The Global Market for Liquefied Natural Gas

Liquefied natural gas (LNG) provides an economic means to transport natural gas over long distances, bringing production from remote gas reserves to market. A large expansion in global LNG trade is currently under way, and Australia is likely to emerge as the second largest supplier globally in coming years. This article describes the functioning of the global LNG market and outlines Australia's position within the market.LNG; liquefied natural gas; natural gas; resources; commodities; energy; exports; fossil fuel; market

The early expansion and evolutionary dynamics of POU class genes.

The POU genes represent a diverse class of animal-specific transcription factors that play important roles in neurogenesis, pluripotency, and cell-type specification. Although previous attempts have been made to reconstruct the evolution of the POU class, these studies have been limited by a small number of representative taxa, and a lack of sequences from basally branching organisms. In this study, we performed comparative analyses on available genomes and sequences recovered through "gene fishing" to better resolve the topology of the POU gene tree. We then used ancestral state reconstruction to map the most likely changes in amino acid evolution for the conserved domains. Our work suggests that four of the six POU families evolved before the last common ancestor of living animals-doubling previous estimates-and were followed by extensive clade-specific gene loss. Amino acid changes are distributed unequally across the gene tree, consistent with a neofunctionalization model of protein evolution. We consider our results in the context of early animal evolution, and the role of POU5 genes in maintaining stem cell pluripotency

WarpNet: Weakly Supervised Matching for Single-view Reconstruction

We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a different object in another. We exploit the structure of the fine-grained dataset to create artificial data for training this network in an unsupervised-discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. On the CUB-200-2011 dataset of bird categories, we improve the AP over an appearance-only network by 13.6%. We further demonstrate that our WarpNet matches, together with the structure of fine-grained datasets, allow single-view reconstructions with quality comparable to using annotated point correspondences.Comment: to appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

Deep Hierarchical Parsing for Semantic Segmentation

This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.Comment: IEEE CVPR 201

Neural Nets via Forward State Transformation and Backward Loss Transformation

This article studies (multilayer perceptron) neural networks with an emphasis on the transformations involved --- both forward and backward --- in order to develop a semantical/logical perspective that is in line with standard program semantics. The common two-pass neural network training algorithms make this viewpoint particularly fitting. In the forward direction, neural networks act as state transformers. In the reverse direction, however, neural networks change losses of outputs to losses of inputs, thereby acting like a (real-valued) predicate transformer. In this way, backpropagation is functorial by construction, as shown earlier in recent other work. We illustrate this perspective by training a simple instance of a neural network