Maximum A Posteriori Research Articles

Estimation of spatial demographic maps from polymorphism data using a neural network.

A fundamental goal in population genetics is to understand how variation is arrayed over natural landscapes. From first principles we know that common features such as heterogeneous population densities and barriers to dispersal should shape genetic variation over space, however there are few tools currently available that can deal with these ubiquitous complexities. Geographically referenced single nucleotide polymorphism (SNP) data are increasingly accessible, presenting an opportunity to study genetic variation across geographic space in myriad species. We present a new inference method that uses geo-referenced SNPs and a deep neural network to estimate spatially heterogeneous maps of population density and dispersal rate. Our neural network trains on simulated input and output pairings, where the input consists of genotypes and sampling locations generated from a continuous space population genetic simulator, and the output is a map of the true demographic parameters. We benchmark our tool against existing methods and discuss qualitative differences between the different approaches; in particular, our program is unique because it infers the magnitude of both dispersal and density as well as their variation over the landscape, and it does so using SNP data. Similar methods are constrained to estimating relative migration rates, or require identity-by-descent blocks as input. We applied our tool to empirical data from North American grey wolves, for which it estimated mostly reasonable demographic parameters, but was affected by incomplete spatial sampling. Genetic based methods like ours complement other, direct methods for estimating past and present demography, and we believe will serve as valuable tools for applications in conservation, ecology and evolutionary biology. An open source software package implementing our method is available from https://github.com/kr-colab/mapNN.

Bayesian image segmentation under varying blur with triplet Markov random field

Abstract In this paper, we place ourselves in the context of the Bayesian framework for image segmentation in the presence of varying blur. The proposed approach is based on Triplet Markov Random Fields (TMRF). This method takes into account, during segmentation, peculiarities of an image such as noise, blur, and texture. We present an unsupervised TMRF method, which jointly deals with the problem of segmentation, and that of depth estimation in order to process fluorescence microscopy images. In addition to the estimation of the depth maps using the Metropolis-Hasting and the Stochastic Parameter Estimation (SPE) algorithms, we also estimate the model parameters using the SPE algorithm. We compare our TMRF method to other MRF models on simulated images, and to an unsupervised method from the state of art on real fluorescence microscopy images. Our method offers improved results, especially when blur is important.

Bayesian improved cross entropy method with categorical mixture models for network reliability assessment

We employ the Bayesian improved cross entropy (BiCE) method for rare event estimation in static networks and choose the categorical mixture (CM) as the parametric family to capture the dependence among network components. The proposed method is termed BiCE-CM. At each iteration of BiCE-CM, the mixture parameters are updated through the weighted maximum a posteriori (MAP) estimate, which mitigates the overfitting issue of the standard improved cross entropy (iCE) method through a novel balanced prior, and we propose a generalized version of the expectation–maximization (EM) algorithm to approximate this weighted MAP estimate. The resulting importance sampling distribution is proved to be unbiased. For choosing a proper number of components K in the mixture, we compute the Bayesian information criterion (BIC) of each candidate K as a by-product of the generalized EM algorithm. The performance of the proposed method is investigated through a simple illustration, a benchmark study, and a practical application. In all these numerical examples, the BiCE-CM method results in an efficient and accurate estimator that significantly outperforms the standard iCE method and the BiCE method with the independent categorical distribution.

A canonical polyadic tensor basis for fast Bayesian estimation of multi-subject brain activation patterns.

Task-evoked functional magnetic resonance imaging studies, such as the Human Connectome Project (HCP), are a powerful tool for exploring how brain activity is influenced by cognitive tasks like memory retention, decision-making, and language processing. A fast Bayesian function-on-scalar model is proposed for estimating population-level activation maps linked to the working memory task. The model is based on the canonical polyadic (CP) tensor decomposition of coefficient maps obtained for each subject. This decomposition effectively yields a tensor basis capable of extracting both common features and subject-specific features from the coefficient maps. These subject-specific features, in turn, are modeled as a function of covariates of interest using a Bayesian model that accounts for the correlation of the CP-extracted features. The dimensionality reduction achieved with the tensor basis allows for a fast MCMC estimation of population-level activation maps. This model is applied to one hundred unrelated subjects from the HCP dataset, yielding significant insights into brain signatures associated with working memory.

Fast and motion-robust saturation transfer MRI with inherent B0 correction using rosette trajectories and compressed sensing.

To implement rosette readout trajectories with compressed sensing reconstruction for fast and motion-robust CEST and magnetization transfer contrast imaging with inherent correction of B0 inhomogeneity. A pulse sequence was developed for fast saturation transfer imaging using a stack of rosette trajectories with a higher sampling density near the k-space center. Each rosette lobe was segmented into two halves to generate dual-echo images. B0 inhomogeneities were estimated using the phase difference between the images and corrected subsequently. The rosette-based imaging was evaluated in comparison to a fully sampled Cartesian trajectory and demonstrated on CEST phantoms (creatine solutions and egg white) and healthy volunteers at 3 T. Compared with the conventional Cartesian acquisition, compressed sensing reconstructed rosette images provided image quality with overall higher contrast-to-noise ratio and significantly faster readout time. Accurate B0 map estimation was achieved from the rosette acquisition with a negligible bias of 0.01 Hz between the rosette and dual-echo Cartesian gradient echo B0 maps, using the latter as ground truth. The water-saturation spectra (Z-spectra) and amide proton transfer weighted signals obtained from the rosette-based sequence were well preserved compared with the fully sampled data, both in the phantom and human studies. Fast, motion-robust, and inherent B0-corrected CEST and magnetization transfer contrast imaging using rosette trajectories could improve subject comfort and compliance, contrast-to-noise ratio, and provide inherent B0 homogeneity information. This work is expected to significantly accelerate the translation of CEST-MRI into a robust, clinically viable approach.

UnWarpME: Unsupervised warping map estimation in real-world scenarios

This paper introduces a novel approach to warp an RGB image to a new target pose, where it learns to inpaint the invisible parts of the image by sampling pixels from the visible parts. This technique is particularly relevant in applications such as novel pose image generation, where the quality of the generated samples heavily relies on the warped images. Conventional methods typically utilize an affine warping map estimator and learn to estimate the warping map through a downstream image generation task. However, this approach results in an affine function being returned regardless of the complexity of the deformation between the source and target poses. In contrast, our proposed method involves estimating the warping map using a convolutional function, which learns through alternating between two downstream tasks. Initially, the estimation treats the warping as part of an image generation task, similar to existing methods but without the constraint of an affine transformation. This encourages the estimation of complex transformations beyond affine deformations. Subsequently, disregarding the image generation task, we supervise the convolutional warping map to learn any potential affine transformation between the guiding poses of the sample. Our experimental results demonstrate the high effectiveness of our method in preserving image textures while transforming it into highly complex poses.

Scalable Bayesian uncertainty quantification with data-driven priors for radio interferometric imaging

Abstract Next-generation radio interferometers like the Square Kilometer Array have the potential to unlock scientific discoveries thanks to their unprecedented angular resolution and sensitivity. One key to unlocking their potential resides in handling the deluge and complexity of incoming data. This challenge requires building radio interferometric (RI) imaging methods that can cope with the massive data sizes and provide high-quality image reconstructions with uncertainty quantification (UQ). This work proposes a method coined QuantifAI to address UQ in RI imaging with data-driven (learned) priors for high-dimensional settings. Our model, rooted in the Bayesian framework, uses a physically motivated model for the likelihood. The model exploits a data-driven convex prior potential, which can encode complex information learned implicitly from simulations and guarantee the log-concavity of the posterior. We leverage probability concentration phenomena of high-dimensional log-concave posteriors to obtain information about the posterior, avoiding MCMC sampling techniques. We rely on convex optimisation methods to compute the MAP estimation, which is known to be faster and better scale with dimension than MCMC strategies. QuantifAI allows us to compute local credible intervals and perform hypothesis testing of structure on the reconstructed image. We propose a novel fast method to compute pixel-wise uncertainties at different scales, which uses 3 and 6 orders of magnitude less likelihood evaluations than other UQ methods like length of the credible intervals and Monte Carlo posterior sampling, respectively. We demonstrate our method by reconstructing RI images in a simulated setting and carrying out fast and scalable UQ, which we validate with MCMC sampling. Our method shows an improved image quality and more meaningful uncertainties than the benchmark method based on a sparsity-promoting prior. QuantifAI’s source code is available from https://github.com/astro-informatics/QuantifAI.

Dynamical system identification, model selection, and model uncertainty quantification by Bayesian inference.

This study presents a Bayesian maximum a posteriori (MAP) framework for dynamical system identification from time-series data. This is shown to be equivalent to a generalized Tikhonov regularization, providing a rational justification for the choice of the residual and regularization terms, respectively, from the negative logarithms of the likelihood and prior distributions. In addition to the estimation of model coefficients, the Bayesian interpretation gives access to the full apparatus for Bayesian inference, including the ranking of models, the quantification of model uncertainties, and the estimation of unknown (nuisance) hyperparameters. Two Bayesian algorithms, joint MAP and variational Bayesian approximation, are compared to the least absolute shrinkage and selection operator (LASSO), ridge regression, and the sparse identification of nonlinear dynamics (SINDy) algorithms for sparse regression by application to several dynamical systems with added Gaussian or Laplace noise. For multivariate Gaussian likelihood and prior distributions, the Bayesian formulation gives Gaussian posterior and evidence distributions, in which the numerator terms can be expressed in terms of the Mahalanobis distance or "Gaussian norm" ||y-y^||M-12=(y-y^)⊤M-1(y-y^), where y is a vector variable, y^ is its estimator, and M is the covariance matrix. The posterior Gaussian norm is shown to provide a robust metric for quantitative model selection for the different systems and noise models examined.

Torture Exposure Modulates Cognitive Control and Attention Neural Network Connectivity During a Go/NoGo Task

BackgroundTorture trauma is characterized by intentional uncontrollable acts, but the long-term effects of torture exposure on cognitive control brain mechanisms are unknown. MethodsA final sample of 33 torture survivors (TSs) and 44 non-TSs, all with a refugee background, completed a Go/NoGo response inhibition task during functional magnetic resonance imaging scanning. Data-driven independent component analysis identified active networks across the task and on Go, NoGo, and error of commission trials. Groups were compared on within-/between-network connectivity while for demographic and psychological symptom covariates were controlled for. Secondary analyses investigated whether network connectivity moderated the associations between torture exposure and severity on fear (e.g., re-experiencing) and dysphoria (e.g., anhedonia) posttraumatic stress disorder symptoms. ResultsThe TS group exhibited decreased connectivity (compared with the non-TS control group) within the posterior default mode network (specifically the left precuneus) and auditory-motor network (specifically the right superior temporal gyrus) and reduced connectivity between the dorsomedial frontal network and dorsal attention network across the Go/NoGo task. The TS group also showed more negative ventral attention network connectivity during NoGo (i.e., inhibition) trials. No behavioral effects were observed. Secondary analyses revealed that the association between torture exposure and elevated posttraumatic stress disorder dysphoria (not fear) symptoms was moderated by reduced connectivity in the right superior temporal gyrus and between the dorsomedial frontal network and the dorsal attention network. ConclusionsResponse inhibition, attention, and motor networks appear to be less connected in TSs, which may be specifically linked to posttraumatic stress disorder dysphoria symptom profiles. The findings suggest that targeting cognitive control processes may hold promise for alleviating posttraumatic symptoms among survivors of torture.

Underwater Image Enhancement by Illumination Map Estimation and Adaptive High-Frequency Gain

Underwater Image Enhancement by Illumination Map Estimation and Adaptive High-Frequency Gain

Deep learning-based prediction of 3-dimensional silver contact shapes enabling improved quality control in solar cell metallization

The industrial metallization of Si solar cells predominantly relies on screen printing, with silver as the preferred electrode material. However, the design of commercial screens often leads to suboptimal silver usage and increased electrical resistance due to print-related inhomogeneities like mesh marks, constrictions and spreading. Real-time monitoring of quality parameters during production has thus become increasingly critical. Current inline optical quality control systems usually only include 2D visualizations of the printed layout, which limits their effectiveness in quality control. Options that allow 3D measurements are usually slow, expensive, and therefore not worth considering in most cases. This research focuses on the development of a model that can estimate the three-dimensional shape of printed contact fingers from a single 2D image without the need of additional hardware using deep learning. Furthermore, a workflow for the generation of training data, which involves the creation of image pairs from a 2D microscope and a 3D confocal laser scanning microscope (CLSM) to accurately represent solar cell fingers, is presented. After model training, the predicted height maps are compared with the ground truth height maps, and the robustness of the model with respect to a paste variation and screen parameter variation is examined. The results confirm the feasibility and reliability of deep learning-based 3D shape estimation, extending its applicability to new, previously unseen data from screen-printed contact fingers. With a structural similarity index (SSIM) score of 0.76, a strong correlation between the estimated and ground truth height maps is established. In summary, our deep learning-based approach for height map estimation offers an effective and reliable solution for fast inline detection and analysis of the cross-sectional area of the printed contact fingers.

Benchmarking building energy consumption for space heating using an empirical Bayesian approach with urban-scale energy model

Calibration of an urban-scale building energy model is crucial for advancing building energy efficiency codes and policies to reduce carbon dioxide emissions whereas the accuracy of the model is challenged by performance gaps resulting from sparse data and uncertainties in modeling. An empirical Bayesian (EB) approach, incorporated a probability-based mixed-effects energy model and Monte Carlo Markov chain simulation, was developed to predict and benchmark building energy use intensity (EUI) for space heating, using the metering data of 2020 stochastically sampled district heating substations from a municipal utility database. The limitations, implementations and performances of different modeling strategy, including EB as well as Maximum Likelihood Estimation (MLE), Maximum A Posteriori (MAP) and Fully Bayes (FB) were compared. The results show that EB strategy achieved superior prediction performance than other strategies on the novel test dataset. The preparation, calibration, and validation efforts for implementing the EB approach were demonstrated. The proposed methodology is recommended as a robust approach for benchmarking urban-scale building EUI, distinguished from other machine learning approaches by requiring fewer training samples, better interpreting uncertainties in the data used, and exhibiting superior generalization capabilities. The proposed methodology was successfully demonstrated through the urban-scale benchmarking of space heating EUI in Beijing.

Hyperspectral Image Denoising by Pixel-Wise Noise Modeling and TV-Oriented Deep Image Prior

Model-based hyperspectral image (HSI) denoising methods have attracted continuous attention in the past decades, due to their effectiveness and interpretability. In this work, we aim at advancing model-based HSI denoising, through sophisticated investigation for both the fidelity and regularization terms, or correspondingly noise and prior, by virtue of several recently developed techniques. Specifically, we formulate a novel unified probabilistic model for the HSI denoising task, within which the noise is assumed as pixel-wise non-independent and identically distributed (non-i.i.d) Gaussian predicted by a pre-trained neural network, and the prior for the HSI image is designed by incorporating the deep image prior (DIP) with total variation (TV) and spatio-spectral TV. To solve the resulted maximum a posteriori (MAP) estimation problem, we design a Monte Carlo Expectation–Maximization (MCEM) algorithm, in which the stochastic gradient Langevin dynamics (SGLD) method is used for computing the E-step, and the alternative direction method of multipliers (ADMM) is adopted for solving the optimization in the M-step. Experiments on both synthetic and real noisy HSI datasets have been conducted to verify the effectiveness of the proposed method.

KACM: A KIS-awared active contour model for low-contrast image segmentation

Low-contrast image segmentation is a challenging task, as it requires distinguishing target objects from the background with minimal intensity differences, while also dealing with factors such as noise and blur. In recent years, the active contour model (ACM) has gained popularity for its high accuracy and efficiency in image segmentation. However, it often struggles with low-contrast images segmentation. To address this issue, we propose a KIS-awared ACM (KACM) in this paper. Firstly, based on the Koschmieder imaging system (KIS), the observed low-contrast image is modeled as a combination of the true image and two imaging factors: the scene transmission function and the global intensity of light in the imaging environment. Next, we pursue the true image in the logarithmic domain using the maximum a posteriori (MAP) criterion, and establish the active contour model. In this model, the true image follows a piecewise lognormal distribution, while the scene transmission function is described as a Markov random field (MRF), with its prior probability being defined as a Gibbs energy function. Lastly, an alternating iterative algorithm that combines variation calculus and gradient descent of the three-step time-splitting method is introduced to solve the proposed model. We validate the proposed model through qualitative and quantitative experiments, demonstrating its effectiveness in low-contrast image segmentation. Compared to several state-of-the-art models, the proposed KACM exhibits competitive performance in terms of both accuracy and efficiency.

LSGDDN-LCD: An appearance-based loop closure detection using local superpixel grid descriptors and incremental dynamic nodes

Loop Closure Detection (LCD) is an essential component of visual Simultaneous Localization and Mapping (SLAM) systems. It enables the recognition of previously visited scenes to eliminate pose and map estimate drifts arising from long-term exploration. However, current appearance-based LCD methods face significant challenges, including high computational costs, viewpoint changes, and dynamic objects in scenes. This paper introduced an online appearance based LCD using Local Superpixel Grids Descriptor (LSGD) and Dynamic Nodes (DN), i.e., LSGDDN-LCD, to find similarities between scenes via handcrafted features extracted from the LSGD. Additionally, we proposed the adaptive mechanism to group similar scenes called DynamicNodes, which incrementally adjusted the database in an online manner, allowing for efficient and online retrieval of previously viewed images without need of the pre-training. Experimental results confirmed that the LSGDDN-LCD significantly improved LCD precision–recall and efficiency, and outperformed several state-of-the-art (SOTA) approaches on public and our own datasets, indicating its great potential as a generic LCD framework. Our implementation of the LSGDDN-LCD approach and the datasets were open-sourced on GitHub (https://github.com/BaoshengZhang0/LSGDDN-LCD.git).

Likelihood Inference for Factor Copula Models with Asymmetric Tail Dependence.

For multivariate non-Gaussian involving copulas, likelihood inference is dominated by the data in the middle, and fitted models might not be very good for joint tail inference, such as assessing the strength of tail dependence. When preliminary data and likelihood analysis suggest asymmetric tail dependence, a method is proposed to improve extreme value inferences based on the joint lower and upper tails. A prior that uses previous information on tail dependence can be used in combination with the likelihood. With the combination of the prior and the likelihood (which in practice has some degree of misspecification) to obtain a tilted log-likelihood, inferences with suitably transformed parameters can be based on Bayesian computing methods or with numerical optimization of the tilted log-likelihood to obtain the posterior mode and Hessian at this mode.

Variant calling in polyploids for population and quantitative genetics.

Advancements in genome assembly and sequencing technology have made whole genome sequence (WGS) data and reference genomes accessible to study polyploid species. Compared to popular reduced-representation sequencing approaches, the genome-wide coverage and greater marker density provided by WGS data can greatly improve our understanding of polyploid species and polyploid biology. However, biological features that make polyploid species interesting also pose challenges in read mapping, variant identification, and genotype estimation. Accounting for characteristics in variant calling like allelic dosage uncertainty, homology between subgenomes, and variance in chromosome inheritance mode can reduce errors. Here, I discuss the challenges of variant calling in polyploid WGS data and discuss where potential solutions can be integrated into a standard variant calling pipeline.

A Bayesian Approach To Locally Varying Regularization In Optical Flow Velocimetry

A recent method for determining accurate, dense velocity fields from a pair of particle images is to solve the optical flow problem, but the ill-posed inverse problem of optical flow velocimetry (OFV) generally entails minimizing a weighted sum of two terms--fidelity and regularization--and the weights in the sum are parameters that require manual tuning based on the flow properties and particle images. This tuning of the weighting parameters makes the experimental applicability of the method much more challenging as the calculated velocity field has demonstrated sensitivity to the value of the weights. This work proposes a hierarchical model for the weighting parameters in the framework of maximum a posteriori (MAP)-based Bayesian optimization approaches. The resulting method is tested on three different synthetic datasets and on experimental particle images. The method is found to be capable of self-adjusting the local weights of the optimization process in real-time while simultaneously determining the velocity field, leading to a more accurate estimate of the velocity field without requiring any dataset-specific manual tuning of parameters.

Enhancements in image quality and block detection performance for Reinforced Soil-Retaining Walls under various illuminance conditions

To ensure continuous monitoring of reinforced soil-retaining walls (RSWs) even under low-illuminance conditions, such as during the night, it is imperative to evaluate the performance of deep learning-based detection. In this study, we constructed a laboratory RSW model and generated a dataset with varying illuminance levels to assess the impact of image enhancement and block detection. Various image enhancement methods were applied to improve image quality and evaluate their effect on deep learning. RGB optimization (RO) was proposed to optimize RGB intensity and compared with gamma correction, histogram equalization, and low-light image enhancement with illumination map estimation. RO demonstrated outstanding image enhancement performance, as evidenced by lightness order error, peak signal-to-noise ratio, and structural similarity index measure, ensuring high image quality. The trained RO model using Mask R-CNN exhibited excellent accuracy, recall, and F1 score, delivering remarkable detection performance under low illuminance conditions, resulting in a 7.44 % improvement in the F1 score. Image enhancement techniques that maintain similarity, such as lightness order error and structural similarity, across varying illuminance conditions contribute to enhancing the block detection performance of Mask R-CNNs.

Adaptive detectors for mismatched subspace target in clutter with lognormal texture

Due to factors such as array misalignment and waveform distortion, the target echo may not be precisely located within the nominal subspace in practical applications, resulting in mismatch issues. In response to this challenge, this paper investigates adaptive detection methods for detecting mismatched subspace targets amidst lognormal clutter background. To enhance the suppression of mismatched signal, we introduce a fictitious signal into the null hypothesis, which is situated within a subspace orthogonal to the nominal subspace in the whitened observation space. Following convention, we allow for the existence of a training dataset that shares the same covariance matrix (CM) structure as the main dataset. Subsequently, we propose two adaptive subspace detectors based on a two-step Generalized Likelihood Ratio Test (GLRT) and a two-step maximum a posteriori (MAP) GLRT. Both novel detectors have been validated to have constant false alarm rate (CFAR) properties for speckle CM. Numerical experiments are carried out using simulation data and measured sea clutter data, which demonstrate that our proposed methods exhibit robustness against non-mismatched signal and effective suppression of mismatches.