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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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A Compact Beam-Forming Network for Switched-Beam Arrays.
Abstract not provided.
MaxReach: Reducing Network Incompleteness through Node Probes
Abstract not provided
Efficient transfer learning for neural network language models.
Abstract not provided.
Physical Security Assessment with Convolutional Neural Network Transfer Learning.
Abstract not provided.
A Game Theoretic Model of Computer Network Exploitation Campaigns.
Abstract not provided.
Adaptive Data Rate Techniques for Energy Constrained Ad Hoc LoRa Networks.
Abstract not provided.
DeepHyper: Asynchronous Hyperparameter Search for Deep Neural Networks
Hyperparameters employed by deep learning (DL) methods play a substantial role in the performance and reliability of these methods in practice. Unfortunately, finding performance-optimizing hyperparameter settings is a notoriously difficult task. Hyperparameter search methods typically have limited production-strength implementations or do not target scalability within a highly parallel machine, portability across different machines, experimental comparison between different methods, and tighter integration with workflow systems. In this paper, we present DeepHyper, a Python package that provides a common interface for the implementation and study of scalable hyperparameter search methods. It adopts the Balsam workflow system to hide the complexities of running large numbers of hyperparameter configurations in parallel on high-performance computing (HPC) systems. We implement and study asynchronous model-based search methods that consist of sampling a small number of input hyperparameter configurations and progressively fitting surrogate models over the input-output space until exhausting a user-defined budget of evaluations. We evaluate the efficacy of these methods relative to approaches such as random search, genetic algorithms, Bayesian optimization, and hyperband on DL benchmarks on CPU- and GPU-based HPC systems.
StressBench: A Configurable Full System Network and I/O Benchmark Framework.
Abstract not provided.
BOOTSTRAPPING GRAPH CONVOLUTIONAL NEURAL NETWORKS FOR AUTISM SPECTRUM DISORDER CLASSIFICATION
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Traffic Signal Control for Large-Scale Urban Traffic Networks: Real-World Experiments using Vision-Based Sensors
Effective control of traffic signals plays a critical role in ensuring smooth vehicle flow in urban areas. Expertly engineered traffic signal controllers can considerably minimize travel delays and enhance sustainability. In this paper, the team proposes the Model Predictive Control (MPC) traffic signal control strategy using real-time traffic flow data from a vision-based camera as feedback information. Also, a realistic signal timing plan that considers National Electrical Manufacturers Association (NEMA) constraints has been developed to be applied to real-world scenarios. The primary aim is to reduce the number of vehicles across all links in the controlled area, thereby optimizing traffic flow and reducing energy consumption. To validate the proposed method, several real-life experiments were conducted at 24 intersections in Chattanooga, Tennessee, by collaborating with traffic field engineers. These experiments demonstrated significant performance improvements in comparison to the existing method.
NiMC: Characterizing and Eliminating Network-Induced Memory Contention.
Abstract not provided.
Level-Spread: A New Job Allocation Policy for Dragonfly Networks.
Abstract not provided.