A Novel Approach for Effective Multi-View Clustering with Information-Theoretic Perspective

Multi-view clustering (MVC) is a popular technique for improving clustering performance using various data sources. However, existing methods primarily focus on acquiring consistent information while often neglecting the issue of redundancy across multiple views. This study presents a new approach called Sufficient Multi-View Clustering (SUMVC) that examines the multi-view clustering framework from an information-theoretic standpoint. Our proposed method consists of two parts. Firstly, we develop a simple and reliable multi-view clustering method SCMVC (simple consistent multi-view clustering) that employs variational analysis to generate consistent information. Secondly, we propose a sufficient representation lower bound to enhance consistent information and minimise unnecessary information among views. The proposed SUMVC method offers a promising solution to the problem of multi-view clustering and provides a new perspective for analyzing multi-view data. To verify the effectiveness of our model, we conducted a theoretical analysis based on the Bayes Error Rate, and experiments on multiple multi-view datasets demonstrate the superior performance of SUMVC

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

Dealloyed porous gold anchored by: In situ generated graphene sheets as high activity catalyst for methanol electro-oxidation reaction

A novel one-step method to prepare the nanocomposites of reduced graphene oxide (RGO)/nanoporous gold (NPG) is realized by chemically dealloying an Al2Au precursor. The RGO nanosheets anchored on the surface of NPG have a cicada wing like shape and act as both conductive agent and buffer layer to improve the catalytic ability of NPG for methanol electro-oxidation reaction (MOR). This improvement can also be ascribed to the microstructure change of NPG in dealloying with RGO. This work inspires a facile and economic method to prepare the NPG based catalyst for MOR

Pig-Posture Recognition Based on Computer Vision: Dataset and Exploration

Posture changes in pigs during growth are often precursors of disease. Monitoring pigs’ behavioral activities can allow us to detect pathological changes in pigs earlier and identify the factors threatening the health of pigs in advance. Pigs tend to be farmed on a large scale, and manual observation by keepers is time consuming and laborious. Therefore, the use of computers to monitor the growth processes of pigs in real time, and to recognize the duration and frequency of pigs’ postural changes over time, can prevent outbreaks of porcine diseases. The contributions of this article are as follows: (1) The first human-annotated pig-posture-identification dataset in the world was established, including 800 pictures of each of the four pig postures: standing, lying on the stomach, lying on the side, and exploring. (2) When using a deep separable convolutional network to classify pig postures, the accuracy was 92.45%. The results show that the method proposed in this paper achieves adequate pig-posture recognition in a piggery environment and may be suitable for livestock farm applications

Deep Multi-View Subspace Clustering with Anchor Graph

Deep multi-view subspace clustering (DMVSC) has recently attracted increasing attention due to its promising performance. However, existing DMVSC methods still have two issues: (1) they mainly focus on using autoencoders to nonlinearly embed the data, while the embedding may be suboptimal for clustering because the clustering objective is rarely considered in autoencoders, and (2) existing methods typically have a quadratic or even cubic complexity, which makes it challenging to deal with large-scale data. To address these issues, in this paper we propose a novel deep multi-view subspace clustering method with anchor graph (DMCAG). To be specific, DMCAG firstly learns the embedded features for each view independently, which are used to obtain the subspace representations. To significantly reduce the complexity, we construct an anchor graph with small size for each view. Then, spectral clustering is performed on an integrated anchor graph to obtain pseudo-labels. To overcome the negative impact caused by suboptimal embedded features, we use pseudo-labels to refine the embedding process to make it more suitable for the clustering task. Pseudo-labels and embedded features are updated alternately. Furthermore, we design a strategy to keep the consistency of the labels based on contrastive learning to enhance the clustering performance. Empirical studies on real-world datasets show that our method achieves superior clustering performance over other state-of-the-art methods

A Deep Learning-Based Electromagnetic Signal for Earthquake Magnitude Prediction

The influence of earthquake disasters on human social life is positively related to the magnitude and intensity of the earthquake, and effectively avoiding casualties and property losses can be attributed to the accurate prediction of earthquakes. In this study, an electromagnetic sensor is investigated to assess earthquakes in advance by collecting earthquake signals. At present, the mainstream earthquake magnitude prediction comprises two methods. On the one hand, most geophysicists or data analysis experts extract a series of basic features from earthquake precursor signals for seismic classification. On the other hand, the obtained data related to earth activities by seismograph or space satellite are directly used in classification networks. This article proposes a CNN and designs a 3D feature-map which can be used to solve the problem of earthquake magnitude classification by combining the advantages of shallow features and high-dimensional information. In addition, noise simulation technology and SMOTE oversampling technology are applied to overcome the problem of seismic data imbalance. The signals collected by electromagnetic sensors are used to evaluate the method proposed in this article. The results show that the method proposed in this paper can classify earthquake magnitudes well

Feasibility study on construction of RCC gravity dam under special climatic conditions in Tibet

According to the experience in the construction of water conservancy and hydropower projects in the Mainland, RCC dam construction has two main advantages: the rapid construction speed of roller compacted concrete can realize early power generation, and the price of fly ash is much lower than that of cement, which can reduce the project cost. Tibet has special geographical environment and climatic conditions, and generally has the characteristics of “high altitude, low pressure, low temperature, large temperature difference between day and night, and dry climate”. Taking the dam-building environment in central Tibet as an example, through investigation and research and analogy of similar projects, this paper analyzes the adaptability of construction of RCC gravity dams in Tibet from the aspects of geographical environment, climatic conditions, material properties, construction progress, and project cost. Adaptability to high altitudes. It provides a reference for choosing a safe, reliable, economical and reasonable dam type in the construction of water conservancy and hydropower projects. It provides reference for selecting safe, reliable, economical and reasonable dam types in water conservancy and hydropower engineering construction

Quercetin Increases Hepatic Homocysteine Remethylation and Transsulfuration in Rats Fed a Methionine-Enriched Diet

This study was aimed at investigating the effects of quercetin on mRNA expression and activity of critical enzymes in homocysteine metabolism in rats fed a methionine-enriched diet. Rats were fed for 6 weeks the following diets, that is, control, 0.5% quercetin, 1.0% methionine, and 1.0% methionine plus 0.5% quercetin diets. Serum homocysteine was significantly increased after methionine treatment and decreased after the addition of quercetin. The mRNA expression of methionine synthase was significantly increased after methionine or methionine plus quercetin supplementation, while its enzymatic activity was significantly increased after methionine plus quercetin supplementation. The mRNA expression and enzymatic activity of cystathionine -synthase and cystathionine -lyase were upregulated after quercetin, methionine, or quercetin plus methionine treatment and a more significant increase was observed for hepatic cystathionine -synthase in the methionine plus quercetin treated rats, suggesting an interaction between methionine and quercetin. Meanwhile, hepatic ratio of S-adenosylmethionine to S-adenosylhomocysteine was significantly decreased in response to methionine supplementation and normalized after the addition of quercetin. It is concluded that quercetin reduces serum homocysteine by increasing remethylation and transsulfuration of homocysteine in rats exposed to a methionine-enriched diet

Effect of Different Protection on Lateral Ankle during Landing: An Instantaneous Impact Analysis

Ankle sprain is the most common injury during parachute landing. The biomechanical behavior of the tissues can help us understand the injury mechanism of ankle inversion. To accurately describe the injury mechanism of tissues and assess the effect of ankle protection, a stable time of landing was obtained through the dynamic stability test. It was used for the boundary condition of the foot finite element (FE). The FE model was provided a static load equal to half of the bodyweight applied at the distal tibia and fibula; a foot-boot-brace FE model was established to simulate the landing of subjects on an inversion inclined platform of 0–20°, including non-, external, and elastic ankle braces. Compared with the non-ankle brace, both the external and elastic ankle braces decreased the peak strains of the cal-fibular, anterior Ta-fibular, and posterior Ta-fibular ligaments (15.2–33.0%), and of the peak stress of the fibula (15.2–24.5%). For the strain decrement of the aforementioned ligaments, the elastic brace performed better than the external ankle brace under the inversion of the 10° condition. The peak stress of the fibula (15.6 MPa) decreased up to 24.5% with an elastic brace and 5.6–10.3% with an external brace. The findings suggested that the behaviors of lateral ankle ligaments and fibula were meaningful for the functional ability of the ankle. This provides some suggestions regarding the optimal design of ankle protection

Procyanidin B2 alleviates uterine toxicity induced by cadmium exposure in rats: The effect of oxidative stress, inflammation, and gut microbiota

Environmental exposure to hazardous materials causes enormous socioeconomic problems due to its deleterious impacts on human beings, agriculture and animal husbandry. As an important hazardous material, cadmium can promote uterine oxidative stress and inflammation, leading to reproductive toxicity. Antioxidants have been reported to attenuate the reproductive toxicity associated with cadmium exposure. In this study, we investigated the potential protective effect of procyanidin oligosaccharide B2 (PC-B2) and gut microbiota on uterine toxicity induced by cadmium exposure in rats. The results showed that the expression levels of glutathione peroxidase (GSH-Px) and superoxide dismutase (SOD) were reduced in utero. Proinflammatory cytokines (including tumor necrosis factor-& alpha;, interleukin-1 & beta; and interleukin-6), the NLRP3 inflammasome, Caspase-1 and pro-IL-1 & beta; were all involved in inflammatory-mediated uterine injury. PC-B2 prevented CdCl2-induced oxidative stress and inflammation in uterine tissue by increasing antioxidant enzymes and reducing proinflammatory cytokines. Additionally, PC-B2 significantly reduced cadmium deposition in the uterus, possibly through its significant increase in MT1, MT2, and MT3 mRNA expression. Interestingly, PC-B2 protected the uterus from CdCl2 damage by increasing the abundance of intestinal microbiota, promoting beneficial microbiota, and inhibiting harmful microbiota. This study provides novel mechanistic insights into the toxicity of environmental cadmium exposure and indicates that PC-B2 could be used in the prevention of cadmium exposure-induced uterine toxicity