The KAI Cognitive Style Inventory: Was it personality all along?

Kirton's Adaption-Innovation Inventory (KAI) is a widely-used measure of "cognitive style." Surprisingly, there is very little research investigating the discriminant and incremental validity of the KAI. In two studies (n = 213), we examined whether (a) we could predict KAI scores with the "big five" personality dimensions and (b) the KAI scores predicted leadership behavior when controlling for personality and ability. Correcting for measurement error, we found that KAI scores were predicted mostly by personality and gender (multiple R = 0.82). KAI scores did not predict variance in leadership while controlling for established predictors. Our findings add to recent literature that questions the uniqueness and utility of cognitive style or similar "style" constructs; researchers using such measures must control for the big five factors and correct for measurement error to avoid confounded interpretations

Many-core applications to online track reconstruction in HEP experiments

Interest in parallel architectures applied to real time selections is growing in High Energy Physics (HEP) experiments. In this paper we describe performance measurements of Graphic Processing Units (GPUs) and Intel Many Integrated Core architecture (MIC) when applied to a typical HEP online task: the selection of events based on the trajectories of charged particles. We use as benchmark a scaled-up version of the algorithm used at CDF experiment at Tevatron for online track reconstruction - the SVT algorithm - as a realistic test-case for low-latency trigger systems using new computing architectures for LHC experiment. We examine the complexity/performance trade-off in porting existing serial algorithms to many-core devices. Measurements of both data processing and data transfer latency are shown, considering different I/O strategies to/from the parallel devices.Comment: Proceedings for the 20th International Conference on Computing in High Energy and Nuclear Physics (CHEP); missing acks adde

3-D Reconstructions and Numerical Simulations of Precarious Rocks in Southern California

Reliable estimates of seismic hazard are essential for the development of resilient communities; however, estimates of rare, yet high intensity earthquakes are highly uncertain due to a lack of observations and recordings. Lacking this data, seismic hazard analyses may be based on extrapolations from earthquakes with more moderate return periods, which can lead to physically unrealistic earthquake scenarios. However, the existence of certain precariously balanced rocks (PBRs) has been identified as an indicator of an upper bound ground motion, which precludes toppling of the balanced rock, over its lifetime. To this end, a survey of PBRs was conducted in proximity to the Elsinore fault east of San Diego, CA. Each identified PBR is modeled using point clouds derived from ground-based laser scanning and images from an unmanned aerial vehicle. The resultant geometric reconstructions are then used in a probabilistic overturning analysis and compared to the anticipated seismic hazard at the site. Accounting for an estimated age range and 50% probability of overturning for the PBRs, approximately half of the surveyed PBRs indicate a potential overestimation of seismic hazard at the site

Effect of Learning to Use a Mobility Aid on Gait and Cognitive Demands in People with Mild to Moderate Alzheimer\u27s Disease: Part I - Cane

BACKGROUND: People with Alzheimer\u27s disease (AD) exhibit balance and walking impairments that increase falls risk. Prescription of a mobility aid is done to improve stability, yet also requires increased cognitive resources. Single-point canes require unique motor sequencing for safe use. The effect of learning to use a single-point cane has not been evaluated in people with AD. OBJECTIVES: In people with AD and healthy adult controls: 1) examine changes in gait while using a cane under various walking conditions; and 2) determine the cognitive and gait costs associated with concurrent cane walking while multi-tasking. METHODS: Seventeen participants with AD (age 82.1±5.6 years) and 25 healthy controls (age 70.8±14.1 years) walked using a single-point cane in a straight (6 meter) and a complex (Figure of 8) path under three conditions: single-task (no aid), dual-task (walking with aid), and multi-task (walking with aid while counting backwards by ones). Velocity and stride time variability were recorded with accelerometers. RESULTS: Gait velocity significantly slowed for both groups in all conditions and stride time variability was greater in the AD group. Overall, multi-tasking produced a decrease in gait and cognitive demands for both groups, with more people with AD self-prioritizing the cognitive task over the gait task. CONCLUSION: Learning to use a cane demands cognitive resources that lead to detrimental changes in velocity and stride time variability. This was most pronounced in people with mild to moderate AD. Future research needs to investigate the effects of mobility aid training on gait performance

Effect of Learning to Use a Mobility Aid on Gait and Cognitive Demands in People with Mild to Moderate Alzheimer\u27s Disease: Part II - 4-Wheeled Walker.

BACKGROUND: Cognitive deficits and gait problems are common and progressive in Alzheimer\u27s disease (AD). Prescription of a 4-wheeled walker is a common intervention to improve stability and independence, yet can be associated with an increased falls risk. OBJECTIVES: 1) To examine changes in spatial-temporal gait parameters while using a 4-wheeled walker under different walking conditions, and 2) to determine the cognitive and gait task costs of walking with the aid in adults with AD and healthy older adults. METHODS: Twenty participants with AD (age 79.1±7.1 years) and 22 controls (age 68.5±10.7 years) walked using a 4-wheeled walker in a straight (6 m) and Figure of 8 path under three task conditions: single-task (no aid), dual-task (walking with aid), and multi-task (walking with aid while counting backwards by ones). RESULTS: Gait velocity was statistically slower in adults with AD than the controls across all conditions (all p values CONCLUSION: Learning to use a 4-wheeled walker is cognitively demanding and any additional tasks increases the demands, further adversely affecting gait. The increased cognitive demands result in a decrease in gait velocity that is greatest in adults with AD. Future research needs to investigate the effects of mobility aid training on gait performance

The experiences of people with Alzheimer’s dementia and their caregivers in acquiring and using a mobility aid_ a qualitative study

Purpose: Cognitive deficits and gait and balance problems are progressive in people with Alzheimer’s dementia. Yet, mobility aids are associated with an increased falls risk in people with dementia. Our objectives were to identify the perceptions of people living with mild-to-moderate Alzheimer’s dementia, and their caregivers, on the use of mobility aids. Methods: A qualitative study using semi-structured, face-to-face interviews was conducted. Community-dwelling older adults with dementia attending a day hospital program were recruited. Thematic analysis was conducted and the text was coded into broad themes aligned with the research questions. The coded text was examined for patterns and similarities, and grouped to form inductive themes. Results: Twenty-four people (12 living with dementia and their 12 caregivers) participated. Five themes were identified: (1) acknowledgement of need; (2) protecting a sense of self; (3) caregiver oversight and relief of burden; (4) healthcare professional involvement; (5) environment and design of aids. Conclusions: The findings suggest that people with Alzheimer’s dementia and their caregivers regard mobility aid use as increasing independence. There is a role for healthcare professionals to be involved in the prescription, provision and training for use of mobility aids among people living with dementia to ensure uptake and safety.IMPLICATIONS FOR REHABILITATION Mobility aid use is regarded as increasing independence by people with Alzheimer’s disease and their caregivers. Falls risk associated with mobility aid use was not well known and caregivers perceived mobility aids as a means to reduce falls. Most people do not see a healthcare professional when they acquire a mobility aid and there is a role for healthcare professionals to be involved in the prescription, provision and training of people living with dementia in the use of mobility aids in order to increase the uptake of aids and their safe use

Implications of the introduction of forage chopper machines

United States Agency for International Developmen

Subsonic Boundary-Layer Wavefront Spectra for a Range of Reynolds Numbers

Aero-optic measurements of turbulent boundary layers were performed in wind tunnels at the University of Notre Dame and California Institute of Technology for heated walls at a range of Reynolds numbers. Temporally resolved measurements of wavefronts were collected at a range of Mach numbers between 0.03 and 0.4 and the range of Re_θ between 1,700 and 20,000. Wavefront spectra for both heated and un-heated walls were extracted and compared to demonstrate that wall heating does not noticeably alter the shape of wavefront spectra in the boundary layer. The effect of Reynolds number on the normalized spectra was also presented, and an empirical spectral model was modified to account for Reynolds number dependence. Measurements of OPD_(rms) for heated walls were shown to be consistent with results from prior experiments, and a method of estimating OPD_(rms) and other boundary layer statistics from wavefront measurements of heated-wall boundary layers was demonstrated and discussed

Assessing the semantic similarity of images of silk fabrics using convolutional neural networks

This paper proposes several methods for training a Convolutional Neural Network (CNN) for learning the similarity between images of silk fabrics based on multiple semantic properties of the fabrics. In the context of the EU H2020 project SILKNOW (http://silknow.eu/), two variants of training were developed, one based on a Siamese CNN and one based on a triplet architecture. We propose different definitions of similarity and different loss functions for both training strategies, some of them also allowing the use of incomplete information about the training data. We assess the quality of the trained model by using the learned image features in a k-NN classification. We achieve overall accuracies of 93-95% and average F1-scores of 87-92%. © 2020 Copernicus GmbH. All rights reserved

IMPROVED CLASSIFICATION OF SATELLITE IMAGERY USING SPATIAL FEATURE MAPS EXTRACTED FROM SOCIAL MEDIA

In this work, we consider the exploitation of social media data in the context of Remote Sensing and Spatial Information Sciences. To this end, we explore a way of augmenting and integrating information represented by geo-located feature vectors into a system for the classification of satellite images. For that purpose, we present a quite general data fusion framework based on Convolutional Neural Network (CNN) and an initial examination of our approach on features from geo-located social media postings on the Twitter and Sentinel images. For this examination, we selected six simple Twitter features derived from the metadata, which we believe could contain information for the spatial context. We present initial experiments using geotagged Twitter data from Washington DC and Sentinel images showing this area. The goal of classification is to determine local climate zones (LCZ). First, we test whether our selected feature maps are not correlated with the LCZ classification at the geo-tag position. We apply a simple boost tree classifier on this data. The result turns out not to be a mere random classifier. Therefore, this data can be correlated with LCZ. To show the improvement by our method, we compare classification with and without the Twitter feature maps. In our experiments, we apply a standard pixel-based CNN classification of the Sentinel data and use it as a baseline model. After that, we expand the input augmenting additional Twitter feature maps within the CNN and assess the contribution of these additional features to the overall F1-score of the classification, which we determine from spatial cross-validation