A vital aspect of human intelligence is the ability to compose increasingly complex concepts out of simpler ideas, enabling both rapid learning and adaptation of knowledge. In this paper we show that energy-based models can exhibit this ability by directly combining probability distributions. Samples from the combined distribution correspond to compositions of concepts. For example, given a distribution for smiling faces, and another for male faces, we can combine them to generate smiling male faces. This allows us to generate natural images that simultaneously satisfy conjunctions, disjunctions, and negations of concepts. We evaluate compositional generation abilities of our model on the CelebA dataset of natural faces and synthetic 3D scene images. We also demonstrate other unique advantages of our model, such as the ability to continually learn and incorporate new concepts, or infer compositions of concept properties underlying an image.

Modeling distributions of covariates, or density estimation, is a core challenge in unsupervised learning. However, the majority of work only considers the joint distribution, which has limited relevance to practical situations. A more general and useful problem is arbitrary conditional density estimation, which aims to model any possible conditional distribution over a set of covariates, reflecting the more realistic setting of inference based on prior knowledge. We propose a novel method, Arbitrary Conditioning with Energy (ACE), that can simultaneously estimate the distribution $p(\mathbf{x}_u \mid \mathbf{x}_o)$ for all possible subsets of unobserved features $\mathbf{x}_u$ and observed features $\mathbf{x}_o$. ACE is designed to avoid unnecessary bias and complexity --- we specify densities with a highly expressive energy function and reduce the problem to only learning one-dimensional conditionals (from which more complex distributions can be recovered during inference). This results in an approach that is both simpler and higher-performing than prior methods. We show that ACE achieves state-of-the-art for arbitrary conditional likelihood estimation and data imputation on standard benchmarks.

ABSTRACT Natural gas (NG) is relatively a clean source of energy, particularly compared to fossil fuels, and worldwide consumption of NG has been increasing almost linearly in the last two decades. A similar trend can also be seen in Turkey, while another similarity is the high dependence on impor ts for the continuous NG supply. It is crucial to accurately forecast future NG demand (NGD) in Turkey, especially, for import contracts; in this respect, forecasts of monthly NGD for the following year are of utmost importance. In the current study, the h istorical monthly NG consumption data between 2014 and 2024 provided by SOCAR, the local residential NG distribution company for two cities in Turkey, Bursa and Kayseri, was used to determine out - of - sample monthly NGD forecasts for a period of one year and nine months using various time series models, including SARIMA and ETS models, and a novel proposed machine learning method. The proposed method, named Just - in - Time - Learning - Gaussia n Process Regression (JITL - GPR), uses a novel feature representation for t he past NG demand values; instead of using past demand values as column - wise separate features, they are placed on a two - dimensional (2 - D) grid of year - month values. For each test point, a kernel function, tailored for the NGD predictions, is used in GPR t o predict the query point. Since a model is constructed separately for each test point, the proposed method is, indeed, an example of JITL. The JITL - GPR method is easy to use and optimize, and offers a reduction in forecast errors compared to traditional t ime series methods and a state - of - the - art combinat ion model; therefore, it is a promising tool for NGD forecasting in similar settings. INTRODUCTION In the last few decades, there has been a shift in energy sources from fossil fuels to cleaner energy sources, such as wind and solar energy, mainly due to environmental concerns and related government regulations . However, these latter sources are depend ent on w eather conditions and require integration with grid technologies for continuous power generation. Natural gas (NG), typically, consists of (up to) ~95% of methane and 2 - 2.5% ethane - hexane+, with the remain der consist ing of nitrogen, CO NG p ower plants are easy to build and highly reliable, mak ing them invaluable for "clean" energy production. On the other hand, m ost countries depend on imports to maintain t heir NG supplies, and there is a delicate balance between import s and domestic demand . S toring excess import ed gas above actual demand is difficult and would result in economic losses, while import ing less than actual demand could result in a nationwide sh ortage.

The heat capacity of a material is a fundamental property of great practical importance. For example, in a carbon capture process, the heat required to regenerate a solid sorbent is directly related to the heat capacity of the material. However, for most materials suitable for carbon capture applications, the heat capacity is not known, and thus the standard procedure is to assume the same value for all materials. In this work, we developed a machine learning approach, trained on density functional theory simulations, to accurately predict the heat capacity of these materials, that is, zeolites, metal–organic frameworks and covalent–organic frameworks. The accuracy of our prediction is confirmed with experimental data. Finally, for a temperature swing adsorption process that captures carbon from the flue gas of a coal-fired power plant, we show that for some materials, the heat requirement is reduced by as much as a factor of two using the correct heat capacity. Heat capacity of nanoporous materials is important for processes such as carbon capture, as this can affect process design energy requirements. Here, a machine learning approach for heat capacity prediction, trained on density functional theory simulations, is presented and experimentally verified.

After a long day of work, you might feel tired or exhilarated. Either way, you are affected by what happened to you in the past. Accelerator magnets are no different. What they went through – or what went through them, like an electric current – affects how they will perform in the future. Without understanding a magnet's past, researchers might need to fully reset them before starting a new experiment, a process that can take 10 or 15 minutes.

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As advances in computing power and the ability to leverage large data sets and complex algorithms have increased in recent years, Federal agencies are embracing artificial intelligence (AI) to gain new insights from data and improve operational efficiencies in everything from healthcare to transportation to citizen services and public safety. NASA used the technology to capture and process data for missions to Mars, using AI to develop improved systems and allow rovers to venture further and faster. The U.S. Patent and Trade Office is using AI tools to enhance the quality and efficiency of the patent and trademark examination process, while the National Oceanic and Atmospheric Administration is utilizing data-centric approaches to predict weather disasters and alert the public in real time. The U.S. Nuclear Regulatory Commission is exploring ways AI can help detect cyberattacks on power plants. Storage solutions from DDN are also critical to a newly announced initiative with the Department of Energy, in which the Pacific Northwest National Laboratory will use AI to enhance the predictive understanding of coastal systems, including the response to short- and long-term changes.

Officials within the Department of Energy are looking to apply practical uses of artificial intelligence technology to helping underserved communities. Speaking during a FedScoop discussion panel, Pamela Isom, the director of the Artificial Intelligence and Technology Office at Energy, explained the importance of using AI technology to strategically help, as it becomes more and more ubiquitous in daily life. Some current use cases for AI tech within Energy are automated loan and application processing. Ipsom elaborated that her office's mission to ensure equitable access to AI technology came from a community discussion where gaps in adequate technological infrastructure were highlighted. "Today, we're looking at AI for instance, to not only help with procurement cycles, but with processing and evaluating [requests for information] for instance," Isom said.

Officials within the Department of Energy are looking to apply practical uses of artificial intelligence technology to helping underserved communities. Speaking during a FedScoop discussion panel, Pamela Isom, the director of the Artificial Intelligence and Technology Office at Energy, explained the importance of using AI technology to strategically help, as it becomes more and more ubiquitous in daily life. Some current use cases for AI tech within Energy are automated loan and application processing. Ipsom elaborated that her office's mission to ensure equitable access to AI technology came from a community discussion where gaps in adequate technological infrastructure were highlighted. "Today, we're looking at AI for instance, to not only help with procurement cycles, but with processing and evaluating [requests for information] for instance," Isom said.

Perovskites are a family of materials that are currently the leading contender to potentially replace today's silicon-based solar photovoltaics. They hold the promise of panels that are far thinner and lighter, that could be made with ultra-high throughput at room temperature instead of at hundreds of degrees, and that are cheaper and easier to transport and install. But bringing these materials from controlled laboratory experiments into a product that can be manufactured competitively has been a long struggle. Manufacturing perovskite-based solar cells involves optimizing at least a dozen or so variables at once, even within one particular manufacturing approach among many possibilities. But a new system based on a novel approach to machine learning could speed up the development of optimized production methods and help make the next generation of solar power a reality.