Modeling Approaches for Addressing Simple Unrelaxable Constraints with Unconstrained Optimization Methods

We explore novel approaches for solving nonlinear optimization problems with unrelaxable bound constraints, which must be satisfied before the objective function can be evaluated. Our method reformulates the unrelaxable bound-constrained problem as an unconstrained optimization problem that is amenable to existing unconstrained optimization methods. The reformulation relies on a domain warping to form a merit function; the choice of the warping determines the level of exactness with which the unconstrained problem can be used to find solutions to the bound-constrained problem, as well as key properties of the unconstrained formulation such as smoothness. We develop theory when the domain warping is a multioutput sigmoidal warping, and we explore the practical elements of applying unconstrained optimization methods to the formulation. We develop an algorithm that exploits the structure of the sigmoidal warping to guarantee that unconstrained optimization algorithms applied to the merit function will find a stationary point to the desired tolerance.Comment: 20 pages, 5 figure

libEnsemble: A Library to Coordinate the Concurrent Evaluation of Dynamic Ensembles of Calculations

Almost all applications stop scaling at some point; those that don't are seldom performant when considering time to solution on anything but aspirational/unicorn resources. Recognizing these tradeoffs as well as greater user functionality in a near-term exascale computing era, we present libEnsemble, a library aimed at particular scalability- and capability-stretching uses. libEnsemble enables running concurrent instances of an application in dynamically allocated ensembles through an extensible Python library. We highlight the structure, execution, and capabilities of the library on leading pre-exascale environments as well as advanced capabilities for exascale environments and beyond

P2–320: Characteristics associated with willingness to participate in technology‐based behavioral clinical trials

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152609/1/alzjjalz201305968.pd

Computer mouse movement patterns: A potential marker of mild cognitive impairment

AbstractIntroductionSubtle changes in cognitively demanding activities occur in mild cognitive impairment (MCI) but are difficult to assess with conventional methods. In an exploratory study, we examined whether patterns of computer mouse movements obtained from routine home computer use discriminated between older adults with and without MCI.MethodsParticipants were 42 cognitively intact and 20 older adults with MCI enrolled in a longitudinal study of in-home monitoring technologies. Mouse pointer movement variables were computed during one week of routine home computer use using algorithms that identified and characterized mouse movements within each computer use session.ResultsMCI was associated with making significantly fewer total mouse moves (P < .01) and making mouse movements that were more variable, less efficient, and with longer pauses between movements (P < .05). Mouse movement measures were significantly associated with several cognitive domains (P values <.01–.05).DiscussionRemotely monitored computer mouse movement patterns are a potential early marker of real-world cognitive changes in MCI

Characteristics associated with willingness to participate in a randomized controlled behavioral clinical trial using home-based personal computers and a webcam

Abstract Background Trials aimed at preventing cognitive decline through cognitive stimulation among those with normal cognition or mild cognitive impairment are of significant importance in delaying the onset of dementia and reducing dementia prevalence. One challenge in these prevention trials is sample recruitment bias. Those willing to volunteer for these trials could be socially active, in relatively good health, and have high educational levels and cognitive function. These participants’ characteristics could reduce the generalizability of study results and, more importantly, mask trial effects. We developed a randomized controlled trial to examine whether conversation-based cognitive stimulation delivered through personal computers, a webcam and the internet would have a positive effect on cognitive function among older adults with normal cognition or mild cognitive impairment. To examine the selectivity of samples, we conducted a mass mail-in survey distribution among community-dwelling older adults, assessing factors associated with a willingness to participate in the trial. Methods Two thousand mail-in surveys were distributed to retirement communities in order to collect data on demographics, the nature and frequency of social activities, personal computer use and additional health-related variables, and interest in the prevention study. We also asked for their contact information if they were interested in being contacted as potential participants in the trial. Results Of 1,102 surveys returned (55.1% response rate), 983 surveys had complete data for all the variables of interest. Among them, 309 showed interest in the study and provided their contact information (operationally defined as the committed with interest group), 74 provided contact information without interest in the study (committed without interest group), 66 showed interest, but provided no contact information (interest only group), and 534 showed no interest and provided no contact information (no interest group). Compared with the no interest group, the committed with interest group were more likely to be personal computer users (odds ratio (OR) = 2.78), physically active (OR = 1.03) and had higher levels of loneliness (OR = 1.16). Conclusion Increasing potential participants’ familiarity with a personal computer and the internet before trial recruitment could increase participation rates and improve the generalizability of future studies of this type. Trial registration The trial was registered on 29 March 2012 at ClinicalTirals.gov (ID number NCT01571427 ).http://deepblue.lib.umich.edu/bitstream/2027.42/111291/1/13063_2013_Article_2385.pd

Characteristics Associated with Willingness to Participate in a Randomized Controlled Behavioral Clinical Trial Using Home-Based Personal Computers and a Webcam

BACKGROUND: Trials aimed at preventing cognitive decline through cognitive stimulation among those with normal cognition or mild cognitive impairment are of significant importance in delaying the onset of dementia and reducing dementia prevalence. One challenge in these prevention trials is sample recruitment bias. Those willing to volunteer for these trials could be socially active, in relatively good health, and have high educational levels and cognitive function. These participants\u27 characteristics could reduce the generalizability of study results and, more importantly, mask trial effects. We developed a randomized controlled trial to examine whether conversation-based cognitive stimulation delivered through personal computers, a webcam and the internet would have a positive effect on cognitive function among older adults with normal cognition or mild cognitive impairment. To examine the selectivity of samples, we conducted a mass mail-in survey distribution among community-dwelling older adults, assessing factors associated with a willingness to participate in the trial. METHODS: Two thousand mail-in surveys were distributed to retirement communities in order to collect data on demographics, the nature and frequency of social activities, personal computer use and additional health-related variables, and interest in the prevention study. We also asked for their contact information if they were interested in being contacted as potential participants in the trial. RESULTS: Of 1,102 surveys returned (55.1% response rate), 983 surveys had complete data for all the variables of interest. Among them, 309 showed interest in the study and provided their contact information (operationally defined as the committed with interest group), 74 provided contact information without interest in the study (committed without interest group), 66 showed interest, but provided no contact information (interest only group), and 534 showed no interest and provided no contact information (no interest group). Compared with the no interest group, the committed with interest group were more likely to be personal computer users (odds ratio (OR) = 2.78), physically active (OR = 1.03) and had higher levels of loneliness (OR = 1.16). CONCLUSION: Increasing potential participants\u27 familiarity with a personal computer and the internet before trial recruitment could increase participation rates and improve the generalizability of future studies of this type. TRIAL REGISTRATION: The trial was registered on 29 March 2012 at ClinicalTirals.gov (ID number NCT01571427)

Weekly observations of online survey metadata obtained through home computer use allow for detection of changes in everyday cognition before transition to mild cognitive impairment

IntroductionSubtle changes in instrumental activities of daily living often accompany the onset of mild cognitive impairment (MCI) but are difficult to measure using conventional tests.MethodsWeekly online survey metadata metrics, annual neuropsychological tests, and an instrumental activity of daily living questionnaire were examined in 110 healthy older adults with intact cognition (mean age = 85 years) followed up for up to 3.6 years; 29 transitioned to MCI during study follow‐up.ResultsIn the baseline period, incident MCI participants completed their weekly surveys 1.4 hours later in the day than stable cognitively intact participants, P = .03, d = 0.47. Significant associations were found between earlier survey start time of day and higher memory (r = −0.34; P < .001) and visuospatial test scores (r = −0.37; P < .0001). Longitudinally, incident MCI participants showed an increase in survey completion time by 3 seconds per month for more than the year before diagnosis compared with stable cognitively intact participants (β = 0.12, SE = 0.04, t = 2.8; P = .006).DiscussionWeekly online survey metadata allowed for detection of changes in everyday cognition before transition to MCI.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/152601/1/alzjjalz201707756.pd