Additive models play an essential role in studying non-linear relationships. Despite many recent advances in estimation, there is a lack of methods and theories for inference in high-dimensional additive models, including confidence interval construction and hypothesis testing. Motivated by inference for non-linear treatment effects, we consider the high-dimensional additive model and make inferences for the function derivative. We propose a novel decorrelated local linear estimator and establish its asymptotic normality. The main novelty is the construction of the decorrelation weights, which is instrumental in reducing the error inherited from estimating the nuisance functions in the high-dimensional additive model. We construct the confidence interval for the function derivative and conduct the related hypothesis testing. We demonstrate our proposed method over large-scale simulation studies and apply it to identify non-linear effects in the motif regression problem. Our proposed method is implemented in the R package DLL available from CRAN.

Despite the proven effectiveness of deep learning technology in pine tree disease segmentation, acquiring a large volume of labeled data remains challenging and inefficient. Few-shot segmentation (FSS) uses a small amount of labeled data to guide the segmentation of unknown categories, further evolving into one-shot segmentation (OSS), which utilizes a single labeled sample to perform segmentation under conditions of extreme data scarcity. However, these methods are mostly applicable to natural images with clear boundaries and have not yet been applied to segmenting pine tree disease in autonomous aerial vehicle (AAV) remote sensing images. For this reason, we have designed the OSS model C2Net for the first time, which includes two main modules: 1) a prior contour awareness module (PCAM) that first generates a query image prior mask with contour response and then uses an iterative feature refinement unit (FRU) to refine features and accurately delineate the segmentation boundaries of pine tree disease and 2) an interclass relationship module (ICRM), which studies the vegetation index features of the support and query images, constructing importance weights that reflect the differences between categories, solving the visual similarity issue. Our experiments on field-collected and publicly available datasets demonstrate that C2Net excels in challenging OSS tasks, showing its ability to generalize across different sensor domains and various disease categories. Especially, on the field acquisition dataset, using just a single labeled pine tree disease image achieves an intersection over union (IoU) of 55.24% and an$F_{1}$of 71.24%.

In point cloud video streaming systems, the field of view (FoV) prediction is critical for selecting the tiles, the objective of which is to optimize the expected long-term quality-of-experience (QoE) from the perspective of a user. On one hand, a satisfactory QoE accounts for not only the playback quality but also the playback smoothness. On the other hand, the large data volume of a selected tile requires the transmission to be adaptive to the system uncertainties. This paper applies a Markov decision process to formulate the problem of tile selection across the infinite discrete time horizon. In particular, a system state includes the FoV information, which is predicted from the Transformer. To alleviate the dependence on system uncertainty statistics, a deep reinforcement learning approach is derived for solving the optimal control policy. Under different settings, we conduct experiments based on the real throughput and head-mounted display data. The results show that compared to the existing baselines, our proposed prediction-control approach achieves a higher FoV prediction accuracy, better playback quality as well as smoothness, and hence a better average QoE for the user.