1,077 research outputs found
Airflow in a Multiscale Subject-Specific Breathing Human Lung Model
The airflow in a subject-specific breathing human lung is simulated with a
multiscale computational fluid dynamics (CFD) lung model. The three-dimensional
(3D) airway geometry beginning from the mouth to about 7 generations of airways
is reconstructed from the multi-detector row computed tomography (MDCT) image
at the total lung capacity (TLC). Along with the segmented lobe surfaces, we
can build an anatomically-consistent one-dimensional (1D) airway tree spanning
over more than 20 generations down to the terminal bronchioles, which is
specific to the CT resolved airways and lobes (J Biomech 43(11): 2159-2163,
2010). We then register two lung images at TLC and the functional residual
capacity (FRC) to specify subject-specific CFD flow boundary conditions and
deform the airway surface mesh for a breathing lung simulation (J Comput Phys
244:168-192, 2013). The 1D airway tree bridges the 3D CT-resolved airways and
the registration-derived regional ventilation in the lung parenchyma, thus a
multiscale model. Large eddy simulation (LES) is applied to simulate airflow in
a breathing lung (Phys Fluids 21:101901, 2009). In this fluid dynamics video,
we present the distributions of velocity, pressure, vortical structure, and
wall shear stress in a breathing lung model of a normal human subject with a
tidal volume of 500 ml and a period of 4.8 s. On exhalation, air streams from
child branches merge in the parent branch, inducing oscillatory jets and
elongated vortical tubes. On inhalation, the glottal constriction induces
turbulent laryngeal jet. The sites where high wall shear stress tends to occur
on the airway surface are identified for future investigation of
mechanotransduction.Comment: This submission is part of the APS DFD Gallery of Fluid Motio
Confidence-Based Feature Imputation for Graphs with Partially Known Features
This paper investigates a missing feature imputation problem for graph
learning tasks. Several methods have previously addressed learning tasks on
graphs with missing features. However, in cases of high rates of missing
features, they were unable to avoid significant performance degradation. To
overcome this limitation, we introduce a novel concept of channel-wise
confidence in a node feature, which is assigned to each imputed channel feature
of a node for reflecting certainty of the imputation. We then design
pseudo-confidence using the channel-wise shortest path distance between a
missing-feature node and its nearest known-feature node to replace unavailable
true confidence in an actual learning process. Based on the pseudo-confidence,
we propose a novel feature imputation scheme that performs channel-wise
inter-node diffusion and node-wise inter-channel propagation. The scheme can
endure even at an exceedingly high missing rate (e.g., 99.5\%) and it achieves
state-of-the-art accuracy for both semi-supervised node classification and link
prediction on various datasets containing a high rate of missing features.
Codes are available at https://github.com/daehoum1/pcfi.Comment: Accepted to ICLR 2023. 28 page
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders
Most of the existing literature regarding hyperbolic embedding concentrate
upon supervised learning, whereas the use of unsupervised hyperbolic embedding
is less well explored. In this paper, we analyze how unsupervised tasks can
benefit from learned representations in hyperbolic space. To explore how well
the hierarchical structure of unlabeled data can be represented in hyperbolic
spaces, we design a novel hyperbolic message passing auto-encoder whose overall
auto-encoding is performed in hyperbolic space. The proposed model conducts
auto-encoding the networks via fully utilizing hyperbolic geometry in message
passing. Through extensive quantitative and qualitative analyses, we validate
the properties and benefits of the unsupervised hyperbolic representations.
Codes are available at https://github.com/junhocho/HGCAE
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
We propose a symmetric graph convolutional autoencoder which produces a
low-dimensional latent representation from a graph. In contrast to the existing
graph autoencoders with asymmetric decoder parts, the proposed autoencoder has
a newly designed decoder which builds a completely symmetric autoencoder form.
For the reconstruction of node features, the decoder is designed based on
Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder,
which allows utilizing the graph structure in the whole processes of the
proposed autoencoder architecture. In order to prevent the numerical
instability of the network caused by the Laplacian sharpening introduction, we
further propose a new numerically stable form of the Laplacian sharpening by
incorporating the signed graphs. In addition, a new cost function which finds a
latent representation and a latent affinity matrix simultaneously is devised to
boost the performance of image clustering tasks. The experimental results on
clustering, link prediction and visualization tasks strongly support that the
proposed model is stable and outperforms various state-of-the-art algorithms.Comment: 10 pages, 3 figures, ICCV 2019 accepte
QUICK: Quantization-aware Interleaving and Conflict-free Kernel for efficient LLM inference
We introduce QUICK, a group of novel optimized CUDA kernels for the efficient
inference of quantized Large Language Models (LLMs). QUICK addresses the shared
memory bank-conflict problem of state-of-the-art mixed precision matrix
multiplication kernels. Our method interleaves the quantized weight matrices of
LLMs offline to skip the shared memory write-back after the dequantization. We
demonstrate up to 1.91x speedup over existing kernels of AutoAWQ on larger
batches and up to 1.94x throughput gain on representative LLM models on various
NVIDIA GPU devices.Comment: 9 pages, 8 figure
CT-based lung motion differences in patients with usual interstitial pneumonia and nonspecific interstitial pneumonia
We applied quantitative CT image matching to assess the degree of motion in the idiopathic ILD such as usual interstitial pneumonia (UIP) and nonspecific interstitial pneumonia (NSIP). Twenty-one normal subjects and 42 idiopathic ILD (31 UIP and 11 NSIP) patients were retrospectively included. Inspiratory and expiratory CT images, reviewed by two experienced radiologists, were used to compute displacement vectors at local lung regions matched by image registration. Normalized three-dimensional and two-dimensional (dorsal-basal) displacements were computed at a sub-acinar scale. Displacements, volume changes, and tissue fractions in the whole lung and the lobes were compared between normal, UIP, and NSIP subjects. The dorsal-basal displacement in lower lobes was smaller in UIP patients than in NSIP or normal subjects (p = 0.03, p = 0.04). UIP and NSIP were not differentiated by volume changes in the whole lung or upper and lower lobes (p = 0.53, p = 0.12, p = 0.97), whereas the lower lobe air volume change was smaller in both UIP and NSIP than normal subjects (p = 0.02, p = 0.001). Regional expiratory tissue fractions and displacements showed positive correlations in normal and UIP subjects but not in NSIP subjects. In summary, lung motionography quantified by image registration-based lower lobe dorsal-basal displacement may be used to assess the degree of motion, reflecting limited motion due to fibrosis in the ILD such as UIP and NSIP
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Imaging-based clusters in current smokers of the COPD cohort associate with clinical characteristics: the SubPopulations and Intermediate Outcome Measures in COPD Study (SPIROMICS)
Background
Classification of COPD is usually based on the severity of airflow, which may not sensitively differentiate subpopulations. Using a multiscale imaging-based cluster analysis (MICA), we aim to identify subpopulations for current smokers with COPD.
Methods
Among the SPIROMICS subjects, we analyzed computed tomography images at total lung capacity (TLC) and residual volume (RV) of 284 current smokers. Functional variables were derived from registration of TLC and RV images, e.g. functional small airways disease (fSAD%). Structural variables were assessed at TLC images, e.g. emphysema and airway wall thickness and diameter. We employed an unsupervised method for clustering.
Results
Four clusters were identified. Cluster 1 had relatively normal airway structures; Cluster 2 had an increase of fSAD% and wall thickness; Cluster 3 exhibited a further increase of fSAD% but a decrease of wall thickness and airway diameter; Cluster 4 had a significant increase of fSAD% and emphysema. Clinically, Cluster 1 showed normal FEV1/FVC and low exacerbations. Cluster 4 showed relatively low FEV1/FVC and high exacerbations. While Cluster 2 and Cluster 3 showed similar exacerbations, Cluster 2 had the highest BMI among all clusters.
Conclusions
Association of imaging-based clusters with existing clinical metrics suggests the sensitivity of MICA in differentiating subpopulations
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