Large-scale questions and small-scale data: empirical and theoretical methods for scaling up in ecology

Oecologia ◽  
2005 ◽  
Vol 145 (2) ◽  
pp. 176-177 ◽  
Author(s):  
N. Underwood ◽  
P. Hambäck ◽  
B. D. Inouye
Keyword(s):  
Large Scale ◽  
Scaling Up ◽  
Small Scale ◽  
Theoretical Methods ◽  
Scale Data

scholarly journals A Fast Multiscale Clustering Approach Based on DBSCAN

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Runzi Chen ◽  
Shuliang Zhao ◽  
Meishe Liang
Keyword(s):  
Large Scale ◽  
Computational Cost ◽  
Scaling Up ◽  
Practical Significance ◽  
Small Scale ◽  
Large Scale Data ◽  
Traditional Algorithm ◽  
Multiscale Clustering ◽  
Clustering Approach ◽  
Scale Data

Multiscale brings great benefits for people to observe objects or problems from different perspectives. It has practical significance for clustering on multiscale data. At present, there is a lack of research on the clustering of large-scale data under the premise that clustering results of small-scale datasets have been obtained. If one does cluster on large-scale datasets by using traditional methods, two disadvantages are as follows: (1) Clustering results of small-scale datasets are not utilized. (2) Traditional method will cause more running overhead. Aims at these shortcomings, this paper proposes a multiscale clustering framework based on DBSCAN. This framework uses DBSCAN for clustering small-scale datasets, then introduces algorithm Scaling-Up Cluster Centers (SUCC) generating cluster centers of large-scale datasets by merging clustering results of small-scale datasets, not mining raw large-scale datasets. We show experimentally that, compared to traditional algorithm DBACAN and leading algorithms DBSCAN++ and HDBSCAN, SUCC can provide not only competitive performance but reduce computational cost. In addition, under the guidance of experts, the performance of SUCC is more competitive in accuracy.

scholarly journals A chromatography-free and aqueous waste-free process for thioamide preparation with Lawesson’s reagent

2021 ◽  
Vol 17 ◽  
pp. 805-812
Author(s):  
Ke Wu ◽  
Yichen Ling ◽  
An Ding ◽  
Liqun Jin ◽  
Nan Sun ◽  
...  
Keyword(s):  
Large Scale ◽  
Scaling Up ◽  
Small Scale ◽  
Substitution Reactions ◽  
Great Opportunity ◽  
Lawesson’S Reagent ◽  
Free Process ◽  
Aqueous Waste ◽  
Scale Preparation ◽  
Lawesson's Reagent

After completing the thio-substitution with Lawesson’s reagent, ethanol was found to be effective in the decomposition of the inherent stoichiometric six-membered-ring byproduct from the Lawesson’s reagent to a highly polarized diethyl thiophosphonate. The treatment significantly simplified the following chromatography purification of the desired thioamide in a small scale preparation. As scaling up the preparation of two pincer-type thioamides, we have successfully developed a convenient process with ethylene glycol to replace ethanol during the workup, including a traditional phase separation, extraction, and recrystallization. The newly developed chromatography-free procedure did not generate P-containing aqueous waste, and only organic effluents were discharged. It is believed that the optimized procedure offers the great opportunity of applying the Lawesson’s reagent for various thio-substitution reactions on a large scale.

scholarly journals Lightweight Label Propagation for Large-Scale Network Data

2018 ◽  
Author(s):  
De-Ming Liang ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Label Propagation ◽  
Stochastic Gradient Descent ◽  
Small Scale ◽  
Warm Start ◽  
Large Scale Data ◽  
Large Scale Network ◽  
Memory Overhead ◽  
Scale Network ◽  
Scale Data

Label propagation spreads the soft labels from few labeled data to a large amount of unlabeled data according to the intrinsic graph structure. Nonetheless, most label propagation solutions work under relatively small-scale data and fail to cope with many real applications, such as social network analysis, where graphs usually have millions of nodes. In this paper, we propose a novel algorithm named \algo to deal with large-scale data. A lightweight iterative process derived from the well-known stochastic gradient descent strategy is used to reduce memory overhead and accelerate the solving process. We also give a theoretical analysis on the necessity of the warm-start technique for label propagation. Experiments show that our algorithm can handle million-scale graphs in few seconds while achieving highly competitive performance with existing algorithms.

scholarly journals Comparing different response time threshold setting methods to detect low effort on a large-scale assessment

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
James Soland ◽  
Megan Kuhfeld ◽  
Joseph Rios
Keyword(s):  
Response Time ◽  
Test Scores ◽  
Large Scale ◽  
Response Times ◽  
The United States ◽  
Small Scale ◽  
Large Scale Assessment ◽  
Different Response ◽  
Threshold Setting ◽  
Scale Data

AbstractLow examinee effort is a major threat to valid uses of many test scores. Fortunately, several methods have been developed to detect noneffortful item responses, most of which use response times. To accurately identify noneffortful responses, one must set response time thresholds separating those responses from effortful ones. While other studies have compared the efficacy of different threshold-setting methods, they typically do so using simulated or small-scale data. When large-scale data are used in such studies, they often are not from a computer-adaptive test (CAT), use only a handful of items, or do not comprehensively examine different threshold-setting methods. In this study, we use reading test scores from over 728,923 3rd–8th-grade students in 2056 schools across the United States taking a CAT consisting of nearly 12,000 items to compare threshold-setting methods. In so doing, we help provide guidance to developers and administrators of large-scale assessments on the tradeoffs involved in using a given method to identify noneffortful responses.

scholarly journals METHODS OF BUILDING A MODEL OF USER BEHAVIOR

2020 ◽  
Vol 2 (1) ◽  
pp. 43-51
Author(s):  
N. B. Shakhovskaya Shakhovskaya ◽  
◽  
N. I. Melnykova ◽  
Keyword(s):  
Data Analysis ◽  
Data Processing ◽  
Large Scale ◽  
Clustering Algorithms ◽  
Free Form ◽  
Small Scale ◽  
Clustering Methods ◽  
User Behaviour ◽  
Input Parameters ◽  
Scale Data

The number of clustering methods and algorithms were analysed and the peculiarities of their application were singled out. The main advantages of density based clustering methods are the ability to detect free-form clusters of different sizes and resistance to noise and emissions, and the disadvantages include high sensitivity to input parameters, poor class description and unsuitability for large data. The analysis showed that the main problem of all clustering algorithms is their scalability with increasing amount of processed data. The main problems of most of them are the difficulty of setting the optimal input parameters (for density, grid or model algorithms), identification of clusters of different shapes and densities (distribution algorithms, grid-based algorithms), fuzzy completion criteria (hierarchical, partition and model-based). Since the clustering procedure is only one of the stages of data processing of the system as a whole, the chosen algorithm should be easy to use and easy to configure the input parameters. Results of researches show that hierarchical clustering methods include a number of algorithms suitable for both small-scale data processing and large-scale data analysis, which is relevant in the field of social networks. Based on the data analysis, information was collected within fill a smart user profile. Much attention is paid to the study of associative rules, based on which an algorithm for extracting associative rules is proposed, which allows to find statistically significant rules and to look only for dependencies defined by a common set of input data, and has high computational complexity if there are many classification rules. An approach has been developed that focuses on creating and understanding models of user behaviour, predicting future behaviour using the created template. Methods of modelling pre-processing of data (clustering) are investigated and regularities of planning of meetings of friends on the basis of the analysis of daily movement of people and their friends are revealed. Methods of creating and understanding models of user behaviour were presented. The k-means algorithm was used to group users to determine how well each object lay in its own cluster. The concept of association rules was introduced; the method of search of dependences is developed. The accuracy of the model was evaluated.

Simulating Large-Scale Population Dynamics Using Small-Scale Data

2002 ◽  
pp. 275-301 ◽  
Author(s):  
Graham E. Forrester ◽  
Mark A. Steele ◽  
Richard R. Vance
Keyword(s):  
Population Dynamics ◽  
Large Scale ◽  
Small Scale ◽  
Scale Population ◽  
Scale Data

scholarly journals Biohybrid Actuators for Soft Robotics: Challenges in Scaling Up

Actuators ◽  
2020 ◽  
Vol 9 (4) ◽  
pp. 96
Author(s):  
Phillip Won ◽  
Seung Hwan Ko ◽  
Carmel Majidi ◽  
Adam W. Feinberg ◽  
Victoria A. Webster-Wood
Keyword(s):  
Large Scale ◽  
Scaling Up ◽  
Living Cells ◽  
Living Systems ◽  
Soft Robotics ◽  
Small Scale ◽  
Self Healing ◽  
Artificial Structures ◽  
Wide Range ◽  
Macro Scale

Living systems have evolved to survive in a wide range of environments and safely interact with other objects and organisms. Thus, living systems have been the source of inspiration for many researchers looking to apply their mechanics and unique characteristics in engineering robotics. Moving beyond bioinspiration, biohybrid actuators, with compliance and self-healing capabilities enabled by living cells or tissue interfaced with artificial structures, have drawn great interest as ways to address challenges in soft robotics, and in particular have seen success in small-scale robotic actuation. However, macro-scale biohybrid actuators beyond the centimeter scale currently face many practical obstacles. In this perspective, we discuss the challenges in scaling up biohybrid actuators and the path to realize large-scale biohybrid soft robotics.

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