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Technical Report for Argoverse Challenges on Unified Sensor-based Detection, Tracking, and Forecasting
We achieved 1st place in Detection, Tracking, and Forecasting on the E2E Forecasting track in Argoverse Challenges at CVPR 2023 WAD.
Interpretable Tsetlin Machine-based Premature Ventricular Contraction Identification
In this study, we develop a Tsetlin machine (TM) based architecture for premature ventricular contraction (PVC) identification by analysing long-term ECG signals.
POI-Transformers: POI Entity Matching through POI Embeddings by Incorporating Semantic and Geographic Information
A general and robust POI embedding framework, the POI-Transformers, is initially proposed in this study to address these problems of POI entity matching.
Sensing Urban Land-Use Patterns By Integrating Google Tensorflow And Scene-Classification Models
To take advantage of the deep-learning method in detecting urban land-use patterns, we applied a transfer-learning-based remote-sensing image approach to extract and classify features.
Extracting urban impervious surface from GF-1 imagery using one-class classifiers
In this study, we investigate several one-class classifiers, such as Presence and Background Learning (PBL), Positive Unlabeled Learning (PUL), OCSVM, BSVM and MAXENT, to extract urban impervious surface area using high spatial resolution imagery of GF-1, China's new generation of high spatial remote sensing satellite, and evaluate the classification accuracy based on artificial interpretation results.
