Zobrazeno 1 - 10
of 635
pro vyhledávání: '"LIU, ANDREA"'
In physical networks trained using supervised learning, physical parameters are adjusted to produce desired responses to inputs. An example is electrical contrastive local learning networks of nodes connected by edges that are resistors that adjust t
Externí odkaz:
http://arxiv.org/abs/2412.19356
Autor:
Yeh, Chun-Hsiao, Wang, Jiayun, Graham, Andrew D., Liu, Andrea J., Tan, Bo, Chen, Yubei, Ma, Yi, Lin, Meng C.
Accurate diagnosis of ocular surface diseases is critical in optometry and ophthalmology, which hinge on integrating clinical data sources (e.g., meibography imaging and clinical metadata). Traditional human assessments lack precision in quantifying
Externí odkaz:
http://arxiv.org/abs/2410.00292
The sudden arrest of flow by formation of a stable arch over an outlet is a unique and characteristic feature of granular materials. Previous work suggests that grains near the outlet randomly sample configurational flow microstates until a clog-caus
Externí odkaz:
http://arxiv.org/abs/2407.05491
Physical networks can develop diverse responses, or functions, by design, evolution or learning. We focus on electrical networks of nodes connected by resistive edges. Such networks can learn by adapting edge conductances to lower a cost function tha
Externí odkaz:
http://arxiv.org/abs/2406.09689
Autor:
Ridout, Sean A., Liu, Andrea J.
The dynamics of supercooled liquids slow down and become increasingly heterogeneous as they are cooled. Recently, local structural variables identified using machine learning, such as "softness", have emerged as predictors of local dynamics. Here we
Externí odkaz:
http://arxiv.org/abs/2406.05868
Autor:
Momeni, Ali, Rahmani, Babak, Scellier, Benjamin, Wright, Logan G., McMahon, Peter L., Wanjura, Clara C., Li, Yuhang, Skalli, Anas, Berloff, Natalia G., Onodera, Tatsuhiro, Oguz, Ilker, Morichetti, Francesco, del Hougne, Philipp, Gallo, Manuel Le, Sebastian, Abu, Mirhoseini, Azalia, Zhang, Cheng, Marković, Danijela, Brunner, Daniel, Moser, Christophe, Gigan, Sylvain, Marquardt, Florian, Ozcan, Aydogan, Grollier, Julie, Liu, Andrea J., Psaltis, Demetri, Alù, Andrea, Fleury, Romain
Physical neural networks (PNNs) are a class of neural-like networks that leverage the properties of physical systems to perform computation. While PNNs are so far a niche research area with small-scale laboratory demonstrations, they are arguably one
Externí odkaz:
http://arxiv.org/abs/2406.03372
Allosteric regulation in proteins is often accompanied by conformational changes that facilitate transmission of mechanical signals between distant ligand binding sites. Typically, these deformations are classified in terms of specific archetypes, in
Externí odkaz:
http://arxiv.org/abs/2401.13861
Autor:
Chacko, Rahul N., Landes, François P., Biroli, Giulio, Dauchot, Olivier, Liu, Andrea J., Reichman, David R.
Publikováno v:
Physical Review X 14.3 (2024): 031012
Convincing evidence of domain growth in the heating of ultrastable glasses suggests that the equilibration dynamics of super-cooled liquids could be driven by a nucleation and growth mechanism. We investigate this possibility by simulating the equili
Externí odkaz:
http://arxiv.org/abs/2312.15069
Autor:
Tah, Indrajit, Haertter, Daniel, Crawford, Janice M., Kiehart, Daniel P., Schmidt, Christoph F., Liu, Andrea J.
Dorsal closure is a process that occurs during embryogenesis of Drosophila melanogaster. During dorsal closure, the amnioserosa (AS), a one-cell thick epithelial tissue that fills the dorsal opening, shrinks as the lateral epidermis sheets converge a
Externí odkaz:
http://arxiv.org/abs/2312.12926
How do cells tune emergent properties at the scale of tissues? One class of such emergent behaviors are rigidity transitions, in which a tissue changes from a solid-like to a fluid-like state or vice versa. Here, we introduce a new way that cells can
Externí odkaz:
http://arxiv.org/abs/2312.11683