Generative Neural Machine Translation

We introduce Generative Neural Machine Translation (GNMT), a latent variable architecture which is designed to model the semantics of the source and target sentences. We modify an encoder-decoder translation model by adding a latent variable as a language agnostic representation which is encouraged to learn the meaning of the sentence. GNMT achieves competitive BLEU scores on pure translation tasks, and is superior when there are missing words in the source sentence. We augment the model to facilitate multilingual translation and semi-supervised learning without adding parameters. This framework significantly reduces overfitting when there is limited paired data available, and is effective for translating between pairs of languages not seen during training

Generating Sentences Using a Dynamic Canvas

We introduce the Attentive Unsupervised Text (W)riter (AUTR), which is a word level generative model for natural language. It uses a recurrent neural network with a dynamic attention and canvas memory mechanism to iteratively construct sentences. By viewing the state of the memory at intermediate stages and where the model is placing its attention, we gain insight into how it constructs sentences. We demonstrate that AUTR learns a meaningful latent representation for each sentence, and achieves competitive log-likelihood lower bounds whilst being computationally efficient. It is effective at generating and reconstructing sentences, as well as imputing missing words.Comment: AAAI 201

Comparing TCP-IPv4TCP-IPv6 network performance

"December 2013.""A Thesis Presented to the Faculty of the Graduate School at the University of Missouri--Columbia In Partial Fulfillment of the Requirements for the Degree Master of Science."Thesis advisor: Dr. Gordon K. Springer.The Internet Protocol version 4 (IPv4) has been the backbone of the Internet since its inception. The growth and success of the Internet has accelerated the consumption of the IPv4 address space and hence its exhaustion is predicted very soon. Despite the use of multiple hidden and private networks to keep things going, a newer version of the protocol, Internet Protocol version 6 (IPv6), is proposed to solve this issue along with many other improvements as part of a better, newer design. For smoother transition and given the decentralized nature of the Internet, both of the protocol stacks, namely IPv4 and IPv6, are expected to be supported by the hosts and hence co-exist for a period of time. Many application programs, especially those involved in large data transfers, currently use the TCP/IP protocol suite. However, there have not been many attempts to leverage the existence of both Internet Protocol versions over a TCP connection. This thesis, through a prototype, is an attempt to improve the network utilization by using either an IPv4 or an IPv6 protocol for a TCP connection based on end-to-end measured performance between two hosts. A measurement tool, named netaware, is developed as part of this thesis to measure the end-to-end network performance for both IPv4 and IPv6 protocols within a single tool. The tool measures two performance parameters, namely the bandwidth and the latency in a multi-threaded environment. The tool utilizes a simple middleware application, also built as part of this thesis, to create and use socket connections for interprocess communication across the network between the two hosts. The middleware application is used as an intermediate level application to take care of creating IPv4 or IPv6 connections between the hosts, needed to transmit measurement and control data while measuring the performance parameters. The use of middleware application facilitates the construction of network applications by having an application developer to deal with minimal code to use either IP protocIncludes bibliographical references (pages 188-190)

Deep Generative Models for Natural Language

Generative models aim to simulate the process by which a set of data is generated. They are intuitive, interpretable, and naturally suited to learning from unlabelled data. This is particularly appealing in natural language processing, where labels are often costly to obtain and can require significant manual input from trained annotators. However, traditional generative modelling approaches can often be inflexible due to the need to maintain tractable maximum likelihood training. On the other hand, deep learning methods are powerful, flexible, and have achieved significant success on a wide variety of natural language processing tasks. In recent years, algorithms have been developed for training generative models that incorporate neural networks to parametrise their conditional distributions. These approaches aim to take advantage of the intuitiveness and interpretability of generative models as well as the power and flexibility of deep learning. In this work, we investigate how to leverage such algorithms in order to develop deep generative models for natural language. Firstly, we present an attention-based latent variable model, trained using unlabelled data, for learning representations of sentences. Experiments such as missing word imputation and sentence similarity matching suggest that the representations are able to learn semantic information about the sentences. We then present an RNN-based latent variable model for per- forming machine translation. Trained using semi-supervised learning, our approach achieves strong results even with very limited labelled data. Finally, we present a locally-contextual conditional random field for performing sequence labelling tasks. Our method consistently outperforms the linear chain conditional random field and achieves state of the art performance on two out of the four tasks evaluated

The study of correlation between thyroid function and blood pressure in hypertensive patients attending out patient department in tertiary care centre

Background: Hypertension may be the initial clinical presentation for at least 15 endocrine disorders, including overt and subclinical hyperthyroidism and hypothyroidism. The correction of thyroid dysfunction may normalize Blood Pressure (BP) in most cases, therefore checking thyroid function is essential during the workup for hypertension. The present study was conducted to find out the association between hypertension and thyroid dysfunction.Methods: It was a retrospective, observational study conducted among patients having hypertension visiting the outpatient department of Medicine in KIMS Karad, during the period of 2 months.Results: The mean values of various thyroid function parameters among hypertensive cases was assessed in the current study, Authors found that the mean Serum T3 level was 93.5917±32.82, Mean Serum T4 level was 6.72±1.64 and the mean Serum TSH level was 2.52±2.71. Among all the cases about 52% cases had deranged thyroid function reports.Conclusions: The results of this study suggest an association between subclinical hypothyroidism and increased blood pressure levels

Nanosuspension: a novel approach to enhance solubility of poorly water soluble drugs- A review

Solubility is the crucial factor for drug effectiveness, independence of the route of administration. Large proportion of newly discovered drugs are water insoluble and therefore poorly bioavailable contributing to desert development effort. Nanosuspensions have emerged as a promising strategy for the efficicent delivery of hydrophilic drugs because of their versatile features and unique advantages. The reduction of drug particles into submicron range leads to a significant increase in dissolution rate and therefore enhances bioavailability. Nanosuspension contain submicron colloidal dispersion of the pharmaceutical active ingredient particles in a liquid phase stabilised by surfactant. Nanosuspensions can be delivered by oral and non-oral route of administration. Study is focused on various methods of preparation with advantages and disadvantages, characterization properties, applications