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Selective network discovery via deep reinforcement learning on embedded spaces

Published in:
Appl. Netw. Sci., Vol. 6, No.1, December 2021, Art. No. 24.

Summary

Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem as a sequential decision-making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called network actor critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. The NAC paradigm utilizes a task-specific network embedding to reduce the state space complexity. A detailed comparative analysis of popular network embeddings is presented with respect to their role in supporting offline planning. Furthermore, a quantitative study is presented on various synthetic and real benchmarks using NAC and several baselines. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms. Finally, we outline learning regimes where planning is critical in addressing sparse and changing reward signals.
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Summary

Complex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream...

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Capacity bounds for frequency-hopped BPSK

Published in:
2021 IEEE Military Communications Conf., MILCOM, 29 November - 2 December 2021.

Summary

In some channels, such as the frequency-hop channel, the transmission may undergo abrupt transitions in phase. This can require the receiver to re-estimate the phase on each hop, or for the system to utilize modulation techniques that lend themselves to noncoherent detection. How well the receiver can estimate the phase depends on the channel signal-to-noise ratio and how long phase coherence can be assumed. Although prior work has shown that using any reference symbols to aid the phase estimation process is suboptimal with respect to capacity, their presence may be useful in practice as they can simplify the receiver processing. In this paper, the effects of per-pulse phase uncertainty are examined for systems using binary modulation. Both the fraction of the transmission that may be devoted to reference symbols without substantially reducing the overall channel capacity and the point at which it is better to forego coherent processing in favor of noncoherent demodulation are examined.
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Summary

In some channels, such as the frequency-hop channel, the transmission may undergo abrupt transitions in phase. This can require the receiver to re-estimate the phase on each hop, or for the system to utilize modulation techniques that lend themselves to noncoherent detection. How well the receiver can estimate the phase...

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Relationships between cognitive factors and gait strategy during exoskeleton-augmented walking

Published in:
Proc. Human Factors and Ergonomics Society Annual Mtg, HFES, Vol. 65, No. 1, 2021.

Summary

Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like normalized stride length or width. Changes indicate shifting priorities like increasing stability or coordination with an exoskeleton. Relationships between cognitive factors and exoskeleton gait characteristics were assessed. Cognitive factors were quantified using a modified Simon task and a speed achievement task on a self-paced treadmill with and without a secondary go/no-go task. Individuals with faster reaction times and decreased ability to maintain a given speed tended to prioritize coordination with an exoskeleton over gait stability. These correlations indicate relationships between cognitive factors and individual exoskeleton-augmented gait strategy that should be further investigated to understand variation in exoskeleton use.
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Summary

Individual variation in exoskeleton-augmented gait strategy may arise from differences in cognitive factors, e.g., ability to respond quickly to stimuli or complete tasks under divided attention. Gait strategy is defined as different approaches to achieving gait priorities (e.g., walking without falling) and is observed via changes in gait characteristics like...

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Scalable and Robust Algorithms for Task-Based Coordination From High-Level Specifications (ScRATCHeS)

Summary

Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We define a specification language, capability temporal logic, to describe rich, temporal properties involving tasks requiring the participation of multiple agents with multiple capabilities, e.g., sensors or end effectors. Arbitrary missions and team dynamics are jointly encoded as constraints in a mixed integer linear program, and solved efficiently using commercial off-the-shelf solvers. ScRATCHeS optionally allows optimization for maximal robustness to agent attrition at the penalty of increased computation time.We include an online replanning algorithm that adjusts the plan after an agent has dropped out. The flexible specification language, fast solution time, and optional robustness of ScRATCHeS provide a first step toward a multipurpose on-the-fly planning tool for tasking large teams of agents with multiple capabilities enacting missions with multiple tasks. We present randomized computational experiments to characterize scalability and hardware demonstrations to illustrate the applicability of our methods.
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Summary

Many existing approaches for coordinating heterogeneous teams of robots either consider small numbers of agents, are application-specific, or do not adequately address common real world requirements, e.g., strict deadlines or intertask dependencies. We introduce scalable and robust algorithms for task-based coordination from high-level specifications (ScRATCHeS) to coordinate such teams. We...

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CoSPA data product description

Published in:
MIT Lincoln Laboratory Report ATC-449

Summary

This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically expressed as rectangular arrays whose elements contain a data value coinciding with uniformly-spaced observations or computed results on a 2-D surface. Gridded data arrays map to the earth's surface through a map projection, for example, Lambert Conformal or Lambert Azimuthal Equal-Area. CoSPA generates only gridded products; there are no non-gridded data for CoSPA.
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Summary

This document contains a description of Consolidated Storm Prediction for Aviation (CoSPA) data products that are packaged and distributed for external users. As described in Rappa and Troxel, 2013 [1] for Corridor Integrated Weather System (CIWS) data products, CoSPA products are categorized as gridded and non-gridded. Gridded products are typically...

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A hybrid algorithm for parameter estimation (HAPE) for dynamic constant power loads

Published in:
IEEE Trans. Ind. Electron., Vol. 68, No. 11, November 2021, pp. 10326-35.
Topic:
R&D group:

Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced. In order to verify the load model and the HAPE, two experiments are conducted with different DCPLs using a Power-Hardwarein-the-Loop (PHiL) testbed. The PHiL testbed consists of a real-time computer working with a programmable power amplifier in order to perturb the input voltage's amplitude and frequency. Each connected DCPL in two separate experiments serves as the device under test (DUT). Using the captured experimental data as a reference, the HAPE is then invoked. The resulting parameter estimates are used to define simulation models. Both resulting DCPL models are simulated to produce waveforms that closely resemble experimental waveforms. Finally, the HAPE's resulting parameter estimates are presented, and the performance of the HAPE is discussed.
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Summary

Low-inertia microgrids may easily have a single load which can make up most of the total load, thereby greatly affecting stability and power quality. Instead of a static load model, a dynamic constant power load (DCPL) model is considered here. Next, a hybrid algorithm for parameter estimation (HAPE) is introduced...

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Utility of inter-subject transfer learning for wearable-sensor-based joint torque prediction models

Published in:
43rd Annual Intl. Conf. of the IEEE Engineering in Medicine & Biology, 31 October 2021-4 November 2021.

Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning techniques applied to machine learning models afford the ability to apply and investigate the viability and utility such knowledge-transfer scenarios. This paper investigates the utility of single- and multi-subject based parameter transfer on LSTM models trained for "sensor-to-joint torque" prediction tasks, with regards to task performance and computational resources required for network training. We find that parameter transfer between both single- and multi-subject models provide useful knowledge transfer, with varying results across specific "source" and "target" subject pairings. This could be leveraged to lower model training time or computational cost in compute-constrained environments or, with further study to understand causal factors of the observed variance in performance across source and target pairings, to minimize data collection and model retraining requirements to select and personalize a generic model for personalized wearable-sensor-based joint torque prediction technologies.
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Summary

Generalizability between individuals and groups is often a significant hurdle in model development for human subjects research. In the domain of wearable-sensor-controlled exoskeleton devices, the ability to generalize models across subjects or fine-tune more general models to individual subjects is key to enabling widespread adoption of these technologies. Transfer learning...

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Application of complex split-activation feedforward networks to beamforming

Published in:
55th Asilomar Conf. on Signals, Systems and Computers, ACSSC, 31 October - 3 November 2021.

Summary

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real sequences. In this paper, neural networks using complex numbers to represent the complex baseband RF signals are considered. A complex backpropagation approach that calculates gradients and a Jacobian, allows for fast optimization of the neural networks. Through simulations, it is shown that complex neural networks require less training samples than their real counterparts and may generalize better in dynamic environments.
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Summary

In increasingly congested RF environments and for jamming at closer ranges, amplifiers may introduce nonlinearities that linear adaptive beamforming techniques can't mitigate. Machine learning architectures are intended to solve such nonlinear least squares problems, but much of the current work and available software is limited to signals represented as real...

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Effect of a wet spherical radome on the reflected power for an S-band planar phased array radar antenna

Published in:
2021 Antenna Measurement Techniques Association Symp., AMTA, 24-29 October 2021.

Summary

An active S-band dual-polarized multifunction phased array radar (MPAR), the Advanced Technology Demonstrator (ATD), has recently been developed for weather sensing and aircraft surveillance. The ATD is an active electronically scanned array (AESA) with 4864 transmit/receive (T/R) modules and was installed in a spherical radome. Simulations and a novel phased array measurement technique have been explored to assess the impact of high reflectivity from a wet radome during rain that can potentially induce voltages exceeding the transmit amplifier breakdown voltage. The measurement technique uses array elements radiating one at a time to illuminate the radome, and uses superposition to quantify the received signal power in a reference antenna on the face of the array. It is shown that when the radome surface is wet and highly reflective, certain electronic steering angles sum to a large reflected signal focused on the array face. This measurement technique can be used prior to high-power phased array radar operation to monitor the magnitude of reflections and help avoid element transmit amplifier failures.
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Summary

An active S-band dual-polarized multifunction phased array radar (MPAR), the Advanced Technology Demonstrator (ATD), has recently been developed for weather sensing and aircraft surveillance. The ATD is an active electronically scanned array (AESA) with 4864 transmit/receive (T/R) modules and was installed in a spherical radome. Simulations and a novel phased...

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Metrics for quantifying cognitive factors that may underlie individual variation in exoskeleton use

Published in:
Proc. of the Human Factors and Ergonomics Society Annual Meeting, Vol. 65, No. 1, 2021, pp. 216-20.

Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and gait strategy. Parameters were measured in 15 subjects via two tasks: (1) a modified Simon task and (2) a speed-achievement task with secondary go/no-go cues on a self-paced treadmill. Outcome metrics were assessed for significantly different intra- versus inter-subject variability. Reaction time measures from the modified Simon task, as well two speed-achievement metrics and one gait-strategy characteristic were found to show significant differences in intra- versus inter-subject variability. These results suggest that select cognitive factors may differentiate between individuals and be potential predictors for individual variation during exoskeleton system operation.
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Summary

Individual differences in adaptation to exoskeletons have been observed, but are not well understood. Kinematic, kinetic, and physiologic factors are commonly used to assess these systems. Parameters from experimental psychology and gait literature wereadapted to probe the lower extremity perception-cognition-action loop using measures of reaction times, gait task performance, and...

READ MORE