Nonlinear mixed-effects models are widely used to analyze longitudinal data, but existing open-source software often supports only a limited subset of the model structures, inference methods, machine-learning components, automatic differentiation techniques, and random-effects distributions required in modern applications. We introduce NoLimits.jl, an open-source Julia package for flexible and composable nonlinear mixed-effects modeling. Its macro-based modeling language enables observation and latent-state models to be constructed from diverse building blocks, including ordinary differential equations, Markov models, and neural networks. NoLimits.jl supports flexible, covariate-dependent observation and random-effects distributions and provides a unified interface to frequentist inference through Laplace approximation, stochastic expectation maximization, and Bayesian Markov chain Monte Carlo methods. We demonstrate the package on three case studies showcasing its workflows, integration of differentiable machine-learning components, and data-driven estimation of random-effects distributions using normalizing flows. Together, these capabilities substantially expand the range of nonlinear mixed-effects models that can be specified, estimated, and compared within a single open-source framework.

Visual assessment of residual plots is a common approach for diagnosing linear models, but it relies on manual evaluation, which does not scale well and can lead to inconsistent decisions across analysts. The lineup protocol, which embeds the observed plot among null plots, can reduce subjectivity but requires even more human effort. In today's data-driven world, such tasks are well suited for automation. We present a new R package that uses a computer vision model to automate the evaluation of residual plots. An accompanying Shiny application is provided for ease of use. Given a sample of residuals, the model predicts a visual signal strength (VSS) and offers supporting information to help analysts assess model fit.

Data augmentation is a simple and model-agnostic approach for exploiting known invariances in learning problems. Given a group acting on the input space, one augments the training set with transformed copies of each sample. Because it exploits symmetries without modifying the underlying learning algorithm, data augmentation can be applied broadly across learning methods. However, this universality comes at a computational cost: when the group is large, full group-sized augmentation quickly becomes computationally infeasible. This raises a fundamental question: Can partial data augmentation achieve the same statistical benefits as full augmentation in terms of generalization and sample complexity? We develop a general framework for investigating this question using Fourier analysis and the representation theory of finite groups. We show that, for a broad class of classical learning problems, partial data augmentation based on a randomly sampled subset of group elements achieves the same minimax rates as full augmentation, up to an approximation error that vanishes as the subset size increases. Our results provide a theoretical explanation for why partial augmentation can retain the statistical benefits of full augmentation despite enforcing symmetry only approximately, and shed light on a recently raised question in learning with symmetries: whether statistically optimal learning under general group invariances can be achieved using computationally scalable methods. Moreover, we prove a complementary impossibility result: enforcing exact invariance via data augmentation requires averaging over the entire group, and cannot be achieved by any strict subset when the hypothesis space is sufficiently expressive. Together, these results provide a unified perspective on full and partial data augmentation, as well as exact and approximate symmetry enforcement.

AI-assisted interviews promise to reduce respondent burden in surveys by allowing respondents to describe experiences naturally while an AI system noisily maps those accounts into structured survey variables. That mapping is a measurement process that is fallible, versioned, adaptive, and potentially behaves differently across subgroups. This paper proposes Adaptive Matrix Validation (AMV), a design in which each respondent completes an AI-assisted interview, which is then mapped into tabular data by the AI. Respondents are also asked a small, randomized set of structured questions, which are used for statistical adjustment. The estimator first calibrates the mapped values using validation answers from other respondents, then corrects the remaining error with the validation answers observed for the target respondent. The paper develops estimators for item means, subgroup estimates, and regression coefficients when outcomes, predictors, or both are mapped from interviews. It also gives planning formulas the number of validation questions required and the sample size. A design-calibration simulation, an American Time Use Survey emulation, and a CHAMPS verbal-autopsy narrative study show when sparse validation can improve precision and when it cannot

Superhuman announced that it has acquired GPTZero. This AI identification business offers services such as hallucination and plagiarism detection as well as a nifty little tool that displays how much of the internet is artificial intelligence. Superhuman said it plans to integrate GPTZero into its Superhuman Go AI assistant to improve the reach of its existing efforts around AI and authenticity. Teachers and students will still be the priority audience for Superhuman following the acquisition. For its part, GPTZero emphasized that Superhuman would also help put its tools in places where people were already reading and writing.

US Olympic legend Bode Miller's alleged illegal drug stash revealed after he was arrested just days before anniversary of daughter's tragic death Trump's inner circle reveal his true feeling on JD Vance... and why the succession war with Rubio is already won: MARK HALPERIN My girlfriend's cuckolding fetish is getting out of hand... Dr. Fauci is subpoenaed after refusing to testify on COVID origins Noah Presgrove's friends hire famous attorney to battle lawsuit claiming teenager was'beat to death' by someone he knew... as fight between'jealous love rivals' emerges Joe Manganiello reveals secret life-threatening health battle which resulted in'amputation' 'Frankenstein' rabbits with tentacles sprouting from their heads invade several US states AMANDA PLATELL: Why Kate must stand firm and protect her family from Sussexes' manipulation - and most of all, her children Shania Twain, 60, slammed for failing to dress age'appropriate' as she hits the stage in VERY racy look Trump's press secretary joins him on Pennsylvania campaign trip less than two months after birth of daughter Vivi Beloved grandma unmasked as killer of autistic granddaughter and second female family member in horror execution-style double murder... as haunting Mother's Day post emerges Dietitians urge caution over'nature's Ozempic' as people take desperate measures to lose weight Aching joints, exhausted, suffering from brain fog... it might not be the menopause says DR PHILIPPA KAYE READ MORE: Scientists blame mothers for Britain's decline in religiosity Religious rituals are practised all around the world - and experts may now know why they're so popular. Researchers have discovered that taking part in ceremonies like baptisms and bat mitzvahs appears to trigger the release of opioids in the brain. These chemicals have been linked to feelings of pain relief, reward and pleasure. They are also released when people take drugs like heroin, morphine and prescription painkillers, producing the'high' that many associate with the experience. The researchers said their findings support the theory that religious rituals evolved as a way for large groups of people to bond.

Things to Do in L.A. Tap to enable a layout that focuses on the article. Fifth grade students work on computers at their South Los Angeles school in 2019. This is read by an automated voice. Please report any issues or inconsistencies here . Los Angeles Unified will ban classroom screen time in preschool through first grade and sharply limit it for older students.

The'Parasite of Parasites' Has Been Discovered in the Tropical Forests of Borneo A newly identified species of fungus attacks the famous "zombie mushrooms" that control ants. Scientists from the Universiti Malaysia Sabah have discovered a newly identified "parasite of parasites " in the tropical forests of Borneo. More specifically, it is what the researchers describe as a hyperparasite--an organism capable of parasitizing other parasites. In this case, its targets are zombie fungi. The new fungal species, named for its distinctive horn-shaped structure, has been described in the journal Phytotaxa.

Image geolocalization is a fundamental yet challenging task, aiming at inferring the geolocation on Earth where an image is taken. State-of-the-art methods employ either grid-based classification or gallery-based image-location retrieval, whose spatial generalizability significantly suffers if the spatial distribution of test images does not align with the choices of grids and galleries. Recently emerging generative approaches, while getting rid of grids and galleries, use raw geographical coordinates and suffer quality losses due to their lack of multi-scale information. To address these limitations, we propose a multi-scale latent diffusion model called LocDiff for image geolocalization. We developed a novel positional encoding-decoding framework called Spherical Harmonics Dirac Delta (SHDD) Representations, which encodes points on a spherical surface (e.g., geolocations on Earth) into a Hilbert space of Spherical Harmonics coefficients and decodes points (geolocations) by mode-seeking on spherical probability distributions. We also propose a novel SirenNet-based architecture (CS-UNet) to learn an image-based conditional backward process in the latent SHDD space by minimizing a latent KL-divergence loss. To the best of our knowledge, LocDiff is the first image geolocalization model that performs latent diffusion in a multi-scale location encoding space and generates geolocations under the guidance of images. Experimental results show that LocDiff can outperform all state-of-the-art grid-based, retrieval-based, and diffusion-based baselines across 5 challenging global-scale image geolocalization datasets, and demonstrates significantly stronger generalizability to unseen geolocations.

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