Weak Clustering Research Articles

Euclid. I. Overview of the Euclid mission

The current standard model of cosmology successfully describes a variety of measurements, but the nature of its main ingredients, dark matter and dark energy, remains unknown. is a medium-class mission in the Cosmic Vision 2015--2025 programme of the European Space Agency (ESA) that will provide high-resolution optical imaging, as well as near-infrared imaging and spectroscopy, over about 14\,000\,deg$^2$ of extragalactic sky. In addition to accurate weak lensing and clustering measurements that probe structure formation over half of the age of the Universe, its primary probes for cosmology, these exquisite data will enable a wide range of science. This paper provides a high-level overview of the mission, summarising the survey characteristics, the various data-processing steps, and data products. We also highlight the main science objectives and expected performance.

GENETIC DIVERSITY AND POPULATION STRUCTURE OF JUNIPERUS SERAVSCHANICA KOM. IN CENTRAL ASIA REVEALED BY MICROSATELLITE MARKERS

Juniperus seravschanica Kom. is a widely distributed and significant tree species in Central Asia, thriving in the mountain ranges from Oman to Uzbekistan, Kyrgyzstan, and Kazakhstan. It plays a vital role in forming shrub-forest massifs in mountainous regions, contributing to soil drainage and stabilization at medium to high altitudes. To gain a detailed understanding of the current status of J. seravschanica and to develop effective conservation strategies, a comprehensive study of the species' genetic diversity and population structure was conducted. Leaf samples were collected from 15 J. seravschanica populations across Uzbekistan, Kyrgyzstan, and Kazakhstan to assess genetic diversity and population structure. Using 11 polymorphic simple sequence repeat (SSR) markers, genetic diversity parameters such as the number of alleles, the number of effective alleles, the percentage of polymorphic loci, and Nei's genetic diversity index were evaluated. Nei's genetic diversity index for J. seravschanica populations averaged 0.450, ranging from 0.407 to 0.566. The AMOVA revealed that 90.3% of the total genetic variation was within populations, indicating a high level of genetic diversity within each population. Gene flow, calculated using alleles from all populations, was estimated at 4.654. Population structure analysis showed weak clustering among the studied populations, supporting the AMOVA findings. This study provides valuable insights into the genetic diversity and population structure of J. seravschanica in Central Asia. The high genetic diversity within populations suggests a strong adaptive capacity to environmental changes. These findings can be effectively utilized to develop conservation strategies for J. seravschanica, ensuring the long-term sustainability of this important tree species in Central Asia. This work was supported by Grant No.AP09259027 of the Science Committee of the Ministry of Science and Higher Education of the Republic of Kazakhstan.

Euclid preparation

Precise and accurate mass calibration is required to exploit galaxy clusters as astrophysical and cosmological probes in the Euclid era. Systematic errors in lensing signals by galaxy clusters can be empirically estimated by comparing different surveys with independent and uncorrelated systematics. To assess the robustness of the lensing results to systematic errors, we carried out end-to-end tests across different data sets. We performed a unified analysis at the catalogue level by leveraging the Euclid combined cluster and weak-lensing pipeline (COMB-CL). Notably, COMB-CL will measure weak lensing cluster masses for the Euclid Survey. Heterogeneous data sets from five recent, independent lensing surveys (CHFTLenS, DES SV1, HSC-SSP S16a, KiDS DR4, and RCSLenS), which exploited different shear and photometric redshift estimation algorithms, were analysed with a consistent pipeline under the same model assumptions. We performed a comparison of the amplitude of the reduced excess surface density and of the mass estimates using lenses from the Planck PSZ2 and SDSS redMaPPer cluster samples. Mass estimates agree with the results in the literature collected in the LC2 catalogues. Mass accuracy was further investigated considering the AMICO-detected clusters in the HSC-SSP XXL-North field. The consistency of the data sets was tested using our unified analysis framework. We found agreement between independent surveys at the level of systematic noise in Stage-III surveys or precursors. This indicates successful control over systematics. If this control continues into Stage IV, Euclid will be able to measure the weak lensing masses of around 13 000 (considering shot noise only) or 3000 (noise from shape and large-scale-structure) massive clusters with a signal-to-noise ratio greater than three.

Quantum wetting transition in the cluster Ising model

The wetting transition in the one-dimensional cluster Ising model with opposite boundary fields is studied. Tuning one boundary field while fixing another leads to the occurrence of phase transitions, where the transition points depend on the cluster coupling. Furthermore, the phase diagram is divided into three regions with different phase transition. For weak and strong cluster coupling, the phase transition is continuous and belongs to the same universality of transverse Ising model with boundary fields. For intermediate cluster coupling, the phase transition is first order. In the strong cluster coupling region, the critical region becomes exponentially small and the asymptotic behavior is absent even for lattice size up to 104. A numerical method to solve the energy gap and the correlation length is proposed on an infinite long spin chain. With this method, one can get the critical behavior as close to the critical point as possible provided that the numerical accuracy is high enough. In the light of this method, we clearly show that there is a preasymptotic regime in which the apparent critical exponents depend on the cluster coupling. Moreover, we obtain the accurate energy gap exponent zν and the correlation length exponent ν in the asymptotic critical region.

Asymmetric population promotes and jeopardizes cooperation in spatial prisoner’s dilemma game

The evolution of cooperation in asymmetric populations has recently garnered widespread attention. In this study, we propose a new asymmetric model to explore the impact of asymmetric behavior on cooperation evolution. Our model assumes that weak players are always forced to cooperate when interacting with strong players, while strong players always receive the temptation payoff. When encountering peers from the same category, players engage in a prisoner’s dilemma game, differing only in the intensity of the dilemma between population categories. Results suggest that in scenarios where traditional network reciprocity can support cooperation, asymmetric interactions between strong and weak players undermine this reciprocity and jeopardize the maintenance of cooperation. Conversely, in scenarios where traditional network reciprocity fails to support cooperation, asymmetric interactions enable strong players to leverage weak cooperative clusters to establish cooperation. However, as the prevalence of asymmetric interactions increases, it becomes detrimental to cooperation within the weak player subgroups, thus undermining overall cooperation. These findings contribute to a deeper understanding of how asymmetry impacts cooperation.

Accretion of warm chondrules in weakly metamorphosed ordinary chondrites and their subsequent reprocessing

To better understand chondrite accretion and subsequent processes, the textures, crystallography, deformation, and compositions of some chondrite constituents in ten lithologies of different cluster texture strength were studied in seven weakly metamorphosed (Type 3) and variably shocked ordinary chondrites (Ragland—LL3 S1, Tieschitz—H/L3 S1, NWA 5421—LL3 S2, NWA 5205—LL3 S2, NWA 11905—LL3-5 S3, NWA 5781—LL3 S3, NWA 11351—LL3-6 S4) using optical and electron microscopy and microtomography techniques.Results support a four-stage model for chondrite formation. This includes 1) limited annealing following collisions during chondrule crystallization and rapid cooling in space prior to accretion, as evidenced by olivine microstructures consistent with dislocation recovery and diffusion; 2) initial accretion of still-warm chondrules into aggregates at an effective chondrite accretion temperature of ∼900-950 °C with nearly in situ impingement deformation between adjacent chondrules in strongly clustered lithologies (NWA 5781, Tieschitz, NWA 5421, NWA 5205 Lithology A), as evidenced by intragranular lattice distortions in olivine consistent with high-temperature slip systems, and by evidence that some olivine-rich objects in Tieschitz accreted while partly molten; 3) syn- or post-accretion bleaching of chondrule mesostases, which transferred feldspathic chondrule mesostasis to an interchondrule glass deposit found in strongly clustered lithologies, as evidenced by chemical data and textures; and 4) post-bleaching weak or strong shocks that resulted in destruction of interchondrule glass and some combination of brecciation, foliation of metal and sulfide, and melting and shock-overprinting effects, as evidenced by poor cluster textures and presence of clastic texture, alignment of metal and sulfide grains caused by shock compression, presence of impact-generated glass, and changes in olivine slip systems. The data support the model of Metzler (2012), who suggested that chondrules in ordinary chondrites accreted while still warm to form cluster chondrite textures as a “primary accretionary rock” (our Stage 2), and that subsequent brecciation destroyed this texture to create chondrites with weak cluster texture (our Stage 4).

Genetic diversity and population structure analysis of a diverse panel of pea (Pisum sativum).

Breeding resilient cultivars with increased tolerance to environmental stress and enhanced resistance to pests and diseases demands pre-breeding efforts that include understanding genetic diversity. This study aimed to evaluate the genetic diversity and population structure of 265 pea accessions. The diversity arrays technology (DArT) genotyping method was employed to identify single-nucleotide polymorphisms (SNPs) and silico markers. After stringent filtering, 6966 SNP and 8,454 silico markers were selected for diversity analysis. Genetic diversity was estimated by grouping accessions based on plant material type, geographic origin, growth habit, and seed color. Generally, diversity estimations obtained using SNPs were similar to those estimated using silico markers. The polymorphism information content (PIC) of the SNP markers ranged from 0.0 to 0.5, with a quarter of them displaying PIC values exceeding 0.4, making them highly informative. Analysis based on plant material type revealed narrow observed heterozygosity (Ho = 0.02-0.03) and expected heterozygosity (He = 0.26-0.31), with landrace accessions exhibiting the highest diversity. Geographic origin-based diversity analysis revealed Ho = 0.02-0.03 and He = 0.22 to 0.30, with European accessions showing the greatest diversity. Moreover, private alleles unique to landrace (4) and European (22) accessions were also identified, which merit further investigation for their potential association with desirable traits. The analysis of molecular variance revealed a highly significant genetic differentiation among accession groups classified by seed color, growth habit, plant material types, and geographic origin (p < 0.01). Principal coordinate analysis and neighbor-joining cluster analysis revealed weak clustering of accessions at different grouping levels. This study underscores the significance of genetic diversity in pea collections, offering valuable insights for targeted breeding and conservation efforts. By leveraging genomic data and exploring untapped genetic resources, pea breeding programs can be fortified to ensure sustainable plant protein production and address future challenges in agriculture.

Probing interacting dark sector models with future weak lensing-informed galaxy cluster abundance constraints from SPT-3G and CMB-S4

Probing interacting dark sector models with future weak lensing-informed galaxy cluster abundance constraints from SPT-3G and CMB-S4

Enhancing Ensemble Clustering with Adaptive High-Order Topological Weights

Ensemble clustering learns more accurate consensus results from a set of weak base clustering results. This technique is more challenging than other clustering algorithms due to the base clustering result set's randomness and the inaccessibility of data features. Existing ensemble clustering methods rely on the Co-association (CA) matrix quality but lack the capability to handle missing connections in base clustering. Inspired by the neighborhood high-order and topological similarity theories, this paper proposes a topological ensemble model based on high-order information. Specifically, this paper compensates for missing connections by mining neighborhood high-order connection information in the CA matrix and learning optimal connections with adaptive weights. Afterward, the learned excellent connections are embedded into topology learning to capture the topology of the base clustering. Finally, we incorporate adaptive high-order connection representation and topology learning into a unified learning framework. To our knowledge, this is the first ensemble clustering work based on topological similarity and high-order connectivity relations. Extensive experiments on multiple datasets demonstrate the effectiveness of the proposed method. The source code of the proposed approach is available at https://github.com/ltyong/awec.

Spatial and seasonal variation in disinfection byproducts concentrations in a rural public drinking water system: A case study of Martin County, Kentucky, USA.

To increase our understanding of the factors that influence formation of disinfection byproducts (DBPs) in rural drinking systems, we investigated the spatial and seasonal variation in trihalomethane (THM) and haloacetic acid (HAA) concentrations in relation to various chemical and physical variables in a rural public drinking water system in Martin County, Kentucky, USA. We collected drinking water samples from 97 individual homes over the course of one year and analyzed them for temperature, electrical conductivity, pH, free chlorine, total chlorine, THMs (chloroform, bromodichloromethane, dibromochloromethane, dichlorobromomethane, and bromoform) and HAAs (monochloroacetic acid, dichloroacetic acid, trichloroacetic acid, bromoacetic acid, and dibromoacetic acid). Spatial autocorrelation analysis showed only weak overall clustering for HAA concentrations and none for THMs. The relationship between modeled water age and TTHM or HAA5 concentrations varied seasonally. In contrast, there was strong variation for both HAA and THMs, with concentrations of HAA peaking in mid-summer and THMs peaking in early fall. Multiple regression analysis revealed that THM concentrations were strongly correlated with conductivity, while HAA concentrations were more strongly correlated with water temperature. Individual DBP species that only contained chlorine halogen groups were strongly correlated with temperature, while compounds containing bromine were more strongly correlated with conductivity. Further investigation revealed that increased drinking water conductivity associated with low discharge of the Tug Fork River, the source water, is highly correlated with increased concentrations of bromide. Discharge and conductivity of the Tug Fork River changed dramatically through the year contributing to a seasonal peak in bromide concentrations in the late summer and early fall and appeared to be a driver of brominated THM concentrations. Brominated DBPs tend to have higher toxicity than DBPs containing only chlorine, therefore this study provides important insight into the seasonal factors driving risk from exposure to DBPs in rural drinking water systems impacted by bromide.

Machine learning of public sentiments towards wind energy in Norway

AbstractAcross Europe, negative public opinion has and may continue to limit the deployment of renewable energy infrastructure required for the transition to net‐zero energy systems. Understanding public sentiment and its spatio‐temporal variations is as such important for decision‐making and for developing socially accepted energy systems. In this study, we apply a sentiment classification model based on a machine learning framework for natural language processing, NorBERT, on data collected from Twitter between 2006 and 2022 to analyse the case of wind power opposition in Norway. From the 68,828 tweets with geospatial information, we show how discussions about wind power intensified in 2018/2019 together with a trend of more negative tweets up until 2020, both on a regional level and for Norway as a whole. Furthermore, we find weak geographical clustering in our data, indicating that discussions are country wide and not dominated by specific regional events or developments. Twitter data allow for detailed insight into the temporal nature of public sentiments and extending this research to additional case studies of technologies, countries and sources of data (e.g. newspapers, other social media) may prove important to complement traditional survey research and the understanding of public sentiment.

Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks

The advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data rates and ultra-reliable, low-latency communications. While machine learning-based approaches have been devised to facilitate D2D communication in 5 G, ensuring robust connectivity and optimal network throughput remains a persisting challenge. Therefore, the bagging ensemble mean-shift Gaussian kernelized clustering-based D2D Connectivity (BEMSGKC-DC) algorithm is designed to enhance device-to-device communication in 5 G Cellular Networks with better packet delivery ratio and throughput. The proposed BEMSGKC-DC algorithm is proposed to enable massive device connectivity while improving the overall network performance. This can be achieved by employing bootstrap aggregating clustering where it clusters the devices via forming a set of base clusters i.e., Mean-Shift Gaussian kernelized clustering algorithm. To further enhance the performance of the algorithm, this paper also computes the resources of the devices in the clustering process using the Gaussian kernel function. All the weak cluster outcomes are merged to acquire the final clustering output with lower error. Through the resultant clustering of the device, the connectivity between D2D is ensured and thereby improves the data communication in the 5 G network. The result demonstrates that the BEMSGKC-DC algorithm optimizes the connectivity with lower energy, latency, and higher data delivery ratio and packet loss rate than the conventional algorithms.

Constructing Interfacial Oxygen Vacancy and Ruthenium Lewis Acid–Base Pairs to Boost the Alkaline Hydrogen Evolution Reaction Kinetics

AbstractSimultaneous optimization of the energy level of water dissociation, hydrogen and hydroxide desorption is the key to achieving fast kinetics for the alkaline hydrogen evolution reaction (HER). Herein, the well‐dispersed Ru clusters on the surface of amorphous/crystalline CeO2‐δ (Ru/ac‐CeO2‐δ) is demonstrated to be an excellent electrocatalyst for significantly boosting the alkaline HER kinetics owing to the presence of unique oxygen vacancy (VO) and Ru Lewis acid–base pairs (LABPs). The representative Ru/ac‐CeO2‐δ exhibits an outstanding mass activity of 7180 mA mgRu−1 that is approximately 9 times higher than that of commercial Pt/C at the potential of −0.1 V (V vs RHE) and an extremely low overpotential of 21.2 mV at a geometric current density of 10 mA cm−2. Experimental and theoretical studies reveal that the VO as Lewis acid sites facilitate the adsorption of H2O and cleavage of H‐OH bonds, meanwhile, the weak Lewis basic Ru clusters favor for the hydrogen desorption. Importantly, the desorption of OH from VO sites is accelerated via a water‐assisted proton exchange pathway, and thus boost the kinetics of alkaline HER. This study sheds new light on the design of high‐efficiency electrocatalysts with LABPs for the enhanced alkaline HER.

Constructing Interfacial Oxygen Vacancy and Ruthenium Lewis Acid-Base Pairs to Boost the Alkaline Hydrogen Evolution Reaction Kinetics.

Simultaneous optimization of the energy level of water dissociation, hydrogen and hydroxide desorption is the key to achieving fast kinetics for the alkaline hydrogen evolution reaction (HER). Herein, the well-dispersed Ru clusters on the surface of amorphous/crystalline CeO2-δ (Ru/ac-CeO2-δ ) is demonstrated to be an excellent electrocatalyst for significantly boosting the alkaline HER kinetics owing to the presence of unique oxygen vacancy (VO ) and Ru Lewis acid-base pairs (LABPs). The representative Ru/ac-CeO2-δ exhibits an outstanding mass activity of 7180 mA mgRu -1 that is approximately 9 times higher than that of commercial Pt/C at the potential of -0.1 V (V vs RHE) and an extremely low overpotential of 21.2 mV at a geometric current density of 10 mA cm-2 . Experimental and theoretical studies reveal that the VO as Lewis acid sites facilitate the adsorption of H2 O and cleavage of H-OH bonds, meanwhile, the weak Lewis basic Ru clusters favor for the hydrogen desorption. Importantly, the desorption of OH from VO sites is accelerated via a water-assisted proton exchange pathway, and thus boost the kinetics of alkaline HER. This study sheds new light on the design of high-efficiency electrocatalysts with LABPs for the enhanced alkaline HER.

Almost Everywhere Ergodicity in Quantum Lattice Models

We rigorously examine, in generality, the ergodic properties of quantum lattice models with short range interactions, in the C^* algebra formulation of statistical mechanics. Ergodicity results, in the context of group actions on C^* algebras, assume that the algebra is asymptotically abelian, which is not generically the case for time evolution. The Lieb-Robinson bound tells us that, in a precise sense, the spatial extent of any time-evolved local operator grows linearly with time. This means that the algebra of observables is asymptotically abelian in a space-like region, and implies a form of ergodicity outside the light-cone. But what happens within it? We show that the long-time limit of the n-th moment of a ray-averaged observable, along space-time rays of almost every speed, converges to the n-th power of its expectation in the state (i.e. its ensemble average). Thus ray averages do not fluctuate in the long time limit. This is a statement of ergodicity, and holds in any state that is invariant under space-time translations and that satisfies weak clustering properties in space. The ray averages can be performed in a way that accounts for oscillations, showing that ray-averaged observables cannot sustainably oscillate in the long time limit. We also show that in the GNS representation of the algebra of observables, for any KMS state with the above properties, the long-time limit of the ray average of any observable converges (in the strong operator topology) to the ensemble average times the identity, again along space-time rays of almost every speed. This is a strong version of ergodicity, and indicates that, as operators, observables get “thinner” almost everywhere within the light-cone. A similar statement holds under oscillatory averaging.

Interpreting Sunyaev–Zel’dovich observations with MillenniumTNG: mass and environment scaling relations

ABSTRACT Sunyaev–Zel’dovich (SZ) measurements can dramatically improve our understanding of the intergalactic medium and the role of feedback processes in galaxy formation, allowing us to calibrate important astrophysical systematics in cosmological constraints from weak lensing galaxy clustering surveys. However, the signal is only measured in a two-dimensional projection, and its correct interpretation relies on understanding the connection between observable quantities and the underlying intrinsic properties of the gas, in addition to the relation between the gas and the underlying matter distribution. One way to address these challenges is through the use of hydrodynamical simulations such as the high-resolution, large-volume MillenniumTNG suite. We find that measurements of the optical depth, τ, and the Compton-y parameter, Y, receive large line-of-sight contributions that can be removed effectively by applying a compensated aperture photometry filter. In contrast with other τ probes (e.g. X-rays and fast radio bursts), the kinematic SZ-inferred τ receives most of its signal from a confined cylindrical region around the halo due to the velocity decorrelation along the line of sight. Additionally, we perform fits to the Y–M and τ–M scaling relations and report best-fitting parameters adopting the smoothly broken power law formalism. We note that subgrid physics modelling can broaden the error bar on these by 30 per cent for intermediate-mass haloes (${\sim }10^{13} \, {\rm M}_{\odot }$). The scatter of the scaling relations can be captured by an intrinsic dependence on concentration and an extrinsic dependence on tidal shear. Finally, we comment on the effect of using galaxies rather than haloes in observations, which can bias the inferred profiles by ∼20 per cent for L* galaxies.

Arabic Text Clustering Using Self-Organizing Maps and Grey Wolf Optimization

Arabic text clustering is an essential topic in Arabic Natural Language Processing (ANLP). Its significance resides in various applications, such as document indexing, categorization, user review analysis, and others. After inspecting the current work on clustering Arabic text, it is observed that most researchers focus on applying K-Means clustering while hindering other clustering techniques. Our evaluation shows that K-Means has a weakness of inconsistent clustering results and weak clustering performance when the data dimensionality increases. Unlike K-Means clustering, Artificial Neural Networks (ANN) models such as Self-Organizing Maps (SOM) demonstrated higher accuracy and efficiency in clustering even with high dimensional datasets. In this paper, we introduce a new clustering model based on an optimization technique called Grey Wolf Optimization (GWO) used conjointly with SOM clustering to enhance its clustering performance and accuracy. The evaluation results of our proposed technique show an improvement in the effectiveness and efficiency in comparison with state-of-the-art approaches.

Evaluation of Pt particles redeposition effect on gas transport in Nafion membrane

During the operation of proton exchange membrane fuel cell, Pt particles in the catalytic layer inevitably dissolve into the membrane, which affects the performance of fuel cell. In this work, molecular dynamics is employed to investigate the effect of the redeposition of Pt particles on gas diffusion and solvation characteristics in the hydration membrane. Gas diffusion, hydrophilic cluster and its connectivity are analyzed. The interaction between Pt particles, Nafion membrane and water molecules is discussed. The influence of Pt particles on the thermal conduction of membrane is also evaluated. The results demonstrate that the presence of Pt particles affects the solvation effect of membrane, leading to a weak water cluster connectivity of membrane, which in turn leads to a poor gas diffusion performance. In addition, the thermal conductivity gradually deteriorates during the redeposition of Pt particles.

Genetic diversity and population structure of Vernonia amygdalina Del. in Uganda based on genome wide markers.

Determining the extent and distribution of genetic diversity is an essential component of plant breeding. In the present study, we explored the genetic diversity and population structure of Vernonia amygdalina, a fodder, vegetable and medicinal species of Africa and some parts of Yemen. Most empirical studies demonstrate that populations that are separated by geographic or ecological factors may experience genetic differentiation resulting from restricted gene flow between populations. A total of 238 individuals were sampled from two populations: i) Lake Victoria crescent (LVC) and ii) Southern and Eastern Lake Kyoga basin (SEK) agroecological zones of Uganda and genotyped using DArT platform. Of the two populations, the overall mean observed heterozygosity (Ho) was low to medium (Ho = 0.07[silicoDArTs] and 0.2[SNPs]). Inbreeding levels were also very low (-0.04 to -0.08) suggesting the presence of random mating. Partitioning of genetic structure in the two populations indicated that SEK exhibited a higher genetic diversity than LVC. The principal coordinates analysis (PCA) showed no geographical structuring, consistent with the low genetic differentiation (Fst = 0.00) and the low Euclidean genetic distance (1.38-1.39) between the LVC and SEK populations. However, STRUCTURE analysis with admixture models revealed weak possible genetic clusters with very small genetic distance among them. Overall, the results suggest low genetic diversity and weak genetic differentiation between the two populations. One possible explanation of the results could be the presence of human assisted gene flow over long distances.

The Spatial Clustering of ROSAT All-Sky Survey Active Galactic Nuclei. V. The Evolution of Broad-line AGN Clustering Properties in the Last 6 Gyr

This is the fifth paper in a series of investigations of the clustering properties of luminous, broad-emission-line active galactic nuclei (AGNs) identified in the ROSAT All-Sky Survey (RASS) and Sloan Digital Sky Survey (SDSS). In this work we measure the cross-correlation function (CCF) between RASS/SDSS Data Release 14 AGNs with the SDSS CMASS galaxy sample at 0.44 < z < 0.64. We apply halo occupation distribution (HOD) modeling to the CCF along with the autocorrelation function of the CMASS galaxies. We find that X-ray-selected and optically selected AGNs at 0.44 < z < 0.64 reside in statistically identical halos with a typical dark matter halo (DMH) mass of . The acceptable HOD parameter space for these two broad-line AGN samples have only statistically marginal differences caused by small deviations of the CCFs in the one-halo-dominated regime on small scales. In contrast to optically selected AGNs, the X-ray AGN sample may contain a larger population of satellites at M DMH ∼ 1013 h −1 M ⊙. We compare our measurements in this work with our earlier studies at lower independent redshift ranges, spanning a lookback time of 6 Gyr. The comparison over this wider redshift range of 0.07 < z < 0.64 reveals (i) no significant difference between the typical DMH masses of X-ray-selected and optically selected AGNs, (ii) weak positive clustering dependencies of with L X and M BH, (iii) no significant dependence of on Eddington ratio, and (iv) the same DMH masses host more-massive accreting black holes at high redshift than at low redshifts.