Browsing by Author "Jiang, Hong"

Now showing 1 - 3 of 3
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    An Efficient Augmented Lagrangian Method with Applications to Total Variation Minimization
    (2012-07) Li, Chengbo; Yin, Wotao; Jiang, Hong; Zhang, Yin
    Based on the classic augmented Lagrangian multiplier method, we propose, analyze and test an algorithm for solving a class of equality-constrained non-smooth optimization problems (chiefly but not necessarily convex programs) with a particular structure. The algorithm effectively combines an alternating direction technique with a nonmonotone line search to minimize the augmented Lagrangian function at each iteration. We establish convergence for this algorithm, and apply it to solving problems in image reconstruction with total variation regularization. We present numerical results showing that the resulting solver, called TVAL3, is competitive with, and often outperforms, other state-of-the-art solvers in the field.
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    A New Compressive Video Sensing Framework for Mobile Broadcast
    (IEEE, 2013-03) Li, Chengbo; Jiang, Hong; Wilford, Paul; Zhang, Yin; Scheutzow, Mike
    A new video coding method based on compressive sampling is proposed. In this method, a video is coded using compressive measurements on video cubes. Video reconstruction is performed by minimization of total variation (TV) of the pixelwise discrete cosine transform coefficients along the temporal direction. A new reconstruction algorithm is developed from TVAL3, an efficient TV minimization algorithm based on the alternating minimization and augmented Lagrangian methods. Video coding with this method is inherently scalable, and has applications in mobile broadcast.
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    Strategic stabilization of arousal boosts sustained attention
    (Elsevier, 2024) de Gee, Jan Willem; Mridha, Zakir; Hudson, Marisa; Shi, Yanchen; Ramsaywak, Hannah; Smith, Spencer; Karediya, Nishad; Thompson, Matthew; Jaspe, Kit; Jiang, Hong; Zhang, Wenhao; McGinley, Matthew J.
    Arousal and motivation interact to profoundly influence behavior. For example, experience tells us that we have some capacity to control our arousal when appropriately motivated, such as staying awake while driving a motor vehicle. However, little is known about how arousal and motivation jointly influence decision computations, including if and how animals, such as rodents, adapt their arousal state to their needs. Here, we developed and show results from an auditory, feature-based, sustained-attention task with intermittently shifting task utility. We use pupil size to estimate arousal across a wide range of states and apply tailored signal-detection theoretic, hazard function, and accumulation-to-bound modeling approaches in a large cohort of mice. We find that pupil-linked arousal and task utility both have major impacts on multiple aspects of task performance. Although substantial arousal fluctuations persist across utility conditions, mice partially stabilize their arousal near an intermediate and optimal level when task utility is high. Behavioral analyses show that multiple elements of behavior improve during high task utility and that arousal influences some, but not all, of them. Specifically, arousal influences the likelihood and timescale of sensory evidence accumulation but not the quantity of evidence accumulated per time step while attending. In sum, the results establish specific decision-computational signatures of arousal, motivation, and their interaction in attention. So doing, we provide an experimental and analysis framework for studying arousal self-regulation in neurotypical brains and in diseases such as attention-deficit/hyperactivity disorder.