Miracle-dz/UniFAR-FTUs huggingface dataset:
GoJournal: a verified, concurrent, crash-safe journaling system

The main contribution of this paper is GoJournal, a verified, concurrent journaling system that provides atomicity for storage applications, together with Perennial 2.0, a framework for formally specifying and verifying concurrent crash-safe systems. GoJournal’s goal is to bring the advantages of jour-naling for code to specs and proofs. Perennial 2.0 makes this possible by introducing several techniques to formalize GoJournal’s specification and to manage the complexity in the proof of GoJournal’s implementation. Lifting predicates and crash framing make the specification easy to use for developers, and logically atomic crash specifications allow for modular reasoning in GoJournal, making the proof tractable despite complex concurrency and crash interleavings. GoJournal is implemented in Go, and Perennial is implemented in the Coq proof assistant. While verifying GoJournal, we found one serious concurrency bug, even though GoJour-nal has many unit tests. We built a functional NFSv3 server, called GoNFS, to use GoJournal. Performance experiments show that GoNFS provides similar performance (e.g., at least 90% throughput across several benchmarks on an NVMe disk) to Linux’s NFS server exporting an ext4 file system, suggesting that GoJournal is a competitive journaling system. We also verified a simple NFS server using GoJournal’s specs, which confirms that they are helpful for application verification: a significant part of the proof doesn’t have to consider concurrency and crashes.
Galcanezumab in chronic migraine

Objective To evaluate the efficacy and safety of galcanezumab, a humanized monoclonal antibody that selectively binds to calcitonin gene-related peptide, in the preventive treatment of chronic migraine. Methods A phase 3, randomized, double-blind, placebo-controlled study of LY2951742 in patients with chronic migraine (Evaluation of Galcanezumab in the Prevention of Chronic Migraine [REGAIN]) was a phase 3 study with a 3-month double-blind, placebo-controlled treatment phase and a 9-month open-label extension. Eligible patients 18 to 65 years of age with chronic migraine were randomized 2:1:1 to monthly subcutaneous injections of placebo (n = 558), galcanezumab 120 mg (with a 240-mg loading dose, n = 278), or galcanezumab 240 mg (n = 277). The primary endpoint was the overall mean change from baseline in the number of monthly migraine headache days (MHDs) during the 3-month double-blind treatment phase. Results Mean number of monthly MHDs at baseline was 19.4 for the total sample. Both galcanezumab dose groups demonstrated greater overall mean reduction in the number of monthly MHDs compared to placebo (placebo −2.7, galcanezumab 120 mg −4.8, galcanezumab 240 mg −4.6) (p < 0.001 for each dose compared to placebo). There were no clinically meaningful differences between galcanezumab doses and placebo on any safety or tolerability outcome except for a higher incidence of treatment-emergent injection-site reaction (p < 0.01), injection-site erythema (p < 0.001), injection-site pruritus (p < 0.01), and sinusitis (p < 0.05) in the galcanezumab 240-mg group relative to placebo. Conclusions Both doses of galcanezumab were superior to placebo in reducing the number of monthly MHDs. Galcanezumab appears efficacious, safe, and well tolerated for the preventive treatment of chronic migraine. ClinicalTrials.gov identifier NCT02614261. Classification of evidence This interventional study provides Class I evidence that galcanezumab is superior to placebo in the reduction of the number of monthly MHDs.
Relationship between IL-6, IL-1Î², and vitamin D on frailty status in elderly women

Relationship between IL-6, IL-1β, and vitamin D on frailty status in elderly women Dwi Ngestiningsih1*, Jessa Kris Dayanti2, Lusiana Batubara3 Background: The aging process is associated with an increase in proinflammatory markers’ serum levels, indicating that a chronic inflammatory process is correlated with an increase in disability, mortality, and frailty. Pro-inflammatory cytokines that increase with the incidence of frailty syndrome are IL-6, IL-1β and TNF-α. 25-hydroxyvitamin D (25 (OH) D) deficiency is also a potential risk factor for frailty, especially in the elderly. Vitamin D deficiency is associated with a risk of falling, which can speed up the frailty process. The aim of the study is to determine the relationship between IL-6, IL-1β and Vitamin D on frailty status in elderly women in elderly integrated health services (posyandu lansia), Semarang City. Methods: This study used a cross sectional design with a consecutive sampling method of 27 subjects. Subjects measured vital signs, weight and height measurements. The study subjects were then taken venous blood to measure the levels of IL-6, IL-1β and vitamin D in blood serum and assessed their frailty status. Data analysis using SPSS 25 with Spearman correlation
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 abies

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. © 2021 International Association for Vegetation Science 1Department of Biological and Environmental Science, University of Jyväskylä, Jyväskylä, Finland 2School of Resource Wisdom, University of Jyväskylä, Jyväskylä, Finland 3Finnish Environment Institute, Helsinki, Finland
KRONOS VST – THE PROGRAMMABLE EFFECT PLUGIN

This paper introduces Kronos VST, an audio effect plugin conforming to the VST 3 standard that can be programmed on the ﬂy by the user, allowing entire signal processors to be deﬁned in real time. A brief survey of existing programmable plugins or development aids for audio effect plugins is given. Kronos VST includes a functional just in time compiler that produces high performance native machine code from high level source code. The features of the Kronos programming language are brieﬂy covered, followed by the special considerations of integrating user programs into the VST infrastructure. Finally, introductory example programs are provided.
A paradoxical approach to instructional leadership

Purpose – Perceptual inhibitors to instructional leadership are based on disagreements with the premises of instructional leadership. This study explored how the paradoxical approach, which advocates “both/and” approach to conflicting demands, may moderate the influence of the perceptual inhibitors of instructional leadership. Design/methodology/approach – The current study is qualitative in nature. Study participants were 30 Israeli school principals, representing the larger body of Israeli principals in terms of sex, age, years of experience, education and school level. Data were collected through semi-structured interviews. Data analysis included a two-step theory-driven coding process. Findings – This study found that the paradoxical approach allowed school principals to hold conflicting perspectives on instructional leadership simultaneously. Thus, it has reduced the effect of the perceptual inhibitors of instructional leadership, as it permitted principals to delay the decision between the expectation to fulfill the role of instructional leader and their disagreements with it. Originality/value – Despite prolonged pressures, school principals demonstrate limited involvement in instructional leadership, in part because of perceptual inhibitors. The findings of this study can be used in dealing with these inhibitors.
A privacy‐preserving framework for location recommendation using decentralized collaborative machine learning

The nowadays ubiquitous location‐aware mobile devices have contributed to the rapid growth of individual‐level location data. Such data are usually collected by location‐based service platforms as training data to improve their predictive models' performance, but the collection of such data may raise public concerns about privacy issues. In this study, we introduce a privacy‐preserving location recommendation framework based on a decentralized collaborative machine learning approach: federated learning. Compared with traditional centralized learning frameworks, we keep users' data on their own devices and train the model locally so that their data remain private. The local model parameters are aggregated and updated through secure multiple‐party computation to achieve collaborative learning among users while preserving privacy. Our framework also integrates information about transportation infrastructure, place safety, and flow‐based spatial interaction to further improve recommendation accuracy. We further design two attack cases to examine the privacy protection effectiveness and robustness of the framework. The results show that our framework achieves a better balance on the privacy–utility trade‐off compared with traditional centralized learning methods. The results and ensuing discussion offer new insights into privacy‐preserving geospatial artificial intelligence and promote geoprivacy in location‐based services.
Understanding Machine Learning Model Updates in Malware Detection Systems Based on Feature Attribution

Machine learning (ML) models are often adopted in malware detection systems. To ensure the detection performance in such ML-based systems, updating ML models with new data is crucial to minimize the inﬂuence of concept drift. After an update, the detection accuracy is commonly used to validate the new model. However, the detection accuracy dose not include detailed information regarding the causes of performance changes or slight changes in updates. When a system does not achieve the detection performance as expected, these information can explain the changes in updates and help with the further improvement. We therefore propose a method for understanding ML model updates in malware detection systems by using a feature attribution method called Shapley additive explanations (SHAP), which interprets the output of a ML model by assigning an importance value called a SHAP value to each feature. Since feature attribution will change as the model changes, we can analyze the model changes by calculating the SHAP value changes before and after updates. Using our method, we can identify the important features related to the performance changes and distinguish the slight changes in models due to updates. We can also analyze the eﬀects of new data and the tendency of new predictions.
Gender Stereotypes of Children from “Extended” Single-Parent Families in Turkey

Basım Tarihi: XX.XX. XXXX Ebeveynler arasındaki ilişkilerin bozulması, boşanma ya da ölüm gibi durumlar ailedeki rollerin yeniden yapılanmasını zorunlu kıldığı için, çocukların toplumsal cinsiyet rollerine ilişkin tutumlarını etkileyebilmektedir. Bu doğrultuda bu çalışmada tek ebeveynli ailelerde yaşayan çocukların toplumsal cinsiyet rollerine ilişkin tutumlarının incelemesi amaçlanmaktadır. Bu amaçla çalışmada nitel araştırma desenlerinden fenomenoloji yaklaşımı kullanılmıştır. Veriler tek ebeveynli ailelerde yaşayan ve yaşları 54-78 ay arasında değişen dokuz çocukla yapılan müdahalesiz etkinlikler aracılığıyla elde edilmiştir. Elde edilen verilerin analizinde içerik analizi yöntemi kullanılmıştır. Müdahalesiz etkinlikler; oyuncaklara, mesleklere ve evdeki sorumluluklara yönelik kalıp yargısal davranışları ortaya çıkarmak amacıyla çeşitli görseller ve bu görsellere ilişkin soruları içermektedir. Araştırma sonuçları, annesi ile yaşayan çocukların evdeki işlere ve mesleklere yönelik tercihlerinde toplumsal cinsiyet kalıplarının dışına çıkabildiklerini ortaya koymuştur. Ayrıca, evdeki amcalar ve büyükanneler gibi sosyal rol modellerin, çocukların ev işleri ile ilgili toplumsal cinsiyet kalıp yargılarını etkileyebileceği görülmüştür. Ancak, bu etkilerin sınırlı olduğu görülmektedir. Buna göre hem anneleri ile birlikte hem de babaları ile birlikte yaşayan tek ebeveynli ailelerdeki çocukların tercihlerinde toplumsal cinsiyet kalıplarının etkili olduğu belirlenmiştir. Çocukların sahip oldukları kalıp yargılar, ilgili alan yazın ve Türkiye’deki aile yapısı bağlamında tartışılmıştır. Gelecek araştırmalar için öneriler ayrıca sunulmuştur. Anahtar Sözcükler: Tek ebeveynli aileler, Toplumsal cinsiyet, Aile
Towards Improvement of Integration Accuracy of Linear Pdes of First Order in Time

We extend the approaches developed in the papers by Kaps and Rentrop and by Schatzman, and suggest a method for highly accurate time integration of linear first-order evolutionary PDEs. The main idea is to generalise the Runge-Kutta methods for complex timesteps, which reduces the problem of accurate integration to the search for an optimal temporal path on the complex plane. Theoretically the method admits integration with timesteps.
Non‐GAAP Reporting: Evidence from Academia and Current Practice

The number of firms reporting earnings on a non&#8208;GAAP basis has increased dramatically over the last decade, and non&#8208;GAAP reporting is now commonplace in capital markets. This proliferation of non&#8208;GAAP reporting has renewed both regulators&#8217; and standard setters&#8217; interests in these alternative performance metrics. For example, the SEC, FASB, and IASB have all recently questioned what this increasing reporting trend means for IFRS&#8208; and US&#8208;GAAP&#8208;based reporting and whether these measures are misleading to investors. This increasing focus on non&#8208;GAAP metrics motivates us to synthesize the nearly two decades of research on non&#8208;GAAP reporting to provide insights on what academics have learned to date about this reporting practice. Then, we utilize a novel dataset of detailed non&#8208;GAAP disclosures to provide new descriptive evidence on current trends in non&#8208;GAAP reporting and its recent proliferation. Finally, we discuss important questions for future researchers to consider in moving the literature forward.
A Note on GameStop, Short Squeezes, and Autodidactic Herding: An Evolution in Financial Literacy?

Abstract This note explores a secondary effect of the GameStop short squeeze event and links the exalted focus of retail investors on meme stocks to financial literacy and autodidacticism. From an overview of stylized facts about the short squeeze of GameStop based on high frequency data, short interest, and key figures of related derivatives, it is shown that these financial concepts are reflected in keyword searches across multiple platforms. This self-education with regard to financial terms, keywords, and products and the understanding of basic market speculation mechanisms such as short sales plays a significant role for the influx of retail investors.
When prosocial investments signal trustworthiness: Theory and Experiment

This paper studies in theory and in a laboratory experiment the conditions under which prosocial investments signal trustworthiness. Results show that the choice alone of a prosocial investment, if the level of prosocial activity is not visible, does not increase investors’ transfers to prosocial investments but a visible high level of prosociality does as long as the prosocial activity is not too costly. The announcement of the level of prosocial activity is a valid signal of trustworthiness as the entrepreneurs who announce a higher level are more trustworthy. However, the prosocial entrepreneurs pay this signal “out-of-pocket” as they carry the cost of the prosocial activity by returning to the investors as much as the entrepreneurs who chose purely financial project. The mechanisms behind investors believing that prosocial entrepreneurs will be more trustworthy is that the mere project type is insufficient and information about the effective spillover is necessary; making that quantitative information visible allows investors to differentiate between investment opportunities. Furthermore, while entrepreneurs are found to respond to fiscal incentives by choosing more often the impact project under tax exemption, they prevent signal dilution by maintaining the level of spillover under tax exemption.
Cross-Predictability of Earnings and Stock Returns: A Latent Factor Approach

I propose a new method, three-pass regression filter developed by Kelly and Pruitt (2015), to predict non-announcing firms’ earnings news using the cross-section of all available early announcers’ earnings news, the number of which can be as large as thousands. The method assumes a set of common latent factors driving the earnings news of non-announcing firms and early announcers and thus efficiently reduces the dimension of announced earnings. Empirical tests show that the extracted measure strongly predicts the earnings surprise and the earnings announcement return with both statistical and economic significance. A long-short trading strategy based on the extracted information realizes a 10% value-weighted alpha annually, indicating a delayed reaction of investors. The return predictability is stronger for small firms or firms with less investor attention. Controlling a series of documented information channels has little impact on the predictive power of this extracted measure.
Parameter Estimation and Random Number Generation from a Zipf-Related Lerch Distribution

The Lerch family of three-parameter, discrete univariate distributions includes as special cases the well known Zipf, Zipf-Mandelbrot, and Good distributions that are used as models in ecology, linguistics, information science, and statistical physics. The Lerch distribution was originally defined on the set of positive integers by Zörnig and Altmann (1995, Comp. Stat. Data An., 19, 461). Here we extend the definition of the Lerch distribution to the set of nonnegative integers, which allows application of the Lerch distribution to problems where the observed random variable can take zero values, e.g. modeling count data and survival and dispersal processes in ecology. Properties of truncated distributions, including the zero-truncated case studied by Zörnig and Altmann, can be easily derived from the general Lerch distribution. We derive conditions on parameters of the general Lerch distribution for it to be strongly unimodal, give formulas for its uncorrected, central, and factorial moments, and derive the moment and maximum likelihood estimators. We also propose an algorithm for random number generation from the Lerch distribution that is based on the method of inversion by correction. Finally, we provide a Mathematica package for calculations related to the Lerch distribution.
How Reliable are the Hurtt Professional Skepticism Scale and the Rotter Interpersonal Trust Scale for Audit Experimental Research?

This study uses an experimental context to compare the results obtained from two well accepted scales that have been designed to measure stable personality traits - the Hurtt Professional Skepticism Scale (HPSS) and the Rotter Interpersonal Trust Scale (RITS). The RITS was developed outside of accounting, whereas the HPSS was developed specifically for measuring professional skepticism in accounting. Recently, the RITS has been proposed by Quadackers, Groot and Wright (2014) as a substitute for the HPSS on the grounds that the inverse of the RITS is a more useful measure of skepticism for audit purposes as it measures the presumptive doubt form of skepticism (Nelson 2009) rather than the neutral form of skepticism measured by the HPSS and is more responsive to risk information. To test this assertion, we created three case scenarios involving three different auditing tasks adapted from current literature. For each scenario, we administered both scales, varying the order of case and scale administration to monitor the stability that would be expected of measures of stable traits. Our findings indicate that HPSS and RITS measures of skepticism and trust, respectively, vary by case presented and order of risk tolerance measurement. Skepticism, as measured per the HPSS is not affected by the order of HPSS administration; however, it is affected by case and order of risk tolerance measurement. Trust, as measured by the RITS, is affected by order (timing) of RITS administration. Specifically, measured Trust decreases when the scale is administered after cases are completed compared to the level obtained when Trust is measured before the case is completed. RITS is also marginally affected by case and order of risk tolerance measurement. This study contributes to our understanding of the HPSS and RITS, challenges the reliability of both scales as measures of stable personality traits, and supports order stability for the HPSS, but not the RITS. Both scales may have components that are sensitive to states rather than being pure measures of traits. Also, HPSS and RITS do not appear to be substitutes for one another, as the RITS does not behave as the inverse of the HPSS across cases and orders of administration. Lack of trust (the inverse of RITS) does not appear to be synonymous with skepticism as measured by the HPSS.
An Empirical Analysis of Analysts’ Capital Expenditure Forecasts: Evidence from Corporate Investment Efficiency

We examine whether the information conveyed in a relatively new analyst research output—capital expenditure (capex) forecasts—affects corporate investment efficiency. We find that firms with analyst capex forecasts exhibit higher investment efficiency. This effect is stronger when the forecasts are issued by analysts with higher ability or greater industry knowledge. Moreover, the effect of capex forecasts on investment efficiency varies with the signals they convey about future growth opportunities—positive-growth signals are more effective in reducing underinvestment, while negative-growth signals are more effective in reducing overinvestment. Cross-sectional tests suggest that these effects operate at least in part through both a financing channel and a monitoring channel. Taken together, our results suggest that analysts’ capex forecasts convey useful information about firms’ growth opportunities to managers and investors, which can facilitate efficient investment.
The Effect of Tariffs on Spanish Goods Exports

This paper investigates the impact of trade protectionism in the form of tariff barriers on Spanish goods exports. The Spanish economy has significantly increased its degree of openness, which improves potential economic growth, but also implies a higher exposure to the protectionist shift in the international environment observed in the last years. With the purpose of assessing exports sensitivity to tariff increases, we obtain a database combining annual tariffs applied to Spanish products over the years 1995-2019 from WITS and bilateral extra-EU Spanish goods exports from Eurostat, with a product disaggregation level at 6 digits. We estimate the effect of tariffs both on exporting probability (i.e. exports extensive margin) through a linear probability model and exports levels (i.e. exports intensive margin) through a gravity equation. The findings of this paper show that higher tariffs reduce exports levels through extensive an intensive margins. A 1% tariff increase reduces on average the probability of exporting to a specific market by nearly 0.08 pp. and exported values by around 1%.
Computation of a Normal Form for Moore-Greitzer Compression System Lumped-Parameter Model

The problem of post-stall behavior of compression systems is studied through numerous investigations during three last decades. Moore-Greitzer model is of special interest due to its nice applicability to these problems. Bifurcation theory is one of the new trends of these studies and is used to explore topology of associated dynamical systems. In this paper, a nonlinear transformation of coordinates, known as normal form, is used there to put governing ordinary differential equations in the most appropriate coordinate system.