Asteroseismology of Vibration Powered Neutron Stars

Chapter from the book "Astrophysics", p.287-308. Edited by Ibrahim Kucuk, ISBN 978-953-51-0473-5, InTech, March 3, 2012.Comment: http://cdn.intechopen.com/pdfs/34269/InTech-Asteroseismology_of_vibration_powered_neutron_stars.pd

What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media

Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few depression detection studies attempt to explain the decision based on the importance score or attention weights, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we follow the computational design science paradigm to develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses using a large-scale dataset show that MSTPNet outperforms state-of-the-art depression detection methods with an F1-score of 0.851. This result also reveals new symptoms that are unnoted in the survey approach, such as sharing admiration for a different life. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media. In practice, our proposed method can be implemented in social media platforms to provide personalized online resources for detected depressed patients.Comment: 56 pages, 10 figures, 21 table

What Symptoms and How Long? An Interpretable AI Approach for Depression Detection in Social Media

Depression is the most prevalent and serious mental illness, which induces grave financial and societal ramifications. Depression detection is key for early intervention to mitigate those consequences. Such a high-stake decision inherently necessitates interpretability. Although a few depression detection studies attempt to explain the decision, these explanations misalign with the clinical depression diagnosis criterion that is based on depressive symptoms. To fill this gap, we develop a novel Multi-Scale Temporal Prototype Network (MSTPNet). MSTPNet innovatively detects and interprets depressive symptoms as well as how long they last. Extensive empirical analyses show that MSTPNet outperforms state-of-the-art depression detection methods. This result also reveals new symptoms that are unnoted in the survey approach. We further conduct a user study to demonstrate its superiority over the benchmarks in interpretability. This study contributes to IS literature with a novel interpretable deep learning model for depression detection in social media

Suicide Risk Prediction for Users with Depression in Question Answering Communities: A Design Based on Deep Learning

In the field of public health, suicide risk prediction is a central and urgent problem. Existing researches mainly focus on user’s current post but overlook historical post. In light of the psychological characteristics, we argue that it is valuable to consider users’ historical post in addition to current post for predicting suicide risk. Based on this rationale, we propose a deep learning-based suicide risk prediction framework - Dynamic Historical Information based Suicide Risk Prediction (DHISRP) - by considering the user’s current post content and historical post content. To capture the dynamic and complicated information of historical post, we design a unit based on long short-term memory (LSTM), named RNLSTM. We also conduct experiments to compare with the benchmark model to prove the effectiveness of our model, and perform ablation experiments to verify the significance of each component in the prediction framework in this study

More User Engagement in Online Community: Effects of Posting and Replying Behaviors on Detection of Depression

Depression is a common and severe mental illness. Early detection can reduce costs and improve treatment outcomes. Previous studies mainly relied on the posting behaviors to build automatic detection model for patients with depression but ignored the replying behaviors. This study systematically analyze the replying behavior, identify various features about content, language style and emotion from user-written replies and user-replied posts, and compare their relative importance. The experimental results using real-world dataset reveal that the replying behavior can significantly improve traditional detection model. Compared with posting behavior, replying behavior be shown to be more important for depression detection. Further analysis for the replying behavior shows that the user-written replies is not effective, while user-replied posts are effective. Considering that there are many users who only have replying behaviors, the detection model proposed will be applicable to a larger number of people

Impacts of thermal and electric contact resistance on the material design in segmented thermoelectric generators

Segmented thermoelectric generators (STEGs) can exhibit present superior performance than those of the conventional thermoelectric generators. Thermal and electrical contact resistances exist between the thermoelectric material interfaces in each thermoelectric leg. This may significantly hinder performance improvement. In this study, a five-layer STEG with three pairs of thermoelectric (TE) materials was investigated considering the thermal and electrical contact resistances on the material contact surface. The STEG performance under different contact resistances with various combinations of TE materials were analyzed. The relationship between the material sequence and performance indicators under different contact resistances is established by machine learning. Based on the genetic algorithm, for each contact resistance combination, the optimal material sequences were identified by maximizing the electric power and energy conversion efficiency. To reveal the underlying mechanism that determines the heat-to-electrical performance, the total electrical resistance, output voltage, ZT value, and temperature distribution under each optimized scenario were analyzed. The STEG can augment the heat-to-electricity performance only at small contact resistances. A large contact resistance significantly reduces the performance. At an electrical contact resistance of RE = 10–3 K·m2·W−1 and thermal contact resistance of RT = 10–8 Ω·m2, the maximum electric power was reduced to 5.71 mW (90.86 mW without considering the contact resistance). And the maximum energy conversion efficiency is lowered to 2.54% (12.59% without considering the contact resistance)

Three New Benzophenone Derivatives from <i>Selaginella tamariscina</i>

Six compounds including three new benzophenones, selagibenzophenones D-F (1–3), two known selaginellins (4–5) and one known flavonoid (6), were isolated from Selaginella tamariscina. The structures of new compounds were established by 1D-, 2D-NMR and HR-ESI-MS spectral analyses. Compound 1 represents the second example of diarylbenzophenone from natural sources. Compound 2 possesses an unusual biphenyl-bisbenzophenone structure. Their cytotoxicity against human hepatocellular carcinoma HepG2 and SMCC-7721 cells and inhibitory activities on lipopolysaccharide-induced nitric oxide (NO) production in RAW264.7 cells were evaluated. Compound 2 showed moderate inhibitory activity against HepG2 and SMCC-7721 cells, and compounds 4 and 5 showed moderate inhibitory activity to HepG2 cells. Compounds 2 and 5 also exhibited inhibitory activities on lipopolysaccharide-induced nitric oxide (NO) production