Trick or treat?: uneven understanding of mind and emotion and executive dysfunction in "hard-to-manage" preschoolers

It is widely recognised that impaired social relations are characteristic of school-aged children with behavioural disorders, and predict a poor long-term outcome (Parker & Asher, 1987). However, little is known about the early antecedents of social impairment in behaviourally disturbed children. The aim of the present study was to explore three areas of potential dysfunction in younger children: theory of mind, emotion understanding, and executive function. Forty preschoolers, rated by their parents on the Strengths and Difficulties Questionnaire (Goodman, 1994) as "hard to manage" (H2M) were compared with a control group on a set of: (1) theory of mind tasks (including an emotion prediction task involving either a nice or a nasty surprise); (2) emotion understanding stories (that required affective perspective-taking skills as well as situational understanding); and (3) simple executive function tasks (adapted for preschoolers, and tapping inhibitory control, attentional set-shifting, and working memory). Small but significant group differences were found in all three cognitive domains. In particular, hard-to-manage preschoolers showed poor understanding of emotion and executive control, poor prediction or recall of a false belief, and better understanding of the belief-dependency of emotion in the context of a trick than a treat. Moreover, executive function was associated with performance on the theory of mind tasks for the hard-to-manage group alone, suggesting both direct and indirect links between executive dysfunction and disruptive behaviour.     

Differences in detection of alcohol use in a prenatal population (on a Northern Plains Indian Reservation) using various methods of ascertainment

Although Fetal Alcohol Syndrome (FAS) rates have been reported to be higher in American Indian populations, no screening tool has been validated for alcohol use in American Indian women. The objectives of this study were to compare the detection of prenatal alcohol use by a self-administered questionnaire to detection by clinical interview; and to ascertain whether the screening tool would increase detection of pregnant women who are abusing alcohol. The hospital records of the women were reviewed for any history of alcohol-related illnesses or injuries to compare with results obtained from the questionnaire. Seventy women attending their first prenatal clinic visit on a reservation were screened for alcohol use. There was a wide range in detection of prenatal alcohol use (20%-71% of the sample detected) depending on the method used. There was a large variation in sensitivities (7%-93%) of the individual questions in identifying patients detected as "high risk" by the clinicians. The T-ACE screening questions significantly increased detection of alcohol use compared to detection by the clinicians (p = 0.04 Fisher's exact test). Due to the large variation between different methods of detection, it is recommended that screening tools that increase detection of alcohol use should be combined with methods of higher specificity such as using questions about quantity and frequency of alcohol intake, medical chart review and clinical interview. We also found that various interpretations of the screening questions by the patients highlighted the need to tailor the wording of individual questions to the particular patient population.     

Segment-constrained regression tree estimation of forest stand height from very high spatial resolution panchromatic imagery over a boreal environment

Mean stand height is an important parameter for forest volume and biomass estimation in support of monitoring and management activities. Information on mean stand height is typically obtained through the manual interpretation of aerial photography, often supplemented by the collection of field calibration data. In remote areas where forest management practices may not be spatially exhaustive or where it is difficult to acquire aerial photography, alternate approaches for estimating stand height are required. One approach is to use very high spatial resolution (VHSR) satellite imagery (pixels sided less than 1 m) as a surrogate for air photos. In this research we demonstrate an approach for modelling mean stand height at four sites in the Yukon Territory, Canada, from QuickBird panchromatic imagery. An object-based approach was used to generate homogenous segments from the imagery (analogous to manually delineated forest stands) and an algorithm was used to automatically delineate individual tree crowns within the segments. A regression tree was used to predict mean stand height from stand-level metrics generated from the image grey-levels and within-stand objects relating individual tree crown characteristics. Heights were manually interpreted from the QuickBird imagery and divided into separate sets of calibration and validation data. The effects of calibration data set size and the input metrics used on the regression tree results were also assessed. The approach resulted in a model with a significant R² of 0.53 and an RMSE of 2.84 m. In addition, 84.6% of the stand height estimates were within the acceptable error for photo interpreted heights, as specified by the forest inventory standards of British Columbia. Furthermore, residual errors from the model were smallest for the stands that had larger mean heights (i.e., > 20 m), which aids in reducing error in subsequent estimates of biomass or volume (since stands with larger trees contribute more to overall estimates of volume or biomass). Estimated and manually interpreted heights were reclassified into 5-metre height classes (a schema frequently used for forest analysis and modelling applications) and compared; classes corresponded in 54% of stands assessed, and all stands had an estimated height class that was within ± 1 class of their actual class. This study demonstrates the capacity of VHSR panchromatic imagery (in this case QuickBird) for generating useful estimates of mean stand heights in unmonitored, remote, or inaccessible forest areas.     

Automated diagnosis of feature model configurations

Software product-lines (SPLs) are software platforms that can be readily reconfigured for different project requirements. A key part of an SPL is a model that captures the rules for reconfiguring the software. SPLs commonly use feature models to capture SPL configuration rules. Each SPL configuration is represented as a selection of features from the feature model. Invalid SPL configurations can be created due to feature conflicts introduced via staged or parallel configuration or changes to the constraints in a feature model. When invalid configurations are created, a method is needed to automate the diagnosis of the errors and repair the feature selections.This paper provides two contributions to research on automated configuration of SPLs. First, it shows how configurations and feature models can be transformed into constraint satisfaction problems to automatically diagnose errors and repair invalid feature selections. Second, it presents empirical results from diagnosing configuration errors in feature models ranging in size from 100 to 5,000 features. The results of our experiments show that our CSP-based diagnostic technique can scale up to models with thousands of features.     

The Malta cistern mapping project: Underwater robot mapping and localization within ancient tunnel systems

This paper documents the development of an underwater robot system enabled with several mapping and localization techniques applied to a particular archaeological expedition. The goal of the expedition was to explore and map ancient cisterns located on the islands of Malta and Gozo. The cisterns of interest acted as water storage systems for fortresses, private homes, and churches. Such cisterns often consisted of several connected chambers, still containing water. A sonar‐equipped remotely operated vehicle (ROV) was deployed into these cisterns to obtain both video footage and sonar range measurements. Six different mapping and localization techniques were employed, including (1) sonar image mosaics using stationary sonar scans, (2) sonar image mosaics using stationary sonar scans with Smart Tether position data, (3) simultaneous localization and mapping (SLAM) while the vehicle was in motion, (4) SLAM using stationary sonar scans, (5) localization using previously created maps, and (6) SLAM while the vehicle was in motion with Smart Tether position data. Top‐down‐view maps of 22 different cisterns were successfully constructed. It is estimated that the cisterns were built as far back as 300 B.C., and few records of their size, shape, and connectivity existed before the expedition. © 2010 Wiley Periodicals, Inc.     

An application of the technology acceptance model to the level of Internet usage by older adults

In this cross-sectional study, the principles of a technology acceptance model were used to identify variables related to the level of Internet usage by older adults. Community-dwelling older adults aged 60–88 years completed a postal questionnaire survey that elicited responses on the use of the Internet. Out of a sample of 592 older adults (236 males and 356 females), 50.7% used the Internet. A multiple linear regression analysis was carried out on the Internet users sample using the self-reported number of hours of Internet usage per week as the dependent variable. The results indicated that attitude toward using the Internet and good health status were statistically significant predictors of the level of Internet usage. A second multiple regression analysis using Internet activity as the dependent variable showed that attitude, usefulness, good health, and gender (males) were significant predictor variables.     

Indoor robot gardening: design and implementation

This paper describes the architecture and implementation of a distributed autonomous gardening system with applications in urban/indoor precision agriculture. The garden is a mesh network of robots and plants. The gardening robots are mobile manipulators with an eye-in-hand camera. They are capable of locating plants in the garden, watering them, and locating and grasping fruit. The plants are potted cherry tomatoes enhanced with sensors and computation to monitor their well-being (e.g. soil humidity, state of fruits) and with networking to communicate servicing requests to the robots. By embedding sensing, computation, and communication into the pots, task allocation in the system is de-centrally coordinated, which makes the system scalable and robust against the failure of a centralized agent. We describe the architecture of this system and present experimental results for navigation, object recognition, and manipulation as well as challenges that lie ahead toward autonomous precision agriculture with multi-robot teams.     

A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS

Investigating the temporal and spatial pattern of landscape disturbances is an important requirement for modeling ecosystem characteristics, including understanding changes in the terrestrial carbon cycle or mapping the quality and abundance of wildlife habitats. Data from the Landsat series of satellites have been successfully applied to map a range of biophysical vegetation parameters at a 30 m spatial resolution; the Landsat 16 day revisit cycle, however, which is often extended due to cloud cover, can be a major obstacle for monitoring short term disturbances and changes in vegetation characteristics through time.The development of data fusion techniques has helped to improve the temporal resolution of fine spatial resolution data by blending observations from sensors with differing spatial and temporal characteristics. This study introduces a new data fusion model for producing synthetic imagery and the detection of changes termed Spatial Temporal Adaptive Algorithm for mapping Reflectance Change (STAARCH). The algorithm is designed to detect changes in reflectance, denoting disturbance, using Tasseled Cap transformations of both Landsat TM/ETM and MODIS reflectance data. The algorithm has been tested over a 185 × 185 km study area in west-central Alberta, Canada. Results show that STAARCH was able to identify spatial and temporal changes in the landscape with a high level of detail. The spatial accuracy of the disturbed area was 93% when compared to the validation data set, while temporal changes in the landscape were correctly estimated for 87% to 89% of instances for the total disturbed area. The change sequence derived from STAARCH was also used to produce synthetic Landsat images for the study period for each available date of MODIS imagery. Comparison to existing Landsat observations showed that the change sequence derived from STAARCH helped to improve the prediction results when compared to previously published data fusion techniques.     

Dart Throwing on Surfaces

In this paper we present dart throwing algorithms to generate maximal Poisson disk point sets directly on 3D surfaces. We optimize dart throwing by efficiently excluding areas of the domain that are already covered by existing darts. In the case of triangle meshes, our algorithm shows dramatic speed improvement over comparable sampling methods. The simplicity of our basic algorithm naturally extends to the sampling of other surface types, including spheres, NURBS, subdivision surfaces, and implicits. We further extend the method to handle variable density points, and the placement of arbitrary ellipsoids without overlap. Finally, we demonstrate how to adapt our algorithm to work with geodesic instead of Euclidean distance. Applications for our method include fur modeling, the placement of mosaic tiles and polygon remeshing.     

A Multi‐Autonomous Underwater Vehicle System for Autonomous Tracking of Marine Life

This paper presents a multi‐autonomous underwater vehicle system capable of cooperatively and autonomously tracking and following marine targets (i.e., fish) tagged with an acoustic transmitter. The AUVs have been equipped with stereo‐hydrophones that receive signals broadcasted by the acoustic transmitter tags to enable real‐time calculation of bearing‐to‐tag and distance‐to‐tag measurements. These measurements are shared between AUVs via acoustic modem and fused within each AUV's particle filter for estimating the target's position. The AUVs use a leader/follower multi‐AUV control system to enable the AUVs to drive toward the estimated target state by following collision‐free paths. Once within the local area of the target, the AUVs circumnavigate the target state until it moves to another area. The system builds on previous work by incorporating a new SmartTag package that can be attached to an individual's dorsal fin. The SmartTag houses a full inertial measurement unit (INU), video logger, acoustic transmitter, and timed release mechanism. After real‐time AUV tracking experiments, the SmartTag is recovered. Logged IMU data are fused with logged AUV‐obtained acoustic tag measurements within a particle filter to improve state estimation accuracy. This improvement is validated through a series of multi‐AUV shark and boat tracking experiments conducted at Santa Catalina Island, California. When compared with previous work that did not use the SmartTag package, results demonstrated a decrease in mean position estimation error of 25–75%, tag orientation estimation errors dropped from 80° to 30° , the sensitivity of mean position error with respect to distance to the tag was less by a factor of 50, and the sensitivity of mean position error with respect to acoustic signal reception frequency to the tag was 25 times less. These statistics demonstrate a large improvement in the system's robustness when the SmartTag package is used.