An intelligent assistant for exploratory data analysis

In this paper we present an account of the main features of SNOUT, an intelligent assistant for exploratory data analysis (EDA) of social science survey data that incorporates a range of data mining techniques. EDA has much in common with existing data mining techniques: its main objective is to help an investigator reach an understanding of the important relationships ina data set rather than simply develop predictive models for selectd variables. Brief descriptions of a number of novel techniques developed for use in SNOUT are presented. These include heuristic variable level inference and classification, automatic category formation, the use of similarity trees to identify groups of related variables, interactive decision tree construction and model selection using a genetic algorithm

Evaluation of Methods for Estimating Time to Steady State with Examples from Phase 1 Studies

An overview is provided of the methodologies used in determining the time to steady state for Phase 1 multiple dose studies. These methods include NOSTASOT (no-statistical-significance-of-trend), Helmert contrasts, spline (quadratic) regression, effective half life for accumulation, nonlinear mixed effects modeling, and Bayesian approach using Markov Chain Monte Carlo (MCMC) methods. For each methodology we describe its advantages and disadvantages. The first two methods do not require any distributional assumptions for the pharmacokinetic (PK) parameters and are limited to average assessment of steady state. Also spline regression which provides both average and individual assessment of time to steady state does not require any distributional assumptions for the PK parameters. On the other hand, nonlinear mixed effects modeling and Bayesian hierarchical modeling which allow for the estimation of both population and subject-specific estimates of time to steady state do require distributional assumptions on PK parameters. The current investigation presents eight case studies for which the time to steady state was assessed using the above mentioned methodologies. The time to steady state estimates obtained from nonlinear mixed effects modeling, Bayesian hierarchal approach, effective half life, and spline regression were generally similar

Dengue epidemics and human mobility

In this work we explore the effects of human mobility on the dispersion of a vector borne disease. We combine an already presented stochastic model for dengue with a simple representation of the daily motion of humans on a schematic city of 20x20 blocks with 100 inhabitants in each block. The pattern of motion of the individuals is described in terms of complex networks in which links connect different blocks and the link length distribution is in accordance with recent findings on human mobility. It is shown that human mobility can turn out to be the main driving force of the disease dispersal.Comment: 24 pages, 13 figure

Further Investigation of the Time Delay, Magnification Ratios, and Variability in the Gravitational Lens 0218+357

High precision VLA flux density measurements for the lensed images of 0218+357 yield a time delay of 10.1(+1.5-1.6)days (95% confidence). This is consistent with independent measurements carried out at the same epoch (Biggs et al. 1999), lending confidence in the robustness of the time delay measurement. However, since both measurements make use of the same features in the light curves, it is possible that the effects of unmodelled processes, such as scintillation or microlensing, are biasing both time delay measurements in the same way. Our time delay estimates result in confidence intervals that are somewhat larger than those of Biggs et al., probably because we adopt a more general model of the source variability, allowing for constant and variable components. When considered in relation to the lens mass model of Biggs et al., our best-fit time delay implies a Hubble constant of H_o = 71(+17-23) km/s-Mpc for Omega_o=1 and lambda_o=0 (95% confidence; filled beam). This confidence interval for H_o does not reflect systematic error, which may be substantial, due to uncertainty in the position of the lens galaxy. We also measure the flux ratio of the variable components of 0218+357, a measurement of a small region that should more closely represent the true lens magnification ratio. We find ratios of 3.2(+0.3-0.4) (95% confidence; 8 GHz) and 4.3(+0.5-0.8) (15 GHz). Unlike the reported flux ratios on scales of 0.1", these ratios are not strongly significantly different. We investigate the significance of apparent differences in the variability properties of the two images of the background active galactic nucleus. We conclude that the differences are not significant, and that time series much longer than our 100-day time series will be required to investigate propagation effects in this way.Comment: 33 pages, 9 figures. Accepted for publication in ApJ. Light curve data may be found at http://space.mit.edu/RADIO/papers.htm

Inference with interference between units in an fMRI experiment of motor inhibition

An experimental unit is an opportunity to randomly apply or withhold a treatment. There is interference between units if the application of the treatment to one unit may also affect other units. In cognitive neuroscience, a common form of experiment presents a sequence of stimuli or requests for cognitive activity at random to each experimental subject and measures biological aspects of brain activity that follow these requests. Each subject is then many experimental units, and interference between units within an experimental subject is likely, in part because the stimuli follow one another quickly and in part because human subjects learn or become experienced or primed or bored as the experiment proceeds. We use a recent fMRI experiment concerned with the inhibition of motor activity to illustrate and further develop recently proposed methodology for inference in the presence of interference. A simulation evaluates the power of competing procedures.Comment: Published by Journal of the American Statistical Association at http://www.tandfonline.com/doi/full/10.1080/01621459.2012.655954 . R package cin (Causal Inference for Neuroscience) implementing the proposed method is freely available on CRAN at https://CRAN.R-project.org/package=ci

A geometric approach to visualization of variability in functional data

We propose a new method for the construction and visualization of boxplot-type displays for functional data. We use a recent functional data analysis framework, based on a representation of functions called square-root slope functions, to decompose observed variation in functional data into three main components: amplitude, phase, and vertical translation. We then construct separate displays for each component, using the geometry and metric of each representation space, based on a novel definition of the median, the two quartiles, and extreme observations. The outlyingness of functional data is a very complex concept. Thus, we propose to identify outliers based on any of the three main components after decomposition. We provide a variety of visualization tools for the proposed boxplot-type displays including surface plots. We evaluate the proposed method using extensive simulations and then focus our attention on three real data applications including exploratory data analysis of sea surface temperature functions, electrocardiogram functions and growth curves

An Experimental Investigation of Colonel Blotto Games

"This article examines behavior in the two-player, constant-sum Colonel Blotto game with asymmetric resources in which players maximize the expected number of battlefields won. The experimental results support all major theoretical predictions. In the auction treatment, where winning a battlefield is deterministic, disadvantaged players use a 'guerilla warfare' strategy which stochastically allocates zero resources to a subset of battlefields. Advantaged players employ a 'stochastic complete coverage' strategy, allocating random, but positive, resource levels across the battlefields. In the lottery treatment, where winning a battlefield is probabilistic, both players divide their resources equally across all battlefields." (author's abstract)"Dieser Artikel untersucht das Verhalten von Individuen in einem 'constant-sum Colonel Blotto'-Spiel zwischen zwei Spielern, bei dem die Spieler mit unterschiedlichen Ressourcen ausgestattet sind und die erwartete Anzahl gewonnener Schlachtfelder maximieren. Die experimentellen Ergebnisse bestätigen alle wichtigen theoretischen Vorhersagen. Im Durchgang, in dem wie in einer Auktion der Sieg in einem Schlachtfeld deterministisch ist, wenden die Spieler, die sich im Nachteil befinden, eine 'Guerillataktik' an, und verteilen ihre Ressourcen stochastisch auf eine Teilmenge der Schlachtfelder. Spieler mit einem Vorteil verwenden eine Strategie der 'stochastischen vollständigen Abdeckung', indem sie zufällig eine positive Ressourcenmenge auf allen Schlachtfeldern positionieren. Im Durchgang, in dem sich der Gewinn eines Schlachtfeldes probabilistisch wie in einer Lotterie bestimmt, teilen beide Spieler ihre Ressourcen gleichmäßig auf alle Schlachtfelder auf." (Autorenreferat

The NIEHS Superfund Research Program: 25 Years of Translational Research for Public Health

BACKGROUND: The Superfund Research Program (SRP) is an academically based, multidisciplinary, translational research program that for 25 years has sought scientific solutions to health and environmental problems associated with hazardous waste sites. SRP is coordinated by the National Institute of Environmental Health Sciences (NIEHS). It supports multi-project grants, undergraduate and postdoctoral training programs, individual research grants, and Small Business Innovation Research (SBIR) and Technology Transfer Research (STTR) grants. RESULTS: SRP has had many successes: discovery of arsenic\u27s toxicity to the developing human central nervous system; documentation of benzene toxicity to hematologic progenitor cells in human bone marrow; development of novel analytic techniques such as the luciferase expression assay and laser fragmentation fluorescence spectroscopy; demonstration that PCBs can cause developmental neurotoxicity at low levels and alter the genomic characteristics of sentinel animals; elucidation of the neurodevelopmental toxicity of organophosphate insecticides; documentation of links between antimicrobial agents and alterations in hormone response; discovery of biological mechanisms through which environmental chemicals may contribute to obesity, atherosclerosis, diabetes, and cancer; tracking the health and environmental effects of the attacks on the World Trade Center and Hurricane Katrina; and development of novel biological and engineering techniques to facilitate more efficient and lower-cost remediation of hazardous waste sites. CONCLUSION: SRP must continue to address the legacy of hazardous waste in the United States, respond to new issues caused by rapid advances in technology, and train the next generation of leaders in environmental health science while recognizing that most of the world\u27s worst toxic hot spots are now located in low- and middle-income countries

The Search for Invariance: Repeated Positive Testing Serves the Goals of Causal Learning

Positive testing is characteristic of exploratory behavior, yet it seems to be at odds with the aim of information seeking. After all, repeated demonstrations of one’s current hypothesis often produce the same evidence and fail to distinguish it from potential alternatives. Research on the development of scientific reasoning and adult rule learning have both documented and attempted to explain this behavior. The current chapter reviews this prior work and introduces a novel theoretical account—the Search for Invariance (SI) hypothesis—which suggests that producing multiple positive examples serves the goals of causal learning. This hypothesis draws on the interventionist framework of causal reasoning, which suggests that causal learners are concerned with the invariance of candidate hypotheses. In a probabilistic and interdependent causal world, our primary goal is to determine whether, and in what contexts, our causal hypotheses provide accurate foundations for inference and intervention—not to disconfirm their alternatives. By recognizing the central role of invariance in causal learning, the phenomenon of positive testing may be reinterpreted as a rational information-seeking strategy

Music notation: a new method for visualizing social interaction in animals and humans

BACKGROUND: Researchers have developed a variety of techniques for the visual presentation of quantitative data. These techniques can help to reveal trends and regularities that would be difficult to see if the data were left in raw form. Such techniques can be of great help in exploratory data analysis, making apparent the organization of data sets, developing new hypotheses, and in selecting effects to be tested by statistical analysis. Researchers studying social interaction in groups of animals and humans, however, have few tools to present their raw data visually, and it can be especially difficult to perceive patterns in these data. In this paper I introduce a new graphical method for the visual display of interaction records in human and animal groups, and I illustrate this method using data taken on chickens forming dominance hierarchies. RESULTS: This new method presents data in a way that can help researchers immediately to see patterns and connections in long, detailed records of interaction. I show a variety of ways in which this new technique can be used: (1) to explore trends in the formation of both group social structures and individual relationships; (2) to compare interaction records across groups of real animals and between real animals and computer-simulated animal interactions; (3) to search for and discover new types of small-scale interaction sequences; and (4) to examine how interaction patterns in larger groups might emerge from those in component subgroups. In addition, I discuss how this method can be modified and extended for visualizing a variety of different kinds of social interaction in both humans and animals. CONCLUSION: This method can help researchers develop new insights into the structure and organization of social interaction. Such insights can make it easier for researchers to explain behavioural processes, to select aspects of data for statistical analysis, to design further studies, and to formulate appropriate mathematical models and computer simulations