MAP Inference Research Articles (Page 1)

While δ Scuti stars --- intermediate-mass stars pulsating with periods of $<0.3$ d --- are the most numerous class of κ-mechanism pulsators in the instability strip, the short periods and small peak-to-peak amplitudes have left them understudied and under-utilized. Recently, large-scale time-domain surveys have significantly increased the number of identified δ Scuti stars, enabling more comprehensive investigations into their properties. Notably, the Tsinghua University–Ma Huateng Telescopes for Survey (TMTS), with its high-cadence observations at 1-minute intervals, has identified thousands of δ Scuti stars, greatly expanding the sample of these short-period pulsating variables. This study makes use of multiband photometric time-series data to refine the period-luminosity (P-L) relations of δ Scuti stars and show how observed P-L relations can be used to simultaneously infer dust obscuration and distance. Using spectroscopy, we also study the dependence of the P-L relations on metallicity. Using the δ Scuti stars from the TMTS catalogs of Periodic Variable Stars, we cross-matched the dataset with Pan-STARRS1, 2MASS, and WISE to obtain photometric measurements across optical (g, r, i, z, and y), near-infrared (J, H, and K_s), and mid-infrared (W1, W2, and W3) bands, respectively. Parallax data, used as Bayesian priors, were retrieved from Gaia DR3, and line-of-sight dust extinction priors were estimated from a 3D dust map. Using PyMC we performed a simultaneous determination of the 11-band P-L relations of δ Scuti stars. The simultaneous determination of multiband P-L relations of δ Scuti stars not only yields precise measurements of these relations, but also greatly improves constraints on the distance moduli and color excesses, as evidenced by the reduced uncertainties in the posterior distributions. Furthermore, our methodology enables an independent estimation of the color excess through the P-L relations, offering a potential complement to existing 3D dust maps. Moreover, by cross-matching with LAMOST DR7, we investigated the influence of metallicity on the P-L relations. Our analysis reveals that incorporating metallicity might reduce the intrinsic scatter at longer wavelengths. However, this result does not achieve 3σ significance, leaving open the possibility that the observed reduction is attributable to statistical fluctuations. We introduce an innovative approach to studying the P-L relations of δ Scuti stars, facilitating more comprehensive investigations into their utility as distance indicators and their significance in understanding stellar evolution. Our extensible methodology also enables the inference of dust extinction using pulsating stars beyond δ Scuti stars. Although the inclusion of metallicity in the P-L relations appears to reduce intrinsic scatter at longer wavelengths, further analysis is required to fully understand the impact of metal abundances on the properties of δ Scuti stars.

Abstract Spectrum map is a database that stores multidimensional representations of spectrum situation information. It provides support for spectrum sensing and endows wireless communication networks with intelligence. However, the ubiquitous deployment of monitoring devices leads to huge costs of operation and maintenance. It indicates that an approach is needed to reduce the number of monitoring devices, but prevent the degradation of data granularity. Therefore, this paper focuses on the accurate construction of the spectrum map. It aims to infer the fine‐grained spectrum situation of the target region based on coarse‐grained observation. In order to solve this problem, an inference framework based on deep residual network is developed in this paper. In the case of rule deployment for sensing nodes, it adopts the idea of super resolution to improve the accuracy of the spectrum map. The framework is composed of two major parts: an inference network, which generates fine‐grained spectrum maps from coarse‐grained counterparts by using feature extraction module and upsampling construction module; and a fusion network, which considers the influence of environmental factors to further improve the performance. A large number of experiments on simulated datasets verify the effectiveness of the proposed method.

We present a novel problem, called MetaEC, which aims to infer gene-species assignments in a collection of partially leaf-labeled gene trees labels by minimizing the size of duplication episode clustering (EC). This problem is particularly relevant in metagenomics, where incomplete data often poses a challenge in the accurate reconstruction of gene histories. To solve MetaEC, we propose a polynomial time dynamic programming (DP) formulation that verifies the existence of a set of duplication episodes from a predefined set of episode candidates. In addition, we design a method to infer distributions of gene-species mappings. We then demonstrate how to use DP to design an algorithm that solves MetaEC. Although the algorithm is exponential in the worst case, we introduce a heuristic modification of the algorithm that provides a solution with the knowledge that it is exact. To evaluate our method, we perform two computational experiments on simulated and empirical data containing whole genome duplication events, showing that our algorithm is able to accurately infer the corresponding events.

Robust and accurate object association is essential for precise 3D object landmark inference in semantic Simultaneous Localization and Mapping (SLAM), and yet remains challenging due to the detection deficiency caused by high miss detection rate, false alarm, occlusion and limited field-of-view, etc. The 2D location of an object is a crucial complementary cue to the appearance feature, especially in the case of associating objects across frames under large viewpoint changes. However, motion model or trajectory pattern based methods struggle to infer object motion reliably with a moving camera. In this paper, by exploiting the local projective warping consistency, a local homography based 2D motion inference method is proposed to sequentially estimate the object location along with uncertainty. By integrating the deep appearance feature and semantic information, an object association method, named HOA, which is robust to detection deficiency is proposed. Experimental evaluations suggest that the proposed motion prediction method is capable of maintaining a low cumulative error over a long duration, which enhances the object association performance in both accuracy and robustness. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —This work aims to consistently associate 2D detection boxes corresponding to the same 3D object across images. In tasks of landmark-based navigation, collision avoidance, grasping and manipulation, objects in the task space are commonly simplified into 3D enveloping surfaces (e.g. cuboid or ellipsoid) by using 2D object detection boxes from multiple image views, and accurate data association is a prerequisite for precise enveloping surface reconstruction. This problem remains challenging considering the imperfect object detections, the appearance similarity of objects and the unpredictable trajectory of the moving camera. This work proposes a long-term reliable 2D location prediction algorithm that is capable of handling the complex motion of the target. Along with the appearance feature extracted by a retrain-free deep learning based model, this work proposes an object association method that can simultaneously deal with multiple objects with unknown object categories under the moving camera scenario.

This paper proposes a distributed version of Determinant Point Processing (DPP) inference to enhance multi-source data diversification under limited communication bandwidth. DPP is a popular probabilistic approach that improves data diversity by enforcing the repulsion of elements in the selected subsets. The well-studied Maximum A Posteriori (MAP) inference in DPP aims to identify the subset with the highest diversity quantified by DPP. However, this approach is limited by the presumption that all data samples are available at one point, which hinders its applicability to real-world applications such as traffic datasets where data samples are distributed across sources and communication between them is band-limited. Inspired by the techniques used in Multiple-Input Multiple-Output (MIMO) communication systems, we propose a strategy for performing MAP inference among distributed sources. Specifically, we show that a lower bound of the diversity-maximized distributed sample selection problem can be decomposed into a sum of MIMOlike sub-problems. A determinant-preserved sparse representation of selected samples is used to perform sample Pre-coding in local sources to be processed by DPP. Our method does not require raw data exchange among sources, but rather a band-limited feedback channel to send lightweight diversity measures, analogous to the Channel State Information (CSI) message in MIMO systems, from the center to data sources. The experiments show that our scalable approach can outperform alternative methods and finally demonstrates the potential to translate the Diversification to the improvement of learning quality in several applications, such as multi-level classification, object detection, and multiple-instance learning.

Reservoir interwell connectivity estimation from small datasets using a probabilistic data driven approach and uncertainty quantification

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference

Abstract We propose a novel graph-based approach for semantic parsing that resolves two problems observed in the literature: (1) seq2seq models fail on compositional generalization tasks; (2) previous work using phrase structure parsers cannot cover all the semantic parses observed in treebanks. We prove that both MAP inference and latent tag anchoring (required for weakly-supervised learning) are NP-hard problems. We propose two optimization algorithms based on constraint smoothing and conditional gradient to approximately solve these inference problems. Experimentally, our approach delivers state-of-the-art results on GeoQuery, Scan, and Clevr, both for i.i.d. splits and for splits that test for compositional generalization.

Considering the worst-case scenario, the junction-tree algorithm remains the most general solution for exact MAP inference with polynomial run-time guarantees. Unfortunately, its main tractability assumption requires the treewidth of a corresponding MRF to be bounded, strongly limiting the range of admissible applications. In fact, many practical problems in the area of structured prediction require modeling global dependencies by either directly introducing global factors or enforcing global constraints on the prediction variables. However, this always results in a fully-connected graph, making exact inferences by means of this algorithm intractable. Previous works focusing on the problem of loss-augmented inference have demonstrated how efficient inference can be performed on models with specific global factors representing non-decomposable loss functions within the training regime of SSVMs. Making the observation that the same fundamental idea can be applied to solve a broader class of computational problems, in this paper, we adjust the framework for an efficient exact inference to allow much finer interactions between the energy of the core model and the sufficient statistics of the global terms. As a result, we greatly increase the range of admissible applications and strongly improve upon the theoretical guarantees of computational efficiency. We illustrate the applicability of our method in several use cases, including one that is not covered by the previous problem formulation. Furthermore, we propose a new graph transformation technique via node cloning, which ensures a polynomial run-time for solving our target problem. In particular, the overall computational complexity of our constrained message-passing algorithm depends only on form-independent quantities such as the treewidth of a corresponding graph (without global connections) and image size of the sufficient statistics of the global terms.

Multiple-view stereo has potential applications in robotic operations and autonomous driving (unstructured environment construction, visual servo). With assisted depth information, inertial navigation systems can achieve precise navigation. It is, especially suitable for GPS failures in complex environments. Accurate depth estimation is a challenge in low-textured or occluded regions. To alleviate the inference of incorrect depth, a multi-stage pixel-visibility learning-based stereo network is presented in this paper. Its improvements are as follows: 1) a new content-adaptive cost volume aggregation mechanism based on neighboring pixel-wise visibility is designed to effectively produce more accurate and smoother depth map predictions in the object boundary. 2) global convolution block and boundary refinement block are developed to regularize its cost volume, they can learn the inherent constraints of feature matching correspondence and effectively mitigate the depth estimation uncertainty in low-textured regions. 3) a new loss function is designed to measure the uncertainty of predicted probability distribution and enhance the reliability of depth map inference. Experimental results on the indoor DTU datasets and the outdoor Tanks & Temples datasets indicate that our method can achieve superior performance and has a powerful generalization ability, which is comparable to state-of-the-art works. Note to Practitioners—Multiple-view stereo (MVS) can estimate dense 3D representations of scenes, which is widely used in autonomous driving, robotic navigation, virtual reality (VR), and augmented reality (AR). Aiming at the problem of incorrect depth inference in low-textured or occluded regions, this work proposes a novel multi-stage depth prediction method based on neighboring pixel-wise visibility. Our method cannot only achieve accurate depth estimation for robot perception but also make no concession to real-time performance. It is clear that the proposed method has good potential in 3D reconstruction, robotic navigation, and VR/AR fields to provide accurate depth estimation in real-time with limited memory consumption.

Sparse mobile crowdsensing (Sparse MCS) is an emerging paradigm for urban-scale sensing applications, which recruits suitable participants to complete sensing tasks in only a few selected cells and then infers data of unsensed cells for saving sensing costs and obtaining high-quality sensing maps. In Sparse MCS, one crucial issue is task assignment, in which the platform selects cells whose sensing data can reduce inferred sensing maps errors (i.e., cell selection) and recruits the participant set with the maximum contribution for performing tasks (i.e., participant recruitment). The research on participant recruitment mainly focuses on single participatory-based or single opportunistic-based sensing mode. Due to the complementarity of two sensing modes, recruiting participants by only one sensing mode would result in wasting sensing resources and compromising the quality of task completion. Thus, combining the advantages of two sensing modes, we propose a task assignment framework based on hybrid sensing modes in Sparse MCS (HSM-SMCS) for achieving a good tradeoff between sensing quality and cost. Specifically, we propose a heuristic two-stage search strategy that simultaneously recruits opportunistic and participatory participants to perform tasks in significant cells within the constraint of total costs, considering their contributions to sensing map inference. Thereinto, for opportunistic participants, mobility prediction greatly affects task assignment effectiveness. However, existing prediction algorithms lead to unsatisfactory outcomes when the historical trajectory data of opportunistic participants are scarce. To effectively improve the predictive accuracy, we design a mobility prediction model based on transfer learning. The experimental evaluation on real trajectory data sets and sensor data sets of corresponding areas demonstrates that our framework outperforms state-of-the-art methods with higher quality reconstructed sensing maps.

We present a field-based signal extraction of weak lensing from noisy observations on the curved and masked sky. We test the analysis on a simulated Euclid-like survey, using a Euclid-like mask and noise level. To make optimal use of the information available in such a galaxy survey, we present a Bayesian method for inferring the angular power spectra of the weak lensing fields, together with an inference of the noise-cleaned tomographic weak lensing shear and convergence (projected mass) maps. The latter can be used for field-level inference with the aim of extracting cosmological parameter information including non-gaussianity of cosmic fields. We jointly infer all-sky $E$-mode and $B$-mode tomographic auto- and cross-power spectra from the masked sky, and potentially parity-violating $EB$-mode power spectra, up to a maximum multipole of $\ell_{\rm max}=2048$. We use Hamiltonian Monte Carlo sampling, inferring simultaneously the power spectra and denoised maps with a total of $\sim 16.8$ million free parameters. The main output and natural outcome is the set of samples of the posterior, which does not suffer from leakage of power from $E$ to $B$ unless reduced to point estimates. However, such point estimates of the power spectra, the mean and most likely maps, and their variances and covariances, can be computed if desired.

This article presents a novel and flexible multitask multilayer Bayesian mapping framework with readily extendable attribute layers. The proposed framework goes beyond modern metric-semantic maps to provide even richer environmental information for robots in a single mapping formalism while exploiting intralayer and interlayer correlations. It removes the need for a robot to access and process information from many separate maps when performing a complex task, advancing the way robots interact with their environments. To this end, we design a multitask deep neural network with attention mechanisms as our front-end to provide heterogeneous observations for multiple map layers simultaneously. Our back-end runs a scalable closed-form Bayesian inference with only logarithmic time complexity. We apply the framework to build a dense robotic map including metric-semantic occupancy and traversability layers. Traversability ground truth labels are automatically generated from exteroceptive sensory data in a self-supervised manner. We present extensive experimental results on publicly available datasets and data collected by a 3D bipedal robot platform and show reliable mapping performance in different environments. Finally, we also discuss how the current framework can be extended to incorporate more information such as friction, signal strength, temperature, and physical quantity concentration using Gaussian map layers. The software for reproducing the presented results or running on customized data is made publicly available.

BackgroundEstimating and accounting for hidden variables is widely practiced as an important step in molecular quantitative trait locus (molecular QTL, henceforth “QTL”) analysis for improving the power of QTL identification. However, few benchmark studies have been performed to evaluate the efficacy of the various methods developed for this purpose.ResultsHere we benchmark popular hidden variable inference methods including surrogate variable analysis (SVA), probabilistic estimation of expression residuals (PEER), and hidden covariates with prior (HCP) against principal component analysis (PCA)—a well-established dimension reduction and factor discovery method—via 362 synthetic and 110 real data sets. We show that PCA not only underlies the statistical methodology behind the popular methods but is also orders of magnitude faster, better-performing, and much easier to interpret and use.ConclusionsTo help researchers use PCA in their QTL analysis, we provide an R package PCAForQTL along with a detailed guide, both of which are freely available at https://github.com/heatherjzhou/PCAForQTL. We believe that using PCA rather than SVA, PEER, or HCP will substantially improve and simplify hidden variable inference in QTL mapping as well as increase the transparency and reproducibility of QTL research.

The online food delivery (OFD) business is booming in China. Owing to the timeliness requirements, delivery personnel in OFD platforms usually use electric bicycles to make deliveries. However, the accuracy and the coverage rate of existing cycling maps are relatively low, as is evidenced by a considerable amount of cycling global positioning system (GPS) trajectories that cannot be matched to existing maps, thus the efficiency of delivery is affected. Although there has been a proliferation of studies on driving or walking map inference using GPS trajectories, to the authors’ knowledge, none of them systematically investigate the cycling scenario. Our study addresses this gap. We work with Meituan—the largest OFD platform in China—and use the GPS trajectories reported by delivery personnel to infer the underlying cycling map. We first adapt three popular map inference algorithms, namely, k-means clustering, kernel density estimation, and trace merging. We also propose a new approach that infers the cycling network. We perform an initial inference of the underlying road network through an iterative process and apply a series of map refinement techniques to further improve the appearance of the inferred road network. The result shows that our algorithm reaches an F-score of 0.41, whereas the best existing algorithm we adapt reaches an F-score of 0.39. We also consider a special case that uses the driving map information in the area. In this case, a map-matching step is included and the overall F-score further increases from 0.41 to 0.70.

Deployment of a deep-learning based multi-view stereo approach for measurement of ship shell plates

Proton therapy requires accurate dose calculation for treatment planning to ensure the conformal doses are precisely delivered to the targets. The conversion of CT numbers to material properties is a significant source of uncertainty for dose calculation. The aim of this study is to develop a physics-informed deep learning (PIDL) framework to derive accurate mass density and relative stopping power maps from dual-energy computed tomography (DECT) images. The PIDL framework allows deep learning (DL) models to be trained with a physics loss function, which includes a physics model to constrain DL models. Five DL models were implemented including a fully connected neural network (FCNN), dual-FCNN (DFCNN), and three variants of residual networks (ResNet): ResNet-v1 (RN-v1), ResNet-v2 (RN-v2), and dual-ResNet-v2 (DRN-v2). An artificial neural network (ANN) and the five DL models trained with and without physics loss were explored to evaluate the PIDL framework. Two empirical DECT models were implemented to compare with the PIDL method. DL training data were from CIRS electron density phantom 062M (Computerized Imaging Reference Systems, Inc., Norfolk, VA). The performance of DL models was tested by CIRS adult male, adult female, and 5-year-old child anthropomorphic phantoms. For density map inference, the physics-informed RN-v2 was 3.3%, 2.9% and 1.9% more accurate than ANN for the adult male, adult female, and child phantoms. The physics-informed DRN-v2 was 0.7%, 0.6%, and 0.8% more accurate than DRN-v2 without physics training for the three phantoms, respectfully. The results indicated that physics-informed training could reduce uncertainty when ANN/DL models without physics training were insufficient to capture data structures or derived significant errors. DL models could also achieve better image noise control compared to the empirical DECT parametric mapping methods. The proposed PIDL framework can potentially improve proton range uncertainty by offering accurate material properties conversion from DECT.

This paper reports on a dynamic semantic mapping framework that incorporates 3D scene flow measurements into a closed-form Bayesian inference model. Existence of dynamic objects in the environment can cause artifacts and traces in current mapping algorithms, leading to an inconsistent map posterior. We leverage state-of-the-art semantic segmentation and 3D flow estimation using deep learning to provide measurements for map inference. We develop a Bayesian model that propagates the scene with flow and infers a 3D continuous (i.e., can be queried at arbitrary resolution) semantic occupancy map outperforming its static counterpart. Extensive experiments using publicly available data sets show that the proposed framework improves over its predecessors and input measurements from deep neural networks consistently.

Despite the plethora of successful Super-Resolution Reconstruction (SRR) models applied to natural images, their application to remote sensing imagery tends to produce poor results. Remote sensing imagery is often more complicated than natural images and has its peculiarities such as being of lower resolution, it contains noise, and often depicting large textured surfaces. As a result, applying non-specialized SRR models like the Enhanced Super Resolution Generative Adversarial Network (ESRGAN) on remote sensing imagery results in artifacts and poor reconstructions. To address these problems, we propose a novel strategy for enabling an SRR model to output realistic remote sensing images: instead of relying on feature-space similarities as a perceptual loss, the model considers pixel-level information inferred from the normalized Digital Surface Model (nDSM) of the image. This allows the application of better-informed updates during the training of the model which sources from a task (elevation map inference) that is closely related to remote sensing. Nonetheless, the nDSM auxiliary information is not required during production i.e., the model infers a super-resolution image without additional data. We assess our model on two remotely sensed datasets of different spatial resolutions that also contain the DSMs of the images: the DFC2018 dataset and the dataset containing the national LiDAR fly-by of Luxembourg. We compare our model with ESRGAN and we show that it achieves better performance and does not introduce any artifacts in the results. In particular, the results for the high-resolution DFC2018 dataset are realistic and almost indistinguishable from the ground truth images.

As a crucial component in intelligent transportation systems, traffic flow prediction has recently attracted widespread research interest in the field of artificial intelligence (AI) with the increasing availability of massive traffic mobility data. Its key challenge lies in how to integrate diverse factors (such as temporal rules and spatial dependencies) to infer the evolution trend of traffic flow. To address this problem, we propose a unified neural network called Attentive Traffic Flow Machine (ATFM), which can effectively learn the spatial-temporal feature representations of traffic flow with an attention mechanism. In particular, our ATFM is composed of two progressive Convolutional Long Short-Term Memory (ConvLSTM <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> ) units connected with a convolutional layer. Specifically, the first ConvLSTM unit takes normal traffic flow features as input and generates a hidden state at each time-step, which is further fed into the connected convolutional layer for spatial attention map inference. The second ConvLSTM unit aims at learning the dynamic spatial-temporal representations from the attentionally weighted traffic flow features. Further, we develop two deep learning frameworks based on ATFM to predict citywide short-term/long-term traffic flow by adaptively incorporating the sequential and periodic data as well as other external influences. Extensive experiments on two standard benchmarks well demonstrate the superiority of the proposed method for traffic flow prediction. Moreover, to verify the generalization of our method, we also apply the customized framework to forecast the passenger pickup/dropoff demands in traffic prediction and show its superior performance. Our code and data are available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/liulingbo918/ATFM</uri> .