Miracle-dz/UniFAR-FTUs huggingface dataset:
Comprehensive literature data-mining analysis reveals a broad genetic network functionally associated with autism spectrum disorder

Previous studies have indicated that genetic factors are the predominate cause of Autism spectrum disorder (ASD). Nevertheless, to the best of our knowledge, to date no systematic study has summarized these data and provided an objective, complete list of genes with demonstrated associations with ASD. The present study included a literature data mining analysis of >2,064 articles including publications from January 2000 to April 2016, which identified 488 ASD target genes. Gene set enrichment analysis (GSEA), sub-network enrichment analysis (SNEA) and network connectivity analysis (NCA) were conducted to assess the functional profile and pathogenic significance of these genes. A total of 2 literature metrics were proposed to prioritize the curated ASD genes with specific significance. This approach resulted in the development of an ASD genetic database. Subsequent analysis indicated that 391 of the 488 genes were enriched in 97 biological pathways (P<1×10−8), demonstrating significant functional associations with each other. The majority of these curated ASD genes also serve significant roles in the pathogenesis of other neuropsychiatric disorders. These results suggest that the genetic causes of ASD are within a large network composed of functionally-associated genes. The genetic database, together with the metric scores developed in the present study, provides a basis for future biological/genetic modeling in the field.
Developmental changes in plasticity, synaptic, glia, and connectivity protein levels in rat medial prefrontal cortex

The medial prefrontal cortex (mPFC) plays a critical role in complex brain functions including decision-making, integration of emotional, and cognitive aspects in memory processing and memory consolidation. Because relatively little is known about the molecular mechanisms underlying its development, we quantified rat mPFC basal expression levels of sets of plasticity, synaptic, glia, and connectivity proteins at different developmental ages. Specifically, we compared the mPFC of rats at postnatal day 17 (PN17), when they are still unable to express long-term contextual and spatial memories, to rat mPFC at PN24, when they have acquired the ability of long-term memory expression and finally to the mPFC of adult rats. We found that, with increased age, there are remarkable and significant decreases in markers of cell activation and significant increases in proteins that mark synaptogenesis and synapse maturation. Furthermore, we found significant changes in structural markers over the ages, suggesting that structural connectivity of the mPFC increases over time. Finally, the substantial biological difference in mPFC at different ages suggest caution in extrapolating conclusions from brain plasticity studies conducted at different developmental stages.
Imagined Intergroup Physical Contact Improves Attitudes Toward Immigrants

In this set of research, we investigated the effects of intergroup physical contact on intergroup attitudes by relying on indirect contact strategies, namely the imagined contact paradigm. We implemented the imagined contact paradigm by leading participants to shape the mental imagery upon pictorial information. Specifically, in Study 1 participants saw a picture of a white hand touching a black hand [i.e., intergroup physical contact condition (InterPC)] or a picture of an outdoor scene (i.e., control condition), and were asked to imagine being either the toucher or in the outdoor scene, respectively. Results demonstrated that InterPC compared to control condition reduced intergroup bias. In Study 2 we compared the InterPC condition to a condition in which participants saw a white hand touching another white hand [i.e., intragroup physical contact (IntraPC)], and imagined to be the toucher. Again, we found that participants in the InterPC condition showed reduced intergroup bias compared to the IntraPC. Study 3 replicated results of Studies 1 and 2 by using an implicit measure of prejudice. Also, Study 3 further showed that asking participants to merely look at the picture of a white hand touching a black hand, without imagining being the toucher was not effective in reducing implicit prejudice. Results were discussed with respect to the literature on physical contact and prejudice reduction processes.
From Swarms to Stars: Task Coverage in Robot Swarms with Connectivity Constraints

Swarm robotics carries the potential of solving complex tasks using simple devices. To do so, however, one must define distributed control algorithms capable of producing globally coordinated behaviours. We propose a methodology to address the problem of the spatial coverage of multiple tasks with a swarm of robots that must not lose global connectivity. Our methodology comprises two layers: (i) a distributed Robot Navigation Controller (RNC) is responsible for simultaneously guaranteeing connectivity and pursuit of multiple tasks; and (ii) a global Task Scheduling Controller approximates the optimal strategy for the RNC with minimal computational load. Our contributions include: (i) a qualitative analysis of the literature on connectivity assessment, (ii) our proposed methodology, (iii) simulations in a multi-physics environment, (iv) real-life robot experiments, and (v) the experimental validation of connectivity, coverage optimality, and fault-tolerance.
The Space-Efficient Core of Vadalog

Vadalog is a system for performing complex reasoning tasks such as those required in advanced knowledge graphs. The logical core of the underlying Vadalog language is the warded fragment of tuple-generating dependencies (TGDs). This formalism ensures tractable reasoning in data complexity, while a recent analysis focusing on a practical implementation led to the reasoning algorithm around which the Vadalog system is built. A fundamental question that has emerged in the context of Vadalog is the following: can we limit the recursion allowed by wardedness in order to obtain a formalism that provides a convenient syntax for expressing useful recursive statements, and at the same time achieves space-efficiency? After analyzing several real-life examples of warded sets of TGDs provided by our industrial partners, as well as recent benchmarks, we observed that recursion is often used in a restricted way: the body of a TGD contains at most one atom whose predicate is mutually recursive with a predicate in the head. We show that this type of recursion, known as piece-wise linear in the Datalog literature, is the answer to our main question. We further show that piece-wise linear recursion alone, without the wardedness condition, is not enough as it leads to the undecidability of reasoning. We finally study the relative expressiveness of the query languages based on (piece-wise linear) warded sets of TGDs.
The rotating Navier-Stokes-Fourier-Poisson system on thin domains

We consider the compressible Navier - Stokes - Fourier - Poisson system describing the motion of a viscous heat conducting rotating fluid confined to a straight layer $ \Omega_{\epsilon} = \omega \times (0,\epsilon) $, where $\omega$ is a 2-D domain. The aim of this paper is to show that the weak solutions in the 3D domain converge to the strong solution of the 2-D Navier - Stokes - Fourier - Poisson system $\omega$ as $\epsilon \to 0$ on the time interval, where the strong solution exists. We consider two different regimes in dependence on the asymptotic behaviour of the Froude number.
Sexually transmitted infections and HIV RNA levels in blood and anogenital compartments among Thai men who have sex with men before and after antiretroviral therapy: implication for Treatment as Prevention programme

Sexually transmitted infections (STIs) are common among HIV‐positive men who have sex with men (MSM). There have been concerns that undiagnosed and untreated STIs could undermine efforts to use antiretroviral therapy (ART) for prevention due to genital secretion infectiousness. We evaluated the correlation between STIs and HIV RNA in anogenital compartments among HIV‐positive MSM before and after ART.
Teach-and-Replay of Mobile Robot with Particle Filter on Episode

A novel method for replaying behavior of a mobile robot from its memory of past experiences is presented in this paper. The method is a version of a particle filter on episode (PFoE), which applies a particle filter on the memory so as to efficiently find some similar situations with the current one. Though the original PFoE was proposed as a reinforcement learning method, we once removed the reward system from the original one so as to apply it to task teaching. In the experiment, we gave several kinds of motion to a micromouse type robot with the proposed method through a gamepad. The robot replayed the behaviors robustly with sensor feedback after several number of repetitive teaching.
Low-Energy Graph Fourier Basis Functions Span Salient Objects

There is an emerging interest aiming at defining principles for signals on general graphs, which are analogous to the basic principles in traditional signal processing. One example is the Graph Fourier Transform which aims at decomposing a graph signal into its components based on a set of basis functions with corresponding graph frequencies. It has been observed that most of the important information of a graph signal is contained inside the low frequency band, which leads to several applications such as denoising, compression, etc. In this paper, we show that the low frequency basis functions span the salient regions in an image, which can also be considered as important regions. Motivated by this, we present a novel simple and unsupervised method to utilize a number of low-energy basis functions and show that it improves the performance of seven state-of-the-art salient object detection methods in five datasets under four different evaluation criteria, with only minor exceptions.
Robust Sequence-Based Localization in Acoustic Sensor Networks

Acoustic source localization in sensor network is a challenging task because of severe constraints on cost, energy, and effective range of sensor devices. To overcome these limitations in existing solutions, this paper formally describes, designs, implements, and evaluates a Half Plane Intersection method to Sequence-Based Localization, i.e., HPI-SBL, in distributed smartphone networks. The localization space can be divided into distinct regions, and each region can be uniquely identified by the node sequence that represents the ranking of distances from the reference nodes to the region. The key idea behind HPI-SBL is to turn the localization problem into half-plane intersection by processing the node sequence. The proposed design is evaluated through extensive simulations and physical experiments in an indoor test-bed with 30 smartphone nodes. Evaluation results show that HPI-SBL can effectively locate the acoustic source with good robustness.
Incidence of unintended pregnancy among female sex workers in low-income and middle-income countries: a systematic review and meta-analysis

Objectives To determine the incidence of unintended pregnancy among female sex workers (FSWs) in low-income and middle-income countries (LMICs). Design We searched MEDLINE, PsychInfo, Embase and Popline for papers published in English between January 2000 and January 2016, and Web of Science and Proquest for conference abstracts. Meta-analysis was performed on the primary outcomes using random effects models, with subgroup analysis used to explore heterogeneity. Participants Eligible studies targeted FSWs aged 15–49 years living or working in an LMIC. Outcome measures Studies were eligible if they provided data on one of two primary outcomes: incidence of unintended pregnancy and incidence of pregnancy where intention is undefined. Secondary outcomes were also extracted when they were reported in included studies: incidence of induced abortion; incidence of birth; and correlates/predictors of pregnancy or unintended pregnancy. Results Twenty-five eligible studies were identified from 3866 articles. Methodological quality was low overall. Unintended pregnancy incidence showed high heterogeneity (I²>95%), ranging from 7.2 to 59.6 per 100 person-years across 10 studies. Study design and duration were found to account for heterogeneity. On subgroup analysis, the three cohort studies in which no intervention was introduced had a pooled incidence of 27.1 per 100 person-years (95% CI 24.4 to 29.8; I2=0%). Incidence of pregnancy (intention undefined) was also highly heterogeneous, ranging from 2.0 to 23.4 per 100 person-years (15 studies). Conclusions Of the many studies examining FSWs’ sexual and reproductive health in LMICs, very few measured pregnancy and fewer assessed pregnancy intention. Incidence varied widely, likely due to differences in study design, duration and baseline population risk, but was high in most studies, representing a considerable concern for this key population. Evidence-based approaches that place greater importance on unintended pregnancy prevention need to be incorporated into existing sexual and reproductive health programmes for FSWs. PROSPERO registration number CRD42016029185
Multiclass Network Attack Classifier Using CNN Tuned with Genetic Algorithms

Intrusion Detection Systems (IDS) are implemented by service providers and network operators to monitor and detect attacks to protect their infrastructures and increase the service availability. Many machine learning algorithms, standalone or combined, have been proposed, including different types of Artificial Neural Networks (ANN). This work evaluates a Convolutional Neural Network (CNN), created for image classification, as a multiclass network attack classifier that can be deployed in a router. A Genetic Algorithm (GA) is used to find a high-quality solution by rearranging the layout of the input features, reducing the amount of different features if required. The tests have been done using two different public datasets with different ratio of attacks: UNSW (10 classes) and NSL-KDD (4 classes). Both classifiers distinguish correctly normal traffic from attack. However, in order to correctly classify the attack, the latter works better because it can be proportionate between the different classes, obtaining a cross-validated multi-class classifier with $K$ of 0.95.
Learned Convolutional Sparse Coding

We propose a convolutional recurrent sparse auto-encoder model. The model consists of a sparse encoder, which is a convolutional extension of the learned ISTA (LISTA) method, and a linear convolutional decoder. Our strategy offers a simple strategy for learning a task-driven sparse convolutional dictionary (CD), and producing an approximate convolutional sparse code (CSC) over the learned dictionary. We trained the model to minimize reconstruction loss via gradient decent with back-propagation and have achieved competitve results to KSVD image denoising and to leading CSC methods in image inpainting requiring only a small fraction of their runtime.
Using Technology to Conduct Focus Groups With a Hard-to-Reach Population: A Methodological Approach Concerning Male Victims of Partner Abuse in Four English-Speaking Countries

Research shows that the experiences of male victims of partner abuse (PA) are often denied by the public and the professionals who are charged to support PA victims. Recruiting female victims for research on PA victimization is relatively easy because there are existing structures to serve this group of victims. Thus, male victims are considered a hard-to-reach (HTR) population, and studying them can be difficult. This article focuses on the use of technology to collect qualitative data from male PA victims in an international study focusing on male victims. The researchers used their own professional networks to recruit and screen a convenience sample of male victims of female-to-male PA, in four different English-speaking countries: Australia, Canada, England, and the United States. Four web-based, video-enabled, focus groups were held for each country—for a total of 12 groups and 41 male participants. This article addresses recruitment methods, the use of technology in data collection, protecting the confidentiality of male victims, methods for informed consent, and lessons learned to facilitate future research.
Assessment of E-Learning Readiness in South African Schools

The delivery of education and/or curriculum has shifted from the traditional method of curriculum delivery to a digital format of delivery using available technology. However, without eLearning readiness, eLearning benefits will not be reaped and the probability of failure in adopting eLearning is high. Therefore, it is imperative to know the level of readiness of educators and learners. In this paper, e-Learning readiness of the South African educators and learners in certain section of Gauteng Schools were investigated. A twenty-nine (29) item questionnaire was used to obtain the data. The results showed that in certain section of Gauteng schools, the educators and the learners are at the expected level of e-Learning readiness. They can now adopt e-learning as a learning method as they will be able to benefit from the new learning method. In conclusion, the nominated schools in Gauteng are ready but need a few improvements.
Double-Blind, Randomized, Placebo-Controlled Phase II Dose-Finding Study To Evaluate High-Dose Rifampin for Tuberculous Meningitis

High doses of rifampin may help patients with tuberculous meningitis (TBM) to survive. Pharmacokinetic pharmacodynamic evaluations suggested that rifampin doses higher than 13 mg/kg given intravenously or 20 mg/kg given orally (as previously studied) are warranted to maximize treatment response. ABSTRACT High doses of rifampin may help patients with tuberculous meningitis (TBM) to survive. Pharmacokinetic pharmacodynamic evaluations suggested that rifampin doses higher than 13 mg/kg given intravenously or 20 mg/kg given orally (as previously studied) are warranted to maximize treatment response. In a double-blind, randomized, placebo-controlled phase II trial, we assigned 60 adult TBM patients in Bandung, Indonesia, to standard 450 mg, 900 mg, or 1,350 mg (10, 20, and 30 mg/kg) oral rifampin combined with other TB drugs for 30 days. The endpoints included pharmacokinetic measures, adverse events, and survival. A double and triple dose of oral rifampin led to 3- and 5-fold higher geometric mean total exposures in plasma in the critical early days (2 ± 1) of treatment (area under the concentration-time curve from 0 to 24 h [AUC0–24], 53.5 mg · h/liter versus 170.6 mg · h/liter and 293.5 mg · h/liter, respectively; P < 0.001), with proportional increases in cerebrospinal fluid (CSF) concentrations and without an increase in the incidence of grade 3 or 4 adverse events. The 6-month mortality was 7/20 (35%), 9/20 (45%), and 3/20 (15%) in the 10-, 20-, and 30-mg/kg groups, respectively (P = 0.12). A tripling of the standard dose caused a large increase in rifampin exposure in plasma and CSF and was safe. The survival benefit with this dose should now be evaluated in a larger phase III clinical trial. (This study has been registered at ClinicalTrials.gov under identifier NCT02169882.)
Deep Multi-class Eye Segmentation for Ocular Biometrics

Segmentation techniques for ocular biometrics typically focus on finding a single eye region in the input image at the time. Only limited work has been done on multi-class eye segmentation despite a number of obvious advantages. In this paper we address this gap and present a deep multi-class eye segmentation model build around the SegNet architecture. We train the model on a small dataset (of 120 samples) of eye images and observe it to generalize well to unseen images and to ensure highly accurate segmentation results. We evaluate the model on the Multi-Angle Sclera Database (MASD) dataset and describe comprehensive experiments focusing on: i) segmentation performance, ii) error analysis, iii) the sensitivity of the model to changes in view direction, and iv) comparisons with competing single-class techniques. Our results show that the proposed model is viable solution for multi-class eye segmentation suitable for recognition (multi-biometric) pipelines based on ocular characteristics.
Multi-hop assortativities for networks classification

Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.
BSS Eval or Peass? Predicting the Perception of Singing-Voice Separation

There is some uncertainty as to whether objective metrics for predicting the perceived quality of audio source separation are sufficiently accurate. This issue was investigated by employing a revised experimental methodology to collect subjective ratings of sound quality and interference of singing-voice recordings that have been extracted from musical mixtures using state-of-the-art audio source separation. A correlation analysis between the experimental data and the measures of two objective evaluation toolkits, BSS Eval and PEASS, was performed to assess their performance. The artifacts-related perceptual score of the PEASS toolkit had the strongest correlation with the perception of artifacts and distortions caused by singing-voice separation. Both the source-to-interference ratio of BSS Eval and the interference-related perceptual score of PEASS showed comparable correlations with the human ratings of interference.
Style Transfer Through Multilingual and Feedback-Based Back-Translation

Style transfer is the task of transferring an attribute of a sentence (e.g., formality) while maintaining its semantic content. The key challenge in style transfer is to strike a balance between the competing goals, one to preserve meaning and the other to improve the style transfer accuracy. Prior research has identified that the task of meaning preservation is generally harder to attain and evaluate. This paper proposes two extensions of the state-of-the-art style transfer models aiming at improving the meaning preservation in style transfer. Our evaluation shows that these extensions help to ground meaning better while improving the transfer accuracy.
MBLLEN: Low-Light Image/Video Enhancement Using CNNs

We present a deep learning based method for low-light image enhancement. This problem is challenging due to the difﬁculty in handling various factors simultaneously including brightness, contrast, artifacts and noise. To address this task, we propose the multi-branch low-light enhancement network (MBLLEN). The key idea is to extract rich features up to different levels, so that we can apply enhancement via multiple subnets and ﬁnally produce the output image via multi-branch fusion. In this manner, image quality is improved from different aspects. Through extensive experiments, our proposed MBLLEN is found to outperform the state-of-art techniques by a large margin. We additionally show that our method can be directly extended to handle low-light videos.