We study continuous-time mean--variance portfolio selection in markets where stock prices are diffusion processes driven by observable factors that are also diffusion processes yet the coefficients of these processes are unknown. Based on the recently developed reinforcement learning (RL) theory for diffusion processes, we present a general data-driven RL algorithm that learns the pre-committed investment strategy directly without attempting to learn or estimate the market coefficients. For multi-stock Black--Scholes markets without factors, we further devise a baseline algorithm and prove its performance guarantee by deriving a sublinear regret bound in terms of Sharpe ratio. For performance enhancement and practical implementation, we modify the baseline algorithm into four variants, and carry out an extensive empirical study to compare their performance, in terms of a host of common metrics, with a large number of widely used portfolio allocation strategies on S\&P 500 constituents. The results demonstrate that the continuous-time RL strategies are consistently among the best especially in a volatile bear market, and decisively outperform the model-based continuous-time counterparts by significant margins.

The electricity sector is undergoing substantial transformations due to the rising electrification of demand, enhanced integration of renewable energy resources, and the emergence of new technologies. These changes are rendering the electric grid more volatile and unpredictable, making it difficult to maintain reliable operations. In order to address these issues, advanced time series prediction models are needed for closing the gap between the forecasted and actual grid outcomes. In this paper, we introduce a multivariate time series prediction model that combines traditional state space models with deep learning methods to simultaneously capture and predict the underlying dynamics of multiple time series. Additionally, we design a time series processing module that incorporates high-resolution external forecasts into sequence-to-sequence prediction models, achieving this with negligible increases in size and no loss of accuracy. We also release an extended dataset spanning five years of load, electricity price, ancillary service price, and renewable generation. To complement this dataset, we provide an open-access toolbox that includes our proposed model, the dataset itself, and several state-of-the-art prediction models, thereby creating a unified framework for benchmarking advanced machine learning approaches. Our findings indicate that the proposed model outperforms existing models across various prediction tasks, improving state-of-the-art prediction error by an average of 7% and decreasing model parameters by 43%.

The efficiency of active learning (AL) approaches to identify materials with desired properties relies on the knowledge of a few parameters describing the property. However, these parameters are unknown if the property is governed by a high intricacy of many atomistic processes. Here, we develop an AL workflow based on the sure-independence screening and sparsifying operator (SISSO) symbolic-regression approach. SISSO identifies the few, key parameters correlated with a given materials property via analytical expressions, out of many offered primary features. Crucially, we train ensembles of SISSO models in order to quantify mean predictions and their uncertainty, enabling the use of SISSO in AL. By combining bootstrap sampling to obtain training datasets with Monte-Carlo feature dropout, the high prediction errors observed by a single SISSO model are improved. Besides, the feature dropout procedure alleviates the overconfidence issues observed in the widely used bagging approach. We demonstrate the SISSO-guided AL workflow by identifying acid-stable oxides for water splitting using high-quality DFT-HSE06 calculations. From a pool of 1470 materials, 12 acid-stable materials are identified in only 30 AL iterations. The materials property maps provided by SISSO along with the uncertainty estimates reduce the risk of missing promising portions of the materials space that were overlooked in the initial, possibly biased dataset.

Small unmanned aerial vehicles (UAVs) have become standard tools in reconnaissance and surveying for both civilian and defense applications. In the future, UAVs will likely play a pivotal role in autonomous package delivery, but current multi-rotor candidates suffer from poor energy efficiency leading to insufficient endurance and range. In order to reduce the power demands of package delivery UAVs while still maintaining necessary hovering capabilities, companies like Amazon are experimenting with hybrid Vertical Take-Off and Landing (VTOL) platforms. Tailsitter VTOLs offer a mechanically simple and cost-effective solution compared to other hybrid VTOL configurations, and while advances in hardware and microelectronics have optimized the tailsitter for package delivery, the software behind its operation has largely remained a critical barrier to industry adoption. Tailsitters currently lack a generic, computationally efficient method of control that can provide strong safety and robustness guarantees over the entire flight domain. Further, tailsitters lack a closed-form method of designing dynamically feasible transition maneuvers between hover and cruise. In this paper, we survey the modeling and control methods currently implemented on small-scale tailsitter UAVs, and attempt to leverage a nonlinear dynamic model to design physically realizable, continuous-pitch transition maneuvers at constant altitude. Primary results from this paper isolate potential barriers to constant-altitude transition, and a novel approach to bypassing these barriers is proposed. While initial results are unsuccessful at providing feasible transition, this work acts as a stepping stone for future efforts to design new transition maneuvers that are safe, robust, and computationally efficient.

Data acquisition in array signal processing (ASP) is costly because achieving high angular and range resolutions necessitates large antenna apertures and wide frequency bandwidths, respectively. The data requirements for ASP problems grow multiplicatively with the number of viewpoints and frequencies, significantly increasing the burden of data collection, even for simulation. Implicit Neural Representations (INRs) -- neural network-based models of 3D objects and scenes -- offer compact and continuous representations with minimal radar data. They can interpolate to unseen viewpoints and potentially address the sampling cost in ASP problems. In this work, we select Synthetic Aperture Radar (SAR) as a case from ASP and propose Radon Implicit Field Transform (RIFT). RIFT consists of two components: a classical forward model for radar (Generalized Radon Transform, GRT), and an INR based scene representation learned from radar signals. This method can be extended to other ASP problems by replacing the GRT with appropriate algorithms corresponding to different data modalities. In our experiments, we first synthesize radar data using the GRT. We then train the INR model on this synthetic data by minimizing the reconstruction error of the radar signal. After training, we render the scene using the trained INR and evaluate our scene representation against the ground truth scene. Due to the lack of existing benchmarks, we introduce two main new error metrics: phase-Root Mean Square Error (p-RMSE) for radar signal interpolation, and magnitude-Structural Similarity Index measure(m-SSIM) for scene reconstruction. These metrics adapt traditional error measures to account for the complex nature of radar signals. Compared to traditional scene models in radar signal processing, with only 10% data footprint, our RIFT model achieves up to 188% improvement in scene reconstruction.

The internal structure of Jupiter is constrained by the precise gravity field measurements by NASA's Juno mission, atmospheric data from the Galileo entry probe, and Voyager radio occultations. Not only are these observations few compared to the possible interior setups and their multiple controlling parameters, but they remain challenging to reconcile. As a complex, multidimensional problem, characterizing typical structures can help simplify the modeling process. We used NeuralCMS, a deep learning model based on the accurate concentric Maclaurin spheroid (CMS) method, coupled with a fully consistent wind model to efficiently explore a wide range of interior models without prior assumptions. We then identified those consistent with the measurements and clustered the plausible combinations of parameters controlling the interior. We determine the plausible ranges of internal structures and the dynamical contributions to Jupiter's gravity field. Four typical interior structures are identified, characterized by their envelope and core properties. This reduces the dimensionality of Jupiter's interior to only two effective parameters. Within the reduced 2D phase space, we show that the most observationally constrained structures fall within one of the key structures, but they require a higher 1 bar temperature than the observed value. We provide a robust framework for characterizing giant planet interiors with consistent wind treatment, demonstrating that for Jupiter, wind constraints strongly impact the gravity harmonics while the interior parameter distribution remains largely unchanged. Importantly, we find that Jupiter's interior can be described by two effective parameters that clearly distinguish the four characteristic structures and conclude that atmospheric measurements may not fully represent the entire envelope.

This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact on AI and Generative Models. Starting with foundational concepts, we delve into how MLLMs integrate various data types, including text, images, video and audio, to enable complex AI systems for cross-modal understanding and generation. It covers essential topics such as training methods, architectural components, and practical applications in various fields, from visual storytelling to enhanced accessibility. Through detailed case studies and technical analysis, the text examines prominent MLLM implementations while addressing key challenges in scalability, robustness, and cross-modal learning. Concluding with a discussion of ethical considerations, responsible AI development, and future directions, this authoritative resource provides both theoretical frameworks and practical insights. It offers a balanced perspective on the opportunities and challenges in the development and deployment of MLLMs, and is highly valuable for researchers, practitioners, and students interested in the intersection of natural language processing and computer vision.

This story originally appeared on Inside Climate News and is part of the Climate Desk collaboration. In the old ranchlands of South Texas, dormant uranium mines are coming back online. A collection of new ones hope to start production soon, extracting radioactive fuel from the region's shallow aquifers. These mines are the leading edge of what government and industry leaders in Texas hope will be a nuclear renaissance, as America's latent nuclear sector begins to stir again. Texas is currently developing a host of high-tech industries that require enormous amounts of electricity, from cryptocurrency mines and artificial intelligence to hydrogen production and seawater desalination.

Chugoku Electric Power on Saturday restarted its Shimane nuclear power station in western Japan, shuttered since shortly after the 2011 Fukushima meltdown, the company said. The long-delayed restart of the 820 plant's megawatt (MW) No. 2 reactor, which was shut down in January 2012, boosts the number of Japan's operational reactors to 14, with a combined capacity of 13,253 MW. Japan's demand for liquefied natural gas and thermal coal is expected to fall next year, with Tohoku Electric Power also recently resuming operations of the 825 MW No. 2 reactor at its Onagawa nuclear power plant in northern Japan. The increased operation of nuclear power plants is expected to help Japan meet the growing power demand from semiconductor plants and data centers that support artificial intelligence applications. The government anticipates power output to grow to between 1.35 trillion and 1.5 trillion kilowatt-hours (kWh) by 2050, from 1 trillion kWh projected for this decade, as Japan establishes more data centers, chip factories and other energy-intensive businesses.

In this work, we develop a novel neural operator, the Solute Transport Operator Network (STONet), to efficiently model contaminant transport in micro-cracked reservoirs. The model combines different networks to encode heterogeneous properties effectively. By predicting the concentration rate, we are able to accurately model the transport process. Numerical experiments demonstrate that our neural operator approach achieves accuracy comparable to that of the finite element method. The previously introduced Enriched DeepONet architecture has been revised, motivated by the architecture of the popular multi-head attention of transformers, to improve its performance without increasing the compute cost. The computational efficiency of the proposed model enables rapid and accurate predictions of solute transport, facilitating the optimization of reservoir management strategies and the assessment of environmental impacts. The data and code for the paper will be published at https://github.com/ehsanhaghighat/STONet.