Low Ambiguity Zone (LAZ) sequences are essential in modern integrated sensing and communication (ISAC) systems. Recently, locally perfect nonlinear functions (LPNFs) have been employed to design LAZ sequences with flexible parameters. In this work, we propose three new classes of LPNFs and use them to construct LAZ sequences that offer additional flexible parameters. Notably, all the proposed LAZ sequences are asymptotically optimal with respect to the Ye-Zhou-Fan-Liu-Lei-Tang bounds derived in [IEEE J. Sel. Areas Commun. 40(6), pp. 1809-1822, 2022].

Advertising expenditures have become the major source of revenue for e-commerce platforms. Providing good advertising experiences for advertisers by reducing their costs of trial and error in discovering the optimal advertising strategies is crucial for the long-term prosperity of online advertising. To achieve this goal, the advertising platform needs to identify the advertiser's optimization objectives, and then recommend the corresponding strategies to fulfill the objectives. In this work, we first deploy a prototype of strategy recommender system on Taobao display advertising platform, which indeed increases the advertisers' performance and the platform's revenue, indicating the effectiveness of strategy recommendation for online advertising. We further augment this prototype system by explicitly learning the advertisers' preferences over various advertising performance indicators and then optimization objectives through their adoptions of different recommending advertising strategies. We use contextual bandit algorithms to efficiently learn the advertisers' preferences and maximize the recommendation adoption, simultaneously. Simulation experiments based on Taobao online bidding data show that the designed algorithms can effectively optimize the strategy adoption rate of advertisers.