Figure 4: Impact of the proportion of injected noise for learning Paragraph Em-beddings on XSum dataset. (Figure 4). The results of the ablation study are presented in Table 5. Embedder in providing clean and denoised reconstructions. In general, it has been observed that generations progress in a coarse-to-fine manner. The early time step, which is close to 1, tends to be less fluent and generic. This was the nicest stay we have ever had. Turtle Bay was a great resort. This was the nicest stay we have ever had.

Wild fluctuations in the price of gold continued on Wednesday as geopolitical tensions reignited after the US downed an Iranian drone . The precious metal, which is seen as a so-called safe haven for investors in times of uncertainty, shot back above $5,000 (£3,650) an ounce following days of sharp falls. Gold prices had been propelled to record highs by rapid changes in US trade policy, ongoing geopolitical uncertainty and conflict and central banks increasing their purchases of bullion. Wednesday's jump, to $5,061 per ounce, left the price of gold around 80% higher than the same time a year ago. A US military spokesman confirmed the Iranian drone had been shot down after it aggressively approached an American aircraft carrier in the Arabian Sea. Tehran has not commented on Tuesday's incident.

In a pine forest on Michigan's Upper Peninsula, the only active nickel mine in the US is nearing the end of its life. At a time when carmakers want the metal for electric-vehicle batteries, nickel concentration at Eagle Mine is falling and could soon drop too low to warrant digging. Demand for nickel, copper, and rare earth elements is rapidly increasing amid the explosive growth of metal-intensive data centers, electric cars, and renewable energy projects. But producing these metals is becoming harder and more expensive because miners have already exploited the best resources. Here's how biotechnology could help . What we've been getting wrong about AI's truth crisis What would it take to convince you that the era of truth decay we were long warned about--where AI content dupes us, shapes our beliefs even when we catch the lie, and erodes societal trust in the process--is now here?

The US Government Is Trying To Make Coal Cute. Trump complained that coal needed better PR. The original Coalie was just a lump of coal with eyes and nothing more. Get your news from a source that's not owned and controlled by oligarchs. Can a lump of coal ever be cute?

Analyzing the planet at scale with satellite imagery and machine learning is a dream that has been constantly hindered by the cost of difficult-to-access highly-representative high-resolution imagery. To remediate this, we introduce here the WorldStratified dataset. The largest and most varied such publicly available dataset, at Airbus SPOT 6/7 satellites' high resolution of up to 1.5 m/pixel, empowered by European Space Agency's Phi-Lab as part of the ESA-funded QueryPlanet project, we curate 10,000 sq km of unique locations to ensure stratified representation of all types of land-use across the world: from agriculture to ice caps, from forests to multiple urbanization densities. We also enrich those with locations typically under-represented in ML datasets: sites of humanitarian interest, illegal mining sites, and settlements of persons at risk.

One of WIRED's Favorite Chef Knives Is Free With a HelloFresh Membership The 8-inch Zwilling Four Star chef's knife is an excellent carbon steel blade that retails around $100. It's free with some food. I don't know if a good knife is hard to find. But they usually cost at least a hundred dollars, so it's worth noting when HelloFresh is offering one of WIRED's favorite chef's knives for the low, low price of free. This is the time of year when a lot of the best meal kit deals start to happen. And so if you hang around for three weeks of meal delivery service from HelloFresh, your third box will include delivery of a Zwilling Four Star 8-inch chef knife, a $100-plus carbon steel blade that WIRED reviewer Molly Higgins lists as her runner-up favorite blade overall--and her favorite carbon-steel for most people.

Part qualification is crucial in additive manufacturing (AM) because it ensures that additively manufactured parts can be consistently produced and reliably used in critical applications. Part qualification aims at verifying that an additively manufactured part meets performance requirements; therefore, predicting the complex stress-strain behaviors of additively manufactured parts is critical. We develop a dynamic time warping (DTW)-transfer learning (TL) framework for additive manufacturing part qualification by transferring knowledge of the stress-strain behaviors of additively manufactured low-cost polymers to metals. Specifically, the framework employs DTW to select a polymer dataset as the source domain that is the most relevant to the target metal dataset. Using a long short-term memory (LSTM) model, four source polymers (i.e., Nylon, PLA, CF-ABS, and Resin) and three target metals (i.e., AlSi10Mg, Ti6Al4V, and carbon steel) that are fabricated by different AM techniques are utilized to demonstrate the effectiveness of the DTW-TL framework. Experimental results show that the DTW-TL framework identifies the closest match between polymers and metals to select one single polymer dataset as the source domain. The DTW-TL model achieves the lowest mean absolute percentage error of 12.41% and highest coefficient of determination of 0.96 when three metals are used as the target domain, respectively, outperforming the vanilla LSTM model without TL as well as the TL model pre-trained on four polymer datasets as the source domain.

Underground mining operations depend on sensor networks to monitor critical parameters such as temperature, gas concentration, and miner movement, enabling timely hazard detection and safety decisions. However, transmitting raw sensor data to a centralized server for machine learning (ML) model training raises serious privacy and security concerns. Federated Learning (FL) offers a promising alternative by enabling decentralized model training without exposing sensitive local data. Yet, applying FL in underground mining presents unique challenges: (i) Adversaries may eavesdrop on shared model updates to launch model inversion or membership inference attacks, compromising data privacy and operational safety; (ii) Non-IID data distributions across mines and sensor noise can hinder model convergence. To address these issues, we propose FedMining--a privacy-preserving FL framework tailored for underground mining. FedMining introduces two core innovations: (1) a Decentralized Functional Encryption (DFE) scheme that keeps local models encrypted, thwarting unauthorized access and inference attacks; and (2) a balancing aggregation mechanism to mitigate data heterogeneity and enhance convergence. Evaluations on real-world mining datasets demonstrate FedMining's ability to safeguard privacy while maintaining high model accuracy and achieving rapid convergence with reduced communication and computation overhead. These advantages make FedMining both secure and practical for real-time underground safety monitoring.

One of the first studies of its kind also showed mining's stark effects on the abyssal plain. Breakthroughs, discoveries, and DIY tips sent every weekday. Researchers completing one of the largest impact studies on the potential environmental impacts of deep-sea mining found a bit more than they bargained for on the ocean floor: 4,350 animals, each at least larger than 0.3 millimeters. From these, they ultimately identified 788 separate species of unique crustaceans, mollusks, marine bristle worms, and other creatures living in this sought after mining zone. While the team found that harvesting rare earth metals from over 13,000 feet below the ocean's surface may not be as destructive as initially theorized, the disruptions are still cause for serious concerns.