Bed-bound Lindsey Vonn reveals pain is'hard to manage' as she speaks out for the first time after FIFTH surgery on her broken leg'Fergie might end up having to tell her story to the police': 'Toxic' Sarah Ferguson is'broke and in a bad way' after Andrew's arrest...and looking to UAE for cash because'everyone is out to get her' The tide of sleaze rolling over Beatrice, Eugenie and Fergie is going to capsize them all. Moment Kate and William revealed their'true feelings' towards Andrew and Fergie: Princess'ignoring' Sarah and Prince'secretly scolding' his uncle... how Duchess of Kent's funeral said it all Kurt Cobain's uncle insists Nirvana legend was murdered and calls on cops to investigate clues that haunt him Kristi Noem's secret escape plan to ditch DHS revealed amid ICE raid fallout and'culture of fear' rumors Winter Olympics chiefs reach verdict on Jutta Leerdam's '$1m underwear-flashing gesture' after Jake Paul's fiancée faced covert marketing claims Country singer Conner Smith's charges DROPPED after he hit and killed a woman, 77, with his truck I ditched weight-loss shots for the new Wegovy pill and am astonished by the difference. The pounds are falling off, I have no side effects and it's cheaper The subtle early warning sign that revealed Eric Dane's illness - as Grey's Anatomy star dies of motor neurone disease Johnny Depp let Eric Dane live'rent-free in one of his LA homes' as he tried to ease Grey's Anatomy star's financial worries in the months before his death from ALS aged 53 Uproar as NYC's'communist' mayor announces crippling tax for ALL homeowners after promising to only go after billionaires Wall Street panics as America's growth stalls while everyday prices refuse to fall I stumbled across my wife's Pornhub search history and it's broken me. She told me it's'just a fantasy lots of women have' but now I fear I'll never be enough Non-binary activist wins compensation after taking year-and-a-half off work with stress because hair salon's online booking form only offered male or female cuts Courtney Love caught on camera fleeing shocking car collision... days after bombshell Kurt Cobain'homicide investigation' Trump-bashing Winter Olympics star Hunter Hess whines about'hardest weeks of his life' after being called a'real loser' by the president In a viral post on X, user @ShishirShelke1 shared their strange discovery about the clock app icon. Normally, the icon on the home screen shows the second hand smoothly gliding around the clock face.

Jean-Paul Thorbjornsen, an Australian man in his mid-30s, with a rural Catholic upbringing, is a founder of THORChain, a blockchain through which users can swap one cryptocurrency for another and earn fees from making those swaps. THORChain is permissionless, so anyone can use it without getting prior approval from a centralized authority. As a decentralized network, the blockchain is built and run by operators located across the globe. During its early days, Thorbjornsen himself hid behind the pseudonym "leena" and used an AI-generated female image as his avatar. But around March 2024, he revealed his true identity as the mind behind the blockchain. If there is a central question around THORChain, it is this: Exactly who is responsible for its operations?

Things to Do in L.A. Tap to enable a layout that focuses on the article. An attendee plays the Monster Hunter Wilds video game on the Sony PlayStation 5 Pro console during the Tokyo Game Show 2024 at Makuhari Messe in 2024 in Chiba, Japan. This is read by an automated voice. Please report any issues or inconsistencies here . With the start of the new year, Californians will pay a new fee every time they buy a product with a nonremovable battery -- whether it's a power tool, a PlayStation or even a singing greeting card.

Speaking with popular AI content creators convinces me that "slop" isn't just the internet rotting in real time, but the early draft of a new kind of pop culture. Lately, everywhere I scroll, I keep seeing the same fish-eyed CCTV view: a grainy wide shot from the corner of a living room, a driveway at night, an empty grocery store. JD Vance shows up at the doorstep in a crazy outfit. A car folds into itself like paper and drives away. A cat comes in and starts hanging out with capybaras and bears, as if in some weird modern fairy tale. This fake-surveillance look has become one of the signature flavors of what people now call AI slop. For those of us who spend time online watching short videos, slop feels inescapable: a flood of repetitive, often nonsensical AI-generated clips that washes across TikTok, Instagram, and beyond. For that, you can thank new tools like OpenAI's Sora (which exploded in popularity after launching in app form in September), Google's Veo series, and AI models built by Runway. Now anyone can make videos, with just a few taps on a screen.

The availability and sophistication degree of such services are fair measures of progress for any city. In this context, Geographic information systems " GIS " offers solutions that support the decision - making processes regarding management, planning and distribution of services, ultimately improving the standard of living in cities (Aziz, 2007, p. 11). Investigating services planning standards is one of the most relevant issues concerning human progress regarding its proper definition and needs. Planning standards can be reconsidered by studying the variation in the distribution of geographical phenomena and the characteristi cs of geographic areas. More effort should be exerted in defining these standards parallel to the characteristics of each region. Such efforts will facilitate appropriate allocation s of services and accurate definitions of future developmental efforts. The problem of the study is that the planning standards are not suitable for the characteristics of the Egyptian cities, which include more population and intensive daily use of services. The solution to this problem is to create new planning standards that suit the rapidly changing nature of cities, and to generate these criteria current services and their intensity and the built - up areas are going to be used to reflect the characteristics of the city, taking this abroach is a new way to generate such criteria. This study attempts to derive planning standards for public services in the city of Qena that are compatible with the characteristics of the city, the geographical distribution of the population, the built - up area, and the services therein.

STEAM education in many parts of the Global South remains abstract and weakly connected to learners sociocultural realities. This study examines how human experts evaluate the capacity of Generative AI (GenAI) to contextualize STEAM instruction in these settings. Using a convergent mixed-methods design grounded in human-centered and culturally responsive pedagogy, four STEAM education experts reviewed standardized Ghana NaCCA lesson plans and GenAI-generated lessons created with a customized Culturally Responsive Lesson Planner (CRLP). Quantitative data were collected with a validated 25-item Culturally Responsive Pedagogy Rubric assessing bias awareness, cultural representation, contextual relevance, linguistic responsiveness, and teacher agency. Qualitative reflections provided additional insight into the pedagogical and cultural dynamics of each lesson. Findings show that GenAI, especially through the CRLP, improved connections between abstract standards and learners lived experiences. Teacher Agency was the strongest domain, while Cultural Representation was the weakest. CRLP-generated lessons were rated as more culturally grounded and pedagogically engaging. However, GenAI struggled to represent Ghana's cultural diversity, often producing surface-level references, especially in Mathematics and Computing. Experts stressed the need for teacher mediation, community input, and culturally informed refinement of AI outputs. Future work should involve classroom trials, broader expert participation, and fine-tuning with Indigenous corpora.

Accurate and efficient seismic response prediction is essential for the design of resilient structures. While the Finite Element Method (FEM) remains the standard for nonlinear seismic analysis, its high computational demands limit its scalability and real time applicability. Recent developments in deep learning, particularly Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Long Short Term Memory (LSTM) models, have shown promise in reducing the computational cost of nonlinear seismic analysis of structures. However, these data driven models often struggle to generalize and capture the underlying physics, leading to reduced reliability. We propose a novel Physics Informed U Net LSTM framework that integrates physical laws with deep learning to enhance both accuracy and efficiency. By embedding domain specific constraints into the learning process, the proposed model achieves improved predictive performance over conventional Machine Learning architectures. This hybrid approach bridges the gap between purely data driven methods and physics based modeling, offering a robust and computationally efficient alternative for seismic response prediction of structures.

We retrieve top 10 passages and use them as input to mGEN. Gettysburg College, where he was a member of the Lambda Chi Alpha fraternity. We further subsample 50% of the synthetically generated questions. For our multilingual retriever, we split each article into 100-token chunks (Karpukhin et al., 2020), The original passage text file is 29GB, and the total index size is around 129 GB. Both two datasets are under the MIT licence.

Large-scale models are at the forefront of time series (TS) forecasting, dominated by two paradigms: fine-tuning text-based Large Language Models (LLM4TS) and training Time Series Foundation Models (TSFMs) from scratch. Both approaches share a foundational assumption that scaling up model capacity and data volume leads to improved performance. However, we observe a \textit{\textbf{scaling paradox}} in TS models, revealing a puzzling phenomenon that larger models do \emph{NOT} achieve better performance. Through extensive experiments on two model families across four scales (100M to 1.7B parameters) and diverse data (up to 6B observations), we rigorously confirm that the scaling paradox is a pervasive issue. We then diagnose its root cause by analyzing internal representations, identifying a phenomenon we call \textit{few-layer dominance}: only a small subset of layers are functionally important, while the majority are redundant, under-utilized, and can even distract training. Based on this discovery, we propose a practical method to automatically identify and retain only these dominant layers. In our models, retaining only 21\% of the parameters achieves up to a 12\% accuracy improvement and a 2.7$\times$ inference speedup. We validate the universality of our method on 8 prominent SOTA models (LLM4TS and TSFMs, 90M to 6B), showing that retaining less than 30\% of layers achieves comparable or superior accuracy in over 95\% of tasks.

The dynamic structural load identification capabilities of the gated recurrent unit, long short-term memory, and convolutional neural networks are examined herein. The examination is on realistic small dataset training conditions and on a comparative view to the physics-based residual Kalman filter (RKF). The dynamic load identification suffers from the uncertainty related to obtaining poor predictions when in civil engineering applications only a low number of tests are performed or are available, or when the structural model is unidentifiable. In considering the methods, first, a simulated structure is investigated under a shaker excitation at the top floor. Second, a building in California is investigated under seismic base excitation, which results in loading for all degrees of freedom. Finally, the International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring benchmark problem is examined for impact and instant loading conditions. Importantly, the methods are shown to outperform each other on different loading scenarios, while the RKF is shown to outperform the networks in physically parametrized identifiable cases.