Sepsis is a life-threatening and serious global health issue. This study
combines knowledge with available hospital data to investigate the potential
causes of Sepsis that can be affected by policy decisions. We investigate the
underlying causal structure of this problem by combining clinical expertise
with score-based, constraint-based, and hybrid structure learning algorithms. A
novel approach to model averaging and knowledge-based constraints was
implemented to arrive at a consensus structure for causal inference. The
structure learning process highlighted the importance of exploring data-driven
approaches alongside clinical expertise. This includes discovering unexpected,
although reasonable, relationships from a clinical perspective. Hypothetical
interventions on Chronic Obstructive Pulmonary Disease, Alcohol dependence, and
Diabetes suggest that the presence of any of these risk factors in patients
increases the likelihood of Sepsis. This finding, alongside measuring the
effect of these risk factors on Sepsis, has potential policy implications.
Recognising the importance of prediction in improving Sepsis related health
outcomes, the model built is also assessed in its ability to predict Sepsis.
The predictions generated by the consensus model were assessed for their
accuracy, sensitivity, and specificity. These three indicators all had results
around 70%, and the AUC was 80%, which means the causal structure of the model
is reasonably accurate given that the models were trained on data available for
commissioning purposes only.

通过结合临床专业知识与基于评分、基于约束和混合结构学习算法，该研究研究了可能受政策决策影响的败血症潜在原因的潜在因果结构，并发现慢阻肺、酒精依赖和糖尿病等风险因素的存在会增加患者患败血症的可能性，从而对政策具有潜在影响。通过评估模型在预测败血症方面的能力，发现共识模型的预测准确度、敏感度和特异度均在 70% 左右，AUC 为 80%，说明该模型的因果结构在只使用可供委托目的的数据训练的情况下是相当准确的。

利用贝叶斯网络结构学习研究败血症潜在原因

Investigating potential causes of Sepsis with Bayesian network structure  learning

Consider public health officials aiming to spread awareness about a new
vaccine in a community interconnected by a social network. How can they
distribute information with minimal resources, ensuring community-wide
understanding that aligns with the actual facts? This concern mirrors numerous
real-world situations. In this paper, we initialize the study of sample
complexity in opinion formation to solve this problem. Our model is built on
the recognized opinion formation game, where we regard each agent's opinion as
a data-derived model parameter, not just a real number as in prior studies.
Such an extension offers a wider understanding of opinion formation and ties
closely with federated learning. Through this formulation, we characterize the
sample complexity bounds for any network and also show asymptotically tight
bounds for specific network structures. Intriguingly, we discover optimal
strategies often allocate samples inversely to the degree, hinting at vital
policy implications. Our findings are empirically validated on both synthesized
and real-world networks.

通过在社交网络中进行样本复杂度研究，该论文探讨了公共卫生官员如何以最少的资源分发信息，确保社区广泛理解与实际事实相一致，我们的发现在合成和实际网络上得到了实证验证，对政策具有重要意义。

网络上观点形成的样本复杂度

Sample Complexity of Opinion Formation on Networks

Large-scale pre-training has recently emerged as a technique for creating
capable, general purpose, generative models such as GPT-3, Megatron-Turing NLG,
Gopher, and many others. In this paper, we highlight a counterintuitive
property of such models and discuss the policy implications of this property.
Namely, these generative models have an unusual combination of predictable loss
on a broad training distribution (as embodied in their "scaling laws"), and
unpredictable specific capabilities, inputs, and outputs. We believe that the
high-level predictability and appearance of useful capabilities drives rapid
development of such models, while the unpredictable qualities make it difficult
to anticipate the consequences of model deployment. We go through examples of
how this combination can lead to socially harmful behavior with examples from
the literature and real world observations, and we also perform two novel
experiments to illustrate our point about harms from unpredictability.
Furthermore, we analyze how these conflicting properties combine to give model
developers various motivations for deploying these models, and challenges that
can hinder deployment. We conclude with a list of possible interventions the AI
community may take to increase the chance of these models having a beneficial
impact. We intend this paper to be useful to policymakers who want to
understand and regulate AI systems, technologists who care about the potential
policy impact of their work, and academics who want to analyze, critique, and
potentially develop large generative models.

通过探讨大规模训练的生成模型的特性，本文发现这些模型表现出可预测的失真以及不可预测的特性，从而提出了文献和实际观察所展示的社会有害行为的实例，并分析了这些相互冲突的特性如何导致模型的开发和部署受到各种挑战。作者提出了可能的干预措施，以便 AI 社区创造有益的影响。

大型生成模型中的可预测性与惊喜

Predictability and Surprise in Large Generative Models

The lack of predictability of citation-based measures frequently used to
gauge impact, from impact factors to short-term citations, raises a fundamental
question: Is there long-term predictability in citation patterns? Here, we
derive a mechanistic model for the citation dynamics of individual papers,
allowing us to collapse the citation histories of papers from different
journals and disciplines into a single curve, indicating that all papers tend
to follow the same universal temporal pattern. The observed patterns not only
help us uncover basic mechanisms that govern scientific impact but also offer
reliable measures of influence that may have potential policy implications.

研究论文提供一种基于机械模型的引用动态模式，具有预测科学影响力和潜在政策意义的可靠测量方式。