Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles

Hao Huang; Shinjae Yoo; Konstantine Kaznatcheev; Kevin G. Yager; Fang Lu; Dantong Yu; Oleg Gang; Andrei Fluerasu; Hong Qin

doi:10.1145/2554850.2554914

Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles

Hao Huang, Shinjae Yoo, Konstantine Kaznatcheev, Kevin G. Yager, Fang Lu, Dantong Yu, Oleg Gang, Andrei Fluerasu, Hong Qin

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

6 Scopus citations

Abstract

Coherent X-ray scattering is an emerging technique for measuring structure at the nanoscale. Data management and analysis is becoming a bottleneck in this technique. We present an unsupervised method which can sort and cluster the scattering snapshots, uncovering patterns inherent in the data. Our algorithm operates without resorting to templates, specific noise models, or user-directed learning. We test our methods using scattering images of two-dimensional nanoparticle assemblies. The experimental results show the effectiveness of our algorithm on real world scientific data.

Original language	English (US)
Title of host publication	Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014
Publisher	Association for Computing Machinery
Pages	85-90
Number of pages	6
ISBN (Print)	9781450324694
DOIs	https://doi.org/10.1145/2554850.2554914
State	Published - 2014
Externally published	Yes
Event	29th Annual ACM Symposium on Applied Computing, SAC 2014 - Gyeongju, Korea, Republic of Duration: Mar 24 2014 → Mar 28 2014

Publication series

Name	Proceedings of the ACM Symposium on Applied Computing

Other

Other	29th Annual ACM Symposium on Applied Computing, SAC 2014
Country/Territory	Korea, Republic of
City	Gyeongju
Period	3/24/14 → 3/28/14

All Science Journal Classification (ASJC) codes

Software

Keywords

AHK
EMD
Nanoparticle Assemblies

Access to Document

10.1145/2554850.2554914

Cite this

Huang, H., Yoo, S., Kaznatcheev, K., Yager, K. G., Lu, F., Yu, D., Gang, O., Fluerasu, A., & Qin, H. (2014). Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014 (pp. 85-90). (Proceedings of the ACM Symposium on Applied Computing). Association for Computing Machinery. https://doi.org/10.1145/2554850.2554914

@inproceedings{41b92dc1205444b5b49a121400471f44,

title = "Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles",

abstract = "Coherent X-ray scattering is an emerging technique for measuring structure at the nanoscale. Data management and analysis is becoming a bottleneck in this technique. We present an unsupervised method which can sort and cluster the scattering snapshots, uncovering patterns inherent in the data. Our algorithm operates without resorting to templates, specific noise models, or user-directed learning. We test our methods using scattering images of two-dimensional nanoparticle assemblies. The experimental results show the effectiveness of our algorithm on real world scientific data.",

keywords = "AHK, EMD, Nanoparticle Assemblies",

author = "Hao Huang and Shinjae Yoo and Konstantine Kaznatcheev and Yager, {Kevin G.} and Fang Lu and Dantong Yu and Oleg Gang and Andrei Fluerasu and Hong Qin",

year = "2014",

doi = "10.1145/2554850.2554914",

language = "English (US)",

isbn = "9781450324694",

series = "Proceedings of the ACM Symposium on Applied Computing",

publisher = "Association for Computing Machinery",

pages = "85--90",

booktitle = "Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014",

note = "29th Annual ACM Symposium on Applied Computing, SAC 2014 ; Conference date: 24-03-2014 Through 28-03-2014",

}

Huang, H, Yoo, S, Kaznatcheev, K, Yager, KG, Lu, F, Yu, D, Gang, O, Fluerasu, A & Qin, H 2014, Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles. in Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Proceedings of the ACM Symposium on Applied Computing, Association for Computing Machinery, pp. 85-90, 29th Annual ACM Symposium on Applied Computing, SAC 2014, Gyeongju, Korea, Republic of, 3/24/14. https://doi.org/10.1145/2554850.2554914

Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles. / Huang, Hao; Yoo, Shinjae; Kaznatcheev, Konstantine et al.
Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Association for Computing Machinery, 2014. p. 85-90 (Proceedings of the ACM Symposium on Applied Computing).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles

AU - Huang, Hao

AU - Yoo, Shinjae

AU - Kaznatcheev, Konstantine

AU - Yager, Kevin G.

AU - Lu, Fang

AU - Yu, Dantong

AU - Gang, Oleg

AU - Fluerasu, Andrei

AU - Qin, Hong

PY - 2014

Y1 - 2014

N2 - Coherent X-ray scattering is an emerging technique for measuring structure at the nanoscale. Data management and analysis is becoming a bottleneck in this technique. We present an unsupervised method which can sort and cluster the scattering snapshots, uncovering patterns inherent in the data. Our algorithm operates without resorting to templates, specific noise models, or user-directed learning. We test our methods using scattering images of two-dimensional nanoparticle assemblies. The experimental results show the effectiveness of our algorithm on real world scientific data.

AB - Coherent X-ray scattering is an emerging technique for measuring structure at the nanoscale. Data management and analysis is becoming a bottleneck in this technique. We present an unsupervised method which can sort and cluster the scattering snapshots, uncovering patterns inherent in the data. Our algorithm operates without resorting to templates, specific noise models, or user-directed learning. We test our methods using scattering images of two-dimensional nanoparticle assemblies. The experimental results show the effectiveness of our algorithm on real world scientific data.

KW - AHK

KW - EMD

KW - Nanoparticle Assemblies

UR - http://www.scopus.com/inward/record.url?scp=84905660324&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84905660324&partnerID=8YFLogxK

U2 - 10.1145/2554850.2554914

DO - 10.1145/2554850.2554914

M3 - Conference contribution

AN - SCOPUS:84905660324

SN - 9781450324694

T3 - Proceedings of the ACM Symposium on Applied Computing

SP - 85

EP - 90

BT - Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014

PB - Association for Computing Machinery

T2 - 29th Annual ACM Symposium on Applied Computing, SAC 2014

Y2 - 24 March 2014 through 28 March 2014

ER -

Huang H, Yoo S, Kaznatcheev K, Yager KG, Lu F, Yu D et al. Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles. In Proceedings of the 29th Annual ACM Symposium on Applied Computing, SAC 2014. Association for Computing Machinery. 2014. p. 85-90. (Proceedings of the ACM Symposium on Applied Computing). doi: 10.1145/2554850.2554914

Diffusion-based clustering analysis of coherent X-Ray scattering patterns of self-assembled nanoparticles

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Keywords

Access to Document

Other files and links

Fingerprint

Cite this