Travel and residual emotional well-being

This study addresses the question of how work commutes change positive versus negative and active versus passive mood experienced after the commutes. Analyses are presented for 230 time-sampled morning commutes to work, made by 146 randomly sampled people in three different Swedish cities, asking them to use smartphones to report mood before, directly after, and later in the work place after the commute. The results show that self-reported positive emotional responses evoked by critical incidents are related to mood changes directly after the commute but not later in the day. It is also shown that satisfaction with the commute, measured retrospectively, is related to travel mode, travel time, as well as both positive and negative emotional responses to critical incidents.     

Applying Deutsch’s concept of good explanations to artificial intelligence and neuroscience – An initial exploration

Artificial intelligence has made great strides since the deep learning revolution, but AI systems remain incapable of learning principles and rules which allow them to extrapolate outside of their training data to new situations. For inspiration we look to the domain of science, where scientists have been able to develop theories which show remarkable ability to extrapolate and sometimes even predict the existence of phenomena which have never been observed before. According to David Deutsch, this type of extrapolation, which he calls “reach”, is due to scientific theories being hard to vary. In this work we investigate Deutsch’s hard-to-vary principle and how it relates to more formalized principles in deep learning such as the bias-variance trade-off and Occam’s razor. We distinguish internal variability, how much a model/theory can be varied internally while still yielding the same predictions, with external variability, which is how much a model must be varied to predict new, out-of-distribution data. We discuss how to measure internal variability using the notion of the Rashomon set and how to measure external variability using Kolmogorov complexity. We explore what role hard-to-vary explanations play in intelligence by looking at the human brain, the only example of highly general purpose intelligence known. We distinguish two learning systems in the brain – the first operates similar to deep learning and likely underlies most of perception while the second is a more creative system capable of generating hard-to-vary models and explanations of the world. We make contact with Popperian epistemology which suggests that the generation of scientific theories is a not an inductive process but rather an evolutionary process which proceeds through conjecture and refutation. We argue that figuring out how replicate this second system, which is capable of generating hard-to-vary explanations, is a key challenge which needs to be solved in order to realize artificial general intelligence.     

关于疾病的预防和早期诊断的全方位思考

从扁鹊见蔡桓公的故事思考疾病预防与早期诊断治疗的意义，分析存在的主要问题，其中很大一部分源于对疾病预防和疾病的早期诊疗的重视程度不够，进而从患者、社会、医院三方面探求其解决方法。通过提高大众的防患意识，充分发挥医院在此的优势，进一步完善医保制度等方式来解决。     

5-HTTLPR基因多态性和早期养育压力对学前儿童问题行为的影响

本研究拟探讨5-HTTLPR基因多态性与母亲早期养育压力对中国汉族学前儿童问题行为的交互作用机制和性别差异。355名6个月婴儿及其父母参加了本研究。在婴儿6个月时，收集婴儿的口腔脱落细胞和母亲养育压力数据；在48个月时，再次邀请父母分别报告儿童的问题行为。结果发现：（1）当母亲养育压力较高时，s/s和s/l型男孩的外显问题行为显著高于l/l型男孩；但当母亲养育压力较低时，两组男孩没有显著差异；（2）当母亲养育压力较高时，l/l型女孩的内隐问题行为显著高于s/s和s/l型女孩；但当母亲养育压力较低时，两组女孩没有显著差异。研究结果表明，5-HTTLPR基因多态性与母亲养育压力在预测男孩外显问题行为和女孩内隐问题行为时的交互作用机制均支持素质-压力模型。