During imbibition, initially connected oil is displaced until it is trapped as immobile clusters. While initial and final states have been well described before, here we image the dynamic transient ...process in a sandstone rock using fast synchrotron‐based X‐ray computed microtomography. Wetting film swelling and subsequent snap off, at unusually high saturation, decreases nonwetting phase connectivity, which leads to nonwetting phase fragmentation into mobile ganglia, i.e., ganglion dynamics regime. We find that in addition to pressure‐driven connected pathway flow, mass transfer in the oil phase also occurs by a sequence of correlated breakup and coalescence processes. For example, meniscus oscillations caused by snap‐off events trigger coalescence of adjacent clusters. The ganglion dynamics occurs at the length scale of oil clusters and thus represents an intermediate flow regime between pore and Darcy scale that is so far dismissed in most upscaling attempts.
Key Points
Ganglion dynamics contributes to nonwetting phase transport
Coalescence of nonwetting phase clusters is triggered by snap off
Ganglion dynamics occurs from 75% nonwetting phase saturation on
Existing approaches to indoor scene understanding formulate the problem as a structured prediction task focusing on estimating the 3D bounding box which best describes the scene layout. ...Unfortunately, these approaches utilize high order potentials which are computationally intractable and rely on ad-hoc approximations for both learning and inference. In this paper we show that the potentials commonly used in the literature can be decomposed into pair-wise potentials by extending the concept of integral images to geometry. As a consequence no heuristic reduction of the search space is required. In practice, this results in large improvements in performance over the state-of-the-art, while being orders of magnitude faster.
When a landowner makes a charitable gift of a conservation easement to a nonprofit organization or government entity and elects to seek a federal tax deduction, both landowner and easement holder are ...subject to federal tax laws and regulations governing the creation, monitoring, amendment, and extinguishment of the easement. A nonprofit easement holder is subject to federal laws governing nonprofit operations. The nonprofit and government holders are also subject to state laws governing the operations of nonprofit organizations and the administration of charitable and other public assets on behalf of the public. All of these laws affect and restrict the ability of nonprofit and government holders to amend and terminate perpetual conservation easements. Contrary to representations made in When Perpetual Is Not Forever: The Challenge of Changing Conditions, Amendment, and Termination of Conservation Easements, 36 Harv. Envtl. L. Rev. 1 (2012), none of these laws can be ignored.
In this paper we propose a distributed message-passing algorithm for inference in large scale graphical models. Our method can handle large problems efficiently by distributing and parallelizing the ...computation and memory requirements. The convergence and optimality guarantees of recently developed message-passing algorithms are preserved by introducing new types of consistency messages, sent between the distributed computers. We demonstrate the effectiveness of our approach in the task of stereo reconstruction from high-resolution imagery, and show that inference is possible with more than 200 labels in images larger than 10 MPixels.
How many people should you ask if you are not sure about your way? We provide an answer to this question for Random Forest classification. The presented method is based on the statistical formulation ...of confidence intervals and conjugate priors for binomial as well as multinomial distributions. We derive appealing decision rules to speed up the classification process by leveraging the fact that many samples can be clearly mapped to classes. Results on test data are provided, and we highlight the applicability of our method to a wide range of problems. The approach introduces only one non-heuristic parameter, that allows to trade-off accuracy and speed without any re-training of the classifier. The proposed method automatically adapts to the difficulty of the test data and makes classification significantly faster without deteriorating the accuracy.
A reliable method to evaluate and follow the course of arthritis is given by examination of the carpal bones within the wrist joint. Humans typically have eight such small angular bones arranged in ...two rows. The small size as well as the number make manual segmentation for an analysis of the disease progression a tedious process. Further, fully automatic approaches are still not very reliable. To support medical treatment we present a fully automatic machine learning approach which (i) finds a bounding box around every bone and (ii) outlines the contour and computes a 3-D model of every carpal. The proposed approach has been successfully evaluated on 110 clinical wrist data sets of arthritis patients. The data consists of 59 T1 and 51 T2 weighted MRI images. With the point-to-mesh error deviating from ground truth an average of 0.48 ± 0.45 mm / 0.59 ± 0.49 mm on T1 / T2 modality, accurate segmentation results have been achieved.
For the single-group multicast scenario, where K users are served with the same data by a base station equipped with N antennas, we present two beamforming algorithms which outperform ...state-of-the-art multicast filters and feature a drastically reduced complexity at the same time. For the power minimization problem, where QoS constraints need to be satisfied, we introduce a successive beamforming filter computation approach aiming at satisfying at least one additional SNR constraint per orthogonal filter update. As long as the number of users K is smaller than the number N of transmit antennas, this procedure delivers excellent results. Our second approach is an iterative update algorithm for the max-min problem subject to a limitation of the transmit power. Given a low-complexity initialization beamformer, we search within the local vicinity of this filter vector for a filter-update preserving the transmit power and achieving a larger minimum SNR. To this end, we improve the weakest user's SNR during each iteration and keep on applying this procedure as long as the updates increase the smallest SNR. Otherwise, we adapt the step-size and continue investigating the local vicinity. It turns out that this novel approach is superior to existing state-of-the-art multicast beamformers for an arbitrary number of users.
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