Resource-Adaptive Newton's Method for Distributed Learning

Distributed stochastic optimization methods based on Newton's method offer significant advantages over first-order methods by leveraging curvature information for improved performance. However, the practical applicability of Newton's method is hindered in large-scale and heterogeneous learning environments due to challenges such as high computation and communication costs associated with the Hessian matrix, sub-model diversity, staleness in training, and data heterogeneity. To address these challenges, this paper introduces a novel and efficient algorithm called RANL, which overcomes the limitations of Newton's method by employing a simple Hessian initialization and adaptive assignments of training regions. The algorithm demonstrates impressive convergence properties, which are rigorously analyzed under standard assumptions in stochastic optimization. The theoretical analysis establishes that RANL achieves a linear convergence rate while effectively adapting to available resources and maintaining high efficiency. Unlike traditional first-order methods, RANL exhibits remarkable independence from the condition number of the problem and eliminates the need for complex parameter tuning. These advantages make RANL a promising approach for distributed stochastic optimization in practical scenarios

The Role of AM Symbiosis in Plant Adaptation to Drought Stress

Symposium paper Part 1: Function and management of soil microorganisms in agro-ecosystems with special reference to arbuscular mycorrhizal fung

Surface charging on HVDC spacers considering time-varying effect of temperature and electric fields

The dynamic behavior of surface charging on spacers in DC-GILs can be influenced by multi-factors including the non-uniform distributed electric field as well as the time-varying temperature gradient. In this paper, the time-varying effect of surface charging phenomenon on spacers is studied and a time-varying mathematical model is established, based on the influence of temperature and electric field on the ion mobility at the gas phase and the bulk conductivity in the solid phase. The results verify that the bulk conductivity can be greatly influenced by temperature, which leads to an increase in the surface charge density on the spacer. This allows the surface charge accumulation to stabilize more quickly. However, the ion mobility from the gas phase is less affected by temperature. When the non-uniform distributed electric field changes from 1.3 to 6.4 kV/mm, ion mobility is less influenced and the surface charge density on the spacer varies slightly. In this case, the effects of the non-uniformly distributed electric field in surface charge density variation is much smaller and can be ignored