Miracle-dz/UniFAR-FTUs huggingface dataset:
Gamabufotalin Inhibits Osteoclastgenesis and Counteracts Estrogen-Deficient Bone Loss in Mice by Suppressing RANKL-Induced NF-κB and ERK/MAPK Pathways

Osteolytic bone disease is a condition of imbalanced bone homeostasis, characterized mainly by excessive bone-resorptive activity, which could predispose these populations, such as the old and postmenopausal women, to developing high risk of skeletal fragility and fracture. The nature of bone homeostasis is the coordination between the osteoblasts (OBs) and osteoclasts (OCs). Abnormal activation of osteoclasts (OCs) could compromise the bone homeostasis, constantly followed by a clutch of osteolytic diseases, including postmenopausal osteoporosis, osteoarthritis, and rheumatoid arthritis. Thus, it is imperatively urgent to explore effective medical interventions for patients. The traditional Chinese medicine (TCM) gamabufotalin (CS-6) is a newly identified natural product from Chansu and has been utilized for oncologic therapies owing to its good clinical efficacy with less adverse events. Previous study suggested that CS-6 could be a novel anti-osteoporotic agent. Nevertheless, whether CS-6 suppresses RANK-(receptor activator of nuclear factor-κ B ligand)/TRAF6 (TNF receptor-associated factor 6)-mediated downstream signaling activation in OCs, as well as the effects of CS-6 on OC differentiation in vivo, remains elusive. Therefore, in this present study, we aimed to explore the biological effects of CS-6 on osteoclastogenesis and RANKL-induced activation of related signaling pathways, and further to examine the potential therapeutic application in estrogen-deficient bone loss in the mice model. The results of in vitro experiment showed that CS-6 can inhibit RANKL-induced OC formation and the ability of bone resorption in a dose-dependent manner at both the early and late stages of osteoclastogenesis. The gene expression of OC-related key genes such as tartrate-resistant acid phosphatase (TRAP), CTSK, DC-STAMP, MMP9, and β3 integrin was evidently reduced. In addition, CS-6 could mitigate the systemic estrogen-dependent bone loss and pro-inframammary cytokines in mice in vivo. The molecular mechanism analysis suggested that CS-6 can suppress RANKL/TRAF6-induced early activation of NF-κB and ERK/MAPK signaling pathways, which consequently suppressed the transcription activity of c-Fos and NFATc1. Taken together, this present study provided ample evidence that CS-6 has the promise to become a therapeutic candidate in treating osteolytic conditions mediated by elevated OC formation and bone resorption.
Reinsurance and Financial Performance of Non-life Insurance Companies in Ghana

The study examines how premiums ceded to a reinsurer affect the profitability of non-life insurance companies in Ghana. Secondary data on reinsurance ceded, combined ratio, assets, liabilities and return on assets for 20 non-life insurance companies over the period 2008–2018 were sourced from National Insurance Commission whilst interest and exchange rates variables were obtained from the Bank of Ghana. Panel regression model was employed for the analysis of the data collected. The results show that purchasing high levels of reinsurance alone does not affect the profitability of non-life insurance companies, but the combined effect of reinsurance and solvency ratio significantly impact their profitability. Managers of non-life insurance companies in Ghana should increase their ability to repay all financial obligations in the short, medium and long term in combination with reinsurance. This will enable insurers to stabilize growth, earn profits and meet their obligations to policyholders in a timely fashion. JEL Code: G22
This Is Who I Am: Instagram as Counterspace for Shared Gendered Ethnic Identity Expressions

Dominant media stereotypes, oppression, and cultural expectations within diasporic communities exert pressure on Western women of South Asian descent. This research examines the way these individuals use Instagram as a counterspace to create and share gendered ethnic identity (GEI) expressions to counter oppression and promote well-being. In doing so, this study answers the call to advance research on genders, markets, and culture by exploring GEI as a sociocultural, intersectional construct. Based on visual data analysis and the extant literature, we posit a framework of social media as a counterspace for shared GEI expressions. This framework introduces three novel challenging processes (acts of intragroup questioning, acts of intragroup alliance, and narratives of possibility) that participants use to foster just representation and collective well-being.
EasyTurk: A User-Friendly Interface for High-Quality Linguistic Annotation with Amazon Mechanical Turk

Amazon Mechanical Turk (AMT) has recently become one of the most popular crowd-sourcing platforms, allowing researchers from all over the world to create linguistic datasets quickly and at a relatively low cost. Amazon provides both a web interface and an API for AMT, but they are not very user-friendly and miss some features that can be useful for NLP researchers. In this paper, we present EasyTurk, a free tool that improves the potential of Amazon Mechanical Turk by adding to it some new features. The tool is free and released under an open source license.
Exploiting Image–Text Synergy for Contextual Image Captioning

Modern web content - news articles, blog posts, educational resources, marketing brochures - is predominantly multimodal. A notable trait is the inclusion of media such as images placed at meaningful locations within a textual narrative. Most often, such images are accompanied by captions - either factual or stylistic (humorous, metaphorical, etc.) - making the narrative more engaging to the reader. While standalone image captioning has been extensively studied, captioning an image based on external knowledge such as its surrounding text remains under-explored. In this paper, we study this new task: given an image and an associated unstructured knowledge snippet, the goal is to generate a contextual caption for the image.
Slav-NER: the 3rd Cross-lingual Challenge on Recognition, Normalization, Classification, and Linking of Named Entities across Slavic Languages

This paper describes Slav-NER: the 3rd Multilingual Named Entity Challenge in Slavic languages. The tasks involve recognizing mentions of named entities in Web documents, normalization of the names, and cross-lingual linking. The Challenge covers six languages and five entity types, and is organized as part of the 8th Balto-Slavic Natural Language Processing Workshop, co-located with the EACL 2021 Conference. Ten teams participated in the competition. Performance for the named entity recognition task reached 90% F-measure, much higher than reported in the first edition of the Challenge. Seven teams covered all six languages, and five teams participated in the cross-lingual entity linking task. Detailed valuation information is available on the shared task web page.
Aggregating and Learning from Multiple Annotators

The success of NLP research is founded on high-quality annotated datasets, which are usually obtained from multiple expert annotators or crowd workers. The standard practice to training machine learning models is to first adjudicate the disagreements and then perform the training. To this end, there has been a lot of work on aggregating annotations, particularly for classification tasks. However, many other tasks, particularly in NLP, have unique characteristics not considered by standard models of annotation, e.g., label interdependencies in sequence labelling tasks, unrestricted labels for anaphoric annotation, or preference labels for ranking texts. In recent years, researchers have picked up on this and are covering the gap. A first objective of this tutorial is to connect NLP researchers with state-of-the-art aggregation models for a diverse set of canonical language annotation tasks. There is also a growing body of recent work arguing that following the convention and training with adjudicated labels ignores any uncertainty the labellers had in their classifications, which results in models with poorer generalisation capabilities. Therefore, a second objective of this tutorial is to teach NLP workers how they can augment their (deep) neural models to learn from data with multiple interpretations.
How do people interact with biased text prediction models while writing?

Recent studies have shown that a bias in thetext suggestions system can percolate in theuser’s writing. In this pilot study, we ask thequestion: How do people interact with text pre-diction models, in an inline next phrase sugges-tion interface and how does introducing senti-ment bias in the text prediction model affecttheir writing? We present a pilot study as afirst step to answer this question.
Identifying negative language transfer in learner errors using POS information

A common mistake made by language learners is the misguided usage of first language rules when communicating in another language. In this paper, n-gram and recurrent neural network language models are used to represent language structures and detect when Chinese native speakers incorrectly transfer rules from their first language (i.e., Chinese) into their English writing. These models make it possible to inform corrective error feedback with error causes, such as negative language transfer. We report the results of our negative language detection experiments with n-gram and recurrent neural network models that were trained using part-of-speech tags. The best performing model achieves an F1-score of 0.51 when tasked with recognizing negative language transfer in English learner data.
Making Use of Latent Space in Language GANs for Generating Diverse Text without Pre-training

Generating diverse texts is an important factor for unsupervised text generation. One approach is to produce the diversity of texts conditioned by the sampled latent code. Although several generative adversarial networks (GANs) have been proposed thus far, these models still suffer from mode-collapsing if the models are not pre-trained. In this paper, we propose a GAN model that aims to improve the approach to generating diverse texts conditioned by the latent space. The generator of our model uses Gumbel-Softmax distribution for the word sampling process. To ensure that the text is generated conditioned upon the sampled latent code, reconstruction loss is introduced in our objective function. The discriminator of our model iteratively inspects incomplete partial texts and learns to distinguish whether they are real or fake by using the standard GAN objective function. Experimental results using the COCO Image Captions dataset show that, although our model is not pre-trained, the performance of our model is quite competitive with the existing baseline models, which requires pre-training.
MATILDA - Multi-AnnoTator multi-language InteractiveLight-weight Dialogue Annotator

Dialogue Systems are becoming ubiquitous in various forms and shapes - virtual assistants(Siri, Alexa, etc.), chat-bots, customer sup-port, chit-chat systems just to name a few.The advances in language models and their publication have democratised advanced NLP.However, data remains a crucial bottleneck.Our contribution to this essential pillar isMATILDA, to the best of our knowledge the first multi-annotator, multi-language dialogue annotation tool. MATILDA allows the creation of corpora, the management of users, the annotation of dialogues, the quick adaptation of the user interface to any language and the resolution of inter-annotator disagreement. We evaluate the tool on ease of use, annotation speed and interannotation resolution for both experts and novices and conclude that this tool not only supports the full pipeline for dialogue annotation, but also allows non-technical people to easily use it. We are completely open-sourcing the tool at https://github.com/wluper/matilda and provide a tutorial video1.
Comparing the Performance of CNNs and Shallow Models for Language Identification

In this work we compare the performance of convolutional neural networks and shallow models on three out of the four language identification shared tasks proposed in the VarDial Evaluation Campaign 2021. In our experiments, convolutional neural networks and shallow models yielded comparable performance in the Romanian Dialect Identification (RDI) and the Dravidian Language Identification (DLI) shared tasks, after the training data was augmented, while an ensemble of support vector machines and Naïve Bayes models was the best performing model in the Uralic Language Identification (ULI) task. While the deep learning models did not achieve state-of-the-art performance at the tasks and tended to overfit the data, the ensemble method was one of two methods that beat the existing baseline for the first track of the ULI shared task.
Training and Domain Adaptation for Supervised Text Segmentation

Unlike traditional unsupervised text segmentation methods, recent supervised segmentation models rely on Wikipedia as the source of large-scale segmentation supervision. These models have, however, predominantly been evaluated on the in-domain (Wikipedia-based) test sets, preventing conclusions about their general segmentation efficacy. In this work, we focus on the domain transfer performance of supervised neural text segmentation in the educational domain. To this end, we first introduce K12Seg, a new dataset for evaluation of supervised segmentation, created from educational reading material for grade-1 to college-level students. We then benchmark a hierarchical text segmentation model (HITS), based on RoBERTa, in both in-domain and domain-transfer segmentation experiments. While HITS produces state-of-the-art in-domain performance (on three Wikipedia-based test sets), we show that, subject to the standard full-blown fine-tuning, it is susceptible to domain overfitting. We identify adapter-based fine-tuning as a remedy that substantially improves transfer performance.
SERAG: Semantic Entity Retrieval from Arabic Knowledge Graphs

Knowledge graphs (KGs) are widely used to store and access information about entities and their relationships. Given a query, the task of entity retrieval from a KG aims at presenting a ranked list of entities relevant to the query. Lately, an increasing number of models for entity retrieval have shown a significant improvement over traditional methods. These models, however, were developed for English KGs. In this work, we build on one such system, named KEWER, to propose SERAG (Semantic Entity Retrieval from Arabic knowledge Graphs). Like KEWER, SERAG uses random walks to generate entity embeddings. DBpedia-Entity v2 is considered the standard test collection for entity retrieval. We discuss the challenges of using it for non-English languages in general and Arabic in particular. We provide an Arabic version of this standard collection, and use it to evaluate SERAG. SERAG is shown to significantly outperform the popular BM25 model thanks to its multi-hop reasoning.
Why Find the Right One?

The present paper investigates the impact of the anaphoric one words in English on the Neural Machine Translation (NMT) process using English-Hindi as source and target language pair. As expected, the experimental results show that the state-of-the-art Google English-Hindi NMT system achieves significantly poorly on sentences containing anaphoric ones as compared to the sentences containing regular, non-anaphoric ones. But, more importantly, we note that amongst the anaphoric words, the noun class is clearly much harder for NMT than the determinatives. This reaffirms the linguistic disparity of the two phenomenon in recent theoretical syntactic literature, despite the obvious surface similarities.
Comparison of Bag-Of-Words and Doc2Vec Sentence-Vector Representations in Graph-Based Summarization of Earnings Call Transcripts

This research illustrates the use of an automatic summarization algorithm in the domain of corporate disclosure. The goal of this study was to compare a graph-based automatic summarization algorithm that incorporates semantic inter-sentence similarities to one that only takes into account lexical similarities, by producing summaries of 20 earnings call transcripts. The two algorithms differ in their sentence-vector representations: the lexical summarization algorithm employs a bag-of-words (BOW) model to obtain sentence-vectors, while the semantic summarization algorithm makes use of sentence embeddings (Doc2Vec). After producing the summaries and evaluating them with a help of six human judges, it has been concluded that the semantic algorithm produces summaries containing information that is more useful for making investment decisions about a company. The views stated in this thesis are those of the author and not necessarily those of the supervisor, second assessor, Erasmus School of Economics or Erasmus University Rotterdam.
Is Energy Subsidy Reform in an Oil-Exporting Small Economy Beneficial to Trade? Illustrations from Kuwait

Abstract This article fills a gap in the literature by quantifying impacts of fossil fuel subsidy reform on trade (inflow and outflow) in an oil-producing, “almost small”, economy, using Kuwait as an example. It employs a two-region economy-wide model with oligopoly behaviour in a general equilibrium framework. The model embodies unique elements of Kuwait's economic structure, idiosyncratic rigidities, and distortions, including oligopolistic industrial structure and labour markets. Simulations show that energy subsidies have minimal effects on trade and on non-energy exports, largely due to the pervasiveness of oligopolies that sustain large markups and their collusive pricing. Reforming energy subsidies generates higher pro-trade effects if implemented during low (not high) oil prices because its negative effects are partially offset by efficiency gains and reduction in oligopoly markups. Yet, contrary to claims by proponents of reforms, these effects remain largely constrained unless appropriate incentives are introduced. These results have important policy implications. In developing oil-exporting economies with pervasive oligopolies, microeconomic reform can be a channel through which to achieve pro-trade effects of energy subsidy reform. Further, benefits beyond export expansion, such as higher economic efficiency, could be better motivators of energy subsidy reform in oil economies.
The Pattern of Life of Spoken Communities: Ethnographic Studies of Communication on Student's Social Life

Students are a social community where the basic views and primary cells grow from the basic views and scientific disciplines in the academic world. Students have their own culture of social life in their main activities, which are seen as very functioning as the main pillar in the development of human resources and as a determinant of a nation's quality level. The research aims to determine the social interaction behavior of students in several faculties at Pattimura University. With a background of good student social interaction behavior in several faculties, a harmonious social relationship will be built between fellow students in the Pattimura University campus environment. This research is qualitative research, designed based on a constructive paradigm in which every speech and action community is not only interpreted from a particular perspective but needs to be understood holistically based on the cultural approach and characteristics of each region. This research is based on an interpretive paradigm that views the world and objects of human life from their experience and subjectivity and has different views from the positivistic paradigm. When the positivistic paradigm uses a basic premise with a short phrase, knowledge is un-problematic, the interpretive paradigm sees the opposite.
Syndiotactic Polymer of Allylcyclopentane by a Metallocene Catalyst

Syndiospecific (co)polymerization of some higher α‐olefins was performed by using a metallocene catalyst. Homopolymers of allylcyclopentane, 4‐methyl‐1‐pentene, and allylcyclohexane are highly syndiotactic. The results of the copolymerization of allylcyclopentane with 1‐hexene, 1‐octene, or 1‐decene showed that the incorporation of a linear higher α‐olefin interfered with the syndiospecificity of the catalyst, and the melting point of the resulting copolymer decreased with the incorporation of the comonomer. In contrast, the copolymer of allylcyclopentane with 4‐methyl‐1‐pentene or allylcyclohexane maintained the high syndiotacticity, and the melting point also remained almost unchanged no matter how much the comonomer was incorporated. It followed that the “shape” of the monomer had a significant influence on the structure of the copolymer. The monomers employed in copolymerization with a similar “shape” gave a copolymer with a similar microstructure.
Extractive desulfurisation of gasoline with tetrabutyl ammonium chloride-based deep eutectic solvents

ABSTRACT In this study, three tetrabutyl ammonium chloride (TBAC)-based deep eutectic solvents (DESs) were synthesised and used for desulfurising model and real gasolines. Extraction efficiencies of different DESs followed the order of TBAC/ethylene glycol > TBAC/glycerol > TBAC/malonic acid. After five extraction cycles, the extraction efficiencies of TBAC/ethylene glycol, TBAC/glycerol and TBAC/malonic acid reached 99.5, 99.3 and 99.2%, respectively. Additionally, the structures of original and recycled DESs were analysed and the results showed that DESs maintained their original structures after regeneration. Finally, the desulfurisation performance of DESs for real gasoline was also investigated. This work will provide new green extractants for gasoline desulfurisation.
Unraveling the Organotellurium Chemistry Applied to the Synthesis of Gold Nanomaterials

Long-term preservation of the properties of gold nanoparticles in both solution and the dry powder form can be difficult. We have overcome this challenge by using organotellurium derivatives as both reducing agents and stabilizers in the synthesis of gold nanoparticles. This new synthetic protocol takes advantage of the photochemical and oxidative properties of diphenyl ditelluride (Ph2Te2), which, so far, have never been exploited in the synthesis of gold nanoparticles. The Au/Te core/shell (inorganic/organic) hybrid nanomaterial can be obtained in a one-step reaction, using only Ph2Te2 and HAuCl4. By modifying the reaction conditions, different resonance conditions of the gold core are achieved due to the formation of external shells with different thicknesses. The organotellurium shell can be easily removed by resuspension of the nanoparticles in environmentally friendly solvents, such as water or ethanol, making the Au core available for subsequent applications. A mechanism for the formation of core/shell nanoparticles has also been discussed.
Electrochemical Analysis of Some Biodegradable Mg-Ca-Mn Alloys

BOGDAN OPRISAN, DECEBAL VASINCU*, STEFAN LUPESCU*, CORNELIU MUNTEANU, BOGDAN ISTRATE*, DIANA POPESCU, CATALIN PLESEA CONDRATOVICI, ALINA RAMONA DIMOFTE, KAMEL EARAR Gr.T. Popa Medicine University of Iasi, Faculty of General Medicine, 16 University Str. , Iasi, Romania University of Medicine and Pharmacy Grigore T. Popa, Faculty of DentalMedicine, 16 Universitatii Str. 700115, Iasi, Romania Gheorghe Asachi University of Iasi, Faculty of Mechanical Engineering Department, 43 DimitrieMangeron Str. , 700050, Iasi University „Dunărea de Jos”of Galați, Faculty of Medicine and Pharmacy, 35 Al. I. Cuza Str. , Galati, Romania