| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Maoshan Liu, Vasile Palade, Zhonglong Zhen. Learning the consensus and complementary information for large-scale multi-view clustering. Neural networks : the official journal of the International Neural Network Society. vol 172. 2024-01-14. PMID:38219678. |
to be specific, representative anchor points are selected for each view to construct corresponding anchor representation matrices, and all views' anchor points are utilized to construct a common representation matrix. |
2024-01-14 |
2024-01-17 |
Not clear |
| Bei Zhu, Hao-Yang Yu, Bing-Xue Du, Jian-Yu Sh. DMGL-MDA: A dual-modal graph learning method for microbe-drug association prediction. Methods (San Diego, Calif.). 2024-01-06. PMID:38184219. |
most computational models usually use a drug similarity matrix as the initial feature representation of drugs and stack the graph neural network layers to extract the features of network nodes. |
2024-01-06 |
2024-01-09 |
human |
| Julio A López-Saldívar, Olga V Man'ko, Margarita A Man'ko, Vladimir I Man'k. Bosonic Representation of Matrices and Angular Momentum Probabilistic Representation of Cyclic States. Entropy (Basel, Switzerland). vol 25. issue 12. 2023-12-23. PMID:38136507. |
the jordan-schwinger map allows us to go from a matrix representation of any arbitrary lie algebra to an oscillator (bosonic) representation. |
2023-12-23 |
2023-12-25 |
cat |
| Xinyun Hua, Chi Zhang, Chaojin Zhang, Lei Cheng, Ting Zhang, Jianlong L. Enhancing the robustness of ocean sound speed profile representation via interpretable deep matrix decomposition. The Journal of the Acoustical Society of America. vol 154. issue 6. 2023-12-19. PMID:38112485. |
enhancing the robustness of ocean sound speed profile representation via interpretable deep matrix decomposition. |
2023-12-19 |
2023-12-23 |
Not clear |
| Xinyun Hua, Chi Zhang, Chaojin Zhang, Lei Cheng, Ting Zhang, Jianlong L. Enhancing the robustness of ocean sound speed profile representation via interpretable deep matrix decomposition. The Journal of the Acoustical Society of America. vol 154. issue 6. 2023-12-19. PMID:38112485. |
to overcome these limitations and effectively learn a large number of basis functions with strong representation power from potentially noisy ssp data, we propose a novel algorithm called deep matrix decomposition (deep md). |
2023-12-19 |
2023-12-23 |
Not clear |
| Mark A Boyer, Edwin L Siber. A general expression for vibrational Hamiltonians expressed in oblique coordinates. The Journal of chemical physics. vol 159. issue 23. 2023-12-18. PMID:38108478. |
oblique coordinates are obtained by making non-orthogonal rotations of the original coordinates that convert the matrix representation of the quadratic hamiltonian operator into a block-diagonal matrix where the blocks are distinguished by the total quanta of vibrational excitation. |
2023-12-18 |
2023-12-21 |
Not clear |
| Li Yuan, Tao Wang, Xiaopeng Zhang, Francis Eng Hock Tay, Zequn Jie, Yonghong Tian, Wei Liu, Jiashi Fen. Learnable Central Similarity Quantization for Efficient Image and Video Retrieval. IEEE transactions on neural networks and learning systems. vol PP. 2023-12-14. PMID:38090871. |
to construct well-separated hash centers, we provide two efficient methods: 1) leveraging the hadamard matrix and bernoulli distributions to generate data-independent hash centers and 2) learning data-dependent hash centers from data representations. |
2023-12-14 |
2023-12-17 |
Not clear |
| Zhi Mao, Mingfang L. Artificial intelligence and cognitive diagnosis based teaching resource recommendation algorithm. PeerJ. Computer science. vol 9. 2023-12-11. PMID:38077591. |
this intricately constructed model consists of two core elements: the multi-layer perceptron (mlp) and the generalized matrix factorization (gmf), operating cohesively through stages of linear representation and nonlinear learning of the interaction function. |
2023-12-11 |
2023-12-17 |
Not clear |
| Phillip P A Staniczenko, Debabrata Panj. Temporal origin of nestedness in interaction networks. PNAS nexus. vol 2. issue 12. 2023-12-11. PMID:38077691. |
in networks with high levels of nestedness, the link positions of low-degree nodes (those with few links) form nested subsets of the link positions of high-degree nodes (those with many links), leading to matrix representations with characteristic upper triangular or staircase patterns. |
2023-12-11 |
2023-12-17 |
Not clear |
| Xu Jin, Xuhu Wang, Yujun Hou, Siyuan Hao, Xinjie Wang, Zhenhua Xu, Qunfei Zhan. Robust Sparse Bayesian Two-Dimensional Direction-of-Arrival Estimation with Gain-Phase Errors. Sensors (Basel, Switzerland). vol 23. issue 23. 2023-12-09. PMID:38067793. |
a sparse representation model with gain-phase errors is constructed using the diagonal element vector of the cross-correlation covariance matrix of two submatrices of the l-shaped sensor array. |
2023-12-09 |
2023-12-17 |
Not clear |
| Bahman Abdi-Sargezeh, Sepehr Shirani, Saeid Sanei, Clive Cheong Took, Oana Geman, Gonzalo Alarcon, Antonio Valenti. A review of signal processing and machine learning techniques for interictal epileptiform discharge detection. Computers in biology and medicine. vol 168. 2023-12-09. PMID:38070202. |
in this review paper, we group ied detection techniques into six categories: (1) template matching, (2) feature representation (mimetic, time-frequency, and nonlinear features), (3) matrix decomposition, (4) tensor factorization, (5) neural networks, and (6) estimation of the ieeg from the concurrent sceeg followed by detection and classification. |
2023-12-09 |
2023-12-17 |
Not clear |
| Zhongwei Sun, Yueli Sun, Changwen M. Comprehensive modeling of annulus fibrosus: from biphasic refined characterization to damage accumulation under viscous loading. Acta biomaterialia. 2023-12-09. PMID:38070844. |
the model accounts for fluid pressure, osmotic pressure, and matrix mechanics, providing a more accurate representation of the af's behavior. |
2023-12-09 |
2023-12-17 |
human |
| Nina Glaser, Alberto Baiardi, Markus Reihe. Flexible DMRG-Based Framework for Anharmonic Vibrational Calculations. Journal of chemical theory and computation. 2023-12-07. PMID:38060309. |
we present a novel formulation of the vibrational density matrix renormalization group (vdmrg) algorithm tailored to strongly anharmonic molecules described by general, high-dimensional model representations of potential energy surfaces. |
2023-12-07 |
2023-12-10 |
Not clear |
| Yusuke Endo, Koujin Taked. Performance Evaluation of Matrix Factorization for fMRI Data. Neural computation. 2023-12-05. PMID:38052077. |
the result suggests the sparse coding hypothesis in information representation in the whole human brain, because extracted features from the sparse matrix factorization (mf) method, sparse principal component analysis (sparsepca), or method of optimal directions (mod) under a high sparsity setting or an approximate sparse mf method, fast independent component analysis (fastica), can classify external visual stimuli more accurately than the nonsparse mf method or sparse mf method under a low sparsity setting. |
2023-12-05 |
2023-12-10 |
human |
| Wangping Xiong, Kaiqi Wang, Shixiong Liu, Zhaoyang Liu, Yimin Zhu, Peng Liu, Ming Yang, Xian Zho. Multiple prescription pattern recognition model based on Siamese network. Mathematical biosciences and engineering : MBE. vol 20. issue 10. 2023-12-05. PMID:38052575. |
secondly, a drug attribute tagging strategy was used to quantify the functional features of each drug in the core prescriptions; finally, a bidirectional long short-term memory network (bilstm) was used to extract the relational features of the core prescriptions, and a vector representation similarity matrix was constructed in combination with the siamese network framework to calculate the similarity between the core prescriptions and the classical prescriptions. |
2023-12-05 |
2023-12-10 |
Not clear |
| Jan Graffelman, Jan de Leeu. Improved approximation and visualization of the correlation matrix. The American statistician. vol 77. issue 4. 2023-12-04. PMID:38045013. |
the graphical representation of the correlation matrix by means of different multivariate statistical methods is reviewed, a comparison of the different procedures is presented with the use of an example data set, and an improved representation with better fit is proposed. |
2023-12-04 |
2023-12-10 |
Not clear |
| Jan Graffelman, Jan de Leeu. Improved approximation and visualization of the correlation matrix. The American statistician. vol 77. issue 4. 2023-12-04. PMID:38045013. |
weighted alternating least squares is a very strong competitor for principal component analysis, in particular if the correlation matrix is the focus of the study, because it improves the representation of the correlation matrix, often at the expense of only a minor percentage of explained variance for the original data matrix, if the latter is mapped onto the correlation biplot by regression. |
2023-12-04 |
2023-12-10 |
Not clear |
| Xiao-Dong Yu, D M Ton. Evolution Operator Can Always Be Separated into the Product of Holonomy and Dynamic Operators. Physical review letters. vol 131. issue 20. 2023-12-01. PMID:38039483. |
based on it, we further derive a matrix representation of this separation formula for cyclic evolution, and give a necessary and sufficient condition for a general evolution being purely holonomic. |
2023-12-01 |
2023-12-10 |
Not clear |
| Daniel Runfola, Heather Baier, Laura Mills, Maeve Naughton-Rockwell, Anthony Stefanidi. Deep learning fusion of satellite and social information to estimate human migratory flows. Transactions in GIS : TG. vol 26. issue 6. 2023-12-01. PMID:38024452. |
we leverage a three-stage approach, in which we (1) construct a matrix-based representation of socioeconomic information for each municipality in mexico, (2) implement a convolutional neural network with both satellite imagery and the constructed socioeconomic matrix, and (3) use the output vectors of information to estimate migratory flows. |
2023-12-01 |
2023-12-07 |
human |
| Chuan Ma, Yingwei Zhang, Chun-Yi S. Graph-Based Multicentroid Nonnegative Matrix Factorization. IEEE transactions on neural networks and learning systems. vol PP. 2023-12-01. PMID:38015683. |
nonnegative matrix factorization (nmf) is a widely recognized approach for data representation. |
2023-12-01 |
2023-12-07 |
Not clear |