Raman fingerprint of two terahertz spin wave branches in a two-dimensional honeycomb Ising ferromagnet

Two-dimensional (2D) magnetism has been long sought-after and only very recently realized in atomic crystals of magnetic van der Waals materials. So far, a comprehensive understanding of the magnetic excitations in such 2D magnets remains missing. Here we report polarized micro-Raman spectroscopy studies on a 2D honeycomb ferromagnet CrI3. We show the definitive evidence of two sets of zero-momentum spin waves at frequencies of 2.28 terahertz (THz) and 3.75 THz, respectively, that are three orders of magnitude higher than those of conventional ferromagnets. By tracking the thickness dependence of both spin waves, we reveal that both are surface spin waves with lifetimes an order of magnitude longer than their temporal periods. Our results of two branches of high-frequency, long-lived surface spin waves in 2D CrI3 demonstrate intriguing spin dynamics and intricate interplay with fluctuations in the 2D limit, thus opening up opportunities for ultrafast spintronics incorporating 2D magnets.Comment: 25 pages, 4 figures + 8 supplementary figure

Activation of autophagy protects against cholestasis-induced hepatic injury

Do higher wages prevent corruption (bribe taking)? We investigate a setting where individuals who apply for public sector jobs are motivated not just by monetary incentives but also by intrinsic motivation and concern for the collective reputation of their profession. We show that an increase in monetary compensation may cause reputation‐concerned individuals to be more prone to participate in corruption due to an “overjustification” effect. The overall effect of monetary incentives on fighting corruption crucially depends on the composition of the pool of public sector workers for two reasons: first, different types of workers react differently to the same policy; second, the composition of the pool of workers affects individual behavior through its effect on collective reputation. These results imply in particular that policies to fight corruption should focus more on increasing the collective reputation of the public sector rather than using monetary incentives, which have perverse effects on some agents.</p

Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification

Federated Learning (FL) enables multiple institutes to train models collaboratively without sharing private data. Current FL research focuses on communication efficiency, privacy protection, and personalization and assumes that the data of FL have already been ideally collected. In medical scenarios, however, data annotation demands both expertise and intensive labor, which is a critical problem in FL. Active learning (AL), has shown promising performance in reducing the number of data annotations in medical image analysis. We propose a federated AL (FedAL) framework in which AL is executed periodically and interactively under FL. We exploit a local model in each hospital and a global model acquired from FL to construct an ensemble. We use ensemble-entropy-based AL as an efficient data-annotation strategy in FL. Therefore, our FedAL framework can decrease the amount of annotated data and preserve patient privacy while maintaining the performance of FL. To our knowledge, this is the first FedAL framework applied to medical images. We validated our framework on real-world dermoscopic datasets. Using only 50% of samples, our framework was able to achieve state-of-the-art performance on a skin-lesion classification task. Our framework performed better than several state-of-the-art AL methods under FL and achieved comparable performance to full-data FL.Comment: 14 pages, 3 figure