Road classification using built-in self-scaling method of Bayesian regression

2022 ◽  
Vol 516 ◽  
pp. 116523
Author(s):  
Ai Hui Tan ◽  
Mathias Foo ◽  
Duu Sheng Ong
Keyword(s):  
Bayesian Regression ◽  
Scaling Method

Integrated Approach for Sclera Recognition and Eye Gaze Detection

2018 ◽  
Vol 6 (7) ◽  
pp. 77
Author(s):  
Snehal S. Rajole ◽  
J. V. Shinde
Keyword(s):  
Near Infrared ◽  
Eye Gaze ◽  
Integrated Approach ◽  
Error Rates ◽  
Infrared Images ◽  
Scaling Method ◽  
Extensive Simulation ◽  
Sclera Recognition ◽  
Gaze Detection ◽  
Biometric Trait

In this paper we proposed unique technique which is adaptive to noisy images for eye gaze detection as processing noisy sclera images captured at-a-distance and on-the-move has not been extensively investigated. Sclera blood vessels have been investigated recently as an efficient biometric trait. Capturing part of the eye with a normal camera using visible-wavelength images rather than near infrared images has provoked research interest. This technique involves sclera template rotation alignment and a distance scaling method to minimize the error rates when noisy eye images are captured at-a-distance and on-the move. The proposed system is tested and results are generated by extensive simulation in java.

RANCANGAN MODEL PENILAIAN KINERJA DENGAN METODE MANAJEMEN BERDASARKAN SASARAN (MANAGEMENT BY OBJECTIVE) DAN PENSKALAAN GRAFIK DI PT ACI

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Linda Mora Siregar
Keyword(s):  
Performance Appraisal ◽  
Job Analysis ◽  
Scaling Method ◽  
Scaling Methods ◽  
Questionnaire Method ◽  
A Performance

The purpose of this study is to design a performance appraisal model more effectively in accordance with the company's vision and mission, namely by combining management methods based on objectives (management by objective) and graph scaling methods. In the preparation of performance appraisal models with management methods based on objectives (management by objective) and graph scaling methods, carried out with several stages, namely the first stage is planning the making of performance appraisal models, the second is job analysis, third is the preparation of management methods based on objectives (management by objective), the fourth is the preparation of the scaling method and the fifth is the preparation of the performance appraisal form. The making of this performance appraisal design will use a questionnaire method, observation and also interviews to obtain and collect information and data.  Keywords: Performance Appraisal, Management Methods, Graph Scaling.   Tujuan penelitian ini adalah untuk merancang model penilaian kinerja secara lebih efektif sesuai dengan visi dan misi perusahaan, yaitu dengan menggabungkan metode manajemen berdasarkan sasaran (management by objective) dan metode penskalaan grafik. Dalam penyusunan model penilaian kinerja dengan metode manajemen berdasarkan sasaran (management by objective) dan metode penskalaan grafik, dilakukan dengan beberapa tahapan yaitu tahap pertama adalah perencanaan pembuatan model penilaian kinerja, kedua analisis jabatan, ketiga penyusunan metode manajemen berdasarkan sasaran (management by objective), keempat penyusunan metode penskalaan grafik dan kelima adalah penyusunan formulir penilaian kinerja. Pembuatan rancangan penilaian kinerja ini akan menggunakan metode kuesioner, observasi dan juga wawancara untuk mendapatkan dan mengumpulkan informasi dan data.   Kata Kunci: Penilaian kinerja, Metode Manajemen, Penskalaan Grafik.

Finite Sample Performance of Robust Bayesian Regression

1997 ◽  
Author(s):  
Michael NMI3 Smith ◽  
Simon Sheather ◽  
Robert Kohn
Keyword(s):  
Bayesian Regression ◽  
Finite Sample

scholarly journals Does Resisted Sled Towing Improve the Physical Qualities of Elite Youth Soccer Players of Differing Maturity Status?

2021 ◽  
Author(s):  
Rhys Morris ◽  
Tony Myers ◽  
Stacey Emmonds ◽  
Dave Singleton ◽  
Kevin Till
Keyword(s):  
Body Mass ◽  
Bayesian Regression ◽  
Soccer Players ◽  
Height Velocity ◽  
Regression To The Mean ◽  
Peak Height Velocity ◽  
Maturity Group ◽  
Youth Soccer ◽  
Professional Soccer ◽  
The Impact

Abstract Purpose Sled towing has been shown to be an effective method to enhance the physical qualities in youth athletes. The aim of this study was to evaluate the impact of a 6-week sled towing intervention on muscular strength, speed and power in elite youth soccer players of differing maturity status. Method Seventy-three male elite youth soccer players aged 12–18 years (Pre-Peak Height Velocity [PHV] n = 25; Circa-PHV n = 24; Post-PHV n = 24) from one professional soccer academy participated in this study. Sprint assessments (10 m and 30 m), countermovement jump and isometric mid-thigh pull were undertaken before (T1) and after (T2) a 6-week intervention. The training intervention consisted of 6 weeks (2 × per week, 10 sprints over 20 m distance) of resisted sled towing (linear progression 10%–30% of body mass) during the competitive season. Bayesian regression models analysed differences between T1 and T2 within each maturity group. Results There were minimal changes in strength, speed and power (P = 0.35–0.80) for each maturity group across the 6-week intervention. Where there were changes with greater certainty, they are unlikely to represent real effect due to higher regression to the mean (RTM). Conclusion It appears that a 6-week sled towing training programme with loadings of 10%–30% body mass only maintains physical qualities in elite youth soccer players pre-, circa-, and post-PHV. Further research is required to determine the effectiveness of this training method in long-term athletic development programmes.

scholarly journals Model-independent calibrations of gamma-ray bursts using machine learning

2021 ◽  
Vol 503 (3) ◽  
pp. 4581-4600
Author(s):  
Orlando Luongo ◽  
Marco Muccino
Keyword(s):  
Machine Learning ◽  
Dark Energy ◽  
Gamma Ray ◽  
Gamma Ray Bursts ◽  
Bayesian Regression ◽  
Machine Learning Techniques ◽  
Acoustic Oscillations ◽  
Chi Square ◽  
Model Independent ◽  
Chi Square Analysis

ABSTRACT We alleviate the circularity problem, whereby gamma-ray bursts are not perfect distance indicators, by means of a new model-independent technique based on Bézier polynomials. We use the well consolidate Amati and Combo correlations. We consider improved calibrated catalogues of mock data from differential Hubble rate points. To get our mock data, we use those machine learning scenarios that well adapt to gamma-ray bursts, discussing in detail how we handle small amounts of data from our machine learning techniques. We explore only three machine learning treatments, i.e. linear regression, neural network, and random forest, emphasizing quantitative statistical motivations behind these choices. Our calibration strategy consists in taking Hubble’s data, creating the mock compilation using machine learning and calibrating the aforementioned correlations through Bézier polynomials with a standard chi-square analysis first and then by means of a hierarchical Bayesian regression procedure. The corresponding catalogues, built up from the two correlations, have been used to constrain dark energy scenarios. We thus employ Markov chain Monte Carlo numerical analyses based on the most recent Pantheon supernova data, baryonic acoustic oscillations, and our gamma-ray burst data. We test the standard ΛCDM model and the Chevallier–Polarski–Linder parametrization. We discuss the recent H0 tension in view of our results. Moreover, we highlight a further severe tension over Ωm and we conclude that a slight evolving dark energy model is possible.

scholarly journals Dynamic model updating (DMU) approach for statistical learning model building with missing data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rahi Jain ◽  
Wei Xu
Keyword(s):  
Missing Data ◽  
Dynamic Model ◽  
Statistical Models ◽  
Missing Values ◽  
Model Building ◽  
Model Updating ◽  
Biological Data ◽  
Bayesian Regression ◽  
Biological Research ◽  
Original Dataset

Abstract Background Developing statistical and machine learning methods on studies with missing information is a ubiquitous challenge in real-world biological research. The strategy in literature relies on either removing the samples with missing values like complete case analysis (CCA) or imputing the information in the samples with missing values like predictive mean matching (PMM) such as MICE. Some limitations of these strategies are information loss and closeness of the imputed values with the missing values. Further, in scenarios with piecemeal medical data, these strategies have to wait to complete the data collection process to provide a complete dataset for statistical models. Method and results This study proposes a dynamic model updating (DMU) approach, a different strategy to develop statistical models with missing data. DMU uses only the information available in the dataset to prepare the statistical models. DMU segments the original dataset into small complete datasets. The study uses hierarchical clustering to segment the original dataset into small complete datasets followed by Bayesian regression on each of the small complete datasets. Predictor estimates are updated using the posterior estimates from each dataset. The performance of DMU is evaluated by using both simulated data and real studies and show better results or at par with other approaches like CCA and PMM. Conclusion DMU approach provides an alternative to the existing approaches of information elimination and imputation in processing the datasets with missing values. While the study applied the approach for continuous cross-sectional data, the approach can be applied to longitudinal, categorical and time-to-event biological data.

scholarly journals The Genuine Resonance of Full-Charm Tetraquarks

Universe ◽  
2021 ◽  
Vol 7 (5) ◽  
pp. 155
Author(s):  
Xiaoyun Chen
Keyword(s):  
Quark Model ◽  
Bound States ◽  
Expansion Method ◽  
Chiral Quark Model ◽  
Stabilization Method ◽  
Scaling Method ◽  
Resonance States ◽  
Gaussian Expansion Method ◽  
Color Structure ◽  
Quantum Numbers

In this work, the genuine resonance states of full-charm tetraquark systems with quantum numbers JPC=0++,1+−,2++ are searched in a nonrelativistic chiral quark model with the help of the Gaussian Expansion Method. In this calculation, two structures, meson-meson and diquark–antidiquark, as well as their mixing with all possible color-spin configurations, are considered. The results show that no bound states can be formed. However, resonances are possible because of the color structure. The genuine resonances are identified by the stabilization method (real scaling method). Several resonances for the full-charm system are proposed, and some of them are reasonable candidates for the full-charm states recently reported by LHCb.

scholarly journals Smartphone GPS Locations of Students’ Movements to and from Campus

2021 ◽  
Vol 10 (8) ◽  
pp. 517
Author(s):  
Patricia K. Doyle-Baker ◽  
Andrew Ladle ◽  
Angela Rout ◽  
Paul Galpern
Keyword(s):  
Student Engagement ◽  
Environmental Factors ◽  
University Students ◽  
Regression Models ◽  
Bayesian Regression ◽  
Daily Movement ◽  
Campus Visit ◽  
Canadian City ◽  
Campus Activities ◽  
Ability And Willingness

For many university students, commuting to and from campus constitutes a large proportion of their daily movement, and therefore it may influence their ability and willingness to spend time on campus or to participate in campus activities. To assess student engagement on campus, we collected smartphone GPS location histories from volunteers (n = 280) attending university in a major Canadian city. We investigated how campus visit length and frequency were related to characteristics of the commute using Bayesian regression models. Slower commutes and commutes over longer distances were associated with more time spent but less frequent visits to campus. Our results demonstrate that exposure to campus life, and therefore the potential for student engagement, may relate not just to whether a student lives on or near campus, but also to urban environmental factors that interact to influence the commuting experience.

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