Since Facebook was renamed Meta, a lot of attention, debate, and exploration have intensified about what the Metaverse is, how it works, and the possible ways to exploit it. It is anticipated that Metaverse will be a continuum of rapidly emerging technologies, usecases, capabilities, and experiences that will make it up for the next evolution of the Internet. Several researchers have already surveyed the literature on artificial intelligence (AI) and wireless communications in realizing the Metaverse. However, due to the rapid emergence and continuous evolution of technologies, there is a need for a comprehensive and in-depth survey of the role of AI, 6G, and the nexus of both in realizing the immersive experiences of Metaverse. Therefore, in this survey, we first introduce the background and ongoing progress in augmented reality (AR), virtual reality (VR), mixed reality (MR) and spatial computing, followed by the technical aspects of AI and 6G. Then, we survey the role of AI in the Metaverse by reviewing the state-of-the-art in deep learning, computer vision, and Edge AI to extract the requirements of 6G in Metaverse. Next, we investigate the promising services of B5G/6G towards Metaverse, followed by identifying the role of AI in 6G networks and 6G networks for AI in support of Metaverse applications, and the need for sustainability in Metaverse. Finally, we enlist the existing and potential applications, usecases, and projects to highlight the importance of progress in the Metaverse. Moreover, in order to provide potential research directions to researchers, we underline the challenges, research gaps, and lessons learned identified from the literature review of the aforementioned technologies.

If robots are ever to achieve autonomous motion comparable to that exhibited by animals, they must acquire the ability to quickly recover motor behaviors when damage, malfunction, or environmental conditions compromise their ability to move effectively. We present an approach which allowed our robots and simulated robots to recover high-degree of freedom motor behaviors within a few dozen attempts. Our approach employs a behavior specification expressing the desired behaviors in terms as rank ordered differential constraints. We show how factoring these constraints through an encoding template produces a recipe for generalizing a previously optimized behavior to new circumstances in a form amenable to rapid learning. We further illustrate that adequate constraints are generically easy to determine in data-driven contexts. As illustration, we demonstrate our recovery approach on a physical 7 DOF hexapod robot, as well as a simulation of a 6 DOF 2D kinematic mechanism. In both cases we recovered a behavior functionally indistinguishable from the previously optimized motion.

This research provides a novel framework to estimate the economic, environmental, and social values of electrifying public transit buses, for cities across the world, based on open-source data. Electric buses are a compelling candidate to replace diesel buses for the environmental and social benefits. However, the state-of-art models to evaluate the value of bus electrification are limited in applicability because they require granular and bespoke data on bus operation that can be difficult to procure. Our valuation tool uses General Transit Feed Specification, a standard data format used by transit agencies worldwide, to provide high-level guidance on developing a prioritization strategy for electrifying a bus fleet. We develop physics-informed machine learning models to evaluate the energy consumption, the carbon emissions, the health impacts, and the total cost of ownership for each transit route. We demonstrate the scalability of our tool with a case study of the bus lines in the Greater Boston and Milan metropolitan areas. Detailed Affiliation: U.Vijay, S.Woo, and S.J.Moura are at Department of Civil and Environmental Engineering, University of California-Berkeley, Davis Hall, Berkeley, California, 94720, USA. A.Jain is at Department of Electrical Engineering and Computer Sciences, University of California-Berkeley, Soda Hall, Berkeley, California, 94720, USA. D.Rodriguez and E.Mellekas are at Enel X, North America, Inc., One Marina Park Drive, Boston, 02210, MA, USA. S. Gambacorta is at Enel X, Innovation and Sustainability Global, Smart City, Viale Tor di Quinto, Rome, 00191, Italy. G.Ferrara is at Enel X, Innovation and Sustainability Global, Smart City, Passo Martino, Catania, 95121, Italy. L.Lanuzza is at Enel X, Innovation and Sustainability B2C & B2B Innovation Factory, Viale Tor di Quinto, Rome, 00191, Italy. C.Zulberti and C.Papa are at Enel Foundation, Via Bellini, Rome, 00198, Italy. Vehicle electrification is crucial for reducing the climate impact of the transportation sector, which currently accounts for 16.2% of the global greenhouse gas emissions [22]. Zero-emission electric vehicles can significantly improve the air quality, health, and environmental equity [23], [24].

A rapid transformation of current electric power and natural gas (NG) infrastructure is imperative to meet the mid-century goal of CO2 emissions reduction requires. This necessitates a long-term planning of the joint power-NG system under representative demand and supply patterns, operational constraints, and policy considerations. Our work is motivated by the computational and practical challenges associated with solving the generation and transmission expansion problem (GTEP) for joint planning of power-NG systems. Specifically, we focus on efficiently extracting a set of representative days from power and NG data in respective networks and using this set to reduce the computational burden required to solve the GTEP. We propose a Graph Autoencoder for Multiple time resolution Energy Systems (GAMES) to capture the spatio-temporal demand patterns in interdependent networks and account for differences in the temporal resolution of available data. The resulting embeddings are used in a clustering algorithm to select representative days. We evaluate the effectiveness of our approach in solving a GTEP formulation calibrated for the joint power-NG system in New England. This formulation accounts for the physical interdependencies between power and NG systems, including the joint emissions constraint. Our results show that the set of representative days obtained from GAMES not only allows us to tractably solve the GTEP formulation, but also achieves a lower cost of implementing the joint planning decisions.

Information loss in numerical physics simulations can arise from various sources when solving discretized partial differential equations. In particular, errors related to numerical precision ("sub-precision errors") can accumulate in the quantities of interest when the simulations are performed using low-precision 16-bit floating-point arithmetic compared to an equivalent 64-bit simulation. On the other hand, low-precision computation is less resource intensive than high-precision computation. Several machine learning techniques proposed recently have been successful in correcting errors due to coarse spatial discretization. In this work, we extend these techniques to improve CFD simulations performed with low numerical precision. We quantify the precision-related errors accumulated in a Kolmogorov forced turbulence test case. Subsequently, we employ a Convolutional Neural Network together with a fully differentiable numerical solver performing 16-bit arithmetic to learn a tightly-coupled ML-CFD hybrid solver. Compared to the 16-bit solver, we demonstrate the efficacy of the hybrid solver towards improving various metrics pertaining to the statistical and pointwise accuracy of the simulation.

On the evening of Sunday, Sept. 23, 1962, millions of American families finished their dinners, turned on their televisions and were introduced to The Jetsons, a cartoon sitcom produced by the legendary team of Hanna-Barbera. Set in 2062, The Jetsons captured the technological optimism of the time and projected it into a space-age, gadget-fueled vision of the future, inviting its viewers to imagine the dazzling possibilities that the current wave of technological achievement could one day realize. In the end, The Jetsons was a rather tame, pedestrian sitcom about a family that reinforced traditional gender and family roles, knew little of the social issues of the time (it was, for example, unbearably white), and effectively glorified the consumerist, suburban lifestyle. But as a template for a technology-driven American future, it was no less than iconic. The Jetsons debuted five years after the Soviets had launched Sputnik, four years after the opening of the first commercial nuclear power plant in the U.S., and 16 months after President John F. Kennedy set a goal of putting a man on the moon by the decade's end. Fifteen years earlier, scientists at AT&T's Bell Labs invented the transistor, and soon after, miniature (by contemporary standards) transistor radios were found in many households.

As part of a major sustainability effort, New York State Gov. Kathy Hochul has issued an executive order mandating that NY state agencies use the NY Power Authority's NY Energy Manager application, a system developed and deployed with the leading enterprise AI software application company. "This is great validation in the work C3 AI has done with our longtime customer, the NY Power Authority, and we look forward to helping Governor Hochul achieve her goal of making New York's public sector operations more sustainable." "We are pleased to receive such broad recognition and confidence in our enterprise AI energy management solution," said Ed Abbo, President and CTO of C3 AI. "This is great validation in the work C3 AI has done with our longtime customer, the NY Power Authority, and we look forward to helping Governor Hochul achieve her goal of making New York's public sector operations more sustainable." Announces Leadership Promotions to Drive Next Stage of Company Growth Among the many other goals spelled out in Executive Order 22, enabled by C3 AI, is a mandate for state operations to run on 100% clean electricity by 2030. The NY Energy Manager application, built on C3 AI Energy Management, has already been deployed to about 1,000 customers, including communities, businesses, municipalities, and electricity providers in New York. It will now serve as the system of record for all energy data from all state agencies.

As more and more countries choose to use nuclear technology for peaceful purposes and adopt nuclear power programmes, the IAEA works continuously to ensure the protection of people and the environment from the potential harmful effects of ionizing radiation. AI can contribute to nuclear security and safety in several ways. It can be used in the processing of data from radiation detection systems to enhance the detection and identification of nuclear and other radioactive material. It can be applied to analyse data from physical protection systems to improve the detection of intruders. It can also help spot anomalies that could indicate a cyber-attack on a nuclear facility.

Forecast combinations have flourished remarkably in the forecasting community and, in recent years, have become part of the mainstream of forecasting research and activities. Combining multiple forecasts produced from single (target) series is now widely used to improve accuracy through the integration of information gleaned from different sources, thereby mitigating the risk of identifying a single "best" forecast. Combination schemes have evolved from simple combination methods without estimation, to sophisticated methods involving time-varying weights, nonlinear combinations, correlations among components, and cross-learning. They include combining point forecasts and combining probabilistic forecasts. This paper provides an up-to-date review of the extensive literature on forecast combinations, together with reference to available open-source software implementations. We discuss the potential and limitations of various methods and highlight how these ideas have developed over time. Some important issues concerning the utility of forecast combinations are also surveyed. Finally, we conclude with current research gaps and potential insights for future research.

The study of robotic flocking has received significant attention in the past twenty years. In this article, we present a constraint-driven control algorithm that minimizes the energy consumption of individual agents and yields an emergent V formation. As the formation emerges from the decentralized interaction between agents, our approach is robust to the spontaneous addition or removal of agents to the system. First, we present an analytical model for the trailing upwash behind a fixed-wing UAV, and we derive the optimal air speed for trailing UAVs to maximize their travel endurance. Next, we prove that simply flying at the optimal airspeed will never lead to emergent flocking behavior, and we propose a new decentralized "anseroid" behavior that yields emergent V formations. We encode these behaviors in a constraint-driven control algorithm that minimizes the locomotive power of each UAV. Finally, we prove that UAVs initialized in an approximate V or echelon formation will converge under our proposed control law, and we demonstrate this emergence occurs in real-time in simulation and in physical experiments with a fleet of Crazyflie quadrotors.