The development of vehicle intelligence and connectivity provides the opportunity to integrate more road information for vehicle control. This article proposes a preview active suspension control method based on model predictive control (MPC) with the aim of suppressing vertical vibration. A key challenge in implementing MPC lies in the heavy computational burden associated with solving Quadratic Programming (QP) problems. To address this, a QP algorithm based on non-negative least squares (NNLS) is utilized to accelerate the controller solving speed, thereby enhancing its practical feasibility. Additionally, to further improve ride comfort, a speed planning method is developed. This method addresses a multi-objective optimization problem (MOP) incorporating both travel time and comfort, then a feasible solution is chosen from the optimal solution set according to passenger preferences. Various experiment and simulation results are provided to validates the effectiveness of the controller, speed planning and the integrated design of both. Compared with traveling at a constant speed, the proposed speed planning method decreases the RMS value of body vertical acceleration by 17.3% while concurrently achieving shorter travel time.

The fraudulent insurance claim is critical for the insurance industry.Insurance companies or agency platforms aim to confidently estimate the fraud risk of claims by gathering data from various sources.Although more data sources can improve the estimation accuracy, they inevitably lead to increased costs.Therefore, a great challenge of fraud risk verification lies in well balancing these two aspects.To this end, this paper proposes a framework named cost-efficient fraud risk optimization with submodularity (CEROS) to optimize the process of fraud risk verification.CEROS efficiently allocates investigation resources across multiple information sources, balancing the trade-off between accuracy and cost.CEROS consists of two parts that we propose: a submodular set-wise classification model * Equal Contribution.

Autoscaling is a critical mechanism in cloud computing, enabling the autonomous adjustment of computing resources in response to dynamic workloads. This is particularly valuable for co-located, long-running applications with diverse workload patterns. The primary objective of autoscaling is to regulate resource utilization at a desired level, effectively balancing the need for resource optimization with the fulfillment of Service Level Objectives (SLOs). Many existing proactive autoscaling frameworks may encounter prediction deviations arising from the frequent fluctuations of cloud workloads. Reactive frameworks, on the other hand, rely on realtime system feedback, but their hysteretic nature could lead to violations of stringent SLOs. Hybrid frameworks, while prevalent, often feature independently functioning proactive and reactive modules, potentially leading to incompatibility and undermining the overall decision-making efficacy. In addressing these challenges, we propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module. The proactive module delivers reliable future workload predictions to the optimization module, while the reactive module offers a self-tuning estimator for real-time updates. By embedding a Model Predictive Control (MPC) mechanism and chance constraints into the optimization module, we further enhance its robustness. Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework, leading to over a 36% reduction in SLO violations compared to prevalent reactive, proactive, or hybrid autoscalers. Notably, OptScaler has been successfully deployed at Alipay, providing autoscaling support for the world-leading payment platform.

Time series forecasting is an important ingredient in the intelligence of business and decision processes. In industrial scenarios, the time series of interest are mostly macroscopic time series that are aggregated from microscopic time series, e.g., the retail sales is aggregated from the sales of different goods, and that are also intervened by certain treatments on the microscopic individuals, e.g., issuing discount coupons on some goods to increase the retail sales. These characteristics are not considered in existing toolkits, which just focus on the "natural" time series forecasting that predicts the future value based on historical data, regardless of the impact of treatments. In this paper, we present AntTS, a time series toolkit paying more attention on the forecasting of the macroscopic time series with underlying microscopic time series and certain treatments, besides the "natural" time series forecasting. AntTS consists of three decoupled modules, namely Clustering module, Natural Forecasting module, and Effect module, which are utilized to study the homogeneous groups of microscopic individuals, the "natural" time series forecasting of homogeneous groups, and the treatment effect estimation of homogeneous groups. With the combinations of different modules, it can exploit the microscopic individuals and the interventions on them, to help the forecasting of macroscopic time series. We show that AntTS helps address many typical tasks in the industry.

Post-processing is crucial to modern recommendation systems to achieve various purposes, e.g., improving diversity, and giving reasonable itineraries which consist of combinations of items, but is merely studied in the literature. We decouple the recommendation system into two modules including a reward estimation module and a post-processing module. Our real-time post-processing module built on Ray abstracts the common post-processing problems in the itinerary recommendation as combinatorial optimization problems. Under the goal of maximizing the click-through rate, the more reasonable recommendation results are obtained by imposing various constraints on the candidate items. However, the optimization problems are typically mixed integer programming problems with quadratic terms in practice, which are NP-hard. In real-time scenarios, there are extremely high requirements for the speed of the solving process. We speed up the problem solving by linearizing and relaxing the original problem and use Ray serving as the underlying service to provide stable and efficient technical support. At last, We provide services to users by deploying the post-processing module in the itinerary recommendation scenario at Alipay's built-in applet named ''What's nearby''. The online A/B experiment shows that the user exposure click rate can be significantly improved.

Time series forecasting is widely used in business intelligence, e.g., forecast stock market price, sales, and help the analysis of data trend. Most time series of interest are macroscopic time series that are aggregated from microscopic data. However, instead of directly modeling the macroscopic time series, rare literature studied the forecasting of macroscopic time series by leveraging data on the microscopic level. In this paper, we assume that the microscopic time series follow some unknown mixture probabilistic distributions. We theoretically show that as we identify the ground truth latent mixture components, the estimation of time series from each component could be improved because of lower variance, thus benefitting the estimation of macroscopic time series as well. Inspired by the power of Seq2seq and its variants on the modeling of time series data, we propose Mixture of Seq2seq (MixSeq), an end2end mixture model to cluster microscopic time series, where all the components come from a family of Seq2seq models parameterized by different parameters. Extensive experiments on both synthetic and real-world data show the superiority of our approach.

In recent years, many studies extract aspects from user reviews and integrate them with ratings for improving the recommendation performance. The common aspects mentioned in a user’s reviews and a product’s reviews indicate indirect connections between the user and product. However, these aspect-based methods suffer from two problems. First, the common aspects are usually very sparse, which is caused by the sparsity of user-product interactions and the diversity of individual users’ vocabularies. Second, a user’s interests on aspects could be different with respect to different products, which are usually assumed to be static in existing methods. In this article, we propose an Attentive Aspect-based Recommendation Model (AARM) to tackle these challenges. For the first problem, to enrich the aspect connections between user and product, besides common aspects, AARM also models the interactions between synonymous and similar aspects. For the second problem, a neural attention network which simultaneously considers user, product, and aspect information is constructed to capture a user’s attention toward aspects when examining different products. Extensive quantitative and qualitative experiments show that AARM can effectively alleviate the two aforementioned problems and significantly outperforms several state-of-the-art recommendation methods on the top-N recommendation task.