GuidedMixup: An Efficient Mixup Strategy Guided by Saliency Maps

Data augmentation is now an essential part of the image training process, as it effectively prevents overfitting and makes the model more robust against noisy datasets. Recent mixing augmentation strategies have advanced to generate the mixup mask that can enrich the saliency information, which is a supervisory signal. However, these methods incur a significant computational burden to optimize the mixup mask. From this motivation, we propose a novel saliency-aware mixup method, GuidedMixup, which aims to retain the salient regions in mixup images with low computational overhead. We develop an efficient pairing algorithm that pursues to minimize the conflict of salient regions of paired images and achieve rich saliency in mixup images. Moreover, GuidedMixup controls the mixup ratio for each pixel to better preserve the salient region by interpolating two paired images smoothly. The experiments on several datasets demonstrate that GuidedMixup provides a good trade-off between augmentation overhead and generalization performance on classification datasets. In addition, our method shows good performance in experiments with corrupted or reduced datasets.Comment: Published at AAAI2023 (Oral

Trend Analysis of Physical Activity Measurement Research Using Text Mining in Big Data Analytics

Measurements of physical activity taken in a valid and reliable way are essential in characterizing the relationship between physical activity and health outcomes. Given the steadily growing interest in the physical activity measurement and the lack of research to identify current trends, this study investigated the research trend of physical activity measurement by applying four text data mining techniques (i.e., future signal, keyword network analysis, keyword trend, and keyword association rule). A total of 54,670 publications from 1982 to 2021 were collected from PubMed. As a result, the current study 1) confirmed two weak signal topics (i.e., “validity of physical activity instrument” and “classification of physical activity patterns using machine learning algorithms”) that are likely to affect future research trends, 2) identified keywords (e.g., “youth,” “adult,” “woman,” “survey,” “questionnaire,” and “monitor”) from the perspective of populations and measurement tools, 3) examined that the relative importance of keyword, “senior” increased rapidly, and 4) indicated that new keywords (i.e., “smartphone,” “wearable device,” “GPS,” “tracker,” and “app”) appeared in the early 2000s. The findings of this study provided implications for the selection of research topics and the use of text mining techniques in physical activity measurement research

Issues and challenges in sedentary behavior measurement

Previous research has shown the negative impact of sedentary behavior on health, including cardiovascular risk factors, chronic disease-related morbidity, and mortality. Accurate measurement of sedentary behavior is thus important to plan effective interventions and to inform public health messages. This article (a) provides an overview of the nature and importance of sedentary behavior, (b) describes measurement methods, including subjective and objective measurement tools, (c) reviews the most important measurement and data processing issues and challenges facing sedentary behavior researchers, and (d) presents key findings from the most recent sedentary behavior measurement-related research. Both subjective and objective measures of sedentary behavior have limitations for obtaining accurate sedentary behavior measurements compliant with the current definitions of sedentary behavior, especially when investigating sedentary behavior as part of the full spectrum of physical behaviors. Regardless of the sedentary behavior measure chosen, researchers must be aware of all possible sources of error inherent to each technique and minimize those errors, thereby increasing validity of the outcome data

FOOD FOR THE CITY: CULTIVATING COMMUNITY IN BALTIMORE CITY

One in four residents of Baltimore City live in a food desert. Food desert disproportionately affects the low income neighborhoods more than the neighborhoods with financial stability. Throughout history, food became a commodity that depends on and dictates the market force. Food sources were being eliminated in the inner city while the suburbs saw rising development of grocery stores. Without grocery stores and other food retailers, communities are missing gathering and commercial hubs that make neighborhoods livable and help the local economy sustain and thrive. This thesis studies why food was further displaced from suffering communities and how an inclusive sustainable urban food system can help create a hub of neighborhood revitalization and promote health, social, safety, stability, and economic well-being of the community

Towards Oracle Knowledge Distillation with Neural Architecture Search

We present a novel framework of knowledge distillation that is capable of learning powerful and efficient student models from ensemble teacher networks. Our approach addresses the inherent model capacity issue between teacher and student and aims to maximize benefit from teacher models during distillation by reducing their capacity gap. Specifically, we employ a neural architecture search technique to augment useful structures and operations, where the searched network is appropriate for knowledge distillation towards student models and free from sacrificing its performance by fixing the network capacity. We also introduce an oracle knowledge distillation loss to facilitate model search and distillation using an ensemble-based teacher model, where a student network is learned to imitate oracle performance of the teacher. We perform extensive experiments on the image classification datasets---CIFAR-100 and TinyImageNet---using various networks. We also show that searching for a new student model is effective in both accuracy and memory size and that the searched models often outperform their teacher models thanks to neural architecture search with oracle knowledge distillation.Comment: accepted by AAAI-2

Conditional Score Guidance for Text-Driven Image-to-Image Translation

We present a novel algorithm for text-driven image-to-image translation based on a pretrained text-to-image diffusion model. Our method aims to generate a target image by selectively editing the regions of interest in a source image, defined by a modifying text, while preserving the remaining parts. In contrast to existing techniques that solely rely on a target prompt, we introduce a new score function, which considers both a source prompt and a source image, tailored to address specific translation tasks. To this end, we derive the conditional score function in a principled manner, decomposing it into a standard score and a guiding term for target image generation. For the gradient computation, we adopt a Gaussian distribution of the posterior distribution, estimating its mean and variance without requiring additional training. In addition, to enhance the conditional score guidance, we incorporate a simple yet effective mixup method. This method combines two cross-attention maps derived from the source and target latents, promoting the generation of the target image by a desirable fusion of the original parts in the source image and the edited regions aligned with the target prompt. Through comprehensive experiments, we demonstrate that our approach achieves outstanding image-to-image translation performance on various tasks

Parameters of walking and jogging in healthy young adults

The purposes of this study were to a) investigate the average heart rate (HR), speed, stride length, and stride rate during moderate intensity walking and jogging in healthy young adults, b) cross validate the walking stride length calculation based on 42% of height and c) provide reliability information for measurement of walking and jogging steps, speed, stride length, and stride rate. Participants (N=130) wore two Yamax SW-200 pedometers and a Polar A-1 HR monitor while performing walking and jogging trials. The correlation between estimated (0.71 ± 0.04 m·stride-1) and actual stride length (0.78 ± 0.05 m·stride-1) was moderate (r = .46). However, a significant difference was observed between the two measurements (t(115) = -14.24, p < .001). The reliability results for speed, stride length, and stride rate showed that two or fewer trials were enough to achieve reliable estimates. In conclusion, when instructed to walk at a moderate pace, healthy young adults tend to walk at an average pace that is greater than that recommended for meeting current public health recommendations (80 m·min-1). Similarly, when instructed to jog at a comfortable pace, healthy young adults tend to jog at a speed greater than that corresponding to vigorous intensity physical activity (134 m·min-1). The results of the reliability analysis indicate that in healthy young adults, to measure typical walking and jogging patterns using a pedometer, only two trials for walking and one trial for jogging are necessary to achieve reliable estimates. Stride rate calculations requires the combination of two trials and one pedometer for both walking and jogging

Measurement considerations of peak stepping cadence measures using national health and nutrition examination survey 2005-2006

Background: This study examined the optimal measurement conditions to obtain reliable peak cadence measures using the accelerometerdetermined step data from the National Health and Nutrition Examination Survey 2005-2006. Methods: A total of 1282 adults (> 17 years) who provided valid accelerometer data for 7 consecutive days were included. The peak 1-and 30-minute cadences were extracted. The sources of variance in peak stepping cadences were estimated using Generalizability theory analysis. A simulation analysis was conducted to examine the effect of the inclusion of weekend days. The optimal number of monitoring days to achieve 80% reliability for peak stepping cadences were estimated. Results: Intraindividual variability was the largest variance component of peak cadences for young and middle-aged adults aged < 60 years (50.55%-59.24%) compared with older adults aged < 60 years (31.62%-41.72%). In general, the minimum of 7 and 5 days of monitoring were required for peak 1-and 30-minute cadences among young and middle-aged adults, respectively, whereas 3 days of monitoring was sufficient for older adults to achieve the desired reliability (0.80). The inclusion of weekend days in the monitoring frame may not be practically important. Conclusions: The findings could be applied in future research as the reference measurement conditions for peak cadences