arXiv subjects

Ling Yin

Publications and source records attributed to Ling Yin.

8 recordsLinked to original sources

arXiv2026

Arbitrary-Order Scattering Exceptional Points in Configurable Non-Hermitian Zero-Index Materials

Scattering exceptional points (EPs) are non-Hermitian degeneracies where the eigenvalues and eigenvectors of scattering matrices coalesce, enabling many intriguing phenomena in optical systems. Higher-order scattering EPs are particularly notable for their ultrasensitive response to perturbations, yet achieving flexible, arbitrary-order control remains challenging. Here, we propose a configurable non-Hermitian zero-index material (ZIM) network that enables arbitrary-order scattering EPs, as rigorously proved theoretically and validated numerically. Specifically, we show that in an N-port non-Hermitian ZIM network embedded with loss/gain dopants, the maximum achievable EP order is N, and the order can be flexibly tuned from 2 to N or completely eliminated by adjusting the dopants. Furthermore, we compare conventional coherent perfect absorption with absorbing EPs of different orders. Although both achieve perfect absorption of all incident waves, a second-order EP already outperforms coherent perfect absorption, and higher-order EPs provide further power-law enhancement. These findings establish a pathway toward realizing arbitrary-order EPs in open scattering systems, holding significant promise for advanced sensing applications.

physics.optics↗

arXiv2025

M-STAR: Multi-Scale Spatiotemporal Autoregression for Human Mobility Modeling

Modeling human mobility is vital for extensive applications such as transportation planning and epidemic modeling. With the rise of the Artificial Intelligence Generated Content (AIGC) paradigm, recent works explore synthetic trajectory generation using autoregressive and diffusion models. While these methods show promise for generating single-day trajectories, they remain limited by inefficiencies in long-term generation (e.g., weekly trajectories) and a lack of explicit spatiotemporal multi-scale modeling. This study proposes Multi-Scale Spatio-Temporal AutoRegression (M-STAR), a new framework that generates long-term trajectories through a coarse-to-fine spatiotemporal prediction process. M-STAR combines a Multi-scale Spatiotemporal Tokenizer that encodes hierarchical mobility patterns with a Transformer-based decoder for next-scale autoregressive prediction. Experiments on two real-world datasets show that M-STAR outperforms existing methods in fidelity and significantly improves generation speed. The data and codes are available at https://github.com/YuxiaoLuo0013/M-STAR.

cs.AI↗

arXiv2024

Deciphering Human Mobility: Inferring Semantics of Trajectories with Large Language Models

Understanding human mobility patterns is essential for various applications, from urban planning to public safety. The individual trajectory such as mobile phone location data, while rich in spatio-temporal information, often lacks semantic detail, limiting its utility for in-depth mobility analysis. Existing methods can infer basic routine activity sequences from this data, lacking depth in understanding complex human behaviors and users' characteristics. Additionally, they struggle with the dependency on hard-to-obtain auxiliary datasets like travel surveys. To address these limitations, this paper defines trajectory semantic inference through three key dimensions: user occupation category, activity sequence, and trajectory description, and proposes the Trajectory Semantic Inference with Large Language Models (TSI-LLM) framework to leverage LLMs infer trajectory semantics comprehensively and deeply. We adopt spatio-temporal attributes enhanced data formatting (STFormat) and design a context-inclusive prompt, enabling LLMs to more effectively interpret and infer the semantics of trajectory data. Experimental validation on real-world trajectory datasets demonstrates the efficacy of TSI-LLM in deciphering complex human mobility patterns. This study explores the potential of LLMs in enhancing the semantic analysis of trajectory data, paving the way for more sophisticated and accessible human mobility research.

cs.AI↗

arXiv2023

SUES-200: A Multi-height Multi-scene Cross-view Image Benchmark Across Drone and Satellite

Cross-view image matching aims to match images of the same target scene acquired from different platforms. With the rapid development of drone technology, cross-view matching by neural network models has been a widely accepted choice for drone position or navigation. However, existing public datasets do not include images obtained by drones at different heights, and the types of scenes are relatively homogeneous, which yields issues in assessing a model's capability to adapt to complex and changing scenes. In this end, we present a new cross-view dataset called SUES-200 to address these issues. SUES-200 contains 24120 images acquired by the drone at four different heights and corresponding satellite view images of the same target scene. To the best of our knowledge, SUES-200 is the first public dataset that considers the differences generated in aerial photography captured by drones flying at different heights. In addition, we developed an evaluation for efficient training, testing and evaluation of cross-view matching models, under which we comprehensively analyze the performance of nine architectures. Then, we propose a robust baseline model for use with SUES-200. Experimental results show that SUES-200 can help the model to learn highly discriminative features of the height of the drone.

cs.CV↗

arXiv2022

Impact of initial outbreak locations on transmission risk of infectious diseases in an intra-urban area

Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, and the structural heterogeneity of human mobility network embedded in space, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of spatiotemporal transmission and reveal the driving force behind high-risk outbreak locations. First, integrating mobile phone location data, we built a SLIR (susceptible-latent-infectious-removed)-based meta-population model to simulate the spreading process of an infectious disease (i.e., COVID-19) across fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City, China). Based on the simulation model, we evaluated the transmission risk caused by different initial outbreak locations by proposing three indexes including the number of infected cases (CaseNum), the number of affected regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally, we investigated the contribution of different influential factors to the transmission risk via machine learning models. Results indicates that different initial outbreak locations would cause similar CaseNum but different RegionNum and SpatialRange. To avoid the epidemic spread quickly to more regions, it is necessary to prevent epidemic breaking out in locations with high population-mobility flow density. While to avoid epidemic spread to larger spatial range, remote regions with long daily trip distance of residents need attention. Those findings can help understand the transmission risk and driving force of initial outbreak locations within cities and make precise prevention and control strategies in advance.

physics.soc-ph↗

arXiv2015

Preprint WebVRGIS Based Traffic Analysis and Visualization System

This is the preprint version of our paper on Advances in Engineering Software. With several characteristics, such as large scale, diverse predictability and timeliness, the city traffic data falls in the range of definition of Big Data. A Virtual Reality GIS based traffic analysis and visualization system is proposed as a promising and inspiring approach to manage and develop traffic big data. In addition to the basic GIS interaction functions, the proposed system also includes some intelligent visual analysis and forecasting functions. The passenger flow forecasting algorithm is introduced in detail.

cs.OH↗

arXiv2015

Preprint Virtual Geographic Environment Based Coach Passenger Flow Forecasting

This is the preprint version of our paper on 2015 IEEE Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA). There are lacks of integrated analysis and visual display of multiple real-time dynamic traffic information. This research proposed a deep research and application examples on this basis which is conducted in virtual geographic environment. Currently, there are many kinds of traffic passenger flow forecasting models, and the common models include regression forecasting model and time series prediction model. The coach passenger flow shows strong regularity and stability without longterm change trend, so this research adopts regression forecasting model to forecast the coach passenger flow

cs.SI↗

arXiv2015

Preprint Traffic Management and Forecasting System Based on 3D GIS

This is the preprint version of our paper on 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid). This paper takes Shenzhen Futian comprehensive transportation junction as the case, and makes use of continuous multiple real-time dynamic traffic information to carry out monitoring and analysis on spatial and temporal distribution of passenger flow under different means of transportation and service capacity of junction from multi-dimensional space-time perspectives such as different period and special period. Virtual reality geographic information system is employed to present the forecasting result.

cs.OH↗