108 research outputs found
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Temporal hybridity: Mixing live video footage with instant replay in real time
Copyright @ 2010 ACMIn this paper we explore the production of streaming media that involves live and recorded content. To examine this, we report on how the production practices and process are conducted through an empirical study of the production of live television, involving the use of live and non-live media under highly time critical conditions. In explaining how this process is managed both as an individual and collective activity, we develop the concept of temporal hybridity to
explain the properties of these kinds of production system and show how temporally separated media are used, understood and coordinated. Our analysis is examined in
the light of recent developments in computing technology and we present some design implications to support amateur video production.The research was partly made possible by a grant from the Swedish Governmental Agency for Innovation Systems to the Mobile Life VinnExcellence Center, in partnership with
SonyEricsson, Ericsson, Microsoft Research, Nokia Research, TeliaSonera and the City of Stockholm
Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health
ChatGPT has drawn considerable attention from both the general public and
domain experts with its remarkable text generation capabilities. This has
subsequently led to the emergence of diverse applications in the field of
biomedicine and health. In this work, we examine the diverse applications of
large language models (LLMs), such as ChatGPT, in biomedicine and health.
Specifically we explore the areas of biomedical information retrieval, question
answering, medical text summarization, information extraction, and medical
education, and investigate whether LLMs possess the transformative power to
revolutionize these tasks or whether the distinct complexities of biomedical
domain presents unique challenges. Following an extensive literature survey, we
find that significant advances have been made in the field of text generation
tasks, surpassing the previous state-of-the-art methods. For other
applications, the advances have been modest. Overall, LLMs have not yet
revolutionized the biomedicine, but recent rapid progress indicates that such
methods hold great potential to provide valuable means for accelerating
discovery and improving health. We also find that the use of LLMs, like
ChatGPT, in the fields of biomedicine and health entails various risks and
challenges, including fabricated information in its generated responses, as
well as legal and privacy concerns associated with sensitive patient data. We
believe this first-of-its-kind survey can provide a comprehensive overview to
biomedical researchers and healthcare practitioners on the opportunities and
challenges associated with using ChatGPT and other LLMs for transforming
biomedicine and health
MultiGBS: A multi-layer graph approach to biomedical summarization
Automatic text summarization methods generate a shorter version of the input
text to assist the reader in gaining a quick yet informative gist. Existing
text summarization methods generally focus on a single aspect of text when
selecting sentences, causing the potential loss of essential information. In
this study, we propose a domain-specific method that models a document as a
multi-layer graph to enable multiple features of the text to be processed at
the same time. The features we used in this paper are word similarity, semantic
similarity, and co-reference similarity, which are modelled as three different
layers. The unsupervised method selects sentences from the multi-layer graph
based on the MultiRank algorithm and the number of concepts. The proposed
MultiGBS algorithm employs UMLS and extracts the concepts and relationships
using different tools such as SemRep, MetaMap, and OGER. Extensive evaluation
by ROUGE and BERTScore shows increased F-measure values
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