Proximity graphs (PG) have gained increasing popularity as the state-of-the-art solutions to k -approximate nearest neighbor ( k -ANN) search on high-dimensional data, which serves as a fundamental function in various fields, e.g., retrieval-augmented generation. Although PG-based approaches have the best k -ANN search performance, their index construction cost is superlinear to the number of points. Such superlinear cost substantially limits their scalability in the era of big data. Hence, the goal of this paper is to accelerate the construction of PG-based methods without compromising their k -ANN search performance. To achieve this goal, two mainstream categories of PG are revisited: relative neighborhood graph (RNG) and navigable small world graph (NSWG). By revisiting their construction process, we find the issues of construction efficiency. To address these issues, we propose a new construction framework with a novel pruning strategy for edge selection, which accelerates RNG construction while keeping its k -ANN search performance. Then, we integrate this framework into NSWG construction to enhance both the construction efficiency and k -ANN search performance of NSWG. Extensive experiments are conducted to validate our construction framework for both RNG and NSWG, and that it significantly reduces the PG construction cost, achieving up to 5.6x speedup, while not compromising the k -ANN search performance.

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

Air pollution seriously affects public health, while effective air quality prediction remains a challenging problem since the complex spatial-temporal couplings exist in multi-area monitoring data of the city. Current approaches rarely consider relative geographical locations when capturing spatial-temporal relations, instead the latent inter-dependencies (i.e., implicit spatial relations) of data as a replacement. However, such relations cannot necessarily reflect the diffusion of air pollutants in the real world, and genuine location-related information could be lost during the implicit relation learning process. In this article, we introduce a new concept, geospatial-temporal data, and propose a novel deep neural network architecture, CoupledGT, to learn the geospatial-temporal couplings within data for air quality prediction. Specifically, the asymmetric diffusion relation of air quality data between two areas is first explicitly represented by the newly developed planar Gaussian diffusion (PGD) equation. And then, a geospatial couplings diffuser (GCD) is designed to parameterize the PGD equation and learn multi-areas diffusion mutually affected geospatial couplings. Besides, the RNN is employed to capture temporal couplings of each area, and incorporated with GCD to learn both shared and unique characteristics of the geospatial-temporal data simultaneously, which empowers the generalization and efficiency of the model. Extensive experiments on two real-world datasets demonstrate our method is robust and outperforms existing baseline methods in air quality prediction tasks.

To meet the growing bike-sharing demands and make people’s travel convenient, the companies need to add new stations at locations where demands exceed supply. Before making reliable decisions on adding new stations, it is required to understand the spatial effects of new stations on the station network. In this paper, we study the deployment of the new station by estimating its varied causal effects on the demands of nearby stations, e.g., how does adding a new station (treatment) causally influence the demands (outcome) of nearby stations? When working with observational data, we should control hidden confounders, which cause spurious relations between treatments and outcomes. However, previous studies use historical data of the individual unit (e.g., the station’s historical demands) to approximate its hidden confounders, which cannot deal with the lack of historical data for new stations. And the conventional methods overlook the differences between units, which cannot be applied to our problem. To overcome the challenges, we propose a novel model (CausalSE) to estimate the varied effects of new stations on nearby stations, which uses the shared knowledge (i.e., similar traveling patterns among stations) to approximate hidden confounders. Experimental results on real-world datasets show that CausalSE outperforms 6 state-of-the-art methods.

In e-commerce platforms, the online descriptive information of products shows significant impacts on the purchase behaviors. To attract potential buyers for product promotion, numerous workers are employed to write the impressive product descriptions. The hand-crafted product descriptions are less-efficient with great labor costs and huge time consumption. Meanwhile, the generated product descriptions do not take consideration into the customization and the diversity to meet users’ interests. To address these problems, we propose one generic framework, namely DeepDepict, to automatically generate the information-rich and personalized product descriptive information. Specifically, DeepDepict leverages the graph attention to retrieve the product-related knowledge from external knowledge base to enrich the diversity of products, constructs the personalized lexicon to capture the linguistic traits of individuals for the personalization of product descriptions, and utilizes multiple pointer-generator network to fuse heterogeneous data from multi-sources to generate informative and personalized product descriptions. We conduct intensive experiments on one public dataset. The experimental results show that DeepDepict outperforms existing solutions in terms of description diversity, BLEU, and personalized degree with significant margin gain, and is able to generate product descriptions with comprehensive knowledge and personalized linguistic traits.

In this paper, we propose a new single-image dehazing method for synthetic and real-world hazy images. Based on the color attenuation prior, this proposed dehazing method improves it in two aspects. First, we estimate the atmospheric light with the haze-lines prior, which is based on the observation that pixel values of a hazy image can be modeled as lines in the RGB color space that intersects at the air-light. Second, the dynamic scattering coefficient, which is an exponential function of image depth, is proposed to replace the constant scattering coefficient. Experimental results demonstrate that the dehazed image of proposed algorithm is clearer and more natural than that of the color attenuation prior. The proposed algorithm can effectively improve the effect of dehazing.