Miracle-dz/UniFAR-FTUs huggingface dataset:
On Black’s Leverage Effect in Firms with No Leverage

One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility. Also known as the leverage effect, this relationship was first documented by Black, who attributed it to the effects of financial or operating leverage. This article documents that firms that had no debt (and thus no financial leverage) from January 1973 to December 2017 exhibit Black’s leverage effect. Moreover, the authors find that the leverage effect of firms in this sample is not driven by operating leverage. On the contrary, in this sample the leverage effect is stronger for firms with low operating leverage as compared to those with high operating leverage. Interestingly, the firms with no debt from the lowest quintile of operating leverage exhibit a leverage effect that is on par with or stronger than that of debt-financed firms. TOPICS: Fundamental equity analysis, equity portfolio management, accounting and ratio analysis Key Findings • The inverse relationship between stock prices and volatility is often attributed to the effects of leverage and is known as the leverage effect. • We find that firms with no debt (and thus no financial leverage) exhibit the leverage effect. • Interestingly, among the firms with no debt, those with the lowest levels of operating leverage exhibit the strongest leverage effect.
An Efficient Classification Technique For Automatic Identification of Emotions Leading To Stress

Facial Expression Recognition (FER) has gained interest among researchers because of its ineludible role in human computer interaction (HCI). It is found to be useful in information security, data privacy, user authentication, person identification and in many areas. FER is also useful in recognizing social signals, social behavior, deceit detection, interactive video and behavior monitoring in the field of human-computer interaction. Aim of this paper is to identify a classifier which is most accurate to identify emotions like sad, disgust, angry and fear. For this the performance of Support Vector Machine (SVM), Random Forest (RF), Radial basis function (RBF), Multilayer Perceptron Neural Network (MLPNN), Decision tree (J48), K-Nearest Neighbor (KNN), Neural Network (NN) classifiers are evaluated. Principal Component Analysis (PCA) is used as the dimensionality reduction technique. Local binary pattern (LBP), Histogram of Gradient (HOG), Gabor wavelet and Chi-Square are used as the feature extraction technique. The evaluation process was conducted on MMI, JAFFE and Extended Cohn Kanade $(\mathbf{CK}+)$ facial expression datasets. It is found that the classifier KNN gives classification accuracy of 93.46 % in identifying the emotions which may lead to stress.
Morocco’s Makhzen and the Challenge of National Development

This article explores the question of national development in Morocco considering the institution of the makhzen. It asserts that to adequately assess Morocco’s national development as a post-colonial country, it is necessary to rely on an economic model based in politics rather than in theories exclusively informed by classical and neoclassical economics. Among the key economists called upon to investigate the validity of politics in discussions of national development and income inequality are the following: Simon Kuznets, Thomas Piketty, W. A. Lewis, and the duo Daron Acemoglu and James A. Robinson, all of whom continue a long tradition of economic sociology that had been established by George Simmel, Max Weber, Emile Durkheim, and Thorstein Veblen. Acemoglu and Robinson offer an especially useful theoretical model to discuss Morocco in light of its pervasive political institution: the makhzen. Overall, the paper demonstrates that inadequate national institutions, such as the makhzen in the case of Morocco, adversely impact national development and increase the level of income inequality.
Pattern of renal impairment among hypertensive subjects in Umuahia, South East, Nigeria

Hypertension is a cause and consequence of chronic kidney disease (CKD). The present study investigated the patterns of renal impairment among hypertensive subjects in Umuahia, South East, Nigeria. A cross-sectional study involving 262 subjects comprising equal number of hypertensive and non-hypertensive was used. Questionnaires were administered; medical, socio-demographic and anthropometric profiles were obtained. Blood samples were taken for creatinine determination and glomerular filtration rate (GFR) was estimated using the modification of diet in renal disease (MDRD) equation. The GFR in hypertensive group was 87.4±30.2 ml/min/1.73 m2 compared to 99.9 ±32.3 ml/min/1.73 m2 in the non-hypertensive. In the hypertensive group, 30.5, 29.0 and 0.8% had mild, moderate and severe renal impairment respectively whereas in the non-hypertensive group, the values were 28.2, 14.5 and 0.8% respectively. The prevalence of CKD in hypertensive subjects was 29.8% while that in the non-hypertensive was 15.3%. 55.6% of male hypertensive subjects had mild to moderate renal impairment compared to 23.5% in the non-hypertensive group; the difference between hypertensive and non-hypertensive in females was not statistically significant. More females had CKD than their male counterparts. Hypertension might have increased incidence of renal impairment and prevalence of CKD in Nigeria. This underscores the need for screening for CKD in the general population. Key words: Essential hypertension, glomerular filtration rate, chronic kidney disease.
Are Secondhand Internal Whistleblowing Reports Credible?

This study examines the characteristics, credibility, and value of internal whistleblowing reports based on secondhand information. We analyze over two million reports submitted to over one thousand publicly traded U.S. firms between 2004 and 2017 and find that claims in secondhand reports are 47.7% more likely than firsthand reports to be substantiated by management, which suggests that management views many secondhand reports as credible. Further, we find negative associations between the number of secondhand reports and negative outcomes including lawsuits and government fines. These findings are consistent with secondhand reports providing valuable information that allows management to identify and address issues before they become more costly to the firm.
Carbon labels on restaurant menus: do people pay attention to them?

Abstract This empirical study explores visual attention to carbon labels on restaurant menus. Carbon labels can support consumers in making deliberate low-carbon menu choices if, and only if, consumers pay attention to such labels. Using eye-tracking, we investigate how much attention consumers pay to carbon labels. We also compare how much attention they pay to carbon labels compared to labels indicating the benefit of each menu item to the local community. Results indicate that both carbon labels and labels communicating local community benefits attract little attention, suggesting that they are insufficient to direct patrons towards ordering low emissions menu items. Directing patrons toward more environmentally friendly menu items could significantly reduce the negative environmental impact of tourism and hospitality.
Determinants of Technology Adoption: Evidence from the Chinese Banking Industry

ABSTRACT The decision on online banking adoption is a strategic choice by banks with multiple purposes. Using panel data on banks with hand-collected information on adoption decisions from 2002 to 2016, this article is the first to examine the determinants of online banking adoption in China. We first construct a theoretical model to illustrate banks’ online service adoption strategy and then test the determinants of online banking adoption decisions. Our model compares the patterns of online banking adoption decisions at large national banks and small banks. The model and empirical results show that banks with a larger geographic market adopt online banking earlier, and banks’ online banking adoption may be motivated by having competitors that have already adopted this technology. We also find evidence that online banking is a substitute for physical branches. Our results shed light on the banks’ effective management of online and offline services.
PositIV:A Configurable Posit Processor Architecture for Image and Video Processing

Image processing is essential for applications such as robot vision, remote sensing, computational photography, augmented reality etc. In the design of dedicated hardware for such applications, IEEE Std 754™ floating point (float) arithmetic units have been widely used. While float-based architectures have achieved favorable results, their hardware is complicated and requires a large silicon footprint. In this paper we propose a Posit-based Image and Video processor (PositIV), a completely pipelined, configurable, image processor using posit arithmetic that guarantees lower power use and smaller silicon footprint than floats. PositIV is able to effectively overlap computation with memory access and supports multidimensional addressing, virtual border handling, prefetching and buffering. It is successfully able to integrate configurability, flexibility, and ease of development with real-time performance characteristics. The performance of PositIV is validated on several image processing algorithms for different configurations and compared against state-of-the-art implementations. Additionally, we empirically demonstrate the superiority of posits in processing images for several conventional algorithms, achieving at least 35–40% improvement in image quality over standard floats.
Automatic Generation of Road Feature Identification Models from Point Cloud Data Using HD Maps

A large amount of point cloud data has been measured and accumulated at various locations for Japanese public works by introducing mobile mapping systems and terrestrial laser scanners. However, since point cloud data are a huge set of points that hold position coordinates, their use is limited in the unprocessed state. For this reason, we have been proposing a method for constructing a product model that structures point cloud data by assigning the meaning of a planimetric feature to the points using a plan of completion drawing that shows the completed shape of a construction object and 3D map data with high precision for autonomous driving. However, the applicable scope of the existing method is limited to the plan of completion drawing and the already developed sections of road maps. Therefore, a method of extracting road features from point cloud data using deep learning has been proposed. However, it requires manual preparation of a huge amount and highly qualified training data. In this study, we propose a method to automatically generate training data for constructing a road feature identification model from point cloud data automatically extracted using road maps. The usefulness of the proposed method has been confirmed through demonstration experiments.
The relationship between public debt accumulation and default risk under the ECB’s conventional vs. non-standard monetary policy: a panel data analysis of 9 Eurozone countries (2000–2015)

Abstract This paper investigates a long-run relationship between public debt accumulation and default risk, represented by Eurozone countries’ bond rates minus German benchmark bond rates for 9 Eurozone countries under the ECB’s conventional vs. nonstandard monetary policy for the period 2000–2015. Along with various unit root tests and cointegration tests, Dynamic OLS (DOLS) and Fully Modified OLS (FMOLS) methods are applied in order to examine a common long-run linkage between bond rates spreads and macro variables concerned without ignoring heterogeneous short-run dynamics. Such techniques directly address the endogeneity issue often encountered in analyses of economic fundamental variables. The empirical evidence reveals that a positive relationship between the bond rates spread and the debt-to-GDP ratio is found during the European Sovereign Debt Crisis and before the Global Financial Crisis in which the conventional monetary policy prevailed, reflecting negative market sentiments on default risk and market discipline. A negative long-run relationship is, by contrast, shown under the effective unconventional monetary policy, implying that overstated (exaggerated) default risk diminished after the ECB’s nonstandard measures. This phenomenon is especially apparent in, but not restricted to, peripheral Eurozone countries where most of the new monetary measures were targeted.
Comparison of four tumor markers at the RNA and protein level for the detection of micrometastases and disseminated tumor cells in lymph nodes of patients with cervical carcinoma

Lymph node status is the key prognostic factor for disease recurrence and patient mortality among cervical cancer patients. Patients with positive lymph nodes (pN1) have a high risk for recurrence. However, 15% of treated patients suffer recurrent disease although their lymph nodes have no evident metastases or micrometastases (pN0). The presence of occult tumor cells or tumor cell clusters smaller than micrometastases (<0.2mm) in lymph nodes could be the reason for poor prognosis of these patients. The goal of this dissertation is the detection of occult tumor cells and clusters in lymph nodes by using different molecular tumor markers and the measurement of the reliability on each marker by comparing them to each other. A highly sensitive, and at the same time, specific detection of these tumor cells is a prerequisite for further research confirming the clinical importance of occult tumor cells in lymph nodes. In this study, immunohistochemical staining (IHC) and reverse transcription nested PCR (RT-PCR) were used to detect metastatic tumor cells or clusters in sentinel lymph nodes (SLN) of 48 patients with primary cervical cancer. 120 lymph nodes were evaluated. IHC was performed with a pan-reactive antibody against cytokeratins (AE1/3), an antibody against CK19 and an antibody against p16INK4a. The latter protein, which is a surrogate marker of viral oncogene activity, is invariably upregulated in cervical cancers. Moreover, the viral oncogene (HPV-mRNA) activity was directly detected by RT-PCR.
Life cycle assessment of emerging environmental technologies in the early stage of development: A case study on nanostructured materials

The use of nanostructured materials has been recently proposed in the field of environmental nanoremediation. This approach consists in using nanomaterials not directly, but as building blocks for the design of nano‐porous micro‐dimensional systems, overcoming the eco‐ and health‐toxicology risks generally associated with the use of nano‐sized technologies. Herein we report the use of life cycle assessment (LCA) as an eco‐design tool for optimizing the production of cellulose nanosponges (CNS), nanostructured materials recently developed for water remediation purposes. LCA was applied from the acquisition of raw materials to the synthesis of CNS (from cradle‐to‐gate), considering three production systems, from the lab‐level to a modeled scale‐up system. The lab‐scale LCA identified the main environmental hotspots, namely the energy‐consuming steps and the final purification of the material (washing step). In a second lab‐scale production, an improvement action could be implemented, switching the washing solvent from methanol to water and decreasing the washing temperature. A second LCA showed a reduced contribution to the impacts from the materials, while the global impacts remained within the same order of magnitude. A simulated scale‐up of the process allowed to optimize the energy‐consuming steps and the water consumption, through internal recycling. A third LCA assessed the resulting benefits and a decrease in the global impacts by two orders of magnitude. Our study contributes to the discussion of LCA community, providing a focus on the importance of scaling‐up of emerging technologies, namely nanostructured porous materials, highlighting the benefits of a LCA based approach since the very beginning of product design (eco‐design).
Analysis on the Application of Presentation-Assimilation-Discussion Teaching Mode in Ideological and Political Education in Colleges and Universities under the Background of New Media

Under the background of new media, the channels of Ideological and Political Education in colleges and universities have been further enriched and expanded. This paper makes a preliminary analysis and exploration on effectively combining the Presentation-Assimilation-Discussion (PAD) teaching mode and new media, fully applying them to the process of Ideological and Political Education in colleges and universities, and effectively improving the quality level of talents training.
Extraction and Classification of Employment-Related Information from Web Data

On the web, there is large amount of information pertaining to various job-hunting activities, and automatically collecting this information would be efficient and convenient. In this study, information related to job hunting was extracted from massive web data and was classified. For extracting employment-related information, the performance of the following three methods was compared: a supervised machine learning method called a support vector machine (SVM), a rule-based method, and a baseline method. The rule-based method obtained superior performance with an F-measure of 0.7. To classify employment-related information, the following seven categories were used: “qualification information,” “vocational information,” “job seeker information,” “job offer information,” “no relationship,” “job hunting support information,” and “job change information. ” The results of the comparison between the three methods revealed the rule-based method displayed superior performance, with an average F-measure of approximately 0.6. In addition, the rule-based method had an F-measure of approximately 0.8 for the categories “qualification information,” “job offer information,” and “job change information. ”
Factors Influencing Employees Trust in AI & its Adoption at Work: Evidence from United Arab Emirates

UAE“s aspiration to be among the leading countries in artificial intelligence by 2031 calls for the identification of facilitators championing the adoption of this advanced technology in enterprises and government bodies across the country. The workforce is a major facilitator or barrier in new technology adoption at work. We need to determine if their trust in the new technology-with the competitive advantage of intelligence- influence their willingness to adopt them at work. This study investigates the impact of individual level (generation, gender, education, knowledge, skills) and organizational level (ownership type, business type) factors on employees' trust in artificial intelligence and its adoption at work. Six hundred and thirty- eight employees from both public and private sectors participated in the survey from the Emirates of Abu Dhabi. The findings confirmed significant influence of employees' generation, knowledge, and skills on their trust in Artificial Intelligence technology, subsequently influencing their intention to adopt the advanced technology at their respective workplaces. The findings have implications for government bodies formulating and implementing UAE's AI strategy in the region as well as for industry practitioners.
With Greater Uncertainty Comes Greater Volatility

Some academics and market participants argue that the growth of indexing causes market volatility. However, while the percentage of assets in indexed strategies has grown over the past twenty-five years, market volatility has risen and fallen in a somewhat random pattern, peaking around economic and financial crises. In this article, we test two measures of market volatility for their potential relationship with growth in indexing assets and selected macroeconomic factors. Our analysis demonstrates that macroeconomic factors have a strong correlation with and are useful predictors of market volatility; on the other hand, growth in indexing assets does not exhibit any causal relationship with market volatility. TOPIC: Volatility measures Key Findings • Macroeconomic factors and market volatility have a strong positive correlation while correlation between market volatility and growth of indexing is negative and relatively small in absolute terms. • Granger causality tests suggest that macroeconomic factors do have a causal relationship with and are useful predictors of market volatility. Growth of indexing, however, does not have such a relationship and is not a useful predictor of market volatility. • Macroeconomic factors such as economic policy uncertainty—not the growth of indexing assets—are responsible for elevated market volatility.
Understanding the Influence of Airport Servicescape on Traveler Dissatisfaction and Misbehavior

Underpinned by complexity theory, this study investigates whether the influence of social and physical servicescape on international travelers’ dissatisfaction and misbehavior differs between two characteristically different international airports in Iran. Partial least squares (PLS) and multi-group analysis (MGA) were employed to test the conceptual model. The results revealed significant differences between the effects of physical servicescape on travelers’ dissatisfaction and misbehavior across both airports. However, the results did not support any differences between the effects of social servicescape on travelers’ dissatisfaction and misbehavior between both airports. Additionally, using fuzzy-set qualitative comparative analysis (fsQCA), this study identified multiple configurations of physical and social servicescape dimensions leading to traveler dissatisfaction and misbehavior. In doing so, the results highlighted the conditions leading to low traveler dissatisfaction and misbehavior scores, confirming the applicability of complexity theory in explaining international traveler behavior in airports, providing implications and directions for future research in the process.
The Impact of No-Fault Unilateral Divorce Laws on Divorce Rates in Mexico

Between 2008 and 2017, Mexican states introduced no-fault unilateral divorce, which allowed married individuals to seek a divorce without the consent of their spouse. In this paper, we exploit variation in the state-level adoption of the reforms to investigate the consequences of the divorce law liberalization. Based on an event study design, our results suggest that no-fault divorce dramatically increased divorce rates in the 3 years following the reform. We next consider how the reform affected divorce filings and divorce settlements. We find that no-fault divorce increased individual divorce filings, especially among women, and lowered the frequency of spousal alimony payments.
Report of KiTS19 competition

As an important organ of the urinary system, the kidney focuses on generating urine, purifying the blood, and also maintaining water, electrolytes and acid-base balance. Kidney tumors, as one of the most common tumors, are extremely harmful. Once they are found, surgery is the widespread treatment. Therefore, accurate segmentation of renal tumors is of great significance to surgeons performing renal tumor resection. To this end, this paper proposes an effective method for segmentation of the kidney and its tumor for the KiTS19 competition. Specifically, our method first designs a 3D ResUNet framework to segment the whole kidney, and then develops a 2.5D segmentation network to segment the tumors based on the result of kidney segmentation. After validation, the performance of our method reaches a good level under the given metric.