Residual Normality Assumption and the Estimation of Multiple Membership Random Effects Models

Data collected in the human and biological sciences often have multilevel structures. While conventional hierarchical linear modeling is applicable to purely hierarchical data, multiple membership random effects modeling is appropriate for non-purely nested data wherein some lower-level units manifest mobility across higher-level units. Fitting a multiple membership random effects model (MMrem) to non-purely nested data may account for lower-level observation interdependencies and the contextual effects of higher-level units on the outcomes of lower-level units. One important assumption in multilevel modeling is normality of the residual distributions. Although a few recent studies have investigated the effect of cluster-level residual non-normality on hierarchical linear modeling estimation for purely hierarchical data, no research has examined MMrem robustness issues given residual non-normality. The purpose of the present research was to extend prior research on the influence of residual non-normality from purely nested data structures to multiple membership data structures. To investigate the statistical performance of an MMrem when the level-two residual distributional assumption was unmet, this research inquiry employed a Monte Carlo simulation study to examine two-level MMrem fixed effect and variance component parameter estimate biases and inferential errors under a fully crossed study design. Simulation factors included the level-two residual distribution, number of level-two clusters, number of level-one units per cluster, intra-cluster correlation coefficient, and mobility rate. The generating parameters for the Monte Carlo simulation study were based on an analysis of a subset of the newly-released publicly-available data of the Early Childhood Longitudinal Study, Kindergarten Class of 2010-11. By building upon previous MMrem methodological studies, this research inquiry sought answers to the following questions: When the level-two residual normality assumption was violated, (1) how accurate were MMrem fixed effect and variance component parameter estimates, and (2) what sample size was adequate with respect to MMrem estimation? The findings should be useful for research in education, public health, psychology, and other fields, and contribute to the literature on the importance of residual normality for the accuracy of MMrem estimates

Reconstruction of compressed spectral imaging based on global structure and spectral correlation

In this paper, a convolution sparse coding method based on global structure characteristics and spectral correlation is proposed for the reconstruction of compressive spectral images. The proposed method uses the convolution kernel to operate the global image, which can better preserve image structure information in the spatial dimension. To take full exploration of the constraints between spectra, the coefficients corresponding to the convolution kernel are constrained by the norm to improve spectral accuracy. And, to solve the problem that convolutional sparse coding is insensitive to low frequency, the global total-variation (TV) constraint is added to estimate the low-frequency components. It not only ensures the effective estimation of the low-frequency but also transforms the convolutional sparse coding into a de-noising process, which makes the reconstructing process simpler. Simulations show that compared with the current mainstream optimization methods (DeSCI and Gap-TV), the proposed method improves the reconstruction quality by up to 7 dB in PSNR and 10% in SSIM, and has a great improvement in the details of the reconstructed image

Analysis of CO2 Emission for the Cement Manufacturing with Alternative Raw Materials: A LCA-based Framework

AbstractThe cement industry is a significant CO2 emitter mainly due to the calcinations of raw materials and the combustions of fuels. Some measures have been considered to reduce the CO2 emissions in cement industry, of which alternative raw materials are the most efficient practicing way. In this study, a LCA-based CO2 accounting framework with alternative raw materials was constructed to analyze the CO2 emissions from concrete with different kinds of low carbon substitution, within which cement production process was divided into six stages associated with the environmental impacts. A better routine is expected to understand the environmental hazards of cement products and to optimize the design to reduce adverse environmental impacts

In Defense of Image Pre-Training for Spatiotemporal Recognition

Image pre-training, the current de-facto paradigm for a wide range of visual tasks, is generally less favored in the field of video recognition. By contrast, a common strategy is to directly train with spatiotemporal convolutional neural networks (CNNs) from scratch. Nonetheless, interestingly, by taking a closer look at these from-scratch learned CNNs, we note there exist certain 3D kernels that exhibit much stronger appearance modeling ability than others, arguably suggesting appearance information is already well disentangled in learning. Inspired by this observation, we hypothesize that the key to effectively leveraging image pre-training lies in the decomposition of learning spatial and temporal features, and revisiting image pre-training as the appearance prior to initializing 3D kernels. In addition, we propose Spatial-Temporal Separable (STS) convolution, which explicitly splits the feature channels into spatial and temporal groups, to further enable a more thorough decomposition of spatiotemporal features for fine-tuning 3D CNNs. Our experiments show that simply replacing 3D convolution with STS notably improves a wide range of 3D CNNs without increasing parameters and computation on both Kinetics-400 and Something-Something V2. Moreover, this new training pipeline consistently achieves better results on video recognition with significant speedup. For instance, we achieve +0.6% top-1 of Slowfast on Kinetics-400 over the strong 256-epoch 128-GPU baseline while fine-tuning for only 50 epochs with 4 GPUs. The code and models are available at https://github.com/UCSC-VLAA/Image-Pretraining-for-Video.Comment: Published as a conference paper at ECCV 202

PO-067 Effects of oral Lycium barbarum juice in red blood cell antioxidant biomarkers and physical function during 8 days of aerobic exercise

Objective Lycium barbarum polysaccharide (LBP) is the main active components of Lycium barbarum, its benefits to anti-aging, vision, kidney, and liver functions. Nevertheless, there is still a scarcity of experimental evidence to support the effect of Lycium barbarum on aerobic exercise.This a randomized controlled trial was observed the effects of oral Lycium barbarum juice in red blood cell antioxidant biomarkers and physical function during 8 days of aerobic exercise. Methods 28 healthy male college students were divided into control group（16）and experimental group（12），and underwent interval running once every other day，total of 8 days. Exercise program: An exercise session includes two 30-minute aerobic exercises at 60%VO2max and a five-minute break. For the duration of the 8 days period, participants exercised one time every other day and the experimental group drank 100ml Lycium barbarum juice (each LBP content 360-440mg%) at bedtime every night. In ninth days, all the experimenters did exhaustive exercise with 80%VO2max on a treadmill with 8°.simultaneous recording of motion duration. The levels of red blood cell SOD, MDA, GSH-PX, serum CAT, serum TAC and other oxygenation stress markers and BLA, Glu, Urea, CK, Urine eight items and other physical function indexes of the subjects were determined before the experiment and after the completion of each intensity exercise. Differences between before and after intervention values were tested using a paired t test.And to compare the mean of outcomes in quantitative variables between the 2 groups, a independent t-test was used. The SPSS software (version 17, SPSS Inc, Chicago, IL, USA) was applied for data analysis and statistical significance was accepted at P < 0.05. Results (1)After 8 days of oral Lycium barbarum juice, the exhaustion time of exercise force in the experimental group was 30.76 ±12.19min, while the control group was 23.64±8.56min. Compared with the control group, the average exercise exhaustion time of the experimental group was prolonged 7.12min. (2)The red blood cell SOD in the two groups after 8 days of aerobic exercise had significant and significant improvement (P < 0.05, P < 0.01), and moreover, the increase of the experimental group was significantly higher than that of the control group (P < 0.05).As well as, the blood erythrocyte GSH-PX and serum TAC were significantly enhanced after the experiment (P < 0.01).It is suggested that increasing the levels of SOD and GSH-PX in vivo is beneficial in scavenging the free radicals produced by body movement. (3)After the 8 days oral Lycium barbarum juice, the decrease of MDA in blood red blood cells in the experimental group was greater than that of the control group (P < 0.01), indicating that the juice of Lycium barbarum could reduce the production of lipid peroxide products. (4) Exhaustion exercise after 8 days of oral Lycium barbarum juice, the physical function indexes of the experimental group, such as BLA, Urea, and CK were reduced. The positive rate of eight urine items was less than that in the control group, 8 in the control group, 2 for bilirubin positive, 3 in the urinary occult blood and 5 in the urine protein, while only 1 in the experimental group were positive for urine protein.   Conclusions Oral Lycium barbarum juice can improve the activity of antioxidant enzymes during aerobic exercise, reduce the formation of lipid peroxides in the body, protect the biological function of red blood cells, improve the physical function and postpone the production of sports fatigue

MARS: Exploiting Multi-Level Parallelism for DNN Workloads on Adaptive Multi-Accelerator Systems

Along with the fast evolution of deep neural networks, the hardware system is also developing rapidly. As a promising solution achieving high scalability and low manufacturing cost, multi-accelerator systems widely exist in data centers, cloud platforms, and SoCs. Thus, a challenging problem arises in multi-accelerator systems: selecting a proper combination of accelerators from available designs and searching for efficient DNN mapping strategies. To this end, we propose MARS, a novel mapping framework that can perform computation-aware accelerator selection, and apply communication-aware sharding strategies to maximize parallelism. Experimental results show that MARS can achieve 32.2% latency reduction on average for typical DNN workloads compared to the baseline, and 59.4% latency reduction on heterogeneous models compared to the corresponding state-of-the-art method.Comment: Accepted by 60th DA

Infection status and etiological characteristics of diarrheogenic Escherichia coli among diarrhea patients in sentinel hospitals of Fujian Province in 2019

Objective To investigate the infectious status, virulence gene, molecular typing and antibiotic resistance of diarrheogenic Escherichia coli (DEC) in sentinel hospitals of Fujian Province in 2019. Methods Fluorescent polymerase chain reaction (PCR) was used to identify 210 fecal samples of diarrhea patients after isolation of E. coli according to GB 4789. 6-2016, and then pulsed field gel electrophoresis (PFGE) molecular traceability and antibiotic resistance test were conducted on the isolated DEC. Results Thirty two strains of bacteria were detected, with a detection rate of 15.2% (32/210). Among them, enteropathogenic E. coli (EPEC) accounted for 37.5% (12/32), enteroaggregative E. coli (EAEC) for 37.5% (12/32), and enterotoxigenic E. coli (ETEC) for 25.0% (8/32). The results of antibiotic resistance test showed that these 32 strains of bacteria were most resistant to ampicillin, with a resistance rate of 78.1% (25/32), followed by tetracycline and trimethoprim/sulfamethoxazole, with resistance rates of 62.5% (20/32) and 59.4% (19/32), respectively. The multiple antibiotic resistance rate was 50.0% (16/32). The results of PFGE showed that 32 strains of bacteria causing diarrhea were divided into 28 PFGE banding patterns. Among them, 12 strains of EPEC and 12 strains of EAEC were divided into 10 PFGE banding patterns, respectively, and 8 strains of ETEC were divided into 8 PFGE banding patterns. The results of cluster analysis showed that two groups of EPEC strains had 100.0% similar banding patterns, one group of EAEC strains had 100.0% similar banding patterns, and ETEC strains did not have completely consistent banding pattern. Conclusion EPEC and EAEC were the main pathogens of diarrhea in surveillance points of Fujian Province in 2019. The genetic diversity of the strains showed that the genetic relationship between them was relatively distant. The antibiotic resistance of DEC was severe, and the rate of multiple antibiotic resistance was high

SwinMM: Masked Multi-view with Swin Transformers for 3D Medical Image Segmentation

Recent advancements in large-scale Vision Transformers have made significant strides in improving pre-trained models for medical image segmentation. However, these methods face a notable challenge in acquiring a substantial amount of pre-training data, particularly within the medical field. To address this limitation, we present Masked Multi-view with Swin Transformers (SwinMM), a novel multi-view pipeline for enabling accurate and data-efficient self-supervised medical image analysis. Our strategy harnesses the potential of multi-view information by incorporating two principal components. In the pre-training phase, we deploy a masked multi-view encoder devised to concurrently train masked multi-view observations through a range of diverse proxy tasks. These tasks span image reconstruction, rotation, contrastive learning, and a novel task that employs a mutual learning paradigm. This new task capitalizes on the consistency between predictions from various perspectives, enabling the extraction of hidden multi-view information from 3D medical data. In the fine-tuning stage, a cross-view decoder is developed to aggregate the multi-view information through a cross-attention block. Compared with the previous state-of-the-art self-supervised learning method Swin UNETR, SwinMM demonstrates a notable advantage on several medical image segmentation tasks. It allows for a smooth integration of multi-view information, significantly boosting both the accuracy and data-efficiency of the model. Code and models are available at https://github.com/UCSC-VLAA/SwinMM/.Comment: MICCAI 2023; project page: https://github.com/UCSC-VLAA/SwinMM

Validation of the digital health literacy assessment among the university students in China

PurposeWith the development of the internet, digital health literacy (DHL) has become increasingly important for managing health. Consequently, various digital health literacy scales have been created for different groups. The purpose of this study was to verify the reliability and validity of the simplified Chinese version of the Digital Health Literacy Assessment (DHLA) scale among university students in China.MethodSnowball sampling was used to recruit the participants via an online platform (Wenjuan.com), and finally 304 university students were included in the survey. Demographic information and the status of DHL were collected through the online questionnaire. Cronbach’s alpha and split-half reliability were used to test the internal consistency of the scale, while the structural validity was verified by exploratory factor analysis and confirmatory factor analysis. Additionally, the convergence of the scale was tested by composite reliability (CR) and average variance extracted (AVE).ResultTwo dimensions were generated from 10 entries in the scale, named Self-rated Digital Health Literacy and Trust Degree of Online Health Information, respectively. The Cronbach’s alpha and split-half reliability of the total scale were 0.912 and 0.828, while the Cronbach’s alpha of the two dimensions were 0.913 and 0.830, respectively. The structural validity-related indexes of the scale met the standards (RMSEA = 0.079, GFI = 0.943, AGFI = 0.902, CFI = 0.971). In each dimension, the CR and AVE also reached critical values (CR > 0.7 and AVE > 0.5).ConclusionThe scale had high reliability and validity, indicating the simplified Chinese DHLA scale could be used to evaluate the DHL of university students in China