Hydrodynamics and the Detection of the QCD Axial Anomaly in Heavy Ion Collisions

We consider the experimental implications of the axial current triangle diagram anomaly in a hydrodynamic description of high density QCD. We propose a signal of an enhanced production of spin-excited hadrons in the direction of the rotation axis in off-central heavy ion collisions.Comment: 15 pages, 19 figures; v2: refs added, minor changes to the plots; v3, comments adde

Problems in Evaluating Grammatical Error Detection Systems

ABSTRACT Many evaluation issues for grammatical error detection have previously been overlooked, making it hard to draw meaningful comparisons between different approaches, even when they are evaluated on the same corpus. To begin with, the three-way contingency between a writer's sentence, the annotator's correction, and the system's output makes evaluation more complex than in some other NLP tasks, which we address by presenting an intuitive evaluation scheme. Of particular importance to error detection is the skew of the data -the low frequency of errors as compared to non-errors -which distorts some traditional measures of performance and limits their usefulness, leading us to recommend the reporting of raw measurements (true positives, false negatives, false positives, true negatives). Other issues that are particularly vexing for error detection focus on defining these raw measurements: specifying the size or scope of an error, properly treating errors as graded rather than discrete phenomena, and counting non-errors. We discuss recommendations for best practices with regard to reporting the results of system evaluation for these cases, recommendations which depend upon making clear one's assumptions and applications for error detection. By highlighting the problems with current error detection evaluation, the field will be better able to move forward

Speaker recognition with hybrid features from a deep belief network

Learning representation from audio data has shown advantages over the handcrafted features such as mel-frequency cepstral coefficients (MFCCs) in many audio applications. In most of the representation learning approaches, the connectionist systems have been used to learn and extract latent features from the fixed length data. In this paper, we propose an approach to combine the learned features and the MFCC features for speaker recognition task, which can be applied to audio scripts of different lengths. In particular, we study the use of features from different levels of deep belief network for quantizing the audio data into vectors of audio word counts. These vectors represent the audio scripts of different lengths that make them easier to train a classifier. We show in the experiment that the audio word count vectors generated from mixture of DBN features at different layers give better performance than the MFCC features. We also can achieve further improvement by combining the audio word count vector and the MFCC features

Carnosine:can understanding its actions on energy metabolism and protein homeostasis inform its therapeutic potential?

The dipeptide carnosine (β-alanyl-L-histidine) has contrasting but beneficial effects on cellular activity. It delays cellular senescence and rejuvenates cultured senescent mammalian cells. However, it also inhibits the growth of cultured tumour cells. Based on studies in several organisms, we speculate that carnosine exerts these apparently opposing actions by affecting energy metabolism and/or protein homeostasis (proteostasis). Specific effects on energy metabolism include the dipeptide's influence on cellular ATP concentrations. Carnosine's ability to reduce the formation of altered proteins (typically adducts of methylglyoxal) and enhance proteolysis of aberrant polypeptides is indicative of its influence on proteostasis. Furthermore these dual actions might provide a rationale for the use of carnosine in the treatment or prevention of diverse age-related conditions where energy metabolism or proteostasis are compromised. These include cancer, Alzheimer's disease, Parkinson's disease and the complications of type-2 diabetes (nephropathy, cataracts, stroke and pain), which might all benefit from knowledge of carnosine's mode of action on human cells. © 2013 Hipkiss et al.; licensee Chemistry Central Ltd

Nanotechnology and global energy demand: challenges and prospects for a paradigm shift in the oil and gas industry.

The exploitation of new hydrocarbon discoveries in meeting the present global energy demand is a function of the availability and application of new technologies. The relevance of new technologies is borne out of the complex subsurface architecture and conditions of offshore petroleum plays. Conventional techniques, from drilling to production, for exploiting these discoveries may require adaption for such subsurface conditions as they fail under conditions of high pressure and high temperature. The oil and gas industry over the past decades has witnessed increased research into the use of nanotechnology with great promise for drilling operations, enhanced oil recovery, reservoir characterization, production, etc. The prospect for a paradigm shift towards the application of nanotechnology in the oil and gas industry is constrained by evolving challenges with its progression. This paper gave a review of developments from nano-research in the oil and gas industry, challenges and recommendations

Spatially Resolving Spin-split Edge States of Chiral Graphene Nanoribbons

A central question in the field of graphene-related research is how graphene behaves when it is patterned at the nanometer scale with different edge geometries. Perhaps the most fundamental shape relevant to this question is the graphene nanoribbon (GNR), a narrow strip of graphene that can have different chirality depending on the angle at which it is cut. Such GNRs have been predicted to exhibit a wide range of behaviour (depending on their chirality and width) that includes tunable energy gaps and the presence of unique one-dimensional (1D) edge states with unusual magnetic structure. Most GNRs explored experimentally up to now have been characterized via electrical conductivity, leaving the critical relationship between electronic structure and local atomic geometry unclear (especially at edges). Here we present a sub-nm-resolved scanning tunnelling microscopy (STM) and spectroscopy (STS) study of GNRs that allows us to examine how GNR electronic structure depends on the chirality of atomically well-defined GNR edges. The GNRs used here were chemically synthesized via carbon nanotube (CNT) unzipping methods that allow flexible variation of GNR width, length, chirality, and substrate. Our STS measurements reveal the presence of 1D GNR edge states whose spatial characteristics closely match theoretical expectations for GNR's of similar width and chirality. We observe width-dependent splitting in the GNR edge state energy bands, providing compelling evidence of their magnetic nature. These results confirm the novel electronic behaviour predicted for GNRs with atomically clean edges, and thus open the door to a whole new area of applications exploiting the unique magnetoelectronic properties of chiral GNRs

Reaction rates and transport in neutron stars

Understanding signals from neutron stars requires knowledge about the transport inside the star. We review the transport properties and the underlying reaction rates of dense hadronic and quark matter in the crust and the core of neutron stars and point out open problems and future directions.Comment: 74 pages; commissioned for the book "Physics and Astrophysics of Neutron Stars", NewCompStar COST Action MP1304; version 3: minor changes, references updated, overview graphic added in the introduction, improvements in Sec IV.A.