scholarly journals Empirical determination of depth-distance corrections for mband Mwfrom global seismograph network stations

1998 ◽  
Vol 25 (9) ◽  
pp. 1451-1454 ◽  
Author(s):  
Guust Nolet ◽  
Steve Krueger ◽  
Robert M. Clouser
Keyword(s):  
Empirical Determination ◽  
Seismograph Network

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.

Moment-tensor solutions for the 24 November 1987 Superstition Hills, California, earthquakes

1989 ◽  
Vol 79 (2) ◽  
pp. 493-499
Author(s):  
Stuart A. Sipkin
Keyword(s):  
Main Shock ◽  
Moment Tensor ◽  
Time Dependent ◽  
Origin Time ◽  
Data Set ◽  
Filter Theory ◽  
Long Period ◽  
Seismograph Network ◽  
Scalar Moment

Abstract The teleseismic long-period waveforms recorded by the Global Digital Seismograph Network from the two largest Superstition Hills earthquakes are inverted using an algorithm based on optimal filter theory. These solutions differ slightly from those published in the Preliminary Determination of Epicenters Monthly Listing because a somewhat different, improved data set was used in the inversions and a time-dependent moment-tensor algorithm was used to investigate the complexity of the main shock. The foreshock (origin time 01:54:14.5, mb 5.7, Ms 6.2) had a scalar moment of 2.3 × 1025 dyne-cm, a depth of 8 km, and a mechanism of strike 217°, dip 79°, rake 4°. The main shock (origin time 13:15:56.4, mb 6.0, Ms 6.6) was a complex event, consisting of at least two subevents, with a combined scalar moment of 1.0 × 1026 dyne-cm, a depth of 10 km, and a mechanism of strike 303°, dip 89°, rake −180°.

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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