Pang‐Ning Tan
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View article: FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models
FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models Open
Deep learning-based weather forecasting models have recently demonstrated significant performance improvements over gold-standard physics-based simulation tools. However, these models are vulnerable to adversarial attacks, which raises con…
View article: Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds
Abrupt changes in algal biomass of thousands of US lakes are related to climate and are more likely in low-disturbance watersheds Open
Climate change is predicted to intensify lake algal blooms globally and result in regime shifts. However, observed increases in algal biomass do not consistently correlate with air temperature or precipitation, and evidence is lacking for …
View article: HPV Vaccine Hesitancy in Rural America and Exploring Artificial-Intelligence Interventions
HPV Vaccine Hesitancy in Rural America and Exploring Artificial-Intelligence Interventions Open
This study illuminates human papillomavirus (HPV) vaccine hesitancy in rural America and explores the potential of using chatbot-enabled social media interventions. The results from our nationally representative survey among US parents of …
View article: Unraveling Block Maxima Forecasting Models with Counterfactual Explanation
Unraveling Block Maxima Forecasting Models with Counterfactual Explanation Open
Disease surveillance, traffic management, and weather forecasting are some of the key applications that could benefit from block maxima forecasting of a time series as the extreme block maxima values often signify events of critical import…
View article: Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups
Population Graph Cross-Network Node Classification for Autism Detection Across Sample Groups Open
Graph neural networks (GNN) are a powerful tool for combining imaging and non-imaging medical information for node classification tasks. Cross-network node classification extends GNN techniques to account for domain drift, allowing for nod…
View article: Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning
Self-Recover: Forecasting Block Maxima in Time Series from Predictors with Disparate Temporal Coverage Using Self-Supervised Learning Open
Forecasting the block maxima of a future time window is a challenging task due to the difficulty in inferring the tail distribution of a target variable. As the historical observations alone may not be sufficient to train robust models to …
View article: The Design of Open Platforms: Towards an Emulation Theory
The Design of Open Platforms: Towards an Emulation Theory Open
The enrolment of third-party developers is essential to leverage the creation and evolution of data ecosystems. When such complementary development takes place without any organizational consent, however, it causes new social and technical…
View article: Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media
Using Deep Learning to Identify Linguistic Features that Facilitate or Inhibit the Propagation of Anti- and Pro-Vaccine Content on Social Media Open
Anti-vaccine content is rapidly propagated via social media, fostering vaccine hesitancy, while pro-vaccine content has not replicated the opponent's successes. Despite this disparity in the dissemination of anti- and pro-vaccine posts, li…
View article: DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data Open
Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) wi…
View article: COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence Open
Normalizing flows—a popular class of deep generative models—often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, charact…
View article: Unsupervised Anomaly Detection by Robust Density Estimation
Unsupervised Anomaly Detection by Robust Density Estimation Open
Density estimation is a widely used method to perform unsupervised anomaly detection. By learning the density function, data points with relatively low densities are classified as anomalies. Unfortunately, the presence of anomalies in trai…
View article: DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events
DeepGPD: A Deep Learning Approach for Modeling Geospatio-Temporal Extreme Events Open
Geospatio-temporal data are pervasive across numerous application domains.These rich datasets can be harnessed to predict extreme events such as disease outbreaks, flooding, crime spikes, etc. However, since the extreme events are rare, pr…
View article: DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data
DeepExtrema: A Deep Learning Approach for Forecasting Block Maxima in Time Series Data Open
Accurate forecasting of extreme values in time series is critical due to the significant impact of extreme events on human and natural systems. This paper presents DeepExtrema, a novel framework that combines a deep neural network (DNN) wi…
View article: COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence
COMET Flows: Towards Generative Modeling of Multivariate Extremes and Tail Dependence Open
Normalizing flows, a popular class of deep generative models, often fail to represent extreme phenomena observed in real-world processes. In particular, existing normalizing flow architectures struggle to model multivariate extremes, chara…
View article: Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators
Automated Analysis of the US Drought Monitor Maps With Machine Learning and Multiple Drought Indicators Open
The US Drought Monitor (USDM) is a hallmark in real time drought monitoring and assessment as it was developed by multiple agencies to provide an accurate and timely assessment of drought conditions in the US on a weekly basis. The map is …
View article: A non-negative matrix factorization based clustering to identify potential tuna fishing zones
A non-negative matrix factorization based clustering to identify potential tuna fishing zones Open
Many nonnegative matrix factorization based clusterings are employed in discovering pattern and knowledge. Considering the sparseness nature of our data set about the daily tuna fishing data, we attempted to utilize a clustering approach, …
View article: RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection
RCA: A Deep Collaborative Autoencoder Approach for Anomaly Detection Open
Unsupervised anomaly detection plays a crucial role in many critical applications. Driven by the success of deep learning, recent years have witnessed growing interests in applying deep neural networks (DNNs) to anomaly detection problems.…
View article: Learning Deep Neural Networks under Agnostic Corrupted Supervision.
Learning Deep Neural Networks under Agnostic Corrupted Supervision. Open
Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm t…
View article: Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study
Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study Open
Background Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading t…
View article: Learning Deep Neural Networks under Agnostic Corrupted Supervision
Learning Deep Neural Networks under Agnostic Corrupted Supervision Open
Training deep neural models in the presence of corrupted supervision is challenging as the corrupted data points may significantly impact the generalization performance. To alleviate this problem, we present an efficient robust algorithm t…
View article: Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App
Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App Open
Background Diet-tracking mobile apps have gained increased interest from both academic and clinical fields. However, quantity-focused diet tracking (eg, calorie counting) can be time-consuming and tedious, leading to unsustained adoption. …
View article: Fairness Perception from a Network-Centric Perspective
Fairness Perception from a Network-Centric Perspective Open
Algorithmic fairness is a major concern in recent years as the influence of machine learning algorithms becomes more widespread. In this paper, we investigate the issue of algorithmic fairness from a network-centric perspective. Specifical…
View article: Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study (Preprint)
Using Machine Learning to Compare Provaccine and Antivaccine Discourse Among the Public on Social Media: Algorithm Development Study (Preprint) Open
BACKGROUND Despite numerous counteracting efforts, antivaccine content linked to delays and refusals to vaccinate has grown persistently on social media, while only a few provaccine campaigns have succeeded in engaging with or persuading …
View article: Developing a Vaccine Informatics to Identify Message Frames Used in Vaccine Debates on Social Media: Combining Automatic Tweet Classification and Clustering Machine-Learning Algorithms with Qualitative Content Analysis (Preprint)
Developing a Vaccine Informatics to Identify Message Frames Used in Vaccine Debates on Social Media: Combining Automatic Tweet Classification and Clustering Machine-Learning Algorithms with Qualitative Content Analysis (Preprint) Open
BACKGROUND Exposure to anti-vaccine content on social media has been associated with delays and refusals of vaccinations, while pro-vaccine campaigns devised to disseminate the preventive benefits of vaccines have not succeeded in increas…
View article: Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App (Preprint)
Improving Heart Disease Risk Through Quality-Focused Diet Logging: Pre-Post Study of a Diet Quality Tracking App (Preprint) Open
BACKGROUND Diet-tracking mobile apps have gained increased interest from both academic and clinical fields. However, quantity-focused diet tracking (eg, calorie counting) can be time-consuming and tedious, leading to unsustained adoption.…
View article: Bursting the Filter Bubble: Fairness-Aware Network Link Prediction
Bursting the Filter Bubble: Fairness-Aware Network Link Prediction Open
Link prediction is an important task in online social networking as it can be used to infer new or previously unknown relationships of a network. However, due to the homophily principle, current algorithms are susceptible to promoting link…
View article: OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting
OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting Open
Hurricanes are powerful tropical cyclones with sustained wind speeds ranging from at least 74 mph (for category 1 storms) to more than 157 mph (for category 5 storms). Accurate prediction of the storm tracks is essential for hurricane prep…
View article: Improving Heart disease risk through quality-focused diet logging: pre-post study of a diet quality tracking app
Improving Heart disease risk through quality-focused diet logging: pre-post study of a diet quality tracking app Open
Diet-tracking mobile apps have been effective in behavior change. At the same time, quantity-focused diet tracking (e.g., calorie counting) can be time-consuming and tedious, leading to unsustained adoption. Diet Quality—focusing on high-q…