Miracle-dz/UniFAR-FTUs huggingface dataset:
Global Evidence on Mandating ESG Disclosure in Annual Reports*

On July 28, 2021, the SEC announced that it plans to release a rulemaking proposal for mandatory climate risk disclosure by the end of 2021. One major objective of this rulemaking is to provide investors with more comparable information. To further accounting researchers’ understanding of the effects of mandatory adoption of ESG reporting standards, and to provide policy-relevant insights to regulators, we conduct an international study of whether mandating ESG reporting leads to harmonization effects in reporting practices in annual reports. Moreover, we investigate the extent to which the comprehensiveness and specificity of the disclosure requirements, the mandated ESG disclosure channels, and the legal enforcement environment moderate the relationship between mandating ESG reporting and the harmonization of disclosure practices. Using Refinitiv Global Filings, we collect 20 years of annual reports (i.e., 2000-2019) from publicly listed firms in 26 different international regions. We estimate ESG disclosure in annual reports using a dictionary-based natural language processing technique, then derive our comparability measure and test our research questions using a standard staggered Difference-in-Differences approach. * We would like to thank Sean Cao, Ciao-Wei Chen, Xia Chen, Frank Heflin, Adam Koch, Heather Li, Yufei Lin, James R. Moon, and workshop participants at the Chinese University of Hong Kong, Shenzhen and the University of Virginia for their helpful comments. The remaining errors are ours. Rui Shen acknowledges financial support from the National Science Foundation of China (NSFC 72072153). Jasmine Wang and Y. Julia Yu are grateful for financial support from McIntire School of Commerce, University of Virginia. We are responsible for all remaining errors and omissions. Global Evidence on Mandating ESG Disclosure in Annual Reports
The interaction between language usage and acoustic correlates of the Kuy register distinction

Contact is often cited as an explanation for the convergence of areal features and has been proposed as an explanation for the emergence of tonal languages in Mainland Southeast Asia. The current production study probes this hypothesis by exploring the relationship between tonal language usage and the acoustic correlates of the register distinction in Kuy, a Katuic language, as spoken in a quadrilingual (Kuy, Thai, Lao, Khmer) Kuy community in Northeast Thailand. The results demonstrate greater persistence of fundamental frequency (f0) differences over the course of the vowel alongside more tonal language experience for male speakers; however, analysis of individual differences finds that H 1 * −H 2 * , a correlate of voice quality, is the primary cue for male speakers with greater tonal language experience. For female speakers, a tradeoff is found between f0 and voice quality cues alongside tonal language experience at both the group and individual levels. These findings provide evidence for a model by which contact may serve to enhance existing, non-primary cues in a phonological contrast by shifting cue distributions, thereby increasing the likelihood that these cues will come to be perceived as prominent and phonologized.
Role of IMF Lending Preconditions In Nigeria

Nigerian government was seeking help from the IMF, because of which the economy has deteriorated considerably as the Institution recommended some structural conditions before the country is supported which was proclaimed as a panacea for solving the development problem within the country. Therefore, the aim of this study was to seek whether the policies contribute to solving the development problems Nigeria is facing. The study utilized Neoliberalism as conceptual framework, using a qualitative research approach, the data were obtained using an in-depth interviews. With the assistance of Qualitative Data Analysis software, NVivo 12 for Microsoft Windows, the collected data was analyzed using content and thematic analysis. The results indicate that the policies prescribed by the IMF are in place to promote Nigeria's socio-economic prosperity. Some of the policies prescribed by the IMF include deregulation, privatization, removal of subsidies, currency devaluation and trade liberalization. The study also revealed that over the years, IMF policies have contributed and impeded Nigerian economic growth tremendously. The study advises that the government should formulate plans and initiatives to cushion the detrimental effects of any policies that can be directly or indirectly enforced. This will guarantee that the country is not politically or financially fully destabilized
Precancerous and Cancerous Lesions of the Cervix in African Environments (Healthdistrict of Thecommune 5 of Bamako in Mali)

Objective: was to take stock of the screening and treatment of precancerous and cancerous lesions of the cervix in the health district of commune V of Bamako in Mali. Patients and Methods: This was a descriptive, cross-sectional, analytical study with retrospectiveand prospective data collection over a period of 08 years from 1 January 2010 to 31 December 2017. Results: From January 1, 2010 to December 31, 2017, 42492 women were screened on a target of 174777 women, a frequency of 24.30%. The median age of women screened was 32 years; 25% were under 25 years of age. A similar proportion were over the age of 40. 3/4 of the women screened belonged to the age group of 20 to 49 years. Women aged 55 and over were the least represented by 5.7%. Screening coverage was over 35% among the 25-49 age groups. It was 31.9% and 23.1% among women aged 50 – 54 and 55 – 59 respectively. Screening coverage was less than 15% among 60-year-olds and older. Of the 22842 women screened, 90.1% had a normal cervix at IVA/IVL. However 4.1% cervix positivity to acetic acid and 5.1% positivity to Lugol. 0.7% of the women screened had clinically cancerous lesions. At the anatomo-pathological level, various histological lesions have been observed with regard to precancerous lesions, squamous cell carcinoma and adenocarcinomas. Indeed, among the women screened 96.5% had a normal cervix with benign lesions. For those with pathological histological results, 2.6% of them had histology with precancerous le sions and 0.8% had squamous cell carcinomas. A low rate of 0.1% of adenocarcinoma was recorded in our series. Conclusion: The improvement of screening indicators with IVA/IVL which does not require large resources is a promising avenue in Africa on which, it can hope for the rate of morbidity and mortality by cervical cancer.
Knowledge Discovery for Early Failure Assessment of Complex Engineered Systems Using Natural Language Processing

Emerging complex engineered systems may have unexpected safety issues due to novel operational environments, increasing autonomy, human-machine interaction, and other factors. To prevent failures in operation or testing that necessitate costly redesign, it is desirable to predict likely failure modes early in the design process. Information about past engineering failures in natural language format presents one possible solution by enabling the retrieval of information that can inform new designs. However, identifying documents containing usable information and extracting the required information can be prohibitively time-consuming when implemented at scale. In this research, an automated natural language processing (NLP) framework is proposed to discover relevant knowledge from documents containing failure-related design information. The framework is applied to NASA’s Lessons Learned Information System (LLIS), which is publicly available. Documents containing usable information are filtered using two different NLP-based models. Next, from the identified usable documents, a failure taxonomy is extracted using a partitioned hierarchical topic modeling approach. Partitions of the document describe different sections of the failure taxonomy — i.e., failure, cause of failure, and recommendations — as indicated by the structure of the original document. The extracted failure taxonomy can be leveraged in early design failure assessment methods. Moreover, the framework can be used to identify documents containing usable failure-related design information from other databases and extract relevant information from these documents.
Agent-based simulation for aircraft stand operations to predict ground time using machine learning

Punctual and reliable aircraft ground handling operations at the airports significantly contribute to efficient traffic flows in the air traffic network. Any improved prediction of aircraft ground times can help to reduce local delays and delay propagation in the network by taking into account the forecast of future operational states for adjusted planning and delay mitigation strategies. In our work, we target to predict aircraft ground times at their stands by machine learning algorithms, where the complete turnaround sub-processes and domain knowledge are input for the models. We develop two types of models, the first type is regression-oriented that intends to forecast the exact aircraft ground time. And the second one is classification-oriented, which attempts to confirm aircraft off-block time adherence. An agent-based approach is applied to generate some synthetic data, besides, we also obtain an actual aircraft ground handling dataset from a certain European airport to validate our models. Finally, the interpretable method for the machine learning models is used to analyse the feature importances, and the feature affections on the prediction results. The results show that our classification model is capable to predict accurate aircraft off-block time adherence.
PREDICATIVITY AND CONSTRUCTIVE MATHEMATICS

Constructive mathematics is a form of mathematics which uses intuitionistic rather than classical logic. Different varieties of mathematics based on intuitionistic logic have been proposed over the years since Brouwer’s inception of intuitionism. In the following, “constructive mathematics” denotes “Bishop–style” mathematics, the mathematics based on intuitionistic logic initiated by Errett Bishop in “Foundations of constructive analysis” [9]. Constructive mathematics has since witnessed substantial advances in analysis, topology and algebra. Starting from the 1970’s, a number of formal systems have been proposed to codify or formalise this form of mathematics. Their aim was to isolate the principles underlying constructive mathematics’ fundamental concepts, especially its concepts of set and function. Among these systems are Martin-Löf Type Theory and Constructive Set Theory. In this article, I consider one aspect in which constructive and classical foundations of mathematics differ. Constructive set and type theories diverge from standard classical set theories such as ZermeloFraenkel set theory in two distinct respects: they employ intuitionistic
Later credits: resourceful reasoning for the later modality

In the past two decades, step-indexed logical relations and separation logics have both come to play a major role in semantics and verification research. More recently, they have been married together in the form of step-indexed separation logics like VST, iCAP, and Iris, which provide powerful tools for (among other things) building semantic models of richly typed languages like Rust. In these logics, propositions are given semantics using a step-indexed model, and step-indexed reasoning is reflected into the logic through the so-called “later” modality. On the one hand, this modality provides an elegant, high-level account of step-indexed reasoning; on the other hand, when used in sufficiently sophisticated ways, it can become a nuisance, turning perfectly natural proof strategies into dead ends. In this work, we introduce later credits, a new technique for escaping later-modality quagmires. By leveraging the second ancestor of these logics—separation logic—later credits turn “the right to eliminate a later” into an ownable resource, which is subject to all the traditional modular reasoning principles of separation logic. We develop the theory of later credits in the context of Iris, and present several challenging examples of proofs and proof patterns which were previously not possible in Iris but are now possible due to later credits.
Effect of priming with proline on the performance of two wheat cultivars

Amino acids (bio-stimulants) are considered as cheap and biodegradable chemicals that increased yield and agronomic performance of several crops. A field experiment was conducted at the experimental station of National Research Centre, Nubaria district El-Behera Governorate-Egypt, during two winter seasons 2017/2018 and 2018/2019. Grains of Misr 3 and Sids 12 cultivars were soaked with proline at 5 or 10 mM for 12 hrs and dried at room temperature before sowing to study the effect of proline treatments on the performance and quality of wheat plants grown under sandy soil conditions. Data show that performance of Misr 3 cv grown under the conditions of sandy soil was more better than Sids 12 cv. Moreover, proline treatments at both concentrations caused significant increases in most parameters measured in this study as plant fresh and dry weight; total photosynthetic pigments; grains weight/fedan. Proline treatment at 5 mM caused the highest significant increase in grains yield and yield components of Misr 3 cv. Where, grains yield/feddan was increased by 21.32%. Meanwhile, the highest significant increase in grains yield of Sids 12 cv was achieved by proline at 10 mM by 16.90%. It was noted that Misr 3cv responded effectively to 5 mM proline, while response of Sids 12 cv was more effective to 10 mM proline. It could be concluded that proline treatments showed promotive effect on quality and quantity of both wheat cultivars grown under sandy soil conditions.
Sentiment Analysis on Covid-19 Twitter Data

—According to the WHO, Covid-19 is an illness caused by a novel coronavirus scientifically known as SARS-CoV2. This virus was first discovered in late December 2019, when a cluster of pneumonia cases was reported from Wuhan, China. Since then, the virus has continued to spread and cases have grown rapidly, leading to 74.3 million cases worldwide and 1.65 million deaths in within the span of a year. According to Dr. Sanil Rege, a psychiatrist, the effects of quarantining and social distancing include multiple stressors affect a person during a time of isolation. For instance, a person could have unpleasant experiences due to loss of freedom, separation from significant people in their lives, fear, financial stability, and lack of supplies. These factors can strongly lead to the development of stress symptoms such as irritability, insomnia, temper issues, emotional burnout, and overall low mental health. As such, the Covid-19 pandemic has affected the normalcy of life throughout the world and many people have taken to social media platforms such as Twitter to express their thoughts and feelings. In order to understand the type of discussions taking place regarding Covid-19 and to recognize major topics of concern, the relationship between tweet sentiment and Covid-19 casualties are analyzed, then, Tweet text is examined to identify frequently used words, hashtags, and mentioned users. Covid-19 keyword containing tweets are downloaded using Tweepy to a database and analyzed for sentiment by NLTK Vader. Results suggest a moderate positive correlation between negative sentiment and Covid-19 cases and deaths.
Elastic Tree Layouts for Interactive Exploration of Mentorship

Mentorship is an important collaborative relationship among scholars. The existing tools to visualize it mainly suffer from a waste of space, lack of overview representation, and less displayed attribute information. To solve these problems, we propose a novel elastic tree layout based on node-link diagrams, in which nodes and edges are represented as elastic rectangles and bands respectively. By stretching, compressing, aggregating, and expanding nodes and edges, we can: get a compact tree layout with high space-efﬁciency, display both the detailed subtree and compressed context in a single view, use labeling, charts, and node opacity to show multiple attributes. Besides, we designed various animated interactions to facilitate the exploration.
Beware the Side Effects: Capital Controls, Misallocation and Welfare

We study the effects of capital controls through the lens of a dynamic model with heterogeneous firms, monopolistic competition, endogenous trade participation and financial frictions. A tax on foreign borrowing worsens misallocation via static effects that reduce capital-labor ratios and increase firm prices. Dynamic effects reduce it via stronger saving incentives and general equilibrium effects worsen it by reducing aggregate prices and output. Quantitative analysis calibrated to the 1990s Chilean encaje predicts higher misallocation with larger effects on high-productivity exporting or young firms that depend more on debt. Social welfare falls sharply, by 2.39%, due to large general equilibrium effects. The costs for high-productivity firms are twice as large. Using an interest-rate hike (a tighter LTV requirement) instead of capital controls yields larger (smaller) effects. Empirical evidence derived from Chilean firm-level data shows that the encaje did increase misallocation and significantly more for high-productivity exporting or young firms.
Generalising to German Plural Noun Classes, from the Perspective of a Recurrent Neural Network

Inflectional morphology has since long been a useful testing ground for broader questions about generalisation in language and the viability of neural network models as cognitive models of language. Here, in line with that tradition, we explore how recurrent neural networks acquire the complex German plural system and reflect upon how their strategy compares to human generalisation and rule-based models of this system. We perform analyses including behavioural experiments, diagnostic classification, representation analysis and causal interventions, suggesting that the models rely on features that are also key predictors in rule-based models of German plurals. However, the models also display shortcut learning, which is crucial to overcome in search of more cognitively plausible generalisation behaviour.
Visibility Graph Network of Multidimensional Time Series Data for Protein Structure Classification

In the last decades, many studies have explored the potential of utilizing complex network approaches to characterize time series generated from dynamical systems. Along these lines, Visibility Graph (VG) and Horizontal Visibility Graph (HVG) networks have contributed to an important yet difficult problem in bioinformatics, the classification of the secondary structure of low-homology proteins. In particular, each protein is presented as a two-dimensional time series that is later transformed, using either VG or HVG, into two independent graphs. However, this is an inefficient way of processing multidimensional time series as it fails to capture the correlation between the two signals while it also increases the time and memory complexity. To address this issue, this work proposes four novel VG and HVG-based frameworks that are able to deal directly with the multidimensional time series. Each of the methods generates a unique graph following a different visibility rule concerning only the relation between pairs of time series intensities of the multidimensional time series. Experimental evaluation on real protein sequences demonstrates the superiority of our best scheme, with respect to both accuracy and computational time, when compared against the state-of-the-art.
Bi-allelic variants in CHKA cause a neurodevelopmental disorder with epilepsy and microcephaly

The Kennedy pathways catalyze the de novo synthesis of phosphatidylcholine and phosphatidylethanolamine, the most abundant components of eukaryotic cell membranes. In recent years, these pathways have moved into clinical focus since four out of ten genes involved have been associated with a range of autosomal recessive rare diseases such as a neurodevelopmental disorder with muscular dystrophy (CHKB), bone abnormalities and cone-rod dystrophy (PCYT1A), and spastic paraplegia (PCYT2, SELENOI). We identified six individuals from five families with bi-allelic variants in CHKA presenting with severe global developmental delay, epilepsy, movement disorders, and microcephaly. Using structural molecular modeling and functional testing of the variants in a in a cell-based S. cerevisiae model, we determined that these variants reduce the enzymatic activity of CHKA and confer a significant impairment of the first enzymatic step of the Kennedy pathway. In summary, we present CHKA as a novel autosomal recessive gene for a neurodevelopmental disorder with epilepsy and microcephaly.
Chinese Development Lending & the Ampliﬁcation Eﬀect ∗

Over the last few decades, China has emerged as a major player in foreign aid and development lending. While a large empirical literature exists on the eﬀects of foreign aid upon recipient countries’ political institutions, less has been written on this with respect to China’s role as an oﬃcial lender and source of foreign aid in the developing world. Since Chinese lending diﬀers signiﬁcantly from conventional lending activities engaged in by multilateral institutions and OECD creditor governments, how much of what we know about the eﬀects of foreign aid on political institutions applies to the Chinese case? This paper investigates the “ampliﬁcation eﬀect” hypothesis—that aid ampliﬁes recipients’ existing set of political institutions—with respect to Chinese development ﬂows. Employing diﬀerent estimators upon panel data for 104 countries between 2002 and 2017 that have been recipients of Chinese development ﬂows, the paper’s ﬁndings support the ampliﬁcation eﬀect hypothesis as applied to the Chinese case, as the average sampled democracy becomes more democratic in consequence, and the average sampled autocracy becomes more authoritarian. However, the ampliﬁcation eﬀect exhibits a greater magnitude in autocratic recipients of Chinese development ﬂows, as autocracies become more autocratic relative to democracies becoming more democratic. Various channels through which these eﬀects take place are explored.
Salp Swarm Algorithm Based Priority Scheduling For Energy-Efficient Power Allocation In MIMO-NOMA System

— Large scale Multiple Input Multiple Output (MIMO) is a key technology used in the wireless networks of the next generation. It uses the multiplexing of few antennas at the same frequency to serve few users for achieving high efficiency in the spectral based on the linear transmit and receive approach. However, the system using co-located antennas suffer from the correlated fading of small scale and identical fading of large scale. Non Orthogonal Multiple Access (NOMA) is a multiple access technology in the communication system for fifth generation (5G). The Energy Efficiency (EE) of MIMO-NOMA system needs to be improved, where an optimization method, called Salp Swarm Algorithm (SSA), schedules the user satisfying maximum power and QoS constraints to offer efficient energy and power allocation in the platform effectively. The scheduling is performed based on the SSA algorithm to prioritize the user using the objective function in an optimal way. The experimentation results are analyzed using the evaluation metrics, like spectral power, achievable rate, Bit Error Ratio (BER), and energy. The effectiveness of the SSA-based power allocation approach is exposed using the lower BER of 0.00054, and higher achievable rate, spectral power, and energy of 96.1985Mbps, 128.198dB, and 19.4672dB, respectively. Index Terms