A General Proximal Alternating Minimization Method with Application to Nonconvex Nonsmooth 1D Total Variation Denoising

We deal with a class of problems whose objective functions are compositions of nonconvex nonsmooth functions, which has a wide range of applications in signal/image processing. We introduce a new auxiliary variable, and an efficient general proximal alternating minimization algorithm is proposed. This method solves a class of nonconvex nonsmooth problems through alternating minimization. We give a brilliant systematic analysis to guarantee the convergence of the algorithm. Simulation results and the comparison with two other existing algorithms for 1D total variation denoising validate the efficiency of the proposed approach. The algorithm does contribute to the analysis and applications of a wide class of nonconvex nonsmooth problems

TDC: Towards Extremely Efficient CNNs on GPUs via Hardware-Aware Tucker Decomposition

Tucker decomposition is one of the SOTA CNN model compression techniques. However, unlike the FLOPs reduction, we observe very limited inference time reduction with Tuckercompressed models using existing GPU software such as cuDNN. To this end, we propose an efficient end-to-end framework that can generate highly accurate and compact CNN models via Tucker decomposition and optimized inference code on GPUs. Specifically, we propose an ADMM-based training algorithm that can achieve highly accurate Tucker-format models. We also develop a high-performance kernel for Tucker-format convolutions and analytical performance models to guide the selection of execution parameters. We further propose a co-design framework to determine the proper Tucker ranks driven by practical inference time (rather than FLOPs). Our evaluation on five modern CNNs with A100 demonstrates that our compressed models with our optimized code achieve up to 3.14X speedup over cuDNN, 1.45X speedup over TVM, and 4.57X over the original models using cuDNN with up to 0.05% accuracy loss.Comment: 12 pages, 8 figures, 3 tables, accepted by PPoPP '2

Inhibition of ERK activation enhances the repair of double-stranded breaks via non-homologous end joining by increasing DNA-PKcs activation

AbstractNon-homologous end joining (NHEJ) is one of the major pathways that repairs double-stranded DNA breaks (DSBs). Activation of DNA-PK is required for NHEJ. However, the mechanism leading to DNA-PKcs activation remains incompletely understood. We provide evidence here that the MEK–ERK pathway plays a role in DNA-PKcs-mediated NHEJ. In comparison to the vehicle control (DMSO), etoposide (ETOP)-induced DSBs in MCF7 cells were more rapidly repaired in the presence of U0126, a specific MEK inhibitor, based on the reduction of γH2AX and tail moments. Additionally, U0126 increased reactivation of luciferase activity, which resulted from the repair of restriction enzyme-cleaved DSBs. Furthermore, while inhibition of ERK activation using the dominant-negative MEK1K97M accelerated the repair of DSBs, enforcing ERK activation with the constitutively active MEK1Q56P reduced DSB repair. In line with MEK activating ERK1 and ERK2 kinases, knockdown of either ERK1 or ERK2 increased DSB repair. Consistent with the activation of DNA-PKcs being required for NHEJ, we demonstrated that inhibition of ERK activation using U0126, MEK1K97M, and knockdown of ERK1 or ERK2 enhanced ETOP-induced activation of DNA-PKcs. Conversely, enforcing ERK activation by MEK1Q56P reduced ETOP-initiated DNA-PKcs activation. Taken together, we demonstrate that ERK reduces NHEJ-mediated repair of DSBs via attenuation of DNA-PKcs activation

Polymorphisms of melatonin receptor genes and their associations with egg production traits in Shaoxing duck

Objective In birds, three types of melatonin receptors (MTNR1A, MTNR1B, and MTNR1C) have been cloned. Previous researches have showed that three melatonin receptors played an essential role in reproduction and ovarian physiology. However, the association of polymorphisms of the three receptors with duck reproduction traits and egg quality traits is still unknown. In this test, we chose MTNR1A, MTNR1B, and MTNR1C as candidate genes to detect novel sequence polymorphism and analyze their association with egg production traits in Shaoxing duck, and detected their mRNA expression level in ovaries. Methods In this study, a total of 785 duck blood samples were collected to investigate the association of melatonin receptor genes with egg production traits and egg quality traits using a direct sequencing method. And 6 ducks representing two groups (3 of each) according to the age at first eggs (at 128 days of age or after 150 days of age) were carefully selected for quantitative real-time polymerase chain reaction. Results Seven novel polymorphisms (MTNR1A: g. 268C>T, MTNR1B: g. 41C>T, and g. 161T>C, MTNR1C: g. 10C>T, g. 24A>G, g. 108C>T, g. 363 T>C) were detected. The single nucleotide polymorphism (SNP) of MTNR1A (g. 268C>T) was significantly linked with the age at first egg (pT and egg production traits: total egg numbers at 34 weeks old of age and age at first egg. In addition, the mRNA expression level of MTNR1A in ovary was significantly higher in late-mature group than in early-mature group, while MTNR1C showed a contrary tendency (p<0.05). Conclusion These results suggest that identified SNPs in MTNR1A and MTNR1C may influence the age at first egg and could be considered as the candidate molecular marker for identify early maturely traits in duck selection and improvement