Tokyo Electric Power Company Holdings Inc. halted its investigation of the inside of the containment vessel of the No. 1 reactor at its stricken Fukushima No. 1 nuclear power plant on Wednesday. The move came after an issue was found during preparation work for the display of data such as radiation levels from dosimeters inside underwater robots to be used in the survey. The preparations began at noon the same day and were halted around two hours later. Tepco said that it will resume the survey once measures to resolve the issue are taken. In the survey, which will continue until around August, Tepco aims to take pictures of melted nuclear fuel debris and other deposits using six types of underwater robots to record their locations and thickness in water that has accumulated at the bottom of the containment vessel.

Fusing artificial intelligence with mathematical optimization will dramatically increase the "brainpower" for the task at hand, whether it's optimizing flight patterns or bringing energy and food to underserved areas. That's the word from the academic researchers who are part of a new interdisciplinary institute that aims to integrate the two fields. The National AI Institute for Advances in Optimization (AI4OPT) is led by a multidisciplinary team from six U.S. universities, including computer science and civil, environmental, electrical, and computer engineering professors. The combined methods will foster no less than a "paradigm shift" in optimization, said Pascal Van Hentenryck, professor of industrial and systems engineering at Georgia Tech and institute lead. According to Hentenryck, tackling problems at the scale and complexity faced by society today requires a fusion of optimization and machine learning, with the two technologies working hand-in-hand.

The Energy Stocks Package is based on the I Know First algorithm and is designed for investors and analysts who need recommendations for the best performing stocks for the whole Energy Industry. Package Name: Energy Stocks Forecast Recommended Positions: Long Forecast Length: 7 Days (1/3/22 – 1/10/22) I Know First Average: 7.43% During the 7 Days forecasted period several picks saw significant returns. The algorithm had correctly predicted 8 out of 10 returns. The greatest return came from PBF at 26.98%. Further notable returns came from PTEN and FET at 18.11% and 8.85%, respectively.

Whether protecting against surges on electric networks, locating designs amongst previous criminal offenses, or even improving sources in the treatment of significantly bad people, Duke University computer system expert Cynthia Rudin desires expert system (AI) to reveal its own job. When it is actually creating choices that profoundly impact individuals's lifestyles, particularly. " I would like to give thanks to AAAI and also Squirrel AI for making this honor that I understand will definitely be actually a game-changer for the area," Rudin pointed out. "To possess a'Nobel Prize' for artificial intelligence to assist culture creates it ultimately crystal clear undeniably that this subject matter -- AI help the advantage for community -- is really significant." Dark container designs are actually the contrast of Rudin's straightforward codes.

The Cloud paradigm is at a critical point in which the existing energy-efficiency techniques are reaching a plateau, while the computing resources demand at Data Center facilities continues to increase exponentially. The main challenge in achieving a global energy efficiency strategy based on Artificial Intelligence is that we need massive amounts of data to feed the algorithms. Nowadays, any optimization strategy must begin with data. However, companies with access to these large amounts of data decide not to share them because it could compromise their security. This paper proposes a time-series data augmentation methodology based on synthetic scenario forecasting within the Data Center. For this purpose, we will implement a powerful generative algorithm: Generative Adversarial Networks (GANs). The use of GANs will allow us to handle multivariate data and data from different natures (e.g., categorical). On the other hand, adapting Data Centers' operational management to the occurrence of sporadic anomalies is complicated due to the reduced frequency of failures in the system. Therefore, we also propose a methodology to increase the generated data variability by introducing on-demand anomalies. We validated our approach using real data collected from an operating Data Center, successfully obtaining forecasts of random scenarios with several hours of prediction. Our research will help to optimize the energy consumed in Data Centers, although the proposed methodology can be employed in any similar time-series-like problem.

We introduce an algorithm for computing geodesics on sampled manifolds that relies on simulation of quantum dynamics on a graph embedding of the sampled data. Our approach exploits classic results in semiclassical analysis and the quantum-classical correspondence, and forms a basis for techniques to learn the manifold from which a dataset is sampled, and subsequently for nonlinear dimensionality reduction of high-dimensional datasets. We illustrate the new algorithm with data sampled from model manifolds and also by a clustering demonstration based on COVID-19 mobility data. Finally, our method reveals interesting connections between the discretization provided by data sampling and quantization.

Wirth Research, an engineering company that specialises in computational fluid dynamics, has become increasingly concerned with environmental sustainability. It initially focused on the design of racing cars, allowing clients to replace expensive wind tunnel work with computerised modelling, but in recent years it has designed equipment that reduces the aerodynamic drag of lorries, and a device which reduces cold air escaping from open-fronted supermarket fridges, cutting energy use by a quarter. The Bicester-based company also wanted to reduce the energy used by its detailed computerised modelling, which for car aerodynamics simulates around half a billion tiny cells of air. It had already adjusted the resolution of cells within each model, with a finer sub-millimetre mesh used near sharp edges. Then, during the pandemic when it realised staff could work effectively from home, Wirth moved its computing from its own site to a renewable energy-powered datacentre in Iceland run by Verne Global.

Energy harvesting (EH) IoT devices that operate intermittently without batteries, coupled with advances in deep neural networks (DNNs), have opened up new opportunities for enabling sustainable smart applications. Nevertheless, implementing those computation and memory-intensive intelligent algorithms on EH devices is extremely difficult due to the challenges of limited resources and intermittent power supply that causes frequent failures. To address those challenges, this paper proposes a methodology that enables fast deep learning with low-energy accelerators for tiny energy harvesting devices. We first propose $RAD$, a resource-aware structured DNN training framework, which employs block circulant matrix and structured pruning to achieve high compression for leveraging the advantage of various vector operation accelerators. A DNN implementation method, $ACE$, is then proposed that employs low-energy accelerators to profit maximum performance with small energy consumption. Finally, we further design $FLEX$, the system support for intermittent computation in energy harvesting situations. Experimental results from three different DNN models demonstrate that $RAD$, $ACE$, and $FLEX$ can enable fast and correct inference on energy harvesting devices with up to 4.26X runtime reduction, up to 7.7X energy reduction with higher accuracy over the state-of-the-art.

Efficient model selection for identifying a suitable pre-trained neural network to a downstream task is a fundamental yet challenging task in deep learning. Current practice requires expensive computational costs in model training for performance prediction. In this paper, we propose a novel framework for neural network selection by analyzing the governing dynamics over synaptic connections (edges) during training. Our framework is built on the fact that back-propagation during neural network training is equivalent to the dynamical evolution of synaptic connections. Therefore, a converged neural network is associated with an equilibrium state of a networked system composed of those edges. To this end, we construct a network mapping $\phi$, converting a neural network $G_A$ to a directed line graph $G_B$ that is defined on those edges in $G_A$. Next, we derive a neural capacitance metric $\beta_{\rm eff}$ as a predictive measure universally capturing the generalization capability of $G_A$ on the downstream task using only a handful of early training results. We carried out extensive experiments using 17 popular pre-trained ImageNet models and five benchmark datasets, including CIFAR10, CIFAR100, SVHN, Fashion MNIST and Birds, to evaluate the fine-tuning performance of our framework. Our neural capacitance metric is shown to be a powerful indicator for model selection based only on early training results and is more efficient than state-of-the-art methods.

Analyzing large-scale data from simulations of turbulent flows is memory intensive, requiring significant resources. This major challenge highlights the need for data compression techniques. In this study, we apply a physics-informed Deep Learning technique based on vector quantization to generate a discrete, low-dimensional representation of data from simulations of three-dimensional turbulent flows. The deep learning framework is composed of convolutional layers and incorporates physical constraints on the flow, such as preserving incompressibility and global statistical characteristics of the velocity gradients. The accuracy of the model is assessed using statistical, comparison-based similarity and physics-based metrics. The training data set is produced from Direct Numerical Simulation of an incompressible, statistically stationary, isotropic turbulent flow. The performance of this lossy data compression scheme is evaluated not only with unseen data from the stationary, isotropic turbulent flow, but also with data from decaying isotropic turbulence, and a Taylor-Green vortex flow. Defining the compression ratio (CR) as the ratio of original data size to the compressed one, the results show that our model based on vector quantization can offer CR $=85$ with a mean square error (MSE) of $O(10^{-3})$, and predictions that faithfully reproduce the statistics of the flow, except at the very smallest scales where there is some loss. Compared to the recent study based on a conventional autoencoder where compression is performed in a continuous space, our model improves the CR by more than $30$ percent, and reduces the MSE by an order of magnitude. Our compression model is an attractive solution for situations where fast, high quality and low-overhead encoding and decoding of large data are required.