The last several years have witnessed the great success of reinforcement learning (RL) including the video game playing [Mnih et al., 2015], robot manipulation [Gu et al., 2017], autonomous driving [Shalev-Shwartz et al., 2016] and many others [Lazic et al., 2018, Dalal et al., 2016]. Most of them focus on the problem where the system of interest evolves continuously with time, e.g., a trajectory of a tennis ball. However, the conventional research in RL may omit a category of system that evolves continuously and may be interrupted by stochastic events abruptly (see the jumps in Figure 1). Such system exists ubiquitously in the social and information science and therefore necessitates the research of reinforcement learning in these domains to extend its applicability in the real-world problems [Farajtabar et al., 2017, Wang et al., 2018], in which the agent seeks an optimal intervention policy so as to improve the future course of events. Concrete examples may include: - Social media. Social media website allows users to create and share content. Retweet can form as users resharing and broadcasting others' tweet to their friends and followers. Such stochastic events would steer the behaviors of other tweet users [Rizoiu et al., 2017]. At the same time, the platform (agent) may want to seek a policy to effectively mitigate the fake news by optimizing the performance of real news propagation over the network Farajtabar et al. [2017].

"This optical system represents a computer hardware architecture that can enhance the creativity of artificial neural networks used in AI and machine learning, but more importantly, it demonstrates the viability for this system at a large scale where noise and errors can be mitigated and even harnessed," Li said. "AI applications are growing so fast that in the future, their energy consumption will be unsustainable. This technology has the potential to help reduce that energy consumption, making AI and machine learning environmentally sustainable -- and very fast, achieving higher performance overall.

We implement a data assimilation framework for integrating ice surface and terminus position observations into a numerical ice-flow model. The model uses the well-known shallow shelf approximation (SSA) coupled to a level set method to capture ice motion and changes in the glacier geometry. The level set method explicitly tracks the evolving ice-atmosphere and ice-ocean boundaries for a marine outlet glacier. We use an Ensemble Transform Kalman Filter to assimilate observations of ice surface elevation and lateral ice extent by updating the level set function that describes the ice interface. Numerical experiments on an idealized marine-terminating glacier demonstrate the effectiveness of our data assimilation approach for tracking seasonal and multi-year glacier advance and retreat cycles. The model is also applied to simulate Helheim Glacier, a major tidewater-terminating glacier of the Greenland Ice Sheet that has experienced a recent history of rapid retreat. By assimilating observations from remotely-sensed surface elevation profiles we are able to more accurately track the migrating glacier terminus and glacier surface changes. These results support the use of data assimilation methodologies for obtaining more accurate predictions of short-term ice sheet dynamics.

Planning based on long and short term time series forecasts is a common practice across many industries. In this context, temporal aggregation and reconciliation techniques have been useful in improving forecasts, reducing model uncertainty, and providing a coherent forecast across different time horizons. However, an underlying assumption spanning all these techniques is the complete availability of data across all levels of the temporal hierarchy, while this offers mathematical convenience but most of the time low frequency data is partially completed and it is not available while forecasting. On the other hand, high frequency data can significantly change in a scenario like the COVID pandemic and this change can be used to improve forecasts that will otherwise significantly diverge from long term actuals. We propose a dynamic reconciliation method whereby we formulate the problem of informing low frequency forecasts based on high frequency actuals as a Markov Decision Process (MDP) allowing for the fact that we do not have complete information about the dynamics of the process. This allows us to have the best long term estimates based on the most recent data available even if the low frequency cycles have only been partially completed. The MDP has been solved using a Time Differenced Reinforcement learning (TDRL) approach with customizable actions and improves the long terms forecasts dramatically as compared to relying solely on historical low frequency data. The result also underscores the fact that while low frequency forecasts can improve the high frequency forecasts as mentioned in the temporal reconciliation literature (based on the assumption that low frequency forecasts have lower noise to signal ratio) the high frequency forecasts can also be used to inform the low frequency forecasts.

This means that the contribution of wind power in power systems is becoming increasingly important. The downside is that detailed schedule plans and reserve capacity must be properly set by power system regulators (Impram et al., 2020) facing the intrinsic problem of the highly intermittent nature of wind, making this very hard to predict. The accuracy of wind forecasts thus becomes an issue of paramount importance for the wind industry. In a recent work by Casciaro et al. (2021), a novel accurate Ensemble Model Output Statistics (EMOS) strategy for calibrating wind speed/power forecasts from an Ensemble Prediction System (EPS) has been proposed and its superiority when compared against more parsimonious strategies in the 0-48 h look-ahead forecast horizon clearly emerged. However, because all global weather models start their run from analysis corresponding to the main synoptic hours 00, 06, 12, and 18 UTC, weather predictions (of any forecast horizons) necessarily remain frozen for six hours.

Owing to the lessons learned throughout the COVID-19 crisis, it is about time to design a better world that is characterized by empathy, personal integrity, no corruption, no racism, respect for nature, working smarter, circularity, a changed role of HR, good governance, and ethical leadership. The significant feature in this better world entails SUSTAINABILITY, that is, sustainability in innovation, design, HR, leadership, diversity & inclusion, family businesses, corporate governance, and higher management education, as shown in this figure. The first rule of sustainability is alignment with yourself, to continuously perceive what you do sustainability and be aware of the influence of your behavior and actions on human beings, animals, plants, and the environment. This is personal integrity, which is the foundation of empathy and eco-design thinking. In my articles "HOW TO DESIGN A BETTER WORLD" and "Sustainability Starts With Yourself", I discussed in detail how to design this better world sustainably. In this article I will focus on sustainability in innovation. This is based on my new book "Eco-Design Thinking for Personal, Corporate and Social Innovation; How to Become a Disruptive Eco-Design Thinker Like Elon Musk". Read the excerpt of this book, which is about reimagining design and innovation. Designing a better world requires a new way of thinking.

Note that the Buckeye Flats location (a) contains greater acoustic activity, a result of the nearby rapid flowing stream that produced considerable geophonic sounds. The inset (b) graphs the same data but with Buckeye Flats removed. These values (b) reflect mostly biophony. Sycamore Creek contained the greatest acoustic activity of these three. The fall contains the greatest activity although there was no consistent pattern across sites. Photos of each landscape are provided in (c).

There are a half dozen refrigerator-sized cabinets inside a lab at Stanford University's Precourt Institute for energy that is designed for killing bacteria as quickly as possible. Each contains around 100 lithium-ion cells in trays in which the batteries could be charged and discharged dozens of times each day. The batteries used in these electrochemical torture chambers would normally be found in electronics or electric vehicles. Instead, energy is transported in and out of these cells as quickly as possible, generating reams of performance data that artificial intelligence can use to learn how to make a better battery. To estimate how a battery would perform in the future, AI would require data from a battery after it had begun to degrade. It could take months to cycle the battery enough times to get the required data.

"We can now study the response of many materials under the same extreme pressures," said Sandia scientist Aidan Thompson, who originated SNAP. "Applications include planetary science questions--for example, what kind of impact stress would have led to the formation of our moon. It also opens the door to design and manufacture of novel materials at extreme conditions." The effect of extreme pressures and temperatures on materials also is important for devising interior models of giant planets. Powerful DOE facilities like Sandia's Z machine and Lawrence Livermore National Laboratory's National Ignition Facility can recreate near-identical conditions of these worlds in earthly experiments that offer close-up examinations of radically compressed materials.

This paper presents a systematic study of the effects of hyperspectral pixel dimensionality reduction on the pixel classification task. We use five dimensionality reduction methods -- PCA, KPCA, ICA, AE, and DAE -- to compress 301-dimensional hyperspectral pixels. Compressed pixels are subsequently used to perform pixel classifications. Pixel classification accuracies together with compression method, compression rates, and reconstruction errors provide a new lens to study the suitability of a compression method for the task of pixel classification. We use three high-resolution hyperspectral image datasets, representing three common landscape types (i.e. urban, transitional suburban, and forests) collected by the Remote Sensing and Spatial Ecosystem Modeling laboratory of the University of Toronto. We found that PCA, KPCA, and ICA post greater signal reconstruction capability; however, when compression rates are more than 90\% these methods show lower classification scores. AE and DAE methods post better classification accuracy at 95\% compression rate, however their performance drops as compression rate approaches 97\%. Our results suggest that both the compression method and the compression rate are important considerations when designing a hyperspectral pixel classification pipeline.