This study examined, within a multiple baseline design, the development of generalized imitative repertoires and the occurrence of contingency testing in children with autism. Generalized imitation refers to an imitative response class maintained by a conditioned reinforcer: similarity. In the case of imitation, a response class is established by reinforcement of sufficient exemplars of different imitations; the results is a general repertoire of imitating novel responses on their first presentation. Generalized imitation was facilitated through reinforcement procedures involving shaping and fading. Multiple experimenters and multiple settings have been involved to promote the generalization of imitation. Contingency testing, which has been noticed to naturally occur in typically developing children when imitated, was targeted in the examined treatment, by employing a procedure where the experimenter imitated the child. It was hypothesized that the imitation training leads to generalized imitation and that the imitation of the child by the therapist leads to the occurrence of contingency testing. The results of the study show that the shaping and reinforcement procedures determined an increase in participants' trained imitative responding but no corresponding increase in novel, probed imitative responses. The data indicate the emergence of an imitative repertoire in need of further training. Additionally, the results suggest that generalization of skills across therapists and settings has occurred. Imitation of the child by the therapist had been employed for a very small number of sessions. Notwithstanding, the results yielded an increase in the contingency testing behaviors for both participants.

This research study is the result of a 3 years CIFRE PhD thesis between the Airbus design office(Aircraft Control domain) and TéSA laboratory in Toulouse. The main goal is to propose, developand validate a software solution for the detection and diagnosis of a specific type of elevator andrudder vibration, called limit cycle oscillation (LCO), based on existing signals available in flightcontrol computers on board in-series aircraft. LCO is a generic mathematical term defining aninitial condition-independent periodic mode occurring in nonconservative nonlinear systems. Thisstudy focuses on the LCO phenomenon induced by mechanical freeplays in the control surface ofa civil aircraft. The LCO consequences are local structural load augmentation, flight handlingqualities deterioration, actuator operational life reduction, cockpit and cabin comfort deteriorationand maintenance cost augmentation. The state-of-the-art for freeplay induced LCO detection anddiagnosis is based on the pilot sensitivity to vibration and to periodic freeplay check on the controlsurfaces. This study is thought to propose a data-driven solution to help LCO and freeplaydiagnosis. The goal is to improve even more aircraft availability and reduce the maintenance costsby providing to the airlines a condition monitoring signal for LCO and freeplays. For this reason,two algorithmic solutions for vibration and freeplay diagnosis are investigated in this PhD thesis. Areal time detector for LCO diagnosis is first proposed based on the theory of the generalized likeli hood ratio test (GLRT). Some variants and simplifications are also proposed to be compliantwith the industrial constraints. In a second part of this work, a mechanical freeplay detector isintroduced based on the theory of Wiener model identification. Parametric (maximum likelihoodestimator) and non parametric (kernel regression) approaches are investigated, as well as somevariants to well-known nonparametric methods. In particular, the problem of hysteresis cycleestimation (as the output nonlinearity of a Wiener model) is tackled. Moreover, the constrainedand unconstrained problems are studied. A theoretical, numerical (simulator) and experimental(flight data and laboratory) analysis is carried out to investigate the performance of the proposeddetectors and to identify limitations and industrial feasibility. The obtained numerical andexperimental results confirm that the proposed GLR test (and its variants/simplifications) is a very appealing method for LCO diagnostic in terms of performance, robustness and computationalcost. On the other hand, the proposed freeplay diagnostic algorithm is able to detect relativelylarge freeplay levels, but it does not provide consistent results for relatively small freeplay levels. Moreover, specific input types are needed to guarantee repetitive and consistent results. Further studies should be carried out in order to compare the GLRT results with a Bayesian approach and to investigate more deeply the possibilities and limitations of the proposed parametric method for Wiener model identification.

The primary objective of the study is to evaluate safety performances of different exit ramps used in Florida and nationally. More specific, the research objectives include the following two parts: (1) to evaluate the impacts of different exit ramp types on safety performance for freeway diverge areas; and (2) to identify the different factors contributing to the crashes happening on the exit ramp sections. To achieve the research objectives, the research team investigated crash history at 424 sites throughout Florida. The study area includes two parts, the freeway diverge area and the exit ramp sections. For the freeway diverge areas, exit ramp types were defined based on the number of lanes used by vehicular traffic to exit freeways. Four exit ramp types were considered here including single-lane exit ramps (Type 1), sing-lane exit ramps without a taper (Type 2), two-lane exit ramps with an optional lane (Type 3), and two-lane exit ramps without an optional lane (Type 4). For the exit ramp sections, four ramp configurations, including diamond, out connection, free-flow loop and parclo loop, were considered. Cross-sectional comparisons were conducted to compare crash frequency, crash rate, crash severity and crash types between different exit ramp groups. Crash predictive models were also built to quantify the impacts of various contributing factors. On the freeway diverge areas, it shows that Type 1 exit ramp has the best safety performance in terms of the lowest crash frequency and crash rate. The crash prediction model shows that for one-lane exit ramp, replacing a Type 1 with a Type 2 will increase crash counts at freeway diverge areas by 15.57% while replacing a Type 3 with a Type 4 will increase crash counts by 10.80% for two-lane ramps. On the exit ramp sections, the out connection ramps appear to have the lowest average crash rate than the other three. The crash predictive model shows that replacing an out connection exit ramp with a diamond, free-flow, and parclo loop will increase crashes counts by 26.90%, 68.47% and 48.72% respectively. The results of this study will help transportation decision makers develop tailored technical guidelines governing the selection of the optimum design combinations on freeway diverge areas and exit ramp sections.

Abstract not available

Multi-instance (MI) learning is a variant of supervised machine learning, where each learning example contains a bag of instances instead of just a single feature vector. MI learning has applications in areas such as drug activity prediction, fruit disease management and image classification. This thesis investigates the case where each instance has a weight value determining the level of influence that it has on its bag's class label. This is a more general assumption than most existing approaches use, and thus is more widely applicable. The challenge is to accurately estimate these weights in order to make predictions at the bag level. An existing approach known as MILES is retroactively identified as an algorithm that uses instance weights for MI learning, and is evaluated using a variety of base learners on benchmark problems. New algorithms for learning instance weights for MI learning are also proposed and rigorously evaluated on both artificial and real-world datasets. The new algorithms are shown to achieve better root mean squared error rates than existing approaches on artificial data generated according to the algorithms' underlying assumptions. Experimental results also demonstrate that the new algorithms are competitive with existing approaches on real-world problems.

This thesis is aimed at analysis of sampled-data feedback systems. Our approach is in the frequency-domain, and stresses the study of sensitivity and complementary sensitivity operators. Frequency-domain methods have proven very successful in the analysis and design of linear time-invariant control systems, for which the importance and utility of sensitivity operators is well-recognized. The extension of these methods to sampled-data systems, however, is not straightforward, since they are inherently time-varying due to the intrinsic sample and hold operations. In this thesis we present a systematic frequency-domain framework to describe sampled-data systems considering full-time information. Using this framework, we develop a theory of design limitations for sampled-data systems. This theory allows us to quantify the essential constraints in design imposed by inherent open-loop characteristics of the analog plant. Our results show that: (i) sampled-data systems inherit the difficulty imposed upon analog feedback design by the plant's non-minimum phase zeros, unstable poles, and time-delays, independently of the type of hold used; (ii) sampled-data systems are subject to additional design limitations imposed by potential non-minimum phase zeros of the hold device; and (iii) sampled-data systems, unlike analog systems, are subject to limits upon the ability of high compensator gain to achieve disturbance rejection. As an application, we quantitatively analyze the sensitivity and robustness characteristics of digital control schemes that rely on the use of generalized sampled-data hold functions, whose frequency-response properties we describe in detail. In addition, we derive closed-form expressions to compute the L2-induced norms of the sampled-data sensitivity and complementary sensitivity operators. These expressions are important both in analysis and design, particularly when uncertainty in the model of the plant is considered. Our methods provide some interesting interpretations in terms of signal spaces, and admit straightforward implementation in a numerically reliable fashion. / PhD Doctorate

The temporal and spatial variability in wind speed and direction was investigated in a study of the mesoscale wind fields in the Canberra region. The statistical description of the winds was based on twelve months of three-hourly data at seventeen sites obtained in a joint program carried out by the Division of Land Use Research, CSIRO, the National Capital Development Commission, and the Bureau of Meteorology. The statistical analysis proceeded in stages. The first two stages were concerned with the determination and examination of averages and measures of dipersion. Information on the temporal variability in regional wind, defined as the average of the winds at the seventeen collection sites, provided the first insight into the important determinants of winds in the region. The data were then categorized on the basis of the information thus obtained, and the averages over time for each site were analysed in each category. The variation between sites revealed the extent of the spatial variability in the region. For each category, for each site, there were perturbations around the average state, and in the last stage of the study, the analysis examined how the perturbations were related across sites using correlation coefficients. Generalized Procrustes Analysis was used, followed by the extensive use of cluster analysis. Linear modelling techniques were used at all stages of the study, not only for wind speed, but also for wind direction which is an angular variate and thus required different modelling procedures. The models related the variables of interest to terrain features such as position, elevation and surface roughness. These models allowed an informed judgement to be made on the likelihood of accurately estimating the winds at other locations in the region using interpolation techniques.

<p>In this thesis a method for solving the Generalized Assignment Problem (GAP) is described. It is based on a reformulation of the original problem into a Set Partitioning Problem (SPP), in which the columns represent partial solutions to the original problem. For solving this problem, column generation, with systematic overgeneration of columns, is used. Conditions that guarantee that an optimal solution to a restricted SPP is optimal also in the original problem are given. In order to satisfy these conditions, not only columns with the most negative Lagrangian reduced costs need to be generated, but also others; this observation leads to the use of overgeneration of columns.</p><p>The Generalized Assignment Problem has shown to be NP-hard and therefore efficient algorithms are needed, especially for large problems. The application of the proposed method decomposes GAP into several knapsack problems via Lagrangian relaxation, and enumerates solutions to each of these problems. The solutions obtained from the knapsack problems form a Set Partitioning Problem, which consists of combining one solution from each knapsack problem to obtain a solution to the original problem. The algorithm has been tested on problems with 10 agents and 60 jobs. This leads to 10 knapsack problems, each with 60 variables.</p>

No description available.

The generalized theory of thermoelasticity was employed to characterize the coupled thermal and mechanical wave propagation in high performance microelectronic packages. Application of a Gaussian heat source of spectral profile similar to high performance devices was shown to induce rapid thermal and mechanical transient phenomena. The stresses and temporal gradient of stresses (power density) induced by the thermal and mechanical disturbances were analyzed using the Gabor Wavelet Transform (GWT). The arrival time of frequency components and their magnitude was studied at various locations in the package. Comparison of the results from the classical thermoelasticity theory and generalized theory was also conducted. It was found that the two theories predict vastly different results in the vicinity of the heat source but that the differences diminish within a larger time window. Results from both theories indicate that the rapid thermal-mechanical waves cause high frequency, broadband stress waves to propagate through the package for a very short period of time. The power density associated with these stress waves was found to be of significant magnitude indicating that even though the effect, titled short time scale effect, is short lived, it could have significant impact on package reliability. The high frequency and high power density associated with the stress waves indicate that the probability of sub-micron cracking and/or delamination due to short time scale effect is high. The findings demonstrate that in processes involving rapid thermal transients, there is a non-negligible transient phenomenon worthy of further investigation.