Implicitly estimating the cost of mental illness in Australia: a standard-of-living approach

Background Estimating the costs of mental illness provides useful policy and managerial information to improve the quality of life of people living with a mental illness and their families. Objective This paper estimates the costs of mental health in Australia using the standard-of-living approach. Methods The cost of mental illness was estimated implicitly using a standard of living approach. We analyse data from 16 waves of the Household, Income and Labour Dynamics in Australia Survey (HILDA) using 209,871 observations. Unobserved heterogeneity was mitigated using an extended random-effects estimator. Results The equivalised disposable income of people with mental illness, measured by a self-reported mental health condition, needs to be 50% higher to achieve a similar living standard as those without a mental illness. The cost estimates vary considerably with measures of mental illness and standard of living. An alternative measure of mental illness using the first quintile of the SF-36 mental health score distribution resulted in an increase of estimated costs to 80% equivalised disposable income. Conclusion People with mental illness need to increase equivalised disposable income, which includes existing financial supports, by 50%-80% to achieve a similar level of financial satisfaction as those without a mental illness. The cost estimate can be substantially higher if the overall life satisfaction is used to proxy for standard of living

The Current Status of Historical Preservation Law in Regularory Takings Jurisprudence: Has the Lucas Missile Dismantled Preservation Programs?

This paper describes our NIHRIO system for SemEval-2018 Task 3 "Irony detection in English tweets". We propose to use a simple neural network architecture of Multilayer Perceptron with various types of input features including: lexical, syntactic, semantic and polarity features.  Our system achieves very high performance in both subtasks of binary and multi-class irony detection in tweets. In particular, we rank at fifth in terms of the accuracy metric and the F1 metric. Our code is available at: https://github.com/NIHRIO/IronyDetectionInTwitte

Estimating the cost of mental illness in Australia: a standard of living approach

This paper estimates the costs of mental health in Australia using the standard-of-living approach. We analyse data from the Household, Income and Labour Dynamics in Australia Survey using an extended random-effects estimator. To the best of our knowledge, this is the first study to examine the cost of mental illness in Australia using the standard of living approach with a nationally representative longitudinal data set. Results from the main specification show that people with a mental illness need to increase their equivalised disposable income by 50% to achieve a similar living standard as those without a mental illness. The cost estimates vary considerably with measures of mental illness and standard of living. An alternative measure of mental illness using the first quintile of the SF-36 mental health score distribution resulted in an increase of estimated costs to 80% equivalised disposable income

Multimodal Graph Learning for Modeling Emerging Pandemics with Big Data

Accurate forecasting and analysis of emerging pandemics play a crucial role in effective public health management and decision-making. Traditional approaches primarily rely on epidemiological data, overlooking other valuable sources of information that could act as sensors or indicators of pandemic patterns. In this paper, we propose a novel framework called MGL4MEP that integrates temporal graph neural networks and multi-modal data for learning and forecasting. We incorporate big data sources, including social media content, by utilizing specific pre-trained language models and discovering the underlying graph structure among users. This integration provides rich indicators of pandemic dynamics through learning with temporal graph neural networks. Extensive experiments demonstrate the effectiveness of our framework in pandemic forecasting and analysis, outperforming baseline methods across different areas, pandemic situations, and prediction horizons. The fusion of temporal graph learning and multi-modal data enables a comprehensive understanding of the pandemic landscape with less time lag, cheap cost, and more potential information indicators

Automatically Detect Software Security Vulnerabilities Based on Natural Language Processing Techniques and Machine Learning Algorithms

Nowadays, software vulnerabilities pose a serious problem, because cyber-attackers often find ways to attack a system by exploiting software vulnerabilities. Detecting software vulnerabilities can be done using two main methods: i) signature-based detection, i.e. methods based on a list of known security vulnerabilities as a basis for contrasting and comparing; ii) behavior analysis-based detection using classification algorithms, i.e., methods based on analyzing the software code. In order to improve the ability to accurately detect software security vulnerabilities, this study proposes a new approach based on a technique of analyzing and standardizing software code and the random forest (RF) classification algorithm. The novelty and advantages of our proposed method are that to determine abnormal behavior of functions in the software, instead of trying to define behaviors of functions, this study uses the Word2vec natural language processing model to normalize and extract features of functions. Finally, to detect security vulnerabilities in the functions, this study proposes to use a popular and effective supervised machine learning algorithm