The new tool has already led to the discovery of four new materials including a new family of solid state materials that conduct lithium. Such solid electrolytes will be key to the development of solid state batteries offering longer range and increased safety for electric vehicles. Further promising materials are in development. The University of Liverpool research team created a collaborative AI tool designed to reduce the time and effort required to discover truly new materials. The tool brings together artificial intelligence with human knowledge to prioritise those parts of unexplored chemical space where new functional materials are most likely to be found.

The steady increase in the number of vehicles operating on the highways continues to exacerbate congestion, accidents, energy consumption, and greenhouse gas emissions. Emerging mobility systems, e.g., connected and automated vehicles (CAVs), have the potential to directly address these issues and improve transportation network efficiency and safety. In this paper, we consider a highway merging scenario and propose a framework for coordinating CAVs such that stop-and-go driving is eliminated. We use a decentralized form of the actor-critic approach to deep reinforcement learning$-$multi-agent deep deterministic policy gradient. We demonstrate the coordination of CAVs through numerical simulations and show that a smooth traffic flow is achieved by eliminating stop-and-go driving. Videos and plots of the simulation results can be found at this supplemental $\href{https://sites.google.com/view/ud-ids-lab/MADRL}{site}$.

Long Short-Term Memory (LSTM) neural networks have been widely used for time series forecasting problems. However, LSTMs are prone to overfitting and performance reduction during test phases. Several different regularization techniques have been shown in literature to prevent overfitting problems in neural networks. In this paper, first, we introduce application of kernel flow methods for time series forecasting in general. Afterward, we examine the effectiveness of applying kernel flow regularization on LSTM layers to avoid overfitting problems. We describe a regularization method by applying kernel flow loss function on LSTM layers. In experimental results, we show that kernel flow outperforms baseline models on time series forecasting benchmarks. We also compare the effect of dropout and kernel flow regularization techniques on LSTMs. The experimental results illustrate that kernel flow achieves similar regularization effect to dropout. It also shows that the best results is obtained using both kernel flow and dropout regularizations with early stopping on LSTM layers on some time series datasets (e.g. power-load demand forecasts).

Pool-based active learning (AL) aims to optimize the annotation process (i.e., labeling) as the acquisition of annotations is often time-consuming and therefore expensive. For this purpose, an AL strategy queries annotations intelligently from annotators to train a high-performance classification model at a low annotation cost. Traditional AL strategies operate in an idealized framework. They assume a single, omniscient annotator who never gets tired and charges uniformly regardless of query difficulty. However, in real-world applications, we often face human annotators, e.g., crowd or in-house workers, who make annotation mistakes and can be reluctant to respond if tired or faced with complex queries. Recently, a wide range of novel AL strategies has been proposed to address these issues. They differ in at least one of the following three central aspects from traditional AL: (1) They explicitly consider (multiple) human annotators whose performances can be affected by various factors, such as missing expertise. (2) They generalize the interaction with human annotators by considering different query and annotation types, such as asking an annotator for feedback on an inferred classification rule. (3) They take more complex cost schemes regarding annotations and misclassifications into account. This survey provides an overview of these AL strategies and refers to them as real-world AL. Therefore, we introduce a general real-world AL strategy as part of a learning cycle and use its elements, e.g., the query and annotator selection algorithm, to categorize about 60 real-world AL strategies. Finally, we outline possible directions for future research in the field of AL.

THE current pandemic has shown the oil and gas sector how dependable enterprise operations can be upended almost overnight. Work force routines at extraction sites and refineries have been disrupted, causing unplanned outages, as we saw at the Sharara oilfield. With supply chains interrupted, parts manufactured in traditional source markets could not be delivered on time, delaying essential maintenance. Border closures and an unprecedented drop in demand have further constricted already tight economic operations. Not only do these conditions look set to continue over the short term, but other challenges loom over the foreseeable future.

Stony Brook University professor Peng Zhang, a SUNY Empire Innovation professor in the Department of Electrical and Computer Engineering, is leading a statewide team of collaborators in developing "AI-Grid," an artificial intelligence-enabled, autonomous grid designed to keep power infrastructure resilient from cyberattacks, faults and disastrous accidents. The work is part of the National Science Foundation's (NSF) Convergence Accelerator Program, which supports and builds upon basic research and discovery that involves multidisciplinary work to accelerate solutions toward societal impact. In September 2020, the program launched the 2020 cohort, which included AI-Grid as a phase 1 awardee and grant funding of a $1 million to further AI-Grid research from an idea to a low-fidelity prototype. The Convergence Accelerator recently selected teams for phase 2, to focus on expanding the solution prototype and to build a sustainability plan beyond the NSF funding. Under phase 2, a new $5 million NSF cooperative agreement will fund the AI-Grid project.

In any form of energy conversion--even with something as green as solar panels--extra heat is generated. But with up to 72 percent of it left unused, there's also great potential to harvest electricity from that waste. A University of Alberta researcher has successfully developed a way to figure out the chemistry behind that process. The finding could ultimately help speed up development of thermoelectric materials--products that, if attached to something like a solar panel system, can recover waste heat that can then be used to generate electrical current. Using two machine learning models he developed, Alexander Gzyl has been able to narrow down the chemical makeup of a group of alloys that could be used to create those materials.

Proteins perform critical processes in all living systems: converting solar energy into chemical energy, replicating DNA, as the basis of highly performant materials, sensing and much more. While an incredible range of functionality has been sampled in nature, it accounts for a tiny fraction of the possible protein universe. If we could tap into this pool of unexplored protein structures, we could search for novel proteins with useful properties that we could apply to tackle the environmental and medical challenges facing humanity. This is the purpose of protein design. Sequence design is an important aspect of protein design, and many successful methods to do this have been developed.

"The pace at which themes are transforming businesses is blistering, but the adoption of many technologies -- like smartphones or renewable energy -- have surpassed experts' forecasts by decades, because we often think linearly but progress occurs exponentially," say the strategists. They say a paradigm shift in the explosion of data, faster processing power and the rise of artificial intelligence will bring about the "fastest rollout of disruptive tech in history." And in the big stock universe, an increasing few are showing investors the money. "Over the past 30 years, just 1.5% of companies generated all the net wealth on the global stock market, meaning that actually only a handful of disrupters ("superstar firms") really influence long-term financial markets," says Israel and the team. Here are the 14 technologies: 6G, brain computer interfacing (BCI), emotional artificial intelligence, synthetic biology, immortality, bionic humans, eVTOL (electrical vertical takeoff and landing vehicles), wireless electricity, holograms, metaverse, next-gen batteries, oceantech (ocean energy, precision fishing, etc.), green mining and CCS (negative-emissions technology that captures and stores carbon dioxide before it can be released).

This work deals with the investigation of bifurcating fluid phenomena using a reduced order modelling setting aided by artificial neural networks. We discuss the POD-NN approach dealing with non-smooth solutions set of nonlinear parametrized PDEs. Thus, we study the Navier-Stokes equations describing: (i) the Coanda effect in a channel, and (ii) the lid driven triangular cavity flow, in a physical/geometrical multi-parametrized setting, considering the effects of the domain's configuration on the position of the bifurcation points. Finally, we propose a reduced manifold-based bifurcation diagram for a non-intrusive recovery of the critical points evolution. Exploiting such detection tool, we are able to efficiently obtain information about the pattern flow behaviour, from symmetry breaking profiles to attaching/spreading vortices, even at high Reynolds numbers.