Constructing and evaluating core inflation measures from component‐level inflation data

This paper undertakes a comprehensive examination of 10 measures of core inflation and evaluates which measure produces the best forecast of headline inflation out‐of‐sample. We use the Personal Consumption Expenditure Price Index as our measure of inflation. We use two sets of components (17 and 50) of the Personal Consumption Expenditure Price Index to construct these core inflation measures and evaluate these measures at the three time horizons (6, 12 and 24 months) most relevant for monetary policy decisions. The best measure of core inflation for both sets of components and over all time horizons uses weights based on the first principal component of the disaggregated (component‐level) prices. Interestingly, the results vary by the number of components used; when more components are used the weights based on the persistence of each component is statistically equivalent to the weights generated by the first principal component. However, those forecasts using the persistence of 50 components are statistically worse than those generated using the first principal component of 17 components. The statistical superiority of the principal component method is due to the fact that it extracts (in the first principal component) the common source of variation in the component level prices that accurately describes trend inflation over the next 6–24 months.     

Aristotle Said “Happiness is a State of Activity” — Predicting Mood Through Body Sensing with Smartwatches

We measure and predict states of Activation and Happiness using a body sensing application connected to smartwatches. Through the sensors of commercially available smartwatches we collect individual mood states and correlate them with body sensing data such as acceleration, heart rate, light level data, and location, through the GPS sensor built into the smartphone connected to the smartwatch. We polled users on the smartwatch for seven weeks four times per day asking for their mood state. We found that both Happiness and Activation are negatively correlated with heart beats and with the levels of light. People tend to be happier when they are moving more intensely and are feeling less activated during weekends. We also found that people with a lower Conscientiousness and Neuroticism and higher Agreeableness tend to be happy more frequently. In addition, more Activation can be predicted by lower Openness to experience and higher Agreeableness and Conscientiousness. Lastly, we find that tracking people’s geographical coordinates might play an important role in predicting Happiness and Activation. The methodology we propose is a first step towards building an automated mood tracking system, to be used for better teamwork and in combination with social network analysis studies.     

浅谈功率因数的补偿及其应用

根据我国当前电力系统运行情况，对电网无功功率补偿的必要性及通用补偿方法及其装置进行了具体阐述。     

我国巨型机发展概述

系统地总结和介绍了我国高性能计算机，包括“银河”系列巨型机、“曙光”系列巨型机、“神威”系列巨型机、“深腾”系列巨型机以及“深超”系列巨型机的开发研制以及应用情况。     

建筑物地基处理的设计与施工探讨

以具体工程为例，介绍了建筑物地基处理的设计、施工中遵循的原则，施工中遇到特殊情况时采取的处理方法和必要的检测方法，对几种桩基的设计要求、施工工艺及过程、质量控制等情况进行了简单阐述。     

顶凌覆盖节水增产技术研究

通过1998年、2000年的试验研究，系统分析了顶凌覆盖技术在高寒区节水增温保温、增产增收的机理及其应用效果。提出该技术可以缓解低温干旱特别是春旱对农业生产的困扰，是一项可以抗旱保春播增产增收的实用技术，在高寒地区具有较高的推广价值。