--Methods for Novel View Synthesis (NVS) have recently found traction in the field of LiDAR simulation and large-scale 3D scene reconstruction. While solutions for faster rendering or handling dynamic scenes have been proposed, LiDAR specific effects remain insufficiently addressed. By explicitly modeling sensor characteristics such as rolling shutter, laser power variations, and intensity falloff, our method achieves more accurate LiDAR simulation compared to existing techniques. We demonstrate the effectiveness of our approach through quantitative and qualitative comparisons with state-of-the-art methods, as well as ablation studies that highlight the importance of each sensor model component. Beyond that, we show that our approach exhibits advanced resimulation capabilities, such as generating high resolution LiDAR scans in the camera perspective. Our code and the resulting dataset are available at https://github.com/richardmarcus/ NVS has become a powerful tool for realistic LiDAR simulation where traditional techniques often fall short.

Fox News anchor Bret Baier examines the U.S. power supply on'Special Report.' Over the next two decades, global electricity demand is expected to double, growth we haven't seen since post-World War II. To meet historic projections, we need to generate abundant, reliable and affordable energy at a massive scale. But new generation won't be enough. We must dramatically modernize the country's electrical grid infrastructure, the invisible backbone of our entire energy system.

Artificial intelligence companies are "fundamentally unprepared" for the consequences of creating systems with human-level intellectual performance, according to a leading AI safety group. The Future of Life Institute (FLI) said none of the firms on its AI safety index scored higher than a D for "existential safety planning". One of the five reviewers of the FLI's report said that, despite aiming to develop artificial general intelligence (AGI), none of the companies scrutinised had "anything like a coherent, actionable plan" to ensure the systems remained safe and controllable. AGI refers to a theoretical stage of AI development at which a system is capable of matching a human in carrying out any intellectual task. OpenAI, the developer of ChatGPT, has said its mission is to ensure AGI "benefits all of humanity".

Predicting the behavior of complex systems in engineering often involves significant uncertainty about operating conditions, such as external loads, environmental effects, and manufacturing variability. As a result, uncertainty quantification (UQ) has become a critical tool in modeling-based engineering, providing methods to identify, characterize, and propagate uncertainty through computational models. However, the stochastic nature of UQ typically requires numerous evaluations of these models, which can be computationally expensive and limit the scope of feasible analyses. To address this, surrogate models, i.e., efficient functional approximations trained on a limited set of simulations, have become central in modern UQ practice. This book chapter presents a concise review of surrogate modeling techniques for UQ, with a focus on the particularly challenging task of capturing the full time-dependent response of dynamical systems. It introduces a classification of time-dependent problems based on the complexity of input excitation and discusses corresponding surrogate approaches, including combinations of principal component analysis with polynomial chaos expansions, time warping techniques, and nonlinear autoregressive models with exogenous inputs (NARX models). Each method is illustrated with simple application examples to clarify the underlying ideas and practical use.

Time series forecasting is a fundamental task with broad applications, yet conventional methods often treat data as discrete sequences, overlooking their origin as noisy samples of continuous processes. Crucially, discrete noisy observations cannot uniquely determine a continuous function; instead, they correspond to a family of plausible functions. Mathematically, time series can be viewed as noisy observations of a continuous function family governed by a shared probability measure. Thus, the forecasting task can be framed as learning the transition from the historical function family to the future function family. This reframing introduces two key challenges: (1) How can we leverage discrete historical and future observations to learn the relationships between their underlying continuous functions? (2) How can we model the transition path in function space from the historical function family to the future function family? To address these challenges, we propose NeuTSFlow, a novel framework that leverages Neural Operators to facilitate flow matching for learning path of measure between historical and future function families. By parameterizing the velocity field of the flow in infinite-dimensional function spaces, NeuTSFlow moves beyond traditional methods that focus on dependencies at discrete points, directly modeling function-level features instead. Experiments on diverse forecasting tasks demonstrate NeuTSFlow's superior accuracy and robustness, validating the effectiveness of the function-family perspective.

Recent variational Bayes methods for geospatial regression, proposed as an alternative to computationally expensive Markov chain Monte Carlo (MCMC) sampling, have leveraged Nearest Neighbor Gaussian processes (NNGP) to achieve scalability. Yet, these variational methods remain inferior in accuracy and speed compared to spNNGP, the state-of-the-art MCMC-based software for NNGP. We introduce spVarBayes, a suite of fast variational Bayesian approaches for large-scale geospatial data analysis using NNGP. Our contributions are primarily computational. We replace auto-differentiation with a combination of calculus of variations, closed-form gradient updates, and linear response corrections for improved variance estimation. We also accommodate covariates (fixed effects) in the model and offer inference on the variance parameters. Simulation experiments demonstrate that we achieve comparable accuracy to spNNGP but with reduced computational costs, and considerably outperform existing variational inference methods in terms of both accuracy and speed. Analysis of a large forest canopy height dataset illustrates the practical implementation of proposed methods and shows that the inference results are consistent with those obtained from the MCMC approach. The proposed methods are implemented in publicly available Github R-package spVarBayes.

Modern digital sky surveys have been acquiring images of billions of galaxies. While these images often provide sufficient details to analyze the shape of the galaxies, accurate analysis of such high volumes of images requires effective automation. Current solutions often rely on machine learning annotation of the galaxy images based on a set of pre-defined classes. Here we introduce a new approach to galaxy image analysis that is based on generative AI. The method simplifies the galaxy images and automatically converts them into a ``skeletonized" form. The simplified images allow accurate measurements of the galaxy shapes and analysis that is not limited to a certain pre-defined set of classes. We demonstrate the method by applying it to galaxy images acquired by the DESI Legacy Survey. The code and data are publicly available. The method was applied to 125,000 DESI Legacy Survey images, and the catalog of the simplified images is publicly available.

Can we acknowledge that our relationship with nature has evolved from human dominance to an intimate interconnectedness, recognizing that nature has genuinely attained a form of "personhood," and that artificial intelligence (AI) can facilitate this transforma - tion, serving as a novel medium for human-nature connection? This article begins by examining the critical role of AI at the heart of the urgent ecological transformation currently underway, exploring the paradigm shift emerging from the intersection of AI and non-human life. The discussion progressively narrows its focus to how this innovative AI-nature paradigm manifests specifically within the fields of art and design, highlighting its distinctiveness from traditional artistic and design media. The article seeks to explore how various artists and designers incorporate AI into ecological, microbiological, and geophysical creative practices. Through a comparative analysis of their creative strategies, it elaborates on the relationship between different applications of AI--such as data analysis, image recognition, and ecological restoration--and their unique artistic expressions, while also considering the extended value inherent in AI-driven art and design. However, the precise value of this emergent design paradigm remains subject to ongoing discourse.

The integration of the Industrial Internet of Things (IIoT) with Artificial Intelligence-Generated Content (AIGC) offers new opportunities for smart manufacturing, but it also introduces challenges related to computation-intensive tasks and low-latency demands. Traditional generative models based on cloud computing are difficult to meet the real-time requirements of AIGC tasks in IIoT environments, and edge computing can effectively reduce latency through task offloading. However, the dynamic nature of AIGC tasks, model switching delays, and resource constraints impose higher demands on edge computing environments. To address these challenges, this paper proposes an AIGC task offloading framework tailored for IIoT edge computing environments, considering the latency and energy consumption caused by AIGC model switching for the first time. IIoT devices acted as multi-agent collaboratively offload their dynamic AIGC tasks to the most appropriate edge servers deployed with different generative models. A model aware AIGC task offloading algorithm based on Multi-Agent Deep Deterministic Policy Gradient (MADDPG-MATO) is devised to minimize the latency and energy. Experimental results show that MADDPG-MATO outperforms baseline algorithms, achieving an average reduction of 6.98% in latency, 7.12% in energy consumption, and a 3.72% increase in task completion rate across four sets of experiments with model numbers ranging from 3 to 6, it is demonstrated that the proposed algorithm is robust and efficient in dynamic, high-load IIoT environments.

The ability to perform reliable long-horizon task planning is crucial for deploying robots in real-world environments. However, directly employing Large Language Models (LLMs) as action sequence generators often results in low success rates due to their limited reasoning ability for long-horizon embodied tasks. In the STEP framework, we construct a subgoal tree through a pair of closed-loop models: a subgoal decomposition model and a leaf node termination model. Within this framework, we develop a hierarchical tree structure that spans from coarse to fine resolutions. The subgoal decomposition model leverages a foundation LLM to break down complex goals into manageable subgoals, thereby spanning the subgoal tree. The leaf node termination model provides real-time feedback based on environmental states, determining when to terminate the tree spanning and ensuring each leaf node can be directly converted into a primitive action. Experiments conducted in both the VirtualHome WAH-NL benchmark and on real robots demonstrate that STEP achieves long-horizon embodied task completion with success rates up to 34% (WAH-NL) and 25% (real robot) outperforming SOTA methods.