We present an initial evaluation of NASA and IBM's Prithvi-EO-2.0 geospatial foundation model on shoreline delineation of small sandy islands using satellite images. We curated and labeled a dataset of 225 multispectral images of two Maldivian islands, which we publicly release, and fine-tuned both the 300M and 600M parameter versions of Prithvi on training subsets ranging from 5 to 181 images. Our experiments show that even with as few as 5 training images, the models achieve high performance (F1 of 0.94, IoU of 0.79). Our results demonstrate the strong transfer learning capability of Prithvi, underscoring the potential of such models to support coastal monitoring in data-poor regions.

--The unabated growth in AI workload demands is driving the need for concerted advances in compute, memory, and interconnect performance. As traditional semiconductor scaling slows, high-speed interconnects have emerged as the new scaling engine, enabling the creation of larger logical GPUs by linking many GPUs into a single, low-latency, high-bandwidth compute domain. While initial scale-up fabrics leveraged copper interconnects for their power and cost advantages, the maximum reach of passive electrical interconnects (approximately 1 meter) effectively limits the scale-up domain to within a single rack. The advent of 3D-stacked optics and logic offers a transformative, power-efficient scale-up solution for connecting hundreds of GPU packages (thousands of GPUs) across multiple data center racks. This work explores the design tradeoffs of scale-up technologies and demonstrates how frontier LLMs necessitate novel photonic solutions to achieve aggressive power and performance targets. We model the benefits of 3D CPO (Passage) enabled GPUs and switches within the scale-up domain when training Frontier Mixture of Experts (MoE) models exceeding one trillion parameters. Our results show that the substantial increases in bandwidth and radix enabled by 3D CPO allow for an 8X increase in scale-up capability. The race to build larger, more sophisticated AI models is pushing the limits of existing infrastructure. At the chip and package level, GPUs are constrained by shoreline, yields and power. These challenges have led to the development of large high-bandwidth, low-latency scale-up pods. These pods effectively combine hundreds of GPUs into a single logical GPU to facilitate a variety of parallelism strategies (e.g. Approaches like Mixture of Experts (MoE) [1] have pushed scale-up networks to their limits due to copper reach (1 meter), which constrains the number of GPUs that can be connected within a single network hop. With MoEs, an ensemble of specialized sub-networks work together through sparse activations to increase model capacity without significantly increasing computational requirements. The output of the selected experts are combined to create the final result.

Breakthroughs, discoveries, and DIY tips sent every weekday. A smelly, sometimes toxic "killer belt of seaweed" might put a damper on Floridians' Memorial Day weekend plans. Sargassum is back just in time for the unofficial start of summer and this year's influx of the brown algae would be record breaking at 31 million tons. Sargassum is a genus of large brown seaweed. As a seaweed, it is also a type of algae.

In the last few years, due to the broad applicability of deep learning to downstream tasks and end-to-end training capabilities, increasingly more concerns about potential biases to specific, non-representative patterns have been raised. Many works focusing on unsupervised debiasing usually leverage the tendency of deep models to learn ``easier'' samples, for example by clustering the latent space to obtain bias pseudo-labels. However, the interpretation of such pseudo-labels is not trivial, especially for a non-expert end user, as it does not provide semantic information about the bias features. To address this issue, we introduce ``Say My Name'' (SaMyNa), the first tool to identify biases within deep models semantically. Unlike existing methods, our approach focuses on biases learned by the model. Our text-based pipeline enhances explainability and supports debiasing efforts: applicable during either training or post-hoc validation, our method can disentangle task-related information and proposes itself as a tool to analyze biases. Evaluation on traditional benchmarks demonstrates its effectiveness in detecting biases and even disclaiming them, showcasing its broad applicability for model diagnosis.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. The National Park Service (NPS) said Wednesday that a body had been recovered near Lake Mead's Boulder Islands. An adult woman had gone missing in Nevada's Lake Mead National Recreation Area on June 30, 2022. Park rangers located and recovered the body with the use of a remotely operated vehicle (ROV).

After his mother got dementia, Bari became forgetful. It was little things, like hanging up the wet laundry on time so it wouldn't stink; spraying pesticide on their patch of sea wall against the adventures of crabs and mutant fish; checking the AQI meter before leading his mother out for her evening walk along New Karachi's polluted shoreline. Did something break in your brain, too, when you took care of people who once held you on their lap, helped you count the last straggling trees in the mohalla courtyard? Overwhelmed by their needs and your grief, perhaps you were split into two halves, each perpetually being run into the ground. It wasn't like he had a sibling or a spouse to lean on.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. A sizeable chunk of shoreline was spotted by boaters this week in Michigan's Muskegon Lake that could be the result of record water levels and erosion. The floating piece of vegetation was featured in an aerial drone video that shows a pontoon boat circling it. "I've lived my whole life in the Muskegon area, and I've never seen anything like it," said Joe Gee, the photographer who captured footage of the islet.

California's largest lake, the Salton Sea, was created by accident in 1905 and was transformed into a vacation location by developers who built up the shoreline with resorts, hotels, yacht clubs and more. By the 1950s, thousands flocked to vacation there, including celebrities like Frank Sinatra and The Beach Boys. At one point, it was the most visited destination in the state - beating out Yosemite National Park. During the 1950s and 60s, celebrities flocked to the area to kick back and relax with some fun in the sun for vacations. Eventually, developers and officials saw an opportunity to bring tourism to the area so they began building fancy properties and yacht clubs around the Salton Sea.