Online advertising has been the mainstream monetization approach for internet-based companies, in which bid optimization plays a crucial role in enhancing advertising performance. Currently, the bid optimization problem has narrowed down to two specific forms: Budget-constrained bidding (BCB) and Multi-constraint bidding (MCB). Existing solutions try to solve BCB/MCB via linear programming solvers, learning methods, or feedback control. However, in large-scale complex e-commerce, they still suffer from inefficiency, poor convergence, or slow adaptation to the changing market. This research presents an online receding optimization method as a solution for practical bid optimization problems. We conduct a theoretical analysis of the optimal bidding strategy's structure. Further, an online receding optimization process is designed based on open-loop feedback control, which periodically updates a constructed optimal bid formulation that can be solved by linear programming. Then, considering large-scale linear programming problems, we propose an efficient down sampling scheme. Besides, a neural-network-based auction scale prediction is used to adapt to the changing market. Finally, a series of online A/B experiments onTaobao Sponsored Searchcompare our work to industrial methods and state-of-the-art from several aspects. The proposed method has been implemented onTaobao, a billion-scaled online advertising business, for over a year.

As the IoT technology becomes well established and sharing economy expands worldwide, the bike-sharing system (BSS) has spread fast in the last decade. When introducing the BSS in a new city, the operator often faces many challenges: e.g., optimizing station siting (physical or electric), constructing bike lanes, and making strategies for the initial distribution and rebalancing of bikes. These challenges require an accurate interblock flow prediction before deploying the BSS. In this article, we derive blocks from the urban road network and extract blocks’ features based on the distribution and types of POI. Then, the interblock flow can be predicted based on the features of both start/end block and neighbor blocks. We propose a unified architecture with a generalized attention mechanism named the relation graph network (RGN) to extract block features and make predictions across cities. The evaluations on real-world data sets show that RGN outperforms other popular graph neural networks in multiple metrics. We further simulate the applications of station site selection and bike lane planning based on the prediction from RGN, and the result provides a noticeable increase in the BSS’s deployment/operation efficiency.

As the sharing economy develops and bike-sharing apps emerge, the dockless bike-sharing system (DLBS) has become a competitive alternative to the docked bike-sharing system because of its convenience of finding and parking without physical docks. Meanwhile, new demands are rapidly increasing as DLBS expands, e.g., crowd-sourced re-balancing and pre-ordering during rush hours. A more fine-grained destination prediction is required to tackle these issues. In this paper, we propose a probabilistic-trip-based destination prediction method named P3M. To overcome the uncertainty due to docks’ absence, we introduce the virtual docks derived from POIs and convert a single trip recorded in GPS into several probabilistic trips among POIs using an innovative user behavior model Walking-Riding-Walking Probabilistic Trip. To deal with sparsity, P3M adapts a trip-wised parameter share strategy together with a statistical-based history-feature extractor for better performance without overfitting. Compared with the baseline method, P3M reduces the mean absolute errors measured with distance by 31.55% (from 1.1036 km to 0.7554 km) and is less sensitive to the sparsity of user’s records. Further, we analyze the application of P3M in different types of DLBS and use two simulations to prove its efficiency under insufficient bike supply circumstances.

We develop a method to learn a bio-inspired motion control policy using data collected from hawkmoths navigating in a virtual forest. A Markov Decision Process (MDP) framework is introduced to model the dynamics of moths and sparse logistic regression is used to learn control policy parameters from the data. The results show that moths do not favor detailed obstacle location information in navigation, but rely heavily on optical flow. Using the policy learned from the moth data as a starting point, we propose an actor-critic learning algorithm to refine policy parameters and obtain a policy that can be used by an autonomous aerial vehicle operating in a cluttered environment. Compared with the moths' policy, the policy we obtain integrates both obstacle location and optical flow. We compare the performance of these two policies in terms of their ability to navigate in artificial forest areas. While the optimized policy can adjust its parameters to outperform the moth's policy in each different terrain, the moth's policy exhibits a high level of robustness across terrains.

Finding the k smallest/largest element of a large array, i.e., k-selection is a fundamental supporting algorithm in data analysis. Due to the fact that big data born in geo-distributed environments, it especially requires communication-efficient distributed k-selection, besides typical computation and memory efficiency. Moreover, sensitive organizations make data privacy a rigorous precondition for their participation in such distributed statistical analysis for common profit. To this end, we propose a Distributed Privacy-Aware Median (DPAM) selection algorithm for median selection in distributed large-scale data while preserving local statistics privacy, and extend it to arbitrary k-selection. DPAM utilizes mean to approximate median, via contraction of the standard deviation. It is the theoretical fastest with a worst computation complexity of O(N), and also highly efficient in communication overhead (in logarithm of data range). To preserve ε-differential privacy of local statistics, DPAM randomly adds dummy elements (the number follows a rounded Laplacian distribution) to local data. The noise does not degrade the estimation precision or convergence rate. Performance of DPAM is compared with centralized/distributed quick select and optimization, in terms of complexity and privacy preserving ability. Extensive simulation and experiment results show the higher efficiency of DPAM.

Different from energy harvesting which generates dynamic energy supplies, the mobile charger is able to provide stable and reliable energy supply for sensor nodes, and thus enables sustainable system operations. While previous mobile charging protocols either focus on the charger travel distance or the charging delay of sensor nodes, in this work we propose a novel Energy Synchronized Charging (ESync) protocol, which simultaneously reduces both of them. Observing the limitation of the Traveling Salesman Problem (TSP)-based solutions when nodes energy consumptions are diverse, we construct a set of nested TSP tours based on their energy consumption rates, and only nodes with low remaining energy are involved in each charging round. Furthermore, we propose the concept of energy synchronization to synchronize the charging requests sequence of nodes with their sequence on the TSP tours.