Classification of Suicide Attempts through a Machine Learning Algorithm Based on Multiple Systemic Psychiatric Scales

Classification and prediction of suicide attempts in high-risk groups is important for preventing suicide. The purpose of this study was to investigate whether the information from multiple clinical scales has classification power for identifying actual suicide attempts. Patients with depression and anxiety disorders (N = 573) were included, and each participant completed 31 self-report psychiatric scales and questionnaires about their history of suicide attempts. We then trained an artificial neural network classifier with 41 variables (31 psychiatric scales and 10 sociodemographic elements) and ranked the contribution of each variable for the classification of suicide attempts. To evaluate the clinical applicability of our model, we measured classification performance with top-ranked predictors. Our model had an overall accuracy of 93.7% in 1-month, 90.8% in 1-year, and 87.4% in lifetime suicide attempts detection. The area under the receiver operating characteristic curve (AUROC) was the highest for 1-month suicide attempts detection (0.93), followed by lifetime (0.89), and 1-year detection (0.87). Among all variables, the Emotion Regulation Questionnaire had the highest contribution, and the positive and negative characteristics of the scales similarly contributed to classification performance. Performance on suicide attempts classification was largely maintained when we only used the top five ranked variables for training (AUROC; 1-month, 0.75, 1-year, 0.85, lifetime suicide attempts detection, 0.87). Our findings indicate that information from self-report clinical scales can be useful for the classification of suicide attempts. Based on the reliable performance of the top five predictors alone, this machine learning approach could help clinicians identify high-risk patients in clinical settings

Valence states and spin structure of spinel FeV2O4 with different orbital degrees of freedom

The electronic structure of spinel FeV2O4, which contains two Jahn-Teller active Fe and V ions, has been investigated by employing soft x-ray absorption spectroscopy (XAS), soft x-ray magnetic circular dichroism (XMCD), and nuclear magnetic resonance (NMR). XAS indicates that V ions are trivalent and Fe ions are nearly divalent. The signs of V and Fe 2p XMCD spectra are opposite to each other. It is found that the effect of the V 3d spin-orbit interaction on the V 2p XMCD spectrum is negligible, indicating that the orbital ordering of V t2g states occurs from the real orbital states and that the orbital moment of a V3+ ion is mostly quenched. NMR shows that V spins are canted to have a Yafet-Kittel-type triangular spin configuration

Asymmetric Cost Behavior and Non-Financial Firms' Risky Financial Investments

Using hand-collected data on non-financial firms’ financial portfolios, I examine how asymmetric cost behavior (or cost stickiness) affects risky financial investments. Sticky costs amplify the downward effect of sales decrease on profits because costs do not fall when sales decrease by as much as they rise when sales increase. I find that firms with sticky costs avoid risky financial investments because of expected liquidity needs and the trade-off between operating and financial risk. Oster’s delta and shock-based instrumental variable design address endogeneity concerns. For firms with sticky costs, investing in risky securities subdues non-financial investments and increases a firm’s risk exposure without creating shareholder value

Constructing Differentiated Educational Materials Using Semantic Annotation for Sustainable Education in IoT Environments

Recently, Internet of Things (IoT) technology has become a hot trend and is used in a wide variety of fields. For instance, in education, this technology contributes to improving learning efficiency in the class by enabling learners to interact with physical devices and providing appropriate learning content based on this interaction. Such interaction data can be collected through the physical devices to define personal data. In the meanwhile, multimedia contents in this environment usually have a wide variety of formats and standards, making it difficult for computers to understand their meaning and reuse them. This could be a serious obstacle to the effective use or sustainable management of educational contents in IoT-based educational systems. In order to solve this problem, in this paper, we propose a semantic annotation scheme for sustainable computing in the IoT environment. More specifically, we first show how to collect appropriate multimedia contents and interaction data. Next, we calculate the readability of learning materials and define the user readability level to provide appropriate contents to the learners. Finally, we describe our semantic annotation scheme and show how to annotate collected data using our scheme. We implement a prototype system and show that our scheme can achieve efficient management of various learning materials in the IoT-based educational system

Frequency Selective Auto-Encoder for Smart Meter Data Compression

With the development of the internet of things (IoT), the power grid has become intelligent using massive IoT sensors, such as smart meters. Generally, installed smart meters can collect large amounts of data to improve grid visibility and situational awareness. However, the limited storage and communication capacities can restrain their infrastructure in the IoT environment. To alleviate these problems, efficient and various compression techniques are required. Deep learning-based compression techniques such as auto-encoders (AEs) have recently been deployed for this purpose. However, the compression performance of the existing models can be limited when the spectral properties of high-frequency sampled power data are widely varying over time. This paper proposes an AE compression model, based on a frequency selection method, which improves the reconstruction quality while maintaining the compression ratio (CR). For efficient data compression, the proposed method selectively applies customized compression models, depending on the spectral properties of the corresponding time windows. The framework of the proposed method involves two primary steps: (i) division of the power data into a series of time windows with specified spectral properties (high-frequency, medium-frequency, and low-frequency dominance) and (ii) separate training and selective application of the AE models, which prepares them for the power data compression that best suits the characteristics of each frequency. In simulations on the Dutch residential energy dataset, the frequency-selective AE model shows significantly higher reconstruction performance than the existing model with the same CR. In addition, the proposed model reduces the computational complexity involved in the analysis of the learning process

Consistency Regularization for Adversarial Robustness

Adversarial training (AT) is currently one of the most successful methods to obtain the adversarial robustness of deep neural networks. However, the phenomenon of robust overfitting, i.e., the robustness starts to decrease significantly during AT, has been problematic, not only making practitioners consider a bag of tricks for a successful training, e.g., early stopping, but also incurring a significant generalization gap in the robustness. In this paper, we propose an effective regularization technique that prevents robust overfitting by optimizing an auxiliary `consistency' regularization loss during AT. Specifically, we discover that data augmentation is a quite effective tool to mitigate the overfitting in AT, and develop a regularization that forces the predictive distributions after attacking from two different augmentations of the same instance to be similar with each other. Our experimental results demonstrate that such a simple regularization technique brings significant improvements in the test robust accuracy of a wide range of AT methods. More remarkably, we also show that our method could significantly help the model to generalize its robustness against unseen adversaries, e.g., other types or larger perturbations compared to those used during training. Code is available at https://github.com/alinlab/consistency-adversarial.Comment: Published as a conference proceeding for AAAI 202

Multistep-Ahead Solar Radiation Forecasting Scheme Based on the Light Gradient Boosting Machine: A Case Study of Jeju Island

Smart islands have focused on renewable energy sources, such as solar and wind, to achieve energy self-sufficiency. Because solar photovoltaic (PV) power has the advantage of less noise and easier installation than wind power, it is more flexible in selecting a location for installation. A PV power system can be operated more efficiently by predicting the amount of global solar radiation for solar power generation. Thus far, most studies have addressed day-ahead probabilistic forecasting to predict global solar radiation. However, day-ahead probabilistic forecasting has limitations in responding quickly to sudden changes in the external environment. Although multistep-ahead (MSA) forecasting can be used for this purpose, traditional machine learning models are unsuitable because of the substantial training time. In this paper, we propose an accurate MSA global solar radiation forecasting model based on the light gradient boosting machine (LightGBM), which can handle the training-time problem and provide higher prediction performance compared to other boosting methods. To demonstrate the validity of the proposed model, we conducted a global solar radiation prediction for two regions on Jeju Island, the largest island in South Korea. The experiment results demonstrated that the proposed model can achieve better predictive performance than the tree-based ensemble and deep learning methods