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32 changes: 32 additions & 0 deletions .github/workflows/ci_tests.yml
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name: CI Tests
on:
# Triggers the workflow on push or pull request events
pull_request:
push:
# Allows you to run this workflow manually from the Actions tab
workflow_dispatch:

jobs:
test:
name: Tests Linux
runs-on: "ubuntu-latest"
defaults:
run:
shell: bash -l {0}
steps:
- uses: actions/checkout@v2
- uses: conda-incubator/setup-miniconda@v2
with:
activate-environment: anaconda-client-env
python-version: 3.8.5
auto-activate-base: True
- name: Install Dependencies and T-ELF
run: |
conda info
conda list
python setup.py install
- name: Run the Unittests
run: |
cd tests && python -m pytest *
148 changes: 148 additions & 0 deletions .gitignore
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# mac
results/
.DS_Store
# contains the data for the project
# data/
# swap files
*.swp
# matlab/output

# Created by https://www.toptal.com/developers/gitignore/api/python
# Edit at https://www.toptal.com/developers/gitignore?templates=python

### Python ###
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
pip-wheel-metadata/
share/python-wheels/
*.egg-info/
.installed.cfg
*.egg
MANIFEST

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.nox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
*.py,cover
.hypothesis/
.pytest_cache/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py
db.sqlite3
db.sqlite3-journal

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

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docs/_build/

# PyBuilder
target/

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.ipynb_checkpoints

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profile_default/
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.python-version

# pipenv
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
# install all needed dependencies.
#Pipfile.lock

# PEP 582; used by e.g. github.com/David-OConnor/pyflow
__pypackages__/

# Celery stuff
celerybeat-schedule
celerybeat.pid

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*.sage.py

# Environments
.env
.venv
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venv/
ENV/
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# Spyder project settings
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# pytype static type analyzer
.pytype/

# End of https://www.toptal.com/developers/gitignore/api/python

24 changes: 24 additions & 0 deletions CITATION.cff
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cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Eren
given-names: Maksim
- family-names: Solovyev
given-names: Nick
- family-names: Barron
given-names: Ryan
- family-names: Bhattarai
given-names: Manish
- family-names: Boureima
given-names: Ismael
- family-names: Skau
given-names: Erik
- family-names: Rasmussen
given-names: Kim
- family-names: Alexandrov
given-names: Boian
title: "Tensor Extraction of Latent Features (T-ELF)"
version: 0.0.1
url: https://github.com/lanl/T-ELF
doi:
date-released: 2023-10-15
24 changes: 24 additions & 0 deletions LICENSE
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This program is open source under the BSD-3 License.
Redistribution and use in source and binary forms, with or without modification, are permitted
provided that the following conditions are met:
1. Redistributions of source code must retain the above copyright notice, this list of conditions and
the following disclaimer.

2.Redistributions in binary form must reproduce the above copyright notice, this list of conditions
and the following disclaimer in the documentation and/or other materials provided with the
distribution.

3.Neither the name of the copyright holder nor the names of its contributors may be used to endorse
or promote products derived from this software without specific prior written permission.

THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS
IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF
ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
144 changes: 144 additions & 0 deletions Publications.md
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<ul>

<li><a name="Solovyev23" href="https://arxiv.org/abs/2309.10772"><b>Interactive Distillation of Large Single-Topic Corpora of Scientific Papers.</b></a>,
<br>N.&nbsp;Solovyev, R.&nbsp;Barron, M.&nbsp;Bhattarai, M.&nbsp;Eren, K.&nbsp;O.&nbsp;Rasmussen, ...
<br><cite>arXiv preprint arXiv:2309.10772:</cite> 2023.</li>

<li><a name="Eren23a" href="https://dl.acm.org/doi/abs/10.1145/3624567"><b>Semi-supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model Selection.</b></a>,
<br>M.&nbsp;Eren, M.&nbsp;Bhattarai, R.&nbsp;J.&nbsp;Joyce, E.&nbsp;Raff, C.&nbsp;Nicholas, B.&nbsp;S.&nbsp;Alexandrov
<br><cite>ACM Transactions on Privacy and Security:</cite> 2023.</li>

<li><a name="Truong23" href="https://arxiv.org/abs/2309.03347"><b>Tensor Networks for Solving Realistic Time-independent Boltzmann Neutron Transport Equation.</b></a>,
<br>D.&nbsp;P.&nbsp;Truong, M.&nbsp;I.&nbsp;Ortega, I.&nbsp;Boureima, G.&nbsp;Manzini, K.&nbsp;Ø.&nbsp;Rasmussen, ...
<br><cite>arXiv preprint arXiv:2309.03347:</cite> 2023.</li>

<li><a name="Alexandrov23a" href="https://patents.google.com/patent/US10776718B2/en"><b>Source identification by non-negative matrix factorization combined with semi-supervised clustering.</b></a>,
<br>B.&nbsp;S.&nbsp;Alexandrov, L.&nbsp;B.&nbsp;Alexandrov, F.&nbsp;L.&nbsp;Iliev, V.&nbsp;G.&nbsp;Stanev, V.&nbsp;V.&nbsp;Vesselinov
<br><cite>US Patent 11,748,657:</cite> 2023.</li>

<li><a name="Bhattarai23a" href="https://arxiv.org/abs/2309.01077"><b>Robust Adversarial Defense by Tensor Factorization.</b></a>,
<br>M.&nbsp;Bhattarai, M.&nbsp;C.&nbsp;Kaymak, R.&nbsp;Barron, B.&nbsp;Nebgen, K.&nbsp;Rasmussen, ...
<br><cite>arXiv preprint arXiv:2309.01077:</cite> 2023.</li>

<li><a name="Manzini23" href="https://www.sciencedirect.com/science/article/pii/S0378475423001313"><b>The tensor-train mimetic finite difference method for three-dimensional Maxwell’s wave propagation equations.</b></a>,
<br>G.&nbsp;Manzini, P.&nbsp;M.&nbsp;D.&nbsp;Truong, R.&nbsp;Vuchkov, B.&nbsp;Alexandrov
<br><cite>Mathematics and Computers in Simulation 210:</cite> 2023.</li>

<li><a name="Eren22" href="https://ieeexplore.ieee.org/abstract/document/10069271"><b>One-Shot Federated Group Collaborative Filtering.</b></a>,
<br>M.&nbsp;E.&nbsp;Eren, M.&nbsp;Bhattarai, N.&nbsp;Solovyev, L.&nbsp;E.&nbsp;Richards, R.&nbsp;Yus, C.&nbsp;Nicholas, ...
<br><cite>IEEE International Conference on Machine Learning and Applications:</cite> 2022.</li>

<li><a name="Alexandrov22" href="https://onlinelibrary.wiley.com/doi/full/10.1002/nla.2443"><b>Nonnegative canonical tensor decomposition with linear constraints: nnCANDELINC.</b></a>,
<br>B.&nbsp;Alexandrov, D.&nbsp;F.&nbsp;DeSantis, G.&nbsp;Manzini, E.&nbsp;W.&nbsp;Skau
<br><cite>Numerical Linear Algebra with Applications 29 (6):</cite> 2022.</li>

<li><a name="Skau22" href="https://arxiv.org/abs/2210.01060"><b>Process Modeling, Hidden Markov Models, and Non-negative Tensor Factorization with Model Selection.</b></a>,
<br>E.&nbsp;Skau, A.&nbsp;Hollis, S.&nbsp;Eidenbenz, K.&nbsp;Rasmussen, B.&nbsp;Alexandrov
<br><cite>arXiv preprint arXiv:2210.01060:</cite> 2022.</li>

<li><a name="Eren22a" href="https://dl.acm.org/doi/abs/10.1145/3558100.3563844"><b>Senmfk-split: Large corpora topic modeling by semantic non-negative matrix factorization with automatic model selection.</b></a>,
<br>M.&nbsp;E.&nbsp;Eren, N.&nbsp;Solovyev, M.&nbsp;Bhattarai, K.&nbsp;Ø.&nbsp;Rasmussen, C.&nbsp;Nicholas, ...
<br><cite>Proceedings of the 22nd ACM Symposium on Document Engineering:</cite> 2022.</li>

<li><a name="Eren22b" href="https://arxiv.org/abs/2205.02359"><b>Fedsplit: One-shot federated recommendation system based on non-negative joint matrix factorization and knowledge distillation.</b></a>,
<br>M.&nbsp;E.&nbsp;Eren, L.&nbsp;E.&nbsp;Richards, M.&nbsp;Bhattarai, R.&nbsp;Yus, C.&nbsp;Nicholas, B.&nbsp;S.&nbsp;Alexandrov
<br><cite>arXiv preprint arXiv:2205.02359:</cite> 2022.</li>

<li><a name="Eren22c" href="https://arxiv.org/abs/2208.09942"><b>SeNMFk-SPLIT: large corpora topic modeling by semantic non-negative matrix factorization with automatic model selection.</b></a>,
<br>B.&nbsp;S.&nbsp;Alexandrov, M.&nbsp;E.&nbsp;Eren, N.&nbsp;Solovyev, M.&nbsp;Bhattarai, K.&nbsp;Ø.&nbsp;Rasmussen, ...
<br><cite>DocEng '22: Proceedings of the 22nd ACM Symposium on Document Engineering:</cite> 2022.</li>

<li><a name="Alexandrov21" href="https://www.osti.gov/biblio/1826495"><b>Tensor Text-Mining Methods for Malware Identification and Detection, Malware Dynamics Characterization, and Hosts Ranking.</b></a>,
<br>B.&nbsp;Alexandrov, M.&nbsp;E.&nbsp;Eren
<br><cite>Los Alamos National Lab.(LANL), Los Alamos, NM (United States):</cite> 2021.</li>

<li><a name="Vangara21" href="https://www.osti.gov/biblio/1826495"><b>Finding the number of latent topics with semantic non-negative matrix factorization.</b></a>,
<br>R.&nbsp;Vangara, M.&nbsp;Bhattarai, E.&nbsp;Skau, G.&nbsp;Chennupati, H.&nbsp;Djidjev, T.&nbsp;Tierney, ...
<br><cite>IEEE Access 9:</cite> 2021.</li>

<li><a name="Truong21" href="https://ieeexplore.ieee.org/abstract/document/9521203"><b>Boolean matrix factorization via nonnegative auxiliary optimization.</b></a>,
<br>D.&nbsp;P.&nbsp;Truong, E.&nbsp;Skau, D.&nbsp;Desantis, B.&nbsp;Alexandrov
<br><cite>IEEE Access 9:</cite> 2021.</li>

<li><a name="Eren21" href="https://dl.acm.org/doi/10.1145/3469096.3474927"><b>COVID-19 multidimensional Kaggle literature organization.</b></a>,
<br>M.&nbsp;Eren, N.&nbsp;Solovyev, C.&nbsp;Hamer, R.&nbsp;McDonald, B.S.&nbsp;Alexandrov, and C.&nbsp;Nicholas.
<br><cite>Proceedings of the 21st ACM Symposium on Document Engineering, pp. 1-4.</cite> 2021.</li>

<li><a name="Pulido21" href="https://diglib.eg.org/bitstream/handle/10.2312/evs20211055/055-059.pdf"><b>Selection of Optimal Salient Time Steps by Non-negative Tucker Tensor Decomposition.</b></a>,
<br>J.&nbsp;Pulido, J.&nbsp;Patchett, M.&nbsp;Bhattarai, B.&nbsp;Alexandrov, and J.&nbsp;Ahrens.
<br><cite>EuroVis 2021. Hosted by University of Zurich in collaboration with FAU Erlangen-Nuremberg and ETH Zurich:</cite> 2021.</li>

<li><a name="AlexandrovPatent" href="https://patents.justia.com/patent/10776718"><b>Source identification by non-negative matrix factorization combined with semi-supervised clustering</b></a>,
<br>B.S.&nbsp;Alexandrov, L.B.&nbsp;Alexandrov et al.
<br><cite>US Patent S10,776,718</cite>, Sep. 2020.</li>

<li><a name="VangaraSemantic" href="https://ieeexplore.ieee.org/document/9356214"><b>Semantic Nonnegative Matrix Factorization with Automatic Model Determination for Topic Modeling</b></a>,
<br>R.&nbsp;Vangara, E.&nbsp;Skau, G.&nbsp;Chennupati, et al.
<br><cite>Proceedings of 19th IEEE International Conference on Machine Learning and Applications</cite>, December 14-17, 2020.</li>

<li><a name="ErenAnomalous" href="https://ieeexplore.ieee.org/abstract/document/9280524"><b>Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization</b></a>,
<br>M.&nbsp;Eren, J.&nbsp;Moore, and B.S.&nbsp;Alexandrov.
<br><cite>Proceedings of 18th IEEE International Conference on Intelligence and Security Informatics (ISI)</cite>, Nov. 9-10, 2020.</li>

<li><a name="NebgenNeural" href="https://iopscience.iop.org/article/10.1088/2632-2153/aba372"><b>A neural network for determination of latent dimensionality in Nonnegative Matrix Factorization</b></a>,
<br>B.&nbsp;Nebgen, R.&nbsp;Vangara, M.A.&nbsp;Hombrados-Herrera, S.&nbsp;Kuksova, and B.S.&nbsp;Alexandrov.
<br><cite>Journal of Machine Learning: Science and Technology</cite>, 2020.</li>

<li><a name="VangaraDiffusion" href="https://journals.aps.org/prresearch/abstract/10.1103/PhysRevResearch.2.023248"><b>Identification of anomalous diffusion sources by unsupervised learning</b></a>,
<br>R.&nbsp;Vangara, KØ.&nbsp;Rasmussen, D.N.&nbsp;Petsev, G.&nbsp;Bel, and B.S.&nbsp;Alexandrov.
<br><cite>Physical Review Research 2 (2)</cite>, 023248.</li>

<li><a name="PrasadTemporal" href="https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11423/114230N/Weak-matching-of-temporal-interval-graphs-of-sensors-for-robust/10.1117/12.2558683.full?SSO=1"><b>Weak matching of temporal interval graphs of sensors for robust multi-modal event detection in noise</b></a>,
<br>L.&nbsp;Prasad, B.S.&nbsp;Alexandrov, and B.T.&nbsp;Nebgen.
<br><cite>Proceedings of Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX</cite>, 2020.</li>

<li><a name="TruongTradeFlow" href="https://iopscience.iop.org/article/10.1088/2632-2153/aba9ee"><b>Determination of Latent Dimensionality in International Trade Flow</b></a>,
<br>D.P.&nbsp;Truong, E.&nbsp;Skau, V.I.&nbsp;Valtchinov, and B.S.&nbsp;Alexandrov.
<br><cite>Mach. Learn.: Sci. Technol. 1</cite>, 045017, 2020.</li>

<li><a name="KarimiMetabolomics" href="https://www.nature.com/articles/s41598-020-60387-7"><b>Metabolomics and the pig model reveal aberrant cardiac energy metabolism in metabolic syndrome</b></a>,
<br>M.&nbsp;Karimi, V.&nbsp;Petkova, J.M.&nbsp;Asara, M.J.&nbsp;Griffin, F.W.&nbsp;Sellke, et al.
<br><cite>Scientific Reports 10 (1)</cite>, 1-11, 2020.</li>

<li><a name="DeSantisCluster" href="https://iopscience.iop.org/article/10.1088/2632-2153/abb676"><b>Coarse-Grain Cluster Analysis of Tensors With Application to Climate Biome Identification</b></a>,
<br>D.&nbsp;DeSantis, P.J.&nbsp;Wolfram, K.&nbsp;Bennett, and B.&nbsp;Alexandrov.
<br><cite>Journal of Machine Learning: Science and Technology</cite>, 2020.</li>

<li><a name="AkhterProtein" href="https://ieeexplore.ieee.org/document/8983409"><b>Non-Negative Matrix Factorization for Selection of Near-Native Protein Tertiary Structures</b></a>,
<br>N.&nbsp;Akhter, R.&nbsp;Vangara, G.&nbsp;Chennupati, B.S.&nbsp;Alexandrov, H.&nbsp;Djidjev, and A.&nbsp;Shehu.
<br><cite>Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)</cite>.</li>

<li><a name="VesselinovReactiveMixing" href="https://www.sciencedirect.com/science/article/pii/S0021999119303833?casa_token=cfAXREvAUB8AAAAA:wJXLiCdMh2DxM7PqO5xdK-fMSqx37-prYueK_RfcqU1QyzwQUMUAlbREt7gue8Ag1Vyt0W6N64Y"><b>Unsupervised machine learning based on non-negative tensor factorization for analyzing reactive-mixing</b></a>,
<br>V.V.&nbsp;Vesselinov, M.K.&nbsp;Mudunuru, S.&nbsp;Karra, D.&nbsp;O'Malley, and B.S.&nbsp;Alexandrov.
<br><cite>Journal of Computational Physics 395</cite>, 85-104, 2019.</li>

<li><a name="LópezLipid" href="https://www.sciencedirect.com/science/article/pii/S0021999119303833?casa_token=cfAXREvAUB8AAAAA:wJXLiCdMh2DxM7PqO5xdK-fMSqx37-prYueK_RfcqU1QyzwQUMUAlbREt7gue8Ag1Vyt0W6N64Y"><b>Unsupervised Machine Learning for Analysis of Phase Separation in Ternary Lipid Mixture</b></a>,
<br>C.A.&nbsp;López, V.V.&nbsp;Vesselinov, S.&nbsp;Gnanakaran, and B.S.&nbsp;Alexandrov.
<br><cite>Journal of Chemical Theory and Computation 15 (11)</cite>, 6343-6357.</li>

<li><a name="AlexandrovMicrophase" href="https://onlinelibrary.wiley.com/doi/full/10.1002/sam.11407"><b>Nonnegative tensor decomposition with custom clustering for microphase separation of block copolymers</b></a>,
<br>B.S.&nbsp;Alexandrov, V.G.&nbsp;Stanev, V.V.&nbsp;Vesselinov, and KØ.&nbsp;Rasmussen.
<br><cite>Statistical Analysis and Data Mining: The ASA Data Science Journal 12</cite>, 2019.</li>

<li><a name="VesselinovContaminantHydrology" href="https://pubmed.ncbi.nlm.nih.gov/30528243/"><b>Nonnegative tensor factorization for contaminant source identification</b></a>,
<br>V.V.&nbsp;Vesselinov, B.S.&nbsp;Alexandrov, and D.&nbsp;O'Malley.
<br><cite>Journal of Contaminant Hydrology 220</cite>, 66-97.</li>

<li><a name="StanevMaterials" href="https://www.nature.com/articles/s41524-018-0099-2"><b>Unsupervised Phase Mapping of X-ray Diffraction Data by Nonnegative Matrix, Factorization Integrated with Custom Clustering</b></a>,
<br>B.S.&nbsp;Alexandrov, V.&nbsp;Stanev, V.V.&nbsp;Vesselinov, A.G.&nbsp;Kusne, and G.&nbsp;Antoszewski.
<br><cite>npj Computational Materials 4 (43)</cite>, 2018.</li>

<li><a name="VesselinovContaminantHydrology2" href="https://www.sciencedirect.com/science/article/pii/S0169772217301201?casa_token=Wh2EdbDh5wkAAAAA:EQnFMXrypgLiEWw2VEIGnGwPeFOuJ5wU6bovJWZovIEmwfW200iLzEBI_3mapenJ8SWR7shsjFs"><b>Contaminant source identification using semi-supervised machine learning</b></a>,
<br>V.V.&nbsp;Vesselinov, B.S.&nbsp;Alexandrov, and D.&nbsp;O’Malley.
<br><cite>Journal of Contaminant Hydrology 212</cite>, 134-142.</li>

<li><a name="IlievDelayedSignals" href="https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0193974"><b>Nonnegative Matrix Factorization for identification of unknown number of sources emitting delayed signals</b></a>,
<br>F.L.&nbsp;Iliev, V.G.&nbsp;Stanev, V.V.&nbsp;Vesselinov, and B.S.&nbsp;Alexandrov.
<br><cite>PloS One 13 (3)</cite>, e0193974, 2018.</li>

<li><a name="StanevMathModelling" href="https://www.sciencedirect.com/science/article/pii/S0307904X18301227"><b>Identification of release sources in advection-diffusion system by machine learning combined with Green’s function inverse method</b></a>,
<br>V.&nbsp;Stanev, F.&nbsp;Iliev, S.&nbsp;Hansen, V.&nbsp;Velimir, and B.&nbsp;Alexandrov.
<br><cite>Applied Mathematical Modelling, Volume 60</cite>, August 2018, Pages 64-76.</li>

</ul>
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