scholarly journals T6D-Direct: Transformers for Multi-object 6D Pose Direct Regression

2021 ◽  
pp. 530-544
Author(s):  
Arash Amini ◽  
Arul Selvam Periyasamy ◽  
Sven Behnke
Keyword(s):  
Direct Regression

Testing for Multiple-Horizon Predictability: Direct Regression Based versus Implication Based

2019 ◽  
Vol 33 (9) ◽  
pp. 4403-4443 ◽  
Author(s):  
Ke-Li Xu
Keyword(s):  
Equity Premium ◽  
Predictive Ability ◽  
Finite Sample ◽  
Simulation Experiments ◽  
Asset Return ◽  
Predictive Regression ◽  
Oxford University ◽  
Short Run ◽  
Direct Regression ◽  
Published Paper

Abstract Research in finance and macroeconomics has routinely employed multiple horizons to test asset return predictability. In a simple predictive regression model, we find the popular scaled test can have zero power when the predictor is not sufficiently persistent. A new test based on implication of the short-run model is suggested and is shown to be uniformly more powerful than the scaled test. The new test can accommodate multiple predictors. Compared with various other widely used tests, simulation experiments demonstrate remarkable finite-sample performance. We reexamine the predictive ability of various popular predictors for aggregate equity premium. Authors have furnished an Internet Appendix, which is available on the Oxford University Press Web site next to the link to the final published paper online.

FORECASTING TIME SERIES USING WAVELETS

2007 ◽  
Vol 05 (05) ◽  
pp. 709-724 ◽  
Author(s):  
MINA AMINGHAFARI ◽  
JEAN-MICHEL POGGI
Keyword(s):  
Time Series ◽  
Prediction Equation ◽  
Stationary Time Series ◽  
Wavelet Coefficients ◽  
Statistical Forecasting ◽  
Stationary Time ◽  
Deterministic Trend ◽  
Wavelet Decompositions ◽  
Direct Regression

This paper deals with wavelets in time series, focusing on statistical forecasting purposes. Recent approaches involve wavelet decompositions in order to handle non-stationary time series in such context. A method, proposed by Renaud et al.,11 estimates directly the prediction equation by direct regression of the process on the Haar non-decimated wavelet coefficients depending on its past values. In this paper, this method is studied and extended in various directions. The new variants are used first for stationary data and after for stationary data contaminated by a deterministic trend.

scholarly journals A direct regression approach to decomposing socioeconomic inequality of health

2019 ◽  
Vol 28 (7) ◽  
pp. 884-905 ◽  
Author(s):  
Roselinde Kessels ◽  
Guido Erreygers
Keyword(s):  
Socioeconomic Inequality ◽  
Regression Approach ◽  
Direct Regression

scholarly journals DL-inferencing for 3D Cephalometric Landmarks Regression task using OpenVINO

2020 ◽  
pp. paper35-1-paper35-11
Author(s):  
Evgeny Vasiliev ◽  
Dmitrii Lachinov ◽  
Alexandra Getmanskaya
Keyword(s):  
Neural Networks ◽  
Great Increase ◽  
Three Dimensional ◽  
Ct Scans ◽  
Cephalometric Analysis ◽  
Skull Shape ◽  
Computing Environments ◽  
Hourglass Model ◽  
Direct Regression ◽  
Tomography Data

In this paper, we evaluate the performance of the Intel Distribution of OpenVINO toolkit in practical solving of the problem of automatic three-dimensional Cephalometric analysis using deep learning methods. This year, the authors proposed an approach to the detection of cephalometric landmarks from CT-tomography data, which is resistant to skull deformities and use convolutional neural networks (CNN). Resistance to deformations is due to the initial detection of 4 points that are basic for the parameterization of the skull shape. The approach was explored on CNN for three architectures. A record regression accuracy in comparison with analogs was obtained. This paper evaluates the perfor- mance of decision making for the trained CNN-models at the inference stage. For a comparative study, the computing environments PyTorch and Intel Distribution of OpenVINO were selected, and 2 of 3 CNN architectures: based on VGG for regression of cephalometric landmarks and an Hourglass-based model, with the RexNext backbone for the land- marks heatmap regression. The experimental dataset was consist of 20 CT of patients with acquired craniomaxillofacial deformities and was in- clude pre- and post-operative CT scans whose format is 800x800x496 with voxel spacing of 0.2x0.2x0.2 mm. Using OpenVINO showed a great increase in performance over the PyTorch, with inference speedup from 13 to 16 times for a Direct Regression model and from 3.5 to 3.8 times for a more complex and precise Hourglass model.

scholarly journals An Empirical Study on the National Heterogeneity of High-End Manufacturing Technology Innovation and GVC’s Division of Labor Status

2021 ◽  
Vol 2021 ◽  
pp. 1-10
Author(s):  
Lei Liang ◽  
Yawen Liang
Keyword(s):  
Developing Countries ◽  
Division Of Labor ◽  
Technological Innovation ◽  
Developed Countries ◽  
Inhibitory Effect ◽  
National Differences ◽  
The Status ◽  
Development Levels ◽  
The Relationship ◽  
Direct Regression

Although the relationship between technological innovation and the status of the global value chain’s (GVC) division of labor has been essentially affirmed by academia, the direct regression of all countries does not account for national differences pertaining to different economic development levels. This paper divides the countries selected for this study into developed and developing countries and then conducts empirical tests on two sample sets to explore the heterogeneity of technological innovation and GVC division of labor status. The results reveal the following: (1) in developed countries, the GVC division of labor status of high-end manufacturing is generally higher than that of developing countries; (2) in developed countries, the technological innovation of high-end manufacturing plays a significant role in promoting GVC’s division of labor, while developing countries have a significant inhibitory effect; and (3) staff input and financial developmental levels have significantly promoted GVC’s status in the division of labor. Earlier studies have shown that, in developing countries, technological innovation in high-end manufacturing industries does not fully serve the goal of exporting intermediate goods. This study’s conclusions offer a new method of explaining the nature of a given country, the logic of technological innovation, and the differences in the GVC division of labor status.

scholarly journals Leveraging multiple datasets for deep leaf counting

2017 ◽  
Author(s):  
Andrei Dobrescu ◽  
Mario Valerio Giuffrida ◽  
Sotirios A Tsaftaris
Keyword(s):  
Deep Learning ◽  
Absolute Difference ◽  
Regression Problem ◽  
Mean Absolute Difference ◽  
Multiple Datasets ◽  
Segmentation Algorithms ◽  
In The Wild ◽  
Number Of Leaves ◽  
Direct Regression ◽  
Leaf Segmentation

AbstractThe number of leaves a plant has is one of the key traits (phenotypes) describing its development and growth. Here, we propose an automated, deep learning based approach for counting leaves in model rosette plants. While state-of-the-art results on leaf counting with deep learning methods have recently been reported, they obtain the count as a result of leaf segmentation and thus require per-leaf (instance) segmentation to train the models (a rather strong annotation). Instead, our method treats leaf counting as a direct regression problem and thus only requires as annotation the total leaf count per plant. We argue that combining different datasets when training a deep neural network is beneficial and improves the results of the proposed approach. We evaluate our method on the CVPPP 2017 Leaf Counting Challenge dataset, which contains images of Arabidopsis and tobacco plants. Experimental results show that the proposed method significantly outperforms the winner of the previous CVPPP challenge, improving the results by a minimum of 50% on each of the test datasets, and can achieve this performance without knowing the experimental origin of the data (i.e. “in the wild” setting of the challenge). We also compare the counting accuracy of our model with that of per leaf segmentation algorithms, achieving a 20% decrease in mean absolute difference in count (|DiC|).

Predicting hourly litter moisture content of larch stands in Daxinganling Region, China using three vapour-exchange methods

2015 ◽  
Vol 24 (1) ◽  
pp. 114 ◽  
Author(s):  
Ping Sun ◽  
Hongzhou Yu ◽  
Sen Jin
Keyword(s):  
Moisture Content ◽  
Forest Fires ◽  
Absolute Error ◽  
Model Parameters ◽  
Observation Data ◽  
Fuel Moisture ◽  
Fire Behaviour ◽  
Accuracy Requirement ◽  
Similar Accuracy ◽  
Direct Regression

Fuel moisture affects fuel ignition potential and fire behaviour. To accurately model fire behaviour, predict fuel ignition potential and plan fuel reduction, fuel moisture content must be assessed regularly and often. To establish models for Daxinganling Region, which has the most severe forest fires in China, hourly measurements were taken of moisture content in litter beds of larch stands sampled under different shading and slope conditions. Models were established using three vapour-exchange methods. The Nelson and Simard methods employed a direct timelag method using a timelag concept and the Nelson and Simard equilibrium moisture content (EMC) functions and estimating model parameters directly from fuel moisture and weather observation data in the field. The direct regression method used equations directly derived from linear regression of fuel moisture and field weather variation. The mean absolute error and mean relative error were determined for the Nelson (0.78%, 4.98%), Simard (1.04%, 5.57%) and direct regression (1.48%, 9.01%) methods. Only the models established using the direct timelag methods met the 1% accuracy requirement using either the Nelson or Simard EMC function, confirming the suitability and robustness of the direct timelag methods. The Simard and Nelson methods had similar accuracy, but Simard was more robust and only needed estimation of one parameter and hence is recommended for predicting litter moisture in this region.

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