VeeAlign: Multifaceted Context Representation Using Dual Attention for Ontology Alignment

Ontology Alignment is an important research problem applied to various fields such as data integration, data transfer, data preparation, etc. State-of-the-art (SOTA) Ontology Alignment systems typically use naive domain-dependent approaches with handcrafted rules or domain-specific architectures, making them unscalable and inefficient. In this work, we propose VeeAlign, a Deep Learning based model that uses a novel dual-attention mechanism to compute the contextualized representation of a concept which, in turn, is used to discover alignments. By doing this, not only is our approach able to exploit both syntactic and semantic information encoded in ontologies, it is also, by design, flexible and scalable to different domains with minimal effort. We evaluate our model on four different datasets from different domains and languages, and establish its superiority through these results as well as detailed ablation studies. The code and datasets used are available at https://github.com/Remorax/VeeAlign.Comment: Duplicate of arXiv:2010.1172

Internal Ballistic Code for Solid Rocket Motors using Minimum Distance Function for Grain Burnback

A computer code has been developed for internal ballistic performance evaluation of solid rocket motors, using minimum distance function (MDF) approach for prediction of geometry evolution. This method can handle any complex geometry without the need to define different geometrical shapes and their evolution as used in several existing analytical geometry evolution-based methodologies. The code is validated with both experimental results published in literature, as well as for solid rocket motors of tactical and strategic missiles and a very good match is obtained with static test results. The output of the code gives p-t (pressure-time) curve as well as the detailed parameters of the flow along the axial direction, and geometries in the form of mesh file, which can be further used as input to codes for CFD analysis.Defence Science Journal, Vol. 65, No. 3, May 2015, pp.181-188, DOI: http://dx.doi.org/10.14429/dsj.65.830

Low cost reactors for redox-active polymer flow batteries

Redox-active small molecules, used traditionally in redox flow batteries (RFBs), are susceptible to parasitic crossover of electroactive species through a porous separator, and require expensive ion exchange membranes (IEMs) to achieve long lifetimes. Redox-active polymer (RAP) solutions show great promise as candidate electrolytes to mitigate crossover through size-exclusion, enabling the use of relatively inexpensive porous separators in place of IEMs. This study holistically evaluates poly(vinylbenzyl ethyl viologen) RAPs as potential active species for RFBs, based on trends in electrolyte transport properties, electrochemical performance, and reactor cost. The ionic conductivity of these solutions is found to be of the same order of magnitude as typical Li-ion battery electrolytes, indicating that RAP macromolecular design does not limit the mobility of conducting ions in solution. The electrochemical performance of a RAP-based RFB is predicted by accounting for capacity losses due to electrolyte mixing, and polarization within its reactor. Techno-economic analysis evaluates the impact of electrolyte transport properties and operating conditions on RFB reactor cost. Minimum reactor cost lies between 11-17 dollars kWh-1 across the entire range of active species concentrations studied, which is comparable to the estimated target mean RFB reactor cost of $13.8 kWh-1. The achievement of low cost reactors is enabled by the deviation in transport properties of RAP solutions from the prediction based on the Stokes-Einstein equation. The methodology used here could potentially serve as an approach for examining other candidate active species for RFBs

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Nations within a nation: variations in epidemiological transition across the states of India, 1990–2016 in the Global Burden of Disease Study

18% of the world's population lives in India, and many states of India have populations similar to those of large countries. Action to effectively improve population health in India requires availability of reliable and comprehensive state-level estimates of disease burden and risk factors over time. Such comprehensive estimates have not been available so far for all major diseases and risk factors. Thus, we aimed to estimate the disease burden and risk factors in every state of India as part of the Global Burden of Disease (GBD) Study 2016

25th annual computational neuroscience meeting: CNS-2016

The same neuron may play different functional roles in the neural circuits to which it belongs. For example, neurons in the Tritonia pedal ganglia may participate in variable phases of the swim motor rhythms [1]. While such neuronal functional variability is likely to play a major role the delivery of the functionality of neural systems, it is difficult to study it in most nervous systems. We work on the pyloric rhythm network of the crustacean stomatogastric ganglion (STG) [2]. Typically network models of the STG treat neurons of the same functional type as a single model neuron (e.g. PD neurons), assuming the same conductance parameters for these neurons and implying their synchronous firing [3, 4]. However, simultaneous recording of PD neurons shows differences between the timings of spikes of these neurons. This may indicate functional variability of these neurons. Here we modelled separately the two PD neurons of the STG in a multi-neuron model of the pyloric network. Our neuron models comply with known correlations between conductance parameters of ionic currents. Our results reproduce the experimental finding of increasing spike time distance between spikes originating from the two model PD neurons during their synchronised burst phase. The PD neuron with the larger calcium conductance generates its spikes before the other PD neuron. Larger potassium conductance values in the follower neuron imply longer delays between spikes, see Fig. 17.Neuromodulators change the conductance parameters of neurons and maintain the ratios of these parameters [5]. Our results show that such changes may shift the individual contribution of two PD neurons to the PD-phase of the pyloric rhythm altering their functionality within this rhythm. Our work paves the way towards an accessible experimental and computational framework for the analysis of the mechanisms and impact of functional variability of neurons within the neural circuits to which they belong

Integrative Analysis and Machine Learning Based Characterization of Single Circulating Tumor Cells

We collated publicly available single-cell expression profiles of circulating tumor cells (CTCs) and showed that CTCs across cancers lie on a near-perfect continuum of epithelial to mesenchymal (EMT) transition. Integrative analysis of CTC transcriptomes also highlighted the inverse gene expression pattern between PD-L1 and MHC, which is implicated in cancer immunotherapy. We used the CTCs expression profiles in tandem with publicly available peripheral blood mononuclear cell (PBMC) transcriptomes to train a classifier that accurately recognizes CTCs of diverse phenotype. Further, we used this classifier to validate circulating breast tumor cells captured using a newly developed microfluidic system for label-free enrichment of CTCs