A life cycle inventory of aluminium die casting

As part of an ongoing project, a life cycle inventory (LCI) of aluminium high pressure die casting (HPDC) has been collected. This has been conducted from the view of an individual product and also the entire process. The objective of the study was to analyse the process and suggest changes to reduce environmental impacts. One modem aluminium high pressure die casting plant located in Victoria, Australia was evaluated and modelled. Site specific data on energy and materials was gathered and the process was modelled using a typical automotive component. The paper also presents our experience and methodology used in this inventory data collection process from the real industry for LCA purposes. The inventory data collected itself reveals that the HPDC process is energy intensive and as such the major emissions were from the use of natural gas fired furnaces and from the brown coal derived electricity. It is also found the large environmental benefits of using secondary aluminium over primary aluminium in the HPDC process. A detailed LCA is being cal1ied out based on the inventory obtained.</div

The sign rule and beyond: Boundary effects, flexibility, and noise correlations in neural population codes

Over repeat presentations of the same stimulus, sensory neurons show variable responses. This "noise" is typically correlated between pairs of cells, and a question with rich history in neuroscience is how these noise correlations impact the population's ability to encode the stimulus. Here, we consider a very general setting for population coding, investigating how information varies as a function of noise correlations, with all other aspects of the problem - neural tuning curves, etc. - held fixed. This work yields unifying insights into the role of noise correlations. These are summarized in the form of theorems, and illustrated with numerical examples involving neurons with diverse tuning curves. Our main contributions are as follows. (1) We generalize previous results to prove a sign rule (SR) - if noise correlations between pairs of neurons have opposite signs vs. their signal correlations, then coding performance will improve compared to the independent case. This holds for three different metrics of coding performance, and for arbitrary tuning curves and levels of heterogeneity. This generality is true for our other results as well. (2) As also pointed out in the literature, the SR does not provide a necessary condition for good coding. We show that a diverse set of correlation structures can improve coding. Many of these violate the SR, as do experimentally observed correlations. There is structure to this diversity: we prove that the optimal correlation structures must lie on boundaries of the possible set of noise correlations. (3) We provide a novel set of necessary and sufficient conditions, under which the coding performance (in the presence of noise) will be as good as it would be if there were no noise present at all.Comment: 41 pages, 5 figure

Structural Embedding of Syntactic Trees for Machine Comprehension

Deep neural networks for machine comprehension typically utilizes only word or character embeddings without explicitly taking advantage of structured linguistic information such as constituency trees and dependency trees. In this paper, we propose structural embedding of syntactic trees (SEST), an algorithm framework to utilize structured information and encode them into vector representations that can boost the performance of algorithms for the machine comprehension. We evaluate our approach using a state-of-the-art neural attention model on the SQuAD dataset. Experimental results demonstrate that our model can accurately identify the syntactic boundaries of the sentences and extract answers that are syntactically coherent over the baseline methods