Miracle-dz/UniFAR-FTUs huggingface dataset:
Population and ecological characteristics of the dice snake, Natrixtessellata(Laurenti, 1768), in lower portions of the Vrbanja River(Republic of Srpska, Bosnia and Herzegovina)

Despite their comparative richness and accessibility in the Republic of Srpska and in Bosnia and Herzegovina in general, population studies of reptiles have not been performed in Srpska until recently. For example, one of the most common snake species in this area is the dice snake; nevertheless, previous studies have only reported its distribution. The aim of the present study was to analyze characteristics of the dice snake population along the Vrbanja River. Animals were processed during 2011 throughout their activity period. In total, 199 individuals of all ages were collected. We observed substantial differences in numbers of animals captured in different habitat types classified according to the level of anthropogenic influence. Unexpectedly, the largest number of snakes was captured in the zone with the highest anthropogenic influence, while the smallest number was observed in the zone with no anthropogenic pressures. The above is probably connected with the observed greater number of their most common prey, as well as the absence of raptors in areas with human impact. In the surveyed area, dice snakes feed predominantly on cyprinid fishes, and their reproductive traits are in accord with literature data.
Multi-UAV Based Crowd Monitoring System

This article presents the development of a multi-unmanned aerial vehicle (UAV) based crowd monitoring system, demonstrating a system that uses UAVs to periodically monitor a group of moving walking individuals. Using auction paradigms to distribute targets among UAVs and genetic algorithms to calculate the best order to visit the targets, the system has shown capabilities to efficiently perform the surveillance, visiting all the targets during a surveillance period and minimizing the time between the visits made to each target. Moreover, the system showed robustness keeping the good performance under a variety of situations.
Tie2-mediated loss of peroxisome proliferator-activated receptor- (cid:1) in mice causes PDGF receptor- (cid:2) -dependent pulmonary arterial muscularization

Tie2-mediated loss of peroxisome proliferator-activated receptor- (cid:1) in mice causes PDGF receptor- (cid:2) -dependent pulmonary arterial muscularization. Am J Physiol Lung Cell Mol Physiol 297: L1082–L1090, 2009. doi:10.1152/ajplung.00199. 2009. —Per-oxisome proliferator-activated receptor (PPAR)- (cid:1) is reduced in pulmonary arteries (PAs) of patients with PA hypertension (PAH), and we reported that deletion of PPAR (cid:1) in smooth muscle cells (SMCs) of transgenic mice results in PAH. However, the sequelae of loss of PPAR (cid:1) in PA endothelial cells (ECs) are unknown. Therefore, we bred Tie2-Cre mice with PPAR (cid:1) ﬂox/ﬂox mice to induce EC loss of PPAR (cid:1) (Tie2 PPAR (cid:1) (cid:3) / (cid:3) ), and we assessed PAH by right ventricular systolic pressure (RVSP), RV hypertrophy (RVH), and muscularized distal PAs in room air (RA), after chronic hypoxia (CH), and after 4 wk of recovery in RA (Rec-RA). The Tie2 PPAR (cid:1) (cid:3) / (cid:3) mice developed spontaneous PAH in RA with increased RVSP, RVH, and muscularized PAs vs. wild type (WT); both genotypes exhibited a similar degree of PAH following chronic hypoxia, but Tie2 PPAR (cid:1) (cid:3) / (cid:3) mice had more residual PAH compared with WT mice after Rec-RA. The Tie2 PPAR (cid:1) (cid:3) / (cid:3) vs. WT mice in RA had increased platelet-derived growth factor receptor- (cid:2) (PDGF-R (cid:2) ) expression and signaling, despite an elevation in the PPAR (cid:1) target apolipoprotein E, an inhibitor of PDGF signaling. Inhibition of PDGF-R (cid:2) signaling with imatinib, however, was sufﬁcient to reverse the PAH observed in the Tie2 PPAR (cid:1) (cid:3) / (cid:3) mice. Thus the disruption of PPAR (cid:1) signaling in EC is sufﬁcient to cause mild PAH and to impair recovery from CH-induced PAH. Inhibition of heightened PDGF-R (cid:2) signaling is sufﬁcient to reverse PAH in this genetic model. formed. One-way ANOVA followed by Bonferroni’s post hoc anal- ysis was used to assess changes in a given genotype related to hypoxia and recovery vs. room air. A P value of 0.05 was considered statistically signiﬁcant.
537 550 Aguinis aug 01

The authors extended a previous examination of the effects of nonverbal behavior on perceptions of a male employee’s power bases (H. Aguinis, M. M. Simonsen, & C. A. Pierce, 1998) by examining the effects of nonverbal behavior on perceptions of a female employee’s power bases. U.S. undergraduates read vignettes describing a female employee engaging in 3 types of nonverbal behavior (i.e., eye contact, facial expression, body posture) and rated their perceptions of the woman’s power bases (i.e., reward, coercive, legitimate, referent, expert, credibility). As predicted, (a) direct eye contact increased perceptions of coercive power, and (b) a relaxed facial expression decreased perceptions of all 6 power bases. Also as predicted, the present results differed markedly from those of Aguinis et al. (1998) regarding a male employee. The authors discuss implications for theory, future research, and the advancement of female employees.
The Impact of Internal Corporate Governance Mechanisms on Liquidity Risk at Jordanian Commercial Banks Listed On the Amman Stock Exchange

The aim of this research is to investigate the relationship between Internal corporate governance mechanisms and on liquidity risk using data from 13 commercial banks listed on the Amman Stock Exchange during the period 2009-2016. Panel data was utilized and the data gathered from 104 annual reports from13 commercial banks in Amman, which analyzed using descriptive statistics, correlation, and regression. The use and analysis of data collected from annual reports were using descriptive statistics, correlation, and regression. Seven main corporate governance variables were analyzed in terms namely: (Board size, Board Independence, CEO /Chairman Separation, Audit Committee Independence, Ownership concentration, Institutional Ownership, and Foreign Ownership) their relative of liquidity risk. On the other hand, bank size and debt Ratio were used as a control variable. Based on the results of the study, it has been observed that Internal corporate governance mechanisms variables have a significant effect on liquidity risk
Designing Co-Creation Platforms

With the emergence of new digital tools, and in a value co-creation perspective, companies are using internet tools and platforms to support interaction and design attributes of the product. However many authors underlined that the way co-creation is implemented and operationalized remain unexplored. The paper aims at exploring the reasons why co-creation leads to reframing the development processes with the implications for learning processes. Results will detail the way development and learning loops occurred. Four main observations are proposed: aCo-creation requires including new actors in the systems, resulting in organizational change bDevelopment processes are separated in two parts. cThe issue of product coherence and image is central to the elaboration of the interactive dispositions with the clients. dThe Design of co-creation encounter is not based on co-creation principles. Discussion follows on the way co-creation transforms existing « learning while innovating » frames.
Do Auditors Listen to the Business Press? Negative Media Sentiment and Audit Pricing

This study investigates the role of business press in the auditor’s assessment of a client’s business risk. In this study, we argue that negative media sentiment affects audit effort as well as audit pricing by increasing litigation and reputation risk of auditor, by signaling lower future profitability, and by affecting investors’ perception about the auditor. Using news analytics data from 2004 to 2015, we find that negative media sentiment is positively associated with audit fees, and the association is stronger for press-initiated negative sentiment than for firm-initiated negative sentiment. Additional analyses reveal that the effect of negative media sentiment is stronger for BigN auditors, during assertive market sentiment, and for firms with lower accruals quality, higher probability of default and weaker governance. Results are robust to alternative definitions of sentiment score – abnormal sentiment or sentiment word count, and in audit fee change models.
The Clinical Characteristics according to the Educational Level in the Elderly Patients with Mild Alzheimer's Disease Dementia

The individual susceptibility to dementia including Alzheimer’s disease (AD) differs. The genetic risk factor like apolipoprotein e4, vascular risk factors, history of head trauma, and chronic depression increase the risk of AD.1 Cognitive reserve is crucial in the aspect that it decreases the individual vulnerability to dementia.2 Among the various determinants of cognitive reserve, the educational level would be of prime importance because it has a high influence on the life-long socioeconomic attainments and other cognitive capacities. An individual with poor education is more likely to develop AD.3,4 Illiteracy is the extreme end of low education, which was not uncommon in aged population of our country due to the specific historical situation.5 It was reported to increase the The Clinical Characteristics according to the Educational Level in the Elderly Patients with Mild Alzheimer’s Disease Dementia
Predicting Reported Cybersecurity Breaches Using Financial Measures

“In recent months, cybersecurity has become a top concern to American companies, regulators, and law enforcement agencies. This is in part because of the mounting evidence that the constant threat of cyberattack is real, lasting, and cannot be ignored. There is no doubt that the SEC must play a role in this area. What is less clear is what that role should be.However, the increased pervasiveness and seriousness of the cybersecurity threat raises questions about whether more should be done to ensure the proper functioning of the capital markets and the protection of investors. ” (SEC Commissioner Luis A. Aguilar, March 26, 2014)
When Big Bang meets Big Data

In observational astronomy, electromagnetic waves originating from cosmic sources, impinging on the earth, are measured with the help of telescopes. Astronomers study the cosmic objects and phenomena based on these measurements. While optical telescopes can only observe the light emitted from the stars, highly sensitive radio telescopes can look back in time. This is done by observing weak radio emissions originating from the hydrogen line and ionized gases, even before the first stars were created. These observations provide us with valuable evidence to study the creation of the universe and formations of the stars. Figure 1 shows the different views of the Centaurus A galaxy at radio versus optical frequencies. Clearly, radio frequencies provide an entirely independent view of the galaxy. During the last century, radio astronomy has been vastly advancing. Important discoveries on the formation of various celestial objects such as pulsars, neutron stars, black holes, radio galaxies and quasars are the result of radio astronomical observations.
A Deep Learning Model for Event Extraction and Classification in Hindi for Disaster Domain

Relevant information extraction in disaster situation plays an important role for crisis management. In this paper we propose a deep learning based method for event extraction in Hindi from the man-made and natural disaster related texts. The overall task is to identify the event triggers from text and then classify them into predefined categories of interest. Our proposed model follows an ensemble architecture where we use Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) network as the base learning models. We crawl the data from various newswire sources, define the event types and its annotation guidelines, annotate the datasets and then create a benchmark setup for event extraction. Experiments on 5-fold cross-validation with approximately 80K token datasets show the macro average and micro average F1-scores of 0.40 and 0.59, respectively for event trigger detection and classification.
The demand for corporate bonds

This paper analyzes the determinants of investor demand for corporate bond issues and show that the level of orderbook demand varies substantially across issues. We compare the extent to which credit risk, information costs, and bond market presence can explain variation in investor demand for issued bonds. We find that demand is increasing with the credit spread, decreasing with credit rating and issue frequency, and that investors demand higher quantities of bonds for issues by firms from outside of major bond markets. These results highlight that bond fund managers place higher order quantities for relatively scarce securities that can potentially offer greater risk-reward combinations as part of a diversified portfolio. Demand is lower for debut issuers, suggesting an asymmetric threshold for investor demand between debut and established bond market issuers. JEL Classification: G11, G12, G24
Analysis of Sentiment Direction Based on Two Centuries of the Hansard Debate Archive

This project was conceived by Nalanda Technology, a text data search and analysis company. This study concerned the following central research question: Can Machine Learning techniques be used to analyze automatically the change in aggregate levels of sentiment polarity within a defined topic in UK parliamentary speeches? We addressed the question by making use of methods of natural language processing (NLP). The project evaluated whether or not opinion-mining techniques can successfully be applied to the textual analysis of parliamentary debates. For data, we used the transcriptions of the speeches made at the plenary parliamentary meetings as reported in the Hansard Archive (digitised debates of the UK Parliament from 1803 to the present). A Sentiment Model was implemented to retrieve meaningful patterns based on the classification results. A substantial selection of parliamentary proceedings was compiled and a Gold Standard Corpus (GSC) created, covering both subjectivity and orientation. The main idea of this study is that an automatic sentiment analysis model can be very useful for rapidly visualizing how the opinion of the parliament – which normally reflects the national sentiment – changes over a period of time. This enables the user to put together an overall picture of the position on a specific issue and to pick up on the underlying trends without having to go through the entire sequence of the Hansard speeches. The visualizations have to be clear and easy to understand, with all ambiguities eliminated. The Sentiment Classifier was built on top of the Nalytics search engine, the core product of Nalanda Technology, to add a new analytical feature to this data-search and discovery platform. The classifier was the outcome of a series of experiments, including an analysis of the performance levels of various supervised learning algorithms, such as Support Vector Machines, Linear Regression, Naive Bayes, etc. These techniques and methodologies were written in Python using the NLTK and SciKit-Learn toolkits. The model achieved a classification accuracy level of 75% when trained and tested with the GSC – this was considered a promising result, in light of the complex nature of parliamentary discourse.
Why Default Rates Cannot Shed Light on Mortgage Discrimination

Some scholars argue that racial discrimination in mortgage lending can be observed in default rates. Since lenders who discriminate require higher standards for minority applicants than whites, successful minority applicants are more creditworthy and should default less often. But this conclusion requires three strong assumptions: (1) that credit characteristics not observed by the lender are uncorrelated with minority status, (2) that whites do not receive more favorable treatment than minorities in foreclosure proceedings, and (3) that the losses on minority defaults are at least equal to those for whites. If any of these assumptions is violated, higher default rates for minorities could be observed even if lenders discriminate. At best, then, default studies yield weak tests for discrimination that are inferior to traditional tests based on mortgage denial. The traditional approach to studying discrimination in mortgage lending is to determine whether African-American or Hispanic applicants are more likely to be turned down for a loan than are comparable white applicants. 2 A study by researchers at the Federal Reserve Bank of Boston based on this approach finds that these minority applicants are about 60 percent more likely to be turned down, controlling for all credit characteristics that lenders say they consider. 3 Several columns in popular journals have proposed an alternative way to test for discrimination in mortgage markets—by examining defaults. 4 This approach also has appeared, with far more qualifications, in the academic literature. 5 Despite all the attention, however, this so-called default approach is fatally flawed as a method for studying discrimination in mortgage lending. The basic idea behind the default approach is simple: Assuming that lenders rank applicants by their creditworthiness, discrimination in the form of a higher standard for minority applicants implies that the lowest-ranking white applicants whose loans are approved are less qualified than the lowest-ranking minority applicants whose loans are approved. As a result, the probability of default should be higher for the least qualified whites than for the least qualified minorities. As it turns out, black loan recipients have higher default rates than their white counterparts, on average. On the basis of this argument, the columns mentioned above conclude that there is no discrimination against blacks. Actually, the columns go considerably Cityscape: A Journal of Policy Development and Research • Volume 2, Number 1 • February 1996 U.S. Department of Housing and Urban Development • Office of Policy Development and Research
FORECASTING OF MONEY DEMAND IN MALAYSIA USING NEURAL NETWORKS AND ECONOMETRIC MODEL

This paper aims to evaluate the out-of-sample forecasting performance of estimated money demand function in Malaysia by comparing Vector Error Correction Model (VECM) with Multilayer Feed Forward Neural Network (FFNN) method. Real income, interest rate and inflation were used as determinant variables for money demand. The period study covers monthly data from 1997 until 2015. Forecast errors were generated using in sample results, 3, 6, 12 and 24 months ahead. Our main finding show FFNN yielded a better out of sample forecast performance than VECM. These findings are strongly recommended to policy makers, academicians and investment firms in Malaysia.
Integrating adaptive inventories into public opinion research∗

One of the most difficult tasks facing survey researchers is balancing the imperative to keep surveys short with the need to measure important concepts accurately. Not only are long batteries prohibitively expensive, but lengthy surveys can also lead to less informative answers from respondents. Yet, scholars often wish to measure traits that require a multi-item battery. To resolve these contradicting constraints, we propose the use of adaptive inventories. This approach uses computerized adaptive testing methods to minimize the number of questions each respondent must answer while maximizing the accuracy of the resulting measurement. We provide evidence supporting the utility of adaptive inventories through an empirically informed simulation study, an experimental study, and a detailed case study using data from the 2016 ANES Pilot. The simulation and experiment illustrate the superior performance of adaptive inventories relative to fixed-reduced batteries in terms of precision and accuracy. The ANES analysis serves as an illustration of how adaptive inventories can be developed and fielded and also validates an adaptive inventory with a nationally representative sample. Critically, we provide extensive software tools that allow researchers with minimal technical expertise to easily incorporate adaptive inventories into their own surveys. Abstract word count: 193 Manuscript word count: 6434 ∗Funding for this project was provided by the Weidenbaum Center on the Economy, Government, and Public Policy and the National Science Foundation (SES-1558907). We are grateful to Josh Cutler, Tom Wilkinson, Haley Acevedo, Alex Weil, Ryden Butler, Matt Malis, and Min Hee Seo for their programming assistance. Valuable feedback for this project was provided by Harold Clarke, Brendan Nyhan, and audience members at Washington University in St. Louis, the University of Chicago, Dartmouth College, New York University, and Princeton University. Previous versions of this paper were presented at the 2015 meeting of the Asian Political Methods meeting in Tapei, Taiwan, as well as the 2015 Annual Summer Meeting of the Society for Political Methodology in Rochester, New York.
The Role of Congruency for Distractor-Response Binding: A Caveat

Responding in the presence of stimuli leads to an integration of stimulus features and response features into event files, which can later be retrieved to assist action control. This integration mechanism is not limited to target stimuli, but can also include distractors (distractor-response binding). A recurring research question is which factors determine whether or not distractors are integrated. One suggested candidate factor is target-distractor congruency: Distractor-response binding effects were reported to be stronger for congruent than for incongruent target-distractor pairs. Here, we discuss a general problem with including the factor of congruency in typical analyses used to study distractor-based binding effects. Integrating this factor leads to a confound that may explain any differences between distractor-response binding effects of congruent and incongruent distractors with a simple congruency effect. Simulation data confirmed this argument. We propose to interpret previous data cautiously and discuss potential avenues to circumvent this problem in the future.
Inexplicable Silence: An Uncomfortable Analysis of the Social Silences

Social silence refers to silences that arise in face-to-face social interaction. Despite having a reputation for being nothing at all, close examination of social silence reveals various kinds of silence, arising under different conditions, playing a variety of roles in the temporal order of social interaction. What distinguishes one silence from another? For instance, why are some moments of silence experienced as uncomfortable and others as unremarkable? Here I introduce a conceptual framework for describing a variety of social silences, built on the work of conversation analysis and an ethnographic study of silence in the religious practices of Quakers. A focus on social silence naturally makes the unspoken dimensions of interaction salient and offers new confirmation for the view that face-to-face learning and communication are always partly constituted by embodied (non-verbal) actions. The included framework, descriptions and empirical data serve as platform for two theses: 1) calibrating to the acoustic environment is a basic way that individual behavior is situated in its social context; 2) group silence regulates collective attention by directing it to the present moment of social interaction.