When Jontay Porter was ten, his father woke him up most mornings before dawn. He and his siblings piled into the family's Sprinter van and drove to an empty field house off the highway. Then, while their friends slept, they ran basketball drills with their father. "All I remember is it still being pitch-black outside, and the two dreaded double doors on the side of the building that would always be left open for us," Jontay told me recently. In the bleary moments just before one of his siblings tugged the field-house doors open, Jontay always secretly wished that they'd be locked. And, one morning, they were. Jontay smiled as he climbed back into the van and drifted off to sleep. But his brother Michael, Jr., who was seventeen months older, was furious. "Michael was the opposite," Jontay said. Jontay grew up in Columbia, Missouri, the fourth of eight children. His father, Michael, Sr., worked in Christian ministry, and his mother, Lisa, homeschooled the children. Both had played college basketball--Lisa, as a high-school senior, had averaged a nearly impossible fifty-eight points per game--and they saw the sport as their children's ticket to college. All eight of the kids eventually got basketball scholarships. By the time Jontay was sixteen, he was six feet ten. In most families, as a five-star high-school recruit, Jontay would have been the main attraction. In 2010, their father got a job on the coaching staff for the women's team at the University of Missouri. The men's coach spotted Michael shooting alone in the arena one night and offered him a full scholarship; he was still in seventh grade. When he accepted it, as a high-school senior, he was the top recruit in the country. His highlight reels had racked up millions of views on YouTube. N.B.A. scouts believed that he could be the next Kevin Durant. "He's a game-changer," a league executive told the New York . Big state schools turn their best athletes into local celebrities, but the hype that followed Michael to Missouri's campus on his recruiting visits was closer to Beatlemania. Fans asked for autographs and took selfies. Jontay, who came along, was seduced by the hysteria. "Frat houses are putting up posters, and he's literally treated like a king," he recalled. "I wanted to be a part of that circus." He skipped his senior year of high school to join the team, too. The brothers were best friends. Still, it was sometimes hard to be one of the top players in the country but just the second best in his family. "He got all the attention," Jontay said.

Journalist hilariously fails claiming WNBA's popularity isn't affected by Caitlin Clark's playoff exit Cooper Gallant slept in his truck for years chasing pro fishing dream: 'I didn't have a Plan B' Deion Sanders dared the media to write the truth about Colorado football: Ok, it's time to end this charade Madison Beer is being accused of ruining Justin Herbert's career, sad Eagles fans & Yankees fans fighting Bears' Devin Bush says win over Jets was'probably one of the easiest NFL games I have played in' WWE great Diamond Dallas Page'would love to see' LA Knight capture title vs Roman Reigns at Money in the Bank El-Sayed responds to Mamdani's feelings about socialism This campaign was never about a'streamer in California': Abdul El-Sayed'LOST THEIR MINDS': Trump rallies Nebraska, Thune warns Republicans Jack Smith wears'No Kings' shirt just days after Trump hearing This man was radicalized by the Taliban. Then he became the US government's secret weapon Art Laffer on socialism threat: 'I don't think these guys are serious' Ken Griffin's $3 BILLION donation sparks NYC vs Miami debate We've seen this playbook before, Gen. Keane says OutKick Sophie Cunningham sends a message to the woman-hating WNBA players with her hat, cranky Prime & Maggie Sajak! Sophie Cunningham is SPOT ON with Criticisms of WNBA Players' Inability to Handle the Spotlight | Don't @ Me w/ Dan Dakich'Nightcaps' is a daily column that catches you up on the biggest sports, news and culture stories you may have missed while pretending to do work at your desk. It runs Monday-Friday on OutKick.com . We've got playoff baseball tonight.

We study the problem of reconstructing the latent geometry of a $d$-dimensional Riemannian manifold from a random geometric graph. While recent works have made significant progress in manifold recovery from random geometric graphs, and more generally from noisy distances, the precision of pairwise distance estimation has been fundamentally constrained by the volumetric barrier, namely the natural sample-spacing scale $n^{-1/d}$ coming from the fact that a generic point of the manifold typically lies at distance of order $n^{-1/d}$ from the nearest sampled point. In this paper, we introduce a novel approach, Orthogonal Ring Distance Estimation Routine (ORDER), which achieves a pointwise distance estimation precision of order $n^{-2/(d+5)}$ up to polylogarithmic factors in $n$ in polynomial time. This strictly beats the volumetric barrier for dimensions $d > 5$. As a consequence of obtaining pointwise precision better than $n^{-1/d}$, we prove that the Gromov--Wasserstein distance between the reconstructed metric measure space and the true latent manifold is of order $n^{-1/d}$. This matches the Wasserstein convergence rate of empirical measures, demonstrating that our reconstructed graph metric is asymptotically as good as having access to the full pairwise distance matrix of the sampled points. Our results are proven in a very general setting which includes general models of noisy pairwise distances, sparse random geometric graphs, and unknown connection probability functions.

This thresholding can further exacerbate the aforementioned degeneracy.

Extreme weather events are widely studied in fields such as agriculture, ecology, and meteorology. The spatio-temporal co-occurrence of extreme events can strengthen or weaken under changing climate conditions. In this paper, we propose a novel approach to model spatio-temporal extremes by integrating climate indices via a conditional variational autoencoder (cXVAE). A convolutional neural network (CNN) is embedded in the decoder to convolve climatological indices with the spatial dependence within the latent space, thereby allowing the decoder to be dependent on the climate variables. There are three main contributions here. First, we demonstrate through extensive simulations that the proposed conditional XVAE accurately emulates spatial fields and recovers spatially and temporally varying extremal dependence with very low computational cost post training. Second, we provide a simple, scalable approach to detecting condition-driven shifts and whether the dependence structure is invariant to the conditioning variable. Third, when dependence is found to be condition-sensitive, the conditional XVAE supports counterfactual experiments allowing intervention on the climate covariate and propagating the associated change through the learned decoder to quantify differences in joint tail risk, co-occurrence ranges, and return metrics. To demonstrate the practical utility and performance of the model in real-world scenarios, we apply our method to analyze the monthly maximum Fire Weather Index (FWI) over eastern Australia from 2014 to 2024 conditioned on the El Niño/Southern Oscillation (ENSO) index.

Background: User reviews of Telehealth and Patient Portal mobile applications (apps) hereon referred to as electronic health (eHealth) apps are a rich source of unsolicited patient feedback, revealing critical insights into patient perceptions. However, the lack of large-scale, annotated datasets specific to privacy and trust has limited the ability of researchers to systematically analyze these concerns using natural language processing (NLP) techniques. Objective: This study aims to develop and benchmark Health App Reviews for Privacy & Trust (HARPT), a large-scale annotated corpus of patient reviews from eHealth apps to advance research in patient privacy and trust. Methods: We employed a multistage data construction strategy. This integrated keyword-based filtering, iterative manual labeling with review, targeted data augmentation, and weak supervision using transformer-based classifiers. A curated subset of 7,000 reviews was manually annotated to support machine learning model development and evaluation. The resulting dataset was used to benchmark a broad range of models. Results: The HARPT corpus comprises 480,000 patient reviews annotated across seven categories capturing critical aspects of trust in the application (TA), trust in the provider (TP), and privacy concerns (PC). We provide comprehensive benchmark performance for a range of machine learning models on the manually annotated subset, establishing a baseline for future research. Conclusions: The HARPT corpus is a significant resource for advancing the study of privacy and trust in the eHealth domain. By providing a large-scale, annotated dataset and initial benchmarks, this work supports reproducible research in usable privacy and trust within health informatics. HARPT is released under an open resource license.

Spin coating polymer thin films to achieve specific mechanical properties is inherently a multi-objective optimization problem. We present a framework that integrates an active Pareto front learning algorithm (PyePAL) with visualization and explainable AI techniques to optimize processing parameters. PyePAL uses Gaussian process models to predict objective values (hardness and elasticity) from the design variables (spin speed, dilution, and polymer mixture), guiding the adaptive selection of samples toward promising regions of the design space. To enable interpretable insights into the high-dimensional design space, we utilize UMAP (Uniform Manifold Approximation and Projection) for two-dimensional visualization of the Pareto front exploration. Additionally, we incorporate fuzzy linguistic summaries, which translate the learned relationships between process parameters and performance objectives into linguistic statements, thus enhancing the explainability and understanding of the optimization results. Experimental results demonstrate that our method efficiently identifies promising polymer designs, while the visual and linguistic explanations facilitate expert-driven analysis and knowledge discovery.

Traditional Chinese Medicine (TCM), with a history spanning over two millennia, plays a role in global healthcare. However, applying large language models (LLMs) to TCM remains challenging due to its reliance on holistic reasoning, implicit logic, and multimodal diagnostic cues. Existing TCM-domain LLMs have made progress in text-based understanding but lack multimodal integration, interpretability, and clinical applicability. To address these limitations, we developed BenCao, a ChatGPT-based multimodal assistant for TCM, integrating structured knowledge bases, diagnostic data, and expert feedback refinement. BenCao was trained through natural language instruction tuning rather than parameter retraining, aligning with expert-level reasoning and ethical norms specific to TCM. The system incorporates a comprehensive knowledge base of over 1,000 classical and modern texts, a scenario-based instruction framework for diverse interactions, a chain-of-thought simulation mechanism for interpretable reasoning, and a feedback refinement process involving licensed TCM practitioners. BenCao connects to external APIs for tongue-image classification and multimodal database retrieval, enabling dynamic access to diagnostic resources. In evaluations across single-choice question benchmarks and multimodal classification tasks, BenCao achieved superior accuracy to general-domain and TCM-domain models, particularly in diagnostics, herb recognition, and constitution classification. The model was deployed as an interactive application on the OpenAI GPTs Store, accessed by nearly 1,000 users globally as of October 2025. This study demonstrates the feasibility of developing a TCM-domain LLM through natural language-based instruction tuning and multimodal integration, offering a practical framework for aligning generative AI with traditional medical reasoning and a scalable pathway for real-world deployment.