Linear-time Online Action Detection From 3D Skeletal Data Using Bags of Gesturelets

Sliding window is one direct way to extend a successful recognition system to handle the more challenging detection problem. While action recognition decides only whether or not an action is present in a pre-segmented video sequence, action detection identifies the time interval where the action occurred in an unsegmented video stream. Sliding window approaches for action detection can however be slow as they maximize a classifier score over all possible sub-intervals. Even though new schemes utilize dynamic programming to speed up the search for the optimal sub-interval, they require offline processing on the whole video sequence. In this paper, we propose a novel approach for online action detection based on 3D skeleton sequences extracted from depth data. It identifies the sub-interval with the maximum classifier score in linear time. Furthermore, it is invariant to temporal scale variations and is suitable for real-time applications with low latency

A Data-Driven Method for Selecting Optimal Models Based on Graphical Visualisation of Differences in Sequentially Fitted ROC Model Parameters

Differences in modelling techniques and model performance assessments typically impinge on the quality of knowledge extraction from data. We propose an algorithm for determining optimal patterns in data by separately training and testing three decision tree models in the Pima Indians Diabetes and the Bupa Liver Disorders datasets. Model performance is assessed using ROC curves and the Youden Index. Moving differences between sequential fitted parameters are then extracted, and their respective probability density estimations are used to track their variability using an iterative graphical data visualisation technique developed for this purpose. Our results show that the proposed strategy separates the groups more robustly than the plain ROC/Youden approach, eliminates obscurity, and minimizes over-fitting. Further, the algorithm can easily be understood by non-specialists and demonstrates multi-disciplinary compliance

Magnetic suspension and balance system advanced study, 1989 design

The objectives are to experimentally confirm several advanced design concepts on the Magnetic Suspension and Balance Systems (MSBS). The advanced design concepts were identified as potential improvements by Madison Magnetics, Inc. (MMI) during 1984 and 1985 studies of an MSBS utilizing 14 external superconductive coils and a superconductive solenoid in an airplane test model suspended in a wind tunnel. This study confirmed several advanced design concepts on magnetic suspension and balance systems. The 1989 MSBS redesign is based on the results of these experiments. Savings of up to 30 percent in supporting magnet ampere meters and 50 percent in energy stored over the 1985 design were achieved

Restoration of Cervical and Lumbar Lordosis: CBP® Methods Overview

Low back and neck pain disorders are among the leading causes for work loss, suffering, and health care expenditures throughout the industrialized world. It has been extensively demonstrated that sagittal plane alignment of the cervical and lumbar spines impacts human health and well-being. Today there are reliable and predictable means through the application of extension spinal traction as part of comprehensive rehabilitation programs to restore the natural curvatures of the spine. High-quality evidence points to Chiropractic BioPhysics® (CBP®) methods offering superior long-term outcomes for treating patients with various craniocervical and lumbosacral disorders. CBP technique is a full spine and posture rehabilitation approach that incorporates mirror image® exercises, spinal and postural adjustments, and unique traction applications in the restoration of normal/ideal spinal alignment. Recent randomized controlled trials using CBP’s unique extension traction methods in conjunction with various conventional physiotherapeutic methods have demonstrated those who restore normal lordosis (cervical or lumbar) get symptomatic relief that lasts up to 2 years after treatment. Comparative groups receiving various ‘cookie-cutter’ conventional treatments experience only temporary symptomatic relief that regresses as early as 3 months after treatment. The economic impact/benefit of CBPs newer sagittal spine rehabilitation treatments demand continued attention from clinicians and researchers alike

A practical introduction to using the drift diffusion model of decision-making in cognitive psychology, neuroscience, and health sciences

Recent years have seen a rapid increase in the number of studies using evidence-accumulation models (such as the drift diffusion model, DDM) in the fields of psychology and neuroscience. These models go beyond observed behavior to extract descriptions of latent cognitive processes that have been linked to different brain substrates. Accordingly, it is important for psychology and neuroscience researchers to be able to understand published findings based on these models. However, many articles using (and explaining) these models assume that the reader already has a fairly deep understanding of (and interest in) the computational and mathematical underpinnings, which may limit many readers’ ability to understand the results and appreciate the implications. The goal of this article is therefore to provide a practical introduction to the DDM and its application to behavioral data – without requiring a deep background in mathematics or computational modeling. The article discusses the basic ideas underpinning the DDM, and explains the way that DDM results are normally presented and evaluated. It also provides a step-by-step example of how the DDM is implemented and used on an example dataset, and discusses methods for model validation and for presenting (and evaluating) model results. Supplementary material provides R code for all examples, along with the sample dataset described in the text, to allow interested readers to replicate the examples themselves. The article is primarily targeted at psychologists, neuroscientists, and health professionals with a background in experimental cognitive psychology and/or cognitive neuroscience, who are interested in understanding how DDMs are used in the literature, as well as some who may to go on to apply these approaches in their own work

Estimation of post-harvest losses of Manfalouty pomegranate fruits

Weight loss considered one of the main causes of quality loss in pomegranate fruits during chain marketing. Therefore, this study was conducted on Manfalouty pomegranate (Punica granatum L.) in a private orchard in El Badary, Assiut Governorate, Egypt in 2017 and 2018 to define the various causes of losses during chain handing and estimate it. The fruits harvested at three periods early (September) mid (October) and late season (November). The total losses at harvest were 5.94%, 9.30% and 23.50% for early, mid and late season, respectively. The main cause of losses is due to cracked and infected pests. The total loss of fruits during chain marketing was highest in retail market in comparison with wholesale during early, mid and late season. The main causes of losses due to weight loss and shrinkage fruits. According to data dealing with storage pomegranate fruits at 5±1°C and relative humidity 85-90%, the highest fruit losses found in the third month and this losses due to fruit weight loss and internal chilling injury (brown discoloration) so the storage life of fruit should be two months. DOI: http://dx.doi.org/10.5281/zenodo.405122