Artikler, rapporter og annet (fysikk og teknologi): Recent submissions
Now showing items 141-160 of 1057
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The Meta-Evaluation Problem in Explainable AI: Identifying Reliable Estimators with MetaQuantus
(Journal article; Tidsskriftartikkel, 2023)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 ... -
DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-15)Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. However, despite the significant progress that has been made in generic image synthesis using diffusion models, producing garment images with garment part level semantics that are well aligned with input text prompts and ... -
Coordinate Transformer: Achieving Single-stage Multi-person Mesh Recovery from Videos
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-15)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 ... -
Exploring the Potential of Sentinel-1 Ocean Wind Field Product for Near-Surface Offshore Wind Assessment in the Norwegian Arctic
(Journal article; Tidsskriftartikkel; Peer reviewed, 2024-01-24)The exploitation of offshore wind resources is a crucial step towards a clean energy future. It requires an advanced approach for high-resolution wind resource evaluations. We explored the suitability of the Sentinel-1 Level-2 OCN ocean wind field (OWI) product for offshore wind resource assessments. The SAR data were compared to in situ observations and three reanalysis products: the global ... -
A Comparison Between Oil-to-Water Volumetric Fractions Derived from L-Band Synthetic Aperture Radar Imagery and in Situ Samples
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-20)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 ... -
Hubs and Hyperspheres: Reducing Hubness and Improving Transductive Few-shot Learning with Hyperspherical Embeddings
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-08-22)Distance-based classification is frequently used in transductive few-shot learning (FSL). However, due to the high-dimensionality of image representations, FSL classifiers are prone to suffer from the hubness problem, where a few points (hubs) occur frequently in multiple nearest neighbour lists of other points. Hubness negatively impacts distance-based classification when hubs from one class appear ... -
A southern, middle, and northern Norwegian offshore wind energy resources analysis by a transfer learning method for Energy Internet
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023)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 ... -
Self-Supervised Few-Shot Learning for Ischemic Stroke Lesion Segmentation
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-09-01)Precise ischemic lesion segmentation plays an essential role in improving diagnosis and treatment planning for ischemic stroke, one of the prevalent diseases with the highest mortality rate. While numerous deep neural network approaches have recently been proposed to tackle this problem, these methods require large amounts of annotated regions during training, which can be impractical in the medical ... -
Improvements in September Arctic Sea Ice Predictions Via Assimilation of Summer CryoSat-2 Sea Ice Thickness Observations
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-15)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 ... -
A Contextually Supported Abnormality Detector for Maritime Trajectories
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-31)The analysis of maritime traffic patterns for safety and security purposes is increasing in importance and, hence, Vessel Traffic Service operators need efficient and contextualized tools for the detection of abnormal maritime behavior. Current models lack interpretability and contextualization of their predictions and are generally not quantitatively evaluated on a large annotated dataset comprising ... -
Mapping the extent of giant Antarctic icebergs with deep learning
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-09)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 ... -
ADNet++: A few-shot learning framework for multi-class medical image volume segmentation with uncertainty-guided feature refinement
(Journal article; Tidsskriftartikkel, 2023-08-02)A major barrier to applying deep segmentation models in the medical domain is their typical data-hungry nature, requiring experts to collect and label large amounts of data for training. As a reaction, prototypical few-shot segmentation (FSS) models have recently gained traction as data-efficient alternatives. Nevertheless, despite the recent progress of these models, they still have some essential ... -
GNSS Scintillations in the Cusp, and the Role of Precipitating Particle Energy Fluxes
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-16)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 ... -
Automated tilt compensation in acoustic microscopy
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-09-12)Scanning acoustic microscopy (SAM) is a potent and nondestructive technique capable of producing three-dimensional topographic and tomographic images of specimens. This is achieved by measuring the differences in time of flight (ToF) of acoustic signals emitted from various regions of the sample. The measurement accuracy of SAM strongly depends on the ToF measurement, which is affected by tilt in ... -
View it like a radiologist: Shifted windows for deep learning augmentation of CT images
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-10-23)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 ... -
On mechanisms for high-frequency pump-enhanced optical emissions at 557.7 and 630.0gnm from atomic oxygen in the high-latitude F-region ionosphere
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-13)The EISCAT (European Incoherent Scatter Scientific Association) Heating facility was used to transmit powerful high-frequency (HF) electromagnetic waves into the Fregion ionosphere to enhance optical emissions at 557.7 and 630.0 nm from atomic oxygen. The emissions were imaged by several stations of ALIS (Auroral Large Imaging System) in northern Sweden, and the EISCAT UHF incoherent scatter radar ... -
Impact of the Nares Strait sea ice arches on the long-term stability of the Petermann Glacier ice shelf
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-12)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 ... -
Discriminative multimodal learning via conditional priors in generative models
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-02)Deep generative models with latent variables have been used lately to learn joint representations and generative processes from multi-modal data, which depict an object from different viewpoints. These two learning mechanisms can, however, conflict with each other and representations can fail to embed information on the data modalities. This research studies the realistic scenario in which all ... -
Sub-ppm Methane Detection with Mid-Infrared Slot Waveguides
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-11-21)Hybrid integration of photonic chips with electronic and micromechanical circuits is projected to bring about miniature, but still highly accurate and reliable, laser spectroscopic sensors for both climate research and industrial applications. However, the sensitivity of chip-scale devices has been limited by immature and lossy photonic waveguides, weak light–analyte interaction, and etalon effects ... -
Machine learning assisted multifrequency AFM: Force model prediction
(Journal article; Tidsskriftartikkel; Peer reviewed, 2023-12-05)Multifrequency atomic force microscopy (AFM) enhances resolving power, provides extra contrast channels, and is equipped with a formalism to quantify material properties pixel by pixel. On the other hand, multifrequency AFM lacks the ability to extract and examine the profile to validate a given force model while scanning. We propose exploiting data-driven algorithms, i.e., machine learning packages, ...