Stability and variability in extinction.

Some studies have found that extinction leaves response structures unaltered; others have found that response variability is increased. Responding by Long-Evans rats was extinguished after 3 schedules. In one, reinforcement depended on repetitions of a particular response sequence across 3 operanda. In another, sequences were reinforced only if they varied. In the third, reinforcement was yoked: not contingent upon repetitions or variations. In all cases, rare sequences increased during extinction— variability increased—but the ordering of sequence probabilities was generally unchanged, the most common sequences during reinforcement continuing to be most frequent in extinction. The rats' combination of generally doing what worked before but occasionally doing something very different may maximize the possibility of reinforcement from a previously bountiful source while providing necessary variations for new learning. The main goal of the present study is to reconcile two competing accounts of extinction. One is that variability increases during extinction of operant responses. Another is that operant structures, manifest in response topographies, sequences, and distributions, are unaffected by extinction and remain intact. How can structure

Creating a Data Dictionary for Pediatric Autonomic Disorders

PURPOSE: Whether evaluating patients clinically, documenting care in the electronic health record, performing research, or communicating with administrative agencies, the use of a common set of terms and definitions is vital to ensure appropriate use of language. At a 2017 meeting of the Pediatric Section of the American Autonomic Society, it was determined that an autonomic data dictionary comprising aspects of evaluation and management of pediatric patients with autonomic disorders would be an important resource for multiple stakeholders. METHODS: Our group created the list of terms for the dictionary. Definitions were prioritized to be obtained from established sources with which to harmonize. Some definitions needed mild modification from original sources. The next tier of sources included published consensus statements, followed by Internet sources. In the absence of appropriate sources, we created a definition. RESULTS: A total of 589 terms were listed and defined in the dictionary. Terms were organized by Signs/Symptoms, Triggers, Co-morbid Disorders, Family History, Medications, Medical Devices, Physical Examination Findings, Testing, and Diagnoses. CONCLUSION: Creation of this data dictionary becomes the foundation of future clinical care and investigative research in pediatric autonomic disorders, and can be used as a building block for a subsequent adult autonomic data dictionary

Robuste Bildverarbeitung für Haushalts Roboter

In this thesis we show novel techniques for the robust estimation and segmentation of indoor room structure using common 2.5d sensors, namely AIT Stereo Vision and Microsoft’s Kinect. The underlying concept of this work is the so-called Manhattan world assumption i.e. the frequently observed dominance of three mutually orthogonal vanishing directions in man-made environments. Our work emphasizes on processing speed and robustness over high-quality segmentation. Many indoor environments can be considered Manhattan-like if the furniture is aligned to the walls and the room is rectangular within limits. Our methods works in three steps: First we estimate the Manhattan world, extract features and fuse them together in a segmentation. The estimation uses three different techniques i.e. 2D vision using vanishing point detection, 3D vision using minimum entropy in histograms and normal vector MSAC estimation. All methods work efficiently and independently from each other and are robust to noise and occlusion. The feature extraction is based on the used estimators and uses geometric constrained line and plane detection. Lines are extracted using histograms and gabor filters while planes are extracted using mean shift clustering and connected component RANSAC estimators. All estimates are fused using a traditional particle filter for a coherent sensor data representation. In a last step we apply multi-label graph segmentation and extract the room structure. We also present applications like geometric constrained visual odometry and mapping. We show that our method is robust and accurate in realistic environments using our own created database. This work can be applied for indoor robot navigation, object recognition and holistic scene understanding. Our approach is not limited to AIT Stereo Vision and Microsoft’s Kinect and can be used with any 2.5d sensor like for example in Google’s Project Tango.In this thesis we show novel techniques for the robust estimation and segmentation of indoor room structure using common 2.5d sensors, namely AIT Stereo Vision and Microsoft’s Kinect. The underlying concept of this work is the so-called Manhattan world assumption i.e. the frequently observed dominance of three mutually orthogonal vanishing directions in man-made environments. Our work emphasizes on processing speed and robustness over high-quality segmentation. Many indoor environments can be considered Manhattan-like if the furniture is aligned to the walls and the room is rectangular within limits. Our methods works in three steps: First we estimate the Manhattan world, extract features and fuse them together in a segmentation. The estimation uses three different techniques i.e. 2D vision using vanishing point detection, 3D vision using minimum entropy in histograms and normal vector MSAC estimation. All methods work efficiently and independently from each other and are robust to noise and occlusion. The feature extraction is based on the used estimators and uses geometric constrained line and plane detection. Lines are extracted using histograms and gabor filters while planes are extracted using mean shift clustering and connected component RANSAC estimators. All estimates are fused using a traditional particle filter for a coherent sensor data representation. In a last step we apply multi-label graph segmentation and extract the room structure. We also present applications like geometric constrained visual odometry and mapping. We show that our method is robust and accurate in realistic environments using our own created database. This work can be applied for indoor robot navigation, object recognition and holistic scene understanding. Our approach is not limited to AIT Stereo Vision and Microsoft’s Kinect and can be used with any 2.5d sensor like for example in Google’s Project Tango

Effects Of Generic Versus Personally Delivered Education And Self-Disclosure On Elementary School Children\u27s Social Attitudes Towards A Peer With Tourette Syndrome

Children with tic disorders are at a higher risk for peer rejection and social withdrawn. Children who face peer rejection are at greater risk for internalizing, externalizing, and social problems, and often experience extreme loneliness and a lack of friendships. The impact of educational videos and self-disclosure have been examined in separate studies and have been shown to increase positive attitudes towards those with TS, but the two methods of education have not yet been directly compared to one another with a student population. In the current study, the differential effects of receiving professional TS education, self-disclosure TS education, and non-TS education was compared across measures of social acceptability and behavioral intentions. 243 school-aged children in grades four and five, enrolled in rural school districts in the upper Midwest, viewed a stimulus video of a same-aged peer either engaging in TS behavior or no TS behavior, followed by one of the education videos. Results suggested informing peers about themselves, regardless of type of self-disclosure, may increase social acceptance of children with TS compared to educational videos. The type of education did not increase positive behavioral intentions, though students may demonstrate increased positive intentions towards a new student with TS symptoms compared to a typical new student. Children with higher empathy may also be more socially accepting and welcoming to children with tic disorders. Implications of these findings and suggestions for future research are discussed

The Effect of Colored Overlays on an Individual\u27s Rate of Reading when the Font is \u27Quiet\u27 versus \u27Busy\u27

The study examined the combined effect of colored overlays and either quiet or busy type fonts upon reading rate and accuracy in 64 college-aged participants. Participants were randomly assigned to read either a quiet or busy font and asked to read two passages, once with a Iterative numerical solvers are essential in many areas of engineering. Most high performance solvers rely on lower-level programming languages for the backbone of the computation. By using newer extensions to programs like Matlab, engineers can save time and energy that would be lost to rewriting code and create more readable code that is also easier to debug. Two such extensions are examined: Star-P and the Distributed Computing Toolbox. We found that while Star-P is very easy to program, there are some applications that Star-P cannot run well. The alternative, DCT, required some knowledge of data handling, but showed better performance for each processor used. The important result is that there are always compromises made when using higher-level languages for high performance computing. Chosen overlay and once without. Overall, participants had similar rates of reading when they took the rate of reading test the first time with or without their chosen overlay. When they took the test the second time, rates of reading for those who used an overlay were faster than the rates of reading for those who did not use an overlay. However, for the busy font condition participants actually performed worse when they were using their colored overlay. Overall, the results of this study support previous research that it is the lines of the text which contribute to visual stress and reading difficulties

Never, never, never, never give up: Resilience among individuals with learning disabilities.

Never, never, never, never give up: Resilience among individuals with learning disabilities