Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of 40 Hz Low-Pass Filtering on the Magnitude of the Spatial Ventricular Gradient

Changes in the magnitude of the spatial ventricular gradient (MSVG) have been associated with an increased risk of ventricular arrhythmias. This association makes the MSVG a potentially attractive parameter for ECG monitoring applications. The MSVG is typically obtained using 150 Hz low-pass filtered resting ECGs. However, monitoring applications typically utilise upon 40 Hz low-pass filtered ECG data. The extend to which the utilization of 40 Hz low-pass monitoring ECG filters over the commonly used 150 Hz low-pass resting ECG filters does affect the MSVG value has not previously been reported. The aim of this research was to quantify the differences between MSVG values computed using 40 Hz low-pass filtered ECG data (MSVG40) and 150 Hz lowpass filtered ECG data (MSVG150). The differences between the MSVG40 and the MSVG150 were quantified as systematic error (mean difference) and random error (span of Bland-Altman 95% limits of agreement) using a study population of 726 subjects. The systematic error was found to be 0.013 mV ms [95% confidence interval: 0.008 mV ms to 0.018 mV ms]. The random error was quantified as 0.282 mV ms [95% confidence interval: 0.266 mV ms to 0.298 mV ms]. Our findings suggest that it is possible to record accurate MSVG values in monitoring applications that require the utilization of 40 Hz low-pass filtered ECG data.
Pictorial review Magnetic resonance cholangiography in children

Magnetic resonance cholangiography has been shown to be useful in the evaluation of hepatobiliary problems in paediatric patients. Its clinical application in biliary atresia, choledochal cyst, cholelithiasis, bile plug syndrome and liver transplant are illustrated by the following cases. Magnetic resonance cholangiography (MRC) is now establishing itself as a viable alternative to endoscopic retrograde cholangiopancreatography (ERCP) in adults, particularly in the diagnosis of choledocholithiasis. In children, even in experienced hands, ERCP is difficult and hazardous, with a 90% success rate and a 33% complica- tion rate compared with adults. Therefore, non-invasive and non-irradiating imaging investigations are important for patient's management. Currently most centres would use ultrasound and/or MRC as non-invasive investigation in hepatobiliary problem in paediatric patients.
Selective Attention: Effects of perceptual load on visual tasks of attention

In the present study, Lavie’s perceptual load theory was tested to settle a long-standing debate between early selection theories, implying that only the attended stimuli are processed because of limited capacity, and late selection theories, implying that all information is fully processed but only selected stimuli are given access to further processing stages such as memory. According to Lavie’s theory, it depends on perceptual load whether information is processed early or late. It was anticipated that during high perceptual load (difficult task), early selection would be the chosen strategy and during low perceptual load (easier task), the chosen strategy would be late selection. Participants consisted of 10 females and 10 males (age 18-23 years), who were undergraduate university students. Participants were tested individually using a computeradministered visual selection task with three levels of difficulty, defined by inter-stimulus interval (speed) and Target Type. Repeated measures MANOVA showed significant main effects of inter-stimulus interval and Target Type for all three assessment factors (reaction time, hit rate and false alarm rate). The hypothesis was confirmed, which supports Lavie’s theory that when a task is more difficult and there is only sufficient capacity to attend to relevant stimuli, early selection is used. However, when the task is easier and all information can be attended to, late
Using the Scattering Transform to Predict Stratigraphic Units from Well Logs

Applications of machine learning (ML) have become ubiquitous across many domains in both academia and industry. Voice and facial recognition are now robust features in smart phones and cameras, while text learning and behaviour tracking on the internet have created a commodity market for data. Self-driving cars and spatially aware robotics are both machine learning techologies that have received considerable publicity. Recommendation engines on social media, commercial websites, and internet advertisements are everyday examples of the impact of machine learning. ML is helping biologists to progress genomics research (Libbrecht et al., 2015) and help increase the throughput of microscopic cell analysis (Sommer and Gerlich, 2013). Uses of machine learning to aid in medical diagnoses is a particular high-impact field of research (Kononenko, 2001). ML is also impacting geophysics work flows and interpretation software. Russell and Hampson (1997) use machine learning to predict well log properties from seismic attributes and Meldahl et al. (2001) use a neural network to classify gas chimneys in seismic images. Both technologies are available in interpretation software packages, Hampson and Russell and OpendTect respectively.
Understanding Developer Commenting in Code Reviews

Modern code review is a well-known practice to assess the quality of software where developers discuss the quality in a web-based review tool. However, this lightweight approach may risk an inefficient review participation, especially when comments becomes either excessive (i.e., too many) or underwhelming (i.e., too few). In this study, we investigate the phenomena of reviewer commenting. Through a large-scale empirical analysis of over 1.1 million reviews from five OSS systems, we conduct an exploratory study to investigate the frequency, size, and evolution of reviewer commenting. Moreover, we also conduct a modeling study to understand the most important features that potentially drive reviewer comments. Our results find that (i) the number of comments and the number of words in the comments tend to vary among reviews and across studied systems; (ii) reviewers change their behaviours in commenting over time; and (iii) human experience and patch property aspects impact the number of comments and the number of words in the comments. key words: modern code review, review comments, mining software repositories, empirical study, machine learning
Effort-driven Fact Checking

The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia, each storing millions of facts about society in general, and specific domains, such as politics or medicine. The open nature of the Web, with content potentially being generated by everyone, however, leads to inaccuracies and misinformation, such as fake news and exaggerated claims. Construction and maintenance of a knowledge base thus relies on fact checking, assessing the credibility of facts. Due to the inherent lack of ground truth information, fact checking cannot be done in a purely automated manner, but requires human involvement. In this paper, we propose a framework to guide users in the validation of facts, striving for a minimisation of the invested effort. Specifically, we present a probabilistic model to identify the facts for which manual validation is most beneficial. As a consequence, our approach yields a high quality knowledge base, even if only a sample of a collection of facts is validated. Our experiments with three large-scale datasets demonstrate the efficiency and effectiveness of our approach, reaching levels of above 90% precision of the knowledge base with only a third of the validation effort required by baseline techniques.
Advances in the study of arrhythmogenic mechanisms

Arrhythmia is a common complication of cardiovascular diseases and a risk factor for human health.Especially,ventricular tachycardia and ventricular fibrillation may not only exacerbate original heart diseases,but also cause cardiac sudden death which has been an important death reason in China.However,anti-arrhythmic drugs nowadays cannot effectively treat these arrhythmias,with an efficiency of only 30%-60% ,which indicates that our knowledge about arrhythmias is limited.Hence,to explore the potential mechanism,look for novel targets,and develop drugs with multiple-channel action are the focus of the research direction.Recent studies displayed that the atrial-specific potassium channels such as IKur and IKAch were involved in atrial fibrillation,which provided a prospective target for atrial fibrillation treatment.Calcium leak,gap junction protein and autoantibody against ICaL channel were shown to participate in arrhythmogenesis.These findings provided a theoretical basis for the development of more effective anti-arrhythmic drugs.Remarkably,as a kind of important RNA regulating gene expression,microRNA(miRNA) was shown to possess anti-arrhythmic activities which may prevent cardiac sudden death.miR-1,miR-133 and miR-590 regulated the arrhythmia in various types of animal models.Because of the multiple-gene regulation actions of miRNA,it has the potential to be developed as novel anti-arrhythmic target.
Being like you improves my comfort space: social human-virtual confederates interactions

This study aims to explore how the comfort distance between a person and a virtual avatar (interpersonal comfort-space, IPS) is modulated by body characteristics during a social interaction. Once immersed in a virtual scenario, participants could either see virtual agents approaching them (passive condition) or moved towards them (active condition). In both conditions, participants had to press a button as soon as they felt that the distance between them and the avatar was uncomfortable. Importantly, participants could have similar or different (i.e. smaller) body-size compared to the avatar. Results showed that participants with different body size preferred larger distances as compared to those with similar body size, particularly in the passive condition. Finally, the different body size participants in the passive condition showed a gradual increase of comfort-dis-tance with the decrease of their arms’ length. These results suggest that the possibility of control modulates IPS: people prefer a larger comfort-distance when can only observe virtual agents with a big body size approaching them. Therefore, the IPS increase may reflect an evolutionary mechanism of self-protection that determines an automatic avoidance reaction to a potential violation of the near body space.
Use OpenFOAM coupled with Finite and Boundary Element Formulations for Computational Aero-Acoustics for Ducted Obstacles

In the coming years, more hybrid and full electric vehicles will be coming to the market and some new challenges will arise for noise and vibration engineers. One of them is the flow induced noise within ducts and vents in HVAC systems leading potentially to customer complains. Several numerical approaches are possible to simulate this problem. First, using a compressible form of equation of state for the fluid, the hydrodynamic source mechanisms and the sound propagation in a compressible liquid domain can be computed directly. In parallel, hybrid methods can also be applied assuming the decoupling of noise generation and propagation. First, an incompressible turbulent flow field is performed delivering aeroacoustic equivalent sources. Secondly, several Acoutic Analogies Finite or Boundary Element formulations are solved in the Fourier space. To save some CPU time and some space disk, the aero-acoustic source terms are transferred from the fluid mesh to the acoustic mesh by a conservative algorithm to guaranty the quality of the aeroacoustic phenomena on the fly, i.e. at the end of each time step during the CFD computation. This approach appears to be very suitable for industrial applications and is applied to a 3D ducted diaphragm with a low Mach number flow. The numerical results are compared between the Curle’s BEM analogy for which only the dipole sources are taken into account through a Fluctuating Surface Pressure representation, both FEM Ribner and Lighthill analogies with volume field source representations and experimental measurements. Even if very good agreements are found in terms of flow dynamic and acoustic results with all the methods compared with measurements (PIV for the flow and downstream microphone for the acoustic), the FEM Ribner or Lighthill’s acoustic analogies are revealed to be the best candidate for industrial applications using less computational resources and a full 3D description of the aero-acoustic sources.
A Fission Yeast Bioassay for Rapid Screening for Antiproliferative Agents from Bauhinia variegata and Leptadenia reticulata

The aim of the present study was to evaluate the antiproliferative activity of crude extracts from leaves of Bauhinia variegata and Leptadenia reticulata using fission yeast Schizosaccharomyces pombe as a model. The crude extracts of both plants were prepared by three experimental conditions (25°C, elevated temperature and ultrasound waves) and four solvent systems [Water, Methanol, Water: Methanol (1:1) and Chloroform: Methanol (1:1)]. MTT assay was performed for estimating the cell viability and the results were expressed as % growth inhibition. Overall based on the MTT assay performed on yeast cells, Water: Methanol, Methanol and Water extracts showed higher antiproliferative activity than the positive control (Paclitaxel) in a dose-dependent manner. For Bauhinia variegata and Leptadenia reticulata, methanol extract (25°C temperature & elevated temperature, respectively) exhibited highest inhibition which was approximately 80%. In conclusion, this study has indicated that Bauhinia variegata and Leptadenia reticulata extracts possess strong antiproliferative activity. Furthermore, it could act as a potential source for identifying new molecules with chemotherapeutic potential against cancer.
What Happened to Depth after Pre-SDM ?

True amplitude preserved seismic image, located in its correct spatial position in the sub-surface is essential for success of any E&P activity. Recent advances in Pre-SDM technology with their capability to include heterogeneity and anisotropy, essential for producing amplitude preserved image in its true spatial position, hold great promise. Images produced by isotropic PSDM generally do not match with well tops and mis-match can be of the order of 5%-10% in presence of anisotropy. To match the isotropic PSDM image with well markers the velocity field used for focusing needs to be scaled in the light of well velocity in the area to obtain a new scaled velocity field. This however is no trivial exercise. The most appropriate solution perhaps is to do Anisotropic PSDM (APSDM). In the present paper we show some examples of 2D/3D APSDM from western offshore to demonstrate that inclusion of anisotropy and vertical heterogeneity reduces the depth uncertainty drastically. We also discuss the issue of offsetdepth ratio, which is crucial to achieve the vertical positioning accuracy of the order of seismic resolution; which may be one of the major PSDM objectives for deciding step-out wells in producing fields.
VisualWorldDB: A DBMS for the Visual World

Many recent video applications—including autonomous driving, traﬃc monitoring, drone analytics, large-scale surveillance networks, and virtual reality—require reasoning about, combining, and operating over many video streams, each with distinct position and orientation. However, modern video data management systems are largely designed to process individual streams of video data as if they were independent and unrelated. In this paper, we present VisualWorldDB, a vision and an initial architecture for a new type of database management system optimized for multi-video applications. VisualWorldDB ingests video data from many perspectives and makes them queryable as a single multidimensional visual object . It incorporates new techniques for optimizing, executing, and storing multi-perspective video data. Our preliminary results suggest that this approach allows for faster queries and lower storage costs, improving the state of the art for applications that operate over this type of video data.
BULGARIAN ACADEMY OF SCIENCES

Ethnicity is a key demographic attribute of human beings and it plays a vital role in automatic facial recognition and have extensive real world applications such as Human Computer Interaction (HCI); demographic based classification; biometric based recognition; security and defense to name a few. In this paper, we present a novel approach for extracting ethnicity from the facial images. The proposed method makes use of a pre trained Convolutional Neural Network (CNN) to extract the features, then Support Vector Machine (SVM) with linear kernel is used as a classifier. This technique uses translational invariant hierarchical features learned by the network, in contrast to previous works, which use hand crafted features such as Local Binary Pattern (LBP); Gabor, etc. Thorough experiments are presented on ten different facial databases, which strongly suggest that our approach is robust to different expressions and illuminations conditions. Here we consider ethnicity classification as a three class problem including Asian, African-American and Caucasian. Average classification accuracy over all databases is 98.28%, 99.66% and 99.05% for Asian, African-American and Caucasian respectively. All the codes are available for reproducing the results on request.
Personality change and associated factors in patients with mild cognitive impairment

Background: Previous studies have shown that a history of personality change might guide the diagnosis of early mild cognitive impairment (MCI), which would lead to earlier treatment and better outcome of dementia. Objectives: To investigate personality change and other factors associated with MCI. Methods: This cross-sectional descriptive study was conducted by recruiting information from 83 patients with MCI at King Chulalongkorn Memorial Hospital using Thai version of questionnaires about demographic data, the Thai Mental State Examination (TMSE), the Montreal Cognitive Assessment (MoCA), the Thai Geriatric Depression Scale, the Neuropsychiatric Inventory Questionnaire (NPI-Q) and the International Personality Item Pool (IPIP). The patients’ caregivers were also asked to evaluate the patients’ personality during the 5 years prior to the diagnosis and their current personality. The statistical analyses were frequency, percentage, mean and t test. Analysis of factors correlated with personality change was performed with logistic regression. Results: Eighty-three patients were included, showing a mean age of 72.1  7.1 years old with 61.4% of them being female. The mean TMSE was 27.6 1.9 and the mean MoCA was 21.6  3.1. Personality change in patients with MCI showed a statistically significant increase in neuroticism and a decrease in openness to experience, agreeableness and conscientiousness (P < 0.05). There was a negative correlation between emotional stability, agreeableness, and NPI-Q (r = 0.442 and 0.227) (P < 0.001 and P = 0.039). A positive correlation between extraversion and MoCA was found (r = 0.336) (P = 0.002). Neuroticism was detected to have a correlation with neuropsychiatric symptoms (NPS), including irritability, anxiety, depression, apathy, disinhibition, and agitation. There was a significant correlation between agreeableness and NPS (apathy) (P < 0.05). Conclusions: Our findings suggest that there was personality change in MCI patients, featuring an increase in neuroticism, openness to experience, and agreeableness and a decrease in extraversion and conscientiousness. Extraversion was found to be positively correlated with cognitive functions and there was a positive and negative correlation with NPS among neuroticism and agreeableness, respectively.
“Yes, I can”: what is the role of perceived self-efficacy in violent online-radicalisation processes of “homegrown” terrorists?

ABSTRACT Radicalisation is influenced by a multitude of factors such as situational, social and psychological factors, including social-cognitive processes. This article explores how homegrown extremists are influenced by their perceived agency and how the beliefs of their own abilities to change their situation are directly shaped by the online-propaganda they consume using ISIS propaganda as a case study. The article serves as an exploratory analysis of the potential explanatory qualities of Bandura’s theory of self-efficacy. This preliminary theoretical work explores how online-propaganda seeks to increase perceived personal self-efficacy to inspire action. The findings indicate that an increased focus on agency beliefs may facilitate a more holistic understanding of the psycho-social processes influencing radicalization and factors driving certain individuals to perpetrate violence while others do not. More research needs to be conducted, but this work is a first exploratory step in advancing our understanding of self-efficacy beliefs in the radicalization of homegrown extremists.
Image Versus Information

We study a Principal interacting with image-conscious agents in a general public-goods provision context. Agents have private signals about the quality of the public good which, when suitably aggregated, precisely reveal its social value. Each chooses how much to contribute, based on his own mix of public-spiritedness and reputational concern for appearing prosocial. The Principal can amplify or dampen these reputational payoffs by making individual behavior more or less visible to the community. While this entails no direct cost, it entails an endogenous, informational one: because societal preferences evolve, the Principal knows only imperfectly the social value of the public good and the importance attached by agents to social esteem or sanctions. Learning about public good quality is important for choosing her own contribution, matching rate or other policy (e.g., law). If the Principal suppresses image motivations by making contributions or compliance anonymous, she can precisely infer quality from agents’ aggregate behavior. However, each of them will free-ride to a greater extent, leaving her with a greater burden in achieving optimal provision. If she leverages social image to encourage prosocial behavior, on the other hand, she faces a signal-extraction problem, not knowing whether to attribute compliance to image concerns or to information. We analyze how the socially optimal degree of privacy/publicity and Principal’s matching rate vary with her and agents’information structures, as well as with the variability of aggregate and individual preferences. We show in particular that in a fast-changing society (greater variability in the “fundamental” or the image-motivated component of average preferences), privacy should be greater than in a more static or traditional one, where preferences over public goods vary mostly across individuals.