Xiaodong Hu

Xujin Chen, Xiaodong Hu, Changjun Wang et al.

This paper studies an online trading variant of the classical secretary problem, called secretary problem variant trading (SPVT), from the perspective of an intermediary who facilitates trade between a seller and $n$ buyers (collectively referred to as agents). The seller has an item, and each buyer demands the item. These agents arrive sequentially in a uniformly random order to meet the intermediary, each revealing their valuation of the item upon arrival. After each arrival, the intermediary must make an immediate and irrevocable decision before the next agent appears. The intermediary's objective is to maximize the price of the agent who ultimately holds the item at the end of the process. We evaluate the performance of online algorithms for SPVT using two notions of competitive ratio: strong and weak. The strong notion benchmarks the online algorithm against a powerful offline optimum: the highest price among the $n+1$ agents. We propose an online algorithm for SPVT achieving a strong competitive ratio of $\frac{4e^2}{e^2+1} \approx 3.523$, which is the best possible even when the seller's price may be zero. This tight ratio closes the gap between the previous best upper bound of $4.189$ and lower bound of $3.258$. In contrast, the weak notion restricts the offline optimal algorithm to the given arrival order. The offline algorithm can no longer alter the predetermined arrival order to always place the item in the hands of the agent offering the highest price. Against this weaker benchmark, we design a simple online algorithm for SPVT, achieving a weak competitive ratio of $2$. We further investigate the special case in which the seller's price is zero. For this special SPVT, we develop a double-threshold algorithm achieving a weak competitive ratio of at most $1.83683$ and establish a lower bound of $1.76239$.

Peng Xiao, Xiaodong Hu, Ke Ma et al.

The lack of efficient segmentation methods and fully-labeled datasets limits the comprehensive assessment of optical coherence tomography angiography (OCTA) microstructures like retinal vessel network (RVN) and foveal avascular zone (FAZ), which are of great value in ophthalmic and systematic diseases evaluation. Here, we introduce an innovative OCTA microstructure segmentation network (OMSN) by combining an encoder-decoder-based architecture with multi-scale skip connections and the split-attention-based residual network ResNeSt, paying specific attention to OCTA microstructural features while facilitating better model convergence and feature representations. The proposed OMSN achieves excellent single/multi-task performances for RVN or/and FAZ segmentation. Especially, the evaluation metrics on multi-task models outperform single-task models on the same dataset. On this basis, a fully annotated retinal OCTA segmentation (FAROS) dataset is constructed semi-automatically, filling the vacancy of a pixel-level fully-labeled OCTA dataset. OMSN multi-task segmentation model retrained with FAROS further certifies its outstanding accuracy for simultaneous RVN and FAZ segmentation.