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Jinyuan Mao

Publications and source records attributed to Jinyuan Mao.

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Size Effect of Monovalent Ions on Polyelectrolyte Brushes

The conformation of polyelectrolyte (PE) brushes is highly sensitive to external conditions, particularly salt concentration and ion-specific effects. As salt concentration increases, PE brushes transition from an osmotic brush regime at low salt ($H \propto c_\mathrm{s}^{0}$) to a salted brush regime at high salt ($H \propto c_\mathrm{s}^{-1/3}$). However, deviations from this ideal scaling behavior are frequently observed in molecular simulations. In this work, we employ coarse-grained molecular dynamics simulations to systematically investigate how the sizes of counterions and co-ions affect the structural evolution and scaling behavior of PE brushes over a broad range of salt concentrations. Our results show that counterion size plays a dominant role in regulating ion penetration and coordination with PE monomers. At low salt concentration, smaller counterions penetrate more easily into the brush, leading to enhanced local charge compensation and stronger brush collapse. At high salt concentration, however, the brush height becomes largely insensitive to counterion size, while deviations from the classical scaling relation emerge. On the other hand, co-ion size mainly affects the system indirectly by modifying ion distributions and the local electrostatic environment. Smaller co-ions weaken local charge compensation and suppress brush collapse, with this effect becoming more pronounced at high salt concentration. When the sizes of counterions and co-ions are reduced simultaneously, the system exhibits a coupled response. Collectively, this work provides a microscopic understanding of how ion size and salt concentration jointly govern the structural response of PE brushes and the emergence of non-classical scaling behavior in realistic solution environments.

cond-mat.soft

BIFE: Better Interaction, Fewer Errors for Minute-Long Video Generation

Long video generation is a critical step toward building realistic world models, requiring both high visual fidelity and long-range interaction consistency. Recent autoregressive diffusion models enable long-horizon generation through KV cache reuse, yet suffer from two fundamental challenges: failure to preserve long-range interactions due to sliding-window KV cache and error accumulation that progressively degrades generation quality over time. To address these issues, we propose BIFE, a framework that introduces a semantic sparse KV cache for retrieval-based long-range conditioning and a Block Forcing training strategy to enforce cross-block consistency. Together, these designs preserve historical interactions while mitigating drift, enabling stable and coherent minute-long video generation. We also introduce InterVBench, a minute-long video benchmark with fine-grained block-level annotations and Video Drift Error metrics. Extensive experiments on InterVBench and VBench-Long demonstrate that BIFE achieves state-of-the-art performance, including a 22.2% improvement on VDE-Subject and a 19.4% improvement on VDE-Clarity over baselines. Website: https://alibaba-damo-academy.github.io/BIFE. Code: https://github.com/alibaba-damo-academy/BIFE.

cs.CV

IntuiTF: MLLM-Guided Transfer Function Optimization for Direct Volume Rendering

Direct volume rendering (DVR) is a fundamental technique for visualizing volumetric data, where transfer functions (TFs) play a crucial role in extracting meaningful structures. However, designing effective TFs remains unintuitive due to the semantic gap between user intent and TF parameter space. Although numerous TF optimization methods have been proposed to mitigate this issue, existing approaches still face two major challenges: the vast exploration space and limited generalizability. To address these issues, we propose IntuiTF, a novel framework that leverages Multimodal Large Language Models (MLLMs) to guide TF optimization in alignment with user intent. Specifically, our method consists of two key components: (1) an evolution-driven explorer for effective exploration of the TF space, and (2) an MLLM-guided human-aligned evaluator that provides generalizable visual feedback on rendering quality. The explorer and the evaluator together establish an efficient Trial-Insight-Replanning paradigm for TF space exploration. We further extend our framework with an interactive TF design system. We demonstrate the broad applicability of our framework through three case studies and validate the effectiveness of each component through extensive experiments. We strongly recommend readers check our cases, demo video, and source code at: https://github.com/wyysteelhead/IntuiTF

cs.GR