Secretary of Energy Chris Wright discusses the economic impact of lowering energy prices, why energy is essential for artificial intelligence dominance, American LNG exports and possible U.S. operation of Ukrainian nuclear plants. Rep. August Pfluger, R-Texas, said that his constituents are feeling optimistic once again about the future of the oil and gas industry in his district and beyond. The Republican represents parts of central Texas that are critical to the industry, including the Permian Basin, as the Trump administration has famously promised to "drill, baby, drill." "Think about the hardworking men and women of the Permian Basin, or the Bakken or the Marcellus, or any other producing area. President Biden said, 'What you do is evil. You producing oil and gas is evil.' I mean, they basically demonized them," he told Fox News Digital in a recent interview.

Energy Secretary Chris Wright discusses the economic impact of lowering energy prices, why energy is essential for artificial intelligence dominance, American LNG exports and possible U.S. operation of Ukrainian nuclear plants. Six House Democrats broke from their party on Thursday to pass a pair of bills blocking Biden administration-era green energy rules. One resolution, led by Rep. Stephanie Bice, R-Okla., seeks to overturn regulations imposed by former President Joe Biden's Department of Energy (DOE) for new clean energy standards targeting walk-in freezers and coolers. Biden speaks during the United Auto Workers union conference at the Marriott Marquis in Washington on Jan. 24, 2024. "I have fought every step of the way to prevent egregious rules from taking effect. These regulations will impose significant financial burdens on small businesses, which will have to absorb major upgrade costs to meet these new, aggressive standards," Bice told Fox News Digital.

It's fairly unusual for high-profile games set in the UK to be set outside London. While indie games - such as the Shropshire-set Everybody's Gone to the Rapture and last year's Barnsley-based laughfest Thank Goodness You're Here! - have ventured further north, bigger games haven't tended to stray beyond the M25. Jason says the US is about 40% of the video games market, so it's important to appeal to players there, and there's a "natural tendency" to follow the norms. Being an independent company, he feels, allows Rebellion to do things differently, and Britain offers lots of inspiration for new settings - if you're prepared to look for them. "The UK, I think, to understand certain aspects of our culture, you've got to dig into it a little bit because we tend to understate things quite a lot."

Land cover classification involves the production of land cover maps, which determine the type of land through remote sensing imagery. Over recent years, such classification is being performed by machine learning classification models, which can give highly accurate predictions on land cover per pixel using large quantities of input training data. However, such models do not currently take account of input measurement uncertainty, which is vital for traceability in metrology. In this work we propose a Bayesian classification framework using generative modelling to take account of input measurement uncertainty. We take the specific case of Bayesian quadratic discriminant analysis, and apply it to land cover datasets from Copernicus Sentinel-2 in 2020 and 2021. We benchmark the performance of the model against more popular classification models used in land cover maps such as random forests and neural networks. We find that such Bayesian models are more trustworthy, in the sense that they are more interpretable, explicitly model the input measurement uncertainty, and maintain predictive performance of class probability outputs across datasets of different years and sizes, whilst also being computationally efficient.

Oceanic processes at fine scales are crucial yet difficult to observe accurately due to limitations in satellite and in-situ measurements. The Surface Water and Ocean Topography (SWOT) mission provides high-resolution Sea Surface Height (SSH) data, though noise patterns often obscure fine scale structures. Current methods struggle with noisy data or require extensive supervised training, limiting their effectiveness on real-world observations. We introduce SIMPGEN (Simulation-Informed Metric and Prior for Generative Ensemble Networks), an unsupervised adversarial learning framework combining real SWOT observations with simulated reference data. SIMPGEN leverages wavelet-informed neural metrics to distinguish noisy from clean fields, guiding realistic SSH reconstructions. Applied to SWOT data, SIMPGEN effectively removes noise, preserving fine-scale features better than existing neural methods. This robust, unsupervised approach not only improves SWOT SSH data interpretation but also demonstrates strong potential for broader oceanographic applications, including data assimilation and super-resolution.

Rigid link flapping mechanisms remain the most practical choice for flapping wing micro-aerial vehicles (MAVs) to carry useful payloads and onboard batteries for free flight due to their long-term durability and reliability. However, to achieve high agility and maneuverability-like insects-MAVs with these mechanisms require significant weight reduction. One approach involves using single-DOF planar rigid linkages, which are rarely optimized dimensionally for high lift and low power so that smaller motors and batteries could be used. We integrated a mechanism simulator based on a quasistatic nonlinear finite element method with an unsteady vortex lattice method-based aerodynamic analysis tool within an optimization routine. We optimized three different mechanism topologies from the literature. As a result, significant power savings were observed up to 42% in some cases, due to increased amplitude and higher lift coefficients resulting from optimized asymmetric sweeping velocity profiles. We also conducted an uncertainty analysis that revealed the need for high manufacturing tolerances to ensure reliable mechanism performance. The presented unified computational tool also facilitates the optimal selection of MAV components based on the payload and flight time requirements.

--Achieving robust and accurate spatial perception under adverse weather and lighting conditions is crucial for the high-level autonomy of self-driving vehicles and robots. However, existing perception algorithms relying on the visible spectrum are highly affected by weather and lighting conditions. A long-wave infrared camera ( i.e., thermal imaging camera) can be a potential solution to achieve high-level robustness. However, the absence of large-scale datasets and standardized benchmarks remains a significant bottleneck to progress in active research for robust visual perception from thermal images. Lastly, we provide in-depth analyses and discuss the challenges revealed by the benchmark results, such as the performance variability for each modality under adverse conditions, domain shift between different sensor modalities, and potential research direction for thermal perception. AUTONOMOUS driving aims to develop intelligent vehicles capable of perceiving their surrounding environments, understanding current contextual information, and making decisions to drive safely without human intervention. Recent advancements in autonomous vehicles, such as Tesla and Waymo, have been driven by deep neural networks and large-scale vehicular datasets, such as KITTI [1], DDAD [2], and nuScenes [3]. Manuscript received March XX, 2025; revised April XX, 2025. This work was supported by the National Research Foundation of Korea(NRF) grant funded by the Korea government(MSIT)(RS-2024-00358935). Ukcheol Shin is with the Robotics Institute, School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, United States of America (e-mail: ushin@andrew.cmu.edu). Jinsun Park is with the School of Computer Science and Engineering, Pusan National University, Busan, Republic of Korea (e-mail: jspark@pusan.ac.kr). Color versions of one or more figures in this article are available at https://doi.org/xx.xxxx/TIV However, a major drawback of existing vehicular datasets is their reliance on visible-spectrum images, which are easily affected by weather and lighting conditions such as rain, fog, dust, haze, and low light. Therefore, recent research has actively explored alternative sensors such as Near-Infrared (NIR) cameras [8], Li-DARs [9], [10], radars [11], [12], and long-wave infrared (LWIR) cameras [13], [14] to achieve reliable and robust visual perception in adverse weather and lighting conditions. Among these sensors, LWIR camera ( i.e., thermal camera) has gained popularity because of its competitive price, adverse weather robustness, and unique modality information ( i.e., temperature).

In edge intelligence systems, deep neural network (DNN) partitioning and data offloading can provide real-time task inference for resource-constrained mobile devices. However, the inference time of DNNs is typically uncertain and cannot be precisely determined in advance, presenting significant challenges in ensuring timely task processing within deadlines. To address the uncertain inference time, we propose a robust optimization scheme to minimize the total energy consumption of mobile devices while meeting task probabilistic deadlines. The scheme only requires the mean and variance information of the inference time, without any prediction methods or distribution functions. The problem is formulated as a mixed-integer nonlinear programming (MINLP) that involves jointly optimizing the DNN model partitioning and the allocation of local CPU/GPU frequencies and uplink bandwidth. To tackle the problem, we first decompose the original problem into two subproblems: resource allocation and DNN model partitioning. Subsequently, the two subproblems with probability constraints are equivalently transformed into deterministic optimization problems using the chance-constrained programming (CCP) method. Finally, the convex optimization technique and the penalty convex-concave procedure (PCCP) technique are employed to obtain the optimal solution of the resource allocation subproblem and a stationary point of the DNN model partitioning subproblem, respectively. The proposed algorithm leverages real-world data from popular hardware platforms and is evaluated on widely used DNN models. Extensive simulations show that our proposed algorithm effectively addresses the inference time uncertainty with probabilistic deadline guarantees while minimizing the energy consumption of mobile devices.

This work explores using Large Language Models (LLMs) to translate user preferences into energy optimization constraints for home appliances. We describe a task where natural language user utterances are converted into formal constraints for smart appliances, within the broader context of a renewable energy community (REC) and in the Italian scenario. We evaluate the effectiveness of various LLMs currently available for Italian in translating these preferences resorting to classical zero-shot, one-shot, and few-shot learning settings, using a pilot dataset of Italian user requests paired with corresponding formal constraint representation. Our contributions include establishing a baseline performance for this task, publicly releasing the dataset and code for further research, and providing insights on observed best practices and limitations of LLMs in this particular domain.

Our framework demonstrates state-of-the-art performance across diverse long-horizon tasks, achieving strong generalization in both simulated and real-world scenarios. Videos and code are available at https://ghiara.github.io/DAHLIA/. I. INTRODUCTION Language-conditioned robotic manipulation is an emerging field at the intersection of robotics, natural language processing, and computer vision, which aims to enable robots to interpret human commands and perform complex tasks using multi-modal sensing [1]. Imitation learning (IL) and reinforcement learning (RL) have traditionally been the dominant approaches for training robotic manipulation policies. However, recent IL and RL methods are often constrained to narrow task distributions, leading to sampling inefficiency and high sensitivity to distributional shifts, which limits their ability to generalize to diverse and complex scenarios. Additionally, both IL and RL are data-driven, requiring large-scale expert demonstrations, yet Internet-scale data collection for embodied AI remains a substantial challenge. In contrast, the natural language processing domain has seen state-of-the-art (SOT A) LLMs like GPT [2] and Llama [3] achieve humanlike semantic understanding and common sense reasoning by training on massive datasets. Within embodied AI, LLMs offer a promising solution to bridge the gap between high-level language instructions and low-level robotic control, 1 Y uan Meng, Xiangtong Y ao, Haihui Y e, Yirui Zhou, and Alois Knoll are with the School of Computation, Information and Technology, Technical University of Munich, Germany. 2 Shengqiang Zhang is with the Center for Information and Language Processing, Ludwig Maximilian University of Munich, Germany. 3 Zhenshan Bing is with the State Key Laboratory for Novel Software Technology, Nanjing University, China.