Reinforcement Learning and Bandits for Speech and Language Processing: Tutorial, Review and Outlook

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the training of deep neural networks with its flexible optimization properties, there are still many grounds to explore to utilize the benefits of reinforcement learning, such as its reward-driven adaptability, state representations, temporal structures and generalizability. In this survey, we present an overview of recent advancements of reinforcement learning and bandits, and discuss how they can be effectively employed to solve speech and natural language processing problems with models that are adaptive, interactive and scalable.Comment: To appear in Expert Systems with Applications. Accompanying INTERSPEECH 2022 Tutorial on the same topic. Including latest advancements in large language models (LLMs

Integrated multidisciplinary optimization of rotorcraft: A plan for development

This paper describes a joint NASA/Army initiative at the Langley Research Center to develop optimization procedures aimed at improving the rotor blade design process by integrating appropriate disciplines and accounting for important interactions among the disciplines. The paper describes the optimization formulation in terms of the objective function, design variables, and constraints. Additionally, some of the analysis aspects are discussed, validation strategies are described, and an initial attempt at defining the interdisciplinary couplings is summarized. At this writing, significant progress has been made, principally in the areas of single discipline optimization. Accomplishments are described in areas of rotor aerodynamic performance optimization for minimum hover horsepower, rotor dynamic optimization for vibration reduction, and rotor structural optimization for minimum weight

Futures Studies in the Interactive Society

This book consists of papers which were prepared within the framework of the research project (No. T 048539) entitled Futures Studies in the Interactive Society (project leader: Éva Hideg) and funded by the Hungarian Scientific Research Fund (OTKA) between 2005 and 2009. Some discuss the theoretical and methodological questions of futures studies and foresight; others present new approaches to or procedures of certain questions which are very important and topical from the perspective of forecast and foresight practice. Each study was conducted in pursuit of improvement in futures fields

Integration of drone data and field investigations to investigate avalanche potential in steep cliffs, with examples from Western Norway

Masteroppgave i geovitenskapGEOV399MAMN-GEO

Deep Learning Techniques for Power System Operation: Modeling and Implementation

The fast development of the deep learning (DL) techniques in the most recent years has drawn attention from both academia and industry. And there have been increasing applications of the DL techniques in many complex real-world situations, including computer vision, medical diagnosis, and natural language processing. The great power and flexibility of DL can be attributed to its hierarchical learning structure that automatically extract features from mass amounts of data. In addition, DL applies an end-to-end solving mechanism, and directly generates the output from the input, where the traditional machine learning methods usually break down the problem and combine the results. The end-to-end mechanism considerably improve the computational efficiency of the DL.The power system is one of the most complex artificial infrastructures, and many power system control and operation problems share the same features as the above mentioned real-world applications, such as time variability and uncertainty, partial observability, which impedes the performance of the conventional model-based methods. On the other hand, with the wide spread implementation of Advanced Metering Infrastructures (AMI), the SCADA, the Wide Area Monitoring Systems (WAMS), and many other measuring system providing massive data from the field, the data-driven deep learning technique is becoming an intriguing alternative method to enable the future development and success of the smart grid. This dissertation aims to explore the potential of utilizing the deep-learning-based approaches to solve a broad range of power system modeling and operation problems. First, a comprehensive literature review is conducted to summarize the existing applications of deep learning techniques in power system area. Second, the prospective application of deep learning techniques in several scenarios in power systems, including contingency screening, cascading outage search, multi-microgrid energy management, residential HVAC system control, and electricity market bidding are discussed in detail in the following 2-6 chapters. The problem formulation, the specific deep learning approaches in use, and the simulation results are all presented, and also compared with the currently used model-based method as a verification of the advantage of deep learning. Finally, the conclusions are provided in the last chapter, as well as the directions for future researches. It’s hoped that this dissertation can work as a single spark of fire to enlighten more innovative ideas and original studies, widening and deepening the application of deep learning technique in the field of power system, and eventually bring some positive impacts to the real-world bulk grid resilient and economic control and operation

Assessment and management of the Arroyo Del Coyote watershed, Sandia National Laboratories and Environs, New Mexico

The Environmental Restoration Project managed by Sandia National Laboratories/New Mexico and sponsored by the U.S. Department of Energy is responsible for the investigation, remediation, and monitoring of sites within the confines of Kirtland Air Force Base. These environmental restoration sites are areas where there is a documented or potential risk for the off-site migration of contaminants due to past uses of the land. Recent implementation of the Clean Water Action Plan has encouraged government agencies to deal with multiple environmental issues and sites as integrated components on a watershed scale as opposed to viewing them as individual problems with little or no relationship. Therefore, this watershed assessment and management study was commissioned for the Arroyo del Coyote watershed in June 1998