4,273 research outputs found
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Bipolar disorder (BD) is closely associated with an increased risk of
suicide. However, while the prior work has revealed valuable insight into
understanding the behavior of BD patients on social media, little attention has
been paid to developing a model that can predict the future suicidality of a BD
patient. Therefore, this study proposes a multi-task learning model for
predicting the future suicidality of BD patients by jointly learning current
symptoms. We build a novel BD dataset clinically validated by psychiatrists,
including 14 years of posts on bipolar-related subreddits written by 818 BD
patients, along with the annotations of future suicidality and BD symptoms. We
also suggest a temporal symptom-aware attention mechanism to determine which
symptoms are the most influential for predicting future suicidality over time
through a sequence of BD posts. Our experiments demonstrate that the proposed
model outperforms the state-of-the-art models in both BD symptom identification
and future suicidality prediction tasks. In addition, the proposed temporal
symptom-aware attention provides interpretable attention weights, helping
clinicians to apprehend BD patients more comprehensively and to provide timely
intervention by tracking mental state progression.Comment: KDD 2023 accepte
Beam scanning by liquid-crystal biasing in a modified SIW structure
A fixed-frequency beam-scanning 1D antenna based on Liquid Crystals (LCs) is designed for application in 2D scanning with lateral alignment. The 2D array environment imposes full decoupling of adjacent 1D antennas, which often conflicts with the LC requirement of DC biasing: the proposed design accommodates both. The LC medium is placed inside a Substrate Integrated Waveguide (SIW) modified to work as a Groove Gap Waveguide, with radiating slots etched on the upper broad wall, that radiates as a Leaky-Wave Antenna (LWA). This allows effective application of the DC bias voltage needed for tuning the LCs. At the same time, the RF field remains laterally confined, enabling the possibility to lay several antennas in parallel and achieve 2D beam scanning. The design is validated by simulation employing the actual properties of a commercial LC medium
PIKS: A Technique to Identify Actionable Trends for Policy-Makers Through Open Healthcare Data
With calls for increasing transparency, governments are releasing greater
amounts of data in multiple domains including finance, education and
healthcare. The efficient exploratory analysis of healthcare data constitutes a
significant challenge. Key concerns in public health include the quick
identification and analysis of trends, and the detection of outliers. This
allows policies to be rapidly adapted to changing circumstances. We present an
efficient outlier detection technique, termed PIKS (Pruned iterative-k means
searchlight), which combines an iterative k-means algorithm with a pruned
searchlight based scan. We apply this technique to identify outliers in two
publicly available healthcare datasets from the New York Statewide Planning and
Research Cooperative System, and California's Office of Statewide Health
Planning and Development. We provide a comparison of our technique with three
other existing outlier detection techniques, consisting of auto-encoders,
isolation forests and feature bagging. We identified outliers in conditions
including suicide rates, immunity disorders, social admissions,
cardiomyopathies, and pregnancy in the third trimester. We demonstrate that the
PIKS technique produces results consistent with other techniques such as the
auto-encoder. However, the auto-encoder needs to be trained, which requires
several parameters to be tuned. In comparison, the PIKS technique has far fewer
parameters to tune. This makes it advantageous for fast, "out-of-the-box" data
exploration. The PIKS technique is scalable and can readily ingest new
datasets. Hence, it can provide valuable, up-to-date insights to citizens,
patients and policy-makers. We have made our code open source, and with the
availability of open data, other researchers can easily reproduce and extend
our work. This will help promote a deeper understanding of healthcare policies
and public health issues
Essays on Fine Particulate Matter, Health and Socioeconomic Factors in China
The thesis contains three empirical essays that investigate the relationship between air pollution, economic growth, and health in China.
The first chapter investigates the relationship between air pollution and economic growth, based on Environmental Kuznets Curve (EKC). We examine the EKC hypothesis based on data in Beijing from 2008 to 2017, with quarterly data. Land use and dummy variables for seasons are controlled. The results confirm an “N” shaped EKC in Beijing, with the first turning point at 60,000 RMB and the second point at 132,000 RMB. The “N” shaped EKC indicates that although air pollution is decreasing now, the pressure for the future is high.
The second chapter explores the effects of income and air pollution on health at individual level. The air pollution includes ambient PM 2.5 concentration level, and household air pollution. Ambient concentration comes from official observing sites, and household air pollution is measured with dummy variables on energy consumption and active and negative smoking. The household air quality data, along with data at individual level, comes from micro dataset called CHARLS (Chinese Health and Retirement Longitude Survey), together with socio-economic factors, Probit models are employed to investigate the health effect of income and air pollution, and spatial probit models are also deployed due to the high spatial correlation of air pollution. It is found that the health of individuals is affected by the local air pollution and income, and the pollution from neighbouring cities.
The third chapter focuses on the effect of income, exposure level of air pollution on health. Compared with concentration level, exposure level is a better description of human interaction with air pollution. With the Mass Balance Equation, household air concentration is a function of ambient concentration and emission of household pollutant sources. Two scenarios, window open and closed, are considered due to the difference of air exchange rate and penetration rate. We find that poor lung health is associated with high exposure level and low income in both scenarios. Exposure reduction should not only include the ambient concentration target set by the government, and improvement on the household emissions, such as kitchen extraction and transfer from coal and crop residual to electricity and natural gas
Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities
With the increasing amount of spatial-temporal~(ST) ocean data, numerous
spatial-temporal data mining (STDM) studies have been conducted to address
various oceanic issues, e.g., climate forecasting and disaster warning.
Compared with typical ST data (e.g., traffic data), ST ocean data is more
complicated with some unique characteristics, e.g., diverse regionality and
high sparsity. These characteristics make it difficult to design and train STDM
models. Unfortunately, an overview of these studies is still missing, hindering
computer scientists to identify the research issues in ocean while discouraging
researchers in ocean science from applying advanced STDM techniques. To remedy
this situation, we provide a comprehensive survey to summarize existing STDM
studies in ocean. Concretely, we first summarize the widely-used ST ocean
datasets and identify their unique characteristics. Then, typical ST ocean data
quality enhancement techniques are discussed. Next, we classify existing STDM
studies for ocean into four types of tasks, i.e., prediction, event detection,
pattern mining, and anomaly detection, and elaborate the techniques for these
tasks. Finally, promising research opportunities are highlighted. This survey
will help scientists from the fields of both computer science and ocean science
have a better understanding of the fundamental concepts, key techniques, and
open challenges of STDM in ocean
Temporal Link Prediction: A Unified Framework, Taxonomy, and Review
Dynamic graphs serve as a generic abstraction and description of the
evolutionary behaviors of various complex systems (e.g., social networks and
communication networks). Temporal link prediction (TLP) is a classic yet
challenging inference task on dynamic graphs, which predicts possible future
linkage based on historical topology. The predicted future topology can be used
to support some advanced applications on real-world systems (e.g., resource
pre-allocation) for better system performance. This survey provides a
comprehensive review of existing TLP methods. Concretely, we first give the
formal problem statements and preliminaries regarding data models, task
settings, and learning paradigms that are commonly used in related research. A
hierarchical fine-grained taxonomy is further introduced to categorize existing
methods in terms of their data models, learning paradigms, and techniques. From
a generic perspective, we propose a unified encoder-decoder framework to
formulate all the methods reviewed, where different approaches only differ in
terms of some components of the framework. Moreover, we envision serving the
community with an open-source project OpenTLP that refactors or implements some
representative TLP methods using the proposed unified framework and summarizes
other public resources. As a conclusion, we finally discuss advanced topics in
recent research and highlight possible future directions
Learning Effective Embeddings for Dynamic Graphs and Quantifying Graph Embedding Interpretability
Graph representation learning has been a very active research area in recent years. The goal of graph representation learning is to generate representation vectors that accurately capture the structure and features of large graphs. This is especially important because the quality of the graph representation vectors will affect the performance of these vectors in downstream tasks such as node classification and link prediction. Many techniques have been proposed for generating effective graph representation vectors. These methods can be applied to both static and dynamic graphs. A static graph is a single fixed graph, while a dynamic graph evolves over time, and its nodes and edges can be added or deleted from the graph. We surveyed the graph embedding methods for both static and dynamic graphs. The majority of the existing graph embedding methods are developed for static graphs. Therefore, since most real-world graphs are dynamic, developing novel graph embedding methods suitable for evolving graphs is essential.
This dissertation proposes three dynamic graph embedding models. In previous dynamic methods, the inputs were mainly adjacency matrices of graphs which are not memory efficient and may not capture the neighbourhood structure in graphs effectively. Therefore, we developed Dynnode2vec based on random walks using the static model Node2vec. Dynnode2vec generates node embeddings in each snapshot by initializing the current model with previous embedding vectors and training the model using a set of random walks obtained for nodes in the snapshot. Our second model, LSTM-Node2vec, is also based on random walks. This method leverages the LSTM model to capture the long-range dependencies between nodes in combination with Node2vec to generate node embeddings. Finally, inspired by the importance of substructures in the graphs, our third model TGR-Clique generates node embeddings by considering the effects of neighbours of a node in the maximal cliques containing the node. Experiments on real-world datasets demonstrate the effectiveness of our proposed methods in comparison to the state-of-the-art models. In addition, motivated by the lack of proper measures for quantifying and comparing graph embeddings interpretability, we proposed two interpretability measures for graph embeddings using the centrality properties of graphs
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