国家预印本平台

大语言模型(Large Language Models, LLMs)的类人行为正成为心理学的研究对象之一。新兴的人工智能行为科学的发展也引发了关于以LLMs为代表的“硅基智能”如何被纳入心理学、行为科学研究的巨大争议。本研究认为，当前争议并非仅源于研究方法的差异，而更深层地体现在对LLMs类人行为解释立场的系统性分化上。本文以个体解释和预测系统行为的不同立场(物理立场、设计立场与意向立场)为理论基础，构建了一个连接研究目的与LLMs类人输出解释层级的理论分析框架，识别出工具性、功能性、类主体三类实践的方法与局限，并进一步讨论LLMs心理化实践背后的偏倚、不稳定性与拟人化等潜在风险。最后，研究从提升LLMs作为认知工具的效度、拓展多模态心理测量形式、建立LLMs与人类认知机制的可比性路径，以及发展基于LLMs主动交互心智模块的智能化软件新范型等方面，提出LLMs心理化实践的未来展望。

大气概念来自于《黄帝内经》。民国医家张锡纯认为胸中大气即宗气，全面整合了《内经》以降历代医家宗气与大气学说，并验之于临床，使其形成了完整的理法方药齐备的学术思想体系。神气即经络之气，就大气与神气关系而言，营卫之气合于心肺为胸中大气（宗气），胸中大气出于心肺，合而言之为一身之大气，分而言之为营卫之气，神气通过络脉接受卫气的护卫与温养、营气的濡养。基于中医大气与神气理论，笔者认为失眠病位在脑，乃脑神寤寐功能失常，病机与大气及神气相关。核心病机为“大气虚滞，阴神失养”，治宜“调补大气，益阴安神”。

Contrastive self-supervised learning has achieved strong performance by learning representations from multiple augmented views of the same image. However, most existing methods construct positive pairs using independently sampled stochastic augmentations, which may alter semantic content and ignore the intrinsic geometry of the data distribution. In this work, we propose OTCLR, an optimal transport-aware framework for contrastive learning representations that generates geometry-consistent positive samples. Instead of directly contrasting two randomly augmented views, we construct intermediate views between the original image and its augmented variants through entropic optimal-transport displacement interpolation. These transport-interpolated samples serve as positive views that better preserve image structure while explicitly modeling spatial distributional geometry. To further promote smooth representation learning, we evaluate auxiliary Sinkhorn regularization terms that encourage transport-interpolated views to remain consistent with their endpoint images. The proposed method can be incorporated into standard contrastive learning pipelines without modifying the encoder architecture. Experiments on multiple benchmark datasets show that our approach improves representation quality and transfer learning performance compared with conventional augmentation-based contrastive learning baselines.

Let $X$ denote the number of positive integers which are a prime plus a square. We prove that $E(X)=O(X^{4/5})$.

Long-shot windsurfing video combines small targets, large camera pans, prolonged overlaps, and rapidly changing backgrounds. The desired output is not a generic MOT trace but a separate, stable rider-relative video for each surfer; one false identity merge can invalidate an otherwise useful result. We present an offline analysis system that detects surfers, forms conservative local tracklets, links them globally with camera-compensated motion and a foreground-masked sail-color descriptor, and uses two pose keypoints on the rig to drive a rider-relative virtual camera. The tracking stage is evaluated on 21 manually reconstructed development videos containing 41,004 retained observations. On this fixed-observation protocol, the production system achieves 0.957 pairwise precision, 0.918 recall, and 0.937 F1, compared with 0.792 F1 for OC-SORT and 0.828 for BoT-SORT. Compared with OC-SORT, it reduces fragmentation excess from 845 to 42, but nine of its 95 output tracks mix rider identities and these errors affect seven of the 21 videos.