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spingal » spinal (Expand Search), singal (Expand Search), spinodal (Expand Search), spinggal (Expand Search), sspingal (Expand Search)
spike » spie (Expand Search)
fine » find (Expand Search), line (Expand Search), five (Expand Search)
spina » spinal (Expand Search), espina (Expand Search), spinae (Expand Search), spinga (Expand Search), sspina (Expand Search)
aina » ainaa (Expand Search), bina (Expand Search), anna (Expand Search)
spingggal » spinggggal (Expand Search), sspingggal (Expand Search)
pin » ping (Expand Search), spin (Expand Search)
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1601
Bio-inspired Flow Sensing and Prediction for Fish-like Undulating Locomotion: A CFD-aided Approach
Published 2015Get full text
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Journal Article -
1602
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1603
Global signal of top-down control of terrestrial plant communities by herbivores
Published 2019Get full text
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Journal Article -
1604
Rapid and punctuated Late Holocene recession of Siling Co, central Tibet
Published 2017Get full text
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Journal Article -
1605
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1606
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1607
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1608
Microbend fiber optic sensor for perioperative pediatric vital signs monitoring
Published 2018Get full text
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Conference Paper -
1609
Barrier property of TiSiN films formed by low frequency, high density inductively coupled plasma process
Published 2012Get full text
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Journal Article -
1610
Investigation of active interface control of pressure driven two-fluid flow in microchannels
Published 2012Get full text
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Journal Article -
1611
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1612
GaussCtrl: multi-view consistent text-driven 3D Gaussian splatting editing
Published 2024Conference item -
1613
Impact of international professional mobility programmes: a systematic literature review
Published 2025Journal article -
1614
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1615
Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-level Backdoor Attacks
Published 2024“…The pre-training-then-fine-tuning paradigm has been widely used in deep learning. …”
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Article -
1616
Structure matching mechanism of nRu/FeCo2O4 for highly-selective oxidation of HMF toward FDCA
Published 2025Get full text
Journal Article -
1617
iTD3-CLN: learn to navigate in dynamic scene through Deep Reinforcement Learning
Published 2022“…In contrast to the conventional methods such as the DWA, our approach is found superior in the following ways: no need for prior knowledge of the environment and metric map, lower reliance on an accurate sensor, learning emergent behavior in dynamic scene that is intuitive, and more remarkably, able to transfer to the real robot without further fine-tuning. Our extensive studies show that in comparison to the original TD3, the proposed approach can obtain approximately 50% reduction in training to get same performance, 50% higher accumulated reward, and 30–50% increase in generalization performance when tested in unseen environments. …”
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Journal Article -
1618
Rethinking Cross-Domain Sequential Recommendation under Open-World Assumptions
Published 2024Get full text
Article -
1619
Point defect–dislocation interactions in BEOL-compatible Ge-on-Si epitaxy
Published 2024Get full text
Article -
1620