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The development of levitated optomechanics has enabled precise force sensors that operate in the quantum measurement regime, opening up unique opportunities to search for new physics whose weak interactions may have evaded existing sensors. We demonstrate the detection of impulsive forces acting on optically levitated nanoparticles, where the dominant noise source is provided by measurement backaction. Using these sensors, we search for momentum transfers that may originate from scattering of passing particlelike dark matter. For dark matter that couples to Standard Model neutrons via a generic long-range interaction, this search constrains a range of models in the mass range 1– ${10}^7\mathrm{Ge}\mathrm{V}/c^2$, placing upper limits on single neutron coupling strength as low as $\le 1\times {10}^{-7}$at the 95% confidence level. We also demonstrate the ability of using the inherent directional sensitivity of these sensors to separate possible dark matter signals from backgrounds. Future extensions of the techniques developed here can enable searches for light dark matter and massive neutrinos that can reach sensitivity several orders of magnitude beyond existing searches. 

Robust optimization (RO) is a popular paradigm for modeling and solving two- and multistage decision-making problems affected by uncertainty. In many real-world applications, such as R&D project selection, production planning, or preference elicitation for product or policy recommendations, the time of information discovery is decision-dependent and the uncertain parameters only become observable after an often costly investment. Yet, most of the literature on robust optimization assumes that the uncertain parameters can be observed for free and that the sequence in which they are revealed is independent of the decision-maker’s actions. To fill this gap in the practicability of RO, we consider two- and multistage robust optimization problems in which part of the decision variables control the time of information discovery. Thus, information available at any given time is decision-dependent and can be discovered (at least in part) by making strategic exploratory investments in previous stages. We propose a novel dynamic formulation of the problem and prove its correctness. We leverage our model to provide a solution method inspired from the K-adaptability approximation, whereby K candidate strategies for each decision stage are chosen here-and-now and, at the beginning of each period, the best of these strategies is selected after the uncertain parameters that were chosen to be observed are revealed. We reformulate the problem as a finite mixed-integer (resp. bilinear) program if none (resp. some) of the decision variables are real-valued. This finite program is solvable with off-the-shelf solvers. We generalize our approach to the minimization of piecewise linear convex functions. We demonstrate the effectiveness of our method in terms of usability, optimality, and speed on synthetic instances of the Pandora box problem, the preference elicitation problem with real-valued recommendations, the best box problem, and the R&D project portfolio optimization problem. Finally, we evaluate it on an instance of the active preference elicitation problem used to recommend kidney allocation policies to policy-makers at the United Network for Organ Sharing based on real data from the U.S. Kidney Allocation System. This paper was accepted by Chung Piaw Teo, optimization. Funding: This work was supported primarily by the Operations Engineering Program of the National Science Foundation under NSF Award No. 1763108. The authors are grateful for this support. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.00160 . 

We synthesized and characterized the advanced multifunctional features of mycelium–coir-based composites as a replacement for fossil-based foams used in building insulation.