arXiv subjects

Jinwei Dong

Publications and source records attributed to Jinwei Dong.

4 recordsLinked to original sources

arXiv2026

Ecosystem service demand relationship and trade-off patterns in urban parks across China

Understanding public demand for urban ecosystem services (ES) is crucial for effective green space management, yet the intricate relationships and potential trade-offs among these diverse demands remain poorly understood. Previous studies have yielded inconsistent findings, often limited by small samples or reliance on indirect proxies. Here, we provide the first national-scale, direct assessment of the relationship among demands for nine urban park ES using a survey dataset comprising 20,075 responses across China and a point-allotment experiment that directly quantifies the trade-off patterns among service demands. We found particularly strong preferences among urban residents in China for air purification and recreation services, at the expense of other services. These preferences were further reflected in three distinct demand bundles: air purification-dominated, recreation-dominated, and balanced demands, each delineating a typical group of people with distinct representative characteristics. Socio-economic and environmental factors, such as age, environmental interest, and mean annual precipitation, significantly influence the trade-off intensity among service demands. Our study pioneers the direct, quantitative analysis of relationships among ecosystem service demands, and the results underscore the need for tailored urban park designs that address diverse service demands to sustainably enhance the quality of city life in China and beyond.

econ.GN↗

arXiv2025

EDSep: An Effective Diffusion-Based Method for Speech Source Separation

Generative models have attracted considerable attention for speech separation tasks, and among these, diffusion-based methods are being explored. Despite the notable success of diffusion techniques in generation tasks, their adaptation to speech separation has encountered challenges, notably slow convergence and suboptimal separation outcomes. To address these issues and enhance the efficacy of diffusion-based speech separation, we introduce EDSep, a novel single-channel method grounded in score matching via stochastic differential equation (SDE). This method enhances generative modeling for speech source separation by optimizing training and sampling efficiency. Specifically, a novel denoiser function is proposed to approximate data distributions, which obtains ideal denoiser outputs. Additionally, a stochastic sampler is carefully designed to resolve the reverse SDE during the sampling process, gradually separating speech from mixtures. Extensive experiments on databases such as WSJ0-2mix, LRS2-2mix, and VoxCeleb2-2mix demonstrate our proposed method's superior performance over existing diffusion and discriminative models, validating its efficacy.

eess.AS↗

arXiv2024

Probing band topology in ABAB and ABBA stacked twisted double bilayer graphene

Twisted graphene moire superlattice has been demonstrated as an exotic platform for investigating correlated states and nontrivial topology. Among the moire family, twisted double bilayer graphene (TDBG) is a tunable flat band system expected to show stacking-dependent topological properties. However, electron correlations and the band topology are usually intertwined in the flat band limit, rendering the unique topological property due to stacking still elusive. Focusing on a large-angle TDBG with weak electron correlations, here we probe the Landau level (LL) spectra in two differently stacked TDBG, i.e. ABBA- and ABAB-TDBG, to unveil their distinct topological properties. For ABBA-TDBG, we observe non-trivial topology at zero electric displacement filed, evident from both the emergence of Chern bands from half fillings and the closure of gap at CNP above a critical magnetic field. For ABAB-TDBG, by contrast, we find that the moire band is topologically trivial, supported by the absence of LLs from half fillings and the persistence of the gap at CNP above the critical magnetic fields. In addition, we also observe an evolution of the trivial-to-nontrivial topological transition at finite D fields, confirmed by the emerged Landau fans originating from quarter filling v = 1. Our result demonstrates, for the first time, the unique stacking-dependent topology in TDBG, offering a promising avenue for future investigations on topological states in correlated systems.

cond-mat.mes-hall↗

arXiv2021

Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach

Land cover classification in remote sensing is often faced with the challenge of limited ground truth. Incorporating historical information has the potential to significantly lower the expensive cost associated with collecting ground truth and, more importantly, enable early- and in-season mapping that is helpful to many pre-harvest decisions. In this study, we propose a new approach that can effectively transfer knowledge about the topology (i.e. relative position) of different crop types in the spectral feature space (e.g. the histogram of SWIR1 vs RDEG1 bands) to generate labels, thereby support crop classification in a different year. Importantly, our approach does not attempt to transfer classification decision boundaries that are susceptible to inter-annual variations of weather and management, but relies on the more robust and shift-invariant topology information. We tested this approach for mapping corn/soybeans in the US Midwest and paddy rice/corn/soybeans in Northeast China using Landsat-8 and Sentinel-2 data. Results show that our approach automatically generates high-quality labels for crops in the target year immediately after each image becomes available. Based on these generated labels from our approach, the subsequent crop type mapping using a random forest classifier reach the F1 score as high as 0.887 for corn as early as the silking stage and 0.851 for soybean as early as the flowering stage and the overall accuracy of 0.873 in Iowa. In Northeast China, F1 scores of paddy rice, corn and soybeans and the overall accuracy can exceed 0.85 two and half months ahead of harvest. Overall, these results highlight unique advantages of our approach in transferring historical knowledge and maximizing the timeliness of crop maps. Our approach supports a general paradigm shift towards learning transferrable and generalizable knowledge to facilitate land cover classification.

cs.CV↗