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Abstract BackgroundPressure-sensitive adhesives (PSAs) are integral to various industrial applications, yet a significant gap remains in accurately assessing their impact properties under dynamic conditions. This limitation hampers the optimization of PSAs for specific uses where impact resistance is critical. ObjectiveThis study aims to develop an experimental method to evaluate the impact properties of PSAs, providing a reliable and reproducible technique to assess their performance. MethodWe designed an experimental setup to simulate real-world impact conditions, incorporating high-speed cameras and an image analysis algorithm to capture the adhesive's behavior under sudden loads. The method's novelty lies in its ability to quantify maximum failure load and adhesion failure mechanisms in the dynamic loading of PSAs. ResultsThe experimental results reveal critical insights into the impact resistance of various PSA formulations, highlighting significant differences in energy dissipation and failure patterns. ConclusionThese findings offer new data not previously available in the literature, enabling a more precise evaluation of PSA performance. The developed method provides a robust framework for assessing the impact properties of PSAs, offering valuable guidance for the design and selection of adhesives in applications requiring enhanced impact resistance. This work bridges the gap between quasi-static testing and realistic dynamic performance, contributing to the advancement of PSA technology. 

Gaussian splatting methods are emerging as a popular approach for converting multi-view image data into scene representations that allow view synthesis. In particular, there is interest in enabling view synthesis for dynamic scenes using only monocular input data---an ill-posed and challenging problem. The fast pace of work in this area has produced multiple simultaneous papers that claim to work best, which cannot all be true. In this work, we organize, benchmark, and analyze many Gaussian-splatting-based methods, providing apples-to-apples comparisons that prior works have lacked. We use multiple existing datasets and a new instructive synthetic dataset designed to isolate factors that affect reconstruction quality. We systematically categorize Gaussian splatting methods into specific motion representation types and quantify how their differences impact performance. Empirically, we find that their rank order is well-defined in synthetic data, but the complexity of real-world data currently overwhelms the differences. Furthermore, the fast rendering speed of all Gaussian-based methods comes at the cost of brittleness in optimization. We summarize our experiments into a list of findings that can help to further progress in this lively problem setting. 

Large language models (LLMs) have achieved remarkable performance on various natural language tasks. However, they are trained on static corpora and their knowledge can become outdated quickly in the fast-changing world. This motivates the development of knowledge editing (KE) to update specific knowledge in LLMs without changing unrelated others or compromising their pre-trained capabilities. Previous efforts sought to update a small amount of parameters of a LLM and proved effective for making selective updates. Nonetheless, the edited LLM often exhibits degraded ability to reason about the new knowledge. In this work, we identify a key issue: \textit{heterogeneous token overfitting} (HTO), where the LLM overfits different tokens in the provided knowledge at varying rates. To tackle this, we propose {\NAME}, a token-level smoothing method that mitigates HTO by adaptively refining the target distribution. Theoretically, {\NAME} offers better parameter updates with negligible computation overhead. It also induces an implicit DPO but does not require preference data pairs. Extensive experiments across four editing methods, two LLMs, and diverse scenarios demonstrate the effectiveness and versatility of our method.