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Journal articles on the topic 'Data-driven synthesis'

Author: Grafiati
Published: 4 June 2021
Last updated: 13 February 2022

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1

Carlson, Rolf, and Björn Granström. "Data-driven multimodal synthesis." Speech Communication 47, no. 1-2 (September 2005): 182–93. http://dx.doi.org/10.1016/j.specom.2005.02.015.

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2

Campbell, Nick. "Data‐driven speech synthesis." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1029–30. http://dx.doi.org/10.1121/1.424923.

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3

Taylor, Sam, Doug A. Edwards, Luis A. Plana, and Luis A. Tarazona D. "Asynchronous Data-Driven Circuit Synthesis." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 18, no. 7 (July 2010): 1093–106. http://dx.doi.org/10.1109/tvlsi.2009.2020168.

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4

Wang, Nannan, Mingrui Zhu, Jie Li, Bin Song, and Zan Li. "Data-driven vs. model-driven: Fast face sketch synthesis." Neurocomputing 257 (September 2017): 214–21. http://dx.doi.org/10.1016/j.neucom.2016.07.071.

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5

Lv, Pei, Mingliang Xu, Bailin Yang, Mingyuan Li, and Bing Zhou. "Data-driven humanlike reaching behaviors synthesis." Neurocomputing 177 (February 2016): 26–32. http://dx.doi.org/10.1016/j.neucom.2015.10.118.

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6

Bohg, Jeannette, Antonio Morales, Tamim Asfour, and Danica Kragic. "Data-Driven Grasp Synthesis—A Survey." IEEE Transactions on Robotics 30, no. 2 (April 2014): 289–309. http://dx.doi.org/10.1109/tro.2013.2289018.

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7

Ravitz, Orr. "Data-driven computer aided synthesis design." Drug Discovery Today: Technologies 10, no. 3 (September 2013): e443-e449. http://dx.doi.org/10.1016/j.ddtec.2013.01.005.

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8

Aghdasi, F. "Controller synthesis using data-driven clocks." Microelectronics Journal 26, no. 5 (July 1995): 449–61. http://dx.doi.org/10.1016/0026-2692(95)98947-p.

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9

Horta, N. C., and J. E. Franca. "Algorithm-driven synthesis of data conversion architectures." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 16, no. 10 (1997): 1116–35. http://dx.doi.org/10.1109/43.662675.

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10

Macon, Michael W. "Waveform models for data‐driven speech synthesis." Journal of the Acoustical Society of America 105, no. 2 (February 1999): 1031. http://dx.doi.org/10.1121/1.424928.

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11

Liow, chi Hao, Seungbum Hong, Moony Na, Kihoon Bang, Hyuck Mo Lee, Hye Ryung Byon, Yoonsu Shim, et al. "Data-Driven Synthesis Optimization of Cathode Materials." ECS Meeting Abstracts MA2020-02, no. 2 (November 23, 2020): 200. http://dx.doi.org/10.1149/ma2020-022200mtgabs.

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12

Yessenov, Kuat, Zhilei Xu, and Armando Solar-Lezama. "Data-driven synthesis for object-oriented frameworks." ACM SIGPLAN Notices 46, no. 10 (October 18, 2011): 65–82. http://dx.doi.org/10.1145/2076021.2048075.

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13

Du, Han, Martin Manns, Erik Herrmann, and Klaus Fischer. "Joint Angle Data Representation for Data Driven Human Motion Synthesis." Procedia CIRP 41 (2016): 746–51. http://dx.doi.org/10.1016/j.procir.2015.12.096.

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14

Xiaopeng Wei, Pengfei Yi, and Qiang Zhang. "Foot-contacting Graphs for Data-Driven Motion Synthesis." International Journal of Advancements in Computing Technology 5, no. 1 (January 15, 2013): 884–91. http://dx.doi.org/10.4156/ijact.vol5.issue1.99.

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15

KEEL, Lee H., Sandipan MITRA, and Shankar P. BHATTACHARYYA. "Data Driven Synthesis of Three Term Digital Controllers." SICE Journal of Control, Measurement, and System Integration 1, no. 2 (2008): 102–10. http://dx.doi.org/10.9746/jcmsi.1.102.

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16

Gudnason, Jon, Mark R. P. Thomas, Daniel P. W. Ellis, and Patrick A. Naylor. "Data-driven voice source waveform analysis and synthesis." Speech Communication 54, no. 2 (February 2012): 199–211. http://dx.doi.org/10.1016/j.specom.2011.08.003.

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17

Chen, Hsin-I., Tse-Ju Lin, Xiao-Feng Jian, I.-Chao Shen, and Bing-Yu Chen. "Data-driven Handwriting Synthesis in a Conjoined Manner." Computer Graphics Forum 34, no. 7 (October 2015): 235–44. http://dx.doi.org/10.1111/cgf.12762.

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18

Ahmadi, Mohamadreza, Arie Israel, and Ufuk Topcu. "Safe Controller Synthesis for Data-Driven Differential Inclusions." IEEE Transactions on Automatic Control 65, no. 11 (November 2020): 4934–40. http://dx.doi.org/10.1109/tac.2020.2969713.

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19

Ji, Chuanyi, and Demetri Psaltis. "Network Synthesis through Data-Driven Growth and Decay." Neural Networks 10, no. 6 (August 1997): 1133–41. http://dx.doi.org/10.1016/s0893-6080(97)00032-4.

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20

Jörg, Sophie, Jessica Hodgins, and Alla Safonova. "Data-driven finger motion synthesis for gesturing characters." ACM Transactions on Graphics 31, no. 6 (November 2012): 1–7. http://dx.doi.org/10.1145/2366145.2366208.

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21

Prothiwa, Michaela, Ismail Syed, Mark O. Huising, Talitha van der Meulen, Cynthia J. Donaldson, Sunia A. Trauger, Barbara B. Kahn, and Alan Saghatelian. "Data-Driven Synthesis of Proteolysis-Resistant Peptide Hormones." Journal of the American Chemical Society 136, no. 51 (December 12, 2014): 17710–13. http://dx.doi.org/10.1021/ja5065735.

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22

Wan, Xianmei, and Xiaogang Jin. "Data-driven facial expression synthesis via Laplacian deformation." Multimedia Tools and Applications 58, no. 1 (January 5, 2011): 109–23. http://dx.doi.org/10.1007/s11042-010-0688-7.

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23

Aryal, Sandesh, and Ricardo Gutierrez-Osuna. "Data driven articulatory synthesis with deep neural networks." Computer Speech & Language 36 (March 2016): 260–73. http://dx.doi.org/10.1016/j.csl.2015.02.003.

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24

Manns, Martin, Michael Otto, and Markus Mauer. "Measuring Motion Capture Data Quality for Data Driven Human Motion Synthesis." Procedia CIRP 41 (2016): 945–50. http://dx.doi.org/10.1016/j.procir.2015.12.068.

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25

Nair, Vishnu H., Philippe Schwaller, and Teodoro Laino. "Data-driven Chemical Reaction Prediction and Retrosynthesis." CHIMIA International Journal for Chemistry 73, no. 12 (December 18, 2019): 997–1000. http://dx.doi.org/10.2533/chimia.2019.997.

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The synthesis of organic compounds, which is central to many areas such as drug discovery, material synthesis and biomolecular chemistry, requires chemists to have years of knowledge and experience. The development of technologies with the potential to learn and support experts in the design of synthetic routes is a half-century-old challenge with an interesting revival in the last decade. In fact, the renewed interest in artificial intelligence (AI), driven mainly by data availability, is profoundly changing the landscape of computer-aided chemical reaction prediction and retrosynthetic analysis. In this article, we briefly review different approaches to predict forward reactions and retrosynthesis, with a strong focus on data-driven ones. While data-driven technologies still need to demonstrate their full potential compared to expert rule-based systems in synthetic chemistry, the acceleration experienced in the last decade is a convincing sign that where we use software today, there will be AI tomorrow. This revolution will help and empower bench chemists, driving the transformation of chemistry towards a high-tech business over the next decades.
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26

Zhang, Yong, Weiming Dong, Chongyang Ma, Xing Mei, Ke Li, Feiyue Huang, Bao-Gang Hu, and Oliver Deussen. "Data-Driven Synthesis of Cartoon Faces Using Different Styles." IEEE Transactions on Image Processing 26, no. 1 (January 2017): 464–78. http://dx.doi.org/10.1109/tip.2016.2628581.

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27

Lotfi, Sogol, and Jakoah Brgoch. "Discovering Intermetallics Through Synthesis, Computation, and Data‐Driven Analysis." Chemistry – A European Journal 26, no. 40 (May 26, 2020): 8689–97. http://dx.doi.org/10.1002/chem.202000742.

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28

Chao, Qianwen, Zhigang Deng, Jiaping Ren, Qianqian Ye, and Xiaogang Jin. "Realistic Data-Driven Traffic Flow Animation Using Texture Synthesis." IEEE Transactions on Visualization and Computer Graphics 24, no. 2 (February 1, 2018): 1167–78. http://dx.doi.org/10.1109/tvcg.2017.2648790.

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29

Pola, Giordano, Tommaso Masciulli, Elena De Santis, and Maria Domenica Di Benedetto. "On data—driven controller synthesis with regular language specifications." IFAC-PapersOnLine 53, no. 2 (2020): 3928–33. http://dx.doi.org/10.1016/j.ifacol.2020.12.2245.

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30

Sun, L. Charles. "Data Inter-Operability Driven by Oceanic Data Assimilation Needs." Marine Technology Society Journal 33, no. 3 (January 1, 1999): 55–66. http://dx.doi.org/10.4031/mtsj.33.3.7.

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An interactive data access and retrieval system, developed at the U.S. National Oceanographic Data Genter (NODG) and available at <ext-link ext-link-type="uri" href="http://www.node.noaa.gov">http://www.node.noaa.gov</ext-link>, is presented in this paper. The purposes of this paper are: (1) to illustrate the procedures of quality control and loading oceanographic data into the NODG ocean databases and (2) to describe the development of a system to manage, visualize, and disseminate the NODG data holdings over the Internet. The objective of the system is to provide ease of access to data that will be required for data assimilation models. With advances in scientific understanding of the ocean dynamics, data assimilation models require the synthesis of data from a variety of resources. Modern intelligent data systems usually involve integrating distributed heterogeneous data and information sources. As the repository for oceanographic data, NOAA’s National Oceanographic Data Genter (NODG) is in a unique position to develop such a data system. In support of the data assimilation needs, NODG has developed a system to facilitate browsing of the oceanographic environmental data and information that is available on-line at NODG. Users may select oceanographic data based on geographic areas, time periods and measured parameters. Once the selection is complete, users may produce a station location plot, produce plots of the parameters or retrieve the data.
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31

Ogata, Kohichi, Kohei Matsumura, and Yusuke Matsuda. "Data-glove-driven vocal tract configuration methods for vowel synthesis." Acoustical Science and Technology 36, no. 6 (2015): 527–36. http://dx.doi.org/10.1250/ast.36.527.

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32

Mabrok, Mohamed A., and Ian R. Petersen. "Controller synthesis for negative imaginary systems: a data driven approach." IET Control Theory & Applications 10, no. 12 (August 8, 2016): 1480–86. http://dx.doi.org/10.1049/iet-cta.2015.0800.

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33

Yi, P., Q. Zhang, and X. Wei. "Laplacian coordinates-based motion transition for data-driven motion synthesis." IET Image Processing 6, no. 9 (December 1, 2012): 1331–37. http://dx.doi.org/10.1049/iet-ipr.2012.0186.

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34

Bloemers, Tom, Roland Tóth, and Tom Oomen. "Frequency-Domain Data-Driven Controller Synthesis for Unstable LPV Systems." IFAC-PapersOnLine 54, no. 8 (2021): 109–15. http://dx.doi.org/10.1016/j.ifacol.2021.08.589.

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35

Ibañez, Ruben, Fanny Casteran, Clara Argerich, Chady Ghnatios, Nicolas Hascoet, Amine Ammar, Philippe Cassagnau, and Francisco Chinesta. "On the Data-Driven Modeling of Reactive Extrusion." Fluids 5, no. 2 (June 15, 2020): 94. http://dx.doi.org/10.3390/fluids5020094.

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This paper analyzes the ability of different machine learning techniques, able to operate in the low-data limit, for constructing the model linking material and process parameters with the properties and performances of parts obtained by reactive polymer extrusion. The use of data-driven approaches is justified by the absence of reliable modeling and simulation approaches able to predict induced properties in those complex processes. The experimental part of this work is based on the in situ synthesis of a thermoset (TS) phase during the mixing step with a thermoplastic polypropylene (PP) phase in a twin-screw extruder. Three reactive epoxy/amine systems have been considered and anhydride maleic grafted polypropylene (PP-g-MA) has been used as compatibilizer. The final objective is to define the appropriate processing conditions in terms of improving the mechanical properties of these new PP materials by reactive extrusion.
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36

Li, Ying, Jiaxin L. Fu, and Nancy S. Pollard. "Data-Driven Grasp Synthesis Using Shape Matching and Task-Based Pruning." IEEE Transactions on Visualization and Computer Graphics 13, no. 4 (July 2007): 732–47. http://dx.doi.org/10.1109/tvcg.2007.1033.

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37

Lee, Seogoo, and Andreas Gerstlauer. "Data-Dependent Loop Approximations for Performance-Quality Driven High-Level Synthesis." IEEE Embedded Systems Letters 10, no. 1 (March 2018): 18–21. http://dx.doi.org/10.1109/les.2017.2764542.

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38

Bellegarda, Jerome R. "A Data-Driven Affective Analysis Framework Toward Naturally Expressive Speech Synthesis." IEEE Transactions on Audio, Speech, and Language Processing 19, no. 5 (July 2011): 1113–22. http://dx.doi.org/10.1109/tasl.2010.2078808.

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39

Chu, Mengyu, and Nils Thuerey. "Data-driven synthesis of smoke flows with CNN-based feature descriptors." ACM Transactions on Graphics 36, no. 4 (July 20, 2017): 1–14. http://dx.doi.org/10.1145/3072959.3073643.

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40

Dosil, Raquel, Xosé M. Pardo, and Xosé R. Fdez-Vidal. "Data-driven synthesis of composite-feature detectors for 3D image analysis." Image and Vision Computing 24, no. 3 (March 2006): 225–38. http://dx.doi.org/10.1016/j.imavis.2005.11.005.

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41

Pola, Giordano, Tommaso Masciulli, Elena De Santis, and Maria Domenica Di Benedetto. "Data-driven controller synthesis for abstract systems with regular language specifications." Automatica 134 (December 2021): 109903. http://dx.doi.org/10.1016/j.automatica.2021.109903.

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42

Basori, Ahmad Hoirul, and Hani Moaiteq Abdullah AlJahdali. "Performance Driven-biped Control for Animated Human Model with Motion Synthesis Data." Journal of Information Systems Engineering and Business Intelligence 4, no. 2 (October 28, 2018): 162. http://dx.doi.org/10.20473/jisebi.4.2.162-168.

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Games and 3D movies are mostly supported by realistic character animation performance. The behaviour of the humanoid character it is depend on the motion data itself. Therefore the complexity of character movement will determine the realism of their behaviour. Generally, Motion Capture device will provide the raw data that previously recorded from human/actor movement. However there are some problems remains challenges such as controller, physic effect, or motion combination. Our proposed approach will read the data from motion capture device then transformed into realistic behaviour in virtual environment. However, there are few difficulties on realizing this idea, such as user objective and the appropriate behaviour of virtual human. Therefore, we solve this issue by providing the biped control to overcome the complexity of motion synthesis data when it will be applied into character animation. The controller is capable to perform motion blending with inverse and forward kinematics, as a result it able to generate the realistic behaviour along with user intention. There is three main behaviour walking, steady and jogging that has value 0-100. As a result of experiment, the biped interface control is able to read data from motion capture then load and control the virtual human by manipulating the joint forces power in every movement of the characters. As future works, the external physical forces can be added as additional forces in humanoid model to provide certain effect such as: falling down, jumping or kicking and punching to generate realistic motion synthesis.
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43

Zhang, Min, Yunhui Shi, Na Qi, and Baocai Yin. "Data-Driven Redundant Transform Based on Parseval Frames." Applied Sciences 10, no. 8 (April 22, 2020): 2891. http://dx.doi.org/10.3390/app10082891.

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The sparsity of images in a certain transform domain or dictionary has been exploited in many image processing applications. Both classic transforms and sparsifying transforms reconstruct images by a linear combination of a small basis of the transform. Both kinds of transform are non-redundant. However, natural images admit complicated textures and structures, which can hardly be sparsely represented by square transforms. To solve this issue, we propose a data-driven redundant transform based on Parseval frames (DRTPF) by applying the frame and its dual frame as the backward and forward transform operators, respectively. Benefitting from this pairwise use of frames, the proposed model combines a synthesis sparse system and an analysis sparse system. By enforcing the frame pair to be Parseval frames, the singular values and condition number of the learnt redundant frames, which are efficient values for measuring the quality of the learnt sparsifying transforms, are forced to achieve an optimal state. We formulate a transform pair (i.e., frame pair) learning model and a two-phase iterative algorithm, analyze the robustness of the proposed DRTPF and the convergence of the corresponding algorithm, and demonstrate the effectiveness of our proposed DRTPF by analyzing its robustness against noise and sparsification errors. Extensive experimental results on image denoising show that our proposed model achieves superior denoising performance, in terms of subjective and objective quality, compared to traditional sparse models.
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44

Hahanov, V. I., I. В. Iemelianov, M. M. Liubarskyi, S. V. Chumachenko, and E. I. Litvinova. "Quantum Memory-Driven Method for Test Synthesis Based on Qubit Data Structures." Èlektronnoe modelirovanie 40, no. 1 (February 27, 2018): 63–80. http://dx.doi.org/10.15407/emodel.40.01.063.

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45

Kawai, Masahide, Tomoyori Iwao, Daisuke Mima, Akinobu Maejima, and Shigeo Morishima. "Data-Driven Speech Animation Synthesis Focusing on Realistic Inside of the Mouth." Journal of Information Processing 22, no. 2 (2014): 401–9. http://dx.doi.org/10.2197/ipsjjip.22.401.

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46

Lu, Pengfei, and Matt Huenerfauth. "Data-Driven Synthesis of Spatially Inflected Verbs for American Sign Language Animation." ACM Transactions on Accessible Computing 4, no. 1 (November 2011): 1–29. http://dx.doi.org/10.1145/2039339.2039343.

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47

Zhai, Ding, Qing-Ling Zhang, and Guo-Yi Liu. "Data-driven criteria synthesis of system with two-degree-of-freedom controller." International Journal of Systems Science 45, no. 11 (February 20, 2013): 2275–81. http://dx.doi.org/10.1080/00207721.2013.768306.

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48

Ma, Yun, Xuan Lu, XuanZhe Liu, XuDong Wang, and M. Brian Blake. "Data-driven synthesis of multiple recommendation patterns to create situational Web mashups." Science China Information Sciences 56, no. 8 (August 2013): 1–16. http://dx.doi.org/10.1007/s11432-013-4908-4.

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49

Marsch, Lisa A. "Digital health data-driven approaches to understand human behavior." Neuropsychopharmacology 46, no. 1 (July 12, 2020): 191–96. http://dx.doi.org/10.1038/s41386-020-0761-5.

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AbstractAdvances in digital technologies and data analytics have created unparalleled opportunities to assess and modify health behavior and thus accelerate the ability of science to understand and contribute to improved health behavior and health outcomes. Digital health data capture the richness and granularity of individuals’ behavior, the confluence of factors that impact behavior in the moment, and the within-individual evolution of behavior over time. These data may contribute to discovery science by revealing digital markers of health/risk behavior as well as translational science by informing personalized and timely models of intervention delivery. And they may help inform diagnostic classification of clinically problematic behavior and the clinical trajectories of diagnosable disorders over time. This manuscript provides a review of the state of the science of digital health data-driven approaches to understanding human behavior. It reviews methods of digital health assessment and sources of digital health data. It provides a synthesis of the scientific literature evaluating how digitally derived empirical data can inform our understanding of health behavior, with a particular focus on understanding the assessment, diagnosis and clinical trajectories of psychiatric disorders. And, it concludes with a discussion of future directions and timely opportunities in this line of research and its clinical application.
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50

Silva, Samuel, Nuno Almeida, Conceição Cunha, Arun Joseph, Jens Frahm, and António Teixeira. "Data-Driven Critical Tract Variable Determination for European Portuguese." Information 11, no. 10 (October 21, 2020): 491. http://dx.doi.org/10.3390/info11100491.

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Technologies, such as real-time magnetic resonance (RT-MRI), can provide valuable information to evolve our understanding of the static and dynamic aspects of speech by contributing to the determination of which articulators are essential (critical) in producing specific sounds and how (gestures). While a visual analysis and comparison of imaging data or vocal tract profiles can already provide relevant findings, the sheer amount of available data demands and can strongly profit from unsupervised data-driven approaches. Recent work, in this regard, has asserted the possibility of determining critical articulators from RT-MRI data by considering a representation of vocal tract configurations based on landmarks placed on the tongue, lips, and velum, yielding meaningful results for European Portuguese (EP). Advancing this previous work to obtain a characterization of EP sounds grounded on Articulatory Phonology, important to explore critical gestures and advance, for example, articulatory speech synthesis, entails the consideration of a novel set of tract variables. To this end, this article explores critical variable determination considering a vocal tract representation aligned with Articulatory Phonology and the Task Dynamics framework. The overall results, obtained considering data for three EP speakers, show the applicability of this approach and are consistent with existing descriptions of EP sounds.
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