Explainable AI (XAI) is a rapidly evolving field that aims to improve transparency and trustworthiness of AI systems to humans. One of the unsolved challenges in XAI is estimating the performance of these explanation methods for neural networks, which has resulted in numerous competing metrics with little to no indication of which one is to be preferred. In this paper, to identify the most reliable ...

Multi-person 3D mesh recovery from videos is a critical first step towards automatic perception of group behavior in virtual reality, physical therapy and beyond. However, existing approaches rely on multi-stage paradigms, where the person detection and tracking stages are performed in a multi-person setting, while temporal dynamics are only modeled for one person at a time. Consequently, their ...

We compare in-situ water volume measurements of mineral oil emulsion sampled from an oil slick in Santa Barbara, California, to acquisitions of airborne UAVSAR data acquired in June 2022. Estimating the water-to-oil fraction using the UAVSAR imagery, we find that low SNR in the co- and cross-polarimetric channels limits this capability above a certain oil-to-water volumetric threshold. Higher SNR ...

Dust particles are one of the major constituents of the interplanetary medium in our solar system. They are presumably formed by fragmentation processes of meteoroids and for a certain range of sizes and parameters the dust particles move radially outwards from the Sun. The Radio plasma Wave (RPW) instrument on Solar Orbiter can measure dust particles that impact the spacecraft. By measuring the ...

As renewable energy sources offshore wind energy develop quickly, countries like Norway with long coastlines are exploring their potential. However, the diverse wind resources across different regions of Norway present challenges for study for effective utilization of offshore wind energy. This study proposes a novel method that utilizes transfer learning techniques to analyse the resource differences ...

Because of a spring predictability barrier, the seasonal forecast skill of Arctic summer sea ice is limited by the availability of melt-season sea ice thickness (SIT) observations. The first year-round SIT observations, retrieved from CryoSat-2 from 2011 to 2020, are assimilated into the GFDL ocean–sea ice model. The model's SIT anomaly field is brought into significantly better agreement with the ...

Icebergs release cold, fresh meltwater and terrigenous nutrients as they drift and melt, influencing the local ocean properties, encouraging sea ice formation and biological production. To locate and quantify the fresh water flux from Antarctic icebergs, changes in their area and thickness have to be monitored along their trajectories. While the locations of large icebergs are operationally tracked ...

Using a large data set of ground-based GNSS scintillation observations coupled with in situ particle detector data, we perform a statistical analysis of both the input energy flux from precipitating particles, and the observed occurrence of density irregularities in the northern hemisphere cusp. By examining trends in the two data sets relating to geomagnetic activity, we conclude that observations ...

Deep learning has the potential to revolutionize medical practice by automating and performing important tasks like detecting and delineating the size and locations of cancers in medical images. However, most deep learning models rely on augmentation techniques that treat medical images as natural images. For contrast-enhanced Computed Tomography (CT) images in particular, the signals producing the ...

One of the last remaining floating tongues of the Greenland ice sheet (GrIS), the Petermann Glacier ice shelf (PGIS), is seasonally shielded from warm Atlantic water (AW) by the formation of sea ice arches in the Nares Strait. However, continued decline of the Arctic sea ice extent and thickness suggests that arch formation is likely to become anomalous, necessitating an investigation into ...