Shifted Quasi-Symmetric Functions and the Hopf algebra of peak functions

In his work on P-partitions, Stembridge defined the algebra of peak functions Pi, which is both a subalgebra and a retraction of the algebra of quasi-symmetric functions. We show that Pi is closed under coproduct, and therefore a Hopf algebra, and describe the kernel of the retraction. Billey and Haiman, in their work on Schubert polynomials, also defined a new class of quasi-symmetric functions --- shifted quasi-symmetric functions --- and we show that Pi is strictly contained in the linear span Xi of shifted quasi-symmetric functions. We show that Xi is a coalgebra, and compute the rank of the n-th graded component.Comment: 9 pages, 4 eps figures, uses epsf.sty. to be presented at FPSAC99 in Barcelona by second autho

A Type Language for Calendars

Time and calendars play an important role in databases, on the Semantic Web, as well as in mobile computing. Temporal data and calendars require (specific) modeling and processing tools. CaTTS is a type language for calendar definitions using which one can model and process temporal and calendric data. CaTTS is based on a "theory reasoning" approach for efficiency reasons. This article addresses type checking temporal and calendric data and constraints. A thesis underlying CaTTS is that types and type checking are as useful and desirable with calendric data types as with other data types. Types enable (meaningful) annotation of data. Type checking enhances efficiency and consistency of programming and modeling languages like database and Web query languages

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a \theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose \axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a "theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose "axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF

Effects of Acute and Chronic Sleep Deprivation on Eating Behavior

Sleep-deprivation is thought to be a factor in the rising American obesity epidemic. Altered sleep cycles affect appetite-regulating hormone levels and enhance the hedonic effect of food consumption. As a result, sleep restriction has been associated with an increased daily energy intake of more than 500 calories and high-carbohydrate/high-fat consumption. However, past studies have not examined the relationship between sleep deprivation and decreased intake of nutrient-dense fruits and vegetables as a factor in increased energy consumption. This paper aims to 1) determine the affect average hours of sleep or acute sleep deprivation (defined as less than 6 hour sleep per night) has on consumption of nutritious foods, measured by examining average fruit and vegetable servings overall and on sleep-deprived days; and 2) examine the effect of average hours of sleep or acute deprivation on consumption of sweet and starchy foods, defined by servings per day of French fries, potato chips, and dessert foods, such as cookies or cake, both overall and on sleep deprived days

A cross-sectional pilot study of the Scottish early development instrument : a tool for addressing inequality

Early childhood is recognised as a key developmental phase with implications for social, academic, health and wellbeing outcomes in later childhood and indeed throughout the adult lifespan. Community level data on inequalities in early child development are therefore required to establish the impact of government early years' policies and programmes on children's strengths and vulnerabilities at local and national level. This would allow local leaders to target tailored interventions according to community needs to improve children's readiness for the transition to school. The challenge is collecting valid data on sufficient samples of children entering school to derive robust inferences about each local birth cohort's developmental status. This information needs to be presented in a way that allows community stakeholders to understand the results, expediting the improvement of preschool programming to improve future cohorts' development in the early years. The aim of the study was to carry out a pilot to test the feasibility and ease of use in Scotland of the 104-item teacher-administered Early Development Instrument, an internationally validated measure of children's global development at school entry developed in Canada. Phase 1 was piloted in an education district with 14 Primary 1 teachers assessing a cohort of 154 children, following which the instrument was adapted for the Scottish context (Scottish Early Development Instrument: SEDI). Phase 2 was then carried out using the SEDI. Data were analysed from a larger sample of 1090 participants, comprising all Primary 1 children within this school district, evaluated by 68 teachers. The SEDI displayed adequate psychometric and discriminatory properties and is appropriate for use across Scotland without any further modifications. Children in the lowest socioeconomic status quintiles were 2-3 times more likely than children in the most affluent quintile to score low in at least one developmental domain. Even in the most affluent quintile though, 17% of children were 'developmentally vulnerable', suggesting that those in need cannot be identified by socioeconomic status alone. The SEDI offers a feasible means of providing communities with a holistic overview of school readiness for targeting early years' interventions

Teaching data science in school: Digital learning material on predictive text systems

Data science and especially machine learning issues are currently the subject of lively discussions in society. Many research areas now use machine learning methods, which, especially in combination with increased computer power, has led to major advances in recent years. One example is natural language processing. A large number of technologies and applications that we use every day are based on methods from this area. For example, students encounter these technologies in everyday life through the use of Siri and Alexa but also when chatting with friends they are supported by assistance systems such as predictive text systems that give suggestions for the next word. This proximity to everyday life is used to give students a motivating approach to data science concepts. In this paper we will show how mathematical modeling of data science problems can be addressed with students from tenth grade or higher using digital learning material on predictive text systems

An Empiricist’s Guide to Nonparametric Analysis in Accounting

Recent advancements in statistical packages and computing power have made various forms of nonparametric estimation accessible to empirical researchers. This study explores several of these nonparametric estimation techniques, focusing on kernel density estimates and locally weighted regression for tractability. We provide a discussion of these research design choices, including their statistical properties, limitations, and key inputs over which researchers have discretion. Then, we provide examples of these techniques, analyzing effective tax rates in the financial service industry using kernel density estimates and the relation between audit fees and size using nonparametric regression analysis. Additionally, we discuss how nonparametric techniques may be used in univariate estimates, to complement ordinary least squares regression, and in other areas in accounting

Data Note: People Served in Community Mental Health Programs and Employment

State mental health agencies provide a wide range of supports, including rehabilitation services and vocational and pre-vocational training, as well as supported and competitive employment supports. This Data Note explores how states vary in number and percentage of individuals who are employed among those served in Community Mental Health Programs (CMHPs), i.e., programs with all services provided in the community, rather than in an inpatient setting. It also explores national trends that occurred from 2002 to 2011