Hardware-aware block size tailoring on adaptive spacetree grids for shallow water waves.

Spacetrees are a popular formalism to describe dynamically adaptive Cartesian grids. Though they directly yield an adaptive spatial discretisation, i.e. a mesh, it is often more efficient to augment them by regular Cartesian blocks embedded into the spacetree leaves. This facilitates stencil kernels working efficiently on homogeneous data chunks. The choice of a proper block size, however, is delicate. While large block sizes foster simple loop parallelism, vectorisation, and lead to branch-free compute kernels, they bring along disadvantages. Large blocks restrict the granularity of adaptivity and hence increase the memory footprint and lower the numerical-accuracy-per-byte efficiency. Large block sizes also reduce the block-level concurrency that can be used for dynamic load balancing. In the present paper, we therefore propose a spacetree-block coupling that can dynamically tailor the block size to the compute characteristics. For that purpose, we allow different block sizes per spacetree node. Groups of blocks of the same size are identied automatically throughout the simulation iterations, and a predictor function triggers the replacement of these blocks by one huge, regularly rened block. This predictor can pick up hardware characteristics while the dynamic adaptivity of the fine grid mesh is not constrained. We study such characteristics with a state-of-the-art shallow water solver and examine proper block size choices on AMD Bulldozer and Intel Sandy Bridge processors

Willingness to Pay for Drug Rehabilitation: Implications for Cost Recovery

Objectives: This study estimates the value that clients place on drug rehabilitation services at the time of intake and how this value varies with the probability of success and availability of social services. Methods: We interviewed 241 heroin users who had been referred to, but had not yet entered, methadone maintenance treatment in Baltimore, Maryland. We asked each subject to state a preference among three hypothetical treatment programs that varied across 3 domains: weekly fee paid by the client out of pocket ($5 to$100), presence/absence of case management, and time spent heroin-free (3 to 24 months). Each subject was asked to complete 18 orthogonal comparisons. Subsequently each subject was asked if they likely would enroll in their preferred choice among the set of three. We computed the expected willingness to pay (WTP) as the probability of enrollment times the fee considered in each choice considered from a multivariate logistic model that controlled for product attributes. We also estimated the price elasticity of demand. Results: We found that 21% of clients preferred programs that were logically dominated by other options. The median expected fee subjects were willing to pay for a program that offered 3 months of heroin-free time was $7.30 per week, rising to$17.11 per week for programs that offered 24 months of heroin-free time. The availability of case management increased median WTP by $5.64 per week. The fee was the most important predictor of the self-reported probability of enrollment with a price elasticity of -0.39 (SE 0.042). Conclusions: Clients' median willingness to pay for drug rehabilitation fell short of the average program costs of$82 per week, which reinforces the need for continued subsidization as drug treatment has high positive externalities. Clients will pay more for higher rates of treatment success and for the presence of case management.

The Impact of School Reform Design, English Speakers of Other Languages (ESOL) Instruction and Socioeconomic Status on ESOL Students\u27 Reading Achievement

The purpose of this study was to examine how school reform design, English speakers of other languages (ESOL) instruction, and socioeconomic status (SES) impact the academic achievement of ESOL students in Grade 2. Gains in lexile scores on the Scholastic Reading Inventory were used to measure one aspect of academic achievement, namely, general reading ability. The primary research question was: To what extent can gains in lexile scores on the Scholastic Reading Inventory be explained by the independent variable set of school reform design (America\u27s Choice/Direct Instruction), ESOL instruction (ESOL instruction/no ESOL instruction), and SES (free and reduced lunch/no free lunch). Participants were 204 ESOL students enrolled in Grade 2 in Duval County Public Schools during the 2003-2004 academic year, including 53 in Direct Instruction and 151 in America\u27s Choice school reform designs; 151 receiving free and reduced lunch and 53 paying full fee for lunch; 139 receiving ESOL instruction and 65 receiving no ESOL instruction. Findings indicated that students in the Direct Instruction school reform design had greater gains in lexile scores on the SRI than students in the America\u27s Choice design. SES and ESOL instruction were not statistically significant predictors of academic achievement. Further, there were no statistically significant interactions among any of the predictor variables (between school reform design and ESOL instruction; between school reform design and SES; between SES and ESOL instruction; or among school reform design, SES, and ESOL instruction)

Functional Slicing-free Inverse Regression via Martingale Difference Divergence Operator

Functional sliced inverse regression (FSIR) is one of the most popular algorithms for functional sufficient dimension reduction (FSDR). However, the choice of slice scheme in FSIR is critical but challenging. In this paper, we propose a new method called functional slicing-free inverse regression (FSFIR) to estimate the central subspace in FSDR. FSFIR is based on the martingale difference divergence operator, which is a novel metric introduced to characterize the conditional mean independence of a functional predictor on a multivariate response. We also provide a specific convergence rate for the FSFIR estimator. Compared with existing functional sliced inverse regression methods, FSFIR does not require the selection of a slice number. Simulations demonstrate the efficiency and convenience of FSFIR

An Examination of the Relationship between Validity and Memory Measures in Retired NFL Players

Neuropsychologists have increasingly become involved in assessing sports-related concussions; however, an important concern is the validity of the evaluations. This study examined the relationship between Performance Validity Tests (PVTs) and memory measures in a comprehensive standardized battery administered to retired NFL players, with the purpose of exploring how predictive PVTs are for memory performance in this population. Hierarchical multiple regression analyses were used to evaluate the relationship between four PVTs (TOMM, MSVT, RDS, and Word Choice) and six memory tasks (WMS-IV LM I and LM II, VPA I and VPA II, VR I and VR II). A regression analysis was conducted for each memory test, for a total of six regression analyses. For each model, years played in the NFL, as well as MMPI-2-RF RCd, RC2, and RC7 scales were entered into the first block and the four PVTs were entered into the second block. Each memory subtest was entered as a dependent variable. Results yielded significant findings for each of the regression models, demonstrating that PVTs accounted for a significant amount of the variance of memory performance beyond the effects of emotional functioning, and years in the NFL. MSVT FR was found to be a significant predictor for each of the memory scales. Reliable Digit Span was a significant predictor for immediate memory subtests. Word Choice was a significant predictor for VPA II, and TOMM was a significant predictor of VR I and II. While the results demonstrated significant relationships between PVTs and memory performance, these relationships may be impacted by cognitive abilities, rather than true effort put forth on performance. This is particularly true for MSVT Free Recall, RDS, and the TOMM. Emotional functioning also appeared to impact memory performance. These results have important implications, including that PVTs may not be valid for individuals with severe cognitive impairment and that alternatives to validity testing may be necessary. Additionally, mood difficulties may exacerbate poor performance on neuropsychological testing. Overall, caution must be taken when evaluating performance on PVTs and cognitive tests in order to differentiate between genuine cognitive impairment, emotional distress, and suboptimal effort

Plug-in Performative Optimization

When predictions are performative, the choice of which predictor to deploy influences the distribution of future observations. The overarching goal in learning under performativity is to find a predictor that has low \emph{performative risk}, that is, good performance on its induced distribution. One family of solutions for optimizing the performative risk, including bandits and other derivative-free methods, is agnostic to any structure in the performative feedback, leading to exceedingly slow convergence rates. A complementary family of solutions makes use of explicit \emph{models} for the feedback, such as best-response models in strategic classification, enabling significantly faster rates. However, these rates critically rely on the feedback model being well-specified. In this work we initiate a study of the use of possibly \emph{misspecified} models in performative prediction. We study a general protocol for making use of models, called \emph{plug-in performative optimization}, and prove bounds on its excess risk. We show that plug-in performative optimization can be far more efficient than model-agnostic strategies, as long as the misspecification is not too extreme. Altogether, our results support the hypothesis that models--even if misspecified--can indeed help with learning in performative settings

Tobacco use and other predictors of successful length of stay in a faith-based substance abuse recovery center: Results of an initial assessment at one facility.

There has been debate as to whether smoking should be allowed in addiction treatment centers as part of recovery programming. A prior study at one facility assessed health promotion needs and found 80% of inpatients were smokers or tobacco users. This is four times the national average. This study assessed predictors of length of stay at a faith-based, inpatient facility in Alabama and included tobacco use as a possible predictor of success. Other potential predictors such as basic demographics, drugs of choice, intravenous drug use, parental marital status, and education levels were also tested. Among the 290 participants completing the survey (100%), 83% were males, most were white, mean age was 33 years, and ages ranged from 18-61. Eighty percent used tobacco, and cocaine use was the most common drug for which patients were under treatment. Although approximately one third of patients completed the entire 52 week program, older patients tended to stay longer in the program and those court-ordered were more likely to complete the program as well. Marijuana use predicted longer stays compared to other drugs of choice, and tobacco use was a borderline significant predictor of length of stay (p=0.05), with users less likely to stay as long. Continued tobacco use did not enhance participants’ length of stay. Modifying program delivery by taking into consideration such factors as age of patients and drugs of choice, and considering a tobacco-free policy are issues that the facility may wish to address. Further studies could include assessment of mandated tobacco cessation and its effects on successful length of stay