New nonlinear dielectric materials: Linear electrorheological fluids under the influence of electrostriction

The usual approach to the development of new nonlinear dielectric materials focuses on the search for materials in which the components possess an inherently large nonlinear dielectric response. In contrast, based on thermodynamics, we have presented a first-principles approach to obtain the electrostriction-induced effective third-order nonlinear susceptibility for the electrorheological (ER) fluids in which the components have inherent linear, rather than nonlinear, responses. In detail, this kind of nonlinear susceptibility is in general of about the same order of magnitude as the compressibility of the linear ER fluid at constant pressure. Moreover, our approach has been demonstrated in excellent agreement with a different statistical method. Thus, such linear ER fluids can serve as a new nonlinear dielectric material.Comment: 11 page

Electronic Structures of CaAlSi with Different Stacking AlSi Layers by First-Principles Calculations

The full-potential linear augmented plane-wave calculations have been applied to investigate the systematic change of electronic structures in CaAlSi due to different stacking sequences of AlSi layers. The present ab-initio calculations have revealed that the multistacking, buckling and 60 degrees rotation of AlSi layer affect the electronic band structure in this system. In particular, such a structural perturbation gives rise to the disconnected and cylindrical Fermi surface along the M-L lines of the hexagonal Brillouin zone. This means that multistacked CaAlSi with the buckling AlSi layers increases degree of two-dimensional electronic characters, and it gives us qualitative understanding for the quite different upper critical field anisotropy between specimens with and without superstructure as reported previously.Comment: 4 pages, 4 figures, to be published in J. Phys. Soc. Jp

AGGREGATE STABILITY AND WATER RETENTION NEAR SATURATION CHARACTERISTICS AS AFFECTED BY SOIL TEXTURE, AGGREGATE SIZE AND POLYACRYLAMIDE APPLICATION

Understanding the effects of soil intrinsic properties and extrinsic conditions on aggregate stability is essential for the development of effective soil and water conservation practices. Our objective was to evaluate the combined role of soil texture, aggregate size and application of a stabilizing agent on aggregate and structure stability indices (composite structure index [SI], the and n parameters of the VG model and the S-index) by employing the high energy (0-5.0 J kg(-1)) moisture characteristic (HEMC) method. We used aggregates of three sizes (0.25-0.5, 0.5-1.0 and 1.0-2.0 mm) from four semi-arid soils treated with polyacrylamide (PAM). An increase in SI was associated with the increase in clay content, aggregate size and PAM application. The value of increased with the increase in aggregate size and with PAM application but was not affected by soil texture. For each aggregate size, a unique exponential type relationship existed between SI and . The value of n and the S-index tended, generally, to decrease with the increase in PAM application; however, an increase in aggregate size had an inconsistent effect on these two indices. The relationship between SI and n or the S-index could not be generalized. Our results suggest that (i) the effects of PAM on aggregate stability are not trivial, and its application as a soil conservation tool should consider field soil condition, and (ii), n and S-index cannot replace the SI as a solid measure for aggregate stability and soil structure firmness when assessing soil conservation practices

Multipartite Entanglement Measures and Quantum Criticality from Matrix and Tensor Product States

We compute the multipartite entanglement measures such as the global entanglement of various one- and two-dimensional quantum systems to probe the quantum criticality based on the matrix and tensor product states (MPSs/TPSs). We use infinite time-evolving block decimation (iTEBD) method to find the ground states numerically in the form of MPSs/TPSs, and then evaluate their entanglement measures by the method of tensor renormalization group (TRG). We find these entanglement measures can characterize the quantum phase transitions by their derivative discontinuity right at the critical points in all models considered here. We also comment on the scaling behaviors of the entanglement measures by the ideas of quantum state renormalization group transformations.Comment: 22 pages, 11 figure

A logarithmic generalization of tensor product theory for modules for a vertex operator algebra

We describe a logarithmic tensor product theory for certain module categories for a ``conformal vertex algebra.'' In this theory, which is a natural, although intricate, generalization of earlier work of Huang and Lepowsky, we do not require the module categories to be semisimple, and we accommodate modules with generalized weight spaces. The corresponding intertwining operators contain logarithms of the variables.Comment: 39 pages. Misprints corrected. Final versio

Inferring short-term volatility indicators from Bitcoin blockchain

In this paper, we study the possibility of inferring early warning indicators (EWIs) for periods of extreme bitcoin price volatility using features obtained from Bitcoin daily transaction graphs. We infer the low-dimensional representations of transaction graphs in the time period from 2012 to 2017 using Bitcoin blockchain, and demonstrate how these representations can be used to predict extreme price volatility events. Our EWI, which is obtained with a non-negative decomposition, contains more predictive information than those obtained with singular value decomposition or scalar value of the total Bitcoin transaction volume

Maximum a Posteriori Adaptation of Network Parameters in Deep Models

We present a Bayesian approach to adapting parameters of a well-trained context-dependent, deep-neural-network, hidden Markov model (CD-DNN-HMM) to improve automatic speech recognition performance. Given an abundance of DNN parameters but with only a limited amount of data, the effectiveness of the adapted DNN model can often be compromised. We formulate maximum a posteriori (MAP) adaptation of parameters of a specially designed CD-DNN-HMM with an augmented linear hidden networks connected to the output tied states, or senones, and compare it to feature space MAP linear regression previously proposed. Experimental evidences on the 20,000-word open vocabulary Wall Street Journal task demonstrate the feasibility of the proposed framework. In supervised adaptation, the proposed MAP adaptation approach provides more than 10% relative error reduction and consistently outperforms the conventional transformation based methods. Furthermore, we present an initial attempt to generate hierarchical priors to improve adaptation efficiency and effectiveness with limited adaptation data by exploiting similarities among senones