Cluster Centers Research Articles

Probing the High-frequency Variability of NGC 5044: The Key to Active Galactic Nucleus Feedback

The active galactic nucleus (AGN) feeding and feedback process in the centers of galaxy clusters and groups is still not well understood. NGC 5044 is the ideal system in which to study AGN feedback. It hosts the largest known reservoir of cold gas in any cool-core galaxy group, and features several past epochs of AGN feedback imprinted as cavities in the X-ray-bright intragroup medium, as well as parsec-scale jets. We present Submillimeter Array, Karl G. Jansky Very Large Array, James Clerk Maxwell Telescope, and Atacama Large Millimeter/submillimeter Array high-frequency observations of NGC 5044 to assess the time variability of the millimeter-wave band emission from the accretion disk, and quantify the spectral energy distribution (SED) from the radio to submillimeter band. The SED is well described by an advection-dominated accretion flow (ADAF) model and self-absorbed jet emission from an aging plasma with τ ∼ 1 kyr. We find a characteristic variability timescale of 150 days, which constrains the ADAF emission region to about 0.1 pc, and the magnetic field to ∼4.7 mG in the jets and 870 G in the accretion disk. Longer monitoring/sampling will allow to understand if the underlying process is truly periodic in nature.

A Review on the Application of Automatic Text Summarization and Its Bias Analysis

Abstract. In the era of information explosion, the Internet produces a large number of text documents daily, providing rich information but also posing a challenge: how to swiftly extract key information. Traditional manual reading is time-consuming and inadequate for handling vast data. Thus, automatic text summarization technology emerges as a crucial solution. This paper reviews the application and deviation analysis of this technology across various fields, focusing on addressing shortcomings of traditional methods, such as initial cluster center selection and redundancy. An automatic text summarization method based on an improved TextRank algorithm and K-Means clustering is introduced. Existing methods often struggle with inaccurate initial clustering center selection and high summary redundancy, especially with long texts, resulting in summaries that fail to reflect core content accurately. Furthermore, the widespread use of pre-trained language models introduces potential biases that can propagate to downstream tasks, affecting summary accuracy and impartiality. To address these issues, this paper proposes an innovative automatic text summarization method that optimizes initial clustering center selection and clustering refinement strategies to enhance summary accuracy and readability. Additionally, it discusses name-nationality bias in pre-trained language models and its propagation in text summary tasks, offering a theoretical foundation and practical guidance for developing a more just and reliable Natural Language Processing (NLP) system.

A Young Super Star Cluster Powering a Nebula of Retained Massive Star Ejecta

We suggest that “Godzilla” of the lensed Sunburst galaxy (z = 2.37) is a young super star cluster powering a nebula of gravitationally trapped stellar ejecta. Employing Hubble Space Telescope photometry and spectroscopy from the Very Large Telescope (VLT) MUSE and VLT/X-Shooter, we infer the physical and chemical properties of the cluster and nebula, finding that Godzilla is young, 4–6 Myr; massive, 2 × 106 M ⊙ (1000/μ); of stellar metallicity, Z ≃ 0.25 Z ⊙; and has a compact far-UV component of ≲1 pc (1000/μ), where μ is the flux magnification factor. The gas is significantly enriched with N and He, indicating stellar wind material, and has highly elevated O relative to the stellar metallicity, indicating entrainment of core-collapse supernova (CCSN) ejecta. The high density, n e ≃ 107−8 cm−3, implies a highly pressurized intracluster environment. We propose that the pressure results from CCSN-driven supersonic turbulence in warm, self-shielding gas, which has accumulated in the cluster center after runaway radiative cooling and is dense enough to resist removal by CCSNe. The nebula gas shows subsolar C/O, Ne/O, and Si/O, which may reflect the CCSN element yields for initial stellar masses >40 M ⊙. A comparison to element yield synthesis models for young star clusters shows that the gas abundances are consistent with complete retention and mixture of stellar winds and CCSN ejecta until the inferred cluster age. The inferred O and He enhancement may have implications for the formation of multiple stellar populations in globular clusters, as stars formed from this gas would contradict the observed abundances of second-population stars.

Expansion kinematics of young clusters. I. Lambda Ori

Most stars form in clusters or associations, but only a small number of these groups are expected to remain bound for longer than a few megayears. Once star formation has ended and the molecular gas around young stellar objects has been expelled via feedback processes, most initially bound young clusters lose the majority of their binding mass and begin to disperse into the Galactic field. This process can be investigated by analysing the structure and kinematic trends in nearby young clusters, particularly by analyzing the trend of expansion, which is a tell-tale sign that a cluster is no longer gravitationally bound and dispersing into the field. We combined Gaia DR3 five-parameter astrometry with calibrated RVs for members of the nearby young cluster lambda Ori (Collinder 69). We characterised the plane-of-sky substructure of the cluster using the $Q$-parameter and the angular dispersion parameter. We find evidence that the cluster contains a significant substructure but that this is preferentially located away from the central cluster core, which is smooth and likely remains bound. We found strong evidence for expansion in lambda Ori in the plane of sky by using a number of metrics, but we also found that the trends are asymmetric at the 5sigma significance level, with the maximum rate of expansion being directed nearly parallel to the Galactic plane. We subsequently inverted the maximum rate of expansion of $0.144^ $ kms$^ $pc$^ $ to give an expansion timescale of $6.944^ \:$Myr, which is slightly larger than the typical literature age estimates for the cluster. We also found asymmetry in the velocity dispersion as well as signatures of cluster rotation, and we calculated the kinematic ages for individual cluster members by tracing their motion back in time to their closest approach to the cluster centre.

Mixed Carboxylate Ligands Bridging Tetra-Pr3+-Encapsulated Antimonotungstate: Syntheses, Structure, and Catalytic Activity for Imidazoles Synthesis.

Multinuclear Pr-containing antimonotungstate [Pr4(H2O)10W6O13(mal)2(OAc)(B-α-SbW9O33)4]21- (Pr-1, mal = malate anion, OAc = acetate anion), bridged by organic carboxylic acid, was synthesized through a one-pot assembly reaction and structurally characterized. Pr-1 is composed of four [B-α-SbW9O33]9- fragments fused together by an organic-inorganic hybrid central [Pr4(H2O)10W6O13(mal)2(OAc)]15+ cluster core through 24 μ2-O atoms. Notably, the central cluster comprises unprecedented decanuclear Pr4(H2O)10W6O13 jointly decorated by two types of carboxylic acid ligands. This integration of rare earth-containing antimonotungstate with mixed organic carboxylate ligands is very rare in POMs chemistry. Pr-1 exhibits excellent catalytic activity in the cyclo-condensation reaction involving benzil, aldehyde, and NH4OAc. A series of 2,4,5-trisubstituted imidazoles were synthesized in remarkable yields using iPrOH as a green solvent.

Evaluation Method of Water Injection Development Effects in Fractured Vuggy Carbonate Reservoirs: Case Study of FI7 Strike-Slip Fault Zone

As the main method of secondary development, water injection has been widely used in fractured vuggy carbonate reservoirs. By using an evaluation index system combined with an integrated approach to objectively evaluate and support targeted and operational adjustments of water injection development effects in fractured vuggy carbonate reservoirs, a comprehensive evaluation method is established in this study. The CRITIC method is used as the main approach, and the analytic hierarchy process, the entropy weight method, and the coefficient of variation method are used as sub-methods. Additionally, the clustering centres are divided using the clustering method to reduce the error caused by the irrational distribution of the relevant evaluation index data. The method is used to evaluate and analyse the effects of water injection development, using the FI7 fault zone of the Halahatang oilfield as an example. The application results show that the evaluation method is feasible and effective for cases with small data volume, fewer computational resources, and less time. This study has a certain reference significance for the evaluation of the effect of water injection development in similar fractured vuggy reservoirs.

A Discrete Brain Storm Optimization Algorithm for Hybrid Flowshop Scheduling Problems with Batch Production at Last Stage in the Steelmaking-Refining-Continuous Casting Process.

The iron and steel industry is energy-intensive due to the large volume of steel produced and its high-temperature and high-weight characteristics, sensors such as high-temperature application sensors can be utilized to collect production data and support the process control and optimization. Steelmaking-refining-continuous casting (SRCC) is a bottleneck in the iron and steel production process. SRCC scheduling problems are worldwide problems and NP-hard. The problems are not only important for iron and steel enterprises to enhance production efficiency, but also play a significant role in saving energy and reducing resource consumption. SRCC scheduling problems can be modeled as hybrid flowshop scheduling problems with batch production at the last stage. In this paper, a Discrete Brain Storm Optimization (DBSO) algorithm is proposed to handle SRCC scheduling problems. In the proposed DBSO, population initialization and cluster center replacement are specially designed to enhance the intensification abilities. Moreover, a perturbation operator is devised to enhance its diversification abilities. Furthermore, a new individual generation operator is devised to improve the intensification and diversification abilities simultaneously. Experimental results have demonstrated that the proposed DBSO is an efficient method for solving SRCC scheduling problems.

LUT enabled low-complexity electrical dispersion pre-compensation scheme for IM-DD systems.

Frequency selective fading caused by chromatic dispersion (CD) and direct detection is the primary impediment for IM-DD systems in achieving a large capacity-distance product. Gerchberg-Saxton (GS) based electrical dispersion pre-compensation (pre-EDC) schemes enhance IM-DD systems' robustness against chromatic dispersion (CD), but their high computational complexity limits practical implementation. In this Letter, a novel, to the best of our knowledge, low-complexity pre-EDC scheme enabled by the cluster-assisting look-up table (CLUT) technique is proposed to further reduce the computation complexity of the non-iterative FIR-based pre-EDC scheme, in which convolution operation is replaced by tables look-up and accumulation processing. And a key strategy is proposed to balance the number and the total size of the LUTs by establishing LUTs for each cluster center after clustering the tap weights. To validate the effectiveness of the proposed scheme, we experimentally demonstrate the transmission of 80 Gbit/s PAM-4 signals and 100 Gbit/s PAM-6 signals over 20 km standard single-mode fiber (SSMF). The experimental results show that the proposed scheme can eliminate the multiplication in convolution operations and reduce the addition times of 58.54% for PAM-4 and 51.22% for PAM-6 signals at the BER of 7% HD-FEC threshold without any penalty.

A Novel k-Means Framework via Constrained Relaxation and Spectral Rotation.

Owing to its simplicity, the traditional k - means (Lloyd heuristic) clustering method plays a vital role in a variety of machine-learning applications. Disappointingly, the Lloyd heuristic is prone to local minima. In this article, we propose k - mRSR, which converts the sum-of-squared error (SSE) (Lloyd) into a combinatorial optimization problem and incorporates a relaxed trace maximization term and an improved spectral rotation term. The main advantage of k - mRSR is that it only needs to solve the membership matrix instead of computing the cluster centers in each iteration. Furthermore, we present a nonredundant coordinate descent method that brings the discrete solution infinitely close to the scaled partition matrix. Two novel findings from the experiments are that k - mRSR can further decrease (increase) the objective function values of the k - means obtained by Lloyd (CD), while Lloyd (CD) cannot decrease (increase) the objective function obtained by k - mRSR. In addition, the results of extensive experiments on 15 datasets indicate that k - mRSR outperforms both Lloyd and CD in terms of the objective function value and outperforms other state-of-the-art methods in terms of clustering performance.

Artificial Algae Algorithm for Clustering of Benchmark Datasets

The best solution found for a problem under specific circumstances is called optimization. Algorithms for optimization can make the best use of the information at their disposal. Numerous optimization algorithms have been created thus far by researchers, and most of these algorithms are based on the characteristics of naturally occurring biological organisms. Optimization algorithms have proven to be highly effective in numerous fields, including finance, engineering, and medical. Apart from these applications, they have also been employed in data mining techniques including clustering and classification. In many different domains, the clustering method is widely applied. Finding the optimum cluster centers is the most crucial step in the clustering process. In this study, the Artificial Algae Algorithm (AAA) is used to perform the clustering procedure by using 12 datasets that were taken from the UCI Machine Learning Repository. For every dataset, the squared distance values between the cluster centers and the data were computed in order to assess the effectiveness of AAA. The study evaluated AAA's performance against that of the ALO, DEA, MFO, PSO, TSA, and WOA algorithms. The clustering performance of AAA on benchmark datasets was measured to be better than the performance of other algorithms.

Performance degradation assessment of rolling bearings for electrostatic monitoring based on IDDAE and ADPC

Abstract To improve the early degradation detection capability of the electrostatic monitoring system for rolling bearings, a performance degradation evaluation method based on improved deep denoising autoencoder and adaptive density peak clustering (ADPC) is proposed in this paper. Firstly, the fusion of electrostatic charge signal features with conventional time-domain, frequency-domain and time–frequency-domain features constitutes the characteristic parameter set of the electrostatic monitoring system indicating the status of the bearings. Then, in order to improve the feature extraction ability of DAE, the deep network DDAE is constructed, and L1 regularisation and Dropout mechanism are applied to avoid overfitting in the deep network, so as to achieve non-linear mapping dimensionality reduction of high-dimensional features. Moreover, to eliminate the error caused by manually selecting the clustering centre, the parameters are adaptively determined by entropy value method and comprehensive optimisation search on the basis of DPC, thus avoiding the ‘chain effect’ that occurs in traditional DPC when data are incorrectly aggregated due to incorrect assignment of clustering centres. Consequently, an improved ADPC algorithm is used to establish a model to measure the health status of bearings, calculate the Mahalanobis distance (MD) between the test set and the cluster centre, and quantitatively characterize the degree of performance degradation of rolling bearings. Finally, combining the 3 δ principle, a repair method that can satisfy online monitoring and adapt to spurious fluctuations in different situations is established on a sliding window to obtain an improved index IMD that can accurately characterise the rolling bearing degradation process. The experimental results show that the proposed method can identify early bearing degradation earlier and has better monotonicity, robustness and tendency than other performance degradation assessment methods.

Multi-Dimensional Fuzzy Clustering-Based Trajectory Initialization Algorithm for Infrared Weak Target Trajectories in Robust Clutter Environments

When conducting maneuver target tracking, trajectory initialization plays a crucial role in enhancing the accuracy of tracking algorithms. During maneuver target tracking, the accuracy of the tracking algorithm can be significantly improved through trajectory initialization. However, the traditional trajectory initialization algorithms face issues such as susceptibility to noise interference, lack of universality, and poor robustness in environments with high clutter levels. To address these issues, this study proposes a trajectory initialization algorithm based on multidimensional fuzzy clustering (MDF-clustering). The algorithm utilizes multidimensional feature information of the target, such as speed and irradiance, to determine point trajectory affiliation by assigning weights based on the clustering center of each feature type. Subsequently, it updates the clustering center and weight assignment using the new target features, ultimately deriving the correct trajectory through iterative processes. Experimental results demonstrate that the proposed method achieves an average stable initialization frame number of 3.12 frames, an average correct trajectory initialization rate of 99.59%, an average false trajectory occupancy rate of 0.04%, and an average missed batch rate of 0.06%. These results indicate improvements of at least 0.87 frames, 27.11%, 60.28%, and 6.48%, respectively, in terms of initialization rate, false trajectory rate, and missed batch rate, when compared to traditional methods. The algorithm enhances the accuracy and robustness of trajectory initialization in challenging environments characterized by solid clutter and target maneuvers, offering significant practical value for target tracking in complex scenarios.

Clustering by detecting skeletal structure and identifying density fluctuation

Clustering is one of the most important techniques for unsupervised learning, it tries to divide points into different clusters without any priori knowledge of data. Therefore, several criterions for clustering algorithm are as follows: 1. Handling clusters with arbitrary shape and various density; 2. Finding cluster centers automatically; 3. Low parameter sensitivity and computational complexity. In this context, a novel algorithm namely clustering by detecting skeletal structure and identifying density fluctuation (CSSDF) was presented. In CSSDF, an efficient strategy based on density and local information of neighborhood is firstly proposed to detect the skeletal structure, which can collect the local information and identify the rough distribution of data. With the identified distribution information, a method takes expanded neighborhood and density fluctuation into consideration is proposed to further collect global information of data, which can assign all skeleton points and find cluster centers. To sum up, CSSDF can not only discover the underlying structure of data regardless of its’ distribution, but also ensure the correct assignment of all skeleton points and thus lead to a satisfying clustering performance. In addition, the computational complexity of the proposed approach is O(nlogn), which makes it possible to deal with some large clustering problem.

Unmanned aerial vehicle image stitching based on multi‐region segmentation

AbstractUnmanned aerial vehicle (UAV) image stitching is a key technology for aerial remote sensing applications. Most existing image stitching methods based on optimal seamline searching algorithms can eliminate defects such as ghosting and distortion in stitched images, which unfortunately suffer from the problem that the seamline may cross those regions with significant geometric misalignment between different images. Therefore, a novel image stitching method based on multi‐region image segmentation is proposed. The algorithm starts by performing a multi‐scale morphological reconstruction in the overlapping regions between UAV images to obtain superpixel images with precise contours. Then, the fast density peaks clustering based on K‐nearest neighbours is applied to automatically determine the clustering centres and the number of clusters. By constructing a cost function, an energy map is generated in the overlapping regions between UAV images. Finally, the optimal seamline can be determined with a graph‐cut method. Compared to several popular image stitching algorithms in real experiments, the proposed method can essentially prevent the seamline from crossing significant ground objects to ensure the integrity of structural objects while achieving satisfactory accuracy and efficiency during the UAV image stitching process.

Electromagnetic signatures of black hole clusters in the center of super-Eddington galaxies

Supermassive black holes (SMBHs) at the centers of active galaxies are fed by accretion disks that radiate from the infrared or optical to the X-ray bands. Several types of objects can orbit SMBHs, including massive stars, neutron stars, clouds from the broad- and narrow-line regions, and X-ray binaries. Isolated black holes with a stellar origin (BHs of ∼10 M⊙) should also be present in large numbers within the central parsec of the galaxies. These BHs are expected to form a cluster around the SMBH as a result of the enhanced star formation rate in the inner galactic region and the BH migration caused by gravitational dynamical friction. However, except for occasional microlensing effects on background stars or gravitational waves from binary BH mergers, the presence of a BH population is hard to verify. In this paper, we explore the possibility of detecting electromagnetic signatures of a central cluster of BHs when the accretion rate onto the central SMBH is greater than the Eddington rate. In these supercritical systems, the accretion disk launches powerful winds that interact with the objects orbiting the SMBH. Isolated BHs can capture matter from this dense wind, leading to the formation of small accretion disks around them. If jets are produced in these single microquasars, they could be sites of particle acceleration to relativistic energies. These particles in turn are expected to cool by various radiative processes. Therefore, the wind of the SMBH might illuminate the BHs through the production of both thermal and nonthermal radiation.

Self-supervised based clustering for retinal optical coherence tomography images.

In response to the inadequacy of manual analysis in meeting the rising demand for retinal optical coherence tomography (OCT) images, a self-supervised learning-based clustering model was implemented. A public dataset was utilized, with 83,484 OCT images with categories of choroidal neovascularization (CNV), diabetic macular edema (DME), drusen, and normal fundus. This study employed the Semantic Pseudo Labeling for Image Clustering (SPICE) framework, a self-supervised learning-based method, to cluster unlabeled OCT images into binary and four categories, and the performances were compared with baseline models. We also analysed feature distribution using t-SNE, and explored the cluster centers, attention maps, and misclassified images. In addition, DME and CNV subsets were clustered binarily, and the results were interpreted by two retinal specialists. SPICE demonstrated superior performance in binary and four categories classification tasks, achieving the accuracy of 0.886 and 0.846, respectively. In t-SNE analysis, the four types exhibited significant clustering into distinct groups. The cluster centers corresponded to the human labels, and the heat map revealed that the model focused on important biomarkers. The misclassified images exposed similar features to the inaccurate classes. The model also grouped DME and CNV into two distinct categories respectively. Self-supervised clustering effectively distinguished disease variances and revealed common features, with a notable capability to detect disease heterogeneity through biomarkers.

Modeling and Subtracting Diffuse Cluster Light in JWST Images: A Relation between the Spatial Distribution of Globular Clusters, Dwarf Galaxies, and Intracluster Light in the Lensing Cluster SMACS 0723

We present a methodology for modeling and removing light from cluster galaxies and intracluster light (ICL) from James Webb Space Telescope images of gravitational lensing clusters. We apply our method to Webb’s First Deep Field the SMACS 0723 Early Release Observations and use the ICL-subtracted images to select a sample of globular clusters (GCs) and dwarf galaxies within the cluster. We compare the spatial distributions of these two samples with our models of the galaxy and ICL light, finding significant similarities. In particular, we find that GCs trace the diffuse ICL, while dwarf galaxies are centrally concentrated near the cluster center We quantify the relationship between the surface density of compact sources and total cluster light, demonstrating a significant, tight correlation. We repeat our methodology and compare distributions of GCs with the dark matter surface density and find a comparable result. Our findings suggest a common origin for GCs and diffuse ICL, with stripping from massive galaxies as they merge with the cluster being a plausible scenario.

Multivariate Cluster Point Process to Quantify and Explore Multi-Entity Configurations: Application to Biofilm Image Data.

Clusters of similar or dissimilar objects are encountered in many fields. Frequently used approaches treat each cluster's central object as latent. Yet, often objects of one or more types cluster around objects of another type. Such arrangements are common in biomedical images of cells, in which nearby cell types likely interact. Quantifying spatial relationships may elucidate biological mechanisms. Parent-offspring statistical frameworks can be usefully applied even when central objects ("parents") differ from peripheral ones ("offspring"). We propose the novel multivariate cluster point process (MCPP) to quantify multi-object (e.g., multi-cellular) arrangements. Unlike commonly used approaches, the MCPP exploits locations of the central parent object in clusters. It accounts for possibly multilayered, multivariate clustering. The model formulation requires specification of which object types function as cluster centers and which reside peripherally. If such information is unknown, the relative roles of object types may be explored by comparing fit of different models via the deviance information criterion (DIC). In simulated data, we compared a series of models' DIC; the MCPP correctly identified simulated relationships. It also produced more accurate and precise parameter estimates than the classical univariate Neyman-Scott process model. We also used the MCPP to quantify proposed configurations and explore new ones in human dental plaque biofilm image data. MCPP models quantified simultaneous clustering of Streptococcus and Porphyromonas around Corynebacterium and of Pasteurellaceae around Streptococcus and successfully captured hypothesized structures for all taxa. Further exploration suggested the presence of clustering between Fusobacterium and Leptotrichia, a previously unreported relationship.

Fully convolutional networks-based particle distribution analysis at multiphase interfaces

The intricate interplay between droplet dynamics and particle characteristics has led to the extensive use of particle-laden droplets in diverse applications, spanning industrial engineering and scientific research. Accurate particle distribution analysis at multiphase interfaces is crucial for understanding the complex behaviors of these soft matters, for example, liquid marbles (LMs). However, traditional image analysis methods for particle-laden interfaces often rely on manual processing, which is both time-consuming and susceptible to human error. In this study, we present a pixel-level image recognition technique based on fully convolutional networks (FCNs) to automatically analyze the interfacial particle distribution of LMs exhibiting clear core-shell structures. The FCNs-based method enables efficient and accurate segmentation of target particles on sessile LMs, utilizing experimental data obtained from an in-situ optical imaging device. This approach significantly expedites the analysis of particle distribution, including proportion, concentration, and cluster centers. We assess the performance of the proposed method using image data of monolayer LMs encapsulated by dispersed polystyrene microspheres, demonstrating its superiority over traditional techniques in terms of accuracy and generalization. This investigation aims to offer novel insights into determining pertinent particulate characteristics within similar solid-liquid hybrid microsystems.

Tscluster: A python package for the optimal temporal clustering framework

Temporal clustering extends the conventional task of data clustering by grouping time series data according to shared temporal trends across sociospatial units, with diverse applications in the social sciences, especially urban science. The two dominant methods are as follows: Time Series Clustering (TSC), with dynamic cluster centres but static labels for each entity, and Sequence Label Analysis (SLA), with static cluster centres but dynamic labels. To implement the universe of models spanning the design space between TSC and SLA, we present tscluster, an open-source Python framework. tscluster offers: (1) several innovative techniques, such as Bounded Dynamic Clustering (BDC), that are not available in existing libraries, allowing users to set an upper bound on the number of label changes and identify the most dynamically evolving time series; (2) a user-friendly interface for applying and comparing these methods; (3) globally optimal solutions for the clustering objective by employing a mixed-integer linear programming formulation, enhancing the reproducibility and robustness of the results in contrast to existing methods based on initialization-sensitive local optimization; and (4) a suite of visualization tools for interpretability and comparison of clustering results. We present our framework using a case study of neighbourhood change in Toronto, comparing two methods available in tscluster. Supplemental materials provide an additional case study of local business development in Chicago and a detailed mathematical exposition of our framework. tscluster can be installed via PyPI (pypi.org/project/tscluster), and the source code is accessible on Github (github.com/tscluster-project/tscluster). Documentation is available online at the tscluster website (tscluster.readthedocs.io).