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方向感是人类与动物进行空间导航的核心能力，以往缺少对其多模态信息整合认知过程及其神经机制的探讨。文献梳理发现，个体通过对视觉、前庭觉、本体觉等多模态信息的独立编码、最优整合以及与空间记忆的交互建立、维持和更新方向感，前庭-视觉通路中多类编码空间信息的细胞通过动态协作为此提供神经基础。未来研究应系统考察方向感的多模态信息动态整合机制；扩大信息整合范畴，探讨听、嗅、触觉对方向感的贡献，拓展其多模态信息整合模型；构建方向感的神经计算模型，为航空、驾驶等领域的导航和定向技术迭代提供参考；探讨个体对方向感的空间言语表述机制，优化人机导航协作的信息传递模式。

[目的] 面向自动字幕和语音翻译及其语料库构建任务中原文字幕与译文字幕对齐及时间同步的问题，提出 Fusion-Align 融合对齐同步方法。[方法] Fusion-Align 以多特征融合与温度标定的对数似然比（LLR）评分器为核心，配合 DTW 对齐算法进行全局最优路径搜索，并通过子句级对齐再优化提升复杂长句的对齐质量。方法从句级与词级嵌入向量中提取 25 维特征（9 维句级语义特征、10 维词无序特征与 6 维词有序特征），经轻量 MLP 输出对数几率并通过温度标定得到可加 LLR 作为统一评分刻度。[结果] 在真实 TED 数据的所评样本上，融合对齐方式（B）的系统均分明显高于直译对齐（A）；配对自助法的置信区间完全为正，95\% 置信水平下支持“B 优于 A”的结论。[局限] 当 ASR 误识/断句漂移显著或跨域分布偏移较大时，刻度温度与阈值的自适应校准仍有空间；单调路径对大幅度重排仍有约束；跨域/跨语种的温标稳健性与特征转移尚待加强。[结论] Fusion-Align 把多源证据凝练为统一可加的 LLR 标尺，将“多特征融合→统一刻度（LLR）→单调全局搜索（DTW）→子句级对齐再优化”的流程闭环，为自动字幕翻译在真实 ASR 场景中的对齐问题提供了新的方法与技术路线。

We explore three applications of Min-Max-Jump distance (MMJ distance). MMJ-based K-means revises K-means with MMJ distance. MMJ-based Silhouette coefficient revises Silhouette coefficient with MMJ distance. We also tested the Clustering with Neural Network and Index (CNNI) model with MMJ-based Silhouette coefficient. In the last application, we tested using Min-Max-Jump distance for predicting labels of new points, after a clustering analysis of data. Result shows Min-Max-Jump distance achieves good performances in all the three proposed applications. In addition, we devise several algorithms for calculating or estimating the distance.

Large language models (LLMs) are used as “digital twins” to replace human respondents, yet their psychometric comparability to humans is uncertain. We propose a construct-validity framework spanning construct representation and the nomological net, benchmarking digital twins against human gold standards across models, tasks and testing how person-specific inputs shape performance. Across studies, digital twins achieved high population-level accuracy and strong within-participant profile correlations, alongside attenuated item-level correlations. In word association tests, LLM-based networks show small-world structure and theory-consistent communities similar to humans, yet diverge lexically and in local structure. In decision-making and contextualized tasks, digital twins under-reproduce heuristic biases, showing normative rationality, compressed variance and limited sensitivity to temporal information. Feature-rich digital twins improve Big Five Personality prediction, but their personality networks show only configural invariance and do not achieve metric invariance. In more applied free-text tasks, feature-rich digital twins better match human narratives, but linguistic differences persist. Together, these results indicate that feature-rich conditioning enhances validity but does not resolve systematic divergences in psychometric comparability. Future work should therefore prioritize delineating the effective boundaries of digital twins, establishing the precise contexts in which they function as reliable proxies for human cognition and behavior.

为解决 Fyne 框架 “轻量自绘” 架构导致的全局刷新、复杂视觉效果缺失、自定义绘制生命周期耦合三大性能瓶颈，提出一种非侵入式折衷优化方案。该方案无需修改框架源码，通过集成 gg 库补充复杂 2D 绘图能力、借助 oksvg/rasterx 库优化矢量图形渲染，搭配 Stack 容器隔离刷新范围，结合 StyleConfig、ColorConfig 实现样式统一管理。在 Windows 11 与 Linux 容器环境中开展性能测试（每组 n=30），结果显示：自定义控件与原生控件帧率持平（约 62 FPS，p>0.05）；Windows 环境下内存使用降低 1.80%、CPU 使用率降低 1.45%；Linux 环境下内存使用降低 6.43%、CPU 仅微增 0.51%，所有性能差异均具有高度统计显著性（p<0.0001）且效应量大。该方案保持 Fyne 轻量化、跨平台核心优势，提升资源使用效率，支持 Material Design 适配与定制化开发，为轻量级 GUI 框架优化提供可复用方法论。