Miracle-dz/UniFAR-FTUs huggingface dataset:
A highly-available move operation for replicated trees

Replicated tree data structures are a fundamental building block of distributed filesystems, such as Google Drive and Dropbox, and collaborative applications with a JSON or XML data model. These systems need to support a move operation that allows a subtree to be moved to a new location within the tree. However, such a move operation is difficult to implement correctly if different replicas can concurrently perform arbitrary move operations, and we demonstrate bugs in Google Drive and Dropbox that arise with concurrent moves. In this paper we present a CRDT algorithm that handles arbitrary concurrent modifications on trees, while ensuring that the tree structure remains valid (in particular, no cycles are introduced), and guaranteeing that all replicas converge towards the same consistent state. Our algorithm requires no synchronous coordination between replicas, making it highly available in the face of network partitions. We formally prove the correctness of our algorithm using the Isabelle/HOL proof assistant, and evaluate the performance of our formally verified implementation in a geo-replicated setting.
RAPID RESOLUTION LIQUID CHROMATOGRAPHY METHOD FOR DETERMINATION OF CHLOROGENIC ACID IN ECHINACEA EXTRACTS

This study presents a development and validation of a new, fast, efficient and cost effective reversed-phase rapid resolution liquid chromatography (RP-RRLC) method for determination of chlorogenic acid in echinacea extracts. The optimum separation with symmetrical peak shape and good index purity of the analyte was achieved on a Poroshell 120 EC-18 (50 mm x 3 mm; 2.7 μm) analytical column, mobile phase consisted of acetonitrile/(water with 1 % phosphoric acid), (10/90, V/V)in isocratic elution with flow rate of 1 mL/min and UV diode-array detection (UV-DAD) at 325 nm. The developed method was validated by testing specificity, selectivity, linearity, precision, accuracy, limit of detection (LOD) and limit of quantification (LOQ). The calibration curve of chlorogenic acid followed Beer’s law within the range 28.97 ng 362.19 ng (R = 0.9994). The LOD was 0.29 pg, while LOQ was 0.96pg. The intra-day precisions was evaluated for the retention time, peak area and peak height and the calculated values for relative standard deviations (RSD) were 0.21 %, 0.11 % and 0.22 %, respectively. The mean recoveries ranged from 98.75 to 104.63 % and RSD was less than 0.23 %. The developed method was successfully applied for identification and quantification of chlorogenic acid in three samples of echinacea extracts, taken from local pharmacies.
ATTITUDES AND SOCIAL COGNITION From Variability to Vulnerability: People Exposed to Greater Variability Judge Wrongdoers More Harshly

Recent decades have seen increased variability in diverse domains, such as the climate and asset prices. As more resources are required to cope with greater variability in the outside world, exposure to greater variability can make people feel that society is more vulnerable. This sense of vulnerability, in turn, can lead people to judge and punish wrongdoers more harshly. Studies 1a–2c found that people who were exposed to graphs representing greater variability were more willing to punish wrongdoers, both in domains that were related to the source of variability and those that were unrelated. Studies 3 and 4 found that people who experienced more variable dice rolls were more likely to punish unethical behaviors in hypothetical scenarios and in experimental games, even at a financial cost to themselves. Studies 5a and 5b provided evidence for the underlying mechanism—sense of vulnerability—using correlational designs. Study 6 provided experimental evidence for the underlying mechanism. These findings suggest that increasing variability in diverse domains can have unexpected psychological consequences.
Generating Negative Commonsense Knowledge

The acquisition of commonsense knowledge is an important open challenge in artificial intelligence. In this work-in-progress paper, we study the task of automatically augmenting commonsense knowledge bases (KBs) with novel statements. We show empirically that obtaining meaningful negative samples for the completion task is nontrivial, and propose NegatER, a framework for generating negative commonsense knowledge, to address this challenge. In our evaluation we demonstrate the intrinsic value and extrinsic utility of the knowledge generated by NegatER, opening up new avenues for future research in this direction.
SUPPORTED BY UNIGESTION “ National Culture , Corporate Governance Practices , and Firm Performance

We contrast the universalist and cultural perspectives on “good” corporate governance practices. Using a new database from Governance Metrics International featuring highly granular measures of corporate governance practices across a large number of countries for 2006-2011, we find that the national cultural dimension of individualism is positively associated with accountability and transparent disclosure, and with corporate behavior standards, and that uncertainty avoidance is negatively associated with accountability and transparent disclosure, and with minority shareholder protection. Within countries, there is a largely positive association between firm-level “good” corporate governance practices and firm performance; however, across countries, the association is largely negative. Monday, April 20, 2015, 10:30-12:00 Room 125, Extranef building at the University of Lausanne National Culture, Corporate Governance Practices, and Firm Performance* First version: January 2012 This version: January 2015 Abstract We contrast the universalist and cultural perspectives on “good” corporate governance practices. Using a new database from Governance Metrics International featuring highly granular measures of corporate governance practices across a large number of countries for 2006-2011, we find that the national cultural dimension of individualism is positively associated with accountability and transparent disclosure, and with corporate behavior standards, and that uncertainty avoidance is negatively associated with accountability and transparent disclosure, and with minority shareholder protection. Within countries, there is a largely positive association between firm-level “good” corporate governance practices and firm performance; however, across countries, the association is largely negative.
Analisis Faktor yang Mempengaruhi Niat Penggunaan Layanan Pendaftaran Nikah Online Pada Simkah Web di Sleman

The use of e-government services today, where millennials who understand technology sometimes still rely on information from others who have used the previous system, causing information to be missed on the benefits of the system that have been done online, resulting in the intention to use the system is low. This research is about online marriage registration services on the Simkah web application owned by the Ministry of Religion of the Republic of Indonesia because there are still many prospective brides who have not used the online service. The model used is to combine the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model and the modified IS DeLone and McLean success model and consider moderating age, gender, and internet experience. In this study, it is necessary to know whether the constructor in both models effect the intention to use online marriage registration services. The study was conducted in Sleman Regency. The construct finding that affects the intention to use online marriage registration services is hedonic motivation so the Ministry of Religion needs to increase hedonic motivation or as an incentive in a person to use technology.
Institutional Quality, Productivity and Patterns of Comparative Advantage

This paper contributes to the recent, but rapidly growing, literature on the impact of institutional environment on firms’ productivity and trade propensity. Analyzing a comparable cross-country, firm-level survey we find that firms’ productivity and trade performance is robustly related to institutional environment. Using a novel set of instrumental variables we show that the chain of causality runs from improved institutional environment to better firm’s productivity and higher trade volumes rather than the reverse. Furthermore, we find that institutional environment is more important for productivity in high value-added industries such as electronics or machinery hence the pattern of trade and specialization. From a policy perspective our results imply that institutional and regulatory reform especially in enforcement of contracts and business regulation may enhance the capacity of poor countries to move up to specialization in highervalued and more complex products and to reap benefits from international integration.
Algorithms for Propagation Effects and Remedies in Microwave Applications

The lower frequency bands becomes saturated with users, it is require to use microwave frequency bands. The problem associated with high frequencies in wireless communication is atmospheric attenuation, propagation delay, signal dispersion, signal scattering, etc. In this paper we developed an algorithm for analyzing the propagation effects and how to overcome this effect to retrieve the maximum signal strength. In this paper we focused on how to calculate the range error due to certain atmospheric parameters such as humidity, dry air, hydrostatic atmospheric. Also we focused on increasing of signal strength at the receiver by calculating the angle of arrival. The developed algorithms were applied and tested by developing the communication system.
Prognosis of Wind Turbine Gearbox Bearing Failures using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.
QuetzalC++ Team - Results for Final Tier 1

—ADR and Alpha pilot are international compe-titions inspired by the dron racer league. The aim of these competitions is to promote the development of artiﬁcial intelligence algorithms so that a drone runs through a complex track as quickly as possible. This has provided the scientiﬁc community with breakthroughs in environmental awareness, location, safe navigation, planning, as well as precise ﬂight control to successfully execute the manoeuvres necessary to run a runway. Now, Game of Drones NeurIPS 2019 competition proposes to take a step closer to the drone racer league, to compete on the same runway against an adversary, and for it designs three levels of difﬁculty, 1) planning, 2) perception and 3) fully autonomous. We present our results of the participation on the ﬁrst tier of teh competition using a trajectory planner.
Application of the 4 Ps Marketing Mix by Micro and Small-Scale Traders in Kenya. Impact on Household Incomes

This article examines the application of the four Ps marketing mix by micro and small-scale traders (MSTs) dealing in fresh (fruit and vegetable) produce in Kenya. The study was necessitated by the need to explain unimproved living standards of the traders despite thousands of Kenyans doing business in this sector. Survey research design was adopted, and stratified random sampling techniques were employed to draw 150 respondents from among the traders in the residential estates, and the main marketing the city of Nakuru, Kenya. Structured questionnaires were used in data collection. Descriptive statistics and regression modeling were applied in analyzing the data. The results indicate that MSTs in the produce industry in Kenya, pay attention to all the 4 Ps marketing mix, i.e., promotion, place, price and product, and, in that order of increasing importance. Yet among these strategies, price and product are the most important determinants of household income. Additionally, bigger MSTs are more likely to experience increase in household income than their smaller counter parts. Finally, the average MST in the produce industry perceives that their household income has increased because of the business.
A Decision-Making Framework for Safe Operations of Unmanned Aerial Vehicles in Urban Environments

This paper develops an ofﬂine decision-making framework to support safe urban operations of individual unmanned aerial vehicles (UAV) ﬂights. The core of the proposed framework is the analysis of the probability of mission failure and the corresponding risk of ﬂight as a function of two factors: (1) collision with obstacles, and (2) crashes attributed to the degraded state of the vehicle. The risk computation is associated with speciﬁc trajectories deﬁned by a set of way points. Our experimental studies consider a UAV mission fails when (1) it collides with other objects, or (2) the battery charge is depleted below a threshold. The decision making system automatically selects the mission plan that minimizes risk of ﬂight by considering the state of the vehicle, the environmental conditions, and a map of the environment.
Integrating standardized patients into bioethics education for freshman nursing students

Available online at: http://npt.tums. ac.ir Background & Aim: Bioethics education nourishes consciousness in bioethics and influences the attitude and perception of death. The purpose of this study was to investigate the effect of bioethics education by employing standardized patients for freshmen nursing students. Methods & Materials: A quasi-experimental single group pretest-posttest design was used to assess the effect of bioethics education employing standardized patients on 33 freshman nursing students’ perception of dignified death and empathy who took the course of bioethics of a university. Results: The perception of dignified death appeared higher in post-education than pre-education, and the subdomain of personal distress under empathy appeared higher in post-education than pre-education. In regard to the replies on the question of “disclosure of diagnosed cancer”, 54.6% of respondents approved, while 90.9% expressed “it should be at the discretion of doctors. ” Conclusion: bioethics education employing standardized patients appeared assisting students experiencing changes in perception of a dignified death. It may be useful in applying the teaching module for students with courses in bioethics.
Fault Diagnostics and Prognostics for Vehicle Springs and Stablizer Bar

Vehicle springs and stabilizer bar are critical suspension components impacting vehicle riding and handling experience. Diagnostics and prognostics of springs and stabilizer bar can improve customer perceived quality, reduce repair cost and increase up-time for fleet vehicles. It’s even more important for autonomous vehicles, since there is no human driver to sense fault symptoms. Currently, there is no production solution to automatically diagnose and prognose spring and stabilizer bar failures, and most research work is suffered by various noise factors. In this work, a novel solution based on static ramp test is proposed to isolate and localize spring and stabilizer bar faults. With limited number of longitudinal and lateral acceleration measurements, the solution can quickly and effectively isolate faulty spring, disconnected stabilizer bar, loose bushing and loose end link. The validation results from a MY17 Bolt EV demonstrate the effectiveness and robustness of the proposed solution.
Network for wildlife health surveillance in Europe

The authors are responsible for the final contents of the card. Please refer to this card when you publish a study for which the APHAEA protocol has been applied. Reference suggestion: «This method is recommended by the EWDA Wildlife Health Network (www.ewda.org)»; citation: Author(s), Year, APHAEA/EWDA Species Card:[name of species / taxonomic group]. Network for wildlife health surveillance in Europe Species Card
Sexual dimorphism in the spinning apparatus of Allocosa senex (Araneae: Lycosidae), a wolf spider with a reversal in typical sex roles

Abstract Allocosa senex (Mello-Leitão, 1945) is a sex role-reversed wolf spider that inhabits sandy water-margin environments of southern South America. Males are larger than females and dig deeper burrows. Females are the courting sex and they prefer to mate with males that build deep burrows, suggesting high selective pressures on male digging behavior. Our aim was to investigate the external morphology and histological constitution of the spinning apparatus of males, females and juveniles of A. senex. Our results showed that A. senex adult males possess more piriform glands and spigots than adult females and juveniles. These glands produce silk for attachment discs that are crucial for the stability of the burrows. The differences according to the sex could be related to females' and males' digging strategies and strong selection on male burrow length in this species.
TRENDS IN RESEARCH ON PARAQUAT-INDUCED PARKINSONISM

Paraquat, a herbicide widely used in agriculture for better crop production, is an environmental toxin and causes oxidative stress-induced systemic damages in plants, animals and humans. Epidemiological studies report that paraquat toxicity is related to the onset of parkinsonism in humans. Animal experimentations indicate that paraquat toxicity causes the cellular and molecular changes associated with parkinsonism. Experimental evidences show that bloodbrain-barrier limits the paraquat entry in brain of primates and humans and therefore, the doubts remain for a direct paraquat-induced parkinsonism in human. To define a remedial measure, extensive studies are required on the cause and prognosis of Parkinson’s Disease induced by such environmental toxicant.
Random variate generation using only finitely many unbiased, independently and identically distributed random bits.

For any discrete probability distributions with bounded entropy, we can generate exactly a random variate using only a finite expected number of perfect coin flips. A perfect coin flip is the outcome of an unbiased Bernoulli random variable. Coin flips are unbiased, independently and identically distributed in all our work. We survey well-known algorithms for the discrete case such as the one from Knuth and Yao as well as the one from Han and Hoshi. We also discuss briefly about a practical implementation for the algorithm proposed by Knuth and Yao. For the continuous case, only approximations can be hoped for. The freedom to choose the accuracy for the approximations matters, and, for that, we propose to measure accuracy in terms of the Wasserstein $L_\infty$-metric. We derive a universal lower bound for the expected number of perfect coin flips required to reach a desired accuracy. We also provide several algorithms for absolutely continuous distributions that come within our universal lower bound.
MGARD+: Optimizing Multilevel Methods for Error-Bounded Scientific Data Reduction

Nowadays, data reduction is becoming increasingly important in dealing with the large amounts of scientific data. Existing multilevel compression algorithms offer a promising way to manage scientific data at scale, but may suffer from relatively low performance and reduction quality. In this paper, we propose MGARD+, a multilevel data reduction and refactoring framework drawing on previous multilevel methods, to achieve high-performance data decomposition and high-quality error-bounded lossy compression. Our contributions are four-fold: 1) We propose to leverage a level-wise coefficient quantization method, which uses different error tolerances to quantize the multilevel coefficients. 2) We propose an adaptive decomposition method which treats the multilevel decomposition as a preconditioner and terminates the decomposition process at an appropriate level. 3) We leverage a set of algorithmic optimization strategies to significantly improve the performance of multilevel decomposition/recomposition. 4) We evaluate our proposed method using four real-world scientific datasets and compare with several state-of-the-art lossy compressors. Experiments demonstrate that our optimizations improve the decomposition/recomposition performance of the existing multilevel method by up to <inline-formula><tex-math notation="LaTeX">$70 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>70</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liang-ieq1-3092201.gif"/></alternatives></inline-formula>, and the proposed compression method can improve compression ratio by up to <inline-formula><tex-math notation="LaTeX">$2 \times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>2</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="liang-ieq2-3092201.gif"/></alternatives></inline-formula> compared with other state-of-the-art error-bounded lossy compressors under the same level of data distortion.
Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising

The explosive rise of the use of Computer tomography (CT) imaging in medical practice has heightened public concern over the patient’s associated radiation dose. On the other hand, reducing the radiation dose leads to increased noise and artifacts, which adversely degrades the scan’s interpretability. In recent times, the deep learning-based technique has emerged as a promising method for low dose CT(LDCT) denoising. However, some common bottleneck still exists, which hinders deep learning-based techniques from furnishing the best performance. In this study, we attempted to mitigate these problems with three novel accretions. First, we propose a novel convolutional module as the first attempt to utilize neighborhood similarity of CT images for denoising tasks. Our proposed module assisted in boosting the denoising by a significant margin. Next, we moved towards the problem of non-stationarity of CT noise and introduced a new noise aware mean square error loss for LDCT denoising. The loss mentioned above also assisted to alleviate the laborious effort required while training CT denoising network using image patches. Lastly, we propose a novel discriminator function for CT denoising tasks. The conventional vanilla discriminator tends to overlook the fine structural details and focus on the global agreement. Our proposed discriminator leverage self-attention and pixel-wise GANs for restoring the diagnostic quality of LDCT images. Our method validated on a publicly available dataset of the 2016 NIH-AAPM-Mayo Clinic Low Dose CT Grand Challenge performed remarkably better than the existing state of the art method. The corresponding source code is available at: https://github.com/reach2sbera/ldct_nonlocal