Regularized Wasserstein Means for Aligning Distributional Data

We propose to align distributional data from the perspective of Wasserstein means. We raise the problem of regularizing Wasserstein means and propose several terms tailored to tackle different problems. Our formulation is based on the variational transportation to distribute a sparse discrete measure into the target domain. The resulting sparse representation well captures the desired property of the domain while reducing the mapping cost. We demonstrate the scalability and robustness of our method with examples in domain adaptation, point set registration, and skeleton layout

A modified lattice Bhatnagar-Gross-Krook model for convection heat transfer in porous media

The lattice Bhatnagar-Gross-Krook (LBGK) model has become the most popular one in the lattice Boltzmann method for simulating the convection heat transfer in porous media. However, the LBGK model generally suffers from numerical instability at low fluid viscosities and effective thermal diffusivities. In this paper, a modified LBGK model is developed for incompressible thermal flows in porous media at the representative elementary volume scale, in which the shear rate and temperature gradient are incorporated into the equilibrium distribution functions. With two additional parameters, the relaxation times in the collision process can be fixed at a proper value invariable to the viscosity and the effective thermal diffusivity. In addition, by constructing a modified equilibrium distribution function and a source term in the evolution equation of temperature field, the present model can recover the macroscopic equations correctly through the Chapman-Enskog analysis, which is another key point different from previous LBGK models. Several benchmark problems are simulated to validate the present model with the proposed local computing scheme for the shear rate and temperature gradient, and the numerical results agree well with analytical solutions and/or those well-documented data in previous studies. It is also shown that the present model and the computational schemes for the gradient operators have a second-order accuracy in space, and better numerical stability of the present modified LBGK model than previous LBGK models is demonstrated.Comment: 38pages,50figure

Volume-averaged macroscopic equation for fluid flow in moving porous media

Darcy's law and the Brinkman equation are two main models used for creeping fluid flows inside moving permeable particles. For these two models, the time derivative and the nonlinear convective terms of fluid velocity are neglected in the momentum equation. In this paper, a new momentum equation including these two terms are rigorously derived from the pore-scale microscopic equations by the volume-averaging method, which can reduces to Darcy's law and the Brinkman equation under creeping flow conditions. Using the lattice Boltzmann equation method, the macroscopic equations are solved for the problem of a porous circular cylinder moving along the centerline of a channel. Galilean invariance of the equations are investigated both with the intrinsic phase averaged velocity and the phase averaged velocity. The results demonstrate that the commonly used phase averaged velocity cannot serve as the superficial velocity, while the intrinsic phase averaged velocity should be chosen for porous particulate systems

Bank market competition and syndicated loan prices

This paper investigates the ‘price-concentration’ relationship in pricing syndicated loans. By measuring bank concentration at a state level in U.S, we show supporting evidence to market power hypothesis that syndicated loan prices are positively associated with the concentration of both borrower’s and lead arranger’s markets but not the concentration of participant lenders’ markets. We also show that loan prices are more sensitively to lead arranger’s market concentration than to borrower’s and a borrower could reduce loan costs by borrowing from a less concentrated bank market. In sharp contrast, loan prices are negatively associated with bank concentration if a loan syndication is led by an investment bank or non-bank financial institution. Our findings are robust to a variety of bank concentration measures and model specification

Bank competition, information specialization and innovation

Complementary to rich existing evidence on bank competition and corporate innovation, this paper aims to investigate the impacts of bank competition on innovation efficiencies, in terms of both R&D input and output at firm level. By acknowledging the role played by information asymmetries in financing innovation, we also examine the moderating effects of information specialization at both industry and firm level on corporate innovation. Analyzing innovation and bank structure data from U.S. between 1992 and 2010, we show novel evidence that increased bank competition improves innovation efficiencies in terms of both R&D input (investment) and output (patents and profits generated by R&D). In addition, we find bank competition has a greater favorable effect on innovation for those firms with more specialized information, such as those operating in an industry with more dispersed productivity growth and those with more concentrated patent types. Overall, our findings support market power hypothesis and banking strategic theory where bank competition improves credit supply to corporate innovation

Effects of different concentrations of topotactic hydrogen impurities on the electronic structure of nickelate superconductors

Infinite-layer nickelate superconductors have recently been discovered to share both similarities and differences with cuprate superconductors. Notably, the incorporation of hydrogen (H) through topotactic reduction has been found to play a critical role in their electronic structure and, consequently, their superconductivity. In this study, we utilized a theoretical approach combining density-functional theory and impurity approximation to design three characteristic multi-orbital Hubbard models representing low, moderate, and high concentrations of topotactic-hydrogen. Consistent with experimental findings, our simulations revealed that both low and high concentrations of topotactic-hydrogen induce high-spin states ($S$=1) that are composed by holes at $d_{x^2-y^2}$ and $d_{z^2}$ orbitals and consequently the emergent inter-site hopping between $d_{z^2}$ to $d_{x^2-y^2}$ is unfavorable for superconductivity. Conversely, an optimal concentration of 25\% H aligns with the single Ni-$d_{x^2-y^2}$ band picture of superconductivity in infinite-layer nickelates, demonstrating its beneficial effect on promoting superconducting behavior.Comment: 9 pages, 6 figure

Numerical and physical simulation of rapid microstructural evolution of gas atomised Ni superalloy powders

The rapid microstructural evolution of gas atomised Ni superalloy powder compacts over timescales of a few seconds was studied using a Gleeble 3500 thermomechanical simulator, finite element based numerical model and electron microscopy. The study found that the microstructural changes were governed by the characteristic temperatures of the alloy. At a temperature below the γ' solvus, the powders maintained dendritic structures. Above the γ' solvus temperature but in the solid-state, rapid grain spheroidisation and coarsening occurred, although the fine-scale microstructures were largely retained. Once the incipient melting temperature of the alloy was exceeded, microstructural change was rapid, and when the temperature was increased into the solid + liquid state, the powder compact partially melted and then re-solidified with no trace of the original structures, despite the fast timescales. The study reveals the relationship between short, severe thermal excursions and microstructural evolution in powder processed components, and gives guidance on the upper limit of temperature and time for powder-based processes if desirable fine-scale features of powders are to be preserved

Fast-R2D2: A Pretrained Recursive Neural Network based on Pruned CKY for Grammar Induction and Text Representation

Recently CKY-based models show great potential in unsupervised grammar induction thanks to their human-like encoding paradigm, which runs recursively and hierarchically, but requires $O(n^3)$ time-complexity. Recursive Transformer based on Differentiable Trees (R2D2) makes it possible to scale to large language model pre-training even with complex tree encoder by introducing a heuristic pruning method. However, the rule-based pruning approach suffers from local optimum and slow inference issues. In this paper, we fix those issues in a unified method. We propose to use a top-down parser as a model-based pruning method, which also enables parallel encoding during inference. Typically, our parser casts parsing as a split point scoring task, which first scores all split points for a given sentence, and then recursively splits a span into two by picking a split point with the highest score in the current span. The reverse order of the splits is considered as the order of pruning in R2D2 encoder. Beside the bi-directional language model loss, we also optimize the parser by minimizing the KL distance between tree probabilities from parser and R2D2. Our experiments show that our Fast-R2D2 improves performance significantly in grammar induction and achieves competitive results in downstream classification tasks.Comment: EMNLP 202