This paper proposes a dual divide-and-optimize algorithm (DualOpt) for solving the large-scale traveling salesman problem (TSP). DualOpt combines two complementary strategies to improve both solution quality and computational efficiency. The first strategy is a grid-based divide-and-conquer procedure that partitions the TSP into smaller sub-problems, solving them in parallel and iteratively refining the solution by merging nodes and partial routes. The process continues until only one grid remains, yielding a high-quality initial solution. The second strategy involves a path-based divide-and-optimize procedure that further optimizes the solution by dividing it into sub-paths, optimizing each using a neural solver, and merging them back to progressively improve the overall solution. Extensive experiments conducted on two groups of TSP benchmark instances, including randomly generated instances with up to 100,000 nodes and real-world datasets from TSPLIB, demonstrate the effectiveness of DualOpt. The proposed DualOpt achieves highly competitive results compared to 10 state-of-the-art algorithms in the literature. In particular, DualOpt achieves an improvement gap up to 1.40% for the largest instance TSP100K with a remarkable 104x speed-up over the leading heuristic solver LKH3. Additionally, DualOpt demonstrates strong generalization on TSPLIB benchmarks, confirming its capability to tackle diverse real-world TSP applications.

Recently, extensive efforts have been devoted to computer vision and machine learning by exploiting big data to explore many practical applications. However, these research fields are still quite limited not only by the sheer volume, but also the versatility and diversity, of the available datasets. In this paper, we target at four challenging and yet important computer vision tasks, namely, human-centered scene classification, attribute based zero-shot learning (recognition), human keypoint detection and image Chinese captioning. Four novel large-scale datasets are collected and annotated to facilitate these tasks of deeper image understanding. Labels, bounding boxes, attributes, keypoints and captions are annotated in corresponding datasets. These rich annotations bridge the semantic gap between low-level images and high-level concepts. Extensive experiments on baseline methods have been implemented and compared, which show that these learning tasks on our datasets are still challenging.

Visible light indoor localization offers promising accuracy and reliability due to dense light deployment and multipath-free propagation. However, current visible light localization schemes rely on either energy-hungry cameras or assumptions about channel responses of light sources, which limits their application. They also require LED lights with specialized circuitry, which increases cost and hinders large scale deployment. We present LiTell2, a scheme that extracts unique features from incumbent fluorescent or LED lights, and uses a simple photodiode sensor to extract AoA information for localization. LiTell2's sensor achieves AoA measurements by leveraging the diversity of photodiodes' angular responses. By comparing signal from photodiodes of different angular response, LiTell2 can derive AoA of each light source, thus achieving high precision localization with unmodified light fixtures and extremely low fingerprinting cost. Our experiments show that LiTell2 provides accurate AoA sensing and enables energy efficient yet robust localization.