Topics in Chinese syntax : word order in synchrony and diachrony.

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Low Resistivity Contrast Gas Bearing Formation Identification from Conventional Logs in Tight Gas Sandstones

It’s a great challenge in identifying gas bearing formation from conventional logs in tight gas sandstones due to the low resistivity contrast caused by high irreducible water saturation. Based on the difference of the principles of three kinds of porosity logs (density, neutron and acoustic logs), three porosities difference method, three porosities ratio method, correlation of neutron and density logs and the overlap method of water-filled porosity and total porosity are introduced to identify tight gas bearing reservoirs. In gas bearing formations, the difference of three porosities is higher than 0.0, the ratio of three porosities is higher than 1.0, the correlation between density and neutron logs is negative, and the water filled porosities are lower than total porosities. On the contrary, in water saturated formations, the difference of three porosities is lower than 0.0, the ratio of three porosities is lower than 1.0, the correlation between density and neutron logs is positive, and the water filled porosities are overlapped with total porosities. Considering the complexity of in-suit formation, when the proposed identification criterion are mainly meet, the pore fluid should be determined, field examples show that the proposed techniques are applicable in tight gas formation identification.Key words: Low resistivity contrast gas bearing formation; Tight gas sandstones;  Identification; Difference of three porosities; Ratio of three porosities; Correlation of neutron and density log

Learning Probabilistic Topological Representations Using Discrete Morse Theory

Accurate delineation of fine-scale structures is a very important yet challenging problem. Existing methods use topological information as an additional training loss, but are ultimately making pixel-wise predictions. In this paper, we propose the first deep learning based method to learn topological/structural representations. We use discrete Morse theory and persistent homology to construct an one-parameter family of structures as the topological/structural representation space. Furthermore, we learn a probabilistic model that can perform inference tasks in such a topological/structural representation space. Our method generates true structures rather than pixel-maps, leading to better topological integrity in automatic segmentation tasks. It also facilitates semi-automatic interactive annotation/proofreading via the sampling of structures and structure-aware uncertainty.Comment: 16 pages, 11 figure

Service Robotics: Robot-Assisted Training for Stroke Rehabilitation

Author name used in this publication: Raymond Kai-yu Tongpublished_fina

Confidence Estimation Using Unlabeled Data

Overconfidence is a common issue for deep neural networks, limiting their deployment in real-world applications. To better estimate confidence, existing methods mostly focus on fully-supervised scenarios and rely on training labels. In this paper, we propose the first confidence estimation method for a semi-supervised setting, when most training labels are unavailable. We stipulate that even with limited training labels, we can still reasonably approximate the confidence of model on unlabeled samples by inspecting the prediction consistency through the training process. We use training consistency as a surrogate function and propose a consistency ranking loss for confidence estimation. On both image classification and segmentation tasks, our method achieves state-of-the-art performances in confidence estimation. Furthermore, we show the benefit of the proposed method through a downstream active learning task. The code is available at https://github.com/TopoXLab/consistency-ranking-lossComment: Accepted by ICLR'2

Draft Genome Sequence of Streptomyces sp. Strain CT34, Isolated from a Ghanaian Soil Sample

Copyright © 2015 Zhai et al. This work was supported by the China “973” program (2012CB721001), the “863” Program (2012AA092201), the National Natural Science Foundation of China (31170467), and the EU FP7 project PharmaSea (312184). K.K., M.J., and H.D. thank the Royal Society–Leverhulme Trust Africa for the financial support (award AA090088) that enabled the sampling of sediments and subsequent isolation of this unique Ghanaian strain.Non peer reviewedPublisher PD