This paper presents a methodology for linear embedding of nonlinear systems that bounds the model error in terms of the prediction horizon and the magnitude of the derivatives of the system states. Using higher-order derivatives of general nonlinear dynamics that need not be known, we construct a Koopman operator-based linear representation and utilize Taylor series accuracy to derive an error bound. The error formula is used to choose the order of derivatives in the basis functions and obtain a data-driven Koopman model using a closed-form expression that can be computed in real time. The Koopman representation of the nonlinear system is then used to synthesize LQR feedback. The efficacy of the embedding approach is demonstrated with simulation and experimental results on the control of a tail-actuated robotic fish. Experimental results show that the proposed data-driven control approach outperforms a tuned PID (Proportional Integral Derivative) controller and that updating the data-driven model online significantly improves performance in the presence of unmodeled fluid disturbance. This paper is complemented with a video: https://youtu.be/9_wx0tdDta0.

The current knowledge system of macroeconomics is built on interactions among a small number of variables, since traditional macroeconomic models can mostly handle a handful of inputs. Recent work using big data suggests that a much larger number of variables are active in driving the dynamics of the aggregate economy. In this paper, we introduce a knowledge graph (KG) that consists of not only linkages between traditional economic variables but also new alternative big data variables. We extract these new variables and the linkages by applying advanced natural language processing (NLP) tools on the massive textual data of academic literature and research reports. As one example of the potential applications, we use it as the prior knowledge to select variables for economic forecasting models in macroeconomics. Compared to statistical variable selection methods, KG-based methods achieve significantly higher forecasting accuracy, especially for long run forecasts.

We investigate a deep reinforcement learning (RL) architecture that supports explaining why a learned agent prefers one action over another. The key idea is to learn action-values that are directly represented via human-understandable properties of expected futures. This is realized via the embedded self-prediction (ESP)model, which learns said properties in terms of human provided features. Action preferences can then be explained by contrasting the future properties predicted for each action. To address cases where there are a large number of features, we develop a novel method for computing minimal sufficient explanations from anESP. Our case studies in three domains, including a complex strategy game, show that ESP models can be effectively learned and support insightful explanations.

Accurate flood detection in near real time via high resolution, high latency satellite imagery is essential to prevent loss of lives by providing quick and actionable information. Instruments and sensors useful for flood detection are only available in low resolution, low latency satellites with region re-visit periods of up to 16 days, making flood alerting systems that use such satellites unreliable. This work presents H2O-Network, a self supervised deep learning method to segment floods from satellites and aerial imagery by bridging domain gap between low and high latency satellite and coarse-to-fine label refinement. H2O-Net learns to synthesize signals highly correlative with water presence as a domain adaptation step for semantic segmentation in high resolution satellite imagery. Our work also proposes a self-supervision mechanism, which does not require any hand annotation, used during training to generate high quality ground truth data. We demonstrate that H2O-Net outperforms the state-of-the-art semantic segmentation methods on satellite imagery by 10% and 12% pixel accuracy and mIoU respectively for the task of flood segmentation. We emphasize the generalizability of our model by transferring model weights trained on satellite imagery to drone imagery, a highly different sensor and domain.

Spiking Neural Networks requires less frequency while communicating, and involves minimum calculations for performing the task. The neural networks are the brain of Artificial Intelligence. Just like the neurons in the human body, these neural networks precede every process of AI. The modern neural networks are efficient in performing tasks but are lacks energy efficiency. That's why performing tasks like speech recognition, ECG and gesture recognition entails consumption of extensive energy.

Cooperation between different data owners may lead to an improvement in forecast quality - for instance by benefiting from spatial-temporal dependencies in geographically distributed time series. Due to business competitive factors and personal data protection questions, said data owners might be unwilling to share their data, which increases the interest in collaborative privacy-preserving forecasting. This paper analyses the state-of-the-art and unveils several shortcomings of existing methods in guaranteeing data privacy when employing Vector Autoregressive (VAR) models. The paper also provides mathematical proofs and numerical analysis to evaluate existing privacy-preserving methods, dividing them into three groups: data transformation, secure multi-party computations, and decomposition methods. The analysis shows that state-of-the-art techniques have limitations in preserving data privacy, such as a trade-off between privacy and forecasting accuracy, while the original data in iterative model fitting processes, in which intermediate results are shared, can be inferred after some iterations.

Machine-learning techniques, best known for teaching self-driving cars to stop at red lights, may soon help researchers around the world improve their control over the most complicated reaction known to science: nuclear fusion. Fusion reactions are typically hydrogen atoms heated to form a gaseous cloud called a plasma that releases energy as the particles bang into each other and fuse. Getting these reactions under better control could create huge amounts of environmentally clean energy from nuclear reactors in fusion power plants of the future. "The connection between machine learning and fusion energy is not obvious," said Sandia researcher Aidan Thompson, principal investigator for a $2.2 million, three-year DOE Office of Science award to make that connection. "Simply put, we have pioneered machine-learning's use to improve simulations of the reactor's wall material as it interacts with the plasma. This has been beyond the scope of atomic-scale simulations of the past."

Deep learning is a subset of machine learning, which both fall under the artificial intelligence (AI) and internet of things (IoT) umbrellas. Without deep learning applications, though, automation and intelligence would not be where they are today. For instance, AI in manufacturing has come a long way with tech like predictive maintenance. Here are 10 examples of the deep learning revolution. One of the most common deep learning applications is with digital assistants.

The graph represents a network of 1,750 Twitter users whose tweets in the requested range contained "#iiot", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Wednesday, 07 October 2020 at 21:11 UTC. The requested start date was Tuesday, 06 October 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 16-hour, 55-minute period from Friday, 02 October 2020 at 21:22 UTC to Monday, 05 October 2020 at 14:18 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.

The graph represents a network of 1,719 Twitter users whose tweets in the requested range contained "iiot ai", or who were replied to or mentioned in those tweets. The network was obtained from the NodeXL Graph Server on Friday, 09 October 2020 at 10:01 UTC. The requested start date was Friday, 09 October 2020 at 00:01 UTC and the maximum number of tweets (going backward in time) was 7,500. The tweets in the network were tweeted over the 2-day, 4-hour, 29-minute period from Tuesday, 06 October 2020 at 12:01 UTC to Thursday, 08 October 2020 at 16:30 UTC. Additional tweets that were mentioned in this data set were also collected from prior time periods.