101 research outputs found
Mechanisms of pelvic floor muscle function and the effect on the urethra during a cough
Background: Current measurement tools have difficulty identifying the automaticphysiologic processes maintaining continence, and many questions still remainabout pelvic floor muscle (PFM) function during automatic events.Objective: To perform a feasibility study to characterise the displacement, velocity,and acceleration of the PFM and the urethra during a cough.Design, setting, and participants: A volunteer convenience sample of 23 continentwomen and 9 women with stress urinary incontinence (SUI) from the generalcommunity of San Francisco Bay Area was studied.Measurements: Methods included perineal ultrasound imaging, motion trackingof the urogenital structures, and digital vaginal examination. Statistical analysisused one-tailed unpaired student t tests, and Welch’s correction was applied whenvariances were unequal.Results and limitations: The cough reflex activated the PFM of continent women tocompress the urogenital structures towards the pubic symphysis, which wasabsent in women with SUI. The maximum accelerations that acted on the PFMduring a cough were generally more similar than the velocities and displacements.The urethras of women with SUI were exposed to uncontrolled transverse accelerationand were displaced more than twice as far ( p = 0.0002), with almost twicethe velocity ( p = 0.0015) of the urethras of continent women. Caution regardingthe generalisability of this study is warranted due to the small number of women inthe SUI group and the significant difference in parity between groups.Conclusions: During a cough, normal PFM function produces timely compressionof the pelvic floor and additional external support to the urethra, reducing displacement,velocity, and acceleration. In women with SUI, who have weakerurethral attachments, this shortening contraction does not occur; consequently,the urethras of women with SUI move further and faster for a longer duratio
Node Embedding from Neural Hamiltonian Orbits in Graph Neural Networks
In the graph node embedding problem, embedding spaces can vary significantly
for different data types, leading to the need for different GNN model types. In
this paper, we model the embedding update of a node feature as a Hamiltonian
orbit over time. Since the Hamiltonian orbits generalize the exponential maps,
this approach allows us to learn the underlying manifold of the graph in
training, in contrast to most of the existing literature that assumes a fixed
graph embedding manifold with a closed exponential map solution. Our proposed
node embedding strategy can automatically learn, without extensive tuning, the
underlying geometry of any given graph dataset even if it has diverse
geometries. We test Hamiltonian functions of different forms and verify the
performance of our approach on two graph node embedding downstream tasks: node
classification and link prediction. Numerical experiments demonstrate that our
approach adapts better to different types of graph datasets than popular
state-of-the-art graph node embedding GNNs. The code is available at
\url{https://github.com/zknus/Hamiltonian-GNN}
On the Robustness of Graph Neural Diffusion to Topology Perturbations
Neural diffusion on graphs is a novel class of graph neural networks that has
attracted increasing attention recently. The capability of graph neural partial
differential equations (PDEs) in addressing common hurdles of graph neural
networks (GNNs), such as the problems of over-smoothing and bottlenecks, has
been investigated but not their robustness to adversarial attacks. In this
work, we explore the robustness properties of graph neural PDEs. We empirically
demonstrate that graph neural PDEs are intrinsically more robust against
topology perturbation as compared to other GNNs. We provide insights into this
phenomenon by exploiting the stability of the heat semigroup under graph
topology perturbations. We discuss various graph diffusion operators and relate
them to existing graph neural PDEs. Furthermore, we propose a general graph
neural PDE framework based on which a new class of robust GNNs can be defined.
We verify that the new model achieves comparable state-of-the-art performance
on several benchmark datasets
Adversarial Robustness in Graph Neural Networks: A Hamiltonian Approach
Graph neural networks (GNNs) are vulnerable to adversarial perturbations,
including those that affect both node features and graph topology. This paper
investigates GNNs derived from diverse neural flows, concentrating on their
connection to various stability notions such as BIBO stability, Lyapunov
stability, structural stability, and conservative stability. We argue that
Lyapunov stability, despite its common use, does not necessarily ensure
adversarial robustness. Inspired by physics principles, we advocate for the use
of conservative Hamiltonian neural flows to construct GNNs that are robust to
adversarial attacks. The adversarial robustness of different neural flow GNNs
is empirically compared on several benchmark datasets under a variety of
adversarial attacks. Extensive numerical experiments demonstrate that GNNs
leveraging conservative Hamiltonian flows with Lyapunov stability substantially
improve robustness against adversarial perturbations. The implementation code
of experiments is available at
https://github.com/zknus/NeurIPS-2023-HANG-Robustness.Comment: Accepted by Advances in Neural Information Processing Systems
(NeurIPS), New Orleans, USA, Dec. 2023, spotligh
Graph Neural Convection-Diffusion with Heterophily
Graph neural networks (GNNs) have shown promising results across various
graph learning tasks, but they often assume homophily, which can result in poor
performance on heterophilic graphs. The connected nodes are likely to be from
different classes or have dissimilar features on heterophilic graphs. In this
paper, we propose a novel GNN that incorporates the principle of heterophily by
modeling the flow of information on nodes using the convection-diffusion
equation (CDE). This allows the CDE to take into account both the diffusion of
information due to homophily and the ``convection'' of information due to
heterophily. We conduct extensive experiments, which suggest that our framework
can achieve competitive performance on node classification tasks for
heterophilic graphs, compared to the state-of-the-art methods. The code is
available at \url{https://github.com/zknus/Graph-Diffusion-CDE}.Comment: Proc. International Joint Conference on Artificial Intelligence
(IJCAI), Macao, China, Aug. 202
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