Large-scale tuberculosis (TB) screening is limited by the high cost and operational complexity of traditional diagnostics, creating a need for artificial-intelligence solutions. We propose DeepGB-TB, a non-invasive system that instantly assigns TB risk scores using only cough audio and basic demographic data. The model couples a lightweight one-dimensional convolutional neural network for audio processing with a gradient-boosted decision tree for tabular features. Its principal innovation is a Cross-Modal Bidirectional Cross-Attention module (CM-BCA) that iteratively exchanges salient cues between modalities, emulating the way clinicians integrate symptoms and risk factors. To meet the clinical priority of minimizing missed cases, we design a Tuberculosis Risk-Balanced Loss (TRBL) that places stronger penalties on false-negative predictions, thereby reducing high-risk misclassifications. DeepGB-TB is evaluated on a diverse dataset of 1,105 patients collected across seven countries, achieving an AUROC of 0.903 and an F1-score of 0.851, representing a new state of the art. Its computational efficiency enables real-time, offline inference directly on common mobile devices, making it ideal for low-resource settings. Importantly, the system produces clinically validated explanations that promote trust and adoption by frontline health workers. By coupling AI innovation with public-health requirements for speed, affordability, and reliability, DeepGB-TB offers a tool for advancing global TB control.

Although it is, at least according to the Library of Congress, the most-watched movie of all time, The Wizard of Oz was a costly failure at the box office, and only became a perennial favorite thanks to the regular TV airings that began in the 1950s. But in the decades since it's become a metonym for the wonder of the big screen, a movie even people who prefer their content streaming will make the effort to see in a movie theater. Beginning on Labor Day weekend, audiences will get to experience the movie on perhaps the largest screen ever created. But it won't be The Wizard of Oz as we've come to know it for the better part of a century. The version of the movie that will fill Las Vegas' Sphere starting Aug. 28 has been retooled to fit the venue's curved shell, its images enhanced and expanded to fill four football fields' worth of 16K LED screens--the foundation of an immersive presentation that also includes flames, gusts of wind, and inflatable flying monkeys piloted by drone. It is, to quote the title of a CBS news report, "The Wizard of Oz as you've never seen it before."

The sea floor is home to all sorts of weird and wonderful creatures. But one in particular has become an online sensation, thanks to its impressive'buttocks'. A big–bottomed sea star has been spotted more than 1,000 metres (3,280ft) below the waves. And it appears to have a backside that will make even the most avid gymgoer jealous. This has led many baffled viewers to compare the creature to Patrick from the animated series Spongebob Squarepants.

Three people were killed in a Russian attack on the Stepnohirsk community in Ukraine's Zaporizhia region, the local military administration said on Telegram. Russia launched 405 attacks on 10 settlements in the region in the past day, the administration said on Monday. Russian drone attacks killed three people in the Chuhuiv district of Ukraine's Kharkiv region, the regional prosecutor's office said. The victims included a man killed when Russian drones caused a fire in his home in the village of Losivka, and a man and a woman who were riding a motorcycle when they were killed. The prosecutor's office said it was investigating the motorcycle attack as a possible war crime.

Machine learning models trained on Earth observation data, such as satellite imagery, have demonstrated significant promise in predicting household-level wealth indices, enabling the creation of high-resolution wealth maps that can be leveraged across multiple causal trials. However, because standard training objectives prioritize overall predictive accuracy, these predictions inherently suffer from shrinkage toward the mean, leading to attenuated estimates of causal treatment effects and limiting their utility in policy. Existing debiasing methods, such as Prediction-Powered Inference, can handle this attenuation bias but require additional fresh ground-truth data at the downstream stage of causal inference, which restricts their applicability in data-scarce environments. Here, we introduce and evaluate two correction methods -- linear calibration correction and Tweedie's correction -- that substantially reduce prediction bias without relying on newly collected labeled data. Linear calibration corrects bias through a straightforward linear transformation derived from held-out calibration data, whereas Tweedie's correction leverages empirical Bayes principles to directly address shrinkage-induced biases by exploiting score functions derived from the model's learning patterns. Through analytical exercises and experiments using Demographic and Health Survey data, we demonstrate that the proposed methods meet or outperform existing approaches that either require (a) adjustments to training pipelines or (b) additional labeled data. These approaches may represent a promising avenue for improving the reliability of causal inference when direct outcome measures are limited or unavailable, enabling a "one map, many trials" paradigm where a single upstream data creation team produces predictions usable by many downstream teams across diverse ML pipelines.

To reach human level intelligence, learning algorithms need to incorporate causal reasoning. But identifying causality, and particularly counterfactual reasoning, remains an elusive task. In this paper, we make progress on this task by utilizing instrumental variables (IVs). IVs are a classic tool for mitigating bias from unobserved confounders when estimating causal effects. While IV methods have been extended to non-separable structural models at the population level, existing approaches to counterfactual prediction typically assume additive noise in the outcome. In this paper, we show that under standard IV assumptions, along with the assumptions that latent noises in treatment and outcome are strictly monotonic and jointly Gaussian, the treatment-outcome relationship becomes uniquely identifiable from observed data. This enables counterfactual inference even in non-separable models. We implement our approach by training a normalizing flow to maximize the likelihood of the observed data, demonstrating accurate recovery of the underlying outcome function. We call our method Flow IV .

Accurate solar energy output prediction is key for integrating renewables into grids, maintaining stability, and improving energy management. However, standard error metrics such as Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Skill Scores (SS) fail to capture the multidimensional nature of solar irradiance forecasting. These metrics lack sensitivity to forecastability, rely on arbitrary baselines (e.g., clear-sky models), and are poorly suited for operational use. To address this, we introduce the NICEk framework (Normalized Informed Comparison of Errors, with k = 1, 2, 3, Sigma), offering a robust and interpretable evaluation of forecasting models. Each NICEk score corresponds to an Lk norm: NICE1 targets average errors, NICE2 emphasizes large deviations, NICE3 highlights outliers, and NICESigma combines all. Using Monte Carlo simulations and data from 68 stations in the Spanish SIAR network, we evaluated methods including autoregressive models, extreme learning, and smart persistence. Theoretical and empirical results align when assumptions hold (e.g., R^2 ~ 1.0 for NICE2). Most importantly, NICESigma consistently shows higher discriminative power (p < 0.05), outperforming traditional metrics (p > 0.05). The NICEk metrics exhibit stronger statistical significance (e.g., p-values from 10^-6 to 0.004 across horizons) and greater generalizability. They offer a unified and operational alternative to standard error metrics in deterministic solar forecasting.

Large Language Models (LLMs) are known to overuse certain terms like "delve" and "intricate." The exact reasons for these lexical choices, however, have been unclear. Using Meta's Llama model, this study investigates the contribution of Learning from Human Feedback (LHF), under which we subsume Reinforcement Learning from Human Feedback and Direct Preference Optimization. We present a straightforward procedure for detecting the lexical preferences of LLMs that are potentially LHF-induced. Next, we more conclusively link LHF to lexical overuse by experimentally emulating the LHF procedure and demonstrating that participants systematically prefer text variants that include certain words. This lexical overuse can be seen as a sort of misalignment, though our study highlights the potential divergence between the lexical expectations of different populations -- namely LHF workers versus LLM users. Our work contributes to the growing body of research on explainable artificial intelligence and emphasizes the importance of both data and procedural transparency in alignment research.

Traditional machine learning met hods, such as support vector machines (SVM), artificial neural networks (ANN), and logistic regression, have been employed to classify dates based on morphological features like color, t exture, and shape. While effective, these approaches often lack the flexibility and comprehensive quality control needed in modern agricultural practices. To address these limitations, the SmartDate system represents a significant technological advancement by integrat ing deep learning with genetic algorithms and reinforcement learning. This AI-driven system not only excels in date fruit classification but also predicts expiration dates, filling a cruci al gap in existing solutions. SmartDate leverages multispectral and hyperspectral imaging, coupled with Visible-Near-Infrared (VisNIR) spectral sensors, to assess key quality indicators such as moisture c ontent, sugar levels, firmness, and internal defects. This allows for a more thorough evaluation of fruit quality compared to co nventional methods. Moreover, the inclusion of reinforcement learning e nables SmartDate to adapt in real-time to production envir onment changes, optimizing sorting accuracy and ensuring t hat only premium quality dates reach the market.

The diagnosis of induction machines has traditionally relied on model-based methods that require the development of complex dynamic models, making them difficult to implement and computationally expensive. To overcome these limitations, this paper proposes a model-free approach using Graph Neural Networks (GNNs) for fault diagnosis in induction machines. The focus is on detecting multiple fault types -- including eccentricity, bearing defects, and broken rotor bars -- under varying severity levels and load conditions. Unlike traditional approaches, raw current and vibration signals are used as direct inputs, eliminating the need for signal preprocessing or manual feature extraction. The proposed GNN-ASE model automatically learns and extracts relevant features from raw inputs, leveraging the graph structure to capture complex relationships between signal types and fault patterns. It is evaluated for both individual fault detection and multi-class classification of combined fault conditions. Experimental results demonstrate the effectiveness of the proposed model, achieving 92.5\% accuracy for eccentricity defects, 91.2\% for bearing faults, and 93.1\% for broken rotor bar detection. These findings highlight the model's robustness and generalization capability across different operational scenarios. The proposed GNN-based framework offers a lightweight yet powerful solution that simplifies implementation while maintaining high diagnostic performance. It stands as a promising alternative to conventional model-based diagnostic techniques for real-world induction machine monitoring and predictive maintenance.