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Machine learning-driven alignment architecture of heterogeneous data with transient varying semantics.

Code ↔ Paper

6 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 6 matches
  1. [1] § Methods › Signal processing ↔ Data preprocessing/1 optical signal/3_dynamic_threshold_adaptivecutted_tif.py, lines 23–33 · score 0.63 · dynamic threshold adaptive, optical signal
  2. [2] § Results and discussion › Promotion of alignment architecture ↔ Demo/Supplementary Figs31a-b/Generate_data_and_align.py, lines 80–143 · score 0.57 · squared error, sample shift, MSE, regression, SVM, transformer
  3. [3] § Methods › Signal processing ↔ Data preprocessing/3 acoustic signal/current-carrying friction/airborne/frequency domain/revised_thinkdsp.py, lines 543–667 · score 0.54 · frequency component, Hamming, frequency domain, window, spectrogram, segment
  4. [4] § Results and discussion › Alignment verification and applications ↔ Class activation mapping/1_obtain_model.py, lines 1–34 · score 0.53 · class activation mapping, detection model, acoustic, 2–3, OS, transformation
  5. [5] § Methods › In situ test of current-carried friction ↔ Data preprocessing/3 acoustic signal/current-carrying friction/airborne/time frequency domain/1Mhz/data_output.py, lines 60–127 · score 0.52 · acoustic signals, carrying friction, Optical, segmented, airborne
  6. [6] § Results and discussion › Alignment verification and applications ↔ Alignment and modeling tasks/K/Obtaining_the_arc_detection_model_based_on_OS_as_input.py, lines 126–168 · score 0.51 · fully connected layer, arc detection model, validation, kernel, trained, OS

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

The paper is loaded when this pane is shown.

The authors' code

Python · 34 lines · 1.1 KB · MIT · 1 match

  1. import pathlib
  2. import os
  3. import pandas as pd
  4. import numpy as np
  5. import regex as re
  6. import numpy as np
  7. import random
  8. import cv2
  10. if not os.path.exists('dynamic_threshold_adaptivecutted_tif'):
  11. os.makedirs('dynamic_threshold_adaptivecutted_tif')
  13. SFT_png = pathlib.Path('cutted_tif')
  16. def sort_by_number_in_filename(filename):#自定义文件排序函数
  17. # 使用正则表达式从文件名中提取数字
  18. match_numbers = re.findall(r'\d+', os.path.basename(filename))
  19. match_numbers = [int(num) for num in match_numbers]
  20. print(match_numbers[1])
  21. return match_numbers[1]
  23. png_list = [str(path) for path in sorted(SFT_png.glob('*.tif'),key=sort_by_number_in_filename)]
  24. print(png_list)
  27. i = 0
  28. for file in png_list:
  29. image = cv2.imread(file, 1)
  30. gray_image = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
  31. adaptive_thresh_image = cv2.adaptiveThreshold(gray_image, 255, cv2.ADAPTIVE_THRESH_MEAN_C,cv2.THRESH_BINARY_INV, 51, 3.9)
  32. cv2.imwrite(os.path.join('dynamic_threshold_adaptivecutted_tif', "33tif_{}.tif".format(i)), adaptive_thresh_image) # 保存图像
  33. i += 1 # 确保 i 自增

3_dynamic_threshold_adaptivecutted_tif.py at commit 0bf8dfd, under MIT · at the source

Overview

Authors: Chaofan Li1, Zhichao Ma1,2, Yangzhi Zeng1, Zaizheng Yang1, Jiakai Li1, Zheng Yang1, Junming Xiong1, Shichao Niu3, Zhe Wang3, Hongwei Zhao1,2, Luquan Ren3
ORCID iDs: Zhichao Ma, Shichao Niu, Zhe Wang, Hongwei Zhao
  1. School of Mechanical and Aerospace Engineering, Jilin University, Changchun, China
  2. Key Laboratory of CNC Equipment Reliability Ministry of Education, Jilin University, Changchun, China
  3. Key Laboratory of Bionic Engineering Ministry of Education, Jilin University, Changchun, China
Institutions: Jilin University (China)
Journal: Nature communications, volume 17, issue 1, article 5604
Dates: received 8 April 2025; accepted 10 April 2026; published online 23 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1038/s41467-026-72377-w · PMID 42026099 · PMCID PMC13316067 · OpenAlex W7155365649
Open access: gold, a free copy (OpenAlex)
Status: code verified
Methods: Spectral & time-frequency, Machine learning, Connectivity, Physiology & signal measures
Keywords: Mechanical engineering, Electrical and electronic engineering
Topic: Ferroelectric and Negative Capacitance Devices (Electrical and Electronic Engineering, Engineering), according to OpenAlex
Funding: National Natural Science Foundation of China (National Science Foundation of China) (52525510, 52550005, 92266206)
Citations: cited by 2 papers (Europe PMC); 53 references in the paper

Abstract

Via cross-correlation algorithms or synchronized acquisition of signals, the alignment of heterogeneous data with unknown semantic time shifts and intermittent semantic variations cannot be solved. The shift is caused by different data acquisition principles of sensors, different response discrimination principles using heterogeneous data, etc. Here, we report an unsupervised alignment architecture with a supervised learning model as the kernel to overcome the limitations of brain cognition, perception, and storage in aligning complex heterogeneous data. A set of data with a time shift is input into the kernel model of the architecture to predict the semantic labels, features or continuous values corresponding to another set of data. The time shift corresponding to the maximum testing accuracy or the minimum mean squared error is the alignment parameter for the two heterogeneous datasets. This architecture is expected to serve as a preprocessing step for semantic mining of signals and for information fusion.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repositories

Its files are read in the Code ↔ Paper reader above, with 6 matches between paragraphs and lines of code.

Zenodo 19679056

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Size: 1 file
Software Heritage: not checked
Found in: the references
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Availability: 1 check, the latest on 29 September 2026: the link answers (HTTP 200)
  • 29 September 2026: the link answers (HTTP 200)
At the source:

chaofanli-jinlinuniversity/alignment-architecture-of-heterogeneous-data

License: MIT
State: the link answers, verified on 29 September 2026
Evidence: files inventoried
Commit: 0bf8dfda39bd55b13a339e3519a59ecb60d10632, 21 April 2026
Languages: Python (121)
Size: 151 files, 121 scripts
Software Heritage: not archived
Found in: the Zenodo archive record
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (121 files), pandas (116 files), Matplotlib (115 files), Pillow (108 files), PyTorch (108 files), scikit-learn (16 files), SciPy (15 files), OpenCV (6 files)
Availability: 1 check, the latest on 29 September 2026: the link answers
  • 29 September 2026: the link answers
123 files
At the source: github.com/ChaofanLi-JinlinUniversity/Alignment-Architecture-of-Heterogeneous-Data/tree/V2.0.1

Code availability

The synchronize triggering software and code for data generation, data processing, data alignment, and obtaining arc detection models have been deposited in the public repository53.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 2 repositories of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 121 scripts, each with its path and the digest of its content;
  • 6 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The authors declare that the main data supporting the findings of this study are available within the article and its Supplementary Information files. Source Data are provided with this paper. All other relevant data are available from the corresponding author upon request. The datasets used for data alignment, as well as training and testing of the arc detection models have been deposited in the public repository (https://www.scidb.cn/en/s/iMnaii). Source data are provided with this paper.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 11 authors, 2 keywords, 1 funder, 14 references.

Cite

This paper

Li, C., Ma, Z., Zeng, Y., Yang, Z., Li, J., Yang, Z., Xiong, J., Niu, S., Wang, Z., Zhao, H., & Ren, L. (2026). Machine learning-driven alignment architecture of heterogeneous data with transient varying semantics. Nature communications, 17(1), 5604. https://doi.org/10.1038/s41467-026-72377-w

BibTeX

@article{li2026machine,
author = {Li, Chaofan and Ma, Zhichao and Zeng, Yangzhi and Yang, Zaizheng and Li, Jiakai and Yang, Zheng and Xiong, Junming and Niu, Shichao and Wang, Zhe and Zhao, Hongwei and Ren, Luquan},
title = {{Machine learning-driven alignment architecture of heterogeneous data with transient varying semantics}},
journal = {Nature communications},
year = {2026},
month = apr,
volume = {17},
number = {1},
pages = {5604},
publisher = {Nature Publishing Group},
issn = {2041-1723},
doi = {10.1038/s41467-026-72377-w},
url = {https://doi.org/10.1038/s41467-026-72377-w},
pmid = {42026099},
pmcid = {PMC13316067}
}

RIS

TY - JOUR
AU - Li, Chaofan
AU - Ma, Zhichao
AU - Zeng, Yangzhi
AU - Yang, Zaizheng
AU - Li, Jiakai
AU - Yang, Zheng
AU - Xiong, Junming
AU - Niu, Shichao
AU - Wang, Zhe
AU - Zhao, Hongwei
AU - Ren, Luquan
TI - Machine learning-driven alignment architecture of heterogeneous data with transient varying semantics
T2 - Nature communications
J2 - Nat Commun
PY - 2026
DA - 2026/04/23
VL - 17
IS - 1
SP - 5604
SN - 2041-1723
PB - Nature Publishing Group
DO - 10.1038/s41467-026-72377-w
UR - https://doi.org/10.1038/s41467-026-72377-w
LA - en
ER -

CSL-JSON

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"PMCID": "PMC13316067",
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The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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