Adaptive multi-view subspace learning based on distributed optimization

As the rapid development of Internet of Things (IoT), the data is collected from different sensors and stored in distributed devices, these data can be regarded as the multi-view data. There are currently numerous clustering algorithms designed to handle multi-view data. However, most of these algorithms still suffer from the following problems: They are designed to operate directly on raw data, which preserves excessive redundant information and increases the computational burden for subsequent tasks. They primarily focus on pairwise relationships between views, neglecting the intricate high-order connections among multiple views. The prior information of singular values is not taken into account in multi-view. Different views are considered to have equal contributions for clustering. To efficiently address the above problems, adaptive multi-view subspace learning based on distributed optimization (AMSLDO) is proposed in this paper. Specifically, the original multi-view data is projected to a low-dimensional space for subspace representation, and multiple representation matrices are stacked in a tensor with weighted tensor nuclear norm to obtain high-order correlations and discover the prior information of singular values. Furthermore, adaptive graph learning automatically assigns weights to obtain a consensus graph. Meanwhile, the samples are partitioned into the ideal number of clusters through Laplacian rank constraint. An efficient distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) framework is designed to solve the proposed model. Extensive experiments are conducted on six datasets, demonstrating the superiority of the proposed model compared with eleven state-of-art methods.</p

Adaptive multi-view subspace learning based on distributed optimization

As the rapid development of Internet of Things (IoT), the data is collected from different sensors and stored in distributed devices, these data can be regarded as the multi-view data. There are currently numerous clustering algorithms designed to handle multi-view data. However, most of these algorithms still suffer from the following problems: They are designed to operate directly on raw data, which preserves excessive redundant information and increases the computational burden for subsequent tasks. They primarily focus on pairwise relationships between views, neglecting the intricate high-order connections among multiple views. The prior information of singular values is not taken into account in multi-view. Different views are considered to have equal contributions for clustering. To efficiently address the above problems, adaptive multi-view subspace learning based on distributed optimization (AMSLDO) is proposed in this paper. Specifically, the original multi-view data is projected to a low-dimensional space for subspace representation, and multiple representation matrices are stacked in a tensor with weighted tensor nuclear norm to obtain high-order correlations and discover the prior information of singular values. Furthermore, adaptive graph learning automatically assigns weights to obtain a consensus graph. Meanwhile, the samples are partitioned into the ideal number of clusters through Laplacian rank constraint. An efficient distributed optimization algorithm based on the Alternating Direction Method of Multipliers (ADMM) framework is designed to solve the proposed model. Extensive experiments are conducted on six datasets, demonstrating the superiority of the proposed model compared with eleven state-of-art methods.</p

Centric graph regularized log-norm sparse non-negative matrix factorization for multi-view clustering

Multi-view non-negative matrix factorization (NMF) provides a reliable method to analyze multiple views of data for low-dimensional representation. A variety of multi-view learning methods have been developed in recent years, demonstrating successful applications in clustering. However, existing methods in multi-view learning often tend to overlook the non-linear relationships among data and the significance of the similarity of internal views, both of which are essential in multi-view tasks. Meanwhile, the mapping between the obtained representation and the original data typically contains complex hidden information that deserves to be thoroughly explored. In this paper, a novel multi-view NMF is proposed that explores the local geometric structure among multi-dimensional data and learns the hidden representation of different attributes through centric graph regularization and pairwise co-regularization of the coefficient matrix. In addition, the proposed model is further sparsified with 2,-(pseudo) norm to efficiently generate sparse solutions. As a result, the model obtains a better part-based representation, enhancing its robustness and applicability in complex noisy scenarios. An effective iterative update algorithm is designed to solve the proposed model, and the convergence of the algorithm is proven to be theoretically guaranteed. The effectiveness of the proposed method is verified by comparing it with nine state-of-the-art methods in clustering tasks of eight public datasets.</p

Tensor Factorization with Sparse and Graph Regularization for Fake News Detection on Social Networks

Social media has a significant influence, which greatly facilitates people to stay up-to-date with information. Unfortunately, a great deal of fake news on social media misleads people and causes a lot of losses. Therefore, fake news detection is necessary to address this issue. Recently, social content category-based methods have become a crucial component of fake news detection. Different from news context-based category, which focuses on word embedding, it tends to explore the potential relationships and structures between users and news. In this article, a third-order tensor, which obtains massive information and connections, is constructed by the social links and engagements of social networks. Then, a sparse and graph-regularized CANDECOMP/PARAFAC (SGCP) tensor decomposition learning method is proposed for fake news detection on social network. In SGCP, a news factor matrix is constructed by CP decomposition of the tensor, which reflects the complex connections among users and news. Furthermore, SGCP retains sparsity of the news factor matrix and preserves the manifold structures from the original space. In addition, an efficient optimization algorithm, which is proven to be monotonically nonincreasing, is proposed to solve SGCP. Finally, abundant experiments are conducted on real-world datasets and demonstrate the effectiveness of the proposed SGCP.</p

Robust multi-view non-negative matrix factorization with adaptive graph and diversity constraints

Multi-view clustering (MVC) has received extensive attention due to its efficient processing of high-dimensional data. Most of the existing multi-view clustering methods are based on non-negative matrix factorization (NMF), which can achieve dimensionality reduction and interpretable representation. However, there are following issues in the existing researches: (1) The existing methods based on NMF using Frobenius norm are sensitive to noises and outliers. (2) Many methods only use the information shared by multi-view data, while ignoring the diverse information between views. (3) The data graph constructed by the conventional K Nearest Neighbors (KNN) method may misclassify neighbors and degrade the clustering performance. To address the above problems, we propose a novel robust multi-view clustering method. Specifically, -norm is introduced to measure the factorization error to improve the robustness of NMF. Additionally, a diversity constraint is utilized to learn the diverse relationship of multi-view data, and an adaptive graph method via information entropy is designed to overcome the shortcomings of misclassifying neighbors. Finally, an iterative updating algorithm is developed to solve the optimization model, which can make the objective function monotonically non-increasing. The effectiveness of the proposed method is substantiated by comparing with eleven state-of-the-art methods on five real-world and four synthetic multi-view datasets for clustering tasks

Enhancing the Performance of Perovskite Solar Cells via the Functional Group Synergistic Effect in Interfacial Passivation Materials

Interfacial defects are considered to be a stumbling block in producing highly efficient perovskite solar cells (PSCs), so a more reasonable design is required for interfacial passivation materials (IPMs) to achieve further improvements in PSC performance. Here, we use fluorine atom (−F) and methoxy (−OCH3) functional groups to modify the same molecular fragment, obtaining three kinds of IPMs named YZ-301, YZ-302, and YZ-303, respectively. Through the subtle combination of −F and −OCH3, the fragment in YZ-302 exhibits an enhanced electronegativity, rendering the correlative IPM with a stronger interaction with the perovskite layer. As a result, YZ-302 shows the best defect passivation and hole transport effect at the interface, and the PSC based on YZ-302 treatment achieves the best efficiency approaching 24%, which is better than the reference and devices with other IPMs, and it also has excellent device stability

Prokaryotic Ubiquitin-Like ThiS Fusion Enhances the Heterologous Protein Overexpression and Aggregation in <i>Escherichia coli</i>

<div><p>Fusion tags are commonly employed to enhance target protein expression, improve their folding and solubility, and reduce protein degradation in expression of recombinant proteins. Ubiquitin (Ub) and SUMO are highly conserved small proteins in eukaryotes, and frequently used as fusion tags in prokaryotic expression. ThiS, a smaller sulfur-carrier protein involved in thiamin synthesis, is conserved among most prokaryotic species. The structural similarity between ThiS and Ub provoked us into expecting that the former could be used as a fusion tag. Hence, ThiS was fused to insulin A and B chains, murine Ribonuclease Inhibitor (mRI) and EGFP, respectively. When induced in <i>Escherichia coli</i>, ThiS-fused insulin A and B chains were overexpressed in inclusion bodies, and to higher levels in comparison to the same proteins fused with Ub. On the contrast, ThiS fusion of mRI, an unstable protein, resulted in enhanced degradation that was not alleviated in protease-deficient strains. While the degradation of Ub- and SUMO-fused mRI was less and seemed protease-dependent. Enhanced degradation of mRI did not occur for the fusions with half-molecules of ThiS. When ThiS-tag was fused to the C-terminus of EGFP, higher expression, predominantly in inclusion bodies, was observed again. It was further found that ThiS fusion of EGFP significantly retarded its refolding process. These results indicated that prokaryotic ThiS is able to promote the expression of target proteins in <i>E. coli</i>, but enhanced degradation may occur in case of unstable targets. Unlike eukaryotic Ub-based tags usually increase the solubility and folding of proteins, ThiS fusion enhances the expression by augmenting the formation of inclusion bodies, probably through retardation of the folding of target proteins.</p></div

Expression of Insulin chains fused with half-molecules of ThiS or Ubiquitin.

<p>Insulin A chain (A) or B chain (B) fused with the N-terminal half (ThN-) or C terminal half (ThC-) of ThiS or the N-terminal half of ubiquitin (UbN-), were expressed in <i>E. coli</i> BL21 (DE3) pLysS. Total cell lysate from uninduced (−) or induced (+) cells with IPTG, and the soluble (S) or insoluble fraction (I) of induced cell were resolved on 15% SDS-PAGE, shown in left panels. M indicates Marker proteins. Western blot probed with anti His-tag antibody were shown in right panels. Arrowheads highlight observed positions of expressed proteins.</p

Guest-Effected Spin-Crossover in a Novel Three-Dimensional Self-Penetrating Coordination Polymer with Permanent Porosity

Porous and nonporous 3D heterobimetallic coordination polymers based on the 1,4-di­(pyridin-4-yl)­benzene ligand (dpb), [Fe­(dpb)­{Ag­(CN)₂}­{Ag₂(CN)₃}]·<i>n</i>Solv (<b>1</b>·<i>n</i>Solv; <i>n</i>Solv = DMF·EtOH, 2DMF·MeCN) and [Fe­(dpb)₂{Ag­(CN)₂}₂] (<b>2</b>), have been synthesized by diffusion technique, respectively. Single-crystal X-ray analysis shows that <b>1</b>·<i>n</i>Solv consists of a 3D self-penetrating network with in-situ-generated [Ag₂(CN)₃]⁻ species and displays one of the largest volume values of porosity (299 ų per iron atom) after desolvation for the Hoffman-like porous SCO coordination polymers to date. In contrast, nonporous compound <b>2</b> is composed of two independent interpenetrated 3D nets with in-situ-generated [Ag­(dpb)­(CN)₂]⁻ species. Their significant distinctions of structural architectures lead to dramatically different magnetic properties: <b>1</b>·<i>n</i>Solv displays two-step guest-effected SCO with hysteresis, whereas <b>2</b> presents characteristic paramagnetic behavior

Enhanced Spin-Crossover Behavior Mediated by Supramolecular Cooperative Interactions

Three one-dimensional (1D) hetereobimetallic coordination polymers [Fe^II(L)₂(AgCN)₂]·Solv (L = bpt^–, <b>1</b>; L = Mebpt^–, Solv = 1.75EtOH, <b>2</b>; L = bpzt^–, <b>3</b>) with in situ generated AgCN species were synthesized by solvothermal reactions of Fe^II salt, K­[Ag­(CN)₂], and the corresponding ligands [bptH = 3,5-bis­(pyridin-2-yl)-1,2,4-triazole, MebptH = 3-(3-methyl-2-pyridyl)-5-(2-pyridyl)-1,2,4-triazole, and bpztH = 3,5-bis­(pyrazin-2-yl)-1,2,4-triazole]. They were further characterized by X-ray crystallography, magnetic and photomagnetic measurements, and differential scanning calorimetry. Single-crystal X-ray analyses show that they are isostructural with 1D zigzag chain structures with rhombus {Fe₂Ag₂} units, in which the substituted bpt^– ligand connects the Fe^II ion and AgCN species in a cis bridging mode. Then the zigzag chains are packed into three-dimensional supramolecular structures by π···π interactions. Most importantly, weak Ag···N interactions (2.750 Å at 150 K) between the π-stacked neighboring chains present in complex <b>3</b>. Magnetic susceptibility measurements exhibit that complex <b>1</b> displays characteristic paramagnetic behavior in the temperature range investigated. Complex <b>2</b> undergoes a gradual spin-crossover (SCO) with critical temperatures <i>T</i>_1/2↓ = 232 K and <i>T</i>_1/2↑ = 235 K, whereas <b>3</b> exhibits an abrupt SCO with critical temperatures <i>T</i>_1/2↓ = 286 K and <i>T</i>_1/2↑ = 292 K. The magnetostructural relationships suggest that the magnetic behaviors can be modulated from paramagnetic behavior to abrupt and hysteretic SCO near room temperature through adjustment of the electronic substituent effect and intermolecular interactions