Zobrazeno 1 - 10
of 8 483
pro vyhledávání: '"Amirali, A"'
Breaking the mold: overcoming the time constraints of molecular dynamics on general-purpose hardware
Autor:
Perez, Danny, Thompson, Aidan, Moore, Stan, Oppelstrup, Tomas, Sharapov, Ilya, Santos, Kylee, Sharifian, Amirali, Kalchev, Delyan Z., Schreiber, Robert, Pakin, Scott, Leon, Edgar A., Laros III, James H., James, Michael, Rajamanickam, Sivasankaran
The evolution of molecular dynamics (MD) simulations has been intimately linked to that of computing hardware. For decades following the creation of MD, simulations have improved with computing power along the three principal dimensions of accuracy,
Externí odkaz:
http://arxiv.org/abs/2411.10532
With the rapid growth of black-box models in machine learning, Shapley values have emerged as a popular method for model explanations due to their theoretical guarantees. Shapley values locally explain a model to an input query using additive feature
Externí odkaz:
http://arxiv.org/abs/2410.19236
Autor:
Ghaffarzadeh-Esfahani, Mohammadreza, Ghaffarzadeh-Esfahani, Mahdi, Salahi-Niri, Arian, Toreyhi, Hossein, Atf, Zahra, Mohsenzadeh-Kermani, Amirali, Sarikhani, Mahshad, Tajabadi, Zohreh, Shojaeian, Fatemeh, Bagheri, Mohammad Hassan, Feyzi, Aydin, Tarighatpayma, Mohammadamin, Gazmeh, Narges, Heydari, Fateme, Afshar, Hossein, Allahgholipour, Amirreza, Alimardani, Farid, Salehi, Ameneh, Asadimanesh, Naghmeh, Khalafi, Mohammad Amin, Shabanipour, Hadis, Moradi, Ali, Zadeh, Sajjad Hossein, Yazdani, Omid, Esbati, Romina, Maleki, Moozhan, Nasr, Danial Samiei, Soheili, Amirali, Majlesi, Hossein, Shahsavan, Saba, Soheilipour, Alireza, Goudarzi, Nooshin, Taherifard, Erfan, Hatamabadi, Hamidreza, Samaan, Jamil S, Savage, Thomas, Sakhuja, Ankit, Soroush, Ali, Nadkarni, Girish, Darazam, Ilad Alavi, Pourhoseingholi, Mohamad Amin, Safavi-Naini, Seyed Amir Ahmad
Background: This study aimed to evaluate and compare the performance of classical machine learning models (CMLs) and large language models (LLMs) in predicting mortality associated with COVID-19 by utilizing a high-dimensional tabular dataset. Materi
Externí odkaz:
http://arxiv.org/abs/2409.02136
In 1988, Erd\H{o}s suggested the question of minimizing the number of edges in a connected $n$-vertex graph where every edge is contained in a triangle. Shortly after, Catlin, Grossman, Hobbs, and Lai resolved this in a stronger form. In this paper,
Externí odkaz:
http://arxiv.org/abs/2409.11216
Autor:
Fontaine, Samuel E., Vendromin, Colin, Steiner, Trevor J., Atrli, Amirali, Thiel, Lillian, Castro, Joshua, Moody, Galan, Bowers, John, Liscidini, Marco, Sipe, J. E.
We explore how III-V semiconductor microring resonators can efficiently generate photon pairs and squeezed vacuum states via spontaneous parametric down-conversion by utilizing their built-in quasi phase matching and modal dispersion. We present an a
Externí odkaz:
http://arxiv.org/abs/2409.08230
Autor:
Xia, Yi, Huang, Guanhao, Beccari, Alberto, Zicoschi, Alessio, Arabmoheghi, Amirali, Engelsen, Nils J., Kippenberg, Tobias J.
The motional sideband asymmetry of a mechanical oscillator interacting with a laser field can be observed when approaching the quantum ground state, where the zero-point energy of the mechanical oscillator becomes a sizable contribution to its motion
Externí odkaz:
http://arxiv.org/abs/2408.06498
Python data science libraries such as Pandas and NumPy have recently gained immense popularity. Although these libraries are feature-rich and easy to use, their scalability limitations require more robust computational resources. In this paper, we pr
Externí odkaz:
http://arxiv.org/abs/2407.11616
In [5], we have designed impulsive and feedback controls for harmonic chains with a point thermostat. In this work, we study the internal control for stochastic lattice dynamics, with the goal of controlling the transition kernel of the kinetic equat
Externí odkaz:
http://arxiv.org/abs/2407.03710
Autor:
Saber, Danial, Salehi-Abari, Amirali
Graph Neural Networks (GNNs) have emerged as the predominant method for analyzing graph-structured data. However, canonical GNNs have limited expressive power and generalization capability, thus triggering the development of more expressive yet compu
Externí odkaz:
http://arxiv.org/abs/2406.11714
Autor:
Santos, Kylee, Moore, Stan, Oppelstrup, Tomas, Sharifian, Amirali, Sharapov, Ilya, Thompson, Aidan, Kalchev, Delyan Z, Perez, Danny, Schreiber, Robert, Pakin, Scott, Leon, Edgar A, Laros III, James H, James, Michael, Rajamanickam, Sivasankaran
Molecular dynamics (MD) simulations have transformed our understanding of the nanoscale, driving breakthroughs in materials science, computational chemistry, and several other fields, including biophysics and drug design. Even on exascale supercomput
Externí odkaz:
http://arxiv.org/abs/2405.07898