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Found 11 papers, 3 papers with code
Spatiotemporal Predictions of Toxic Urban Plumes Using Deep Learning
Industrial accidents, chemical spills, and structural fires can release large amounts of harmful materials that disperse into urban atmospheres and impact populated areas.
ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data.
Order Estimation of Linear-Phase FIR Filters for DAC Equalization in Multiple Nyquist Bands
In addition, the letter discusses the appropriateness of the four types of linear-phase FIR filters, for the different equalizer cases, as well as the corresponding properties of the equalized systems.
H2G2-Net: A Hierarchical Heterogeneous Graph Generative Network Framework for Discovery of Multi-Modal Physiological Responses
Discovering human cognitive and emotional states using multi-modal physiological signals draws attention across various research applications.
Flow Completion Network: Inferring the Fluid Dynamics from Incomplete Flow Information using Graph Neural Networks
This paper introduces a novel neural network - flow completion network (FCN) - to infer the fluid dynamics, includ-ing the flow field and the force acting on the body, from the incomplete data based on Graph Convolution AttentionNetwork.
An Intelligent End-to-End Neural Architecture Search Framework for Electricity Forecasting Model Development
Although several recent studies have considered the application of the neural architecture search (NAS) technique for obtaining a network with an optimized structure in the electricity forecasting sector, their training process is computationally expensive and their search strategies are not flexible, indicating that the NAS application in this area is still at an infancy stage.
WOOD: Wasserstein-based Out-of-Distribution Detection
When part of the test samples are drawn from a distribution that is sufficiently far away from that of the training samples (a. k. a.
NP-ODE: Neural Process Aided Ordinary Differential Equations for Uncertainty Quantification of Finite Element Analysis
Finite element analysis (FEA) has been widely used to generate simulations of complex and nonlinear systems.
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses.
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control.
Gmail Smart Compose: Real-Time Assisted Writing
In this paper, we present Smart Compose, a novel system for generating interactive, real-time suggestions in Gmail that assists users in writing mails by reducing repetitive typing.
