Cluster Boundaries Research Articles

Inversion of magnetotelluric data with fuzzy cluster petrophysical and boundary constraints

Inverse magnetotelluric (MT) problems are naturally ill-posed and smoothing criteria are typically added to stabilize the process. Smoothing and geo-electrical equivalency tend to produce unrealistic geological models. In reality the subsurface geology is differentiated by distinct rock units that are often better defined by boundaries rather diffuse or smooth boundaries. We present the application of fuzzy clustering as an added constraint within the inversion process to guide model updates toward earth models that resemble geological units. Fuzzy clustering divides the simulated model into clusters based on the similarity of model features. Moreover, fuzzy clustering enables the inclusion of additional prior information in the inversion process such as structural and/or petrophysical information. The inclusion of this information produces geo-electrical distributions that more closely reflect the true rock units and unit boundaries. This is demonstrated through several synthetic examples. The simulations show that by including prior petrophysical and/or boundary location information within the inversion the original conductivity distribution is well resolved.

Mining Bacterial Genomes for Secondary Metabolite Gene Clusters.

With the emergence of bacterial resistance against frequently used antibiotics, novel antibacterial compounds are urgently needed. Traditional bioactivity-guided drug discovery strategies involve laborious screening efforts and display high rediscovery rates. With the progress in next generation sequencing methods and the knowledge that the majority of antibiotics in clinical use are produced as secondary metabolites by bacteria, mining bacterial genomes for secondary metabolites with antimicrobial activity is a promising approach, which can guide a more time and cost-effective identification of novel compounds. However, what sounds easy to accomplish, comes with several challenges. To date, several tools for the prediction of secondary metabolite gene clusters are available, some of which are based on the detection of signature genes, while others are searching for specific patterns in gene content or regulation.Apart from the mere identification of gene clusters, several other factors such as determining cluster boundaries and assessing the novelty of the detected cluster are important. For this purpose, comparison of the predicted secondary metabolite genes with different cluster and compound databases is necessary. Furthermore, it is advisable to classify detected clusters into gene cluster families. So far, there is no standardized procedure for genome mining; however, different approaches to overcome all of these challenges exist and are addressed in this chapter. We give practical guidance on the workflow for secondary metabolite gene cluster identification, which includes the determination of gene cluster boundaries, addresses problems occurring with the use of draft genomes, and gives an outlook on the different methods for gene cluster classification. Based on comprehensible examples a protocol is set, which should enable the readers to mine their own genome data for interesting secondary metabolites.

Spatio-Temporal Structure, Path Characteristics, and Perceptual Grouping in Immediate Serial Spatial Recall.

Immediate serial spatial recall measures the ability to retain sequences of locations in short-term memory and is considered the spatial equivalent of digit span. It is tested by requiring participants to reproduce sequences of movements performed by an experimenter or displayed on a monitor. Different organizational factors dramatically affect serial spatial recall but they are often confounded or underspecified. Untangling them is crucial for the characterization of working-memory models and for establishing the contribution of structure and memory capacity to spatial span. We report five experiments assessing the relative role and independence of factors that have been reported in the literature. Experiment 1 disentangled the effects of spatial clustering and path-length by manipulating the distance of items displayed on a touchscreen monitor. Long-path sequences segregated by spatial clusters were compared with short-path sequences not segregated by clusters. Recall was more accurate for sequences segregated by clusters independently from path-length. Experiment 2 featured conditions where temporal pauses were introduced between or within cluster boundaries during the presentation of sequences with the same paths. Thus, the temporal structure of the sequences was either consistent or inconsistent with a hierarchical representation based on segmentation by spatial clusters but the effect of structure could not be confounded with effects of path-characteristics. Pauses at cluster boundaries yielded more accurate recall, as predicted by a hierarchical model. In Experiment 3, the systematic manipulation of sequence structure, path-length, and presence of path-crossings of sequences showed that structure explained most of the variance, followed by the presence/absence of path-crossings, and path-length. Experiments 4 and 5 replicated the results of the previous experiments in immersive virtual reality navigation tasks where the viewpoint of the observer changed dynamically during encoding and recall. This suggested that the effects of structure in spatial span are not dependent on perceptual grouping processes induced by the aerial view of the stimulus array typically afforded by spatial recall tasks. These results demonstrate the independence of coding strategies based on structure from effects of path characteristics and perceptual grouping in immediate serial spatial recall.

Real-space pseudopotential method for computing the vibrational Stark effect.

The vibrational Stark shift is an important effect in determining the electrostatic environment for molecular or condensed matter systems. However, accurate ab initio calculations of the vibrational Stark effect are a technically demanding challenge. We make use of density functional theory constructed on a real-space grid to expedite the computation of this effect. Our format is especially advantageous for the investigation of small molecules in finite fields as cluster boundary conditions eliminate spurious supercell interactions and allow for charged systems, while convergence is controlled by a single parameter, the grid spacing. The Stark tuning rate is highly sensitive to the interaction between anharmonicity in a vibrational mode and the applied field. To ensure this subtle interaction is fully captured, we apply three parallel approaches: a direct finite field, a perturbative method, and a molecular dynamics method. We illustrate this method by applying it to several small molecules containing C-O and C-N bonds and show that a consistent result can be obtained.

KNCM: Kernel Neutrosophic c-Means Clustering

Data clustering is an important step in data mining and machine learning. It is especially crucial to analyze the data structures for further procedures. Recently a new clustering algorithm known as ‘neutrosophic c-means’ (NCM) was proposed in order to alleviate the limitations of the popular fuzzy c-means (FCM) clustering algorithm by introducing a new objective function which contains two types of rejection. The ambiguity rejection which concerned patterns lying near the cluster boundaries, and the distance rejection was dealing with patterns that are far away from the clusters. In this paper, we extend the idea of NCM for nonlinear-shaped data clustering by incorporating the kernel function into NCM. The new clustering algorithm is called Kernel Neutrosophic c-Means (KNCM), and has been evaluated through extensive experiments. Nonlinear-shaped toy datasets, real datasets and images were used in the experiments for demonstrating the efficiency of the proposed method. A comparison between Kernel FCM (KFCM) and KNCM was also accomplished in order to visualize the performance of both methods. According to the obtained results, the proposed KNCM produced better results than KFCM.

Electric field induced by vortex transport in percolation superconductors

The influence of fractal normal phase clusters on the electric field induced by the flow and creep of the magnetic flux in percolation superconductors has been considered. The current–voltage characteristics of such superconductors with allowance for the influence of the fractal dimension of cluster boundaries and the pinning barrier height have been obtained. The vortex dynamics in percolation superconductors with a fractal cluster structure in a viscous flow of the magnetic flux, the Anderson–Kim creep, and the collective flux creep has been analyzed. It has been discovered that the fractality of normal phase clusters reduces the electric field arising in the initial stage of the resistive transition.

Spatial Competition: Roughening of an Experimental Interface.

Limited dispersal distance generates spatial aggregation. Intraspecific interactions are then concentrated within clusters, and between-species interactions occur near cluster boundaries. Spread of a locally dispersing invader can become motion of an interface between the invading and resident species, and spatial competition will produce variation in the extent of invasive advance along the interface. Kinetic roughening theory offers a framework for quantifying the development of these fluctuations, which may structure the interface as a self-affine fractal, and so induce a series of temporal and spatial scaling relationships. For most clonal plants, advance should become spatially correlated along the interface, and width of the interface (where invader and resident compete directly) should increase as a power function of time. Once roughening equilibrates, interface width and the relative location of the most advanced invader should each scale with interface length. We tested these predictions by letting white clover (Trifolium repens) invade ryegrass (Lolium perenne). The spatial correlation of clover growth developed as anticipated by kinetic roughening theory, and both interface width and the most advanced invader’s lead scaled with front length. However, the scaling exponents differed from those predicted by recent simulation studies, likely due to clover’s growth morphology.

Rearrangement Clustering: Pitfalls, Remedies, and Applications

Given a matrix of values in which the rows correspond to objects and the columns correspond to features of the objects, rearrangement clustering is the problem of rearranging the rows of the matrix such that the sum of the similarities between adjacent rows is maximized. Referred to by various names and reinvented several times, this clustering technique has been extensively used in many fields over the last three decades. In this paper, we point out two critical pitfalls that have been previously overlooked. The first pitfall is deleterious when rearrangement clustering is applied to objects that form natural clusters. The second concerns a similarity metric that is commonly used. We present an algorithm that overcomes these pitfalls. This algorithm is based on a variation of the Traveling Salesman Problem. It offers an extra benefit as it automatically determines cluster boundaries. Using this algorithm, we optimally solve four benchmark problems and a 2,467-gene expression data clustering problem. As expected, our new algorithm identifies better clusters than those found by previous approaches in all five cases. Overall, our results demonstrate the benefits of rectifying the pitfalls and exemplify the usefulness of this clustering technique. Our code is available at our websites.

The state of vortex glass induced by creep of vortices in percolation superconductors

The influence of magnetic flux creep on the dynamics of vortices in percolation superconductors containing fractal clusters of the normal phase has been considered. Dependences of the resistance of these superconductors on the transport current are obtained for different fractal dimensions of cluster boundaries. It is established that the vortex-glass state is implemented in percolation superconductors with a fractal cluster structure under collective creep of vortices.

Modeling of hydrodynamic cavitating flows considering the bubble-bubble interaction

A cavitation model considering the second-order derivative and bubble-bubble interaction is proposed. The bubble wall velocities are obtained by integrating the modified Rayleigh equation. The bubble-bubble interaction is measured by the coupling strength, which is modified for the hydrodynamic cavitating flows during the evaporation and decreases sharply at the cluster boundary during the condensation. The phase change rates are greatly reduced when the bubble-bubble interaction is considered, except at extremely low vapor volume fraction. Numerical results are given for the collapse of cavitation clusters and the unsteady cavitating flows around two NACA0015 hydrofoils. Comparisons are made with the experimental data and Zwart–Gerber–Belamri cavitation model.

Optimal Cluster Analysis for Objective Regionalization of Seasonal Precipitation in Regions of High Spatial–Temporal Variability: Application to Western Ethiopia

Abstract Defining homogeneous precipitation regions is fundamental for hydrologic applications, yet nontrivial, particularly for regions with highly varied spatial–temporal patterns. Traditional approaches typically include aspects of subjective delineation around sparsely distributed precipitation stations. Here, hierarchical and nonhierarchical (k means) clustering techniques on a gridded dataset for objective and automatic delineation are evaluated. Using a spatial sensitivity analysis test, the k-means clustering method is found to produce much more stable cluster boundaries. To identify a reasonable optimal k, various performance indicators, including the within-cluster sum of square errors (WSS) metric, intra- and intercluster correlations, and postvisualization are evaluated. Two new objective selection metrics (difference in minimum WSS and difference in difference) are developed based on the elbow method and gap statistics, respectively, to determine k within a desired range. Consequently, eight homogenous regions are defined with relatively clear and smooth boundaries, as well as low intercluster correlations and high intracluster correlations. The underlying physical mechanisms for the regionalization outcomes not only help justify the optimal number of clusters selected, but also prove informative in understanding the local- and large-scale climate factors affecting Ethiopian summertime precipitation. A principal component linear regression model to produce cluster-level seasonal forecasts also proves skillful.

Biosynthetic Genes for the Tetrodecamycin Antibiotics.

We recently described 13-deoxytetrodecamycin, a new member of the tetrodecamycin family of antibiotics. A defining feature of these molecules is the presence of a five-membered lactone called a tetronate ring. By sequencing the genome of a producer strain, Streptomyces sp. strain WAC04657, and searching for a gene previously implicated in tetronate ring formation, we identified the biosynthetic genes responsible for producing 13-deoxytetrodecamycin (the ted genes). Using the ted cluster in WAC04657 as a reference, we found related clusters in three other organisms: Streptomyces atroolivaceus ATCC 19725, Streptomyces globisporus NRRL B-2293, and Streptomyces sp. strain LaPpAH-202. Comparing the four clusters allowed us to identify the cluster boundaries. Genetic manipulation of the cluster confirmed the involvement of the ted genes in 13-deoxytetrodecamycin biosynthesis and revealed several additional molecules produced through the ted biosynthetic pathway, including tetrodecamycin, dihydrotetrodecamycin, and another, W5.9, a novel molecule. Comparison of the bioactivities of these four molecules suggests that they may act through the covalent modification of their target(s). The tetrodecamycins are a distinct subgroup of the tetronate family of secondary metabolites. Little is known about their biosynthesis or mechanisms of action, making them an attractive subject for investigation. In this paper we present the biosynthetic gene cluster for 13-deoxytetrodecamycin in Streptomyces sp. strain WAC04657. We identify related clusters in several other organisms and show that they produce related molecules.

Semiclassical asymptotic approximation of the two-dimensional Hartree operator spectrum near the upper boundaries of spectral clusters

We consider an eigenvalue problem for the two-dimensional Hartree operator with a small parameter at the nonlinearity. We obtain the asymptotic eigenvalues and the asymptotic eigenfunctions near the upper boundaries of the spectral clusters formed near the energy levels of the unperturbed operator and construct an asymptotic expansion around the circle where the solution is localized.

Clustering Through Hybrid Network Architecture With Support Vectors.

In this paper, we propose a clustering algorithm based on a two-phased neural network architecture. We combine the strength of an autoencoderlike network for unsupervised representation learning with the discriminative power of a support vector machine (SVM) network for fine-tuning the initial clusters. The first network is referred as prototype encoding network, where the data reconstruction error is minimized in an unsupervised manner. The second phase, i.e., SVM network, endeavors to maximize the margin between cluster boundaries in a supervised way making use of the first output. Both the networks update the cluster centroids successively by establishing a topology preserving scheme like self-organizing map on the latent space of each network. Cluster fine-tuning is accomplished in a network structure by the alternate usage of the encoding part of both the networks. In the experiments, challenging data sets from two popular repositories with different patterns, dimensionality, and the number of clusters are used. The proposed hybrid architecture achieves comparatively better results both visually and analytically than the previous neural network-based approaches available in the literature.

Probing dark energy via galaxy cluster outskirts

We present a Bayesian approach to combine $Planck$ data and the X-ray physical properties of the intracluster medium in the virialization region of a sample of 320 galaxy clusters ($0.056<z<1.24$, $kT>3$ keV) observed with $Chandra$. We exploited the high-level of similarity of the emission measure in the cluster outskirts as cosmology proxy. The cosmological parameters are thus constrained assuming that the emission measure profiles at different redshift are weakly self-similar, that is their shape is universal, explicitly allowing for temperature and redshift dependence of the gas fraction. This cosmological test, in combination with $Planck$+SNIa data, allows us to put a tight constraint on the dark energy models. For a constant-$w$ model, we have $w=-1.010\pm0.030$ and $\Omega_m=0.311\pm0.014$, while for a time-evolving equation of state of dark energy $w(z)$ we have $\Omega_m=0.308\pm 0.017$, $w_0=-0.993\pm0.046$ and $w_a=-0.123\pm0.400$. Constraints on the cosmology are further improved by adding priors on the gas fraction evolution from hydrodynamic simulations. Current data favour the cosmological constant with $w\equiv-1$, with no evidence for dynamic dark energy. We checked that our method is robust towards different sources of systematics, including background modelling, outlier measurements, selection effects, inhomogeneities of the gas distribution and cosmic filaments. We also provided for the first time constraints on which definition of cluster boundary radius is more tenable, namely based on a fixed overdensity with respect to the critical density of the Universe. This novel cosmological test has the capacity to provide a generational leap forward in our understanding of the equation of state of dark energy.

Clustering boundary detection for high dimensional space based on space inversion and Hopkins statistics

Physicists research the symmetry of particle space through the contrast of motion law in the real space and inversion space which is created by space inversion techniques. Inspired by this theory, we propose the idea of using local space transformation and dynamic relative position to detect the clustering boundary in high dimensional space. Due to the curse of dimensionality, global space transformation approaches are not only time-consuming, but also fail to keep the original distribution characteristics. So, we inverse the space positions of the k nearest neighbors and project them on the high dimensional space coordinate system. To address the lack of statistics that can describe the uniformity of high dimensional space, we propose the Symmetry Statistics based on the Hopkins Statistics. It is employed to judge the uniformity of k nearest neighbor space of coordinate origin. Moreover, we introduce a filter function to remove some special noises and isolated points. Finally, we use boundary and filter ratios to detect the clustering boundary and propose the corresponding detection algorithm, called Spinver. Experimental results from synthetic and real data sets demonstrate the effectiveness of this algorithm.

Electric field induced by collective vortex creep in superconductors with fractal clusters of normal phase

The influence of the collective creep of magnetic flux on the electric field induced in a superconducting composite with fractal cluster structure is considered. Current–voltage (I–V) characteristics of these superconductors are determined with allowance for the influence of the fractal dimensionality of boundaries of the normal phase clusters and the height of pinning barriers on the nonlinearity of I–V characteristics at small transport currents. A relationship is established between the collective pinning and vortex glass state that is formed in superconductors with a fractal cluster structure. It is shown that the intensity of an electric field induced in the case of collective creep is smaller than that for Anderson–Kim creep.

Asymptotics of the Hartree-type operator spectrum near the lower boundaries of spectral clusters

We consider the eigenvalue problem for a perturbed two-dimensional resonance oscillator. The excitation potential is given by a Hartree-type nonlinearity with a smooth self-action potential. We use asymptotic formulas for the quantum averages to obtain asymptotic eigenvalues and asymptotic eigenfunctions near the lower boundaries of spectral clusters which are formed near the energy levels of the unperturbed operator.

A cluster boundary detection algorithm based on shadowed set

A cluster boundary detection algorithm based on shadowed set

Acoustic response of bubbles inside a cylindrical cavitationbubble cluster generated by low-frequency ultrasound

An ultrasonic horn can radiate a strong ultrasonic wave into viscous liquid contained in a tank or cylindrical cup, and bubble clusters could be generated by the high-intensity ultrasound in the liquid. In the bubble clusters, interaction of bubbles exists because of the secondary radiation of bubbles. Therefore, the oscillations of bubbles are coupled. On the other hand, the surrounding liquid pressure of the bubbles in the cluster is influenced by the oscillations of the bubbles, which induces a pressure gradient on the boundary of the cluster. Therefore, the oscillation of a bubble inside a cluster is contracted by the formation of the cluster and its structure evolution. In this paper, a cylindrical cavitation bubble cluster is considered as a mixture drop of bubbles and liquids, and the motion of the cluster boundary is proposed with a second two-dimensional (2D) Rayleigh equation related to the difference between the inner mixture pressure and the outside liquid pressure on the boundary. Based on the bubble cluster boundary dynamical equation, a new mathematical model is developed to describe the motion of cavitation bubbles inside a cylindrical cluster when the effects of coupled oscillation are included. Comparing the new model equation with the Rayleigh-Plesset equation of single bubble in unbounded liquid, it is easy to draw the conclusion that the contraction of oscillating bubbles is strengthened by the coupled oscillation of bubbles and the boundary motion. In the cylindrical cluster, the oscillation of bubbles is suppressed, and the natural frequency of bubbles is reduced. The proposed model is used as a basis for the numerical investigation of the nonlinear acoustic response of bubbles. The suppression of the bubble oscillation is strengthened by increasing the number density of bubbles. Comparing numerical curves of the maximum radius of the oscillating bubble, it is shown that there are local peaks which are related to the resonance response of bubbles. In some unstable parameter regions, the maximum radius of the oscillating bubble varies sensitively with the tiny change of the parameters. The parameter space distribution of the unstable regions is related to the initial bubble radius and driving frequency of ultrasound. According to the numerical results related to the parameters, such as bubble number density, initial radius, driving frequency and pressure amplitude of ultrasound, it is found that the unstable acoustic response could be amplified for bubbles of smaller initial radius driven by a low-frequency ultrasound. For cavitation bubbles of initial radii ranging from 1 m to 10 m in low-frequency ultrasonic field, the unstable regions of parameter spaces related to the evolution of maximum radius become broader with the decrease of bubble initial radius and driving frequency of ultrasound. Therefore, the tiny bubbles inside cylindrical clusters have stronger nonlinear properties and the change of the parameters in the dynamical model equation has greater influence on the tiny bubbles.