Estimates of genetic variability and gene action in two maize (Zea mays L.) populations

The objectives of this study were to: (1) estimate the genetic variances, average levels of dominance, and genetic effects in two maize (Zea mays L.) populations that descended from a high performance single cross, B73 x Mo17, and a related line single cross, B73 x B84; (2) study the relationships between genetic effects and heterosis; and (3) estimate the effects of linkage disequilibrium on the estimates of genetic parameters by using the advanced random mating generations. The parental lines, F1 single crosses, F2 populations of the two F1\u27s and backcross and S1 progenies randomly sampled from each of the F2 and F2 Syn5 populations were evaluated;Heterosis was greater in B73 x Mo17 than in B73 x B84 for yield, ear height, ear length, and ear diameter. In design III and combined design III and S1 analysis, genetic variances estimated for B73 x Mo17 populations were greater than for B73 x B84 populations for yield and other traits. Additive variance for yield estimated for the B73 x Mo17 populations was about two and three times greater than for the B73 x B84 F2 and F2 Syn5 populations, respectively. Estimates of average levels of dominance were in partial to complete dominance range in both populations for all traits;In generation mean analysis, dominance effects were more important in the expression of heterosis for yield and other traits in both B73 x Mo17 and B73 x B84. The greater heterosis observed for yield and other traits in B73 x Mo17 than in B73 x B84 was due to the greater positive dominance effects and smaller negative dominance x dominance effects in B73 x Mo17;Estimates of dominance variance for yield decreased from the F2 to the F2 Syn5 populations for both hybrids. Estimates of additive variance of yield for F2 populations were not significantly different from the estimates for F2 Syn5 populations for both hybrids. This suggested that the effect of coupling and repulsion phase linkages cancelled each other. Linkage also had an important effect on the estimates of epistatic effects of ear height and yield in generation mean analysis for both hybrids

Estimates of Genetic Variability in F2 Maize Populations

Maize (Zea mays L.) breeders emphasize selection within F2 populations derived from crosses of inbred lines: Studies of the inheritance of quantitative traits in maize have been conducted primarily for generically broad-based populations. Objectives of our study were to estimate the generic variability in F2 populations developed from crosses of related and unrelated lines and to determine the effects of five generations of random intermating of plants within F2 populations on the estimates of genetic variability. Estimates of additive genetic variability were greater in the unrelated line crosses, but the estimates were not significantly different before and after random intermating within both crosses. Estimates of dominance variance decreased with random mating, suggesting that linkage effects were affecting the estimates. For applied breeding programs, it seems that adequate genetic variability was available in both types of crosses and that five generations of random intermating were not effective for increasing genetic variability

Universum-inspired Supervised Contrastive Learning

As an effective data augmentation method, Mixup synthesizes an extra amount of samples through linear interpolations. Despite its theoretical dependency on data properties, Mixup reportedly performs well as a regularizer and calibrator contributing reliable robustness and generalization to deep model training. In this paper, inspired by Universum Learning which uses out-of-class samples to assist the target tasks, we investigate Mixup from a largely under-explored perspective - the potential to generate in-domain samples that belong to none of the target classes, that is, universum. We find that in the framework of supervised contrastive learning, Mixup-induced universum can serve as surprisingly high-quality hard negatives, greatly relieving the need for large batch sizes in contrastive learning. With these findings, we propose Universum-inspired supervised Contrastive learning (UniCon), which incorporates Mixup strategy to generate Mixup-induced universum as universum negatives and pushes them apart from anchor samples of the target classes. We extend our method to the unsupervised setting, proposing Unsupervised Universum-inspired contrastive model (Un-Uni). Our approach not only improves Mixup with hard labels, but also innovates a novel measure to generate universum data. With a linear classifier on the learned representations, UniCon shows state-of-the-art performance on various datasets. Specially, UniCon achieves 81.7% top-1 accuracy on CIFAR-100, surpassing the state of art by a significant margin of 5.2% with a much smaller batch size, typically, 256 in UniCon vs. 1024 in SupCon using ResNet-50. Un-Uni also outperforms SOTA methods on CIFAR-100. The code of this paper is released on https://github.com/hannaiiyanggit/UniCon.Comment: Accepted by IEEE Transactions on Image Processin

Experimental research on dynamic characteristics of a hybrid gas bearing-rotor system for high-speed permanent magnet machine

An experiment on the vibrational characteristics of a hybrid gas bearing-rotor system in a 45 kW high-speed permanent magnet machine test rig is conducted. Nonlinear methods of measurements and analyses, including bifurcation maps, frequency spectra, and axis orbits, are adopted to evaluate sub-synchronous vibration in rotor acceleration. The effects of bearing supply pressure and speed accelerating rates on the stability of the gas bearing-rotor system are determined. Experimental results show that half-speed whirling of the gas film is eliminated and the start of gas film whipping is delayed by using the appropriate bearing supply pressure plan, thereby improving stability. Meanwhile power frequency vibrational amplitude is the smallest during the acceleration process, including the critical speed, when the appropriate speed accelerating rates are employed

Modeling the High-Pressure Solid and Liquid Phases of Tin from Deep Potentials with ab initio Accuracy

Constructing an accurate atomistic model for the high-pressure phases of tin (Sn) is challenging because properties of Sn are sensitive to pressures. We develop machine-learning-based deep potentials for Sn with pressures ranging from 0 to 50 GPa and temperatures ranging from 0 to 2000 K. In particular, we find the deep potential, which is obtained by training the ab initio data from density functional theory calculations with the state-of-the-art SCAN exchange-correlation functional, is suitable to characterize high-pressure phases of Sn. We systematically validate several structural and elastic properties of the {\alpha} (diamond structure), {\beta}, bct, and bcc structures of Sn, as well as the structural and dynamic properties of liquid Sn. The thermodynamics integration method is further utilized to compute the free energies of the {\alpha}, {\beta}, bct, and liquid phases, from which the deep potential successfully predicts the phase diagram of Sn including the existence of the triple-point that qualitatively agrees with the experiment

Is SN 2006X from a WD + MS system with optically thick wind?

The single-degenerate channel is widely accepted as the progenitors of type Ia supernovae (SNe Ia). Following the work of Meng, Chen and Han (2009), we reproduced the birth rate and age of supernovae like SN 2006X by the single-degenerate model (WD + MS) with an optically thick wind, which may imply that the progenitor of SN 2006X is a WD + MS system.Comment: 10 pages, 2 figures, accepted for publication in New

Efficient Diffusion Training via Min-SNR Weighting Strategy

Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence. In this paper, we discovered that the slow convergence is partly due to conflicting optimization directions between timesteps. To address this issue, we treat the diffusion training as a multi-task learning problem, and introduce a simple yet effective approach referred to as Min-SNR-$\gamma$. This method adapts loss weights of timesteps based on clamped signal-to-noise ratios, which effectively balances the conflicts among timesteps. Our results demonstrate a significant improvement in converging speed, 3.4$\times$ faster than previous weighting strategies. It is also more effective, achieving a new record FID score of 2.06 on the ImageNet $256\times256$ benchmark using smaller architectures than that employed in previous state-of-the-art. The code is available at https://github.com/TiankaiHang/Min-SNR-Diffusion-Training