GFlowNet policies can efficiently find high-quality solutions.

The left side is a zoom-in of the right side. AB at point E, that is, PE is perpendicular to AB . Table 1: Monocular 3D detection performance of Car category on KITTI test set. All results are evaluated on KITTI testing server. Same as KITTI leaderboard, methods are ranked under the moderate difficulty level.

Internal measures that are used to assess the quality of a clustering usually take into account intra-group and/or inter-group criteria.

Artificial intelligence (AI) has been used to find two previously undiscovered possible cave entrances on the Moon, which could support human survival on future space missions. Daniel Le Corre, a PhD researcher at the University of Kent, surveyed less than 0.3% of the lunar surface before finding the two pits. The South Marius Hills Pit, which the university said was previously overlooked by researchers, is in an area thought to be rich in lava tubes, while the Bel'kovich A Pit is close to the Moon's north pole and more likely to be a source of water. The pits were detected using an AI model that was trained to scan publicly available Nasa images and identify pits based on their distinctive shape. The AI model is named Essa, which is short for entrances to sub-surface areas and a nod to the Cornish name of Mr Le Corre's hometown, Saltash.

Unscrupulous foreign firms are using AI-generated images and false back stories to pose as family-run UK businesses to lure in shoppers. Customers say they feel completely ripped off after believing they were buying from independent boutiques in England but were delivered cheap clothes and jewellery, mass-shipped from warehouses in east Asia. Among the websites is C'est La Vie, a shop purporting to be run by couple Eileen and Patrick for 29 years and based in Birmingham's historic Jewellery Quarter - but with a returns address in China. Consumer guide Which? said the growing use of AI tools was making it possible for fraudsters to mislead the public on an unprecedented scale. Another website appearing to use AI-generated images is Mabel & Daisy, a seemingly quintessential, mother and daughter-owned clothing firm, which claims to be based in Bristol but has an address in Hong Kong.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? Russian President Vladimir Putin said his forces have captured almost 5,000 square kilometres (1,930sq miles) of Ukrainian territory so far this year, and Moscow retains the strategic initiative on the battlefield. Russian troops have captured the Ukrainian villages of Novovasylivka in the southeastern Zaporizhia region and Fedorivka in the eastern Donetsk region, Russia's defence ministry said.

Feature attributions are post-training analysis methods that assess how various input features of a machine learning model contribute to an output prediction. Their interpretation is straightforward when features act independently, but becomes less direct when the predictive model involves interactions such as multiplicative relationships or joint feature contributions. In this work, we propose a general theory of higher-order feature attribution, which we develop on the foundation of Integrated Gradients (IG). This work extends existing frameworks in the literature on explainable AI. When using IG as the method of feature attribution, we discover natural connections to statistics and topological signal processing. We provide several theoretical results that establish the theory, and we validate our theory on a few examples.

Monitoring greenhouse gas emissions and evaluating national inventories require efficient, scalable, and reliable inference methods. Top-down approaches, combined with recent advances in satellite observations, provide new opportunities to evaluate emissions at continental and global scales. However, transport models used in these methods remain a key source of uncertainty: they are computationally expensive to run at scale, and their uncertainty is difficult to characterise. Artificial intelligence offers a dual opportunity to accelerate transport simulations and to quantify their associated uncertainty. We present an ensemble-based pipeline for estimating atmospheric transport "footprints", greenhouse gas mole fraction measurements, and their uncertainties using a graph neural network emulator of a Lagrangian Particle Dispersion Model (LPDM). The approach is demonstrated with GOSAT (Greenhouse Gases Observing Satellite) observations for Brazil in 2016. The emulator achieved a ~1000x speed-up over the NAME LPDM, while reproducing large-scale footprint structures. Ensembles were calculated to quantify absolute and relative uncertainty, revealing spatial correlations with prediction error. The results show that ensemble spread highlights low-confidence spatial and temporal predictions for both atmospheric transport footprints and methane mole fractions. While demonstrated here for an LPDM emulator, the approach could be applied more generally to atmospheric transport models, supporting uncertainty-aware greenhouse gas inversion systems and improving the robustness of satellite-based emissions monitoring. With further development, ensemble-based emulators could also help explore systematic LPDM errors, offering a computationally efficient pathway towards a more comprehensive uncertainty budget in greenhouse gas flux estimates.

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