ASL Recognition by the Layered Learning Model Using Clustered Groups

American Sign Language(ASL)images can be used as a communication tool by determining numbers and letters using the shape of the fingers.Particularly,ASL can have an key role in communication for hearing-impaired persons and conveying information to other persons,because sign language is their only channel of expression.Representative ASL recognition methods primarily adopt images,sensors,and pose-based recognition techniques,and employ various gestures together with hand-shapes.This study briefly reviews these attempts at ASL recognition and provides an improved ASL classification model that attempts to develop a deep learning method with meta-layers.In the proposed model,the collected ASL images were clustered based on similarities in shape,and clustered group classification was first performed,followed by reclassification within the group.The experiments were conducted with various groups using different learning layers to improve the accuracy of individual image recognition.After selecting the optimized group,we proposed a meta-layered learning model with the highest recognition rate using a deep learning method of image processing.The proposed model exhibited an improved performance compared with the general classification model.

Improvisation of Node Mobility Using Cluster Routing-based Group Adaptive in MANET

In today's Internet routing infrastructure,designers have addressed scal-ing concerns in routing constrained multiobjective optimization problems examining latency and mobility concerns as a secondary constrain.In tactical Mobile Ad-hoc Network(MANET),hubs can function based on the work plan in various social affairs and the internally connected hubs are almost having the related moving standards where the topology between one and the other are tightly coupled in steady support by considering the touchstone of hubs such as a self-sorted out,self-mending and self-administration.Clustering in the routing process is one of the key aspects to increase MANET performance by coordinat-ing the pathways using multiple criteria and analytics.We present a Group Adaptive Hybrid Routing Algorithm(GAHRA)for gathering portability,which pursues table-driven directing methodology in stable accumulations and on-request steering strategy for versatile situations.Based on this aspect,the research demonstrates an adjustable framework for commuting between the table-driven approach and the on-request approach,with the objectives of enhancing the out-put of MANET routing computation in each hub.Simulation analysis and replication results reveal that the proposed method is promising than a single well-known existing routing approach and is well-suited for sensitive MANET applications.

Group decision-making method based on entropy and experts cluster analysis

According to the aggregation method of experts＇ evaluation information in group decision-making,the existing methods of determining experts＇ weights based on cluster analysis take into account the expert＇s preferences and the consistency of expert＇s collating vectors,but they lack of the measure of information similarity.So it may occur that although the collating vector is similar to the group consensus,information uncertainty is great of a certain expert.However,it is clustered to a larger group and given a high weight.For this,a new aggregation method based on entropy and cluster analysis in group decision-making process is provided,in which the collating vectors are classified with information similarity coefficient,and the experts＇ weights are determined according to the result of classification,the entropy of collating vectors and the judgment matrix consistency.Finally,a numerical example shows that the method is feasible and effective.

Form Gene Clustering Method about Pan-Ethnic-Group Products Based on Emotional Semantic

The use of pan-ethnic-group products form knowledge primarily depends on a designer＇s subjective experience without user participation. The majority of studies primarily focus on the detection of the perceptual demands of consumers from the target product category. A pan-ethnic-group products form gene clustering method based on emotional semantic is constructed. Consumers＇ perceptual images of the pan-ethnic-group products are obtained by means of product form gene extraction and coding and computer aided product form clustering technology. A case of form gene clustering about the typical pan-ethnic-group products is investigated which indicates that the method is feasible. This paper opens up a new direction for the future development of product form design which improves the agility of product design process in the era of Industry 4.0.

Radar group target recognition based on HRRPs and weighted mean shift clustering

When range high-resolution radar is applied to target recognition,it is quite possible for the high-resolution range profiles(HRRPs)of group targets in a beam to overlap,which reduces the target recognition performance of the radar.In this paper,we propose a group target recognition method based on a weighted mean shift(weighted-MS)clustering method.During the training phase,subtarget features are extracted based on the template database,which is established through simulation or data acquisition,and the features are fed to the support vector machine(SVM)classifier to obtain the classifier parameters.In the test phase,the weighted-MS algorithm is exploited to extract the HRRP of each subtarget.Then,the features of the subtarget HRRP are extracted and used as input in the SVM classifier to be recognized.Compared to the traditional group target recognition method,the proposed method has the advantages of requiring only a small amount of computation,setting parameters automatically,and having no requirement for target motion.The experimental results based on the measured data show that the method proposed in this paper has better recognition performance and is more robust against noise than other recognition methods.

Symptom Cluster Research in Women with Breast Cancer: A Comparison of Three Subgrouping Techniques

Aims: To examine how symptom cluster subgroups defined by extreme discordant composite scores, cut-off scores, or a median split influence statistical associations with peripheral cytokine levels in women with breast cancer. Background: Systemic cytokine dysregulation has been posited as a potential biological mechanism underlying symptom clusters in women with breast cancer. Symptom characteristics may play an important role in identifying cytokines of significant etiological importance, however, there is no consensus regarding to the ideal subgrouping technique to use. Design: A secondary analysis of data collected from a cross-sectional descriptive study of women with stage I-II breast cancer was used to examine and compare the relationships between peripheral cytokine levels and symptom subgroups defined by extreme discordant composite scores, cut-off scores, or a median split. Methods: Participant symptom scores were transformed into a composite score to account for variability in symptom intensity, frequency and interference. Cytokine levels in subgroups defined by composite scores within the highest and lowest 20% were contrasted with those composed from cut-off scores and a median split. Results: Subgroups defined by the composite score or cut-off scores resulted in similar statistical relationships with cytokine levels in contrast to the median split technique. The use of a median split for evaluating relationships between symptoms clusters and cytokine levels may increase the risk of a type I error. Conclusion: Composite and cut-off scores represent best techniques for defining symptom cluster subgroups in women with breast cancer. Using a consistent approach to define symptom clusters across studies may assist in identifying relevant biological mechanisms.

Cluster algebra structure on the finite dimensional representations of affine quantum group U_q(_3)

In this paper, we prove one case of conjecture given by Hemandez and Leclerc. We give a cluster algebra structuure on the Grothendieck ring of a full subcategory of the finite dimensional representations of affine quantum group Uq（A3）. As a conclusion, for every exchange relation of cluster algebra, there exists an exact sequence of the full subcategory corresponding to it.

Scalable group management in large-scale virtualized clusters

To save cost, more and more users choose provision resources at the granularity of virtual machines in cluster systems, especially data centres. Maintaining a consistent member view is the foundation of reliable cluster managements, and it also raises several challenge issues for large scale cluster systems deployed with virtual machines （which we call virtualized clusters）. In this paper, we introduce our experience in design and implementation of scalable member view management on large-scale virtual clusters. Our research contributions include three-aspects ： 1 ） we propose a scalable and reliable management infrastructure that combines a peer-to-peer structure and a hierarchy structure to maintain a consistent member view in virtual clusters; 2 ） we present a light-weighted group membership algorithm that can reach the consistent member view within a single round of message exchange; 3 ） we design and implement a scalable membership service that can provide virtual machines and maintain a consistent member view in virtual clusters. Our work is verified on Dawning 5000A, which ranked No. 10 of Top 500 super computers in November, 2008.

Analyzing Differences between Online Learner Groups during the COVID-19 Pandemic through K-Prototype Clustering

Online learning is a very important means of study, and has been adopted in many countries worldwide. However, only recently are researchers able to collect and analyze massive online learning datasets due to the COVID-19 epidemic. In this article, we analyze the difference between online learner groups by using an unsupervised machine learning technique, i.e., k-prototypes clustering. Specifically, we use questionnaires designed by domain experts to collect various online learning data, and investigate students’ online learning behavior and learning outcomes through analyzing the collected questionnaire data. Our analysis results suggest that students with better learning media generally have better online learning behavior and learning result than those with poor online learning media. In addition, both in economically developed or undeveloped regions, the number of students with better learning media is less than the number of students with poor learning media. Finally, the results presented here show that whether in an economically developed or an economically undeveloped region, the number of students who are enriched with learning media available is an important factor that affects online learning behavior and learning outcomes.

Ant colony ATTA clustering algorithm of rock mass structural plane in groups

Based on structural surface normal vector spherical distance and the pole stereographic projection Euclidean distance,two distance functions were established.The cluster analysis of structure surface was conducted by the use of ATTA clustering methods based on ant colony piles,and Silhouette index was introduced to evaluate the clustering effect.The clustering analysis of the measured data of Sanshandao Gold Mine shows that ant colony ATTA-based clustering method does better than K-mean clustering analysis.Meanwhile,clustering results of ATTA method based on pole Euclidean distance and ATTA method based on normal vector spherical distance have a great consistence.The clustering results are most close to the pole isopycnic graph.It can efficiently realize grouping of structural plane and determination of the dominant structural surface direction.It is made up for the defects of subjectivity and inaccuracy in icon measurement approach and has great engineering value.

Synthesis and Crystal Structure of a Novel Hetero-six-nuclear Copper-iron Cluster Containing Ferroceylphenyl Groups

A new hetero-six-nuclear cluster was synthesized and determined by X-ray diffraction technique. The four-carboxylate groups are bound to the Cu(II) atoms to form Cu(OR)4Cu paddle-wheel-type cage between two DMF as the basis for the cluster. The distance of two copper(II) atoms is 2.642 ?, and they are bridged by the carboxylate groups. A huge system plane was auto-assembled by four host molecules and two Cu (II) ions, which was observed in the crystal structure.

Central gas entropy excess as direct evidence for AGN feedback in galaxy groups and clusters

By analyzing Chandra X-ray data of a sample of 21 galaxy groups and 19 galaxy clusters, we find that in 31 sample systems there exists a significant central （R ≤ 10 h^-171 kpc） gas entropy excess （AK0）, which corresponds to = 0.1 - 0.5 keV per gas particle, beyond the power-law model that best fits the radial entropy profile of the outer regions. We also find a distinct correlation between the central entropy excess △K0 and K-band luminosity LK of the central dominating galaxies （CDGs）, which is scaled as △K0 ∝ L K 1.6±04, where LK is tightly associated with the mass of the supermassive black hole hosted in the CDG. In fact, if an effective mass-to-energy conversionefficiency of 0.02 is assumed for the accretion process, the cumulative AGN feedback E AGN feedack=ηMBHc2 yields an extra heating of = 0.5 - 17.0keV per particle, which feedback is sufficient to explain the central entropy excess. In most cases, the AGN contribution can compensate the radiative loss of the X-ray gas within the cooling radius （= 0.002 - 2.2 keV per particle）, and apparently exceeds the energy required to cause the scaling relations to deviate from the self-similar predictions （=0.2 - 1.0 keV per particle）. In contrast to the AGN feedback, the extra heating provided by supernova explosions accounts for = 0.01 - 0.08 keV per particle in groups and is almost negligible in clusters. Therefore, the observed correlation between △K0 and Lx can be considered as direct evidence for AGN feedback in galaxy groups and clusters.

Sub-Grouping Healthy Subjects’ Sensitivity to Pain and Its Relationship to Personality Traits: Results of a Cluster Analysis

Objective: Individual differences in the sensitivity to pain and the factors that may contribute to these differences are well studied. Nevertheless, there is no single test that can reliably classify subjects as being sensitive or insensitive to pain. Methods: In the present study, hierarchical clustering and K-means cluster analysis was used to identify subgroups among 191 healthy subjects (105 females, 86 males) according to their sensitivity to pain. Group determination was based on the subjects’ response to experimental noxious stimuli of heat (pain intensity), cold (cold pain threshold, tolerance, and intensity), and conditioned pain modulation (CPM, tested by co-administering repeated short painful heat stimuli and a conditioning tonic cold pain stimulation). In addition, in order to determine if the subjects in these subgroups differed on personality traits scores on Cloninger’s Tridimensional Personality Questionnaire (TPQ, outcome measure) for the three dimensions of personality: Novelty Seeking (NS);Harm Avoidance (HA);and Reward Dependence (RD) were calculated. Results: Based on pain scores, subjects were grouped as low pain (57%) with a low level of sensitivity in pain parameters, or high pain (43%) cluster members. The high pain had significant higher scores of HA (p = 0.05) and RD (p = 0.05) than the low pain group. Conclusions: This method of sub-grouping may be useful for identifying the mechanisms underlying individual variability in the sensitivity to pain and may point to groups at risk for experiencing high levels of clinical pain.

Weak Merging Scenario of CLASH Cluster A209

We study the structural and dynamical properties of A209 based on Chandra and XMM-Newton observations.We obtain detailed temperature,pressure,and entropy maps with the contour binning method,and find a hot region in the NW direction.The X-ray brightness residual map and corresponding temperature profiles reveal a possible shock front in the NW direction and a cold front feature in the SE direction.Combined with the galaxy luminosity density map we propose a weak merger scenario.A young sub-cluster passing from the SE to NW direction could explain the optical subpeak,the intracluster medium temperature map,the X-ray surface brightness excess,and the X-ray peak offset together.

Preparation of Highly Dispersed Supported Metal Catalysts via Solvated Metal Atom Impregnation ( V) --The Effects of Hydroxyl Group on the Surface Concentrations of SiO2 on the Properties of Supported Fe Clusters

Two kinds of small iron clusters supported on SiO2-200 (dehydroxylated at 200℃ and SiO2-600 (de-hydroxylated at 600℃) were prepared by Solvated Metal Atom Impregnation (SMAI) techniques. The iron atom precursor complex, bis (toluene) iron(0) formed in the metal atom reactor, was impregnated into SiO2 having different concentrations of surface hydroxyl groups to study the effect of surface hydroxylation on the crucial stage of iron cluster formation. Catalysts prepared in this way were characterized by THM, Mosbauer and chemisorption measurements, and the resules show that higher concentration of surface hydroxyl groups of SiO2-200 favours the formation of more positively charged support iron cluster Fen/SiO2-200 and the lower concentration of surface hydroxyl groups of SiO2-600 favours the formation of basically neutral supported iron cluster Fe2/SiO2-600. The measured results also indicate that the higher concentration of surface hydroxyl groups causes the precursor complex,bis(toluene) fron(0), to decompose more rapidly, and favours the formation of relatively large iron cluster. As a consequence, these two kinds of catalysts show different catalytic properties in Fischer-Tropsch reaction. The catalytic pattern of Fe/SiO2-200 in F-T reaction is similar to that of the unreduced a-Fe2O2, while Fe2/SiO2 -600 is similar to that of reduced α-Fe2O2.

The kinematical and space structures of IC 2391 open cluster and moving group with Gaia-DR2

The kinematical parameters,spatial shape and structure of the open cluster IC 2391 and the associated stellar stream are studied here using Gaia Data Release 2(GDR2) astrometry data.The apex positions are determined for the open cluster IC 2391(data taken from Cantat-Gaudin et al.) and for the kinematical stream’s stars mentioned in Montes et al.employing both convergent point and AD-diagram methods.The values of apex coordinates are:(A,D)CP=(6.~h17 ± 0.~h004,-6.°88 ± 0.°381;for cluster) and(6.~h07 ± 0.~h007,-5.°00 ± 0.°447;stream),and(A0,D0) =(6.~h12 ± 0.~h004,-3.°4 ± 0.°3;cluster) and(6.~h21 ±0.~h007,-11.°895 ± 0.°290;stream).The results are in good agreement with the previously calculated values.The positions of the stars in the disk and the spatial dispersion velocities are determined.The paths of cluster and associated stream are traced in the disk by orbit calculation back in time to their places of formation.A possible genetic relationship between the cluster and stream has been detected.The approximation of the spatial and kinematical shape of the stream and the cluster is made.According to this study,even though currently the cluster and stream seem to have a spatial difference in their locations,they appear to have formed in the same region of the Galactic disk.

Cooperative Particle Swarm Optimization in Distance-Based Clustered Groups

TCPSO (Two-swarm Cooperative Particle Swarm Optimization) has been proposed by Sun and Li in 2014. TCPSO divides the swarms into two groups with different migration rules, and it has higher performance for high-dimensional nonlinear optimization problems than traditional PSO and other modified method of PSO. This paper proposes a particle swarm optimization by modifying TCPSO to avoid inappropriate convergence onto local optima. The quite feature of the proposed method is that two kinds of subpopulations constructed based on the scheme of TCPSO are divided into some clusters based on distance measure, k-means clustering method, to maintain both diversity and centralization of search process are maintained. This paper conducts numerical experiments using several types of functions, and the experimental results indicate that the proposed method has higher performance than the TCPSO for large-scale optimization problems.

Integrated HI emission in galaxy groups and clusters

The integrated HI emission from hierarchical structures such as groups and clusters of galax- ies can be detected by FAST at intermediate redshifts. Here we propose to use FAST to study the evolution of the global HI content of clusters and groups over cosmic time by measuring their integrated HI emissions. We use the Virgo Cluster as an example to estimate the detection limit of FAST, and have estimated the integration time to detect a Virgo type cluster at different redshifts （from z = 0.1 to z ---- 1.5）. We have also employed a semi-analytic model （SAM） to simulate the evolution of HI contents in galaxy clusters. Our simulations suggest that the HI mass of a Virgo-like cluster could be 2-3 times higher and the physical size could be more than 50% smaller when redshift increases from z = 0.3 to z = 1. Thus the integration time could be reduced significantly and gas rich clusters at intermediate redshifts can be detected by FAST in less than 2 hours of integration time. For the local Universe, we have also used SAM simulations to create mock catalogs of clusters to predict the outcomes from FAST all sky surveys. Comparing with the optically selected catalogs derived by cross matching the galaxy catalogs from the SDSS survey and the ALFALFA survey, we find that the HI mass distribution of the mock catalog with 20 s of integration time agrees well with that of observations. However, the mock catalog with 120 s of integration time predicts many more groups and clusters that contain a population of low mass HI galaxies not detected by the ALFALFA survey. A future deep HI blind sky survey with FAST would be able to test such prediction and set constraints on the numerical simulation models. The observational strategy and sample selections for future FAST observations of galaxy clusters at high redshifts are also discussed.

A Study of Triangle Inequality Violations in Social Network Clustering

Clustering a social network is a process of grouping social actors into clusters where intra-cluster similarities among actors are higher than inter-cluster similarities. Clustering approaches, i.e. , k-medoids or hierarchical, use the distance function to measure the dissimilarities among actors. These distance functions need to fulfill various properties, including the triangle inequality (TI). However, in some cases, the triangle inequality might be violated, impacting the quality of the resulting clusters. With experiments, this paper explains how TI violates while performing traditional clustering techniques: k-medoids, hierarchical, DENGRAPH, and spectral clustering on social networks and how the violation of TI affects the quality of the resulting clusters.

金融机构ESG投资的“漂绿”与“反漂绿”——基于DWSGroup的案例分析

深化绿色金融发展、助力经济绿色发展,是实现“双碳”目标的重要途径,特别是在ESG理念盛行的今天,金融机构的ESG投资行为因其深远影响而颇受投资者青睐,但层出不穷的“漂绿”现象也牢牢制约着绿色金融的发展,制约着ESG投资的发展。西方国家作为绿色金融发展先驱,从信息披露、制度监管、实践探索等方面对“漂绿”现象进行了研究和治理。本文在学习其先进经验的基础上,基于舞弊三角理论对DWSGroup案例进行分析,从压力、机会和自我合理化因素角度对金融机构ESG投资“漂绿”行为的动因进行分析,并据此提出我国“反漂绿”体系框架,以期为绿色金融的平稳发展和“双碳”目标的实现作出贡献。