Book review: fashioning diaspora: beauty, femininity and South Asian American culture by Vanita Reddy

In Fashioning Diaspora: Beauty, Femininity and South Asian American Culture, Vanita Reddy examines how beauty is a mobilising and socialising force implicated in the shaping of South Asian American identities, focusing on a range of cultural and literary texts drawn from the 1990s to the present. In showing beauty to be an active force in constructions of the social, this is a nuanced and timely analysis of the experiences of South Asian American women that bridges the gaps between fashion, racialisation, aesthetics and politics, finds Rajat Singh

An Improved Fatigue Detection System Based on Behavioral Characteristics of Driver

In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.Comment: 4 pages, 2 figures, edited version of published paper in IEEE ICITE 201

Minimizing Supervision in Multi-label Categorization

Multiple categories of objects are present in most images. Treating this as a multi-class classification is not justified. We treat this as a multi-label classification problem. In this paper, we further aim to minimize the supervision required for providing supervision in multi-label classification. Specifically, we investigate an effective class of approaches that associate a weak localization with each category either in terms of the bounding box or segmentation mask. Doing so improves the accuracy of multi-label categorization. The approach we adopt is one of active learning, i.e., incrementally selecting a set of samples that need supervision based on the current model, obtaining supervision for these samples, retraining the model with the additional set of supervised samples and proceeding again to select the next set of samples. A crucial concern is the choice of the set of samples. In doing so, we provide a novel insight, and no specific measure succeeds in obtaining a consistently improved selection criterion. We, therefore, provide a selection criterion that consistently improves the overall baseline criterion by choosing the top k set of samples for a varied set of criteria. Using this criterion, we are able to show that we can retain more than 98% of the fully supervised performance with just 20% of samples (and more than 96% using 10%) of the dataset on PASCAL VOC 2007 and 2012. Also, our proposed approach consistently outperforms all other baseline metrics for all benchmark datasets and model combinations.Comment: Accepted in CVPR-W 202

Neural Network Architecture for Credibility Assessment of Textual Claims

Text articles with false claims, especially news, have recently become aggravating for the Internet users. These articles are in wide circulation and readers face difficulty discerning fact from fiction. Previous work on credibility assessment has focused on factual analysis and linguistic features. The task's main challenge is the distinction between the features of true and false articles. In this paper, we propose a novel approach called Credibility Outcome (CREDO) which aims at scoring the credibility of an article in an open domain setting. CREDO consists of different modules for capturing various features responsible for the credibility of an article. These features includes credibility of the article's source and author, semantic similarity between the article and related credible articles retrieved from a knowledge base, and sentiments conveyed by the article. A neural network architecture learns the contribution of each of these modules to the overall credibility of an article. Experiments on Snopes dataset reveals that CREDO outperforms the state-of-the-art approaches based on linguistic features.Comment: Best Paper Award at 19th International Conference on Computational Linguistics and Intelligent Text Processing, March 2018, Hanoi, Vietna

Chemical Synthesis of Oligosaccharides as Basis for the Development of Carbohydrate-Based Vaccine Candidates against Streptococcus suis Serotype 18 and Candida auris

Capsular polysaccharides (CPSs) on the surface of bacteria play an essential role in the virulence of bacteria by protecting them from the host's immune system. Because of their important role in bacterial virulence, CPSs are often targeted by vaccines and antimicrobial therapies. Carbohydrates-based vaccines can be developed to stimulate the immune system to produce antibodies against specific CPSs, which can then prevent or reduce the severity of infections caused by the targeted bacteria. The first part of the dissertation focused on synthesizing five novel synthetic oligosaccharides resembling the capsular polysaccharides of Streptococcus suis serotype 18 using solution-phase chemistry. The second part of the dissertation focused on the fungi Candida auris and describes the synthesis of oligosaccharides resembling the key immunogenic structure of Candida auris KCTC 17810

Emotions are Universal: Learning Sentiment Based Representations of Resource-Poor Languages using Siamese Networks

Machine learning approaches in sentiment analysis principally rely on the abundance of resources. To limit this dependence, we propose a novel method called Siamese Network Architecture for Sentiment Analysis (SNASA) to learn representations of resource-poor languages by jointly training them with resource-rich languages using a siamese network. SNASA model consists of twin Bi-directional Long Short-Term Memory Recurrent Neural Networks (Bi-LSTM RNN) with shared parameters joined by a contrastive loss function, based on a similarity metric. The model learns the sentence representations of resource-poor and resource-rich language in a common sentiment space by using a similarity metric based on their individual sentiments. The model, hence, projects sentences with similar sentiment closer to each other and the sentences with different sentiment farther from each other. Experiments on large-scale datasets of resource-rich languages - English and Spanish and resource-poor languages - Hindi and Telugu reveal that SNASA outperforms the state-of-the-art sentiment analysis approaches based on distributional semantics, semantic rules, lexicon lists and deep neural network representations without shComment: Accepted Long Paper at 19th International Conference on Computational Linguistics and Intelligent Text Processing, March 2018, Hanoi, Vietnam. arXiv admin note: text overlap with arXiv:1804.0080

Azidothymidine induces severe hematological toxicity and hepatic injury in Charles Foster rats

Background: The present study aimed at evaluating the effects of azidothymidine (AZT) on hematologic and biochemical parameters in Charles Foster rats.Methods: Twelve adult healthy Charles Foster rats comprising of six male and six females were selected for study. Test rodents were divided into four groups containing three rodents each. Three males and three females served as control and remaining received AZT drug. Rodents were acclimatized for 10 days and drug was administered for 28 days. After the completion of drug administration, blood samples were collected and analyzed for hematologic parameters, i.e., Hemoglobin (Hb), Packed cell volume (PCV), red blood cell (RBC), Mean corpuscular hemoglobin concentration (MCHC), total leukocyte count (TLC), Mean corpuscular volume (MCV), Platelet count (Plt) using a Fully Automatic Fully Digital Hematology Cell Counter. In addition, biochemical parameters, were measured to assess the effects of AZT on rodent physiology. In-vivo histopathological studies were also performed on vital organs of rodents to understand the effects of drug at tissue level.Results: When compared with the control group, the data indicated a outstanding decrease in Hb, PCV, RBC, TLC and platelets in all test groups, whereas MCHC did not show any major reduction but MCV data suggested a slight increase. Among biochemical parameters, aspartate aminotransferase (AST), alanine aminotransferase (ALT) and alkaline phosphatase (ALP). were found to be remarkably elevated along with elevated bilirubin and reduced albumin, pointing towards a possible liver damage which was later corroborated by liver histopathological study.Conclusions: Above results indicate azidothymidine to be a myelosuppresive and hepatotoxic drug and its usage as an anti-retro viral during highly active anti-retro viral therapy (HAART) regime should be strictly monitored