Zobrazeno 1 - 10
of 26 195
pro vyhledávání: '"Demetri, A."'
Autor:
Liang, Hanwen, Cao, Junli, Goel, Vidit, Qian, Guocheng, Korolev, Sergei, Terzopoulos, Demetri, Plataniotis, Konstantinos N., Tulyakov, Sergey, Ren, Jian
This paper addresses a challenging question: How can we efficiently create high-quality, wide-scope 3D scenes from a single arbitrary image? Existing methods face several constraints, such as requiring multi-view data, time-consuming per-scene optimi
Externí odkaz:
http://arxiv.org/abs/2412.12091
Collaboration is a cornerstone of society. In the real world, human teammates make use of multi-sensory data to tackle challenging tasks in ever-changing environments. It is essential for embodied agents collaborating in visually-rich environments re
Externí odkaz:
http://arxiv.org/abs/2412.05255
A major challenge for Multi-Agent Systems is enabling agents to adapt dynamically to diverse environments in which opponents and teammates may continually change. Agents trained using conventional methods tend to excel only within the confines of the
Externí odkaz:
http://arxiv.org/abs/2410.21794
Despite almost a century of anticipation, polar uniaxial nematics were only recently discovered and shown to adopt a chiral supramolecular structure. Monte Carlo molecular simulations of curve-shaped rods show the propensity of such shapes to polymor
Externí odkaz:
http://arxiv.org/abs/2408.13325
Autor:
Guo, Danfeng, Terzopoulos, Demetri
Large Vision-Language Models (LVLMs) have achieved significant success in recent years, and they have been extended to the medical domain. Although demonstrating satisfactory performance on medical Visual Question Answering (VQA) tasks, Medical LVLMs
Externí odkaz:
http://arxiv.org/abs/2407.21368
Autor:
Oguz, Ilker, Dinc, Niyazi Ulas, Yildirim, Mustafa, Ke, Junjie, Yoo, Innfarn, Wang, Qifei, Yang, Feng, Moser, Christophe, Psaltis, Demetri
Diffusion models generate new samples by progressively decreasing the noise from the initially provided random distribution. This inference procedure generally utilizes a trained neural network numerous times to obtain the final output, creating sign
Externí odkaz:
http://arxiv.org/abs/2407.10897
Autor:
Hung, Alex Ling Yu, Zheng, Haoxin, Zhao, Kai, Pang, Kaifeng, Terzopoulos, Demetri, Sung, Kyunghyun
Current deep learning-based models typically analyze medical images in either 2D or 3D albeit disregarding volumetric information or suffering sub-optimal performance due to the anisotropic resolution of MR data. Furthermore, providing an accurate un
Externí odkaz:
http://arxiv.org/abs/2407.01146
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
Optical diffraction tomography (ODT) has emerged as an important label-free tool in biomedicine to measure the three-dimensional (3D) structure of a biological sample. In this paper, we describe ODT using second-harmonic generation (SHG) which is a c
Externí odkaz:
http://arxiv.org/abs/2405.11398
Autor:
Meena, Muralikrishnan Gopalakrishnan, Liousas, Demetri, Simin, Andrew D., Kashi, Aditya, Brewer, Wesley H., Riley, James J., Kops, Stephen M. de Bruyn
We develop time-series machine learning (ML) methods for closure modeling of the Unsteady Reynolds Averaged Navier Stokes (URANS) equations applied to stably stratified turbulence (SST). SST is strongly affected by fine balances between forces and be
Externí odkaz:
http://arxiv.org/abs/2404.16141