Street lamps constitute the densest electrically operated public infrastructure in urban areas. Their changeover to energy-friendly LED light quickly amortizes and is increasingly leveraged for smart city projects, where LED street lamps double, for example, as wireless networking or sensor infrastructure. We make the case for a new paradigm called SLaaP--street lamps as a platform. SLaaP is proposed as an open, enabling platform, fostering innovative citywide services for the full range of stakeholders and end users--seamlessly extending from everyday use to emergency response. In this article, we first describe the role and potential of street lamps and introduce one novel base service as a running example. We then discuss citywide infrastructure design and operation, followed by addressing the major layers of a SLaaP infrastructure: hardware, distributed software platform, base services, value-added services and applications for users and'things.' Finally, we discuss the crucial roles and participation of major stakeholders: citizens, city, government, and economy. Recent years have seen the emergence of smart street lamps, with very different meanings of'smart'--sometimes related to the original purpose as with usage-dependent lighting, but mostly as add-on capabilities like urban sensing, monitoring, digital signage, WiFi access, or e-vehicle charging.a The future holds even more use cases: for example, after a first wave of 5G mobile network rollouts from 2020 onward, a second wave shall apply mm-wave frequencies for which densely deployed light poles can be appropriate'cell towers.'

This paper studies the problem of globally optimizing a variable of interest that is part of a causal model in which a sequence of interventions can be performed. This problem arises in biology, operational research, communications and, more generally, in all fields where the goal is to optimize an output metric of a system of interconnected nodes. Our approach combines ideas from causal inference, uncertainty quantification and sequential decision making. In particular, it generalizes Bayesian optimization, which treats the input variables of the objective function as independent, to scenarios where causal information is available. We show how knowing the causal graph significantly improves the ability to reason about optimal decision making strategies decreasing the optimization cost while avoiding suboptimal solutions. We propose a new algorithm called Causal Bayesian Optimization (CBO). CBO automatically balances two trade-offs: the classical exploration-exploitation and the new observation-intervention, which emerges when combining real interventional data with the estimated intervention effects computed via do-calculus. We demonstrate the practical benefits of this method in a synthetic setting and in two real-world applications.

But 2019 was the year the earth burned. In Australia, the world watched in horror as bushfires destroyed 10.3 million hectares, marking the continent's most intense and destructive fire season in over 40 years. Earlier that fall, California saw more than 101,000 hectares destroyed, with damages upward of $80 billion. Alaska saw nearly a million. Record-breaking fires also hit Indonesia, Russia, Lebanon -- but nowhere saw the sheer mass of media coverage as the fires that tore through the Amazon nearly all last summer. By year's end, thousands of global media outlets had reported that Brazil's largest rainforest played host to more than 80,000 individual forest fires in 2019, resulting in an estimated 906,000 square hectares of environmental destruction. At the time, Brazil's National Institute for Space Research reported it was the fastest rate of burning since record keeping began in 2013. But amid the charred ruins of one of the largest oxygen-producing environments on the planet, a secret lies buried beneath the soil.

Such a disappearance is a fundamental consequence not of technology but of human psychology. Whenever people learn something sufficiently well, they cease to be aware of it. Thus, Weiser's vision is even broader: as this technology becomes truly embedded in human activity we won't be aware of it at all. As the field of ubiquitous computing has evolved, with computation embedded in walls, clothes, and so forth, the materiality to support it is often physically and intentionally hidden from the user. Indeed, this material disappearance is often considered evidence of good design. The "agent" metaphor, in particular in its early presentations such as the Knowledge Navigator and Starfire, is also another utopian vision. These virtual agents are typically accessible via peripherals such as screens or phones, doing the bidding of those they serve.

Machine Learning methods and, in particular, Artificial Neural Networks (ANNs) have demonstrated promising capabilities in material constitutive modeling. One of the main drawbacks of such approaches is the lack of a rigorous frame based on the laws of physics. This may render physically inconsistent the predictions of a trained network, which can be even dangerous for real applications. Here we propose a new class of data-driven, physics-based, neural networks for constitutive modeling of strain rate independent processes at the material point level, which we define as Thermodynamics-based Artificial Neural Networks (TANNs). The two basic principles of thermodynamics are encoded in the network's architecture by taking advantage of automatic differentiation to compute the numerical derivatives of a network with respect to its inputs. In this way, derivatives of the free-energy, the dissipation rate and their relation with the stress and internal state variables are hardwired in the network. Consequently, our network does not have to identify the underlying pattern of thermodynamic laws during training, reducing the need of large data-sets. Moreover the training is more efficient and robust, and the predictions more accurate. Finally and more important, the predictions remain thermodynamically consistent, even for unseen data. Based on these features, TANNs are a starting point for data-driven, physics-based constitutive modeling with neural networks. We demonstrate the wide applicability of TANNs for modeling elasto-plastic materials, with strain hardening and strain softening. Detailed comparisons show that the predictions of TANNs outperform those of standard ANNs. TANNs ' architecture is general, enabling applications to materials with different or more complex behavior, without any modification.

Modeling multivariate time series has long been a subject that has attracted researchers from a diverse range of fields including economics, finance, and traffic. A basic assumption behind multivariate time series forecasting is that its variables depend on one another but, upon looking closely, it is fair to say that existing methods fail to fully exploit latent spatial dependencies between pairs of variables. In recent years, meanwhile, graph neural networks (GNNs) have shown high capability in handling relational dependencies. GNNs require well-defined graph structures for information propagation which means they cannot be applied directly for multivariate time series where the dependencies are not known in advance. In this paper, we propose a general graph neural network framework designed specifically for multivariate time series data. Our approach automatically extracts the uni-directed relations among variables through a graph learning module, into which external knowledge like variable attributes can be easily integrated. A novel mix-hop propagation layer and a dilated inception layer are further proposed to capture the spatial and temporal dependencies within the time series. The graph learning, graph convolution, and temporal convolution modules are jointly learned in an end-to-end framework. Experimental results show that our proposed model outperforms the state-of-the-art baseline methods on 3 of 4 benchmark datasets and achieves on-par performance with other approaches on two traffic datasets which provide extra structural information.

Researchers from U of T Engineering and Carnegie Mellon University are using electrolyzers like this one to convert waste CO2 into commercially valuable chemicals. Their latest catalyst, designed in part through the use of AI, is the most efficient in its class. Researchers at University of Toronto Engineering and Carnegie Mellon University are using artificial intelligence (AI) to accelerate progress in transforming waste carbon into a commercially valuable product with record efficiency. They leveraged AI to speed up the search for the key material in a new catalyst that converts carbon dioxide (CO2) into ethylene -- a chemical precursor to a wide range of products, from plastics to dish detergent. The resulting electrocatalyst is the most efficient in its class.

The research report on artificial intelligence market, in substance, presents an exclusive understanding of the vast expanse of the business space in question. The report comprises a gist of the industry by means of providing an executive summary, industry insights, industry ecosystem analysis, market segmentation, and global trends. Furthermore, the study also provides deliverables pertaining to the regulatory and competitive landscapes and the strategic perspectives of various industry contenders with respect to the artificial intelligence indutry . However, the major challenges faced by industry players are the low return on investment and the complexity involved in the creation of AI mechanisms and models. Lack of energy-efficient and cost-effective hardware restricts the adoption of such technology in small and medium enterprises, thereby restricting the artificial intelligence market growth during the forecast timeline.

This paper uses the peridynamic theory, which is well-suited to crack studies, to predict the crack patterns in a moving disk and classify them according to the modes and finally perform regression analysis. In that way, the crack patterns are obtained according to each mode by Molecular Dynamic (MD) simulation using the peridynamics. Image classification and regression studies are conducted through Convolutional Neural Networks (CNNs) and the neural processes. First, we increased the amount and quality of the data using peridynamics, which can theoretically compensate for the problems of the finite element method (FEM) in generating crack pattern images. Second, we did the case study for the PMB, LPS, and VES models that were obtained using the peridynamic theory. Case studies were performed to classify the images using CNNs and determine the PMB, LBS, and VES models' suitability. Finally, we performed the regression analysis for the images of the crack patterns with neural processes to predict the crack patterns. In the regression problem, by representing the results of the variance according to the epochs, it can be confirmed that the result of the variance is decreased by increasing the epoch numbers through the neural processes. The most critical point of this study is that the neural processes make an accurate prediction even if there are missing or insufficient training data.

A deep-learning based hybrid strategy for short-term load forecasting is presented. The strategy proposes a novel tree-based ensemble method Warm-start Gradient Tree Boosting (WGTB). Current strategies either ensemble submodels of a single type, which fail to take advantage of statistical strengths of different inference models. Or they simply sum the outputs from completely different inference models, which doesn't maximize the potential of ensemble. WGTB is thus proposed and tailored to the great disparity among different inference models in accuracy, volatility and linearity. The complete strategy integrates four different inference models (i.e., auto-regressive integrated moving average, nu support vector regression, extreme learning machine and long short-term memory neural network), both linear and nonlinear models. WGTB then ensembles their outputs by hybridizing linear estimator ElasticNet and nonlinear estimator ExtraTree via boosting algorithm. It is validated on the real historical data of a grid from State Grid Corporation of China of hourly resolution. The result demonstrates the effectiveness of the proposed strategy that hybridizes statistical strengths of both linear and nonlinear inference models.