Accurate subglacial bed topography is essential for ice-sheet modeling, yet radar observations are sparse and uneven. We propose a physics-guided residual learning framework that predicts bed thickness residuals over a BedMachine prior and reconstructs bed from the observed surface. A DeepLabV3+ decoder over a standard encoder (e.g., ResNet-50) is trained with lightweight physics and data terms: multi-scale mass conservation, flow-aligned total variation, Laplacian damping, non-negativity of thickness, a ramped prior-consistency term, and a masked Huber fit to radar picks modulated by a confidence map. To measure real-world generalization, we adopt leakagesafe block-wise hold-outs (vertical/horizontal) with safety buffers and report metrics only on held-out cores. Across two Greenland sub-regions, our approach achieves strong test accuracy (RMSE 3.05–10.54m; R2= 0.993–0.999) and high structural fidelity (SSIM ≥ 0.998, PSNR up to 52.9dB), outperforming U-Net, Attention U-Net, FPN, and a plain CNN. The residual-over-prior design, combined with physics, yields spatially coherent, physically plausible beds suitable for operational mapping under domain shift.

Mapping Greenland's subglacial topography is critical for projecting the future mass loss of the ice sheet and its contribution to global sea-level rise. However, the complex and sparse nature of observational data, particularly information about the bed topography under the ice sheet, significantly increases the uncertainty in model projections. Bed topography is traditionally measured by airborne ice-penetrating radars that measure the ice thickness directly underneath the aircraft, leaving data gaps of tens of kilometers in between flight lines. This study introduces a deep learning framework, DeepTopoNet, that integrates radar-derived ice thickness observations and BedMachine Greenland data through a novel dynamic loss-balancing mechanism. Among all efforts to reconstruct bed topography, BedMachine has emerged as one of the most widely used datasets, combining mass conservation principles and ice thickness measurements to generate high-resolution bed elevation estimates. The proposed loss function adaptively adjusts the weighting between radar and BedMachine's bed, ensuring robustness in areas with limited radar coverage while leveraging the high spatial resolution of BedMachine's bed estimates. Our approach incorporates gradient-based and trend surface features to enhance model performance and utilizes a convolutional neural network (CNN) architecture (i.e., BedTopoCNN) designed for subgrid-scale predictions. By systematically testing on the Upernavik Isstrøm) region in West Greenland, the model achieves high accuracy (MAE: 12.49 m, RMSE: 19.38 m, and R2: 0.99), outperforming baseline methods in reconstructing subglacial terrain. This work demonstrates the potential of deep learning in bridging observational gaps, providing a scalable and efficient solution to inferring subglacial topography. This framework paves the way for improved predictions of ice sheet flow and sea level rise.

The increasing threat of sea level rise due to climate change necessitates a deeper understanding of ice sheet structures. This study addresses the need for accurate ice sheet data interpretation by introducing a suite of quantitative metrics designed to validate ice sheet annotation techniques. Focusing on both manual and automated methods, including ARESELP and its modified version, MARESELP, we assess their accuracy against expert annotations. Our methodology incorporates several computer vision metrics, traditionally under-utilized in glaciological research, to evaluate the continuity and connectivity of ice layer annotations. The results demonstrate that while manual annotations provide invaluable expert insights, automated methods, particularly MARESELP, improve layer continuity and alignment with expert labels.

Ice-penetrating radar surveys have been conducted across the Greenland Ice Sheet since the 1960s, producing radargrams that measure ice thickness and detect the ice sheet’s radiostratigraphy. However, these radargrams are relatively under-explored and not yet fully annotated, mapped, or interpreted glaciologically. We aim to move towards automatic radargram annotation using deep learning-based methods. To provide a training set for these methods, we develop a two-step semi-supervised annotation (TSSA) approach that uses an existing unsupervised layer annotation (ARESELP) method and a deep learning-based segmentation approach (U-Net) to detect surface, and bottom reflectors (representing the bedrock) layers in radargrams. Here we focus on two evaluations of our approach: 1. Surface and bottom annotations; and 2. Data augmentation and transfer learning techniques for improving the performance of deep learning methods. Our study is a foundation for improving the efficacy of AI-based methods for auto-annotation of radargrams, where the training set is generated seamlessly through unsupervised learning.

Ice layers in glaciers, such as those covering Greenland and Antarctica, are deformed over time. The deformations of these layers provide a record of climate history and are useful in predicting future ice flow and ice loss. Cross sectional images of the ice can be captured by airborne radar and layers in the images then annotated by glaciologists. Recent advances in semi-automated and automated annotation allow for significantly more annotations, but the validity of these annotations is difficult to determine because ground-truth (GT) data is scarce. In this paper, we (1) propose GT-dependent and GT-independent metrics for layer annotations and (2) present results from our implementation and initial testing of GT-independent metrics, such as layer breakpoints, local layer density, spatial frequency, and layer orientation agreement.

Any person can post arbitrary strings on the bulletin board. Following publication on bulletin board, a party receives a "evidence" that the intended data were posted. The bulletin board is open to the public, which means that anyone can view its contents. The fundamental security criteria for a BB are that its contents cannot be deleted, and that no evidence of publication can be falsified. BB was commonly perceived as a trusted and publicly verifiable channel. It formed the backbone of many important protocols such as e-voting, secure multi-party computation etc. and quite often it was assumed to exist to support the execution of the main protocol. After the advent of blockchain technology, BB systems has found a strong footing regarding its implementation in a distributed manner. In this paper, we aim to provide a concise summary of the evolution of bulletin board, its implementation issues and security analysis with an emphasis on its impact to e-voting systems.

There is a growing need for empirical benchmarks that support researchers and practitioners in selecting the best machine learning technique for given prediction tasks. In this article, we consider the next event prediction task in business process predictive monitoring, and we extend our previously published benchmark by studying the impact on the performance of different encoding windows and of using ensemble schemes. The choice of whether to use ensembles and which scheme to use often depends on the type of data and classification task. While there is a general understanding that ensembles perform well in predictive monitoring of business processes, next event prediction is a task for which no other benchmarks involving ensembles are available. The proposed benchmark helps researchers to select a high-performing individual classifier or ensemble scheme given the variability at the case level of the event log under consideration. Experimental results show that choosing an optimal number of events for feature encoding is challenging, resulting in the need to consider each event log individually when selecting an optimal value. Ensemble schemes improve the performance of low-performing classifiers in this task, such as SVM, whereas high-performing classifiers, such as tree-based classifiers, are not better off when ensemble schemes are considered.