Spatial temporal Fourier single-pixel imaging.

Generally, the imaging quality of Fourier single-pixel imaging (FSI) will severely degrade while achieving high-speed imaging at a low sampling rate (SR). To tackle this problem, a new, to the best of our knowledge, imaging technique is proposed: firstly, the Hessian-based norm constraint is introduced to deal with the staircase effect caused by the low SR and total variation regularization; secondly, based on the local similarity prior of consecutive frames in the time dimension, we designed the temporal local image low-rank constraint for the FSI, and combined the spatiotemporal random sampling method, the redundancy image information of consecutive frames can be utilized sufficiently; finally, by introducing additional variables to decompose the optimization problem into multiple sub-problems and analytically solving each one, a closed-form algorithm is derived for efficient image reconstruction. Experimental results show that the proposed method improves imaging quality significantly compared with state-of-the-art methods.

Identifying vital nodes for influence maximization in attributed networks.

Identifying a set of vital nodes to achieve influence maximization is a topic of general interest in network science. Many algorithms have been proposed to solve the influence maximization problem in complex networks. Most of them just use topology information of networks to measure the node influence.

Working memory asymmetrically modulates auditory and linguistic processing of speech.

Working memory load can modulate speech perception. However, since speech perception and working memory are both complex functions, it remains elusive how each component of the working memory system interacts with each speech processing stage. To investigate this issue, we concurrently measure how the working memory load modulates neural activity tracking three levels of linguistic units, i.

High-speed three-dimensional shape measurement based on tripartite complementary Gray-coded light.

In phase-shifting profilometry based on the Gray code, the jump error is inevitably generated and is further amplified in dynamic scenes. To tackle this problem, we propose the robust tripartite complementary Gray code method (TCG). Without projecting additional patterns, TCG uses different combinations of Gray code to calculate three complementary orders able to avoid jump error in the unwrapping process.

Fast and high-quality single-pixel imaging.

The imaging quality of the conventional single-pixel-imaging (SPI) technique seriously degrades at a low sampling rate. To tackle this problem, we propose an efficient sampling method and a high-quality real-time image reconstruction strategy: first, different from the conventional simple circular path sampling strategy or variable density random sampling technique, the proposed method samples the Fourier spectrum using the spectrum distribution of the image, that is, sampling the significant spectrum coefficients first, which will help to improve the image quality at a relevantly low sampling rate; second, to handle the long image reconstruction time caused by the iterative algorithm, the sparsity of the image and the alternating direction optimization strategy are combined to ameliorate the reconstruction process in the image gradient space. Compared with the state-of-the-art techniques, the proposed method significantly improves the imaging quality and achieves real-time reconstruction on the time scale of milliseconds.

Association Between MTHFR C677T Polymorphism and Susceptibility to Autism Spectrum Disorders: A Meta-Analysis in Chinese Han Population.

Prior studies have examined the influence of MTHFR C677T on autism susceptibility, however, there are no consensus conclusions and specific analyses of a Chinese population. This meta-analysis included a false-positive report probability (FPRP) test to comprehensively evaluate the association of MTHFR C677T polymorphism with autism susceptibility among a Chinese Han population. A large-scale literature retrieval was conducted using various databases including PubMed, Embase, Wan Fang, and the Chinese National Knowledge Infrastructure (CNKI) up to July 31, 2020, with a total of 2,258 cases and 2,073 controls included.

High quality and fast compressed sensing MRI reconstruction via edge-enhanced dual discriminator generative adversarial network.

Generative adversarial networks (GAN) are widely used for fast compressed sensing magnetic resonance imaging (CSMRI) reconstruction. However, most existing methods are difficult to make an effective trade-off between abstract global high-level features and edge features. It easily causes problems, such as significant remaining aliasing artifacts and clearly over-smoothed reconstruction details.

Optical frequency and phase information-based fusion approach for image rotation symmetry detection.

Detecting an object using rotation symmetry property is widely applicable as most artificial objects have this property. However, current known techniques often fail due to using single symmetry energy. To tackle this problem, this paper proposes a novel method which consists of two steps: 1) Based on an optical image, two independent symmetry energies are extracted from the optical frequency space (RSS - Rotation Symmetry Strength) and phase space (SSD - Symmetry Shape Density).

Why Smoggy Days Suppress Our Mood: Automatic Association Between Clarity and Valence.

The intuition of clarity-valence association seems to be pervasive in daily life, however, whether there exists a potential association between clarity (i.e., operationalized as visual resolution) and affect in human cognition remains unknown.

Quantifying Interpreting Types: Language Sequence Mirrors Cognitive Load Minimization in Interpreting Tasks.

Most interpreting theories claim that different interpreting types should involve varied processing mechanisms and procedures. However, few studies have examined their underlying differences. Even though some previous results based on quantitative approaches show that different interpreting types yield outputs of varying lexical and syntactic features, the grammatical parsing approach is limited.

A Fractional-Order Variational Framework for Retinex: Fractional-Order Partial Differential Equation-Based Formulation for Multi-Scale Nonlocal Contrast Enhancement with Texture Preserving.

This paper discusses a novel conceptual formulation of the fractional-order variational framework for retinex, which is a fractional-order partial differential equation (FPDE) formulation of retinex for the multi-scale nonlocal contrast enhancement with texture preserving. The well-known shortcomings of traditional integer-order computation-based contrast-enhancement algorithms, such as ringing artefacts and staircase effects, are still in great need of special research attention. Fractional calculus has potentially received prominence in applications in the domain of signal processing and image processing mainly because of its strengths like long-term memory, nonlocality, and weak singularity, and because of the ability of a fractional differential to enhance the complex textural details of an image in a nonlinear manner.

Geometry Guided Multi-Scale Depth Map Fusion via Graph Optimization.

In depth discontinuous and untextured regions, depth maps created by multiple view stereopsis are with heavy noises, but existing depth map fusion methods cannot handle it explicitly. To tackle the problem, two novel strategies are proposed: 1) a more discriminative fusion method, which is based on geometry consistency, measuring the consistency, and stability of surface geometry computed on both partial and global surfaces, different from traditional methods only using visibility consistency; 2) a graph optimization method which fuses pyramids of depth maps as mutual complementary information is available in different scales, and differs from existing multi-scale fusion methods. The method considers both sampling scale of a point and relations among points, and is proven to be solvable by graph cuts.

L₁-Norm Low-Rank Matrix Decomposition by Neural Networks and Mollifiers.

The L1-norm cost function of the low-rank approximation of the matrix with missing entries is not smooth, and also cannot be transformed into a standard linear or quadratic programming problem, and thus, the optimization of this cost function is still not well solved. To tackle this problem, first, a mollifier is used to smooth the cost function. High closeness of the smoothed function to the original one can be obtained by tuning the parameters contained in the mollifier.

Accurate and Fast Convergent Initial-Value Belief Propagation for Stereo Matching.

The belief propagation (BP) algorithm has some limitations, including ambiguous edges and textureless regions, and slow convergence speed. To address these problems, we present a novel algorithm that intrinsically improves both the accuracy and the convergence speed of BP. First, traditional BP generally consumes time due to numerous iterations.

Robust Prostate Segmentation Using Intrinsic Properties of TRUS Images.

Accurate segmentation is usually crucial in transrectal ultrasound (TRUS) image based prostate diagnosis; however, it is always hampered by heavy speckles. Contrary to the traditional view that speckles are adverse to segmentation, we exploit intrinsic properties induced by speckles to facilitate the task, based on the observations that sizes and orientations of speckles provide salient cues to determine the prostate boundary. Since the speckle orientation changes in accordance with a statistical prior rule, rotation-invariant texture feature is extracted along the orientations revealed by the rule.

An improved SOM algorithm and its application to color feature extraction.

Reducing the redundancy of dominant color features in an image and meanwhile preserving the diversity and quality of extracted colors is of importance in many applications such as image analysis and compression. This paper presents an improved self-organization map (SOM) algorithm namely MFD-SOM and its application to color feature extraction from images. Different from the winner-take-all competitive principle held by conventional SOM algorithms, MFD-SOM prevents, to a certain degree, features of non-principal components in the training data from being weakened or lost in the learning process, which is conductive to preserving the diversity of extracted features.

Complex-valued filtering based on the minimization of complex-error entropy.

In this paper, we consider the training of complex-valued filter based on the information theoretic method. We first generalize the error entropy criterion to complex domain to present the complex error entropy criterion (CEEC). Due to the difficulty in estimating the entropy of complex-valued error directly, the entropy bound minimization (EBM) method is used to compute the upper bounds of the entropy of the complex-valued error, and the tightest bound selected by the EBM algorithm is used as the estimator of the complex-error entropy.

Supervised methods for symptom name recognition in free-text clinical records of traditional Chinese medicine: an empirical study.

Clinical records of traditional Chinese medicine (TCM) are documented by TCM doctors during their routine diagnostic work. These records contain abundant knowledge and reflect the clinical experience of TCM doctors. In recent years, with the modernization of TCM clinical practice, these clinical records have begun to be digitized.

Statistical virtual eye model based on wavefront aberration.

Wavefront aberration affects the quality of retinal image directly. This paper reviews the representation and reconstruction of wavefront aberration, as well as the construction of virtual eye model based on Zernike polynomial coefficients. In addition, the promising prospect of virtual eye model is emphasized.

A framework and its empirical study of automatic diagnosis of traditional Chinese medicine utilizing raw free-text clinical records.

Automatic diagnosis is one of the most important parts in the expert system of traditional Chinese medicine (TCM), and in recent years, it has been studied widely. Most of the previous researches are based on well-structured datasets which are manually collected, structured and normalized by TCM experts. However, the obtained results of the former work could not be directly and effectively applied to clinical practice, because the raw free-text clinical records differ a lot from the well-structured datasets.

k-NS: a classifier by the distance to the nearest subspace.

To improve the classification performance of k-NN, this paper presents a classifier, called k -NS, based on the Euclidian distances from a query sample to the nearest subspaces. Each nearest subspace is spanned by k nearest samples of a same class. A simple discriminant is derived to calculate the distances due to the geometric meaning of the Grammian, and the calculation stability of the discriminant is guaranteed by embedding Tikhonov regularization.

On the almost periodic solution of cellular neural networks with distributed delays.

By exponential dichotomy about differential equations, a formal almost periodic solution (APS) of a class of cellular neural networks (CNNs) with distributed delays is obtained. Then, within different normed spaces, several sufficient conditions guaranteeing the existence and uniqueness of an APS are proposed using two fixed-point theorems. Based on the continuity property and some inequality techniques, two theorems insuring the global stability of the unique APS are given.

A functional neural network computing some eigenvalues and eigenvectors of a special real matrix.

How to quickly compute eigenvalues and eigenvectors of a matrix, especially, a general real matrix, is significant in engineering. Since neural network runs in asynchronous and concurrent manner, and can achieve high rapidity, this paper designs a concise functional neural network (FNN) to extract some eigenvalues and eigenvectors of a special real matrix. After equivalent transforming the FNN into a complex differential equation and obtaining the analytic solution, the convergence properties of the FNN are analyzed.