Cueing animations: Dynamic signaling aids information extraction and comprehension

The effectiveness of animations containing two novel forms of animation cueing that target relations between event units rather than individual entities was compared with that of animations containing conventional entity-based cueing or no cues. These relational event unit cues (progressive path and local coordinated cues) were specifically designed to support key learning processes posited by the Animation Processing Model (Lowe & Boucheix, 2008). Four groups of undergraduates (N = 84) studied a user-controllable animation of a piano mechanism and then were assessed for mental model quality (via a written comprehension test) and knowledge of the mechanism's dynamics (via a novel non-verbal manipulation test). Time-locked eye tracking was used to characterize participants' obedience to cues (initial engagement versus ongoing loyalty) across the learning period. For both output measures, participants in the two relational event unit cueing conditions were superior to those in the entity-based and uncued conditions. Time-locked eye tracking analysis of cue obedience revealed that initial cue engagement did not guarantee ongoing cue loyalty. The findings suggest that the Animation Processing Model provides a principled basis for designing more effective animation support

A Local Convergence Proof for the Minvar Algorithm for Computing Continuous Piecewise Linear Approximations

The class of continuous piecewise linear (PL) functions represents a useful family of approximants because invertibility can be readily imposed, and if a PL function is invertible, then it can be inverted in closed form. Many applications, arising, for example, in control systems and robotics, involve the simultaneous construction of a forward and inverse system model from data. Most approximation techniques require that separate forward and inverse models be trained, whereas an invertible continuous PL affords, simultaneously, the forward and inverse system model in a single representation. The minvar algorithm computes a continuous PL approximation to data. Local convergence of minvar is proven for the case when the data generating function is itself a PL function and available directly rather than through data

A Framework for the Coordination of Legged Robot Gaits

This paper introduces a framework for representing, generating, and then tuning gaits of legged robots. We introduce a convenient parametrization of gait generators as dynamical systems possessing designer specified stable limit cycles over an appropriate torus. This parametrization affords a continuous selection of operation within a coordination design plane, inspired by biology, spanned by axes that determine the mix of feedforward/feedback and centralized/decentralized control. Tuning the gait generator parameters through repeated physical experiments with our robot hexapod, RHex, determines the appropriate operating point - the mix of feedback and degree of control decentralization - to achieve significantly increased performance relative to the centralized feedforward operating point that has governed its previous behavior. The present preliminary experiments with these new gaits suggest that they may permit for the first time locomotion over extremely rough terrain that is almost as reliable, rapid, and energy efficient as the very fastest or most efficient outcomes centralized feedforward gaits can achieve on level ground

Invertible Piecewise Linear Approximations for Color Reproduction

We consider the use of linear splines with variable knots for the approximation of unknown functions from data, motivated by control and estimation problems arising in color systems management. Unlike most popular nonlinear-in-parameters representations, piecewise linear (PL) functions can be simply inverted in a closed form. For the one-dimensional case, we present a study comparing PL and neural network (NN) approximations for several function families. Preliminary results suggest that PL, in addition to their analytical benefits, are at least competitive with NN in terms of sum square error, computational effort and training time

Piecewise Linear Homeomorphisms: The Scalar Case

The class of piecewise linear homeomorphisms (PLH) provides a convenient functional representation for many applications wherein an approximation to data is required that is invertible in closed form. In this paper we introduce the graph intersection (GI) algorithm for learning piecewise linear scalar functions in two settings: approximation, where an oracle outputs accurate functional values in response to input queries; and estimation, where only a fixed discrete data base of input-output pairs is available. We provide a local convergence result for the approximation version of the GI algorithm as well as a study of its numerical performance in the estimation setting. We conclude that PLH offers accuracy closed to that of a neural net while requiring, via our GI algorithm, far shorter training time and preserving desired invariant properties unlike any other presently popular basis family

AN ANALYSIS OF CONSUMER PERCEPTIONS OF FRESH FISH AND SEAFOOD IN THE DELMARVA REGION

Consumer/Household Economics,

The Ursinus Weekly, January 10, 1938

Alumna gets post on faculty as voice teacher • College to add drama course to roster in September • WSGA appoints Ruth Roth Lorelei chairman; Alspach names Wilson to head soph hop • Quarantine follows single scarlet fever case • Frosh elect Jacobs • MSC distributes manners pamphlets to men • Seventy couples truck at Greek letter ball • Seven from Ursinus attend conference in Ohio • Leading roles chosen for Mikado • Weekly correspondent brings to light conditions in beleaguered South Hall • Error in spring vacation changes junior weekend • Vespers program presents films on Soviet Russia, Russian music by Bach accompanist • Kellett to speak at smoker of Philadelphia alumni • Freshman composer writes song for class • Mertz and Haas debate Penn over WFIL • Interfraternity council sets dates of freshman rushing parties • Santo Domingan student speaks to university women group • World conferences subject of IRC speeches • Ouderkirk takes coach\u27s place as Phys. Ed. Club sponsor • Dr. Sibbald gives tips on play judging to manuscripters • Bullets ineffective as Hashagenmen take opener 36-28 • Wrestlers open Sat. with Penn • Dormitory bright lights begin to shine • Kellett starts boys tomorrow night • Soccer crown announced at sports banquet • Sheeder finds variety, spice in Lantern • Doggie roast, barn dance planned by YM-YW • Cancellations this year double normal amounthttps://digitalcommons.ursinus.edu/weekly/1886/thumbnail.jp

Gait Generation and Optimization for Legged Robots

This paper presents a general framework for representing and generating gaitsfor legged robots. We introduce a convenient parametrization of gait generators as dynamical systems possessing specified stable limit cycles over an appropriate torus. Inspired by biology, this parametrization affords a continuous selection of operation within a coordination design plane spanned by axes that determine the mix of ”feedforward/feedback” and centralized/decentralized” control. Applying optimization to the parameterized gait generation system allowed RHex, our robotic hexapod, to learn new gaits demonstrating significant performance increases. For example, RHex can now run at 2.4m/s (up from 0.8m/s), run with a specific resistance of 0.6 (down from 2.0), climb 45◦ inclines (up from 25◦), and traverse 35◦ inclines (up from 15◦)

Modeling and Control of Color Xerographic Processes

The University of Michigan and Xerox\u27s Wilson Research Center have been collaborating on problems in color management systems since 1996, supported in part by an NSF GOALI grant. The paper is divided into three sections. The first discusses the basics of xerography and areas where systems methodology can have a potential impact. The second section describes the authors\u27 approach to the approximation of color space transformations using piecewise linear approximants and the graph intersection algorithm, with a brief review of some of the analytical and numerical results. The last section expounds on some of the benefits and difficulties of industry-university-government collaboration

Representation of Color Space Transformations for Effective Calibration and Control

We propose the minvar algorithm for computing continuous, continuously invertible, piecewise linear (PL) approximations of color space transformations that can serve as functional replacements wherever look-up tables are presently used. After motivating the importance of invertible approximants in color space management applications, we review the parameterization and computational implementation of PL functions as representing one useful instance of this notion. Finally, we describe the present version of the minvar algorithm and compare the approximations it yields with standard industrial practice — interpolation of look-up table data