nils-herrmann/clean_scirepeval huggingface dataset:
Accuracy feedback improves word learning from context: evidence from a meaning generation task

The present study asked whether accuracy feedback on a meaning generation task would lead to improved contextual word learning (CWL) Active generation can facilitate learning by increasing task engagement and memory retrieval, which strengthens new word representations. However, forced generation results in increased errors, which can be detrimental for learning if they are not corrected. The goal of this study was to determine whether immediate feedback on response accuracy would ameliorate this risk. The study was conducted using an intelligent tutoring system, which presents target words in multiple contexts and prompts users to generate a target word meaning after each context. One group of participants (feedback group) received immediate feedback based on Markov Estimation of Semantic Association (MESA) which estimates the distance between the learner response and the target word meaning. The control group did not receive feedback. Results from conventional (pre/post test) measures showed greater gains in accuracy and confidence for the feedback group. Moreover, when contextual support was decreased mid way through the training (from trial 3 to trial 4) MESA measures showed a corresponding drop in accuracy, but only for the No Feedback group. These findings suggest that accuracy feedback can improve outcomes in CWL, particularly when there is an increased risk of errors. This strengthens the case for meaning generation as a tool to build high quality lexical representations.
Feature identification for predicting community evolution in dynamic social networks
Venue: Eng. Appl. Artif. Intell.

In parallel with the increasing popularity of commercial social networking systems, the scales of such systems have grown notably, now with sizes ranging from hundreds of millions to more than a billion users. Besides being large, these systems also have a dynamic, temporal nature, with evolving structures. Thus, one of the main challenges is to understand and model the evolution of the meso scale structures such as community structures within these networks. Most previous studies have concentrated on determining community events based on the community features extracted at different time points. However, both the huge volume of data and the dynamic structure of the networks hinder effective computation of these features. In this paper, we propose a novel framework that examines various structural features of the network and detects the most prominent subset of community features in order to predict the future direction of community evolution. Our approach is to extract the network structure and use it to determine the subset of community features that leads to accurate community event prediction. Unlike traditional approaches that harvest a large number of features at each time point, the proposed framework requires extraction of a minimal number of community features to effectively determine whether a community will remain stable or undergo certain events such as shrink, merge or split. Moreover, the extracted community features vary depending on the network structure, capturing network specific characteristics. Several experiments conducted on four publicly available datasets verified the effectiveness of the proposed framework.
La Idea del Purgatorio en la Edad Media: Organizacion y definicion de una tradicion

At the XIIIth Century, Catholic Church oficially accepts the belief in Purgatory. This belief in an intermediate Place to where some souls go after death, to atone for his venial sins before entering Heaven, is neither accepted by the Greek Ortodox Church, nor by the Protestant Churches. However, for Catolics, this belief is part of a tradition traced to the origins of the Church. Taking as a starting point Jacques Le Goff's investigation about this issue, this paper describes the development of this belief along the medieval centuries.
Boreal and Temperate Trees in a Changing Climate
Venue: Biometeorology

Boreal and temperate trees grow under climatic conditions in which the ambient air temperature displays pronounced seasonal variation. Unlike herbs and grasses, trees overwinter without a sheltering snow cover, so that they are exposed to all the harsh climatic conditions. That is why their climatic adaptation is based on their annual cycle of development, whereby the frost hardy dormant phase and the susceptible growth phase are synchronised with the seasonality of the climate. The main aspects of this adaptive strategy of trees are briefly discussed, emphasising both the geographical and the year to year variation of the seasonal air temperature conditions. Many boreal and temperate tree species have large ranges of geographical distribution, so that their different provenances have adapted to the particular local climate prevailing at their native growing site. The extent of the geographical variation in air temperature crucial for this adaptation is highlighted by examining the climatic records of four locations within the European distribution range of Pinus sylvestris. The extent of the year to year variation is similarly highlighted by examining a 92 year climatic record from Jyvaskyla, central Finland. In the coolest summer, the temperature sum in Jyvaskyla was similar to the average temperature sum 600 km north of Jyvaskyla; and in the warmest summer it was similar to the average temperature sum 600 km south of Jyvaskyla. This limited analysis suffices to reveal the extent of the climatic year to year variation that trees need to acclimate to at their native growing site.
Boreal and temperate trees show strong acclimation of respiration to warming
Venue: Nature

Plant respiration results in an annual flux of carbon dioxide (CO2) to the atmosphere that is six times as large as that due to the emissions from fossil fuel burning, so changes in either will impact future climate. As plant respiration responds positively to temperature, a warming world may result in additional respiratory CO2 release, and hence further atmospheric warming. Plant respiration can acclimate to altered temperatures, however, weakening the positive feedback of plant respiration to rising global air temperature, but a lack of evidence on long term (weeks to years) acclimation to climate warming in field settings currently hinders realistic predictions of respiratory release of CO2 under future climatic conditions. Here we demonstrate strong acclimation of leaf respiration to both experimental warming and seasonal temperature variation for juveniles of ten North American tree species growing for several years in forest conditions. Plants grown and measured at 3.4 degC above ambient temperature increased leaf respiration by an average of 5% compared to plants grown and measured at ambient temperature; without acclimation, these increases would have been 23% Thus, acclimation eliminated 80% of the expected increase in leaf respiration of non acclimated plants. Acclimation of leaf respiration per degree temperature change was similar for experimental warming and seasonal temperature variation. Moreover, the observed increase in leaf respiration per degree increase in temperature was less than half as large as the average reported for previous studies, which were conducted largely over shorter time scales in laboratory settings. If such dampening effects of leaf thermal acclimation occur generally, the increase in respiration rates of terrestrial plants in response to climate warming may be less than predicted, and thus may not raise atmospheric CO2 concentrations as much as anticipated.
Birds of a feather flock together: Using trait groups to understand the effect of macropod grazing on birds in grassy habitats

Restoration of appropriate disturbance regimes is a high conservation priority. However, for most species, little is known about appropriate disturbance regimes to achieve defined conservation outcomes. In this context, trait based approaches can offer a means to generalize responses to environmental change across multiple species. Here, we investigated the potential of a trait based approach to predict the preference of birds utilizing the grassy layers for different levels of grazing by a native grazer within grassy habitats in south eastern Australia. We tested three hypotheses: 1) birds with particular traits (i.e. large ground foraging, small ground foraging, aerial insectivore, and ground nesting/concealment) will show preferences for certain levels of grazing: 2) species within the same trait group will show preferences for a similar level of grazing intensity: and 3) different bird trait groups will favor different grazing intensities Overall, we found a significant relationship between grazing intensity and the richness of aerial insectivore and large ground foraging trait groups utilizing the grassy layer, but not for the richness of small ground foraging and ground nesting/concealment trait groups. We also found that the likelihood of 3/3 aerial insectivores, 4/7 large ground foragers, 3/10 small ground foragers, and 1/3 ground nesting/concealment species using the grassy layer was significantly related to grazing intensity. However, we found no significant relationship between the probability of 12 species using the grassy layer and grazing intensity, with other environmental factors potentially masking grazing response. Importantly, species within the same trait group showed a preference for similar grazing intensities, and different trait groups showed preference for different grazing intensities. For example, aerial insectivores, and a single ground nesting/concealment species were more likely to use the grassy layer at lower grazing intensities, whereas large ground foraging birds and small ground foraging birds were more likely to use the grassy layer at higher grazing intensities. To maintain optimal grass structure for birds with varying grass structure preferences, landscapes should contain a heterogeneous mosaic of grazing intensities.
Adaptive Training Environment without Prior Knowledge: Modeling Feedback Selection as a Multi armed Bandit Problem
Venue: UMAP

Pedagogical Action Selection (PAS) is a major issue for intelligent tutoring and training systems. Expert knowledge provides useful insights to build strategies that relate students representation to PAS, but it can be difficult to collect. Furthermore, the influence of a specific action may vary across students, which is rarely reflected in expert knowledge. As part of an automatic gesture training system, we propose to model the co evolution between a student and a training environment in order to provide personalized action selection. The proposed approach is based on three models representing the student, the environment, and the interactions between these two entities. The latter model sees the PAS as a multi armed bandit problem, each arm representing a possible action. Thus, PAS personalization only relies on the interactions between the student and the learning environment, without any prior knowledge. Two experiments, one in a simulated environment and a second in a calligraphy training environment, highlight the model ability to personalize action selection, and the benefits of this ability on students skill acquisition.
Action selection in growing state spaces: Control of Network Structure Growth
Venue: ArXiv

The dynamical processes taking place on a network depend on its topology. Influencing the growth process of a network therefore has important implications on such dynamical processes. We formulate the problem of influencing the growth of a network as a stochastic optimal control problem in which a structural cost function penalizes undesired topologies. We approximate this control problem with a restricted class of control problems that can be solved using probabilistic inference methods. To deal with the increasing problem dimensionality, we introduce an adaptive importance sampling method for approximating the optimal control. We illustrate this methodology in the context of formation of information cascades, considering the task of influencing the structure of a growing conversation thread, as in Internet forums. Using a realistic model of growing trees, we show that our approach can yield conversation threads with better structural properties than the ones observed without control.
Definition and diagnostic criteria of sleep related hypermotor epilepsy
Venue: Neurology

The syndrome known as nocturnal frontal lobe epilepsy is recognized worldwide and has been studied in a wide range of clinical and scientific settings (epilepsy, sleep medicine, neurosurgery, pediatric neurology, epidemiology, genetics) Though uncommon, it is of considerable interest to practicing neurologists because of complexity in differential diagnosis from more common, benign sleep disorders such as parasomnias, or other disorders like psychogenic nonepileptic seizures. Moreover, misdiagnosis can have substantial adverse consequences on patients' lives. At present, there is no consensus definition of this disorder and disagreement persists about its core electroclinical features and the spectrum of etiologies involved. To improve the definition of the disorder and establish diagnostic criteria with levels of certainty, a consensus conference using formal recommended methodology was held in Bologna in September 2014. It was recommended that the name be changed to sleep related hypermotor epilepsy (SHE) reflecting evidence that the attacks are associated with sleep rather than time of day, the seizures may arise from extrafrontal sites, and the motor aspects of the seizures are characteristic. The etiology may be genetic or due to structural pathology, but in most cases remains unknown. Diagnostic criteria were developed with 3 levels of certainty: witnessed (possible) SHE, video documented (clinical) SHE, and video EEG documented (confirmed) SHE. The main research gaps involve epidemiology, pathophysiology, treatment, and prognosis.
Stacked Hourglass Networks for Human Pose Estimation
Venue: ECCV

This work introduces a novel convolutional network architecture for the task of human pose estimation. Features are processed across all scales and consolidated to best capture the various spatial relationships associated with the body. We show how repeated bottom up, top down processing used in conjunction with intermediate supervision is critical to improving the performance of the network. We refer to the architecture as a "stacked hourglass" network based on the successive steps of pooling and upsampling that are done to produce a final set of predictions. State of the art results are achieved on the FLIC and MPII benchmarks outcompeting all recent methods.
A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Venue: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Recent work has shown that optical flow estimation can be formulated as a supervised learning task and can be successfully solved with convolutional networks. Training of the so called FlowNet was enabled by a large synthetically generated dataset. The present paper extends the concept of optical flow estimation via convolutional networks to disparity and scene flow estimation. To this end, we propose three synthetic stereo video datasets with sufficient realism, variation, and size to successfully train large networks. Our datasets are the first large scale datasets to enable training and evaluation of scene flow methods. Besides the datasets, we present a convolutional network for real time disparity estimation that provides state of the art results. By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.
The impact of affective contexts on working memory capacity in healthy populations and in individuals with PTSD.
Venue: Emotion

Individual differences in working memory capacity (WMC) strongly predict variations in real world cognitive functioning. However, little is known about how WMC is influenced by the ubiquitously present affective information in our everyday environments. Here, we present a series of 3 experiments investigating a novel WMC paradigm performed in affective (vs. neutral) contexts. The paradigm requires simultaneous performance of a visuospatial search and a verbal storage task. These tasks are performed in the presence of either neutral or negative emotional distractor images. Experiments 1 and 2 confirmed our prediction that WMC would be reduced in the context of emotional compared with neutral distractors in student and community samples. Experiment 3 extended these findings to a clinical sample. WMC in motor vehicle accident survivors with a history of posttraumatic stress disorder (PTSD) was selectively reduced in the presence of trauma related emotional distraction compared with survivors without a history of PTSD. Implications of these findings for affective cognitive science are discussed.
Can Community Social Cohesion Prevent Posttraumatic Stress Disorder in the Aftermath of a Disaster? A Natural Experiment From the 2011 Tohoku Earthquake and Tsunami
Venue: American journal of epidemiology

In the aftermath of a disaster, the risk of posttraumatic stress disorder (PTSD) is high. We sought to examine whether the predisaster level of community social cohesion was associated with a lower risk of PTSD after the earthquake and tsunami in Tohoku, Japan, on March 11, 2011. The baseline for our natural experiment was established in a survey of older community dwelling adults who lived 80 kilometers west of the epicenter 7 months before the earthquake and tsunami. A follow up survey was conducted approximately 2.5 years after the disaster. We used a spatial Durbin model to examine the association of community level social cohesion with the individual risk of PTSD. Among our analytic sample (n 3,567) 11.4% of respondents reported severe PTSD symptoms. In the spatial Durbin model, individual and community level social cohesion before the disaster were significantly associated with lower risks of PTSD symptoms (odds ratio 0.87, 95% confidence interval: 0.77, 0.98 and odds ratio 0.75, 95% confidence interval: 0.63, 0.90, respectively) even after adjustment for depression symptoms at baseline and experiences during the disaster (including loss of loved ones, housing damage, and interruption of access to health care) Community level social cohesion strengthens the resilience of community residents in the aftermath of a disaster.
Productive and reproductive performance of Black Bengal Goat under farming condition in Bangladesh

The study was undertaken to evaluate the productive and reproductive performances of Black Bengal Goat (BBG) under farming condition. Data were collected from Bangladesh Livestock Research Institute (BLRI) goat research farm during 2006 to June 2013. Data were analyzed by SPSS version 17.0 statistical computerprogram. The mean of birth weight of kid irrespective ofsex was 1.31 kg. Live weights at 3, 6, 9 and 12 months were 5.65, 9.63, 14.20 and 17.70 kg, respectively. Weaning weight was heavier in second generation (G2) (5.34 kg) followed by 5.15 kg in first generation (G1) 4.87 kg in third generation (G3) and 4.17 kg in foundation stock (G0) with an overall mean of 4.88 kg. Body weight gain at 0 3 month, 3 6 month, 6 9 month, and 9 12 month ages were 66.34+ 1.69 g/d, 41.54+ 2.02 g/d, 49.97+ 3.10 g/d and 39.28+ 4.35 g/d, respectively. Body weight gain from 0 12 month age was 56.31+ 1.40 g/d. Average daily milk yield, lactation length and total milk per lactation were 287.7 ml/d, 61.5 days and 19129.40 ml, respectively. Average age and weight at puberty were 182.7+ 7.25 days and 9.81+ 0.34 kg, respectively. Age and weight at 1 st service were 187.56+ 8.33 days and 9.91+ 0.39 kg, respectively. Number of services per conception was 1.37+ 0.03. Average litter size, gestation length, post partum heat period, days open and kidding interval were 1.75+ 0.03, 142.45+ 0.31 days, 33.39+ 1.82 days, 46.62+ 2.86 days and 188.01+ 2.14 days, respectively. The results indicate that despite of having promising prolificacy and breeding and reproductive efficiency of BBG, still there need to be improved for birth weight, weight gain and milk yield of dam.
The Effectiveness of Assertiveness Training on the Levels of Stress, Anxiety, and Depression of High School Students
Venue: Iranian Red Crescent medical journal

Adolescence is a transition period from childhood to early adulthood. Because of the immense pressure imposed on adolescents due to the complications and ambiguities of this transition, their level of excitement increases and sometimes it appears in the form of sensitivity and intense excitement.  This study aimed at determining the effectiveness of assertiveness training on the levels of stress, anxiety, and depression of high school students.  This quasi experimental study was conducted on high school students of Isfahan in academic year 2012 13. A total of 126 second grade high school students were collected according to simple random sampling method and divided into two groups: experimental with 63 participants and control with the same number. Data gathering instruments included a demographic questionnaire, Gambill Richey assertiveness scale, and depression anxiety stress scales (DASS 21) Assertiveness training was carried out on the experimental group in 8 sessions; after 8 weeks, posttest was carried out on both groups. Statistical tests such as independent t test, repeated measures ANOVA, Chi square test, and the Mann Whitney test were used to interpret and analyze the data.  The Chi square and Mann Whitney tests did not show significant statistical differences between the two groups in terms of demographic variables (P 0.05) Repeated measures ANOVA showed no significant difference between the mean scores for assertiveness before (100.23 7.37) immediately after (101.57 16.06) and 2 months after (100.77 12.50) the intervention in the control group. However, the same test found a significant difference between the mean score for assertiveness in the experimental group before (101.6 9.1) immediately after (96.47 10.84) and 2 months after (95.41 8.37) implementing the training program (P 0.002) The independent t test showed no significant difference in the mean score for anxiety and stress between two groups before the assertiveness training program; however, 2 months after the intervention, the mean score for anxiety in the experimental group was found significantly lower than the control group. As for the mean score for depression, the independent t test showed no significant difference between two groups before training; however, despite the decrease in the mean scores for depression in the experimental group following the intervention, the difference was not significant (P 0.09)  The results of the current study show that conducting assertive training in high school students decreases their anxiety, stress, and depression. Given that high school years are among the most sensitive stages of one's life plus the fact that conducting such training programs besides their safe and low cost nature are effective and practical, it is highly recommended that such programs be carried out among high school adolescents.