scholarly journals VESPA: static profiling for binary optimization

2021 ◽  
Vol 5 (OOPSLA) ◽  
pp. 1-28
Author(s):  
Angélica Aparecida Moreira ◽  
Guilherme Ottoni ◽  
Fernando Magno Quintão Pereira
Keyword(s):  
Regression Model ◽  
Binary Optimization ◽  
The Past ◽  
Original Proposal ◽  
Runtime Profiling ◽  
Dynamic Profiling

Over the past few years, there has been a surge in the popularity of binary optimizers such as BOLT, Propeller, Janus and HALO. These tools use dynamic profiling information to make optimization decisions. Although effective, gathering runtime data presents developers with inconveniences such as unrepresentative inputs, the need to accommodate software modifications, and longer build times. In this paper, we revisit the static profiling technique proposed by Calder et al. in the late 90’s, and investigate its application to drive binary optimizations, in the context of the BOLT binary optimizer, as a replacement for dynamic profiling. A few core modifications to Calder et al.’s original proposal, consisting of new program features and a new regression model, are sufficient to enable some of the gains obtained through runtime profiling. An evaluation of BOLT powered by our static profiler on four large benchmarks (clang, GCC, MySQL and PostgreSQL) yields binaries that are 5.47 % faster than the executables produced by clang -O3.

The relationship between disability and suicide: prospective evidence from the Ten to Men cohort

2018 ◽  
Vol 41 (4) ◽  
pp. 707-713 ◽  
Author(s):  
Allison Milner ◽  
Anne-Marie Bollier ◽  
Eric Emerson ◽  
Anne Kavanagh
Keyword(s):  
Mental Health ◽  
Logistic Regression ◽  
Regression Model ◽  
Logistic Regression Model ◽  
People With Disabilities ◽  
Cross Sectional ◽  
Longitudinal Cohort ◽  
The Past ◽  
The Relationship

Abstract Background People with disabilities often face a range of social and economic adversities. Evidence suggests that these disadvantages result in poorer mental health. Some research also indicates that people with disabilities are more likely experience thoughts about suicide than people without disability, although most of this research is based on small cross-sectional samples. Methods We explored the relationship between self-reported disability (measured at baseline) and likelihood of reporting thoughts of suicide (measured at follow up) using a large longitudinal cohort of Australian males. A logistic regression model was conducted with thoughts of suicide within the past 12 months (yes or no) as the outcome and disability as the exposure. The models adjusted for relevant confounders, including mental health using the SF-12 MCS, and excluded males who reported thoughts of suicide at baseline. Results After adjustment, there was a 1.48 (95% CI: 0.98–2.23, P = 0.063) increase in the odds of thoughts of suicide among men who also reported a disability. The size of association was similar to that of being unemployed. Conclusions Males reporting disability may also suffer from thoughts of suicide. We speculate that discrimination may be one explanation for the observed association. More research on this topic is needed.

scholarly journals A POWER REGRESSION MODEL FOR LONG RANGE FORECAST OF SOUTHWEST MONSOON RAINFALL OVER INDIA

MAUSAM ◽  
2021 ◽  
Vol 42 (2) ◽  
pp. 125-130
Author(s):  
VASANT GOWARIKER ◽  
V. THAPLIYAL ◽  
S. M. KULSHRESTHA ◽  
G. S. MANDAL ◽  
N. SEN ROY ◽  
...  
Keyword(s):  
Regression Model ◽  
Detailed Analysis ◽  
Long Range ◽  
Monsoon Rainfall ◽  
India Meteorological Department ◽  
Southwest Monsoon ◽  
Climatic Conditions ◽  
The Past ◽  
Interactive Nature ◽  
Southwest Monsoon Rainfall

A detailed analysis of southwest monsoon (June to September) rainfall over India of several decades vis-a-vis the regional and global antecedent signals in numerous permutations and combinations has led the authors to conclude that a long range forecast based on one, two, three or four parameters as attempted by several workers in the past, cannot be reliable on all occasions as indeed has proved to be the case. The parametric and power regression models utilizing 16 parameters, described in the present paper, suggest that it is a tapestry of several parameters and interactive nature of the regional and global climatic forcings that govern the quality and quantity of the monsoon. A detailed analysis of non-linear interactions among the antecedent climatic conditions and the monsoon has led the authors to introduce the concept of proportionate weightage to the signals of different parameters. This has led to the development of a power regression model, which is able to quantify the effect of each parameter. Details of the model are presented, Based on the model, the India Meteorological Department has been issuing the operational long range forecast of monsoon rainfall over India as a whole during the past 3 years, 1988 to 1990, and these forecasts have proved to be correct.

scholarly journals Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: Development of a prediction model based on data from Ischgl, Austria

2020 ◽  
Author(s):  
Jens Lehmann ◽  
Johannes M Giesinger ◽  
Gerhard Rumpold ◽  
Wegene Borena ◽  
Ludwig Knabl ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Multivariate Analysis ◽  
Prediction Model ◽  
Regression Model ◽  
Web Application ◽  
Binary Logistic Regression ◽  
Population Level ◽  
Limb Pain ◽  
The Past ◽  
Model Based

We report the development of a regression model to predict prevalence of SARS-CoV-2 antibodies on a population level based on self-reported symptoms.We assessed participant-reported symptoms in the past twelve weeks, as well as presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n=451) were on average 47.4 years old (SD 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n=197 (43.7%) participants. In the multivariate analysis, three significant predictors were included: Odds ratios (OR) for the most predictive categories were: cough (OR 3.34, CI 1.70 - 6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90 - 32.17), and limb pain (OR 2.55, CI 1.20 - 6.50). The AUC was 0.773 (95% CI: 0.727-0.820).Our regression model may be used to estimate seroprevalence on a population-level and a web application is being developed to facilitate use of the model.

Regression Model for Resilient Modulus of Subgrade Soils in Shanghai

2011 ◽  
Vol 374-377 ◽  
pp. 1796-1799
Author(s):  
Hong Xi Liu ◽  
Liang Zhou
Keyword(s):  
Regression Model ◽  
Prediction Models ◽  
Resilient Modulus ◽  
Soil Samples ◽  
Soil Types ◽  
Model Parameters ◽  
Cohesive Soils ◽  
Subgrade Soils ◽  
The Past ◽  
Effective Design

Subgrade resilient modulus (MR) is very important for effective design of pavements. Several methods to estimate the resilient modulus were suggested in the past years. The main objective of this paper was to validate the correlation of MR with other physical properties of the subgrade soils. Cohesive soils representing major soil types in Shanghai were selected. The resilient modulus tests were conducted with UTM. Additional analysis was then performed to develop correlations between the model parameters and other soil properties. To verify the prediction models independently, laboratory MR tests were conducted on new subgrade soils. It was observed that the predicted MR values compared well with the laboratory measured values for the soil samples.

Prediction of Sea of Japan (East Sea) acidification over the past 40 years using a multiparameter regression model

2010 ◽  
Vol 24 (3) ◽  
pp. n/a-n/a ◽  
Author(s):  
Tae-Wook Kim ◽  
Kitack Lee ◽  
Richard A. Feely ◽  
Christopher L. Sabine ◽  
Chen-Tung Arthur Chen ◽  
...  
Keyword(s):  
Regression Model ◽  
Sea Of Japan ◽  
East Sea ◽  
The Past

Government Subsidies to Non-profit Organisations and its Impact on Private Donations: Regional and Subsector Models

2017 ◽  
Vol 15 (2) ◽  
pp. 283-301 ◽  
Author(s):  
Marie Hladká ◽  
Vladimír Hyánek
Keyword(s):  
Czech Republic ◽  
Regression Model ◽  
Government Funding ◽  
Data Sources ◽  
The Czech Republic ◽  
Non Profit ◽  
Government Subsidies ◽  
Private Giving ◽  
The Past ◽  
Private Donations

Government subsidies to the non-profit sector are a significant source of income for non-profit organisations. One significant impact of these subsidies is on the changing scope of private giving. The objective of this paper is to use a regression model to test whether government funding in the Czech Republic encourages private gifts and large amounts of government funding discourages gifts. However, rather than focusing on aggregate data sources, this study examines how these impacts vary among regions and sub-sectors. These models help explain why studies conducted in the past frequently differed and were inconsistent in their findings.

scholarly journals PERAN EKSISTENSI SAHAM DI LQ45 PADA KINERJA PROFITABILITAS PERUSAHAAN

2019 ◽  
Vol 3 (1) ◽  
pp. 72-94
Author(s):  
Herman Karamoy ◽  
Hizkia H. D. Tasik
Keyword(s):  
Panel Data ◽  
Regression Model ◽  
Financial Performance ◽  
Random Effect ◽  
Previous Period ◽  
The Past ◽  
Percentage Points ◽  
Panel Data Regression ◽  
Data Regression ◽  
Net Profit

Many companies in Indonesia compete to improve the financial performance to be listed in LQ45 index. LQ45 index is the house of 45 stocks with high liquidity, big market capitalization and good financial performance. This paper aims to investigate whether the existence status of stocks in LQ45 index in the past affects the current profitability performance of the companies. This study relies on semesterly data from 19 companies dated from 2012 to 2015. Using panel data regression model of Net Profit Margin (NPM), the findings suggests that the existence of stocks in LQ45 index in previous period significantly affect the NPM. The results are robust and consistent across specifications either using fixed effect or random effect approaches. The magnitude ranges from 22,77 to 26,87 percentage points.

scholarly journals Estimating seroprevalence of SARS-CoV-2 antibodies using three self-reported symptoms: development of a prediction model based on data from Ischgl, Austria

2021 ◽  
Vol 149 ◽  
Author(s):  
Jens Lehmann ◽  
Johannes M. Giesinger ◽  
Gerhard Rumpold ◽  
Wegene Borena ◽  
Ludwig Knabl ◽  
...  
Keyword(s):  
Multivariate Analysis ◽  
Regression Model ◽  
Web Application ◽  
Operating Characteristic ◽  
Characteristic Curve ◽  
Binary Logistic Regression ◽  
Population Level ◽  
Limb Pain ◽  
The Past ◽  
Operating Characteristic Curve

Abstract We report the development of a regression model to predict the prevalence of severe acute respiratory syndrome coronavirus (SARS-CoV-2) antibodies on a population level based on self-reported symptoms. We assessed participant-reported symptoms in the past 12 weeks, as well as the presence of SARS-CoV-2 antibodies during a study conducted in April 2020 in Ischgl, Austria. We conducted multivariate binary logistic regression to predict seroprevalence in the sample. Participants (n = 451) were on average 47.4 years old (s.d. 16.8) and 52.5% female. SARS-CoV-2 antibodies were found in n = 197 (43.7%) participants. In the multivariate analysis, three significant predictors were included and the odds ratios (OR) for the most predictive categories were cough (OR 3.34, CI 1.70–6.58), gustatory/olfactory alterations (OR 13.78, CI 5.90–32.17) and limb pain (OR 2.55, CI 1.20–6.50). The area under the receiver operating characteristic curve was 0.773 (95% CI 0.727–0.820). Our regression model may be used to estimate the seroprevalence on a population level and a web application is being developed to facilitate the use of the model.

scholarly journals ANALISIS RASIO PROFITABILITAS GROSS PROFIT MARGIN DAN RETURN ON ASSET SEBAGAI ALAT UKUR UNTUK MENILAI KEEFEKTIFAN KINERJA KEUANGAN PADA PT. KIMIA FARMA (PERSERO), TBK TAHUN 2013-2017

2020 ◽  
Vol 13 (2) ◽  
pp. 85-92
Author(s):  
Putri Sarirati ◽  
Devi Choirinnisa
Keyword(s):  
Regression Model ◽  
Financial Performance ◽  
Profit Margin ◽  
Operational Performance ◽  
Measurement Tool ◽  
Average Increase ◽  
Return On Assets ◽  
The Past ◽  
Normality Test ◽  
Regression Test

Based on the calculation of Gross Profit Margin tends to show an increase every year, namely from 2013 to 2017, overall an average increase of 1.55%, with an R Square level of 0.85 or 85%. Return on Assets experienced instability in the period 2013 - 2017 due to the increase in cost of goods sold. The overall decrease is an average of 0.81% with the level of R Square 0.94 or 94%. A decrease of 0.51% occurred in 2013 of 8.58% until 2014 it became 8.07%. In 2015 it decreased by 0.46% to 7.61%. And again, decreased in 2016 by 1.81% to 5.80%. Kimia Farma declined again in 2017 by 0.44% to 5.36%. Based on the results of the Regression Test for Gross Profit Margin and Return on Assets variables meet the Normality Test, in terms of Autocorrelation Test R Square level of 0.960 or 96% effect as a measurement tool to assess the effectiveness of financial performance, in terms of Homoscedasticity Test Homoscedasticity did not occur and in terms of Multicollinearity, Test Multicollinearity disorder does not occur or in other words the Regression model is free from the symptoms of Multicollinearity. The final result of this study is the profitability of PT. Kimia Farma (Perseo) Tbk, during the last five years experienced a fluctuation or recollection and decline. The level of profitability or the company's ability to make a profit has decreased in the past year. The best level of profitability in years 2013. However, PT. Kimia Farma (Persero) Tbk has been able to pass 2017 which is full of challenges, with a record that the company's operational performance is very satisfying even though in terms of financial performance has decreased compared to the realization of performance in 2013.

scholarly journals Reconstructing the Past Wind Stresses over the Tropical Pacific Ocean from 1875 to 1947

2009 ◽  
Vol 48 (6) ◽  
pp. 1181-1198 ◽  
Author(s):  
Ziwang Deng ◽  
Youmin Tang
Keyword(s):  
Pacific Ocean ◽  
Climate Variability ◽  
Regression Model ◽  
Wind Stress ◽  
Tropical Pacific ◽  
Tropical Pacific Ocean ◽  
The Past ◽  
Past Climate Variability ◽  
The Tropical Pacific

Abstract An important step in understanding the climate system is simulating and studying the past climate variability, using oceanic models, atmospheric models, or both. Toward this goal, long-term wind stress data, as the forcing of oceanic or climate models, are often required. In this study, the possibility of reconstructing the past winds of the tropical Pacific Ocean using historical sea surface temperature (SST) and sea level pressure (SLP) datasets was explored. Four statistical models, based on principal component (PC) regression and singular vector decomposition (SVD), were developed for reconstructing monthly pseudo wind stress over the tropical Pacific for the period 1875–1947. The high-frequency noise was removed from the raw data prior to the reconstruction. These models are SST-based PC regression (model 1), SLP-based PC regression (model 2), SST-based SVD (model 3), and SLP-based SVD (model 4). The results show that reconstructed wind stresses from all models can account for more than one-half of the total variances. In general, the SLP is better than SST as a predictor and the SVD method is superior to the PC regression. Forced by these reconstructed wind stresses, an oceanic general circulation model can simulate realistic interannual variability of the tropical Pacific SST. However, the wind stress reconstructed by SST-based models leads to better simulation skill in comparison with that from SLP-based models. Last, a long-term wind stress dataset was constructed for the period from 1875 to 1947 by the SST-based SVD model, which provides a useful tool for studying the past climate variability over the tropical Pacific, especially for El Niño–Southern Oscillation.

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