Zhenning Wang | Searcharxiv

arXiv2026

Non-Hermitian Mosaic Maryland model

We introduce the non-Hermitian mosaic Maryland model, where a discrete modulation period and a non-Hermitian phase are incorporated into the potential, rendering the originally exactly solvable system generally non-integrable. This model provides a unique platform to investigate how structural modulation governs localization in complex quasiperiodic potentials. Using Avila's global theory, we analytically derive the exact Lyapunov exponent and obtain explicit formulas for the complex mobility edges. Remarkably, for modulation periods kappa >= 2, the system intrinsically hosts kappa-1 robust extended bands that persist independently of the potential strength and non-Hermiticity. We further characterize the topological nature of these phases via the spectral winding number. Unlike the standard Maryland model, the mosaic modulation induces mobility edges, and the resulting phase transitions are continuous, reflecting the non-integrable nature of the system. Numerical calculations of the inverse participation ratio and fractal dimension confirm the analytical predictions for the asymptotic form of the mobility edges in the large non-Hermiticity limit. This work establishes structural design as a powerful degree of freedom for engineering wave transport and enhancing the robustness of extended states in non-Hermitian systems.

cond-mat.dis-nn↗

arXiv2025

A Synthetic Data-Driven Radiology Foundation Model for Pan-tumor Clinical Diagnosis

AI-assisted imaging made substantial advances in tumor diagnosis and management. However, a major barrier to developing robust oncology foundation models is the scarcity of large-scale, high-quality annotated datasets, which are limited by privacy restrictions and the high cost of manual labeling. To address this gap, we present PASTA, a pan-tumor radiology foundation model built on PASTA-Gen, a synthetic data framework that generated 30,000 3D CT scans with pixel-level lesion masks and structured reports of tumors across ten organ systems. Leveraging this resource, PASTA achieves state-of-the-art performance on 45 of 46 oncology tasks, including non-contrast CT tumor screening, lesion segmentation, structured reporting, tumor staging, survival prediction, and MRI-modality transfer. To assess clinical applicability, we developed PASTA-AID, a clinical decision support system, and ran a retrospective simulated clinical trial across two scenarios. For pan-tumor screening on plain CT with fixed reading time, PASTA-AID increased radiologists' throughput by 11.1-25.1% and improved sensitivity by 17.0-31.4% and precision by 10.5-24.9%; additionally, in a diagnosis-aid workflow, it reduced segmentation time by up to 78.2% and reporting time by up to 36.5%. Beyond gains in accuracy and efficiency, PASTA-AID narrowed the expertise gap, enabling less-experienced radiologists to approach expert-level performance. Together, this work establishes an end-to-end, synthetic data-driven pipeline spanning data generation, model development, and clinical validation, thereby demonstrating substantial potential for pan-tumor research and clinical translation.

eess.IV↗