Adaptive Bayesian Beamforming for Imaging by Marginalizing the Speed of Sound

Imaging methods based on array signal processing often require a fixed propagation speed of the medium, or speed of sound (SoS) for methods based on acoustic signals. The resolution of the images formed using these methods is strongly affected by the assumed SoS, which, due to multipath, nonlinear propagation, and non-uniform mediums, is challenging at best to select. In this letter, we propose a Bayesian approach to marginalize the influence of the SoS on beamformers for imaging. We adapt Bayesian direction-of-arrival estimation to an imaging setting and integrate a popular minimum variance beamformer over the posterior of the SoS. To solve the Bayesian integral efficiently, we use numerical Gauss quadrature. We apply our beamforming approach to shallow water sonar imaging where multipath and nonlinear propagation is abundant. We compare against the minimum variance distortionless response (MVDR) beamformer and demonstrate that its Bayesian counterpart achieves improved range and azimuthal resolution while effectively suppressing multipath artifacts

Linear Convergence of Black-Box Variational Inference: Should We Stick the Landing?

We prove that black-box variational inference (BBVI) with control variates, particularly the sticking-the-landing (STL) estimator, converges at a geometric (traditionally called "linear") rate under perfect variational family specification. In particular, we prove a quadratic bound on the gradient variance of the STL estimator, one which encompasses misspecified variational families. Combined with previous works on the quadratic variance condition, this directly implies convergence of BBVI with the use of projected stochastic gradient descent. We also improve existing analysis on the regular closed-form entropy gradient estimators, which enables comparison against the STL estimator and provides explicit non-asymptotic complexity guarantees for both

The Behavior and Convergence of Local Bayesian Optimization

A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to traditional global strategies. The "folk wisdom" in the literature is that the focus on local optimization sidesteps the curse of dimensionality; however, little is known concretely about the expected behavior or convergence of Bayesian local optimization routines. We first study the behavior of the local approach, and find that the statistics of individual local solutions of Gaussian process sample paths are surprisingly good compared to what we would expect to recover from global methods. We then present the first rigorous analysis of such a Bayesian local optimization algorithm recently proposed by M\"uller et al. (2021), and derive convergence rates in both the noisy and noiseless settings.Comment: 25 page

Practical and Matching Gradient Variance Bounds for Black-Box Variational Bayesian Inference

Understanding the gradient variance of black-box variational inference (BBVI) is a crucial step for establishing its convergence and developing algorithmic improvements. However, existing studies have yet to show that the gradient variance of BBVI satisfies the conditions used to study the convergence of stochastic gradient descent (SGD), the workhorse of BBVI. In this work, we show that BBVI satisfies a matching bound corresponding to the $ABC$ condition used in the SGD literature when applied to smooth and quadratically-growing log-likelihoods. Our results generalize to nonlinear covariance parameterizations widely used in the practice of BBVI. Furthermore, we show that the variance of the mean-field parameterization has provably superior dimensional dependence.Comment: Accepted to ICML'23 for live oral presentatio

Black-Box Variational Inference Converges

We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference. While preliminary investigations worked on simplified versions of BBVI (e.g., bounded domain, bounded support, only optimizing for the scale, and such), our setup does not need any such algorithmic modifications. Our results hold for log-smooth posterior densities with and without strong log-concavity and the location-scale variational family. Also, our analysis reveals that certain algorithm design choices commonly employed in practice, particularly, nonlinear parameterizations of the scale of the variational approximation, can result in suboptimal convergence rates. Fortunately, running BBVI with proximal stochastic gradient descent fixes these limitations, and thus achieves the strongest known convergence rate guarantees. We evaluate this theoretical insight by comparing proximal SGD against other standard implementations of BBVI on large-scale Bayesian inference problems.Comment: under revie

New insights in the pathogenesis of alcohol-related liver disease: The metabolic, immunologic, and neurologic pathways☆

Alcohol-related liver disease (ALD) became an important health issue worldwide. Following chronic alcohol consumption, the development of ALD might be caused by metabolic and immunologic factors, such as reactive oxygen species (ROS) and pro-inflammatory cytokines. For example, hepatic cytochrome P450 2E1 enzyme increases ROS production and stimulates de novo lipogenesis after alcohol exposure. In addition, damage- and pathogen-associated molecular patterns stimulate their specific receptors in non-parenchymal cells, including Kupffer cells, hepatic stellate cells (HSCs), and lymphocytes, which result in hepatocyte death and infiltration of pro-inflammatory cells (e.g., neutrophils and macrophages) in the liver. Moreover, our studies have suggested the novel involvement of neurologic signaling pathways (e.g., endocannabinoid and glutamate) through the metabolic synapse between hepatocytes and HSCs in the development of alcohol-related hepatic steatosis. Additionally, agouti-related protein and beta2-adrenergic receptors aggravate hepatic steatosis. Furthermore, organ-crosstalk has emerged as a critical issue in ALD. Chronic alcohol consumption induces dysbiosis and barrier disruption in the gut, leading to endotoxin leakage into the portal circulation, or lipolysis-mediated transport of triglycerides from the adipose tissue to the liver. In summary, this review addresses multiple pathogeneses of ALD, provides novel neurologic signaling pathways, and emphasizes the importance of organ-crosstalk in the development of ALD

Ginsenoside F2 Restrains Hepatic Steatosis and Inflammation by Altering the Binding Affinity of Liver X Receptor Coregulators

Background: Ginsenoside F2 (GF2), the protopanaxadiol-type constituent in Panax ginseng, has been reported to attenuate metabolic dysfunction-associated steatotic liver disease (MASLD). However, the mechanism of action is not fully understood. Here, this study investigates the molecular mechanism by which GF2 regulates MASLD progression through liver X receptor (LXR). Methods: To demonstrate the effect of GF2 on LXR activity, computational modeling of protein-ligand binding, Time-resolved fluorescence resonance energy transfer (TR-FRET) assay for LXR cofactor recruitment, and luciferase reporter assay were performed. LXR agonist T0901317 was used for LXR activation in hepatocytes and macrophages. MASLD was induced by high-fat diet (HFD) feeding with or without GF2 administration in WT and LXRα−/− mice. Results: Computational modeling showed that GF2 had a high affinity with LXRα. LXRE-luciferase reporter assay with amino acid substitution at the predicted ligand binding site revealed that the S264 residue of LXRα was the crucial interaction site of GF2. TR-FRET assay demonstrated that GF2 suppressed LXRα activity by favoring the binding of corepressors to LXRα while inhibiting the accessibility of coactivators. In vitro, GF2 treatments reduced T0901317-induced fat accumulation and pro-inflammatory cytokine expression in hepatocytes and macrophages, respectively. Consistently, GF2 administration ameliorated hepatic steatohepatitis and improved glucose or insulin tolerance in WT but not in LXRα−/− mice. Conclusion: GF2 alters the binding affinities of LXRα coregulators, thereby interrupting hepatic steatosis and inflammation in macrophages. Therefore, we propose that GF2 might be a potential therapeutic agent for the intervention in patients with MASLD.11Nsciescopuskc

xCT-mediated glutamate excretion in white adipocytes stimulates interferon-γ production by natural killer cells in obesity

Summary: Obesity-mediated hypoxic stress underlies inflammation, including interferon (IFN)-γ production by natural killer (NK) cells in white adipose tissue. However, the effects of obesity on NK cell IFN-γ production remain obscure. Here, we show that hypoxia promotes xCT-mediated glutamate excretion and C-X-C motif chemokine ligand 12 (CXCL12) expression in white adipocytes, resulting in CXCR4+ NK cell recruitment. Interestingly, this spatial proximity between adipocytes and NK cells induces IFN-γ production in NK cells by stimulating metabotropic glutamate receptor 5 (mGluR5). IFN-γ then triggers inflammatory activation of macrophages and augments xCT and CXCL12 expression in adipocytes, forming a bidirectional pathway. Genetic or pharmacological inhibition of xCT, mGluR5, or IFN-γ receptor in adipocytes or NK cells alleviates obesity-related metabolic disorders in mice. Consistently, patients with obesity showed elevated levels of glutamate/mGluR5 and CXCL12/CXCR4 axes, suggesting that a bidirectional pathway between adipocytes and NK cells could be a viable therapeutic target in obesity-related metabolic disorders