Miracle-dz/UniFAR-FTUs huggingface dataset:
Concomitant Tricuspid Annuloplasty in Functional Tricuspid Regurgitation: De Vega Suture Annuloplasty versus Ring Annuloplasty Along With Mitral Valve Replacement-A Comparative Study

Introduction : Our understanding towards the management of functional tricuspid regurgitation has evolved over time, from being of “benign” neglect to the one with huge prognostic importance. About 30% to 50% of mitral valve disease patients have significant functional tricuspid regurgitation. Functional tricuspid regurgitation when addressed along with left heart pathology is a safe procedure and results in better prognosis. Various methods of tricuspid annuloplasty have been described, the most commonly used being DeVega and Ring annuloplasties. In our present study we have attempted a comparison between the two annuloplasty techniques. Material & Methods. : Patients requiring functional tricuspid annuloplasty along with mitral valve replacement were enrolled and were randomized into two groups to receive either DeVega or Ring annuloplasty. Data from clinical examination, patient symptoms and echocardiography were collected at pre-operatively, pre-discharge, 3 and 6 months post discharge and were statistically analyzed. Results : When compared to DeVega annuloplasty we found that Carpentier Edwards rigid ring annuloplasty gives better freedom from recurrence of tricuspid regurgitation, better control of Pulmonary Arterial Hypertension, more decrease in Right Atrial transverse Diameter, better improvement in Right Ventricular function (TAPSE), better functional status of patients and same mortality percentage. The only downside of using Carpentier Edwards’s rigid ring tricuspid annuloplasty over DeVega Tricuspid annuloplasty is significantly longer operating time Conclusions : For a post-operative period of 6 months the Carpentier Edwards rigid ring tricuspid annuloplasty appears to be superior to DeVega Tricuspid annuloplasty for addressing functional tricuspid regurgitation when done along with mitral valve replacement.
Simple Creative Video Explainer for Teacher, Support Education in Industrial 4.0

The study aim to making videos supported by rich literacy is capable of producing creative and meaningful media. Making video explainer media can be done together with students as project assignments, so the meaningfulness of the material becomes deeper, as a form of subject collaboration with IT skills. The Metodology using instructional research and development in the Dick and Carey model. It consists of ten stages, namely: 1) Analysis of learning needs and goals. 2) Analysis of learning. 3) Analysis of learners and context. 4) Formulate performance goals. 5) Developing instruments. 6) Develop learning strategies. 7) Develop and choose learning materials. 8) Designing and conducting formative evaluations. 9) Make revisions. 10) Conduct summative evaluation. The making of this video explainer has been the author of previous research and trials and was declared feasible to be applied to the world of education through the assessment of instructional and IT aspects using a Likert scale 5. Through validation by three media expert lecturers, three material expert lecturers, three supporting teachers with a mean score of 4.05 is included in the excellent category. Furthermore, trials to students in small groups to three students, medium groups to six students and large groups to 26 students with a mean score of 4.24 fall into the excellent category.
The Potentiality of Dynamic Assessment in Massive Open Online Courses (MOOCs): The Case of Listening Comprehension MOOCs

Massive Open Online Courses (MOOCs) as a new shaking educational development provide the scene for achieving social inclusion and dissemination of knowledge. Anyhow, facilitating network learning experiences through creating an adaptive learning environment can pave the way for this open and energetic way to learning. The present study aimed to explore the possible role of Dynamic Assessment (DA) as an evaluation tool for providing an adaptive learning to the diversity of online platforms. 453 Second Language (L2) learners participated in the course. Similar to the typical MOOCs, learners watched lecture videos, answered quizzes, posted responses to forums, and communicated with others. Dissimilar to the typical MOOCs, the quizzes were prepared and assessed based on DA tenets and the learners were required to take a listening comprehension test at the end of each module. Quantitative analyses were made to find out each individual's Zone of Actual Development (ZAD), Zone of Proximal Development (ZPD), and Learning Potential Score (LPS). The findings of the study revealed that DA was promising for giving insight into the development of the learner’s listening comprehension and the assessment of listening ability in MOOC. The results of the study pinpointed that scores on what a learner could do alone and without assistance, ZAD scores, could not provide a clear picture of the learner’s listening ability, while mediation helped to reveal his/her potential abilities (ZPD) and diagnose his/her difficulties. It was also shown that DA brought to light the areas the learner needed more support and instruction, and consequently provided a profound feedback and evaluation on the learner’s learning and performance, the elements missed in MOOC.
Radiological Anatomy of the Lumbar Canal: A MRI study

Stenosis due to decreased mid-sagittal diameter has been reported in the cervical spine as well as in the lumbar spine. It has also been suggested that reduced interpedicular distance is one of the cause of primary narrowing of the spinal canal. [1] Shobeiri et al reported the prevalence of LSS as 37% among patients with sciatica and 11% among patients with low back pain (LBP) (p value < 0.001). [2] According to the North American Spinal Society (NASS), around 20% of the adult population suffers from this pathology (5% central stenosis and 15% lateral stenosis).
STUDY ON RELATIONSHIPS BETWEEN MACROSEISMIC INTENSITY AND PEAK GROUND ACCELERATION, PEAK GROUND VELOCITY IN CHINA

This paper has collected the data of approximately 793 strong ground motion records, along with a comparable macroseismic intensity data from 28 China earthquakes, 52 America earthquakes, 9 Iran earthquakes and 8 Mexico earthquakes respectively, to develop relationships between macroseismic intensity (I) and peak ground acceleration (PGA), and peak ground velocity (PGV), by using weighted least squares method of set the weight of AHP and utilizing the boxplot to test the outlier of the data. Outlier test is used to ensure that raw data is a statistical real and effective. We then derived relationships between I and each ground-motion parameter by using a weighted least squares regression to fit a log-linear function, considering appropriate increase the weight in high intensity area and decrease the weight in low intensity area. The analysis results show that for peak acceleration with V≤I≤IX, logPGA=0.268I+0.330, and for peak velocity with V≤I≤IX, logPGV=0.277I-0.753. The statistic results indicate that PGA values of this paper is as about 1.3 times as that of the Chinese seismic intensity scale(CSIS2008) models with V≤I≤IX, meanwhile, PGV values of this paper is as about 1.24 times as CSIS2008 models with V≤I≤IX. Compared to the corresponding values in the ShakeMap system, the results of this paper are lower. It indicated that he CSIS2008 models, however, are conservative to earthquake hazard assessment and seismic design in China.
The food and feeding habits of the Indian oil sardineSardinella longiceps Valenciennes

nformation on the food of the Indian oil sardine, Sardinella longiceps, known as Mathi in Malayalam, forming one of the foremost commercially important fisheries of the west coast of India, is fragmentary. As stated by Homell (1910) the food of the oil sardine from the fish curing yards of the Madras State -was reported to be 'mud' but, he described for the first time some of the food constituents of the species. Further, in 1924 he along with Nayudu gave a detailed account of the food composition and its seasonal fluctuations. Information about the nature of the food of this fish is available from the works of John and Menon (1942), Chidambaram (1950), Devanesan and Chidambaram (1953) and Dhulkhed (1962). Nair and Subrahmanyam (1955) and Nair (1960) are of the opinion that the diatom, Fragilaria oceanica is an indicator of the abundance of the Indian oil sardine.
Causes of epilepsy: FLAIR MRIbased study

Epilepsy is a neurological disorder in which brain activity becomes abnormal, causing seizures or periods of unusual behavior. Flair MRI was used to characterize the brain in 55 SA patients diagnosed with epilepsy in EEG,in100% of patients; 56% was unremarkable MRI, while 44% present with abnormal MRI findings.The patients whom diagnosed as lesion and brain atrophy was (21&10) respectively, and a significant correlation between them has been noted at p=0.01. Bilateral temporal lobes were the most common location of lesion in MRI (12.7%). Cystic lesion followed by ischemia(9.1% and 5.5% respectively) were the most common lesions type, a significant relationship was observe between lesions location and type in MRI at p=0.01. Study also found that all patients diagnosed with ventricular system dilatation, had lesions and increasing in brain volume at significant correlation equal to p=0.01 for both variables. MRI flair was not able to diagnose all causes of epilepsy, fMRI has been recommended for that epilepsy of unknown cause in flair MRI.
A Comparative Study between Intracervical Dinoprostone Gel and Intravenous Oxytocin in Induction of Labour- Fetal and Maternal Outcome

BACKGROUNDInduction of Labour is the artificial initiation of labour before spontaneous onset of labour sets in. The frequency of induction varies by the different drugs used for it. The objective of this study was to study the fetomaternal outcome after induction with Intracervical Dinoprostone (PGE2 Gel) and Intravenous Oxytocin. METHOD-. A prospective study was done at B.S.M.C. H, W.B, India using data of deliveries conducted during the period of Jan,2016 to Dec 2017. Eligible patients included those at term, 37 weeks of gestation . All patients had singleton pregnancy with vertex presentation. A total of 150 patients were studied. They were distributed into two groups. 85 patients were in Group A were induced with single intra-cervical application of 0.5 mg PGE2 gel and remaining 65 patients of Group B were induced with intravenous Oxytocin and efficacy of the two methods of induction was compared. RESULTS-Total number of deliveries during this period was 21091. Labour was established within 24 hours in 71.4% of primigravida and 94% of multigravida in PGE2 treated group compared to 68% primigravida and 90% multigravida in the Oxytocin treaed group. The study found substantial improvement in cervical score 12 hrs after application of PGE2 gel with no major adverse effect to mother or neonate. CONCLUSIONIntra-cervical PGE2 gel is safe and effective for inducing labour in patients with unfavorable cervix. In our study we had high rate of vaginal delivery with no major adverse effect to mother or neonate. KeywordsInduction, Intracervical Dinoprostone (PGE2 gel) , Oxytocin. ------------------------------------------------------------------------------------------------------------------------------------Date of Submission: 12-06-2018 Date Of Acceptance: 27-06-2018 -------------------------------------------------------------------------------------------------------------------------------------
Mining Robust Overlapping Co-Clustering in the Presence of Noise

Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great achievement. However, due to the various structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, the problem of revealing robust overlapping co-clustering is identified in the presence of noise. Instead of requiring all objects in a cluster have identical attribute order, this system requires that (1) at least a certain fraction of the objects have identical attribute order; (2) other objects in the cluster may deviate from the consensus order by up to a certain fraction of attributes.
Rethinking post-traumatic stress disorder – A predictive processing perspective

Predictive processing has become a popular framework in neuroscience and computational psychiatry, where it has provided a new understanding of various mental disorders. Here, we apply the predictive processing account to post-traumatic stress disorder (PTSD). We argue that the experience of a traumatic event in Bayesian terms can be understood as a perceptual hypothesis that is subsequently given a very high a-priori likelihood due to its (life-) threatening significance; thus, this hypothesis is re-selected although it does not fit the actual sensory input. Based on this account, we re-conceptualise the symptom clusters of PTSD through the lens of a predictive processing model. We particularly focus on re-experiencing symptoms as the hallmark symptoms of PTSD, and discuss the occurrence of flashbacks in terms of perceptual and interoceptive inference. This account provides not only a new understanding of the clinical profile of PTSD, but also a unifying framework for the corresponding pathologies at the neurobiological level. Finally, we derive directions for future research and discuss implications for psychological and pharmacological interventions.
The Direct and Spillover Effects of a Nationwide Socio-Emotional Learning Program for Disruptive Students

Social and emotional learning (SEL) programs teach disruptive students to improve their classroom behavior. Small-scale programs in high-income countries have been shown to improve treated students’ behavior and academic outcomes. Using a randomized experiment, we show that a nationwide SEL program in Chile has no effect on eligible students. We find evidence that very disruptive students may hamper the program’s effectiveness. ADHD, a disorder correlated with disruptiveness, is much more prevalent in Chile than in high-income countries, so very disruptive students may be more present in Chile than in the contexts where SEL programs have been shown to work.
High-resolution VLA Imaging of Obscured Quasars: Young Radio Jets Caught in a Dense ISM

We present new subarcsecond-resolution Karl G. Jansky Very Large Array (VLA) imaging at 10 GHz of 155 ultraluminous (Lbol ∼ 1011.7–1014.2 L⊙) and heavily obscured quasars with redshifts z ∼ 0.4–3. The sample was selected to have extremely red mid-infrared–optical color ratios based on data from the Wide-Field Infrared Survey Explorer (WISE) along with a detection of bright, unresolved radio emission from the NRAO VLA Sky Survey (NVSS) or Faint Images of the Radio Sky at Twenty cm Survey. Our high-resolution VLA observations have revealed that the majority of the sources in our sample (93 out of 155) are compact on angular scales <0. ″2 (≤1.7 kpc at z ∼ 2). The radio luminosities, linear extents, and lobe pressures of our sources are similar to young radio active galactic nuclei (e.g., gigahertz-peaked spectrum [GPS] and compact steep-spectrum [CSS] sources), but their space density is considerably lower. Application of a simple adiabatic lobe expansion model suggests relatively young dynamical ages (∼104–7 yr), relatively high ambient ISM densities (∼1–104 cm−3), and modest lobe expansion speeds (∼30–10,000 km s−1). Thus, we find our sources to be consistent with a population of newly triggered, young jets caught in a unique evolutionary stage in which they still reside within the dense gas reservoirs of their hosts. Based on their radio luminosity function and dynamical ages, we estimate that only ∼20% of classical large-scale FR I/II radio galaxies could have evolved directly from these objects. We speculate that the WISE-NVSS sources might first become GPS or CSS sources, of which some might ultimately evolve into larger radio galaxies.
Geometry-aware domain adaptation for unsupervised alignment of word embeddings

We propose a novel manifold based geometric approach for learning unsupervised alignment of word embeddings between the source and the target languages. Our approach formulates the alignment learning problem as a domain adaptation problem over the manifold of doubly stochastic matrices. This viewpoint arises from the aim to align the second order information of the two language spaces. The rich geometry of the doubly stochastic manifold allows to employ efficient Riemannian conjugate gradient algorithm for the proposed formulation. Empirically, the proposed approach outperforms state-of-the-art optimal transport based approach on the bilingual lexicon induction task across several language pairs. The performance improvement is more significant for distant language pairs.
How to Teach DNNs to Pay Attention to the Visual Modality in Speech Recognition

Audio-Visual Speech Recognition (AVSR) seeks to model, and thereby exploit, the dynamic relationship between a human voice and the corresponding mouth movements. A recently proposed multimodal fusion strategy, AV Align, based on state-of-the-art sequence to sequence neural networks, attempts to model this relationship by explicitly aligning the acoustic and visual representations of speech. This study investigates the inner workings of AV Align and visualises the audio-visual alignment patterns. Our experiments are performed on two of the largest publicly available AVSR datasets, TCD-TIMIT and LRS2. We find that AV Align learns to align acoustic and visual representations of speech at the frame level on TCD-TIMIT in a generally monotonic pattern. We also determine the cause of initially seeing no improvement over audio-only speech recognition on the more challenging LRS2. We propose a regularisation method which involves predicting lip-related Action Units from visual representations. Our regularisation method leads to better exploitation of the visual modality, with performance improvements between 7% and 30% depending on the noise level. Furthermore, we show that the alternative Watch, Listen, Attend, and Spell network is affected by the same problem as AV Align, and that our proposed approach can effectively help it learn visual representations. Our findings validate the suitability of the regularisation method to AVSR and encourage researchers to rethink the multimodal convergence problem when having one dominant modality.
Rigid Formats Controlled Text Generation

Neural text generation has made tremendous progress in various tasks. One common characteristic of most of the tasks is that the texts are not restricted to some rigid formats when generating. However, we may confront some special text paradigms such as Lyrics (assume the music score is given), Sonnet, SongCi (classical Chinese poetry of the Song dynasty), etc. The typical characteristics of these texts are in three folds: (1) They must comply fully with the rigid predefined formats. (2) They must obey some rhyming schemes. (3) Although they are restricted to some formats, the sentence integrity must be guaranteed. To the best of our knowledge, text generation based on the predefined rigid formats has not been well investigated. Therefore, we propose a simple and elegant framework named SongNet to tackle this problem. The backbone of the framework is a Transformer-based auto-regressive language model. Sets of symbols are tailor-designed to improve the modeling performance especially on format, rhyme, and sentence integrity. We improve the attention mechanism to impel the model to capture some future information on the format. A pre-training and fine-tuning framework is designed to further improve the generation quality. Extensive experiments conducted on two collected corpora demonstrate that our proposed framework generates significantly better results in terms of both automatic metrics and the human evaluation.
Data‐driven flood emulation: Speeding up urban flood predictions by deep convolutional neural networks

Computational complexity has been the bottleneck for applying physically based simulations in large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessment. To overcome the issue of long computational time and accelerate the prediction process, this paper proposes that the prediction of maximum water depth can be considered an image‐to‐image translation problem in which water depth rasters are generated using the information learned from data instead of by conducting simulations. The proposed data‐driven urban pluvial flood approach is based on a deep convolutional neural network trained using flood simulation data obtained from three catchments and 18 hyetographs. Multiple tests to assess the accuracy and validity of the proposed approach were conducted with both design and real hyetographs. The results show that flood prediction based on neural networks use only 0.5% of the time compared with that of physically based models, with promising accuracy and generalizability. The proposed neural network can also potentially be applied to different but relevant problems, including flood analysis for flood‐safe urban layout planning.
Duplication Detection in Knowledge Graphs: Literature and Tools

In recent years, an increasing amount of knowledge graphs (KGs) have been created as a means to store cross-domain knowledge and billion of facts, which are the basis of costumers' applications like search engines. However, KGs inevitably have inconsistencies such as duplicates that might generate conflicting property values. Duplication detection (DD) aims to identify duplicated entities and resolve their conflicting property values effectively and efficiently. In this paper, we perform a literature review on DD methods and tools, and an evaluation of them. Our main contributions are a performance evaluation of DD tools in KGs, improvement suggestions, and a DD workflow to support future development of DD tools, which are based on desirable features detected through this study.
Machine learning reveals complex behaviours in optically trapped particles

Since their invention in the 1980s, optical tweezers have found a wide range of applications, from biophotonics and mechanobiology to microscopy and optomechanics. Simulations of the motion of microscopic particles held by optical tweezers are often required to explore complex phenomena and to interpret experimental data. For the sake of computational efficiency, these simulations usually model the optical tweezers as an harmonic potential. However, more physically-accurate optical-scattering models are required to accurately model more onerous systems; this is especially true for optical traps generated with complex fields. Although accurate, these models tend to be prohibitively slow for problems with more than one or two degrees of freedom (DoF), which has limited their broad adoption. Here, we demonstrate that machine learning permits one to combine the speed of the harmonic model with the accuracy of optical-scattering models. Specifically, we show that a neural network can be trained to rapidly and accurately predict the optical forces acting on a microscopic particle. We demonstrate the utility of this approach on two phenomena that are prohibitively slow to accurately simulate otherwise: the escape dynamics of swelling microparticles in an optical trap, and the rotation rates of particles in a superposition of beams with opposite orbital angular momenta. Thanks to its high speed and accuracy, this method can greatly enhance the range of phenomena that can be efficiently simulated and studied.