Search for: All records

Abstract Running quantum algorithms protected by quantum error correction requires a real time, classical decoder. To prevent the accumulation of a backlog, this decoder must process syndromes from the quantum device at a faster rate than they are generated. Most prior work on real time decoding has focused on an isolated logical qubit encoded in the surface code. However, for surface code, quantum programs of utility will require multi-qubit interactions performed via lattice surgery. A large merged patch can arise during lattice surgery—possibly as large as the entire device. This puts a significant strain on a real time decoder, which must decode errors on this merged patch and maintain the level of fault-tolerance that it achieves on isolated logical qubits. These requirements are relaxed by using spatially parallel decoding, which can be accomplished by dividing the physical qubits on the device into multiple overlapping groups and assigning a decoder module to each. We refer to this approach asspatially parallel windows. While previous work has explored similar ideas, none have addressed system-specific considerations pertinent to the task or the constraints from using hardware accelerators. In this work, we demonstrate how to configure spatially parallel windows, so that the scheme (1) is compatible with hardware accelerators, (2) supports general lattice surgery operations, (3) maintains the fidelity of the logical qubits, and (4) meets the throughput requirement for real time decoding. Furthermore, our results reveal the importance of optimally choosing the buffer width to achieve a balance between accuracy and throughput—a decision that should be influenced by the device’s physical noise. 

A convergence of technology advancements including spatial computing, augmented reality (AR), and artificial intelligence (AI) can now support the personalization of learning environments and dynamically respond to learner performance data with personalized feedback. Augmented Learning for Environmental Robotics (ALERT), leverages advances in technology to research, develop, and test an augmented reality-enhanced (AR) curriculum for learning how to develop and use robotic environmental monitoring tools for collecting data on environmentally sensitive construction sites. With this project, our research team aims to develop the ALERT curriculum as an immersive learning environment, implement automation processes that dynamically adjust to learner performance, and address a pressing problem in the construction sector with recent advances in small robotics and remote sensing. 

Abstract. The building industry is facing environmental, technological, and economic challenges, placing significant pressure on preparing the workforce for Industry 4.0 needs. The fields of Architecture, Engineering, and Construction (AEC) are being reshaped by robotics technologies which demand new skills and creating disruptive change to job markets. Addressing the learning needs of AEC students, professionals, and industry workers is critical to ensuring the competitiveness of the future workforce. In recent years advancements in Information Technology, Augmented Reality (AR), Virtual Reality (VR), and Artificial Intelligence (AI) have led to new research and theories on virtual learning environments. In the AEC fields researchers are beginning to rethink current robotics training to counteract costly and resource-intensive in-person learning. However, much of this work has been focused on simulation physics and has yet to adequately address how to engage AEC learners with different learning abilities, styles, and diverse backgrounds.This paper presents the advantages and difficulties associated with using new technologies to develop virtual reality (VR) learning games for robotics. It describes an ongoing project for creating performance driven curriculum. Drawing on the Constructivist Learning Theory, the affordances of Adaptive Learning Systems, and data collection methods from the VR game environment, the project provides a customized and performance-oriented approach to carrying out practical robotics tasks in real-world scenarios. 

4. Eric L. Peterson, Trond I. Andersen, Giovanni Scuri, Andrew Y. Joe, Andrés M. Mier Valdivia, Xiaoling Liu, Alexander A. Zibrov, Bumho Kim, Takashi Taniguchi, Kenji Watanabe, James Hone, Valentin Walther, Hongkun Park, Philip Kim, Mikhail D. Lukin 

The co-occurrence of uranyl and arsenate in contaminated water caused by natural processes and mining is a concern for impacted communities, including in Native American lands in the U.S. Southwest. We investigated the simultaneous removal of aqueous uranyl and arsenate after the reaction with limestone and precipitated hydroxyapatite (HAp, Ca10(PO4)6(OH)2). In benchtop experiments with an initial pH of 3.0 and initial concentrations of 1 mM U and As, uranyl and arsenate coprecipitated in the presence of 1 g L−1 limestone. However, related experiments initiated under circumneutral pH conditions showed that uranyl and arsenate remained soluble. Upon addition of 1 mM PO43− and 3 mM Ca2+ in solution (initial concentration of 0.05 mM U and As) resulted in the rapid removal of over 97% of U via Ca−U−P precipitation. In experiments with 2 mM PO4 3− and 10 mM Ca2+ at pH rising from 7.0 to 11.0, aqueous concentrations of As decreased (between 30 and 98%) circa pH 9. HAp precipitation in solids was confirmed by powder X-ray diffraction and scanning electron microscopy/energy dispersive X-ray. Electron microprobe analysis indicated U was coprecipitated with Ca and P, while As was mainly immobilized through HAp adsorption. The results indicate that natural materials, such as HAp and limestone, can effectively remove uranyl and arsenate mixtures. 

The increasing environmental concerns call for more sophisticated and integrated educational methods. For sustainable outcomes, understanding and navigating complex environmental factors is essential. By imparting knowledge about environmental data and its applications, students can be better prepared to address environmental issues.The Augmented Learning for Environmental Robotics Technologies (ALERT) program introduces an educational method using augmented reality (AR) and artificial intelligence (AI). It provides students, particularly those in architecture, engineering, and construction (AEC), with an immersive learning experience focused on environmental data and robotics. Considering the significant environmental footprint of the AEC sector—emanating from energy-intensive buildings, roads, and infrastructures—the ALERT initiative strives to instill a comprehensive understanding of environmental data collection and visualization. This is done with the aim of promoting data-centric design and construction for a more eco-friendly built environment.In the ALERT program, AR is employed to fashion an augmented learning space where students can engage with both real-time and past environmental data. They learn to set up environmental sensors, collect data, and visualize it to unearth hidden trends and connections. Additionally, AI ensures a tailored learning journey for each student, offering optimal challenges and support. This innovative blend of AR and AI not only offers an enriching learning experience but also prepares AEC students to be at the forefront of transformative shifts, especially those influenced by advancements like robotic automation, fostering a profound understanding of environmental data.This paper outlines the preliminary stages of the ALERT project, detailing its foundational research. Topics include the educational theories guiding the creation of a groundbreaking Intelligent Learning System (ILS) and curriculum, as well as the projected impact of the program. ALERT emerges as a promising venture, potentially empowering students with the expertise to reduce the ecological footprint of infrastructure, paving the way for a greener future. 

AI, robotics, and automation are reshaping many industries, including the Architecture, Engineering, and Construction (AEC) industries. For students aiming to enter these evolving fields, comprehensive and accessible training in high-tech roles is becoming increasingly important. Traditional robotics education, while often effective, usually necessitates small class sizes and specialized equipment. On-the-job training introduces safety risks, particularly for inexperienced individuals. The integration of advanced technologies for training presents an alternative that reduces the need for extensive physical resources and minimizes safety concerns. This paper introduces the Intelligent Learning Platform for Robotics Operations (IL-PRO), an innovative project that integrates the use of Artificial Intelligence (AI), Virtual Reality (VR), and game-assisted learning for teaching robotic arms operations. The goal of this project is to address the limitations of traditional training through the implementation of personalized learning strategies supported by Adaptive Learning Systems (ALS). These systems hold the potential to transform education by customizing content to cater to various levels of understanding, preferred learning styles, past experiences, and diverse linguistic and socio-cultural backgrounds.Central to IL-PRO is the development of its ALS, which uses student progress variables and multimodal machine learning to infer students’ level of understanding and automate task and feedback delivery. The curriculum is organized into modules, starting with fundamental robotic concepts, and advancing to complex motion planning and programming. The curriculum is guided by a learner model that is continuously refined through data collection. Furthermore, the project incorporates gaming elements into its VR learning approach to create an engaging educational environment. Thus, the learning content is designed to engage students with simulated robots and input devices to solve sequences of game-based challenges. The challenge sequences are designed similarly to levels in a game, each with increasing complexity, in order to systematically incrementally build students' knowledge, skills, and confidence in robotic operations. The project is conducted by a team of interdisciplinary faculty from Florida International University (FIU), the University of California Irvine (UCI), the University of Hawaii (UH) and the University of Kansas-Missouri (UKM). The collaboration between these institutions enables the sharing of resources and expertise that are essential for the development of this comprehensive learning platform.