Phase conjugation by degenerate four wave mixing in disodium fluorescein solution in methanol

Organic dyes are known to show the resonant type of nonlinear optical properties, including phase conjugation. In the present work, disodium fluorescein in methanol is used as an organic nonlinear medium for degenerate four wave mixing at 532 nm to see the intensity dependence of the phase conjugate signal at different concentrations of the solution. It is observed that the maximum reflectivity of the signal occurs in a concentration range of 5 x 10(exp -3)/cu cm to 1.2 x 10(exp -2) g/cu cm. It is also observed that the intensity of the signal drops suddenly to less than half of its maximum outside the concentration range mentioned above. An investigation of the phase conjugate signal intensity by changing the delay time between probe signal and the forward pump is also examined. Briefly discussed is the possibility of population grating in dye liquids as a source of enhancing the third order susceptibility besides the other techniques mentioned in reference. The experiment is done by beam splitting the second harmonic (532 nm) of Nd:YAG laser, Q-switched at 20 pulses/sec (pulse width is approximately 8 and 200 mJ per pulse)

Improving accuracy metric with precision and recall metrics for optimizing stochastic classifier

All stochastic classifiers attempt to improve their classification performance by constructing an optimized classifier.Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution.However, the use of accuracy metric could lead the solution towards the sub-optimal solution due less discriminating power.Moreover, the accuracy metric also unable to perform optimally when dealing with imbalanced class distribution. In this study, we propose a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects.We refer the new evaluation metric as optimized accuracy with recall-precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric and able to perform optimally when dealing with imbalanced class distribution using one simple counter-example.We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the accuracy and FMeasure metrics.Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two selected metrics for almost five medical data sets

A Review On Evaluation Metrics For Data Classification Evaluations

Evaluation metric plays a critical role in achieving the optimal classifier during the classification training. Thus, a selection of suitable evaluation metric is an important key for discriminating and obtaining the optimal classifier. This paper systematically reviewed the related evaluation metrics that are specifically designed as a discriminator for optimizing generative classifier. Generally, many generative classifiers employ accuracy as a measure to discriminate the optimal solution during the classification training. However, the accuracy has several weaknesses which are less distinctiveness, less discriminability, less informativeness and bias to majority class data. This paper also briefly discusses other metrics that are specifically designed for discriminating the optimal solution. The shortcomings of these alternative metrics are also discussed. Finally, this paper suggests five important aspects that must be taken into consideration in constructing a new discriminator metric

Inequality in hospitalization due to non-communicable diseases in Sweden: Age-cohort analysis of the Uppsala Birth Cohort Multigenerational Study

We aimed to investigate cohort differences in age trajectories of hospitalization due to non-communicable conditions, and if these varied by paternal socioeconomic position. We used the Uppsala Birth Cohort Multigenerational Study—including virtually complete information on medical diagnoses. Our sample constituted 28,448 individuals (103,262 observations). The outcome was five-year prevalence of hospitalization due to major non-communicable conditions in 1989–2008. The exposures were age (19–91), year-of-birth (1915–1929; 1938–1972), gender (man vs woman), and parental socioeconomic position (low, medium, and high). We used multilevel logit models to examine associations between exposures and the hospitalization outcome. Younger cohorts had a higher prevalence of hospitalization at overlapping ages than those born earlier, with inter-cohort differences emerging from early-adulthood and increasing with age. For instance, at age 40 predicted probability of hospitalization increased across birth-cohorts—from 1.2% (born in 1948-52) to 2.0% (born in 1963-67)—whereas at age 50 it was 2.9% for those born in 1938-42 compared with 4.6% among participants born in 1953-57. Those with medium and low socioeconomic position had 13.0% and 20.0% higher odds of experiencing hospitalization during the observation period, respectively—when age, year-of-birth and gender were accounted for. We found that no progress was made in reducing the socioeconomic inequalities in hospitalization across cohorts born between 1915 and 1972. Hence, more effective policies and interventions are needed to reduce the overall burden of morbidity—particularly among the most vulnerable

Towards Guidelines for Assessing Qualities of Machine Learning Systems

Nowadays, systems containing components based on machine learning (ML) methods are becoming more widespread. In order to ensure the intended behavior of a software system, there are standards that define necessary quality aspects of the system and its components (such as ISO/IEC 25010). Due to the different nature of ML, we have to adjust quality aspects or add additional ones (such as trustworthiness) and be very precise about which aspect is really relevant for which object of interest (such as completeness of training data), and how to objectively assess adherence to quality requirements. In this article, we present the construction of a quality model (i.e., evaluation objects, quality aspects, and metrics) for an ML system based on an industrial use case. This quality model enables practitioners to specify and assess quality requirements for such kinds of ML systems objectively. In the future, we want to learn how the term quality differs between different types of ML systems and come up with general guidelines for specifying and assessing qualities of ML systems.Comment: Has been accepted at the 13th International Conference on the Quality of Information and Communications Technology QUATIC2020 (https://2020.quatic.org/). QUATIC 2020 proceedings will be included in a volume of Springer CCIS Series (Communications in Computer and Information Science

OAERP : A Better Measure than Accuracy in Discriminating a Better Solution for Stochastic Classification Training

The use of accuracy metric for stochastic classification training could lead the solution selecting towards the sub-optimal solution due to its less distinctive value and also unable to perform optimally when confronted with imbalanced class problem. In this study, a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects was proposed. This new evaluation metric is known as Optimized Accuracy with Extended Recall-precision (OAERP). By using two examples, the results has shown that the OAERP metric has produced more distinctive and discriminating values as compared to accuracy metric. This paper also empirically demonstrates that Monte Carlo Sampling (MCS) algorithm that is trained by OAERP metric was able to obtain better predictive results than the one trained by the accuracy metric alone, using nine medical data sets. In addition, the OAERP metric also performed effectively when dealing with imbalanced class problems. Moreover, the t-test analysis also shows a clear advantage of the MCS model trained by the OAERP metric against its previous metric over five out of nine medical data sets. From the abovementioned results, it is clearly indicates that the OAERP metric is more likely to choose a better solution during classification training and lead towards a better trained classification model

Associations between family social circumstances and psychological distress among the university students of Bangladesh : to what extent do the lifestyle factors mediate?

Background: While there is a growing body of empirical studies focusing on the social and behavioral predictors of psychological health, the mechanisms that may underlie the reported associations have not been adequately explored. This study aimed to examine the association of social and lifestyle factors with psychological distress, and the potential mediating role of the lifestyle factors in the estimated associations between social circumstances and psychological distress. Methods: A total of 742 tertiary level students (53% females) from a range of socio-economic backgrounds and multiple educational institutions participated in this cross-sectional study. The 12-items General Health Questionnaire (GHQ-12) was utilized for measuring psychological distress. Data related to students’ socio-demographic characteristics, family social circumstances, and lifestyle factors were also collected. Modified Poisson regression analysis was used to estimate the risk ratios (RR) and their 95% confidence intervals (CI). Results: The multivariable regression analysis suggests heightened risks of psychological distress associated with low parental Socio-Economic Position (SEP) (RR: 1.36; 95% CI: 1.07, 1.76), childhood poverty (RR: 1.31; 95% CI: 1.11, 1.55), and living away from the family (RR: 1.28; 95% CI: 1.07, 1.54). Among the lifestyle factors, past smoking, physical inactivity, inadequate fruit intake, and poor sleep quality were strongly associated with psychological distress and these associations persisted when the family social circumstances and lifestyle factors were mutually adjusted for. The lifestyle factors did not considerably mediate the estimated associations between family social circumstances and psychological distress. Conclusion: The social and lifestyle factors operated independently to increase students’ risk of psychological distress. Accordingly, while promoting students’ healthy lifestyles may reduce the overall burden of psychological distress, any equity initiative aiming to minimize the social inequalities in psychological health should be targeted to improving the living conditions in early life

Improving Accuracy Metric with Precision and Recall Metrics for Optimizing Stochastic Classifier

All stochastic classifiers attempt to improve their classifica-tion performance by constructing an optimized classifier. Typically, all of stochastic classification algorithms employ accuracy metric to discriminate an optimal solution. However, the use of accuracy metric could lead the so-lution towards the sub-optimal solution due less discriminating power. Moreover, the accuracy metric also unable to perform optimally when deal-ing with imbalanced class distribution. In this study, we propose a new evaluation metric that combines accuracy metric with the extended precision and recall metrics to negate these detrimental effects. We refer the new evaluation metric as optimized accuracy with recall-precision (OARP). This paper demonstrates that the OARP metric is more discriminating than the accuracy metric and able to perform optimally when dealing with imba-lanced class distribution using one simple counter-example. We also dem-onstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the accuracy and F-Measure metrics. Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the two se-lected metrics for almost five medical data sets

A Novel Performance Metric for Building an Optimized Classifier

Problem statement: Typically, the accuracy metric is often applied for optimizing the heuristic or stochastic classification models. However, the use of accuracy metric might lead the searching process to the sub-optimal solutions due to its less discriminating values and it is also not robust to the changes of class distribution. Approach: To solve these detrimental effects, we propose a novel performance metric which combines the beneficial properties of accuracy metric with the extended recall and precision metrics. We call this new performance metric as Optimized Accuracy with Recall-Precision (OARP). Results: In this study, we demonstrate that the OARP metric is theoretically better than the accuracy metric using four generated examples. We also demonstrate empirically that a naïve stochastic classification algorithm, which is Monte Carlo Sampling (MCS) algorithm trained with the OARP metric, is able to obtain better predictive results than the one trained with the conventional accuracy metric. Additionally, the t-test analysis also shows a clear advantage of the MCS model trained with the OARP metric over the accuracy metric alone for all binary data sets. Conclusion: The experiments have proved that the OARP metric leads stochastic classifiers such as the MCS towards a better training model, which in turn will improve the predictive results of any heuristic or stochastic classification models