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Found 18 papers, 1 papers with code
Correlation of obesity and osteoporosis: Effect of free fatty acids on bone marrow-derived mesenchymal stem cell differentiation
In mouse BMSCs, the Sox9 and Runx2 genes were significantly up-regulated after exposure to CM from FFA-treated adipocytes, while PPARpparγ and CEBP-α were significantly down-regulated.
The BeiHang Keystroke Dynamics Authentication System
Keystroke Dynamics is an important biometric solution for person authentication.
L0-norm Sparse Graph-regularized SVD for Biclustering
Motivated by the development of sparse coding and graph-regularized norm, we propose a novel sparse graph-regularized SVD as a powerful biclustering tool for analyzing high-dimensional data.
Network-regularized Sparse Logistic Regression Models for Clinical Risk Prediction and Biomarker Discovery
To address it, we introduce a novel network-regularized sparse LR model with a new penalty $\lambda \|\bm{w}\|_1 + \eta|\bm{w}|^T\bm{M}|\bm{w}|$ to consider the difference between the absolute values of the coefficients.
Sparse Weighted Canonical Correlation Analysis
Given two data matrices $X$ and $Y$, sparse canonical correlation analysis (SCCA) is to seek two sparse canonical vectors $u$ and $v$ to maximize the correlation between $Xu$ and $Yv$.
Group-sparse SVD Models and Their Applications in Biological Data
We employ an alternating direction method of multipliers (ADMM) to solve the proximal operator.
Non-locally Encoder-Decoder Convolutional Network for Whole Brain QSM Inversion
Quantitative Susceptibility Mapping (QSM) reconstruction is a challenging inverse problem driven by ill conditioning of its field-to -susceptibility transformation.
MRI Tissue Magnetism Quantification through Total Field Inversion with Deep Neural Networks
Quantitative susceptibility mapping (QSM) utilizes MRI signal phase to infer estimates of local tissue magnetism (magnetic susceptibility), which has been shown useful to provide novel image contrast and as biomarkers of abnormal tissue.
Meta-QSM: An Image-Resolution-Arbitrary Network for QSM Reconstruction
In this work, we proposed a novel method called Meta-QSM to firstly solve QSM reconstruction of arbitrary image resolution with a single model.
Medical Physics Image and Video Processing
COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis
We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety.
Weakly-supervised Learning for Single-step Quantitative Susceptibility Mapping
wTFI uses the BFR method RESHARP local fields as supervision to perform a multi-task learning of local tissue fields and QSM, and is capable of recovering magnetic susceptibility estimates near the edges of the brain where are eroded in RESHARP and realize whole brain QSM estimation.
The dynamic energy balance in earthquakes expressed by fault surface morphology
The fault surface morphology is the direct result of the microscopic processes near the crack tip or on the frictional interface.
Geophysics
Improved Padding in CNNs for Quantitative Susceptibility Mapping
Recently, deep learning methods have been proposed for quantitative susceptibility mapping (QSM) data processing: background field removal, field-to-source inversion, and single-step QSM reconstruction.
Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement
High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images.
StoryTrans: Non-Parallel Story Author-Style Transfer with Discourse Representations and Content Enhancing
Moreover, to enhance content preservation, we design a mask-and-fill framework to explicitly fuse style-specific keywords of source texts into generation.
Deeply Supervised Layer Selective Attention Network: Towards Label-Efficient Learning for Medical Image Classification
Labeling medical images depends on professional knowledge, making it difficult to acquire large amount of annotated medical images with high quality in a short time.
Unsupervised Deep Probabilistic Approach for Partial Point Cloud Registration
Deep point cloud registration methods face challenges to partial overlaps and rely on labeled data.
