Overcoming forgetting in fine-grained urban flow inference via adaptive knowledge replay
Fine-grained urban flow inference (FUFI) problem aims at inferring the high-resolution flow
maps from the coarse-grained ones, which plays an important role in sustainable and …
maps from the coarse-grained ones, which plays an important role in sustainable and …
GSAPSO-MQC: medical image encryption based on genetic simulated annealing particle swarm optimization and modified quantum chaos system
S Yin, H Li - Evolutionary Intelligence, 2021 - Springer
Due to the large amount of image information data, high redundancy and high pixel
correlation, the traditional medical image encryption algorithm is easy to be attacked by …
correlation, the traditional medical image encryption algorithm is easy to be attacked by …
Score-based Graph Learning for Urban Flow Prediction
P Wang, X Luo, W Tai, K Zhang, G Trajcevski… - ACM Transactions on …, 2024 - dl.acm.org
Accurate urban flow prediction (UFP) is crucial for a range of smart city applications such as
traffic management, urban planning, and risk assessment. To capture the intrinsic …
traffic management, urban planning, and risk assessment. To capture the intrinsic …
FDTI: Fine-Grained Deep Traffic Inference with Roadnet-Enriched Graph
This paper proposes the fine-grained traffic prediction task (eg interval between data points
is 1 min), which is essential to traffic-related downstream applications. Under this setting …
is 1 min), which is essential to traffic-related downstream applications. Under this setting …
Dynamic auto-structuring graph neural network: A joint learning framework for origin-destination demand prediction
Solving the demand prediction problem is an important part of improving the efficiency and
reliability of ride-hailing services. Spatial-temporal graph learning methods have shown …
reliability of ride-hailing services. Spatial-temporal graph learning methods have shown …
DSTnet: A new discrete shearlet transform-based CNN model for image denoising
Z Lyu, C Zhang, M Han - Multimedia Systems, 2021 - Springer
Due to the superior performance and fast running speed, deep learning methods have been
widely employed in image processing fields. However, most deep learning-based denoising …
widely employed in image processing fields. However, most deep learning-based denoising …
Revisiting convolutional neural networks for citywide crowd flow analytics
Citywide crowd flow analytics is of great importance to smart city efforts. It aims to model the
crowd flow (eg, inflow and outflow) of each region in a city based on historical observations …
crowd flow (eg, inflow and outflow) of each region in a city based on historical observations …
Diffusion probabilistic modeling for fine-grained urban traffic flow inference with relaxed structural constraint
Inferring the citywide urban traffic flows is critical for numerous smart city applications such
as urban planning, traffic control, and transportation management. Urban traffic flow …
as urban planning, traffic control, and transportation management. Urban traffic flow …
Fine-grained urban flow inference with incomplete data
Fine-grained urban flow inference, which aims to infer the fine-grained urban flows of a city
given the coarse-grained urban flow observations, is critically important to various smart city …
given the coarse-grained urban flow observations, is critically important to various smart city …
Spatio-Temporal Pivotal Graph Neural Networks for Traffic Flow Forecasting
W Kong, Z Guo, Y Liu - Proceedings of the AAAI Conference on Artificial …, 2024 - ojs.aaai.org
Traffic flow forecasting is a classical spatio-temporal data mining problem with many real-
world applications. Recently, various methods based on Graph Neural Networks (GNN) …
world applications. Recently, various methods based on Graph Neural Networks (GNN) …