The vehicle traveled 1,806 miles on a single tank of hydrogen. Hydrogen-powered trucks are making waves in the world of clean transportation, and the H2Rescue truck just set a new Guinness World Record to prove it. This impressive vehicle, developed by Cummins Accelera in collaboration with the U.S. Department of Energy and Department of Defense, traveled an astounding 1,806 miles on a single tank of hydrogen. The H2Rescue truck embarked on its record-setting trip in California, carrying 386 pounds of hydrogen fuel. Throughout the journey, it navigated rush hour traffic, maintained speeds between 50 and 55 mph and operated in temperatures ranging from 60 to 80 degrees Fahrenheit.

Russia's Ministry of Defence said the army gained control of the settlement of Shevchenko, near the logistical centre of Pokrovsk, a key target in its advance through Ukraine's eastern Donetsk region. Ukraine has yet to acknowledge the loss of the town. Ukraine's General Staff of the Armed Forces said it repelled 46 of 56 Russian attacks around a dozen towns in the Pokrovsk sector and several clashes were ongoing. A Ukrainian drone hit one of Russia's largest oil refineries – in Taneko, Tatarstan – according to Russian Telegram channel ASTRA. Fuel oil that spilled from wrecked Russian tankers has spread into the Sea of Azov and reached the shores of Ukraine's partly Russian-occupied Zaporizhia region, a Moscow-installed official said.

Catastrophe brings a search for accountability. As fires wreak havoc in California, Sen. Ed Markey, D-Mass., claimed in a post on X the catastrophe is "what a climate emergency looks like." He took aim at President-elect Trump, asserting the incoming president has been bought off by the oil industry. "Trump has been bought for 1 billion by Big Oil. Just a payoff to kill the IRA and the Green New Deal. We know what will happen. The LA fires are preview of coming atrocities," Markey declared in a post on X. Markey, who claims there is a "climate crisis," has also warned about the potential effects of artificial intelligence (AI).

Data-driven machine learning (ML) has demonstrated tremendous potential in material property predictions. However, the scarcity of materials data with costly property labels in the vast chemical space presents a significant challenge for ML in efficiently predicting properties and uncovering structure-property relationships. Here, we propose a novel hierarchy-boosted funnel learning (HiBoFL) framework, which is successfully applied to identify semiconductors with ultralow lattice thermal conductivity ($\kappa_\mathrm{L}$). By training on only a few hundred materials targeted by unsupervised learning from a pool of hundreds of thousands, we achieve efficient and interpretable supervised predictions of ultralow $\kappa_\mathrm{L}$, thereby circumventing large-scale brute-force calculations without clear objectives. As a result, we provide a list of candidates with ultralow $\kappa_\mathrm{L}$ for potential thermoelectric applications and discover a new factor that significantly influences structural anharmonicity. This study offers a novel practical pathway for accelerating the discovery of functional materials.

Deep Reinforcement Learning (DRL) has been extensively used to address portfolio optimization problems. The DRL agents acquire knowledge and make decisions through unsupervised interactions with their environment without requiring explicit knowledge of the joint dynamics of portfolio assets. Among these DRL algorithms, the combination of actor-critic algorithms and deep function approximators is the most widely used DRL algorithm. Here, we find that training the DRL agent using the actor-critic algorithm and deep function approximators may lead to scenarios where the improvement in the DRL agent's risk-adjusted profitability is not significant. We propose that such situations primarily arise from the following two problems: sparsity in positive reward and the curse of dimensionality. These limitations prevent DRL agents from comprehensively learning asset price change patterns in the training environment. As a result, the DRL agents cannot explore the dynamic portfolio optimization policy to improve the risk-adjusted profitability in the training process. To address these problems, we propose a novel multi-agent Hierarchical Deep Reinforcement Learning (HDRL) algorithmic framework in this research. Under this framework, the agents work together as a learning system for portfolio optimization. Specifically, by designing an auxiliary agent that works together with the executive agent for optimal policy exploration, the learning system can focus on exploring the policy with higher risk-adjusted return in the action space with positive return and low variance. In this way, we can overcome the issue of the curse of dimensionality and improve the training efficiency in the positive reward sparse environment.

We introduce Neural Discrete Equilibrium (NeurDE), a machine learning (ML) approach for long-term forecasting of flow phenomena that relies on a "lifting" of physical conservation laws into the framework of kinetic theory. The kinetic formulation provides an excellent structure for ML algorithms by separating nonlinear, non-local physics into a nonlinear but local relaxation to equilibrium and a linear non-local transport. This separation allows the ML to focus on the local nonlinear components while addressing the simpler linear transport with efficient classical numerical algorithms. To accomplish this, we design an operator network that maps macroscopic observables to equilibrium states in a manner that maximizes entropy, yielding expressive BGK-type collisions. By incorporating our surrogate equilibrium into the lattice Boltzmann (LB) algorithm, we achieve accurate flow forecasts for a wide range of challenging flows. We show that NeurDE enables accurate prediction of compressible flows, including supersonic flows, while tracking shocks over hundreds of time steps, using a small velocity lattice-a heretofore unattainable feat without expensive numerical root finding.

We develop a procedure for substantially improving the quality of segmented 3D micro-Computed Tomography (micro-CT) images of rocks with a Machine Learning (ML) Generative Model. The proposed model enhances the resolution eightfold (8x) and addresses segmentation inaccuracies due to the overlapping X-ray attenuation in micro-CT measurement for different rock minerals and phases. The proposed generative model is a 3D Deep Convolutional Wasserstein Generative Adversarial Network with Gradient Penalty (3D DC WGAN-GP). The algorithm is trained on segmented 3D low-resolution micro-CT images and segmented unpaired complementary 2D high-resolution Laser Scanning Microscope (LSM) images. The algorithm was demonstrated on multiple samples of Berea sandstones. We achieved high-quality super-resolved 3D images with a resolution of 0.4375 micro-m/voxel and accurate segmentation for constituting minerals and pore space. The described procedure can significantly expand the modern capabilities of digital rock physics.

Bayesian Neural Networks (BNNs) offer robust uncertainty quantification in model predictions, but training them presents a significant computational challenge. This is mainly due to the problem of sampling multimodal posterior distributions using Markov Chain Monte Carlo (MCMC) sampling and variational inference algorithms. Moreover, the number of model parameters scales exponentially with additional hidden layers, neurons, and features in the dataset. Typically, a significant portion of these densely connected parameters are redundant and pruning a neural network not only improves portability but also has the potential for better generalisation capabilities. In this study, we address some of the challenges by leveraging MCMC sampling with network pruning to obtain compact probabilistic models having removed redundant parameters. We sample the posterior distribution of model parameters (weights and biases) and prune weights with low importance, resulting in a compact model. We ensure that the compact BNN retains its ability to estimate uncertainty via the posterior distribution while retaining the model training and generalisation performance accuracy by adapting post-pruning resampling. We evaluate the effectiveness of our MCMC pruning strategy on selected benchmark datasets for regression and classification problems through empirical result analysis. We also consider two coral reef drill-core lithology classification datasets to test the robustness of the pruning model in complex real-world datasets. We further investigate if refining compact BNN can retain any loss of performance. Our results demonstrate the feasibility of training and pruning BNNs using MCMC whilst retaining generalisation performance with over 75% reduction in network size. This paves the way for developing compact BNN models that provide uncertainty estimates for real-world applications.

Most modern LLMs use Transformers [30] as their backbones, which demonstrate significant advantages over many existing neural network models. Transformers achieve many state-of-the-art performances in learning tasks including natural language processing [33] and computer vision [18]. However, the underlying mechanism for the success of Transformers remains largely a mystery to theoretical researchers. It has been discussed in a line of recent works [2, 4, 15, 38] that, instead of learning simple prediction rules (such as a linear model) Transformers are capable of learning to perform learning algorithms that can automatically generate new prediction rules. For instance, when a new dataset is organized as the input of a Transformer, the model can automatically perform linear regression on this new dataset to produce a newly fitted linear model and make predictions accordingly. This idea of treating Transformers as "algorithm approximators" has provided insights into the power of large language models. However, these existing works only provide guarantees for the in-context supervised learning capacities of Transformers. It remains unclear whether Transformers are capable of handling unsupervised tasks as well.

Online trajectory optimization and optimal control methods are crucial for enabling sustainable unmanned aerial vehicle (UAV) services, such as agriculture, environmental monitoring, and transportation, where available actuation and energy are limited. However, optimal controllers are highly sensitive to model mismatch, which can occur due to loaded equipment, packages to be delivered, or pre-existing variability in fundamental structural and thrust-related parameters. To circumvent this problem, optimal controllers can be paired with parameter estimators to improve their trajectory planning performance and perform adaptive control. However, UAV platforms are limited in terms of onboard processing power, oftentimes making nonlinear parameter estimation too computationally expensive to consider. To address these issues, we propose a relaxed, affine-in-parameters multirotor model along with an efficient optimal parameter estimator. We convexify the nominal Moving Horizon Parameter Estimation (MHPE) problem into a linear-quadratic form (LQ-MHPE) via an affine-in-parameter relaxation on the nonlinear dynamics, resulting in fast quadratic programs (QPs) that facilitate adaptive Model Predictve Control (MPC) in real time. We compare this approach to the equivalent nonlinear estimator in Monte Carlo simulations, demonstrating a decrease in average solve time and trajectory optimality cost by 98.2% and 23.9-56.2%, respectively.