Online ride-hailing platforms aim to deliver efficient mobility-on-demand services, often facing challenges in balancing dynamic and spatially heterogeneous supply and demand. Existing methods typically fall into two categories: reinforcement learning (RL) approaches, which suffer from data inefficiency, oversimplified modeling of real-world dynamics, and difficulty enforcing operational constraints; or decomposed online optimization methods, which rely on manually designed high-level objectives that lack awareness of low-level routing dynamics. To address this issue, we propose a novel hybrid framework that integrates large language model (LLM) with mathematical optimization in a dynamic hierarchical system: (1) it is training-free, removing the need for large-scale interaction data as in RL, and (2) it leverages LLM to bridge cognitive limitations caused by problem decomposition by adaptively generating high-level objectives. Within this framework, LLM serves as a meta-optimizer, producing semantic heuristics that guide a low-level optimizer responsible for constraint enforcement and real-time decision execution. These heuristics are refined through a closed-loop evolutionary process, driven by harmony search, which iteratively adapts the LLM prompts based on feasibility and performance feedback from the optimization layer. Extensive experiments based on scenarios derived from both the New York and Chicago taxi datasets demonstrate the effectiveness of our approach, achieving an average improvement of 16% compared to state-of-the-art baselines.

Accurate segmentation of skin lesion from dermoscopic images is a crucial part of computer-aided diagnosis of melanoma. It is challenging due to the fact that dermoscopic images from different patients have non-negligible lesion variation, which causes difficulties in anatomical structure learning and consistent skin lesion delineation. In this paper, we propose a novel bi-directional dermoscopic feature learning (biDFL) framework to model the complex correlation between skin lesions and their informative context. By controlling feature information passing through two complementary directions, a substantially rich and discriminative feature representation is achieved. Specifically, we place biDFL module on the top of a CNN network to enhance high-level parsing performance. Furthermore, we propose a multi-scale consistent decision fusion (mCDF) that is capable of selectively focusing on the informative decisions generated from multiple classification layers. By analysis of the consistency of the decision at each position, mCDF automatically adjusts the reliability of decisions and thus allows a more insightful skin lesion delineation. The comprehensive experimental results show the effectiveness of the proposed method on skin lesion segmentation, achieving state-of-the-art performance consistently on two publicly available dermoscopic image databases.

Accurate skin lesion segmentation plays an important role in the computer aided analysis of melanoma. It is a challenging task due to the variation of skin lesion appearance, the low contrast with background, and the existence of the artifacts in dermoscopic images. In this paper, we try to boost the skin lesion segmentation performance based on the fully convolutional neural network. To this end, we first propose an enhanced high-level parsing (EHP) module to generate meaningful feature representation for skin lesion and make more precise delineation of the detailed lesion structure. Furthermore, to handle the imbalance data distribution of skin lesion and background, we propose a class weighted loss (CWL) to achieve more consistent lesion prediction. Experiment results evaluated on ISBI 2017 database demonstrate the effectiveness and robustness of the proposed architecture on skin lesion segmentation, achieving new state-of-the-art prediction performance.

Accurate segmentation of retinal vessel plays an important role in the computer-aided diagnosis of eye diseases. Existing supervised methods extract features only from green channel due to its much higher contrast between vessel and background than in red and blue channels. However, red and blue channels also contain useful information for distinguishing vessel from background. This work investigates various ways of combining information in all 3 color channels to enhance the segmentation performance, based on which an effective color fusion scheme is proposed in this paper. Its performance is evaluated on two publicly available databases DRIVE and STARE. Results demonstrate that the proposed feature fusion with dimensionality reduction by asymmetric PCA visibly enhances the segmentation performance consistently on both databases, rendering better performance than state-of-the-art methods in dealing with healthy and pathological retinal images.