Forecasting the Subject Trend of International Library and Information Science Research by 2030 Using the Deep Learning Approach

This study seeks to forecast the subject trend of library and information science research until 2030 based on modeling previous research topics in this field, which has been done with a text mining and in-depth learning approach. After pre-processing and thematic classification of the studies, deep neural network algorithms were used to model previous studies and forecast future topics. The study population included 90,311 journal articles in library and information science publications indexed on the Web of Science website from 1945-2020. All research processes were implemented in the Python programming language. The findings showed that the largest number of studies in the future would be related to Internet and web studies, and the growth rate of these topics will be higher in the future. However, topics related to libraries and their work processes and other traditional disciplines such as theoretical foundations will have a lower growth rate in library and information science studies. As a result, knowledge of important future issues, while helping to plan for future research, can identify study gaps and investment opportunities in the R&D sector, thereby assisting researchers, universities, and relevant research institutes in selecting projects intelligently.https://dorl.net/dor/ 20.1001.1.20088302.2022.20.1.26.

Summary of high-efficiency solar-cell research

High-efficiency solar-cell activities supporting efforts to achieve the DOE Five-Year Plan goals are summarized. Specific objectives are to identify and resolve key generic problems that limit cell efficiency to below theoretically predicted values and to design and fabricate cells having efficiences equal to or greater than 20% (AM1.5). Theoretical curves for various p-n junction cells were shown. The effects of practical barriers on cell efficiency was depicted along with the modeling parameters. Cell design parameters used in the analyses were described. The usefulness and present limitations of the existing modeling capabilities were presented. The historical evolution of the efficiencies of cells made from web and edge-defined film-fed growth (EFG) silicon ribbons were also described. The status of contemporary higher-efficiency technical capabilities and future activities to raise efficiencies were stated

LGDN: Language-Guided Denoising Network for Video-Language Modeling

Video-language modeling has attracted much attention with the rapid growth of web videos. Most existing methods assume that the video frames and text description are semantically correlated, and focus on video-language modeling at video level. However, this hypothesis often fails for two reasons: (1) With the rich semantics of video contents, it is difficult to cover all frames with a single video-level description; (2) A raw video typically has noisy/meaningless information (e.g., scenery shot, transition or teaser). Although a number of recent works deploy attention mechanism to alleviate this problem, the irrelevant/noisy information still makes it very difficult to address. To overcome such challenge, we thus propose an efficient and effective model, termed Language-Guided Denoising Network (LGDN), for video-language modeling. Different from most existing methods that utilize all extracted video frames, LGDN dynamically filters out the misaligned or redundant frames under the language supervision and obtains only 2--4 salient frames per video for cross-modal token-level alignment. Extensive experiments on five public datasets show that our LGDN outperforms the state-of-the-arts by large margins. We also provide detailed ablation study to reveal the critical importance of solving the noise issue, in hope of inspiring future video-language work.Comment: Accepted by NeurIPS202

The Lifecycles of Apps in a Social Ecosystem

Apps are emerging as an important form of on-line content, and they combine aspects of Web usage in interesting ways --- they exhibit a rich temporal structure of user adoption and long-term engagement, and they exist in a broader social ecosystem that helps drive these patterns of adoption and engagement. It has been difficult, however, to study apps in their natural setting since this requires a simultaneous analysis of a large set of popular apps and the underlying social network they inhabit. In this work we address this challenge through an analysis of the collection of apps on Facebook Login, developing a novel framework for analyzing both temporal and social properties. At the temporal level, we develop a retention model that represents a user's tendency to return to an app using a very small parameter set. At the social level, we organize the space of apps along two fundamental axes --- popularity and sociality --- and we show how a user's probability of adopting an app depends both on properties of the local network structure and on the match between the user's attributes, his or her friends' attributes, and the dominant attributes within the app's user population. We also develop models that show the importance of different feature sets with strong performance in predicting app success.Comment: 11 pages, 10 figures, 3 tables, International World Wide Web Conferenc

EcoCyc: fusing model organism databases with systems biology.

EcoCyc (http://EcoCyc.org) is a model organism database built on the genome sequence of Escherichia coli K-12 MG1655. Expert manual curation of the functions of individual E. coli gene products in EcoCyc has been based on information found in the experimental literature for E. coli K-12-derived strains. Updates to EcoCyc content continue to improve the comprehensive picture of E. coli biology. The utility of EcoCyc is enhanced by new tools available on the EcoCyc web site, and the development of EcoCyc as a teaching tool is increasing the impact of the knowledge collected in EcoCyc