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  1. Abstract

    Heuristics are essential for addressing the complexities of engineering design processes. The goodness of heuristics is context-dependent. Appropriately tailored heuristics can enable designers to find good solutions efficiently, and inappropriate heuristics can result in cognitive biases and inferior design outcomes. While there have been several efforts at understanding which heuristics are used by designers, there is a lack of normative understanding about when different heuristics are suitable. Towards addressing this gap, this paper presents a reinforcement learning-based approach to evaluate the goodness of heuristics for three sub-problems commonly faced by designers: (1) learning the map between the design space and the performance space, (2) acquiring sequential information, and (3) stopping the information acquisition process. Using a multi-armed bandit formulation and simulation studies, we learn the suitable heuristics for these individual sub-problems under different resource constraints and problem complexities. Additionally, we learn the optimal heuristics for the combined problem (i.e., the one composing all three sub-problems), and we compare them to ones learned at the sub-problem level. The results of our simulation study indicate that the proposed reinforcement learning-based approach can be effective for determining the quality of heuristics for different problems, and how the effectiveness of the heuristics changes as a function of the designer’s preference (e.g., performance versus cost), the complexity of the problem, and the resources available.

     
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  2. We studied the molecular gas properties of AzTEC/C159, a star-forming disk galaxy at $z=4.567$. We secured $^{12}$CO molecular line detections for the $J=2\to1$ and $J=5\to4$ transitions using the Karl G. Jansky VLA and the NOEMA interferometer. The broad (FWHM$\sim750\,{\rm km\,s}^{-1}$) and tentative double-peaked profiles of both $^{12}$CO lines are consistent with an extended molecular gas reservoir, which is distributed in a rotating disk as previously revealed from [CII] 158$\mu$m line observations. Based on the $^{12}$CO(2$\to$1) emission line we derived $L'_{\rm{CO}}=(3.4\pm0.6)\times10^{10}{\rm \,K\,km\,s}^{-1}{\rm \,pc}^{2}$, that yields a molecular gas mass of $M_{\rm H_2 }(\alpha_{\rm CO}/4.3)=(1.5\pm0.3)\times 10^{11}{\rm M}_\odot$ and unveils a gas-rich system with $\mu_{\rm gas}(\alpha_{\rm CO}/4.3)\equiv M_{\rm H_2}/M_\star=3.3\pm0.7$. The extreme star formation efficiency (SFE) of AzTEC/C159, parametrized by the ratio $L_{\rm{IR}}/L'_{\rm{CO}}=(216\pm80)\, {\rm L}_{\odot}{\rm \,(K\,km\,s}^{-1}{\rm \,pc}^{2})^{-1}$, is comparable to merger-driven starbursts such as local ultra-luminous infrared galaxies (ULIRGs) and SMGs. Likewise, the $^{12}$CO(5$\to$4)/CO(2$\to$1) line brightness temperature ratio of $r_{52}= 0.55\pm 0.15$ is consistent with high excitation conditions, similar to that observed in SMGs. We constrained the value for the $L'_{\text{CO}}-{\rm H}_2$ mass conversion factor in AzTEC/C159, i.e. $\alpha_{\text{CO}}=3.9^{+2.7}_{-1.3}{\rm \,M}_{\odot}{\rm \,K}^{-1}{\rm \,km}^{-1}{\rm \,s\,pc}^{-2}$, that is consistent with a self-gravitating molecular gas distribution as observed in local star-forming disk galaxies. Cold gas streams from cosmological filaments might be fueling a gravitationally unstable gas-rich disk in AzTEC/C159, which breaks into giant clumps forming stars as efficiently as in merger-driven systems and generate high gas excitation. 
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