'It Was Nuts': The Extreme Tests that Show Why Hail Is a Multibillion-Dollar Problem The costs of a hail damage have ballooned over the past two decades, prompting researchers to resort to extreme measures to understand how these storms destroy buildings. The scars left on houses look like shotgun blasts, sometimes. In the aftermath of major storms, Andrew Shick, owner and chief executive of Illinois-based firm Roofing USA, has driven through suburbs blasted by hail and been left stunned by the damage. Earlier this year, he visited a farm complex in western Illinois where roofs, even sturdy metal ones, were left pockmarked and perforated after 3-inch balls of ice fell from the sky. "It was nuts," he recalls.

Group Relative Policy Optimization (GRPO) has demonstrated great utilization in post-training of Large Language Models (LLMs). In GRPO, prompts are answered by the model and, through reinforcement learning, preferred completions are learnt. Owing to the small communication volume, GRPO is inherently suitable for decentralised training as the prompts can be concurrently answered by multiple nodes and then exchanged in the forms of strings. In this work, we present the first adversarial attack in decentralised GRPO. We demonstrate that malicious parties can poison such systems by injecting arbitrary malicious tokens in benign models in both out-of-context and in-context attacks. Using empirical examples of math and coding tasks, we show that adversarial attacks can easily poison the benign nodes, polluting their local LLM post-training, achieving attack success rates up to 100% in as few as 50 iterations. We propose two ways to defend against these attacks, depending on whether all users train the same model or different models. We show that these defenses can achieve stop rates of up to 100%, making the attack impossible.

Hail nowcasting is a considerable contributor to meteorological disasters and there is a great need to mitigate its socioeconomic effects through precise forecast that has high resolution, long lead times and local details with large landscapes. Existing medium-range weather forecasting methods primarily rely on changes in upper air currents and cloud layers to predict precipitation events, such as heavy rainfall, which are unsuitable for hail nowcasting since it is mainly caused by low-altitude local strong convection associated with terrains. Additionally, radar captures the status of low cloud layers, such as water vapor, droplets, and ice crystals, providing rich signals suitable for hail nowcasting. To this end, we introduce a Spatial-Temporal gEnerAtive Model called SteamCast for hail nowcasting with radar echo extrapolation, it is a deep probabilistic diffusion model based on spatial-temporal representations including radar echoes as well as their position/time embeddings, which we trained on historical reanalysis archive from Yan'an Meteorological Bureau in China, where the crop yield like apple suffers greatly from hail damage. Considering the short-term nature of hail, SteamCast provides 30-minute nowcasts at 6-minute intervals for a single radar reflectivity variable, across 9 different vertical angles, on a latitude-longitude grid with approximately 1 km * 1 km resolution per pixel in Yan'an City, China. By successfully fusing the spatial-temporal features of radar echoes, SteamCast delivers competitive, and in some cases superior, results compared to other deep learning-based models such as PredRNN and VMRNN.

Can't you be more like ChatGPT? You know me better than I know myself More charming and in far better health You're a sociopath who answers either way You can, like, program math stuff, you can write a play I'm Salieri, you're Mozart You don't take days, deadlines, or amphetamines to start You don't second-guess yourself, or spout self-doubt You just sit there, you smug son-of-a- . . . Can't you be more like that Meta A.I.? You'd be a far more impressive guy All hail A.I.; All hail A.I. The robots have arrived, and my logic board's fried I sorta thought I'd seen a lot of change in my life My beard's not fully gray, yet I've seen all the strife Algorithms, iPhones, TikToks, and tweets Now there's a smart-ass robot stealing my blankets and sheets Keepin' me outta the jobs I already couldn't land Even writes better songs Got me kicked outta my band Can't you be more like ChatGPT? Welcome to the singularity ChatGPT, ChatGPT Boy, do I regret my English degree . . .

Hierarchical time-series forecasting (HTSF) is an important problem for many real-world business applications where the goal is to simultaneously forecast multiple time-series that are related to each other via a hierarchical relation. Recent works, however, do not address two important challenges that are typically observed in many demand forecasting applications at large companies. First, many time-series at lower levels of the hierarchy have high sparsity i.e., they have a significant number of zeros. Most HTSF methods do not address this varying sparsity across the hierarchy. Further, they do not scale well to the large size of the real-world hierarchy typically unseen in benchmarks used in literature. We resolve both these challenges by proposing HAILS, a novel probabilistic hierarchical model that enables accurate and calibrated probabilistic forecasts across the hierarchy by adaptively modeling sparse and dense time-series with different distributional assumptions and reconciling them to adhere to hierarchical constraints. We show the scalability and effectiveness of our methods by evaluating them against real-world demand forecasting datasets. We deploy HAILS at a large chemical manufacturing company for a product demand forecasting application with over ten thousand products and observe a significant 8.5\% improvement in forecast accuracy and 23% better improvement for sparse time-series. The enhanced accuracy and scalability make HAILS a valuable tool for improved business planning and customer experience.

For machine learning-based prognosis and diagnosis of rare diseases, such as pediatric brain tumors, it is necessary to gather medical imaging data from multiple clinical sites that may use different devices and protocols. Deep learning-driven harmonization of radiologic images relies on generative adversarial networks (GANs). However, GANs notoriously generate pseudo structures that do not exist in the original training data, a phenomenon known as "hallucination". To prevent hallucination in medical imaging, such as magnetic resonance images (MRI) of the brain, we propose a one-shot learning method where we utilize neural style transfer for harmonization. At test time, the method uses one image from a clinical site to generate an image that matches the intensity scale of the collaborating sites. Our approach combines learning a feature extractor, neural style transfer, and adaptive instance normalization. We further propose a novel strategy to evaluate the effectiveness of image harmonization approaches with evaluation metrics that both measure image style harmonization and assess the preservation of anatomical structures. Experimental results demonstrate the effectiveness of our method in preserving patient anatomy while adjusting the image intensities to a new clinical site. Our general harmonization model can be used on unseen data from new sites, making it a valuable tool for real-world medical applications and clinical trials.

If your real-life dog isn't as obedient as you'd like, a Chinese firm may have a perfect robotic replacement for you. Called Go2, the'intelligent quadruped robot' can dance, do a handstand while wiggling its legs in the air and even rush to greet its owner – just like a real pooch. It can also climb the stairs, play fetch, emit music from a built-in speaker and even take photos on command, which are sent straight to the owner's smartphone. A new promo clip shows the bot showing off its tricks, including jumping between rocks and even working its way around a hedge maze. Go2 is similar to the Spot robot dog from rival Boston Dynamics, although it's been designed for consumers at a hefty price tag of $1,600 (£1,240).

Cleotha Abston, the career criminal accused of kidnapping and murdering Eliza Fletcher, a mother of two, in Memphis in September returns to court for a hearing on Thursday. HAPPENING TODAY - Cleotha Abston, who is accused of kidnapping and murdering jogger Eliza Fletcher, returns to court for a hearing. SENT PACKING - Judge rules in favor of'Yellowstone' star Kevin Costner during divorce from estranged wife. 'GROWING RISKS' - President Biden's crackdown on power plants is sounding off alarms. SOCIAL SHOWDOWN - Meta's new site'Threads' gives Twitter a run for its money within hours of debut.

Predictions of thunderstorm-related hazards are needed in several sectors, including first responders, infrastructure management and aviation. To address this need, we present a deep learning model that can be adapted to different hazard types. The model can utilize multiple data sources; we use data from weather radar, lightning detection, satellite visible/infrared imagery, numerical weather prediction and digital elevation models. We demonstrate the ability of the model to predict lightning, hail and heavy precipitation probabilistically on a 1 km resolution grid, with a temporal resolution of 5 min and lead times up to 60 min. Shapley values quantify the importance of the different data sources, showing that the weather radar products are the most important predictors for all three hazard types.

Hail risk assessment is necessary to estimate and reduce damage to crops, orchards, and infrastructure. Also, it helps to estimate and reduce consequent losses for businesses and, particularly, insurance companies. But hail forecasting is challenging. Data used for designing models for this purpose are tree-dimensional geospatial time series. Hail is a very local event with respect to the resolution of available datasets. Also, hail events are rare - only 1% of targets in observations are marked as "hail". Models for nowcasting and short-term hail forecasts are improving. Introducing machine learning models to the meteorology field is not new. There are also various climate models reflecting possible scenarios of climate change in the future. But there are no machine learning models for data-driven forecasting of changes in hail frequency for a given area. The first possible approach for the latter task is to ignore spatial and temporal structure and develop a model capable of classifying a given vertical profile of meteorological variables as favorable to hail formation or not. Although such an approach certainly neglects important information, it is very light weighted and easily scalable because it treats observations as independent from each other. The more advanced approach is to design a neural network capable to process geospatial data. Our idea here is to combine convolutional layers responsible for the processing of spatial data with recurrent neural network blocks capable to work with temporal structure. This study compares two approaches and introduces a model suitable for the task of forecasting changes in hail frequency for ongoing decades.