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28 results for “MOS”
Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor (raw data from journal article)
<p>This upload contains raw data from the manuscript "Floating-Gate MOS Transistor with Dynamic Biasing as a Radiation Sensor". The manuscript was published in Sensors 20, no. 11 (2020): 3329; DOI: https://doi.org/10.3390/s20113329</p> <p>The upload consists of .pdf file of the manuscript and .vsz files with raw data related to the figures in the manuscript. Each .vsz file is linked with raw data from the text files (.txt) and placed in a folder with the name and ordinal number of the figure in the publication. Additionally, a .pdf output file of the Veusz program (freely available) is placed in each folder.</p> <p>This work was supported in part by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558) and the Ministry of Education, Science and Technology Development of the Republic of Serbia (Project No. 43011).</p>
SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis
<p>This is the public release of the Samsung Open Mean Opinion Scores (SOMOS) dataset for the evaluation of neural text-to-speech (TTS) synthesis, which consists of audio files generated with a public domain voice from trained TTS models based on bibliography, and numbers assigned to each audio as quality (naturalness) evaluations by several crowdsourced listeners.<br><br><strong>Description</strong><br><br>The SOMOS dataset contains 20,000 synthetic utterances (wavs), 100 natural utterances and 374,955 naturalness evaluations (human-assigned scores in the range 1-5). The synthetic utterances are single-speaker, generated by training several Tacotron-like acoustic models and an LPCNet vocoder on the LJ Speech voice public dataset. 2,000 text sentences were synthesized, selected from Blizzard Challenge texts of years 2007-2016, the LJ Speech corpus as well as Wikipedia and general domain data from the Internet.<br>Naturalness evaluations were collected via crowdsourcing a listening test on Amazon Mechanical Turk in the US, GB and CA locales. The records of listening test participants (workers) are fully anonymized. Statistics on the reliability of the scores assigned by the workers are also included, generated through processing the scores and validation controls per submission page.</p> <p>To listen to audio samples of the dataset, please see <a href="https://innoetics.github.io/publications/somos-dataset/index.html">our Github page</a>.</p> <p>The dataset release comes with a carefully designed train-validation-test split (70%-15%-15%) with unseen systems, listeners and texts, which can be used for experimentation on MOS prediction.</p> <p><em>This version also contains the necessary resources to obtain the transcripts corresponding to all dataset audios.</em></p> <p><strong>Terms of use</strong></p> <ul> <li>The dataset may be used for <strong>research</strong> purposes only, for <strong>non-commercial</strong> purposes only, and may be distributed with the same terms.</li> <li>Every time you produce research that has used this dataset, please <strong>cite </strong>the dataset appropriately.</li> </ul> <p>Cite as:</p> <pre><code>@inproceedings{maniati22_interspeech, author={Georgia Maniati and Alexandra Vioni and Nikolaos Ellinas and Karolos Nikitaras and Konstantinos Klapsas and June Sig Sung and Gunu Jho and Aimilios Chalamandaris and Pirros Tsiakoulis}, title={{SOMOS: The Samsung Open MOS Dataset for the Evaluation of Neural Text-to-Speech Synthesis}}, year=2022, booktitle={Proc. Interspeech 2022}, pages={2388--2392}, doi={10.21437/Interspeech.2022-10922} } </code></pre> <p><br><strong>References of resources & models used</strong></p> <p>Voice & synthesized texts:<br>K. Ito and L. Johnson, “The LJ Speech Dataset,” https://keithito.com/LJ-Speech-Dataset/, 2017.</p> <p>Vocoder:<br>J.-M. Valin and J. Skoglund, “LPCNet: Improving neural speech synthesis through linear prediction,” in Proc. ICASSP, 2019.<br>R. Vipperla, S. Park, K. Choo, S. Ishtiaq, K. Min, S. Bhattacharya, A. Mehrotra, A. G. C. P. Ramos, and N. D. Lane, “Bunched lpcnet: Vocoder for low-cost neural text-to-speech systems,” in Proc. Interspeech, 2020.</p> <p>Acoustic models:<br>N. Ellinas, G. Vamvoukakis, K. Markopoulos, A. Chalamandaris, G. Maniati, P. Kakoulidis, S. Raptis, J. S. Sung, H. Park, and P. Tsiakoulis, “High quality streaming speech synthesis with low, sentence-length-independent latency,” in Proc. Interspeech, 2020.<br>Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio et al., “Tacotron: Towards End-to-End Speech Synthesis,” in Proc. Interspeech, 2017.<br>J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al., “Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions,” in Proc. ICASSP, 2018.<br>J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling,” arXiv preprint arXiv:2010.04301, 2020.<br>M. Honnibal and M. Johnson, “An Improved Non-monotonic Transition System for Dependency Parsing,” in Proc. EMNLP, 2015.<br>M. Dominguez, P. L. Rohrer, and J. Soler-Company, “PyToBI: A Toolkit for ToBI Labeling Under Python,” in Proc. Interspeech, 2019.<br>Y. Zou, S. Liu, X. Yin, H. Lin, C. Wang, H. Zhang, and Z. Ma, “Fine-grained prosody modeling in neural speech synthesis using ToBI representation,” in Proc. Interspeech, 2021.<br>K. Klapsas, N. Ellinas, J. S. Sung, H. Park, and S. Raptis, “WordLevel Style Control for Expressive, Non-attentive Speech Synthesis,” in Proc. SPECOM, 2021.<br>T. Raitio, R. Rasipuram, and D. Castellani, “Controllable neural text-to-speech synthesis using intuitive prosodic features,” in Proc. Interspeech, 2020.</p> <p>Synthesized texts from the Blizzard Challenges 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2016:<br>M. Fraser and S. King, "The Blizzard Challenge 2007," in Proc. SSW6, 2007.<br>V. Karaiskos, S. King, R. A. Clark, and C. Mayo, "The Blizzard Challenge 2008," in Proc. Blizzard Challenge Workshop, 2008.<br>A. W. Black, S. King, and K. Tokuda, "The Blizzard Challenge 2009," in Proc. Blizzard Challenge, 2009.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2010," 2010.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2011," 2011.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2012," 2012.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2013," 2013.<br>S. King and V. Karaiskos, "The Blizzard Challenge 2016," 2016.</p> <p><strong>Contact</strong></p> <p>Alexandra Vioni - a.vioni@samsung.com</p> <ul> <li>If you have any questions or comments about the dataset, please feel free to write to us.</li> <li>We are interested in knowing if you find our dataset useful! If you use our dataset, please email us and tell us about your research.</li> </ul>
MOS Prebiotic Potential in Older Adults
ClinicalTrials.gov study NCT05939336. IPD Sharing: NO. Countries: 1. Publications: 1.
Development and Health of Rural Chinese Children Fed Meat as a Daily Complementary Food From 6-18 Mos of Age
ClinicalTrials.gov study NCT00726102. IPD Sharing: Not stated. Countries: 1. Publications: 2.
MicroOrganoSphere (MOS) Drug Screen Pilot Trial in Colorectal Cancer
ClinicalTrials.gov study NCT05189171. IPD Sharing: NO. Countries: 1. Publications: 32.
HMDSO Poisoning of UST1530 MOS Sensor
<p>The dataset was created at the Lab for Measurement Technology (Saarland University). It consists of several characterization measurements of a UST1530 (UST Umweltsensortechnik GmBH) with 2 ppm acetone, 2 ppm carbon monoxide and 2 ppm hydrogen. Between these characterizations the sensor was exposed to 10 ppm of HMDSO (hexamethyldisiloxane) for different times and at different temperatures, which is known to be a sensor poison.</p> <p>An exact description of the measurement setup and its results can be found in the open access article:</p> <p>Schultealbert, C., Uzun, I., Baur, T., Sauerwald, T., and Schütze, A.: Siloxane treatment of metal oxide semiconductor gas sensors in temperature-cycled operation – sensitivity and selectivity, J. Sens. Sens. Syst., 9, 283–292, https://doi.org/10.5194/jsss-9-283-2020, 2020.</p> <p>The h5-file comprises 13 datasets, each containing 48 sensor cycles (ln(G)) with 2500 data points (@ 18 Hz). All measurements were performed consecutively with the same sensor.</p> <p>Gas profile:</p> <p>Cycles 16-20: 2 ppm carbon monoxide</p> <p>Cycles 25-29: 2 ppm acetone</p> <p>Cycles 34-38: 2 ppm hydrogen</p> <p>Sensor operation mode:</p> <p>30 s 450 °C, 120 s 150 °C, 30 s 450 °C, 120 s 200 °C, 30 s 450 °C, 120 s 250 °C, 30 s 450 °C, 120 s 300 °C, 30 s 450 °C, 120 s 350 °C,</p> <p>Datasets:</p> <p>x_0h: Characterization before poisoning</p> <p>x300_3h: Characterization after 3 h at 300 °C at 11 ppm HMDSO</p> <p>x300_9h: Characterization after additional 6 h at 300 °C at 11 ppm HMDSO</p> <p>x300_12h: Characterization after additional 3 h at 300 °C at 11 ppm HMDSO</p> <p>x400_3h: Characterization after additional 3 h at 400 °C at 13 ppm HMDSO</p> <p>x400_6h: Characterization after additional 3 h at 400 °C at 13 ppm HMDSO</p> <p>x400_12h: Characterization after additional 6 h at 400 °C at 13 ppm HMDSO</p> <p>x400_24h: Characterization after additional 12 h at 400 °C at 13 ppm HMDSO</p> <p>x500_3h: Characterization after additional 3 h at 500 °C at 13 ppm HMDSO</p> <p>x500_6h: Characterization after additional 3 h at 500 °C at 13 ppm HMDSO</p> <p>x500_12h: Characterization after additional 6 h at 500 °C at 13 ppm HMDSO</p> <p>x500_24h: Characterization after additional 12 h at 500 °C at 13 ppm HMDSO</p> <p>x700: Characterization after additional 12 minutes at 700 °C at 2500 ppm HMDSO</p>
USE OF THE MOS SF-36 QUESTIONNAIRE IN THE ASSESSMENT OF QUALITY OF LIFE IN SURGERY
Open the record for dataset details and reuse information.
Recharging process of commercial floating-gate MOS transistor in dosimetry application (raw data from journal article)
<p>This upload contains raw data from the manuscript "Recharging process of commercial floating-gate MOS transistor in dosimetry application". The manuscript was published in Microelectronics Reliability, vol. 126, p.114322, 2021; DOI: https://doi.org/10.1016/j.microrel.2021.114322</p> <p>The upload consists of a .pdf file of the manuscript and .vsz files with raw data related to the figures in the manuscript. Each .vsz file is linked with raw data from the text files (.txt) and placed in a folder with the name and an ordinal number of the figure in the publication. Additionally, a .pdf output file of the Veusz program (freely available) is placed in each folder.</p> <p>This work was partly supported by the European Union’s Horizon 2020 research and innovation programme (Grant No. 857558) and the Ministry of Education, Science and Technology Development of the Republic of Serbia (Project No. 43011 and Project No.451-03-9/2021-14/200026).</p>
ASVspoof 2019 LA Listening Test Data for Partial Rank Similarity MOS Prediction
<p>This dataset is a derivitave work of the ASVSpoof 2019 LA condition listening test data found here:<br> https://datashare.ed.ac.uk/handle/10283/3336<br> -> LA.zip</p> <p>"ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech" <br> Xin Wang, Junichi Yamagishi, Massimiliano Todisco, Héctor Delgado, Andreas Nautsch, Nicholas Evans, Md Sahidullah, Ville Vestman, Tomi Kinnunen, Kong Aik Lee, Lauri Juvela, Paavo Alku, Yu-Huai Peng, Hsin-Te Hwang, Yu Tsao, Hsin-Min Wang, Sébastien Le Maguer, Markus Becker, Fergus Henderson, Rob Clark, Yu Zhang, Quan Wang, Ye Jia, Kai Onuma, Koji Mushika, Takashi Kaneda, Yuan Jiang, Li-Juan Liu, Yi-Chiao Wu, Wen-Chin Huang, Tomoki Toda, Kou Tanaka, Hirokazu Kameoka, Ingmar Steiner, Driss Matrouf, Jean-François Bonastre, Avashna Govender, Srikanth Ronanki, Jing-Xuan Zhang, Zhen-Hua Ling.<br> Computer Speech and Language Colume 64, 2020.</p> <p>This form of the data was used for the PRS paper accepted to ASRU 2023:</p> <p>"Partial Rank Similarity Minimization Method for Quality MOS Prediction of <br> Unseen Speech Synthesis Systems in Zero-shot and Semi-supervised Setting."<br> Hemant Yadav, Erica Cooper, Junichi Yamagishi, Sunayana Sitaram, Rajiv Ratn Shah.</p> <p>Modifications to the original data include converting audio from flac -> wav, sv56 normalization, conversion of labels from an 0-9 rating scale to a 1-5 scale, and creation of training/development/testing splits.</p>
Mos-FED (Mosaicism in Focal Epilepsy Cortical Dysplasia Tissue)
ClinicalTrials.gov study NCT06053671. IPD Sharing: YES. Countries: 1. Publications: 0.
Supplementary information for: Deterministic assembly of arrays of lithographically defined WS_2 and MoS_2 monolayer features directly from multilayer sources into van der Waals heterostructures
Open the record for dataset details and reuse information.
NLDAS Mosaic Land Surface Model L4 Monthly Climatology 0.125 x 0.125 degree V2.0 (NLDAS_MOS0125_MC) at GES DISC
This monthly climatology data set contains a series of land surface parameters simulated from the Mosaic land-surface model (LSM) for Phase 2 of the North American Land Data Assimilation System (NLDAS-2). The data are in 1/8th degree grid spacing. The temporal resolution is monthly, ranging from January to December. The NLDAS-2 monthly climatology data are the monthly data averaged over forty years (1981 - 2020). The file format is netCDF. The previous version of this dataset (NLDAS_MC 002) was a 30-year average and was stored in GRIB file format.A brief description about the NLDAS-2 hourly and monthly Mosaic LSM data can be found from the NLDAS_MOS0125_H_2.0 and NLDAS_MOS0125_M_2.0 landing pages.Details about the NLDAS-2 configuration of the Mosaic LSM can be found in Xia et al. (2012).For more information, please see the README Document.
Patient-derived micro-organospheres (MOS) enable clinical precision oncology
GEO Series GSE184242. Homo sapiens. 17 samples. Type: Expression profiling by high throughput sequencing.
Gene expression profile at single cell level of monocytes (MOs) and T cells from the lungs and from broncho-alveolar lavage fluids (BALF) from Mock and MuHV-4 infected WT and CCR2 KO mice.
GEO Series GSE205923. Mus musculus. 6 samples. Type: Expression profiling by high throughput sequencing.
Transcriptome analysis of LPS-stimulated BMDMs pretreated with Ctrl MO or Regnase-1-targeting MOs (Reg1-MOs)
GEO Series GSE182641. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
HRT Versus MOS for Endometrial Preparation Prior to FET in Non PCOS Patients
ClinicalTrials.gov study NCT02330757. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Comparing of Different Visual Aids Like Video,info-graphics and Pamphlets in Enhancing Community Awareness and Promoting Behavioral Change Towards the Prevention of Hypertension in Basilica of the Mos
ClinicalTrials.gov study NCT06761508. IPD Sharing: NO. Countries: 1. Publications: 0.
Monitor Orthopaedic Footwear Questionnaire (MOS) Validity and Reliability Study
ClinicalTrials.gov study NCT07033390. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
HRT Versus MOS for Endometrial Preparation Prior to FET in PCOS Patients
ClinicalTrials.gov study NCT02273791. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Maternal and Postnatal Outcomes Study (MOS): A Global Observational Registry Assessing the Safety of Elfabrio® in Women With Fabry Disease and Their Infants During Pregnancy and Breastfeeding
ClinicalTrials.gov study NCT06941025. IPD Sharing: Not stated. Countries: 5. Publications: 0.
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