92,496 research outputs found
Evidence of instability in previously-mapped landslides as measured using GPS, optical, and SAR data between 2007 and 2017: A case study in the Portuguese Bend Landslide Complex, California
Velocity dictates the destructive potential of a landslide. A combination of synthetic aperture radar (SAR), optical, and GPS data were used to maximize spatial and temporal coverage to monitor continuously-moving portions of the Portuguese Bend landslide complex on the Palos Verdes Peninsula in Southern California. Forty SAR images from the COSMO-SkyMed satellite, acquired between 19 July 2012 and 27 September 2014, were processed using Persistent Scatterer Interferometry (PSI). Eight optical images from the WorldView-2 satellite, acquired between 20 February 2011 and 16 February 2016, were processed using the Co-registration of Optically Sensed Images and Correlation (COSI-Corr) technique. Displacement measurements were taken at GPS monuments between September 2007 and May 2017. Incremental and average deformations across the landslide complex were measured using all three techniques. Velocity measured within the landslide complex ranges from slow (\u3e 1.6 m/year) to extremely slow (\u3c 16 mm/year). COSI-Corr and GPS provide detailed coverage of m/year-scale deformation while PSI can measure extremely slow deformation rates (mm/year-scale), which COSI-Corr and GPS cannot do reliably. This case study demonstrates the applicability of SAR, optical, and GPS data synthesis as a complimentary approach to repeat field monitoring and mapping to changes in landslide activity through time
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Risk as reward: Reinforcement sensitivity theory and psychopathic personality perspectives on everyday risk-taking
This study updates and synthesises research on the extent to which impulsive and antisocial disposition predicts everyday pro- and antisocial risk-taking behaviour. We use the Reinforcement Sensitivity Theory (RST) of personality to measure approach, avoidance, and inhibition dispositions, as well as measures of Callous-Unemotional and psychopathic personalities. In an international sample of 454 respondents, results showed that RST, psychopathic personality, and callous-unemotional measures accounted for different aspects of risk-taking behaviour. Specifically, traits associated with âfearlessnessâ related more to âprosocialâ (recreational and social) risk-taking, whilst traits associated with âimpulsivityâ related more to âantisocialâ (ethical and health) risk-taking. Further, we demonstrate that psychopathic personality may be demonstrated by combining the RST and callous-unemotional traits (high impulsivity, callousness, and low fear). Overall this study showed how impulsive, fearless and antisocial traits can be used in combination to identify pro- and anti-social risk-taking behaviours; suggestions for future research are indicated
Towards lightweight convolutional neural networks for object detection
We propose model with larger spatial size of feature maps and evaluate it on
object detection task. With the goal to choose the best feature extraction
network for our model we compare several popular lightweight networks. After
that we conduct a set of experiments with channels reduction algorithms in
order to accelerate execution. Our vehicle detection models are accurate, fast
and therefore suit for embedded visual applications. With only 1.5 GFLOPs our
best model gives 93.39 AP on validation subset of challenging DETRAC dataset.
The smallest of our models is the first to achieve real-time inference speed on
CPU with reasonable accuracy drop to 91.43 AP.Comment: Submitted to the International Workshop on Traffic and Street
Surveillance for Safety and Security (IWT4S) in conjunction with the 14th
IEEE International Conference on Advanced Video and Signal based Surveillance
(AVSS 2017
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What motivates academic dishonesty in students? A reinforcement sensitivity theory explanation
BACKGROUND: Academic dishonesty (AD) is an increasing challenge for universities worldwide. The rise of the Internet has further increased opportunities for students to cheat.
AIMS: In this study, we investigate the role of personality traits defined within Reinforcement Sensitivity Theory (RST) as potential determinants of AD. RST defines behaviour as resulting from approach (Reward Interest/reactivity, goal-drive, and Impulsivity) and avoidance (behavioural inhibition and Fight-Flight-Freeze) motivations. We further consider the role of deep, surface, or achieving study motivations in mediating/moderating the relationship between personality and AD.
SAMPLE: A sample of UK undergraduates (NÂ =Â 240).
METHOD: All participants completed the RST Personality Questionnaire, a short-form version of the study process questionnaire and a measure of engagement in AD, its perceived prevalence, and seriousness.
RESULTS: Results showed that RST traits account for additional variance in AD. Mediation analysis suggested that GDP predicted dishonesty indirectly via a surface study approach while the indirect effect via deep study processes suggested dishonesty was not likely. Likelihood of engagement in AD was positively associated with personality traits reflecting Impulsivity and Fight-Flight-Freeze behaviours. Surface study motivation moderated the Impulsivity effect and achieving motivation the FFFS effect such that cheating was even more likely when high levels of these processes were used.
CONCLUSIONS: The findings suggest that motivational personality traits defined within RST can explain variance in the likelihood of engaging in dishonest academic behaviours
Sim-to-Real Transfer of Robotic Control with Dynamics Randomization
Simulations are attractive environments for training agents as they provide
an abundant source of data and alleviate certain safety concerns during the
training process. But the behaviours developed by agents in simulation are
often specific to the characteristics of the simulator. Due to modeling error,
strategies that are successful in simulation may not transfer to their real
world counterparts. In this paper, we demonstrate a simple method to bridge
this "reality gap". By randomizing the dynamics of the simulator during
training, we are able to develop policies that are capable of adapting to very
different dynamics, including ones that differ significantly from the dynamics
on which the policies were trained. This adaptivity enables the policies to
generalize to the dynamics of the real world without any training on the
physical system. Our approach is demonstrated on an object pushing task using a
robotic arm. Despite being trained exclusively in simulation, our policies are
able to maintain a similar level of performance when deployed on a real robot,
reliably moving an object to a desired location from random initial
configurations. We explore the impact of various design decisions and show that
the resulting policies are robust to significant calibration error
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