Alternating Direction Method of Multipliers Based on ℓ2,0\ell_{2,0}-norm for Multiple Measurement Vector Problem

In this paper, we propose an alternating direction method of multipliers (ADMM)-based optimization algorithm to achieve better undersampling rate for multiple measurement vector (MMV) problem. The core is to introduce the $\ell_{2,0}$-norm sparsity constraint to describe the joint-sparsity of the MMV problem, which is different from the widely used $\ell_{2,1}$-norm constraint in the existing research. In order to illustrate the better performance of $\ell_{2,0}$-norm, first this paper proves the equivalence of the sparsity of the row support set of a matrix and its $\ell_{2,0}$-norm. Afterward, the MMV problem based on $\ell_{2,0}$-norm is proposed. Moreover, building on the Kurdyka-Lojasiewicz property, this paper establishes that the sequence generated by ADMM globally converges to the optimal point of the MMV problem. Finally, the performance of our algorithm and comparison with other algorithms under different conditions is studied by simulated examples.Comment: 24 pages, 5 figures, 4 table

Network Analysis-Based Approach for Exploring the Potential Diagnostic Biomarkers of Acute Myocardial Infarction

Acute myocardial infarction (AMI) is a severe cardiovascular disease that is a serious threat to human life. However, the specific diagnostic biomarkers have not been fully clarified and candidate regulatory targets for AMI have not been identified. In order to explore the potential diagnostic biomarkers and possible regulatory targets of AMI, we used a network analysis-based approach to analyze microarray expression profiling of peripheral blood in patients with AMI. The significant differentially-expressed genes (DEGs) were screened by Limma and constructed a gene function regulatory network (GO-Tree) to obtain the inherent affiliation of significant function terms. The pathway action network was constructed, and the signal transfer relationship between pathway terms was mined in order to investigate the impact of core pathway terms in AMI. Subsequently, constructed the transcription regulatory network of DEGs. Weighted gene co-expression network analysis (WGCNA) was employed to identify significantly altered gene modules and hub genes in two groups. Subsequently, the transcription regulation network of DEGs was constructed. We found that specific gene modules may provide a better insight into the potential diagnostic biomarkers of AMI. Our findings revealed and verified that NCF4, AQP9, NFIL3, DYSF, GZMA, TBX21, PRF1 and PTGDR genes by RT-qPCR. TBX21 and PRF1 may be potential candidates for diagnostic biomarker and possible regulatory targets in AMI

Effects of laser fluence on silicon modification by four-beam laser interference

This paper discusses the effects of laser fluence on silicon modification by four-beam laser interference. In this work, four-beam laser interference was used to pattern single crystal silicon wafers for the fabrication of surface structures, and the number of laser pulses was applied to the process in air. By controlling the parameters of laser irradiation, different shapes of silicon structures were fabricated. The results were obtained with the single laser fluence of 354 mJ/cm, 495 mJ/cm, and 637 mJ/cm, the pulse repetition rate of 10 Hz, the laser exposure pulses of 30, 100, and 300, the laser wavelength of 1064 nm, and the pulse duration of 7-9 ns. The effects of the heat transfer and the radiation of laser interference plasma on silicon wafer surfaces were investigated. The equations of heat flow and radiation effects of laser plasma of interfering patterns in a four-beam laser interference distribution were proposed to describe their impacts on silicon wafer surfaces. The experimental results have shown that the laser fluence has to be properly selected for the fabrication of well-defined surface structures in a four-beam laser interference process. Laser interference patterns can directly fabricate different shape structures for their corresponding applications

Retrieving Ground-Level PM2.5 Concentrations in China (2013–2021) with a Numerical Model-Informed Testbed to Mitigate Sample Imbalance-Induced Biases

Ground-level PM2.5 data derived from satellites with machine learning are crucial for health and climate assessments, however, uncertainties persist due to the absence of spatially covered observations. To address this, we propose a novel testbed using untraditional numerical simulations to evaluate PM2.5 estimation across the entire spatial domain. The testbed emulates the general machine-learning approach, by training the model with grids corresponding to ground monitor sites and subsequently testing its predictive accuracy for other locations. Our approach enables comprehensive evaluation of various machine-learning methods’ performance in estimating PM2.5 across the spatial domain for the first time. Unexpected results are shown in the application in China, with larger PM2.5 biases found in densely populated regions with abundant ground observations across all benchmark models, challenging conventional expectations and are not explored in the recent literature. The imbalance in training samples, mostly from urban areas with high emissions, is the main reason, leading to significant overestimation due to the lack of monitors in downwind areas where PM2.5 is transported from urban areas with varying vertical profiles. Our proposed testbed also provides an efficient strategy for optimizing model structure or training samples to enhance satellite-retrieval model performance. Integration of spatiotemporal features, especially with CNN-based deep-learning approaches like the ResNet model, successfully mitigates PM2.5 overestimation (by 5–30 µg m-3) and corresponding exposure (by 3 million people • µg m-3) in the downwind area over the past nine years (2013–2021) compared to the traditional approach. Furthermore, the incorporation of 600 strategically positioned ground-measurement sites identified through the testbed is essential to achieve a more balanced distribution of training samples, thereby ensuring precise PM2.5 estimation and facilitating the assessment of associated impacts in China. In addition to presenting the retrieved surface PM2.5 concentrations in China from 2013 to 2021, this study provides a testbed dataset derived from physical modeling simulations which can serve to evaluate the performance of data-driven methodologies, such as machine learning, in estimating spatial PM2.5 concentrations for the community

DiffMimic: Efficient Motion Mimicking with Differentiable Physics

Motion mimicking is a foundational task in physics-based character animation. However, most existing motion mimicking methods are built upon reinforcement learning (RL) and suffer from heavy reward engineering, high variance, and slow convergence with hard explorations. Specifically, they usually take tens of hours or even days of training to mimic a simple motion sequence, resulting in poor scalability. In this work, we leverage differentiable physics simulators (DPS) and propose an efficient motion mimicking method dubbed DiffMimic. Our key insight is that DPS casts a complex policy learning task to a much simpler state matching problem. In particular, DPS learns a stable policy by analytical gradients with ground-truth physical priors hence leading to significantly faster and stabler convergence than RL-based methods. Moreover, to escape from local optima, we utilize a Demonstration Replay mechanism to enable stable gradient backpropagation in a long horizon. Extensive experiments on standard benchmarks show that DiffMimic has a better sample efficiency and time efficiency than existing methods (e.g., DeepMimic). Notably, DiffMimic allows a physically simulated character to learn Backflip after 10 minutes of training and be able to cycle it after 3 hours of training, while the existing approach may require about a day of training to cycle Backflip. More importantly, we hope DiffMimic can benefit more differentiable animation systems with techniques like differentiable clothes simulation in future research.Comment: ICLR 2023 Code is at https://github.com/jiawei-ren/diffmimic Project page is at https://diffmimic.github.io

Cancer burden in China: a Bayesian approach

BACKGROUND Cancer is a serious health issue in China, but accurate national counts for cancer incidence are not currently available. Knowledge of the cancer burden is necessary for national cancer control planning. In this study, national death survey data and cancer registration data were used to calculate the cancer burden in China using a Bayesian approach. METHODS Cancer mortality and incidence rates for 2004-2005 were obtained from the National Cancer Registration database. The third National Death Survey (NDS), 2004-2005 database provided nationally representative cancer mortality rates. Bayesian modeling methods were used to estimate mortality to incidence (MI) ratios from the registry data and national incidence from the NDS for specific cancer types by age, sex and urban or rural location. RESULTS The total estimated incident cancer cases in 2005 were 2,956,300 (1,762,000 males, 1,194,300 females). World age standardized incidence rates were 236.2 per 100,000 in males and 168.9 per 100,000 in females in urban areas and 203.7 per 100,000 and 121.8 per 100,000 in rural areas. CONCLUSIONS MI ratios are useful for estimating national cancer incidence in the absence of representative incidence or survival data. Bayesian methods provide a flexible framework for smoothing rates and representing statistical uncertainty in the MI ratios. Expansion of China's cancer registration network to be more representative of the country would improve the accuracy of cancer burden estimates.This study used the data from National Central Cancer Registry database. The authors acknowledge the contributions of local cancer registries providing registration data and working group of the Third National Death Survey