Privacy Impact Assessments for Digital Repositories

Trustworthy data repositories ensure the security of their collections. We argue they should also ensure the security of researcher and human subject data. Here we demonstrate the use of a privacy impact assessment (PIA) to evaluate potential privacy risks to researchers using the ICPSR’s Open Badges Research Credential System as a case study. We present our workflow and discuss potential privacy risks and mitigations for those risks. [This paper is a conference pre-print presented at IDCC 2020 after lightweight peer review.]&nbsp

Strong electron correlations in the normal state of FeSe0.42Te0.58

We investigate the normal state of the '11' iron-based superconductor FeSe0.42Te0.58 by angle resolved photoemission. Our data reveal a highly renormalized quasiparticle dispersion characteristic of a strongly correlated metal. We find sheet dependent effective carrier masses between ~ 3 - 16 m_e corresponding to a mass enhancement over band structure values of m*/m_band ~ 6 - 20. This is nearly an order of magnitude higher than the renormalization reported previously for iron-arsenide superconductors of the '1111' and '122' families but fully consistent with the bulk specific heat.Comment: 5 pages, 4 figures, to appear in Phys. Rev. Let

Comparison of Artificial Intelligence based approaches to cell function prediction

Predicting Retinal Pigment Epithelium (RPE) cell functions in stem cell implants using non-invasive bright field microscopy imaging is a critical task for clinical deployment of stem cell therapies. Such cell function predictions can be carried out using Artificial Intelligence (AI) based models. In this paper we used Traditional Machine Learning (TML) and Deep Learning (DL) based AI models for cell function prediction tasks. TML models depend on feature engineering and DL models perform feature engineering automatically but have higher modeling complexity. This work aims at exploring the tradeoffs between three approaches using TML and DL based models for RPE cell function prediction from microscopy images and at understanding the accuracy relationship between pixel-, cell feature-, and implant label-level accuracies of models. Among the three compared approaches to cell function prediction, the direct approach to cell function prediction from images is slightly more accurate in comparison to indirect approaches using intermediate segmentation and/or feature engineering steps. We also evaluated accuracy variations with respect to model selections (five TML models and two DL models) and model configurations (with and without transfer learning). Finally, we quantified the relationships between segmentation accuracy and the number of samples used for training a model, segmentation accuracy and cell feature error, and cell feature error and accuracy of implant labels. We concluded that for the RPE cell data set, there is a monotonic relationship between the number of training samples and image segmentation accuracy, and between segmentation accuracy and cell feature error, but there is no such a relationship between segmentation accuracy and accuracy of RPE implant labels

Lessons to be learned by comparing integrated fisheries stock assessment models (SAMs) with integrated population models (IPMs)

AEP was partially funded by the Cooperative Institute for Climate, Ocean, & Ecosystem Studies (CICOES) under NOAA Cooperative Agreement NA15OAR4320063, Contribution No. 2023-1331.Integrated fisheries stock assessment models (SAMs) and integrated population models (IPMs) are used in biological and ecological systems to estimate abundance and demographic rates. The approaches are fundamentally very similar, but historically have been considered as separate endeavors, resulting in a loss of shared vision, practice and progress. We review the two approaches to identify similarities and differences, with a view to identifying key lessons that would benefit more generally the overarching topic of population ecology. We present a case study for each of SAM (snapper from the west coast of New Zealand) and IPM (woodchat shrikes from Germany) to highlight differences and similarities. The key differences between SAMs and IPMs appear to be the objectives and parameter estimates required to meet these objectives, the size and spatial scale of the populations, and the differing availability of various types of data. In addition, up to now, typical SAMs have been applied in aquatic habitats, while most IPMs stem from terrestrial habitats. SAMs generally aim to assess the level of sustainable exploitation of fish populations, so absolute abundance or biomass must be estimated, although some estimate only relative trends. Relative abundance is often sufficient to understand population dynamics and inform conservation actions, which is the main objective of IPMs. IPMs are often applied to small populations of conservation concern, where demographic uncertainty can be important, which is more conveniently implemented using Bayesian approaches. IPMs are typically applied at small to moderate spatial scales (1 to 104 km2), with the possibility of collecting detailed longitudinal individual data, whereas SAMs are typically applied to large, economically valuable fish stocks at very large spatial scales (104 to 106 km2) with limited possibility of collecting detailed individual data. There is a sense in which a SAM is more data- (or information-) hungry than an IPM because of its goal to estimate absolute biomass or abundance, and data at the individual level to inform demographic rates are more difficult to obtain in the (often marine) systems where most SAMs are applied. SAMs therefore require more 'tuning' or assumptions than IPMs, where the 'data speak for themselves', and consequently techniques such as data weighting and model evaluation are more nuanced for SAMs than for IPMs. SAMs would benefit from being fit to more disaggregated data to quantify spatial and individual variation and allow richer inference on demographic processes. IPMs would benefit from more attempts to estimate absolute abundance, for example by using unconditional models for capture-recapture data.Publisher PDFPeer reviewe

A transboundary transport episode of nitrogen dioxide as observed from GOME and its impact in the Alpine region

High tropospheric NO₂ amounts are occasionally detected by space-borne spectrometers above cloudy scenes. For monitoring of near-ground air pollution such data are not directly applicable because clouds shield the highly polluted planetary boundary layer (PBL). We present a method based on trajectories which implicitly estimates the additional sub-cloud NO₂ distribution in order to model concentrations at ground stations. The method is applied to a transboundary pollution transport episode which led to high NO₂ vertical tropospheric column densities (VTCs) over middle Europe observed by the Global Ozone Monitoring Experiment (GOME) instrument above clouds on 17 February 2001. The case study shows that pollution originally residing near the ground in central Germany, the Ruhr area and adjacent parts of the Netherlands and Belgium has been advected to higher tropospheric levels by a passing weather front. Combining the above-cloud NO₂ VTCs with trajectory information covering the GOME columns and including their sub-cloud part yields an estimate of the total NO₂ distribution within the tropospheric columns. The highly polluted air masses are then traced by forward trajectories starting from the GOME columns to move further to the Alpine region and their impact there is assessed. Considering ground-based in-situ measurements in the Alpine region, we conclude that for this episode, at least 50% of the NO₂ concentration recorded at the sites can be attributed to transboundary transport during the frontal passage. This study demonstrates the potential of using NO₂ VTCs from GOME detected above clouds when combined with transport modelling

Late glacial 14C ages from a floating, 1382-ring pine chronology

Author Posting. © Arizona Board of Regents on behalf of the University of Arizona, 2004. This article is posted here by permission of Dept. of Geosciences, University of Arizona for personal use, not for redistribution. The definitive version was published in Radiocarbon 46 (2004): 1203-1209.We built a floating, 1382-ring pine chronology covering the radiocarbon age interval of 12,000 to 10,650 BP. Based on the strong rise of Δ14C at the onset of the Younger Dryas (YD) and wiggle-matching of the decadal-scale Δ14C fluctuations, we can anchor the floating chronology to the Cariaco varve chronology. We observe a marine reservoir correction higher than hitherto assumed for the Cariaco site, of up to 650 yr instead of 400 yr, for the full length of the comparison interval. The tree-ring Δ14C shows several strong fluctuations of short duration (a few decades) at 13,800; 13,600; and 13,350 cal BP. The amplitude of the strong Δ14C rise at the onset of the YD is about 40‰, whereas in the marine data set the signal appears stronger due to a re-adjustment of the marine mixed-layer Δ14C towards the atmospheric level.B K and M F received funding for this work from the German Ministry of Education and Research (BMBF, DEKLIM program) and from the German Research Foundation (DFG; KU 592/29-1)

Swarm Keeping Strategies for Spacecraft under J_2 and Atmospheric Drag Perturbations

This paper presents several new open-loop guidance methods for spacecraft swarms composed of hundreds to thousands of agents with each spacecraft having modest capabilities. These methods have three main goals: preventing relative drift of the swarm, preventing collisions within the swarm, and minimizing the propellant used throughout the mission. The development of these methods progresses by eliminating drift using the Hill-Clohessy-Wiltshire equations, removing drift due to nonlinearity, and minimizing the J_2 drift. In order to verify these guidance methods, a new dynamic model for the relative motion of spacecraft is developed. These dynamics include the two main disturbances for spacecraft in Low Earth Orbit (LEO), J_2 and atmospheric drag. Using this dynamic model, numerical simulations are provided at each step to show the effectiveness of each method and to see where improvements can be made. The main result is a set of initial conditions for each spacecraft in the swarm which provides the trajectories for hundreds of collision-free orbits in the presence of J_2. Finally, a multi-burn strategy is developed in order to provide hundreds of collision-free orbits under the influence of atmospheric drag. This last method works by enforcing the initial conditions multiple times throughout the mission thereby providing collision-free trajectories for the duration of the mission