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Operation of an optical atomic clock with a Brillouin laser subsystem

Summary

Microwave atomic clocks have traditionally served as the 'gold standard' for precision measurements of time and frequency. However, over the past decade, optical atomic clocks have surpassed the precision of their microwave counterparts by two orders of magnitude or more. Extant optical clocks occupy volumes of more than one cubic metre, and it is a substantial challenge to enable these clocks to operate in field environments, which requires the ruggedization and miniaturization of the atomic reference and clock laser along with their supporting lasers and electronics. In terms of the clock laser, prior laboratory demonstrations of optical clocks have relied on the exceptional performance gained through stabilization using bulk cavities, which unfortunately necessitates the use of vacuum and also renders the laser susceptible to vibration-induced noise. Here, using a stimulated Brillouin scattering laser subsystem that has a reduced cavity volume and operates without vacuum, we demonstrate a promising component of a portable optical atomic clock architecture. We interrogate a 88Sr+ ion with our stimulated Brillouin scattering laser and achieve a clock exhibiting short-term stability of 3.9 × 10−14 over one second—an improvement of an order of magnitude over state-of-the-art microwave clocks. This performance increase within a potentially portable system presents a compelling avenue for substantially improving existing technology, such as the global positioning system, and also for enabling the exploration of topics such as geodetic measurements of the Earth, searches for dark matter and investigations into possible long-term variations of fundamental physics constants.
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Summary

Microwave atomic clocks have traditionally served as the 'gold standard' for precision measurements of time and frequency. However, over the past decade, optical atomic clocks have surpassed the precision of their microwave counterparts by two orders of magnitude or more. Extant optical clocks occupy volumes of more than one cubic...

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Ultrasound diagnosis of COVID-19: robustness and explainability

Published in:
arXiv:2012.01145v1 [eess.IV]

Summary

Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an AI model for diagnosis that obtains high sensitivity. Due to the high stakes application we propose the use of robust and explainable techniques. We demonstrate experimentally that robust models have more stable predictions and offer improved interpretability. A framework of contrastive explanations based on adversarial perturbations is used to explain model predictions that aligns with human visual perception.
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Summary

Diagnosis of COVID-19 at point of care is vital to the containment of the global pandemic. Point of care ultrasound (POCUS) provides rapid imagery of lungs to detect COVID-19 in patients in a repeatable and cost effective way. Previous work has used public datasets of POCUS videos to train an...

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A multi-task LSTM framework for improved early sepsis prediction

Summary

Early detection for sepsis, a high-mortality clinical condition, is important for improving patient outcomes. The performance of conventional deep learning methods degrades quickly as predictions are made several hours prior to the clinical definition. We adopt recurrent neural networks (RNNs) to improve early prediction of the onset of sepsis using times series of physiological measurements. Furthermore, physiological data is often missing and imputation is necessary. Absence of data might arise due to decisions made by clinical professionals which carries information. Using the missing data patterns into the learning process can further guide how much trust to place on imputed values. A new multi-task LSTM model is proposed that takes informative missingness into account during training that effectively attributes trust to temporal measurements. Experimental results demonstrate our method outperforms conventional CNN and LSTM models on the PhysioNet-2019 CiC early sepsis prediction challenge in terms of area under receiver-operating curve and precision-recall curve, and further improves upon calibration of prediction scores.
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Summary

Early detection for sepsis, a high-mortality clinical condition, is important for improving patient outcomes. The performance of conventional deep learning methods degrades quickly as predictions are made several hours prior to the clinical definition. We adopt recurrent neural networks (RNNs) to improve early prediction of the onset of sepsis using...

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Towards a distributed framework for multi-agent reinforcement learning research

Summary

Some of the most important publications in deep reinforcement learning over the last few years have been fueled by access to massive amounts of computation through large scale distributed systems. The success of these approaches in achieving human-expert level performance on several complex video-game environments has motivated further exploration into the limits of these approaches as computation increases. In this paper, we present a distributed RL training framework designed for super computing infrastructures such as the MIT SuperCloud. We review a collection of challenging learning environments—such as Google Research Football, StarCraft II, and Multi-Agent Mujoco— which are at the frontier of reinforcement learning research. We provide results on these environments that illustrate the current state of the field on these problems. Finally, we also quantify and discuss the computational requirements needed for performing RL research by enumerating all experiments performed on these environments.
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Summary

Some of the most important publications in deep reinforcement learning over the last few years have been fueled by access to massive amounts of computation through large scale distributed systems. The success of these approaches in achieving human-expert level performance on several complex video-game environments has motivated further exploration into...

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A hardware root-of-trust design for low-power SoC edge devices

Published in:
2020 IEEE High Performance Extreme Computing Conf., HPEC, 22-24 September 2020.

Summary

In this work, we introduce a hardware root-of-trust architecture for low-power edge devices. An accelerator-based SoC design that includes the hardware root-of-trust architecture is developed. An example application for the device is presented. We examine attacks based on physical access given the significant threat they pose to unattended edge systems. The hardware root-of-trust provides security features to ensure the integrity of the SoC execution environment when deployed in uncontrolled, unattended locations. E-fused boot memory ensures the boot code and other security critical software is not compromised after deployment. Digitally signed programmable instruction memory prevents execution of code from untrusted sources. A programmable finite state machine is used to enforce access policies to device resources even if the application software on the device is compromised. Access policies isolate the execution states of application and security-critical software. The hardware root-of-trust architecture saves energy with a lower hardware overhead than a separate secure enclave while eliminating software attack surfaces for access control policies.
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Summary

In this work, we introduce a hardware root-of-trust architecture for low-power edge devices. An accelerator-based SoC design that includes the hardware root-of-trust architecture is developed. An example application for the device is presented. We examine attacks based on physical access given the significant threat they pose to unattended edge systems...

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Fast training of deep neural networks robust to adversarial perturbations

Published in:
2020 IEEE High Performance Extreme Computing Conf., HPEC, 22-24 September 2020.

Summary

Deep neural networks are capable of training fast and generalizing well within many domains. Despite their promising performance, deep networks have shown sensitivities to perturbations of their inputs (e.g., adversarial examples) and their learned feature representations are often difficult to interpret, raising concerns about their true capability and trustworthiness. Recent work in adversarial training, a form of robust optimization in which the model is optimized against adversarial examples, demonstrates the ability to improve performance sensitivities to perturbations and yield feature representations that are more interpretable. Adversarial training, however, comes with an increased computational cost over that of standard (i.e., nonrobust) training, rendering it impractical for use in largescale problems. Recent work suggests that a fast approximation to adversarial training shows promise for reducing training time and maintaining robustness in the presence of perturbations bounded by the infinity norm. In this work, we demonstrate that this approach extends to the Euclidean norm and preserves the human-aligned feature representations that are common for robust models. Additionally, we show that using a distributed training scheme can further reduce the time to train robust deep networks. Fast adversarial training is a promising approach that will provide increased security and explainability in machine learning applications for which robust optimization was previously thought to be impractical.
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Summary

Deep neural networks are capable of training fast and generalizing well within many domains. Despite their promising performance, deep networks have shown sensitivities to perturbations of their inputs (e.g., adversarial examples) and their learned feature representations are often difficult to interpret, raising concerns about their true capability and trustworthiness. Recent...

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Human balance models optimized using a large-scale, parallel architecture with applications to mild traumatic brain injury

Published in:
2020 IEEE High Performance Extreme Computing Conf., HPEC, 22-24 September 2020.

Summary

Static and dynamic balance are frequently disrupted through brain injuries. The impairment can be complex and for mild traumatic brain injury (mTBI) can be undetectable by standard clinical tests. Therefore, neurologically relevant modeling approaches are needed for detection and inference of mechanisms of injury. The current work presents models of static and dynamic balance that have a high degree of correspondence. Emphasizing structural similarity between the domains facilitates development of both. Furthermore, particular attention is paid to components of sensory feedback and sensory integration to ground mechanisms in neurobiology. Models are adapted to fit experimentally collected data from 10 healthy control volunteers and 11 mild traumatic brain injury volunteers. Through an analysis by synthesis approach whose implementation was made possible by a state-of-the-art high performance computing system, we derived an interpretable, model based feature set that could classify mTBI and controls in a static balance task with an ROC AUC of 0.72.
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Summary

Static and dynamic balance are frequently disrupted through brain injuries. The impairment can be complex and for mild traumatic brain injury (mTBI) can be undetectable by standard clinical tests. Therefore, neurologically relevant modeling approaches are needed for detection and inference of mechanisms of injury. The current work presents models of...

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Sensorimotor conflict tests in an immersive virtual environment reveal subclinical impairments in mild traumatic brain injury

Summary

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor perturbations, delivered in a highly instrumented, immersive virtual environment, would challenge sensory subsystems recruited for balance through conflicting multi-sensory evidence, and therefore reveal that not all subsystems are performing optimally. The results show that, as compared to standard clinical tests, the provocative perturbations illuminate balance impairments in subjects who have had mild traumatic brain injuries. Perturbations delivered while subjects were walking provided greater discriminability (average accuracy ≈ 0.90) than those delivered during standing (average accuracy ≈ 0.65) between mTBI subjects and healthy controls. Of the categories of features extracted to characterize balance, the lower limb accelerometry-based metrics proved to be most informative. Further, in response to perturbations, subjects with an mTBI utilized hip strategies more than ankle strategies to prevent loss of balance and also showed less variability in gait patterns. We have shown that sensorimotor conflicts illuminate otherwise-hidden balance impairments, which can be used to increase the sensitivity of current clinical procedures. This augmentation is vital in order to robustly detect the presence of balance impairments after mTBI and potentially define a phenotype of balance dysfunction that enhances risk of injury.
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Summary

Current clinical tests lack the sensitivity needed for detecting subtle balance impairments associated with mild traumatic brain injury (mTBI). Patient-reported symptoms can be significant and have a huge impact on daily life, but impairments may remain undetected or poorly quantified using clinical measures. Our central hypothesis was that provocative sensorimotor...

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Antennas and RF components designed with graded index composite materials

Summary

Antennas and RF components in general, can greatly benefit with the recent development of low-loss 3D print graded index materials. The additional degrees of freedom provided by graded index materials can result in the design of antennas and other RF components with superior performance than currently available designs based on conventional constant permittivity materials. Here we discuss our work designing flat lenses for antennas and RF matching networks as well as filters based on graded index composite materials.
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Summary

Antennas and RF components in general, can greatly benefit with the recent development of low-loss 3D print graded index materials. The additional degrees of freedom provided by graded index materials can result in the design of antennas and other RF components with superior performance than currently available designs based on...

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Graded index dielectric superstrate for phased array scan compensation

Published in:
2020 IEEE Intl. Symp. on Antennas and Propagation and North American Radio Science Meeting, 5-10 July 2020.

Summary

This paper presents the use of additively manufactured graded index materials to improve the scan impedance variation of phased arrays. Solvent-based extrusion permits the programmatically controlled printing of chosen material properties with relative permittivity ranging from 2 to 24.5. This low-loss material shows promise for improvement of scan impedance variation for high scan angles in a smaller footprint than discrete layers.
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Summary

This paper presents the use of additively manufactured graded index materials to improve the scan impedance variation of phased arrays. Solvent-based extrusion permits the programmatically controlled printing of chosen material properties with relative permittivity ranging from 2 to 24.5. This low-loss material shows promise for improvement of scan impedance variation...

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