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Department of Computer Science and EngineeringImage processing is an important and inevitable pipeline for a wide range of industies such as autonomous car, manufacturing, search engine, and healthcare, to solve existing problems. Recently, the image processing in health care has focused on developing more precise prediction methods due to advancements in computing speeds and deep learning methods.Even though all of these challenges are non-trivial in the biomedical field, the most important issue among the existing problems is to determine proper targeted cancer therapy for individual patient in order to achieve precision medicine.Specially, progressing to the rapid acquisition times necessary to generate plenty of microscopy images for biomedical samples, which cannot be observed with the naked eye. Based on these microscopy images, various drug responses to patient-derived cell cultures can be analyzed by stained individual cells with various biomarkers to gain a more detailed understanding through high-content screening (HCS). In this dissertation research, several novel image translation contributions for achieving software-based HCS for precision medicine. First, a novel image translation method, DeepHCS, for transforming bright-field microscopy images into synthetic fluorescence images of cell nuclei biomarkers is introduced. The main motivation of the proposed work is to automatically generate virtual biomarker images from conventional bright-field images, which can greatly reduce time-consuming and laborious tissue preparation efforts and improve the throughput of the screening process. DeepHCS uses bright-field images and their corresponding cell nuclei staining (DAPI) fluorescence images as a set of image pairs to train a series of end-to-end deep convolutional neural networks. Second, a novel microscopy image translation method is proposed, DeepHCS++, for transforming a bright-field microscopy image into three different fluorescence images to observe apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. Thus, the main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright-field image using multi-task learning with adversarial lossesthis can greatly reduce the time-consuming and laborious tissue preparation process as well as improve throughput of the screening process. DeepHCS++ uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. Third, an image translation method with structure-aware features is proposed for the acquisition of more realistic fluorescence microscopy images. This method integrates multi-task learning and cyclic consistency. In order to attain such realistic microscopy images, this proposed method employs an autoencoder that generates cell profile feature maps, in which include satisfactory cell textures and revise feature maps from the translation network by cooperating with the mixture network over these two different feature modalities.ope

Smooth Model Predictive Path Integral Control without Smoothing

We present a sampling-based control approach that can generate smooth actions for general nonlinear systems without external smoothing algorithms. Model Predictive Path Integral (MPPI) control has been utilized in numerous robotic applications due to its appealing characteristics to solve non-convex optimization problems. However, the stochastic nature of sampling-based methods can cause significant chattering in the resulting commands. Chattering becomes more prominent in cases where the environment changes rapidly, possibly even causing the MPPI to diverge. To address this issue, we propose a method that seamlessly combines MPPI with an input-lifting strategy. In addition, we introduce a new action cost to smooth control sequence during trajectory rollouts while preserving the information theoretic interpretation of MPPI, which was derived from non-affine dynamics. We validate our method in two nonlinear control tasks with neural network dynamics: a pendulum swing-up task and a challenging autonomous driving task. The experimental results demonstrate that our method outperforms the MPPI baselines with additionally applied smoothing algorithms.Comment: Accepted to IEEE Robotics and Automation Letters (and IROS 2022). Our video can be found at https://youtu.be/ibIks6ExGw

Family satisfaction and self-efficacy among low-income adolescents during the COVID-19 pandemic: A comparative analysis of parents' educational attainment

PurposesGiven that the period from middle to high school is important to develop and cultivate self-efficacy, reduced support in low-income families might negatively influence the development of self-efficacy among low-income students since COVID-19. This study aims to investigate the association between family satisfaction and self-efficacy among low-income students since COVID-19 and the moderating effect of parents' educational attainment on the relationship.Methods255 low-income students in South-Korea were selected for the final sample. The PROCESS macro 3.4 for Statistical Product and Service Solutions was used to analyze the data.ResultsFamily satisfaction was positively related to self-efficacy among low-income students. There was a significant moderating effect of parents' educational attainment on the relationship between family satisfaction and self-efficacy among low-income students during the COVID-19 pandemic.DiscussionFinancial support and COVID-19 benefits should be prioritized to low-income families with adolescents to improve family relationships, leading to increase self-efficacy among low-income students. Social welfare programs targeting family relationships in low-income households should be especially targeted toward low-income households without a parent who received higher education. Life-long education should be provided to parents in low-income families who did not gain higher education as their educational attainment influences the self-efficacy of their adolescent children

Fast Estimation of NTT/INTT Accelerator Costs for RNS-Based Homomorphic Encryption

This paper proposes a hardware estimator for accelerating number theoretic transform (NTT) for fast polynomial operations in the ring learning with error (RLWE) based homomorphic encryption (HE). By modifying the number of unit processing elements, modulus bit-width, and residue number system (RNS) parameters, we present a systematic way for rapidly calculating the hardware complexity without realizing the target accelerator. Compared with the actual synthesis results in 28nm CMOS technology, experimental results show that the proposed work successfully estimates the NTT accelerator area even under the large-valued parameters with acceptable errors, greatly relaxing the overheads for the design space exploration. Homomorphic encryption, residue number system, hardware accelerator2

Determinants of Behavioral Changes Since COVID-19 among Middle School Students

Middle school students are of particular interest when examining the impact of the COVID-19 pandemic because they are in a formative period for socioemotional development, and because they are not as mature as adults, making them more vulnerable to the effects of the current pandemic. This study seeks to examine determinants of protective behavior changes since COVID-19 among middle school students. Participants were recruited through an official online flatform used by public schools. The final sample included 328 middle school students in South Korea. A multiple linear regression was conducted to explore what factors influence protective behavior changes since COVID-19. Gender and health status were associated with protective behavior changes since COVID-19. Family satisfaction was positively associated with protective behavior changes. Levels of sanitation since COVID-19 and perceptions regarding the risk of COVID-19 were significantly related to protective behavior changes. This study suggests to consider three factors–individual, family, and environmental—in order to prevent middle school students from contracting and spreading the virus

Effects of Learning Attitudes and COVID-19 Risk Perception on Poor Academic Performance among Middle School Students

The purpose of this study is to examine the effects of middle school students’ learning attitudes and risk perception toward COVID-19 on their poor academic performance since the COVID-19 pandemic began. This study limited the sample to middle school students who responded that their academic performance was an A or B grade during the last academic year in 2019. For this study, 268 respondents were selected and logistic regression was employed. Self-motivated studying time and positive attitudes toward online learning predicted consistent academic performance since the COVID-19 pandemic began. Middle school students’ preference toward an in-person classroom format was related to poor academic performance since the COVID-19 pandemic began. A risk perception toward COVID-19 was related to poor academic performance since the COVID-19 pandemic began. It is imperative to provide educational programs which help students develop self-motivated studying habits to maintain their academic performance during COVID-19. Policymakers in schools should consider providing in-person options for students who are more academically successful in such an environment

Smartphone Addiction and Depression among Low-Income Boys since COVID-19: The Moderating Effect of Being an Only Child

Even though boys’ depression has become important, and their smartphone use has increased since COVID-19, little is known about low-income middle and high school boys’ depression in the context of whether they have siblings. Thus, this study investigates the relationship between smartphone addiction and depression as well as the moderating effect of being an only child on the relationship. Participants were limited to middle and high school students whose families were regarded as having a low-income. A total of 129 low-income boys were selected for the final sample. The PROCESS macro 3.4 for Statistical Product and Service Solutions was used to identify the moderating effect. Smartphone addiction was positively related to depression among low-income male students. Being an only child significantly moderated the relationship between smartphone addiction and depression. This study contributes to understanding the importance of examining mental health problems among middle school boys since COVID-19, particularly among low-income boys. It is necessary to provide tailored mental health services for middle school boys in low-income families. Alternative activities and social programs should be provided for adolescent boys who are only children to safely socialize with friends and peers without a smartphone

Sleep Quality and Attention May Correlate With Hand Grip Strength: FARM Study

Objective To determine the socio-demographic, psychologic, hematologic, or other relevant factors associated with hand grip strength in Korean farmers. Methods A total of 528 healthy Korean farmers were enrolled. Hand grip strength was measured in both hands using a hydraulic dynamometer. Socio-demographic characteristics were assessed and anthropometric measurements were obtained. Psycho-cognitive measurements such as sleep quality (Pittsburgh Sleep Quality Index) and Go/No-Go test response time were conducted. In addition to physical measurements, serologic parameters including insulin-like growth factor 1 were measured. The factors associated with hand grip strength were analyzed using multiple linear regression analysis after adjusting for age, height, and weight. Results The mean hand grip strength was associated with the Pittsburgh Sleep Quality Index total score (β=-0.12, p=0.01), the Go/No-Go test response time (β=-0.18, p=0.001), vitamin D (β=0.12, p=0.02), and insulin-like growth factor 1 levels (β=0.1, p=0.045). In female farmers, hand grip strength was only associated with the Pittsburgh Sleep Quality Index total score (β=-0.32, p<0.001). Conclusion The results of this study demonstrate that hand grip strength was associated with sleep quality and attention in Korean farmers

DeepHCS plus plus : Bright-field to fluorescence microscopy image conversion using multi-task learning with adversarial losses for label-free high-content screening

In this paper, we propose a novel microscopy image translation method for transforming a bright-field microscopy image into three different fluorescence images to observe the apoptosis, nuclei, and cytoplasm of cells, which visualize dead cells, nuclei of cells, and cytoplasm of cells, respectively. These biomarkers are commonly used in high-content drug screening to analyze drug response. The main contribution of the proposed work is the automatic generation of three fluorescence images from a conventional bright field image; this can greatly reduce the time-consuming and laborious tissue preparation process and improve throughput of the screening process. Our proposed method uses only a single bright-field image and the corresponding fluorescence images as a set of image pairs for training an end-to-end deep convolutional neural network. By leveraging deep convolutional neural networks with a set of image pairs of bright-field and corresponding fluorescence images, our proposed method can produce synthetic fluorescence images comparable to real fluorescence microscopy images with high accuracy. Our proposed model uses multi-task learning with adversarial losses to generate more accurate and realistic microscopy images. We assess the efficacy of the proposed method using real bright-field and fluorescence microscopy image datasets from patient-driven samples of a glioblastoma, and validate the method&apos;s accuracy with various quality metrics including cell number correlation (CNC), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), cell viability correlation (CVC), error maps, and R 2 correlation

Synthesis of alloyed 393-InZnP Clusters

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