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This paper presents a general method to extract a surface pressure field from a skin friction field in complex flows as an inverse problem, focusing on its application to global luminescent oil-film (GLOF) skin friction measurements. The main technical aspects of this method are described, including the basic equation relating surface pressure to skin friction, variational method, numerical algorithm, and approximate method with the constant boundary enstrophy flux (BEF). The proposed method is evaluated through simulations in the Falkner–Skan flow and the flow over a 70°-delta wing to investigate the effects of the Lagrange multiplier, downsampling rate, noise level, and the value of a constant source term in the approximate method. Further, the approximate method is applied to the skin friction fields obtained by GLOF measurements in the flow over a 65°-delta wing and the square junction flow to obtain the normalized surface pressure fields. The proposed method provides a useful tool to obtain the high-resolution fields of both surface pressure and skin friction by GLOF measurements in complex flows (particularly at low speeds). The normalized surface pressure field extracted from the skin friction field in the square junction flow, where skin friction lines are superposed. [ABSTRACT FROM AUTHOR]

The majority of supervised models estimate optical flow through minimizing the numerical difference between the predicted flow and the ground truth, resulting in the loss of positional and geometric characteristics of the calculated flow fields. In addition, these models require a large number of parameters and high computational cost when computing optical flow. To address these issues, this paper presents a novel loss function and a lightweight framework for optical flow estimation. The proposed loss function, called the random epipolar constraint loss function (RECLoss), incorporates epipolar geometry into supervised optimization to transform the numerical difference into geometry constraint. The RECLoss can make the optical flow estimation models more effective and enhance their generalization abilities. Moreover, the design of RECLoss is more interpretable and the estimated optical flow fields from RECLoss have clearly defined mathematical meanings. A lightweight recurrent neural network for optical flow estimation (LRFlow) that balances computational cost and estimation accuracy is also proposed. The LRFlow, containing only 3.0M parameters, consists of a feature extractor, a correlation matching module, and an iterative update unit. The proposed lightweight network achieves state-of-the-art results compared to all other lightweight networks on the challenging MPI-Sintel and KITTI2015 datasets. The effectiveness of RECLoss in improving the accuracy of LRFlow and other state-of-the-art methods such as RAFT and GMA has been validated through extensive experiments. The source code of the project are available at https://github.com/Eryo-iPython/RECLoss. • A loss function incorporates epipolar geometry into supervised optical flow models. • More interpretable and clear mathematical meaning for optical flow estimation. • A lightweight recurrent neural network for optical flow calculation. [ABSTRACT FROM AUTHOR]

This paper proposes a variational formulation with a smoothness constraint for extraction of pressure from two-dimensional velocity field. The method of characteristics is first described as a reduced case to obtain pressure data by a line integral along a given line. Then, a global optimization method is developed, and the Euler–Lagrange equation is obtained to determine a pressure field from velocity data in a domain with suitable boundary conditions. Numerical algorithms are developed to solve the Euler–Lagrange equation with suitable boundary conditions. The accuracy of the developed methods and the effects of the relevant parameters on extraction of pressure are evaluated on a simulated two-dimensional oblique Hiemenz flow. Subsequently, in an experimental case, pressure fields near a freely flying hawkmoth are estimated from high-resolution velocity fields that are obtained by applying the optical flow method to high-speed Schlieren photography images. Pressure field near the hawkmoth wingtip obtained by using (a) Direct-QR method. And (b) iterative method. [ABSTRACT FROM AUTHOR]

Highlights • The numerical inverse heat transfer method is developed and validated through simulations. • This numerical method is useful to extract heat-flux fields in temperature-sensitive-coating measurements. • The developed method is particularly useful for tests in hypersonic wind tunnels. Abstract The one-dimensional unsteady heat transfer of a polymer layer on a finite base is studied. A numerical inverse method is developed to calculate heat flux from surface-temperature images obtained using temperature-sensitive coating particularly in high-enthalpy wind tunnels. It is found that the effect of the finite thickness of the base is significant as time increases such that the semi-infinite base assumption made in many methods is no longer accurate in heat-flux calculation. The method developed in this work can take into account the effects of the finite thickness of a base and the temperature-dependencies of the thermal properties of the materials. This method has been validated through simulations and processing the experimental temperature-sensitive-paint (TSP) images. [ABSTRACT FROM AUTHOR]

The physics-based optical flow (PBOF) model was derived from typical flow visualizations and it provided a more solid physical foundation for optical flow calculation. However, like many recent variational optical flow methods, it is not formulated to deal with the effect of illumination variation. In this paper, we propose a new efficient illumination-invariant optical flow estimation method, which improves the physics-based optical flow model. The proposed method introduces a multiplicative product of the velocity gradient and intensity from PBOF and an additive diffusion component to relax the brightness constancy assumption. The proposed method employs a linear transformation to reduce the influence of varying illumination on the data term. Then, a smoothness-sparsity regularization is used to constrain the minimization problem to have good edge-preserving for optical flow. Furthermore, the numerical implementation is given to solve the optimization problem. The accuracy of the developed method and the robustness against varying illumination are evaluated. The proposed method can provide motion estimation with acceptable accuracy for three optical flow datasets and cloud images of Jupiter's great red spot, which have challenging illumination variations. • More solid physical foundation for optical flow estimation. • A linear transformation to reduce the influence of varying illumination. • A smoothness-sparsity regularization to achieve good edge-preserving. • Flow patterns extracted from Jupiter's great red spot. [ABSTRACT FROM AUTHOR]

Fast and accurate optical flow estimation is a challenging problem in computer vision. In this paper, we present a novel model to solve the optical flow problem by combining the strengths of the Split Bregman method with the advantages of an efficient variational framework. It allows us to employ different regularization tensors for the Split Bregman regularizer to preserve motion discontinuities. Simultaneously, a novel nonlocal Split Bregman method with adaptive support weights is also developed for the smoothness regularization to preserve motion details, repel outliers, enhance contrast, and reduce motion blurring and staircase effect. The proposed algorithm shows significant improvements in preserving sharp flow edges and important motion details. Most of all, it requires only a few iterations to achieve fast convergence and runs faster than the classic TV-L1 flow method. Our method significantly outperforms the current state-of-the-art methods on the challenging MPI-Sintel dataset and shows good performance on the Kitti flow 2015 and Middlebury datasets. [ABSTRACT FROM PUBLISHER]

A variational solution of the physics-based optical flow equation is studied for extraction of high-resolution velocity fields from flow visualization images. The solution to preserve discontinuities in a fluid velocity field is mathematically justified. The uniqueness of the solution and the convergence of a successive approximation sequence are proven. A numerical algorithm is developed and examined through numerical experiments. [ABSTRACT FROM AUTHOR]

Temporal action detection is an important yet challenging task in video understanding task. Temporal action proposals generation is a common module in action detection, and it effects the performance of action detection greatly. The module requires methods not only generating proposals with accurate temporal boundaries, but also retrieving proposals to cover action instances with high recall using relative fewer proposals. To address these difficulties, we propose an Actionness Score Optimization Model to improve the accuracy of generated proposals by capturing global contextual information of untrimmed videos. Firstly, a deconvolution layer is utilized to learn a nonlinear upsampling for the extracted features, in both spatial and temporal domains. In order to reveal the contextual information, then we introduce the bidirectional gated recurrent unit to the network. Moreover, an attention mechanism is applied to the network so that it can focus on the most relevant parts of the information to obtain more reliable actionness scores. Finally, we validate the effectiveness of our proposed network on three challenging benchmark datasets, ActivityNet v1.2, ActivityNet v1.3, and THUMOS'14. [ABSTRACT FROM AUTHOR]

In recent years, considerable efforts have been made in the research of sparse representation for signals over overcomplete dictionaries. The dictionaries can be either pre-specified transforms or designed by learning from a set of training signals. In the paper, the dictionary learning problem was extended into a quadratic programming framework. A projected gradient with line search method was presented for solving this large-scale box-constrained quadratic program. The non-negative dictionary learned using this method was applied to image de-noising. Experimental results demonstrated that this learning-based method had better performance than the wavelet-based, the variation-based and the K-SVD methods. [ABSTRACT FROM AUTHOR]

Nowadays, with the increased numbers of video cameras, the amount of recorded video is growing. Efficient video browsing and retrieval are critical issues when considering the amount of raw video data to be condensed. Activity-based video synopsis is a popular approach to solving the video condensation problem. However, conventional synopsis methods always consists of complicated and pairwise energy terms that involve a time-consuming optimization problem. In this paper, we propose a simple online video synopsis framework in which the number of collisions of objects is classified first. Different optimization strategies are applied according to different collision situations to maintain a balance among the computational cost, condensation ratio, and collision cost. Secondly, tube-resizing coefficients that are dynamic in different frames are adaptively assigned to a newly generated tube. Therefore, a suitable mapping result can be obtained in order to represent the proper size of the activity in each frame of the synopsis video. The maximum number of activities can be displayed in one frame with minimal collisions. Finally, in order to remove motion anti-facts and improve the visual quality of the condensed video, a smooth term is introduced to constrain the resizing coefficients. Experimental results on extensive videos validate the efficiency of the proposed method. [ABSTRACT FROM AUTHOR]

Atherosclerosis (AS) is a chronic inflammatory disease accompanied by complex pathological changes, such as endothelial dysfunction, foam cell formation, and vascular smooth muscle cell proliferation. Many approaches, including regulating AS-related gene expression in the transcriptional or post-transcriptional level, contribute to alleviating AS development. The DNA methylation is a crucial epigenetic modification in regulating cell function by silencing the relative gene expression. The microRNA (miRNA) is a type of noncoding RNA that plays an important role in gene post-transcriptional regulation and disease development. The DNA methylation and the miRNA are important epigenetic factors in AS. However, recent studies have found a mutual regulation between these two factors in AS development. In this study, recent insights into the roles of miRNA and DNA methylation and their interaction in the AS progression are reviewed. [ABSTRACT FROM AUTHOR]

This review provides a systematical description of the inverse heat transfer solutions applied to global heat-flux measurements using temperature-sensitive-coating techniques in hypersonic wind tunnels. The analytical inverse heat transfer solutions for a two-layer structure (typically a polymer sensor coating on a base) are focused to elucidate the effects of the relevant parameters including the polymer and base thicknesses, the thermal properties of materials, and the convective heat transfer at the base backside. Then, the numerical inverse heat transfer method is developed by incorporating the temperature-dependencies of the thermal properties in heat-flux calculation. Furthermore, correcting the lateral heat conduction effect is formulated as an image deconvolution problem based on a convolution-type integral equation with a Gaussian kernel. Other relevant topics are also discussed, including determining the non-uniform paint thickness distribution, in-situ correction for the effect of the temperature-dependent diffusivity, and in-situ calibration for the thermal penetration parameter of the polymer coating. Examples are given to demonstrate the applications of these inverse solutions in temperature-sensitive-paint (TSP) measurements. [ABSTRACT FROM AUTHOR]

An analytical inverse heat transfer solution is given for calculating surface heat flux from temperature-sensitive coating measurements on a base with a finite thickness, which is particularly relevant to high enthalpy hypersonic wind tunnel experiments. This solution is validated through simulations for several time histories of the heat flux imposed on the surface of the base, focusing on the effects of the relevant parameters including the control parameter in the inverse Laplace transform, base thickness and convective heat transfer on the backside of the base. Further, this solution is used to extract the heat-flux fields from temperature-sensitive-paint images obtained on a flat surface of an aluminum base in a transonic obliquely impinging jet. The experimental data exhibit the self-similar evolution of the surface temperature and heat-flux fields. The Nusselt number distribution evaluated by using this analytical method is consistent with the previous results. [ABSTRACT FROM AUTHOR]

Abstract: Existing face hallucination methods assume that the face images are well-aligned. However, in practice, given a low-resolution face image, it is very difficult to perform precise alignment. As a result, the quality of the super-resolved image is degraded dramatically. In this paper, we propose a near frontal-view face hallucination method which is robust to face image mis-alignment. Based on the discriminative nature of sparse representation, we propose a global face sparse representation model that can reconstruct images with mis-alignment variations. We further propose an iterative method combining the global sparse representation and the local linear regression using the Expectation Maximization (EM) algorithm, in which the face hallucination is converted into a parameter estimation problem with incomplete data. Since the proposed algorithm is independent of the face similarity resulting from precise alignment, the proposed algorithm is robust to mis-alignment. In addition, the proposed iterative manner not only combines the merits of the global and local face hallucination, but also provides a convenient way to integrate different strategies to handle the mis-alignment problem. Experimental results show that the proposed method achieves better performance than existing methods, especially for mis-aligned face images. [Copyright &y& Elsevier]

Polybrominated diphenyl ethers (PBDEs) are a class of widely used flame retardants, and their residue in the environment may threaten the ecosystem and human health. The neurodevelopmental toxic effects of PBDEs have been verified in previous studies, but the mechanisms are still unclear. Behavioral analysis and transcriptomics were performed in this study to assess the neurodevelopmental toxic effects of PBDEs on zebrafish embryos and larvae, and the potential mechanisms. The embryos were collected after fertilization and exposed to control (0.05% DMSO), 10, 50, 100 (ug/L) 2, 2′, 4, 4′-tetrabromodiphenyl ether (BDE-47) for 7 days. The locomotion parameters of larvae were recorded and analyzed by a behavioral analysis system (EthoVision XT, Noldus). Enrichment of functions and signaling pathways of differentially expressed genes (DEGs) were analyzed by GO and DAVID database. The comparison with the control group showed adverse developments such as low hatching rate, high mortality rate, alterative heart rate, and abnormal spontaneous tail coiling frequency of embryos (24hpf). For the zebrafish larvae, behavioral analyses results suggested decreased activities and movements of the treatment in the light-dark period at 120, 144 and 168hpf, especially the 50 and 100μg/L groups. The affected functions included steroid hormone regulation, neuro regulation, circadian regulation, cardioblast differentiation, immune-related regulation. The enrichment of KEGG pathways were Hedgehog signaling (Shh), Toll-like receptor signaling, FoxO signaling, and Steroid biosynthesis pathway. Hedgehog signaling pathway was further verified via RT-qPCR for its major role in the development of neurogenesis. The mRNA levels of Shh pathway indicated the inhibition of Shh signal in our study since shha, patched1, gli1 and gli2 genes were significantly down-regulated. In summary, PBDEs might influence the neurodevelopment of zebrafish in the early life stage by multiple toxic signaling pathways alteration. Unlabelled Image • Behavioral analysis and RNA-seq were used to explore toxicity effects of BDE-47. • BDE-47 affected survival, hatch, and heart rates of zebrafish embryo-larvae. • BDE-47 exposure caused developmental neurotoxicity to zebrafish larval. • Four KEGG pathways were identified as key neurotoxic pathways after BDE-47 exposure. • BDE-47 exposure inhibited Shh signal of Hedgehog signaling pathway. [ABSTRACT FROM AUTHOR]