检索结果-内蒙古大学图书馆

Learn and Link: Learning Critical Regions for Efficient Planning

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Molina, Daniel Kumar, Kislay Srivastava, Siddharth School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

In this paper, we present a new approach to learning for motion planning (MP) where critical regions of an environment with low probability measure are learned from a given set of motion plans and used to improve performance on new problem instances. We show that convolutional neural networks (CNNs) can be used to identify critical regions for motion planning problems. We also introduce a new suite of sampling-based motion planners, Learn and Link. Our planners leverage critical region locations identified by our CNN to overcome the limitations of uniform sampling, while still maintaining guarantees of correctness inherent to sampling-based algorithms. We evaluate Learn and Link against planners from the Open Motion Planning Library (OMPL) using an extensive suite of experiments on challenging motion planning problems. We show that our approach requires far less planning time than existing samplingbased planners. Copyright © 2019, The Authors. All rights reserved.

关键词： Planning

International Symposium for Intelligent Transportation and Smart City (ITASC) 2019 Proceedings 1

学校读者我要写书评

暂无评论

丛书名： Smart Innovation, systems and Technologies

2019年

作者： Xiaoqing Zeng Xiongyao Xie Jian Sun Limin Ma Yinong Chen

ISBN: (数字)9789811375422

ISBN: (纸本)9789811375415

This book presents research advances in intelligent transportation and smart cities in detail, mainly focusing on green traffic and urban utility tunnels, presented at the 4th International Symposium for Intelligent Transportation and Smart City (ITASC) held at Tongji University, Shanghai, on May 8–10, 2019. It discusses a number of hot topics, such as the 2BMW system (Bus, Bike, Metro and Walking), transportation safety and environmental protection, urban utility design and application, as well as the application of BIM (Building Information Modeling) in city design. By connecting the theory and applications of intelligent transportation in smart cities, it enhances traffic efficiency and quality. The book gathers numerous selected papers and lectures, including contributions from respected scholars and the latest engineering advances, to provide guidance to researchers in the field of transportation and urban planning at universities and in related industries.

关键词： Transportation Technology and Traffic engineering Geotechnical engineering and Applied Earth Sciences Sustainability Communications engineering, Networks

Balancing goal obfuscation and goal legibility in settings with cooperative and adversarial observers

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Kulkarni, Anagha Srivastava, Siddharth Kambhampati, Subbarao School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85281 United States

In order to be useful in the real world, AI agents need to plan and act in the presence of others, who may include adversarial and cooperative entities. In this paper, we consider the problem where an autonomous agent needs to act in a manner that clarifies its objectives to cooperative entities while preventing adversarial entities from inferring those objectives. We show that this problem is solvable when cooperative entities and adversarial entities use different types of sensors and/or prior knowledge. We develop two new solution approaches for computing such plans. One approach provides an optimal solution to the problem by using an IP solver to provide maximum obfuscation for adversarial entities while providing maximum legibility for cooperative entities in the environment, whereas the other approach provides a satisficing solution using heuristic-guided forward search to achieve preset levels of obfuscation and legibility for adversarial and cooperative entities respectively. We show the feasibility and utility of our algorithms through an extensive empirical evaluation on problems derived from planning benchmarks. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous agents

Woody: Low-Cost, Open-Source Humanoid Torso Robot

学校读者我要写书评

暂无评论

Woody: Low-Cost, Open-Source Humanoid Torso Robot

International Conference on Ubiquitous Robots and Ambient Intelligence (URAI)

作者： Daniel Hayosh Xiao Liu Kiju Lee Department of Engineering Technology and Industrial Distribution and Department of Mechanical Engineering Texas A&M University TX USA Department of Mechanical and Aerospace Engineering Case Western Reserve University Cleveland OH USA School of Computing Informatics and Decision Systems Engineering Arizona State University Tempe AZ USA

ISBN: (数字)9781728157153

ISBN: (纸本)9781728157160

This paper presents a humanoid torso robot, named Woody. It has two arms, each with five degrees of freedom (DoF) and a 2-DoF neck supporting the head with two moving eyebrows. Woody's hardware is made primarily from laser-cut plywood. Two cameras, a microphone, a speaker, and a microprocessor board are embedded in the robot. The processor board also contains a wireless communication module for remote control and access to networked or cloudbased resources. The interactive functions include face tracking, facial emotion recognition, and several pre-programmed default gestures. Woody's graphical user interface (GUI) provides its users with instructions on hardware construction and initial setup. Through this GUI, the user can also program the robot and record new gestures by its motion recording function. Woody is developed as an open-source hardware platform that can also utilize open-source software for educational purposes.

关键词： Robots Torso Hardware Graphical user interfaces Open source software Eyebrows Servomotors

Model-free model reconciliation

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Sreedharan, Sarath Olmo, Alberto Mishra, Aditya Prasad Kambhampati, Subbarao School of Computing Informatics Decision Systems Engineering Arizona State University TempeAZ85281 United States

Designing agents capable of explaining complex sequential decisions remain a significant open problem in automated decision-making. Recently, there has been a lot of interest in developing approaches for generating such explanations for various decision-making paradigms. One such approach has been the idea of explanation as model-reconciliation. The framework hypothesizes that one of the common reasons for the user's confusion could be the mismatch between the user's model of the task and the one used by the system to generate the decisions. While this is a general framework, most works that have been explicitly built on this explanatory philosophy have focused on settings where the model of user's knowledge is available in a declarative form. Our goal in this paper is to adapt the model reconciliation approach to the cases where such user models are no longer explicitly provided. We present a simple and easy to learn labeling model that can help an explainer decide what information could help achieve model reconciliation between the user and the agent. Copyright © 2019, The Authors. All rights reserved.

关键词： decision making

RadGenNets: Deep learning-based radiogenomics model for gene mutation prediction in lung cancer

学校读者我要写书评

暂无评论

informatics in Medicine Unlocked 2022年 33卷

作者： Tripathi, Satvik Moyer, Ethan Jacob Augustin, Alisha Isabelle Zavalny, Alex Dheer, Suhani Sukumaran, Rithvik Schwartz, Daniel Gorski, Brandon Dako, Farouk Kim, Edward College of Computing and Informatics Drexel University Philadelphia 19104 PA United States College of Arts and Sciences Drexel University Philadelphia 19104 PA United States School of Biomedical Engineering Science and Health Systems Drexel University Philadelphia 19104 PA United States College of Engineering Drexel University Philadelphia 19104 PA United States Department of Radiology University of Pennsylvania School of Medicine Philadelphia 19102 PA United States

In this paper, we present our methodology that can be used for predicting gene mutation in patients with non-small cell lung cancer (NSCLC). There are three major types of gene mutations that a NSCLC patient's gene structure can change to: epidermal growth factor receptor (EGFR), Kirsten rat sarcoma virus (KRAS), and Anaplastic lymphoma kinase (ALK). We worked with the clinical and genomics data for each of the 130 patients as well as their corresponding PET/CT scans. We preprocessed all of the data and then built a novel pipeline to integrate both the image and tabular data. We built a novel pipeline that used a fusion of Convolutional Neural Networks and Dense Neural Networks. Also, using a search approach, we picked an ensemble of deep learning models to classify the separate gene mutations. These models include EfficientNets, SENet, and ResNeXt WSL, among others. Our model achieved a high area under curve (AUC) score of 94% in predicting gene mutation. © 2022 The Author(s)

关键词： Deep learning EfficientNet Gene prediction Lung cancer Radiogenomics

Asking the right questions: Learning interpretable action models through query answering

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Verma, Pulkit Marpally, Shashank Rao Srivastava, Siddharth School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85281 United States

This paper develops a new approach for estimating an interpretable, relational model of a black-box autonomous agent that can plan and act. Our main contributions are a new paradigm for estimating such models using a rudimentary query interface with the agent and a hierarchical querying algorithm that generates an interrogation policy for estimating the agent's internal model in a user-interpretable vocabulary. Empirical evaluation of our approach shows that despite the intractable search space of possible agent models, our approach allows correct and scalable estimation of interpretable agent models for a wide class of black-box autonomous agents. Our results also show that this approach can use predicate classifiers to learn interpretable models of planning agents that represent states as images. Copyright © 2019, The Authors. All rights reserved.

关键词： Autonomous agents

All-in-Memory Stochastic computing using ReRAM

学校读者我要写书评

暂无评论

arXiv 2025年

作者： de Lima, João Paulo C. Moghadam, Mehran Shoushtari Aygun, Sercan Castrillon, Jeronimo Hassan Najafi, M. Khan, Asif Ali Dresden University of Technology Dresden Germany Dresden Germany Electrical Computer and Systems Engineering Department Case Western Reserve University ClevelandOH United States School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA United States Barkhausen Institut Dresden Germany

As the demand for efficient, low-power computing in embedded and edge devices grows, traditional computing methods are becoming less effective for handling complex tasks. Stochastic computing (SC) offers a promising alternative by approximating complex arithmetic operations, such as addition and multiplication, using simple bitwise operations, like majority or AND, on random bit-streams. While SC operations are inherently fault-tolerant, their accuracy largely depends on the length and quality of the stochastic bit-streams (SBS). These bit-streams are typically generated by CMOS-based stochastic bit-stream generators that consume over 80% of the SC system’s power and area. Current SC solutions focus on optimizing the logic gates but often neglect the high cost of moving the bit-streams between memory and processor. This work leverages the physics of emerging ReRAM devices to implement the entire SC flow in place: generating low-cost true random numbers and SBSs, conducting SC operations, and converting SBSs back to binary. Considering the low reliability of ReRAM cells, we demonstrate how SC’s robustness to errors copes with ReRAM’s variability. Our evaluation shows significant improvements in throughput (1.39×, 2.16×) and energy consumption (1.15×, 2.8×) over state-of-the-art (CMOS- and ReRAM-based) solutions, respectively, with an average image quality drop of 5% across multiple SBS lengths and image processing tasks. © 2025, CC BY.

关键词： Logic gates

Predicting The Future: Diverse Perspectives on The Transformation of Healthcare Delivery over the Next 15 Years

学校读者我要写书评

暂无评论

Proceedings of the Human Factors and Ergonomics Society Annual Meeting 2021年第1期64卷 671 - 674页

作者： Emily S. Patterson Shilo Anders Laura G. Militello Jason J. Saleem Shawna Perry Clayton Rothwell Dustin Weiler School of Health and Rehabilitation Sciences The Ohio State University Columbus OH USA Departments of Anesthesiology and Biomedical Informatics Vanderbilt University School of Medicine Nashville TN USA Center for Research and Innovation in Systems Safety Vanderbilt University Medical Center Nashville TN US Applied Decision Science LLC Dayton OH USA Department of Industrial Engineering J.B. Speed School of Engineering University of Louisville Louisville KY USA Department of Emergency Medicine University of Florida College of Medicine Jacksonville FL USA Human Autonomy Systems Department Infoscitex Corporation Dayton OH USA Department of Industrial and Systems Engineering University of Wisconsin-Madison Madison WI USA

Over the last 15 years, healthcare delivery has experienced enormous changes across every aspect of clinical care, both for participants in it and affiliated stakeholders. The plethora and breadth of these changes and ongoing struggles with their incorporation into the work of healthcare begs the question: In 2035, what emerging innovations and considerations will we wish we had prepared for with regard to human factors engineering research, technology development, and system redesign efforts? Panelists and co-chairs have deep experience with steering, and being impacted by, innovations in information technology in healthcare. The panelists represent diverse academic, clinical, government contract research and development, and small entrepreneurial business. Perhaps the biggest benefit of this session will be the explicit inclusion of the audience in brainstorming and discussion activities, which will be facilitated digitally and verbally during the session and shared afterwards electronically with participants.

关键词：