Taylor Sheridan's new war movie gets major update, legendary director attached LPGA star Nelly Korda sizzles on the beach, Dems won't stop dancing & Gia Duddy whips up a bikini lunch Paige Spiranac provides an update on'Great Cans' saga, fan's still MIA but others have picked up the slack Ivanka Trump has the angry libs on high alert as she slides into an amazing dress, Waffle House chaos & MEAT! Donald Trump makes odd'hair' comment to Danica Patrick at TPUSA event Islamabad enters'red zone' lockdown ahead of expected US-Iran peace talks Holocaust survivor known as'Crossing Guard Diva' goes viral for glam style House Ethics Committee weighs action against Rep. Cherfilus-McCormick'Sinister' links suspected in mysterious deaths of scientists Welcome to the numerous new Screencaps readers - trust me, you have to give this column two weeks to understand what's going on If you are one of the hundreds of thousands of new Screencaps readers who found this column on Monday, welcome back. You're about to become hooked. Just go ahead and clear your daily schedule at 9 a.m. for America's Best Daily Column, as named by the readers who've been with me for years. In some cases, readers have been with me for over a decade. This column is their talk radio.

FBI Director Kash Patel is expected to tout Trump-era reforms at a Senate hearing Wednesday, many of which Patel thinks most American's don't know about.

One icy morning in February, nearly 200 people gathered in a church in downtown Richmond, Va. Most had awakened before dawn and driven in from across the state. There were Republicans and Democrats from rural farms and D.C. exurbs. They shared one goal: to fight back against AI development in a region with the largest concentration of data centers in the world. "Aren't you tired of being ignored by both parties, and having your quality of life and your environment absolutely destroyed by corporate greed?" state senator Danica Roem said, to a standing ovation. The activists--wearing homemade shirts with slogans like Boondoggle: Data Center in Botetourt County--marched to the state capitol and spent the day testifying to lawmakers about their fears over data centers' impacts on electricity, water, noise pollution, and more. Some lawmakers pledged to help: "You're getting a sh-t deal," state delegate John McAuliff told activists. The phrase captured many people's feelings toward the AI industry as a whole. Not much unites Americans these days.

Spatial boundaries, such as ecological transitions or climatic regime interfaces, capture steep environmental gradients, and shifts in their structure can signal emerging environmental changes. Quantifying uncertainty in spatial boundary locations and formally testing for temporal shifts remains challenging, especially when boundaries are derived from noisy, gridded environmental data. We present a unified framework that combines heteroskedastic Gaussian process (GP) regression with a scaled Maximum Absolute Difference (MAD) Global Envelope Test (GET) to estimate spatial boundary curves and assess whether they evolve over time. The heteroskedastic GP provides a flexible probabilistic reconstruction of boundary lines, capturing spatially varying mean structure and location specific variability, while the test offers a rigorous hypothesis testing tool for detecting departures from expected boundary behaviors. Simulation studies show that the proposed method achieves the correct size under the null and high power for detecting local boundary shifts. Applying our framework to the Sahel Sahara transition zone, using annual Koppen Trewartha climate classifications from 1960 to 1989, we find no statistically significant decade scale changes in the arid and semi arid or semi arid and non arid interfaces. However, the method successfully identifies localized boundary shifts during the extreme drought years of 1983 and 1984, consistent with climate studies documenting regional anomalies in these interfaces during that period.

The singer leaves behind two of the greatest albums of the century--and generations of artists still struggling to keep up. Great artists who are the opposite of prolific are always a thorny subject. Many of our most romantic ideas about creativity tend to view "genius" as a kind of vessel state, from which beauty and inspiration simply flow forth, effortlessly and boundlessly: It's deflating to be confronted with the reality that this isn't always how it works. And, of course, when such artists come to be the subjects of intense devotion and scrutiny, it often provokes a demand for more and more, faster and faster, which usually has the counterproductive effect of further pressurizing an already fraught creative process. And yet these artists are distinctively precious in their own way, necessary reminders (particularly in our age of pathological, parasocial standom) that even stars don't exist solely as objects for our consumption, that sharing a world with people who provide us with beautiful things is a privilege to be cherished and cared for, rather than an entitlement to be hoarded or otherwise fetishized.

Beta regression is commonly employed when the outcome variable is a proportion. Since its conception, the approach has been widely used in applications spanning various scientific fields. A series of extensions have been proposed over time, several of which address variable selection and penalized estimation, e.g., with an $\ell_1$-penalty (LASSO). However, a theoretical analysis of this popular approach in the context of Beta regression with high-dimensional predictors is lacking. In this paper, we aim to close this gap. A particular challenge arises from the non-convexity of the associated negative log-likelihood, which we address by resorting to a framework for analyzing stationary points in a neighborhood of the target parameter. Leveraging this framework, we derive a non-asymptotic bound on the $\ell_1$-error of such stationary points. In addition, we propose a debiasing approach to construct confidence intervals for the regression parameters. A proximal gradient algorithm is devised for optimizing the resulting penalized negative log-likelihood function. Our theoretical analysis is corroborated via simulation studies, and a real data example concerning the prediction of county-level proportions of incarceration is presented to showcase the practical utility of our methodology.

This study investigates the usage of "slave" and "servant" in the 19th century US newspapers using computational methods. While both terms were used to refer to enslaved African Americans, they were used in distinct ways. In the Chronicling America corpus, we included possible OCR errors by using FastText embedding and excluded text reprints to consider text reprint culture in the 19th century. Word2vec embedding was used to find semantically close words to "slave" and "servant" and log-odds ratio was calculated to identify over-represented discourse words in the Southern and Northern newspapers. We found that "slave" is associated with socio-economic, legal, and administrative words, however, "servant" is linked to religious words in the Northern newspapers while Southern newspapers associated "servant" with domestic and familial words. We further found that slave discourse words in Southern newspapers are more prevalent in Northern newspapers while servant discourse words from each side are prevalent in their own region. This study contributes to the understanding of how newspapers created different discourses around enslaved African Americans in the 19th century US.

Local governments around the world are making consequential decisions on behalf of their constituents, and these constituents are responding with requests, advice, and assessments of their officials at public meetings. So many small meetings cannot be covered by traditional newsrooms at scale. We propose PUBLICSPEAK, a probabilistic framework which can utilize meeting structure, domain knowledge, and linguistic information to discover public remarks in local government meetings. We then use our approach to inspect the issues raised by constituents in 7 cities across the United States. We evaluate our approach on a novel dataset of local government meetings and find that PUBLICSPEAK improves over state-of-the-art by 10% on average, and by up to 40%.

Currently, depression treatment relies on closely monitoring patients response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, burdensome, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. Our results show that this domain adaptation approach can lead to significantly better prediction than that with no domain adaptation. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.67, comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome.