Role of low Gd concentrations in the maximum pulse duration for thermally induced switching.

Thermally induced magnetization switching (TIMS) relying solely on a single laser without any applied magnetic field is a key research direction of current spintronics. Most studies on TIMS so far have focused on GdFeCo with Gd concentration above 20%. In this work, we observe the TIMS at low Gd concentration excited by picosecond laser through atomic spin simulations. The results show that the maximum pulse duration for switching can be increased by an appropriate pulse fluence at the intrinsic damping in low Gd concentrations. At the appropriate pulse fluence, TIMS with pulse duration longer than one picosecond is possible for Gd concentration of only 12%. Our simulation results provide new insights for the exploration of the physical mechanism of ultrafast TIMS.

Fine-Grained Butterfly Recognition via Peer Learning Network with Distribution-Aware Penalty Mechanism.

Automatic species recognition plays a key role in intelligent agricultural production management and the study of species diversity. However, fine-grained species recognition is a challenging task due to the quite diverse and subtle interclass differences among species and the long-tailed distribution of sample data. In this work, a peer learning network with a distribution-aware penalty mechanism is proposed to address these challenges. Specifically, the proposed method employs the two-stream ResNeSt-50 as the backbone to obtain the initial predicted results. Then, the samples, which are selected from the instances with the same predicted labels by knowledge exchange strategy, are utilized to update the model parameters via the distribution-aware penalty mechanism to mitigate the bias and variance problems in the long-tailed distribution. By performing such adaptive interactive learning, the proposed method can effectively achieve improved recognition accuracy for head classes in long-tailed data and alleviate the adverse effect of many head samples relative to a few samples of the tail classes. To evaluate the proposed method, we construct a large-scale butterfly dataset (named Butterfly-914) that contains approximately 72,152 images belonging to 914 species and at least 20 images for each category. Exhaustive experiments are conducted to validate the efficiency of the proposed method from several perspectives. Moreover, the superior Top-1 accuracy rate (86.2%) achieved on the butterfly dataset demonstrates that the proposed method can be widely used for agricultural species identification and insect monitoring.

Influence of a damping parameter on helicity-independent all-optical switching.

Ultrafast magnetization switching has aroused much interest in recent years. Due to the complicated physical mechanisms, helicity-independent all-optical switching (HI-AOS) still lacks comprehensive understanding. In this article, we revealed the influence of damping on HI-AOS based on the simulation of the semiclassical atomic spin dynamics model. The results suggested that the smaller damping not only contributes to the increase to the maximum required pulse duration and the pulse fluence threshold for switching but also slows down the rate of magnetization dynamics. Our simulation results could provide some theoretical foundation to explore the optimization parameters of HI-AOS.