Evidential Deep Learning: Enhancing Predictive Uncertainty Estimation for Earth System Science Applications

Robust quantification of predictive uncertainty is critical for understanding factors that drive weather and climate outcomes. Ensembles provide predictive uncertainty estimates and can be decomposed physically, but both physics and machine learning ensembles are computationally expensive. Parametric deep learning can estimate uncertainty with one model by predicting the parameters of a probability distribution but do not account for epistemic uncertainty.. Evidential deep learning, a technique that extends parametric deep learning to higher-order distributions, can account for both aleatoric and epistemic uncertainty with one model. This study compares the uncertainty derived from evidential neural networks to those obtained from ensembles. Through applications of classification of winter precipitation type and regression of surface layer fluxes, we show evidential deep learning models attaining predictive accuracy rivaling standard methods, while robustly quantifying both sources of uncertainty. We evaluate the uncertainty in terms of how well the predictions are calibrated and how well the uncertainty correlates with prediction error. Analyses of uncertainty in the context of the inputs reveal sensitivities to underlying meteorological processes, facilitating interpretation of the models. The conceptual simplicity, interpretability, and computational efficiency of evidential neural networks make them highly extensible, offering a promising approach for reliable and practical uncertainty quantification in Earth system science modeling. In order to encourage broader adoption of evidential deep learning in Earth System Science, we have developed a new Python package, MILES-GUESS (https://github.com/ai2es/miles-guess), that enables users to train and evaluate both evidential and ensemble deep learning

Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer‐reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state‐of‐the‐art handbook for basic and clinical researchers.DFG, 389687267, Kompartimentalisierung, Aufrechterhaltung und Reaktivierung humaner Gedächtnis-T-Lymphozyten aus Knochenmark und peripherem BlutDFG, 80750187, SFB 841: Leberentzündungen: Infektion, Immunregulation und KonsequenzenEC/H2020/800924/EU/International Cancer Research Fellowships - 2/iCARE-2DFG, 252623821, Die Rolle von follikulären T-Helferzellen in T-Helferzell-Differenzierung, Funktion und PlastizitätDFG, 390873048, EXC 2151: ImmunoSensation2 - the immune sensory syste

Measurements of ion velocity and wave propagation in a hollow cathode plume

The mechanism responsible for the production of energetic ions in the plume of hollow cathodes for electric propulsion is still an open issue. These ions are of concern to cathode and thruster lifetime, particularly for cathodes operating at high (>20 A) discharge currents. Recent theoretical and experimental investigations suggest that there is a correlation between ion energy gain and ion acoustic turbulence. In this paper we present measurements of the evolution of the ion velocity distribution function in the near plume of a 100 A-class hollow cathode, operated in a regime in which the dominant mode is ion acoustic turbulence. Ion flow and thermal properties were related to measurements of the background plasma, fluctuation spectra, and dispersion relations obtained from an array of Langmuir probes. We found ions to flow outward from the cathode and accelerate downstream, to supersonic speeds, approximately aligned with the acoustic wave group velocity vectors. The directions of the ion flow and wave propagation were similar for a range of discharge currents and mass flow rates in the jet region of the plume. One operating condition showed a significant temperature increase, also in the direction of acoustic wave propagation, corresponding to the highest wave energy condition. These results are interpreted in the context of ion acoustic turbulence as a contributing mechanism for ion energy gain

Stakeholders’ Perspectives on Flood Risk and Vulnerability in Peru

This project is part of a larger interdisciplinary initiative to evaluate the efficacy of season-ahead flood predictions, proactive management strategies, and communication efforts in disaster planning and relief efforts. The research seeks to better understand: 1) how individuals working on flood management in Peru perceive flood-related risk and vulnerability; and 2) how these perceptions impact disaster preparedness and risk communication. This information, alongside the technical tools for hazard management, is critical for developing proactive preparation and response strategies. In partnership with the Red Cross Climate Center, a team surveyed stakeholders from across Peru, including workers in disaster management, public health, climate science, engineering, forestry, and academia. The authors compared results between two groups of stakeholders involved in different aspects of disaster management: those working in climate modelling and those involved in disaster preparation and response. Since geography frequently shapes political context, we also compared responses from stakeholders working within and outside of Lima, Peru's capital. Of the 150 potential stakeholders solicited, 56 responded, and 36 completed most of the survey online between December 2019 and January 2020. The findings discussed in this report are based on the perceptions of these respondents and should be interpreted with caution, as this was not a representative sample. They do, however, contribute to a greater understanding of how Peruvian stakeholders might view flood impacts and vulnerability, how stakeholders’ perceptions might shape disaster preparation and response, of where discord in stakeholder perceptions may exist, and of the potential barriers that may limit the implementation of early warning tools.UW2020, Wisconsin Alumni Research Foundatio

Research Data Management Services: A Primer

<p>Presentation file from the Introduction to Research Data Managment Services worskhop at the Charleston Conference 2015, co-sponsored by ALCTS and the Charleston Conference.  </p

Rethinking Social Amplification of Risk: Social Media and Zika in Three Languages

Submitted by Marcus Vinícius Silva ([email protected]) on 2019-03-15T13:22:23Z No. of bitstreams: 1 Wirz_et_al-2018-Risk_Analysis.pdf: 799374 bytes, checksum: c710acd352f60f995fcdebc801e39f05 (MD5)Approved for entry into archive by Marcus Vinícius Silva ([email protected]) on 2019-03-15T13:36:21Z (GMT) No. of bitstreams: 1 Wirz_et_al-2018-Risk_Analysis.pdf: 799374 bytes, checksum: c710acd352f60f995fcdebc801e39f05 (MD5)Made available in DSpace on 2019-03-15T13:36:21Z (GMT). No. of bitstreams: 1 Wirz_et_al-2018-Risk_Analysis.pdf: 799374 bytes, checksum: c710acd352f60f995fcdebc801e39f05 (MD5) Previous issue date: 2018University of Wisconsin-Madison, Department of Life Sciences Communication, Madison, WI, USA.University of Wisconsin-Madison, Department of Life Sciences Communication, Madison, WI, USA.University of Wisconsin-Madison, Department of Life Sciences Communication, Madison, WI, USA / Morgridge Institute for Research, Madison, WI, USA.University of Wisconsin-Madison, Department of Life Sciences Communication, Madison, WI, USA / Morgridge Institute for Research, Madison, WI, USA.University of Wisconsin-Madison, Department of Life Sciences Communication, Madison, WI, USA.Fundação Oswaldo Cruz. Instituto Nacional de Comunicação Pública da Ciência e Tecnologia. Rio de Janeiro, RJ, Brasil / Fundação Oswaldo Cruz. Casa de Oswaldo Cruz. Programa de Pós-Graduação em Divulgação da Ciência, Tecnologia e Saúde, Rio de Janeiro, RJ, Brasil.Using the Zika outbreak as a context of inquiry, this study examines how assigning blame on social media relates to the social amplification of risk framework (SARF). Past research has discussed the relationship between the SARF and traditional mass media, but the role of social media platforms in amplification or attenuation of risk perceptions remains understudied. Moreover, the communication and perceptions of Zika-related risk are not limited to discussions in English. To capture conversations in languages spoken by affected countries, this study combines data in English, Spanish, and Portuguese. To better understand the assignment of blame and perceptions of risk in new media environments, we looked at three different facets of conversations surrounding Zika on Facebook and Twitter: the prominence of blame in each language, how specific groups were discussed throughout the Zika outbreak, and the sentiment expressed about genetically engineered (GE) mosquitoes. We combined machine learning with human coding to analyze public discourse in all three languages. We found differences between languages and platforms in the amount of blame assigned to different groups. We also found more negative sentiments expressed about GE mosquitoes on Facebook than on Twitter. These meaningful differences only emerge from analyses across the three different languages and platforms, pointing to the importance of multilingual approaches for risk communication research. Specific recommendations for outbreak and risk communication practitioners are also discussed

Quantitative Content Analysis Data for Hand Labeling Road Surface Conditions in New York State Department of Transportation Camera Images

Traffic camera images from the New York State Department of Transportation (511ny.org) are used to create a hand-labeled dataset of images classified into to one of six road surface conditions: 1) severe snow, 2) snow, 3) wet, 4) dry, 5) poor visibility, or 6) obstructed. Six labelers (authors Sutter, Wirz, Przybylo, Cains, Radford, and Evans) went through a series of four labeling trials where reliability across all six labelers were assessed using the Krippendorff’s alpha (KA) metric (Krippendorff, 2007). The online tool by Dr. Freelon (Freelon, 2013; Freelon, 2010) was used to calculate reliability metrics after each trial, and the group achieved inter-coder reliability with KA of 0.888 on the 4th trial. This process is known as quantitative content analysis, and three pieces of data used in this process are shared, including: 1) a PDF of the codebook which serves as a set of rules for labeling images, 2) images from each of the four labeling trials, including the use of New York State Mesonet weather observation data (Brotzge et al., 2020), and 3) an Excel spreadsheet including the calculated inter-coder reliability metrics and other summaries used to asses reliability after each trial. The broader purpose of this work is that the six human labelers, after achieving inter-coder reliability, can then label large sets of images independently, each contributing to the creation of larger labeled dataset used for training supervised machine learning models to predict road surface conditions from camera images. The xCITE lab (xCITE, 2023) is used to store camera images from 511ny.org, and the lab provides computing resources for training machine learning models