Miracle-dz/UniFAR-FTUs huggingface dataset:
Bayesian reconstruction of R-fMRI from K-T undersampled data using a robust, subject-invariant, spatially-regularized dictionary prior

Faster resting-state functional magnetic resonance imaging (R-fMRI) can improve spatiotemporal resolution and functional sensitivity. To speedup scans, current methods rely on complex pulse-sequence design or straightforward undersampling along with (weak) priors on the signal. We propose a Bayesian graphical R-fMRI reconstruction framework relying on learning data-adaptive prior models through dictionaries that we design to be robust to large physiological fluctuations typical in R-fMRI signals. Our dictionary adapts to multiple subjects through an optimal similarity transform. Our reconstructions on simulated and real-world R-fMRI give more accurate functional networks and better spatial resolution than the state of the art.
Cascade Centrality with heterogeneous nodal influence in a noisy environment

Centrality is a fundamental building block in network analysis. Existing centrality measures focus on the network topology without considering nodal characteristics. However, this ignorance is perilous if the cascade payoff does not grow monotonically with the size of the cascade. In this paper, we propose a new centrality measure, Cascade Centrality, which integrates network position, the diffusion process, and nodal characteristics. It nests and spans the gap between degree, eigenvector, Katz, and diffusion centrality. Interestingly, when $p\lambda_1 > 1$, eigenvector, Katz and diffusion centrality all collapse to Cascade Centrality with a scaling factor determined by the distribution of eigenvector and the nodal influence vector. Furthermore, the presence of homophily in social networks enables the de-noising of real-world observations. Therefore, we propose a unified framework to simultaneously learn the actual nodal influence vector and network structure with an iterative learning algorithm. Experiments on synthetic and real data show that Cascade Centrality outperforms existing centrality measures in generating cascade payoffs. Moreover, with the proposed algorithm, the de-noised centrality measure is more correlated with the actual Cascade Centrality than merely computing from the observations. Cascade Centrality can capture more complex behaviors and processes in the network and has significant implications for theoretical studies in influence maximization and practical applications in viral marketing and political campaign.
Stress and coping among consultant physicians working in Saudi Arabia

BACKGROUND Exposure to stressful working conditions without adequate stress-coping strategies may lead to stress and even psychiatric morbidity. There are a dearth of studies on stress-coping strategies among consultant physicians in the Arabian Gulf. OBJECTIVES Determine stress-coping strategies among consultants in Saudi Arabia and the relationship of strategies to level of stress. DESIGN Analytical cross-sectional study. SETTINGS Conducted between November 2014 and March 2015 among physician consultants registered at the Saudi Commission for Health Specialties. SUBJECTS AND METHODS Text messages were used to directly ask consultants to complete an online questionnaire. MAIN OUTCOME MEASURES The 28-item Brief COPE inventory and the Perceived Stress Scale. SAMPLE SIZE 582. RESULTS The consultants were largely males (71%) and Saudi (56%), and their mean age was 46.9 (7.9) years. Adaptive stress-coping strategies were more frequently used than maladaptive stress-coping strategies (68% versus 49%). Stress levels were positively correlated with maladaptive stress-coping strategies (r=0.41, P<.001) and negatively correlated with adaptive stress-coping strategies (r=-0.09, P=.026). Religion was the most frequently reported stress-coping strategy (79.6%) while alcohol drinking or substance use was the least frequently reported stress-coping strategy (28.0%). Females used both adaptive and maladaptive stress-coping strategies more than males (P=.002 and P<.001, respectively). Stress management education/training was positively associated with frequent use of adaptive stress-coping strategies. CONCLUSIONS Physician consultants generally cope well with work stressors. Nevertheless, there is still a critical need for stress management programs targeting consultants in order to further improve coping strategies. LIMITATIONS The low response rate may negatively impact the validity and the generalizability of the current findings. The cross-sectional study design precluded the finding of any causal association.
Face Recognition in Low Quality Images: A Survey

Low-resolution face recognition (LRFR) has received increasing attention over the past few years. Its applications lie widely in the real-world environment when high-resolution or high-quality images are hard to capture. One of the biggest demands for LRFR technologies is video surveillance. As the the number of surveillance cameras in the city increases, the videos that captured will need to be processed automatically. However, those videos or images are usually captured with large standoffs, arbitrary illumination condition, and diverse angles of view. Faces in these images are generally small in size. Several studies addressed this problem employed techniques like super resolution, deblurring, or learning a relationship between different resolution domains. In this paper, we provide a comprehensive review of approaches to low-resolution face recognition in the past five years. First, a general problem definition is given. Later, systematically analysis of the works on this topic is presented by catogory. In addition to describing the methods, we also focus on datasets and experiment settings. We further address the related works on unconstrained low-resolution face recognition and compare them with the result that use synthetic low-resolution data. Finally, we summarized the general limitations and speculate a priorities for the future effort.
Crowdsourcing Analytics With CrowdCur

We propose to demonstrate CrowdCur \xspace, a system that allows platform administrators, requesters, and workers to conduct various analytics of interest. CrowdCur \xspace includes a worker curation component that relies on explicit feedback elicitation to best capture workers' preferences, a task curation component that monitors task completion and aggregates their statistics, and an OLAP-style component to query and combine analytics by a worker, by task type, etc. Administrators can fine tune their system's performance. Requesters can compare platforms and better choose the set of workers to target. Workers can compare themselves to others and find tasks and requesters that suit them best.
Metrological evaluation of modeling method between Kinect Fusion and 3D Scan with Kinect V2

In this paper an experimental study of real-time modeling method with Kinect Fusion and 3D Scan respectively was carried out from the visual effect and the accuracy of scanning to obtain point cloud data. Cauliflower, pitaya and carrot, which size, shape, and surface roughness are all different, were selected as the research crops and Kinect V2 (Kinect for windows V2) developed by Microsoft was selected as the sensor. The acquisition environment was designed and processed to remove a lot of noise points around the scene. The experimental results show that the visual effect is the best and the modeling time is the shortest with Kinect Fusion method when the distance between object and Kinect is 83.5cm, VMIW is 200, and VVPM is 512. According to the global fluctuations at different angles of the three crops' point cloud data, it can be concluded that the accuracy of point cloud data is higher, and the details of the objects is more complete and detailed obtained with Kinect Fusion method than with 3D Scan method. However, with the improvement of surface roughness the capability of modeling method is reduce. The proposed modeling method based on Kinect V2 can be used as reference for other crops.
Fenfluramine diminishes NMDA receptor-mediated seizures via its mixed activity at serotonin 5HT2A and type 1 sigma receptors

Fenfluramine exhibits antiepileptic properties and thus diminishes epileptiform discharges in experimental animal models of Dravet syndrome. Fenfluramine is metabolized into norfenfluramine in vivo, which shows greater affinity and agonist activity at serotonin 5HT2 receptors (5HT2R) than fenfluramine. In this study, we found that fenfluramine and norfenfluramine disrupted the regulatory association of the sigma 1 receptor (σ1R) with NR1 subunits of glutamate N-methyl-D-aspartate receptors (NMDAR), an effect that was also produced by σ1R antagonists such as S1RA and prevented by σ1R agonists such as PPCC. The antagonists removed σ1R bound to NMDAR NR1 subunits enabling calcium-regulated calmodulin (CaM) to bind to those subunits. As a result, CaM may inhibit calcium permeation through NMDARs. The serotoninergic activity of fenfluramine at 5HT2AR, and likely also at 5HT2CR, collaborated with its activity at σ1Rs to prevent the convulsive syndrome promoted by NMDAR overactivation. Notably, fenfluramine enhanced the inhibitory coupling of G protein-coupled receptors such as 5HT1AR and cannabinoid type 1 receptor with NMDARs, thus allowing the more effective restrain of NMDAR activity. Thus, fenfluramine circumvents the negative side effects of direct NMDAR antagonists and may improve the quality of life of subjects affected by such proconvulsant dysfunctions.
A fourier spectral pattern analysis to design credit scoring models

The increase of consumer credit has made it necessary to research more and more effective models for the credit scoring. Such models are usually trained by using the past loan applications, evaluating the new ones on the basis of certain criteria. Although the state of the art offers several different approaches for their definition, this process represents a hard challenge due to several reasons. The most important ones are the data unbalance between the default and the non-default cases that reduces the effectiveness of almost all techniques, and the data heterogeneity, which makes it difficult the definition of a model able to effectively evaluate all the new loan applications. The approach proposed in this paper faces the aforementioned problems by moving the evaluation process from the canonical time domain to a frequency one, using a model based on the past non-default loan applications. It allows us to overcome the data unbalance problem by exploiting only a class of data, also defining a model that is less influenced by the data heterogeneity. The performed experiments show interesting results, since the proposed approach achieves performance closer or better than that of one of the best state-of-the-art approaches of credit scoring, such as random forests, although it operates in a proactive way, only by exploiting the past non-default cases.
Learning To Assess School Mathematics: Context, Multimedia and Transfer

The study set out to explore the use of a multimedia program on assessment strategies within a preservice teacher mathematics method unit, and to investigate the extent of transfer to classroom practice. A multimedia program was designed to incorporate characteristics of ‘situated’ learning environments. Preservice teachers used the program in their mathematics method classes, and they and their supervising teachers were interviewed regarding their use of assessment strategies while on professional practice. Results show that all students used a variety of assessment strategies, and according to the beliefs of the students themselves, were influenced in their use of strategies by the multimedia learning environment.
#MeToo: Neural Detection and Explanation of Language in Personal Abuse Stories

The detection and classification of domestic abuse stories shared online has ever-increasing importance in today’s social activism sphere. With massive numbers of stories shared, automatic detection can aggregate stories from around the internet and help push forward the fight against domestic abuse from a social campaign to social change. We develop CNN, LSTM-RNN, and CNN-LSTM neural models to detect domestic abuse stories in the Reddit Domestic Abuse dataset. We achieved 95.8% accuracy in classifying posts as containing abuse stories versus not containing abuse stories, outperforming the current state-of-the-art. More importantly, we next present sentimentonly classification feasibility as well as interpretable and explainable analysis of the neural model’s predictions using activation clustering techniques to automatically discover linguistic features.
EPUTION at SemEval-2018 Task 2: Emoji Prediction with User Adaption

This paper describes our approach, called EPUTION, for the open trial of the SemEval- 2018 Task 2, Multilingual Emoji Prediction. The task relates to using social media — more precisely, Twitter — with its aim to predict the most likely associated emoji of a tweet. Our solution for this text classification problem explores the idea of transfer learning for adapting the classifier based on users’ tweeting history. Our experiments show that our user-adaption method improves classification results by more than 6 per cent on the macro-averaged F1. Thus, our paper provides evidence for the rationality of enriching the original corpus longitudinally with user behaviors and transferring the lessons learned from corresponding users to specific instances.
Utilising GPS Data to Quantify Torso Range of Motion in Field Hockey Athletes

Global Positioning System (GPS) along with micro-electrical mechanical system (MEMS), technology is now commonplace in today’s sporting environment. Little has been published utilising this combination of technologies to quantify the postures of athletes during match-play. This paper presents the results of a preliminary investigation into developing a methodology that allows practitioners to use the technology readily available to them to quantify the postural demands of field hockey players during match-play.
Cyber-Physical Scheduling for Predictable Reliability of Inter-Vehicle Communications

Predictable reliability of inter-vehicle communications is a basis for the paradigm shift from single-vehicle-oriented safety and efficiency control to networked vehicle control. The lack of predictable interference control in existing vehicular communication mechanisms, however, makes them incapable of ensuring predictable communication reliability. To address the gap, we propose the Cyber-Physical Scheduling (CPS) framework that leverages the PRK interference model and addresses the challenges of vehicle mobility and broadcast to PRK-based scheduling. To address vehicle mobility, CPS leverages the physical locations of vehicles to define the gPRK interference model, a geometric approximation of the PRK model, for lightweight control signaling and effective interference relation estimation, and CPS leverages cyber-physical structures of vehicle traffic flows for effective instantiation and use of the gPRK model. To support predictable broadcast, CPS controls the interactions between gPRK model adaptations of the links of the same sender to ensure predictable broadcast reliability in the presence of vehicle mobility. Through experimental analysis with high-fidelity ns-3 and SUMO simulation, we observe that CPS enables predictable reliability while achieving high throughput and low delay in communication. To the best of our knowledge, CPS is the first field-deployable method that ensures predictable interference control and thus reliability in inter-vehicle communications.
A Survey of Aquatic Invertebrate Communities in Nebraska Sandhill Lakes Reveals Potential Alternative Ecosystem States

Abstract Aquatic invertebrate communities are important to shallow lake ecosystem form and function, providing vital components to the food web and thereby important to achieving lake management goals. We characterized lake invertebrate communities and physicochemical variables in six Nebraska Sandhill lakes and examined these characteristics within an alternative stable state framework. Surveys were conducted during 2005 within each of these lakes by sampling aquatic macroinvertebrate abundance, zooplankton abundance and biomass, phytoplankton biomass, and physicochemical variables. When placed within an alternative stable state framework, the response variables exhibited a gradient of different ecosystem states. Two lakes appeared congruent with the clear water state (dense submergent vegetation, high invertebrate abundance and diversity, and low phytoplankton), two lakes were congruent with the turbid water state (high phytoplankton, low vegetation coverage, and low invertebrate abundance and diversity.
Efficacy of ferrous bis-glycinate versus ferrous glycine sulfate in the treatment of iron deficiency anemia with pregnancy: a randomized double-blind clinical trial

Abstract Objective: The aim of this study is to compare the efficacy and tolerability of oral ferrous bis-glycinate versus ferrous glycine sulfate in the treatment of iron deficiency anemia (IDA) with pregnancy. Methods: A randomized double-blind clinical trial (NCT02590224) conducted at a tertiary University Hospital in the period between 1 January 2016 and 31 July 2017 included pregnant women at 14–18 weeks of gestation with mild to moderate IDA. Patients were randomized into two groups: (Group I) received oral ferrous bis-glycinate tablets once daily for eight consecutive weeks and (Group II) received oral ferrous glycine sulfate capsules in the same dose and duration. The primary outcome of the study was the rate of increase of hemoglobin (HB) level after 8 weeks of iron treatment. Results: The study included 187 women in the final analysis. The mean increase in HB level after 8 weeks of treatment in ferrous bis-glycinate group was 2.48 ± 0.12 g/dL versus 1.32 ± 0.18 g/dL in ferrous glycine sulfate group (p ≤ .0001). The percentage of women with HB level more than 11 g/dL after 8 weeks of treatment was 89.2% in ferrous bis-glycinate group versus 71.3% in ferrous glycine sulfate group (p < .0001). The rate of adverse effects was significantly higher in ferrous glycine sulfate group (p = .001). Conclusions: Pregnant women with second trimester IDA could be supplied with ferrous bis-glycinate which is more efficient in increasing HB level. Moreover, it has tolerable adverse effects and high compliance than ferrous glycine sulfate.
The influence of ethnicity in facial gender estimation

Facial gender estimation is very famous nowadays. It has been widely applied in many applications such as biometrics, targeted advertising and human-computer interaction. Malaysia is a developed country that begins to utilize the mentioned technology. Until today, there are plenty of algorithms for facial gender estimation have been developed. For our best knowledge, no researcher ever publish a benchmark of existing algorithms with Malaysian ethnics' database. Moreover, there is no publicly available Malaysia ethnics' database. Hence, the performance of the existing algorithms towards Malaysian is unknown. Other than that, different ethnics have differences in physical look. Malaysia is a multiracial country that consists of multiple races. Thus, Malaysian ethnics may look different with other ethnics such as westerners for computer point of view. The paper provides an experimental study to shows the gender estimation performance of the existing algorithms towards Malaysian ethnics. Most importantly is to find out the influence of ethnicity in gender estimation. To find out the influences of ethnics, Facial Recognition Technology (FERET) Database and Malaysian Ethnics Facial Database (MEFD) are used to represent non-Malaysian ethnics and Malaysian ethnics. Two feature extraction methods namely Principle Component Analysis (PCA) and Multi-Level Local Binary Patterns (ML-LBP) are chosen to conduct the experiments independently with Support Vector Machine (SVM) as a classifier. All the experiments are conducted without face alignment. The result shows that ML-LBP achieved better accuracy than PCA in gender estimation for both Malaysian ethnics and non-Malaysian ethnics. Last but not least, the ethnicity does affect the gender estimation. The training set has to involve the target's ethnicity in order to have a good accuracy.
Blood Shift During Cough: Negligible or Significant?

Rationale: It was reported how forceful rhythmic coughing can provide effective blood flow during ventricular fibrillation without direct chest compression. This mechanism of cough-assisted cardiopulmonary resuscitation constitutes a form of “cardiac massage” secondary to the intrathoracic and intra-abdominal pressure changes during cough. We have previously shown that significant blood shifts (BSs) occurs from the thorax to the extremities during expulsive maneuvers and that abdominal pressure controls the outflow of blood from the splanchnic vasculature. This mechanism was called abdominal circulatory pump. BS was quantified by using double body plethysmography (DBP), which combines total body plethysmography and opto-electronic plethysmography. Aim: We hypothesized that coughing activates also the abdominal circulatory pump, being an additional mechanism that displaces a circulatory output sufficient to maintain consciousness in a patient with a non-beating heart. Methods and Results: We studied seven healthy subjects (age: 28.6 ± 2.5 years) during series of voluntary coughs at three different operating volumes: after a spontaneous tidal volume, at total lung capacity (TLC) and at an intermediate volume. BS from the thorax to the extremities were measured by DBP during quiet breathing and during cough at each operating lung volume. BS during cough resulted significantly higher than during quiet breathing (p < 0.05). During the compressive phase, the blood outflow is around 200 ml, whereas during the expulsive phase BS increased (p < 0.05) with increasing operating volume, being almost 700 ml at TLC. At lower operating volume it is almost 400 ml. Conclusion: Deep, vigorous coughing and the consequent fluctuations in intra-thoracic and intra-abdominal pressure activate both the thoracic and the abdominal pump mechanism. The former leads the low-resistance pulmonary veins to empty into the left heart. The latter can generate a circulatory output from the splanchnic region, which acts as a blood reservoir, to other body tissues. These findings might help to better understand the cardiopulmonary interactions during cough, particularly in patients with unstable cardiac function, and the mechanism by which coughing during unstable cardiac rhythms can maintain consciousness in human subjects.
Accurate 3D PSF estimation from a wide-field microscopy image

The 3D point-spread function (PSF) plays a fundamental role in wide-field fluorescence microscopy. An accurate PSF estimation can significantly improve the performance of deconvolution algorithms. In this work, we propose a calibration-free method to obtain the PSF directly from the image obtained. Specifically, we first parametrize the spherically aberrated PSF as a linear combination of few basis functions. The coefficients of these basis functions are then obtained iteratively by minimizing a novel criterion, which is derived from the mixed Poisson-Gaussian noise statistics. Experiments demonstrate that the proposed approach results in highly accurate PSF estimations.