scholarly journals Quantifying the rarity of the local super-volume

2021 ◽  
Author(s):  
Stephen Stopyra ◽  
Hiranya V Peiris ◽  
Andrew Pontzen ◽  
Jens Jasche ◽  
Priyamvada Natarajan
Keyword(s):  
Cosmological Model ◽  
The Other ◽  
Order Unity ◽  
Number Of Clusters ◽  
Cluster Mass ◽  
Systematic Uncertainties

Abstract We investigate the extent to which the number of clusters of mass exceeding 1015 M⊙ h−1 within the local super-volume (<135 Mpc h−1) is compatible with the standard ΛCDM cosmological model. Depending on the mass estimator used, we find that the observed number N of such massive structures can vary between 0 and 5. Adopting N = 5 yields ΛCDM likelihoods as low as 2.4 × 10−3 (with σ8 = 0.81) or 3.8 × 10−5 (with σ8 = 0.74). However, at the other extreme (N = 0), the likelihood is of order unity. Thus, while potentially very powerful, this method is currently limited by systematic uncertainties in cluster mass estimates. This motivates efforts to reduce these systematics with additional observations and improved modelling.

scholarly journals Determination of Fertility-Related Traits in Muscadine Grape Population

Plants ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 1175
Author(s):  
Jiovan Campbell ◽  
Pranavkumar Gajjar ◽  
Ahmed Ismail ◽  
Fariborz Habibi ◽  
Ahmed G. Darwish ◽  
...  
Keyword(s):  
Fruit Set ◽  
The Other ◽  
Average Yield ◽  
Number Of Clusters ◽  
Breeding Lines ◽  
Muscadine Grape

In this study, fertility-related traits of 90 muscadine grape genotypes were evaluated. Selected genotypes included 21 standard cultivars, 60 breeding lines, and nine Vitis × Muscadinia hybrids (VM hybrids). The first fruiting bud (FFB), bud fertility (BF), bud fertility coefficient (BFC), number of flowers/flower cluster (N.F/FC), fruit-set efficiency (FSE), number of clusters/vine (N.C/V), and yield/vine (Y/V) traits were evaluated. The FFB trait did not show significant differences among genotypes. The muscadine genotype O28-4-2-2 (1.6 ± 0.2) displayed the FFB closest to the base; however, O17-16-2-1, O18-2-1, and VM A12-10-2 genotypes had the most distant FFB (3.6 ± 0.3). All the other fertility-related traits varied widely among the population. The BF, BFC, N.F/FC, FSE, N.C/V, and Y/V exhibited a range estimated at 35.1%, 81.5%, 259.7, 63.3%, 177 C/V, and 22.3 kg/V, respectively. The muscadine genotypes O42-3-1 (36.7% ± 1.3) and Majesty (34% ± 1.2) exhibited the highest BF; however, the VM A12-10-2 (1.6% ± 0.1) recorded the lowest BF. The VM genotype O15-16-1 (82.8% ± 4.1) displayed the highest BFC; however, the VM A12-10-2 (1.3% ± 0.1) showed the lowest BFC. The muscadine genotypes D7-1-1 (280.3 F/FC ± 21.7) and O17-17-1 (20.7 F/FC ± 5.5) showed the highest and lowest N.F/FC, respectively. The maximum and minimum FSE was observed for the Rosa cultivar (65.7% ± 2.4) and muscadine genotype D7-1-1 (2.4% ± 0.2), respectively. The minimum N.C/V was recorded for VM genotype A12-10-2 (6 C/V ± 0.2) and maximum noted for muscadine genotypes B20-18-2 (183 C/V ± 7.5) and O44-14-1 (176 C/V ± 7.3). Muscadine genotype O23-11-2 (22.6 kg ± 1.1) produced the highest Y/V; however, the lowest yield was recorded for O15-17-1, Fry Seedless, Sugargate, and the VM genotypes and A12-10-2, with an average yield among them estimated at 0.4 kg ± 0.2.

scholarly journals Utjecaj prorjeđivanja grozdova na kakvoću mošta kultivara Chardonnay u kutjevačkom vinogorju

2021 ◽  
Vol 82 (4) ◽  
pp. 173-184
Author(s):  
Valentina Obradović ◽  
Brankica Svitlica ◽  
Maja Ergović Ravančić ◽  
Svjetlana Škrabal ◽  
Helena Marčetić ◽  
...  
Keyword(s):  
Lactic Acid ◽  
Sugar Content ◽  
Amino Nitrogen ◽  
Ammonia Nitrogen ◽  
Ammonium Nitrogen ◽  
Total Acidity ◽  
Number Of Clusters ◽  
Cluster Mass ◽  
Significant Difference ◽  
Cluster Thinning

Kutjevo wine-hills are located on southern slopes of Papuk and Krndija mountains. The area is the most famous by production of Graševina grapes, but increasing share of other varieties cannot be ignored. Chardonnay is the most widespread variety all over the world, and in Požeško-slavonska county is represented by 5 % of total vineyards area. The aim of this research was to determine the influence of cluster thinning in Kutjevo wine-hills on maturation and must quality of Chardonnay grapes. Research was conducted in 2020 in Podgorje location (Kutjevo wine-hills). Experiment was established by a randomized block schedule in two treatments with three repetitions. Five vines in a row makes one repetition. The following parameters have been determined: sugar content and total acidity in grapes in period of one month before harvest, number of clusters per vine, cluster mass, mass of 100 berries, density, total acidity, volatile acidity, tartaric acid, malic acid, lactic acid, pH, reducing sugars, extract, glucose, fructose, glycerol, alfa amino nitrogen, ammonia nitrogen and potassium. Results have showed that cluster thinning had a significant influence on cluster mass and number of clusters per vine, but majority of chemical parameters were not significantly different between two treatments. Statistically significant difference was only in case of pH, lactic acid and ammonium nitrogen

Efficient text feature extraction by integrating the average linkage and K-medoids clustering

2021 ◽  
pp. 2150151
Author(s):  
Dasong Sun
Keyword(s):  
Feature Extraction ◽  
Text Classification ◽  
Experimental Results ◽  
The Other ◽  
Central Feature ◽  
Number Of Clusters ◽  
Average Linkage ◽  
Text Feature

By clustering feature words, we can not only simplify the dimension of feature subsets, but also eliminate the redundancy of the feature. However, for a feature set with very large dimensions, the traditional [Formula: see text]-medoids algorithm is difficult to accurately estimate the value of [Formula: see text]. Moreover, the clustering results of the average linkage (AL) algorithm cannot be divided again, and the AL algorithm cannot be directly used for text classification. In order to overcome the limitations of AL and [Formula: see text]-medoids, in this paper, we combine the two algorithms together so as to be mutually complementary to each other. In particular, in order to meet the purpose of text classification, we improve the AL algorithm and propose the [Formula: see text] testing statistics to obtain the approximate number of clusters. Finally, the central feature words are preserved, and the other feature words are deleted. The experimental results show that the new algorithm largely eliminates the redundancy of the feature. Compared with the traditional TF-IDF algorithms, the performance of the text classification of the new algorithm is improved.

scholarly journals Catalogue of Clusters that are Members of Superclusters

1983 ◽  
Vol 104 ◽  
pp. 185-186
Author(s):  
M. Kalinkov ◽  
K. Stavrev ◽  
I. Kuneva
Keyword(s):  
Standard Deviation ◽  
Correlation Coefficient ◽  
The Other ◽  
Clusters Of Galaxies ◽  
Number Of Clusters ◽  
Abell Clusters ◽  
The Mean ◽  
Abell Cluster ◽  
Image Position

An attempt is made to establish the membership of Abell clusters in superclusters of galaxies. The relation is used to calibrate the distances to the clusters of galaxies with two redshift estimates. One is m10, the magnitude of the ten-ranked galaxy, and the other is the “mean population,” P, defined by: where p = 40, 65, 105 … galaxies for richness groups 0, 1, 2 …, and r is the apparent radius in degrees given by: The first iteration for redshift, z1, is obtained from m10 alone: The standard deviation for Eq. (1) is 0.105, the number of clusters with known velocities is 342 and the correlation coefficient between observed and fitted values is 0.921. With zi from Eq. (1), we define Cartesian galactic coordinates Xi = Rih−1 cosBi cosLi, Yi = Rih−1 cosBi sinLi, Zi = Rih−1 sinBi for each Abell cluster, i = 1, …, 2712, where Ri is the distance to the cluster (Mpc), and Ho = 100 h km s−1 Mpc−1.

A GRAPH-BASED ALGORITHM FOR CLUSTER DETECTION

2008 ◽  
Vol 22 (05) ◽  
pp. 843-860 ◽  
Author(s):  
PASQUALE FOGGIA ◽  
GENNARO PERCANNELLA ◽  
CARLO SANSONE ◽  
MARIO VENTO
Keyword(s):  
Computer Vision ◽  
State Of The Art ◽  
Cluster Detection ◽  
The Other ◽  
Actual Number ◽  
Number Of Clusters ◽  
Size And Shape ◽  
Computer Vision Applications

In some Computer Vision applications there is the need for grouping, in one or more clusters, only a part of the whole dataset. This happens, for example, when samples of interest for the application at hand are present together with several noisy samples. In this paper we present a graph-based algorithm for cluster detection that is particularly suited for detecting clusters of any size and shape, without the need of specifying either the actual number of clusters or the other parameters. The algorithm has been tested on data coming from two different computer vision applications. A comparison with other four state-of-the-art graph-based algorithms was also provided, demonstrating the effectiveness of the proposed approach.

Mathematics and Time

KronoScope ◽  
2003 ◽  
Vol 3 (2) ◽  
pp. 153-167 ◽  
Author(s):  
Keyword(s):  
Broad Context ◽  
The Other ◽  
Evolutionary Development ◽  
Structure And Properties ◽  
Other Hand ◽  
Systematic Uncertainties ◽  
Human Capacity ◽  
The Rich ◽  
The One ◽  
Axiomatic Systems

AbstractThe structure of mathematics, as revealed by the exploration of axiomatic systems, bears striking similarities to the structure of nature, as revealed by the hierarchical theory of time. It is assumed that this isomorphism is not accidental but reflects the evolutionary development of the human capacity of handling numbers. This assumption permits a conjecture. Namely, if mathematics is found to possess certain systematic uncertainties, than nature must also possess corresponding qualities which may be identified. The paper proposes that the theme of the conference, "time and uncertainty," be understood in this broad context. I would like to demonstrate the existence of certain striking similarities between the structure and properties of mathematics on the one hand and, on the other hand, the structures and processes of nature at large, as revealed by the hierarchical theory of time. Then, using these correspondences, I propose a framework that promises to provide a unified perspective for the rich program we have ahead.

scholarly journals Clustering Multiple Hydrographs Using Mathematical Optimization

10.29007/6r61 ◽  
2018 ◽  
Author(s):  
Kazuhiro Matsumoto ◽  
Mamoru Miyamoto
Keyword(s):  
Combinatorial Optimization ◽  
Water Level ◽  
Continuous Optimization ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
The Other ◽  
Flood Events ◽  
Number Of Clusters ◽  
Different Characteristics ◽  
Insight Into

A mathematical optimization procedure is presented to group multiple hydrographs into a small number of clusters for the purpose of helping to understand various runoff behaviors observed in flood events in a basin. In grouping, the hydrographs belonging to each cluster can be estimated within the specified accuracy by the corresponding parameter set. The effectiveness is demonstrated using twenty-seven hydrographs observed in nine flood events and at three water level stations in the Abe River basin in Japan. The optimization results illustrate that eight sets of parameters are necessary to estimate such hydrographs within the specified accuracy. One parameter set commonly estimates as many as seven out of twenty-seven hydrographs while some other parameter sets estimate the other hydrographs with different characteristics specific to flood events or water level stations. Most of the previous research is based on continuous optimization; however, a presenting procedure such as clustering is based on combinatorial optimization. Thus, new insight into understanding the runoff behaviors is brought by combinatorial optimization which is not often used in previous research.

scholarly journals Korelasi Antara Profil dan Nilai Akademis Siswa dengan Menggunakan Algoritma K-Means

2021 ◽  
Vol 8 (4) ◽  
pp. 845
Author(s):  
Surohman Surohman ◽  
Luky Fabrianto ◽  
Faiza Riza ◽  
Novianti M Faizah
Keyword(s):  
Parental Education ◽  
The Other ◽  
Vocational High School ◽  
Academic Values ◽  
Number Of Clusters ◽  
Student Profile ◽  
Transportation Equipment ◽  
Attribute Data ◽  
Ideal Number ◽  
Gender Type

<p>Hampir setiap pelajar di Indonesia terdaftar dengan atribut profil yang lengkap, seperti : Nama, Jenis Kelamin, Jenis Tinggal, Alat Transportasi, Usia Orangtua, Pendidikan Orangtua, Pekerjaan Orangtua, Penghasilan Orangtua dan atribut lainnya. Dari data atribut profil tersebut dapat diklasterisasi berdasarkan kedekatan nilai antara atribut yang dimiliki masing-masing siswa. Disisi lain siswa juga memiliki data yang berisi nilai akademis yang juga dapat dibuat klasterisasi.</p><p>Data yang dipakai dalam penelitian ini melibatkan 512 instances yang didapat dari sebuah Sekolah Menengah Kejuruan (SMK)  di Jakarta. Metode yang pakai untuk klasterisasi menggunakan algoritma <em>K-Means.</em> Penelitian ini akan mencari korelasi klasterisasi profil siswa terhadap nilai akademisnya.</p><p>Tahapan penelitian diawali dengan persiapan dataset profil dan dataset nilai siswa, atribut dari dataset profil yang dipakai hanya atribut yang dianggap dapat merepresentasikan profil siswa dan keluarganya. Tahap berikutnya adalah mentrasformasi data atribut non numerik  (kategorik dan interval) menjadi numerik. Dilanjutkan dengan tahap perhitungan jarak antar data dan tahap terakhir mencari pola korelasi antara klaster profil dan klaster nilai akademis yang terbentuk.</p><p>Dengan metode <em>elbow</em> jumlah klaster yang paling ideal dalam penelitian ini adalah antara 3 dan 4 klaster, dimana nilai <em>Silhoutte Coefficient</em> tertinggi adalah 0,8103 untuk penglompokan 3 klaster.</p><p> </p><p><em><strong>Abstract</strong></em></p><p class="Judul2"><em>Almost every student in Indonesia is registered with complete profile attributes, such as: Name, Gender, Type of Stay, Transportation Equipment, Parents' Age, Parental Education, Parents' Work, Parents' Earnings and other attributes. From the profile attribute data it can be clustered based on the closeness of the values between the attributes possessed by each student. On the other hand students also have attribute data that contains academic values that can also be clustered.</em></p><p class="Judul2"><em>The data used in this study involved 512 instances obtained from a Vocational High School (SMK) in Jakarta. The method used for clustering is using the K-Means algorithm. This research will look for correlation of student profile clustering to its academic value.</em></p><p class="Judul2"><em>The stages of the research began with the preparation of the profile dataset and the student value dataset, the attributes of the profile dataset used were only those attributes that were considered to represent the profiles of students and their families. The next step is to transform non-numeric attribute data (categorical and interval) into numeric. Followed by the stage of calculating the distance between data and the final stage looking for patterns of correlation between profile clusters and academic value clusters that are formed.</em></p><p class="Judul2"><em>With the elbow method, the most ideal number of clusters in this study is between 3 and 4 clusters, where the highest Silhoutte Coe</em><em>f</em><em>ficient value is 0.8103 for grouping 3 clusters.</em></p><p><em><strong><br /></strong></em></p>

A Hybrid Validity Index to Determine K Parameter Value of k-Means Algorithm for Time Series Clustering

2021 ◽  
pp. 1-22
Author(s):  
Fatma Ozge Ozkok ◽  
Mete Celik
Keyword(s):  
Time Series ◽  
Clustering Algorithms ◽  
Real Life ◽  
Internal Validity ◽  
The Other ◽  
Sequential Data ◽  
Data Mining Technique ◽  
Validity Index ◽  
Number Of Clusters ◽  
Benchmark Datasets

Time series is a set of sequential data point in time order. The sizes and dimensions of the time series datasets are increasing day by day. Clustering is an unsupervised data mining technique that groups objects based on their similarities. It is used to analyze various datasets, such as finance, climate, and bioinformatics datasets. [Formula: see text]-means is one of the most used clustering algorithms. However, it is challenging to determine the value of [Formula: see text] parameter, which is the number of clusters. One of the most used methods to determine the number of clusters (such as [Formula: see text]) is cluster validity indexes. Several internal and external validity indexes are used to find suitable cluster numbers based on characteristics of datasets. In this study, we propose a hybrid validity index to determine the value of [Formula: see text] parameter of [Formula: see text]-means algorithm. The proposed hybrid validity index comprises four internal validity indexes, such as Dunn, Silhouette, C index, and Davies–Bouldin indexes. The proposed method was applied to nine real-life finance and benchmarks time series datasets. The financial dataset was obtained from Yahoo Finance, consisting of daily closing data of stocks. The other eight benchmark datasets were obtained from UCR time series classification archive. Experimental results showed that the proposed hybrid validity index is promising for finding the suitable number of clusters with respect to the other indexes for clustering time-series datasets.

Tachyonic braneworld mimetic cosmology

2019 ◽  
Vol 34 (21) ◽  
pp. 1950162 ◽  
Author(s):  
S. Davood Sadatian ◽  
Alireza Sepehri
Keyword(s):  
Cosmological Model ◽  
Separation Distance ◽  
The Other ◽  
Late Time ◽  
Moduli Stabilization ◽  
Inflation Model ◽  
Braneworld Model ◽  
And Behavior ◽  
The Universe ◽  
Mimetic Gravity

Recently, some authors have generalized the idea of mimetic gravity to a Randall–Sundrum II braneworld model [L. Randall and R. Sundrum, Phys. Rev. Lett. 83, 3370 (1999); L. Randall and R. Sundrum, Phys. Rev. Lett. 83, 4690 (1999)] and introduced Braneworld Mimetic Cosmology. In this paper, we extend their new cosmological model to brane–anti-brane systems and obtain the explicit form of potential which appeared in their action. This potential depends on the tachyonic fields, the separation distance between two branes and time. On the other hand, our universe is located on one of the branes and its evolution is controlled by the potential between two branes. By passing time and decreasing the separation distance between branes, more energy dissolves into branes and the universe expands. In the following, we presented the physical applications such as late time accelerating phase, inflation model and behavior of perturbations, with respect to brane–anti-brane system. Finally, we briefly discussed the moduli stabilization of the model.

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