Wheat Prices and Trade in the Early Roman Empire

2012 ◽  
Author(s):  
Peter Temin
Keyword(s):  
Roman Empire ◽  
Roman Republic ◽  
Small Data ◽  
Data Sets ◽  
Data Points ◽  
Small Data Sets ◽  
Few Data ◽  
Late Roman

This chapter discusses how there is little of what economists call data on markets in Roman times, despite lots of information about prices and transactions. Data, as economists consider it, consist of a set of uniform prices that can be compared with each other. According to scholars, extensive markets existed in the late Roman Republic and early Roman Empire. Even though there is a lack of data, there are enough observations for the price of wheat, the most extensively traded commodity, to perform a test. The problem is that there is only a little bit of data by modern standards. Consequently, the chapter explains why statistics are useful in interpreting small data sets and how one deals with various problems that arise when there are only a few data points.

Parameter Estimation in Models of Cell Survival Using Scaled Time

2013 ◽  
Author(s):  
Neil T. Wright
Keyword(s):  
Cell Survival ◽  
Treatment Time ◽  
Treatment Temperature ◽  
Separate Experiment ◽  
Small Data ◽  
Data Sets ◽  
Data Points ◽  
Isothermal Analysis ◽  
Time Required ◽  
Few Data

Many individual samples are needed to measure cell survival following heating at multiple temperatures and multiple heating durations. For example, if eight time points are considered for each of seven treatment temperatures with three replicates at each condition, then 168 separate samples are needed. In addition, physical considerations may limit the number of points that can be measured, especially as treatment temperature increases and the heating duration decreases. For a reasonable sample size, there may be a limit to the treatment temperature as the time required to heat the culture to the target temperature becomes comparable to the treatment time. Then, using an isothermal analysis of the data introduces error and the temperature must be considered time varying, requiring estimates of the very parameters being sought. Conversely, for long treatment times, it may be difficult to insure that the temperature remains constant and that the temperature is the only modified experimental condition in the culture medium. These challenges typically lead to relatively small data sets. Furthermore, treating each temperature as a separate experiment leads to challenging statistical analysis of the data, as the few data points lead to difficulty in finding the confidence intervals of the parameters in a given model.

Characteristics of Dimension Functions Associated With Cutting Measurements

1995 ◽  
Vol 117 (3A) ◽  
pp. 259-264
Author(s):  
M. Rokni ◽  
B. S. Berger ◽  
I. Minis
Keyword(s):  
Reference Points ◽  
Coordinate Space ◽  
Small Data ◽  
Data Sets ◽  
Qualitative Properties ◽  
Acceleration Data ◽  
Orthogonal Turning ◽  
Generic Data ◽  
Data Points ◽  
Small Data Sets

The information dimension, D(0), of attractors associated with orthogonal turning is determined from experimental tool-workpiece relative acceleration data. Let E≡dimension of a delay coordinate space, n≡number of generic data points and m≡ number of reference points on the attractor. It is shown that properties of D(0) as a function of E, denoted by D(0):E, are unchanging, invariant, over large intervals of n and m. The qualitative properties of D(0):E discriminate between various cutting cases. This discrimination can be based on relatively small data sets. The computation of D(0) is shown to be robust in the sense that estimated values of D(0) are invariant or slowly varying over intervals of n and m.

scholarly journals Classification of jujube defects in small data sets based on transfer learning

2021 ◽  
Author(s):  
Jianping Ju ◽  
Hong Zheng ◽  
Xiaohang Xu ◽  
Zhongyuan Guo ◽  
Zhaohui Zheng ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Loss Function ◽  
Training Model ◽  
Parameter Distribution ◽  
Test Accuracy ◽  
Small Data ◽  
Data Sets ◽  
Data Set ◽  
Small Data Sets

AbstractAlthough convolutional neural networks have achieved success in the field of image classification, there are still challenges in the field of agricultural product quality sorting such as machine vision-based jujube defects detection. The performance of jujube defect detection mainly depends on the feature extraction and the classifier used. Due to the diversity of the jujube materials and the variability of the testing environment, the traditional method of manually extracting the features often fails to meet the requirements of practical application. In this paper, a jujube sorting model in small data sets based on convolutional neural network and transfer learning is proposed to meet the actual demand of jujube defects detection. Firstly, the original images collected from the actual jujube sorting production line were pre-processed, and the data were augmented to establish a data set of five categories of jujube defects. The original CNN model is then improved by embedding the SE module and using the triplet loss function and the center loss function to replace the softmax loss function. Finally, the depth pre-training model on the ImageNet image data set was used to conduct training on the jujube defects data set, so that the parameters of the pre-training model could fit the parameter distribution of the jujube defects image, and the parameter distribution was transferred to the jujube defects data set to complete the transfer of the model and realize the detection and classification of the jujube defects. The classification results are visualized by heatmap through the analysis of classification accuracy and confusion matrix compared with the comparison models. The experimental results show that the SE-ResNet50-CL model optimizes the fine-grained classification problem of jujube defect recognition, and the test accuracy reaches 94.15%. The model has good stability and high recognition accuracy in complex environments.

scholarly journals 45 A permutation test for validation of genomic estimated breeding values

2020 ◽  
Vol 98 (Supplement_4) ◽  
pp. 8-9
Author(s):  
Zahra Karimi ◽  
Brian Sullivan ◽  
Mohsen Jafarikia
Keyword(s):  
Permutation Test ◽  
Breeding Value ◽  
Small Data ◽  
Data Sets ◽  
Type I ◽  
Future Performance ◽  
Small Data Sets ◽  
Estimated Breeding Value ◽  
Estimated Breeding Values ◽  
Top 40

Abstract Previous studies have shown that the accuracy of Genomic Estimated Breeding Value (GEBV) as a predictor of future performance is higher than the traditional Estimated Breeding Value (EBV). The purpose of this study was to estimate the potential advantage of selection on GEBV for litter size (LS) compared to selection on EBV in the Canadian swine dam line breeds. The study included 236 Landrace and 210 Yorkshire gilts born in 2017 which had their first farrowing after 2017. GEBV and EBV for LS were calculated with data that was available at the end of 2017 (GEBV2017 and EBV2017, respectively). De-regressed EBV for LS in July 2019 (dEBV2019) was used as an adjusted phenotype. The average dEBV2019 for the top 40% of sows based on GEBV2017 was compared to the average dEBV2019 for the top 40% of sows based on EBV2017. The standard error of the estimated difference for each breed was estimated by comparing the average dEBV2019 for repeated random samples of two sets of 40% of the gilts. In comparison to the top 40% ranked based on EBV2017, ranking based on GEBV2017 resulted in an extra 0.45 (±0.29) and 0.37 (±0.25) piglets born per litter in Landrace and Yorkshire replacement gilts, respectively. The estimated Type I errors of the GEBV2017 gain over EBV2017 were 6% and 7% in Landrace and Yorkshire, respectively. Considering selection of both replacement boars and replacement gilts using GEBV instead of EBV can translate into increased annual genetic gain of 0.3 extra piglets per litter, which would more than double the rate of gain observed from typical EBV based selection. The permutation test for validation used in this study appears effective with relatively small data sets and could be applied to other traits, other species and other prediction methods.

A Comparison Study of Mahalanobis-Taguchi System and Neural Network for Multivariate Pattern Recognition

2005 ◽  
Author(s):  
Jungeui Hong ◽  
Elizabeth A. Cudney ◽  
Genichi Taguchi ◽  
Rajesh Jugulum ◽  
Kioumars Paryani ◽  
...  
Keyword(s):  
Neural Network ◽  
Small Data ◽  
Data Sets ◽  
Comparison Study ◽  
Data Set ◽  
Set Size ◽  
Breast Cancer Study ◽  
Discriminant Ability ◽  
Small Data Sets ◽  
Multivariate Pattern

The Mahalanobis-Taguchi System is a diagnosis and predictive method for analyzing patterns in multivariate cases. The goal of this study is to compare the ability of the Mahalanobis-Taguchi System and a neural network to discriminate using small data sets. We examine the discriminant ability as a function of data set size using an application area where reliable data is publicly available. The study uses the Wisconsin Breast Cancer study with nine attributes and one class.

scholarly journals Density Estimation on Small Data Sets

2018 ◽  
Vol 121 (16) ◽  
Author(s):  
Wei-Chia Chen ◽  
Ammar Tareen ◽  
Justin B. Kinney
Keyword(s):  
Density Estimation ◽  
Small Data ◽  
Data Sets ◽  
Small Data Sets

scholarly journals Reshaping Text Data for Efficient Processing on Amazon EC2

2011 ◽  
Vol 19 (2-3) ◽  
pp. 133-145
Author(s):  
Gabriela Turcu ◽  
Ian Foster ◽  
Svetlozar Nestorov
Keyword(s):  
Cost Effective ◽  
Performance Model ◽  
Small Data ◽  
Data Sets ◽  
Performance Measurements ◽  
File Size ◽  
Application Performance ◽  
Execution Plan ◽  
Small Data Sets ◽  
Amazon Ec2

Text analysis tools are nowadays required to process increasingly large corpora which are often organized as small files (abstracts, news articles, etc.). Cloud computing offers a convenient, on-demand, pay-as-you-go computing environment for solving such problems. We investigate provisioning on the Amazon EC2 cloud from the user perspective, attempting to provide a scheduling strategy that is both timely and cost effective. We derive an execution plan using an empirically determined application performance model. A first goal of our performance measurements is to determine an optimal file size for our application to consume. Using the subset-sum first fit heuristic we reshape the input data by merging files in order to match as closely as possible the desired file size. This also speeds up the task of retrieving the results of our application, by having the output be less segmented. Using predictions of the performance of our application based on measurements on small data sets, we devise an execution plan that meets a user specified deadline while minimizing cost.

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