Relativistic spin-orbit interaction drastically modifies electronic band and endows emergent functionalities. One of the example is the Rashba effect 1,2 . In noncentrosymmetric systems such as interface 3 and polar materials 4,5 , the electronic band is spin-splitted depending on the momentum direction owing to the spin-orbit interaction, which is useful for the electric manipulation of spin current. Similar relativistic band-modification is also emergent for spin wave (magnon) in magnetic materials. The asymmetric magnon band dispersion induced by the Dzyaloshinskii-Moriya interaction 6,7 , which is antisymmetric exchange interaction originating from the spinorbit interaction, is theoretically expected 8,9 , and experimentally observed recently in noncentrosymmetric ferromagnets 10,11 . Here, we demonstrate that the nonreciprocal microwave response can be induced by the asymmetric magnon band in a noncentrosymmetric ferrimagnet LiFe 5 O 8 . This result may pave a new path to designing magnonic device based on the relativistic band engineering.

A 2.5-dimensional MHD simulation based on the quadruple magnetic source model (Uchida et al.) was performed to deal with a dark Ðlament eruption and the associated arcade Ñare. The numerical results are summarized as follows :1. A partition-like dark Ðlament containing longitudinal Ðelds (in some cases, helical Ðelds) is conÐned in the thin current sheet in a quadruple magnetic source Ðeld. The dark Ðlament separates the oppositely directed magnetic loop systems squeezed from both sides and thus prevents the release of the stored magnetic energy via magnetic reconnection. Thinning of the current sheet Ðnally leads to the tearing of the dark Ðlament, and the onset of anomalous resistivity at the tearing point induces fast magnetic reconnection.2. In the region above the reconnection point, the upward plasma acceleration occurs at the slow shock where stressed magnetic Ðeld lines are allowed to expand upward as the result of the magnetic reconnection. The dark Ðlament with helical Ðelds is accelerated upward in this expanding Ðeld structure with a rounded shape, which may correspond to coronal mass ejection (CME) containing the erupted dark Ðlament. On the other hand, reconnected Ðeld lines below the reconnection point shrink to form magnetic arcade loops heated by adiabatic compression, which may correspond to the associated arcade Ñare.3. It is shown that the greater part of the stored magnetic energy goes into the kinetic energy of the upward coronal magnetic expansion, and the rest is used for the heating of the magnetic arcade. This may explain the fact that CME carries greater energy compared with the energy of the associated arcade Ñare.

Although allogeneic hematopoietic stem cell transplantation (allo‐HSCT) is a curative therapy for high‐risk acute leukemia (AL), some patients still relapse. Since patients simultaneously have many prognostic factors, difficulties are associated with the construction of a patient‐based prediction algorithm of relapse. The alternating decision tree (ADTree) is a successful classification method that combines decision trees with the predictive accuracy of boosting. It is a component of machine learning (ML) and has the capacity to simultaneously analyze multiple factors. Using ADTree, we attempted to construct a prediction model of leukemia relapse within 1 year of transplantation. With the model of training data (n = 148), prediction accuracy, the AUC of ROC, and the κ‐statistic value were 78.4%, 0.746, and 0.508, respectively. The false positive rate (FPR) of the relapse prediction was as low as 0.134. In an evaluation of the model with validation data (n = 69), prediction accuracy, AUC, and FPR of the relapse prediction were similar at 71.0%, 0.667, and 0.216, respectively. These results suggest that the model is generalized and highly accurate. Furthermore, the output of ADTree may visualize the branch point of treatment. For example, the selection of donor types resulted in different relapse predictions. Therefore, clinicians may change treatment options by referring to the model, thereby improving outcomes. The present results indicate that ML, such as ADTree, will contribute to the decision‐making process in the diversified allo‐HSCT field and be useful for preventing the relapse of leukemia.

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