM-UKCA chemistry-climate model was used to investigate the wider implications of the missing reactivity on the oxidising capacity of the atmosphere. Simulations of the present-day atmosphere were performed and the model was evaluated against an array of field measurements to verify that the known OH sinks were reproduced well, with a resulting good agreement found for most species. Following this, an additional sink was introduced to simulate the missing OH reactivity as an emission of a hypothetical molecule, X, which undergoes rapid reaction with OH. The magnitude and spatial distribution of this sink were underpinned by observations of the missing reactivity. Model runs showed that the missing reactivity accounted for on average 6 % of the total OH loss flux at the surface and up to 50 % in regions where emissions of the additional sink were high. The lifetime of the hydroxyl radical was reduced by 3 % in the boundary layer, whilst tropospheric methane lifetime increased by 2 % when the additional OH sink was included. As no OH recycling was introduced following the initial oxidation of X, these results can be interpreted as an upper limit of the effects of the missing reactivity on the oxidising capacity of the troposphere. The UM-UKCA simulations also allowed us to establish the atmospheric implications of the newly characterised reactions of peroxy radicals (RO2) with OH. Whilst the effects of this chemistry on kOH were minor, the reaction of the simplest peroxy radical, CH3O2, with OH was found to be a major sink for CH3O2 and source of HO2 over remote regions at the surface and in the free troposphere. Inclusion of this reaction in the model increased tropospheric methane lifetime by up to 3 %, depending on its product branching. Simulations based on the latest kinetic and product information showed that this reaction cannot reconcile models with observations of atmospheric methanol, in contrast to recent suggestions

The benefits of investing into improved carbon flux monitoring

Operationalizing a Global Carbon Observing and Analysis System (www.geocarbon.net) would provide a sound basis for monitoring actual carbon fluxes and thus getting quantities right when pricing carbon – be it in a cap-and-trade scheme or under a tax regime. However, such monitoring systems are expensive and—especially in times of economic weakness—budgets for science and environmental policy are under particular scrutiny. In this study, we attempt to demonstrate the magnitude of benefits of improved information about actual carbon fluxes. Such information enables better-informed policy-making and thus paves the way for a more secure investment environment when decarbonizing the energy sector. The numerical results provide a robust indication of a positive social value of improving carbon monitoring systems when compared to their cost, especially for the more ambitious climate policies

On Performance Discrepancies Across Local Homophily Levels in Graph Neural Networks

Research on GNNs has highlighted a relationship between high homophily (i.e., the tendency for nodes of a similar class to connect) and strong predictive performance in node classification. However, recent research has found the relationship to be more nuanced, demonstrating that even simple GNNs can learn in certain heterophilous settings. To bridge the gap between these findings, we revisit the assumptions made in previous works and identify that datasets are often treated as having a constant homophily level across nodes. To align closer to real-world datasets, we theoretically and empirically study the performance of GNNs when the local homophily level of a node deviates at test-time from the global homophily level of its graph. To aid our theoretical analysis, we introduce a new parameter to the preferential attachment model commonly used in homophily analysis to enable the control of local homophily levels in generated graphs, enabling a systematic empirical study on how local homophily can impact performance. We additionally perform a granular analysis on a number of real-world datasets with varying global homophily levels. Across our theoretical and empirical results, we find that (a)~ GNNs can fail to generalize to test nodes that deviate from the global homophily of a graph, (b)~ high local homophily does not necessarily confer high performance for a node, and (c)~ GNN models designed to handle heterophily are able to perform better across varying heterophily ranges irrespective of the dataset's global homophily. These findings point towards a GNN's over-reliance on the global homophily used for training and motivates the need to design GNNs that can better generalize across large local homophily ranges