Matrix Embedding Research Articles

Mechanical Properties of Interfaces between Mg and SiC: An Ab Initio Study

Covalently bonded particles may exhibit extremely high strength, but their performance in the reinforcement of metal alloys crucially depends on the properties of their interfaces with the embedding matrix. Here, density functional theory is used for investigating a range of interface configurations between magnesium and silicon carbide in view of their mechanical properties. Interfaces are analyzed not only in terms of interface energy/work of separation but also in terms of the interfacial shear stresses required to induce interface-parallel displacements. These properties are studied for bilayer systems with different orientations of the Mg and SiC layers and for different terminations of the SiC layer (Si or C atoms located at the interface). The results are discussed in terms of their implication for mechanical behavior of SiC reinforced Mg alloys.

Blood-Based CNT Nanofluid Flow Over Rotating Discs for the Impact of Drag Using Darcy–Forchheimer Model Embedding in Porous Matrix

Blood-Based CNT Nanofluid Flow Over Rotating Discs for the Impact of Drag Using Darcy–Forchheimer Model Embedding in Porous Matrix

Non-iridescent magnetite photonic crystal films and pigments with enhanced magnetic coupling effect

Non-iridescent structural color films and pigments with novel enhanced microwave absorption characteristics are successfully fabricated at room temperature for the first time. The quasi-uniform magnetite nanoparticles (CV = 0.10 ∼ 0.20) with high refractive index (n = 2.42) and broad light absorption provide evident low-angle-independent property for the structural color pigments. More importantly, a universal technical protocol is developed for preparation of photonic crystals with inorganic nanoparticles, i.e., through designing of the binding strength between nanoparticles and mediated molecules, the slurry modulus is increased to 105 Pa, which effectively drives the nanoparticles to rapidly assemble into closely packed ordered arrays by our-designed powerful assembly technique. Additionally, the structural color materials are subjected as photonic crystal pigments (∼20 wt%) embedding in a transparent polymer matrix to form color coatings. Remarkably, the photonic crystal pigments exhibit strong magnetic coupling effect and enhanced microwave absorption performance due to the regular arrangement, with superior reflection loss (RLmin) values of −14.97 dB (increase by 65.8 % compared with randomized magnetic nanoparticles), and the effective absorption bandwidth (EAB) of 2.15 GHz. This new kind of photonic crystal pigment, combining visual color and radar wave absorption, provides a novel idea for developing of multi-wave-band high-performance stealth materials.

Towards a unified framework for graph-based multi-view clustering

Recently, clustering data collected from various sources has become a hot topic in real-world applications. The most common methods for multi-view clustering can be divided into several categories: Spectral clustering algorithms, subspace multi-view clustering algorithms, matrix factorization approaches, and kernel methods. Despite the high performance of these methods, they directly fuse all similarity matrices of all views and separate the affinity learning process from the multiview clustering process. The performance of these algorithms can be affected by noisy affinity matrices. To overcome this drawback, this paper presents a novel method called One Step Multi-view Clustering via Consensus Graph Learning and Nonnegative Embedding (OSMGNE). Instead of directly merging the similarity matrices of different views, which may contain noise, a step of learning a consensus similarity matrix is performed. This step forces the similarity matrices of different views to be too similar, which eliminates the problem of noisy data. Moreover, the use of the nonnegative embedding matrix (soft cluster assignment matrix makes it possible to directly obtain the final clustering result without any extra step. The proposed method can solve five subtasks simultaneously. It jointly estimates the similarity matrix of all views, the similarity matrix of each view, the corresponding spectral projection matrix, the unified clustering indicator matrix, and automatically gives the weight of each view without the use of hyper-parameters. In addition, another version of our method is also studied in this paper. This method differs from the first one by using a consensus spectral projection matrix and a consensus Laplacian matrix over all views. An iterative algorithm is proposed to solve the optimization problem of these two methods. The two proposed methods are tested on several real datasets, which prove their superiority.

Recognizing and detecting COVID-19 in chest X-ray images using constrained multi-view spectral clustering

AbstractMachine learning, particularly classification algorithms, has been widely employed for diagnosing COVID-19 cases. However, these methods typically rely on labeled datasets and analyze a single data view. With the vast amount of patient data available without labels, this paper addresses the novel challenge of unsupervised COVID-19 diagnosis. The goal is to harness the abundant data without labels effectively. In recent times, multi-view clustering has garnered considerable attention in the research community. Spectral clustering, known for its robust theoretical framework, is a key focus. However, traditional spectral clustering methods generate only nonlinear data projections, necessitating additional clustering steps. The quality of these post-processing steps can be influenced by various factors, such as initialization procedures and outliers. This paper introduces an enhanced version of the recent “Multiview Spectral Clustering via integrating Nonnegative Embedding and Spectral Embedding” method. While retaining the benefits of the original technique, the proposed model integrates two essential constraints: (1) a constraint for ensuring the consistent smoothness of the nonnegative embedding across all views and (2) an orthogonality constraint imposed on the columns of the nonnegative embedding matrix. The effectiveness of this approach is demonstrated using COVIDx datasets. Additionally, the method is evaluated on other image datasets to validate its suitability for this study.

A Similarity Matrix Low-Rank Approximation and Inconsistency Separation Fusion Approach for Multiview Clustering

In recent years, multi-view clustering algorithms have achieved promising performance by exploiting the com-plementarity and consistency of different views. However, many multi-view spectral clustering methods only focus on the con-sistent information of views, and the time cost of feature decomposition is expensive. Moreover, these methods also require post-processing (e.g., k-means) to obtain the final clustering results. To overcome these limitations simultaneously, we propose a novel multi-view clustering algorithm. Firstly, the method removes the inconsistent information of the views through cross-view measurement to maintain consistent information. These inconsistencies may be caused by noise, corruptions, or view-specific properties and will affect the quality of the similarity matrix. Then, we learn a consensus embedding matrix with non-negative constraints by performing a low-rank decomposition of the consistency information. In this way, we can replace the eigendecomposition of the n × n Laplacian matrix in spectral clustering with the singular value decomposition of a n × c low-rank matrix to reduce the computational burden, where c ≪ n. Furthermore, due to the non-negative constraint, we can directly obtain the clustering results. Also, to consider the diversity of views, adaptive weighting is applied to different view data. Compared to state-of-the-art multi-view clustering methods on five benchmark multi-view datasets, we demonstrate the superiority and effectiveness of our approach. We release the source code at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hulu88/FAMvC</uri> .

Enhancing Temporal Knowledge Graph Alignment in News Domain With Box Embedding

In many fields, such as social networks and recommendation systems with high time requirements, fake news and false information are often released in real time, impacting on people’s daily life. Entity alignment (EA) in temporal knowledge graph (TKG) can fuse the information contained in entities by finding equivalent entities, thus helping to determine the regular pattern of disinformation under time change. The existing methods either ignore the use of temporal attributes’ information and structural information or the modeling of that is insufficient, which has become a major obstacle to the further and wider application of TKG EA. In this article, we put forward a new idea of training for the processing of time attributes and relational structure information, to further enhance the ability in the EA process of TKGs. By forming box embedding matrix and name embedding matrix, and adaptively fusing the above information, we propose a new TKG EA solution. We carry out comparative experiments on standard news media and social media datasets collected from the real world, which validates the effectiveness of our proposal.

Studying the impact of geometrical and cellular cues on myogenesis with a skeletal muscle-on-chip.

In the skeletal muscle tissue, cells are organized following an anisotropic architecture, which is both required during myogenesis when muscle precursor cells fuse to generate myotubes and for its contractile function. To build an in vitro skeletal muscle tissue, it is therefore essential to develop methods to organize cells in an anisotropic fashion, which can be particularly challenging, especially in 3D. In this study, we present a versatile muscle-on-chip system with adjustable collagen hollow tubes that can be seeded with muscle precursor cells. The collagen acts both as a tube-shaped hollow mold and as an extracellular matrix scaffold that can house other cell types for co-culture. We found that the diameter of the channel affects the organization of the muscle cells and that proper myogenesis was obtained at a diameter of 75 μm. In these conditions, muscle precursor cells fused into long myotubes aligned along these collagen channels, resulting in a fascicle-like structure. These myotubes exhibited actin striations and upregulation of multiple myogenic genes, reflecting their maturation. Moreover, we showed that our chip allowed muscle tissue culture and maturation over a month, with the possibility of fibroblast co-culture embedding in the collagen matrix.

JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer.

JPEG compression adopts the quantization of Discrete Cosine Transform (DCT) coefficients for effective bit-rate reduction, whilst the quantization could lead to a significant loss of important image details. Recovering compressed JPEG images in the frequency domain has recently garnered increasing interest, complementing the multitude of restoration techniques established in the pixel domain. However, existing DCT domain methods typically suffer from limited effectiveness in handling a wide range of compression quality factors or fall short in recovering sparse quantized coefficients and the components across different colorspaces. To address these challenges, we propose a DCT domain spatial-frequential Transformer, namely DCTransformer, for JPEG quantized coefficient recovery. Specifically, a dual-branch architecture is designed to capture both spatial and frequential correlations within the collocated DCT coefficients. Moreover, we incorporate the operation of quantization matrix embedding, which effectively allows our single model to handle a wide range of quality factors, and a luminance-chrominance alignment head that produces a unified feature map to align different-sized luminance and chrominance components. Our proposed DCTransformer outperforms the current state-of-the-art JPEG artifact removal techniques, as demonstrated by our extensive experiments.

Tailored synthesis and optimization of nickel-molybdenum carbide-graphite nanofiber composite for enhanced ethanol electrooxidation.

A novel nickel-molybdenum carbide-graphite nanofiber composite is introduced as an electrocatalyst for ethanol electrooxidation. The proposed nanofibers have been prepared by calcinating electrospun nanofibers composed of nickel acetate tetrahydrate, molybdenum chloride, and polyvinyl alcohol. The calcination process was conducted at different temperatures (700, 850, and 1000°C) under a nitrogen gas atmosphere with a heating rate of 2.5 deg/min and a holding time of 5 h. Physicochemical characterizations have indicated that nickel acetate is entirely reduced to nickel metal during the sintering process, and molybdenum has bonded with carbon to produce molybdenum carbide. At the same time, the used polymer has been pyrolyzed to produce a carbon nanofiber matrix embedding formed inorganic nanoparticles. Electrochemical measurements concluded that molybdenum content and calcination temperature should be controlled to maximize the electrocatalytic activity of the proposed catalyst. Typically, the oxidation peak current density was 28.5, 28.8, 51.5, 128.3, 25.6, and 3 mA/cm2 for nanofibers prepared from an electrospun solution containing 0, 5, 10, 15, 25, and 35 wt% molybdenum carbide, respectively. Moreover, it was observed that increasing the calcination temperature distinctly improves the electrocatalytic activity. Kinetic studies have indicated that the reaction order is close to zero with a reaction temperature-dependent value. Moreover, it was detected that the electrooxidation reaction of ethanol over the proposed nanofiber composite follows the Arrhenius equation. The determined activation energy is 33 kJ/mol, which indicates good catalytic activity for the introduced nanofibers. Through the application of a set of visualization-based tools and the general linear model (GLM), the optimal conditions that generate the highest current density were identified. The computations unveiled that the optimal parameter settings are as follows: Mo content at 15 wt.%, methanol concentration of 1.55 M, and reaction temperature of 59°C.

Biogenic Vaterite Calcium Carbonate-Silver/Poly(Vinyl Alcohol) Film for Wound Dressing.

Vaterite, a spherical polymorph of CaCO3, shows potential as a carrier for the stable and controlled release of silver nanoparticles (AgNPs), preventing their aggregation or loss of efficacy during application. Furthermore, the embedding of CaCO3-Ag in a poly(vinyl alcohol) (PVA) matrix helps effectively encapsulate and protect the CaCO3-Ag microspheres and provides mechanical stability for better contact with the wound surface. This article focuses on the fabrication of an antimicrobial and biocompatible absorbent film embedded with precipitated biogenic vaterite CaCO3-Ag microspheres. The impact of vaterite CaCO3-Ag on the physical, chemical, nanomechanical, biocompatibility, and antimicrobial properties of the PVA films was investigated. The morphology study revealed a bilayer film structure with an inactive and active surface containing homogeneously distributed vaterite CaCO3-Ag. The X-ray photoelectron spectroscopy (XPS) analysis of the spin-orbit splitting in the Ag 3d5/2 and Ag 3d3/2 peaks indicated the presence of both metallic and ionic states of silver in vaterite CaCO3-Ag prior to its incorporation into the PVA polymer matrix. However, upon embedding in the PVA matrix, a subsequent transformation to solely ionic states was observed. The nanomechanical properties of PVA improved, and the reduced modulus and hardness increased to 14.62 ± 5.23 and 0.64 ± 0.29 GPa, respectively. The films demonstrate a significant activity toward Gram-negative Escherichia coli bacteria. The release of AgNPs was studied in both open and closed systems at pH 6, mimicking the pH environment of the wound, and it demonstrated a dependency on the type of capping agent used for synthesis and loading of AgNPs. The results further revealed the biocompatibility of the prepared films with human dermal fibroblast cells at a concentration of ≤5 mg/mL, making them applicable and functional for wound dressing applications.

Contribution to inaccessibility as resource impact method: A base for sustainable resource management along the life cycle

The goal of this paper is to present and demonstrate a proof of concept for a novel method that quantifies the impact of human activities that compromise the accessibility to the instrumental value of resources. It identifies ten key matrices along six life cycle stages. Starting from Time To Accessibility as a base, accessibility factors for resources are developed in function of the embedding matrix, scaled in between 0 and 1. The method provides value-based characterization factors for 45 resources as a Contribution To Inaccessibility - Life Cycle Impact Assessment (CTI-LCIA) method and involves price to capture the value. It points to hotspots along the life cycle (e.g. inaccessibility generation by storing in landfills and tailings deposits), the importance of process efficiencies, and the value of circular economy strategies. The latter is illustrated with a case study on four resources in a battery case study.

Evolution of tensile fractures in feldspar porphyroclast and its implication in paleostress estimation

Fractures are ubiquitous brittle features of the upper crust. Syntectonic granites are often replete with fractures of various attitudes, cross-cutting each other, which develop under the prevalent states of stress. In this article, we study the origin and mechanism of formation of tensile fractures restricted within rectangular alkali feldspar porphyroclasts found in Closepet granite (Dharwar Craton, India), and venture on the possibility of using them as potential paleostress marker. The study is conducted using finite-element-method (FEM) to assess the distribution of induced stresses within the porphyroclasts considering the geometry and varying mechanical properties of the porphyroclast and the embedding matrix. We infer that fractures in the porphyroclast develop due to amplification and concentration of far-field stresses within it. The direction of maximum principal compressive stress as indicated by the fractured porphyroclasts lies in close proximity to the paleostress direction derived from fault-slip analysis of adjacent bodies such as Chitradurga Schist Belt and Koppal syenite (Dharwar craton). Therefore, we advocate that fractured feldspar porphyroclasts are a reliable indicator of paleostress orientations.

Classification of Palm Fruit Ripeness Level Using Learning Vector Quantization (LVQ) Method

The implementation of Learning Vector Quantization (LVQ) to classify the ripeness of oil palm fruits is investigated in this study. In addition, this study provides a comprehensive set of procedures ranging from data collection and pre-processing to training and testing of the LVQ model, and finally, the proposed method has been validated by testing it on previously unseen data. Three feature extraction methods, specifically Gray-Level Co-occurrence Matrix (GLCM), Hue, Saturation, and Value (HSV), and t-Distributed Stochastic Neighbor Embedding (t-SNE), were assessed for their performance. The results show that the chosen feature extraction method strongly influences the classification performance. The accuracy of the model employing t-SNE features is notably the highest at 50%, indicating its efficacy in identifying the ripeness level of palm fruits by extracting pertinent features. On the other hand, the GLCM feature has a 40% accuracy in the test data, suggesting that although it captures information on texture, it may not comprehensively encapsulate ripeness characteristics. Additionally, the HSV feature achieves an accuracy of 45%, which is less precise than that of t-SNE. To conclude, this study elucidates the appropriateness of various feature extraction techniques in categorizing the degree of ripeness in palm fruits. The t-SNE feature extraction model stands out as the most efficient option, exhibiting greater precision in comparison to other methodologies.

Multi-step prediction of chlorophyll concentration based on adaptive graph-temporal convolutional network with series decomposition

Chlorophyll concentration can well reflect the nutritional status and algal blooms of water bodies, and is an important indicator for evaluating water quality. The prediction of chlorophyll concentration change trend is of great significance to environmental protection and aquaculture. However, there is a complex and indistinguishable nonlinear relationship between many factors affecting chlorophyll concentration. In order to effectively mine the nonlinear features contained in the data. This paper proposes a time-series decomposition adaptive graph-time convolutional network prediction model. Firstly, the original sequence is decomposed into trend component and periodic component by moving average method. Secondly, based on the graph convolutional neural network, the water quality parameter data is modeled, and a parameter embedding matrix is defined. The idea of matrix decomposition is used to assign weight parameters to each node. The adaptive graph convolution learns the relationship between different water quality parameters, updates the state information of each parameter, and improves the learning ability of the update relationship between nodes. Finally, time dependence is captured by time convolution to achieve multi-step prediction of chlorophyll concentration. The validity of the model is verified by the water quality data of the coastal city Beihai. The results show that the prediction effect of this method is better than other methods. It can be used as a scientific resource for environmental management decision-making.

Lipidomic Phenotyping Of Human Small Intestinal Organoids Using Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging.

In the past decade, interest in organoids for biomedical research has surged, resulting in a higher demand for advanced imaging techniques. Traditional specimen embedding methods pose challenges, such as analyte delocalization and histological assessment. Here, we present an optimized sample preparation approach utilizing an Epredia M-1 cellulose-based embedding matrix, which preserves the structural integrity of fragile small intestinal organoids (SIOs). Additionally, background interference (delocalization of analytes, nonspecific (histological) staining, matrix ion clusters) was minimized, and we demonstrate the compatibility with matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). With our approach, we can conduct label-free lipid imaging at the single-cell level, thereby yielding insights into the spatial distribution of lipids in both positive and negative ion modes. Moreover, M-1 embedding allows for an improved coregistration with histological and immunohistochemical (IHC) stainings, including MALDI-IHC, facilitating combined untargeted and targeted spatial information. Applying this approach, we successfully phenotyped crypt-like (CL) and villus-like (VL) SIOs, revealing that PE 36:2 [M - H]- (m/z 742.5) and PI 38:4 [M - H]- (m/z 885.5) display higher abundance in CL organoids, whereas PI 36:1 [M - H]- (m/z 863.6) was more prevalent in VL organoids. Our findings demonstrate the utility of M-1 embedding for advancing organoid research and unraveling intricate biological processes within these in vitro models.

Contrastive learning and dynamics embedding neural network for label-free interpretable machine fault diagnosis

Industrial machinery often produces vibration signals that can serve as indicators of underlying faults. However, these signals often need to be labeled, presenting a challenge for accurate and interpretable fault diagnosis. While supervised learning methods, such as deep neural networks, have been applied for fault diagnosis, they need help in effectively distinguishing between different vibration-related faults. In response to this issue, our study introduces an innovative approach for automatic fault diagnosis through the application of the Bootstrap Your Own Latent and Dynamical Systems Model Discovery algorithm (BYOLDIS). This method not only addresses the challenge of unlabelled signals but also provides readily interpretable results. The proposed methodology consists of three fundamental steps. First, we derive a matrix of differential equations to capture the dynamic behavior of faulty bearings. Second, we employ a contrastive learning network alongside a time-delay embedding matrix to reconstruct the coordinates of the fault-dynamical system. Lastly, we construct a library of fault machine dynamic polynomial equations, incorporating prior constraints based on physical models. To assess the effectiveness and robustness of our proposed method, we conducted both simulations and experiments. The results of these case studies affirm that BYOLDIS can accurately diagnose bearing faults and offer dynamic explanations for the diagnostic outcomes. This suggests that BYOLDIS holds substantial promise as a diagnostic tool for processing unlabelled vibrational data.

Low-Rank Tensor Learning for Incomplete Multiview Clustering

Incomplete multiview clustering (IMVC) is an effective way to identify the underlying structure of incomplete multiview data. Most existing algorithms based on matrix factorization, graph learning or subspace learning have at least one of the following limitations: (1) the global and local structures of high-dimensional data are not effectively explored simultaneously; (2) the high-order correlations among multiple views are ignored. In this paper, we propose a low-rank tensor learning (LRTL) method that learns a consensus low-dimensional embedding matrix for IMVC. We first take advantage of the self-expressiveness property of high-dimensional data to construct sparse similarity matrices for individual views under low-rank and sparsity constraints. Individual low-dimensional embedding matrices can be obtained from the sparse similarity matrices using spectral embedding techniques. This approach simultaneously explores the global and local structures of incomplete multiview data. Then, we present a multiview embedding matrix fusion model that incorporates individual low-dimensional embedding matrices into a third-norm tensor to achieve a consensus low-dimensional embedding matrix. The fusion model exploits complementary information by finding the high-order correlations among multiple views. In addition, the computational cost of an improved fusion strategy is dramatically reduced. Extensive experimental results demonstrate that the proposed LRTL method outperforms several state-of-the-art approaches.

Unitary transformations within density matrix embedding approaches: A perspective on the self-consistent scheme for electronic structure calculation

Unitary transformations within density matrix embedding approaches: A perspective on the self-consistent scheme for electronic structure calculation

A Versatile Unitary Transformation Framework for an Optimal Bath Construction in Density-Matrix Based Quantum Embedding Approaches

The performance of embedding methods is directly tied to the quality of the bath orbital construction. In this paper, we develop a versatile framework, enabling the investigation of the optimal construction of the orbitals of the bath. As of today, in state-of-the-art embedding methods, the orbitals of the bath are constructed by performing a Singular Value Decomposition (SVD) on the impurity-environment part of the one-body reduced density matrix, as originally presented in Density Matrix Embedding Theory. Recently, the equivalence between the SVD protocol and the use of unitary transformation, the so-called Block-Householder transformation, has been established. We present a generalization of the Block-Householder transformation by introducing additional flexible parameters. The additional parameters are optimized such that the bath-orbitals fulfill physically motivated constraints. The efficiency of the approach is discussed and exemplified in the context of the half-filled Hubbard model in one-dimension.