巨視的量子オプトメカニクスのための超低損失振子の研究

Tohoku University枝松圭一課

Methods for Data-centric Small Satellite Anomaly Detection and Fault Prediction

Autonomy can increase reaction speed, flexibility, and accuracy of satellite operations, especially in uncertain environments caused by delayed communication and/or adversarial conditions. An increased focus on small satellites makes the development of satellite autonomy even more salient, given fewer operators per satellite. Anomaly detection automates satellite health monitoring, ensuring it functions as designed. This is typically achieved using various forms of recurrent neural networks (RNN). While many of these model-based works show promise, a majority use simulated data or assume lossless communication. In contrast, raw satellite telemetry often has dropped packets, sampling frequency mismatches, noise from electrical systems and radiation, and a lack of clear labels for training. This work demonstrates how data-centric artificial intelligence (AI) can be utilized in satellite autonomy, using telemetry from the Very Low Frequency Propagation Mapper (VPM) small satellite flown by the Air Force Research Lab Space Vehicle Directorate in 2020. We introduce simple, but effective, tools for extracting fault labels from system parameters, resampling outliers to a common, uniform timeline, and evaluating outlier fault predictability. Results find that detected outliers were able to predict faults 1-10 minutes before they occurred with high accuracy

Granger causality for circular variables

In this letter we discuss use of Granger causality to the analyze systems of coupled circular variables, by modifying a recently proposed method for multivariate analysis of causality. We show the application of the proposed approach on several Kuramoto systems, in particular one living on networks built by preferential attachment and a model for the transition from deeply to lightly anaesthetized states. Granger causalities describe the flow of information among variables.Comment: 4 pages, 5 figure

Peptidoglycan editing provides immunity to Acinetobacter baumannii during bacterial warfare

Peptidoglycan (PG) is essential in most bacteria. Thus, it is often targeted by various assaults, including interbacterial attacks via the type VI secretion system (T6SS). Here, we report that the Gram-negative bacteriu

STP-H7-CASPR: A Transition from Mission Concept to Launch

The Configurable and Autonomous Sensor Processing Research (CASPR) project is a university-led experiment developed by student and faculty researchers at the NSF Center for Space, High-performance, and Resilient Computing (SHREC) at the University of Pittsburgh for the Space Test Program – Houston 7 (STP-H7) mission to the International Space Station (ISS). Autonomous sensor processing, the mission theme of the CASPR experiment, is enabled by combining novel sensor technologies with innovative computing techniques on resilient and high-performance flight hardware in a small satellite (SmallSat) form-factor. CASPR includes the iSIM-90, an innovative, high-resolution optical payload for Earth-observation missions developed by SATLANTIS MICROSATS SL. For the CASPR mission, the opto-mechanics of iSIM-90 will be mounted atop a gimbal-actuated platform for agile, low-GRD (ground-resolved distance), and multispectral Earth-observation imaging. This mission will also feature the Prophesee Sisley neuromorphic, event-driven sensor for space situational awareness applications. The CASPR avionics system consists of the following: three radiation-tolerant, reconfigurable space computers, including one flight-proven CSP and two next-gen SSPs; one μCSP Smart Module; one power card; and one backplane. CASPR also features a sub-experiment with an AMD GPU to evaluate new accelerator technologies for space. CASPR is a highly versatile experiment combining a variety of compute and sensor technologies to demonstrate on-orbit capabilities in onboard data analysis, mission operations, and spacecraft autonomy. As a research sandbox, CASPR enables new software and hardware to be remotely uploaded to further enhance mission capabilities. Finally, as a university-led mission, cost is a limiting constraint, leading to budget-driven design decisions and the use of affordable methods and procedures. Other factors, such as a power budget and limited equipment, facilities, and engineering resources, pose additional challenges to the CASPR mission. To address these challenges, we describe cost-effective procedures and methods used in the assembly, integration, and testing of the CASPR experiment

A collaborative approach to combining service, teaching, and research

Objective. To describe a faculty-student collaborative model and its outcomes on teaching, service, and scholarship. Design. A Medicare Part D elective course was offered that consisted of classroom and experiential learning where pharmacy students participated in community outreach events to assist Medicare beneficiaries with Part D plan selection. The course training was expanded to include medication therapy management (MTM) and the administration of immunizations. At the completion of the course, students collaborated with faculty members on research endeavors. Evaluation. During the first 6 years of this course, the class size more than doubled from 20 to 42 students, and all students participating in the course met the IPPE requirements for community outreach. Over that same period, the number of beneficiaries receiving assistance with their Part D plan grew from 72 to 610; and with the help of students starting in 2011, faculty members had 28 poster presentations at national conferences, 7 invited podium presentations at national/international meetings, and published 8 manuscripts in peer-reviewed journals. Conclusion. Through collaborative efforts, this model took an elective course and provided classroom and experiential learning for students, needed health services for the community, and opportunities to pursue wide ranging research projects for faculty members and students

Closing the gaps in care of dyslipidemia: Revolutionizing management with digital health and innovative care models

Although great progress has been made in the diagnostic and treatment options for dyslipidemias, unawareness, underdiagnosis and undertreatment of these disorders remain a significant global health concern. Growth in digital applications and newer models of care provide novel tools to improve the management of chronic conditions such as dyslipidemia. In this review, we discuss the evolving landscape of lipid management in the 21st century, current treatment gaps and possible solutions through digital health and new models of care. Our discussion begins with the history and development of value-based care and the national establishment of quality metrics for various chronic conditions. These concepts on the level of healthcare policy not only inform reimbursements but also define the standard of care. Next, we consider the advances in atherosclerotic cardiovascular disease risk score calculators as well as evolving imaging modalities. The impact and growth of digital health, ranging from telehealth visits to online platforms and mobile applications, will also be explored. We then evaluate the ways in which machine learning and artificial intelligence-driven algorithms are being utilized to address gaps in lipid management. From an organizational perspective, we trace the redesign of medical practices to incorporate a multidisciplinary team model of care, recognizing that atherosclerotic cardiovascular disease risk is multifaceted and requires a comprehensive approach. Finally, we anticipate the future of dyslipidemia management, assessing the many ways in which atherosclerotic cardiovascular disease burden can be reduced on a population-wide scale

Selective vulnerability of tripartite synapses in Amyotrophic Lateral Sclerosis

Authors would like to acknowledge the following funders: Motor Neurone Disease (MND) Association UK (Miles/Apr18/863-791), the Euan MacDonald Centre and Chief Scientist Office, The European Research Council (ERC) under the European Union’s Horizon 2020 Research and Innovation Programme (695568 SYNNOVATE), Simons Foundation Autism Research Initiative (529085), and the Wellcome Trust (Technology Development grant 202932).Amyotrophic Lateral Sclerosis (ALS) is a fatal neurodegenerative disorder. Separate lines of evidence suggest that synapses and astrocytes play a role in the pathological mechanisms underlying ALS. Given that astrocytes make specialised contacts with some synapses, called tripartite synapses, we hypothesise that tripartite synapses could act as the fulcrum of disease in ALS. To test this hypothesis, we have performed an extensive microscopy-based investigation of synapses and tripartite synapses in the spinal cord of ALS model mice and post-mortem human tissue from ALS cases. We reveal widescale synaptic changes at the early symptomatic stages of the SOD1G93a mouse model. Super-resolution microscopy reveals that large complex postsynaptic structures are lost in ALS mice. Most surprisingly, tripartite synapses are selectively lost, while non-tripartite synapses remain in equal number to healthy controls. Finally, we also observe a similar selective loss of tripartite synapses in human post-mortem ALS spinal cords. From these data we conclude that tripartite synaptopathy is a key hallmark of ALS.Publisher PDFPeer reviewe

The free energy principle for action and perception: A mathematical review

The ‘free energy principle’ (FEP) has been suggested to provide a unified theory of the brain, integrating data and theory relating to action, perception, and learning. The theory and implementation of the FEP combines insights from Helmholtzian ‘perception as inference’, machine learning theory, and statistical thermodynamics. Here, we provide a detailed mathematical evaluation of a suggested biologically plausible implementation of the FEP that has been widely used to develop the theory. Our objectives are (i) to describe within a single article the mathematical structure of this implementation of the FEP; (ii) provide a simple but complete agent-based model utilising the FEP and (iii) to disclose the assumption structure of this implementation of the FEP to help elucidate its significance for the brain sciences