Vascular Hemodynamics CFD Modeling

Three dimensional pulsatile blood flow CFD simulations in geometrically genuine normal and non-normal (aneurysm) human neck-head vascular systems nominally spanning the aortic arch to the circle of Willis has been performed and studied. CT scans of the human aortic arch and the carotid arteries were interpreted to obtain geometric data defining the boundary for a vascular CFD simulation. This was accomplished by reconstructing the surface from the anatomical slices and by imposing pertinent boundary conditions at the various artery termini. Following automated formation of a non-conformal CFD mesh, steady and unsteady laminar and low turbulent simulations were performed both for the normal and aneurysm models. Atherosclerosis and atherosclerotic induced aneurysms can occur in the ascending aorta. The results showed marked differences in the flow dynamics for the two models. Secondary flow is induced in both of the models due to the curvature of the aortic arch which is distorted in three dimensions. Counter clockwise rotating vortex formation was seen at the aneurysm segment in the ascending aorta for the aneurysm model which was absent for the normal case. The effect of the aneurysm bulge was seen in regions proximal to it at peak reverse flow causing secondary flow. These secondary aortic blood flows are though to have an effect on the wall shear stress distribution. Maximum pressure regions for the aneurysm were observed at regions distal to it indicating the possible location for rupture. Wall shear force (WSF) values for the normal case at the aortic bend were low indicating the possible reason for the formation of the aneurysm in the first place. The WSF values at the aneurysm segment for the aneurysm case were also low supporting the low shear stress induced atherosclerotic aneurysms theory. These results may act as a precursor for a multiscale Large eddy simulation model (LES) for pulsatile blood flow eliminating the need for a priori definition of the flow as laminar or turbulent

Context-Adaptive Deep Neural Networks via Bridge-Mode Connectivity

The deployment of machine learning models in safety-critical applications comes with the expectation that such models will perform well over a range of contexts (e.g., a vision model for classifying street signs should work in rural, city, and highway settings under varying lighting/weather conditions). However, these one-size-fits-all models are typically optimized for average case performance, encouraging them to achieve high performance in nominal conditions but exposing them to unexpected behavior in challenging or rare contexts. To address this concern, we develop a new method for training context-dependent models. We extend Bridge-Mode Connectivity (BMC) (Garipov et al., 2018) to train an infinite ensemble of models over a continuous measure of context such that we can sample model parameters specifically tuned to the corresponding evaluation context. We explore the definition of context in image classification tasks through multiple lenses including changes in the risk profile, long-tail image statistics/appearance, and context-dependent distribution shift. We develop novel extensions of the BMC optimization for each of these cases and our experiments demonstrate that model performance can be successfully tuned to context in each scenario.Comment: Accepted to the NeurIPS 2022 ML Safety Worksho

Kullback-Leibler Divergence-Guided Copula Statistics-Based Blind Source Separation of Dependent Signals

In this paper, we propose a blind source separation of a linear mixture of dependent sources based on copula statistics that measure the non-linear dependence between source component signals structured as copula density functions. The source signals are assumed to be stationary. The method minimizes the Kullback-Leibler divergence between the copula density functions of the estimated sources and of the dependency structure. The proposed method is applied to data obtained from the time-domain analysis of the classical 11-Bus 4-Machine system. Extensive simulation results demonstrate that the proposed method based on copula statistics converges faster and outperforms the state-of-the-art blind source separation method for dependent sources in terms of interference-to-signal ratio.Comment: Submitted to the ISGT NA 202

Smoothening block rewards: How much should miners pay for mining pools?

The rewards a blockchain miner earns vary with time. Most of the time is spent mining without receiving any rewards, and only occasionally the miner wins a block and earns a reward. Mining pools smoothen the stochastic flow of rewards, and in the ideal case, provide a steady flow of rewards over time. Smooth block rewards allow miners to choose an optimal mining power growth strategy that will result in a higher reward yield for a given investment. We quantify the economic advantage for a given miner of having smooth rewards, and use this to define a maximum percentage of rewards that a miner should be willing to pay for the mining pool services.Comment: 15 pages, 1 figur