We present the first results of our spatially axisymmetric core-collapse supernova simulations with full Boltzmann neutrino transport, which amount to a time-dependent five-dimensional (two in space and three in momentum space) problem. Special relativistic effects are fully taken into account with a two-energy-grid technique. We performed two simulations for a progenitor of 11.2 M _☉, employing different nuclear equations of state (EOSs): Lattimer and Swesty’s EOS with the incompressibility of K = 220 MeV (LS EOS) and Furusawa’s EOS based on the relativistic mean field theory with the TM1 parameter set (FS EOS). In the LS EOS, the shock wave reaches ∼700 km at 300 ms after bounce and is still expanding, whereas in the FS EOS it stalled at ∼200 km and has started to recede by the same time. This seems to be due to more vigorous turbulent motions in the former during the entire postbounce phase, which leads to higher neutrino-heating efficiency in the neutrino-driven convection. We also look into the neutrino distributions in momentum space, which is the advantage of the Boltzmann transport over other approximate methods. We find nonaxisymmetric angular distributions with respect to the local radial direction, which also generate off-diagonal components of the Eddington tensor. We find that the rθ component reaches ∼10% of the dominant rr component and, more importantly, it dictates the evolution of lateral neutrino fluxes, dominating over the θθ component, in the semitransparent region. These data will be useful to further test and possibly improve the prescriptions used in the approximate methods.

We present a newly developed moving-mesh technique for the multi-dimensional Boltzmann-Hydro code for the simulation of core-collapse supernovae (CCSNe). What makes this technique different from others is the fact that it treats not only hydrodynamics but also neutrino transfer in the language of the 3 + 1 formalism of general relativity (GR), making use of the shift vector to specify the time evolution of the coordinate system. This means that the transport part of our code is essentially general relativistic, although in this paper it is applied only to the moving curvilinear coordinates in the flat Minknowski spacetime, since the gravity part is still Newtonian. The numerical aspect of the implementation is also described in detail. Employing the axisymmetric two-dimensional version of the code, we conduct two test computations: oscillations and runaways of proto-neutron star (PNS). We show that our new method works fine, tracking the motions of PNS correctly. We believe that this is a major advancement toward the realistic simulation of CCSNe.

This paper proposes a data collaboration analysis framework for distributed data sets. The proposed framework involves centralized machine learning while the original data sets and models remain distributed over a number of institutions. Recently, data has become larger and more distributed with decreasing costs of data collection. Centralizing distributed data sets and analyzing them as one data set can allow for novel insights and attainment of higher prediction performance than that of analyzing distributed data sets individually. However, it is generally difficult to centralize the original data sets because of a large data size or privacy concerns. This paper proposes a data collaboration analysis framework that does not involve sharing the original data sets to circumvent these difficulties. The proposed framework only centralizes intermediate representations constructed individually rather than the original data set. The proposed framework does not use privacy-preserving computations or model centralization. In addition, this paper proposes a practical algorithm within the framework. Numerical experiments reveal that the proposed method achieves higher recognition performance for artificial and real-world problems than individual analysis.

The demand for the privacy-preserving survival analysis of medical data integrated from multiple institutions or countries has been increased. However, sharing the original medical data is difficult because of privacy concerns, and even if it could be achieved, we have to pay huge costs for cross-institutional or cross-border communications. To tackle these difficulties of privacy-preserving survival analysis on multiple parties, this study proposes a novel data collaboration Cox proportional hazards (DC-COX) model based on a data collaboration framework for horizontally and vertically partitioned data. By integrating dimensionality-reduced intermediate representations instead of the original data, DC-COX obtains a privacy-preserving survival analysis without iterative cross-institutional communications or huge computational costs. DC-COX enables each local party to obtain an approximation of the maximum likelihood model parameter, the corresponding statistic, such as the p-value, and survival curves for subgroups. Based on a bootstrap technique, we introduce a dimensionality reduction method to improve the efficiency of DC-COX. Numerical experiments demonstrate that DC-COX can compute a model parameter and the corresponding statistics with higher performance than the local party analysis. Particularly, DC-COX demonstrates outstanding performance in essential feature selection based on the p-value compared with the existing methods including the federated learning-based method.