Pathwise Sensitivity Analysis in Transient Regimes

The instantaneous relative entropy (IRE) and the corresponding instanta- neous Fisher information matrix (IFIM) for transient stochastic processes are pre- sented in this paper. These novel tools for sensitivity analysis of stochastic models serve as an extension of the well known relative entropy rate (RER) and the corre- sponding Fisher information matrix (FIM) that apply to stationary processes. Three cases are studied here, discrete-time Markov chains, continuous-time Markov chains and stochastic differential equations. A biological reaction network is presented as a demonstration numerical example

Mean-field Study of Charge, Spin, and Orbital Orderings in Triangular-lattice Compounds ANiO2 (A=Na, Li, Ag)

We present our theoretical results on the ground states in layered triangular-lattice compounds ANiO2 (A=Na, Li, Ag). To describe the interplay between charge, spin, orbital, and lattice degrees of freedom in these materials, we study a doubly-degenerate Hubbard model with electron-phonon couplings by the Hartree-Fock approximation combined with the adiabatic approximation. In a weakly-correlated region, we find a metallic state accompanied by \sqroot3x\sqroot3 charge ordering. On the other hand, we obtain an insulating phase with spin-ferro and orbital-ferro ordering in a wide range from intermediate to strong correlation. These phases share many characteristics with the low-temperature states of AgNiO2 and NaNiO2, respectively. The charge-ordered metallic phase is stabilized by a compromise between Coulomb repulsions and effective attractive interactions originating from the breathing-type electronphonon coupling as well as the Hund's-rule coupling. The spin-orbital-ordered insulating phase is stabilized by the cooperative effect of electron correlations and the Jahn-Teller coupling, while the Hund's-rule coupling also plays a role in the competition with other orbital-ordered phases. The results suggest a unified way of understanding a variety of low-temperature phases in ANiO2. We also discuss a keen competition among different spin-orbital-ordered phases in relation to a puzzling behavior observed in LiNiO2

Anisotropic phonon conduction and lattice distortions in CMR-type bilayer manganite (La1−z_{1-z}Prz_{z})1.2_{1.2}Sr1.8_{1.8}Mn2_{2}O7_{7} (z=0,0.2,0.4 and 0.6) single crystals

We have undertaken a systematic study of thermal conductivity as a function of temperature and magnetic field of single crystals of the compound (La$_{1-z}$Pr$_{z}$)$_{1.2}$Sr$_{1.8}$Mn$_{2}$O$_{7}$ for $z$(Pr) =0.2,0.4. and 0.6. The lattice distortion due to Pr-substitution and anisotropic thermal conductivity in bilayer manganites are discussed on the basis of different relaxation models of local lattice distortions in metal and insulating states proposed by Maderda et al. The giant magnetothermal effect is scaled as a function of magnetization and discussed on the basis of a systematic variation of the occupation of the $e_g$-electron orbital states due to Pr-substitution.Comment: 7 pages, 6 figures, in press in Phys.Rev.

Towards a Practical Cluster Analysis over Encrypted Data

Cluster analysis is one of the most significant unsupervised machine learning tasks, and it is utilized in various fields associated with privacy issues including bioinformatics, finance and image processing. In this paper, we propose a practical solution for privacy-preserving cluster analysis based on homomorphic encryption~(HE). Our work is the first HE solution for the mean-shift clustering algorithm. To reduce the super-linear complexity of the original mean-shift algorithm, we adopt a novel random sampling method called dust sampling which perfectly fits in HE and achieves the linear complexity. We also substitute non-polynomial kernels by a new polynomial kernel so that it can be efficiently computed in HE. The HE implementation of our modified mean-shift clustering algorithm based on the approximate HE scheme HEAAN shows prominent performance in terms of speed and accuracy. It takes about $30$ minutes with $99\%$ accuracy over several public datasets with hundreds of data, and even for the dataset with $262,144$ data it takes only $82$ minutes applying SIMD operations in HEAAN. Our results outperform the previously best known result (SAC 2018) over $400$ times