Finger Vein Image Deblurring Using Neighbors-Based Binary-GAN (NB-GAN)

Vein contraction and venous compression typically caused by low temperature and excessive placement pressure can blur the captured finger vein images and severely impair the quality of extracted features. To improve the quality of captured finger vein image, this paper proposes a 26-layer generator network constrained by Neighbors-based Binary Patterns (NBP) texture loss to recover the clear image (guessing the original clear image). Firstly, by analyzing various types and degrees of blurred finger vein images captured in real application scenarios, a method to mathematically model the local and global blurriness using a pair of defocused and mean blur kernels is proposed. By iteratively and alternatively convoluting clear images with both kernels in a multi-scale window, a polymorphic blur training set is constructed for network training. Then, NBP texture loss is used for training the generator to enhance the deblurring ability of the network on images. Lastly, a novel network structure is proposed to retain more vein texture feature information, and two residual connections are added on both sides of the residual module of the 26-layer generator network to prevent degradation and overfitting. Theoretical analysis and simulation results show that the proposed neighbors-based binary-GAN (NB-GAN) can achieve better deblurring performance than the the-state-of-the-art approaches

“I knew where help was.” Identifying Substance Use Patterns, Associated Predictors of Harm, and Barriers to Help Seeking among Music Festival Attendees: The Development of a Targeted Harm Reduction Intervention

This thesis explores substance use among music festival attendees alongside experiences of harm, with an overarching aim to develop a novel preventative intervention rooted within harm-reduction principles. The integrative systematic literature review (Chapter 4) examined a spectrum of harm-reduction interventions targeting substance use at music festivals and similar settings. This review highlighted the lack of psychoeducational harm-reduction interventions which target attendee substance use. An online quantitative study (Chapter 5) with festival attendees (N=773) collected data about substance use during music festivals aiming to develop models of predictors associated with harm to be identified, highlighting the impact of alcohol use, and polysubstance use. The subsequent qualitative study (Chapter 6) explored the experiences of 21 frontline festival workers aiming to determine barriers to effective service delivery namely, law enforcement presence, perceived stigma, environmental factors, and a lack of education for music festival attendees. Findings from the review and the two empirical studies described above were used to create a novel, online video promoting harm-reduction through a psychoeducational format. A two-part pilot study with individuals planning festival attendance (N = 468) was conducted. Pre-intervention, data on intended substance use and behaviours were recorded. Following festival attendance post-intervention, recalled substance use was reported. Data from participants who completed both study components (N=68) supported efficacy of the intervention in reducing harm and increasing receptiveness to help-seeking. Ways to improve engagement and efficacy were also identified. This research demonstrates the potential effectiveness of a short psychoeducational intervention targeting music festivals attendees. This approach will likely benefit individuals and public health agencies, and is also economically advantageous, able to reach large numbers of people, reducing harm with low financial and resource costs. This approach now requires widescale testing to confirm its potential public health impact. An extended abstract is appended (Appendix A)

Performance Validity Assessment Of Bona Fide And Malingered Traumatic Brain Injury Using Novel Eye-Tracking Systems

Purposeful presentation of neurocognitive impairment (i.e., dissimulation) in assessment of brain injury is a primary pitfall to accurate psychological assessment, especially among individuals seeking compensation. Current methods used to evaluate effort test failure (EFT; Webb et al., 2012) and dissimulation in brain injury assessment has advanced over the past few decades, but remains unacceptably inaccurate. In diagnostic decision-making, current methods identify obvious cases of purposefully poor performance, but they are considerably less accurate in subtle cases typically seen clinically; more important, they are vulnerable to coaching. Oculomotor behavior during visual tasks may be a promising avenue in the assessment of performance validity. Oculomotor patterns observed after brain injury have been well documented, and patterns characteristic of normal decision-making have been studied in healthy adults, but findings from these endeavors have not been applied to performance validity assessment. Accordingly, this study evaluated contributions of oculomotor patterns to detection of purposeful poor performance using state-of-the-science eye-tracking equipment by studying the predictive ability of a gold-standard performance validity test: The Test of Memory Malingering (TOMM). The study examined 39 adults with moderate to severe traumatic brain injury (TBI), 42 healthy adults coached to simulate memory impairment (SIM), and 50 healthy adults providing full effort (HC). The results supported the main hypothesis: One index derived using oculomotor patterns of performance provided a reliable increase to the predicative accuracy of the TOMM in differentiating bona fide TBI from simulated TBI. Numerous other oculomotor indexes showed promise, both in their relationships to key cognitive constructs and in their ability to differentiate dissimulation from healthy adults and bona fide TBI. The predicative ability of these measures was insignificant, however, due to an underpowered sample size and violations of the assumptions of pivotal statistical models. As such, future research is needed to replicate these findings and should strive to increase sample sizes to more accurately assess those visual patterns that showed predictive potential

Dynamics of LDL accumulation leading to atherosclerosis initiation

Objective – Atherosclerosis is caused by the accumulation of LDL at atherosclerosissusceptible sites. This requires LDLs to pass through the endothelium and be retained in the arterial intima, but which process is rate-limiting and predicts atherosclerosis has remained controversial. To answer this question, we performed high-resolution mapping for LDL entry and retention in healthy, pre-atherosclerotic and atherosclerotic mouse arteries. Approach and Results – Rates of LDL entry and retention were measured by injecting fluorescent LDL into mice followed by en face scanning and whole-mount confocal microscopy of the aortic arch after 1 hour (entry) and 18 hours (retention). Measurements were performed in groups of pre-atherosclerotic mice after short-term hypercholesterolemia and in normocholesterolemic mice with a similar clearance rate of the fluorescent LDL probe. We found that rates of LDL entry and retention under normal and pre-atherosclerotic conditions are dissociated and divide the aortic arch into three zones: an outer arch zone with low LDL entry and LDL retention, and subdivisions of the inner arch region consisting of a border zone with high LDL entry and high LDL retention and a central inner arch zone with intermediate LDL entry and saturable LDL retention. These characteristics predicted susceptibility to atherosclerosis, which was low in the outer zone, high in the border zone, and intermediate in the central inner zone. Saturation of LDL retention in the central inner zone was intrinsic to the arterial wall and was lost with the onset of atherosclerosis. Conclusion – Rates of LDL entry and retention in arteries are dissociated and provide distinct information on atherosclerosis susceptibility. Combined, they predict where and when atherosclerosis develops in the mouse aortic arch.N

Thermal comfort and perceptions of the ecosystem services and disservices of urban trees in florence

Modern urban lifestyles have most likely generated a loss of awareness of the bio-cultural benefits derived from the presence of trees and forests in cities. The present study aimed at understanding the level of awareness and the ability to express significant relationships, both positive and negative, on ecosystem services and disservices by the citizens of a Mediterranean city where thermal comfort during the summer period can be particularly problematic. A questionnaire consisting of multiple-choice and open-ended questions was disseminated to citizens of Florence, Italy. The open questions allowed respondents space to describe what they perceive are the benefits and disbenefits of urban trees. Meanwhile, geospatial and climate data were processed in order to check the vegetation and microclimate conditions of the city areas where the 592 respondents live. The vast majority of respondents felt Florence is unbearably hot in summer with 93% agreeing the city needs more trees, and shaded places were perceived as the most important feature of urban green space. The results reveal many positive and negative associations to different species of trees and bring out a rich mosaic of perceptions towards urban green spaces and the features they contain. People are generally aware of a wide range of the benefits trees provide to communities and a good knowledge of the microclimate modification properties was revealed. Many of the popular public tree genera in the city, such as Tilia, Platanus and Pinus were favoured by residents however there was some overlap with trees that provoke negative experiences, and this information can be useful to city planners aiming to maximise ecosystem services and minimise ecosystem disservices

Crown-level mapping of tree species and health from remote sensing of rural and urban forests

Tree species composition and health are key attributes for rural and urban forest biodiversity, and ecosystem services preservation. Remote sensing has facilitated extraordinary advances in estimating and mapping tree species composition and health. Yet previous sensors and algorithms were largely unable to resolve individual tree crowns and discriminate tree species or health classes at this essential spatial scale due to the low image spectral and spatial resolution. However, current available very high spatial resolution (VHR) remote sensing data can begin to resolve individual tree crowns and measure their spectral and structural qualities with unprecedented precision. Moreover, various machine learning algorithms are now available to apply these new data sources toward the discrimination and the mapping of tree species and health classes. The dissertation includes an introductory chapter, three stand-alone manuscripts, and a concluding chapter, each of which support the overarching theme of mapping tree species composition and health using remote sensing images. The first manuscript, now published in the International Journal of Remote Sensing, confirms the utility of combining VHR multi-temporal satellite data with LiDAR datasets for tree species classification using machine learning classifiers at the crown level in a rural forest the Fernow Experimental Forest, West Virginia. This research also evaluates the contribution of each type of spectral, phenological and structural feature for discriminating four tree species: red oak (Quercus rubra), sugar maple (Acer saccharum), tulip poplar (Liriodendron tulipifera), and black cherry (Prunus serotina). The second manuscript investigates the performance of tree species classification in urban settings with three contributions: 1) 12 very high resolution WorldView-3 images (WV-3), whose image acquisition date covering the growing season from April to November; 2) a large forest inventory providing sufficient calibration/validation datasets in Washington D.C.; 3) object-based tree species classification using the RandomForest machine learning algorithm. This manuscript identifies the incremental losses in classification accuracy caused by iteratively expanding the classification to 19 species and 10 genera. It also identifies the optimum pheno-phases and spectral bands for discriminating trees species in urban settings. Building on these promising results from the second manuscript, the third manuscript detect a signal of statistical difference among individual tree health conditions using WorldView-3 images from June 11th, July 30th and August 30th , 2017 in Washington D.C.. It examines six vegetation indices calculated from WorldView-3 images to describe three health condition levels in good, fair and poor, and discusses the effects of green-down phenology for tree health analysis. Overall, this dissertation research contributes to remote sensing research by combining data from both active and passive sensors to discriminate tree species in rural forest. For the species-rich urban settings, this dissertation illustrates the importance of phenology for tree species classification at crown level using VHR remote sensing images. Finally, this dissertation provides important insights on detecting statistical differences among tree health conditions at individual crown-level in the urban environment using VHR remote sensing images

Application of a generative adversarial network for multi-featured fermentation data synthesis and artificial neural network (ANN) modeling of bitter gourd–grape beverage production.

Artificial neural networks (ANNs) have in recent times found increasing application in predictive modelling of various food processing operations including fermentation, as they have the ability to learn nonlinear complex relationships in high dimensional datasets, which might otherwise be outside the scope of conventional regression models. Nonetheless, a major limiting factor of ANNs is that they require quite a large amount of training data for better performance. Obtaining such an amount of data from biological processes is usually difficult for many reasons. To resolve this problem, methods are proposed to inflate existing data by artificially synthesizing additional valid data samples. In this paper, we present a generative adversarial network (GAN) able to synthesize an infinite amount of realistic multi-dimensional regression data from limited experimental data (n = 20). Rigorous testing showed that the synthesized data (n = 200) significantly conserved the variances and distribution patterns of the real data. Further, the synthetic data was used to generalize a deep neural network. The model trained on the artificial data showed a lower loss (2.029 ± 0.124) and converged to a solution faster than its counterpart trained on real data (2.1614 ± 0.117)

Improving Group Decision Making with Collaborative Brain-Computer Interfaces

Groups are generally superior to individuals in making decisions. However, time constraints and authoritarian leaders could nullify the potential advantages provided by groups. This thesis proposes a hybrid collaborative Brain-Computer Interface (cBCI) for improving performance in group decision-making. Neural signals recorded via electroencephalography are integrated with other physiological and behavioural measures to predict the likelihood of the user being correct in a decision, i.e., decision confidence. Behavioural responses from multiple users are then weighed according to these confidence estimates to obtain group decisions. The proposed cBCI has been tested with a variety of decision-making tasks, including visual matching, visual search with traditional and realistic stimuli, face recognition from multiple viewpoints, and speech perception. Groups assisted by the cBCI were significantly superior in making decisions than both individuals and traditional equally-sized groups making decisions using the majority method. This thesis also investigates the impact that a constrained form of communication has on individual and group performance in a visual-search experiment. When decision makers are able to exchange information during the experiment, their performance dramatically decreases. However, the cBCI yields superior group decisions even in this context. The confidence estimated by the cBCI is also a more reliable predictor of correctness than the confidence reported by participants after making a decision. When group members were allowed to communicate during visual search, their reported confidence was totally unrelated to the decision correctness, while in a speech perception task reported confidences were very good predictors of correctness. On the contrary, the cBCI?s confidence estimates correlated with correctness in all experiments. When critical decisions involving substantial risks have to be made (e.g., in defence), the proposed cBCI could be a useful tool to reduce the number of erroneous group decisions, thereby saving money and lives