Zobrazeno 1 - 10
of 11 179
pro vyhledávání: '"Ahmadabadi, A."'
Autor:
Medvidović, Matija, Umana, Jaylyn C., Ahmadabadi, Iman, Di Sante, Domenico, Flick, Johannes, Rubio, Angel
Density functional theory (DFT) offers a desirable balance between quantitative accuracy and computational efficiency in practical many-electron calculations. Its central component, the exchange-correlation energy functional, has been approximated wi
Externí odkaz:
http://arxiv.org/abs/2410.16408
Extremum Seeking Control (ESC) is a well-known set of continuous time algorithms for model-free optimization of a cost function. One issue for ESCs is the convergence rates of parameters to extrema of unknown cost functions. The local convergence rat
Externí odkaz:
http://arxiv.org/abs/2409.12290
We present an ab initio method for computing vibro-polariton and phonon-polariton spectra of molecules and solids coupled to the photon modes of optical cavities. We demonstrate that if interactions of cavity photon modes with both nuclear and electr
Externí odkaz:
http://arxiv.org/abs/2407.14613
Autor:
Ghaemi, Hafez, Jamshidi, Shirin, Mashreghi, Mohammad, Ahmadabadi, Majid Nili, Kebriaei, Hamed
Markov games (MGs) and multi-agent reinforcement learning (MARL) are studied to model decision making in multi-agent systems. Traditionally, the objective in MG and MARL has been risk-neutral, i.e., agents are assumed to optimize a performance metric
Externí odkaz:
http://arxiv.org/abs/2406.06041
For a map that is strictly but not strongly convex, model-based gradient extremum seeking has an eigenvalue of zero at the extremum, i.e., it fails at exponential convergence. Interestingly, perturbation-based model-free extremum seeking has a negati
Externí odkaz:
http://arxiv.org/abs/2405.12908
Publikováno v:
Journal of Subcontinent Researches. Spring/Summer2024, Vol. 16 Issue 46, p119-134. 16p.
This paper presents a study on improving human action recognition through the utilization of knowledge distillation, and the combination of CNN and ViT models. The research aims to enhance the performance and efficiency of smaller student models by t
Externí odkaz:
http://arxiv.org/abs/2311.01283
This study addresses the application of deep learning techniques in joint sound signal classification and localization networks. Current state-of-the-art sound source localization deep learning networks lack feature aggregation within their architect
Externí odkaz:
http://arxiv.org/abs/2310.19063
Autor:
Hale, C. L., Schwarz, D. J., Best, P. N., Nakoneczny, S. J., Alonso, D., Bacon, D., Böhme, L., Bhardwaj, N., Bilicki, M., Camera, S., Heneka, C. S., Pashapour-Ahmadabadi, M., Tiwari, P., Zheng, J., Duncan, K. J., Jarvis, M. J., Kondapally, R., Magliocchetti, M., Rottgering, H. J. A., Shimwell, T. W.
Covering $\sim$5600 deg$^2$ to rms sensitivities of $\sim$70$-$100 $\mu$Jy beam$^{-1}$, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency ($\sim$150 MHz) radio catalogue to date, making it an excellent tool
Externí odkaz:
http://arxiv.org/abs/2310.07627
Publikováno v:
Majallah-i Dānishgāh-i ’Ulūm-i Pizishkī-i Shahīd Ṣadūqī Yazd, Vol 32, Iss 7, Pp 8067-8079 (2024)
Introduction: Ischemic heart diseases are one of the most common diseases that cause high mortality worldwide. This article has identified various factors affecting heart disease and identified susceptible people using various machine learning method
Externí odkaz:
https://doaj.org/article/9929b5e288364d0f89be6d55eb7cc1e0