Is Dropping out of High School More Likely after Stressful Life Events?

High school dropout is typically viewed as the result of long-held vulnerabilities such as learning problems. This research brief, by PRC visiting scholars Véronique Dupéré and Eric Dion and PRC faculty research associate Robert Crosnoe and colleagues, shows that recent stressful life events can lead to a student dropping out.Population Research Cente

Enhancing Reading Comprehension Among Students With High-Functioning Autism Spectrum Disorder: A Randomized Pilot Study

Reading with comprehension is a challenge for students with high-functioning autism spectrum disorder. Unfortunately, research has little to offer to teachers trying to help these students. The present study pilots a new intervention targeting vocabulary, main idea identification, anaphoric relations, and text structure. Students (N = 13, M age = 9 years) were randomly assigned to either a control or an intervention condition. Descriptive analyses suggest that the intervention is effective; compared with their control condition peers, students in the intervention condition apparently made more progress on the vocabulary, main idea identification, and comprehension measures

Neural Network Burst Pressure Prediction in Composite Overwrapped Pressure Vessels

Acoustic emission data were collected during the hydroburst testing of eleven 15 inch diameter filament wound composite overwrapped pressure vessels. A neural network burst pressure prediction was generated from the resulting AE amplitude data. The bottles shared commonality of graphite fiber, epoxy resin, and cure time. Individual bottles varied by cure mode (rotisserie versus static oven curing), types of inflicted damage, temperature of the pressurant, and pressurization scheme. Three categorical variables were selected to represent undamaged bottles, impact damaged bottles, and bottles with lacerated hoop fibers. This categorization along with the removal of the AE data from the disbonding noise between the aluminum liner and the composite overwrap allowed the prediction of burst pressures in all three sets of bottles using a single backpropagation neural network. Here the worst case error was 3.38 percent

Utiliser le tutorat par les pairs pour favoriser l’apprentissage de la lecture en milieu défavorisé. Une pré-expérimentation avec examen des caractéristiques des non-répondants

This pilot study examines the effectiveness of peer-mediated reading instruction in first grade classrooms. One class was assigned to the control condition, and three others to the intervention condition. Students who participated in the peer-mediated activities seem to have learned to pronounce letter sounds and to read words more quickly in comparison with their control counterparts, and also demonstrated better comprehension. Students who did not make progress were inattentive and had difficulties developing a good relationship with their teacher.Keywords: Pilot study, reading, peer-tutoring, non-responder.Cette étude pilote examine l’efficacité des activités de tutorat par les pairs en lecture en classe de première année du primaire. Un groupe a été assigné à la condition contrôle, trois autres à la condition intervention. En comparaison avec leurs vis-à-vis de la condition contrôle, les élèves qui ont participé aux activités de tutorat semblent avoir appris plus rapidement à prononcer les sons des lettres et à lire les mots, en plus de démontrer une meilleure compréhension. Les élèves qui n’ont pas réalisé de progrès se démarquent par leur inattention et par leurs difficultés à établir une bonne relation avec l’enseignante.Mots-clés : Étude pilote, lecture, tutorat par les pairs, non-répondants

New Results on LMVDR Estimators for LDSS Models

In the context of linear discrete state-space (LDSS) models, we generalize a result lately introduced in the restricted case of invertible state matrices, namely that the linear minimum variance distortionless response (LMVDR) filter shares exactly the same recursion as the linear least mean squares (LLMS) filter, aka the Kalman filter (KF), except for the initialization. An immediate benefit is the introduction of LMVDR fixed-point and fixed-lag smoothers (and possibly other smoothers or predictors), which has not been possible so far. This result is particularly noteworthy given the fact that, although LMVDR estimators are sub-optimal in mean-squared error sense, they are infinite impulse response distortionless estimators which do not depend on the prior knowledge on the mean and covariance matrix of the initial state. Thus the LMVDR estimators may outperform the usual LLMS estimators in case of misspecification of the prior knowledge on the initial state. Seen from this perspective, we also show that the LMVDR filter can be regarded as a generalization of the information filter form of the KF. On another note, LMVDR estimators may also allow to derive unexpected results, as highlighted with the LMVDR fixed-point smoother

On LMVDR Estimators for LDSS Models: Conditions for Existence and Further Applications

For linear discrete state-space models, under certain conditions, the linear least mean squares (LLMS) filter estimate has a recursive format, a.k.a. the Kalman filter (KF). Interestingly, the linear minimum variance distortionless response (LMVDR) filter, when it exists, shares exactly the same recursion as the KF, except for the initialization. If LMVDR estimators are suboptimal in mean-squared error sense, they do not depend on the prior knowledge on the initial state. Thus, the LMVDR estimators may outperform the usual LLMS estimators in case of misspecification of the prior knowledge on the initial state. In this perspective, we establish the general conditions under which existence of the LMVDRF is guaranteed. An immediate benefit is the introduction of LMVDR fixed-point and fixed-lag smoothers (and possibly other smoothers or predictors), which has not been possible so far. Indeed, the LMVDR fixed-point smoother can be used to compute recursively the solution of a generalization of the deterministic least-squares problem

Minimum Variance Distortionless Response Estimators for Linear Discrete State-Space Models

For linear discrete state-space models, under certain conditions, the linear least-mean-squares filter estimate has a convenient recursive predictor/corrector format, aka the Kalman filter. The purpose of this paper is to show that the linear minimum variance distortionless response (MVDR) filter shares exactly the same recursion, except for the initialization which is based on a weighted least-squares estimator. If the MVDR filter is suboptimal in mean-squared error sense, it is an infinite impulse response distortionless filter (a deconvolver) which does not depend on the prior knowledge (first- and second-order statistics) on the initial state. In other words, the MVDR filter can be pre-computed and its behaviour can be assessed in advance independently of the prior knowledge on the initial state

Prior Medications and the Cardiovascular Benefits From Combination Angiotensin‐Converting Enzyme Inhibition Plus Calcium Channel Blockade Among High‐Risk Hypertensive Patients

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/142520/1/jah32856_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/142520/2/jah32856.pd

Spectral method for the time-dependent Gross-Pitaevskii equation with a harmonic trap

We study the numerical resolution of the time-dependent Gross-Pitaevskii equation, a non-linear Schroedinger equation used to simulate the dynamics of Bose-Einstein condensates. Considering condensates trapped in harmonic potentials, we present an efficient algorithm by making use of a spectral Galerkin method, using a basis set of harmonic oscillator functions, and the Gauss-Hermite quadrature. We apply this algorithm to the simulation of condensate breathing and scissors modes.Comment: 23 pages, 5 figure