Uncertainty quantification (UQ) has increasing importance in building robust high-performance and generalizable materials property prediction models. It can also be used in active learning to train better models by focusing on getting new training data from uncertain regions. There are several categories of UQ methods each considering different types of uncertainty sources. Here we conduct a comprehensive evaluation on the UQ methods for graph neural network based materials property prediction and evaluate how they truly reflect the uncertainty that we want in error bound estimation or active learning. Our experimental results over four crystal materials datasets (including formation energy, adsorption energy, total energy, and band gap properties) show that the popular ensemble methods for uncertainty estimation is NOT the best choice for UQ in materials property prediction. For the convenience of the community, all the source code and data sets can be accessed freely at \url{https://github.com/usccolumbia/materialsUQ}.

As robotic systems continue to address emerging issues in areas such as logistics, mobility, manufacturing, and disaster response, it is increasingly important to rapidly generate safe and energy-efficient trajectories. In this article, we present a new approach to plan energy-optimal trajectories through cluttered environments containing polygonal obstacles. In particular, we develop a method to quickly generate optimal trajectories for a double-integrator system, and we show that optimal path planning reduces to an integer program. To find an efficient solution, we present a distance-informed prefix search to efficiently generate optimal trajectories for a large class of environments. We demonstrate that our approach, while matching the performance of RRT* and Probabilistic Road Maps in terms of path length, outperforms both in terms of energy cost and computational time by up to an order of magnitude. We also demonstrate that our approach yields implementable trajectories in an experiment with a Crazyflie quadrotor.

In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even be used in the presence of unknown classes. For the first time, we demonstrate the successful use of domain adaptation on two very different observational datasets (from SDSS and DECaLS). We show that our method is capable of bridging the gap between two astronomical surveys, and also performs well for anomaly detection and clustering of unknown data in the unlabeled dataset. We apply our model to two examples of galaxy morphology classification tasks with anomaly detection: 1) classifying spiral and elliptical galaxies with detection of merging galaxies (three classes including one unknown anomaly class); 2) a more granular problem where the classes describe more detailed morphological properties of galaxies, with the detection of gravitational lenses (ten classes including one unknown anomaly class).

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Peggy and Tony Schmitz, professor, University of Tennessee, Knoxville, and joint faculty, Oak Ridge National Laboratory, talk about the ACE (America’s Cutting Edge) program and what brought him to the University of Tennessee. He explains that East Tennessee is an exploding ecosystem right now. They also discuss: Why it has the ACE program and the importance of machine tools for defense and our economic security. How machine tool technology has improved and the challenge of the lack of workforce to make the best use of that equipment. The result of outsourcing manufacturing to other countries and what needs to happen next.   (11/8/22 - 796) IoT, Internet of Things, Peggy Smedley, artificial intelligence, machine learning, big data, digital transformation, cybersecurity, blockchain, 5G, cloud, sustainability, future of work, podcast, Tony Schmitz, University of Tennessee, Knoxville, Oak Ridge National Laboratory This episode is available on all major streaming platforms. If you enjoyed this segment, please consider leaving a review on Apple Podcasts.

Phase equilibrium calculations are an essential part of numerical simulations of multi-component multi-phase flow in porous media, accounting for the largest share of the computational time. In this work, we introduce a fast and parallel framework, PTFlash, that vectorizes algorithms required for two-phase flash calculation using PyTorch, and can facilitate a wide range of downstream applications. Vectorization promotes parallelism and consequently leads to attractive hardware-agnostic acceleration. In addition, to further accelerate PTFlash, we design two task-specific neural networks, one for predicting the stability of given mixtures and the other for providing estimates of the distribution coefficients, which are trained offline and help shorten computation time by sidestepping stability analysis and reducing the number of iterations to reach convergence. The evaluation of PTFlash was conducted on three case studies involving hydrocarbons, CO2 and N2, for which the phase equilibrium was tested over a large range of temperature, pressure and composition conditions, using the Soave-Redlich-Kwong (SRK) equation of state. We compare PTFlash with an in-house thermodynamic library, Carnot, written in C++ and performing flash calculations one by one on CPU. Results show speed-ups of up to two order of magnitude on large scale calculations, while maintaining perfect precision with the reference solution provided by Carnot.

Zhang is principal investigator of the Advanced Manufacturing and Energy Lab at Notre Dame. Dowling, assistant professor of chemical and biomolecular engineering, and Luo, the Dorini Family Professor for Energy Studies – both experts in machine learning – contributed to this research, along with doctoral student Mortaza Saeidi-Javash (now assistant professor at California State Long Beach), doctoral student Ke Wang and postdoctoral associate Minxiang Zeng (now assistant professor at Texas Tech University).

Hazardous Lighting Market size was valued at $1.8 billion in 2020 and it is estimated to grow at a CAGR of 2.29% during 2021-2026. The growth is mainly attributed to the increasing investment on various industries, high penetration of internet of things (IoT), increasing demand for efficient advanced lighting solutions across industries and rapid industrialization in emerging economies. Furthermore, the constant innovation in advanced technologies such as artificial intelligence (AI), machine learning (ML), radio-frequency identification (RFID) along with other wireless technologies, which are being used for producing advanced connected hazardous lighting system; and awareness regarding energy conservation boost the growth of hazardous lighting market. Furthermore, government's initiatives for greener strategies to support sustainable development across the world, is one of the major driving factors of hazardous lighting industry. Hence, the above mentioned factors will drive the adoption rate of various hazardous lighting solutions such as industrial LED lighting, fluorescent lighting, high-intensity discharge lamps and others, during the forecast period 2021-2026.

This week, Microsoft President and vice chair Brad Smith is heading to Egypt for the United Nation's annual climate conference with a mission: show the world that the tech giant is "consistent and committed" in its climate goals, as well as communicate the "vital role" that the tech industry as a whole has to play in battling the climate crisis. The Microsoft leader has been busy in recent months since the departure of chief environmental officer Lucas Joppa, stepping in to lead the company's climate initiatives (something Smith has always been intimately involved with, as Joppa's boss prior to his departure). Last week at the Web Summit tech conference, he spoke about the urgency of the workforce transformation the world needs to reach net zero, as well as the current skills gap. "The key to the future is going to be a new generation of people with a new generation of technology coming from a new generation of companies," he said, highlighting the work of startups like the India-based SEEDS, which is using satellite data and AI to identify homes that would be most susceptible to extreme heat, then helping them adapt. Using AI and data to help the Global South adapt to climate change is one of Microsoft's main focuses going into the COP27 climate talks.

Recent advancements in the rollout of 5G and 6G have led to the emergence of a new range of latency-critical applications delivered via a Network Function Virtualization (NFV) enabled paradigm of flexible and softwarized communication networks. Evolving verticals like telecommunications, smart grid, virtual reality (VR), industry 4.0, automated vehicles, etc. are driven by the vision of low latency and high reliability, and there is a wide gap to efficiently bridge the Quality of Service (QoS) constraints for both the service providers and the end-user. In this work, we look to tackle the over-provisioning of latency-critical services by proposing a proactive SLA management framework leveraging Graph Neural Networks (GNN) and Deep Reinforcement Learning (DRL) to balance the trade-off between efficiency and reliability. To summarize our key contributions: 1) we compose a graph-based spatio-temporal multivariate time-series forecasting model with multiple time-step predictions in a multi-output scenario, delivering 74.62% improved performance over the established baseline state-of-art model on the use-case; and 2) we leverage realistic SLA definitions for the use-case to achieve a dynamic SLA-aware oversight for scaling policy management with DRL.