Revealed: The message Norwegian PM sent Trump that sparked president's outburst saying Nobel Peace Prize snub justified Greenland land-grab The Kristi Noem photo that reveals why anti-ICE mob stormed Minnesota church as terrified child worshipper sobbed in father's arms We were $460,000 in debt. We weren't high earners and paid it off using simple but life-changing tricks... anyone can do it Idyllic city was hit by a surge in cancers and miscarriages when Trump's'beautiful baby' arrived. Joseph Gordon-Levitt was the hottest actor in Hollywood... then vanished: Unearthing family tragedy that sparked disappearance and has left'lasting' scars Mayor of gorgeous Oregon city that's home to Nike HQ explains simple reasons why it is thriving while neighboring Portland circles the drain Spanish train disaster victims flew through windows and were found hundreds of yards away, with more than 39 feared dead - as mystery over what caused'truly strange' crash grows Dark side of America's favorite vacation hotspot... where women are subjected to the most horrific sex attacks imaginable Dietitian reveals the game-changing supplements that work like Ozempic... and will super-charge your weight loss without side-effects Pierce Brosnan fans defend star's wife Keely Shaye Smith, 62, after cruel troll posts a photo of her when she met the 72-year-old Bond star alongside a recent snap as a'reminder to avoid marriage' My husband was acting odd for months. But nothing prepared me for what was hidden under the couch... undeniable proof of my worst fear John Barrowman breaks down in tears while cradling his dog's body after the beloved pet'waited until I got home' before dying peacefully in his arms Country singer Karley Scott Collins responds to rumors she's living with Keith Urban after Nicole Kidman split Scientists discover Greenland's'Achilles heel' that could force Trump to rethink his Arctic playbook MORE: I'm a Risk board game champion... Here's how Trump's tactics are a winning strategy for global domination Scientists have identified a hidden geological weakness beneath Greenland's ice sheet that could accelerate its collapse, and complicate US ambitions in the Arctic. A new study discovered a hidden layer of sediment, composed of soft dirt and sand, which has driven more of the Danish territory's glaciers to melt, break apart, and fall into the ocean.

Accurate yet efficient surrogate models are essential for large-scale simulations of partial differential equations (PDEs), particularly for uncertainty quantification (UQ) tasks that demand hundreds or thousands of evaluations. We develop a physics-inspired graph neural network (GNN) surrogate that operates directly on unstructured meshes and leverages the flexibility of graph attention. To improve both training efficiency and generalization properties of the model, we introduce a domain decomposition (DD) strategy that partitions the mesh into subdomains, trains local GNN surrogates in parallel, and aggregates their predictions. We then employ transfer learning to fine-tune models across subdomains, accelerating training and improving accuracy in data-limited settings. Applied to ice sheet simulations, our approach accurately predicts full-field velocities on high-resolution meshes, substantially reduces training time relative to training a single global surrogate model, and provides a ripe foundation for UQ objectives. Our results demonstrate that graph-based DD, combined with transfer learning, provides a scalable and reliable pathway for training GNN surrogates on massive PDE-governed systems, with broad potential for application beyond ice sheet dynamics.

Understanding the thickness and variability of internal ice layers in radar imagery is crucial for monitoring snow accumulation, assessing ice dynamics, and reducing uncertainties in climate models. Radar sensors, capable of penetrating ice, provide detailed radargram images of these internal layers. In this work, we present ST-GRIT, a spatio-temporal graph transformer for ice layer thickness, designed to process these radargrams and capture the spatiotemporal relationships between shallow and deep ice layers. ST-GRIT leverages an inductive geometric graph learning framework to extract local spatial features as feature embeddings and employs a series of temporal and spatial attention blocks separately to model long-range dependencies effectively in both dimensions. Experimental evaluation on radargram data from the Greenland ice sheet demonstrates that ST-GRIT consistently outperforms current state-of-the-art methods and other baseline graph neural networks by achieving lower root mean-squared error. These results highlight the advantages of self-attention mechanisms on graphs over pure graph neural networks, including the ability to handle noise, avoid oversmoothing, and capture long-range dependencies. Moreover, the use of separate spatial and temporal attention blocks allows for distinct and robust learning of spatial relationships and temporal patterns, providing a more comprehensive and effective approach.

Gaining a deeper understanding of the thickness and variability of internal ice layers in Radar imagery is essential in monitoring the snow accumulation, better evaluating ice dynamics processes, and minimizing uncertainties in climate models. Radar sensors, capable of penetrating ice, capture detailed radargram images of internal ice layers. In this work, we introduce GRIT, graph transformer for ice layer thickness. GRIT integrates an inductive geometric graph learning framework with an attention mechanism, designed to map the relationships between shallow and deeper ice layers. Compared to baseline graph neural networks, GRIT demonstrates consistently lower prediction errors. These results highlight the attention mechanism's effectiveness in capturing temporal changes across ice layers, while the graph transformer combines the strengths of transformers for learning long-range dependencies with graph neural networks for capturing spatial patterns, enabling robust modeling of complex spatiotemporal dynamics.

Grounded ice thickness plays a critical role in understanding the behavior and stability of ice sheets, particularly in polar regions such as Greenland, Antarctica, and the Canadian Arctic. Ice sheet dynamics are governed by complex interactions between ice flow, surface accumulation, and bedrock topography, making the accurate modeling of these processes essential for predicting long-term ice sheet behavior and their contributions to global sea level rise [14, 18]. In particular, the Shallow Ice Approximation (SIA) provides a framework for modeling grounded ice, where ice flow is driven by internal deformation and the base is often assumed to be frozen, constraining the ice thickness by bedrock topography [12, 15]. A key challenge in modeling grounded ice involves solving the partial differential equations (PDEs) that govern ice thickness evolution, while incorporating these constraints. This leads to a free boundary problem, where the ice thickness must remain non-negative and above the bedrock, giving rise to an obstacle problem [21, 3].

Microbes living beneath Antarctica's ice sheet may survive on methane generated by geothermal heat rising from deep below Earth's surface. The discovery could have implications for assessing the potential for life to survive on icy worlds beyond Earth. "These could be hotspots for microbes that are adapted to live in these areas," says Gavin Piccione at Brown University in Rhode Island. We already know that there is methane beneath Antarctica's ice sheet.

A six-year investigation into the vast Thwaites glacier in Antarctica has concluded with a grim outlook on its future. Often dubbed the "doomsday glacier", this huge mass of ice is comparable in size to Britain or Florida and its collapse alone would raise sea levels by 65 centimetres. Worse still, this is expected to trigger a more widespread loss of the ice sheet covering West Antarctica, causing a calamitous sea level rise of 3.3 metres and threatening cities like New York, Kolkata and Shanghai. It is an extremely remote and difficult area to get to, but the International Thwaites Glacier Collaboration (ITGC), a joint UK-US research programme, has managed to deploy 100 scientists there over the past six years, using planes, ships and underwater robots to study the dynamics of this ice in detail. "It was a tremendous challenge, and yet we really learned a lot," says Ted Scambos at University of Colorado Boulder.

Although numerical models provide accurate solutions for ice sheet dynamics based on physics laws, they accompany intensified computational demands to solve partial differential equations. In recent years, convolutional neural networks (CNNs) have been widely used as statistical emulators for those numerical models. However, since CNNs operate on regular grids, they cannot represent the refined meshes and computational efficiency of finite-element numerical models. Therefore, instead of CNNs, this study adopts an equivariant graph convolutional network (EGCN) as an emulator for the ice sheet dynamics modeling. EGCN reproduces ice thickness and velocity changes in the Helheim Glacier, Greenland, and Pine Island Glacier, Antarctica, with 260 times and 44 times faster computation time, respectively. Compared to the traditional CNN and graph convolutional network, EGCN shows outstanding accuracy in thickness prediction near fast ice streams by preserving the equivariance to the translation and rotation of graphs.

Natural language processing has seen rapid progress over the past decade. Due to the speed of developments, some practices get established without proper evaluation. Considering one such case and focusing on reading comprehension, we ask our first research question: 1) How does the order of inputs -- i.e., question and context -- affect model performance? Additionally, given recent advancements in input emphasis, we ask a second research question: 2) Does emphasizing either the question, the context, or both enhance performance? Experimenting with 9 large language models across 3 datasets, we find that presenting the context before the question improves model performance, with an accuracy increase of up to $31\%$. Furthermore, emphasizing the context yields superior results compared to question emphasis, and in general, emphasizing parts of the input is particularly effective for addressing questions that models lack the parametric knowledge to answer. Experimenting with both prompt-based and attention-based emphasis methods, we additionally find that the best method is surprisingly simple: it only requires concatenating a few tokens to the input and results in an accuracy improvement of up to $36\%$, allowing smaller models to outperform their significantly larger counterparts.

In its first experiment, radar on the drone will fire radio waves at an ice sheet called Fuchs Piedmont. Some will go into the ice sheet, hit the ground at the base and bounce back. The drone will listen for those reflections and use them to draw the shape of the land.