In Location-Based Services (LBS), such as food delivery, a fundamental task is segmenting Areas of Interest (AOIs), aiming at partitioning the urban geographical spaces into non-overlapping regions. Traditional AOI segmentation algorithms primarily rely on road networks to partition urban areas. While promising in modeling the geo-semantics, road network-based models overlooked the service-semantic goals (e.g., workload equality) in LBS service. In this article, we point out that the AOI segmentation problem can be naturally formulated as a Markov Decision Process (MDP), which gradually chooses a nearby AOI for each grid in the current AOI’s border. Based on the MDP, we present the first attempt to generalize Deep Reinforcement Learning (DRL) for AOI segmentation, leading to a novel DRL-based framework called DRL4AOI. The DRL4AOI framework introduces different service-semantic goals in a flexible way by treating them as rewards that guide the AOI generation. To evaluate the effectiveness of DRL4AOI, we develop and release an AOI segmentation system. We also present a representative implementation of DRL4AOI—TrajRL4AOI—for AOI segmentation in the logistics service. It introduces a Double Deep Q-learning Network (DDQN) to gradually optimize the AOI generation for two specific semantic goals: (i) trajectory modularity, i.e., maximize tightness of the trajectory connections within an AOI and the sparsity of connections between AOIs, (ii) matchness with the road network, i.e., maximizing the matchness between AOIs and the road network. Quantitative and qualitative experiments conducted on synthetic and real-world data demonstrate the effectiveness and superiority of our method. The code and system is publicly available at https://github.com/Kogler7/AoiOpt .

Real-Time Bidding (RTB) is a trading mechanism that allocates advertising (ad) requests through online auctions. Participants in these auctions typically include an ad exchange (AdX) and several demand-side platforms (DSPs). When an RTB auction begins, the AdX first establishes the reserve price set by publishers as the starting bid, after which the DSPs bid to compete for potential ad impressions. The reserve price strategy is crucial to the ad revenue of publishers; however, due to the strategic and dynamic bidding behavior of DSPs, optimizing the reserve price presents a significant challenge. In this work, we report a novel adaptive reserve price strategy based on reinforcement learning (RL). In our scheme, value bucket identification is leveraged to estimate the intrinsic values of ad inventories. Following this estimation, specialized reward functions are utilized to generate informative reward signals for RL models. Furthermore, we study the issue of risk management on the publisher side and develop a risk-aware instantiation to model risk tendency, considering both empirical expert knowledge and real-time trading conditions. Extensive experiments using real-world datasets collected from operational environments have demonstrated the effectiveness of the proposed method.

The query-POI matching task involves interpreting noisy textual addresses to retrieve corresponding Points-of-Interest (POIs), which is crucial for location-based service providers. However, existing methods typically rely on annotated user search logs, limiting their generalization. This paper address the query-POI matching problem through geographical data alignment using a contrastive learning framework. Our model, MoCo-GA (Momentum Contrastive Geographical Alignment), learns similar representations for various geographical data elements of the same POI, including query address, POI address, and geolocation. We developed a method to create a cross-modal geographical dataset from crowd-sourced data for training. Our MoCo-GA employs the momentum contrastive instance discrimination mechanism to learn representations for textual addresses. We further proposed a siamese contrastive learning module to to for geographical data alignment. Experimental results demonstrate that MoCo-GA can consistently outperform baseline methods on query-POI matching task, particularly in zero-shot scenarios. Our code is available at https://github.com/CainiaoTechAi/TextGeoAlign

In the logistics network, accurately estimating packages' Travel Time Distribution (TTD) given the routes greatly benefits both consumers and platforms. Although recent works perform well in predicting an expected time or a time distribution in a road network, they could not be well applied to estimate TTD in logistics networks. Because TTD prediction in the logistics network requires modeling packages' multimodal TTD (MTTD, i.e., there can be more than one likely output with a given input) while leveraging the complex correlations in the logistics network. To this end, this work opens appealing research opportunities in studying MTTD learning conditioned on graph-structure data by investigating packages' travel time distribution in the logistics network. We propose a Graph-based Mixture Density Network, named GMDNet, which takes the benefits of both graph neural network and mixture density network for estimating MTTD conditioned on graph-structure data (i.e., the logistics network). Furthermore, we adopt the Expectation-Maximization (EM) framework in the training process to guarantee local convergence and thus obtain more stable results than gradient descent. Extensive experiments on two real-world datasets demonstrate the superiority of our proposed model.

Last-mile delivery, which refers to delivering packages from the depot to customers, is a crucial step for logistics service. The Route and Time Prediction (RTP) in last-mile package delivery is beneficial to improve customers’ experience and supervise couriers’ behavior. However, the limited raw information brings great challenges to accurately predict the route and delivery time. In this paper, we propose a deep model named I2RTP, which explores the heterogeneous representation of the package’s community to help predict the delivery route and estimate the arrival time of each package. Specifically, for the entire delivery route prediction, we model the inter- and intra-community information to learn the route features from global and local perspectives. Besides, by integrating the community representation with package features, our model could make more accurate predictions of the next-delivery package and its time duration. Experiments on the offline dataset and the online deployment on Cainiao’s Delivery System demonstrate the effectiveness of our proposed method, as well as validate the rationality of the global and local prediction pipeline.

Few-shot deep learning is a topical challenge area for scaling visual recognition to open ended growth of unseen new classes with limited labeled examples. A promising approach is based on metric learning, which trains a deep embedding to support image similarity matching. Our insight is that effective general purpose matching requires non-linear comparison of features at multiple abstraction levels. We thus propose a new deep comparison network comprised of embedding and relation modules that learn multiple non-linear distance metrics based on different levels of features simultaneously. Furthermore, to reduce over-fitting and enable the use of deeper embeddings, we represent images as distributions rather than vectors via learning parameterized Gaussian noise regularization. The resulting network achieves excellent performance on both miniImageNet and tieredImageNet.

Monte Carlo (MC) methods for light transport simulation are flexible and general but typically suffer from high variance and slow convergence. Gradientdomain rendering alleviates this problem by additionally generating image gradients and reformulating rendering as a screened Poisson image reconstruction problem. To improve the quality and performance of the reconstruction, we propose a novel and practical deep learning based approach in this paper. The core of our approach is a multi-branch auto-encoder, termed GradNet, which end-to-end learns a mapping from a noisy input image and its corresponding image gradients to a high-quality image with low variance. Once trained, our network is fast to evaluate and does not require manual parameter tweaking. Due to the difficulty in preparing ground-truth images for training, we design and train our network in a completely unsupervised manner by learning directly from the input data. This is the first solution incorporating unsupervised deep learning into the gradient-domain rendering framework. The loss function is defined as an energy function including a data fidelity term and a gradient fidelity term. To further reduce the noise of the reconstructed image, the loss function is reinforced by adding a regularizer constructed from selected rendering-specific features. We demonstrate that our method improves the reconstruction quality for a diverse set of scenes, and reconstructing a high-resolution image takes far less than one second on a recent GPU.

Researchers often summarize their work in the form of posters. Posters provide a coherent and efficient way to convey core ideas from scientific papers. Generating a good scientific poster, however, is a complex and time consuming cognitive task, since such posters need to be readable, informative, and visually aesthetic. In this paper, for the first time, we study the challenging problem of learning to generate posters from scientific papers. To this end, a data-driven framework, that utilizes graphical models, is proposed. Specifically, given content to display, the key elements of a good poster, including panel layout and attributes of each panel, are learned and inferred from data. Then, given inferred layout and attributes, composition of graphical elements within each panel is synthesized. To learn and validate our model, we collect and make public a Poster-Paper dataset, which consists of scientific papers and corresponding posters with exhaustively labelled panels and attributes. Qualitative and quantitative results indicate the effectiveness of our approach.