This paper presents a three-stage approach for the automated analysis of close-range optical images containing ice objects. The proposed system is based on an ensemble of deep learning models and conditional random field postprocessing. The following surface ice formations were considered: Icebergs, Deformed ice, Level ice, Broken ice, Ice floes, Floebergs, Floebits, Pancake ice, and Brash ice. Additionally, five non-surface ice categories were considered: Sky, Open water, Shore, Underwater ice, and Melt ponds. To find input parameters for the approach, the performance of 12 different neural network architectures was explored and evaluated using a 5-fold cross-validation scheme. The best performance was achieved using an ensemble of models having pyramid pooling layers (PSPNet, PSPDenseNet, DeepLabV3+, and UPerNet) and convolutional conditional random field postprocessing with a mean intersection over union score of 0.799, and this outperformed the best single-model approach. The results of this study show that when per-class performance was considered, the Sky was the easiest class to predict, followed by Deformed ice and Open water. Melt pond was the most challenging class to predict. Furthermore, we have extensively explored the strengths and weaknesses of our approach and, in the process, discovered the types of scenes that pose a more significant challenge to the underlying neural networks. When coupled with optical sensors and AIS, the proposed approach can serve as a supplementary source of large-scale ‘ground truth’ data for validation of satellite-based sea-ice products. We have provided an implementation of the approach here.

Ship collisions with floating ice in which the ship sustains damage are in the shared-energy regime – both the ice and the ship dissipate energy through inelastic deformations. The physics of these events are rarely studied. Experience on the conditions of shared-energy collision tests is limited. The aim of this paper is to present an experimental study on a scenario where the impacted structure undergoes permanent deformations together with ice failure. The paper describes a series of laboratory-scale impact tests of freshwater granular ice blocks against stiffened steel panels, and presents the analysis of the main test results and lessons-learned.

Convolutional neural networks (CNNs) have been shown to be excellent at performing image analysis tasks in recent years. Even so, ice object classification using close-range optical images is an area where their use has barely been touched upon, and how well CNNs perform this classification task is still an open question, especially in the challenging visual conditions often found in the High Arctic. The present study explores the use of CNNs for such ice object classification, including analysis of how visual distortion of optical images impacts their performance and comparisons to human experts and novices. To account for the model’s tendency to predict the presence of very few classes for any given image, the use of a loss-weighting scheme pushing a model towards predicting a higher number of classes is proposed. The results of this study show that on clean images, given the class definitions and labeling scheme used, the networks perform better than some humans. At least for some classes of ice objects, the results indicate that the network learned meaningful features. However, the results also indicate that humans are much better at adapting to new visual conditions than neural networks.

Using next-generation immunoglobulin (IGH) sequencing and flow cytometry, we characterized the composition, diversity and dynamics of non-malignant B cells in patients undergoing treatment with the Bruton tyrosine kinase (BTK) inhibitor ibrutinib or chemo-immunotherapy with fludarabine, cyclophosphamide, and rituximab (FCR). During ibrutinib therapy, non-malignant B cell numbers declined, but patients maintained stable IGH diversity and constant fractions of IGH-mutated B cells. This indicates partial preservation of antigen-experienced B cells during ibrutinib therapy, but impaired replenishment of the normal B cell pool with naïve B cells. In contrast, after FCR we noted a recovery of normal B cells with a marked predominance of B cells with unmutated IGH. This pattern is compatible with a deletion of pre-existing antigen-experienced B cells followed by repertoire renewal with antigen-naïve B cells. These opposite patterns in B cell dynamics may result in different responses towards neoantigens versus recall antigens, which need to be further defined.

Ship-based sea ice analysis algorithms rely on optical images captured in optimal weather conditions with high visibility. However, Arctic imagery is often affected by weather-related degradation due to haze, snow, and rain, impacting the efficacy of deep learning tasks for sea ice analysis, such as segmentation and classification. This paper introduces and evaluates two strategies to address weather-induced degradation in optical sea ice images (RGB). Strategy 1 employs a two-step pipeline: first, removal of weather degradation using deep learning based de-weathering algorithms, then analysis of images as a part of sea ice segmentation/classification tasks. Strategy 2 proposes a ’weather as augmentation’ training approach to create all-in-one weather-resilient segmentation and classification models. Furthermore, we introduce the first open-source ice image dataset (SeaIceWeather) with paired images - one clean and one weather-degraded. Such a dataset allows for training and validation of supervised deep learning based de-weathering algorithms. Using this dataset, we show that the proposed strategies are effective against weather-degraded images, achieving parity with the segmentation and classification performance on clean images. Additionally, we demonstrate de-weathering models capable of removing degradations due to four different weather conditions, including rain, haze, snow, and raindrops on a camera lens with a single set of weights. The presented strategies lay the foundation for robust ship-borne sea ice analysis systems resilient to adverse weather conditions. Furthermore, we hope that the dataset introduced in this study ignites further interest in the analysis of weather-degraded sea ice images. The dataset can be downloaded from our project web page https://sites.google.com/view/seaiceweather/home.