Search Results for author:
Found 17 papers, 9 papers with code
HITSZ-ICRC: A Report for SMM4H Shared Task 2020-Automatic Classification of Medications and Adverse Effect in Tweets
This is the system description of the Harbin Institute of Technology Shenzhen (HITSZ) team for the first and second subtasks of the fifth Social Media Mining for Health Applications (SMM4H) shared task in 2020.
In silico bioactivity prediction of proteins interacting with graphene-based nanomaterials guides rational design of biosensor
Graphene based nanomaterials have attracted significant attention for their potentials in biomedical and biotechnology applications in recent years, owing to the outstanding physical and chemical properties.
A dynamic model to study the potential TB infections and assessment of control strategies in China
Based on the analysis of TB infection data, we develop a model of TB transmission dynamics that include potentially infected individuals and BCG vaccination, fit the model parameters to the data on new TB cases, calculate the basic reproduction number \mathcal{R}_v= 0. 4442.
Multi-fidelity prediction of fluid flow and temperature field based on transfer learning using Fourier Neural Operator
Therefore, this work proposes a novel multi-fidelity learning method based on the Fourier Neural Operator by jointing abundant low-fidelity data and limited high-fidelity data under transfer learning paradigm.
SportsMOT: A Large Multi-Object Tracking Dataset in Multiple Sports Scenes
We expect SportsMOT to encourage the MOT trackers to promote in both motion-based association and appearance-based association.
Ranked #3 on
Multi-Object Tracking
on SportsMOT
(using extra training data)
Uncertainty Guided Ensemble Self-Training for Semi-Supervised Global Field Reconstruction
Recovering a globally accurate complex physics field from limited sensor is critical to the measurement and control in the aerospace engineering.
RecFNO: a resolution-invariant flow and heat field reconstruction method from sparse observations via Fourier neural operator
The MLP embedding is propitious to more sparse input, while the others benefit from spatial information preservation and perform better with the increase of observation data.
Multi-fidelity surrogate modeling for temperature field prediction using deep convolution neural network
Then, a self-supervised learning method for training the physics-driven deep multi-fidelity model (PD-DMFM) is proposed, which fully utilizes the physics characteristics of the engineering systems and reduces the dependence on large amounts of labeled low-fidelity data in the training process.
Semi-supervision semantic segmentation with uncertainty-guided self cross supervision
However, the wrong pseudo labeling information generated by cross supervision would confuse the training process and negatively affect the effectiveness of the segmentation model.
Contrastive Enhancement Using Latent Prototype for Few-Shot Segmentation
This paper proposes a contrastive enhancement approach using latent prototypes to leverage latent classes and raise the utilization of similarity information between prototype and query features.
Deep Monte Carlo Quantile Regression for Quantifying Aleatoric Uncertainty in Physics-informed Temperature Field Reconstruction
However, a lot of labeled data is needed to train CNN, and the common CNN can not quantify the aleatoric uncertainty caused by data noise.
A deep learning method based on patchwise training for reconstructing temperature field
To solve the problem, this work proposes a novel deep learning method based on patchwise training to reconstruct the temperature field of electronic equipment accurately from limited observation.
Physics-informed Convolutional Neural Networks for Temperature Field Prediction of Heat Source Layout without Labeled Data
As the construction of data pairs in most engineering problems is time-consuming, data acquisition is becoming the predictive capability bottleneck of most deep surrogate models, which also exists in surrogate for thermal analysis and design.
A Machine Learning Surrogate Modeling Benchmark for Temperature Field Reconstruction of Heat-Source Systems
To overcome this problem, this work develops a machine learning modelling benchmark for TFR-HSS task.
IDRLnet: A Physics-Informed Neural Network Library
Physics Informed Neural Network (PINN) is a scientific computing framework used to solve both forward and inverse problems modeled by Partial Differential Equations (PDEs).
A Deep Neural Network Surrogate Modeling Benchmark for Temperature Field Prediction of Heat Source Layout
Thermal issue is of great importance during layout design of heat source components in systems engineering, especially for high functional-density products.
Deep Feature Augmentation for Occluded Image Classification
To alleviate the dependency on large-scale occluded image datasets, we propose a novel approach to improve the classification accuracy of occluded images by fine-tuning the pre-trained models with a set of augmented deep feature vectors (DFVs).
