Care during the COVID-19 pandemic hinges upon the existence of fast, safe, and highly sensitive diagnostic tools. Considering significant practical advantages of lung ultrasound (LUS) over other imaging techniques, but difficulties for doctors in pattern recognition, we aim to leverage machine learning toward guiding diagnosis from LUS. We release the largest publicly available LUS dataset for COVID-19 consisting of 202 videos from four classes (COVID-19, bacterial pneumonia, non-COVID-19 viral pneumonia and healthy controls). On this dataset, we perform an in-depth study of the value of deep learning methods for the differential diagnosis of lung pathologies. We propose a frame-based model that correctly distinguishes COVID-19 LUS videos from healthy and bacterial pneumonia data with a sensitivity of $0.90\pm 0.08$ and a specificity of $0.96\pm 0.04$. To investigate the utility of the proposed method, we employ interpretability methods for the spatio-temporal localization of pulmonary biomarkers, which are deemed useful for human-in-the-loop scenarios in a blinded study with medical experts. Aiming for robustness, we perform uncertainty estimation and demonstrate the model to recognize low-confidence situations which also improves performance. Lastly, we validated our model on an independent test dataset and report promising performance (sensitivity $0.806$, specificity $0.962$). The provided dataset facilitates the validation of related methodology in the community and the proposed framework might aid the development of a fast, accessible screening method for pulmonary diseases. Dataset and all code are publicly available at: https://github.com/BorgwardtLab/covid19_ultrasound.

Driven by foundation models, recent progress in AI and machine learning has reached unprecedented complexity. For instance, the GPT-3 language model consists of 175 billion parameters and a training-data size of 570 GB. While it has achieved remarkable performance in generating text that is difficult to distinguish from human-authored content, a single training of the model is estimated to produce over 550 metric tons of CO₂ emissions. Likewise, we see advances in GeoAI research improving large-scale prediction tasks like satellite image classification and global climate modeling, to name but a couple. While these models have not yet reached comparable complexity and emissions levels, spatio-temporal models differ from language and image-generation models in several ways that make it necessary to (re)train them more often, with potentially large implications for sustainability. While recent work in the machine learning community has started calling for greener and more energy-efficient AI alongside improvements in model accuracy, this trend has not yet reached the GeoAI community at large. In this work, we bring this issue to not only the attention of the GeoAI community but also present ethical considerations from a geographic perspective that are missing from the broader, ongoing AI-sustainability discussion. To start this discussion, we propose a framework to evaluate models from several sustainability-related angles, including energy efficiency, carbon intensity, transparency, and social implications. We encourage future AI/GeoAI work to acknowledge its environmental impact as a step towards a more resource-conscious society. Similar to the current push for reproducibility, future publications should also report the energy/carbon costs of improvements over prior work.

Pain serves vital protective functions. To fulfill these functions, a noxious stimulus might induce a percept which, in turn, induces a behavioral response. Here, we investigated an alternative view in which behavioral responses do not exclusively depend on but themselves shape perception. We tested this hypothesis in an experiment in which healthy human subjects performed a reaction time task and provided perceptual ratings of noxious and tactile stimuli. A multi-level moderated mediation analysis revealed that behavioral responses are significantly involved in the translation of a stimulus into perception. This involvement was significantly stronger for noxious than for tactile stimuli. These findings show that the influence of behavioral responses on perception is particularly strong for pain which likely reflects the utmost relevance of behavioral responses to protect the body. These observations parallel recent concepts of emotions and entail implications for the understanding and treatment of pain.

Traffic analysis is crucial for urban operations and planning, while the availability of dense urban traffic data beyond loop detectors is still scarce. We present a large-scale floating vehicle dataset of per-street segment traffic information, (), available for 10 global cities with a 15-minute resolution for collection periods ranging between 108 and 361 days in 2019–2021 and covering more than 1500 square kilometers per metropolitan area. features traffic speed information at all street levels from main arterials to local streets for Antwerp, Bangkok, Barcelona, Berlin, Chicago, Istanbul, London, Madrid, Melbourne , and Moscow . The dataset leverages the industrial-scale floating vehicle 4c data with speeds and vehicle counts provided in a privacy-preserving spatio-temporal aggregation. We detail the efficient matching approach mapping the data to the OpenStreetMap (OSM) road graph. We evaluate the dataset by comparing it with publicly available stationary vehicle detector data (for Berlin, London, and Madrid) and the Uber traffic speed dataset (for Barcelona, Berlin, and London). The comparison highlights the differences across datasets in spatio-temporal coverage and variations in the reported traffic caused by the binning method. enables novel, city-wide analysis of mobility and traffic patterns for ten major world cities, overcoming current limitations of spatially sparse vehicle detector data. The large spatial and temporal coverage offers an opportunity for joining the with other datasets, such as traffic surveys in traffic planning studies or vehicle detector data in traffic control settings.