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Creators/Authors contains: "Huang, H."

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  1. ; ; ; ; ; ( , Conference on Robot Learning)
    Free, publicly-accessible full text available January 1, 2023
  2. ( , IISE Annual Virtual Conference & Expo 2021)
    Accepted Manuscript Full Text Available
  3. ; ( , Conference on Lasers & Electro-Optics)
    Accepted Manuscript Full Text Available
  4. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review X)
    Free, publicly-accessible full text available November 1, 2022
  5. ( , Geoscientific model development)
    Accepted Manuscript Full Text Available
  6. ( , Geoscientific model development)
    Fire causes abrupt changes in vegetation properties and modifies flux exchanges between land and atmosphere at subseasonal to seasonal scales. Yet these shortterm fire effects on vegetation dynamics and surface energy balance have not been comprehensively investigated in the fire-coupled vegetation model. This study applies the SSiB4/TRIFFID-Fire (the Simplified Simple Biosphere Model coupled with the Top-down Representation of Interactive Foliage and Flora Including Dynamics with fire) model to study the short-term fire impact in southern Africa. Specifically, we aim to quantify how large impacts fire exerts on surface energy through disturbances on vegetation dynamics, how fire effects evolve during themore »fire season and the subsequent rainy season, and how surface-darkening effects play a role besides the vegetation change effects. We find fire causes an annual average reduction in grass cover by 4 %–8% for widespread areas between 5–20 S and a tree cover reduction by 1% at the southern periphery of tropical rainforests. The regional fire effects accumulate during June–October and peak in November, the beginning of the rainy season. After the fire season ends, the grass cover quickly returns to unburned conditions, while the tree fraction hardly recovers in one rainy season. The vegetation removal by fire has reduced the leaf area index (LAI) and gross primary productivity (GPP) by 3 %–5% and 5 %–7% annually. The exposure of bare soil enhances surface albedo and therefore decreases the absorption of shortwave radiation. Annual mean sensible heat has dropped by 1.4Wm−2, while the latent heat reduction is small (0.1Wm−2/ due to the evaporation. Surface temperature is increased by as much as 0.33K due to the decrease of sensible heat fluxes, and the warming would be enhanced when the surface-darkening effect is incorporated. Our results suggest that fire effects in grass-dominant areas diminish within 1 year due to the high resilience of grasses after fire. Yet fire effects in the periphery of tropical forests are irreversible within one growing season and can cause large-scale deforestation if accumulated for hundreds of years.« less
    Accepted Manuscript Full Text Available
  7. ; ; ( , 3DV)
    Full Text Available 
  8. ; ; ( , 49th International Conference on Parallel Processing - ICPP : Workshops)
    By modelling how the probability distributions of individuals’ states evolve as new information flows through a network, belief propagation has broad applicability ranging from image correction to virus propagation to even social networks. Yet, its scant implementations confine themselves largely to the realm of small Bayesian networks. Applications of the algorithm to graphs of large scale are thus unfortunately out of reach. To promote its broad acceptance, we enable belief propagation for both small and large scale graphs utilizing GPU processing. We therefore explore a host of optimizations including a new simple yet extensible input format enabling belief propagation tomore »operate at massive scale, along with significant workload processing updates and meticulous memory management to enable our implementation to outperform prior works in terms of raw execution time and input size on a single machine. Utilizing a suite of parallelization technologies and techniques against a diverse set of graphs, we demonstrate that our implementations can efficiently process even massive networks, achieving up to nearly 121x speedups versus our control yet optimized single threaded implementations while supporting graphs of over ten million nodes in size in contrast to previous works’ support for thousands of nodes using CPU-based multi-core and host solutions. To assist in choosing the optimal implementation for a given graph, we provide a promising method utilizing a random forest classifier and graph metadata with a nearly 95% F1-score from our initial benchmarking and is portable to different GPU architectures to achieve over an F1-score of over 72% accuracy and a speedup of nearly 183x versus our control running in this new environment.« less
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  9. ; ; ; ; ; ; ; ; ; ; et al ( , Few-Body Systems)
    Free, publicly-accessible full text available June 1, 2023
  10. ; ( , Proceedings of the 29th International Symposium on High-Performance Parallel and Distributed Computing)
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