However, when IoT devices are increasingly being implemented with neural network DNN models come to on-board, there is a grand challenge to accommodate models to enable smart applications. Energy harvesting (EH) the giant models to tiny IoT devices with limited memory technology that harvests energy from ambient environment is a and computing resources [3, 11-13, 20, 22]. Particularly, first, embedded promising alternative to batteries for powering those devices due IoT devices have limited computational units and low CPU to the low maintenance cost and wide availability of the energy frequency (e.g., 1-16MHZ). Since DNNs are computationally expensive, sources. However, the power provided by the energy harvester is DNN algorithm takes long on-board execution time. Second, low and has an intrinsic drawback of instability since it varies with embedded IoT devices are equipped with small memory (e.g., hundreds the ambient environment. This paper proposes EVE, an automated of KBs) which can not even afford tiny DNN models (e.g., machine learning (autoML) co-exploration framework to search Tens of MBs). Third, these battery-powered devices naturally have for desired multi-models with shared weights for energy harvesting a limited standby time.

While the distribution patterns of cold-water corals, such as Paragorgia arborea, have received increasing attention in recent studies, little is known about their in situ activity patterns. In this paper, we examine polyp activity in P. arborea using machine learning techniques to analyze high-resolution time series data and photographs obtained from an autonomous lander cluster deployed in the Stjernsund, Norway. An interactive illustration of the models derived in this paper is provided online as supplementary material. We find that the best predictor of the degree of extension of the coral polyps is current direction with a lag of three hours. Other variables that are not directly associated with water currents, such as temperature and salinity, offer much less information concerning polyp activity. Interestingly, the degree of polyp extension can be predicted more reliably by sampling the laminar flows in the water column above the measurement site than by sampling the more turbulent flows in the direct vicinity of the corals. Our results show that the activity patterns of the P. arborea polyps are governed by the strong tidal current regime of the Stjernsund. It appears that P. arborea does not react to shorter changes in the ambient current regime but instead adjusts its behavior in accordance with the large-scale pattern of the tidal cycle itself in order to optimize nutrient uptake.

In a grid with a significant share of renewable generation, operators will need additional tools to evaluate the operational risk due to the increased volatility in load and generation. The computational requirements of the forward uncertainty propagation problem, which must solve numerous security-constrained economic dispatch (SCED) optimizations, is a major barrier for such real-time risk assessment. This paper proposes a Just-In-Time Risk Assessment Learning Framework (JITRALF) as an alternative. JITRALF trains risk surrogates, one for each hour in the day, using Machine Learning (ML) to predict the quantities needed to estimate risk, without explicitly solving the SCED problem. This significantly reduces the computational burden of the forward uncertainty propagation and allows for fast, real-time risk estimation. The paper also proposes a novel, asymmetric loss function and shows that models trained using the asymmetric loss perform better than those using symmetric loss functions. JITRALF is evaluated on the French transmission system for assessing the risk of insufficient operating reserves, the risk of load shedding, and the expected operating cost.

Which are the key determinants of oil prices, and what role do financial factors play in Brent price formation? This paper sheds a new light on these fundamental questions relying on a widely used machine learning technique (random forests, based on 1,000 regression trees). As the article shows, the use of this technique leads to very large gains in oil price forecasting performance. Besides strong forecasting performance, this powerful data-driven method also uncovers how economic and financial variables relate to oil prices. The benchmark model relies on 11 explanatory variables, which are firmly grounded on economic theory and measured on a daily frequency.

The concept of small modular reactor has changed the outlook for tackling future energy crises. This new reactor technology is very promising considering its lower investment requirements, modularity, design simplicity, and enhanced safety features. The application of artificial intelligence-driven multi-scale modeling (neutronics, thermal hydraulics, fuel performance, etc.) incorporating Digital Twin and associated uncertainties in the research of small modular reactors is a recent concept. In this work, a comprehensive study is conducted on the multiscale modeling of accident-tolerant fuels. The application of these fuels in the light water-based small modular reactors is explored. This chapter also focuses on the application of machine learning and artificial intelligence in the design optimization, control, and monitoring of small modular reactors. Finally, a brief assessment of the research gap on the application of artificial intelligence to the development of high burnup composite accident-tolerant fuels is provided. Necessary actions to fulfill these gaps are also discussed.

The availability of well-curated datasets has driven the success of Machine Learning (ML) models. Despite the increased access to earth observation data for agriculture, there is a scarcity of curated, labelled datasets, which limits the potential of its use in training ML models for remote sensing (RS) in agriculture. To this end, we introduce a first-of-its-kind dataset, SICKLE, having time-series images at different spatial resolutions from 3 different satellites, annotated with multiple key cropping parameters for paddy cultivation for the Cauvery Delta region in Tamil Nadu, India. The dataset comprises of 2,398 season-wise samples from 388 unique plots distributed across 4 districts of the Delta. The dataset covers multi-spectral, thermal and microwave data between the time period January 2018-March 2021. The paddy samples are annotated with 4 key cropping parameters, i.e. sowing date, transplanting date, harvesting date and crop yield. This is one of the first studies to consider the growing season (using sowing and harvesting dates) as part of a dataset. We also propose a yield prediction strategy that uses time-series data generated based on the observed growing season and the standard seasonal information obtained from Tamil Nadu Agricultural University for the region. The consequent performance improvement highlights the impact of ML techniques that leverage domain knowledge that are consistent with standard practices followed by farmers in a specific region. We benchmark the dataset on 3 separate tasks, namely crop type, phenology date (sowing, transplanting, harvesting) and yield prediction, and develop an end-to-end framework for predicting key crop parameters in a real-world setting.

As artificial intelligence (AI) steadily grows, so do concerns about its environmental footprint. Today's emerging natural language processing (NLP) models, such as GPT-3 can consume as much energy as five cars, according to a 2019 study. To reduce their environmental and climate impact, researchers in the United Arab Emirates are proposing a new development approach for these models that takes energy consumption into account at every stage, aiming to boost energy efficiency wherever possible. Last April, Abu Dhabi's Technology Innovation Institute (TII) launched NOOR, the largest Arabic-language NLP model to date. NOOR – Arabic for "light" – is trained on 10 billion parameters including books, poetry, news, and technical information, reinforcing the model's broad applicability, according to its creators.

In an extant population, how much information do extant individuals provide on the pedigree of their ancestors? Recent work by Kim, Mossel, Ramnarayan and Turner (2020) studied this question under a number of simplifying assumptions, including random mating, fixed length inheritance blocks and sufficiently large founding population. They showed that under these conditions if the average number of offspring is a sufficiently large constant, then it is possible to recover a large fraction of the pedigree structure and genetic content by an algorithm they named REC-GEN. We are interested in studying the performance of REC-GEN on simulated data generated according to the model. As a first step, we improve the running time of the algorithm. However, we observe that even the faster version of the algorithm does not do well in any simulations in recovering the pedigree beyond 2 generations. We claim that this is due to the inbreeding present in any setting where the algorithm can be run, even on simulated data. To support the claim we show that a main step of the algorithm, called ancestral reconstruction, performs accurately in a idealized setting with no inbreeding but performs poorly in random mating populations. To overcome the poor behavior of REC-GEN we introduce a Belief-Propagation based heuristic that accounts for the inbreeding and performs much better in our simulations.

As the thickness of the material decreases compared to the other two dimensions, the surface characteristics dominate the bulk properties of the material and then decide its overall physical and chemical behavior [1]. With the advancement of the thin film technology, now it has become possible to create a wide range of variations in the characteristics of the thin-films by controlling the vital parameters of the growth process paving their way of use in the most technologically advanced applications and industries. As in such applications, almost all the properties of a particular thin film depend on its thickness, hence an accurate estimate of the thickness has been one of the most important deciding factor in the application of thin films in industrial sectors. Some examples of such sectors are display industry, semiconductor devices, eye glasses, stents, solar cells, polymer coatings, photoresists, solar panels, LCD, MEMS, thin-film packaging etc. There have been different methods in use for the measurement of the thin film thicknesses. Ion beam analysis, TEM, ellipsometry, surface profilometry etc. are few examples to mention about. In the present work we have proposed to automate the estimation of the thickness of a growing thin-film by applying an artificial neural network (ANN).

In view of the problems of large consumption of communication and computing resources in the control process, this note studies a fundamental property for a class of multi-agent systems under event-triggered strategy: the S-stabilizability of a group of multi-agent systems with general linear dynamics under weakly connected directed topology. The results indicate that the S-stabilizability can be described in some way that the stabilizability region and feedback gain can evaluate the performance of the protocol. Firstly, a new distributed event-triggered protocol is proposed. Under this protocol, a kind of hybrid static and dynamic event-triggered strategy are presented, respectively. In particular, by using Lyapunov stability theory and graph partition tool, it is proved that the proposed event-triggered control strategy can guarantee the closed-loop system achieve S-stabilizability effectively, if at least one vertex in each iSCC cell receives information from the leader, which reflects the ability of distributed control law. Further, we demonstrate that the stabilizability can be realized if the initial system matrix A is Hurwitz. Moreover, it is confirmed that the designed static event-triggered condition is a limit case of dynamic event condition and can guarantee Zeno-free behavior. Finally, the validity of the theoretical results is proved by numerical simulation.