Winter wonderlands are only possible thanks to a sparkly light trick. Few languages have as many distinct words for snow as Japanese, which has words like miyuki or beautiful snow. Breakthroughs, discoveries, and DIY tips sent six days a week. When someone says " as white as snow," it's easy to envision what they're talking about. We often think of snow as a dazzling white, the same way we immediately conjure up a color when someone says "blood red" or "ocean blue."

The tiny insects are older than dinosaurs. Breakthroughs, discoveries, and DIY tips sent every weekday. Not eating yellow snow is obviously wise advice, but how about snow that looks like a poppy seed bagel? You should also avoid that too, because those "seeds" may actually be tiny critters commonly called snow fleas. As a video taken at the Monomoy National Wildlife Refuge in Massachusetts shows, these little black specks bounce across the snow.

In this paper, we propose a neural network-based approach for learning to represent the behavior of plastic solid materials ranging from rubber and metal to sand and snow. Unlike elastic forces such as spring forces, these plastic forces do not result from the positional gradient of any potential energy, imposing great challenges on the stability and flexibility of their simulation. Our method effectively resolves this issue by learning a generalizable plastic energy whose derivative closely matches the analytical behavior of plastic forces. Our method, for the first time, enables the simulation of a wide range of arbitrary elasticity-plasticity combinations using time step-independent, unconditionally stable optimization-based time integrators. We demonstrate the efficacy of our method by learning and producing challenging 2D and 3D effects of metal, sand, and snow with complex dynamics.

Collecting operationally realistic data to inform machine learning models can be costly. Before collecting new data, it is helpful to understand where a model is deficient. For example, object detectors trained on images of rare objects may not be good at identification in poorly represented conditions. We offer a way of informing subsequent data acquisition to maximize model performance by leveraging the toolkit of computer experiments and metadata describing the circumstances under which the training data was collected (e.g., season, time of day, location). We do this by evaluating the learner as the training data is varied according to its metadata. A Gaussian process (GP) surrogate fit to that response surface can inform new data acquisitions. This meta-learning approach offers improvements to learner performance as compared to data with randomly selected metadata, which we illustrate on both classic learning examples, and on a motivating application involving the collection of aerial images in search of airplanes.

What does'chance of precipitation' really mean? Here's how to figure out if you can leave the umbrella at home. It's not always "when it rains, it pours." Breakthroughs, discoveries, and DIY tips sent every weekday. Understanding the weather forecast can sometimes feel like reading tea leaves.

Avoid injury and improve efficiency with tips from a physical therapist. Don't be a snow hero. Breakthroughs, discoveries, and DIY tips sent every weekday. You know, for life's most essential resource, water knows a hundred ways to kill you if you're not careful. When it's not trying to drown you in its pools and coastlines during the summer, it shape-shifts to snow in the winter, piling up emergency room visits for those forced to shovel it.

The 1,500-year-old site was hidden beneath the dark, damp ice. Breakthroughs, discoveries, and DIY tips sent every weekday. In the fall of 2024, a hiker named Helge Titland was trekking through Aurlandsfjellet, a mountainous region and plateau in Norway and got a little more than just some time with nature. Titland found some strange wooden stakes peaking out of melting snow. He wisely reported it to local archaeologists, but snow returned before the team could investigate.

Dick Cheney dead: Vice President who served with George W. Bush and took leading role in'war on terror' dies at 84 Why Tuesday's races aren't as close as you think: White House analyst CRAIG KESHISHIAN reveals what the polls always miss How Charles reacted when'difficult' William asked if he could do fewer engagements: Biographer ROBERT JOBSON reveals the'tension', secrets of Kate's family life - and how couple are making Prince George'strong' 'She used it to freshen up... it killed her': My wife died of cancer at 63. She never smoked or drank. Taylor Swift enjoys girls' night with squad member Gigi Hadid in NYC after Travis Kelce's ex took swipe Warren Buffett's $6billion stock exit is his loudest warning yet Father reveals'radical faith' spiral of American son killed in hail of arrows by reclusive tribe as new believers consider following him to isolated island My ex is the international fugitive Democrat who's fled to Europe with our nine-year-old son. Here are the disgraceful secrets she'd hate the world to know Woke Gen-Z's revenge: Poll reveals staggering number of under-30s who back Mamdani while their parents are terrified of a return to 1980s New York Coca-Cola faces huge backlash for using AI in its'Holidays are Coming' Christmas advert for the second year in a row - as one viewer calls it the'best ad I've ever seen for Pepsi' Explosive war sparked by Hollywood agent's nepo-baby son dismembering his wife It's the trendy new diagnosis for everything from fatigue to brain fog... but here's the truth about your gut problem - and how to fix it: DR EMILY LEEMING Dallas Cowboys agree huge trade for rival's defensive captain just hours before the NFL deadline Republicans join joyous liberals in dumping on Dick Cheney's death: 'Hell is hot!' AMANDA PLATELL: Fergie's delusions have reached a new low. I can't believe Beatrice and Eugenie are egging her on.

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The NYSDOT evaluates road conditions by driving on roads and observing live cameras, tasks which are labor-intensive but necessary for making critical operational decisions during winter weather events. However, machine learning models can provide additional support for the NYSDOT by automatically classifying current road conditions across the state. In this study, convolutional neural networks and random forests are trained on camera images and weather data to predict road surface conditions. Models are trained on a hand-labeled dataset of 22,000 camera images, each classified by human labelers into one of six road surface conditions: severe snow, snow, wet, dry, poor visibility, or obstructed. Model generalizability is prioritized to meet the operational needs of the NYSDOT decision makers, and the weather-related road surface condition model in this study achieves an accuracy of 81.5% on completely unseen cameras. Keywords Winter weather Co-design Artificial intelligence Risk communication Hand-labeled dataset Highlights Developed a model to classify road surface conditions using image and weather data Achieved accuracy of 81.5% on completely unseen cameras for weather-related classes Integrated co-design with end-users and interdisciplinary collaboration Designed methods that prioritize model generalizability for operational applicability