Maximum A Posteriori Research Articles

Bad Local Minima Exist in the Stochastic Block Model

AbstractWe study the disassortative stochastic block model with three communities, a well-studied model of graph partitioning and Bayesian inference for which detailed predictions based on the cavity method exist (Decelle et al. in Phys Rev E 84:066106, 2011). We provide strong evidence that for a part of the phase where efficient algorithms exist that approximately reconstruct the communities, inference based on maximum a posteriori (MAP) fails. In other words, we show that there exist modes of the posterior distribution that have a vanishing agreement with the ground truth. The proof is based on the analysis of a graph colouring algorithm from Achlioptas and Moore (J Comput Syst Sci 67:441–471, 2003).

Almost exact recovery in noisy semi-supervised learning

Abstract Graph-based semi-supervised learning methods combine the graph structure and labeled data to classify unlabeled data. In this work, we study the effect of a noisy oracle on classification. In particular, we derive the maximum a posteriori (MAP) estimator for clustering a degree corrected stochastic block model when a noisy oracle reveals a fraction of the labels. We then propose an algorithm derived from a continuous relaxation of the MAP, and we establish its consistency. Numerical experiments show that our approach achieves promising performance on synthetic and real data sets, even in the case of very noisy labeled data.

Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging

Objective.Deep learning (DL) is becoming increasingly important in generating attenuation maps for accurate attenuation correction (AC) in cardiac perfusion SPECT imaging. Typically, DL models take inputs from initial reconstructed SPECT images, which are performed on the photopeak window and often also on scatter windows. While prior studies have demonstrated improvements in DL performance when scatter window images are incorporated into the DL input, the comprehensive analysis of the impact of employing different scatter windows remains unassessed. Additionally, existing research mainly focuses on applying DL to SPECT scans obtained at clinical standard count levels. This study aimed to assess utilities of DL from two aspects: (1) investigating the impact when different scatter windows were used as input to DL, and (2) evaluating the performance of DL when applied on SPECT scans acquired at a reduced count level.Approach.We utilized 1517 subjects, with 386 subjects for testing and the remaining 1131 for training and validation.Main results.The results showed that as scatter window width increased from 4% to 30%, a slight improvement was observed in DL estimated attenuation maps. The application of DL models to quarter-count (¼-count) SPECT scans, compared to full-count scans, showed a slight reduction in performance. Nonetheless, discrepancies across different scatter window configurations and between count levels were minimal, with all normalized mean square error (NMSE) values remaining within 2.1% when comparing the different DL attenuation maps to the reference CT maps. For attenuation corrected SPECT slices using DL estimated maps, NMSE values were within 0.5% when compared to CT correction.Significance.This study, leveraging an extensive clinical dataset, showed that the performance of DL seemed to be consistent across the use of varied scatter window settings. Moreover, our investigation into reduced count studies indicated that DL could provide accurate AC even at a ¼-count level.

Foxtrot migration and dynamic over-wintering range of an Arctic raptor

Advances in tracking technologies have revealed the diverse migration patterns of birds, which are critical for range mapping and population estimation. Population trends are usually estimated in breeding ranges where birds remain stationary, but for species that breed in remote areas like the Arctic, these trends are often assessed in over-wintering ranges. Assessing population trends during the wintering season is challenging due to the extensive movements of birds in these ranges, which requires a deep understanding of the movement dynamics. However, these movements remain understudied, particularly in the mid-latitudes, where many Arctic breeders overwinter, increasing uncertainty in their ranges and numbers. Here, we show that the Arctic breeding raptor Rough-legged buzzard, which overwinters in the mid-latitudes, has a specific wintering strategy. After migrating ca. 1500 km from the Arctic to mid-latitudes, the birds continue to move throughout the entire over-wintering period, traveling another 1000 km southwest and then back northeast as the snowline advances. This continuous movement makes their wintering range dynamic throughout the season. In essence, this movement represents an extension of the quick migration process, albeit at a slower pace, and we have termed this migration pattern ‘foxtrot migration’, drawing an analogy to the alternating fast and slow movements of the foxtrot dance. These results highlight the potential errors in range mapping from single mid-winter surveys and emphasize the importance of this migration pattern in assessing the conservation status of bird species. Understanding this migration pattern could help to correctly estimate bird populations in over-wintering ranges, which is especially important for species that nest in hard-to-reach regions such as the Arctic.

Foxtrot migration and dynamic over-wintering range of an Arctic raptor.

Advances in tracking technologies have revealed the diverse migration patterns of birds, which are critical for range mapping and population estimation. Population trends are usually estimated in breeding ranges where birds remain stationary, but for species that breed in remote areas like the Arctic, these trends are often assessed in over-wintering ranges. Assessing population trends during the wintering season is challenging due to the extensive movements of birds in these ranges, which requires a deep understanding of the movement dynamics. However, these movements remain understudied, particularly in the mid-latitudes, where many Arctic breeders overwinter, increasing uncertainty in their ranges and numbers. Here, we show that the Arctic breeding raptor Rough-legged buzzard, which overwinters in the mid-latitudes, has a specific wintering strategy. After migrating ca. 1500 km from the Arctic to mid-latitudes, the birds continue to move throughout the entire over-wintering period, traveling another 1000 km southwest and then back northeast as the snowline advances. This continuous movement makes their wintering range dynamic throughout the season. In essence, this movement represents an extension of the quick migration process, albeit at a slower pace, and we have termed this migration pattern 'foxtrot migration', drawing an analogy to the alternating fast and slow movements of the foxtrot dance. These results highlight the potential errors in range mapping from single mid-winter surveys and emphasize the importance of this migration pattern in assessing the conservation status of bird species. Understanding this migration pattern could help to correctly estimate bird populations in over-wintering ranges, which is especially important for species that nest in hard-to-reach regions such as the Arctic.

Estimation of direction and zero errors of satellite laser terminals in low-light conditions based on machine learning

In the assembly, launch, and on-orbit operation of satellite optical communication terminals, small deviations are difficult to avoid, which can lead to pointing errors and challenges to the establishment of optical communication links. To estimate the pointing errors of on-orbit satellite terminals, a calibration algorithm is developed based on lunar surface imagery. First, a feature extraction algorithm for low-light images is employed to process consecutive frames of low-light images to obtain a lunar surface feature map. Then, by combining the feature map and error estimation model, predictions of direction errors and zero errors were achieved. The ground validation results demonstrate the effectiveness and feasibility of the proposed on-orbit error estimation algorithm under low-signal-to-noise-ratio conditions.

Quantifying residents' exposure to agricultural pesticides using new geospatial approaches

A better understanding of environmental exposure to agricultural pesticides is crucial for public health, regulatory and management purposes. Residents in close vicinity to agricultural fields are likely to be more exposed to pesticides. In that context, an innovative geospatial approach for mapping estimates of agricultural pesticide exposure was developed in this study.Data on pesticide application rates, high-resolution annual datasets of the geographic distribution of crops, and the 100x100m grid population dataset were utilized to complete this analysis in Wallonia (Belgium) over the period 2015-2019. Pesticide exposure metrics were estimated using a buffer-based exposure model by conducting neighborhood analysis within a 1000m buffer radius. Subsequently, a population weighted method was used to ‘up-scale’ the exposure data to administrative levels. In a limited validation effort, model estimates were compared with pesticide measurements in air collected at 12 stations during the period 2015-2016.The results present the first modeling map of environmental exposure to agricultural pesticides for the whole of the Walloon region. The northern part of the Sambre-Meuse axis demonstrates more intensive agriculture and the highest pesticide weighted exposure indices. The majority of the population resides near agricultural areas, with only 4% living in regions that lack crops within a 1000m radius, primarily in the central areas of large cities. A positive trend association between pesticide measures in air and the index was observed at the different stations, nevertheless further validation efforts are needed to accurately compare the same active ingredients.This work gives a valuable basis for research and environmental health actions in Belgium. Maps highlight areas where human biomonitoring and epidemiological studies should be implemented to investigate the impact of potential environmental exposure to pesticides. This information helps policy-makers to take measures to control and reduce the load of agricultural pesticides in the environment.

High Density Isochronal Repolarisation Mapping (iREM) and Reentry Vulnerability Estimation for Scar-related VT Ablation: Mechanistic Basis, Clinical Application and Challenges.

Alterations in repolarisation gradients and increased heterogeneity are key electrophysiological determinants of ventricular arrhythmogenesis across a variety of aetiologies with and without structural heart disease. High-density repolarisation mapping to localise these repolarisation abnormalities could improve characterisation of the individual arrhythmogenic substrate and inform more targeted ablation. Yet, due to challenges posed by intrinsic features of human cardiac repolarisation itself as well as technical and practical limitations, they are not routinely assessed, and traditional substrate mapping techniques remain strictly limited to determining conduction abnormalities. Here, we provide an overview of the mechanistic role of repolarisation alterations in ventricular re-entry arrhythmias followed by a description of a clinical workflow that enables high-density repolarisation mapping during VT ablations using existing clinical tools. We describe step-by-step guidance of how-to set-up and generate repolarisation maps illustrating the approach in case examples of structural normal and abnormal hearts. Furthermore, we discuss how repolarisation mapping could be combined with existing substrate mapping approaches, including isochronal late activation mapping, to delineate sites of increased re-entry vulnerability, that may represent targets for ablation without the requirement for VT induction. Finally, we review challenges and pitfalls and ongoing controversies in relation to repolarisation mapping and discuss the need for future technical and analytical improvements in repolarisation mapping to integrate into ventricular substrate mapping strategies. Repolarisation mapping remains investigational and future research efforts need to be focused on prospective trials to establish the additional diagnostic value and its role in clinical ablation procedures.

Bayesian detection for distributed targets in compound Gaussian sea clutter with lognormal texture

This article investigates the Bayesian detection problem for the distributed targets in the compound Gaussian (CG) sea clutter. The CG sea clutter is formulated as a product of lognormal texture and speckle component with an inverse Wishart distribution covariance matrix (CM). A generalized likelihood ratio test (GLRT) based Bayesian detector, which can operate without training data, is proposed by integrating the speckle CM and estimating the texture using the maximum a posteriori (MAP) criterion. Additionally, three other Bayesian detectors are designed for distributed targets by exploiting the two-step GLRT, the complex-valued Rao, and Wald tests. We first derive the test statistics assuming known texture and speckle CM. Then, by incorporating the MAP-estimated texture components and speckle CM into the test statistics, we present three Bayesian detectors for distributed targets. Finally, simulation experiments validate the detection performance of the proposed Bayesian detectors using both simulated and real sea clutter data.

A convolutional neural network based method for fork type wing fuselage docking

The fork-ear connection attachment method is a widely used technique for attaching the fuselage to the wing. During the fork-ear wing-fuselage docking process, it is essential to ensure that the fork-ear holes in the fuselage and wing are precisely aligned before inserting the mounting pins to complete the final connection. Thus, the alignment accuracy of the fork-ear holes is crucial during the assembly process. When employing conventional visual servo methods to determine the pose adjustment for fork-type wing-fuselage docking, it is necessary to establish a conversion relationship between the image, camera, and posture adjustment mechanism. However, this process can introduce various nonlinear errors that are challenging to compensate for through mathematical modeling. To address this issue, this paper proposes a method for nonlinear mapping estimation of the pose adjustment amounts and develops a pose adjustment estimation convolutional neural network (PAAECNN) with superior performance validated through experiments and discussions. PAAECNN achieves end-to-end mapping from the fork-ear intersection hole images to the pose adjustment amounts for fork-ear wing-fuselage docking. The experimental results demonstrate that the proposed method for estimating the pose adjustment amounts in fork-ear wing-fuselage docking is highly accurate and suitable for visually guided docking tasks.

Exploring the links among brain iron accumulation, cognitive performance, and dietary intake in older adults: A longitudinal MRI study

This study evaluated longitudinal brain iron accumulation in older adults, its association with cognition, and the role of specific nutrients in mitigating iron accumulation. MRI-based, quantitative susceptibility mapping estimates of brain iron concentration were acquired from seventy-two healthy older adults (47 women, ages 60–86) at a baseline timepoint (TP1) and a follow-up timepoint (TP2) 2.5–3.0 years later. Dietary intake was evaluated at baseline using a validated questionnaire. Cognitive performance was assessed at TP2 using the uniform data set (Version 3) neuropsychological tests of episodic memory (MEM) and executive function (EF). Voxel-wise, linear mixed-effects models, adjusted for longitudinal gray matter volume alterations, age, and several non-dietary lifestyle factors revealed brain iron accumulation in multiple subcortical and cortical brain regions, which was negatively associated with both MEM and EF performance at T2. However, consumption of specific dietary nutrients at TP1 was associated with reduced brain iron accumulation. Our study provides a map of brain regions showing iron accumulation in older adults over a short 2.5-year follow-up and indicates that certain dietary nutrients may slow brain iron accumulation.

A petrophysically driven seismic inversion method for the total organic carbon content of source rocks

Organic carbon content is the main index for evaluating organic matter abundance of source rocks, and it is still difficult to quantitatively predict total organic carbon (TOC) in source rocks based on seismic data. The study of seismic fluid identification driven by petrophysics can help to understand the fluid characteristics and distribution patterns of subsurface oil and gas reservoirs. First, this paper clarifies the sweet spot parameters (parameters characterizing hydrocarbon enrichment) and sensitive elastic parameters (a parameter characterizing the nature of an ideal elastic body) of source rocks through theoretical petrophysical modeling. Next, the paper establishes the relationship between sensitive elastic parameters and sweet spot parameters TOC and constructs a statistical petrophysical model that can characterize the relationship between the two on this basis. And then we construct the joint distribution of TOC and elastic impedance through the Bayesian theoretical framework to obtain the maximum posterior probability estimate as the final TOC inversion results of source rocks. Our method successfully predicts the spreading of high-quality source rocks in the Wen4 Section of Lufeng 13 Subsag, and the inversion results are within an uncertainty range of ±14 m for well data, which proves the reliability of the method. The prediction results show that the organic matter abundance of source rocks in the Wen4 Section is high, and the organic carbon content is generally higher than 2%, which provides a reliable basis for the further implementation of the resource scale of the depression and the clarification of the hydrocarbon-rich area, which provides technical support for the evaluation of the source rocks of the new depression in the new area.

A Crowded Object Counting System with Self-Attention Mechanism

Traditional object counting systems use object detection methods to count objects. However, when objects are small, crowded, and dense, object detection may fail, leading to inaccuracies in counting. To address this issue, we propose a crowded object counting system based on density map estimation. While most density map estimation models employ encoder–decoder or multi-branch approaches to generate feature maps at different scales for obtaining an accurate density map, improving the accuracy of crowded object counting remains a challenge. In this paper, we propose a novel model that can generate more accurate density maps, utilizing the context-aware network as the primary structure and integrating the self-attention mechanism. There are three main contributions in this paper. Firstly, the self-attention mechanism is employed to improve the accuracy of density map estimation. Secondly, the missing vehicle labels in the TRANCOS database are relabeled, ensuring that the ground truth data are more complete than the original TRANCOS database, thus enabling the proposed novel model to have higher crowded object counting accuracy. Thirdly, the parameters of the self-attention mechanism are analyzed to obtain the optimum parameter combination. The experimental results demonstrate that the accuracy of crowded object counting can reach 85.9%, 90.0%, 83.4%, and 92.6% for the TRANCOS, relabeled TRANCOS, ShanghaiTech Part A, and Part B datasets, respectively. Furthermore, the ablation study for the context-aware network with self-attention mechanism analyzes the optimum parameter combination.

FaSS-MVS: Fast Multi-View Stereo with Surface-Aware Semi-Global Matching from UAV-Borne Monocular Imagery.

With FaSS-MVS, we present a fast, surface-aware semi-global optimization approach for multi-view stereo that allows for rapid depth and normal map estimation from monocular aerial video data captured by unmanned aerial vehicles (UAVs). The data estimated by FaSS-MVS, in turn, facilitate online 3D mapping, meaning that a 3D map of the scene is immediately and incrementally generated as the image data are acquired or being received. FaSS-MVS is composed of a hierarchical processing scheme in which depth and normal data, as well as corresponding confidence scores, are estimated in a coarse-to-fine manner, allowing efficient processing of large scene depths, such as those inherent in oblique images acquired by UAVs flying at low altitudes. The actual depth estimation uses a plane-sweep algorithm for dense multi-image matching to produce depth hypotheses from which the actual depth map is extracted by means of a surface-aware semi-global optimization, reducing the fronto-parallel bias of Semi-Global Matching (SGM). Given the estimated depth map, the pixel-wise surface normal information is then computed by reprojecting the depth map into a point cloud and computing the normal vectors within a confined local neighborhood. In a thorough quantitative and ablative study, we show that the accuracy of the 3D information computed by FaSS-MVS is close to that of state-of-the-art offline multi-view stereo approaches, with the error not even an order of magnitude higher than that of COLMAP. At the same time, however, the average runtime of FaSS-MVS for estimating a single depth and normal map is less than 14% of that of COLMAP, allowing us to perform online and incremental processing of full HD images at 1-2 Hz.

An Adaptive Strong Tracking Volume Kalman Filter Algorithm

Abstract Based on dental implant technology, this paper studies the position and posture tracking of surgical tools during the treatment of patients. A strong tracking adaptive volume Kalman filter (STF-ACKF) algorithm is proposed to meet the requirements of high accuracy, low time delay and strong robustness due to the complexity of the oral space and the narrow motion environment, to improve the measurement accuracy and reduce the noise interference. The adaptive module is added to the algorithm, and the real-time correction of process noise variance is realized by using MAP estimator [1-3]. The variance of the measured noise [4-5] was estimated using the variational Bayes (VB) method. Finally, the theoretical analysis and simulation results show that the mean error of STF-ACKF on X-axis, Y-axis and yaw angle is reduced by 1.17 mm, 0.76 mm and 0.62 ° respectively, the estimation accuracy and stability are improved significantly.

Predictive modeling using Copula Particle Filter and Adaptive Network-Based Fuzzy Inference

This paper introduces a novel prediction algorithm, CPF-ANFIS, designed to overcome the challenges posed by high-dimensional input data in Adaptive Neuro-Fuzzy Inference Systems (ANFIS). ANFIS's performance deteriorates with increasing input dimensionality due to the distortion of its membership functions. To address this limitation, CPF-ANFIS leverages a two-stage approach: A Copula Particle Filter (CPF) for robust state estimation and ANFIS for nonlinear mapping. By incorporating copulas, CPF effectively addresses the impoverishment and degeneracy problems commonly encountered in traditional particle filters. This enhanced robustness allows for more accurate state estimation, which in turn improves the overall performance of the CPF-ANFIS algorithm. By decoupling state estimation from nonlinear modeling, CPF-ANFIS effectively mitigates the curse of dimensionality. The proposed method is evaluated on real-world applications, such as hybrid PV-wind systems and SLAM. Experimental results demonstrate that CPF-ANFIS consistently outperforms ANFIS and the Copula Particle Filter individually, as well as previously proposed methods such as ANFIS-PF, highlighting its effectiveness in achieving accurate predictions under challenging conditions. The results show that the CPF-ANFIS algorithm increases prediction accuracy by at least 5% compared to using each algorithm separately.

I feel your pain: higher empathy is associated with higher posterior default mode network activity

Abstract Empathy is characterized as the ability to share one's experience and is associated with altruism. Previous work using blood oxygen level–dependent (BOLD) functional MRI (fMRI) has found that empathy is associated with greater activation in brain mechanisms supporting mentalizing (temporoparietal junction), salience (anterior cingulate cortex; insula), and self-reference (medial prefrontal cortex; precuneus). However, BOLD fMRI has some limitations that may not reliably capture the tonic experience of empathy. To address this, the present study used a perfusion-based arterial spin labeling fMRI approach that provides direct a quantifiable measurement of cerebral blood flow (1 mL/100 g tissue/min) and is less susceptible to low-frequency fluctuations and empathy-based “carry-over” effects that may be introduced by BOLD fMRI–based block designs. Twenty-nine healthy females (mean age = 29 years) were administered noxious heat (48°C; left forearm) during arterial spin labeling fMRI. In the next 2 fMRI scans, female volunteers viewed a stranger (laboratory technician) and their romantic partner, respectively, receive pain-evoking heat (48°C; left forearm) in real-time and positioned proximal to the scanner during fMRI acquisition. Visual analog scale (0 = “not unpleasant”; 10 = “most unpleasant sensation imaginable”) empathy ratings were collected after each condition. There was significantly (P = 0.01) higher empathy while viewing a romantic partner in pain and greater cerebral blood flow in the right temporoparietal junction, amygdala, anterior insula, orbitofrontal cortex, and precuneus when compared with the stranger. Higher empathy was associated with greater precuneus and primary visual cortical activation. The present findings indicate that brain mechanisms supporting the embodiment of another's experience is associated with higher empathy.

Measurement method of flexible component pose based on discrete motion actuators

Abstract In order to address the limitations of traditional large discrete motion actuator mechanisms in realizing high-precision inter-axis relationship calibration in manufacturing environments, this paper proposes a new convolutional neural network-based attitude mapping estimation method, component pose using convolutional neural network (CPCNN). The method implicitly encodes the inter-axis relation matrix into the weight parameters of the training neural network, which results in a high degree of integration with existing large discrete motion actuator mechanisms. The CPCNN-based method directly obtains the attitude change of the adjustment cabin by reading the change of each axis of the current motion actuator mechanism in its own coordinate system. This method can overcome the limitations of the experimental process in the traditional calibration methods and improve the accuracy of attitude mapping under the influence of self-weight by selecting better motion parameters through redundant degrees of freedom. The application of this new method will provide an effective solution for the high-precision inter-axis relationship calibration of large discrete motion actuator mechanisms in manufacturing environments, offering new possibilities for improving production efficiency and product quality.

Joint [Formula: see text] and Image Reconstruction in Low-Field MRI by Physics-Informed Deep-Learning.

We present a model-based image reconstruction approach based on unrolled neural networks which corrects for image distortion and noise in low-field ( B0 ∼ 50 mT) MRI. Utilising knowledge about the underlying physics, a novel network architecture (SH-Net) is introduced which involves the estimation of spherical harmonic coefficients to guarantee a spatially smooth field map estimate. The SH-Net is integrated in an end-to-end trainable model which jointly estimates the B0-field map as well as the image. Experiments were conducted on retrospectively simulated low-field data of human knees. We compare our model to different model-based approaches at distinct noise levels and various B0-field distributions. Our results show that our physics-informed neural network approach outperforms the purely model-based methods by improving the PSNR up to 11.7% and the RMSE up to 86.3%. Our end-to-end trained model-based approach outperforms existing methods in reconstructing image and B0-field maps in the low-field regime. low-field MRI is becoming increasingly more popular as it enables access to MR in challenging situations such as intensive care units or resource poor areas. Our method allows for fast and accurate image reconstruction in such low-field imaging with B0-inhomogeneity compensation under a wide range of various environmental conditions.

Basement Depth Map Estimation and Linear Features Detection Depending on Interpretation of Magnetic Data at Qarun Oil Field and Surrounding Areas, North Western Desert, Egypt

This investigation primarily targets a specific area situated in the northeastern Western Desert and northwest Beni-Suef, covering Wadi Al Rayan area, Gindi basin, Kattaniya inversion, and El-Fayum depression. It extends to the east of the river Nile. This study aims to Evaluate the depth of the basement complex in the area under investigation. The total aeromagnetic intensity data was reduced to the north pole (RTP) for clarity. 2D modeling of this RTP data was then used to evaluate the basement depth. The analysis of basement depth maps revealed significant variations in the subsurface structure of the study area, showing both elevated features (horsts) and depressed areas (grabens). The elevated basement (horsts) was found to be at a depth of approximately 813 meters, while the deepest depression (grabens) reached down to 7600 meters. Based on magnetic data combined with eight wells drilled into the basement complex, a basement lineament map of the area is delineated as a result of the investigation. This map shows a series of large alternating highs and valleys in ENE-WSW, NE-SW and E-W trending bands. Together with other minor trends, these major trends have shaped the basement surface into several tilted fault blocks, creating anticlinal and syncline zones of different depths and directions to form the Qattrani-El Gindi low trends. Whereas the high trend is located in El Fayum, El-Sagha, and Abu Rawash areas. The study helped to re-evaluate the subsurface structures affecting the Qarun oil field's hydrocarbon accumulation.