Addressing climate change is everyone's responsibility and AI has the potential to make a significant impact, yet only a fraction of organizations are actively using this technology to its full potential,

Devastated by industrial crisis, America's former "steel city" has reinvented itself as an innovation hub. But today its main challenge is to keep its "One Pittsburgh" promise by ensuring that everybody in its diverse population shares the benefits of new growth. Pittsburgh is back from the brink. A flagship of triumphant industrialisation in the early 20th century, the city has since seen its steel mills decline and then shut down. As the economy lurched from one crisis to another, Pennsylvania's rusting "steel city" became an emblem of decline, like other urban "dead stars" in the rustbelt of America's Middle West. But Pittsburgh never gave up.

Sensor power loss is the scourge of IoT. Deploying millions of sensors is pretty much a useless endeavor if the devices continually run out of power. IoT sensors can't collect or transmit data without power. That's one reason researchers are exploring ambient energy harvesting. Numerous projects have shown that small amounts of power can be generated by converting ambient energy in the environment – from stray magnetic fields, humidity, waste heat, and even unwanted wireless radio noise, for example – into usable electrical energy to power the IoT.

AVEVA, a global leader in engineering and industrial software, announced that it will be extending its long-standing strategic collaboration with Microsoft to focus on accelerating digital transformation in the industrial sector. AVEVA will help maximize the value that customers can derive from the integration of AVEVA's portfolio with Microsoft cloud services and especially Microsoft Azure (infrastructure, data and AI services), helping them achieve implementations quicker, connect teams more readily and drive growth opportunities throughout their integrated portfolio. AVEVA's key focus areas will revolve around cloud as well as transforming the workforce (connected worker), and building a common Asset Strategy (Asset Performance). Working with Microsoft, AVEVA will continue to focus on three key areas, already proven with customers including Total, Veolia and SCG Chemicals - platform integration, a multi-solution engagement approach, and a shared go-to-market strategy. The platform integration approach can help generate new ways to increase business value for customers.

We consider the problem of designing an algorithm to allow a car to autonomously merge on to a highway from an on-ramp. Two broad classes of techniques have been proposed to solve motion planning problems in autonomous driving: Model Predictive Control (MPC) and Reinforcement Learning (RL). In this paper, we first establish the strengths and weaknesses of state-of-the-art MPC and RL-based techniques through simulations. We show that the performance of the RL agent is worse than that of the MPC solution from the perspective of safety and robustness to out-of-distribution traffic patterns, i.e., traffic patterns which were not seen by the RL agent during training. On the other hand, the performance of the RL agent is better than that of the MPC solution when it comes to efficiency and passenger comfort. We subsequently present an algorithm which blends the model-free RL agent with the MPC solution and show that it provides better trade-offs between all metrics -- passenger comfort, efficiency, crash rate and robustness.

We introduce a new variational inference (VI) framework, called energetic variational inference (EVI). It minimizes the VI object function based on a prescribed energy-dissipation law. Using the EVI framework, we can derive many existing Particle-based Variational Inference (ParVI) methods, including the popular Stein Variational Gradient Descent (SVGD) approach. More importantly, many new ParVI schemes can be created under this framework. For illustration, we propose a new particle-based EVI scheme, which performs the particle-based approximation of the density first and then uses the approximated density in the variational procedure, or "Approximation-then-Variation" for short. Thanks to this order of approximation and variation, the new scheme can maintain the variational structure at the particle level and can significantly decrease the KL-divergence in each iteration. Numerical experiments show the proposed method outperforms some existing ParVI methods in terms of fidelity to the target distribution.

A Theory-guided Auto-Encoder (TgAE) framework is proposed for surrogate construction and is further used for uncertainty quantification and inverse modeling tasks. The framework is built based on the Auto-Encoder (or Encoder-Decoder) architecture of convolutional neural network (CNN) via a theory-guided training process. In order to achieve the theory-guided training, the governing equations of the studied problems can be discretized and the finite difference scheme of the equations can be embedded into the training of CNN. The residual of the discretized governing equations as well as the data mismatch constitute the loss function of the TgAE. The trained TgAE can be used to construct a surrogate that approximates the relationship between the model parameters and responses with limited labeled data. In order to test the performance of the TgAE, several subsurface flow cases are introduced. The results show the satisfactory accuracy of the TgAE surrogate and efficiency of uncertainty quantification tasks can be improved with the TgAE surrogate. The TgAE also shows good extrapolation ability for cases with different correlation lengths and variances. Furthermore, the parameter inversion task has been implemented with the TgAE surrogate and satisfactory results can be obtained.

Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks (GANs) to develop a new framework for synthesizing earthquake acceleration time histories. Our approach extends the Wasserstein GAN formulation to allow for the generation of ground-motions conditioned on a set of continuous physical variables. Our model is trained to approximate the intrinsic probability distribution of a massive set of strong-motion recordings from Japan. We show that the trained generator model can synthesize realistic 3-Component accelerograms conditioned on magnitude, distance, and $V_{s30}$. Our model captures the expected statistical features of the acceleration spectra and waveform envelopes. The output seismograms display clear P and S-wave arrivals with the appropriate energy content and relative onset timing. The synthesized Peak Ground Acceleration (PGA) estimates are also consistent with observations. We develop a set of metrics that allow us to assess the training process's stability and tune model hyperparameters. We further show that the trained generator network can interpolate to conditions where no earthquake ground motion recordings exist. Our approach allows the on-demand synthesis of accelerograms for engineering purposes.

Artificial intelligence (AI) is playing an increasingly significant role in our everyday lives. This trend is expected to continue, especially with recent pushes to move more AI to the edge. However, one of the biggest challenges associated with AI on edge devices (mobile phones, unmanned vehicles, sensors, etc.) is their associated size, weight, and power constraints. In this work, we consider the scenario where an AI system may need to operate at less-than-maximum accuracy in order to meet application-dependent energy requirements. We propose a simple function that divides the cost of using an AI system into the cost of the decision making process and the cost of decision execution. For simple binary decision problems with convolutional neural networks, it is shown that minimizing the cost corresponds to using fewer than the maximum number of resources (e.g. convolutional neural network layers and filters). Finally, it is shown that the cost associated with energy can be significantly reduced by leveraging high-confidence predictions made in lower-level layers of the network.

Understanding the reasoning behind the behavior of an automated scheduling system is essential to ensure that it will be trusted and consequently used to its full capabilities in critical applications. In cases where a scheduler schedules activities in an invalid location, it is usually easy for the user to infer the missing constraint by inspecting the schedule with the invalid activity to determine the missing constraint. If a scheduler fails to schedule activities because constraints could not be satisfied, determining the cause can be more challenging. In such cases it is important to understand which constraints caused the activities to fail to be scheduled and how to alter constraints to achieve the desired schedule. In this paper, we describe such a scheduling system for NASA's Mars 2020 Perseverance Rover, as well as Crosscheck, an explainable scheduling tool that explains the scheduler behavior. The scheduling system and Crosscheck are the baseline for operational use to schedule activities for the Mars 2020 rover. As we describe, the scheduler generates a schedule given a set of activities and their constraints and Crosscheck: (1) provides a visual representation of the generated schedule; (2) analyzes and explains why activities failed to schedule given the constraints provided; and (3) provides guidance on potential constraint relaxations to enable the activities to schedule in future scheduler runs.