Personality, Lifetime Earnings, and Retirement Wealth

Studies of adolescents and young adults have shown that schooling impacts economic outcomes beyond its impact on cognitive ability. Research has also shown that the personality trait of conscientiousness predicts health outcomes, academic outcomes, and divorce. Using the Big Five taxonomy of personality traits, this study examines whether non-cognitive traits are related to economic success over the life course. Examining Health and Retirement Study survey data linked to Social Security records on over 10,000 adults age 50 and over, we investigate the relationship of personality traits to economic outcomes. Controlling for cognitive ability and background variables, do more conscientious and emotionally stable adults have higher lifetime earnings, and is this due to higher annual earnings, longer work lives, or both? Do more conscientious adults save a higher proportion of their earnings for retirement, and does conscientiousness of each partner in a married couple matter? Do conscientiousness and emotional stability interact such that the effects of conscientiousness are greater among less emotionally stable adults?

The MUSE Machine -- an Architecture for Structured Data Flow Computation

Computers employing some degree of data flow organisation are now well established as providing a possible vehicle for concurrent computation. Although data-driven computation frees the architecture from the constraints of the single program counter, processor and global memory, inherent in the classic von Neumann computer, there can still be problems with the unconstrained generation of fresh result tokens if a pure data flow approach is adopted. The advantages of allowing serial processing for those parts of a program which are inherently serial, and of permitting a demand-driven, as well as data-driven, mode of operation are identified and described. The MUSE machine described here is a structured architecture supporting both serial and parallel processing which allows the abstract structure of a program to be mapped onto the machine in a logical way

Personality and Response to the Financial Crisis

Social Security Administrationhttp://deepblue.lib.umich.edu/bitstream/2027.42/89939/1/wp260.pd

Translating expert system rules into Ada code with validation and verification

The purpose of this ongoing research and development program is to develop software tools which enable the rapid development, upgrading, and maintenance of embedded real-time artificial intelligence systems. The goals of this phase of the research were to investigate the feasibility of developing software tools which automatically translate expert system rules into Ada code and develop methods for performing validation and verification testing of the resultant expert system. A prototype system was demonstrated which automatically translated rules from an Air Force expert system was demonstrated which detected errors in the execution of the resultant system. The method and prototype tools for converting AI representations into Ada code by converting the rules into Ada code modules and then linking them with an Activation Framework based run-time environment to form an executable load module are discussed. This method is based upon the use of Evidence Flow Graphs which are a data flow representation for intelligent systems. The development of prototype test generation and evaluation software which was used to test the resultant code is discussed. This testing was performed automatically using Monte-Carlo techniques based upon a constraint based description of the required performance for the system

Comparison of residual salivary fluoride retention using amine fluoride toothpastes in caries-free and caries-prone children.

This was to compare the salivary fluoride levels following tooth brushing with amine fluoride toothpastes containing three different concentrations of F (250 ppm F, 500 ppm F and 1250 ppm F) and to evaluate the effect of rinsing with water on the oral fluoride levels up to 90 min.A double blind randomised six-arm crossover study was conducted with 32 child participants. Patients were divided into two groups depending on their caries experience with caries-free group (n = 17, mean age = 72.9 months) and caries-prone group (n = 15, mean age = 69.6 months, mean dmfs = 12.3). Each participant brushed their teeth with a smear of dentifrice containing (250 ppm, 500 ppm and 1250 ppm F toothpastes) for 60 s. After spitting out the dentifrice/saliva slurry, participants either rinsed with water or did not rinse at all. Samples of whole mixed unstimulated saliva were collected at 0 (baseline), 1, 15, 30, 45, 60 and 90 mins post-brushing/rinsing.After completing the study on residual fluoride concentration it was found that caries was not a significant variable (p = 0.567) while every other variable was (all p values 1000 ppm F concentration in children with an increased caries risk in addition to spitting excess toothpaste with no rinsing following brushing

A Simulator Program for Evaluating and Improving the Nottingham Muse Architecture.

This paper describes the modelling and simulation of the Nottingham MUSE (MUltiple Stream Evaluator) machine. MUSE is a data flow machine capable of supporting structured parallel computation. The simulator described in this paper was designed to enable alterations, improvements and additions to be made to the prototype MUSE architecture. The stages through which the model has progressed, and the implementation details of this model as a program, are discussed. The validation experiments are explained, and future plans for alterations and modifications to the basic model are suggested

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.Comment: 12 pages, 1 figures, 2 tables, 10th International Conference, AMT 2014, Warsaw, Poland, August 11-14, 2014. Proceeding