scholarly journals Empirical Determination of Nuclear Moments of Inertia and Intrinsic Quadrupole Moments

1956 ◽  
Vol 103 (3) ◽  
pp. 833-834 ◽  
Author(s):  
E. D. Klema ◽  
R. K. Osborn
Keyword(s):  
Quadrupole Moments ◽  
Nuclear Moments ◽  
Moments Of Inertia ◽  
Empirical Determination

The Deformation Structure of the Nuclei 32S and 36Ar

2017 ◽  
Vol 13 (2) ◽  
pp. 4678-4688
Author(s):  
K. A. Kharroube
Keyword(s):  
Single Particle ◽  
Electric Quadrupole ◽  
Quadrupole Moments ◽  
Moments Of Inertia ◽  
Deformation Structures ◽  
Particle Potential ◽  
Nilsson Model ◽  
Single Particle Potential ◽  
Axes Of Symmetry ◽  
Good Agreement

We applied two different approaches to investigate the deformation structures of the two nuclei S32 and Ar36 . In the first approach, we considered these nuclei as being deformed and have axes of symmetry. Accordingly, we calculated their moments of inertia by using the concept of the single-particle Schrödinger fluid as functions of the deformation parameter β. In this case we calculated also the electric quadrupole moments of the two nuclei by applying Nilsson model as functions of β. In the second approach, we used a strongly deformed nonaxial single-particle potential, depending on Î² and the nonaxiality parameter γ , to obtain the single-particle energies and wave functions. Accordingly, we calculated the quadrupole moments of S32 and Ar36 by filling the single-particle states corresponding to the ground- and the first excited states of these nuclei. The moments of inertia of S32 and Ar36 are then calculated by applying the nuclear superfluidity model. The obtained results are in good agreement with the corresponding experimental data.

scholarly journals Combine-Net: An Improved Filter Pruning Algorithm

Information ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 264
Author(s):  
Jinghan Wang ◽  
Guangyue Li ◽  
Wenzhao Zhang
Keyword(s):  
Structured Model ◽  
Compression Algorithms ◽  
Pruning Algorithm ◽  
Model Compression ◽  
The Neural Network ◽  
Empirical Determination ◽  
Knowledge Distillation ◽  
Resource Constrained Devices ◽  
Constrained Devices

The powerful performance of deep learning is evident to all. With the deepening of research, neural networks have become more complex and not easily generalized to resource-constrained devices. The emergence of a series of model compression algorithms makes artificial intelligence on edge possible. Among them, structured model pruning is widely utilized because of its versatility. Structured pruning prunes the neural network itself and discards some relatively unimportant structures to compress the model’s size. However, in the previous pruning work, problems such as evaluation errors of networks, empirical determination of pruning rate, and low retraining efficiency remain. Therefore, we propose an accurate, objective, and efficient pruning algorithm—Combine-Net, introducing Adaptive BN to eliminate evaluation errors, the Kneedle algorithm to determine the pruning rate objectively, and knowledge distillation to improve the efficiency of retraining. Results show that, without precision loss, Combine-Net achieves 95% parameter compression and 83% computation compression on VGG16 on CIFAR10, 71% of parameter compression and 41% computation compression on ResNet50 on CIFAR100. Experiments on different datasets and models have proved that Combine-Net can efficiently compress the neural network’s parameters and computation.

Semi-empirical determination of the long-range molecular states dissociating to C(3PJ)+O(3PJ′)

1989 ◽  
Vol 134 (1) ◽  
pp. 7-18 ◽  
Author(s):  
B. Bussery ◽  
M.E. Rosenkrantz ◽  
D.D. Konowalow ◽  
M. Aubert-frécon
Keyword(s):  
Long Range ◽  
Empirical Determination ◽  
Semi Empirical

scholarly journals Empirical determination of the shape of dust attenuation curves in star-forming galaxies

2011 ◽  
Vol 417 (3) ◽  
pp. 1760-1786 ◽  
Author(s):  
Vivienne Wild ◽  
Stéphane Charlot ◽  
Jarle Brinchmann ◽  
Timothy Heckman ◽  
Oliver Vince ◽  
...  
Keyword(s):  
Star Forming ◽  
Empirical Determination ◽  
Dust Attenuation
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