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Flexibility is a cornerstone of operations management, crucial to hedge stochasticity in product demands, service requirements, and resource allocation. In two-sided platforms, flexibility is also two-sided and can be viewed as the compatibility of agents on one side with agents on the other side. Platform actions often influence the flexibility on either the demand or the supply side. But how should flexibility be jointly allocated across different sides? Whereas the literature has traditionally focused on only one side at a time, our work initiates the study of two-sided flexibility in matching platforms. We propose a parsimonious matching model in random graphs and identify the flexibility allocation that optimizes the expected size of a maximum matching. Our findings reveal that flexibility allocation is a first-order issue: for a given flexibility budget, the resulting matching size can vary greatly depending on how the budget is allocated. Moreover, even in the simple and symmetric settings we study, the quest for the optimal allocation is complicated. In particular, easy and costly mistakes can be made if the flexibility decisions on the demand and supply side are optimized independently (e.g., by two different teams in the company), rather than jointly. To guide the search for optimal flexibility allocation, we uncover two effects, flexibility cannibalization, and flexibility abundance, that govern when the optimal design places the flexibility budget only on one side or equally on both sides. In doing so we identify the study of two-sided flexibility as a significant aspect of platform efficiency.

In this work, the detection of terahertz (THz) pulse via different schemes of THz time-domain spectroscopy (THz-TDS), including electro-optic sampling (EOS) and photoconductive sampling (PCS), has been reviewed by insisting on its principles and characteristics. Furthermore, four developments of THz-TDS, i.e. THz air-breakdown/biased coherent detection (THz-ABCD), THz-radiation-enhanced emission of fluorescence (THz-REEF), single-shot THz-TDS, and THz asynchronous optical sampling (THz-ASOPS) have also been introduced.

Given a graph $G$, a community structure $\mathcal{C}$, and a budget $k$, the fair influence maximization problem aims to select a seed set $S$ ($|S|\leq k$) that maximizes the influence spread while narrowing the influence gap between different communities. While various fairness notions exist, the welfare fairness notion, which balances fairness level and influence spread, has shown promising effectiveness. However, the lack of efficient algorithms for optimizing the welfare fairness objective function restricts its application to small-scale networks with only a few hundred nodes. In this paper, we adopt the objective function of welfare fairness to maximize the exponentially weighted summation over the influenced fraction of all communities. We first introduce an unbiased estimator for the fractional power of the arithmetic mean. Then, by adapting the reverse influence sampling (RIS) approach, we convert the optimization problem to a weighted maximum coverage problem. We also analyze the number of reverse reachable sets needed to approximate the fair influence at a high probability. Further, we present an efficient algorithm that guarantees $1-1/e - \varepsilon$ approximation.

Effective load balancing is at the heart of many applications in operations. Often tackled via the balls-into-bins paradigm, seminal results have shown that a limited amount of flexibility goes a long way in order to maintain (approximately) balanced loads throughout the decision-making horizon. This paper is motivated by the fact that balance across time is too stringent a requirement for some applications; rather, the only desideratum is approximate balance at the end of the horizon. In this work we design ``limited-flexibility'' algorithms for three instantiations of the end-of-horizon balance problem: the balls-into-bins problem, opaque selling strategies for inventory management, and parcel delivery for e-commerce fulfillment. For the balls-into-bins model, we show that a simple policy which begins exerting flexibility toward the end of the time horizon (i.e., when $\Theta\left(\sqrt{T\log T}\right)$ periods remain), suffices to achieve an approximately balanced load (i.e., a maximum load within ${O}(1)$ of the average load). Moreover, with just a small amount of adaptivity, a threshold policy achieves the same result, while only exerting flexibility in ${O}\left(\sqrt{T}\right)$ periods, matching a natural lower bound. We then adapt these algorithms to develop order-wise optimal policies for the opaque selling problem. Finally, we show via a data-driven case study that the adaptive policy designed for the balls-into-bins model can be modified to (i) achieve approximate balance at the end of the horizon and (ii) yield significant cost savings relative to policies which either never exert flexibility, or exert flexibility aggressively enough to achieve anytime balance. The unifying motivation behind our algorithms is the observation that exerting flexibility at the beginning of the horizon is likely wasted when system balance is only evaluated at the end.

Bayesian optimization (BO) is widely used to optimize expensive-to-evaluate black-box functions.BO first builds a surrogate model to represent the objective function and assesses its uncertainty. It then decides where to sample by maximizing an acquisition function (AF) based on the surrogate model. However, when dealing with high-dimensional problems, finding the global maximum of the AF becomes increasingly challenging. In such cases, the initialization of the AF maximizer plays a pivotal role, as an inadequate setup can severely hinder the effectiveness of the AF. This paper investigates a largely understudied problem concerning the impact of AF maximizer initialization on exploiting AFs' capability. Our large-scale empirical study shows that the widely used random initialization strategy often fails to harness the potential of an AF. In light of this, we propose a better initialization approach by employing multiple heuristic optimizers to leverage the historical data of black-box optimization to generate initial points for the AF maximize. We evaluate our approach with a range of heavily studied synthetic functions and real-world applications. Experimental results show that our techniques, while simple, can significantly enhance the standard BO and outperform state-of-the-art methods by a large margin in most test cases., Comment: Accepted by Transactions on Machine Learning Research (TMLR)

In the world of epsilon-near-zero (ENZ) materials, the plasma is unique for its natural ENZ properties at the plasma frequency (wp). However, for the air plasma during femtosecond laser filamentation with wp in terahertz (THz) band, which is also known as a broadband THz emitter, the possible ENZ effect has long been neglected. In this work, interactions between the laser plasma in air and the radiated THz waves were investigated, and the THz resonance absorption at the ENZ point, which gave rise to the generation of surface plasmon waves, has been theoretically and experimentally demonstrated for the first time, to the best of our knowledge. Specifically, this ENZ effect was accompanied with THz modulations along the plasma filament in multiple domains, including the linear-to-elliptical THz polarization conversion and the multi-ring pattern of far-field THz profiles, etc. Thanks to these novel ENZ-induced phenomena, the understanding of this important plasma-based THz source has been enriched. Moreover, by extending the laser plasma to the role of ENZ material, it is promising to expedite related applications benefiting from the ENZ nature, e.g., the strong spatial confinement of THz waves inside the ENZ region between the bi-filaments array, which realized all-optical time-domain integration of broadband THz pulses as we recently displayed in [arXiv].

We study a classical problem in revenue management: quantity-based single-resource revenue management with no-shows. In this problem, a firm observes a sequence of $T$ customers requesting a service. Each arrival is drawn independently from a known distribution of $k$ different types, and the firm needs to decide irrevocably whether to accept or reject requests in an online fashion. The firm has a capacity of resources $B$, and wants to maximize its profit. Each accepted service request yields a type-dependent revenue and has a type-dependent probability of requiring a resource once all arrivals have occurred (or, be a no-show). If the number of accepted arrivals that require a resource at the end of the horizon is greater than $B$, the firm needs to pay a fixed compensation for each service request that it cannot fulfill. With a clairvoyant, that knows all arrivals ahead of time, as a benchmark, we provide an algorithm with a uniform additive loss bound, i.e., its expected loss is independent of $T$. This improves upon prior works achieving $\Omega(\sqrt{T})$ guarantees.