Distinctive-attribute Extraction for Image Captioning

Image captioning, an open research issue, has been evolved with the progress of deep neural networks. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are employed to compute image features and generate natural language descriptions in the research. In previous works, a caption involving semantic description can be generated by applying additional information into the RNNs. In this approach, we propose a distinctive-attribute extraction (DaE) which explicitly encourages significant meanings to generate an accurate caption describing the overall meaning of the image with their unique situation. Specifically, the captions of training images are analyzed by term frequency-inverse document frequency (TF-IDF), and the analyzed semantic information is trained to extract distinctive-attributes for inferring captions. The proposed scheme is evaluated on a challenge data, and it improves an objective performance while describing images in more detail.Comment: 14 main pages, 4 supplementary page

Translocation of residual ethoprophos and tricyclazole from soil to spinach

The dissipation of ethoprophos and tricyclazole in soil and their translocation tendency to spinach were investigated. Prior to field trials, the analytical method for the determination of these pesticide residues was optimized and validated on soil and spinach. The field trial was conducted under greenhouse conditions for two different pretreatment periods with the pesticides. After treating with pesticides 30 (PBI-30) and 60 days (PBI-60) before seeding, soil samples were collected on different days for the dissipation study of soil. Spinach samples were harvested from the soil, and 50% and 100% mature spinach samples were collected. The initial amounts of ethoprophos residue in the PBI-60 and PBI-30 soils were 0.21 and 2.74 mg/kg, respectively, and these both decreased to less than 0.01 mg/kg on the day of spinach harvest. Similar initial residues of tricyclazole were observed in the PBI-60 (0.87 mg/kg) and PBI-30 soils (0.84 mg/kg), and these decreased to 0.44 and 0.34 mg/kg, respectively. The half-lives of ethoprophos in the soils were calculated as 7.6 and 4.8 days, respectively, while relatively long half-lives of 36.5 and 77.0 days were calculated for tricyclazole. According to the pesticide residue amounts in the spinach, the translocation rate from the soil to the spinach was determined. In the case of ethoprophos, the residual amount was already rapidly degraded in the soil, and the translocation rate could not be confirmed. On the other hand, for tricyclazole, it was confirmed that 1.19 to 1.61% of the residual amount in soil was transferred to spinach. According to these results, safe management guidelines for tricyclazole in soil were suggested considering the maximum residue limit on spinach.This work was supported by the Rural Development Administration (PJ0152772020)

Variational Autoencoder-Based Multiple Image Captioning Using a Caption Attention Map

Image captioning is a promising research topic that is applicable to services that search for desired content in a large amount of video data and a situation explanation service for visually impaired people. Previous research on image captioning has been focused on generating one caption per image. However, to increase usability in applications, it is necessary to generate several different captions that contain various representations for an image. We propose a method to generate multiple captions using a variational autoencoder, which is one of the generative models. Because an image feature plays an important role when generating captions, a method to extract a Caption Attention Map (CAM) of the image is proposed, and CAMs are projected to a latent distribution. In addition, methods for the evaluation of multiple image captioning tasks are proposed that have not yet been actively researched. The proposed model outperforms in the aspect of diversity compared with the base model when the accuracy is comparable. Moreover, it is verified that the model using CAM generates detailed captions describing various content in the image

Associations of Air Pollution and Gait Speed in Older Adults

Thesis (Master's)--University of Washington, 2022Background: Air pollution is widely recognized as a threat to public health. The impact of long-term exposure to air pollution on gait speed trajectories over time have not been fully explored among US older adults. The specific aims of this study were to 1) examine the relationship between long-term exposure to air pollution and decline in gait speed among older adults, and 2) explore effect modification by cardiovascular disease status on the association.Methods: We analyzed data from 3,022 older adults in a prospective cohort study conducted from 2000 to 2008. Long term exposure to fine particulate matter (PM2.5) and nitrogen dioxide (NO2) prior to study enrollment was estimated using state of the art prediction models. Gait speed at usual and rapid pace was assessed annually using a 15-feet timed walk test. Mixed effect models with random intercepts and slopes were fitted, adjusting for demographic and socioeconomic factors. Results: Greater long-term PM2.5 exposure was related to faster gait speed decline at usual pace: one interquartile range higher 5-year average PM2.5 exposure was related to 0.048 m/s decline in gait speed (95% CI: -0.084, -0.024) over a 6-year study period adjusted for age, gender, race and ethnicity, education, smoking status, alcohol consumption, study site, and year since enrollment. Greater long-term NO2 exposure was associated with faster gait speed decline at both usual and rapid pace: one interquartile range higher 5-year average NO2 was associated with 0.078 m/s decline in usual pace gait (95% CI: -0.120, -0.042) and 0.042 m/s decline in rapid pace gait speed (95% CI: -0.078, -0.006) over 6-year period in adjusted models. The longitudinal association between air pollution and rapid gait speed decline was significant only in individuals with cardiovascular disease. Conclusions: Long-term exposure to air pollution appears to be associated with faster progression in gait speed decline among older adults in four different locations in the US. Older adults with cardiovascular disease are more susceptible to the adverse effects of long-term exposure of air pollution and the progression in gait speed decline may be even faster among those with cardiovascular disease. Policies to reduce emission of air pollutants and interventions of avoid air pollution exposure or manage cardiovascular disease could contribute to the reduction in the burden of preventable institutionalization and hospitalization

Neighborhood Attributes and Cognitive Function in Older Adults

Thesis (Ph.D.)--University of Washington, 2021Background: Neighborhood environments are a potential modifiable factor for improving cognitive function among older adults by providing opportunities for physical activity and destinations for social interaction. Lifestyle factors enhance resilience to the development of brain pathology. However, the impact of neighborhoods on cognitive function and its mechanism among older adults is inconclusive. Objectives: This dissertation consists of three studies. The aim of the first study was to determine the association of objective neighborhood attributes (land-use mix, residential density, intersection density, presence of trails, sidewalk coverage, gradient of walkways, and areas covered by parks) with decline in cognitive function over a 2-year period among older adults. The aim of the second study was to examine the associations of perceived neighborhood attributes (residential density, land-use mix, transit ace\ss, bicycling infrastructure, recreation facilities, sidewalk coverage, crime safety, traffic safety, and physically active neighbors) with cognitive function among older adults. The aim of the third study was to test a mediating role of walking on the association between objective walkability and cognitive function or perceived walkability and cognitive function among older adults. Research Design: This dissertation employed a secondary data analysis method using the Adult Changes in Thought (ACT) study, a prospective cohort study. Data on neighborhood characteristics from 2016 King County Assessor, 2016 US Census TIGER/Line road, King County Geographic Information Systems Center, UW Urban Form Lab, and USGS digital elevation raster model (DEM) were combined with the ACT dataset. The first study was a longitudinal analysis in a sample of 1,302 older adults living in King County. Change in cognitive function was measured over 2 years by the Cognitive Ability Screening Instrument (CASI). Objective neighborhood attributes (land-use mix, residential density, intersection density, presence of trail, presence of sidewalk, gradient of walkways, and park area) were measured by geographic information systems (GIS). Multivariate linear regression models were fitted. The second study was a cross-sectional analysis in 821 adults aged 65 or older. Perceived neighborhood attributes were measured by the Physical Activity Neighborhood Environment Scale (PANE). The associations were tested using linear regression. The third study was a cross-sectional analysis in 799 older adults for the associations between objective walkability and cognitive function and in 680 older adults for the associations between perceived walkability and cognitive function. Walking was measured using an accelerometer. Associations were tested using linear regression. Indirect effects were tested using causal mediation analysis. Results: The first study found that greater objective park area within an 800 m buffer was associated with positive change in cognitive function. However, the effect size was small. Other objective neighborhood attributes were not associated with cognitive function change. The second study found that greater perceived access to public transit was associated with better cognitive function, and greater perceived sidewalk coverage was also related to better cognitive function. Perceived land use-mix and recreational facilities, crime, safety, safety from traffic, and neighbors physically active were associated with cognitive function in only unadjusted models. The third study revealed that walking had an indirect effect on the association between perceived walkability and cognitive function but not on the association between objective walkability and cognitive function. Conclusions: Strategies targeting both environmental factors as well as individual behavioral factors should be considered to improve cognitive function in older adults. Improving the perception of neighborhood attributes alongside modifying physical infrastructure may positively impact cognitive function in older adults. Modifying neighborhood infrastructure may not be sufficient to improve perceived walkability. Educational and social support programs are required to improve perceived walkability. The improved perceived walkability may encourage older adults to be more physically active and the benefits of physical activity may improve cognitive function in older adults

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models

Supervised Machine Learning for Understanding and Improving the Computational Performance of Chemical Production Scheduling MIP Models

We adopt a supervised learning approach to predict runtimes of batch production scheduling mixed-integer programming (MIP) models with the aim of understanding what instance features make a model computationally expensive. We introduce novel features to characterize instance difficulty according to problem type. The developed machine learning models trained on runtime data obtained from a wide variety of instances show good predictive performances. Then, we discuss informative features and their effects on computational performance. Finally, based on the derived insights, we propose solution methods for improving the computational performance of batch scheduling MIP models