Title: Longitudinal Deep Kernel Gaussian Process Regression
Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, i.e., irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods. L-DKGPR effectively learns the multilevel correlation with a novel additive kernel that simultaneously accommodates both time-varying and the time-invariant effects. We derive an efficient algorithm to train L-DKGPR using latent space inducing points and variational inference. Results of extensive experiments on several benchmark data sets demonstrate that L-DKGPR significantly outperforms the state-of-the-art longitudinal data analysis (LDA) methods. more »« less
A cell exhibits a variety of responses to internal and external cues. These responses are possible, in part, due to the presence of an elaborate gene regulatory network (GRN) in every single cell. In the past 20 years, many groups worked on reconstructing the topological structure of GRNs from large-scale gene expression data using a variety of inference algorithms. Insights gained about participating players in GRNs may ultimately lead to therapeutic benefits. Mutual information (MI) is a widely used metric within this inference/reconstruction pipeline as it can detect any correlation (linear and non-linear) between any number of variables (n-dimensions). However, the use of MI with continuous data (for example, normalized fluorescence intensity measurement of gene expression levels) is sensitive to data size, correlation strength and underlying distributions, and often requires laborious and, at times, ad hoc optimization.
Results
In this work, we first show that estimating MI of a bi- and tri-variate Gaussian distribution usingk-nearest neighbor (kNN) MI estimation results in significant error reduction as compared to commonly used methods based on fixed binning. Second, we demonstrate that implementing the MI-based kNN Kraskov–Stoögbauer–Grassberger (KSG) algorithm leads to a significant improvement in GRN reconstruction for popular inference algorithms, such as Context Likelihood of Relatedness (CLR). Finally, through extensive in-silico benchmarking we show that a new inference algorithm CMIA (Conditional Mutual Information Augmentation), inspired by CLR, in combination with the KSG-MI estimator, outperforms commonly used methods.
Conclusions
Using three canonical datasets containing 15 synthetic networks, the newly developed method for GRN reconstruction—which combines CMIA, and the KSG-MI estimator—achieves an improvement of 20–35% in precision-recall measures over the current gold standard in the field. This new method will enable researchers to discover new gene interactions or better choose gene candidates for experimental validations.
Jiang, Xi; Xiao, Guanghua; Li, Qiwei(
, Statistics in Medicine)
A recent technology breakthrough in spatial molecular profiling (SMP) has enabled the comprehensive molecular characterizations of single cells while preserving spatial information. It provides new opportunities to delineate how cells from different origins form tissues with distinctive structures and functions. One immediate question in SMP data analysis is to identify genes whose expressions exhibit spatially correlated patterns, called spatially variable (SV) genes. Most current methods to identify SV genes are built upon the geostatistical model with Gaussian process to capture the spatial patterns. However, the Gaussian process models rely on ad hoc kernels that could limit the models' ability to identify complex spatial patterns. In order to overcome this challenge and capture more types of spatial patterns, we introduce a Bayesian approach to identify SV genes via a modified Ising model. The key idea is to use the energy interaction parameter of the Ising model to characterize spatial expression patterns. We use auxiliary variable Markov chain Monte Carlo algorithms to sample from the posterior distribution with an intractable normalizing constant in the model. Simulation studies using both simulated and synthetic data showed that the energy‐based modeling approach led to higher accuracy in detecting SV genes than those kernel‐based methods. When applied to two real spatial transcriptomics (ST) datasets, the proposed method discovered novel spatial patterns that shed light on the biological mechanisms. In summary, the proposed method presents a new perspective for analyzing ST data.
In this thesis we propose novel estimation techniques for localization and planning problems, which are key challenges in long-term autonomy. We take inspiration in our methods from non-parametric estimation and use tools such as kernel density estimation, non-linear least-squares optimization, binary masking, and random sampling. We show that these methods, by avoiding explicit parametric models, outperform existing methods that use them. Despite the seeming differences between localization and planning, we demonstrate in this thesis that the problems share core structural similarities. When real or simulation-sampled measurements are expensive, noisy, or high variance, non-parametric estimation techniques give higher-quality results in less time. We first address two localization problems. In order to permit localization with a set of ad hoc-placed radios, we propose an ultra-wideband (UWB) graph realization system to localize the radios. Our system achieves high accuracy and robustness by using kernel density estimation for measurement probability densities, by explicitly modeling antenna delays, and by optimizing this combination with a non-linear least squares formulation. Next, in order to then support robotic navigation, we present a flexible system for simultaneous localization and mapping (SLAM) that combines elements from both traditional dense metric SLAM and topological SLAM, using a binary "masking function" to focus attention. This masking function controls which lidar scans are available for loop closures. We provide several masking functions based on approximate topological class detectors. We then examine planning problems in the final chapter and in the appendix. In order to plan with uncertainty around multiple dynamic agents, we describe Monte-Carlo Policy-Tree Decision Making (MCPTDM), a framework for efficiently computing policies in partially-observable, stochastic, continuous problems. MCPTDM composes a sequence of simpler closed-loop policies and uses marginal action costs and particle repetition to improve cost estimates and sample efficiency by reducing variance. Finally, in the appendix we explore Learned Similarity Monte-Carlo Planning (LSMCP), where we seek to enhance the sample efficiency of partially observable Monte Carlo tree search-based planning by taking advantage of similarities in the final outcomes of similar states and actions. We train a multilayer perceptron to learn a similarity function which we then use to enhance value estimates in the planning. Collectively, we show in this thesis that non-parametric methods promote long-term autonomy by reducing error and increasing robustness across multiple domains.
Isolating application components is crucial to limit the exposure of sensitive data and code to vulnerabilities in the untrusted components. Process-based isolation is the de facto isolation used in practice, e.g., web browsers. However, it incurs significant performance overhead and is typically infeasible when frequent switches between isolation domains are expected. To address this problem, many intra-process memory isolation techniques have been proposed using novel kernel abstractions, recent CPU extensions (e.g., Intel ® MPK), and software-based fault isolation (e.g., WebAssembly). However, these techniques insufficiently isolate kernel resources, such as file descriptors, or do so by incurring high overheads when resources are accessed. Other work virtualizes the kernel context inside a privileged user space domain, but this is ad-hoc, error-prone, and provides only limited kernel functionalities. We propose μSwitch, an efficient kernel context isolation mechanism with memory protection that addresses these limitations. We use a protected structure, shared by the kernel and the user space, for context switching and propose implicit context switching to improve its performance by deferring the kernel resource switch to the next system call. We apply μSWITCH to isolate libraries in the Firefox web browser and an HTTP server, and reduce the overhead of isolation by 32.7% to 98.4% compared with other isolation techniques.
Roy, Kaushik; Lokala, Usha; Gaur, Manas; Sheth, Amit P(
, Proceedings of the Second International Conference on AI-ML Systems)
Artificial Intelligence (AI) systems for mental healthcare (MHCare) have been ever-growing after realizing the importance of early interventions for patients with chronic mental health (MH) conditions. Social media (SocMedia) emerged as the go-to platform for supporting patients seeking MHCare. The creation of peer-support groups without social stigma has resulted in patients transitioning from clinical settings to SocMedia supported interactions for quick help. Researchers started exploring SocMedia content in search of cues that showcase correlation or causation between different MH conditions to design better interventional strategies. User-level Classification-based AI systems were designed to leverage diverse SocMedia data from various MH conditions, to predict MH conditions. Subsequently, researchers created classification schemes to measure the severity of each MH condition. Such ad-hoc schemes, engineered features, and models not only require a large amount of data but fail to allow clinically acceptable and explainable reasoning over the outcomes. To improve Neural-AI for MHCare, infusion of clinical symbolic knowledge that clinicans use in decision making is required. An impactful use case of Neural-AI systems in MH is conversational systems. These systems require coordination between classification and generation to facilitate humanistic conversation in conversational agents (CA). Current CAs with deep language models lack factual correctness, medical relevance, and safety in their generations, which intertwine with unexplainable statistical classification techniques. This lecture-style tutorial will demonstrate our investigations into Neuro-symbolic methods of infusing clinical knowledge to improve the outcomes of Neural-AI systems to improve interventions for MHCare:(a) We will discuss the use of diverse clinical knowledge in creating specialized datasets to train Neural-AI systems effectively. (b) Patients with cardiovascular disease express MH symptoms differently based on gender differences. We will show that knowledge-infused Neural-AI systems can identify gender-specific MH symptoms in such patients. (c) We will describe strategies for infusing clinical process knowledge as heuristics and constraints to improve language models in generating relevant questions and responses.
Liang, Junjie, Wu, Yanting, Xu, Dongxuan, and Honavar, Vasant. Longitudinal Deep Kernel Gaussian Process Regression. Retrieved from https://par.nsf.gov/biblio/10287280. Proceedings of the AAAI Conference on Artificial Intelligence .
Liang, Junjie, Wu, Yanting, Xu, Dongxuan, & Honavar, Vasant. Longitudinal Deep Kernel Gaussian Process Regression. Proceedings of the AAAI Conference on Artificial Intelligence, (). Retrieved from https://par.nsf.gov/biblio/10287280.
Liang, Junjie, Wu, Yanting, Xu, Dongxuan, and Honavar, Vasant.
"Longitudinal Deep Kernel Gaussian Process Regression". Proceedings of the AAAI Conference on Artificial Intelligence (). Country unknown/Code not available. https://par.nsf.gov/biblio/10287280.
@article{osti_10287280,
place = {Country unknown/Code not available},
title = {Longitudinal Deep Kernel Gaussian Process Regression},
url = {https://par.nsf.gov/biblio/10287280},
abstractNote = {Gaussian processes offer an attractive framework for predictive modeling from longitudinal data, i.e., irregularly sampled, sparse observations from a set of individuals over time. However, such methods have two key shortcomings: (i) They rely on ad hoc heuristics or expensive trial and error to choose the effective kernels, and (ii) They fail to handle multilevel correlation structure in the data. We introduce Longitudinal deep kernel Gaussian process regression (L-DKGPR) to overcome these limitations by fully automating the discovery of complex multilevel correlation structure from longitudinal data. Specifically, L-DKGPR eliminates the need for ad hoc heuristics or trial and error using a novel adaptation of deep kernel learning that combines the expressive power of deep neural networks with the flexibility of non-parametric kernel methods. L-DKGPR effectively learns the multilevel correlation with a novel additive kernel that simultaneously accommodates both time-varying and the time-invariant effects. We derive an efficient algorithm to train L-DKGPR using latent space inducing points and variational inference. Results of extensive experiments on several benchmark data sets demonstrate that L-DKGPR significantly outperforms the state-of-the-art longitudinal data analysis (LDA) methods.},
journal = {Proceedings of the AAAI Conference on Artificial Intelligence},
author = {Liang, Junjie and Wu, Yanting and Xu, Dongxuan and Honavar, Vasant},
editor = {null}
}
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