Molecular-level insights of microplastic-derived soluble organic matter and heavy metal interactions in different environmental occurrences through EEM-PARAFAC and FT-ICR MS #JHAzardMater www.sciencedirect.com/science/arti...
Molecular-level insights of microplastic-derived soluble organic matter and heavy metal interactions in different environmental occurrences through EEM-PARAFAC and FT-ICR MS
The interactions between microplastic-derived dissolved organic matter (MPs-DOM) and heavy metals (Cu, Pb, and Cd) regulate the complex environmental …
www.sciencedirect.com
January 18, 2025 at 4:20 PM
Molecular-level insights of microplastic-derived soluble organic matter and heavy metal interactions in different environmental occurrences through EEM-PARAFAC and FT-ICR MS #JHAzardMater www.sciencedirect.com/science/arti...
Latent Functional PARAFAC for modeling multidimensional longitudinal data https://arxiv.org/abs/2410.18696 arXiv:2410.18696v1 Announce Type: new Abstract: In numerous settings, it is increasingly common to deal with longitudinal data organized as high-dimensional multi-dimensional arrays, also k 📈🤖
October 25, 2024 at 4:38 PM
Latent Functional PARAFAC for modeling multidimensional longitudinal data https://arxiv.org/abs/2410.18696 arXiv:2410.18696v1 Announce Type: new Abstract: In numerous settings, it is increasingly common to deal with longitudinal data organized as high-dimensional multi-dimensional arrays, also k 📈🤖
We're organizing another run of our #PARAFAC for #DissolvedOrganicMatter fluorescence course starting in March 2026. If you're really interested, DM me with an email address to get reminders about registration (opening in January 2026). Read more: dreem.gitlab.io/coursepage/
Parallel Factor Analysis for DOM fluorescence
Parallel Factor Analysis for DOM fluorescence
dreem.gitlab.io
October 25, 2025 at 12:16 PM
We're organizing another run of our #PARAFAC for #DissolvedOrganicMatter fluorescence course starting in March 2026. If you're really interested, DM me with an email address to get reminders about registration (opening in January 2026). Read more: dreem.gitlab.io/coursepage/
one more
n-way operations on tensors a la parafac etc
They are virtually the same in spectral hemistry, neuro (EEG, fMRI), BI, psychology etc all rare differently named etc
There is plenty more as we lack ability to properly search by "meaning" not name or semantics
Custom embeddings might help
n-way operations on tensors a la parafac etc
They are virtually the same in spectral hemistry, neuro (EEG, fMRI), BI, psychology etc all rare differently named etc
There is plenty more as we lack ability to properly search by "meaning" not name or semantics
Custom embeddings might help
November 27, 2024 at 1:44 AM
one more
n-way operations on tensors a la parafac etc
They are virtually the same in spectral hemistry, neuro (EEG, fMRI), BI, psychology etc all rare differently named etc
There is plenty more as we lack ability to properly search by "meaning" not name or semantics
Custom embeddings might help
n-way operations on tensors a la parafac etc
They are virtually the same in spectral hemistry, neuro (EEG, fMRI), BI, psychology etc all rare differently named etc
There is plenty more as we lack ability to properly search by "meaning" not name or semantics
Custom embeddings might help
Yukai Cai, Hang Liu, Xiulin Wang, Hongjin Li, Ziyi Wang, Chuanshuai Yang, Fengyu Cong
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
https://arxiv.org/abs/2404.11890
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
https://arxiv.org/abs/2404.11890
April 19, 2024 at 9:14 AM
Yukai Cai, Hang Liu, Xiulin Wang, Hongjin Li, Ziyi Wang, Chuanshuai Yang, Fengyu Cong
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
https://arxiv.org/abs/2404.11890
FCNCP: A Coupled Nonnegative CANDECOMP/PARAFAC Decomposition Based on Federated Learning
https://arxiv.org/abs/2404.11890
this work, we propose a Multiple Linked Tensors Factorization (MULTIFAC) method extending the CANDECOMP/PARAFAC (CP) decomposition to simultaneously reduce the dimension of multiple multi-way arrays and approximate [4/8 of https://arxiv.org/abs/2502.20286v1]
February 28, 2025 at 6:18 AM
this work, we propose a Multiple Linked Tensors Factorization (MULTIFAC) method extending the CANDECOMP/PARAFAC (CP) decomposition to simultaneously reduce the dimension of multiple multi-way arrays and approximate [4/8 of https://arxiv.org/abs/2502.20286v1]
CANDECOMP/PARAFAC decomposition (CPD-LRTC) to enhance the quality of the measurement data of the DNs. Firstly, the causes and the associated characteristics of the missing data are analyzed, and a third-order standard tensor [3/6 of https://arxiv.org/abs/2502.16188v1]
February 25, 2025 at 6:07 AM
CANDECOMP/PARAFAC decomposition (CPD-LRTC) to enhance the quality of the measurement data of the DNs. Firstly, the causes and the associated characteristics of the missing data are analyzed, and a third-order standard tensor [3/6 of https://arxiv.org/abs/2502.16188v1]
parafac mal meine eier
January 20, 2025 at 3:43 PM
parafac mal meine eier
Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization https://www.biorxiv.org/content/10.1101/2024.07.05.602272v1
Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization https://www.biorxiv.org/content/10.1101/2024.07.05.602272v1
Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These
www.biorxiv.org
July 10, 2024 at 4:04 PM
Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization https://www.biorxiv.org/content/10.1101/2024.07.05.602272v1
Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization https://www.biorxiv.org/content/10.1101/2024.07.05.602272v1
Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization https://www.biorxiv.org/content/10.1101/2024.07.05.602272v1
Tensor factorization is a dimensionality reduction method applied to multidimensional arrays. These
www.biorxiv.org
July 10, 2024 at 4:04 PM
Censored Least Squares for Imputing Missing Values in PARAFAC Tensor Factorization https://www.biorxiv.org/content/10.1101/2024.07.05.602272v1
(7/8) PARAFAC reveals distinct microbial growth patterns: Proteobacteria show exponential growth in case subjects, while Actinobacteria exhibit a sigmoid growth curve in controls. This method interprets time-resolved microbial differences between groups more effectively than PCA.
January 31, 2025 at 7:01 PM
(7/8) PARAFAC reveals distinct microbial growth patterns: Proteobacteria show exponential growth in case subjects, while Actinobacteria exhibit a sigmoid growth curve in controls. This method interprets time-resolved microbial differences between groups more effectively than PCA.
Using 3D fluorescence spectroscopy, specific ratios of #PARAFAC components were identified for municipal & various industrial #wastewater treatment plants in a case study region, and such fingerprints were traced in the fluorescence signal of the receiving river.
2/3
2/3
January 9, 2025 at 1:33 AM
Using 3D fluorescence spectroscopy, specific ratios of #PARAFAC components were identified for municipal & various industrial #wastewater treatment plants in a case study region, and such fingerprints were traced in the fluorescence signal of the receiving river.
2/3
2/3
Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering
Kristina P. Sinaga
http://arxiv.org/abs/2509.16101
Kristina P. Sinaga
http://arxiv.org/abs/2509.16101
September 22, 2025 at 3:48 AM
Personalized Federated Learning with Heat-Kernel Enhanced Tensorized Multi-View Clustering
Kristina P. Sinaga
http://arxiv.org/abs/2509.16101
Kristina P. Sinaga
http://arxiv.org/abs/2509.16101
ParaFac? More like ParaFuck amirite
January 20, 2025 at 3:42 PM
ParaFac? More like ParaFuck amirite
systems. In this paper, we propose a novel angle-delay-Doppler estimation scheme for AFDM-ISAC system in mixed NF and FF scenarios. Specifically, we model the received ISAC signals as a third-order tensor that admits a low-rank CANDECOMP/PARAFAC (CP) [3/6 of https://arxiv.org/abs/2504.06567v1]
April 10, 2025 at 6:01 AM
systems. In this paper, we propose a novel angle-delay-Doppler estimation scheme for AFDM-ISAC system in mixed NF and FF scenarios. Specifically, we model the received ISAC signals as a third-order tensor that admits a low-rank CANDECOMP/PARAFAC (CP) [3/6 of https://arxiv.org/abs/2504.06567v1]
challenges of over-parametrization and computational burden. We address these challenges by building on recently proposed Tensor VAR models to represent the time-varying coefficient matrix as a third-order tensor with CANDECOMP/PARAFAC (CP) [2/7 of https://arxiv.org/abs/2505.07975v1]
May 14, 2025 at 6:17 AM
challenges of over-parametrization and computational burden. We address these challenges by building on recently proposed Tensor VAR models to represent the time-varying coefficient matrix as a third-order tensor with CANDECOMP/PARAFAC (CP) [2/7 of https://arxiv.org/abs/2505.07975v1]
Complexation of Ln(III) Ions by Gluconate: Joint Investigation Applying TRLFS, CE-ICP-MS, NMR, and DF Calculations | Inorganic Chemistry pubs.acs.org/doi/10.1021/... Kretzschmar and co-workers @InorgChem #lanthanides #gluconate #TRLFS #PARAFAC #CEICPMS #NMR #DF #pH
April 19, 2025 at 7:10 AM
Complexation of Ln(III) Ions by Gluconate: Joint Investigation Applying TRLFS, CE-ICP-MS, NMR, and DF Calculations | Inorganic Chemistry pubs.acs.org/doi/10.1021/... Kretzschmar and co-workers @InorgChem #lanthanides #gluconate #TRLFS #PARAFAC #CEICPMS #NMR #DF #pH
arXiv:2505.16305v1 Announce Type: new
Abstract: Tensor CANDECOMP/PARAFAC decomposition (CPD) is a fundamental model for tensor reconstruction. Although the Bayesian framework allows for principled uncertainty quantification and automatic [1/4 of https://arxiv.org/abs/2505.16305v1]
Abstract: Tensor CANDECOMP/PARAFAC decomposition (CPD) is a fundamental model for tensor reconstruction. Although the Bayesian framework allows for principled uncertainty quantification and automatic [1/4 of https://arxiv.org/abs/2505.16305v1]
May 23, 2025 at 6:05 AM
arXiv:2505.16305v1 Announce Type: new
Abstract: Tensor CANDECOMP/PARAFAC decomposition (CPD) is a fundamental model for tensor reconstruction. Although the Bayesian framework allows for principled uncertainty quantification and automatic [1/4 of https://arxiv.org/abs/2505.16305v1]
Abstract: Tensor CANDECOMP/PARAFAC decomposition (CPD) is a fundamental model for tensor reconstruction. Although the Bayesian framework allows for principled uncertainty quantification and automatic [1/4 of https://arxiv.org/abs/2505.16305v1]
signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares [2/4 of https://arxiv.org/abs/2503.09393v1]
March 13, 2025 at 6:02 AM
signal, we decompose the generalized channel into three components: transmitter hardware characteristics, path gains, and angles of arrival. By modelling the received signals as parallel factor (PARAFAC) tensors, we develop alternating least squares [2/4 of https://arxiv.org/abs/2503.09393v1]
Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus
Latent Functional PARAFAC for modeling multidimensional longitudinal data
https://arxiv.org/abs/2410.18696
Latent Functional PARAFAC for modeling multidimensional longitudinal data
https://arxiv.org/abs/2410.18696
October 25, 2024 at 4:31 AM
Lucas Sort, Laurent Le Brusquet, Arthur Tenenhaus
Latent Functional PARAFAC for modeling multidimensional longitudinal data
https://arxiv.org/abs/2410.18696
Latent Functional PARAFAC for modeling multidimensional longitudinal data
https://arxiv.org/abs/2410.18696
My second paper on applying Parallel Factor Analysis (PARAFAC) to microbiome datasets is submitted and available on @biorxivpreprint.bsky.social : https://buff.ly/3PNae61. It features my R package, parafac4microbiome, downloadable from CRAN: https://buff.ly/4apC9CT.
#AcademicTwitter #rStats
#AcademicTwitter #rStats
parafac4microbiome: Exploratory analysis of longitudinal microbiome data using Parallel Factor Analysis
Studies investigating microbial temporal dynamics are increasingly common, leveraging longitudinal designs that collect microbial abundance data across multiple time points from the same subjects.…
buff.ly
January 19, 2025 at 11:00 AM
My second paper on applying Parallel Factor Analysis (PARAFAC) to microbiome datasets is submitted and available on @biorxivpreprint.bsky.social : https://buff.ly/3PNae61. It features my R package, parafac4microbiome, downloadable from CRAN: https://buff.ly/4apC9CT.
#AcademicTwitter #rStats
#AcademicTwitter #rStats
new polarforming-based user localization method that uses a structured time-domain pattern of pilot-polarforming vectors to extract the common stable components in the IPA channel across different polarizations based on the parallel factor (PARAFAC) [6/8 of https://arxiv.org/abs/2505.08070v1]
May 14, 2025 at 5:57 AM
new polarforming-based user localization method that uses a structured time-domain pattern of pilot-polarforming vectors to extract the common stable components in the IPA channel across different polarizations based on the parallel factor (PARAFAC) [6/8 of https://arxiv.org/abs/2505.08070v1]
Care about fluorescence of #DissolvedOrganicMatter and #PARAFAC? 🚨Update alert 🚨 drEEM version 2 is out! Things have gotten simpler, much faster, FAIR'er, and more interactive. Check it out: dreem.openfluor.org
October 25, 2025 at 12:15 PM
Care about fluorescence of #DissolvedOrganicMatter and #PARAFAC? 🚨Update alert 🚨 drEEM version 2 is out! Things have gotten simpler, much faster, FAIR'er, and more interactive. Check it out: dreem.openfluor.org
The analysis of so-called threeway arrays is pretty common in chemometrics and psychometrics. The Parafac/Candecomp-algorithms are quite useful for this, but give a lot of results which are hard to digest.
Biplots are a very useful (and underused) tool to visualise interactions.
1/3
Biplots are a very useful (and underused) tool to visualise interactions.
1/3
May 13, 2025 at 6:05 PM
The analysis of so-called threeway arrays is pretty common in chemometrics and psychometrics. The Parafac/Candecomp-algorithms are quite useful for this, but give a lot of results which are hard to digest.
Biplots are a very useful (and underused) tool to visualise interactions.
1/3
Biplots are a very useful (and underused) tool to visualise interactions.
1/3
Bayesian Adaptive Tucker Decompositions for Tensor Factorization https://arxiv.org/abs/2411.10218 arXiv:2411.10218v1 Announce Type: new Abstract: Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibili 📈🤖
November 18, 2024 at 4:27 PM
Bayesian Adaptive Tucker Decompositions for Tensor Factorization https://arxiv.org/abs/2411.10218 arXiv:2411.10218v1 Announce Type: new Abstract: Tucker tensor decomposition offers a more effective representation for multiway data compared to the widely used PARAFAC model. However, its flexibili 📈🤖