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  1. ( , Mechanics of Composite, Hybrid and Multifunctional Materials , Volume 6. Conference Proceedings of the Society for Experimental Mechanics Series)
    Singh R.P., Chalivendra V. (Ed.)
    Thin-walled structures have been widely used in automotive and aerospace industries to improve the system crashworthiness and impact protection. However, during manufacturing, transporting and handling processes, initial geometric imperfections are inevitably introduced to the thin-walled structures, which imposes negative impacts to the mechanical performance and service life of the thin-walled structures. In this study, we have introduced structural imperfection with controlled geometry and dimension to thin-walled steel tubes and characterized the mechanical response of these empty tubes and LN-filled tubes by quasi-static compression tests. Results show, the structural imperfection reduces the energy absorption capacity of empty tubes by about 20%.more »As the tube is filled with LN, the structural imperfection does not affect the energy absorption capacity of LN filled tube. The enhanced imperfection resistance is attributed to the suppression of imperfection growth caused by the strong liquid-solid interaction between the LN and tube wall. These findings suggest that the LN filling material can effectively reduce the adverse impact of structural imperfection and shed light on future design of thin-walled energy absorption devices.« less
    Full Text Available 
  2. ; ; ; ; ; ( , IEEE/CVF Conference on Computer Vision and Pattern Recognition)
    Accepted Manuscript Full Text Available
  3. ; ; ; ; ; ; ( , Physica A: Statistical Mechanics and its Applications)
    Full Text Available 
  4. ; ; ; ; ( , Physical Review A)
    Full Text Available 
  5. ; ; ; ( , 35th International Conference on Logic Programming)
    Recent successes of Reinforcement Learning (RL) allow an agent to learn policies that surpass human experts but suffers from being time-hungry and data-hungry. By contrast, human learning is significantly faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a Planner-Actor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses its prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions, and also integrates the Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. This work is the first unified framework where knowledge-based planning, RL,more »and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback.« less
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  6. ; ; ; ( , AAAI Fall Symposium)
    Conventional reinforcement learning (RL) allows an agent to learn policies via environmental rewards only, with a long and slow learning curve, especially at the beginning stage. On the contrary, human learning is usually much faster because prior and general knowledge and multiple information resources are utilized. In this paper, we propose a PlannerActor-Critic architecture for huMAN-centered planning and learning (PACMAN), where an agent uses prior, high-level, deterministic symbolic knowledge to plan for goal-directed actions. PACMAN integrates Actor-Critic algorithm of RL to fine-tune its behavior towards both environmental rewards and human feedback. To the best our knowledge, This is the firstmore »unified framework where knowledge-based planning, RL, and human teaching jointly contribute to the policy learning of an agent. Our experiments demonstrate that PACMAN leads to a significant jump-start at the early stage of learning, converges rapidly and with small variance, and is robust to inconsistent, infrequent, and misleading feedback.« less
    Accepted Manuscript Full Text Available
  7. ; ; ; ; ; ; ; ; ( , Energy & environmental science)
    Unbiased photoelectrochemical hydrogen production with high efficiency and durability is highly desired for solar energy storage. Here, we report a microbial photoelectrochemical (MPEC) system that demonstrated superior performance when equipped with bioanodes and black silicon photocathode with a unique ‘‘Swiss-cheese’’ interface. The MPEC utilizes the chemical energy embedded in wastewater organics to boost solar H2 production, which overcomes barriers on anode H2O oxidation. Without any bias, the MPEC generates a record photocurrent (up to 23 mA cm2) and retains prolonged stability for over 90 hours with high Faradaic efficiency (96–99%). The calculated turnover number for MoSx catalyst during a 90more »h period is 495 471 with an average frequency of 1.53 s1 . The system replaced pure water on the anode with actual wastewater and achieved waste organic removal up to 16 kg COD m2 photocathode per day. Cost credits from concurrent wastewater treatment and low-cost design make photoelectrochemical H2 production practical for the first time« less
    Accepted Manuscript Full Text Available
  8. ; ; ; ; ; ; ; ( , Physical Review B)
    Full Text Available 
  9. ; ; ; ; ; ; ; ( , Physical Review E)
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  10. ; ; ; ; ; ; ; ; ; ; et al ( , Physical Review D)
    Free, publicly-accessible full text available May 1, 2023