Randomized clinical trials, while often viewed as the highest evidentiary bar
by which to judge the quality of a medical intervention, are far from perfect.
In silico imaging trials are computational studies that seek to ascertain the
performance of a medical device by collecting this information entirely via
computer simulations. The benefits of in silico trials for evaluating new
technology include significant resource and time savings, minimization of
subject risk, the ability to study devices that are not achievable in the
physical world, allow for the rapid and effective investigation of new
technologies and ensure representation from all relevant subgroups. To conduct
in silico trials, digital representations of humans are needed. We review the
latest developments in methods and tools for obtaining digital humans for in
silico imaging studies. First, we introduce terminology and a classification of
digital human models. Second, we survey available methodologies for generating
digital humans with healthy and diseased status and examine briefly the role of
augmentation methods. Finally, we discuss the trade-offs of four approaches for
sampling digital cohorts and the associated potential for study bias with
selecting specific patient distributions.

通过计算机模拟收集信息对医疗设备性能进行评估的计算研究在评估新技术方面具有显著的资源和时间节约优势，需要数字化人体模型来进行，本文回顾了获取数字人体的最新方法和工具的发展。

随机数字人现已开始招募进行体外成像试验 —— 生成数字队列的方法和工具

The stochastic digital human is now enrolling for in silico imaging trials -- Methods and tools for generating digital cohorts

Self-disclosed mental health diagnoses, which serve as ground truth
annotations of mental health status in the absence of clinical measures,
underpin the conclusions behind most computational studies of mental health
language from the last decade. However, psychiatric conditions are dynamic; a
prior depression diagnosis may no longer be indicative of an individual's
mental health, either due to treatment or other mitigating factors. We ask: to
what extent are self-disclosures of mental health diagnoses actually relevant
over time? We analyze recent activity from individuals who disclosed a
depression diagnosis on social media over five years ago and, in turn, acquire
a new understanding of how presentations of mental health status on social
media manifest longitudinally. We also provide expanded evidence for the
presence of personality-related biases in datasets curated using self-disclosed
diagnoses. Our findings motivate three practical recommendations for improving
mental health datasets curated using self-disclosed diagnoses: 1) Annotate
diagnosis dates and psychiatric comorbidities; 2) Sample control groups using
propensity score matching; 3) Identify and remove spurious correlations
introduced by selection bias.

通过分析社交媒体上五年前披露的抑郁症诊断者的最近活动，我们得出了关于社交媒体上心理健康状况的纵向表现的新理解，并提出了三个实际建议，以改善使用自我披露诊断构建的精神健康数据集的特定问题。

过去和现在：自我披露抑郁症诊断的纵向有效性的量化

Then and Now: Quantifying the Longitudinal Validity of Self-Disclosed Depression Diagnoses

We present an n-ary constraint for the stable marriage problem. This
constraint acts between two sets of integer variables where the domains of
those variables represent preferences. Our constraint enforces stability and
disallows bigamy. For a stable marriage instance with $n$ men and $n$ women we
require only one of these constraints, and the complexity of enforcing
arc-consistency is $O(n^2)$ which is optimal in the size of input. Our
computational studies show that our n-ary constraint is significantly faster
and more space efficient than the encodings presented in \cite{cp01}. We also
introduce a new problem to the constraint community, the sex-equal stable
marriage problem.

论文介绍了一种针对稳定婚姻问题的 n 元约束，其能够保持稳定且禁止重婚，在处理 n 个男人和 n 个女人的稳定婚姻实例时，只需要一个该约束，并且强制执行弧一致性的复杂性是 O (n^2)，其计算研究表明该 n-ary constraint 比文献中的编码更快且更节省空间，同时提出了一个新的约束问题，即性别平等的稳定婚姻问题。