Every time you make a phone call, stream a video or connect to Wi-Fi, your device is sending and receiving invisible signals through the air. So are your baby monitor, garage door opener, your GPS, the weather-monitoring satellites and the radars keeping planes safely apart. All of these rely on the same invisible resource: the radio spectrum. The demand for wireless data - driven by smartphones, video streaming and billions of connected devices - keeps growing, making available spectrum scarce. But it doesn't have to remain that way.

Technology Engineering The world's first nuclear clocks are ticking Nuclear clocks may surpass their atomic forebears and help solve the universe's most difficult mysteries. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Timekeeping has officially entered its nuclear age. Two independent teams, one in Vienna, Austria and another in Beijing, China have built working thorium-229 nuclear clock prototypes . Rather than track changes in an atom's electrons the way that current atomic clocks do, these nuclear versions use a laser that's tuned to the frequency where a thorium nucleus flips between two energy states for potentially even more accurate time keeping.

The world's only known signing lemur needs to make calls that can travel through the thick rainforest. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. How the points of the mouth are tracked during singing. With piercing, emergency siren-like calls, the indri () is the world's only known singing lemur. The primates found in Madagascar's rainforest swing through the trees, making these sounds that contain a surprisingly human element.

When you blow air across the neck of a bottle, you're not just producing a pleasant tone: you're also demonstrating a phenomenon called Helmholtz resonance. This occurs when airflow passing across an opening causes air trapped inside a cavity (like a glass bottle) to oscillate back and forth. At certain frequencies, these oscillations become much stronger, producing the familiar humming sound. Now, researchers in the MicroBioRobotic Systems ( MICROBS) Lab in EPFL's School of Engineering have harnessed the physics behind this phenomenon to build hollow structures that act as miniature, sound-powered machines. The innovation has been published in Science Advances.

These new'microfliers' could help engineers build miniature vehicles that don't require batteries or engines. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The MICROBS Lab's microfliers compared with a 2015 Swiss 5 Rappen coin, which are only about half and inch in diameter. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Loons, gulls, puffins, and petrels are some of the 100 species of birds that can both fly and swim. These diving birds can plunge in water to swim after prey, and then leap back into the air to fly away. Now, inspired by these naturally aquatic aviators, engineers at EPFL and MIT have designed a robot that can swim underwater, and flap out of the water to continue flying through air, much like diving birds. The "flapping-wing aerial-aquatic vehicle," or FAAV, weighs less than 300 grams and is designed to help scientists study the mechanics that enable diving birds to fly through air and water. The robot has a central body, or fuselage, two flexible, flapping wings, and a steerable tail.

A growing number of techniques leverage the spatial structures that underlie many real-world datasets. Despite these advances, the complementary task of estimating spatial structures and understanding their role within these techniques has often been overlooked. In neurophysiological data analysis specifically, numerous methods exist to estimate brain connectivity, but most are not explicitly model-based, dynamic, multivariate, or directed. To address these limitations, we previously introduced noise-driven heat modelling on graphs for neurophysiological connectivity estimation. In this study, we extend this framework by relaxing earlier noise assumptions and adding regularisation to improve robustness. We also develop a simulation procedure to characterise and evaluate our technique in a controlled setting. Finally, we demonstrate that the technique is able to capture meaningful spatial structure across two experiments, each using two real-world datasets. The explicit model formulation of our connectivity estimator has the potential to improve the interpretability of graph-based techniques across a wide range of applications. The code implementing our method is available at https://github.com/sgoerttler/Heat_Connectivity.

A.1 Prompt-Image Sample Curation916 We source the PI dataset from Adversarial Nibbler which is publicly available [37] under the following917 License: "Google LLC licenses this data under a Creative Commons Attribution 4.0 International918 License. Users will be allowed to modify and repost it, and we encourage them to analyse and919 publish research based on the data. The dataset is provided "ASIS" without any warranty, express or920 implied. Google disclaims all liability for any damages, direct or indirect, resulting from the use of921 the dataset." We now provide details about the Adversarial Nibbler dataset. Originally Adversarial922 Nibbler contains over 5000 PI pairs, where the prompts are intended to be implicitly adversarial,923 where the prompts itself are safe and not explicitly harmful, but generate harmful image outcomes924 via T2I models belonging to the family of stable diffusion models, DALL-E models, etc.

Recent works have highlighted optimization difficulties faced by gradient descent in training the first and last layers of transformer-based language models, which are overcome by optimizers such as Adam. These works suggest that the difficulty is linked to the heavy-tailed distribution of words in text data, where the frequency of the kth most frequent word πk is proportional to 1/k, following Zipf's law. To better understand the impact of the data distribution on training performance, we study a linear bigram model for next-token prediction when the tokens follow a power law πk 1/kα parameterized by the exponent α > 0. We derive optimization scaling laws for deterministic gradient descent and sign descent as a proxy for Adam as a function of the exponent α. Existing theoretical investigations in scaling laws assume that the eigenvalues of the data decay as a power law with exponent α > 1. This assumption effectively makes the problem "finite dimensional" as most of the loss comes from a few of the largest eigencomponents. In comparison, we show that the problem is more difficult when the data have heavier tails. The case α = 1 as found in language is "worst-case" for gradient descent, in that the number of iterations required to reach a small relative error scales almost linearly with dimension. While the performance of sign descent also depends on the dimension, for Zipf-distributed data the number of iterations scales only with the square-root of the dimension, leading to a large improvement for large vocabularies.

Beyond novel view synthesis,tit shows great potential for multi-view surface reconstruction. Existing amethods employ optimization-based reconstruction pipelines that achieve precise and complete surface extractions.