Miracle-dz/UniFAR-FTUs huggingface dataset:
Chapter 2 The Takeover Bids Directive ’ s flawed economics jeopardises our wealth

The European Union’s Takeover Bids Directive endeavours to facilitate takeover bids ‘through reinforcement of the freedom to deal in and vote on securities of companies and the prevention of operations which could frustrate a bid’ (EC 2012: 2). Innocent as this objective may seem, it appears to be based on highly questionable theoretical assumptions and assessments of the empirical evidence. As a consequence, the Directive allows plenty of room for perverse takeovers, and more generally lubricates a system that frequently destroys wealth rather than creating it. It therefore jeopardises our wealth. The argument follows from two key issues: the first is the inadequacy of the Directive’s underlying theory of the market for corporate control; the second is the empirical evidence on the performance of mergers and acquisitions, including takeovers. This evidence suggests that most takeovers in the domain for which the Directive has been issued are economically undesirable, so that a regulatory framework that discourages those takeovers would be more efficient than the current Directive. I will discuss some of the characteristics of such a framework in the last section before summarising the paper.
AVERAGING METHODS FOR STUDYING THE PERIODIC ORBITS OF DISCONTINUOUS DIFFERENTIAL SYSTEMS

The main objective of this work is to extend the averaging method for studying the periodic orbits of a class of differential equations with discontinuous second member. Thus, overall results are presented to ensure the existence of limit cycles of such systems. Certainly these results represent new insights in averaging, in particular its relation with non smooth dynamical systems theory. An application is presented in careful detail.
Investigating green supply chain practices for economic growth

Article history: Received November 4, 2018 Received in revised format December 20, 2018 Accepted January 6 2019 Available online January 6 2019 Indonesia has recorded tremendous growth in this decade given a new dimension to her economy. Presently Indonesian economy is the fourth largest in East Asia, after China, South Korea and Japan – and one of the largest in terms of purchasing power parity (PPP), worldwide. This performance can be further improved by employing better supply chain practices. This study observes how green supply chain (GSC) practices can improve foreign direct investment (FDI) and export on Indonesian economic growth. Data is collected from Indonesian economists through a survey questionnaire and is analyzed using PLS-SEM. The results of the study indicate that GSC had positive impact on the economic growth of Indonesia. GSC has also maintained a positive impact on exports and FDI and it is considered crucial for the economic growth. The study shall have broad implications in strategic formulations to boost economic growth.
Challenges of Change: An Experiment Promoting Women to Managerial Roles in the Bangladeshi Garment Sector

Women remain disadvantaged in access to management positions around the world. We conduct a field experiment with 24 large garment factories in Bangladesh to test for inefficient representation of women among line supervisors. We identify the marginal female and male candidates for supervisory positions and randomly assign them to manage production lines. Three sets of results emerge: (i) extensive diagnostic testing at baseline reveal few skill differences between marginal female and male supervisor candidates; (ii) initially, marginal female candidates have lower productivity and evaluations from sub-ordinate workers, though after four to six months, these gaps disappear; and (iii) the share of the female candidates retained as line supervisor after the trial is significantly higher than the share of female supervisors in the factories at baseline. This suggests that factories previously promoted fewer women than would have been optimal. Additional surveys and a lab-in-the-field experiment suggest that the initially worse performance stems from negative beliefs of workers about the abilities of female supervisors.r.
Intriguing properties of adversarial training

Adversarial training is one of the main defenses against adversarial attacks. In this paper, we provide the first rigorous study on diagnosing elements of adversarial training, which reveals two intriguing properties. First, we study the role of normalization. Batch normalization (BN) is a crucial element for achieving state-of-the-art performance on many vision tasks, but we show it may prevent networks from obtaining strong robustness in adversarial training. One unexpected observation is that, for models trained with BN, simply removing clean images from training data largely boosts adversarial robustness, i.e., 18.3%. We relate this phenomenon to the hypothesis that clean images and adversarial images are drawn from two different domains. This two-domain hypothesis may explain the issue of BN when training with a mixture of clean and adversarial images, as estimating normalization statistics of this mixture distribution is challenging. Guided by this two-domain hypothesis, we show disentangling the mixture distribution for normalization, i.e., applying separate BNs to clean and adversarial images for statistics estimation, achieves much stronger robustness. Additionally, we find that enforcing BNs to behave consistently at training and testing can further enhance robustness. Second, we study the role of network capacity. We find our so-called "deep" networks are still shallow for the task of adversarial learning. Unlike traditional classification tasks where accuracy is only marginally improved by adding more layers to "deep" networks (e.g., ResNet-152), adversarial training exhibits a much stronger demand on deeper networks to achieve higher adversarial robustness. This robustness improvement can be observed substantially and consistently even by pushing the network capacity to an unprecedented scale, i.e., ResNet-638.
Are the Effects of Informational Interventions Driven by Salience?

Informational interventions have been shown to significantly change behavior across a variety of settings. Is that because they lead subjects to merely update beliefs in the right direction? Or, alternatively, is it to a large extent because they increase the salience of the decision they target, affecting behavior even in the absence of inputs for belief updating? We study this question in the context of an informational intervention with school parents in Brazil. We randomly assign parents to either an information group, who receives text messages with weekly data on their child’s attendance and school effort, or a salience group, who receives messages that try to redirect their attention without child-specific information. While information has large impacts on attendance, test scores and grade promotion relative to the control group, outcomes in the salience group improve by at least as much, and to a greater extent among students with lower attendance at baseline. Our results suggest that alternative interventions that manipulate attention can generate larger impacts at lower costs, and have implications for the design of informational interventions across a range of domains.
The Impact of Consumer’s Bounded Rationality on Retailer’s Price and Location Decisions

As is the case with most OR models, the original maximal covering location problem with price decision (PrMAXCOV) assumes perfect rationality on the part of the consumer. This is clearly contradicted by the evidence found in marketing literature. Starting from this experimental evidence, we illustrate through the PrMAXCOV how these mathematical models can be adapted in order to better reflect the consumer’s bounded rationality that appears in real life. First, we examine the impact of rounded pricing on the solution of the model. Our results show that simply rounding the solution of a given problem may not always be advisable. In some cases it is advantageous to restrict the possible solutions to rounded values in advance. Based on the rounded optimal solution, we show that expected revenue is actually an underestimation due to the drop-off mechanism. Secondly, we illustrate that when retailers limit their prices to predetermined ranges, this will almost always result in a loss of revenue. Finally, we show that when the model considers the fact that certain customers pay little attention to the price, firms will generally charge higher prices and locate closer to these inattentive and/or uninformed customers.
Explaining the gender gap in job satisfaction

ABSTRACT In general, women report greater job satisfaction than men. The existing literature cannot fully explain the nature of this difference, as the gap tends to persist even when controlling for job characteristics. In this paper, we study job satisfaction using recent data for 28 EU countries. Women, on average, are more satisfied than men and the gap remains even when we account for a wide range of personal, job and family characteristics. However, the gap disappears when we include job preferences, as women place greater importance on work-life balance and the intrinsic desirability of the work.
THE INFLUENCE OF TRAINING ON DETECTING LYING IN LANGUAGE: A CASE STUDY OF NATIVE SPEAKERS OF POLISH USING ENGLISH AS A LEARNED LANGUAGE

Deception among individuals seems to be so common that it appears to be an inseparable part of life. Deception is “an act that is intended to foster in another person a belief or understanding which the deceiver considers false” (Zuckerman, DePaulo, Rosenthal 1981, p. 3). The techniques and methods employed to deceive others also become more complex while technology has advanced. A review of the literature allows a suggestion that lie detection accuracy, while different from perfect, is somewhat greater than would be anticipated by a flip of a coin (DePaulo, Pfeifer 1986; DePaulo, Kirkendol, Tang, O’Brien 1988). However, some researchers have observed that the average lie detection capability is no better than that achieved by random chance (Ekman, O’Sullivan 1991). The correctness of lie detection differs depending on the techniques exploited to determine judgments (i.e., wrong Cues), or even the personality of the lie catcher. For example, Bella DePaulo (1994) observed that individuals who are regarded high in social anxiety achieved worse results at lie detection than those with lower ratings. Assuming that the capability and means to detect deception are crucial in various contexts and across disciplines, examination of the detection of deception conveys vast implications. Research has indicated that some ‘lie catchers’ are constantly better judges of interpersonal deception than others, assuming that a person who gains characteristics or skills of assessing deceit will subsequently become more accurate at deception evaluations. Yet, three deception studies (DePaulo, Pfeifer 1986; Ekman, O’Sullivan 1991) engaging qualified police officers showed that the officers were not able to detect deception above the chance level. Only the accuracy rate was verified in these
Learning by Sharing: Monetary Policy and Common Knowledge

A common view states that central bank releases decrease central banks' own information about the economy and are harmful if about inefficient disturbances, such as cost-push shocks. This paper shows how neither is true in a microfounded macroeconomic model in which households and firms learn from central bank releases and the central bank learns from the observation of firm prices. Central bank releases make private sector and central bank expectations closer to common knowledge. This helps transmit dispersed information between the private sector and the central bank. As a result, the release of additional central bank information decreases the central bank's own uncertainty and can be beneficial, irrespective of the efficacy of macroeconomic fluctuations. A calibrated example suggests that the benefits of disclosure are substantial. (JEL D82, D83, D84, E12, E52, E58)
CMAR_A_181439 7537..7556

Cristina Saavedra Jorge Barriuso Mairéad G McNamara Juan W Valle Angela Lamarca 1Medical Oncology Department, The Christie NHS Foundation Trust, Manchester, UK; 2Medical Oncology Department, Ramon Y Cajal University Hospital, Madrid, Spain; 3Division of Cancer Sciences, University of Manchester, Manchester, UK Abstract: Neuroendocrine tumors (NETs) are rare cancers with an associated prolonged survival in some patients. A proportion of patients diagnosed with NETs will present with carcinoid syndrome symptoms, characterized by diarrhea, flushing and/or wheezing. This review summarizes the current treatment options for carcinoid syndrome, focusing on the latest novel treatment option, telotristat ethyl. In addition, information on patient-reported outcomes and impact of carcinoid syndrome on quality of life (QOL) and improvement of following treatment with telotristat ethyl are reviewed. This article also provides an overview of the current QOL questionnaires for patients with NETs and addresses unmet needs in this field of patient-reported outcomes.
Online Student Engagement: A Case Study in Teaching of Programming

Student engagement is a key indicator of student academic success, especially in the online environment, where students can study anywhere, anytime. In this paper, we analyze track records of the novice programmers’ interaction with an online learning environment. The results show indicators of poor and good engagement and can guide teachers in choosing the most appropriate teaching strategy according to the needs of each individual. Resumo. O envolvimento do aluno é um indicador fundamental para o sucesso acadêmico do aluno, principalmente no ambiente on-line, onde os alunos podem estudar em qualquer lugar e a qualquer momento. Neste trabalho, analisamos registros da interação do aluno iniciante em programação com um ambiente de aprendizado on-line. Os resultados apresentam indicadores de fraco e bom engajamento, e podem guiar os professores na escolha de estratégia de ensino mais adequadas de acordo com a necessidade de cada indíviduo.
Comparisons of citations among clusters of medical subject headings usingvisualizing topic burst on neuropsychiatry: a bibliometric analysis

Background: PubMed is a primary source of biomedical information comprising search tool function and the biomedical literature from MEDLINE. A new method using Medical Subject Headings (MdSH) is needed for searching the knowledge in the neuropsychiatry field. Methods: A new method is proposed in this study for visualizing the recent research trends based on the retrieved MeSH terms corresponding to a search query given by the user. MeSH terms are extracted from Pubmed Central (PMC) based on the keyword of neuropsychiatry [All Fields] through a series of calculations on correlations between terms using Social Network Analysis (SNA). We illustrated four bibliometrics to compare differences among MeSH clusters and verified whether article types might be disparate regarding MeSH terms and citations. We programmed Microsoft Excel VBA routines to extract data. Google Maps and Pajek software were used for displaying graphical representations. Results: We found that (1) the dominant nation on the topic of neuropsychiatry is the US; (2) the MeSH terms of neuropsychiatry, diagnosis, and physiopathology gain the top degree centralities based on SNA; (3) the Mesh term of physiology owns the highest metrics with Impact Factor (IF)=30.37, h-index=6, and x=14.97, respectively; (4) differences were found significantly among MeSH clusters on neuropsychiatry (p<0.05) using 95% confidence intervals of the bootstrapping method; (5) the article(PMID=2190539) published in 1990 was cited most (with 285 times and two MeSH term of diagnosis and psychiatric status rating scales. Conclusions: The application of the MeSH cluster analysis, which could be used as a “guide map for travelers,” allows users to quickly and easily acquire the knowledge of research trends. Combination of PubMed and MeSH citations is expected to be an effective complementary way for the researchers in the biomedical field experiencing difficulties with search and information analysis.
Maternal Stress and Birth Outcomes: Evidence from an Unexpected Earthquake Swarm

We examine the impact of a major earthquake that unexpectedly affected the Canterbury region of New Zealand on a wide-range of birth outcomes, including birth weight, gestational age, and an indicator of general newborn health. We control for observed and unobserved differences between pregnant women in the area affected by the earthquake and other pregnant women by including mother fixed effects in all of our regression models. We extend the previous literature by comparing the impact of the initial unexpected earthquake to the impacts of thousands of aftershocks that occurred in the same region over the 18 months following the initial earthquake. We find that exposure to these earthquakes reduced gestational age, increased the likelihood of having a late birth, and negatively affected newborn health-with the largest effects for earthquakes that occurred in the first and third trimesters of pregnancy. Our estimates are similar when we focus on just the impact of the initial earthquake or, in contrast, on all earthquakes controlling for endogenous location decisions using an instrumental variables approach. This suggests that the previous estimates in the literature that use this approach are likely unbiased and that treatment effects are homogenous in the population. We present supporting evidence that the likely channel for these adverse effects is maternal stress.
Incorporating Conditional Morality into Economic Decisions

We present a framework that incorporates both moral motivations and fairness considerations into utility. The main idea is that individuals face a preference trade-off between their material individual interest and their desire to follow moral norms. In our model, we assume that moral motivation is conditional and may be influenced by others’ actions. Specifically, in our framework moral obligation is a combination of two main components: an autonomous component and a social influence component that captures the influence of others. Our framework is able to explain many stylized results in the literature and to improve theories of economic behavior.
Challenges in Implementing E-Learning in the Language Education Context: Case Study at an English Education Department in Indonesia

At present, the world of education including the sector of English language teaching, has been witnessing the massive development of information technology and using it as a means of improving the effectiveness of the teaching and learning process. Studies confirm that e-learning improves students’ learning achievement. However, despite several benefits offered by e-learning, many lecturers and students still encounter obstacles and difficulties. This research aimed to explore the challenges faced by English education lecturers and students at an English Education Department in a private university in Yogyakarta in using e-learning. This study used a qualitative method. Case study was employed as a strategy of inquiry. Five lecturers and 20 students were recruited as research participants. The interview was deployed as a strategy to collect data from the lecturers and focus group discussions would be utilized to gather information from students. In addition to interviews and focus group discussions, this research also employed document analysis as the strategy to collect the data. The data revealed that the challenges faced by lecturers and students of English education department in implementing learning included cognitive, metacognitive as well as learning styles and preferences challenges. Several strategies to overcome the challenges in the e-learning implementation were proposed by the participants, namely rehearsal, selection, rehearsal and comprehension, learning plan, preparation, evaluation, goal setting, self-management, time management, self-evaluation and synchronous online learning. Keywords—e-learning, English language Teaching, information technology
Creating Cybersecurity Knowledge Graphs From Malware After Action Reports

After Action Reports (AARs) provide incisive analysis of cyber-incidents. Extracting cyber-knowledge from these sources would provide security analysts with credible information, which they can use to detect or find patterns indicative of a cyber-attack. In this paper, we describe a system to extract information from AARs, aggregate the extracted information by fusing similar entities together, and represent that extracted information in a Cybersecurity Knowledge Graph (CKG). We extract entities by building a customized named entity recognizer called ‘Malware Entity Extractor’ (MEE). We then build a neural network to predict how pairs of ‘malware entities’ are related to each other. When we have predicted entity pairs and the relationship between them, we assert the ‘entity-relationship set’ in a CKG. Our next step in the process is to fuse similar entities, to improve our CKG. This fusion helps represent intelligence extracted from multiple documents and reports. The fused CKG has knowledge from multiple AARs, with relationships between entities extracted from separate reports. As a result of this fusion, a security analyst can execute queries and retrieve better answers on the fused CKG, than a knowledge graph with no fusion. We also showcase various reasoning capabilities that can be leveraged by a security analyst using our fused CKG.
Contributions of Bioinformatics for computing education in the detection of programming assignment plagiarism

Any source code can be modified in several ways to confuse plagiarism detection systems. Such diverse modifications require the usage of systems which can handle different types of plagiarism. The usage of automatic source code plagiarism detectors has implications for computing education. This paper extends Pedersen’s work, a Bioinformatics method, by performing the application of this method on programming plagiarism domain, and by analyzing the usage of such tools through a discussion associated with the support for professors in assessing students’ assignments. The application results are compared to a commonly used solution for the same purpose, the JPLAG tool. As a result of the evaluating study, the applied method showed a higher rate of similarity for specific types of plagiarism. Also, as a result of the analysis involving the use of an automatic tool for plagiarism in programming showed the benefits for computing education.
Isolation and identification of facultative mixotrophic ammonia-oxidizing bacteria from Bone Regency, Indonesia

Autotrophic ammonia-oxidizing bacteria (AOB) have been widely documented in intensive aquaculture systems, while facultative mixotrophic AOB have been less comprehensively studied. Although facultative mixotrophic bacteria have been isolated from a variety of aquatic environments, their contribution to biogeochemical processes in waters remains neglected. In this study, heterotrophic bacteria were isolated from intensive shrimp ponds and the estuary in Bone Regency, South Sulawesi, Indonesia and then cultivated in a mixotrophic medium containing ammonium and acetic acid. The characterization of the bacteria was performed using morphological features, gram stain, motility, catalase, and other biochemical tests. Results showed the presence of 12 bacteria, among 48 initially heterotrophic isolates. Among these potentially facultative mixotrophic AOB, three isolates were morphologically and biochemically related to Firmicutes (Bacillus and Lactobacillus), six isolates were affiliated to Actinobacteria (Acinetobacter, Arthrobacter, and Gordonia), three other isolates were associated with Proteobacteria (Paracoccus, Pseudomonas, Vibrio). Pre-screening and screening of these bacteria were conducted to determine the potential for microbial ammonia oxidation.