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| 1 | + |
| 2 | +<ul> |
| 3 | + |
| 4 | +<li><a name="Solovyev23" href="https://arxiv.org/abs/2309.10772"><b>Interactive Distillation of Large Single-Topic Corpora of Scientific Papers.</b></a>, |
| 5 | +<br>N. Solovyev, R. Barron, M. Bhattarai, M. Eren, K. O. Rasmussen, ... |
| 6 | +<br><cite>arXiv preprint arXiv:2309.10772:</cite> 2023.</li> |
| 7 | + |
| 8 | +<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>, |
| 9 | +<br>M. Eren, M. Bhattarai, R. J. Joyce, E. Raff, C. Nicholas, B. S. Alexandrov |
| 10 | +<br><cite>ACM Transactions on Privacy and Security:</cite> 2023.</li> |
| 11 | + |
| 12 | +<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>, |
| 13 | +<br>D. P. Truong, M. I. Ortega, I. Boureima, G. Manzini, K. Ø. Rasmussen, ... |
| 14 | +<br><cite>arXiv preprint arXiv:2309.03347:</cite> 2023.</li> |
| 15 | + |
| 16 | +<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>, |
| 17 | +<br>B. S. Alexandrov, L. B. Alexandrov, F. L. Iliev, V. G. Stanev, V. V. Vesselinov |
| 18 | +<br><cite>US Patent 11,748,657:</cite> 2023.</li> |
| 19 | + |
| 20 | +<li><a name="Bhattarai23a" href="https://arxiv.org/abs/2309.01077"><b>Robust Adversarial Defense by Tensor Factorization.</b></a>, |
| 21 | +<br>M. Bhattarai, M. C. Kaymak, R. Barron, B. Nebgen, K. Rasmussen, ... |
| 22 | +<br><cite>arXiv preprint arXiv:2309.01077:</cite> 2023.</li> |
| 23 | + |
| 24 | +<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>, |
| 25 | +<br>G. Manzini, P. M. D. Truong, R. Vuchkov, B. Alexandrov |
| 26 | +<br><cite>Mathematics and Computers in Simulation 210:</cite> 2023.</li> |
| 27 | + |
| 28 | +<li><a name="Eren22" href="https://ieeexplore.ieee.org/abstract/document/10069271"><b>One-Shot Federated Group Collaborative Filtering.</b></a>, |
| 29 | +<br>M. E. Eren, M. Bhattarai, N. Solovyev, L. E. Richards, R. Yus, C. Nicholas, ... |
| 30 | +<br><cite>IEEE International Conference on Machine Learning and Applications:</cite> 2022.</li> |
| 31 | + |
| 32 | +<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>, |
| 33 | +<br>B. Alexandrov, D. F. DeSantis, G. Manzini, E. W. Skau |
| 34 | +<br><cite>Numerical Linear Algebra with Applications 29 (6):</cite> 2022.</li> |
| 35 | + |
| 36 | +<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>, |
| 37 | +<br>E. Skau, A. Hollis, S. Eidenbenz, K. Rasmussen, B. Alexandrov |
| 38 | +<br><cite>arXiv preprint arXiv:2210.01060:</cite> 2022.</li> |
| 39 | + |
| 40 | +<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>, |
| 41 | +<br>M. E. Eren, N. Solovyev, M. Bhattarai, K. Ø. Rasmussen, C. Nicholas, ... |
| 42 | +<br><cite>Proceedings of the 22nd ACM Symposium on Document Engineering:</cite> 2022.</li> |
| 43 | + |
| 44 | +<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>, |
| 45 | +<br>M. E. Eren, L. E. Richards, M. Bhattarai, R. Yus, C. Nicholas, B. S. Alexandrov |
| 46 | +<br><cite>arXiv preprint arXiv:2205.02359:</cite> 2022.</li> |
| 47 | + |
| 48 | +<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>, |
| 49 | +<br>B. S. Alexandrov, M. E. Eren, N. Solovyev, M. Bhattarai, K. Ø. Rasmussen, ... |
| 50 | +<br><cite>DocEng '22: Proceedings of the 22nd ACM Symposium on Document Engineering:</cite> 2022.</li> |
| 51 | + |
| 52 | +<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>, |
| 53 | +<br>B. Alexandrov, M. E. Eren |
| 54 | +<br><cite>Los Alamos National Lab.(LANL), Los Alamos, NM (United States):</cite> 2021.</li> |
| 55 | + |
| 56 | +<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>, |
| 57 | +<br>R. Vangara, M. Bhattarai, E. Skau, G. Chennupati, H. Djidjev, T. Tierney, ... |
| 58 | +<br><cite>IEEE Access 9:</cite> 2021.</li> |
| 59 | + |
| 60 | +<li><a name="Truong21" href="https://ieeexplore.ieee.org/abstract/document/9521203"><b>Boolean matrix factorization via nonnegative auxiliary optimization.</b></a>, |
| 61 | +<br>D. P. Truong, E. Skau, D. Desantis, B. Alexandrov |
| 62 | +<br><cite>IEEE Access 9:</cite> 2021.</li> |
| 63 | + |
| 64 | +<li><a name="Eren21" href="https://dl.acm.org/doi/10.1145/3469096.3474927"><b>COVID-19 multidimensional Kaggle literature organization.</b></a>, |
| 65 | +<br>M. Eren, N. Solovyev, C. Hamer, R. McDonald, B.S. Alexandrov, and C. Nicholas. |
| 66 | +<br><cite>Proceedings of the 21st ACM Symposium on Document Engineering, pp. 1-4.</cite> 2021.</li> |
| 67 | + |
| 68 | +<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>, |
| 69 | +<br>J. Pulido, J. Patchett, M. Bhattarai, B. Alexandrov, and J. Ahrens. |
| 70 | +<br><cite>EuroVis 2021. Hosted by University of Zurich in collaboration with FAU Erlangen-Nuremberg and ETH Zurich:</cite> 2021.</li> |
| 71 | + |
| 72 | +<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>, |
| 73 | +<br>B.S. Alexandrov, L.B. Alexandrov et al. |
| 74 | +<br><cite>US Patent S10,776,718</cite>, Sep. 2020.</li> |
| 75 | + |
| 76 | +<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>, |
| 77 | +<br>R. Vangara, E. Skau, G. Chennupati, et al. |
| 78 | +<br><cite>Proceedings of 19th IEEE International Conference on Machine Learning and Applications</cite>, December 14-17, 2020.</li> |
| 79 | + |
| 80 | +<li><a name="ErenAnomalous" href="https://ieeexplore.ieee.org/abstract/document/9280524"><b>Multi-Dimensional Anomalous Entity Detection via Poisson Tensor Factorization</b></a>, |
| 81 | +<br>M. Eren, J. Moore, and B.S. Alexandrov. |
| 82 | +<br><cite>Proceedings of 18th IEEE International Conference on Intelligence and Security Informatics (ISI)</cite>, Nov. 9-10, 2020.</li> |
| 83 | + |
| 84 | +<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>, |
| 85 | +<br>B. Nebgen, R. Vangara, M.A. Hombrados-Herrera, S. Kuksova, and B.S. Alexandrov. |
| 86 | +<br><cite>Journal of Machine Learning: Science and Technology</cite>, 2020.</li> |
| 87 | + |
| 88 | +<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>, |
| 89 | +<br>R. Vangara, KØ. Rasmussen, D.N. Petsev, G. Bel, and B.S. Alexandrov. |
| 90 | +<br><cite>Physical Review Research 2 (2)</cite>, 023248.</li> |
| 91 | + |
| 92 | +<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>, |
| 93 | +<br>L. Prasad, B.S. Alexandrov, and B.T. Nebgen. |
| 94 | +<br><cite>Proceedings of Signal Processing, Sensor/Information Fusion, and Target Recognition XXIX</cite>, 2020.</li> |
| 95 | + |
| 96 | +<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>, |
| 97 | +<br>D.P. Truong, E. Skau, V.I. Valtchinov, and B.S. Alexandrov. |
| 98 | +<br><cite>Mach. Learn.: Sci. Technol. 1</cite>, 045017, 2020.</li> |
| 99 | + |
| 100 | +<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>, |
| 101 | +<br>M. Karimi, V. Petkova, J.M. Asara, M.J. Griffin, F.W. Sellke, et al. |
| 102 | +<br><cite>Scientific Reports 10 (1)</cite>, 1-11, 2020.</li> |
| 103 | + |
| 104 | +<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>, |
| 105 | +<br>D. DeSantis, P.J. Wolfram, K. Bennett, and B. Alexandrov. |
| 106 | +<br><cite>Journal of Machine Learning: Science and Technology</cite>, 2020.</li> |
| 107 | + |
| 108 | +<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>, |
| 109 | +<br>N. Akhter, R. Vangara, G. Chennupati, B.S. Alexandrov, H. Djidjev, and A. Shehu. |
| 110 | +<br><cite>Proceedings of 2019 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)</cite>.</li> |
| 111 | + |
| 112 | +<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>, |
| 113 | +<br>V.V. Vesselinov, M.K. Mudunuru, S. Karra, D. O'Malley, and B.S. Alexandrov. |
| 114 | +<br><cite>Journal of Computational Physics 395</cite>, 85-104, 2019.</li> |
| 115 | + |
| 116 | +<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>, |
| 117 | +<br>C.A. López, V.V. Vesselinov, S. Gnanakaran, and B.S. Alexandrov. |
| 118 | +<br><cite>Journal of Chemical Theory and Computation 15 (11)</cite>, 6343-6357.</li> |
| 119 | + |
| 120 | +<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>, |
| 121 | +<br>B.S. Alexandrov, V.G. Stanev, V.V. Vesselinov, and KØ. Rasmussen. |
| 122 | +<br><cite>Statistical Analysis and Data Mining: The ASA Data Science Journal 12</cite>, 2019.</li> |
| 123 | + |
| 124 | +<li><a name="VesselinovContaminantHydrology" href="https://pubmed.ncbi.nlm.nih.gov/30528243/"><b>Nonnegative tensor factorization for contaminant source identification</b></a>, |
| 125 | +<br>V.V. Vesselinov, B.S. Alexandrov, and D. O'Malley. |
| 126 | +<br><cite>Journal of Contaminant Hydrology 220</cite>, 66-97.</li> |
| 127 | + |
| 128 | +<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>, |
| 129 | +<br>B.S. Alexandrov, V. Stanev, V.V. Vesselinov, A.G. Kusne, and G. Antoszewski. |
| 130 | +<br><cite>npj Computational Materials 4 (43)</cite>, 2018.</li> |
| 131 | + |
| 132 | +<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>, |
| 133 | +<br>V.V. Vesselinov, B.S. Alexandrov, and D. O’Malley. |
| 134 | +<br><cite>Journal of Contaminant Hydrology 212</cite>, 134-142.</li> |
| 135 | + |
| 136 | +<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>, |
| 137 | +<br>F.L. Iliev, V.G. Stanev, V.V. Vesselinov, and B.S. Alexandrov. |
| 138 | +<br><cite>PloS One 13 (3)</cite>, e0193974, 2018.</li> |
| 139 | + |
| 140 | +<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>, |
| 141 | +<br>V. Stanev, F. Iliev, S. Hansen, V. Velimir, and B. Alexandrov. |
| 142 | +<br><cite>Applied Mathematical Modelling, Volume 60</cite>, August 2018, Pages 64-76.</li> |
| 143 | + |
| 144 | +</ul> |
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