Miracle-dz/UniFAR-FTUs huggingface dataset:
Generative Adversarial Networks for the Creation of Realistic Artificial Brain Magnetic Resonance Images

Even as medical data sets become more publicly accessible, most are restricted to specific medical conditions. Thus, data collection for machine learning approaches remains challenging, and synthetic data augmentation, such as generative adversarial networks (GAN), may overcome this hurdle. In the present quality control study, deep convolutional GAN (DCGAN)–based human brain magnetic resonance (MR) images were validated by blinded radiologists. In total, 96 T1-weighted brain images from 30 healthy individuals and 33 patients with cerebrovascular accident were included. A training data set was generated from the T1-weighted images and DCGAN was applied to generate additional artificial brain images. The likelihood that images were DCGAN-created versus acquired was evaluated by 5 radiologists (2 neuroradiologists [NRs], vs 3 non-neuroradiologists [NNRs]) in a binary fashion to identify real vs created images. Images were selected randomly from the data set (variation of created images, 40%–60%). None of the investigated images was rated as unknown. Of the created images, the NRs rated 45% and 71% as real magnetic resonance imaging images (NNRs, 24%, 40%, and 44%). In contradistinction, 44% and 70% of the real images were rated as generated images by NRs (NNRs, 10%, 17%, and 27%). The accuracy for the NRs was 0.55 and 0.30 (NNRs, 0.83, 0.72, and 0.64). DCGAN-created brain MR images are similar enough to acquired MR images so as to be indistinguishable in some cases. Such an artificial intelligence algorithm may contribute to synthetic data augmentation for “data-hungry” technologies, such as supervised machine learning approaches, in various clinical applications.
Detection of Mycobacterium tuberculosis purified ESAT-6 (Rv3875) by magnetic bead-coupled gold nanoparticle-based immuno-PCR assay

Purpose Immuno-PCR (I-PCR), an ultrasensitive method, combines the versatility of ELISA with the exponential amplification capacity of PCR. Coupling of detection antibodies with the reporter DNA is a critical step of I-PCR. Gold nanoparticles (GNPs) and magnetic beads (MBs) are relatively easy to attach with the antibodies and DNA. Therefore, we designed MB-coupled GNP-based I-PCR (MB-GNP-I-PCR) assay for the detection of Mycobacterium tuberculosis antigen. Methods GNPs were synthesized by chemical reduction and seed-mediated synthesis. Functionalized GNPs were prepared by coupling GNPs with the detection antibodies and reporter DNA and were characterized. Detection limit of M. tuberculosis-specific purified early secreted antigenic target-6 (ESAT-6) (Rv3875) was determined by MB-GNP-I-PCR. Results Transmission electron microscopy revealed spherical and slightly polydispersed GNPs of ~20 and ~60 nm size. Coupling of antibodies to GNPs was indicated by a shift in absorption maxima from 524 to 534 nm, which was confirmed by transmission electron microscopy. A color reaction with ELISA and the presence of 76 bp product by PCR further validated the coupling of detection antibodies and signal DNA to the functionalized GNPs. Also, attachment of capture antibodies with MBs was confirmed by magneto-ELISA. Detection limit of purified ESAT-6 by MB-GNP-I-PCR was determined to be 10 fg/mL, 105-fold lower than analogous ELISA. Notably, no sample matrix effect was observed in the saliva samples of healthy individuals spiked with the purified ESAT-6. Conclusion Unlike conventional I-PCR (solid format), MB-GNP-I-PCR (liquid format) is relatively simple with the reduced background signals, which can be further exploited for the clinical diagnosis of tuberculosis.
Jian-Pi-Zhi-Dong-Decoction regulates the expression of glutamate transporters to attenuate glutamate excitotoxicity and exerts anti-tics effects in Tourette syndrome model rats

Purpose This study explored whether Jian-Pi-Zhi-Dong-Decoction (JPZDD) could regulate the metabolism of glutamate (GLU) and its transporters in the striatum to exert anti-tics effects in Tourette syndrome (TS) rats. Materials and methods We randomly assigned 56 Sprague Dawley rats into four groups, each with 14 rats: control, model, tiapride (Tia), and JPZDD. TS groups (model, Tia, and JPZDD) received intraperitoneal injection of 3,3′-iminodipropionitrile for 7 days to establish TS model. Thereafter, rats in the four groups were treated differently once a day for 6 weeks. Behavioral evaluation was performed each week by using stereotypy recording and autonomic activity test. The level of GLU in the striatum was examined by high-performance liquid chromatography. Expression of EAAT1 and VGLUT1 were measured by quantitative real-time PCR (qRT-PCR) and laser scanning confocal microscope. Results Compared with the model group, the stereotypy score and autonomic activity were decreased in Tia and JPZDD groups. Notably, the model group had increased concentration of GLU, which decreased after JPZDD and Tia treatments. In the model group, EAAT1 and glial cells were highly co-expressed and the relative fluorescence intensity (FI) of EAAT1 was significantly lower than that in the control group. Treatment with JPZDD and Tia increased the relative FI of EAAT1. The mRNA level of EAAT1 decreased in the model group compared to that in the control group, although it was significantly elevated following JPZDD or Tia treatment. In the model group, there was low co-expression of VGLUT1 and axon cells and the FI of VGLUT1 was remarkably increased relative to that in the control group and reduced following treatment with JPZDD and Tia. A similar trend was observed in the mRNA and protein expression of VGLUT1, although it was not statistically significant. Conclusion The mechanism by which JPZDD alleviated behavioral dysfunction of TS rats may be associated with maintaining normal GLU transport by upregulating EAAT1 and down-regulating VGLUT1 in the striatum.
A molecular phylogeny of Porcellionidae (Isopoda, Oniscidea) reveals inconsistencies with present taxonomy

Porcellionidae is one of the richest families of Oniscidea globally distributed but we still lack a comprehensive and robust phylogeny of the taxa that are assigned to it. Employing five genetic markers (two mitochondrial and three nuclear) we inferred phylogenetic relationships among the majority of Porcellionidae genera. Phylogenetic analyses conducted via Maximum Likelihood and Bayesian Inference resulted in similar tree topologies. The mtDNA genes cytochrome oxidase I (COI) and 16s rRNA (16s) were used for clade dating using previously published mutation rates. Our results provide evidence against the monophyly of both Porcellionidae and the largest genus of the family Porcellio. These results are compared to previous published work based on morphological evidence. The genera Leptotrichus and Brevurus are not grouped with the rest of Porcellionidae whereas Agnaridae are grouped with part of Porcellionidae. Armadillidium and Schizidium (Armadillidiidae) occupy a basal position on the phylogenetic tree. Even though the African genera Tura and Uramba (distributed in East Africa) are grouped together there is no general geographical pattern in other sub-clades. Additional taxonomic issues that arise in this work such as the assignment of the recently described genus Levantoniscus, arealso discussed. The status of Porcellionidae should be further revised and morphological characters traditionally used in Oniscidea taxonomy should be reconsidered in view of molecular evidence. The origin of the monophyletic clade within Porcellionidae as indicated in the present work is dated back to the Oligocene (~32 mya).
Effectiveness of Eccentric Exercise and a Vibration or Cryotherapy Program in Enhancing Rectus Abdominis Muscle Thickness and Inter-Rectus Distance in Patients with Chronic Mid-Portion Achilles Tendinopathy: A Randomized Clinical Trial

Purpose: Abdominal muscles are key in maintaining body stability and balance and an improvement in the functioning of these muscles could influence the rehabilitation process in lower limb pathologies such as Achilles Tendinopathy (AT). The aim was to explore whether calf eccentric exercise (EE) with vibration training was more effective at causing adaptation to the rectus anterior (RA) thickness and inter-rectus distance (IRD) than calf EE with cryotherapy. Methods: The investigation was a single-blinded, randomized, controlled clinical trial (NCT03515148). Sixty-one individuals diagnosed with mid-portion AT were recruited and divided in two groups: group A (n = 30) followed an EE with vibration program and group B (n = 31) an EE program with cryotherapy, for 12-weeks. RA muscle thickness and IRD were measured in maximal isometric contraction and at rest as an indication of superficial abdominal muscle activation. Results: IRD measures showed a significant (P < 0.05) decrease at baseline, 4 and at 12-weeks in both groups, but no significant differences were observed between the intervention groups. RA thickness was significantly increased (P < 0.05) in measures at baseline, 4 and 12-weeks showed a significant increase in maximal isometric contraction and at rest in favor of the EE vibration program group. Conclusions: The present study showed a RA thickness increase in both groups in favor of the EE vibration program with respect to cryotherapy added to EE in short and mid term in maximal isometric contraction and at rest in subjects with chronic mid-portion AT. IRD was decreased in both groups without between-groups differences.
Image-based 3D model retrieval using manifold learning

We propose a new framework for image-based three-dimensional (3D) model retrieval. We first model the query image as a Euclidean point. Then we model all projected views of a 3D model as a symmetric positive definite (SPD) matrix, which is a point on a Riemannian manifold. Thus, the image-based 3D model retrieval is reduced to a problem of Euclid-to-Riemann metric learning. To solve this heterogeneous matching problem, we map the Euclidean space and SPD Riemannian manifold to the same high-dimensional Hilbert space, thus shrinking the great gap between them. Finally, we design an optimization algorithm to learn a metric in this Hilbert space using a kernel trick. Any new image descriptors, such as the features from deep learning, can be easily embedded in our framework. Experimental results show the advantages of our approach over the state-of-the-art methods for image-based 3D model retrieval.
CodedSketch: A Coding Scheme for Distributed Computation of Approximated Matrix Multiplication

In this paper, we propose CodedSketch, as a distributed straggler-resistant scheme to compute an approximation of the multiplication of two massive matrices. The objective is to reduce the recovery threshold, defined as the total number of worker nodes that the master node needs to wait for to be able to recover the final result. To exploit the fact that only an approximated result is required, in reducing the recovery threshold, some sorts of pre-compression are required. However, compression inherently involves some randomness that would lose the structure of the matrices. On the other hand, considering the structure of the matrices is crucial to reduce the recovery threshold. In CodedSketch, we use count–sketch, as a hash-based compression scheme, on the rows of the first and columns of the second matrix, and a structured polynomial code on the columns of the first and rows of the second matrix. This arrangement allows us to exploit the gain of both in reducing the recovery threshold. To increase the accuracy of computation, multiple independent count–sketches are needed. This independency allows us to theoretically characterize the accuracy of the result and establish the recovery threshold achieved by the proposed scheme. To guarantee the independency of resulting count–sketches in the output, while keeping its cost on the recovery threshold minimum, we use another layer of structured codes. The proposed scheme provides an upper-bound on the recovery threshold as a function of the required accuracy of computation and the probability that the required accuracy can be violated. In addition, it provides an upper-bound on the recovery threshold for the case that the result of the multiplication is sparse, and the exact result is required.
Estimation and Restoration of Compositional Degradation Using Convolutional Neural Networks

Image restoration from a single image degradation type, such as blurring, hazing, random noise, and compression has been investigated for decades. However, image degradations in practice are often a mixture of several types of degradation. Such compositional degradations complicate restoration because they require the differentiation of different degradation types and levels. In this paper, we propose a convolutional neural network (CNN) model for estimating the degradation properties of a given degraded image. Furthermore, we introduce an image restoration CNN model that adopts the estimated degradation properties as its input. Experimental results show that the proposed degradation estimation model can successfully infer the degradation properties of compositionally degraded images. The proposed restoration model can restore degraded images by exploiting the estimated degradation properties and can achieve both blind and nonblind image restorations.
Attribute-Aware Face Aging With Wavelet-Based Generative Adversarial Networks

Since it is difficult to collect face images of the same subject over a long range of age span, most existing face aging methods resort to unpaired datasets to learn age mappings. However, the matching ambiguity between young and aged face images inherent to unpaired training data may lead to unnatural changes of facial attributes during the aging process, which could not be solved by only enforcing identity consistency like most existing studies do. In this paper, we propose an attribute-aware face aging model with wavelet based Generative Adversarial Networks (GANs) to address the above issues. To be specific, we embed facial attribute vectors into both the generator and discriminator of the model to encourage each synthesized elderly face image to be faithful to the attribute of its corresponding input. In addition, a wavelet packet transform (WPT) module is incorporated to improve the visual fidelity of generated images by capturing age-related texture details at multiple scales in the frequency space. Qualitative results demonstrate the ability of our model in synthesizing visually plausible face images, and extensive quantitative evaluation results show that the proposed method achieves state-of-the-art performance on existing datasets.
The CORAL+ Algorithm for Unsupervised Domain Adaptation of PLDA

State-of-the-art speaker recognition systems comprise an x-vector (or i-vector) speaker embedding front-end followed by a probabilistic linear discriminant analysis (PLDA) backend. The effectiveness of these components relies on the availability of a large collection of labeled training data. In practice, it is common that the domains (e.g., language, demographic) in which the system is deployed differ from that we trained the system. To close the gap due to the domain mismatch, we propose an unsupervised PLDA adaptation algorithm to learn from a small amount of unlabeled in-domain data. The proposed method was inspired by a prior work on feature-based domain adaptation technique known as the correlation alignment (CORAL). We refer to the model-based adaptation technique proposed in this paper as CORAL+. The efficacy of the proposed technique is experimentally validated on the recent NIST 2016 and 2018 Speaker Recognition Evaluation (SRE’16, SRE’18) datasets.
Extraction of Behavioral Features from Smartphone and Wearable Data

The rich set of sensors in smartphones and wearable devices provides the possibility to passively collect streams of data in the wild. The raw data streams, however, can rarely be directly used in the modeling pipeline. We provide a generic framework that can process raw data streams and extract useful features related to non-verbal human behavior. This framework can be used by researchers in the field who are interested in processing data from smartphones and Wearable devices.
Network topology of neural systems supporting avalanche dynamics predicts stimulus propagation and recovery

Many neural systems display avalanche behavior characterized by uninterrupted sequences of neuronal firing whose distributions of size and durations are heavy-tailed. Theoretical models of such systems suggest that these dynamics support optimal information transmission and storage. However, the unknown role of network structure precludes an understanding of how variations in network topology manifest in neural dynamics and either support or impinge upon information processing. Here, using a generalized spiking model, we develop a mechanistic understanding of how network topology supports information processing through network dynamics. First, we show how network topology determines network dynamics by analytically and numerically demonstrating that network topology can be designed to propagate stimulus patterns for long durations. We then identify strongly connected cycles as empirically observable network motifs that are prevalent in such networks. Next, we show that within a network, mathematical intuitions from network control theory are tightly linked with dynamics initiated by node-specific stimulation and can identify stimuli that promote long-lasting cascades. Finally, we use these network-based metrics and control-based stimuli to demonstrate that long-lasting cascade dynamics facilitate delayed recovery of stimulus patterns from network activity, as measured by mutual information. Collectively, our results provide evidence that cortical networks are structured with architectural motifs that support long-lasting propagation and recovery of a few crucial patterns of stimulation, especially those consisting of activity in highly controllable neurons. Broadly, our results imply that avalanching neural networks could contribute to cognitive faculties that require persistent activation of neuronal patterns, such as working memory or attention.
Generalized Lottery Trees: Budget-Consistent Incentive Tree Mechanisms for Crowdsourcing

Incentive mechanism design has aroused extensive attention for crowdsourcing applications in recent years. Most research assumes that participants are already in the system and aware of the existence of crowdsourcing tasks. Whereas in real life scenarios without this assumption, it is a more effective way to leverage incentive tree mechanisms that incentivize both users' direct contributions and solicitations to other users. Although some such mechanisms have been investigated, we are the first to propose budget-consistent incentive tree mechanisms, called generalized lottrees, which require the total payout to all participants to be consistent with the announced budget, while guaranteeing several other desirable properties including continuing contribution incentive, continuing solicitation incentive, value proportional to contribution, unprofitable solicitor bypassing, and unprofitable sybil attack. Moreover, we present three types of generalized lottree mechanisms, 1-Pachira, K-Pachira, and Sharing-Pachira, which support more diversified requirements. A solid theoretical guidance to the mechanism selection is provided as well based on the Cumulative Prospect Theory. Both extensive simulations and realistic experiments with 82 users have been conducted to confirm our theoretical analysis.
A Survey to Deep Facial Attribute Analysis

Facial attribute analysis has received considerable attention when deep learning techniques make remarkable breakthroughs in this field over the past few years. Deep learning based facial attribute analysis is comprised of two basic sub-issues: Facial Attribute Estimation (FAE), which recognizes whether facial attributes are present in given images, and Facial Attribute Manipulation (FAM), which synthesizes or removes desired facial attributes. In this paper, we provide a comprehensive survey on deep facial attribute analysis from the perspectives of both estimation and manipulation. First, we summarize that deep facial atXin Zheng School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China E-mail: zhengxin@mail.dlut.edu.cn Yanqing Guo* School of Information and Communication Engineering, Dalian University of Technology, Dalian 116024, China E-mail: guoyq@dlut.edu.cn Huaibo Huang School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China E-mail: huaibo.huang@cripac.ia. ac.cn Yi Li National Laboratory of Pattern Recognition, CASIA Center for Research on Intelligent Perception and Computing, CASIA University of Chinese Academy of Sciences, Beijing 100190, China E-mail: yi.li@cripac.ia. ac.cn Ran He National Laboratory of Pattern Recognition, CASIA Center for Research on Intelligent Perception and Computing, CASIA Center for Excellence in Brain Science and Intelligence Technology, CAS University of Chinese Academy of Sciences, Beijing 100190, China E-mail: rhe@nlpr.ia. ac.cn tribute analysis follows a general pipeline, which comprises two stages, i.e., data pre-processing and model construction. Meanwhile, the underlying theories of the two-stage pipeline are provided for both FAE and FAM, respectively. Second, we introduce commonly used datasets and performance metrics in facial attribute analysis. Third, we create a taxonomy of the state-of-the-arts and review FAE and FAM algorithms in detail. Furthermore, several additional facial attribute related issues are introduced, as well as relevant real-world applications. Finally, we discuss possible challenges and promising future research directions.
Joint Embedding Learning and Low-Rank Approximation: A Framework for Incomplete Multiview Learning

In real-world applications, not all instances in the multiview data are fully represented. To deal with incomplete data, incomplete multiview learning (IML) rises. In this article, we propose the joint embedding learning and low-rank approximation (JELLA) framework for IML. The JELLA framework approximates the incomplete data by a set of low-rank matrices and learns a full and common embedding by linear transformation. Several existing IML methods can be unified as special cases of the framework. More interestingly, some linear transformation-based complete multiview methods can be adapted to IML directly with the guidance of the framework. Thus, the JELLA framework improves the efficiency of processing incomplete multiview data, and bridges the gap between complete multiview learning and IML. Moreover, the JELLA framework can provide guidance for developing new algorithms. For illustration, within the framework, we propose the IML with the block-diagonal representation (IML-BDR) method. Assuming that the sampled examples have an approximate linear subspace structure, IML-BDR uses the block-diagonal structure prior to learning the full embedding, which would lead to more correct clustering. A convergent alternating iterative algorithm with the successive over-relaxation optimization technique is devised for optimization. The experimental results on various datasets demonstrate the effectiveness of IML-BDR.