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Creators/Authors contains: "Huang, L."

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  1. ( , IEEE Asilomar Conference on Signals, Systems, and Computers)
    Free, publicly-accessible full text available November 1, 2022
  2. ; ; ; ( , Harvard business review)
    Collaborations that require information sharing and mutual trust between companies, suppliers, and clients can be tough, particularly in the remote era. But blockchain’s distributed ledger — and its use of smart contracts — can simplify the process, creating a common, reliable record of transactions and avoiding costly disputes. In doing so, blockchain changes how deals are made: Partner selection is made simpler, as establishing trust is less important; agreement formation is more important, because protocols are hard to alter once put in place; and execution is made easier, because outcomes can be automated. Blockchain isn’t a magic bullet — itmore »works much better in some situations than others — but it can fundamentally change how collaborations work.« less
    Accepted Manuscript Full Text Available
  3. ; ( , International Conference on Decision and Game Theory for Security (GameSec 2021))
    Bosansky, B. ; Gonzalez, C. ; Rass, S. ; Sinha, A. (Ed.)
    Full Text Available 
  4. ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  5. ; ; ; ( , Journal of machine learning research)
    Accepted Manuscript Full Text Available
  6. ; ; ; ; ; ; ( , The IEEE International Conference on Robotics and Automation)
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ( , CVPR Workshop - Mutual Benefits of Cognitive and Computer Vision)
    The prediction of human shifts of attention is a widely-studied question in both behavioral and computer vision, especially in the context of a free viewing task. However, search behavior, where the fixation scanpaths are highly dependent on the viewer's goals, has received far less attention, even though visual search constitutes much of a person's everyday behavior. One reason for this is the absence of real-world image datasets on which search models can be trained. In this paper we present a carefully created dataset for two target categories, microwaves and clocks, curated from the COCO2014 dataset. A total of 2183 imagesmore »were presented to multiple participants, who were tasked to search for one of the two categories. This yields a total of 16184 validated fixations used for training, making our microwave-clock dataset currently one of the largest datasets of eye fixations in categorical search. We also present a 40-image testing dataset, where images depict both a microwave and a clock target. Distinct fixation patterns emerged depending on whether participants searched for a microwave (n=30) or a clock (n=30) in the same images, meaning that models need to predict different search scanpaths from the same pixel inputs. We report the results of several state-of-the-art deep network models that were trained and evaluated on these datasets. Collectively, these datasets and our protocol for evaluation provide what we hope will be a useful test-bed for the development of new methods for predicting category-specific visual search behavior.« less
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal Supplement Series)
    Free, publicly-accessible full text available August 16, 2022
  9. ; ; ; ; ; ; ; ; ; ; et al ( , The Astrophysical Journal Supplement Series)
    Free, publicly-accessible full text available July 27, 2022
  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review B)
    Full Text Available