Enhancing Domain Word Embedding via Latent Semantic Imputation

We present a novel method named Latent Semantic Imputation (LSI) to transfer external knowledge into semantic space for enhancing word embedding. The method integrates graph theory to extract the latent manifold structure of the entities in the affinity space and leverages non-negative least squares with standard simplex constraints and power iteration method to derive spectral embeddings. It provides an effective and efficient approach to combining entity representations defined in different Euclidean spaces. Specifically, our approach generates and imputes reliable embedding vectors for low-frequency words in the semantic space and benefits downstream language tasks that depend on word embedding. We conduct comprehensive experiments on a carefully designed classification problem and language modeling and demonstrate the superiority of the enhanced embedding via LSI over several well-known benchmark embeddings. We also confirm the consistency of the results under different parameter settings of our method.Comment: ACM SIGKDD 201

Large Deflection Analysis of Beam-Columns with General Sections Using Gaussian Line-element Method

The finite-element analysis method implementing line-elements is extensively adopted for the analysis and design of large-scale structures. The robustness of the element derivations and formulations decidedly impact the accuracy of capturing the actual member behaviors, thereby the structure deformations. This paper develops a new and innovative beam-column element that accounts for different member complexities. The section being nonsymmetric and the twisting deformations along the element length significantly affect the section stiffness and, as a result, the element stiffness matrix. The inclined angle between the cross-section’s and the element’s local axes is varied along the element length, making the cross-section properties apparently different. To this end, the Gaussian quadrature method is adopted to summate the section stiffness; moreover, a refined Updated-Lagrangian method is introduced to account for the element total deformations. Accordingly, fewer elements to simulate the structural member can be adopted, thereby dramatically enhancing the numerical efficiency. Detailed derivations are provided, and their implementations are elaborated. Finally, several validation examples are presented to verify the accuracy and examine the robustness of the proposed method.The first author would like to express his gratitude to Johns Hopkins University for providing an excellent research environment for conducting this research. The last author is grateful for the financial support from the Hong Kong Polytechnic University. The authors want to sincerely acknowledge Professor Ronald D. Ziemian for his guidance and helping on conducting this research

Recurrent Convolutional Neural Networks for Text Classification

Text classification is a foundational task in many NLP applications. Traditional text classifiers often rely on many human-designed features, such as dictionaries, knowledge bases and special tree kernels. In contrast to traditional methods, we introduce a recurrent convolutional neural network for text classification without human-designed features. In our model, we apply a recurrent structure to capture contextual information as far as possible when learning word representations, which may introduce considerably less noise compared to traditional window-based neural networks. We also employ a max-pooling layer that automatically judges which words play key roles in text classification to capture the key components in texts. We conduct experiments on four commonly used datasets. The experimental results show that the proposed method outperforms the state-of-the-art methods on several datasets, particularly on document-level datasets

Wall shear stress and wall heat flux in a supersonic turbulent boundary layer

We report the characteristics of wall shear stress (WSS) and wall heat flux (WHF) from direct numerical simulation (DNS) of a spatially developing zero-pressure-gradient supersonic turbulent boundary layer at a free-stream Mach number M-& INFIN; = 2.25 and a Reynolds number Re-tau = 769 with a cold-wall thermal condition (a ratio of wall temperature to recovery temperature T-w/T-r = 0.75). A comparative analysis is performed on statistical data, including fluctuation intensity, probability density function, frequency spectra, and space-time correlation. The root mean square fluctuations of the WHF exhibit a logarithmic dependence on Re-tau similar to that for the WSS, the main difference being a larger constant. Unlike the WSS, the probability density function of the WHF does not follow a lognormal distribution. The results suggest that the WHF contains more energy in the higher frequencies and propagates downstream faster than the WSS. A detailed conditional analysis comparing the flow structures responsible for extreme positive and negative fluctuation events of the WSS and WHF is performed for the first time, to the best of our knowledge. The conditioned results for the WSS exhibit closer structural similarities with the incompressible DNS analysis documented by Pan and Kwon [ "Extremely high wall-shear stress events in a turbulent boundary layer, " J. Phys.: Conf. Ser. 1001, 012004 (2018)] and Guerrero et al. [ "Extreme wall shear stress events in turbulent pipe flows: Spatial characteristics of coherent motions, " J. Fluid Mech. 904, A18 (2020)]. Importantly, the conditionally averaged flow fields of the WHF exhibit a different mechanism, where the extreme positive and negative events are generated by a characteristic two-layer structure of temperature fluctuations under the action of a strong Q4 event or a pair of strong oblique vortices. Nevertheless, we use the bi-dimensional empirical decomposition method to split the fluctuating velocity and temperature structures into four different modes with specific spanwise length scales, and we quantify their influence on the mean WSS and WHF generation. It is shown that the mean WSS is mainly related to small-scale structures in the near-wall region, whereas the mean WHF is associated with the combined action of near-wall small-scale structures and large-scale structures in the logarithmic and outer regions

Effect of interaction strength on recovery downstream of incident shock interactions

Direct numerical simulations of a supersonic turbulent boundary layer on a flat plate interacting with an impinging shock wave are carried out with two different incident shock angles at Mach 2.25. The effect of the interaction strength on the recovery process in the downstream region is systematically studied, including the turbulence evolution, the statistical and structural properties of wall pressure fluctuations, and the generation of mean skin friction and wall heat flux. The variations of the Reynolds stress components, the anisotropy tensor, and the turbulent kinetic energy budget in the two flow cases highlight a slow reversal tendency and an increasingly pronounced importance of the outer-layer large-scale structures in the relaxation region of the strong interaction. We find that the effect of increasing the interaction strength on the fluctuating wall pressure is reflected by a decrease in the characteristic frequencies, an increase in the spatial extent, and a decrease in the convection velocity. We decompose the mean skin friction and wall heat flux into different physically informed contributions and reveal that the mean wall heat flux generation is the same regardless of the interaction strength; in contrast, the generation mechanism of mean skin friction is found to be fundamentally changed. A novel scale-decomposition method is used to quantify the effect of the increased interaction strength on the leading components, and it is demonstrated that the energetic outer-layer large-scale structures are the dominant contributor in the recovery process as the interaction strength is increased. Published under an exclusive license by AIP Publishing

Direct numerical simulation of supersonic bump flow with shock impingement

Direct numerical simulations are carried out to identify the effects of shock impingement on the behavior of bump flow at freestream Mach number of 2.25. Two cosine-shaped bump cases, with and without an impinging oblique shock at an angle of 33.2 degrees, are compared. The shock impingement exhibits a remarkable influence on the pattern of the shock system and on the size of the separation region. A spectral analysis finds that low-frequency unsteadiness is significantly enhanced by the impingement interaction, and the proper orthogonal decomposition highlights the low-frequency breathing motion of the separation bubble, which is accurately reconstructed using only the first ten low-order modes. Downstream of the bump, both the Reynolds stress components and the turbulence kinetic energy exhibit a general amplification, with the peaks reoccurring at outer wall-normal locations. A turbulent kinetic energy budget analysis shows the greatly increased production in the outer layer which is balanced by turbulent transport and dissipation. An anisotropy-invariant map analysis identifies enhanced isotropic turbulence in the vicinity of the bump, which is qualitatively modified into a two-component axisymmetric state around the reattachment point. In addition, the mean skin friction decomposition suggests that the shock impingement has little influence on the predominant contribution of turbulence kinetic energy production, apart from the spatial growth dominance at the bump summit in the absence of the impinging shock. Interestingly, a scale-decomposed analysis quantitatively demonstrates that the contributions of small-scale structures are attenuated, but those of large-scale ones are relatively increased, with a contribution of more than 80% with shock impingement. Published under an exclusive license by AIP Publishing

Large Deflection Analysis of Beam-Columns with General Sections Using Gaussian Line-element Method

The finite-element analysis method implementing line-elements is extensively adopted for the analysis and design of large-scale structures. The robustness of the element derivations and formulations decidedly impact the accuracy of capturing the actual member behaviors, thereby the structure deformations. This paper develops a new and innovative beam-column element that accounts for different member complexities. The section being nonsymmetric and the twisting deformations along the element length significantly affect the section stiffness and, as a result, the element stiffness matrix. The inclined angle between the cross-section’s and the element’s local axes is varied along the element length, making the cross-section properties apparently different. To this end, the Gaussian quadrature method is adopted to summate the section stiffness; moreover, a refined Updated-Lagrangian method is introduced to account for the element total deformations. Accordingly, fewer elements to simulate the structural member can be adopted, thereby dramatically enhancing the numerical efficiency. Detailed derivations are provided, and their implementations are elaborated. Finally, several validation examples are presented to verify the accuracy and examine the robustness of the proposed method.The first author would like to express his gratitude to Johns Hopkins University for providing an excellent research environment for conducting this research. The last author is grateful for the financial support from the Hong Kong Polytechnic University. The authors want to sincerely acknowledge Professor Ronald D. Ziemian for his guidance and helping on conducting this research