11,615 research outputs found

    Towards Real-Time Detection and Tracking of Spatio-Temporal Features: Blob-Filaments in Fusion Plasma

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    A novel algorithm and implementation of real-time identification and tracking of blob-filaments in fusion reactor data is presented. Similar spatio-temporal features are important in many other applications, for example, ignition kernels in combustion and tumor cells in a medical image. This work presents an approach for extracting these features by dividing the overall task into three steps: local identification of feature cells, grouping feature cells into extended feature, and tracking movement of feature through overlapping in space. Through our extensive work in parallelization, we demonstrate that this approach can effectively make use of a large number of compute nodes to detect and track blob-filaments in real time in fusion plasma. On a set of 30GB fusion simulation data, we observed linear speedup on 1024 processes and completed blob detection in less than three milliseconds using Edison, a Cray XC30 system at NERSC.Comment: 14 pages, 40 figure

    Authentication of Moving Top-k Spatial Keyword Queries

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    Controllable Abstractive Dialogue Summarization with Sketch Supervision

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    In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control. Our model has two primary components and stages: 1) a two-stage generation strategy that generates a preliminary summary sketch serving as the basis for the final summary. This summary sketch provides a weakly supervised signal in the form of pseudo-labeled interrogative pronoun categories and key phrases extracted using a constituency parser. 2) A simple strategy to control the granularity of the final summary, in that our model can automatically determine or control the number of generated summary sentences for a given dialogue by predicting and highlighting different text spans from the source text. Our model achieves state-of-the-art performance on the largest dialogue summarization corpus SAMSum, with as high as 50.79 in ROUGE-L score. In addition, we conduct a case study and show competitive human evaluation results and controllability to human-annotated summaries

    Successful strategies for engaging Chinese breast cancer survivors in a randomized controlled trial

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    This is the author accepted manuscript. The final version is available from the American Psychological Association via the DOI in this record.Chinese immigrant breast cancer survivors face various challenges due to cultural and socioecological factors. Research efforts to develop culturally sensitive interventions have been limited by lack of knowledge regarding successful recruitment and implementation practices among Chinese immigrant populations. This paper documents strategies utilized during the development and implementation of a randomized controlled trial of a culturally sensitive psychosocial intervention for Chinese immigrant breast cancer survivors. In partnership with a community agency, we developed culturally and linguistically appropriate research materials, recruited participants from community channels, and conducted longitudinal data collection. Key strategies include building equitable research partnerships with community agencies to engage participants; being responsive to the needs of community agencies and participants; considering within-group diversity of the research population; utilizing recruitment as an opportunity for relationship-building with participants; and developing key strategies to promote retention. Successful participant engagement in cancer intervention research is the result of collaboration among breast cancer survivors, community leaders and agencies, and academic researchers. The engagement process for this study is novel because we have emphasized cultural factors in the process and taken a relational approach to recruitment and retention

    Characterization of the chronobiological signals based on the continuous wavelet transform

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    Chronobiology, which studies periodic patterns or rhythms of the living beings, often needs to characterize the observed chronobiological time series (CTS) and to study the stability and adaptability of the periodic patterns in different environmental conditions. Fourier transform (FT) based methods and complex demodulation (CD) approach have been widely used in such study. However, the former lacks temporal resolution and the later needs to extract the temporal behaviors of individual frequencies. In this paper, we proposed a new approach to characterize the CTS based on the continuous wavelet transform (CWT). It allows us to investigate the time-frequency dynamics of different rhythmic-band activities in the CTS simultaneously. Two application results have been presented to illustrate the proposed method.published_or_final_versio
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