286 research outputs found
Avoiding Chinglish on the Lexical Level by Acquiring Lexical Phrases
Chinglish is a kind of abnormal English with Chinese characteristics which hinders cross-cultural communication. In this paper, Chinglish on the lexical level is divided into three categories and the sources of this problem are explored. Using lexical phrases to avoid Chinglish on the lexical level is suggested and specific suggestions and measures are offered from the pedagogical point of view
Variational approach to time-dependent fluorescence of a driven qubit
We employ the Dirac-Frenkel variational principle and multiple Davydov ansatz
to study time-dependent fluorescence spectra of a driven qubit in the weak- to
strong qubit-reservoir coupling regimes, where both the Rabi frequency and
spontaneous decay rate are comparable to the transition frequency of the qubit.
Our method agrees well with the time-local master-equation approach in the
weak-coupling regime, and offers a flexible way to compute the spectra from the
bosonic dynamics instead of two-time correlation functions. While the
perturbative master equation breaks down in the strong-coupling regime, our
method actually becomes more accurate due to the use of bosonic coherent states
under certain conditions. We show that the counter-rotating coupling between
the qubit and the reservoir has considerable contributions to the photon number
dynamics and the spectra under strong driving conditions even though the
coupling is moderately weak. The time-dependent spectra are found to be
generally asymmetric, a feature that is derived from photon number dynamics. In
addition, it is shown that the spectral profiles can be dramatically different
from the Mollow triplet due to strong dissipation and/or multiphoton processes
associated with the strong driving. Our formalism provides a unique perspective
to interpret time-dependent spectra.Comment: 19 pages, 8 figure
Do matrix metalloproteinase and cathepsin K inhibitors work synergistically to reduce dentin erosion?
Objectives: To evaluate the effects of matrix metalloproteinase (MMP) and cathepsin K (catK) inhibitors on resistance to dentin erosion. Methodology: A total of 96 dentin specimens (3×3×2 mm) were prepared and randomly assigned into four groups (n=24): deionized water (DW); 1 µM odanacatib (ODN, catK inhibitor); 1 mM 1,10-phenanthroline (PHEN, MMP inhibitor); and 1 µM odanacatib + 1 mM 1,10-phenanthroline (COM). Each group was further divided into two subgroups for the application of treatment solutions before (PRE) and after erosive challenges (POST). All specimens were subjected to four daily erosive challenges for 5 d. For each erosive challenge, the specimens in subgroup PRE were immersed in the respective solutions before cola drinks, while the specimens in subgroup POST were immersed in the respective solutions after cola drinks (the immersion duration was 5 min in both cases). All specimens were stored in artificial saliva at 37°C between erosive challenges. The erosive dentin loss (EDL) was measured by profilometry. The residual demineralized organic matrix (DOM) of specimens was removed using type VII collagenase and evaluated by profilometry. Both the EDL and thickness of the residual DOM were statistically analyzed by two-way analysis of variance (ANOVA) and Bonferroni’s test (α=0.05). The surface topography and transverse sections of the specimens were observed using SEM. MMPs and catK were immunolabeled in the eroded dentin and in situ zymography was performed to evaluate the enzyme activity. Results: Significantly lower EDL was found in the groups ODN, PHEN, and COM than in the control group (all p<0.05), while no significant difference in EDL was found among the groups ODN, PHEN, and COM (all p>0.05). The application sequence showed no significant effect on the EDL of the tested groups (p=0.310). A significantly thicker DOM was observed in the group ODN than in the control group regardless of the application sequence (both p<0.05). The treatment with ODN, PHEN, and COM inhibited the gelatinolytic activity by approximately 46.32%, 58.6%, and 74.56%, respectively. Conclusions: The inhibition of endogenous dentinal MMPs and catK increases the acid resistance of human dentin but without an apparent synergistic effect. The inhibition of MMPs and catK is equally effective either before or after the acid challenge
Multiscale Geometric Modeling of Macromolecules I: Cartesian Representation
This paper focuses on the geometric modeling and computational algorithm development of biomolecular structures from two data sources: Protein Data Bank (PDB) and Electron Microscopy Data Bank (EMDB) in the Eulerian (or Cartesian) representation. Molecular surface (MS) contains non-smooth geometric singularities, such as cusps, tips and self-intersecting facets, which often lead to computational instabilities in molecular simulations, and violate the physical principle of surface free energy minimization. Variational multiscale surface definitions are proposed based on geometric flows and solvation analysis of biomolecular systems. Our approach leads to geometric and potential driven Laplace–Beltrami flows for biomolecular surface evolution and formation. The resulting surfaces are free of geometric singularities and minimize the total free energy of the biomolecular system. High order partial differential equation (PDE)-based nonlinear filters are employed for EMDB data processing. We show the efficacy of this approach in feature-preserving noise reduction. After the construction of protein multiresolution surfaces, we explore the analysis and characterization of surface morphology by using a variety of curvature definitions. Apart from the classical Gaussian curvature and mean curvature, maximum curvature, minimum curvature, shape index, and curvedness are also applied to macromolecular surface analysis for the first time. Our curvature analysis is uniquely coupled to the analysis of electrostatic surface potential, which is a by-product of our variational multiscale solvation models. As an expository investigation, we particularly emphasize the numerical algorithms and computational protocols for practical applications of the above multiscale geometric models. Such information may otherwise be scattered over the vast literature on this topic. Based on the curvature and electrostatic analysis from our multiresolution surfaces, we introduce a new concept, the polarized curvature, for the prediction of protein binding sites
VQ-NeRF: Vector Quantization Enhances Implicit Neural Representations
Recent advancements in implicit neural representations have contributed to
high-fidelity surface reconstruction and photorealistic novel view synthesis.
However, the computational complexity inherent in these methodologies presents
a substantial impediment, constraining the attainable frame rates and
resolutions in practical applications. In response to this predicament, we
propose VQ-NeRF, an effective and efficient pipeline for enhancing implicit
neural representations via vector quantization. The essence of our method
involves reducing the sampling space of NeRF to a lower resolution and
subsequently reinstating it to the original size utilizing a pre-trained VAE
decoder, thereby effectively mitigating the sampling time bottleneck
encountered during rendering. Although the codebook furnishes representative
features, reconstructing fine texture details of the scene remains challenging
due to high compression rates. To overcome this constraint, we design an
innovative multi-scale NeRF sampling scheme that concurrently optimizes the
NeRF model at both compressed and original scales to enhance the network's
ability to preserve fine details. Furthermore, we incorporate a semantic loss
function to improve the geometric fidelity and semantic coherence of our 3D
reconstructions. Extensive experiments demonstrate the effectiveness of our
model in achieving the optimal trade-off between rendering quality and
efficiency. Evaluation on the DTU, BlendMVS, and H3DS datasets confirms the
superior performance of our approach.Comment: Submitted to the 38th Annual AAAI Conference on Artificial
Intelligenc
Combinatorial and Hodge Laplacians: Similarity and Difference
As key subjects in spectral geometry and combinatorial graph theory
respectively, the (continuous) Hodge Laplacian and the combinatorial Laplacian
share similarities in revealing the topological dimension and geometric shape
of data and in their realization of diffusion and minimization of harmonic
measures. It is believed that they also both associate with vector calculus,
through the gradient, curl, and divergence, as argued in the popular usage of
"Hodge Laplacians on graphs" in the literature. Nevertheless, these Laplacians
are intrinsically different in their domains of definitions and applicability
to specific data formats, hindering any in-depth comparison of the two
approaches.
To facilitate the comparison and bridge the gap between the combinatorial
Laplacian and Hodge Laplacian for the discretization of continuous manifolds
with boundary, we further introduce Boundary-Induced Graph (BIG) Laplacians
using tools from Discrete Exterior Calculus (DEC). BIG Laplacians are defined
on discrete domains with appropriate boundary conditions to characterize the
topology and shape of data. The similarities and differences of the
combinatorial Laplacian, BIG Laplacian, and Hodge Laplacian are then examined.
Through an Eulerian representation of 3D domains as level-set functions on
regular grids, we show experimentally the conditions for the convergence of BIG
Laplacian eigenvalues to those of the Hodge Laplacian for elementary shapes.Comment: 26 page
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