Miracle-dz/UniFAR-FTUs huggingface dataset:
A hearer-based analysis of Turkish–Azerbaijani receptive multilingual communication

Aims and Objectives/Purpose/Research Questions: Receptive multilingualism (RM), a mode of multilingual communication in which speakers of different languages use their own native language to communicate and still understand each other, is considered to be a relatively under-investigated area in multilingual research. This paper examines features of Turkish–Azerbaijani RM and, focusing on a hearer-based analysis, looks into strategies applied for achieving understanding in the receptive multilingual communication (RMC). Design/Methodology/Approach: The study is carried out within the conceptual framework of RM. Data and Analysis: Authentic data obtained from oral communication between an Azerbaijani and a Turkish native speaker are analyzed and a post-interview was conducted with the participants in order to investigate to what extent they were able to understand each other in the RMC and what strategies they applied to achieve understanding. Findings/Conclusions: The study reveals that the Turkish and Azerbaijani participants could communicate with each other in RM mode without much difficulty thanks to the typological proximity of their languages. In a few instances in which lack of understanding occurred, the participants were found to apply strategies such as asking questions for clarification, conveying confirmation, repeating, rephrasing, resorting to their lexical and world knowledge as well as utilizing the let-it-pass strategy. Originality and Significance/Implications: The significance of the study is twofold: first, it demonstrates that RMC between Turkish and Azerbaijani can be utilized as an alternative to lingua franca communication; and second, the study expands the taxonomy of hearer-based strategies in RMC.
ADAPTIVE PHENOTYPIC PLASTICITY AND GENETICS OF LARVAL LIFE HISTORIES IN TWO RANA TEMPORARIA POPULATIONS

Abstract Phenotypic plasticity provides means for adapting to environmental unpredictability. In terms of accelerated development in the face of pond-drying risk, phenotypic plasticity has been demonstrated in many amphibian species, but two issues of evolutionary interest remain unexplored. First, the heritable basis of plastic responses is poorly established. Second, it is not known whether interpopulational differences in capacity to respond to pond-drying risk exist, although such differences, when matched with differences in desiccation risk would provide strong evidence for local adaptation. We investigated sources of within- and among-population variation in plastic responses to simulated pond-drying risk (three desiccation treatments) in two Rana temporaria populations originating from contrasting environments: (1) high desiccation risk with weak seasonal time constraint (southern population); and (2) low desiccation risk with severe seasonal time constraint (northern population). The larvae originating from the environment with high desiccation risk responded adaptively to the fast decreasing water treatment by accelerating their development and metamorphosing earlier, but this was not the case in the larvae originating from the environment with low desiccation risk. In both populations, metamorphic size was smaller in the high-desiccation-risk treatment, but the effect was larger in the southern population. Significant additive genetic variation in development rate was found in the northern and was nearly significant in the southern population, but there was no evidence for genetic variation in plasticity for development rates in either of the populations. No genetic variation for plasticity was found either in size at metamorphosis or growth rate. All metamorphic traits were heritable, and additive genetic variances were generally somewhat higher in the southern population, although significantly so in only one trait. Dominance variances were also significant in three of four traits, but the populations did not differ. Maternal effects in metamorphic traits were generally weak in both populations. Within-environment phenotypic correlations between larval period and metamorphic size were positive and genetic correlations negative in both populations. These results suggest that adaptive phenotypic plasticity is not a species-specific fixed trait, but evolution of interpopulational differences in plastic responses are possible, although heritability of plasticity appears to be low. The lack of adaptive response to desiccation risk in northern larvae is consistent with the interpretation that selection imposed by shorter growing season has favored rapid development in north (∼8% faster development in north as compared to south) or a minimum metamorphic size at the expense of phenotypic plasticity. Corresponding Editor: G. Wallis
Electrophysiological Correlates of Emotion-Induced Recognition Bias

The question of how emotions influence recognition memory is of interest not only within basic cognitive neuro-science but from clinical and forensic perspectives as well. Emotional stimuli can induce at recognition bias such that individuals are more likely to respond old to a negative item than to an emotionally neutral item, whether the item is actually old or new. We investigated this bias using event-related brain potential (ERP) measures by comparing the processing of words given old responses with accurate recognition of old/new differences. For correctly recognized items, the ERP difference between old items (hits) and new items (correct rejections, CR) was largely unaffected by emotional violence. That is, regardless of emotional valence, the ERP associated with hits was characterized by a widespread positivity between 300 and 700 msec relative to that for CRs. By contrast, the analysis of ERPs to old and new items that were judged old (hits and false alarms [FAs], respectively) revealed a differential effect of valence by 300 msec: Neutral items showed a large old/new difference over prefrontal sites, whereas negative items did not. These results are the first clear demonstration of response bias effects on ERPs linked to recognition memory. They are consistent with the idea that frontal cortex areas may be responsible for relaxing the retrieval criterion for negative stimuli so as to ensure that emotional events are not as easily missed or forgotten as neutral events.
Importance of Semantic Representation: Dataless Classification

Traditionally, text categorization has been studied as the problem of training of a classifier using labeled data. However, people can categorize documents into named categories without any explicit training because we know the meaning of category names. In this paper, we introduce Dataless Classification, a learning protocol that uses world knowledge to induce classifiers without the need for any labeled data. Like humans, a dataless classifier interprets a string of words as a set of semantic concepts. We propose a model for dataless classification and show that the label name alone is often sufficient to induce classifiers. Using Wikipedia as our source of world knowledge, we get 85.29% accuracy on tasks from the 20 Newsgroup dataset and 88.62% accuracy on tasks from a Yahoo! Answers dataset without any labeled or unlabeled data from the data sets. With unlabeled data, we can further improve the results and show quite competitive performance to a supervised learning algorithm that uses 100 labeled examples.
An Enhanced Fuzzy Min–Max Neural Network for Pattern Classification

An enhanced fuzzy min-max (EFMM) network is proposed for pattern classification in this paper. The aim is to overcome a number of limitations of the original fuzzy min-max (FMM) network and improve its classification performance. The key contributions are three heuristic rules to enhance the learning algorithm of FMM. First, a new hyperbox expansion rule to eliminate the overlapping problem during the hyperbox expansion process is suggested. Second, the existing hyperbox overlap test rule is extended to discover other possible overlapping cases. Third, a new hyperbox contraction rule to resolve possible overlapping cases is provided. Efficacy of EFMM is evaluated using benchmark data sets and a real medical diagnosis task. The results are better than those from various FMM-based models, support vector machine-based, Bayesian-based, decision tree-based, fuzzy-based, and neural-based classifiers. The empirical findings show that the newly introduced rules are able to realize EFMM as a useful model for undertaking pattern classification problems.
A Layered Fault Tree Model for Reliability Evaluation of Smart Grids

The smart grid concept has emerged as a result of the requirement for renewable energy resources and application of new techniques. It is proposed as a practical future form of power distribution system. Evaluating the reliability of smart grids is of great importance and significance. Focusing on the perspective of the consumers, this paper proposes a layered fault tree model to distinguish and separate two different smart grid power supply modes. Revised importance measures for the components in the fault tree are presented considering load priority, aiming to find the weak parts of the system and to improve the design and using. A corresponding hierarchical Monte Carlo simulation procedure for reliability evaluation is proposed based on the layered fault tree model. The method proposed in this paper is tested on a case of reliability assessment for the Future Renewable Electric Energy Delivery and Management (FREEDM) system. The proposed technique can be applicable to other forms of smart grids.
The POSSUM system of surgical audit.

Surgical audit is not a new phenomenon. As early as 1750 BC, King Hammurabi of Babylon issued decrees for the punishment of negligent physicians, particularly surgeons. In such a decree discovered at Susa in Iran and inscribed on a 2-m-high black diorite stone, Hammurabi states that: If a doctor inflicts a serious wound with his operation knife on a free man’s slave and kills him, the doctor must replace the slave with another. If a doctor has treated a free man but caused a serious injury from which the man dies, or if he has opened an abscess and the man goes blind, the man is to cut off his hands. Not surprisingly, internal medicine rather than surgery was popular at that time. Indeed, to many surgeons today, this edict still seems to be exacted in a sublimated way. The outcome of surgical intervention, whether death or uncomplicated survival, complications, or long-term morbidity, is not solely dependent on the abilities of the surgeon in isolation. The patient’s physiological status, the disease that requires surgical correction, the nature of the operation, and the preoperative and postoperative support services have a major effect on the ultimate outcome. It is evident to surgeons worldwide that raw mortality and morbidity rates do little to expound these differences, and that the use of such statistics is at best inaccurate and at worst dangerous. When taken to an extreme, mortality rates can achieve what appears to be a self-fulfilling prophecy. The unit that selects only low-risk cases achieves a low mortality rate and therefore attracts more patients, perhaps undeservedly, whereas the unit that cannot select only low-risk cases is left with a worsening case mix, and their performance as judged by mortality rate will appear to deteriorate still further over time.
An Alternative Unifying Measure of Welfare Gains from Risk-Sharing

Following Lucas's (1987) standard approach, welfare gains from international risk-sharing have been measured as the percentage increase in consumption levels that leaves individuals indifferent between, autarky and risk-sharing. The author proposes to measure welfare gains as the increase in consumption growth, instead of consumption levels. When the consumption process is non-stationary, the author's proposed measure has several attractive features: it does not depend on the horizon, and it is robust to alternative specifications of the consumption stochastic processes (from geometric Brownian processes, to Orstein-Ulhenbeck mean-reverting processes), and preferences (from constant relative risk aversion preferences to Kreps-Porteus preferences). The author then uses this measure to estimate potential welfare gains from international risk-sharing for a representative U.S. consumer. The author finds that if international risk-sharing leads only to a complete elimination of aggregate consumption volatility (with no impact on consumption growth), it represents gains to a U.S. consumer of only $ 12 a year on average. But if international risk-sharing also permits an increase in consumption growth, it may have a sizable impact on welfare. Each 0.5 percentage point increase in consumption growth, represents gains to a U.S. consumer of about $ 160 a year on average.
Paradoxical Effects of Experience: Past Behavior Both Strengthens and Weakens the Intention-Behavior Relationship

Experience has a paradoxical effect on intention-behavior consistency. In some studies greater experience is associated with weaker intention-behavior relations (due to habit formation), whereas in other studies experience strengthens the relationship between intention and behavior (by stabilizing intentions). The present research tests the idea that both of these findings are possible—because experience produces a quadratic relationship between intentions and behavior. Findings from a longitudinal study of blood donors (N = 2,389) indicated that the intention-behavior relation exhibited the predicted inverted U-shaped curve as a function of lifetime donation experience. Greater experience of donation enhanced the predictive validity of intention up to a point; thereafter, increasing experience was associated with weaker prediction of donation behavior by intention. These findings are consistent with the idea that experience both strengthens and weakens the intention-behavior relation and helps to resolve a long-standing paradox in research on behavioral prediction.
Finite-state morphological transducers for three Kypchak languages

This paper describes the development of free/open-source finite-state morphological transducers for three Turkic languages―Kazakh, Tatar, and Kumyk―representing one language from each of the three sub-branches of the Kypchak branch of Turkic. The finite-state toolkit used for the work is the Helsinki Finite-State Toolkit (HFST). This paper describes how the development of a transducer for each subsequent closely-related language took less development time. An evaluation is presented which shows that the transducers all have a reasonable coverage―around 90\%―on freely available corpora of the languages, and high precision over a manually verified test set.
Evolutionary inference across eukaryotes identifies specific pressures favoring mtDNA gene retention

Since their endosymbiotic origin, mitochondria have lost most of their genes. Despite the central cellular importance of mtDNA, selective mechanisms underlying the evolution of its content across eukaryotes remain contentious, with no data-driven exploration of different hypotheses. We developed HyperTraPS, a powerful methodology coupling stochastic modelling with Bayesian inference to identify the ordering and causes of evolutionary events. Using a large dataset from over 2000 complete mitochondrial genomes, we inferred evolutionary trajectories of mtDNA gene loss across the eukaryotic tree of life. We find that proteins fulfilling central energetic roles in the assembly of mitochondrial complexes are prefentially retained in mitochondria across eukary-otes, biophysically supporting ‘colocalization for redox regulation’. We also provide the first quantitative evidence that a combination of GC content and protein hydrophobicity is required to explain mtDNA gene retention, and predicts the success of artificial gene transfer experiments. Our results demonstrate that a combination of these three characteristics explains most mtDNA gene variability, addressing the long-debated question of why particular genes are retained in mitochondrial genomes.
The Effects of Imagery on Muscle Performance and Pain Perception Associated with Delayed-Onset Muscle Soreness

Research has indicated that imagery may aid in recovery from injuries and reduce the perception of pain in athletes during and after competitions. There is little research concerning acute pain and soreness, such as delayed-onset muscle soreness (DOMS). The purpose of this investigation was to determine whether the use of guided imagery for healing will affect soreness, pressure pain threshold (PPT), and muscle performance associated with DOMS. It was hypothesized that those who listened to a healing imagery script following inducement of DOMS would report less soreness, higher pain threshold, and have better muscle performance than a control group. Participants (thirty-eight) completed two days of testing with 48 hours between them. At the end of the first day, DOMS was induced by performing 6 sets of 15 reps of isokinetic eccentric exercise at -1.05 rad/sec. 20 participants listened to a healing imagery script 10 times within the 48 hours after inducement of DOMS while 18 listened to relaxing music. Each day consisted of tests of soreness, PPT, vertical jump, and a Wingate test. Healing imagery had no significant impact on perception of soreness, PPT, or vertical jump. The healing imagery group performed significantly better than the control group on the Wingate test in minimum power and power drop results following DOMS. Thus, it appears that imagery could impact long duration anaerobic performance as opposed to short duration explosive performance. Further research is needed to determine if healing imagery can improve performance on longer anaerobic and possibly aerobic activities. Additionally, it is possible that the limited exposure to the imagery in this investigation limited its ability to impact muscle performance and pain perception.
A Comparison Study of Classifier Algorithms for Cross-Person Physical Activity Recognition

Physical activity is widely known to be one of the key elements of a healthy life. The many benefits of physical activity described in the medical literature include weight loss and reductions in the risk factors for chronic diseases. With the recent advances in wearable devices, such as smartwatches or physical activity wristbands, motion tracking sensors are becoming pervasive, which has led to an impressive growth in the amount of physical activity data available and an increasing interest in recognizing which specific activity a user is performing. Moreover, big data and machine learning are now cross-fertilizing each other in an approach called “deep learning”, which consists of massive artificial neural networks able to detect complicated patterns from enormous amounts of input data to learn classification models. This work compares various state-of-the-art classification techniques for automatic cross-person activity recognition under different scenarios that vary widely in how much information is available for analysis. We have incorporated deep learning by using Google’s TensorFlow framework. The data used in this study were acquired from PAMAP2 (Physical Activity Monitoring in the Ageing Population), a publicly available dataset containing physical activity data. To perform cross-person prediction, we used the leave-one-subject-out (LOSO) cross-validation technique. When working with large training sets, the best classifiers obtain very high average accuracies (e.g., 96% using extra randomized trees). However, when the data volume is drastically reduced (where available data are only 0.001% of the continuous data), deep neural networks performed the best, achieving 60% in overall prediction accuracy. We found that even when working with only approximately 22.67% of the full dataset, we can statistically obtain the same results as when working with the full dataset. This finding enables the design of more energy-efficient devices and facilitates cold starts and big data processing of physical activity records.
A Novel Molecular Strategy for Surveillance of Multidrug Resistant Tuberculosis in High Burden Settings

Background In South Africa and other high prevalence countries, transmission is a significant contributor to rising rates of multidrug resistant tuberculosis (MDR-TB). Thus, there is a need to develop an early detection system for transmission clusters suitable for high burden settings. We have evaluated the discriminatory power and clustering concordance of a novel and simple genotyping approach, combining spoligotyping with pncA sequencing (SpoNC), against two well-established methods: IS6110-RFLP and 24-loci MIRU-VNTR. Methods A total of 216 MDR-TB isolates collected from January to June 2010 from the NHLS Central TB referral laboratory in Braamfontein, Johannesburg, representing a diversity of strains from South Africa, were included. The isolates were submitted for genotyping, pncA sequencing and analysis to the Centre for Tuberculosis in South Africa and the Public Health Research Institute Tuberculosis Center at Rutgers University in the United States. Clustering rates, Hunter-Gaston Discriminatory Indexes (HGI) and Wallace coefficients were compared between the methods. Results Overall clustering rates were high by both IS6110-RFLP (52.8%) and MIRU-VNTR (45.8%), indicative of on-going transmission. Both 24-loci MIRU-VNTR and IS6110-RFLP had similar HGI (0.972 and 0.973, respectively), with close numbers of unique profiles (87 vs. 70), clustered isolates (129 vs. 146), and cluster sizes (2 to 26 vs. 2 to 25 isolates). Spoligotyping alone was the least discriminatory (80.1% clustering, HGI 0.903), with 28 unique types. However, the discriminatory power of spoligotyping was improved when combined with pncA sequencing using the SpoNC approach (61.8% clustering, HGI 0.958). A high proportion of MDR-TB isolates had mutations in pncA (68%, n = 145), and pncA mutations were significantly associated with clustering (p = 0.007 and p = 0.0013 by 24-loci MIRU-VNTR and IS6110-RFLP, respectively), suggesting high rates of resistance to pyrazinamide among all MDR-TB cases and particularly among clustered cases. Conclusion We conclude that SpoNC provides good discrimination for MDR-TB surveillance and early identification of outbreaks in South Africa, with 24-loci MIRU-VNTR applied for pncA wild-type strains as needed.
Fusion of Talking Face Biometric Modalities for Personal Identity Verification

We describe a personal identity verification system based on lip dynamics biometric. The lip shape is represented in terms of a B-spline model, tracked over time. The coordinates of the 11 control points of the B-spline model are used as features for each frame. An utterance consisting of N frames produces a sequence of 22 dimensional feature vectors that is matched to the template using dynamic time warping. The verification error rate achieved by the systems on the XM2VTS database is about 14%. By fusing the system with face and voice biometrics the error rate is reduced to a fraction of one percent