| Publication |
Sentence |
Publish Date |
Extraction Date |
Species |
| Qiang Zhang, Yaming Zheng, Qiangqiang Yuan, Meiping Song, Haoyang Yu, Yi Xia. Hyperspectral Image Denoising: From Model-Driven, Data-Driven, to Model-Data-Driven. IEEE transactions on neural networks and learning systems. vol PP. 2023-06-06. PMID:37279128. |
later, we comprehensively review existing hsi denoising methods, from model-driven strategy (nonlocal mean, total variation, sparse representation, low-rank matrix approximation, and low-rank tensor factorization), data-driven strategy 2-d convolutional neural network (cnn), 3-d cnn, hybrid, and unsupervised networks, to model-data-driven strategy. |
2023-06-06 |
2023-08-14 |
Not clear |
| Stiv Llenga, Ganna Gryn'ov. Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions. The Journal of chemical physics. vol 158. issue 21. 2023-06-02. PMID:37265212. |
matrix of orthogonalized atomic orbital coefficients representation for radicals and ions. |
2023-06-02 |
2023-08-14 |
Not clear |
| Stiv Llenga, Ganna Gryn'ov. Matrix of orthogonalized atomic orbital coefficients representation for radicals and ions. The Journal of chemical physics. vol 158. issue 21. 2023-06-02. PMID:37265212. |
here, we present the matrix of orthogonalized atomic orbital coefficients (maoc) as a quantum-inspired molecular and atomic representation containing both structural (composition and geometry) and electronic (charge and spin multiplicity) information. |
2023-06-02 |
2023-08-14 |
Not clear |
| Yunbo Tang, Dan Chen, Jia Wu, Weiping Tu, Jessica J M Monaghan, Paul Sowman, David Mcalpin. Corrigendum to "Functional Connectivity Learning via Siamese-based SPD Matrix Representation of Brain Imaging Data" [Neural Networks 163 (2023) 272-285]. Neural networks : the official journal of the International Neural Network Society. vol 164. 2023-05-25. PMID:37229929. |
corrigendum to "functional connectivity learning via siamese-based spd matrix representation of brain imaging data" [neural networks 163 (2023) 272-285]. |
2023-05-25 |
2023-08-14 |
Not clear |
| Mugang Lin, Kunhui Wen, Xuanying Zhu, Huihuang Zhao, Xianfang Su. Graph Autoencoder with Preserving Node Attribute Similarity. Entropy (Basel, Switzerland). vol 25. issue 4. 2023-05-16. PMID:37190356. |
the cross-entropy loss of the reconstructed adjacency matrix and the mean-squared error loss of the reconstructed node attribute similarity matrix are used to update the model parameters and ensure that the node representation preserves the original structural and node attribute similarity information. |
2023-05-16 |
2023-08-14 |
Not clear |
| Mustafa Temiz, Burcu Bakir-Gungor, Pınar Güner Şahan, Mustafa Cosku. Topological feature generation for link prediction in biological networks. PeerJ. vol 11. 2023-05-15. PMID:37187525. |
due to the high dimensionality of the matrix obtained after the embedding process, the data are transformed into a smaller representation by applying feature regularization techniques. |
2023-05-15 |
2023-08-14 |
Not clear |
| Andero Uusberg, Brett Ford, Helen Uusberg, James J Gros. Reappraising reappraisal: an expanded view. Cognition & emotion. 2023-05-10. PMID:37161355. |
we demonstrate that the 2 × 2 × 2 matrix formed by crossing the three distinctions between reconstrual and repurposing, between object-level and meta-level representations, and between decommitment and commitment operations forms a useful map of different reappraisal tactics. |
2023-05-10 |
2023-08-14 |
Not clear |
| Yunyun Liang, Rongguo Yang, Jing Zhang, Tiancai Zhan. Hexapartite steering based on a four-wave-mixing process with a spatially structured pump. Optics express. vol 31. issue 7. 2023-05-09. PMID:37155804. |
matrix representation is used to express the steerings for the first time, which is very useful to understand the monogamy relations intuitively. |
2023-05-09 |
2023-08-14 |
Not clear |
| Cornelia L A Dewald, Alina Balandis, Lena S Becker, Jan B Hinrichs, Christian von Falck, Frank K Wacker, Hans Laser, Svetlana Gerbel, Hinrich B Winther, Johanna Apfel-Stark. Automated Classification of Free-Text Radiology Reports: Using Different Feature Extraction Methods to Identify Fractures of the Distal Fibula. RoFo : Fortschritte auf dem Gebiete der Rontgenstrahlen und der Nuklearmedizin. 2023-05-09. PMID:37160146. |
this data was used to establish a machine learning pipeline, which implemented the text representation methods bag-of-words (bow), term frequency-inverse document frequency (tf-idf), principal component analysis (pca), non-negative matrix factorization (nmf), latent dirichlet allocation (lda), and document embedding (doc2vec). |
2023-05-09 |
2023-08-14 |
Not clear |
| Jerome Riedel, Patrick Gelß, Rupert Klein, Burkhard Schmid. WaveTrain: A Python package for numerical quantum mechanics of chain-like systems based on tensor trains. The Journal of chemical physics. vol 158. issue 16. 2023-04-28. PMID:37114709. |
the python package is centered around tensor train (tt, or matrix product) format representations of hamiltonian operators and (stationary or time-evolving) state vectors. |
2023-04-28 |
2023-08-14 |
Not clear |
| Devan Becker, David Champredon, Connor Chato, Gopi Gugan, Art Poo. SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications. NAR genomics and bioinformatics. vol 5. issue 2. 2023-04-27. PMID:37101658. |
our method (which we have dubbed sequence uncertainty propagation, or sup) uses a probabilistic matrix representation of individual sequences which incorporates base quality scores as a measure of uncertainty that naturally lead to resampling and replication as a framework for uncertainty propagation. |
2023-04-27 |
2023-08-14 |
human |
| Devan Becker, David Champredon, Connor Chato, Gopi Gugan, Art Poo. SUP: a probabilistic framework to propagate genome sequence uncertainty, with applications. NAR genomics and bioinformatics. vol 5. issue 2. 2023-04-27. PMID:37101658. |
with the matrix representation, resampling possible base calls according to quality scores provides a bootstrap- or prior distribution-like first step towards genetic analysis. |
2023-04-27 |
2023-08-14 |
human |
| Yinli Tian, Wenjian Qin, Fei Xue, Ricardo Lambo, Meiyan Yue, Songhui Diao, Lequan Yu, Yaoqin Xie, Hailin Cao, Shuo L. ARR-GCN: Anatomy-Relation Reasoning Graph Convolutional Network for Automatic Fine-grained Segmentation of Organ's Surgical Anatomy. IEEE journal of biomedical and health informatics. vol PP. 2023-04-26. PMID:37099476. |
most importantly, to explicitly learn the anatomic relations, the prior anatomic-relations among the sub-regions are encoded in the form of an adjacency matrix and embedded into the intermediate node representations to guide framework learning. |
2023-04-26 |
2023-08-14 |
Not clear |
| Yunbo Tang, Dan Chen, Jia Wu, Weiping Tu, Jessica J M Monaghan, Paul Sowman, David Mcalpin. Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data. Neural networks : the official journal of the International Neural Network Society. vol 163. 2023-04-22. PMID:37086544. |
functional connectivity learning via siamese-based spd matrix representation of brain imaging data. |
2023-04-22 |
2023-08-14 |
human |
| Yunbo Tang, Dan Chen, Jia Wu, Weiping Tu, Jessica J M Monaghan, Paul Sowman, David Mcalpin. Functional connectivity learning via Siamese-based SPD matrix representation of brain imaging data. Neural networks : the official journal of the International Neural Network Society. vol 163. 2023-04-22. PMID:37086544. |
to bridge the technical gap, this study proposes a siamese-based symmetric positive definite (spd) matrix representation framework (siamesespd-mr) to derive the functional connectivity of brain imaging data (bid) such as electroencephalography (eeg), thus the alternative application-independent measure (in the form of spd matrix) can be automatically learnt: (1) siamesespd-mr first exploits graph convolution to extract the representative features of bid with the adjacency matrix computed considering the anatomical structure; (2) adaptive gaussian kernel function then applies to obtain the functional connectivity representations from the deep features followed by spd matrix transformation to address the intrinsic functional characteristics; and (3) two-branch (siamese) networks are combined via an element-wise product followed by a dense layer to derive the similarity between the pairwise inputs. |
2023-04-22 |
2023-08-14 |
human |
| Rodrigo Martínez-Peña, Juan-Pablo Orteg. Quantum reservoir computing in finite dimensions. Physical review. E. vol 107. issue 3-2. 2023-04-19. PMID:37072987. |
more explicitly, system isomorphisms are established that unify the density matrix approach to qrc with the representation in the space of observables using bloch vectors associated with gell-mann bases. |
2023-04-19 |
2023-08-14 |
Not clear |
| Bruna Moreira da Silva, David B Ascher, Douglas E V Pire. epitope1D: accurate taxonomy-aware B-cell linear epitope prediction. Briefings in bioinformatics. 2023-04-11. PMID:37039696. |
to address these limitations, we have developed epitope1d, an explainable machine learning method capable of accurately identifying linear b-cell epitopes, leveraging two new descriptors: a graph-based signature representation of protein sequences, based on our well-established cutoff scanning matrix algorithm and organism ontology information. |
2023-04-11 |
2023-08-14 |
Not clear |
| Yue Wang, Han Sun, Haodong Wang, Dandan Li, Weizhong Zhao, Xingpeng Jiang, Xianjun She. An Effective Model for Predicting Phage-host Interactions via Graph Embedding Representation Learning with Multi-head Attention Mechanism. IEEE journal of biomedical and health informatics. vol PP. 2023-04-08. PMID:37030796. |
more specifically, a module of gat with talking-heads is employed to learn representations of phages and bacteria, on which neural induction matrix completion is conducted to reconstruct the phage-host association matrix. |
2023-04-08 |
2023-08-14 |
human |
| Phu Pham, Loan T T Nguyen, Ngoc-Thanh Nguyen, Witold Pedrycz, Unil Yun, Jerry Chun-Wei Lin, Bay V. An Approach to Semantic-Aware Heterogeneous Network Embedding for Recommender Systems. IEEE transactions on cybernetics. vol PP. 2023-04-06. PMID:37021984. |
these rich-structural user and item representations are then used to facilitate the matrix factorization (mf) process. |
2023-04-06 |
2023-08-14 |
Not clear |
| Shouzhi Chen, Yanhong Liao, Jianping Zhao, Yannan Bin, Chunhou Zhen. PACVP: Prediction of Anti-Coronavirus Peptides Using A Stacking Learning Strategy with Effective Feature Representation. IEEE/ACM transactions on computational biology and bioinformatics. vol PP. 2023-04-06. PMID:37022025. |
in the first layer, we use nine feature encoding methods with different feature representation angles to characterize the rich sequence information and fuse them into a feature matrix. |
2023-04-06 |
2023-08-14 |
Not clear |