Investigate the interaction between dark matter and dark energy

In this paper we investigate the interaction between dark matter and dark energy by considering two different interacting scenarios, i.e. the cases of constant interaction function and variable interaction function. By fitting the current observational data to constrain the interacting models, it is found that the interacting strength is non-vanishing, but weak for the case of constant interaction function, and the interaction is not obvious for the case of variable interaction function. In addition, for seeing the influence from interaction we also investigate the evolutions of interaction function, effective state parameter for dark energy and energy density of dark matter. At last some geometrical quantities in the interacting scenarios are discussed.Comment: 14 pages, 6 figure

Distilling Word Embeddings: An Encoding Approach

Distilling knowledge from a well-trained cumbersome network to a small one has recently become a new research topic, as lightweight neural networks with high performance are particularly in need in various resource-restricted systems. This paper addresses the problem of distilling word embeddings for NLP tasks. We propose an encoding approach to distill task-specific knowledge from a set of high-dimensional embeddings, which can reduce model complexity by a large margin as well as retain high accuracy, showing a good compromise between efficiency and performance. Experiments in two tasks reveal the phenomenon that distilling knowledge from cumbersome embeddings is better than directly training neural networks with small embeddings.Comment: Accepted by CIKM-16 as a short paper, and by the Representation Learning for Natural Language Processing (RL4NLP) Workshop @ACL-16 for presentatio

Impact of capillary pressure on micro-fracture propagation pressure during hydraulic fracturing in shales: An analytical model

The presence of micro-fractures in shale reservoirs is vital for economic production. While a number of models have been proposed to predict the propagation pressure of pre-existing micro-fractures, few models have considered capillary pressure, which may play a significant role in the presence of micro-fractures with nano-scale width. In this study, a new model was developed to predict the propagation pressure of microfractures. It is assumed that pre-existing micro-fractures are arbitrarily intersected with the propagated hydraulic fractures. The model was derived based upon linear elastic fracture mechanics under the condition of mode I fracture propagation coupled with capillary pressure. Furthermore, this paper also conducted sensitivity analyses to predict the micro-fracture propagation pressure as a function of the contact angle, surface tension and the width of micro-fracture. The results demonstrated that decreasing the contact angle reduces the propagation pressure of micro-fractures, implying that a hydrophilic system may yield a lower fracture propagation pressure compared with the hydrophobic counterpart. Moreover, for a hydrophilic system, further decreasing the contact angle shifts the propagation pressure to a negative value, implying that the capillary pressure may induce the propagation of micro-fractures without external fluid injection. The propagation pressure is also affected by the surface tension and the width of micro-fracture.Document Type: Original articleCited as: Lu, Y., Jin, Y., Li, H. Impact of capillary pressure on micro-fracture propagation pressure during hydraulic fracturing in shales: An analytical model. Capillarity, 2023, 8(3): 45-52. https://doi.org/10.46690/capi.2023.09.0