Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined Donor-Recipient Obesity and the Risk of Graft Loss After Kidney Transplantation

Background: As the prevalence of obesity increases globally, appreciating the effect of donor and recipient (DR) obesity on graft outcomes is of increasing importance. Methods: In a cohort of adult, kidney transplant recipients (2000–2017) identified using the SRTR, we used Cox proportional hazards models to examine the association between DR obesity pairing (body mass index (BMI) >30 kg/m2), and death-censored graft loss (DCGL) or all-cause graft loss, and logistic regression to examine risk of delayed graft function (DGF) and ≤30 days graft loss. We also explored the association of DR weight mismatch (>30 kg, 10-30 kg (D>R; D<R) and <10 kg (D = R)) with each outcome, stratifying by DR obesity pairing. Results: Relative to non-obese DR, obese DR were highest risk for all outcomes (DCGL: HR 1.26, 95% CI 1.22–1.32; all-cause graft loss: HR 1.09, 95% CI 1.06–1.12; DGF: OR 1.98, 95% CI 1.89–2.08; early graft loss: OR 1.34, 95% CI 1.19–1.51). Donor obesity modified the risk of recipient obesity and DCGL [p = 0.001] and all-cause graft loss [p < 0.001] but not DGF or early graft loss. The known association of DR weight mismatch with DCGL was attenuated when either the donor or recipient was obese. Conclusion: DR obesity status impacts early and late post-transplant outcomes. GRAPHICAL ABSTRACT
Open Research Online Development and cross-national validation of the Emotional Eﬀort Scale (EEF)

Background: Researchers define Emotional Labour (EL) as the effort associated with meeting the emotional requirements of the job, yet nobody has ever directly tested this effort. Building on classic stress and ego depletion theory, this study develops the Emotional Effort scale (EEF). Methods: In Study 1, exploratory (N=197) and confirmatory factor analysis (N=182) were conducted with a British sample. In Study 2, the instrument was adapted to Spanish and measurement invariance was tested (N=304). In Study 3, (N=185), we tested convergent and divergent validity with the EL strategies (i.e. surface acting and deep acting) and the relationship between EEF and emotional exhaustion. Results: The final scale is a two-dimensional measure (explicit and implicit emotional effort) with good reliability levels in all samples (N=818). Additionally, it shows adequate convergent, divergent and nomological validity. Conclusions: The Emotional Effort construct adds unique value to the literature. Thus, explicit effort seems to be the mechanism that explains the association between EL and exhaustion. Additionally, this study adapts and translates the measure to two of the most used languages in the world, enabling the emergence of cross-national studies in the field of emotions at work.
Ontology Integrated Recommender System: A Review

— Today’s world is an informative society where need is associated with the information. Recommender system is one of the software tools to spread the information among the society. Ontology is one of the pillars of semantic web to store information in semantic nature. The pace of information on the internet is growing rapidly every second due to the fact that the demand for semantic information is increasing. This paper focuses on the mechanism of integrating semantic information with the recommender system studied by the various researchers.
Features of collisionless turbulence in the intracluster medium from simulated Faraday Rotation maps

Observations of the intracluster medium (ICM) in galaxy clusters suggest for the presence of turbulence and the magnetic fields existence has been proved through observations of Faraday Rotation and synchrotron emission. The ICM is also known to be filled by a rarefied weakly collisional plasma. In this work we study the possible signatures left on Faraday Rotation maps by collisionless instabilities. For this purpose we use a numerical approach to investigate the dynamics of the turbulence in collisionless plasmas based on an magnetohydrodynamical (MHD) formalism taking into account different levels of pressure anisotropy. We consider models covering the sub/super-Alfv\'enic and trans/supersonic regimes, one of them representing the fiducial conditions corresponding to the ICM. From the simulated models we compute Faraday Rotation maps and analyze several statistical indicators in order to characterize the magnetic field structure and compare the results obtained with the collisionless model to those obtained using standard collisional MHD framework. We find that important imprints of the pressure anisotropy prevails in the magnetic field and also manifest in the associated Faraday Rotation maps which evidence smaller correlation lengths in the collisionless MHD case. These points are remarkably noticeable for the case mimicking the conditions prevailing in ICM. Nevertheless, in this study we have neglected the decrease of pressure anisotropy due to the feedback of the instabilities that naturally arise in collisionless plasmas at small scales. This decrease may not affect the statistical imprint differences described above, but should be examined elsewhere.
A System to Filter out Unwanted Social Media Content in Real-time on iPhones

Social media users are often harassed. This paper presents a patented system to filter out harassing content before it reaches the recipient. Our first version is for the iPhone. To detect harassment, we adopted sentiment analysis with a supervised learning approach that combines Machine Learning (ML) text classifiers with a lexicon approach that provides a feedback loop to retrain the ML model with unknown terms. Because good data is essential to obtain the best output of any system, we focused on validating our labeled data. Our results on static and real-time data have an accuracy of, respectively, 90% and 94%. Our labeled data validation allows us to correct labels; we also realized the need to increase the number of sets in our lexicons. Our prototype demonstrates that we are able to build an AI infrastructure to filter out harassment on an iPhone in real-time with good results.
Open Journal of Pulmonology and Respiratory Medicine Biologics for the treatment of severe united airways disease

The united airways disease refers to the coexistence of allergic rhinitis (AR), eosinophilic asthma, and chronic inosinate’s with nasal polyps (CRSwNP). United airways diseases (UAD) are common inflammatory conditions, which contribute to significant morbidity, and economical costs. Eosinophilic asthma affects more than 50% of patients with asthma, and AR is the most common immunological disease. CRSwNP is a complex inflammatory disease affecting the nasal and paranasal sinuses, and is reported in approximately 4.3% of the general population. Eosinophilic asthma is often associated with AR, and CRSwNP, and share similar immunopathological mechanisms, characterised by eosinophilia, and raised immunoglobulin E. Patients with eosinophilic asthma and CRSwNP have very severe UAD, characterized by frequent exacerbations, hospitalisation, poor lung function, persistent nasal obstruction, and worse health-related quality of life. T helper type 2 (Th2) lymphocytes, and epithelial cells play an important role in the pathogenesis of eosinophilic asthma, AR, and CRSwNP. Activation of Th2 cells and epithelial dysfunction lead to secretion of cytokines, chemokines, and adhesion molecules which orchestrate airway eosinophilic inflammation. Th2 cytokines, such as interleukin-5 (IL-5), IL-4, IL-13, and epithelial-derived cytokines, including IL-25, IL-33, and thymic stromal lymphopoietin (TSLP) play a key role in the pathogenesis of airway inflammation and remodelling. Treatment of severe united airways disease is difficult because of the severity of the syndrome. There is urgent need to develop novel biologics for uniform treatment of the UAD. Biologics targeting the inciting interleukins, such as mepolizumab and reslizumab (anti-IL-5), benralizumab (anti-IL-5Rα), dupilumab (anti-4Rα), and tezelizumab (anti-TSLP) have been shown to significantly reduce annualized exacerbation rates, improve asthma control, and lung function. Currently, there are two biologics which have been approved for the treatment of severe eosinophilic asthma and CRSwNP, including omalizumab, and dupilumab. Both biologics have been shown to concomitantly improve the nasal congestion score, Sino-Nasal Outcome Test-22 score, endoscopic nasal polyp score, Lund-Mackay computed tomography score, peak nasal inspiratory flow, and quality of life. Mepolizumab, reslizumab, benralizumab, and tezepelumab are in phase III clinical trials for the treatment of the UAD, and have been shown to be effective in the treatment of both severe eosinophilic asthma and CRwNP.
A Polynomial approach for simultaneous channel estimation and data detection

This work investigates the simultaneous estimate of both the impulse response samples and the transmitted signal in a digital communication channel. The minimum variance approach applied to a suitably de¯ned extended state-space representation, which includes both the channel samples and the signal, is here adopted. Such a problem cannot be solved by the use of linear algorithms. It is shown how the proposed polynomial approach overcomes the previously mentioned drawback. Numerical simulations referred to a telecommunication framework support theoretical results.
Relevance in Dialogue: An empirical comparison of existing metrics, and a novel simple metric

In this work, we evaluate various existing di-001 alogue relevance metrics, ﬁnd strong depen-002 dencies on the dataset, often with poor cor-003 relation with human scores of relevance, and 004 propose modiﬁcations to reduce data require-005 ments while improving correlation. With these 006 changes, our metric achieves a new state-of-007 the-art on the HUMOD dataset (Merdivan 008 et al., 2020). We achieve this without ﬁne-009 tuning, using only 3750 unannotated human 010 dialogues and a single negative example. De-011 spite these limitations, we demonstrate com-012 petitive performance on three datasets from 013 different domains. Our code including our 014 metric and data processing is open sourced 1 . 015
Last to Learn Bias: Analyzing and Mitigating a Shortcut in Question Matching

Recent studies report that even if deep neural 001 models make correct predictions, models may 002 be relying on shortcut rather than understand-003 ing the semantics of the text. Previous stud-004 ies indicate that the shortcut deriving from the 005 biased data distribution in training set makes 006 spurious correlations between features and la-007 bels. In this paper, we focus on analyzing 008 and mitigating the biased data distribution in 009 question matching by exploring the model be-010 havior and performance. In particular, we de-011 ﬁne bias-word as the shortcut, and explore 012 the following questions: (1) Will the bias af-013 fect the model? (2) How does the bias affect 014 the model’s decision? Our analysis reveals 015 that bias-words make signiﬁcantly higher con-016 tributions to model predictions than random 017 words, and the models tend to assign labels 018 that are highly correlated to the bias-words. To 019 mitigate the effects of shortcut, we propose 020 a simple approach that learns more no-bias-021 examples ﬁrst and more bias-examples last. 022 The experiments demonstrate the effectiveness 023 of the proposed approach. 024
The Students ’ Ability in Reading Comprehension of TOEFL for EFL Learners

The objectives of the research are to know the students’ ability in reading comprehension of TOEFL test for the EFL Learners. This study is categorized as descriptive quantitative research. This study was conducted at the eighth semester students of Hamzanwadi University. The population of this study was 144 students from 3 classes and the sampling method was purposive sampling. The sample of this study was 30 students. The research instrument used to collect data was reading comprehension of TOEFL. The collected data were submitted to descriptive statistic by using SPSS 22 for windows. The mean score of the test was 4.17, which is categorized into low. Based on that result, the EFL Learners did not have good ability in finding main ideas, references, and meaning of words on reading comprehension of TOEFL. Meanwhile, the mean score in finding information was 7.2 and inference was 6.4. Therefore, the EFL Learners did not have good ability in finding information and inference.
Towards Focused and Connected Document-Level Event Extraction

Document-level event extraction (DEE) is in-001 dispensable when events are naturally de-002 scribed in the form of a document. Although 003 previous methods have made great success 004 on DEE, they are limited by two bottlenecks: 005 losing focus and losing the connection. In 006 this paper, to break through the above bot-007 tlenecks, we annotated a new dataset, named 008 WIKIEVENT++, towards focused and con-009 nected DEE. Besides, we propose two differ-010 ent models to approach this task: the extractive 011 model and the generative model. Experimen-012 tal results verify the effectiveness of our pro-013 posed methods. We further present a promis-014 ing case study to explore the performance bot-015 tleneck for this task. Data and code will be 016 released at http://anonymized to advance 017 the research on document-level event extrac-018 tion. 019
NEWSFARM: the Largest Chinese Corpus for Long News Summarization Anonymous ACL submission

Recently, driven by a large number of datasets, 001 the ﬁeld of natural language processing(NLP) 002 has developed rapidly. However, the lack of 003 large-scale and high-quality Chinese datasets 004 is still a critical bottleneck for further research 005 on automatic text summarization. To close 006 this gap, we searched Chinese news websites 007 of domestic and abroad media, designed the 008 algorithm HSS (hidden topic, semantic, and 009 syntactic) to crawl and ﬁlter these records to 010 construct NEWSFARM . NEWSFARM is the 011 largest highest quality Chinese long news sum-012 marization corpus, containing more than 200K 013 Chinese long news and summaries written by 014 professional editors or authors, which are all 015 released to the public. Based on the corpus, 016 we calculated the static metrics and designed 017 many experiments with the baseline models. 018 By comparing with the common datasets, the 019 experiment results show that the high quality 020 of our dataset and training effect of the mod-021 els, which not only demonstrates the useful-022 ness and challenges of the proposed corpus 023 for automatic text summarization but also pro-024 vides a benchmark for further research. 025
Splash in a Flash: Sharpness-aware minimization for efficient liquid splash simulation

We present sharpness-aware minimization (SAM) for fluid dynamics which can efficiently learn the plausible dynamics of liquid splashes. Due to its ability to achieve robust and generalizing solutions, SAM efficiently converges to a parameter set that predicts plausible dynamics of elusive liquid splashes. Our training scheme requires 6 times smaller number of epochs to converge and, 4 times shorter wall-clock time. Our result shows that sharpness of loss function has a close connection to the plausibility of fluid dynamics and suggests further applicability of SAM to machine learning based fluid simulation.
Parsing Natural Language into Propositional and First-Order Logic with Dual Reinforcement Learning

Semantic parsing converts natural language utterances into structured logical expressions. We consider two such formal representations: Propositional Logic (PL) and First-order Logic (FOL). The paucity of labeled data is a major challenge in this field. In previous works, dual reinforcement learning has been proposed as an approach to reduce dependence on labeled data. However, this method has the following limitations: 1) The reward needs to be set manually and is not applicable to all kinds of logical expressions. 2) The training process easily collapses when models are trained with only the reward from dual reinforcement learning. In this paper, we propose a scoring model to automatically learn a model-based reward, and an effective training strategy based on curriculum learning is further proposed to stabilize the training process. In addition to the technical contribution, a Chinese-PL/FOL dataset is constructed to compensate for the paucity of labeled data in this field. Experimental results show that the proposed method outperforms competitors on several datasets. Furthermore, by introducing PL/FOL generated by our model, the performance of existing Natural Language Inference (NLI) models is further enhanced.
Towards a Fast Response Selection: Selecting the Optimal Dialogue Response Once for All

Response selector, as an essential component of 001 dialogue systems, aims to pick out an optimal 002 response in a candidate pool to continue the dia-003 logue. The current state-of-the-art methods are 004 mainly based on an encoding paradigm called 005 Cross-Encoder (Urbanek et al., 2019), which 006 separately encodes each context-response pair 007 and ranks the responses according to their fit-008 ness scores. However, such a paradigm is 009 both inefficient and ineffective. Specifically, 010 it has to repeatedly encode the same context for 011 each response, which results in heavy inference 012 cost. Also, without considering the relation-013 ship among the candidates, it is difficult to tell 014 which one is the best candidate purely based 015 on the fitness score of each candidate. To ad-016 dress this problem, we propose a new model 017 called Panoramic-Encoder , which accepts all 018 candidates and the context as inputs at once and 019 allows them to interact with each other through 020 a specially designed attention mechanism. Our 021 method also allows us to naturally integrate 022 some of the effective training techniques, such 023 as the in-batch negative training. Extensive ex-024 periments across four benchmark datasets show 025 that our new method significantly outperforms 026 the current state-of-the-art while achieving ap-027 proximately 3 × speed-up at inference time. 028
AIS Electronic Library (AISeL) Exploring Sharing Economy Success: Resource-Based View and the Role of Resource Complementarity in Business Value Co-Creation

In recent years, the sharing economy has been grown increasingly as an important means to achieve sustainable competitive advantage. With the support of mobile phone devices and the ubiquitous application of “SOLOMO (social, location, mobile)” concept, the emerging sharing economy hubs increase the pool of potential service providers and sellers by leveraging networked technology to change how market participants engage in a specific transaction. Several successful examples, such as Uber, Airbnb or live streaming platforms, demonstrate that such phenomenon will continue to generate immense interests and receive growing practical and academic attentions for the next decades. In order to meet the needs of heterogeneous network users or buyers, platform owners seek to promote and exploit the network resources from providers and facilitate the transactions. Therefore, the holistic network performance improves and values are co-created when the presence of platform owner complements network members each other. Though the importance of the above mentioned resource complementarity can be well-recognized from many practical evidences and academic studies, the role and empirical evidence of resource complementarity in facilitating the cocreation value and the impact on subsequent performance under sharing economy context is still not well understood. Moreover, sharing economy network is a new and distinct type of organization form and separates from markets and hierarchies, it still requires unique theories and research approaches for providing deeper insights. Value can be co-created by complementary alignment of mutual resources, but the degree to which value generation occurs is still subject to contextual factors. Therefore, the objective of this study is to present a holistic view to illuminate relationships among resource complementarity, relational capabilities (including relational embeddedness and ambidextrous competence), subsequent performance and cooperation continuance intention based on the perspective of resource-based view. 367 respondents from well-known online streaming platform were collected. We find that all hypotheses are supported, except that relational embeddedness has no any significant effect on financial performance. From this study, we hope to contribute nascent knowledge for sharing economy phenomenon and value co-creation with online marketing and information management disciplines scholarly, and provide fruitful insights to the design of an effective value-creating ecosystem application platform through our study for practitioners.
MODELING THE NEAR-UV BAND OF GK STARS. II. NON-LTE MODELS

We present a grid of atmospheric models and synthetic spectral energy distributions (SEDs) for late-type dwarfs and giants of solar and 1/3 solar metallicity with many opacity sources computed in self-consistent non-local thermodynamic equilibrium (NLTE), and compare them to the LTE grid of Short & Hauschildt (Paper I). We describe, for the first time, how the NLTE treatment affects the thermal equilibrium of the atmospheric structure (T(τ) relation) and the SED as a finely sampled function of Teff, log g, and [A/H] among solar metallicity and mildly metal-poor red giants. We compare the computed SEDs to the library of observed spectrophotometry described in Paper I across the entire visible band, and in the blue and red regions of the spectrum separately. We find that for the giants of both metallicities, the NLTE models yield best-fit Teff values that are 30–90 K lower than those provided by LTE models, while providing greater consistency between log g values, and, for Arcturus, Teff values, fitted separately to the blue and red spectral regions. There is marginal evidence that NLTE models give more consistent best-fit Teff values between the red and blue bands for earlier spectral classes among the solar metallicity GK giants than they do for the later classes, but no model fits the blue-band spectrum well for any class. For the two dwarf spectral classes that we are able to study, the effect of NLTE on derived parameters is less significant. We compare our derived Teff values to several other spectroscopic and photometric Teff calibrations for red giants, including one that is less model dependent based on the infrared flux method (IRFM). We find that the NLTE models provide slightly better agreement to the IRFM calibration among the warmer stars in our sample, while giving approximately the same level of agreement for the cooler stars.
The Final Merger of Black-Hole Binaries

Recent breakthroughs in the field of numerical relativity have led to dramatic progress in understanding the predictions of general relativity for the dynamical interactions of two black holes in the regime of very strong gravitational fields. Such black-hole binaries are important astrophysical systems and are a key target of current and developing gravitational-wave detectors. The waveform signature of strong gravitational radiation emitted as the black holes fall together and merge provides a clear, observable record of the process. After decades of slow progress, these mergers and the gravitational-wave signals they generate can now be routinely calculated using the methods of numerical relativity. We review recent advances in understanding the predicted physics of events and the consequent radiation and discuss some of the impacts of this new knowledge in various areas of astrophysics.