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114. Enhancement of edge turbulence concomitant with ELM suppression during boron powder injection in EAST

Author(s):
Sun, Zhen; Maingi, Rajesh; Diallo, Ahmed; Xu, Wei; Qian, Yuzhong; Tritz, Kevin; Ye, Yang; Li, Chenglong; Xu, Zong; Wang, Yifeng; Ye, Kaixuan; Bortolon, A.; Nagy, Alex; Zhang, Ling; Duan, Yanmin; Lu, Zhiyuan; Wang, Huiqian; Shi, Tonghui; Zhao, Hailin; Wei, Gao; Xu, Jichan; Chen, Ran; Huang, Ming; Zuo, Guizhong; Xu, Guosheng; Gong, Xianzu; Hu, Jiansheng
Type:
Dataset
Issue Date:
2021

118. Experimental data for paper "hydraulic transmissivity inferred from ice-sheet relaxation following Greenland supraglacial lake drainages"

Author(s):
Lai, Ching-Yao
Abstract:
This setup mimics ice lying above the drainage system. In the experiment, a fluid-filled blister is generated via liquid injection into the interface between a transparent elastic layer and a porous substrate. After injection of liquid, the fluid permeates from the blister through the porous substrate, the blister volume V(t) relaxes exponentially with time. Our lab experiments show that varying the permeability of the porous substrate k significantly impacts the relaxation timescale in the experiments.
Type:
Dataset
Issue Date:
18 May 2021

122. Fast animal pose estimation using deep neural networks

Author(s):
Pereira, Talmo D.; Aldarondo, Diego E.; Willmore, Lindsay; Kislin, Mikhail; Wang, Samuel S.-H.; Murthy, Mala; Shaevitz, Joshua W.
Abstract:
Recent work quantifying postural dynamics has attempted to define the repertoire of behaviors performed by an animal. However, a major drawback to these techniques has been their reliance on dimensionality reduction of images which destroys information about which parts of the body are used in each behavior. To address this issue, we introduce a deep learning-based method for pose estimation, LEAP (LEAP Estimates Animal Pose). LEAP automatically predicts the positions of animal body parts using a deep convolutional neural network with as little as 10 frames of labeled data for training. This framework consists of a graphical interface for interactive labeling of body parts and software for training the network and fast prediction on new data (1 hr to train, 185 Hz predictions). We validate LEAP using videos of freely behaving fruit flies (Drosophila melanogaster) and track 32 distinct points on the body to fully describe the pose of the head, body, wings, and legs with an error rate of <3% of the animal's body length. We recapitulate a number of reported findings on insect gait dynamics and show LEAP's applicability as the first step in unsupervised behavioral classification. Finally, we extend the method to more challenging imaging situations (pairs of flies moving on a mesh-like background) and movies from freely moving mice (Mus musculus) where we track the full conformation of the head, body, and limbs.
Type:
Dataset
Issue Date:
30 May 2018

125. Femtosecond X-ray Diffraction of Laser-shocked Forsterite (Mg2SiO4) to 122 GPa

Author(s):
Kim, Donghoon; Tracy, Sally J.; Smith, Raymond F.; Gleason, Arianna E.; Bolme, Cindy A.; Prakapenka, Vitali B.; Appel, Karen; Speziable, Sergio; Wicks, June K.; Berryman, Eleanor J.; Han, Sirus K.; Schoelmerich, Markus O.; Lee, Hae Ja; Nagler, Bob; Cunningham, Eric F.; Akin, Minta C.; Asimow, Paul D.; Eggert, Jon H.; Duffy, Thomas S.
Abstract:
The behavior of forsterite, Mg2SiO4, under dynamic compression is of fundamental importance for understanding its phase transformations and high-pressure behavior. Here, we have carried out an in situ X-ray diffraction study of laser-shocked poly- and single-crystal forsterite (a-, b-, and c- orientations) from 19 to 122 GPa using the Matter in Extreme Conditions end-station of the Linac Coherent Light Source. Under laser-based shock loading, forsterite does not transform to the high-pressure equilibrium assemblage of MgSiO3 bridgmanite and MgO periclase, as was suggested previously. Instead, we observe forsterite and forsterite III, a metastable polymorph of Mg2SiO4, coexisting in a mixed-phase region from 33 to 75 GPa for both polycrystalline and single-crystal samples. Densities inferred from X-ray diffraction data are consistent with earlier gas-gun shock data. At higher stress, the behavior observed is sample-dependent. Polycrystalline samples undergo amorphization above 79 GPa. For [010]- and [001]-oriented crystals, a mixture of crystalline and amorphous material is observed to 108 GPa, whereas the [100]-oriented crystal adopts an unknown crystal structure at 122 GPa. The Q values of the first two sharp diffraction peaks of amorphous Mg2SiO4 show a similar trend with compression as those observed for MgSiO3 glass in both recent static and laser-compression experiments. Upon release to ambient pressure, all samples retain or revert to forsterite with evidence for amorphous material also present in some cases. This study demonstrates the utility of femtosecond free-electron laser X-ray sources for probing the time evolution of high-pressure silicates through the nanosecond-scale events of shock compression and release.
Type:
Dataset
Issue Date:
November 2020

126. First impurity powder injection experiments in LHD

Author(s):
Nespoli, F; Ashikawa, N.; Gilson, E. P.; Lunsford, R.; Masuzaki, S.; Shoji, M.; Oishi, T.; Suzuki, C.; Nagy, A.; Mollén, A.; Pablant, N. A.; Ida, K.; Yoshinuma, M.; Tamura, N.; Gates, D. A.; Morisaki, T.; experiment group, LHD
Type:
Dataset
Issue Date:
2020

130. Fusion Nuclear Science Facilities and Pilot Plants Based on the Spherical Tokamak

Author(s):
Menard, J. E.; Brown, T.; El-Guebaly, L.; Boyer, M.; Canik, J.; Colling, B.; Raman, R.; Wang, Z.; Zhai, Y.; Buxton, P.; Covele, B.; D'Angelo, C.; Davis, A.; Gerhardt, S.; Gryaznevich, M.; Harb, M.; Hender, T. C.; Kaye, S.; Kingham, D.; Kotschenreuther, M.; Mahajan, S.; Maingi, R.; Marriott, E.; Meier, E. T.; Mynsberge, L.; Neumeyer, C.; Ono, M.; Park, J. -K.; Sabbagh, S. A.; Soukhanovskii, V.; Valanju, P.; Woolley, R.
Type:
Dataset
Issue Date:
2016

149. Integrative Mechanisms of Social Attention

Author(s):
Bio, Branden; Graziano, Michael
Abstract:
Monitoring the attention of others is fundamental to social cognition. Most of the literature on the topic assumes that our social cognitive machinery is tuned specifically to the gaze direction of others as a proxy for attention. This standard assumption reduces attention to an externally visible parameter. Here we show that this assumption is wrong and a deeper, more meaningful representation is involved. We presented subjects with two cues about the attentional state of a face: direction of gaze and emotional expression. We tested whether people relied predominantly on one cue, the other, or both. If the traditional view is correct, then the gaze cue should dominate. Instead, people employed a variety of strategies, some relying on gaze, some on expression, and some on an integration of cues. We also assessed people’s social cognitive ability using two, independent, standard tests. If the traditional view is correct, then social cognitive ability, as assessed by the independent tests, should correlate with the degree to which people successfully use the gaze cue to judge the attention state of the face. Instead, social cognitive ability correlated best with the degree to which people successfully integrated the cues together, instead of with the use of any one specific cue. The results suggest a rethink of a fundamental component of social cognition: monitoring the attention of others involves constructing a deep model that is informed by a combination of cues. Attention is a rich process and monitoring the attention of others involves a similarly rich representation.
Type:
Dataset
Issue Date:
22 July 2021

161. Lower Hybrid Drift Waves During Guide Field Reconnection

Author(s):
Yoo, Jongsoo; Ji, Jeong-Young; Ambat, M.V.; Wang, Shan; Ji, Hantao; Lo, Jenson; Li, Bowen; Ren, Yang; Jara-Almonte, J.; Fox, William; Yamada, Masaaki; Alt, Andrew; Goodman, Aaron
Type:
Dataset
Issue Date:
2020

163. MHD-blob correlations in NSTX

Author(s):
Zweben, S. J.; Fredrickson, E. D.; Myra, J. R.; Podesta, M.; Scotti, F.
Type:
Dataset
Issue Date:
2020

183. Neural pattern change during encoding of a narrative predicts retrospective duration estimates

Author(s):
Lositsky, Olga; Chen, Janice; Toker, Daniel; Honey, Christopher; Hasson, Uri; Norman, Kenneth
Abstract:
What mechanisms support our ability to estimate durations on the order of minutes? Behavioral studies in humans have shown that changes in contextual features lead to overestimation of past durations. Based on evidence that the medial temporal lobes and prefrontal cortex represent contextual features, we related the degree of fMRI pattern change in these regions with people's subsequent duration estimates. After listening to a radio story in the scanner, participants were asked how much time had elapsed between pairs of clips from the story. Our ROI analysis found that the neural pattern distance between two clips at encoding was correlated with duration estimates in the right entorhinal cortex and right pars orbitalis. Moreover, a whole-brain searchlight analysis revealed a cluster spanning the right anterior temporal lobe. Our findings provide convergent support for the hypothesis that retrospective time judgments are driven by 'drift' in contextual representations supported by these regions.
Type:
Dataset
Issue Date:
12 March 2016

193. Observation of a reduced-turbulence regime with boron powder injection in a stellarator

Author(s):
Nespoli, Federico; Masuzaki, Suguru; Tanaka, Kenji; Ashikawa, Naoko; Shoji, Mamoru; Gilson, Erik; Oishi, Tetsutarou; Ida, Katsumi; Yoshinuma, Mikirou; Takemura, Yuki; Kinoshita, Toshiki; Motojima, Gen; Kenmochi, Naoki; Kawamura, Gakushi; Suzuki, Chihiro; Nagy, Alex; Bortolon, Alessandro; Pablant, Novimir; Mollén, Albert; Tamura, Naoki; Gates, David; Morisaki, Tomohiro
Type:
Dataset
Issue Date:
2022

199. Overview of NSTX Upgrade Initial Results and Modelling Highlights

Author(s):
Menard, J. E.; Allain, J. P.; Battaglia, D. J.; Bedoya, F.; Bell, R. E.; Belova, E.; Berkery, J. W.; Boyer, M. D.; Crocker, N.; Diallo, A.; Ebrahimi, F.; Ferraro, N.; Fredrickson, E.; Frerichs, H.; Gerhardt, S.; Gorelenkov, N.; Guttenfelder, W.; Heidbrink, W.; Kaita, R.; Kaye, S. M.; Kriete, D. M.; Kubota, S.; LeBlanc, B. P.; Liu, D.; Lunsford, R.; Mueller, D.; Myers, C. E.; Ono, M.; Park, J. -K.; Podesta, M.; Raman, R.; Reinke, M.; Ren, Y.; Sabbagh, S. M.; Schmitz, O.; Scotti, F.; Sechrest, Y.; Skinner, C. H.; Smith, D. R.; Soukhanovskii, V.; Stoltzfus-Dueck, T.; Yuh, H.; Wang, Z.; Waters, I.; Ahn, J. -W.; Andre, A.; Barchfeld, R.; Beiersdorfer, P.; Bertelli, N.; Bhattacharjee, A.; Boyle, D.; Brennan, D.; Buttery, R.; Capece, A.; Canal, G.; Canik, J.; Chang, C. S.; Darrow, D.; Delgado-Aparicio, L.; Domier, C.; Ethier, S.; Evans, T.; Ferron, J.; Finkenthal, M.; Fonck, R.; Gan, K.; Gates, D.; Goumiri, I.; Gray, T.; Hosea, J.; Humphreys, D.; Jarboe, T.; Jardin, S.; Jaworski, M. A.; Koel, B.; Kolemen, E.; Ku, S.; LaHaye, R. J.; Levinton, F.; Luhmann Jr., N.; Maingi, R.; Maqueda, R.; McKee, G.; Meier, E.; Myra, J.; Perkins, R.; Poli, F.; Rhodes, T.; Riquezes, J.; Rowley, C.; Russell, D.; Schuster, E.; Stratton, B.; Stutman, D.; Taylor, G.; Tritz, K.; Wang, W.; Wirth, B.; Zweben, S. J.
Type:
Dataset
Issue Date:
2017