Publications
Impact of WSR-88D intra-volume low-level scans on sever weather warning performance
July 1, 2022
Journal Article
Published in:
Weather Forecast., Vol. 37, No. 7, July 2022, p. 1169-98.
Summary
The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus SAILS-off. Within the three possible SAILS modes of one (SAILSx1), two (SAILSx2), and three (SAILSx3) additional base scans per volume, for SVR, SAILSx2 and SAILSx3 are associated with better warning performance compared to SAILSx1; for FF and TOR, SAILSx3 is associated with better warning performance relative to SAILSx1 and SAILSx2. Two severe storm cases (one that spawned a tornado, one that did not) are presented where SAILS usage helped forecasters make the correct TOR warning decision, lending real-life credence to the statistical results. Furthermore, a statistical analysis of automated volume scan evaluation and termination effects, parsed by SAILS usage and mode, yield a statistically significant association between volume scan update rate and SVR warning lead time.
Summary
The statistical relationship between supplemental adaptive intra-volume low-level scan (SAILS) usage on the Weather Surveillance Radar-1988 Doppler and National Weather Service severe storm warning performance during 2014–20 is analyzed. Results show statistically significant improvement in severe thunderstorm (SVR), flash flood (FF), and tornado (TOR) warning performance associated with SAILS-on versus...
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Impact of haptic cues and an active ankle exoskeleton on gait characteristics
July 1, 2022
Journal Article
Published in:
Hum. Factors, Vol. 0, No. 0, July 2022, p. 1-12.
Summary
Objective This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. Background An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. Methods Participants (N = 16) were provided combinations of torque assistance and vibrotactile cues at pre-specified time points in late swing and early stance while walking on a self-paced treadmill. Participants were either given explicit instructions (N = 8) or were allowed to freely interpret (N=8) how to coordinate with cues. Results For the free interpretation group, the data support an 8% increase in stride length and 14% increase in speed with exoskeleton torque across cue timing, as well as a 5% increase in stride length and 7% increase in speed with only vibrotactile cues. When given explicit instructions, participants modulated speed according to cue timing-increasing speed by 17% at cues in late swing and decreasing speed 11% at cues in early stance compared to no cue when exoskeleton torque was off. When torque was on, participants with explicit instructions had reduced changes in speed. Conclusion These findings support that the presence of torque mitigates how cues were used and highlights the importance of explicit instructions for haptic cuing. Interpreting cues while walking with an exoskeleton may increase cognitive load, influencing overall human-exoskeleton performance for novice users. Application Interactions between haptic feedback and exoskeleton use during gait can inform future feedback designs to support coordination between users and exoskeletons.
Summary
Objective This study examined the interaction of gait-synchronized vibrotactile cues with an active ankle exoskeleton that provides plantarflexion assistance. Background An exoskeleton that augments gait may support collaboration through feedback to the user about the state of the exoskeleton or characteristics of the task. Methods Participants (N = 16) were...
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Advances in cross-lingual and cross-source audio-visual speaker recognition: The JHU-MIT system for NIST SRE21
June 28, 2022
Conference Paper
Published in:
Speaker and Language Recognition Workshop, Odyssey 2022, pp. 213-220.
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Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE, MIT-LL and AGH for NIST SRE21. NIST SRE21 consisted of speaker detection over multilingual conversational telephone speech (CTS) and audio from video (AfV). Besides the regular audio track, the evaluation also contains visual (face recognition) and multi-modal tracks. This evaluation exposes new challenges, including cross-source–i.e., CTS vs. AfV– and cross-language trials. Each speaker can speak two or three languages among English, Mandarin and Cantonese. For the audio track, we evaluated embeddings based on Res2Net and ECAPA-TDNN, where the former performed the best. We used PLDA based back-ends trained on previous SRE and VoxCeleb and adapted to a subset of Mandarin/Cantonese speakers. Some novel contributions of this submission are: the use of neural bandwidth extension (BWE) to reduce the mismatch between the AFV and CTS conditions; and invariant representation learning (IRL) to make the embeddings from a given speaker invariant to language. Res2Net with neural BWE was the best monolithic system. We used a pre-trained RetinaFace face detector and ArcFace embeddings for the visual track, following our NIST SRE19 work. We also included a new system using a deep pyramid single shot face detector and face embeddings trained on Crystal loss and probabilistic triplet loss, which performed the best. The number of face embeddings in the test video was reduced by agglomerative clustering or weighting the embedding based on the face detection confidence. Cosine scoring was used to compare embeddings. For the multi-modal track, we just added the calibrated likelihood ratios of the audio and visual conditions, assuming independence between modalities. The multi-modal fusion improved Cprimary by 72% w.r.t. audio.
Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE, MIT-LL and AGH for NIST SRE21. NIST SRE21 consisted of speaker detection over multilingual conversational telephone speech (CTS) and audio from video (AfV). Besides the regular audio track, the evaluation also contains visual (face recognition) and multi-modal tracks. This...
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Advances in speaker recognition for multilingual conversational telephone speech: the JHU-MIT system for NIST SRE20 CTS challenge
June 27, 2022
Conference Paper
Published in:
Speaker and Language Recognition Workshop, Odyssey 2022, pp. 338-345.
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Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE and MIT-LL for NIST SRE20. NIST SRE20 CTS consisted of multilingual conversational telephone speech. The set of languages included in the evaluation was not provided, encouraging the participants to develop systems robust to any language. We evaluated x-vector architectures based on ResNet, squeeze-excitation ResNets, Transformers and EfficientNets. Though squeeze-excitation ResNets and EfficientNets provide superior performance in in-domain tasks like VoxCeleb, regular ResNet34 was more robust in the challenge scenario. On the contrary, squeeze-excitation networks over-fitted to the training data, mostly in English. We also proposed a novel PLDA mixture and k-NN PLDA back-ends to handle the multilingual trials. The former clusters the x-vector space expecting that each cluster will correspond to a language family. The latter trains a PLDA model adapted to each enrollment speaker using the nearest speakers–i.e., those with similar language/channel. The k-NN back-end improved Act. Cprimary (Cp) by 68% in SRE16-19 and 22% in SRE20 Progress w.r.t. a single adapted PLDA back-end. Our best single system achieved Act. Cp=0.110 in SRE20 progress. Meanwhile, our best fusion obtained Act. Cp=0.110 in the progress–8% better than single– and Cp=0.087 in the eval set.
Summary
We present a condensed description of the joint effort of JHUCLSP/HLTCOE and MIT-LL for NIST SRE20. NIST SRE20 CTS consisted of multilingual conversational telephone speech. The set of languages included in the evaluation was not provided, encouraging the participants to develop systems robust to any language. We evaluated x-vector architectures...
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AAM-Gym: Artificial intelligence testbed for advanced air mobility
June 9, 2022
Journal Article
Published in:
arXiv, 9 June 2022.
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Summary
We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of AI algorithms require representative AAM scenarios, as well as a fast time simulation testbed to evaluate their performance. Until now, there has been no such testbed available for AAM to enable a common research platform for individuals in government, industry, or academia. MIT Lincoln Laboratory has developed AAM-Gym to address this gap by providing an ecosystem to develop, train, and validate new and established AI algorithms across a wide variety of AAM use-cases. In this paper, we use AAM-Gym to study the performance of two reinforcement learning algorithms on an AAM use-case, separation assurance in AAM corridors. The performance of the two algorithms is demonstrated based on a series of metrics provided by AAM-Gym, showing the testbed’s utility to AAM research.
Summary
We introduce AAM-Gym, a research and development testbed for Advanced Air Mobility (AAM). AAM has the potential to revolutionize travel by reducing ground traffic and emissions by leveraging new types of aircraft such as electric vertical take-off and landing (eVTOL) aircraft and new advanced artificial intelligence (AI) algorithms. Validation of...
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Toward improving EN adoption: Bridging the gap between stated intention and actual use
June 3, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-7
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact Tracing (PACT) protocol and related projects to assist the public health objective of slowing the spread of SARS-CoV-2 through digital contact tracing. The joint Google and Apple deployed protocol (Google-Apple Exposure Notifications, also known as GAEN or EN), which became the de facto standard in the U.S., employs the same features as detailed by PACT. The protocol leverages smartphone Bluetooth communications to alert users of potential contact with those carrying the COVID-19 virus in a way that preserves the privacy of both the known-infected individual, and the users receiving the alert. Contact tracing and subsequent personal precautions are more effective at reducing disease spread when more of the population participates, but there are known difficulties with the adoption of novel technology. In order to help the U.S. Centers for Disease Control and Prevention (CDC) and U.S. state-level public health teams address these difficulties, a team of staff from MIT's Lincoln Laboratory (MIT LL) and Computer Science and Artificial Intelligence Laboratory (MIT CSAIL) focused on studying user perception and information needs.
Summary
As the COVID-19 pandemic swept the globe in the spring of 2020, technologists looked to enlist technology to assist public health authorities (PHAs) and help stem the tide of infections. As part of this technology push, experts in health care, cryptography, and other related fields developed the Private Automated Contact...
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The thundering herd: Amplifying kernel interference to attack response times
May 4, 2022
Conference Paper
Published in:
2022 IEEE 28th Real-Time and Embedded Technology and Applications Symp., RTAS, 4-6 May 2022.
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Summary
Embedded and real-time systems are increasingly attached to networks. This enables broader coordination beyond the physical system, but also opens the system to attacks. The increasingly complex workloads of these systems include software of varying assurance levels, including that which might be susceptible to compromise by remote attackers. To limit the impact of compromise, u-kernels focus on maintaining strong memory protection domains between different bodies of software, including system services. They enable limited coordination between processes through Inter-Process Communication (IPC). Real-time systems also require strong temporal guarantees for tasks, and thus need temporal isolation to limit the impact of malicious software. This is challenging as multiple client threads that use IPC to request service from a shared server will impact each other's response times. To constrain the temporal interference between threads, modern u-kernels often build priority and budget awareness into the system. Unfortunately, this paper demonstrates that this is more challenging than previously thought. Adding priority awareness to IPC processing can lead to significant interference due to the kernel's prioritization logic. Adding budget awareness similarly creates opportunities for interference due to the budget tracking and management operations. In both situations, a Thundering Herd of malicious threads can significantly delay the activation of mission-critical tasks. The Thundering Herd effects are evaluated on seL4 and results demonstrate that high-priority threads can be delayed by over 100,000 cycles per malicious thread. This paper reveals a challenging dilemma: the temporal protections u-kernels add can, themselves, provide means of threatening temporal isolation. Finally, to defend the system, we identify and empirically evaluate possible mitigations, and propose an admission-control test based upon an interference-aware analysis.
Summary
Embedded and real-time systems are increasingly attached to networks. This enables broader coordination beyond the physical system, but also opens the system to attacks. The increasingly complex workloads of these systems include software of varying assurance levels, including that which might be susceptible to compromise by remote attackers. To limit...
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Modeling probability of alert of Bluetooth low energy-based automatic exposure notifications
April 27, 2022
Project Report
Published in:
MIT Lincoln Laboratory Report ACTA-4
Summary
BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy (BLE) signal attenuation domains. However, many public health definitions of "exposure" to a disease are based upon the distance between an index case and another person. To bridge the conceptual gap for public health authorities (PHAs) from the familiar distance-and-duration space to the signal attenuation-and-duration space, BLEMUR uses BLE signal attenuation as a proxy for distance between people, albeit an imprecise one. This paper will discuss the EN settings that can be manipulated, the BLE data collected, how data support a model of the relationship between measured attenuation and distance between phones, and how BLEMUR calculates the probability of alert for a distance and duration based on the settings and data.
Summary
BLEMUR, or Bluetooth Low Energy Model of User Risk, is a model of the probability of alert at a given duration and distance of an index case for a specific configuration of settings for an Exposure Notification (EN) system.The Google-Apple EN framework operates in the duration and Bluetooth Low Energy...
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Nearfield anechoic chamber and farfield on-site antenna calibration pattern comparison of an S-band planar phased array radar
April 27, 2022
Conference Paper
Published in:
IEEE Annual Conf. on Wireless and Microwave Technology, WAMICON, 27-28 April 2022.
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Summary
The Advanced Technology Demonstrator (ATD) is an active, S-band, dual-polarization phased array radar developed for weather sensing. The ATD is an active electronically scanned array (AESA) with a 4-m aperture comprised of 4,864 individual transmit/receive (T/R) modules. The antenna was calibrated at the element, subarray, and array levels. Calibration, validation, and verification testing was completed in two main stages, first in an anechoic chamber and second after it was installed on site in its permanent location. This paper describes the procedure used to collect antenna patterns at each stage and compares three key performance metrics: beamwidth, mean-squared sidelobe level (MSSL), and integrated sidelobe level (ISL).
Summary
The Advanced Technology Demonstrator (ATD) is an active, S-band, dual-polarization phased array radar developed for weather sensing. The ATD is an active electronically scanned array (AESA) with a 4-m aperture comprised of 4,864 individual transmit/receive (T/R) modules. The antenna was calibrated at the element, subarray, and array levels. Calibration, validation...
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Graph-guided network for irregularly sampled multivariate time series
April 25, 2022
Conference Paper
Published in:
International Conference on Learning Representations, ICLR 2022.
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In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also learning the dynamics of sensors purely from observational data. RAINDROP represents every sample as a separate sensor graph and models time-varying dependencies between sensors with a novel message passing operator. It estimates the latent sensor graph structure and leverages the structure together with nearby observations to predict misaligned readouts. This model can be interpreted as a graph neural network that sends messages over graphs that are optimized for capturing time-varying dependencies among sensors. We use RAINDROP to classify time series and interpret temporal dynamics on three healthcare and human activity datasets. RAINDROP outperforms state-of-the-art methods by up to 11.4% (absolute F1-score points), including techniques that deal with irregular sampling using fixed discretization and set functions. RAINDROP shows superiority in diverse setups, including challenging leave-sensor-out settings.
Summary
In many domains, including healthcare, biology, and climate science, time series are irregularly sampled with varying time intervals between successive readouts and different subsets of variables (sensors) observed at different time points. Here, we introduce RAINDROP, a graph neural network that embeds irregularly sampled and multivariate time series while also...
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