Federated Bayesian Deep Learning: The Application of Statistical Aggregation Methods to Bayesian Models

Federated learning (FL) is an approach to training machine learning models that takes advantage of multiple distributed datasets while maintaining data privacy and reducing communication costs associated with sharing local datasets. Aggregation strategies have been developed to pool or fuse the weights and biases of distributed deterministic models; however, modern deterministic deep learning (DL) models are often poorly calibrated and lack the ability to communicate a measure of epistemic uncertainty in prediction, which is desirable for remote sensing platforms and safety-critical applications. Conversely, Bayesian DL models are often well calibrated and capable of quantifying and communicating a measure of epistemic uncertainty along with a competitive prediction accuracy. Unfortunately, because the weights and biases in Bayesian DL models are defined by a probability distribution, simple application of the aggregation methods associated with FL schemes for deterministic models is either impossible or results in sub-optimal performance. In this work, we use independent and identically distributed (IID) and non-IID partitions of the CIFAR-10 dataset and a fully variational ResNet-20 architecture to analyze six different aggregation strategies for Bayesian DL models. Additionally, we analyze the traditional federated averaging approach applied to an approximate Bayesian Monte Carlo dropout model as a lightweight alternative to more complex variational inference methods in FL. We show that aggregation strategy is a key hyperparameter in the design of a Bayesian FL system with downstream effects on accuracy, calibration, uncertainty quantification, training stability, and client compute requirements.Comment: 22 pages, 9 figure

Improving image contrast using coded excitation for ultrasonic imaging

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Complex shear modulus reconstruction using ultrasound shear-wave imaging

Many pathological processes in tissues are recognized by morphological changes that reflect alterations of the soft tissue mechanical properties. Ultrasound shear-wave imaging can provide quantitative information about soft tissue mechanical properties, specifically the complex shear modulus. Advancing this field has the potential to bridge molecular, cellular, and tissue biology and to influence medical diagnoses and patient treatment. This dissertation describes several quantitative developments in the field of ultrasound shear-wave imaging. The initial study is a time-domain method for quantitative reconstruction of the complex shear modulus, estimated from the tracked displacement of the embedded spherical scatterer. This study also established a methodology for independent experimental verification of estimated material properties using rheometer measurements. The second study presents a technique for shear-wave imaging using a vibrating needle source for shear wave excitation. An advantage of such an approach is extended bandwidth of the measurement and a well-defined shear wave propagation that can be advantageous in the complex shear modulus reconstruction. This method was used to explore viscoelastic mechanisms in liver tissue and to explore different modeling approaches. It was found that the shear dynamic viscosity provides more contrast in imaging thermal damage in porcine liver, as compared to the shear elastic modulus. The third study was to develop an FDTD 3D viscoelastic solver capable of accurate modeling of shear wave propagation in heterogeneous media. Numerical results are experimentally validated. Furthermore, this numerical framework is used to study complex modulus imaging, specifically a direct algebraic Helmholtz inversion. The practical limitations and complex shear modulus reconstruction artifacts were studied, where it was found that distortions can be minimized simply by imaging the magnitude of the complex shear modulus. The final study was a recursive Bayesian solution to complex shear modulus reconstruction. A result of this is a stochastic filtering approach that uses a priori information about spatio-temporal dynamics of wave propagation to provide low variance estimates of the complex shear modulus. The stochastic filtering approach is studied both in simulation and experiments. The benefit of such an approach is low variance online reconstruction of the complex shear modulus per imaging frequency

Uncertainty-Aware Aerial Coastal Imagery Pattern Recognition Through Transfer Learning With ImageNet-1K Variational Embeddings

Monitoring coastal landscape changes is crucial for understanding the impact of extreme weather events on coastal communities as well as longer term trends in landscape change. However, continuous monitoring remains challenging given the dynamic nature of these environments as well as the diversity of landscape characteristics (for example, not all beaches have the same features). While previous work has primarily focused on semantic segmentation of satellite imagery, oblique aerial imagery offers superior temporal and spatial resolution for coastal monitoring, which can help identify regions of greatest change. However, the variability in image quality and lack of consistent coverage hinder the application of semantic segmentation to oblique aerial imagery across broad geographic regions. In this study, we demonstrate the effectiveness of whole-image classification using transfer learning on a novel dataset of 8,800 oblique coastal images from the U.S. East, Gulf, and West Coasts. We evaluate the performance of fifteen convolutional neural networks and two vision transformers, including both deterministic and probabilistic models, with all networks achieving over 90% accuracy. After pre-training Bayesian variants of ResNet50 on ImageNet-1K and transferring them to our coastal dataset, we perform uncertainty decomposition analysis to enhance model explainability. We make our pre-trained Bayesian ImageNet-1K checkpoints, fine-tuned Bayesian checkpoints, and curated dataset publicly available to facilitate reproducibility and further research in this domain. The resulting models could later be used to classify and map coastal landscapes on a global level, which would allow for longer term determination of landscape change associated with climate variability

Harnessing Artificial Intelligence - Supervised Learning AI (Lecture #5) [video]

Harnessing Artificial Intelligence - Supervised Learning AI (Lecture #5), Oct. 15. 2019); By Dr. Marko Orescanin, Assistant Professor, NPS Department of Computer Scienc

VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition

Transfer learning (TL) is an increasingly popular approach to training deep learning (DL) models that leverages the knowledge gained by training a foundation model on diverse, large-scale datasets for use on downstream tasks where less domain- or task-specific data is available. The literature is rich with TL techniques and applications; however, the bulk of the research makes use of deterministic DL models which are often uncalibrated and lack the ability to communicate a measure of epistemic (model) uncertainty in prediction. Unlike their deterministic counterparts, Bayesian DL (BDL) models are often well-calibrated, provide access to epistemic uncertainty for a prediction, and are capable of achieving competitive predictive performance. In this study, we propose variational inference pre-trained audio neural networks (VI-PANNs). VI-PANNs are a variational inference variant of the popular ResNet-54 architecture which are pre-trained on AudioSet, a large-scale audio event detection dataset. We evaluate the quality of the resulting uncertainty when transferring knowledge from VI-PANNs to other downstream acoustic classification tasks using the ESC-50, UrbanSound8K, and DCASE2013 datasets. We demonstrate, for the first time, that it is possible to transfer calibrated uncertainty information along with knowledge from upstream tasks to enhance a model’s capability to perform downstream tasks

Enhancing PMW Satellite Precipitation Estimation: Detecting Convective Class

The article of record as published may be found at https://doi.org/10.1175/JTECH-D-19-0008.1A decades-long effort in observing precipitation from space has led to continuous improvements of satellite-derived passive microwave (PMW) large-scale precipitation products. However, due to a limited ability to relate observed radiometric signatures to precipitation type (convective and stratiform) and associated precipitation rate variability, PMW retrievals are prone to large systematic errors at instantaneous scales. The present study explores the use of deep learning approach in extracting the information content from PMW observation vectors to help identify precipitation types. A deep learning neural network model (DNN) is developed to retrieve the convective type in precipitating systems from PMW observations. A 12-month period of Global Precipitation Measurement mission Microwave Imager (GMI) observations is used as a dataset for model development and verification. The proposed DNN model is shown to accurately predict precipitation types for 85% of total precipitation volume. The model reduces precipitation rate bias associated with convective and stratiform precipitation in the GPM operational algorithm by a factor of 2 while preserving the correlation with reference precipitation rates, and is insensitive to surface type variability. Based on comparisons against currently used convective schemes, it is concluded that the neural network approach has the potential to address regime-specific PMW satellite precipitation biases affecting GPM operations.This study was supported by the following grants: NNX16AQ66G, NA19NES4320002, 80NSSC19K0681

Network Anomaly Detection Using Quantum Neural Networks on Noisy Quantum Computers

The escalating threat and impact of network-based attacks necessitate innovative intrusion detection systems. Machine learning has shown promise, with recent strides in quantum machine learning offering new avenues. However, the potential of quantum computing is tempered by challenges in current noisy intermediate-scale quantum era machines. In this article, we explore quantum neural networks (QNNs) for intrusion detection, optimizing their performance within current quantum computing limitations. Our approach includes efficient classical feature encoding, QNN classifier selection, and performance tuning leveraging current quantum computational power. This study culminates in an optimized multilayered QNN architecture for network intrusion detection. A small version of the proposed architecture was implemented on IonQ's Aria-1 quantum computer, achieving a notable 0.86 F1 score using the NF-UNSW-NB15 dataset. In addition, we introduce a novel metric, certainty factor, laying the foundation for future integration of uncertainty measures in quantum classification outputs. Moreover, this factor is used to predict the noise susceptibility of our quantum binary classification system