Optimal Lower Bounds for Projective List Update Algorithms

The list update problem is a classical online problem, with an optimal competitive ratio that is still open, known to be somewhere between 1.5 and 1.6. An algorithm with competitive ratio 1.6, the smallest known to date, is COMB, a randomized combination of BIT and the TIMESTAMP algorithm TS. This and almost all other list update algorithms, like MTF, are projective in the sense that they can be defined by looking only at any pair of list items at a time. Projectivity (also known as "list factoring") simplifies both the description of the algorithm and its analysis, and so far seems to be the only way to define a good online algorithm for lists of arbitrary length. In this paper we characterize all projective list update algorithms and show that their competitive ratio is never smaller than 1.6 in the partial cost model. Therefore, COMB is a best possible projective algorithm in this model.Comment: Version 3 same as version 2, but date in LaTeX \today macro replaced by March 8, 201

Quality of life of survivors of paediatric intensive care

Objective: The mortality rate in paediatric intensive care units (PICU) has fallen over the last two decades. More advanced treatment is offered to children with life-threatening disease and there is substantial interest in knowing whether long term outcome and quality of life after intensive care are acceptable

Health Outcomes and Cost of Care Among Older Adults with Schizophrenia: A 10-Year Study Using Medical Records across the Continuum of Care

Objectives The population of older patients with schizophrenia is increasing. This study describes health outcomes, utilization, and costs over 10 years in a sample of older patients with schizophrenia compared to older patients without schizophrenia. Design, Setting, Participants An observational cohort study of 31,588 older adults (mean age 70.44 years) receiving care from an urban public health system, including a community mental health center, during 1999–2008. 1635 (5.2%) were diagnosed with schizophrenia and 757 (2.4%) had this diagnosis confirmed in the community mental health center. Patients’ electronic medical records were merged with Medicare claims, Medicaid claims, the Minimum Dataset, and the Outcome and Assessment Information Set. Information on medication use was not available. Measurements Rates of comorbid conditions, health care utilization, costs, and mortality. Results Patients with schizophrenia had significantly higher rates of congestive heart failure (45.05% v. 38.84%), chronic obstructive pulmonary disease (52.71% v. 41.41%), and hypothyroidism (36.72% v. 26.73%) than the patients without schizophrenia (p<0.001). They had significantly lower rates of cancer (30.78% v. 43.18%) and significantly higher rates of dementia (64.46% v. 32.13%). The patients with schizophrenia had significantly higher mortality risk (HR: 1.25, CI: 1.07–1.47) than the patients without schizophrenia. They also had significantly higher rates of health care utilization. The mean costs for Medicare and Medicaid were significantly higher for the patients with schizophrenia than for the patients without schizophrenia. Conclusions The management of older adult patients with schizophrenia is creating a serious burden for our health care system, requiring the development of integrated models of health care

Investigating Genetic Algorithm Optimization Techniques in Video Games

Immersion is essential for player experience in video games. Artificial Intelligence serves as an agent that can generate human-like responses and intelligence to reinforce a player’s immersion into their environment. The most common strategy involved in video game AI is using decision trees to guide chosen actions. However, decision trees result in repetitive and robotic actions that reflect an unrealistic interaction. This experiment applies a genetic algorithm that explores selection, crossover, and mutation functions for genetic algorithm implementation in an isolated Super Mario Bros. pathfinding environment. An optimized pathfinding AI can be created by combining an elitist selection strategy with a uniform distribution crossover and minimal mutation rate

Reliable automatic model reduction combining a posteriori error analysis and a posteriori model reduction

Mathematical models are used for simulations and predictions of various phenomena and processes that can be translated into mathematical language. Nowadays, with easy access to powerful computers and specialised software, even complicated models can be handled. Model output can be generated even by users that may not have specialised mathematical training. A consequence of the increase in complexity of the models on the one hand, and the relative ease of numerical output generation on the other hand, can be a loss of insight into the mathematical model. One way to regain this is to reduce the original model to a simpler form that will nevertheless capture the key features of the model and elucidate the processes that are modelled, especially those that are of interest to the user. In this thesis I develop a model reduction algorithm that is based on a technique borrowed from finite element methods. An a posteriori error estimate, where the error is defined as the difference between the unknown solution and a FE approximation thereof, is used to ensure that the mesh on the computational domain is fine enough such that the discretised scheme approximates the differential operator sufficiently accurately. Model reduction on the other hand uses an a posteriori error estimate, where the error is defined as the difference between the solutions to the original model and a reduced model, to generate a differential operator that is a close enough approximation of the original operator. The main research chapter focuses on the mathematical analysis that this automated method entails. The model reduction technique is then applied to model problems to validate the numerical methods that are employed. An enzyme kinetic reaction model is discussed in detail as the application of the developed model reduction method satisfies the aim of this thesis to a great extent. In particular, I can show with that example that the reduced models which were automatically generated correspond to those obtained by asymptotic analysis. The same elucidation of the processes that are modelled could therefore be achieved with the automatic model reduction technique as with methods that usually need considerable mathematical expertise

Age Differences in the Association Between Body Mass Index Class and Annualized Medicare Expenditures

OBJECTIVE: The aim of the study is to assess the relationship between body mass index (BMI) class and Medicare claims among young-old (65-69), old (70-74), and old-old (75+) adults over a 10-year period. METHOD: We assessed costs by BMI class and age group among 9,300 respondents to the 1998 Health and Retirement Study (HRS) with linked 1998-2008 Medicare claims data. BMI was classified as normal (18.5-24.9), overweight (25-29.9), mild obesity (30-34.9), or severe obesity (35 or above). RESULTS: Annualized total Medicare claims adjusted for age, gender, ethnicity, education, and smoking history were 109% greater for severely obese young-old adults in comparison with normal weight young-old adults (US$9,751 vs. US$4,663). Total annualized claim differences between the normal weight and severely obese in the old and old-old groups were not statistically significant. DISCUSSION: Excess Medicare expenditures related to obesity may be concentrated among severely obese young-old adults. Preventing severe obesity among middle and older aged adults may have large cost implications for society

An experimental test of whether financial incentives constitute undue inducement in decision-making

Around the world, laws limit the incentives that can be paid for transactions such as human research participation, egg donation or gestational surrogacy. A key reason is concerns about ‘undue inducement’ - the influential but empirically untested hypothesis that incentives can cause harm by distorting individual decision-making. Here I present two experiments (n = 671 and n = 406), including one based on a highly visceral transaction (eating insects). Incentives caused biased information search - participants offered a higher incentive to comply more often sought encouragement to do so. However, I demonstrate theoretically that such behaviour does not prove that incentives have harmful effects; it is consistent with Bayesian rationality. Empirically, although a substantial minority of participants made bad decisions, incentives did not magnify them in a way that would suggest allowing a transaction but capping incentives. Under the conditions of this experiment, there was no evidence that higher incentives could undermine welfare for transactions that are permissible at low incentives

Contextualized Measurement Scale Adaptation: A 4-Step Tutorial for Health Psychology Research.

Health psychology research is inherently context specific: Different health behaviors are executed by different target groups (e.g., gender, age) in different social structures, cultures, and environments. This asks for the adaptation of research instruments to enhance specificity. For example, when using measurement scales in new contexts, translation and psychometric validation of the instruments are necessary but not sufficient if the validity of the psychological concept behind a measurement scale has not been researched. In this study, we build on existing guidelines of translation as well as psychometric validation and present four steps on how to adapt measurement scales to a new context: Step 1 asks whether the psychological concept is found in the new context. Step 2 asks whether the measurement scale and its items are understood in the new context. Step 3 asks whether a measurement scale is valid and reliable. Step 4 asks how the items of the measurement scale perform individually. Following these four steps, measurement scales are carefully translated, adapted, and validated and can therefore be transferred to very different contexts

Interpreting the will of the people: a positive analysis of ordinal preference aggregation

We investigate how individuals think groups should aggregate members’ ordinal preferences – that is, how they interpret “the will of the people.” In an experiment, we elicit revealed attitudes toward ordinal preference aggregation and classify subjects according to the rules they apparently deploy. Majoritarianism is rare. Instead, people employ rules that place greater weight on compromise options. The classification’s fit is excellent, and clustering analysis reveals that it does not omit important rules. We ask whether rules are stable across domains, whether people impute cardinal utility from ordinal ranks, and whether attitudes toward aggregation differ across countries with divergent traditions

Antihypertensive Medication and Dementia Risk in Older Adult African Americans with Hypertension: A Prospective Cohort Study

BACKGROUND: African Americans are especially at risk of hypertension and dementia. Antihypertensive medications reduce the risk of cardiovascular events, but may also reduce the risk of dementia. OBJECTIVE: To assess the longitudinal effects of antihypertensive medications and blood pressure on the onset of incident dementia in a cohort of African Americans. DESIGN: Prospective cohort. PARTICIPANTS: 1236 community-dwelling patients from an inner-city public health care system, aged 65 years and older, with a history of hypertension but no history of dementia, and who had at least three primary care visits and a prescription filled for any medication. MAIN MEASURES: Blood pressure was the average of three seated measurements. Dementia was diagnosed using a two-stage design, with a screening evaluation every 2 to 3 years followed by a comprehensive in-home clinical evaluation for those with a positive screen. Laboratory, inpatient and outpatient encounter data, coded diagnoses and procedures, and medication records were derived from a health information exchange. KEY RESULTS: Of the 1236 hypertensive participants without dementia at baseline, 114 (9%) developed incident dementia during follow-up. Individuals prescribed any antihypertensive medication (n = 816) were found to have a significantly reduced risk of dementia (HR = 0.57, 95% CI 0.37-0.88, p = 0.0114) compared to untreated hypertensive participants (n = 420). When this analysis was repeated including a variable indicating suboptimally treated blood pressure (> 140 mmHg systolic or >90 mmHg diastolic), the effect of antihypertensive medication was no longer statistically significant (HR = 0.65, 95% CI 0.32-1.30, p = 0.2217). CONCLUSIONS: Control of blood pressure in older adult African American patients with hypertension is a key intervention for preventing dementia, with similar benefits from most of the commonly available antihypertensive medications