Multi-level comparisons of input–output tables using cross-entropy indicators

2021 ◽  
pp. 1-20
Author(s):  
Muhammad Daaniyall Abd Rahman ◽  
Bart Los ◽  
Anne Owen ◽  
Manfred Lenzen
Keyword(s):  
Cross Entropy ◽  
Input Output ◽  
Multi Level

scholarly journals A new approach to modelling the input–output structure of regional economies using non-survey methods

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Anthony T. Flegg ◽  
Guiseppe R. Lamonica ◽  
Francesco M. Chelli ◽  
Maria C. Recchioni ◽  
Timo Tohmo
Keyword(s):  
Unknown Parameter ◽  
Survey Methods ◽  
Cross Entropy ◽  
Regional Economies ◽  
Regional Data ◽  
Input Output ◽  
New Approach ◽  
South Korean ◽  
Sectoral Output ◽  
Output Structure

AbstractThis paper proposes a new approach to the regionalization of national input–output tables where suitable regional data are scarce and analysts are considering using location quotients (LQs). We focus on the FLQ formula, which frequently yields the best results of the pure LQ-based methods, and develop an enhanced way of implementing this approach. We use a modified cross-entropy (MCE) method, along with a regression model, to estimate values of the unknown parameter δ in the FLQ formula, specific to both region and country. An analysis of survey-based data for 16 South Korean regions reveals that the proposed FLQ+ approach yields more accurate estimates of both input coefficients and sectoral output multipliers than those from simpler LQ-based methods or the MCE approach alone. Sectoral outputs (or employment) are the only regional data required. The MCE method also clearly outperforms GRAS.

scholarly journals Adaptive Feature Pyramid Network to Predict Crisp Boundaries via NMS Layer and ODS F-Measure Loss Function

Information ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 32
Author(s):  
Gang Sun ◽  
Hancheng Yu ◽  
Xiangtao Jiang ◽  
Mingkui Feng
Keyword(s):  
Edge Detection ◽  
Loss Function ◽  
State Of The Art ◽  
Cross Entropy ◽  
Post Processing ◽  
Multi Scale ◽  
Feature Pyramid ◽  
Multi Level ◽  
Different Levels ◽  
F Measure

Edge detection is one of the fundamental computer vision tasks. Recent methods for edge detection based on a convolutional neural network (CNN) typically employ the weighted cross-entropy loss. Their predicted results being thick and needing post-processing before calculating the optimal dataset scale (ODS) F-measure for evaluation. To achieve end-to-end training, we propose a non-maximum suppression layer (NMS) to obtain sharp boundaries without the need for post-processing. The ODS F-measure can be calculated based on these sharp boundaries. So, the ODS F-measure loss function is proposed to train the network. Besides, we propose an adaptive multi-level feature pyramid network (AFPN) to better fuse different levels of features. Furthermore, to enrich multi-scale features learned by AFPN, we introduce a pyramid context module (PCM) that includes dilated convolution to extract multi-scale features. Experimental results indicate that the proposed AFPN achieves state-of-the-art performance on the BSDS500 dataset (ODS F-score of 0.837) and the NYUDv2 dataset (ODS F-score of 0.780).

scholarly journals Proposal for a Nucleus I/O System

1974 ◽  
Vol 3 (23) ◽  
Author(s):  
Robert F. Rosin
Keyword(s):  
Computer Science ◽  
Computer System ◽  
General Nature ◽  
Specific Reference ◽  
Science Department ◽  
Input Output ◽  
Computer Science Department ◽  
Multi Level

<p>Since the typical so-called microprogrammed system provides only hardware oriented input-output facilities, it is highly desirable to supplement this with a nucleus of software supported I/O routines.</p><p>A proposal is set forth for such a nucleus system to be implemented at the lowest level of a multi-level computer system.</p><p>Although the proposal is of a general nature, specific reference is also made to the Rikke-1 hardware being developed in the Computer Science Department at Aarhus University.</p>

scholarly journals The efficiency of the cross-entropy method when estimating the technical coefficients of input–output tables

2019 ◽  
Vol 15 (1) ◽  
pp. 62-91 ◽  
Author(s):  
Giuseppe R. Lamonica ◽  
Maria C. Recchioni ◽  
Francesco M. Chelli ◽  
Luca Salvati
Keyword(s):  
Entropy Method ◽  
Cross Entropy ◽  
Input Output ◽  
Cross Entropy Method ◽  
The Cross ◽  
Technical Coefficients

scholarly journals Separation from Brain Magnetic Resonance images (MRI) using Multistage Thresholding Technique

2015 ◽  
Vol 2 (3) ◽  
pp. 9-12
Author(s):  
Siya bonga ◽  
Shi ra
Keyword(s):  
Brain Mri ◽  
Search Space ◽  
Magnetic Resonance Images ◽  
Cross Entropy ◽  
Threshold Values ◽  
Optimal Result ◽  
New Process ◽  
Fcm Clustering ◽  
Image Separation ◽  
Multi Level

Image separation is a significant task concerned in dissimilar areas from image dispensation to picture examination. One of the simplest methods for image segmentation is thresholding. Though, many thresholding methods are based on a bi-level thresholding process. These methods can be extensive to form multi-level thresholding, but they become computationally expensive since a large number of iterations would be necessary for computing the most select threshold values. In order to conquer this difficulty, a new process based on a Shrinking Search Space (3S) algorithm is proposed in this paper. The method is applied on statistical bi-level thresholding approaches including Entropy, Cross-entropy, Covariance, and Divergent Based Thresholding (DBT), to attain multi-level thresholding and used for separation from brain MRI images. The paper demonstrates that the collision of the proposed 3S method on the DBT method is more important than the other bi-level thresholding approaches. Comparing the results of using the proposed approach against those of the Fuzzy C-Means (FCM) clustering method demonstrates a better segmentation performance by improving the comparison index from 0.58 in FCM to 0.68 in the 3S method. Also, this method has a lower calculation impediment of around 0.37s with admiration to 157s dispensation time in FCM. In addition, the FCM approach does not always guarantee the convergence, whilst the 3S method always converges to the optimal result.

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