Tertiary Hierarchical Complexity in Assemblies of Sulfur-Bridged Metal Chiral Clusters
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99 GENERAL AND MISCELLANEOUS↗
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Results for “Hierarchical clustering”
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Topological grain boundary segregation transitions
Engineering the structure of grain boundaries (GBs) by solute segregation is a promising strategy to tailor the properties of polycrystalline materials. Solute segregation triggering phase transitions at GBs has been suggested theoretically to offer different pathways to design interfaces, but an understanding of their intrinsic atomistic nature is missing. Here, we combined atomic resolution electron microscopy and atomistic simulations to discover that iron segregation to GBs in titanium stabilizes icosahedral units (“cages”) that form robust building blocks of distinct GB phases. Owing to their five-fold symmetry, the iron cages cluster and assemble into hierarchical GB phases characterized by a different number and arrangement of the constituent icosahedral units. Our advanced GB structure prediction algorithms and atomistic simulations validate the stability of these observed phases and the high excess of iron at the GB that is accommodated by the phase transitions.
36 MATERIALS SCIENCE↗
Identifying Vehicle Signals in Continuous Seismic Data Using Unsupervised Machine-Learning Techniques
Seismic sensors deployed near roadways effectively capture ground vibrations generated by passing vehicles. Although both traditional and machine‐learning algorithms have been utilized for analyzing such signals, independent validation of detected vehicle events remains limited. We applied two unsupervised machine‐learning algorithms, uniform manifold approximation and projection for dimension reduction, and hierarchical density‐based spatial clustering of applications with noise, to continuous seismic data collected along a road on the main campus of Oak Ridge National Laboratory. The algorithms identified seven distinct cluster labels across the entire dataset. By comparing these cluster labels with precipitation records from a nearby weather station and image‐derived labels from a local camera system, we identified one cluster associated with rainfall and another with vehicle activity. Our algorithms identified a greater number of vehicle‐related labels compared to the camera‐derived labels because seismic data are unaffected by poor lighting conditions. The arrival times of the newly detected vehicle signals corresponded well with the road’s speed limit, supporting our findings. Our algorithm outperformed the short‐term average/long‐term average method and k‐means clustering. Our results suggest that seismic data, when analyzed with machine‐learning algorithms, can complement existing vehicle monitoring systems, particularly under challenging environmental conditions.
Chai, Chengping [Oak Ridge National Laboratory (OR↗
Cloud Classification in Polar and Desert Regions and Smoke Classification from Biomass Burning Using a Hierarchical Neural Network
This research focuses on a new neural network scene classification technique. The task is to identify scene elements in Advanced Very High Resolution Radiometry (AVHRR) data from three scene types: polar, desert and smoke from biomass burning in South America (smoke). The ultimate goal of this research is to design and implement a computer system which will identify the clouds present on a whole-Earth satellite view as a means of tracking global climate changes. Previous research has reported results for rule-based systems (Tovinkere et at 1992, 1993) for standard back propagation (Watters et at. 1993) and for a hierarchical approach (Corwin et al 1994) for polar data. This research uses a hierarchical neural network with don't care conditions and applies this technique to complex scenes. A hierarchical neural network consists of a switching network and a collection of leaf networks. The idea of the hierarchical neural network is that it is a simpler task to classify a certain pattern from a subset of patterns than it is to classify a pattern from the entire set. Therefore, the first task is to cluster the classes into groups. The switching, or decision network, performs an initial classification by selecting a leaf network. The leaf networks contain a reduced set of similar classes, and it is in the various leaf networks that the actual classification takes place. The grouping of classes in the various leaf networks is determined by applying an iterative clustering algorithm. Several clustering algorithms were investigated, but due to the size of the data sets, the exhaustive search algorithms were eliminated. A heuristic approach using a confusion matrix from a lightly trained neural network provided the basis for the clustering algorithm. Once the clusters have been identified, the hierarchical network can be trained. The approach of using don't care nodes results from the difficulty in generating extremely complex surfaces in order to separate one class from all of the others. This approach finds pairwise separating surfaces and forms the more complex separating surface from combinations of simpler surfaces. This technique both reduces training time and improves accuracy over the previously reported results. Accuracies of 97.47%, 95.70%, and 99.05% were achieved for the polar, desert and smoke data sets.
Alexander, June↗
Supramolecular Assembly of U(IV) Clusters and Superatoms with Unconventional Countercations
Superatoms are nanometer-sized molecules or particles that form ordered lattices, mimicking their atomic counterparts. Hierarchical assembly of superatoms gives rise to emergent properties in lattices of quantum dots, p-block clusters, and fullerenes. In this report, we introduce a family of uranium-oxysulfate cluster anions whose hierarchical assembly in water is controlled by two parameters: acidity and the lanthanide or transition-metal countercation. In acid, larger Ln III (Ln = La–Ho) link hexamer (U 6 ) oxoclusters into body-centered cubic frameworks, while smaller Ln III (Ln = Er–Lu and Y) promote linking of 14 U 6 clusters into hollow superclusters (U 84 superatoms). U 84 assembles into superlattices including cubic-closest packed, body-centered cubic, and interpenetrating networks, bridged by interstitial countercations and U 6 clusters. Divalent transition metals (TM = Mn II and Zn II ) charge-balance and promote the fusion of 10 U 6 and 10 U monomers into a wheel-shaped cluster (U 70 ). Dissolution of U 70 in organic media shows (by small-angle X-ray scattering) that differing supramolecular assemblies are accessed, controlled by TM II -linking of U 70 clusters. Magnetic measurements of these assemblies reveal Curie–Weiss behavior at high temperatures, without pairing of the 5f 2 -electrons down to 2 K.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Marker-Based Hierarchical Segmentation and Classification Approach for Hyperspectral Imagery
The Hierarchical SEGmentation (HSEG) algorithm, which is a combination of hierarchical step-wise optimization and spectral clustering, has given good performances for hyperspectral image analysis. This technique produces at its output a hierarchical set of image segmentations. The automated selection of a single segmentation level is often necessary. We propose and investigate the use of automatically selected markers for this purpose. In this paper, a novel Marker-based HSEG (M-HSEG) method for spectral-spatial classification of hyperspectral images is proposed. First, pixelwise classification is performed and the most reliably classified pixels are selected as markers, with the corresponding class labels. Then, a novel constrained marker-based HSEG algorithm is applied, resulting in a spectral-spatial classification map. The experimental results show that the proposed approach yields accurate segmentation and classification maps, and thus is attractive for hyperspectral image analysis.
Tarabalka, Yuliya↗
Hydrogen bond arrangements in (H 2 O) 20, 24, 28 clathrate hydrate cages: Optimization and many-body analysis
Here we provide a detailed study of hydrogen bonding arrangements, relative stability, residual entropy, and an analysis of the many-body effects in the (H 2 O) 20 (D-cage), (H 2 O) 24 (T-cage), and (H 2 O) 28 (H-cage) hollow cages making up structures I (sI) and II (sII) of clathrate hydrate lattices. Based on the enumeration of the possible hydrogen bonding networks for a fixed oxygen atom scaffold, the residual entropy (S 0 ) of these three gas phase cages was estimated at 0.754 82, 0.754 44, and 0.754 17 · Nk b , where N is the number of molecules and k b is Boltzmann’s constant. A previously identified descriptor of enhanced stability based on the relative arrangement and connectivity of nearest-neighbor fragments on the polyhedral water cluster [strong-weak-effective-bond model] also applies to the larger hollow cages. The three cages contain a maximum of 7, 9, and 11 such preferable arrangements of trans nearest dimer pairs with one “free” OH bond on the donor molecule (t1d dimers). The Many-Body Expansion (MBE) up to the 4-body suggests that the many-body terms vary nearly linearly with the cluster binding energy. Using a hierarchical approach of screening the relative stability of networks starting from optimizations with the TIP4P, TTM2.1-F, and MB-pol classical potentials, subsequently refining at more accurate levels of electronic structure theory (DFT and MP2), and finally correcting for zero-point energy, we were able to identify a group of four low-lying isomers of the (H 2 O) 24 T-cage, two of which are antisymmetric and the other two form a pair of antipode configurations.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Exploring Continuous Seismic Data at an Industry Facility Using Unsupervised Machine Learning
Seismic data recorded at industrial sites contain valuable information on anthropogenic activities. With advances in machine learning and computing power, new opportunities have emerged to explore the seismic wavefield in these complex environments. We applied two unsupervised machine learning algorithms to analyze continuous seismic data collected from an industrial facility in Texas, United States. The Uniform Manifold Approximation and Projection for Dimension Reduction algorithm was used to reduce the dimensionality of the data and generate 2D embeddings. Then, the Hierarchical Density-Based Spatial Clustering of Applications with Noise method was employed to automatically group these embeddings into distinct signal clusters. Our analysis of over 1400 hr (around 59 days) of continuous seismic data revealed five and seven signal clusters at two separate stations. At both stations, we identified clusters associated with background noise and vehicle traffic, with the latter’s temporal patterns aligning closely with the facility’s work schedule. Furthermore, the algorithms detected signal clusters from unknown sources and underline the ability of unsupervised machine learning for uncovering previously unrecognized patterns. Our analysis demonstrates the effectiveness of unsupervised approaches in examining continuous seismic data without requiring prior knowledge or pre-existing labels.
58 GEOSCIENCES↗
Non-dilute helium-related defect interactions in the near-surface region of plasma-exposed tungsten
We report a systematic energetic analysis of helium-related defect interactions that mediate helium (He) segregation on surfaces of plasma-exposed tungsten at different levels of He ion implantation. We focus on high He fluences that increase the He content in the plasma-exposed material well beyond the dilute limit of He concentration and employ atomic configurations generated by large-scale molecular dynamics simulations of He-implanted tungsten. We perform systematic molecular statics computations of cluster– defect interaction energetics in the highly defect-rich near-surface region of plasma-exposed tungsten for small mobile helium clusters as a function of the cluster distance from the surface. In this region, mobile clusters are also subjected to the stress fields generated by defects such as helium bubbles and other clusters, which govern cluster–defect interactions in addition to the cluster–surface interaction. Based on systematic investigation of individual cluster–defect interactions, we develop a mathematical framework to describe the interaction energy landscapes consisting of elastic interaction potential perturbations to finite-width square-well potentials, where the potential well accounts for cluster trapping by the defect at close range and subsequent coalescence and the perturbation potential is parameterized according to elastic inclusion theory. Superposition of all the relevant interaction potentials provides a comprehensive description of the interaction energy landscape that would be experienced by a small mobile cluster along its migration path toward the plasma-exposed surface at high He fluence. Such descriptions are particularly important for developing atomistically-informed, hierarchical multi-scale models of helium cluster dynamics in plasma-facing materials.
36 MATERIALS SCIENCE↗
Evidence for evolution of the luminosity function of clusters of galaxies
From an all sky, x-ray flux limited sample of clusters of galaxies evidence for a significant deficit in the number of high luminosity clusters is found in the redshift range z approximately 0.1 to 0.2 compared with numbers of nearby clusters. This indicates that the x-ray luminous clusters are undergoing strong evolution. The strength of the effect is consistent with hierarchical merging models. The implications of such strong evolution for clusters are discussed.
Edge, Alastair C.↗
Evidence for evolution of the luminosity function of clusters of galaxies
From an all sky, X-ray flux limited sample of clusters of galaxies evidence for a significant deficit in the number of high luminosity clusters is found in the redshift range z approximately 0.1 to 0.2 compared with numbers of nearby clusters. This indicates that the X-ray luminous clusters are undergoing strong evolution. The strength of the effect is consistent with hierarchical merging models. The implications of such strong evolution for clusters are discussed.
Edge, A. C.↗
CODEX weak lensing mass catalogue and implications on the mass–richness relation
The COnstrain Dark Energy with X-ray clusters (CODEX) sample contains the largest flux limited sample of X-ray clusters at 0.35 < z < 0.65. It was selected from ROSAT data in the 10 000 square degrees of overlap with BOSS, mapping a total number of 2770 high-z galaxy clusters. We present here the full results of the CFHT CODEX programme on cluster mass measurement, including a reanalysis of CFHTLS Wide data, with 25 individual lensing-constrained cluster masses. We employ lensfit shape measurement and perform a conservative colour–space selection and weighting of background galaxies. Using the combination of shape noise and an analytic covariance for intrinsic variations of cluster profiles at fixed mass due to large-scale structure, miscentring, and variations in concentration and ellipticity, we determine the likelihood of the observed shear signal as a function of true mass for each cluster. We combine 25 individual cluster mass likelihoods in a Bayesian hierarchical scheme with the inclusion of optical and X-ray selection functions to derive constraints on the slope α, normalization β, and scatter σln λ|μ of our richness–mass scaling relation model in log-space: ${\langle {\rm In}\,\, \lambda\!\!\mid\!\!\mu\rangle = \alpha\mu + \beta,} $ with μ = ln (M 200c /M piv ), and M piv = 10 14.81 M ⊙ . We find a slope $\alpha = 0.49^{+0.20}_{-0.15}$, normalization $\exp (\beta) = 84.0^{+9.2}_{-14.8}$, and $\sigma _{\ln \lambda | \mu } = 0.17^{+0.13}_{-0.09}$ using CFHT richness estimates. In comparison to other weak lensing richness–mass relations, we find the normalization of the richness statistically agreeing with the normalization of other scaling relations from a broad redshift range (0.0 < z < 0.65) and with different cluster selection (X-ray, Sunyaev–Zeldovich, and optical).
72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
A New Member of the Milky Way’s Family Tree: Characterizing the Pontus Merger of Our Galaxy
We study the Pontus structure—a recently discovered merger that brought in ~7 globular clusters in the course of the hierarchical buildup of the Milky Way’s halo. Here, we analyze the stellar population of Pontus and examine (1) its phase-space distribution using the ESA/Gaia data set, (2) its metallicity and chemical abundances (i.e., [Fe/H], [ α /Fe], [Mg/Fe], and [Al/Fe]) using the spectroscopic catalog of APOGEE DR17, and (3) the color–magnitude diagram that shows interesting features, including a possibly double horizontal branch and a small population of blue stragglers. In sum, the Pontus stars show some unique properties that suggest they likely originated from the merging of an independent satellite galaxy; however, future analysis will shed more light on the true nature of this structure. This chemodynamical analysis of Pontus stars is another step forward in our bigger quest to characterize all the merging events of our Milky Way.
79 ASTRONOMY AND ASTROPHYSICS↗
A new clustering algorithm applicable to multispectral and polarimetric SAR images
We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.
Wong, Yiu-Fai↗
Do the major axes of rich clusters of galaxies point toward their neighbors?
The major axis orientation of rich clusters of galaxies, determined from an analysis of X-ray images, is used to investigate whether these clusters point toward their nearest neighbors. No statistical significance is found for a pointing effect between clusters and their nearest neighbors in either X-ray, optical, or combined X-ray and optical samples. Using updated redshifts and permitting nonstatistical sample Abell clusters as nearest neighbors does not affect this conclusion. The lack of statistical significance for a pointing effect favors hierarchical models in which galaxies form first, followed by clusters and then superclusters. For clusters with well-defined X-ray orientations, it is found that cluster position angles determined from X-ray and optical data are in general agreement.
Ulmer, M. P.↗
Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems
Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. Here this paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated subregion. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack ‘coarse’ localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a ‘fine’ cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.
42 ENGINEERING↗
The hierarchical growth of bright central galaxies and intracluster light as traced by the magnitude gap
Using a sample of 2800 galaxy clusters identified in the Dark Energy Survey across the redshift range 0.20 < z < 0.60, we characterize the hierarchical assembly of bright central galaxies (BCGs) and the surrounding intracluster light (ICL). To quantify hierarchical formation we use the stellar mass–halo mass (SMHM) relation, comparing the halo mass, estimated via the mass–richness relation, to the stellar mass within the BCG + ICL system. Moreover, we incorporate the magnitude gap (M14), the difference in brightness between the BCG (measured within 30 kpc) and fourth brightest cluster member galaxy within 0.5 $R_{200,c}$, as a third parameter in this linear relation. The inclusion of M14, which traces BCG hierarchical growth, increases the slope and decreases the intrinsic scatter, highlighting that it is a latent variable within the BCG + ICL SMHM relation. Moreover, the correlation with M14 decreases at large radii. However, the stellar light within the BCG + ICL transition region (30 –80 kpc) most strongly correlates with halo mass and has a statistically significant correlation with M14. Since the transition region and M14 are independent measurements, the transition region may grow due to the BCG’s hierarchical formation. Additionally, as M14 and ICL result from hierarchical growth, we use a stacked sample and find that clusters with large M14 values are characterized by larger ICL and BCG + ICL fractions, which illustrates that the merger processes that build the BCG stellar mass also grow the ICL. Furthermore, this may suggest that M14 combined with the ICL fraction can identify dynamically relaxed clusters.
79 ASTRONOMY AND ASTROPHYSICS↗
X-ray luminosity functions of clusters of galaxies
Clusters of galaxies must have a considerable intrinsic spread in their X-ray luminosities at given mass if they are formed bottom-up by direct gravitational instability. The distributions of luminosities at given mass take on the general form of a flat power law with a sharp upper cutoff, consistent with the recently obtained luminosity functions for Abell clusters of given richness classes. The quantitative features depend on the specific hierarchical cosmogony, with models including mass accretion after first collapse providing the best agreement. The same clustering mechanism, after integrating over mass, yields a steep overall luminosity function consistent with existing measurements.
Cavaliere, A.↗