Fire Performance of Steel Reinforced Concrete (SRC) Structures

AbstractThis paper summarizes some of the recent research published on steel reinforced concrete (SRC) structures under or after exposure to fire. The contents include: 1) Fire resistance and post-fire behavior of SRC columns; 2) Fire performance of SRC column to beam joints, by adopting a loading sequence including initial loading, heating, cooling and post-fire loading; 3) Fire resistance and post-fire behavior of SRC composite frames

Threshold current of field-free perpendicular magnetization switching using anomalous spin-orbit torque

Spin-orbit torque (SOT) is a candidate technique in next generation magnetic random-access memory (MRAM). Recently, experiments show that some material with low-symmetric crystalline or magnetic structures can generate anomalous SOT that has an out-of-plane component, which is crucial in switching perpendicular magnetization of adjacent ferromagnetic (FM) layer in the field-free condition. In this work, we analytically derive the threshold current of field-free perpendicular magnetization switching using the anomalous SOT. And we numerically calculate the track of the magnetic moment in a FM free layer when an applied current is smaller and greater than the threshold current. After that, we study the applied current dependence of the switching time and the switching energy consumption, which shows the minimum energy consumption decreases as out-of-plane torque proportion increases. Then we study the dependences of the threshold current on anisotropy strength, out-of-plane torque proportion, FM free layer thickness and Gilbert damping constant, and the threshold current shows negative correlation with the out-of-plane torque proportion and positive correlation with the other three parameters. Finally, we demonstrate that when the applied current is smaller than the threshold current, although it cannot switch the magnetization of FM free layer, it can still equivalently add an effective exchange bias field H_{bias} on the FM free layer. The H_{bias} is proportional to the applied current J_{SOT}, which facilitates the determination of the anomalous SOT efficiency. This work helps us to design new spintronic devices that favor field-free switching perpendicular magnetization using the anomalous SOT, and provides a way to adjust the exchange bias field, which is helpful in controlling FM layer magnetization depinning

On Penalty-based Bilevel Gradient Descent Method

Bilevel optimization enjoys a wide range of applications in hyper-parameter optimization, meta-learning and reinforcement learning. However, bilevel optimization problems are difficult to solve. Recent progress on scalable bilevel algorithms mainly focuses on bilevel optimization problems where the lower-level objective is either strongly convex or unconstrained. In this work, we tackle the bilevel problem through the lens of the penalty method. We show that under certain conditions, the penalty reformulation recovers the solutions of the original bilevel problem. Further, we propose the penalty-based bilevel gradient descent (PBGD) algorithm and establish its finite-time convergence for the constrained bilevel problem without lower-level strong convexity. Experiments showcase the efficiency of the proposed PBGD algorithm.Comment: Improved Section 4 by removing a critical assumption; Added Section 5 and citation

A Region-Shrinking-Based Acceleration for Classification-Based Derivative-Free Optimization

Derivative-free optimization algorithms play an important role in scientific and engineering design optimization problems, especially when derivative information is not accessible. In this paper, we study the framework of classification-based derivative-free optimization algorithms. By introducing a concept called hypothesis-target shattering rate, we revisit the computational complexity upper bound of this type of algorithms. Inspired by the revisited upper bound, we propose an algorithm named "RACE-CARS", which adds a random region-shrinking step compared with "SRACOS" (Hu et al., 2017).. We further establish a theorem showing the acceleration of region-shrinking. Experiments on the synthetic functions as well as black-box tuning for language-model-as-a-service demonstrate empirically the efficiency of "RACE-CARS". An ablation experiment on the introduced hyperparameters is also conducted, revealing the mechanism of "RACE-CARS" and putting forward an empirical hyperparameter-tuning guidance

Utopia point method based robust vector polynomial optimization scheme

In this paper, we focus on a class of robust vector polynomial optimization problems (RVPOP in short) without any convex assumptions. By combining/improving the utopia point method (a nonlinear scalarization) for vector optimization and "joint+marginal" relaxation method for polynomial optimization, we solve the RVPOP successfully. Both theoratical and computational aspects are considered