Miracle-dz/UniFAR-FTUs huggingface dataset:
Clinical Study Myocardial Injury in Critically Ill Children : A Case Control Study

Objectives. The aim of this study was to investigate the occurrence of myocardial injury in critically ill children through assessment of cardiac troponin T levels and whether levels are associated with disease severity and myocardial dysfunction measured by echocardiography. Methods. Over a 6-month period, this case control study included 50 patients admitted to Pediatric Intensive Care Unit of Zagazig University Children’s Hospital. Twenty-five healthy children were included as a control group. Demographic and clinical data including the pediatric index of mortality II score were recorded. Echocardiographic examination was done and level of cardiac troponin T was measured using Elecsys Troponin T STAT Immunoassay. Results. Cardiac troponin T levels were significantly higher in critically ill in comparison to healthy children (median 22 (18–28) pg/mL versus 10 (10-10) pg/mL, P < 0.05). Cardiac troponin T levels correlated positively with duration of ventilation as well as with disease severity and correlated negatively with left ventricular fractional shortening. Moreover, cardiac troponin T levels were significantly higher in nonsurvivors when compared to survivors (median 34.5 (27.5–41.5) pg/mL versus 20 (18–24) pg/mL, P < 0.05). Conclusion. In critically ill children, cardiac troponin T levels were elevated and were associated with duration of ventilation and disease severity.
How Much Spam Can You Take? An Analysis of Crowdsourcing Results to Increase Accuracy

Crowdsourcing is used to obtain relevance judgments for query-document pairs. To obtain accurate judgments, each query-document pair is judged by several workers. Consensus is usually obtained by majority voting and spam most commonly reduced by injecting gold set questions. This study puts the performance of gold sets and majority voting to the test. Based on the analysis of crowdsourcing results for a relevance judgment task, we propose an alternative to reduce spam and increase accuracy. Simulations were used to compare performance between different algorithms, inspecting accuracy and costs for different experimental settings. The results show that gold sets and majority voting are less spam-resistant than many believe and can easily be outperformed.
Assessing BERT ’ s Ability to Judge Moral Scenarios Using Reddit ’ s “ Am I The Asshole ” as a Database Exposé

Sentiment Analysis is a field of Natural Language Processing (NLP) which focuses on extracting subjective opinions from natural language texts. It has been used to gauge public opinion on brands [1], assess internet marketing possibilities [2], and analyze app user-reviews to gauge which features are liked/disliked [3] to name a few examples. This project will use BERT [4], a new technology from Google which is currently setting records in Sentiment Analysis, to predict and mimic human moral judgment using the Reddit community http://www.reddit. com/r/amitheasshole​ as a dataset.
To Mend a Broken Heart : Searching the Human Heart for Novel Binding Partners of Mox Utilizing the Yeast-2-Hybrid System

Strict regulation of cellular proliferation, cellular differentiation, and apoptosis is required during embryonic cardiogenesis to ensure the proper development of the cardiovascular system. Regulation of these processes is also important during the injury response of the diseased adult heart and blood vessels. Homeobox genes, such as the Mox family, have been found to play important roles in both of these processes. The Mox2 homeobox gene has been implicated in negatively regulating the cell cycle of both vascular smooth muscle cells and cardiomyocytes, but the effect of a related protein Mox1 has not been extensively studied. Although Mox1 and Mox2 are 98% identical at the amino acid level in their homeodomains, this degree of homology is not maintained in adjacent regions of the proteins. Through the use of a yeast two-hybrid (Y2H) system, a human heart cDNA library will be screened to search for proteins that interact with Mox proteins and modulate their function. Testing of the Gal4-based Matchmaker3 Y2H system has revealed Mox1 to be a much stronger transcriptional activator of Gal4 reporter genes relative to Mox2, but weaker than that of a Gal4 positive control. This is shown by activation of the GAL4 dependent reporter genes Ade2, His3, Mel1, and LacZ in the AH109 yeast strain.
Profiling complex word usage in the speech of English preschool children: frequency patterns and transparency characteristics

This corpus-based study provides a baseline of complex word usage patterns in the spontaneous speech of English preschool children to ascertain the characteristics of their derivative vocabulary before literacy development affects language skills. Frequencies of suffixed derivatives produced by (N=243) children aged 2-5 and their caregivers were extracted for 58 suffix variants, yielding 558 types from the former and 1,364 from the latter. Between the youngest and oldest groups, 11 suffix categories increased significantly in type frequency, compared with 22 in the caregiver dataset. All derivative types were classified for transparency of meaning and simplicity of form on a 5-point analysability scale. Around 59% of both the child and caregiver derivative vocabulary sets were classified as transparent regardless of age, suggesting that the potential analysability of the preschool child’s input remains surprisingly invariant over time. The study provides baseline data for future studies on the development of morphological awareness in English-speaking schoolchildren.
ON THE FORWARD IN TIME SOLUTION OF THE UNSTEADY ADJOINT EQUATIONS

Abstract. A new algorithm for the solution of the unsteady adjoint equations is proposed in this paper aiming at overcoming the excessive computational cost and memory requirements of the conventional adjoint approach for the optimization of unsteady problems in computational mechanics. The proposed algorithm consists of two phases: the first one is based on the backward in time solution of the adjoint equations based on average in time state quantities and the second one solves the exact unsteady adjoint equations forward in time, based on the initial adjoint condition derived from the averaging technique of the first phase. The proposed algorithm is compared to other approaches in the case of the 1D unsteady Burgers equation with non-smooth source terms which resembles the modelling of unsteady turbulent flows with very fine temporal discretization modeled using LES, DES or DNS methods. The adjoint variables are computed with acceptable accuracy at a cost of four times solving the unsteady state equations with negligible additional memory requirements.
Critical Strategies for Safe Laparoscopic Cholecystectomy

*Correspondence: Bulent Kaya, Department of General Surgery, Health Science University Fatih Sultan Mehmet Training and Resarch Hospital, Atasehir, Istanbul, Turkey, Tel: 5056822101; E-mail: drbkaya@yahoo.com Received Date: 11 Sep 2017 Accepted Date: 20 Nov 2017 Published Date: 30 Nov 2017 Citation: Kaya B, Bulut NE. Critical Strategies for Safe Laparoscopic Cholecystectomy. Clin Surg. 2017; 2: 1779.
Grid Resource Discovery Using Web Services

Grids hold the promise to provide global interoperability and interconnectivity involving networks of heterogeneous resources, working in collaboration, to solve problems that cannot be solved using the resources of any single organization. Opportunistic sharing of Internet-connected resources is a low cost method for obtaining access to unprecedented-scale collections of resources. An essential service in any resource-sharing environment is resource discovery: given a description of the resources desired, a resource discovery mechanism returns locations of resources that match the description. Web Services are an XML based set of standards and technologies to integrate software application systems using Internet technology. In practice, there is not much difference between the existing Grid and web infrastructure. The implementation of web based Grid is one of the potential solution to Grid infrastructure based problems. This can be done by sharing Grid services across the Grid infrastructure using the underlying web service as vehicles or transporters of these services. Using web services based resource discovery is a standards based technique for querying resource discovery information which to date, has received surprisingly little analysis as a discovery mechanism for web service based grids. In this thesis, we proposed, designed and implemented Grid resource discovery using web services on a . NET-based Grid computing framework so as to utilize the computing resources of the vast majority of desktop computers i.e. those running variants of the Microsoft Windows operating system. Using this framework, a language independent and platform independent support is provided via a web services interface. Furthermore we can conclude that on the design principles proposed in this thesis work, a complete Grid Middleware can be implemented on the same lines of which only the Resource Discovery portion has been presented here.
Partnership for Advanced Computing in Europe Numerical Simulation of Accidental Fires with a Spillage of Oil in Large Buildings

BAC’s (BAC Engineering Consultancy Group) participation in PRACE programme has made possible to increase its knowledge in the field of fire engineering and the application of advanced computing strategies. These fields may be applied directly to a fire engineering analysis (Performance-Based design) of the steel structure buildings that belong to the International Thermonuclear Experimental Reactor industrial complex in France, which is devoted to research in the field of Nuclear Fusion. In particular, the project could be focused on the building that hosts the cryogenic system of the Tokamak. This building is characterized by its large size. The building hosts a large number of helium and nitrogen compressors, as well as the charcoal filter system, that is necessary for the cryogenic system. Numerical simulations of the accidental fires that can take place inside of the building have been carried out, some of them as complex as considering the combination of a liquid combustible spillage added to an oil cloud due to the breakage of a high pressure pipe.
Panoramic imaging for text spotting

Author(s): Ren, Peng | Advisor(s): Manduchi, Roberto | Abstract: Visually impaired people are a group of people who have some degree of vision loss. In order to help these people getting text information of surrounding space, I build an app that can detect text for visually impaired people. At first, the app takes images automatically, and then the google OCR API is used to find text spots and perform word recognition. After that, my app will notify the user of the text contents and positions. Since there is overlapping between adjacent images, the app might detect one text twice if the text exists in both photos. To solve this problem, homography matrices among pictures are calculated using feature detection and matching techniques. Besides, the panorama is used to illustrate my results. As a result, the app can detect text efficiently and reduce various difficulties that visually impaired people might meet in walking, navigating and so on.
Bayesian analysis of the covariance matrix of a multivariate normal distribution with a new class of priors

Bayesian analysis for the covariance matrix of a multivariate normal distribution has received a lot of attention in the last two decades. In this paper, we propose a new class of priors for the covariance matrix, including both inverse Wishart and reference priors as special cases. The main motivation for the new class is to have available priors – both subjective and objective – that do not “force eigenvalues apart,” which is a criticism of inverse Wishart and Jeffreys priors. Extensive comparison of these ‘shrinkage priors’ with inverse Wishart and Jeffreys priors is undertaken, with the new priors seeming to have considerably better performance. A number of curious facts about the new priors are also observed, such as that the posterior distribution will be proper with just three vector observations from the multivariate normal distribution – regardless of the dimension of the covariance matrix – and that useful inference about features of the covariance matrix can be possible. Finally, a new MCMC algorithm is developed for this class of priors and is shown to be computationally effective for matrices of up to 100 dimensions.
Opioid Relapse Prediction with GAN

Opioid addiction is a severe public health threat in the U.S, causing massive deaths and many social problems. Accurate relapse prediction is of practical importance for recovering patients since relapse prediction promotes timely relapse preventions that help patients stay clean. In this paper, we introduce a Generative Adversarial Networks (GAN) model to predict the addiction relapses based on sentiment images and social influences. Experimental results on real social media data from Reddit.com demonstrate that the GAN model delivers a better performance than comparable alternative techniques. The sentiment images generated by the model show that relapse is closely connected with two emotions ‘joy’ and ‘negative’. This work is one of the first attempts to predict relapses using massive social media (Reddit.com) data and generative adversarial nets. The proposed method, combined with knowledge of social media mining, has the potential to revolutionize the practice of opioid addiction prevention and treatment.
Computations with quasiseparable polynomials and matrices T

In this paper, we survey several recent results that highlight an interplay between a relatively new class of quasiseparablematrices and univariate polynomials. Quasiseparable matrices generalize two classical matrix classes, Jacobi (tridiagonal) matrices and unitary Hessenberg matrices that are known to correspond to real orthogonal polynomials and Szegö polynomials, respectively. The latter two polynomial families arise in a wide variety of applications, and their short recurrence relations are the basis for a number of efficient algorithms. For historical reasons, algorithm development is more advanced for real orthogonal polynomials. Recent variations of these algorithms tend to be valid only for the Szegö polynomials; they are analogues and not generalizations of the original algorithms. Herein,we survey several recent results for the ‘‘superclass’’ of quasiseparablematrices, which includes both Jacobi and unitary Hessenberg matrices as special cases. The interplay between quasiseparable matrices and their associated polynomial sequences (which contain both real orthogonal and Szegö polynomials) allows one to obtain true generalizations of several algorithms. Specifically, we discuss the Björck–Pereyra algorithm, the Traub algorithm, certain newdigital filter structures, aswell asQR anddivide and conquer eigenvalue algorithms. © 2008 Elsevier B.V. All rights reserved.
DECOMPOSING PERFORMANCE

We present a new methodology for decomposing the (risk-adjusted) performance in empirical finance. Our technique offers the same straightforward economic intuition and all the statistical benefits of the portfolio sorts approach, in particular robustness to cross-sectional correlation, and in addition resolves the major drawbacks of portfolio sorts. Most importantly, our regression-based methodology handles multiple dimensions and continuous firm, fund, or investor characteristics. Moreover, the technique allows for relying on standard Wald-tests as an alternative to the popular Gibbons, Ross, and Shanken (1989) test. We illustrate our methodology with an asset pricing application and a long-horizon event study.
Determination of the optimal pre-enrichment period for the detection of Salmonella enterica in artificially inoculated meat products using culture , PCR and LAMP assays

etermination of the minimum enrichment period and most suitable detection assay for Salmonella in meat is essential in food safety and prevention of S. enterica-related outbreaks. Using artificially inoculated raw and processed meat products, the optimal pre-enrichment period for culture, polymerase chain reaction (PCR), and loop-mediated isothermal amplification (LAMP) assays to detect Salmonella in meat was evaluated. Optimal pre-enrichment period is herein defined as the shortest period of incubation to reliably detect the target organism. The three detection assays were conducted in a time-point manner, specifically the first six hours and 24th hour of pre-enrichment and the 48th hour of selective enrichment phase. LAMP assay had an optimal pre-enrichment period of 24 hours, i.e. 100% detection, on beef, chicken, pork, and bacon. Alternatively, the PCR assay had an optimal enrichment period of 48 hours covering the complete pre-enrichment and selective enrichment phases. The culture assay failed to achieve 100% detection, even after a 24-hour incubation or end of pre-enrichment period but had a higher tolerance to inhibitory substances than PCR. Of the three detection assays evaluated, LAMP assay had the shortest optimal enrichment period, highest sensitivity to Salmonella in meat products, and highest tolerance to inhibitory substances.
Research on News Keyword Extraction Based on TF-IDF and Chinese Features

Keyword extraction technology is the basis of corpus construction, text analysis processing, and information retrieval. For the special carrier of Chinese news text, the traditional TF-IDF algorithm is too dependent on word frequency and cannot handle the drawbacks of Chinese grammar accurately. This paper elaborates on the characteristics of Chinese news text keywords. On the basis of TF-IDF algorithm, it integrates Chinese special features such as part of speech, word length and lexical, and constructs an improved TF-IDF weighting formula that comprehensively considers text features. A scoring method for keyword matching is proposed, and the keywords that are "cut off" by the Chinese word segmentation are reconstituted into formal keywords. Cross-comparison experiments show that the improved algorithm is superior to the traditional algorithm in accuracy, recall and F value, and can correctly and effectively extract Chinese keywords.
Integrated Trajectory Planning and Torque Vectoring for Autonomous Emergency Collision Avoidance

This paper proposes an integrated trajectory planning based on Model Predictive Control (MPC) for designing collision-free evasive trajectory and a torque vectoring controller based on optimal control to ensure lateral-yaw stabilization in autonomous emergency collision avoidance under low friction and crosswinds on highways. The trajectory for performing the evasive manoeuvre is designed to minimise the deviation of the vehicle from the lane center while ensuring the vehicle remains within the road boundaries. The steering input computed from the MPC is used to steer the vehicle along the reference trajectory while the torque vectoring controller provides additional lateral-yaw stability. The integrated control framework was implemented on IPG Carmaker-MATLAB co-simulation platform and its efficacy was evaluated under different scenarios. Simulations performed for emergency collision avoidance at high speeds with low road friction and heavy crosswinds confirm the ability of the proposed closed-loop framework at successfully avoiding collisions with moving obstacles while ensuring that the controlled vehicle remains within its limits of stability. Furthermore, the robustness of the proposed control framework to variations in road friction changes is demonstrated by simulating an evasive manoeuvre at high-speeds for wide range of road friction conditions. Comparing the performance of the proposed control framework to a vehicle without the corrective actions available via torque vectoring highlight the additional benefits provided by the proposed closed-loop scheme at ensuring lateral-yaw stability under emergency scenarios.
EFFECT OF BI ADDITION ON THE OPTICAL BAND GAP OF Se 85 Te 15 CHALCOGENIDE THIN FILMS

This paper reports the influence of bismuth (Bi) addition on the optical properties of thin films of Se85-xTe15Bix (x = 0, 1, 2, 3, 4, 5) chalcogenide glassy alloys. This has been done by analyzing the transmission spectra, measured at normal incidence, in the spectral range 400 to 1500 nm. Optical band gap (Eg) is estimated using Tauc’s extrapolation and is found to decrease from 1.46 to 1.24 eV with the Bi addition. This behavior of optical band gap is interpreted in terms of electronegativity difference of the atoms involved and cohesive energy of the system.
Related Party Transactions : A Second Source for Earnings Management – Evidence from Chinese IPOs

Prior literature has identified IPO firms abuse the use of discretionary accruals before the IPO for the purpose of raising the IPO offering price and others. This study examines earnings management behaviors of Chinese IPOs in the pre-IPO period. We argue that there are two ways for IPO firms to manipulate pre-IPO reported earnings: one is to manipulate discretionary accruals, and the other is to structure artificial operating RPTs (non-loan) with controlling shareholders to boost sales and/or profits. Besides some evidence of accrual-based earnings management in the pre-IPO period, we also find that controlling shareholders structure a large percentage of operating RPTs with IPO firms in the pre-IPO year and the IPO year, which are positively associated with the operating performance of IPO firms. In the post-IPO period, controlling shareholders discontinue these RPT-based earnings management practices, so that the positive relation between operating performance of IPO firms and the size of operating RPTs disappears. We find that long-run IPO stock performance is significantly associated with the change in operating RPTs from before to after the IPO (at 1% significance level). IPOs reporting a decline in operating RPTs significantly underperform their industry peers.
Frictionless Indoor Location using BLE

Indoor location of people is a wide studied topic as it is a powerful tool that can be used to many tasks like helping to navigate better inside a large space like an airport, understanding better the trajectories of people on a certain place, or tailoring marketing strategies depending on the current location of an individual. Currently, indoor location using Bluetooth low energy is very used because of the low power consume and the low prices of the beacons. But an issue of usability is still present and is that every space need a new app, and that discourage the users to use the system. This paper describes the work in progress of a distributed indoor location system using BLE that migrates the processing and calibration requirements from the smartphone to external hardware and the cloud so there is only one free application that can handle all the location based services of every place.
An efficient intrusion detection system based on data mining and evolutionary algorithm

This paper proposed an efficient approach for intrusion detection which is based on data mining and evolutionary algorithm. The standard NSL-KDD dataset has been used for the experimentation and comparative study. This includes the data separation along with the k-clusters. So that proper classification has been applied and the classification accuracy can be improved. The parameters used are content, traffic and the host. Particle swarm optimization has been applied based on the maximum threshold and the objective function. The results shows that the proposed approach has 97% average accuracy in case of denial of service (DoS), user to root (U2R), remote to user (R2L) and probe attacks.