5,696 research outputs found
The quasi-particle gap in a disordered boson Hubbard model in two dimensions
We investigate the behavior of the quasi-particle energy gap near quantum
phase transitions in a two-dimensional disordered boson Hubbard model at a
commensurate filling. Via Monte Carlo simulations of ensembles with fixed
numbers of particles, we observe the behavior of the gap as a function of the
tuning parameter for various strength of diagonal disorder. For weak disorder,
we find that gapped Mott insulating phase is sustained up to the transition
point and disappears only in a superfluid, strongly supporting a direct
Mott-insulator-to-superfluid transition. Bose glass behavior, insulating with
vanishing gap, appears only when the strength of disorder is bigger than a
critical value
Click-aware purchase prediction with push at the top
Eliciting user preferences from purchase records for performing purchase
prediction is challenging because negative feedback is not explicitly observed,
and because treating all non-purchased items equally as negative feedback is
unrealistic. Therefore, in this study, we present a framework that leverages
the past click records of users to compensate for the missing user-item
interactions of purchase records, i.e., non-purchased items. We begin by
formulating various model assumptions, each one assuming a different order of
user preferences among purchased, clicked-but-not-purchased, and non-clicked
items, to study the usefulness of leveraging click records. We implement the
model assumptions using the Bayesian personalized ranking model, which
maximizes the area under the curve for bipartite ranking. However, we argue
that using click records for bipartite ranking needs a meticulously designed
model because of the relative unreliableness of click records compared with
that of purchase records. Therefore, we ultimately propose a novel
learning-to-rank method, called P3Stop, for performing purchase prediction. The
proposed model is customized to be robust to relatively unreliable click
records by particularly focusing on the accuracy of top-ranked items.
Experimental results on two real-world e-commerce datasets demonstrate that
P3STop considerably outperforms the state-of-the-art implicit-feedback-based
recommendation methods, especially for top-ranked items.Comment: For the final published journal version, see
https://doi.org/10.1016/j.ins.2020.02.06
Effect of geopolymer composition and curing conditions on efflorescence in lightweight porous geopolymers
Lightweight geopolymers are more readily produced and give higher fire resistant performance than foam cement concrete. The fly ash-based lightweight geopolymers stones could be used for decoration due to the similarity in appearance and color to natural vesicular basalts. Efflorescence can be even more nettlesome issue in commercialization of lightweight porous geopolimers. Lowering the density of solid geopolymers can be achieved by inducing chemical reactions that entrain gases to foam the geopolymer structure. The apparent density of lightweight porous geopolymers has a range from 0.7 to 1.2 g/m3 with 0.025, 0.05 and 0.10 wt% of Al powder concentration, which corresponds to about 37~60 % of the apparent density, 1.96 g/cm3, of solid geopolymers. The compressive strength of cellular structured geopolymers decreased to 6~18 % of the compressive strength, 45 MPa of solid geopolymers. The microstructure of geopolymers gel was equivalent for both solid and cellular structured geopolymers. The efflorescence can be controlled by reducing targeted Na/Al molar ratio less than 1.0 or water content in geopolymers or curing at high temperature.
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Reynolds Averaged Solutions of the Navier-Stokes Equation
The mean of Young measure solutions for the Navier-Stokes equations with
general initial conditions are PDE solutions of the Navier-Stokes equation of
the class considered by Leray and Hopf.Comment: arXiv admin note: substantial text overlap with arXiv:2212.1473
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