An industry-backed researcher who has forged a career sowing doubt about the dangers of pollutants is attempting to use artificial intelligence (AI) to amplify his perspective. Louis Anthony "Tony" Cox Jr, a Denver-based risk analyst and former Trump adviser who once reportedly claimed there is no proof that cleaning air saves lives, is developing an AI application to scan academic research for what he sees as the false conflation of correlation with causation. Cox has described the project as an attempt to weed "propaganda" out of epidemiological research and perform "critical thinking at scale" in emails to industry researchers, which were obtained via Freedom of Information Act requests by the Energy and Policy Institute, a non-profit advocacy group, and exclusively reviewed by the Guardian. He has long leveled accusations of flimsiness at research linking exposure to chemical compounds with health dangers, including on behalf of polluting interests such as cigarette manufacturer Philip Morris and the American Petroleum Institute – a fossil fuel lobbying group he has even allowed to "copy edit" his findings. Both the tobacco and oil industries have a history of weaponizing scientific uncertainty, experts say, with some arguing that similar tactics drive the Trump administration's current deregulatory efforts. The president's May "gold standard" science order, for instance, empowered his appointees to "correct scientific information" and "discipline" those who breach the administration's views, prompting outrage from some scientists. Cox has obtained funding to develop the new AI reviewer from the American Chemistry Council (ACC), the nation's largest chemical industry advocacy group, which counts oil and chemical giants such as Exxon and DuPont as members.

Environmental sustainability is a critical global imperative and existential challenge for humanity. While computing professionals tend to think of computing as a positive technology, there's no doubt it also has significant negative impacts, such as growing environmental damage. Firstly, computing is a rapidly growing consumer of environmental resources (for example, minerals, water), a producer of greenhouse-gas emissions (for example, operational, embodied), a creator of environmental pollution (for example, e-waste), and an enabler of environmentally harmful activities. This damage has grown steadily over decades with little prospect of slowing (see the recent Communications article by Eeckhout2). But secondly, computing has an important role in understanding climate change and reducing greenhouse gas emissions and other environmental damage in a broad array of societal activities (for example, agriculture, transportation, manufacturing, facility management, power generation, and more) and other applications that hope to promote environmental sustainability.

US Republicans are pushing to pass a major spending bill that includes provisions to prevent states from enacting regulations on artificial intelligence. Such untamed growth in AI will take a heavy toll upon the world's dangerously overheating climate, experts have warned. About 1bn tons of planet-heating carbon dioxide are set to be emitted in the US just from AI over the next decade if no restraints are placed on the industry's enormous electricity consumption, according to estimates by researchers at Harvard University and provided to the Guardian. This 10-year timeframe, a period of time in which Republicans want a "pause" of state-level regulations upon AI, will see so much electricity use in data centers for AI purposes that the US will add more greenhouse gases to the atmosphere than Japan does annually, or three times the yearly total from the UK. The exact amount of emissions will depend on power plant efficiency and how much clean energy will be used in the coming years, but the blocking of regulations will also be a factor, said Gianluca Guidi, visiting scholar at the Harvard TH Chan School of Public Health.

The event marked the launch of the company's new business line, Redwood Energy, which will initially repurpose (rather than recycle) batteries with years of remaining life to create renewable-powered microgrids. Such small-scale energy systems can operate on or off the larger electricity grid, providing electricity for businesses or communities. Redwood Materials says many of the batteries it takes in for processing retain more than half their capacity. "We can extract a lot more value from that material by using it as an energy storage project before recycling it," JB Straubel, Redwood's founder and chief executive, said at the event. This first microgrid, housed at the company's facility in the Tahoe Reno Industrial Center, is powered by solar panels and capable of generating 64 megawatt-hours of electricity, making it one of the nation's largest such systems.

Ensemble learning has proven effective in boosting predictive performance, but traditional methods such as bagging, boosting, and dynamic ensemble selection (DES) suffer from high computational cost and limited adaptability to heterogeneous data distributions. To address these limitations, we propose Hellsemble, a novel and interpretable ensemble framework for binary classification that leverages dataset complexity during both training and inference. Hellsemble incrementally partitions the dataset into circles of difficulty by iteratively passing misclassified instances from simpler models to subsequent ones, forming a committee of specialised base learners. Each model is trained on increasingly challenging subsets, while a separate router model learns to assign new instances to the most suitable base model based on inferred difficulty. Hellsemble achieves strong classification accuracy while maintaining computational efficiency and interpretability. Experimental results on OpenML-CC18 and Tabzilla benchmarks demonstrate that Hellsemble often outperforms classical ensemble methods. Our findings suggest that embracing instance-level difficulty offers a promising direction for constructing efficient and robust ensemble systems.

Vision-based scientific foundation models hold significant promise for advancing scientific discovery and innovation. This potential stems from their ability to aggregate images from diverse sources such as varying physical groundings or data acquisition systems and to learn spatio-temporal correlations using transformer architectures. However, tokenizing and aggregating images can be compute-intensive, a challenge not fully addressed by current distributed methods. In this work, we introduce the Distributed Cross-Channel Hierarchical Aggregation (D-CHAG) approach designed for datasets with a large number of channels across image modalities. Our method is compatible with any model-parallel strategy and any type of vision transformer architecture, significantly improving computational efficiency. We evaluated D-CHAG on hyperspectral imaging and weather forecasting tasks. When integrated with tensor parallelism and model sharding, our approach achieved up to a 75% reduction in memory usage and more than doubled sustained throughput on up to 1,024 AMD GPUs on the Frontier Supercomputer.

Drones can inspect overhead power lines while they remain energized, significantly simplifying the inspection process. However, localizing a drone relative to all conductors using an onboard LiDAR sensor presents several challenges: (1) conductors provide minimal surface for LiDAR beams limiting the number of conductor points in a scan, (2) not all conductors are consistently detected, and (3) distinguishing LiDAR points corresponding to conductors from other objects, such as trees and pylons, is difficult. This paper proposes an estimation approach that minimizes the error between LiDAR measurements and a single geometric model representing the entire conductor array, rather than tracking individual conductors separately. Experimental results, using data from a power line drone inspection, demonstrate that this method achieves accurate tracking, with a solver converging under 50 ms per frame, even in the presence of partial observations, noise, and outliers. A sensitivity analysis shows that the estimation approach can tolerate up to twice as many outlier points as valid conductors measurements.

Multivariate time series anomaly detection (MTS-AD) is critical in domains like healthcare, cybersecurity, and industrial monitoring, yet remains challenging due to complex inter-variable dependencies, temporal dynamics, and sparse anomaly labels. We introduce mTSBench, the largest benchmark to date for MTS-AD and unsupervised model selection, spanning 344 labeled time series across 19 datasets and 12 diverse application domains. mTSBench evaluates 24 anomaly detection methods, including large language model (LLM)-based detectors for multivariate time series, and systematically benchmarks unsupervised model selection techniques under standardized conditions. Consistent with prior findings, our results confirm that no single detector excels across datasets, underscoring the importance of model selection. However, even state-of-the-art selection methods remain far from optimal, revealing critical gaps. mTSBench provides a unified evaluation suite to enable rigorous, reproducible comparisons and catalyze future advances in adaptive anomaly detection and robust model selection.

Modern observatories are designed to deliver increasingly detailed views of astrophysical signals. To fully realize the potential of these observations, principled data-analysis methods are required to effectively separate and reconstruct the underlying astrophysical components from data corrupted by noise and instrumental effects. In this work, we introduce a novel multi-frequency Bayesian model of the sky emission field that leverages latent-space tension as an indicator of model misspecification, enabling automated separation of diffuse, point-like, and extended astrophysical emission components across wavelength bands. Deviations from latent-space prior expectations are used as diagnostics for model misspecification, thus systematically guiding the introduction of new sky components, such as point-like and extended sources. We demonstrate the effectiveness of this method on synthetic multi-frequency imaging data and apply it to observational X-ray data from the eROSITA Early Data Release (EDR) of the SN1987A region in the Large Magellanic Cloud (LMC). Our results highlight the method's capability to reconstruct astrophysical components with high accuracy, achieving sub-pixel localization of point sources, robust separation of extended emission, and detailed uncertainty quantification. The developed methodology offers a general and well-founded framework applicable to a wide variety of astronomical datasets, and is therefore well suited to support the analysis needs of next-generation multi-wavelength and multi-messenger surveys.

Transition path sampling (TPS), which involves finding probable paths connecting two points on an energy landscape, remains a challenge due to the complexity of real-world atomistic systems. Current machine learning approaches use expensive, task-specific, and data-free training procedures, limiting their ability to benefit from high-quality datasets and large-scale pre-trained models. In this work, we address TPS by interpreting candidate paths as trajectories sampled from stochastic dynamics induced by the learned score function of pre-trained generative models, specifically denoising diffusion and flow matching. Under these dynamics, finding high-likelihood transition paths becomes equivalent to minimizing the Onsager-Machlup (OM) action functional. This enables us to repurpose pre-trained generative models for TPS in a zero-shot manner, in contrast with bespoke, task-specific approaches in previous work. We demonstrate our approach on varied molecular systems, obtaining diverse, physically realistic transition pathways and generalizing beyond the pre-trained model's original training dataset. Our method can be easily incorporated into new generative models, making it practically relevant as models continue to scale and improve with increased data availability. Code is available at github.com/ASK-Berkeley/OM-TPS.