Evidence map›Paper›PMID 35571607›Full record

ArticleNational science review2022

Harvesting random embedding for high-frequency change-point detection in temporal complex systems.

Jia-Wen Hou, Huan-Fei Ma, Dake He, Jie Sun, Qing Nie, Wei Lin

Abstract read
In one paragraph

Article in National science review, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. Tipping Point Detection Using Reservoir Computing.Research (Washington, D.C.) · 2023
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Jia-Wen HouResearch Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.ORCID https://orcid.org/0000-0003-4267-5316
Huan-Fei MaSchool of Mathematical Sciences, Soochow University, Suzhou 215006, China.
Dake HeXinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai 200092, China.
Jie SunResearch Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.
Qing NieDepartment of Mathematics, Department of Developmental and Cell Biology, and NSF-Simons Center for Multiscale Cell Fate Research, University of California, Irvine, CA 92697-3875, USA.ORCID https://orcid.org/0000-0002-8804-3368
Wei LinResearch Institute of Intelligent Complex Systems, Fudan University, Shanghai 200433, China.ORCID https://orcid.org/0000-0002-1863-4306

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Recent investigations have revealed that dynamics of complex networks and systems are crucially dependent on the temporal structures. Accurate detection of the time instant at which a system changes its internal structures has become a tremendously significant mission, beneficial to fully understanding the underlying mechanisms of evolving systems, and adequately modeling and predicting the dynamics of the systems as well. In real-world applications, due to a lack of prior knowledge on the explicit equations of evolving systems, an open challenge is how to develop a practical and

Indexed as

change-point detectioncomplex dynamical systemstemporal systemstime series

Identifiers

PMID35571607
PMCPMC9097594

What OpenQuestion holds

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.