Quasi-aromaticity in Cluster Chemistry Ⅱ. Localized Molecular Orbital Studies on Co_6 (CO)_(14)^(4-) and Ni_2Co_4 (CO)_(14)^(2-) Cluster Anions

The energy-localized CNDO/2 molecular orbitals have been calculated for the cluster anions of [Co_6 (CO)_(14)]￣(4-) and [Ni_2Co_4 (CO)_(14)]￣(2-)in order to get a deeper insight into the nature of their skeletal bonding. The bonding characteristics of these hexanuclear carbonyl cobaltates are described from a localization bonding viewpoint. There are two typical M-CO bondings, one of which is formed by electron donation from the terminal and capping carbonyl ligands into the vacant hybrid orbitals on the metal atoms , leading to formation ofσ(C_t→Co) and σ(C_b→{Co_3})bonds. The other typical M-CO bonding is back donation of the lone d-electron pairs on the metal atoms into the carbonyl ligands, forming π(Co→C_t) bonds, σ(Co→{C_(b2)}) bonds and π(Co→{C_(b4)} ) bonds. It is found that there are no direct metalmetal bondings in the skeletons of these two cluster anions.The delocalization situation of the skeletal bonding electrons is briefly discussed.

Index-adaptive Triangle-Based Graph Local Clustering

Motif-based graph local clustering(MGLC)algorithms are gen-erally designed with the two-phase framework,which gets the motif weight for each edge beforehand and then conducts the local clustering algorithm on the weighted graph to output the result.Despite correctness,this frame-work brings limitations on both practical and theoretical aspects and is less applicable in real interactive situations.This research develops a purely local and index-adaptive method,Index-adaptive Triangle-based Graph Local Clustering(TGLC+),to solve the MGLC problem w.r.t.triangle.TGLC+combines the approximated Monte-Carlo method Triangle-based Random Walk(TRW)and deterministic Brute-Force method Triangle-based Forward Push(TFP)adaptively to estimate the Personalized PageRank(PPR)vector without calculating the exact triangle-weighted transition probability and then outputs the clustering result by conducting the standard sweep procedure.This paper presents the efficiency of TGLC+through theoretical analysis and demonstrates its effectiveness through extensive experiments.To our knowl-edge,TGLC+is the first to solve the MGLC problem without computing the motif weight beforehand,thus achieving better efficiency with comparable effectiveness.TGLC+is suitable for large-scale and interactive graph analysis tasks,including visualization,system optimization,and decision-making.

Unveiling the early stage evolution of local atomic structures in the crystallization process of a metallic glass

The early stage evolution of local atomic structures in a multicomponent metallic glass during its crystallization process has been investigated via molecular dynamics simulation.It is found that the initial thermal stability and earliest stage evolution of the local atomic clusters show no strong correlation with their initial short-range orders,and this leads to an observation of a novel symmetry convergence phenomenon,which can be understood as an atomic structure manifestation of the ergodicity.Furthermore,in our system we have quantitatively proved that the crucial factor for the thermal stability against crystallization exhibited by the metallic glass is not the total amount of icosahedral clusters,but the degree of global connectivity among them.

A LOCAL DYNAMIC CLUSTER SELF-ORGANIZATION ALGORITHM IN WIRELESS SENSOR NETWORKS FOR RAINFALL MONITORING

Wireless Sensor Networks for Rainfall Monitoring (RM-WSNs) is a sensor network for the large-scale regional and moving rainfall monitoring,which could be controlled deployment. Delivery delay and cross-cluster calculation leads to information inaccuracy by the existing dynamic collabo-rative self-organization algorithm in WSNs. In this letter,a Local Dynamic Cluster Self-organization algorithm (LDCS) is proposed for the large-scale regional and moving target monitoring in RM-WSNs. The algorithm utilizes the resource-rich node in WSNs as the cluster head,which processes target information obtained by sensor nodes in cluster. The cluster head shifts with the target moving in chance and re-groups a new cluster. The target information acquisition is limited in the dynamic cluster,which can reduce information across-clusters transfer delay and improve the real-time of information acquisition. The simulation results show that,LDCS can not only relieve the problem of "too frequent leader switches" in IDSQ,also make full use of the history monitoring information of target and con-tinuous monitoring of sensor nodes that failed in DCS.

The LAMOST spectroscopic survey of globular clusters in M31and M33.I.catalog and new identifications

We present a catalog of 908 objects observed with the Large Sky Area Multi-Object Fiber Spectroscopic Telescope（LAMOST） in fields in the vicinity of M31 and M33, targeted as globular clusters（GCs） and candidates. The targets include known GCs and candidates selected from the literature, as well as new candidates selected from the Sloan Digital Sky Survey（SDSS）. Analysis shows that 356 of them are likely GCs with various confidence levels, while the remaining ones turn out to be background galaxies and quasars, stars and H II regions in M31 or foreground Galactic stars. The 356 likely GCs include 298 bona fide GCs and 26 candidates known in the literature. Three candidates, selected from the Revised Bologna Catalog of M31 GCs and candidates（RBC） and one possible cluster from Johnson et al., are confirmed to be bona fide clusters. We search for new GCs in the halo of the M31 among the new candidates selected from the SDSS photometry. Based on radial velocities yielded by LAMOST spectra and visual examination of the SDSS images, we find 28 objects, 5bona fide and 23 likely GCs. Among the five bona fide GCs, three have been recently discovered independently by others, and the remaining 25 are our new identifications,including two bona fide ones. The newly identified objects fall at projected distances ranging from 13 to 265 kpc from M31. Of the two newly discovered bona fide GCs,one is located near M33, probably a GC belonging to M33. The other bona fide GC falls on the Giant Stream with a projected distance of 78 kpc from M31. Of the 23 newly identified likely GCs, one has a projected distance of about 265 kpc from M31 and could be an intergalactic cluster.

Winding sense of galaxies around the local supercluster

We present an analysis of the winding sense（S and Z-shapes） of 1 621 field galaxies that have radial velocity between 3 000 km s-1 and 5 000 km s-1.The preferred alignments of S-and Z-shaped galaxies are studied using chi-square,autocorrelation and Fourier series tests.We classify all the galaxies into 32 subsamples and notice a good agreement between the position angle（PA） distribution of the S-and Zshaped galaxies.The homogeneous distribution of the S-and Z-shaped galaxies is more noticeable for the late-type spirals（Sc,Scd,Sd and Sm） than for the early-types（Sa,Sab,Sb and Sbc） .A significant dominance of S-mode galaxies is apparent in the barred spirals.A random alignment is evident in the PA-distribution of Z-and S-mode spirals.In addition,a homogeneous distribution of the S-and Z-shaped galaxies is found to be invariant under global expansion.The PA-distribution of the total S-mode galaxies is found to be random,whereas a preferred alignment is clear for all the Zmode galaxies.It is found that the galactic planes of Z-mode galaxies tend to lie in the equatorial plane.

Index-free triangle-based graph local clustering

Motif-based graph local clustering(MGLC)is a popular method for graph mining tasks due to its various applications.However,the traditional two-phase approach of precomputing motif weights before performing local clustering loses locality and is impractical for large graphs.While some attempts have been made to address the efficiency bottleneck,there is still no applicable algorithm for large scale graphs with billions of edges.In this paper,we propose a purely local and index-free method called Index-free Triangle-based Graph Local Clustering(TGLC^(*))to solve the MGLC problem w.r.t.a triangle.TGLC^(*)directly estimates the Personalized PageRank(PPR)vector using random walks with the desired triangleweighted distribution and proposes the clustering result using a standard sweep procedure.We demonstrate TGLC^(*)’s scalability through theoretical analysis and its practical benefits through a novel visualization layout.TGLC^(*)is the first algorithm to solve the MGLC problem without precomputing the motif weight.Extensive experiments on seven real-world large-scale datasets show that TGLC^(*)is applicable and scalable for large graphs.

Impacts of changing scale on Getis-Ord Gi* hotspots of CPUE:a case study of the neon flying squid (Ommastrephes bartramii)in the northwest Pacific Ocean

We examined the scale impacts on spatial hot and cold spots of CPUE for Ommastrephes bartramii in the northwest Pacific Ocean. The original fishery data were tessellated to 18 spatial scales from 5′×5′ to 90′×90′ with a scale interval of 5′ to identify the local clusters. The changes in location, boundaries, and statistics regarding the Getis-Ord Gi＊ hot and cold spots in response to the spatial scales were analyzed in detail. Several statistics including Min, mean, Max, SD, CV, skewness, kurtosis, first quartile（Q1）, median, third quartile（Q3）, area and centroid were calculated for spatial hot and cold spots. Scaling impacts were examined for the selected statistics using linear, logarithmic, exponential, power law and polynomial functions. Clear scaling relations were identified for Max, SD and kurtosis for both hot and cold spots. For the remaining statistics, either a difference of scale impacts was found between the two clusters, or no clear scaling relation was identified. Spatial scales coarser than 30′ are not recommended to identify the local spatial patterns of fisheries because the boundary and locations of hot and cold spots at a coarser scale are significantly different from those at the original scale.

A Novel Unsupervised Change Detection Method with Structure Consistency and GFLICM Based on UAV Images

With the rapid development of Unmanned Aerial Vehicle(UAV)technology,change detection methods based on UAV images have been extensively studied.However,the imaging of UAV sensors is susceptible to environmental interference,which leads to great differences of same object between UAV images.Overcoming the discrepancy difference between UAV images is crucial to improving the accuracy of change detection.To address this issue,a novel unsupervised change detection method based on structural consistency and the Generalized Fuzzy Local Information C-means Clustering Model(GFLICM)was proposed in this study.Within this method,the establishment of a graph-based structural consistency measure allowed for the detection of change information by comparing structure similarity between UAV images.The local variation coefficient was introduced and a new fuzzy factor was reconstructed,after which the GFLICM algorithm was used to analyze difference images.Finally,change detection results were analyzed qualitatively and quantitatively.To measure the feasibility and robustness of the proposed method,experiments were conducted using two data sets from the cities of Yangzhou and Nanjing.The experimental results show that the proposed method can improve the overall accuracy of change detection and reduce the false alarm rate when compared with other state-of-the-art change detection methods.

Local and long-range transport influences on PM_(2.5) at a cities-cluster in northern China,during summer 2008

Hourly PM2.5 concentrations were observed simultaneously at a cities-cluster comprising 10 cities/towns in Hebei province in China from July 1 to 31, 2008. Among the 10 cities/towns, Baoding showed the high- est average concentration level （161.57μg/m3） and Yanjiao exhibited the lowest （99.35 μg/m3 ）. These observed data were also studied using the joint potential source contribution function with 24-h and 72-h backward trajectories, to identify more clearly the local and countrywide-scale long-range transport sources. For the local sources, three important influential areas were found, whereas five important influential areas were defined for long-range transport sources. Spatial characteristics of PM2.5 were determined by multivariate statistical analyses. Soil dust, coal combustion, and vehicle emissions might be the potential contributors in these areas. The results of a hierarchical cluster analysis for back trajectory endpoints and PM2.s concentrations datasets show that the spatial characteristics of PM2.5 in the cities-cluster were influenced not only by local sources, but also by long-range transport sources. Different cities in the cities-cluster obtained different weighted contributions from local or long-range transport sources. Cangzhou, Shijiazhuang, and Baoding are near the source areas in the south of Hebei province, whereas Zhuozhou, Yangfang, Yanjiao, Xianghe, and Langfang are close to the sources areas near Beijing and Tianjin.

Coordinated target localization base on pseudo measurement for clustered space robot

This paper presents a coordinated target localization method for clustered space robot.According to the different measuring capabilities of cluster members,the master-slave coordinated relative navigation strategy for target localization with respect to slavery space robots is proposed;then the basic mathematical models,including coordinated relative measurement model and cluster centralized dynamics,are established respectively.By employing the linear Kalman flter theorem,the centralized estimator based on truth measurements is developed and analyzed frstly,and with an intention to inhabit the initial uncertainties related to target localization,the globally stabilized estimator is designed through introduction of pseudo measurements.Furthermore,the observability and controllability of stochastic system are also analyzed to qualitatively evaluate the convergence performance of pseudo measurement estimator.Finally,on-orbit target approaching scenario is simulated by using semi-physical simulation system,which is used to verify the convergence performance of proposed estimator.During the simulation,both the known and unknown maneuvering acceleration cases are considered to demonstrate the robustness of coordinated localization strategy.

A novel texture clustering method based on shift invariant DWT and locality preserving projection

We propose a novel texture clustering method. A classical type of(approximate) shift invariant discrete wavelet transform(DWT),dual tree DWT,is used to decompose texture images. Multiple signatures are generated from the obtained high-frequency bands. A locality preserving approach is applied subsequently to project data from high-dimensional space to low-dimensional space. Shift invariant DWT can represent image texture information efficiently in combination with a histogram signature,and the local geometrical structure of the dataset is preserved well during clustering. Experimental results show that the proposed method remarkably outperforms traditional ones.

Time-varying clustering for local lighting and material design

This paper presents an interactive graphics processing unit （GPU）-based relighting system in which local lighting condition, surface materials and viewing direction can all be changed on the fly. To support these changes, we simulate the lighting transportation process at run time, which is normally impractical for interactive use due to its huge computational burden. We greatly alleviate this burden by a hierarchical structure named a transportation tree that clusters similar emitting samples together within a perceptually acceptable error bound. Furthermore, by exploiting the coherence in time as well as in space, we incrementally adjust the clusters rather than computing them from scratch in each frame. With a pre-computed visibility map, we are able to efficiently estimate the indirect illumination in parallel on graphics hardware, by simply summing up the radiance shoots from cluster representatives, plus a small number of operations of merging and splitting on clusters. With relighting based on the time-varying clusters, interactive update of global illumination effects with multi-bounced indirect lighting is demonstrated in applications to material animation and scene decoration.

A novel multimode process monitoring method integrating LDRSKM with Bayesian inference

A local discriminant regularized soft k-means （LDRSKM） method with Bayesian inference is proposed for multimode process monitoring. LDRSKM extends the regularized soft k-means algorithm by exploiting the local and non-local geometric information of the data and generalized linear discriminant analysis to provide a better and more meaningful data partition. LDRSKM can perform clustering and subspace selection simultaneously, enhancing the separability of data residing in different clusters. With the data partition obtained, kernel support vector data description （KSVDD） is used to establish the monitoring statistics and control limits. Two Bayesian inference based global fault detection indicators are then developed using the local monitoring results associated with principal and residual subspaces. Based on clustering analysis, Bayesian inference and manifold learning methods, the within and cross-mode correlations, and local geometric information can be exploited to enhance monitoring performances for nonlinear and non-Gaussian processes. The effectiveness and efficiency of the proposed method are evaluated using the Tennessee Eastman benchmark process.

Indoor Localization with a Crowdsourcing Based Fingerprints Collecting

Fingerprint matching is adopted by a large family of indoor localization schemes,where collecting fingerprints is inevitable but all consuming.While the increasingly popular crowdsourcing based approach provides an opportunity to relieve the burden of fingerprints collecting,a number of formidable challenges for such an approach have yet been studied.For instance,querying in a large fingerprints database for matching process takes a lot of time and calculation;fingerprints collected by crowdsourcing lacks of robustness because of heterogeneous devices problem.Those are important challenges which impede practical deployment of the fingerprint matching indoor localization system.In this study,targeting on effectively utilizing and mining large amount fingerprint data,enhancing the robustness of fingerprints under heterogeneous devices' collection and realizing the real time localization response,we propose a crowdsourcing based fingerprints collecting mechanism for indoor localization systems.With the proposed approach,massive raw fingerprints will be divided into small clusters while diverse devices' uploaded fingerprints will be merged for overcoming device heterogeneity,both of which will contribute to reduce response time.We also build a mobile cloud testbed to verify the proposed scheme.Comprehensive real world experiment results indicate that the scheme can provide comparable localization accuracy.