Cluster Phase Research Articles

Intrinsic origin of room temperature ferromagnetic ordering in (In0.97-xCoxMg0.03)2O3 thin films

The (In0.97-xCoxMg0.03)2O3 films with different Co concentration are prepared using RF magnetron sputtering. The XRD and XPS measurements show that the films are highly oriented and both Co and Mg elements present valence state of +2. The EXAFS spectra show that only substitutional Co ions exist in the film with x = 0.02. However, the films with higher Co concentration (x > 0.02) form separate phases of substitutional Co and metallic Co clusters. All the films exhibit n-type conductivity and the carrier concentration firstly decreases and then increases with Co doping. The transport mechanisms are dominated using Mott variable range hopping at low temperature and Hard bandgap hopping at high temperature. The Co dopant can effectively alter the MR characters, which undergoes a clear transition from negative MR to positive MR. All the films exhibit room temperature ferromagnetic hysteresis loop. Clear increase of ferromagnetic hysteresis loop and saturation magnetic moment are observed due to the incorporation of Co ions. The ferromagnetism is ascribed to oxygen vacancies-induced bound magnetic polarizations. The relative percentage of substitutional Co ions decrease with Co doping, which leads to the decreasing of number of bound magnetic polarons induced by oxygen vacancies, and then weaken the Ms.

Lie Algebraic Quantum Phase Reduction.

We introduce a general framework of phase reduction theory for quantum nonlinear oscillators. By employing the quantum trajectory theory, we define the limit-cycle trajectory and the phase according to a stochastic Schrödinger equation. Because a perturbation is represented by unitary transformation in quantum dynamics, we calculate phase response curves with respect to generators of a Lie algebra. Our method shows that the continuous measurement yields phase clusters and alters the phase response curves. The observable clusters capture the phase dynamics of individual quantum oscillators, unlike indirect indicators obtained from density operators. Furthermore, our method can be applied to finite-level systems that lack classical counterparts.

Effect of Phase Clustering Bias on Phase-Amplitude Coupling for Emotional EEG.

Emotions are thought to be related to distinct patterns of neural oscillations, but the interactions among multi-frequency neural oscillations during different emotional states lack full exploration. Phase-amplitude coupling is a promising tool for understanding the complexity of the neurophysiological system, thereby playing a crucial role in revealing the physiological mechanisms underlying emotional electroencephalogram (EEG). However, the non-sinusoidal characteristics of EEG lead to the non-uniform distribution of phase angles, which could potentially affect the analysis of phase-amplitude coupling. Removing phase clustering bias (PCB) can uniform the distribution of phase angles, but the effect of this approach is unknown on emotional EEG phase-amplitude coupling. This study aims to explore the effect of PCB on cross-frequency phase-amplitude coupling for emotional EEG. The technique of removing PCB was implemented on a publicly accessible emotional EEG dataset to calculate debiased phase-amplitude coupling. Statistical analysis and classification were conducted to compare the difference in emotional EEG phase-amplitude coupling prior to and post the removal of PCB. Emotional EEG phase-amplitude coupling values are overestimated due to PCB. Removing PCB enhances the difference in coupling strength between fear and happy emotions in the frontal lobe. Comparable emotion recognition performance was achieved with fewer features after removing PCB. These findings suggest that removing PCB enhances the difference in emotional EEG phase-amplitude coupling patterns and generates features that contain more emotional information. Removing PCB may be advantageous for analyzing emotional EEG phase-amplitude coupling and recognizing human emotions.

Evaluation of classical correlation functions from 2/3D images on CPU and GPU architectures: Introducing CorrelationFunctions.jl

Correlation functions are becoming one of the major tools for quantification of structural information that is usually represented as 2D or 3D images. In this paper we introduce ▪ open-source package developed in Julia and capable of computing all classical correlation functions based on imaging input data. Images include both binary and multi-phase representations. Our code is capable of evaluating two-point probability S2, phase cross-correlation ρij, cluster C2, lineal-path L2, surface-surface Fss, surface-void Fsv, pore-size P and chord-length p distribution functions on both CPU and GPU architectures. Where possible, we presented two types of computations: full correlation map (correlations of each point with other points on the image, that also allows obtaining ensemble averaged CF) and directional correlation functions (currently in major orthogonal and diagonal directions). Such an implementation allowed for the first time to assemble a completely free solution to evaluate correlation functions under any operating system with well documented application programming interface (API). Our package includes automatic tests against analytical solutions that are described in the paper. We measured execution times for all CPU and GPU implementations and as a rule of thumb full correlation maps on GPU are faster than other methods. However, full maps require more RAM and, thus, are limited to available RAM resources. On the other hand, directional CFs are memory efficient and can be evaluated for huge datasets – this way they are the first candidates for structural data compression of feature extraction. The package itself is available through Julia package ecosystem and on GitHub, the latter source also contains documentation and additional helpful resources such as tutorials. We believe that a single powerful computational tool such as ▪ presented in this paper will significantly facilitate the usage of correlation functions in numerous areas of structural description and research of porous materials, as well as in machine learning applications. We also present some examples as applied to ceramic, soil composite and oil-bearing rock samples based on their 3D X-ray tomography and 2D scanning electron microscope images. Finally, we conclude our paper with discussion of possible ways to further improve presented computational framework. Program summaryProgram Title: CorrelationFunctions.jlCPC Library link to program files:https://doi.org/10.17632/6gb9gfm3dw.1Developer's repository link:https://github.com/fatimp/CorrelationFunctions.jlLicensing provisions: MITProgramming language: JuliaSupplementary material: Numerous Jupiter notebooks with examples are available on the GitHub pageNature of problem: Correlation functions are invaluable universal statistical descriptors of structures used in numerous scientific fields such as astronomy, material science, rock and soil physics, hydrology and biology, to name just a handful of examples. While computational approaches are available in the literature for some functions, they are fragmented and are usually implemented in proprietary interpreted languages for CPU architecture alone.Solution method: We contribute an open source and cross-platform solution with well documented API for computation of all classical correlation functions from both 2D and 3D images on CPU and GPU architectures. The package computes correlation functions using two approaches: computation of correlation maps and computation along predefined directions. These two approaches can be thought of as an execution time - memory trade-off, but the choice may also depend on the application. The computations are based on a) fast Fourier transform with preprocessing steps such as cluster labeling or edge detection, and b) linear scan approach to evaluate correlation functions along predefined directions. Where justified, the algorithms can be executed on both CPU and GPU which results in high execution speed on modern hardware.

Quantum cluster equilibrium theory applied to liquid ammonia.

Through this paper, the authors propose using the quantum cluster equilibrium (QCE) theory to reinvestigate ammonia clusters in the liquid phase. The ammonia clusters from size monomer to hexadecamer were considered to simulate the liquid ammonia in this approach. The clusterset used to model the liquid ammonia is an ensemble of different structures of ammonia clusters. After studious research of the representative configurations of ammonia clusters through the cluster research program ABCluster, the configurations have been optimized at the MN15/6-31++G(d,p) level of theory. These optimizations lead to geometries and frequencies as inputs for the Peacemaker code. The QCE study of this molecular system permits us to get the liquid phase populations in a temperature range of 190-260 K, covering the temperatures from the melting point to the boiling point. The results show that the population of liquid ammonia comprises mainly the ammonia hexadecamer followed by pentadecamer, tetradecamer, and tridecamer. We noted that the small-sized ammonia clusters do not contribute to the population of liquid ammonia. In addition, the thermodynamic properties, such as heat of vaporization, heat capacity, entropy, enthalpy, and free energies, obtained by the QCE theory have been compared to the experiment given some relatively good agreements in the gas phase and show considerable discrepancies in liquid phase except the density. Finally, based on the predicted population, we calculated the infrared spectrum of liquid ammonia at 215 K temperature. It comes out that the calculated infrared spectrum qualitatively agrees with the experiment.

Adaptation rules inducing synchronization of heterogeneous Kuramoto oscillator network with triadic couplings.

A class of adaptation functions is found for which a synchronous mode with different number of phase clusters exists in a network of phase oscillators with triadic couplings. This mode is implemented in a fairly wide range of initial conditions and the maximum number of phase clusters is four. The joint influence of coupling strength and adaptation parameters on synchronization in the network has been studied. The desynchronization transition under variation of the adaptation parameter occurs abruptly and begins with the highest-frequency oscillator, spreading hierarchically to all other elements.

Inner-shell photoelectron spectroscopy unveils the interplay between hydrogen bonds and π−π stacking in clusters of biomolecules in the gas phase: hypoxanthine clusters as a case study

We present a joint theoretical-experimental study on the inner-shell photoionization of hypoxanthine clusters in the gas phase. Simulations were performed using a computational strategy that combines molecular dynamics to explore the conformers of the clusters, density functional theory for geometry optimization and inner shell photoionization calculations. Two main intermolecular interactions are observed: hydrogen-bonds (HB) and π−π stacking. When increasing the cluster size, a combination of both kinds of interaction occurs. We show that such intermolecular interactions play a role in the chemical shift observed in X-ray photoemission spectroscopy experiments. In particular, we highlight the interplay between charge depletion and charge accumulation in regions where HBs stabilize the clusters.

Dual interfacing with metallic cobalt boosts the electron shuttle of CdS-carbide nanoassemblies

Harnessing accelerated interfacial redox, thus boosting charge separation, is of great importance in photocatalytic solar hydrogen generation. In effect, nanoassembling non-noble metallic phases in CdS-based systems and elucidating their role in photocatalysis hold the key to eventually boosting electron shuttle in the field. Here we combine an efficient in-situ exsoluted metallic Co0 nanoparticles on a carbides matrix (CMG) with CdS (CdS@CoCMG) for photogeneration of hydrogen. The metallic cobalt phase exhibits strong binding at the CdS-carbide dual interfaces, forming the accelerated “electron converter” mechanism validated by charge transfer kinetics and achieving two orders of magnitude faster hydrogen production (44.42 mmol g−1 h−1) relative to CdS (0.43 mmol g−1 h−1). We propose that the unique catalyst configuration enable the directional electron-relay photocatalysis via harnessing interfaces between Co0 phase, carbides, and CdS clusters, which eventually boosts the redox process and charge separation of the integrated system, leading to high H2 production rates in the suspension.

Alternative splicing of latrophilin-3 controls synapse formation.

The assembly and specification of synapses in the brain is incompletely understood1-3. Latrophilin-3 (encoded by Adgrl3, also known as Lphn3)-a postsynaptic adhesion G-protein-coupled receptor-mediates synapse formation in the hippocampus4 but the mechanisms involved remain unclear. Here we show in mice that LPHN3 organizes synapses through a convergent dual-pathway mechanism: activation of Gαs signalling and recruitment of phase-separated postsynaptic protein scaffolds. We found that cell-type-specific alternative splicing of Lphn3 controls the LPHN3 G-protein-coupling mode, resulting in LPHN3 variants that predominantly signal through Gαs or Gα12/13. CRISPR-mediated manipulation of Lphn3 alternative splicing that shifts LPHN3 from a Gαs- to a Gα12/13-coupled mode impaired synaptic connectivity as severely as the overall deletion of Lphn3, suggesting that Gαs signalling by LPHN3 splice variants mediates synapse formation. Notably, Gαs-coupled, but not Gα12/13-coupled, splice variants of LPHN3 also recruit phase-transitioned postsynaptic protein scaffold condensates, such that these condensates are clustered by binding of presynaptic teneurin and FLRT ligands to LPHN3. Moreover, neuronal activity promotes alternative splicing of the synaptogenic Gαs-coupled variant of LPHN3. Together, these data suggest that activity-dependent alternative splicing of a key synaptic adhesion molecule controls synapse formation by parallel activation of two convergent pathways: Gαs signalling and clustered phase separation of postsynaptic protein scaffolds.

Improved LEACH Protocol Based on Underwater Energy Propagation Model, Parallel Transmission, and Replication Computing for Underwater Acoustic Sensor Networks.

Underwater acoustic sensor networks (UASNs) are critical to a range of applications from oceanographic data collection to submarine surveillance. In these networks, efficient energy management is critical due to the limited power resources of underwater sensors. The LEACH protocol, a popular cluster-based protocol, has been widely used in UASNs to minimize energy consumption. Despite its widespread use, the conventional LEACH protocol faces challenges such as an unoptimized cluster number and low transmission efficiency, which hinder its performance. This paper proposes an improved LEACH protocol for cluster-based UASNs, where the cluster number is optimized with an underwater energy propagation model to reduce energy consumption, and a transmission scheduling algorithm is also employed to achieve conflict-free parallel data transmission. Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and data transmission phases. The simulation results show that the proposed protocol outperforms several conventional methods in terms of normalized average residual energy, average number of surviving nodes, average round when the first death node occurs, and the number of packets received by the base station.

Smart Log Analyzer for Anomaly Detection in Distributed Systems

Abstract—This research work outlines a novel approach to fortify the stability and security of distributed systems through the implementation of an AI-enabled smart log analyzer. The escalating proliferation of distributed systems has led to an unprecedented surge in the volume of generated log data, which holds crucial insights into system performance, security, and dependability. However, the substantial challenges associated with managing this data—such as its overwhelming volume, diverse nature, and real-time processing requirements—have posed significant hurdles. The proposed AI-enabled smart log analyzer, detailed in this report, harnesses the power of advanced machine learning and natural language processing techniques to address these challenges effectively. The methodology is struc- tured into three fundamental phases, namely, Data Preprocessing, Anomaly Detection, and Clustering. The Data Preprocessing phase encompasses the collection, parsing, filtering, and feature extraction of log data. Anomaly Detection integrates machine learning models to discern various anomalies, encompassing irregular access patterns, log flooding, error messages, suspicious content, and outliers in resource requests. The Clustering phase categorizes log entries into meaningful groups based on attributes such as log level, component, event, error code, and resource usage, facilitating a comprehensive understanding of system behavior. This holistic approach holds the promise of significantly enhancing system stability and security. Index Terms—Anomaly Detection, Clustering, Data Prepro- cessing, Distributed Systems, Log analyzer, System security, System stability

Diagnosing awareness in disorders of consciousness with gamma-band auditory responses.

The prolonged disorders of consciousness (pDOC) describe a group of neurological conditions characterized by severe impairment of consciousness resulting from the injury of the central nervous system. As the behavioral diagnosis of pDOC remains challenging, the methods based on observing brain activity appear as promising alternatives. One of these methods is electroencephalography, which allows for noninvasive assessment of brain function. In this study, we evaluated evoked auditory responses to the chirp-modulated auditory stimulation as a potential biomarker of awareness in pDOC. Chirp-modulated stimulation is based on the repetitive presentation of auditory stimuli with a changing frequency over time. Two protocols were tested: amplitude-modulated narrow-band chirps (frequency range 25-55 Hz) and click-based wide-band chirps (30-100 Hz). The studied pDOC patient group included 62 patients (19 females and 43 males, mean age 40.72 years) diagnosed with Coma Recovery Scale-Revised. Envelope-following responses to stimulation were examined using the intertrial phase clustering coefficient. For both types of stimulation, the strength of the response in the low-gamma range (around 40 Hz) was related to the diagnosis of pDOC. Patients diagnosed with unresponsive wakefulness syndrome exhibited diminished responses, while more favorable diagnoses, suggesting awareness (minimally conscious state or emergence from the minimally conscious state), showed elevated responses. The variations in the integrity of the auditory pathway and the etiology of brain injury altered the observed response strength. Narrow-band stimulation yielded a more systematic relationship between low-gamma response and pDOC diagnosis. The results suggest the potential role of low gamma-band responses to chirp-modulated stimulation as the supportive diagnostic tool to detect awareness in the pDOC patient group.

Multiphase Solar Photovoltaic Prediction Model Based on Season, Hierarchical k‐Means Clustering, GRA‐PCC, SVM, and Neural Network

Solar photovoltaic (PV) has accounted for the highest percentage of power generation capacity among other renewables. However, solar PV power generation is highly variable because of different factors; therefore, accurate forecasting is critical for reliable integration into the power system. This paper proposes a multiphase solar PV prediction model that includes grouping, clustering, linking, classifying, and predicting using historical solar PV power and weather data. Seasonal variation is considered in the grouping phase, followed by hybrid hierarchical k‐means clustering to enhance data division in the clustering phase. A hybrid gray relational analysis‐Pearson correlation coefficient identifies significant weather factors impacting solar PV power in the linking phase. The classification phase employs a support vector machine to establish the relationship between the clusters and the relevant weather factors. Lastly, a neural network (NN) is trained to predict solar PV power. The solar PV power profiles are presented to show the variability in season and time. The simulation results of the proposed model showed relatively accurate forecasting results, including MAE of 0.408 MW, MSE of 460.51 MW, RMSE of 0.679 MW, nRMSE of 4.345%, and MRE of 2.266%. These results represent that the uncertainties of the proposed model are 6 and 12 times lower than those of the conventional methods (i.e., conventional NN and ARMAX). These results assure that the proposed model can provide more accurate solar PV power profiles for reliable power system integration.

Phase separation kinetics and cluster dynamics in two-dimensional active dumbbell systems.

Molecular dynamics simulations were employed to investigate the phase separation process of a two-dimensional active Brownian dumbbell model. We evaluated the time dependence of the typical size of the dense component using the scaling properties of the structure factor, along with the averaged number of clusters and their radii of gyration. The growth observed is faster than in active disk models, and this effect is further enhanced under stronger activity. Next, we focused on studying the hexatic order of the clusters. The length associated with the orientational order increases algebraically with time and faster than for spherical active Brownian particles. Under weak active forces, most clusters exhibit a uniform internal orientational order. However, under strong forces, large clusters consist of domains with different orientational orders. We demonstrated that the latter configurations are not stable, and given sufficient time to evolve, they eventually achieve homogeneous configurations as well. No gas bubbles are formed within the clusters, even when there are patches of different hexatic order. Finally, attention was directed towards the geometry and motion of the clusters themselves. By employing a tracking algorithm, we showed that clusters smaller than the typical size at the observation time exhibit regular shapes, while larger ones display fractal characteristics. In between collisions or break-ups, the clusters behave as solid bodies. Their centers of mass undergo circular motion, with radii increasing with the cluster size. The angular velocity of the center of mass equals that of the constituents with respect to their center of mass. These observations were rationalised with a simple mechanical model.

Synchronized rotations of active particles on chemical substrates.

Many microorganisms use chemical 'signaling' - a quintessential self-organizing strategy in non-equilibrium - that can induce spontaneous aggregation and coordinated motion. Using synthetic signaling as a design principle, we construct a minimal model of active Brownian particles (ABPs) having soft repulsive interactions on a chemically quenched patterned substrate. The interplay between chemo-phoretic interactions and activity is numerically investigated for a proposed variant of the Keller-Segel model for chemotaxis. Such competition not only results in a chemo-motility-induced phase-separated state, but also results in a new cohesive clustering phase with synchronized rotations. Our results suggest that rotational order can emerge in systems by virtue of activity and repulsive interactions alone without an explicit alignment interaction. These rotations can also be exploited by designing mechanical devices that can generate reorienting torques using active particles.

Nuclear clustering over the nuclear chart from a covariant EDF approach

Nuclear clustering has been studied with covariant Energy Density Functional approaches for more than a decade. It recently allowed to bridge microscopically the description of cluster states in light nuclei with the one of cluster and alpha decays in heavy ones. The proper use of theoretical tools, such as the density and the nucleonic localization function, allows to shed light on the mechanisms of formation and identification of clusters in nuclei. Finally, a global analysis of nuclear clustering is discussed, in order to identify control parameters for a nuclear cluster phase.

Expeditious Dynamic Clustering in Preprocessing for High‐Performance Classification

The accuracy of classification algorithms across different applications is significantly influenced by data preprocessing. A well‐executed data preprocessing phase can notably enhance the outcomes of classification. This research introduces a swift data preprocessing technique characterized by its simplicity, ease of comprehension, and remarkable efficacy in data classification tasks. The algorithm proposed herein streamlines the process of data comprehension, simultaneously ensuring high efficiency in classification outcomes. Moreover, it demonstrates versatility across diverse large datasets. Termed as expeditious dynamic clustering (EDC), this innovative data preprocessing algorithm amalgamates several conceptual algorithms into a cohesive framework. The approach employs a top‐down data clustering technique, focusing on clusters with ascending values (initial clustering phase of EDC) to determine the optimal number of clusters for each feature. This involves comparing member dissimilarity within the feature set. Subsequently, it integrates low‐frequency clusters into the clustering process dynamically to refine the cluster count. This integration selectively combines clusters based on a low‐frequency criterion value, optimizing the clustering of the dataset. The EDC execution method operates without the need for a computational distance function, thereby revolutionizing the clustering paradigm for high‐performance data classification. To assess the effectiveness of data preprocessing methods employing the novel EDC concept, its performance was evaluated with various data classification algorithms, including neural networks and decision trees. The experiments encompassed large datasets featuring diverse data types and classes, totalling 13 datasets. The results showcased strong classification performance and high accuracy. Furthermore, in addition to EDC data preprocessing, appending it to the classification model alongside the aforementioned two models can facilitate feature selection without compromising classification accuracy.

Classification of dementia from spoken speech using feature selection and the bag of acoustic words model

<p>Memory disorders and dementia are a central factor in the decline of functioning and daily activities in older individuals. The workload related to standardized speech tests in clinical settings has led to a growing emphasis on developing automatic machine learning techniques for analyzing naturally spoken speech. This study presented a bag of acoustic words approach for distinguishing dementia patients from control individuals based on audio speech recordings. In this approach, each individual's speech was segmented into voiced periods, and these segments were characterized by acoustic features using the open-source openSMILE library. Word histogram representations were formed from the characterized speech segments of each speaker, which were used for classifying subjects. The formation of word histograms involved a clustering phase where feature vectors were quantized. It is well-known that partitional clustering involves instability in clustering results due to the selection of starting points, which can cause variability in classification outcomes. This study aimed to address instability by utilizing robust K-spatial-medians clustering, efficient K-means$ ++ $ clustering initialization, and selecting the smallest clustering error from repeated clusterings. Additionally, the study employed feature selection based on the Wilcoxon signed-rank test to achieve computational efficiency in the methods. The results showed that it is possible to achieve a consistent 75% classification accuracy using only twenty-five features, both with the external ADReSS 2020 test data and through leave-one-subject-out cross-validation of the entire dataset. The results rank at the top compared to international research, where the same dataset and only acoustic features have been used to diagnose patients.</p>

From Pain to Progress: Comprehensive Analysis of Musculoskeletal Disorders Worldwide.

Comprehensive analyses of the burden of musculoskeletal disorders (MSDs) are crucial for deepening our understanding of their impact on population health and for evaluating progress toward achieving international health goals. A comprehensive analysis was performed using data from the Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021. We gathered data on the number of prevalent cases, incidences, deaths, and disability-adjusted life years (DALYs) for MSDs from 1990 to 2021. Subsequently, crude indicators, age-standardized rates (ASRs) and estimated annual percentage change (EAPC) were calculated for MSDs from 1990 to 2021 at the global, regional, national, age and gender levels. Finally, decomposition analysis, frontier analysis, and EAPC cluster analysis were employed to examine changes in the indicators and the factors influencing them. In 2021, there were 367,193,430 incident cases and 1,686,561,517 prevalent cases of MSDs globally. Between 1990 and 2021, the age-standardized incidence rates (ASIR) declined marginally from 4641.5 to 4351.79 cases per 100,000 population [EAPC: -0.16 (-0.19 to -0.13)], while the age-standardized DALY rates (ASDR) increased from 1886.2 to 1908.87 per 100,000 population [EAPC: 0.09 (0.07 to 0.11)]. The ASRs were markedly elevated in the high SDI regions in comparison to the remaining SDI regions. The EAPC and its phase clustering analyses indicated a notable upward trajectory in the burden of MSDs in the low and middle SDI regions, particularly in Central Asia. The prevalence of MSDs is highest among individuals aged 50-59 years and among women. At the global level, the burden of MSDs is 1.43 to 2.30 times higher for women than for men. From 1990 to 2021, the burden of MSDs has been concentrated in high SDI regions, with a gradual shift towards low and middle SDI regions.

One-step Multi-view Clustering with Consensus Graph and Data Representation Convolution

Multi-view clustering aims to partition unlabeled patterns into disjoint clusters using consistent and complementary information derived from features of patterns in multiple views. Downstream methods perform this clustering sequentially: estimation of individual or consistent similarity matrices, spectral embedding, and clustering. In this article, we present an approach that can address some of the shortcomings of previous multiview clustering methods. We propose a single objective function whose optimization can jointly provide the consistent graph matrix for all views, the unified spectral data representation, the cluster assignments, and the view weights. We propose a new constraint term that sets the cluster index matrix to the convolution of the consistent spectral projection matrix over the consistent graph. Our proposed scheme has two interesting properties that the recent works do not have simultaneously. First, the cluster assignments can be estimated directly without the need for an additional clustering phase, which depends heavily on initialization. Second, the soft cluster assignments are directly linked to the kernel representation of the features of the views. Moreover, our method automatically computes the weights of each view, requiring fewer hyperparameters. We have conducted a series of experiments on real datasets. These demonstrate the effectiveness of the proposed approach, which compares favorably to many competing multi-view clustering methods.