The incidence and outcome of respiratory distress syndrome in preterm babies in relation to administration of antenatal corticosteroids

Background: Premature infants have a higher incidence of respiratory distress syndrome (RDS), which is one of the main causes of early neonatal mortality. Objectives: The objective is to study the incidence and outcome of RDS in preterm babies <34 weeks of gestation born to mothers who had received antenatal corticosteroids (ACS). Methodology: A prospective observational study was conducted among preterm babies from January 2015 to December 2015 in a tertiary care hospital of South India. Details of the mothers with a period of gestation 34 weeks or less who had received ACS were recorded. Results: The study population included 749 preterm babies (<34 weeks) delivered in our hospital. Among them, 698 (93.2%) mothers received two doses of ACS and 51 (6.8%) received only a single dose of ACS. Neonates whose mothers received two doses of ACS had a significantly lower incidence of RDS (27.6% vs. 100%, p<0.001), lower rate of mechanical ventilation (45% vs. 72.5%, p<0.001), and higher survival rate (87% vs. 68.6%, p=0.001) than neonates whose mothers received a single dose of ACS. The occurrence of RDS is highest in 2

A Factored Relevance Model for Contextual Point-of-Interest Recommendation

The challenge of providing personalized and contextually appropriate recommendations to a user is faced in a range of use-cases, e.g., recommendations for movies, places to visit, articles to read etc. In this paper, we focus on one such application, namely that of suggesting 'points of interest' (POIs) to a user given her current location, by leveraging relevant information from her past preferences. An automated contextual recommendation algorithm is likely to work well if it can extract information from the preference history of a user (exploitation) and effectively combine it with information from the user's current context (exploration) to predict an item's 'usefulness' in the new context. To balance this trade-off between exploration and exploitation, we propose a generic unsupervised framework involving a factored relevance model (FRLM), comprising two distinct components, one corresponding to the historical information from past contexts, and the other pertaining to the information from the local context. Our experiments are conducted on the TREC contextual suggestion (TREC-CS) 2016 dataset. The results of our experiments demonstrate the effectiveness of our proposed approach in comparison to a number of standard IR and recommender-based baselines

UMass at TREC 2004: Novelty and HARD

For the TREC 2004 Novelty track, UMass participated in all four tasks. Although finding relevant sentences was harder this year than last, we continue to show marked improvements over the baseline of calling all sentences relevant, with a variant of tfidf being the most successful approach. We achieve 5–9%improvements over the baseline in locating novel sentences, primarily by looking at the similarity of a sentence to earlier sentences and focusing on named entities. For the High Accuracy Retrieval from Documents (HARD) track, we investigated the use of clarification forms, fixed- and variable-length passage retrieval, and the use of metadata. Clarification form results indicate that passage level feedback can provide improvements comparable to user supplied related-text for document evaluation and outperforms related-text for passage evaluation. Document retrieval methods without a query expansion component show themost gains fromrelated-text. We also found that displaying the top passages for feedback outperformed displaying centroid passages. Named entity feedback resulted in mixed performance. Our primary findings for passage retrieval are that document retrieval methods performed better than passage retrieval methods on the passage evaluation metric of binary preference at 12,000 characters, and that clarification forms improved passage retrieval for every retrieval method explored. We found no benefit to using variable-length passages over fixed-length passages for this corpus. Our use of geography and genremetadata resulted in no significant changes in retrieval performance

SENSEYE: A MULTI-TIER HETEROGENEOUS CAMERA Approved as to style and content by:

My six-year stay at Amherst for my doctoral degree has been a memorable experience— made possible by a lot of people. I am immensely grateful to you all. First and foremost, I am grateful to the the Computer Science Department at the Univer-sity of Massachusetts, Amherst which gave me an opportunity to pursue the Ph.D. program. I am indebted to my advisers Prof. Prashant Shenoy and Prof. Deepak Ganesan. Prashant provided valuable guidance and mentoring throughout my stay at Amherst. I am also grate-ful to Deepak for advising me for my dissertation. I have learnt several aspects of research and teaching from both, which I hope to follow. I would like to thank my thesis committee members — Prof. Jim Kurose, Prof. C. Mani Krishna and Prof. Mark Corner, for agreeing to be part of my dissertation and for their feedback

UMass at TREC 2004: Novelty and HARD

For the TREC 2004 Novelty track, UMass participated in all four tasks. Although finding relevant sentences was harder this year than last, we continue to show marked improvements over the baseline of calling all sentences relevant, with a variant of tfidf being the most successful approach. We achieve 5–9%improvements over the baseline in locating novel sentences, primarily by looking at the similarity of a sentence to earlier sentences and focusing on named entities. For the High Accuracy Retrieval from Documents (HARD) track, we investigated the use of clarification forms, fixed- and variable-length passage retrieval, and the use of metadata. Clarification form results indicate that passage level feedback can provide improvements comparable to user supplied related-text for document evaluation and outperforms related-text for passage evaluation. Document retrieval methods without a query expansion component show themost gains fromrelated-text. We also found that displaying the top passages for feedback outperformed displaying centroid passages. Named entity feedback resulted in mixed performance. Our primary findings for passage retrieval are that document retrieval methods performed better than passage retrieval methods on the passage evaluation metric of binary preference at 12,000 characters, and that clarification forms improved passage retrieval for every retrieval method explored. We found no benefit to using variable-length passages over fixed-length passages for this corpus. Our use of geography and genremetadata resulted in no significant changes in retrieval performance