Statistical modeling of violin bowing parameter contours

We present a framework for modeling right-hand gestures in bowed-string instrument playing, applied to violin. Nearly non-intrusive sensing techniques allow for accurate acquisition of relevant timbre-related bowing gesture parameter cues. We model the temporal contour of bow transversal velocity, bow pressing force, and bow-bridge distance as sequences of short segments, in particular B´ezier cubic curve segments. Considering different articulations, dynamics, andcontexts, a number of note classes is defined. Gesture parameter contours of a performance database are analyzed at note-level by following a predefined grammar that dictatescharacteristics of curve segment sequences for each of the classes into consideration. Based on dynamic programming, gesture parameter contour analysis provides an optimal curve parameter vector for each note. The informationpresent in such parameter vector is enough for reconstructing original gesture parameter contours with significant fidelity. From the resulting representation vectors, weconstruct a statistical model based on Gaussian mixtures, suitable for both analysis and synthesis of bowing gesture parameter contours. We show the potential of the modelby synthesizing bowing gesture parameter contours from an annotated input score. Finally, we point out promising applicationsand developments.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, April 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, March 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Evaluation of Gap-Graded Asphalt Concrete Mixtures Part II: Statistical Design and Analysis, HR-157, 1973

This report presents the results of a comparative laboratory study between well- and gap-graded aggregates used in asphalt concrete paving mixtures. A total of 424 batches of asphalt concrete mixtures and 3, 960 Marshall and Hveem specimens were examined. The main thrust of the statistical analysis conducted in this experiment was in the calibration study and in Part I of the experiment. In the former study, the compaction procedure between the Iowa State University Lab and the Iowa Highway Commission Lab was calibrated. By an analysis of the errors associated with the measurements we were able to separate the "preparation" and "determination" errors for both laboratories as well as develop the calibration curve which describes the relationship between the compaction procedures at the two labs. In Part I, the use of a fractional factorial design in a split plot experiment in measuring the effect of several factors on asphalt concrete strength and weight was exhibited. Also, the use of half normal plotting techniques for indicating significant factors and interactions and for estimating errors in experiments with only a limited number of observations was outlined,

Statistical Summaries of Selected Iowa Streamflow Data Through September 1996, HR-395, 1998

Statistical summaries of streamflow data collected at 156 streamflow-gaging stations in Iowa are presented in this report. All gaging stations included for analysis have at least 10 years of continuous record collected before or through September 1996. The statistical summaries include (1) statistics of monthly and annual mean discharges; (2) monthly and annual flow durations; (3) magnitudes and frequencies of instantaneous peak discharges (flood frequencies); and (4) magnitudes and frequencies of high and low discharges. Also presented for each gaging station is a graph of the annual mean flows and, for most stations, selected values from the most-recent stage-discharge rating table.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, May 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Statistical mechanics of multi-edge networks

Statistical properties of binary complex networks are well understood and recently many attempts have been made to extend this knowledge to weighted ones. There are, however, subtle yet important considerations to be made regarding the nature of the weights used in this generalization. Weights can be either continuous or discrete magnitudes, and in the latter case, they can additionally have undistinguishable or distinguishable nature. This fact has not been addressed in the literature insofar and has deep implications on the network statistics. In this work we face this problem introducing multiedge networks as graphs where multiple (distinguishable) connections between nodes are considered. We develop a statistical mechanics framework where it is possible to get information about the most relevant observables given a large spectrum of linear and nonlinear constraints including those depending both on the number of multiedges per link and their binary projection. The latter case is particularly interesting as we show that binary projections can be understood from multiedge processes. The implications of these results are important as many real-agent-based problems mapped onto graphs require this treatment for a proper characterization of their collective behavior.

Development of Self-Cleaning Box Culvert Designs, TR-545, 2009

The main function of a roadway culvert is to effectively convey drainage flow during normal and extreme hydrologic conditions. This function is often impaired due to the sedimentation blockage of the culvert. This research sought to understand the mechanics of sedimentation process at multi-box culverts, and develop self-cleaning systems that flush out sediment deposits using the power of drainage flows. The research entailed field observations, laboratory experiments, and numerical simulations. The specific role of each of these investigative tools is summarized below: a) The field observations were aimed at understanding typical sedimentation patterns and their dependence on culvert geometry and hydrodynamic conditions during normal and extreme hydrologic events. b) The laboratory experiments were used for modeling sedimentation process observed insitu and for testing alternative self-cleaning concepts applied to culverts. The major tasks for the initial laboratory model study were to accurately replicate the culvert performance curves and the dynamics of sedimentation process, and to provide benchmark data for numerical simulation validation. c) The numerical simulations enhanced the understanding of the sedimentation processes and aided in testing flow cases complementary to those conducted in the model reducing the number of (more expensive) tests to be conducted in the laboratory. Using the findings acquired from the laboratory and simulation works, self-cleaning culvert concepts were developed and tested for a range of flow conditions. The screening of the alternative concepts was made through experimental studies in a 1:20 scale model guided by numerical simulations. To ensure the designs are effective, performance studies were finally conducted in a 1:20 hydraulic model using the most promising design alternatives to make sure that the proposed systems operate satisfactory under closer to natural scale conditions.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, June 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Superpave Mix Designs for Low-Volume Roads, TR-414, Final Report, 2004

Most research current to the time of these projects was focused on use of Superpave mix designs on higher volume roads. Low volume roads have different requirements in terms of mix design, aggregate types, aggregate sources and project budgets. The purpose of this research was to determine if the Superpave mix design strategy for low volume roads was practical and economical. Eight projects were selected in five counties. The projects were completed in the summer of 1998. Performance evaluation of the resulting pavements was carried out annually. There was no significant increase in costs related to the use of Superpave. Nor were there any significant construction issues. There were some differences noted in placement and compaction in the field, but these were not serious.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, July 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, August 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program

Statistical properties of population differentiation estimators under stepwise mutation in a finite island model.

Microsatellite loci mutate at an extremely high rate and are generally thought to evolve through a stepwise mutation model. Several differentiation statistics taking into account the particular mutation scheme of the microsatellite have been proposed. The most commonly used is R(ST) which is independent of the mutation rate under a generalized stepwise mutation model. F(ST) and R(ST) are commonly reported in the literature, but often differ widely. Here we compare their statistical performances using individual-based simulations of a finite island model. The simulations were run under different levels of gene flow, mutation rates, population number and sizes. In addition to the per locus statistical properties, we compare two ways of combining R(ST) over loci. Our simulations show that even under a strict stepwise mutation model, no statistic is best overall. All estimators suffer to different extents from large bias and variance. While R(ST) better reflects population differentiation in populations characterized by very low gene-exchange, F(ST) gives better estimates in cases of high levels of gene flow. The number of loci sampled (12, 24, or 96) has only a minor effect on the relative performance of the estimators under study. For all estimators there is a striking effect of the number of samples, with the differentiation estimates showing very odd distributions for two samples.

A-1 - Monthly Public Assistance Statistical Report Family Investment Program, September 2015

A-1 - Monthly Public Assistance Statistical Report Family Investment Program