清华大学

In this article, we propose a novel model for time series of counts called the hysteretic Poisson autoregressive (HPART) model with thresholds by extending the linear Poisson autoregressive model into a nonlinear model. Unlike other approaches that bear the adjective “hysteretic", our model incorporates a scientifically relevant controlling factor that produces genuine hysteresis. Further, we re-analyse the buffered Poisson autoregressive (BPART) model with thresholds. Although the two models share the convenient piecewise linear structure, the HPART model probes deeper into the intricate dynamics that governs regime switching. We study the maximum likelihood estimation of the parameters of both models and their asymptotic properties in a unified manner, establish tests of separate families of hypotheses for the non-nested case involving a BPART model and a HPART model, and demonstrate the finite-sample efficacy of parameter estimation and tests with Monte Carlo simulation. We showcase advantages of the HPART model with two real time series, including plausible interpretations and improved out-of-sample predictions.

The identification of dq-asymmetric impedances presents a challenging estimation problem due to the strong cross-coupling between axes, which is especially prominent at low frequencies. Conventional identification schemes are often hampered by the requirement for sequential perturbations or the difficulty of selecting appropriate global parametric models. This paper introduces an active non-parametric frequency-domain approach that utilizes complex transfer functions and local parametric approximations. This formulation eliminates the requirement for periodic steady-state measurements while providing precision comparable to that of correctly specified parametric models. By solving localized linear least-squares problems, the approach maintains high computational efficiency while remaining agnostic to the specific grid structure. Electromagnetic transient (EMT) simulations demonstrate that the proposed method achieves high-fidelity identification with a 1 Hz frequency resolution using only one second of noise-corrupted measurements. The results confirm a significant reduction in measurement time compared to available techniques. Additionally, the approach shows superior robustness against model-order selection errors, ensuring accurate wideband modeling of grid dynamics.

Pathological image segmentation faces numerous challenges, particularly due to ambiguous semantic boundaries and the high cost of pixel-level annotations. Although recent semi-supervised methods based on consistency regularization (e.g., UniMatch) have made notable progress, they mainly rely on perturbation-based consistency within the image modality, making it difficult to capture high-level semantic priors, especially in structurally complex pathology images. To address these limitations, we propose MPAMatch - a novel segmentation framework that performs pixel-level contrastive learning under a multimodal prototype-guided supervision paradigm. The core innovation of MPAMatch lies in the dual contrastive learning scheme between image prototypes and pixel labels, and between text prototypes and pixel labels, providing supervision at both structural and semantic levels. This coarse-to-fine supervisory strategy not only enhances the discriminative capability on unlabeled samples but also introduces the text prototype supervision into segmentation for the first time, significantly improving semantic boundary modeling. In addition, we reconstruct the classic segmentation architecture (TransUNet) by replacing its ViT backbone with a pathology-pretrained foundation model (Uni), enabling more effective extraction of pathology-relevant features. Extensive experiments on GLAS, EBHI-SEG-GLAND, EBHI-SEG-CANCER, and KPI show MPAMatch's superiority over state-of-the-art methods, validating its dual advantages in structural and semantic modeling.

For H⊆([n]k), let ν(H) denote its matching number and let dH(E) denote the codegree of a (k−1)-set E. We study the codegree moments cop(H)=∑E∈([n]k−1)dH(E)p and the number of copies of the k-uniform l-petal sunflower Sk,lk−1 whose core has size k−1. For fixed positive integers k,l≥2 and s,p≥1, and all sufficiently large n, we prove that among all k-uniform families with matching number at most s, both quantities are uniquely maximized, up to permutation, byHn,k,s={F∈([n]k):F∩[s]≠∅}. This determines the generalized Turán number exk(n,Sk,lk−1,Ms+1), where Ms+1 consists of s+1 pairwise disjoint edges. When k=3, the conclusion for codegree moments holds for n≥6s+3, uniformly for all positive integers p. The proof combines a theorem of Frankl and Kupavskii on families with bounded matching number, an analysis of (k−1)-sets of large codegree, and a moment inequality.

Effective tourism management requires understanding how attraction attributes and supporting factors shape tourists’ perceived attractiveness, and how these in turn translate into visitation. We develop a tourist-centered framework using 610,136 reviews across 2128 heritage attractions in 143 cities, measuring multi-scale perceived attractiveness with a large language model pipeline and visitation with review volume, then identifying the factors that influence both metrics using XGBoost and SHAP. Results show that (1) perceived attractiveness and visitation share a multi-core spatial structure, while visitation displays a polarized long-tail distribution; (2) attraction attributes, especially official designation, visual quality, and ticket pricing, dominate both metrics but shape them differently; (3) positively perceived social, economic, scientific, historic and aesthetical values increase visitation, whereas political value is negatively associated; and (4) accessibility and spatial agglomeration also contribute positively. Practically, multi-scale benchmarking identifies underperforming attractions, while attraction-scale value profiles and the identified influencing factors guide targeted strategies.