Dr. Deeb is committed to providing the highest quality patient care in cosmetic and reconstructive surgery of the face, head and neck including a special interest in cosmetic and revision rhinoplasty, middle-eastern rhinoplasty, African American rhinoplasty, rejuvenation of the face, eyelid surgery, reconstructive surgery, and skin cancer surgery.

Dr. Robert H. Deeb is an ENT-otolaryngologist in Detroit, Michigan and is affiliated with multiple hospitals in the area, including John D. Dingell VA Medical Center and Henry Ford Hospital. He...

2023年7月15日 · In this work, we focus on investigating the performance characteristics of SACM Ge-on-Si APDs, specifically their dependence on the thicknesses of the multiplication and absorption layers. Breakdown voltage, multiplication gain, bandwidth, responsivity, and quantum efficiency are among the performance parameters examined.

in vitro anti-angiogenic properties of ethanolic crude extract of Vernonia amygdalina. Ibrahim Al-deeb, Julia Joseph, Amin Malik Shah Abdul Majid, Aman Shah Abdul ...

Professor of Marketing, German University in Cairo. International Journal of Social Media and Interactive Learning Environments … Computer-Generated Imagery Influencer Marketing—Which Ends of the...

DeepH-pack is the official implementation of the DeepH (Deep H amiltonian) method described in the paper Deep-learning density functional theory Hamiltonian for efficient ab initio electronic-structure calculation and in the Research Briefing. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA and will support HONPAS soon.

This code is the implementation of the DeepH-E3 method described in the paper General framework for E (3)-equivariant neural network representation of density functional theory Hamiltonian (arXiv:2210.13955). You can find demo input files and instructions in these repositories: Dataset 1, Dataset 2 and Dataset 3.

在最新的工作中，徐勇、段文晖研究组提出了xDeepH（extended DeepH）方法，用于学习磁性材料的DFT哈密顿量对原子结构和磁结构的依赖关系，并高效预测其电子结构与物性。 将物理先验知识融入神经网络架构设计，对深度学习的性能至关重要。 DFT哈密顿量对原子结构和磁结构的依赖关系，在对称操作下（如旋转和时间反演）具有等变性。 为此，该研究提出了一种拓展的等变神经网络，能考虑电子自旋和轨道自由度，使得神经网络保持在欧几里得群和时间反演操作下的对 …

2024年8月26日 · DeepH-pack 是一个专为基于局部坐标和基变换预测密度泛函理论 (DFT)哈密顿矩阵设计的深度神经网络应用包。 以下是项目的主要目录结构概述： ./ 根目录，包含了项目 …

DeepH-pack is a package for the application of deep neural networks to the prediction of density functional theory (DFT) Hamiltonian matrices based on local coordinates and basis transformation 1. DeepH-pack supports DFT results made by ABACUS, OpenMX, FHI-aims or SIESTA, and will support HONPAS soon.