Miracle-dz/UniFAR-FTUs huggingface dataset:
Symptoms of Anxiety and Depression in Young Athletes Using the Hospital Anxiety and Depression Scale

Elite young athletes have to cope with multiple psychological demands such as training volume, mental and physical fatigue, spatial separation of family and friends or time management problems may lead to reduced mental and physical recovery. While normative data regarding symptoms of anxiety and depression for the general population is available (Hinz and Brähler, 2011), hardly any information exists for adolescents in general and young athletes in particular. Therefore, the aim of this study was to assess overall symptoms of anxiety and depression in young athletes as well as possible sex differences. The survey was carried out within the scope of the study “Resistance Training in Young Athletes” (KINGS-Study). Between August 2015 and September 2016, 326 young athletes aged (mean ± SD) 14.3 ± 1.6 years completed the Hospital Anxiety and Depression Scale (HAD Scale). Regarding the analysis of age on the anxiety and depression subscales, age groups were classified as follows: late childhood (12–14 years) and late adolescence (15–18 years). The participating young athletes were recruited from Olympic weight lifting, handball, judo, track and field athletics, boxing, soccer, gymnastics, ice speed skating, volleyball, and rowing. Anxiety and depression scores were (mean ± SD) 4.3 ± 3.0 and 2.8 ± 2.9, respectively. In the subscale anxiety, 22 cases (6.7%) showed subclinical scores and 11 cases (3.4%) showed clinical relevant score values. When analyzing the depression subscale, 31 cases (9.5%) showed subclinical score values and 12 cases (3.7%) showed clinically important values. No significant differences were found between male and female athletes (p ≥ 0.05). No statistically significant differences in the HADS scores were found between male athletes of late childhood and late adolescents (p ≥ 0.05). To the best of our knowledge, this is the first report describing questionnaire based indicators of symptoms of anxiety and depression in young athletes. Our data implies the need for sports medical as well as sports psychiatric support for young athletes. In addition, our results demonstrated that the chronological classification concerning age did not influence HAD Scale outcomes. Future research should focus on sports medical and sports psychiatric interventional approaches with the goal to prevent anxiety and depression as well as teaching coping strategies to young athletes.
Ionospheric local model and climatology from long‐term databases of multiple incoherent scatter radars

Empirical ionospheric local models have been developed from long‐term data sets of seven incoherent scatter radars spanning invariant latitudes from 25° to 75° in American, European and Asian longitudes at Svalbard, Tromsø, Sondrestrom, Millstone Hill, St. Santin, Arecibo and Shigaraki. These models, as important complements to global models, represent electron density, ion and electron temperatures, and ion drifts in the E and F regions, giving a comprehensive quantitative description of ionospheric properties. A case study of annual ionospheric variations in electron density and ion temperature is presented based on some of these models. Clear latitudinal, longitudinal, and altitude dependency of annual and semiannual components are found.
Examining Daily Electronic Cigarette Puff Topography Among Established and Nonestablished Cigarette Smokers in their Natural Environment

Introduction Understanding exposures and potential health effects of e-cigarettes is complex. Users' puffing behavior, or topography, affects function of e-cigarette devices (eg, coil temperature) and composition of their emissions. Users with different topographies are likely exposed to different amounts of any harmful or potentially harmful constituents (HPHCs). In this study, we compare e-cigarette topographies of established cigarette smokers and nonestablished cigarette smokers. Methods Data measuring e-cigarette topography were collected using a wireless hand-held monitoring device in users' everyday lives over 1 week. Young adult (aged 18-25) participants (N = 20) used disposable e-cigarettes with the monitor as they normally would and responded to online surveys. Topography characteristics of established versus nonestablished cigarette smokers were compared. Results On average, established cigarette smokers in the sample had larger first puff volume (130.9 mL vs. 56.0 mL, p < .05) and larger puff volume per session (1509.3 mL vs. 651.7 mL, p < .05) compared with nonestablished smokers. At marginal significance, they had longer sessions (566.3 s vs. 279.7 s, p = .06) and used e-cigarettes more sessions per day (5.3 s vs. 3.5 s, p = .14). Established cigarette smokers also used e-cigarettes for longer puff durations (3.3 s vs. 1.8 s, p < .01) and had larger puff volume (110.3 mL vs. 54.7 mL, p < .05) compared with nonestablished smokers. At marginal significance, they had longer puff interval (38.1 s vs. 21.7 s, p = .05). Conclusions Our results demonstrate that topography characteristics differ by level of established cigarette smoking. This suggests that exposure to constituents of e-cigarettes depends on user characteristics and that specific topography parameters may be needed for different user populations when assessing e-cigarette health effects. Implications A user's topography affects his or her exposure to HPHCs. As this study demonstrates, user characteristics, such as level of smoking, can influence topography. Thus, it is crucial to understand the topography profiles of different user types to assess the potential for population harm and to identify potentially vulnerable populations. This study only looked at topography of cigarette smokers using disposable e-cigarettes. Further research is needed to better understand potential variation in e-cigarette topography and resulting exposures to HPHCs among users of different e-cigarette devices and liquids.
Semantic and Phonological Encoding Times in Adults Who Stutter: Brain Electrophysiological Evidence.

Purpose Some psycholinguistic theories of stuttering propose that language production operates along a different time course in adults who stutter (AWS) versus typically fluent adults (TFA). However, behavioral evidence for such a difference has been mixed. Here, the time course of semantic and phonological encoding in picture naming was compared in AWS (n = 16) versus TFA (n = 16) by measuring 2 event-related potential (ERP) components: NoGo N200, an ERP index of response inhibition, and lateralized readiness potential, an ERP index of response preparation. Method Each trial required a semantic judgment about a picture in addition to a phonemic judgment about the target label of the picture. Judgments were mapped onto a dual-choice (Go-NoGo/left-right) push-button response paradigm. On each trial, ERP activity time-locked to picture onset was recorded at 32 scalp electrodes. Results NoGo N200 was detected earlier to semantic NoGo trials than to phonemic NoGo trials in both groups, replicating previous evidence that semantic encoding generally precedes phonological encoding in language production. Moreover, N200 onset was earlier to semantic NoGo trials in TFA than in AWS, indicating that semantic information triggering response inhibition became available earlier in TFA versus AWS. In contrast, the time course of N200 activity to phonemic NoGo trials did not differ between groups. Lateralized readiness potential activity was influenced by strategic response preparation and, thus, could not be used to index real-time semantic and phonological encoding. Conclusion NoGo N200 results point to slowed semantic encoding in AWS versus TFA. Discussion considers possible factors in slowed semantic encoding in AWS and how fluency might be impacted by slowed semantic encoding.
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression

In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by learning a population-level model using multi-modal data from previously seen patients using the base Gaussian Process (GP) regression. Then, this model is adapted sequentially over time to a new patient using domain adaptive GPs to form the patient's pGP. We show that this new approach, together with an auto-regressive formulation, leads to significant improvements in forecasting future clinical status and cognitive scores for target patients when compared to modeling the population with traditional GPs.
Forest Mapping of Central America and Mexico with AVHRR Data

Abstract Concerns over changes in global forest resource distributions have prompted a number of studies to examine and map forest areas at continental scales with various types of satellite data. The work described here details the use of Advanced Very High Resolution Radiometer (AVHRR) data in concert with Landsat Thematic Mapper (TM) and Systeme Probatoire d'Observation de la Terre (SPOT) imagery to identify and map forest land in Mexico and Central America. A two stage approach was used to accomplish the study objectives. First, a modeling procedure was used to estimate percent forest cover in AVHRR pixels based on enumeration of forest area with Landsat TM and SPOT data of the same region covered by the AVHRR data. This product was used to subset the original AVHRR data into areas of probable forest lands. The AVHRR spectral data of the subset area was then classified by forest type and the results were compiled to produce the final product. The implications of this work are discussed in the context of global forest monitoring and landscape management.
Series Beauty Contests and the Term Structure

A novel decomposition highlights the scope for information to influence the term structure of interest rates. Based on the law of total covariance, we show that real term premia in macroeconomic models contain a component that depends on covariances of realised stochastic discount factors and a component that depends on covariances of expectations of those stochastic discount factors. The impact of different informational assumptions can then be identified by looking at their effect on the second, expectational, component. If agents have full information about technology in a simple macro-finance model then the conditional covariance of expectations is low, which contributes to the real term premia implied by the model being at least an order of magnitude too small, a result that is unchanged if some components of technology are unobservable or observed with noise. To generate realistic term premia, we draw on the beauty contest literature by differentiating between private and public information and introducing the possibility of strategic complementarities in the formation of expectations. A quantitative version of the model is found to explain a significant proportion of observed term premia when estimated using data on expectations of productivity growth from the Survey of Professional Forecasters.
Affordances, response conflict, and enhanced-action tendencies in obsessive-compulsive disorder: an ERP study

Abstract Background Obsessive-compulsive disorder (OCD) is characterized by recurrent, intrusive thoughts and/or behaviors. OCD symptoms are often triggered by external stimuli. Therefore, it has been suggested that difficulty inhibiting responses to stimuli associated with strong action tendencies may underlie symptoms. The present electrophysiological study examined whether stimuli evoking a strong automatic response are associated with enhanced action tendencies in OCD participants relative to healthy controls. Methods The lateralized readiness potential (LRP) and the N2 event-related potential (ERP) components were used as measures of action tendencies and inhibition, respectively. ERPs were recorded while 38 participants diagnosed with OCD and 38 healthy controls performed a variation of the Stroop task using colored arrows. Results The OCD group presented with larger LRP amplitudes than the control group. This effect was found specifically in the incongruent condition. Furthermore, an interaction effect was found between group and congruency such that the OCD group showed a reduced N2 in the incongruent condition compared to the congruent condition, whereas the control group demonstrated the opposite effect. Results support the hypothesis that OCD is characterized by stronger readiness-for-action and impaired inhibitory mechanisms, particularly when the suppression of a dominant response tendency is required. Our results were supported by source localization analyses for the LRP and N2 components. These findings were specific to OCD and not associated with anxiety and depression symptoms. Conclusions The present results support the notion of stronger habitual behavior and embodiment tendencies in OCD and impaired inhibitory control under conditions of conflict.
A Communication-Efficient Parallel Algorithm for Decision Tree

Decision tree (and its extensions such as Gradient Boosting Decision Trees and Random Forest) is a widely used machine learning algorithm, due to its practical effectiveness and model interpretability. With the emergence of big data, there is an increasing need to parallelize the training process of decision tree. However, most existing attempts along this line suffer from high communication costs. In this paper, we propose a new algorithm, called \emph{Parallel Voting Decision Tree (PV-Tree)}, to tackle this challenge. After partitioning the training data onto a number of (e.g., $M$) machines, this algorithm performs both local voting and global voting in each iteration. For local voting, the top-$k$ attributes are selected from each machine according to its local data. Then, the indices of these top attributes are aggregated by a server, and the globally top-$2k$ attributes are determined by a majority voting among these local candidates. Finally, the full-grained histograms of the globally top-$2k$ attributes are collected from local machines in order to identify the best (most informative) attribute and its split point. PV-Tree can achieve a very low communication cost (independent of the total number of attributes) and thus can scale out very well. Furthermore, theoretical analysis shows that this algorithm can learn a near optimal decision tree, since it can find the best attribute with a large probability. Our experiments on real-world datasets show that PV-Tree significantly outperforms the existing parallel decision tree algorithms in the tradeoff between accuracy and efficiency.
On benefits of network coding in bidirected networks and hyper-networks

Network coding is a technique that allows information flows to be encoded while routed across a data network. It was shown that network coding helps increase the throughput and reduce the cost of data transmission, especially for one-to-many multicast applications. An important direction in network coding research is to understand and quantify the coding advantage and cost advantage, i.e., the potential benefits of network coding, as compared to routing, in terms of increasing throughput and reducing transmission cost, respectively. Two classic network models were considered in previous studies of coding advantage: directed networks and undirected networks. The study of coding advantage in this work further focuses on two types of parameterized networks, including bidirected networks and hyper-networks, which generalizes the directed and the undirected network models, respectively. With proper parameter setting, more realistic modeling of networks in practice can be achieved. We prove upper-bounds and lower-bounds on the coding advantage for multicast in these models. Some of our bounds are new and unknown before, some improve upon previously proven bounds, and some answer open questions in the literature.
Flexibility to contingency changes distinguishes habitual and goal-directed strategies in humans

Decision-making in the real world presents the challenge of requiring flexible yet prompt behavior, a balance that has been characterized in terms of a trade-off between a slower, prospective goal-directed model-based (MB) strategy and a fast, retrospective habitual model-free (MF) strategy. Theory predicts that flexibility to changes in both reward values and transition contingencies can determine the relative influence of the two systems in reinforcement learning, but few studies have manipulated the latter. Therefore, we developed a novel two-level contingency change task in which transition contingencies between states change every few trials; MB and MF control predict different responses following these contingency changes, allowing their relative influence to be inferred. Additionally, we manipulated the rate of contingency changes in order to determine whether contingency change volatility would play a role in shifting subjects between a MB and MF strategy. We found that human subjects employed a hybrid MB/MF strategy on the task, corroborating the parallel contribution of MB and MF systems in reinforcement learning. Further, subjects did not remain at one level of MB/MF behavior but rather displayed a shift towards more MB behavior over the first two blocks that was not attributable to the rate of contingency changes but rather to the extent of training. We demonstrate that flexibility to contingency changes can distinguish MB and MF strategies, with human subjects utilizing a hybrid strategy that shifts towards more MB behavior over blocks, consequently corresponding to a higher payoff. Author Summary To make good decisions, we must learn to associate actions with their true outcomes. Flexibility to changes in action/outcome relationships, therefore, is essential for optimal decision-making. For example, actions can lead to outcomes that change in value – one day, your favorite food is poorly made and thus less pleasant. Alternatively, changes can occur in terms of contingencies – ordering a dish of one kind and instead receiving another. How we respond to such changes is indicative of our decision-making strategy; habitual learners will continue to choose their favorite food even if the quality has gone down, whereas goal-directed learners will soon learn it is better to choose another dish. A popular paradigm probes the effect of value changes on decision making, but the effect of contingency changes is still unexplored. Therefore, we developed a novel task to study the latter. We find that humans used a mixed habitual/goal-directed strategy in which they became more goal-directed over the course of the task, and also earned more rewards with increasing goal-directed behavior. This shows that flexibility to contingency changes is adaptive for learning from rewards, and indicates that flexibility to contingency changes can reveal which decision-making strategy is used.
Breaking the Single-Cycle Barrier Generation of 1 . 6 fs pulses opens the gateway to a new regime of nonlinear optics and coherent control

Recently, we synthesized ultrawide bandwidth optical pulses, which have the shortest duration ever achieved using optical frequencies, 1.6 fs, and a repetition rate of 11 fs. These pulses are single-cycle, consisting of a single electrical field oscillation, and have over one megawatt of peak power. In addition, we were able to vary the shape of the pulse to generate different prescribed waveforms.
Animal‐borne sensors successfully capture the real‐time thermal properties of ocean basins

Climate change is perhaps the most pressing and urgent environmental issue facing the world today. However our ability to predict and quantify the consequences of this change is severely limited by the paucity of in situ oceanographic measurements. Marine animals equipped with sophisticated oceanographic data loggers to study their behavior offer one solution to this problem because marine animals range widely across the world's ocean basins and visit remote and often inaccessible locations. However, unlike the information being collected from conventional oceanographic sensing equipment, which has been validated, the data collected from instruments deployed on marine animals over long periods has not. This is the first long‐term study to validate in situ oceanographic data collected by animal oceanographers. We compared the ocean temperatures collected by leatherback turtles (Dermochelys coriacea) in the Atlantic Ocean with the ARGO network of ocean floats and could find no systematic errors that could be ascribed to sensor instability. Animal‐borne sensors allowed water temperature to be monitored across a range of depths, over entire ocean basins, and, importantly, over long periods and so will play a key role in assessing global climate change through improved monitoring of global temperatures. This finding is especially pertinent given recent international calls for the development and implementation of a comprehensive Earth observation system (see http://iwgeo.ssc.nasa.gov/documents.asp? s=review) that includes the use of novel techniques for monitoring and understanding ocean and climate interactions to address strategic environmental and societal needs.
Forward induction and entry deterrence: an experiment

The Dixit (Econ J 90:95–106, 1980) hypothesis that incumbents use investment in capacity to deter potential entrants has found little empirical support. Bagwell and Ramey (J Econ 27:660–680, 1996) propose a model where, in the unique game-theoretic prediction based on forward induction or iterated elimination of weakly-dominated strategies, the incumbent does not have the strategic advantage. We conduct an experiment with games inspired by these models. In the Dixit-style game, the incumbent monopolizes the market most of the time even without the investment in capacity. In our Bagwell-and-Ramey-style game, the incumbent also tends to keep the market, in contrast to the predictions of an entrant advantage. Nevertheless, we find strong evidence that forward induction affects the behavior of most participants. The results of our games suggest that players perceive that the first mover has an advantage without having to pre-commit capacity. In our Bagwell–Ramey game, evolution and learning do not drive out this perception. We back these claims with data analysis and a theoretical framework for dynamics.
Feature extraction using convolutional neural networks for multi-atlas based image segmentation

Multi-atlas based image segmentation in conjunction with pattern recognition based label fusion strategies has achieved promising performance in a variety of image segmentation problems, including hippocampus segmentation from MR images. The pattern recognition based label fusion consists of image feature extraction and pattern recognition components. Since the feature extraction component plays an important role in the pattern recognition based label fusion, a variety of feature extraction methods have been proposed to extract image features, including texture features and random projection features. However, these feature extraction methods are not adaptive to different segmentation problems. Following the success of convolutional neural networks in image feature extraction, we propose a feature extraction method based on convolutional neural networks for multi-atlas based image segmentation. The proposed method has been validated based on 135 T1 magnetic resonance imaging (MRI) scans and their hippocampus labels provided by the EADC-ADNI harmonized segmentation protocol. We also compared our method with state-of-the-art pattern recognition based MAIS methods, including Local Label Learning and Random Local Binary Patterns. The experimental results have demonstrated that our method could achieve competitive hippocampus segmentation performance over the alternative methods under comparison.