Inference Framework Research Articles

Causal inference and machine learning in endocrine epidemiology.

With the rapid development of computer science, there is an increasing demand for the use of causal inference methods and machine learning in the research of endocrine disorders and their long-term health outcomes. However, studies on the effective and appropriate applications of these approaches in real-world data and clinical settings are still limited. This review will illustrate the use of causal inference and machine learning in epidemiological research within the field of endocrinology and metabolism. It will examine each concept of causal inference and machine learning through application examples of endocrine disorders. Subsequently, the paper will discuss the integration of machine learning within the causal inference framework, including (i) the estimation of treatment effects or the causal relationship between exposure and outcomes, and (ii) the evaluation of heterogeneity in such treatment effects (or exposure-outcome causal relationship) based on individuals' characteristics. Accurately assessing causal relationships and their heterogeneity across different individuals is crucial not only for determining effective interventions, but also for the appropriate allocation of medical resources and reducing healthcare disparities. By illustrating some application examples in endocrinology, this review aims to enhance readers' understanding and application of causal inference and machine learning in future epidemiological studies focusing on endocrine disorders.

Part-based Tracking via Discriminative Correlation Filters

In order to better deal with the partial occlusion issue, part-based trackers are widely used in visual object tracking recently. However, it is still difficult to realize fast and robust tracking, due to complicated online training and updating process. Correlation filters have been used in visual object tracking tasks recently because of their high efficiency. However, the traditional correlation filter based tracking methods do not deal with occlusion well. In this paper, we propose a novel tracking method which tracks objects based on parts with multiple correlation filters. The Bayesian inference framework and a structural constraint mask are adopted to enable our tracker to be robust to various appearance changes. Additionally, a discriminative part selection scheme is adopted to further improve performance and accelerate our method. Experimental results demonstrate that our multiple part tracker can significantly improve tracking performance on benchmark datasets.

Pratical Privacy-preserving Convolutional Neural Network Inference Framework with Edge Computing for Health Monitoring

Pratical Privacy-preserving Convolutional Neural Network Inference Framework with Edge Computing for Health Monitoring

Hastening Stream Offloading of Inference via Multi-Exit DNNs in Mobile Edge Computing

As the primary driver of intelligent mobile applications, deep neural networks (DNNs) have gradually deployed to millions of mobile devices, producing massive latency-sensitive and computation-intensive tasks daily. Mobile edge computing facilitates the deployment of computing resources at the edge, which enables fine-grained offloading of DNN inference tasks from mobile devices to edge nodes. However, most existing studies have not systematically considered three crucial performance aspects: scheduling multiple streams of DNN inference tasks, leveraging multi-exit models to hasten task processing, and partitioning inference models for partial offloading. To this end, this paper proposes an adaptive inference framework in mobile edge computing, which can dynamically select the exit point and partition point for multiple inference task streams. We design a dynamic programming algorithm to obtain an efficient solution under the ideal condition that task arrival information is known. Further, we design a learning-based algorithm for online scheduling, whose training efficiency is improved based on historical experience initialization and priority experience replay. Experimental results show that compared with the Greedy algorithm, the online algorithm improves the performance on two environmental parameters by an average of 5.9% and 32%, respectively.

Bayesian phylogenetic analysis of pitch-accent systems based on accentual class merger: a new method applied to Japanese dialects

Abstract Unlike studies of the evolutionary relationship between languages, the dialect-level variation within a language has seldom been studied within the framework of a phylogenetic tree, because frequent lexical borrowing muddles the evidence of shared ancestry. The phonological history of Japanese is an exceptional case study where the phenomenon called accentual class merger enables the phylogenetic analysis of dialectal pitch-accent systems in a way that is not subject to borrowing. However, previous studies have lacked statistical analysis and failed to evaluate the relative credence of alternative hypotheses. Here we developed a novel substitution model that describes the mutation of pitch-accent systems driven by accentual class merger and integrated the model into the framework of Bayesian phylogenetic inference with geographical diffusion. Applying the method to data on the pitch-accent variation in modern Japanese dialects and historical documents collected from literature, we reconstructed the evolutionary history and spatial diffusion of pitch-accent systems. Our result supports the monophyly of each of three groups of pitch-accent systems in conventional categorization, namely Tokyo type, Keihan type, and N-kei (N-pattern) type of Kyushu, whereas the monophyly of the Tokyo type has been highly controversial in previous studies. The divergence time of the mainland pitch-accent systems was estimated to be from mid-Kofun to early Heian period. Also, it is suggested that the modern Kyoto dialect did not inherit its accent patterns from Bumoki but from an unrecorded lineage which survived from the Muromachi period. Analyses on geographical diffusion suggest that the most recent common ancestor (MRCA) of all the taxa and that of Keihan type were located in or around the Kinki region, whereas the MRCA of N-kei type was located in northern to central Kyushu. The geographical location of the MRCA of Tokyo type remains unclear, but the Kinki and Kanto regions are the most plausible candidates.

Data and knowledge collaborative-driven fault identification and self-healing control action inference framework for blast furnace

The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. The blast furnace reactor has a large volume, and smelting occurs at high temperatures and high-pressure environments. Considering the small samples of fault data and non-stationarity of the production process with a considerable time lag, it is difficult to diagnose and eliminate the faults timely. This paper proposes for the first time a data and knowledge collaborative-driven framework to identify fault and perform self-healing control. Following the adaptive two-scale sequence division method, the prior knowledge matrix refined K2 algorithm is used to construct Bayesian networks to model the control process. When a new abnormal condition arises, the most similar historical sequence will be found, and inference on the corresponding Bayesian network can lead to fault identification results and self-healing control action. The proposed framework has achieved satisfactory results in simulation experiments and real data experiments. In the simulation data, the Bayesian network (BN) modeling achieved an accuracy of 98.75%, and the time required for structure learning of the BN was reduced by over 75.69%. In real data, the accuracy of identifying blast furnace faults reached 97.1%, and the results of self-healing control reasoning surpassed those of traditional BN reasoning methods in terms of correctness, order, completeness, and necessity.

Nonlinear bias toward complex contagion in uncertain transmission settings.

Current epidemics in the biological and social domains are challenging the standard assumptions of mathematical contagion models. Chief among them are the complex patterns of transmission caused by heterogeneous group sizes and infection risk varying by orders of magnitude in different settings, like indoor versus outdoor gatherings in the COVID-19 pandemic or different moderation practices in social media communities. However, quantifying these heterogeneous levels of risk is difficult, and most models typically ignore them. Here, we include these features in an epidemic model on weighted hypergraphs to capture group-specific transmission rates. We study analytically the consequences of ignoring the heterogeneous transmissibility and find an induced superlinear infection rate during the emergence of a new outbreak, even though the underlying mechanism is a simple, linear contagion. The dynamics produced at the individual and group levels are therefore more similar to complex, nonlinear contagions, thus blurring the line between simple and complex contagions in realistic settings. We support this claim by introducing a Bayesian inference framework to quantify the nonlinearity of contagion processes. We show that simple contagions on real weighted hypergraphs are systematically biased toward the superlinear regime if the heterogeneity of the weights is ignored, greatly increasing the risk of erroneous classification as complex contagions. Our results provide an important cautionary tale for the challenging task of inferring transmission mechanisms from incidence data. Yet, it also paves the way for effective models that capture complex features of epidemics through nonlinear infection rates.

The UNCOVER Survey: A First-look HST+JWST Catalog of Galaxy Redshifts and Stellar Population Properties Spanning 0.2 ≲ z ≲ 15

The recent UNCOVER survey with the James Webb Space Telescope (JWST) exploits the nearby cluster A2744 to create the deepest view of our Universe to date by leveraging strong gravitational lensing. In this work, we perform photometric fitting of more than 50,000 robustly detected sources out to z ∼ 15. We show the redshift evolution of stellar ages, star formation rates, and rest-frame colors across the full range of 0.2 ≲ z ≲ 15. The galaxy properties are inferred using the Prospector Bayesian inference framework using informative Prospector-β priors on the masses and star formation histories to produce joint redshift and stellar populations posteriors. Additionally, lensing magnification is performed on the fly to ensure consistency with the scale-dependent priors. We show that this approach produces excellent photometric redshifts with σ NMAD ∼ 0.03, of a similar quality to the established photometric redshift code EAzY. In line with the open-source scientific objective of this Treasury survey, we publicly release the stellar population catalog with this paper, derived from our photometric catalog adapting aperture sizes based on source profiles. This release (the catalog and all related documentation are accessible via the UNCOVER survey web page: https://jwst-uncover.github.io/DR2.html#SPSCatalogs with a copy deposited to Zenodo at doi:10.5281/zenodo.8401181) includes posterior moments, maximum likelihood spectra, star formation histories, and full posterior distributions, offering a rich data set to explore the processes governing galaxy formation and evolution over a parameter space now accessible by JWST.

Bayesian filter for failure times identification of moving heat sources in 2D geometry

This study investigates the application of the Bayesian filter method for identifying failure times in a 2D parabolic partial differential equation system. The identification of failure times in thermal systems, which are subject to partial differential equations, presents significant difficulties, especially due to their ill-posed nature, which makes them highly sensitive to measurement errors. A Bayesian inference framework was previously developed in a related study, aiming to solve inverse heat conduction problems by utilising temperature measurements from sensors to estimate failure times or potential restarts of fixed heat sources. This paper focuses on the case of mobile sources, where a set of fixed sensors is considered and the trajectories of the heating sources are known and follow a constant velocity. The main objective is to accurately identify the failing heat sources and determine the exact failure time, as well as the possibility of resuming normal operation. A Monte Carlo simulation is performed to assess the impact of sensor measurements.

Resolution-independent generative models based on operator learning for physics-constrained Bayesian inverse problems

The Bayesian inference approach is widely used to solve inverse problems due to its versatile and natural ability to handle ill-posedness. However, there are often challenges when dealing with situations involving continuous fields or parameters with large-resolution discrete representations. Furthermore, the prior distribution of the unknown parameters is also commonly difficult to determine. Therefore, in this study, an operator learning-based generative adversarial network (OL-GAN) is proposed and integrated into the Bayesian inference framework to address these issues. Compared with classical Bayesian approaches, the distinctive characteristic of the proposed method is that it learns the joint distributions of parameters and responses. By using the trained generative model to handle the prior in Bayes’ rule, the posteriors of the unknown parameters can theoretically be approximated by any sampling algorithms (e.g., Markov chain Monte Carlo, MCMC) under the proposed framework. More importantly, efficient sampling can be implemented in a low-dimensional latent space shared by the components of the joint distribution. The latent space is typically a simple and easy-to-sample distribution (e.g., Gaussian, uniform), which significantly reduces the computational cost associated with the Bayesian inference while avoiding prior selection. Furthermore, the generator is resolution-independent due to the incorporation of operator learning. Predictions can thus be obtained at desired coordinates, and inversions can be performed even if the observation data are misaligned with the training data. Finally, the effectiveness of the proposed method is validated through several numerical experiments.

Color Semantics in Human Cognition

People have associations between colors and concepts that influence the way they interpret color meaning in information visualizations (e.g., charts, maps, diagrams). These associations are not limited to concrete objects (e.g., fruits, vegetables); even abstract concepts, like sleeping and driving, have systematic color-concept associations. However, color-concept associations and color meaning (color semantics) are not the same thing, and sometimes they conflict. This article describes an approach to understanding color semantics called the color inference framework. The framework shows how color semantics is highly flexible and context dependent, which makes color an effective medium for communication.

Gaussian process metamodel and Markov chain Monte Carlo-based Bayesian inference framework for stochastic nonlinear model updating with uncertainties

The estimation of the posterior probability density function (PDF) of unknown parameters remains a challenge in stochastic nonlinear model updating with uncertainties; thus, a novel Bayesian inference framework based on the Gaussian process metamodel (GPM) and the advanced Markov chain Monte Carlo (MCMC) method is proposed in this paper. The instantaneous characteristics (ICs) of the decomposed measurement response, calculated using the Hilbert transform and the discrete analytical mode decomposition methodology, are extracted as nonlinear indices and further used to construct the likelihood function. Then, the posterior PDFs of structural nonlinear model parameters are derived based on the Bayesian theorem. To precisely calculate the posterior PDF, an advanced MCMC approach, i.e., delayed rejection adaptive Metropolis-Hastings (DRAM) algorithm, is adopted with the advantages of a high acceptance ratio and strong robustness. However, as a common shortage in most MCMC methods, the resampling technology is still applied, and numerous iterations of nonlinear simulations are conducted to ensure accuracy, thus directly reducing the computational efficiency of the DRAM. To address the abovementioned issue, a mathematical regression metamodel of the GPM with a polynomial kernel function is adopted in this paper instead of the traditional finite element model (FEM) to simulate a nonlinear response for the reduction of computational cost, and the hyperparameters are further estimated using the conjugate gradient optimization methodology. Finally, numerical simulations concerning a Giuffré–Menegotto–Pinto modeled steel-frame structure and a seven-storey base-isolated structure are conducted. Furthermore, a shake-table experimental test of a nonlinear steel framework is investigated to validate the accuracy of the Bayesian inference method. Both simulations and experiment demonstrate that the proposed GPM and DRAM-based Bayesian method effectively estimates the posterior PDF of unknown parameters and is appropriate for stochastic nonlinear model updating even with multisource uncertainties.

Robust Queue Inference from Waiting Times

Modeling and decision making for queueing systems have been one of fundamental topics in operations research. For these problems, uncertainty models are established by estimation of key parameters such as expected interarrival and service times. In practice, however, their distributions are unknown, and decision makers are only given historical records of waiting times, which contain relevant but indirect information on the uncertainties. Their complex temporal dependence on the queueing dynamics and the absence of distributional information on the model primitives render estimation of queueing systems remarkably challenging. In the paper “Robust Queue Inference from Waiting Times” by Chaithanya Bandi, Eojin Han, and Alexej Proskynitopoulos, a new inference framework based on robust optimization is proposed to estimate unknown service times from waiting time observations. This new framework allows data-driven, distribution-free estimation on unknown model primitives by solving tractable optimization problems.

Distributed estimation and inference for spatial autoregression model with large scale networks

The rapid growth of online network platforms generates large-scale network data and it poses great challenges for statistical analysis using the spatial autoregression (SAR) model. In this work, we develop a novel distributed estimation and statistical inference framework for the SAR model on a distributed system. We first propose a distributed network least squares approximation (DNLSA) method. This enables us to obtain a one-step estimator by taking a weighted average of local estimators on each worker. Afterwards, a refined two-step estimation is designed to further reduce the estimation bias. For statistical inference, we utilize a random projection method to reduce the expensive communication cost. Theoretically, we show the consistency and asymptotic normality of both the one-step and two-step estimators. In addition, we provide theoretical guarantee of the distributed statistical inference procedure. The theoretical findings and computational advantages are validated by several numerical simulations implemented on the Spark system. Lastly, an experiment on the Yelp dataset further illustrates the usefulness of the proposed methodology.

PopSED: Population-level Inference for Galaxy Properties from Broadband Photometry with Neural Density Estimation

We present PopSED , a framework for the population-level inference of galaxy properties from photometric data. Unlike the traditional approach of first analyzing individual galaxies and then combining the results to determine the physical properties of the entire galaxy population, we directly make the population distribution the inference objective. We train normalizing flows to approximate the population distribution by minimizing the Wasserstein distance between the synthetic photometry of the galaxy population and the observed data. We validate our method using mock observations and apply it to galaxies from the GAMA survey. PopSED reliably recovers the redshift and stellar mass distribution of 105 galaxies using broadband photometry within <1 GPU hr, being 105–6 times faster than the traditional spectral energy distribution modeling method. From the population posterior, we also recover the star-forming main sequence for GAMA galaxies at z < 0.1. With the unprecedented number of galaxies in upcoming surveys, our method offers an efficient tool for studying galaxy evolution and deriving redshift distributions for cosmological analyses.

Controlling the Flexibility of Non-Gaussian Processes Through Shrinkage Priors

The normal inverse Gaussian (NIG) and generalized asymmetric Laplace (GAL) distributions can be seen as skewed and semi-heavy-tailed extensions of the Gaussian distribution. Models driven by these more flexible noise distributions are then regarded as flexible extensions of simpler Gaussian models. Inferential procedures tend to overestimate the degree of non-Gaussianity in the data and therefore we propose controlling the flexibility of these non-Gaussian models by adding sensible priors in the inferential framework that contract the model towards Gaussianity. In our venture to derive sensible priors, we also propose a new intuitive parameterization of the non-Gaussian models and discuss how to implement them efficiently in Stan. The methods are derived for a generic class of non-Gaussian models that include spatial Matérn fields, autoregressive models for time series, and simultaneous autoregressive models for aerial data. The results are illustrated with a simulation study and geostatistics application, where priors that penalize model complexity were shown to lead to more robust estimation and give preference to the Gaussian model, while at the same time allowing for non-Gaussianity if there is sufficient evidence in the data.

GENERALIZED MATRIX DECOMPOSITION REGRESSION: ESTIMATION AND INFERENCE FOR TWO-WAY STRUCTURED DATA.

Motivated by emerging applications in ecology, microbiology, and neuroscience, this paper studies high-dimensional regression with two-way structured data. To estimate the high-dimensional coefficient vector, we propose the generalized matrix decomposition regression (GMDR) to efficiently leverage auxiliary information on row and column structures. GMDR extends the principal component regression (PCR) to two-way structured data, but unlike PCR, GMDR selects the components that are most predictive of the outcome, leading to more accurate prediction. For inference on regression coefficients of individual variables, we propose the generalized matrix decomposition inference (GMDI), a general high-dimensional inferential framework for a large family of estimators that include the proposed GMDR estimator. GMDI provides more flexibility for incorporating relevant auxiliary row and column structures. As a result, GMDI does not require the true regression coefficients to be sparse, but constrains the coordinate system representing the regression coefficients according to the column structure. GMDI also allows dependent and heteroscedastic observations. We study the theoretical properties of GMDI in terms of both the type-I error rate and power and demonstrate the effectiveness of GMDR and GMDI in simulation studies and an application to human microbiome data.

BAYESIAN LEARNING OF COVID-19 VACCINE SAFETY WHILE INCORPORATING ADVERSE EVENTS ONTOLOGY.

While vaccines are crucial to end the COVID-19 pandemic, public confidence in vaccine safety has always been vulnerable. Many statistical methods have been applied to VAERS (Vaccine Adverse Event Reporting System) database to study the safety of COVID-19 vaccines. However, none of these methods considered the adverse event (AE) ontology. AEs are naturally related; for example, events of retching, dysphagia, and reflux are all related to an abnormal digestive system. Explicitly bringing AE relationships into the model can aid in the detection of true AE signals amid the noise while reducing false positives. We propose a Bayesian graph-assisted signal selection (BGrass) model to simultaneously estimate all AEs while incorporating the network of dependence between AEs. Under a fully Bayesian inference framework, we also propose a negative control approach to mitigate the reporting bias and an enrichment approach to detecting AE groups of concern. For posterior computation we construct an equivalent model representation and develop an efficient Gibbs sampler. We evaluate the performance of BGrass via extensive simulations. To study the safety of COVID-19 vaccines, we apply BGrass to analyze approximately one million VAERS reports (01/01/2016-12/24/2021) involving more than 800 AEs. In particular, we found that blood clots (including deep vein thrombosis, thrombosis, and pulmonary embolism) are more likely to be reported after COVID-19 vaccination, compared to influenza vaccines. They are also reported more often for Johnson & Johnson-Janssen vaccine, compared to mRNA-based COVID-19 vaccines. A user-friendly R package BGrass that implements the proposed methods to assess vaccine safety is included in the Supplementary Material and is publicly available at https://github.com/BangyaoZhao/BGrass.

Gene regulatory network inference based on causal discovery integrating with graph neural network

AbstractGene regulatory network (GRN) inference from gene expression data is a significant approach to understanding aspects of the biological system. Compared with generalized correlation‐based methods, causality‐inspired ones seem more rational to infer regulatory relationships. We propose GRINCD, a novel GRN inference framework empowered by graph representation learning and causal asymmetric learning, considering both linear and non‐linear regulatory relationships. First, high‐quality representation of each gene is generated using graph neural network. Then, we apply the additive noise model to predict the causal regulation of each regulator‐target pair. Additionally, we design two channels and finally assemble them for robust prediction. Through comprehensive comparisons of our framework with state‐of‐the‐art methods based on different principles on numerous datasets of diverse types and scales, the experimental results show that our framework achieves superior or comparable performance under various evaluation metrics. Our work provides a new clue for constructing GRNs, and our proposed framework GRINCD also shows potential in identifying key factors affecting cancer development.

EdgeCompress: Coupling Multidimensional Model Compression and Dynamic Inference for EdgeAI

Convolutional neural networks (CNNs) have demonstrated encouraging results in image classification tasks. However, the prohibitive computational cost of CNNs hinders the deployment of CNNs onto resource-constrained embedded devices. To address this issue, we propose, a comprehensive compression framework to reduce the computational overhead of CNNs. In, we first introduce dynamic image cropping, where we design a lightweight foreground predictor to accurately crop the most informative foreground object of input images for inference, which avoids redundant computation on background regions. Subsequently, we present compound shrinking to collaboratively compress the three dimensions (depth, width, and resolution) of CNNs according to their contribution to accuracy and model computation. Dynamic image cropping and compound shrinking together constitute a multi-dimensional CNN compression framework, which is able to comprehensively reduce the computational redundancy in both input images and neural network architectures, thereby improving the inference efficiency of CNNs. Further, we present a dynamic inference framework to efficiently process input images with different recognition difficulties, where we cascade multiple models with different complexities from our compression framework and dynamically adopt different models for different input images, which further compresses the computational redundancy and improves the inference efficiency of CNNs, facilitating the deployment of advanced CNNs onto embedded hardware. Experiments on ImageNet-1K demonstrate that reduces the computation of ResNet-50 by 48.8% while improving the top-1 accuracy by 0.8%. Meanwhile, we improve the accuracy by 4.1% with similar computation compared to HRank. the state-of-the-art compression framework. The source code and models are available at