Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment. The ability of the systems to detect any behavior that can lead to disruption can help in alerting the system beforehand and prevent its harmful effects. Many machine learning techniques have already been in use at large tokamaks like JET and ASDEX, but are not suitable for ADITYA, which is comparatively small. Through this work, we discuss a new real-time approach to predict the time of disruption in ADITYA tokamak and validate the results on an experimental dataset. The system uses selected diagnostics from the tokamak and after some pre-processing steps, sends them to a time-sequence Long Short-Term Memory (LSTM) network. The model can make the predictions 12 ms in advance at less computation cost that is quick enough to be deployed in real-time applications.

Commercial buildings account for approximately 36% of US electricity consumption, of which nearly two-thirds is met by fossil fuels [1] resulting in an adverse impact on the environment. Reducing this impact requires improving energy efficiency and lowering energy consumption. Most existing studies focus on designing methods to regulate and reduce HVAC and lighting energy consumption. However, few studies have focused on the control of occupant plugload energy consumption. In this study, we conducted multiple experiments to analyze changes in occupant plugload energy consumption due to monetary incentives and/or feedback. The experiments were performed in government office and university buildings at NASA Research Park located in Moffett Field, CA. Analysis of the data reveal significant plugload energy reduction can be achieved via feedback and/or incentive mechanisms. Autoregressive models are used to predict expected plugload savings in the presence of exogenous variables. The results of this study suggest that occupant-in-the-loop control architectures have the potential to reduce energy consumption and hence lower the carbon footprint of commercial buildings.

The new technology provides'actual' thermal fabric performance data Knauf Insulation EXCLUSIVE: System that uses machine learning to measure the'actual' fabric thermal performance of a home within three months could provide evidence base for national retrofit programme Knauf Insulation has developed a technology that uses machine learning systems to accurately measure the'actual' energy performance of individual homes, an innovation that could drastically enhance the accuracy of energy performance certificates (EPC), BusinessGreen can reveal. The new technology, which can generate an assessment of fabric performance of a home within three months, could provide the evidence base for an energy efficiency retrofit programme for the nation's homes, the company said. The "discreet, scalable and cost-efficient" measurement tool ensures the building fabric component of a building's EPC rating can be backed by real evidence, rather than "notional Standard Assessment Procedure calculations", according to Knauf Insulation. The company stressed the tool marked a major departure from other available techniques to measure'actual' fabric thermal performance, which it said were "intrusive and expensive". Steven Heath, technical and strategy director of Knauf Insulation, celebrated the launch of the product, noting that the UK's 2050 net zero emissions ambition depended on the country's housing …

Bluware Corp, the digital innovation platform that enables the oil and gas industry to accelerate digital transformation initiatives using deep learning, is pleased to announce a new agreement with BP (NYSE: BP). Bluware's technology will help BP to improve quality and speed when delivering seismic interpretation products. "BP recognizes the significant impact advances in digital technology can bring and we are pleased to implement Bluware InteractivAI, a new and innovative deep learning technology, augmenting our geoscientists' ability to accelerate subsurface data interpretation," says Ahmed Hashmi, Upstream Chief Digital and Technology Officer at BP. Large seismic data sets are difficult to move and use in workflows and time consuming to interpret. InteractivAI, powered by Bluware Volume Data Store (VDS) cloud-native data environment, enables the acceleration of detailed interpretation tasks. With this tool geoscientists can now train and correct deep learning results interactively, significantly improving structural interpretation workflows.

We present OtoWorld, an interactive environment in which agents must learn to listen in order to solve navigational tasks. The purpose of OtoWorld is to facilitate reinforcement learning research in computer audition, where agents must learn to listen to the world around them to navigate. OtoWorld is built on three open source libraries: OpenAI Gym for environment and agent interaction, PyRoomAcoustics for ray-tracing and acoustics simulation, and nussl for training deep computer audition models. OtoWorld is the audio analogue of GridWorld, a simple navigation game. OtoWorld can be easily extended to more complex environments and games. To solve one episode of OtoWorld, an agent must move towards each sounding source in the auditory scene and "turn it off". The agent receives no other input than the current sound of the room. The sources are placed randomly within the room and can vary in number. The agent receives a reward for turning off a source. We present preliminary results on the ability of agents to win at OtoWorld. OtoWorld is open-source and available.

Estimation of the soil organic carbon content is of utmost importance in understanding the chemical, physical, and biological functions of the soil. This study proposes machine learning algorithms of support vector machines, artificial neural networks, regression tree, random forest, extreme gradient boosting, and conventional deep neural network for advancing prediction models of SOC. Models are trained with 1879 composite surface soil samples, and 105 auxiliary data as predictors. The genetic algorithm is used as a feature selection approach to identify effective variables. The results indicate that precipitation is the most important predictor driving 15 percent of SOC spatial variability followed by the normalized difference vegetation index, day temperature index of moderate resolution imaging spectroradiometer, multiresolution valley bottom flatness and land use, respectively. Based on 10 fold cross validation, the DNN model reported as a superior algorithm with the lowest prediction error and uncertainty. In terms of accuracy, DNN yielded a mean absolute error of 59 percent, a root mean squared error of 75 percent, a coefficient of determination of 0.65, and Lins concordance correlation coefficient of 0.83. The SOC content was the highest in udic soil moisture regime class with mean values of 4 percent, followed by the aquic and xeric classes, respectively. Soils in dense forestlands had the highest SOC contents, whereas soils of younger geological age and alluvial fans had lower SOC. The proposed DNN is a promising algorithm for handling large numbers of auxiliary data at a province scale, and due to its flexible structure and the ability to extract more information from the auxiliary data surrounding the sampled observations, it had high accuracy for the prediction of the SOC baseline map and minimal uncertainty.

Earth observation resources are becoming increasingly indispensable in disaster relief, damage assessment and related domains. Many unpredicted factors, such as the change of observation task requirements, to the occurring of bad weather and resource failures, may cause the scheduled observation scheme to become infeasible. Therefore, it is crucial to be able to promptly and maybe frequently develop high-quality replanned observation schemes that minimize the effects on the scheduled tasks. A bottom-up distributed coordinated framework together with an improved contract net are proposed to facilitate the dynamic task replanning for heterogeneous Earth observation resources. This hierarchical framework consists of three levels, namely, neighboring resource coordination, single planning center coordination, and multiple planning center coordination. Observation tasks affected by unpredicted factors are assigned and treated along with a bottom-up route from resources to planning centers. This bottom-up distributed coordinated framework transfers part of the computing load to various nodes of the observation systems to allocate tasks more efficiently and robustly. To support the prompt assignment of large-scale tasks to proper Earth observation resources in dynamic environments, we propose a multiround combinatorial allocation (MCA) method. Moreover, a new float interval-based local search algorithm is proposed to obtain the promising planning scheme more quickly. The experiments demonstrate that the MCA method can achieve a better task completion rate for large-scale tasks with satisfactory time efficiency. It also demonstrates that this method can help to efficiently obtain replanning schemes based on original scheme in dynamic environments.

Reinforcement learning (RL) agents are often designed specifically for a particular problem and they generally have uninterpretable working processes. Statistical methods-based agent algorithms can be improved in terms of generalizability and interpretability using symbolic Artificial Intelligence (AI) tools such as logic programming. In this study, we present a model-free RL architecture that is supported with explicit relational representations of the environmental objects. For the first time, we use the PrediNet network architecture in a dynamic decision-making problem rather than image-based tasks, and Multi-Head Dot-Product Attention Network (MHDPA) as a baseline for performance comparisons. We tested two networks in two environments ---i.e., the baseline Box-World environment and our novel environment, Relational-Grid-World (RGW). With the procedurally generated RGW environment, which is complex in terms of visual perceptions and combinatorial selections, it is easy to measure the relational representation performance of the RL agents. The experiments were carried out using different configurations of the environment so that the presented module and the environment were compared with the baselines. We reached similar policy optimization performance results with the PrediNet architecture and MHDPA; additionally, we achieved to extract the propositional representation explicitly ---which makes the agent's statistical policy logic more interpretable and tractable. This flexibility in the agent's policy provides convenience for designing non-task-specific agent architectures. The main contributions of this study are two-fold ---an RL agent that can explicitly perform relational reasoning, and a new environment that measures the relational reasoning capabilities of RL agents.

A team of scientists has developed a technique that automatically makes written sentences more polite. Why it matters: As the authors themselves note in the paper, it is "imperative to use the appropriate level of politeness for smooth communication in conversations." And what better to determine the appropriate level of politeness than an unfeeling machine-learning algorithm? What's new: In a paper presented this week at the annual meeting of the Association for Computational Linguistics, researchers from Carnegie Mellon University analyzed a dataset of 1.39 million sentences, each of which was labeled with a politeness score. Of note: The researchers used the "Enron Corpus" as a dataset -- hundreds of thousands of emails exchanged by Enron employees and preserved by the federal government during its investigation of the now-defunct energy firm.

Can artificial intelligence be deployed to slow down global warming, or is AI one of the greatest climate sinners ever? That is the interesting debate that finds (not surprisingly) representatives from the AI industry and academia on opposite sides of the issue. While PwC and Microsoft published a report concluding that using AI could reduce world-wide greenhouse gas emissions by 4% in 2030, researchers from the University of Amherst Massachusetts have calculated that training a single AI model can emit more than 626,000 pounds of carbon dioxide equivalent--nearly five times the lifetime emissions of the average American car. The big players have clearly understood that the public sensibility towards climate change offers a wonderful marketing opportunity. IBM has launched its Green Horizons project to analyze environmental data and predict pollution.