TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD

Genetic Evidence for an Indispensable Role of Somatic Embryogenesis Receptor Kinases in Brassinosteroid Signaling

The authors are grateful to the Arabidopsis Biological Resource Center for providing the T-DNA insertion lines discussed in this work. We thank Dr. Yanhai Yin (Iowa State University) for providing anti-BES1 antibody, Dr. Jiayang Li (Institute of Genetics and Developmental Biology, Chinese Academy of Sciences) for bri1-301 seeds, and Dr. Xing-wang Deng (Yale University) for cop1-4 and cop1-6 seeds as controls.Author Summary Brassinosteroids (BRs) are a group of plant hormones critical for plant growth and development. BRs are perceived by a cell-surface receptor complex including two distinctive receptor kinases, BRI1 and BAK1. Whereas BRI1 is a true BR-binding receptor, BAK1 does not appear to have BR-binding activity. Therefore, BAK1 is likely a co-receptor in BR signal transduction. The genetic significance of BAK1 was not clearly demonstrated in previous studies largely due to functional redundancy of BAK1 and its closely related homologues. It was not clear whether BAK1 plays an essential role or only an enhancing role in BR signaling. In this study, we identified all possible BAK1 redundant genes in the Arabidopsis thaliana genome and generated single, double, triple, and quadruple mutants. Detailed analysis indicated that, without BAK1 and its functionally redundant proteins, BR signaling is completely disrupted, largely because BRI1 has lost its ability to activate downstream components. These studies provide the first piece of loss-of-functional genetic evidence that BAK1 is indispensable to the early events of the BR signaling pathway.Yeshttp://www.plosgenetics.org/static/editorial#pee

EXISTENCE AND ASYMPTOTIC BEHAVIOR OF POSITIVE SOLUTIONS FOR A CLASS OF (p(x), q(x))-LAPLACIAN SYSTEMS

Abstract. In this paper, our main purpose is to establish the existence of positive solution of the following system where Ω ⊂ R N is a bounded domain with C 2 boundary, p(x),q(x) are functions which satisfy some conditions, We give the existence results of positive solutions and consider the asymptotic behavior of the solutions near the boundary. The approach is based on the sub-and super-solution method

Existence of multiple solutions for quasilinear elliptic equations in R^N

In this article, we establish the multiplicity of positive weak solution for the quasilinear elliptic equation \displaylines{ -\Delta_p u+\lambda|u|^{p-2}u=f(x) |u|^{s-2 }u+h(x)|u|^{r-2}u\quad x\in \mathbb{R}^N,\cr u>0\quad x\in \mathbb{R}^N,\cr u\in W^{1,p}(\mathbb{R}^N) } We show how the shape of the graph of f affects the number of positive solutions. Our results extend the corresponding results in [21]

Periodic Solution Problems for a Class of Hebbian-Type Networks with Time-Varying Delays

By using Gronwall’s inequality and coincidence degree theory, the sufficient conditions of the globally exponential stability and existence are given for a Hebbian-type network with time-varying delays. The periodic behavior phenomenon is one of the hot topics in network systems research, from which we can discover the symmetric characteristics of certain neurons. The main theorems in the present paper are illustrated using a numerical example

Contrast metric enhancement based on memory extraction for online class-incremental learning in image classification

In view of the catastrophic forgetting of previous knowledge in class incremental learning for image classification, existing replay-based methods focus on memory updating and sampling, while overlooking the feature relationships between old and new samples. To this end, the paper proposes a method called contrast metric enhancement based on memory extraction(cME2) for Online Class-incremental Learning in Image Classification, which designs two new types of positive and negative sample-pairs, enhances the reuse of old sample feature information, and strengthens the ability of model to express redundant features and common features. It improves the distribution of samples in embedding space based on the nearest class mean classifier. Finally, the effectiveness and efficiency of the proposed method are verified by comparison experiment and ablation experiment