Using social robots to study abnormal social development

Social robots recognize and respond to human social cues with appropriate behaviors. Social robots, and the technology used in their construction, can be unique tools in the study of abnormal social development. Autism is a pervasive developmental disorder that is characterized by social and communicative impairments. Based on three years of integration and immersion with a clinical research group which performs more than 130 diagnostic evaluations of children for autism per year, this paper discusses how social robots will make an impact on the ways in which we diagnose, treat, and understand autism

An integrated remote sensing approach for identifying ecological range sites

A model approach for identifying ecological range sites was applied to high elevation sagebrush-dominated rangelands on Parker Mountain, in south-central Utah. The approach utilizes map information derived from both high altitude color infrared photography and LANDSAT digital data, integrated with soils, geological, and precipitation maps. Identification of the ecological range site for a given area requires an evaluation of all relevant environmental factors which combine to give that site the potential to produce characteristic types and amounts of vegetation. A table is presented which allows the user to determine ecological range site based upon an integrated use of the maps which were prepared. The advantages of identifying ecological range sites through an integrated photo interpretation/LANDSAT analysis are discussed

Veterinary student competence in equine lameness recognition and assessment: a mixed methods study

The development of perceptual skills is an important aspect of veterinary education. The authors investigated veterinary student competency in lameness evaluation at two stages, before (third year) and during (fourth/fifth year) clinical rotations. Students evaluated horses in videos, where horses were presented during trot on a straight line and in circles. Eye-tracking data were recorded during assessment on the straight line to follow student gaze. On completing the task, students filled in a structured questionnaire. Results showed that the experienced students outperformed inexperienced students, although even experienced students may classify one in four horses incorrectly. Mistakes largely arose from classifying an incorrect limb as lame. The correct detection of sound horses was at chance level. While the experienced student cohort primarily looked at upper body movement (head and sacrum) during lameness assessment, the inexperienced cohort focused on limb movement. Student self-assessment of performance was realistic, and task difficulty was most commonly rated between 3 and 4 out of 5. The inexperienced students named a considerably greater number of visual lameness features than the experienced students. Future dedicated training based on the findings presented here may help students to develop more reliable lameness assessment skills

Nested Partially-Latent Class Models for Dependent Binary Data; Estimating Disease Etiology

The Pneumonia Etiology Research for Child Health (PERCH) study seeks to use modern measurement technology to infer the causes of pneumonia for which gold-standard evidence is unavailable. The paper describes a latent variable model designed to infer from case-control data the etiology distribution for the population of cases, and for an individual case given his or her measurements. We assume each observation is drawn from a mixture model for which each component represents one cause or disease class. The model addresses a major limitation of the traditional latent class approach by taking account of residual dependence among multivariate binary outcome given disease class, hence reduces estimation bias, retains efficiency and offers more valid inference. Such "local dependence" on a single subject is induced in the model by nesting latent subclasses within each disease class. Measurement precision and covariation can be estimated using the control sample for whom the class is known. In a Bayesian framework, we use stick-breaking priors on the subclass indicators for model-averaged inference across different numbers of subclasses. Assessment of model fit and individual diagnosis are done using posterior samples drawn by Gibbs sampling. We demonstrate the utility of the method on simulated and on the motivating PERCH data.Comment: 30 pages with 5 figures and 1 table; 1 appendix with 4 figures and 1 tabl

Signature extension using transformed cluster statistics and related techniques

There are no author-identified significant results in this report

Use of computer-aided detection (CAD) tools in screening mammography: a multidisciplinary investigation

We summarise a set of analyses and studies conducted to assess the effects of the use of a computer-aided detection (CAD) tool in breast screening. We have used an interdisciplinary approach that combines: (a) statistical analyses inspired by reliability modelling in engineering; (b) experimental studies of decisions of mammography experts using the tool, interpreted in the light of human factors psychology; and (c) ethnographic observations of the use of the tool both in trial conditions and in everyday screening practice. Our investigations have shown patterns of human behaviour and effects of computer-based advice that would not have been revealed by a standard clinical trial approach. For example, we found that the negligible measured effect of CAD could be explained by a range of effects on experts' decisions, beneficial in some cases and detrimental in others. There is some evidence of the latter effects being due to the experts using the computer tool differently from the intentions of the developers. We integrate insights from the different pieces of evidence and highlight their implications for the design, evaluation and deployment of this sort of computer tool

Combining forecasts based on multiple encompassing tests in a macroeconomic core system

Copyright © 2010 John Wiley & Sons, Ltd. This is the accepted version of the following article: Costantini, M. and Kunst, R. M. (2011), Combining forecasts based on multiple encompassing tests in a macroeconomic core system. J. Forecast., 30: 579–596, which has been published in final form at http://onlinelibrary.wiley.com/doi/10.1002/for.1190/abstract.This paper investigates whether and to what extent multiple encompassing tests may help determine weights for forecast averaging in a standard vector autoregressive setting. To this end we consider a new test-based procedure, which assigns non-zero weights to candidate models that add information not covered by other models. The potential benefits of this procedure are explored in extensive Monte Carlo simulations using realistic designs that are adapted to UK and to French macroeconomic data, to which trivariate vector autoregressions (VAR) are fitted. Thus simulations rely on potential data-generating mechanisms for macroeconomic data rather than on simple but artificial designs. We run two types of forecast ‘competitions’. In the first one, one of the model classes is the trivariate VAR, such that it contains the generating mechanism. In the second specification, none of the competing models contains the true structure. The simulation results show that the performance of test-based averaging is comparable to uniform weighting of individual models. In one of our role model economies, test-based averaging achieves advantages in small samples. In larger samples, pure prediction models outperform forecast averages

Cone-Beam Computed Tomography Accuracy for Morphological and Morphometric Evaluation of Mandibular Condyles Using Small FOV and Small Voxel Size

  The objective of this study is to evaluate the accuracy of cone beam computed tomography (CBCT) in determining and visualizing the morphology and morphometry of the mandibular condyle. Narrative reviews with article searches were carried out through NCBI's PubMed database and Scopus from September 2021–October 2021, with the inclusion criteria articles published in 2011–2021.  The temporomandibular joint (TMJ) has a crucial role and is closely related to the masticatory system. The diagnosis of temporomandibular disorder (TMD) is not easy and is complex enough to require a comprehensive clinical and radiographic examination. Pathological changes such as erosion of the condyle, fracture, ankylosis, dislocation, and osteophyte can be well seen using CBCT imaging. CBCT images obtained with smaller field of view (FOV) have smaller a voxel size and a higher image resolution. FOV or scan volume refers to the anatomical area that will be included in the data volume or the area of the patient that will be irradiated. The dimension of FOV depends on the detector size and shape, the beam projection geometry, and the ability to collimate the beam. Voxel size is an important component of image quality, related to both the pixel size and the image matrix. Selection of small FOV and small voxel size is recommended because they provide better visualization and detail for the evaluation of morphology and morphometry of the condyle, especially the detection of erosion and defects on the condyle surface