Web records are structured data on a Web page that embeds records retrieved from an underlying database according to some templates. Mining data records on the Web enables the integration of data from multiple Web sites for providing value-added services. Most existing works on Web record extraction make two key assumptions: (1) records are retrieved from databases with uniform schemas and (2) records are displayed in a linear structure on a Web page. These assumptions no longer hold on the modern Web. A Web page may present records of diverse entity types with different schemas and organize records hierarchically, in nested structures, to show richer relationships among records. In this paper, we revisit these assumptions and modify them to reflect Web pages on the modern Web. Based on the reformulated assumptions, we introduce the concept of invariant in Web data records and propose Miria (Mining record invariant), a bottom-up, recursive approach to construct the Web records from the invariants. The proposed approach is both effective and efficient, consistently outperforming the state-of-the-art Web record extraction methods on modern Web pages.
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Abstract Real-time magnetic resonance imaging (RT-MRI) of human speech production is enabling significant advances in speech science, linguistics, bio-inspired speech technology development, and clinical applications. Easy access to RT-MRI is however limited, and comprehensive datasets with broad access are needed to catalyze research across numerous domains. The imaging of the rapidly moving articulators and dynamic airway shaping during speech demands high spatio-temporal resolution and robust reconstruction methods. Further, while reconstructed images have been published, to-date there is no open dataset providing raw multi-coil RT-MRI data from an optimized speech production experimental setup. Such datasets could enable new and improved methods for dynamic image reconstruction, artifact correction, feature extraction, and direct extraction of linguistically-relevant biomarkers. The present dataset offers a unique corpus of 2D sagittal-view RT-MRI videos along with synchronized audio for 75 participants performing linguistically motivated speech tasks, alongside the corresponding public domain raw RT-MRI data. The dataset also includes 3D volumetric vocal tract MRI during sustained speech sounds and high-resolution static anatomical T2-weighted upper airway MRI for each participant.
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Purpose To demonstrate a tagging method compatible with RT‐MRI for the study of speech production.
Methods Tagging is applied as a brief interruption to a continuous real‐time spiral acquisition. Tagging can be initiated manually by the operator, cued to the speech stimulus, or be automatically applied with a fixed frequency. We use a standard 2D 1‐3‐3‐1 binomial SPAtial Modulation of Magnetization (SPAMM) sequence with 1 cm spacing in both in‐plane directions. Tag persistence in tongue muscle is simulated and validated in vivo. The ability to capture internal tongue deformations is tested during speech production of American English diphthongs in native speakers.
Results We achieved an imaging window of 650‐800 ms at 1.5T, with imaging signal to noise ratio ≥ 17 and tag contrast to noise ratio ≥ 5 in human tongue, providing 36 frames/s temporal resolution and 2 mm in‐plane spatial resolution with real‐time interactive acquisition and view‐sharing reconstruction. The proposed method was able to capture tongue motion patterns and their relative timing with adequate spatiotemporal resolution during the production of American English diphthongs and consonants.
Conclusion Intermittent tagging during real‐time MRI of speech production is able to reveal the internal deformations of the tongue. This capability will allow new investigations of valuable spatiotemporal information on the biomechanics of the lingual subsystems during speech without reliance on binning speech utterance repetition.
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Objectives To evaluate a novel method for real‐time tagged MRI with increased tag persistence using phase sensitive tagging (REALTAG), demonstrated for speech imaging.
Methods Tagging is applied as a brief interruption to a continuous real‐time spiral acquisition. REALTAG is implemented using a total tagging flip angle of 180° and a novel frame‐by‐frame phase sensitive reconstruction to remove smooth background phase while preserving the sign of the tag lines. Tag contrast‐to‐noise ratio of REALTAG and conventional tagging (total flip angle of 90°) is simulated and evaluated in vivo. The ability to extend tag persistence is tested during the production of vowel‐to‐vowel transitions by American English speakers.
Results REALTAG resulted in a doubling of contrast‐to‐noise ratio at each time point and increased tag persistence by more than 1.9‐fold. The tag persistence was 1150 ms with contrast‐to‐noise ratio >6 at 1.5T, providing 2 mm in‐plane resolution, 179 frames/s, with 72.6 ms temporal window width, and phase sensitive reconstruction. The new imaging window is able to capture internal tongue deformation over word‐to‐word transitions in natural speech production.
Conclusion Tag persistence is substantially increased in intermittently tagged real‐time MRI by using the improved REALTAG method. This makes it possible to capture longer motion patterns in the tongue, such as cross‐word vowel‐to‐vowel transitions, and provides a powerful new window to study tongue biomechanics.
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