Miracle-dz/UniFAR-FTUs huggingface dataset:
Does Emotion-Regulation Moderate Ostracism and Risk-taking?

Ostracism, feeling excluded or ignored, has been associated with increased risk-taking behavior on a number of self-report and lab-based measures (Buelow & Wirth, 2017; Duclos, Wan, & Jiang, 2012; Falk et al., 2014; Svetieva et al. 2016). Anger mediates the relationship between ostracism and risk-taking (Svetieva et al., 2016), and it is possible that emotion-regulation strategies to reduce anger may minimize this relationship. However, research has yet to test if emotion-regulation strategies can reduce affective responses following ostracism. The purpose of the current study is to examine the effects of ostracism via Cyberball and emotion-regulation strategies on risk-taking behavior using the Columbia Card Task (CCT), an objective risk-taking measure. It was predicted that participants in the ostracism condition would uniquely vary on risk-taking based on social condition (included vs. ostracism), emotion-regulation instructions (reappraisal vs. no instructions), and CCT version (Hot vs. Cold). Sixty-five participants were assigned to one of eight conditions in which they were instructed to either reappraise or given no instructions to regulate their emotional responses, were either socially included or ostracized, and then completed the affective (Hot) version of the CCT or the deliberative (Cold) version of the CCT. Although we found that ostracized participants self-reported greater feelings of anger compared to socially included participants, we found no difference between social conditions or emotion-regulation conditions for risk-taking. However, given the limited sample size and several indicators suggesting the Cold CCT was not an effective measure of risk-taking in the current study, these results should be interpreted with caution. Research should continue to explore the relationship between ostracism and anger on affective and deliberative risk-taking.
Comparing Generative Adversarial Networks to Deep RNN Language Models

Generating long sequences of text is a challenging problem in natural language processing. Although current models using deep recurrent neural networks (RNNs) have attained high performance on the generation of short text sequences, these models break down when generating longer text. In this work we examine the use of Generative Adversarial Networks (GANs), as a learned metric of the quality of produced text with which to train a generative model. We demonstrate the effective training of this model with policy gradient methods, which circumvent the non-differentiable sampling procedure required for use with a sequence level metric. We then compare the performance of the GAN with that of a state of the art deep learning model on the task of creating a simple conversational agent.
An Interactive System for Tree Alignment and Reconstruction

This application software system of Tree Alignment and Reconstruction (TAAR) is an X Window based interactive system developed mainly for sequence analysis. It provides a user-friendly graphical interface and convenient operations for performing Pairwise Alignment, 3-Star Alignment, Phylogeny Reconstruction and Generalized Tree Alignment algorithms. The algorithms for Phylogeny Reconstruction and Generalized Tree Alignment are designed based on heuristic stepwise addition and internal node sequence alignment induction methods. All above algorithms are space-efficiently implemented. For each algorithm, both fast and optimal versions are provided for user's convenience. TAAR can be used for DNA, RNA and PROTEIN sequences analysis. In general, as long as the sequence characters are in the range of a-z and A-Z, and the score matrix alphabet contains the sequence alphabet, the sequence would be accepted by this application and all calculations could be carried out. This software system is programmed in C, Motif and X Window Library functions. It is intended to be running in a UNIX and X Window environment.
Týr : a dependent type based code transformation for spatial memory safety in LLVM

The C programming language does not enforce spatial memory safety: it does not ensure that memory accessed through a pointer to an object, such as an array, actually belongs to that object. Rather, the programmer is responsible for keeping track of allocations and bounds information and ensuring that only valid memory accesses are performed by the program. On the one hand, this provides flexibility: the programmer has full control over the layout of data in memory, and when checks are performed. On the other hand, this is a frequent source of bugs and security vulnerabilities in C programs. A number of techniques have been proposed to provide memory safety in C. Typically such systems keep their own bounds information and instrument the program to ensure that memory safety is not violated. This has a number of drawbacks, such as changing the memory layout of data structures and thus breaking binary compatibility with external libraries and/or increased memory usage. A different approach is to use dependent types to describe the bounds information already latent in C programs and thus allow the compiler to use that information to enforce spatial memory safety. Although such systems have been proposed before, they are tied specifically to the C programming language. Other languages such as C++ suffer from similar memory safety problems, and thus could benefit from a more language-agnostic approach. This work proposes Týr, a program transformation based on dependent types for ensuring spatial memory safety of C programs at the LLVM IR level. It allows programmers to describe at the type level the relationships between pointers and bounds information already present in C programs. In this way, Týr ensures spatial memory safety by checking the consistent usage of this pre-existing metadata, through run-time checks inserted in the program guided by the dependent type information. By targeting the lower LLVM IR level, Týr aims to be usable as a foundation for spatial memory which could be easily extended in the future to other languages that can be compiled to LLVM IR, such as C++ and Objective C. We show that Týr is effective at protecting against spatial memory safety violations, with a reasonably low execution time overhead and nearly zero memory consumption overhead, thus achieving performance competitive with other systems for spatial memory safety, in a more language-agnostic way.
Non-linear beam theory in context of bio-inspired sensing of flows

The thesis at hand is part of a research project that attempts to study and develop vibrissa inspired tactile sensors for object and fluid flow detection. The main focus of the thesis is on the development of a model for a vibrissa-like sensor for obstacle contour recognition under fluid loads. To this end, a mechanical model – based on the non-linear Euler-Bernoulli beam theory – is established. The model includes the main characteristics found in a natural vibrissa, such as elasticity of the base, that acts as the vibrissa follicle; the intrinsic curvature; and conicity. The characteristics are represented as parameters of the model. The model is subjected to a contact load and a fluid flow load, represented by a concentrated load and a distributed load, respectively. Then, the model is transformed into a dimensionless representation for further studies to achieve more general assertions. A variation of the magnitude of these loads, as well as the vibrissa parameters is also analyzed. A direct numerical approximation using the finite difference method, along with the shooting method, is used to obtain a solution of the model. Subsequently, the model is used to simulate an ideal contact between an obstacle and the vibrissa. This simulation considers a quasi-static sweep of the artificial vibrissa with the contour of a profile, while measuring and recording the forces and moment at the base. This procedure is then repeated in combination of a distributed force acting on the vibrissa, simulating the effect of a fluid flow. Two types of contact phases are identified and the conditions for each one are set. Finally, the measured quantities, which represent the observables an animal solely relies on, are used to obtain the magnitude of the fluid load and to reconstruct the profile contour of the obstacle. The developed model is used again for the reconstruction, an analysis of the observables is performed to identify and predict which contact phase the vibrissa is in. The results successfully show identification of the fluid flow load as well as reconstruction of the profile, the difference between the reconstructed profile and the original profile is then calculated as a measure of reconstruction quality.
Veri cation of Pointers

Our recent work links type checking in programming languages to veri cation based on automata. In this survey, we give an overview of our methods and suggest directions for furture rearch. In our approach, we view data types as invariants and devise a logical and decidable framework for expressing global properties of a store consisting of records and pointers. We can express common properties, for example about doubly-linked lists and their algorithms. Such properties seemed to have called for a full Hoare logic beyond the reach of type checking and decidability. Our work is based on monadic second-order logic. Thus veri cation boils down to calculations on nite-state automata. This raises speci c questions about combinatorial techniques for representing state spaces if these calculations are ever to be carried out on more than simple examples. Topics: program veri cation, data types. The author is supported by a fellowship irom the Danish Research Council. The author is partially supported by the BRICS Center under the Danish Research Foundation.
Real-time image reconstruction for low-dose CT using deep convolutional generative adversarial networks (GANs)

This paper introduces a deep learning network that reconstructs low-dose CT images into CT images of a high quality comparable to adaptive statistical iterative reconstruction (ASIR) as fast as filtered backprojection (FBP). Fully convolutional networks (FCNs) are adopted to denoise the low-dose CT images reconstructed with FBP. In contrast to patch-based convolutional neural networks (CNNs), we train the FCN-based denoising network with full-size images, which is computationally efficient due to the reuse of feature maps from the lower layers. To guarantee that the resultant high-quality images are consistent with the input images, a CNN-based classifier is added to the denoising network during the training phase. The classifier incorporates the CT noise model and evaluates the consistency between the images reconstructed with FBP and those of the denoising network. This supplementary structure makes the entire network a class of generative adversarial networks (GANs). For training and testing the network, we use a dataset of 18 patients who have undergone abdominal low-dose CT with both FBP and ASIR, which we split into a training set of 12 patients and a validation set of the remaining 6 patients. After being trained with FBP and ASIR image pairs, the GAN successfully recovers the high-quality images from the noisy CT images reconstructed with FBP. The network, by using a moderate GPU, is computationally efficient in recovering each image within 0.1 second. It is also remarkable that the GAN successfully preserves the image details, whereas ASIR is known for its occasional failure to recover small low-contrast features.
To Know or not To Know Information Value in a Semantic Game

This paper establishes that the information value, when defi ned in many different ways depending on player’s knowledge, is positive for some specific types of messages and some specific classes of games. More precisely, it is shown that the information value is positive (i) for the receiver of a secret message, (ii) for the receiver of a private message in a zero-sum game and (iii) for all players when they receive a public message in a pure coordination game. Journal of Economic Literature Classification Number: C72.
Machine Learning Predicts Aperiodic Laboratory Earthquakes

In this paper we ﬁnd a pattern of aperiodic seismic signals that precede earthquakes at any time in a laboratory earthquake’s cycle using a small window of time. We use a data set that comes from a classic laboratory experiment having several stick-slip displacements (earthquakes), a type of experiment which has been studied as a simulation of seismologic faults for decades. This data exhibits similar behavior to natural earthquakes, so the same approach may work in predicting the timing of them. Here we show that by applying random forest machine learning technique to the acoustic signal emitted by a laboratory fault, we can predict the time remaining before failure with 1.61 seconds mean absolute error at any moment of earthquake’s cycle. These predictions are based solely on the acoustical signal’s statistical features derived from the local, moving 0.3 second time windows and do not make use of its history. Essential improvements in providing new understanding of fault physics may be brought by applying this technique to acoustic seismic data.
FAMILY BACKGROUND OR LANGUAGE DISADVANTAGES? FACTORS FOR UNDERACHIEVEMENT IN HIGH STAKES TESTS

Group for the Psychology of Mathematics Education, Vol. 4, pp. 49-56. Kiel, Germany: PME. FAMILY BACKGROUND OR LANGUAGE DISADVANTAGES? FACTORS FOR UNDERACHIEVEMENT IN HIGH STAKES TESTS Susanne Prediger, Nadine Renk, Andreas Büchter, Erkan Gürsoy, Claudia Benholz TU Dortmund University, Mathematics Education; University of Cologne, Mathematics Education; University Duisburg-Essen, German as second language, Germany
Natural Plant Compounds with Possible Interaction with Anesthetics

The use of medicinal products is becoming more and more modern. Although little is known about the exact composition and effects of various herbal preparations, their plant and natural origin serve as evidence of their safety. The doctor is rarely notified of their use, while there is increasing body of evidence on the effects, side effects and interactions with synthetically produced drugs.
xperimental validation of a resilient monitoring and control system

Complex, high performance, engineering systems have to be closely monitored and controlled to ensure safe operation and protect public from potential hazards. One of the main challenges in designing monitoring and control algorithms for these systems is that sensors and actuators may be malfunctioning due to malicious or natural causes. To address this challenge, this paper addresses a resilient monitoring and control (ReMAC) system by expanding previously developed resilient condition assessment monitoring systems and Kalman filter-based diagnostic methods and integrating them with a supervisory controller developed here. While the monitoring and diagnostic algorithms assess plant cyber and physical health conditions, the supervisory controller selects, from a set of candidates, the best controller based on the current plant health assessments. To experimentally demonstrate its enhanced performance, the developed ReMAC system is then used for monitoring and control of a chemical reactor with a water cooling system in a hardware-in-the-loop setting, where the reactor is computer simulated and the water cooldaptive supervisory controls
Fish oil: MedlinePlus Supplements

Fish oil is used for a wide range of conditions. It is most often used for conditions related to the heart and blood system. Some people use fish oil to lower blood pressure or triglyceride levels (fats related to cholesterol). Fish oil has also been tried for preventing heart disease or stroke. The scientific evidence suggests that fish oil really does lower high triglycerides, and it also seems to help prevent heart disease and stroke when taken in the recommended amounts. Ironically, taking too much fish oil can actually increase the risk of stroke.
SEOM – SERAM – SEMNIM guidelines on the use of functional and molecular imaging techniques in advanced non ‐ small ‐ cell lung

Imaging in oncology is an essential tool for patient management but its potential is being profoundly underutilized. Each of the techniques used in the diagnostic process also conveys functional information that can be relevant in treatment decisionmaking. New imaging algorithms and techniques enhance our knowledge about the phenotype of the tumor and its potential response to different therapies. Functional imaging can be defined as the one that provides information beyond the purely morphological data, and include all the techniques that make it possible to measure specific physiological functions of the tumor, whereas molecular imaging would include techniques that allow us to measure metabolic changes. Functional and molecular techniques included in this document are based on multi-detector computed tomography (CT), 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET), magnetic resonance imaging (MRI), and hybrid equipments, integrating PET with CT (PET/CT) or MRI (PET-MRI). Lung cancer is one of the most frequent and deadly tumors although survival is increasing thanks to advances in diagnostic methods and new treatments. This increased survival poises challenges in terms of proper follow-up and definitions of response and progression, as exemplified by immune therapy-related pseudoprogression. In this consensus document, the use of functional and molecular imaging techniques will be addressed to exploit their current potential and explore future applications in the diagnosis, evaluation of response and detection of recurrence of advanced NSCLC.
The MuCoW Test Suite at WMT 2019: Automatically Harvested Multilingual Contrastive Word Sense Disambiguation Test Sets for Machine Translation

Supervised Neural Machine Translation (NMT) systems currently achieve impressive translation quality for many language pairs. One of the key features of a correct translation is the ability to perform word sense disambiguation (WSD), i.e., to translate an ambiguous word with its correct sense. Existing evaluation benchmarks on WSD capabilities of translation systems rely heavily on manual work and cover only few language pairs and few word types. We present MuCoW, a multilingual contrastive test suite that covers 16 language pairs with more than 200 thousand contrastive sentence pairs, automatically built from word-aligned parallel corpora and the wide-coverage multilingual sense inventory of BabelNet. We evaluate the quality of the ambiguity lexicons and of the resulting test suite on all submissions from 9 language pairs presented in the WMT19 news shared translation task, plus on other 5 language pairs using NMT pretrained models. The MuCoW test suite is available at http://github.com/Helsinki-NLP/MuCoW.
Developing Students ’ Speaking Competence in “ Food and Beverage Subject ” in Palembang

The title of this research is developing students’ speaking competence in “food and beverage subject” in state polytechnics of sriwijaya. This research aimed at (1) finding out the most applicable teaching model to develop students’ speaking competence, and (2) finding out if the teaching model was effective in developing students’ speaking competence in food and beverage services subjects. The method of this study is Research and Development method. Forty three students of English Department of State Polytechnics of Sriwijaya were chosen purposively as the sample of the study. The data were collected through test, post test, questionnaires, and tryouts. By the help of the four raters, the data were analyzed using t-test. The findings showed that firstly, the students and lecturers of English Department of SPS believed that the new teaching model was effective to develop their competency both in food and beverage and speaking subjects. Secondly, the new teaching provided the students more fun in the teaching and learning process. Finally, there was some improvement in the students’ gained scores between the pre test and the post test which indicated that team teaching model was effective in developing students’ speaking competence in food and beverage subjects.
The Utility of Urine-Based Sampling for Cervical Cancer Screening in Low-Resource Settings

Background: WHO has recommended Visual Inspection with Acetic acid (VIA) or Human Papillomavirus (HPV) DNA testing if feasible, for cervical cancer screening in low income countries. However, the number of women undergoing screening is very low as a result of limited information, inadequate infrastructure and invasive nature of sampling. Methods: A cross sectional study was carried out comparing HPV DNA detection by Polymerase Chain Reaction (PCR) in paired cervical and urine samples procured from histologically confirmed cervical cancer cases. Results: Amongst the samples collected from 114 cervical cancer cases, HPV DNA was tested positive in cervical samples of 89 (78.1%) and urine samples of 55 (48.2%) patients. The agreement between the two sampling methods was 66.7% and the kappa value was 0.35 indicating a fair agreement. The sensitivity of HPV detection using urine samples was 59.6% (95% confidence interval 49.16%-69.15%) and the specificity was 92% (95% confidence interval 75.0%-97.8%). Conclusion: Even though not acceptable as an HPV DNA screening tool due to low sensitivity, the urine sampling method is inexpensive and more socially acceptable for large epidemiological surveys in developing countries to estimate the burden.
A Survey on Cyber-Insurance

Cyber insurance is a young and rapidly developing area which draws more and more attention of practitioners and researchers. Insurance, an alternative way to deal with residual risks (next to simple risk acceptance), was only recently applied to cyber world. The immature cyber insurance market faces a number of unique challenges on the way of its development. In this paper we summarise the basic knowledge about cyber insurance available so far from both market and scientific perspectives. We provide a common background explaining basic terms and formalisation of the area. We discuss the issues which make this type of insurance unique and show how different technologies are affected by these issues. We compare the available scientific approaches to analysis of cyber insurance market and summarise their findings with a common view. Finally, we propose directions for further advances in the research on cyber insurance. ∗Electronic address: angelica.marotta@iit.cnr. it †Electronic address: Fabio.Martinelli@iit.cnr. it ‡Electronic address: artsiom.yautsiukhin@iit.cnr. it; Corresponding author §Electronic address: Stefano.Nanni@unipolsai.it