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Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing

Overview of attention for article published in IEEE Transactions on Neural Networks and Learning Systems, November 2023
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About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (86th percentile)
  • High Attention Score compared to outputs of the same age and source (98th percentile)

Mentioned by

twitter
16 X users

Citations

dimensions_citation
6 Dimensions

Readers on

mendeley
11 Mendeley
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Title
Fast-Converging Simulated Annealing for Ising Models Based on Integral Stochastic Computing
Published in
IEEE Transactions on Neural Networks and Learning Systems, November 2023
DOI 10.1109/tnnls.2022.3159713
Pubmed ID
Authors

Naoya Onizawa, Kota Katsuki, Duckgyu Shin, Warren J. Gross, Takahiro Hanyu

X Demographics

X Demographics

The data shown below were collected from the profiles of 16 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 11 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 18%
Student > Ph. D. Student 1 9%
Unspecified 1 9%
Student > Bachelor 1 9%
Professor > Associate Professor 1 9%
Other 0 0%
Unknown 5 45%
Readers by discipline Count As %
Computer Science 3 27%
Engineering 2 18%
Unspecified 1 9%
Unknown 5 45%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 12. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 11 April 2022.
All research outputs
#3,156,325
of 25,708,267 outputs
Outputs from IEEE Transactions on Neural Networks and Learning Systems
#95
of 3,412 outputs
Outputs of similar age
#47,594
of 363,704 outputs
Outputs of similar age from IEEE Transactions on Neural Networks and Learning Systems
#3
of 220 outputs
Altmetric has tracked 25,708,267 research outputs across all sources so far. Compared to these this one has done well and is in the 87th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,412 research outputs from this source. They receive a mean Attention Score of 2.7. This one has done particularly well, scoring higher than 97% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 363,704 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 220 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 98% of its contemporaries.