Academic literature on the topic 'Non overlapping clusters'
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Journal articles on the topic "Non overlapping clusters"
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Qing, Huan. "Studying Asymmetric Structure in Directed Networks by Overlapping and Non-Overlapping Models." Entropy 24, no. 9 (August 30, 2022): 1216. http://dx.doi.org/10.3390/e24091216.
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We consider the problem of modeling and estimating communities in directed networks. Models to this problem in the previous literature always assume that the sending clusters and the receiving clusters have non-overlapping property or overlapping property simultaneously. However, previous models cannot model the directed network in which nodes in sending clusters have overlapping property, while nodes in receiving clusters have non-overlapping property, especially for the case when the number of sending clusters is no larger than that of the receiving clusters. This kind of directed network exists in the real world for its randomness, and by the fact that we have little prior knowledge of the community structure for some real-world directed networks. To study the asymmetric structure for such directed networks, we propose a flexible and identifiable Overlapping and Non-overlapping model (ONM). We also provide one model as an extension of ONM to model the directed network, with a variation in node degree. Two spectral clustering algorithms are designed to fit the models. We establish a theoretical guarantee on the estimation consistency for the algorithms under the proposed models. A small scale computer-generated directed networks are designed and conducted to support our theoretical results. Four real-world directed networks are used to illustrate the algorithms, and the results reveal the existence of highly mixed nodes and the asymmetric structure for these networks.
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Wu, Mary, Byung Chul Ahn, and Chong Gun Kim. "A Channel Reuse Procedure in Clustering Sensor Networks." Applied Mechanics and Materials 284-287 (January 2013): 1981–85. http://dx.doi.org/10.4028/www.scientific.net/amm.284-287.1981.
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Sensor nodes having the limited resource, energy efficiency is an important issue. Clustering on the sensor networks reduces the volume of inter-node communications and raises energy efficiency by transmitting the data collected from members by a cluster head to a sink node. But, due to radio frequency characteristics, interference and collision can occur between neighbor clusters, the resulted re-transmission is more energy consuming. The interference and collision occurred among adjacent clusters can be resolved by assigning non-overlapping channels among neighbor clusters. In this paper, we propose a channel reuse procedure which shows practical steps to assign dynamically channels among adjacent clusters in sensor networks. This method is expected to perform successfully the allocation process of non-overlapping channels for various cluster topologies.
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Vats, Divyanshu, and José M. F. Moura. "Finding Non-Overlapping Clusters for Generalized Inference Over Graphical Models." IEEE Transactions on Signal Processing 60, no. 12 (December 2012): 6368–81. http://dx.doi.org/10.1109/tsp.2012.2214216.
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Azizah, Anestasya Nur, Tatik Widiharih, and Arief Rachman Hakim. "Kernel K-Means Clustering untuk Pengelompokan Sungai di Kota Semarang Berdasarkan Faktor Pencemaran Air." Jurnal Gaussian 11, no. 2 (August 28, 2022): 228–36. http://dx.doi.org/10.14710/j.gauss.v11i2.35470.
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K-Means Clustering is one of the types of non-hierarchical cluster analysis which is frequently used, but has a weakness in processing data with non-linearly separable (do not have clear boundaries) characteristic and overlapping cluster, that is when visually the results of a cluster are between other clusters. The Gaussian Kernel Function in Kernel K-Means Clustering can be used to solve data with non-linearly separable characteristic and overlapping cluster. The difference between Kernel K-Means Clustering and K-Means lies on the input data that have to be plotted in a new dimension using kernel function. The real data used are the data of 47 rivers and 18 indicators of river water pollution from Dinas Lingkungan Hidup (DLH) of Semarang City in the first semester of 2019. The cluster results evaluation is used the Calinski-Harabasz, Silhouette, and Xie-Beni indexes. The goals of this study are to know the step concepts and analysis results of Kernel K-Means Clustering for the grouping of rivers in Semarang City based on water pollution factors. Based on the results of the study, the cluster results evaluation show that the best number of clusters K=4
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Laskhmaiah, K., S. Murali Krishna, and B. Eswara Reddy. "An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases." International Journal of Computer Network and Information Security 13, no. 6 (December 8, 2021): 70–82. http://dx.doi.org/10.5815/ijcnis.2021.06.06.
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From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but non-overlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With non-overlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized K-Means with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.
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Balaguer-Núñez, L., M. López del Fresno, E. Solano, D. Galadí-Enríquez, C. Jordi, F. Jimenez-Esteban, E. Masana, J. Carbajo-Hijarrubia, and E. Paunzen. "Clusterix 2.0: a virtual observatory tool to estimate cluster membership probability." Monthly Notices of the Royal Astronomical Society 492, no. 4 (February 11, 2020): 5811–43. http://dx.doi.org/10.1093/mnras/stz3610.
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ABSTRACT Clusterix 2.0 is a web-based, Virtual Observatory compliant, interactive tool for the determination of membership probabilities in stellar clusters based on proper-motion data using a fully non-parametric method. In an area occupied by a cluster, the frequency function is made up of two contributions: cluster and field stars. The tool performs an empirical determination of the frequency functions from the vector point diagram without relying on any previous assumption about their profiles. Clusterix 2.0 allows us to search the appropriate spatial areas in an interactive way until an optimal separation of the two populations is obtained. Several parameters can be adjusted to make the calculation computationally feasible without interfering with the quality of the results. The system offers the possibility to query different catalogues, such as Gaia, or upload a user’s own data. The results of the membership determination can be sent via Simple Application Messaging Protocol (SAMP) to Virtual Observatory (VO) tools such as Tool for OPerations on Catalogues And Tables (TOPCAT). We apply Clusterix 2.0 to several open clusters with different properties and environments to show the capabilities of the tool: an area of five degrees radius around NGC 2682 (M67), an old, well-known cluster; a young cluster NGC 2516 with a striking elongated structure extended up to four degrees; NGC 1750 and NGC 1758, a pair of partly overlapping clusters; the area of NGC 1817, where we confirm a little-known cluster, Juchert 23; and an area with many clusters, where we disentangle two overlapping clusters situated where only one was previously known: Ruprecht 26 and the new Clusterix 1.
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Richette, Pascal, Marijn Vis, Sarah Ohrndorf, William Tillett, Julio Ramírez, Marlies Neuhold, Michel van Speybroeck, et al. "Identification of PsA phenotypes with machine learning analytics using data from two phase III clinical trials of guselkumab in a bio-naïve population of patients with PsA." RMD Open 9, no. 1 (March 2023): e002934. http://dx.doi.org/10.1136/rmdopen-2022-002934.
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ObjectivesPsoriatic arthritis (PsA) phenotypes are typically defined by their clinical components, which may not reflect patients’ overlapping symptoms. This post hoc analysis aimed to identify hypothesis-free PsA phenotype clusters using machine learning to analyse data from the phase III DISCOVER-1/DISCOVER-2 clinical trials.MethodsPooled data from bio-naïve patients with active PsA receiving guselkumab 100 mg every 8/4 weeks were retrospectively analysed. Non-negative matrix factorisation was applied as an unsupervised machine learning technique to identify PsA phenotype clusters; baseline patient characteristics and clinical observations were input features. Minimal disease activity (MDA), disease activity index for psoriatic arthritis (DAPSA) low disease activity (LDA) and DAPSA remission at weeks 24 and 52 were evaluated.ResultsEight clusters (n=661) were identified: cluster 1 (feet dominant), cluster 2 (male, overweight, psoriasis dominant), cluster 3 (hand dominant), cluster 4 (dactylitis dominant), cluster 5 (enthesitis, large joints), cluster 6 (enthesitis, small joints), cluster 7 (axial dominant) and cluster 8 (female, obese, large joints). At week 24, MDA response was highest in cluster 2 and lowest in clusters 3, 5 and 6; at week 52, it was highest in cluster 2 and lowest in cluster 5. At weeks 24 and 52, DAPSA LDA and remission were highest in cluster 2 and lowest in clusters 4 and 6, respectively. All clusters improved with guselkumab treatment over 52 weeks.ConclusionsUnsupervised machine learning identified eight PsA phenotype clusters with significant differences in demographics, clinical features and treatment responses. In the future, such data could help support individualised treatment decisions.
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Ben N'Cir, Chiheb-Eddine, and Nadia Essoussi. "Using Sequences of Words for Non-Disjoint Grouping of Documents." International Journal of Pattern Recognition and Artificial Intelligence 29, no. 03 (April 27, 2015): 1550013. http://dx.doi.org/10.1142/s0218001415500135.
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Grouping documents based on their textual content is an important application of clustering referred to as text clustering. This paper deals with two issues in text clustering which are the detection of non-disjoint groups and the representation of textual data. In fact, a text document can discuss several topics and then, it must belong to several groups. The learning algorithm must be able to produce non-disjoint clusters and assigns documents to several clusters. Given that text documents are considered as unstructured data, the application of a learning algorithm requires to prepare a set of documents for numerical analysis by using the vector space model (VSM). This representation of text avoids correlation between terms and does not give importance to the order of words in the text. Therefore, we present in this paper an unsupervised learning method, based on the word sequence kernel, where the correlation between adjacent words in text and the possibility of document to belong to more than one cluster are not ignored. In addition, to facilitate the use of this method in text-analytic practice, we present the "DocCO" software which is publicly available. Experiments performed on several text collections show that the proposed method outperforms existing overlapping methods using VSM representation in terms of clustering accuracy.
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Himmelfarb, Sarah Talia, Nell Bond, Adaora Okoli, John Schieffelin, Jeffrey Shaffer, Robert J. Samuels, and Emily J. Engel. "31. Post-ebola Syndrome Presents with Multiple Overlapping Symptom Clusters: Evidence from an Ongoing Cohort Study in Eastern Sierra Leone." Open Forum Infectious Diseases 7, Supplement_1 (October 1, 2020): S16—S17. http://dx.doi.org/10.1093/ofid/ofaa417.030.
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Abstract Background Since the outbreak of Ebola Virus Disease (EVD) in West Africa from 2013–2016, a large cohort of survivors with persistent health complaints has emerged. This constellation of issues is termed post-Ebola syndrome. Here we characterize the symptoms and physical exam findings of this syndrome in a cohort of survivors from Sierra Leone 2.6 years after resolution of disease. Ebola survivors present with clusters of symptoms that represent sub phenotypes of post-Ebola syndrome Methods Potential survivor participants in Eastern Sierra Leone were identified and recruited through the Sierra Leone Association of Ebola Survivors. Household contacts of survivors were identified by enrolled survivors. Both groups were administered a questionnaire assessing self-reported symptoms. A physical exam was performed by a limited number of trained providers. Symptoms were then compared using hierarchical clustering. Statistical analysis of the correlations between clusters was conducted using conditional logistic regression. Both SPICE and principal component (PCA) analyses were performed to explore the relationships between symptom clusters. Results Between March 2016 and January 2019, 375 Ebola survivors and 1040 contacts were enrolled. At enrollment, Ebola survivors of all age groups reported significantly more symptoms than their contacts in all categories. Six symptom clusters were identified representing distinct organ systems. SPICE revealed 2 general phenotypes: with or without rheumatologic symptoms. Clusters including rheumatologic symptoms were correlated with one another (r = 0.63) but not with other clusters (r < 0.35). Ophthalmologic/auditory symptoms were moderately correlated with the non-rheumatologic clusters (r > 0.5). Interestingly, psychologic/neurologic, cardiac/GI and constitutional clusters correlated with one another (r > 0.6) p < 0.0001 in all cases. The symptom clusters were then mapped onto a PCA. Each symptom cluster separated from the remainder along PC1, particularly the phenotypes with rheumatologic symptoms. Conclusion This study presents an in-depth characterization of post-Ebola syndrome in Sierra Leonean survivors. The interrelationship between symptom clusters indicates that post-Ebola syndrome is a heterogeneous disease. The phenotypes identified may have unique mechanisms of pathogenesis, and require distinct therapies. Disclosures John Schieffelin, MD, MSPH, Wolters-Kluwer (Independent Contractor)
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Akhi, AH, S. Ahmed, ANMS Karim, F. Begum, and MM Rohman. "Genetic divergence of exotic inbred lines of maize (Zea mays. L)." Bangladesh Journal of Agricultural Research 42, no. 4 (February 27, 2018): 665–71. http://dx.doi.org/10.3329/bjar.v42i4.35793.
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Sixty exotic inbred lines of maize from CIMMYT were characterized for a few morphological attributes and grain yield at the experimental field of Bangladesh Agricultural Research Institute (BARI) during 2013-14. The inbred lines of the existing investigation were grouped into five distinct non-overlapping clusters based on D2 analysis. Cluster II was comprised of the highest number of inbreds whilst cluster III and IV included the lowest number of inbreds. The inter cluster distance was higher than intra cluster distance suggesting wider genetic diversity among the genotypes of different groups. The highest inter-cluster distance was exhibited between clusters II and V (D2 = 15.40) and the lowest inter-cluster distance was observed between clusters I and II (D2 = 2.82). Cluster II exhibited the highest mean values for cob length and cob diameter, cluster V for number of grain /cob and total grain weight. The lowest mean value for plant height & ear height were found in cluster II and cluster IV for days to pollen shedding and days to silking. Days to silking, plant height, cob length (cm), number of rows /cob, number of grains /cob showed maximum contribution towards total divergence among different characters. The inbred lines were characterized for their morphological traits and kernel yield to achieve more heterotic partners to get higher heterosis.Bangladesh J. Agril. Res. 42(4): 665-671, December 2017
Dissertations / Theses on the topic "Non overlapping clusters"
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Beka, Sylvia Enobong. "The genomics of Type 1 Diabetes susceptibility regions and effect of regulatory SNPs." Thesis, University of Hertfordshire, 2016. http://hdl.handle.net/2299/17200.
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Human complex diseases, like Diabetes and Cancer, affect many people worldwide today. Despite existing knowledge, many of these diseases are still not preventable. Complex diseases are known to be caused by a combination of genetic factors, as well as environmental and life style factors. The scope of this investigation covered the genomics of Type 1 Diabetes (T1D). There are 49 human genomic regions that are known to carry markers (disease-associated single nucleotide mutations) for T1D, and these were extensively studied in this research. The aim was to find out in how far this disease may be caused by problems in gene regulation rather than in gene coding. For this, the genetic factors associated with T1D, including the single point mutations and susceptibility regions, were characterised on the basis of their genomic attributes. Furthermore, mutations that occur in binding sites for transcription factors were analysed for change in the conspicuousness of their binding region, caused by allele substitution. This is called SNP (Single nucleotide polymorphism) sensitivity. From this study, it was found that the markers for T1D are mostly non-coding SNPs that occur in introns and non-coding gene transcripts, these are structures known to be involved in gene regulatory activity. It was also discovered that the T1D susceptibility regions contain an abundance of intronic, non-coding transcript and regulatory nucleotides, and that they can be split into three distinct groups on the basis of their structural and functional genomic contents. Finally, using an algorithm designed for this study, thirty-seven SNPs that change the representation of their surrounding region were identified. These regulatory mutations are non-associated T1D-SNPs that are mostly characterised by Cytosine to Thymine (C-T) transition mutations. They were found to be closer in average distance to the disease-associated SNPs than other SNPs in binding sites, and also to occur frequently in the binding motifs for the USF (Upstream stimulatory factor) protein family which is linked to problems in Type 2 diabetes.
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Tribou, Michael John. "Relative Pose Estimation Using Non-overlapping Multicamera Clusters." Thesis, 2014. http://hdl.handle.net/10012/8141.
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This thesis considers the Simultaneous Localization and Mapping (SLAM) problem using a set of perspective cameras arranged such that there is no overlap in their fields-of-view. With the known and fixed extrinsic calibration of each camera within the cluster, a novel real-time pose estimation system is presented that is able to accurately track the motion of a camera cluster relative to an unknown target object or environment and concurrently generate a model of the structure, using only image-space measurements. A new parameterization for point feature position using a spherical coordinate update is presented which isolates system parameters dependent on global scale, allowing the shape parameters of the system to converge despite the scale parameters remaining uncertain. Furthermore, a flexible initialization scheme is proposed which allows the optimization to converge accurately using only the measurements from the cameras at the first time step. An analysis is presented identifying the configurations of the cluster motions and target structure geometry for which the optimization solution becomes degenerate and the global scale is ambiguous. Results are presented that not only confirm the previously known critical motions for a two-camera cluster, but also provide a complete description of the degeneracies related to the point feature constellations. The proposed algorithms are implemented and verified in experiments with a camera cluster constructed using multiple perspective cameras mounted on a quadrotor vehicle and augmented with tracking markers to collect high-precision ground-truth motion measurements from an optical indoor positioning system. The accuracy and performance of the proposed pose estimation system are confirmed for various motion profiles in both indoor and challenging outdoor environments.
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Huang, Ying-Shuo, and 黃盈碩. "Non-exhaustive Clustering for Overlapping Patent Clusters Analysis." Thesis, 2007. http://ndltd.ncl.edu.tw/handle/15970598844632266402.
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碩士國立清華大學
工業工程與工程管理學系
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Facing the challenges from a knowledge-based economy, having a comprehensive understanding insights of technology and industry development is the basic and necessary requirement to gain competitive edges for enterprises. Consequently, enterprises apply technology forecasting techniques to assist generating useful information for further R&D strategic decisions. Nonetheless, current technology forecasting analyses base mostly on macro-indicators such as market share and growth rate rather than on specific technology development information, such as invention and patents of certain technology. In Campbell’s (1983) research, he found that patent documents often better expresses the development trend of technology when compare with ordinary scientific journals. Moreover, according to the report of WIPO (1996), patent documents can better reveal the core technology and innovation than other knowledge documents such as journal papers and technical reports. As a result, we try to incorporate patent analysis while proceed technology forecasting. In this research, a non-exhaustive clustering methodology is proposed as the basis for a novel technology forecasting system. Non-exhaustive clustering methodology allows overlapping of patent documents, which is plausible when any patent can claim multiple key technical inventions. The characteristic of non-exhaustivity emphasizes that one patent contains multiple technology breakthroughs. We use Radio Frequency Identification (RFID) as case example in this research. RFID ontology is constructed. Afterward, refined Normalized Term Frequency/Inverse Document Frequency (NTF-IDF) key-phrase extraction methodology is developed to extract representative key phrases for following clustering procedure. Finally, the non-exhaustive clustering methodology is applied to generate overlapping clusters of patents. The clustering results and analysis of growth trend for each cluster provide users a clear view of patent distribution in a given broad technology area (e.g., RFID). The expected results of this research contain extracting domain key phrases precisely, using the non-exhaustive clustering results as input data of technology forecasting and finally visualize the technology trend. This system enables R&D engineers and managers to find the existing patents related to their interested technical domains (clusters) and enable them to strengthen their R&D efforts offensively and defensively.
Books on the topic "Non overlapping clusters"
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Palfrey, Simon. Formaction. Edited by Henry S. Turner. Oxford University Press, 2017. http://dx.doi.org/10.1093/oxfordhb/9780199641352.013.18.
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This chapter examines formactions—the working parts and craft materials of playworlds—that are often simultaneous, clustered, overlapping, and invisible and do not simply mediate or re-present things in the world, but are themselves vitally immanent with possible life. It argues that ‘theatricality’ describes not a technology of mimesis or even a kind of enacted philosophy, but rather a kind of physics: a world in which bodies, ideas, affects, and figures combine and recombine to generate the plays we watch, read, react to, and think about today. It highlights the value of the category of ‘form’ and uses it to address some of the major methodological problems associated with early modern theatre, including the problem of the ontology of theatre and its creations. It considers the metaphysics of Gottfried Wilhelm Leibniz, with particular emphasis on his philosophy of monads, to think about theatrical life.
Book chapters on the topic "Non overlapping clusters"
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Singh, Sarjinder. "Non-Overlapping, Overlapping, Post, and Adaptive Cluster Sampling." In Advanced Sampling Theory with Applications, 765–828. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-007-0789-4_9.
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Sarkar, Anasua, and Ujjwal Maulik. "Cancer Gene Expression Data Analysis Using Rough Based Symmetrical Clustering." In Handbook of Research on Computational Intelligence for Engineering, Science, and Business, 699–715. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2518-1.ch027.
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Identification of cancer subtypes is the central goal in the cancer gene expression data analysis. Modified symmetry-based clustering is an unsupervised learning technique for detecting symmetrical convex or non-convex shaped clusters. To enable fast automatic clustering of cancer tissues (samples), in this chapter, the authors propose a rough set based hybrid approach for modified symmetry-based clustering algorithm. A natural basis for analyzing gene expression data using the symmetry-based algorithm is to group together genes with similar symmetrical patterns of microarray expressions. Rough-set theory helps in faster convergence and initial automatic optimal classification, thereby solving the problem of unknown knowledge of number of clusters in gene expression measurement data. For rough-set-theoretic decision rule generation, each cluster is classified using heuristically searched optimal reducts to overcome overlapping cluster problem. The rough modified symmetry-based clustering algorithm is compared with another newly implemented rough-improved symmetry-based clustering algorithm and existing K-Means algorithm over five benchmark cancer gene expression data sets, to demonstrate its superiority in terms of validity. The statistical analyses are also performed to establish the significance of this rough modified symmetry-based clustering approach.
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"Graph Mining Approaches to Study Volunteer Relationships in Sourceforge.net." In Free and Open Source Software in Modern Data Science and Business Intelligence, 117–39. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-3707-6.ch007.
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The contribution of volunteers in the development of Free and Open Source Software in Sourceforge.net is studied in this paper. Using Social Network analysis, the small set of developers who can maximize the information flow in the network are discovered. The propagation of top developers across past three years are also studied. The four algorithms used to find top influential developers gives almost similar results. The movement of top developers over past years was also consistent. Influential nodes in a network are very important to diffuse influence on the rest of the network. They are most often highly connected within the network. The existing algorithms are efficient to identify them. However, the challenge is in selecting a seed set that can spread the influence instantaneously with least effort. In this paper, a method is defined to spread influence on the entire network by selecting the least number of non-overlapping influential nodes faster than a well known existing algorithm. Further to this, the number of clusters in the network is also determined simultaneously from the seed set of the networks.
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Variego, Jorge. "Harmony (Exercises 21–40)." In Composing with Constraints, 23–42. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190057237.003.0003.
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This chapter focuses on sounds that occur simultaneously. The concept of harmony here is broad and has a far-reaching scope; it includes functional and non-functional harmonies, color, and any other approach to simultaneous sonorities. Exercise 21 suggests the implementation of voice leading as a constructive principle through stylistic imitation. Number 22, in which “melody becomes harmony,” proposes the harmonization of a melody using exclusively its pitch content. Exercise 23 incorporates symmetrical harmonies; 24 and 25 use the harmonic series to create harmonies. In 26 the harmonies are built around major and minor triads with pitches in common. Exercise 27 incorporates the use of integer notation; 28 uses scale degrees but not triads. The base of exercise 29 is the 12-tone row; 30 uses the concept of “circle” progression or harmonies that move around the circle of fifths; 31 and 32 incorporate harmonies that move in major and minor thirds; 33 and 34 discuss overlapping triads and polytonality; 35 provides an opportunity to work with pedal tones; 36 incorporates the church modes and 37 the use of clusters (i.e., harmonies based exclusively on minor a major seconds). Exercise 38 is based on harmonic sequences; 39 and 40 discuss implied harmonies and writing “contrafacts.”
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Peneder, Michael, and Andreas Resch. "Finance and economic evolution." In Schumpeter's Venture Money, 138–68. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780198804383.003.0007.
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The final chapter of Part II highlights Schumpeter's seminal theory of economic development as a deliberate monetary conception and its genuine account of the nexus between finance and the real economy. Innovation and creative destruction critically depend on the creation and reallocation of purchasing power through finance. The chapter offers a brief reconstruction, which assimilates Schumpeter's further elaborations on selected themes in his later publications along several basic premises, such as open opportunity sets, entrepreneurship and innovation, temporary monopoly profits, or discontinuous change. Furthermore, venture investors must trust the entrepreneurs' vision and provide them with the purchasing power needed to control the means of production in advance. Successful innovations induce imitation and their progressive diffusion annihilates the temporary monopoly surplus, thereby decreasing prices, raising demand and fostering the overall growth of real income. Moreover, innovations do not occur independently, but are clustered, generating overlapping waves of business fluctuations.
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Schmidt, Vivien A. "Conclusion How to (Re-)Envision Eurozone Governance." In Europe's Crisis of Legitimacy, 291–304. Oxford University Press, 2020. http://dx.doi.org/10.1093/oso/9780198797050.003.0011.
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After briefly setting the Eurozone crisis in international context, this Conclusion asks how the EU may move forward, to re-envision Eurozone governance while resolving the EU’s (euro) crisis of legitimacy in ways that ensure greater input, output, and throughput legitimacy. To do so, it recommends reforming Eurozone governance through a more fine-tuned approach to EU-level macroeconomic coordination via more differentiated country-specific targets debated by EU actors. And it proposes decentralizing the administration of the European Semester to ensure a more bottom-up process that is more sensitive to national varieties of capitalism and growth models. Only in so doing, the chapter argues, can there be greater democratic input at both EU and national levels, which could serve to reduce the citizen discontent driving populism. But for this to work, more solidarity mechanisms along with more resources are required not just for the Eurozone but equally across crisis areas, to ensure successful output performance. Finally, the chapter suggests that EU governance itself requires rethinking through more differentiated integration. Rather than a “hard core” moving forward, or Europe “à la carte,” the most effective future integration would be by seeing recognizing the EU as it already is: a “soft core” Europe of clusters of member states in overlapping policy communities, administered by one overall set of institutions.
Conference papers on the topic "Non overlapping clusters"
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tava, Martin, and Pavel Tvrdík. "Overlapping Non-dedicated Clusters Architecture." In 2009 International Conference on Computer Engineering and Technology (ICCET 2009). IEEE, 2009. http://dx.doi.org/10.1109/iccet.2009.66.
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Stava, Martin, and Pavel Tvrdik. "Security System for Overlapping Non-dedicated Clusters." In 2009 IEEE International Symposium on Parallel and Distributed Processing with Applications. IEEE, 2009. http://dx.doi.org/10.1109/ispa.2009.19.
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Akyamac, Ahmet A., and Thomas P. Chu. "Non-overlapping rings: A new architecture for designing switch clusters in data centers." In 2013 IEEE Globecom Workshops (GC Wkshps). IEEE, 2013. http://dx.doi.org/10.1109/glocomw.2013.6825023.
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Peng, Jinjia, Yang Wang, Huibing Wang, Zhao Zhang, Xianping Fu, and Meng Wang. "Unsupervised Vehicle Re-identification with Progressive Adaptation." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/127.
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Vehicle re-identification (reID) aims at identifying vehicles across different non-overlapping cameras views. The existing methods heavily relied on well-labeled datasets for ideal performance, which inevitably causes fateful drop due to the severe domain bias between the training domain and the real-world scenes; worse still, these approaches required full annotations, which is labor-consuming. To tackle these challenges, we propose a novel Progressive Adaptation Learning method for vehicle reID, named PAL, which infers from the abundant data without annotations. For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as “pseudo target samples”. These pseudo samples are combined with the unlabeled samples that are selected by a dynamic sampling strategy to make training faster. We further proposed a weighted label smoothing (WLS) loss, which considers the similarity between samples with different clusters to balance the confidence of pseudo labels. Comprehensive experimental results validate the advantages of PAL on both VehicleID and VeRi-776 dataset.
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Kuchumov, R., and V. Korkhov. "HPC WORKLOAD BALANCING ALGORITHM FOR CO- SCHEDULING ENVIRONMENTS." In 9th International Conference "Distributed Computing and Grid Technologies in Science and Education". Crossref, 2021. http://dx.doi.org/10.54546/mlit.2021.21.34.001.
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The goal of this research work is to reduce wait time of HPC (high performance computing) applications in schedulers queue by applying a co-scheduling strategy. This strategy allows the execution of more than one task with different non-overlapping requirements for computational resources simultaneously. Co-scheduling strategy reduces task queue wait time and improves utilization of cluster resources when compared to the scheduling strategies that do not allow for parallel task execution on the same machine. We have proposed a method for measuring application processing speed in its run-time, which can be used as a feedback for scheduling strategies. In this work, we have formalized the co-scheduling problem and proposed strategies for solving it. For some strategies we have shown analytically the upper bounds values of their competitive ratios. Besides that for the proposed scheduling strategies we ran numerical experiments using imitation models to show how they compare to the optimal strategy.
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Mahdavi, Arash, and Eric Mockensturm. "A New Hierarchical Technique for the Multiscale Modeling of Carbon Nanostructures." In ASME 2005 International Mechanical Engineering Congress and Exposition. ASMEDC, 2005. http://dx.doi.org/10.1115/imece2005-82988.
Full textAbstract:
We present a new hierarchical modeling technique called the Consistent Atomic-scale Finite Element (CAFE´) method [1]. Unlike traditional approaches for linking the atomic structure to its equivalent continuum [2-7], this method directly connects the atomic degrees of freedom to a reduced set of finite element degrees of freedom without passing through an intermediate homogenized continuum. As a result, there is no need to introduce stress and strain measures at the atomic level. This technique partitions atoms to masters and salves and reduces the total number of degrees of freedom by establishing kinematic constraints between them [5-6]. The Tersoff-Brenner interatomic potential [8] is used to calculate the consistent tangent stiffness matrix of the structure. In this finite element formulation, all local and non-local interactions between carbon atoms are taken into account using overlapping finite elements (Figure 1b). In addition, a consistent hierarchical finite element modeling technique is developed for adaptively coarsening and refining the mesh over different parts of the model (Figure 2a, 2b). The stiffness of higher-rank elements is approximated using the stiffness of lower-rank elements and kinematic constraints. This process is consistent with the underlying atomic structure and, by refining the mesh, molecular dynamic results will be recovered. This method is valid across the scales and can be used to concurrently model atomistic and continuum phenomena so, in contrast with most other multiscale methods [4-7], there is no need to introduce artificial boundaries for coupling atomistic and continuum regions. Effect of the length scale of the nanostructure is also included in the model by building the hierarchy of elements from bottom up using a finite size atom cluster as the building block (Figures 2a, 2b). In this method by introducing two independent field variables, the so-called inner displacement is taken into account (Fig. 3b). Applicability of the method is shown with several examples of deformation of carbon nanostructures such as graphene sheet, nanotube, and nanocone, subjected to different loads and boundary conditions.