Miracle-dz/UniFAR-FTUs huggingface dataset:
When Suddenly Nothing Works Anymore Within a Team – Causes of Collective Sport Team Collapse

Collective team collapse occurs when multiple players of a sport team experience a sudden and extreme underperformance within a game. To date, minimal research has been conducted on the causes of collective team collapse. Thus, goals of this study were to explore perceived causes of collective team collapse in different sports and to define team collapse in contrast to negative momentum. To investigate factors causing and maintaining collective sport team collapse, an inductive, exploratory qualitative analysis of individual interviews was conducted. Semi-structured interviews were carried out with 10 athletes of professional German teams of various sports playing in between first and fourth division. Participants were interviewed about a team collapse event they had experienced with their team during the past year. Data were collected and analyzed using a grounded theory methodology. Collective team collapse appeared to be induced by a temporal cascade of causes rather than by single triggers. This cascade included antecedents, which represent factors that make the occurrence of a team collapse more likely; critical events, which include specific events within the game that trigger a team collapse; as well as affective, cognitive, and behavioral outcomes that foster a maintenance of the collapse. Within this theoretical framework, social factors, such as decreased performance contagion or emotional contagion, played crucial roles in causing a team collapse. These results illustrate that collective team collapse is more than the sum of individual choking of multiple players at the same time. In conclusion, a new definition, differentiating team collapse from negative momentum, is introduced. Furthermore, a process model of causes of collective team collapse is proposed. The results provide first insights into causes of collective collapse in a variety of team sports. The developed model is supposed to help future research to better connect to practice and to support athletes, coaches, and sport psychologists.
Submodular Maximization with Nearly Optimal Approximation, Adaptivity and Query Complexity

Submodular optimization generalizes many classic problems in combinatorial optimization and has recently found a wide range of applications in machine learning (e.g., feature engineering and active learning). For many large-scale optimization problems, we are often concerned with the adaptivity complexity of an algorithm, which quantifies the number of sequential rounds where polynomially-many independent function evaluations can be executed in parallel. While low adaptivity is ideal, it is not sufficient for a distributed algorithm to be efficient, since in many practical applications of submodular optimization the number of function evaluations becomes prohibitively expensive. Motivated by these applications, we study the adaptivity and query complexity of adaptive submodular optimization. Our main result is a distributed algorithm for maximizing a monotone submodular function with cardinality constraint $k$ that achieves a $(1-1/e-\varepsilon)$-approximation in expectation. This algorithm runs in $O(\log(n))$ adaptive rounds and makes $O(n)$ calls to the function evaluation oracle in expectation. The approximation guarantee and query complexity are optimal, and the adaptivity is nearly optimal. Moreover, the number of queries is substantially less than in previous works. Last, we extend our results to the submodular cover problem to demonstrate the generality of our algorithm and techniques.
Prenatal Alcohol Exposure and Child Psychosocial Behavior: A Sibling Fixed-Effects Analysis

Background: The association between low levels of alcohol consumption during pregnancy and children's health remains unclear because of the difficulty in ruling out residual genetic and environmental confounding factors. In this study, using a within-family sibling fixed effects design, we sought to examine the association between low prenatal alcohol exposures (PAE) and children's overall psychosocial behavior in a Japanese cohort. Methods: We used maternal and sibling data from the Japanese Study of Stratification, Health, Income and Neighborhood 2012-2013. Households were recruited from the Tokyo metropolitan area through clustered random sampling. Children under 18 years old who have siblings (n = 1,600) and their mothers were selected. PAE status was retrospectively measured, and classified by binominal and continuous measurements. Outcome measures of children's psychosocial behavior were assessed with the Child Behavior Checklist T-score. Results: Low PAE was significantly associated with the offspring's anxiety problems (β = 1.54, 95%CI = 0.26, 2.82) and internalizing problems (β = 2.73, 95%CI = 0.87, 4.60), and marginally significant with the offspring's total problem scores (β = 2.34, 95%CI = −0.24, 4.92). There was no significant difference in PAE between boys and girls when it comes to behavioral problems. Conclusions: Low PAE was associated with children's anxiety, internalizing problems and overall problems, taking into account possible unobserved genetic and environmental confounding influences.
A current approach to the control of filamentous fungal growth in media: microparticle enhanced cultivation technique

Abstract Various strategies have been carried out to date in order to overcome the problem of the adverse effects of bulk fungal growth in bioreactors. Nevertheless, previous conventional methods such as modifying the cultivation temperature or pH resulted in limited biomass production and consequently lower yields. In recent years microparticle enhanced cultivation (MPEC) techniques are one of the most remarkable and novel methods employed for submerged fungal production to overcome bulk microbial growth. In addition to low cost advantages, MPEC also provides benefits such as not interfering with fungal metabolism, enhancing final product concentration and improving homogeneity in the fermentation broth. In this review, a comparison of conventional and novel methods to control fungal morphology has been discussed. Additionally, the application of microparticles in fungal fermentations, their benefits to the process in terms of fungal morphology, biomass accumulation, substrate consumption, and product formation also effect mechanisms of microparticle function are discussed in detail.
Open Relation Extraction by Matrix Factorization and Universal Schemas

Universal schemas are a remarkable approach used to solve relation extraction, constructed by a union of all usable schemas. However, in free word order languages where surface form predicates are difficult to extract, original universal schemas cannot be applied. We introduce a novel extension: using dependency paths and entity types instead of surface form predicates. Based on the extension, we could cluster similar ontological relations, which broaden the coverage of question answering. We verified the performance of our extended model by constructing and evaluating our model in Korean based on Korean DBpedia and Korean Wikipedia distant supervision data.
No clear benefit of muscle heating on hypertrophy and strength with resistance training

ABSTRACT Heat is a major stressor during exercise, though its value in driving adaptation is not well understood. Muscle heating can upregulate pathways facilitating protein synthesis and could thereby enhance effects of exercise training, however, few studies have investigated this possibility. We examined whether heating active muscle during resistance training differentially affected physical and functional adaptations. Within a randomised contralateral-limb control study, ten healthy, resistance-untrained individuals (21 ± 3 y; 5 female) completed 30 sessions of progressive resistance training (12 weeks), performing 4 × 8 unilateral knee extensions at 70% of 1RM. One randomly-allocated thigh was heated during, and for 20 min after, each session using an electric pad eliciting muscle temperatures of >38 °C (HOT); the contralateral limb remained unheated (CON). Training intensity was progressed using 4-weekly strength assessments. Quadricep lean mass (measured using DXA) increased by 15 ± 7% in HOT (p = 0.00) and 15 ± 6% in CON (p = 0.00); the difference being trivial (p = 0.94). Peak isokinetic torque at 90°. s−1 increased by 30 ± 25% (HOT; p = 0.00) and 34 ± 33% (CON; p = 0.01), with no difference (p = 0.84) between limbs. Rate of torque development increased ∼40%, with no difference between limbs (p = 0.73). The increase in 3-RM strength was also similar in HOT (75 ± 16%) and CON (71 ± 14%; p = 0.80 for difference). No differences in mass or strength changes were evident between sexes. In conclusion, supplemental heating of active muscle during and after each bout of resistance training showed no clear positive (or negative) effect on training-induced hypertrophy or function.
Variability in Groundwater Flow and Chemistry in the Houzhai Karst Basin, Guizhou Province, China

Understanding how karst aquifers store and transmit water and contaminants is an ongoing problem in hydrogeology. Multiple flow paths and recharge heterogeneity contribute to the complexity of these systems. This study explored karst-conduit connectivity and water-chemistry variability within the Houzhai catchment in Guizhou Province, China. Artificial tracer tests were conducted during both the monsoon and dry seasons to understand temporal variability in connectivity and water velocity between karst features. Multiple flow paths through the catchment were activated during the monsoon season and partially abandoned during the dry season. Additionally, gradient reversals during monsoonal high-flow events and as a result of pumping were observed. Synoptic water samples from several karst features taken during both monsoon and dry seasons elucidated spatial and temporal variability within the catchment. Water residence time was generally longer during the dry season, and flow within the Houzhai catchment was determined to be temporally dependent. Time-series sampling at the outlet spring following a monsoonal storm event captured chemical variability and identified multiple flow paths. Overall, this study refines widely applicable methods for studying karst systems to this catchment and provides a foundation for future studies in similar settings.
sST2 as a New Biomarker of Chronic Kidney Disease-Induced Cardiac Remodeling: Impact on Risk Prediction

Heart failure is the most frequent cardiac complication of chronic kidney disease (CKD). Biomarkers help identify high-risk patients. Natriuretic peptides (BNP and NT-proBNP) are largely used for monitoring patients with cardiac failure but are highly dependent on glomerular filtration rate (GFR). Soluble suppression of tumorigenicity 2 (sST2) biomarker is well identified in risk stratification of cardiovascular (CV) events in heart failure. Furthermore, sST2 is included in a bioclinical score to stratify mortality risk. The aims of this study were to evaluate (i) the interest of circulating sST2 level in heart dysfunction and (ii) the bioclinical score (Barcelona Bio-Heart Failure risk calculator) to predict the risk of composite outcome (major adverse coronary events) and mortality in the CKD population. A retrospective study was carried out on 218 CKD patients enrolled from 2004 to 2015 at Montpellier University Hospital. sST2 was measured by ELISA (Presage ST2® kit). GFR was estimated by the CKD-EPI equation (eGFR). Indices of cardiac parameters were performed by cardiac echography. No patient had reduced ejection fraction. 112 patients had left ventricular hypertrophy, and 184 presented cardiac dysfunction, with structural, functional abnormalities or both. sST2 was independent of age and eGFR (ρ = 0.05, p = 0.44, and ρ = −0.07, p = 0.3, respectively). Regarding echocardiogram data, sST2 was correlated with left ventricular mass index (ρ = 0.16, p = 0.02), left atrial diameter (ρ = 0.14, p = 0.04), and volume index (ρ = 0.13, p = 0.05). sST2 alone did not change risk prediction of death and/or CV events compared to natriuretic peptides. Included in the Barcelona Bio-Heart Failure (BCN Bio-HF) score, sST2 added value and better stratified the risk of CV events and/or death in CKD patients (p < 0.0001). To conclude, sST2 was associated with cardiac remodeling independently of eGFR, unlike other cardiac biomarkers. Added to the BCN Bio-HF score, the risk stratification of death and/or CV events in nondialyzed CKD patients was highly improved.
Leveraging Random Label Memorization for Unsupervised Pre-Training

We present a novel approach to leverage large unlabeled datasets by pre-training state-of-the-art deep neural networks on randomly-labeled datasets. Specifically, we train the neural networks to memorize arbitrary labels for all the samples in a dataset and use these pre-trained networks as a starting point for regular supervised learning. Our assumption is that the "memorization infrastructure" learned by the network during the random-label training proves to be beneficial for the conventional supervised learning as well. We test the effectiveness of our pre-training on several video action recognition datasets (HMDB51, UCF101, Kinetics) by comparing the results of the same network with and without the random label pre-training. Our approach yields an improvement - ranging from 1.5% on UCF-101 to 5% on Kinetics - in classification accuracy, which calls for further research in this direction.
Neighborhood Poverty Increases Risk for Cigarette Smoking From Age 30 to 39.

Background Lower socioeconomic status (SES) has been associated with higher rates of smoking. Few longitudinal studies have examined indicators of SES at both the neighborhood- and individual-level over time in conjunction with proximal risk factors of cigarette smoking. Purpose To examine associations of time-varying measures of SES, demographic factors, and proximal risk factors for smoking net of average trajectories of smoking behavior from ages 30 to 39 in a community sample. Methods Data from the Seattle Social Development Project (N = 752), a theory-driven longitudinal study originating in Seattle, WA, were used to estimate trajectories of smoking from age 30 to 39. Time-varying measures of neighborhood poverty, coworker smoking, partner smoking, depression, anxiety, education, income, marital status, and parenthood were associated with smoking over time using latent growth curve modeling. Results Results indicated that living in higher poverty neighborhoods was uniquely associated with a greater likelihood of smoking net of average trajectories of smoking from age 30 to 39, gender and race/ethnicity, time-varying measures of SES and demographics, and time-varying measures of proximal risk factors for smoking. Conclusions Living in higher poverty neighborhoods presents a unique risk for smoking among adults aged 30 to 39 above and beyond multiple aspects of SES and other potential mechanisms relating SES to smoking.
Glucose Metabolism Changes in Patients with Chronic Hepatitis C Treated with Direct Acting Antivirals

Background and Aims Chronic hepatitis C is a systemic disease and type 2 diabetes mellitus (T2DM) belongs to more common extrahepatic. The aim of this study was to (i) explore the prevalence of impaired fasting glucose (IFG) and T2DM in patients with chronic hepatitis C, (ii) explore the effect of direct acting antivirals (DAA) treatment on the glycemia, and (iii) explore the factors that modulate the effect of DAA treatment on glycemia in patients with chronic hepatitis C. Methods We performed a longitudinal retrospective observational study focused on the patients undergoing DAA treatment of chronic hepatitis C. Data about glycemia, history of diabetes, hepatitis C virus, treatment, and liver status, including elastography, were obtained at baseline (before treatment start), at the end of treatment and 12 weeks after the end of treatment. Patients were treated with various regimens of direct acting antivirals. Results We included 370 patients; 45.9% had F4 fibrosis. At baseline, the prevalence of T2DM increased with the degree of fibrosis (F0-F2 14.4%, F3 21.3%, and F4 31.8%, p=0.004). Fasting glycemia also increased with the degree of fibrosis (F0-F2 5.75±0.18 F3 5.84±0.17, and F4 6.69±0.2 mmol/L, p=0.001). We saw significant decrease of glycemia after treatment in all patients, but patients without T2DM or IFG from 6.21±0.12 to 6.08±0.15 mmol/L (p=0.002). The decrease was also visible in treatment experienced patients and patients with Child-Pugh A cirrhosis. Conclusion We confirmed that the prevalence of either T2DM or IFG increases in chronic hepatitis C patients with the degree of fibrosis. The predictive factors for T2DM were, besides F4, fibrosis also higher age and BMI. Significant decrease of fasting glycemia after the DAA treatment was observed in the whole cohort and in subgroups of patients with T2DM, IFG, cirrhotic, and treatment experienced patients.
Coffee Consumption and Risk of Dementia and Alzheimer’s Disease: A Dose-Response Meta-Analysis of Prospective Studies

Coffee consumption is associated with a reduced risk of several diseases but uncertainty remains about the influence of coffee consumption on the risk of dementia. We performed a dose-response meta-analysis to summarize the prospective data on coffee consumption and associated risk of dementia and Alzheimer’s disease. We identified studies by searching PubMed (from January 1966) and Web of Science (from January 1945) through 4 October 2018 and by scrutinizing the reference lists of pertinent publications. Two researchers independently reviewed the literature. Results were combined using a restricted cubic spline random-effects dose-response meta-analysis based on a one-stage approach. Eight relevant prospective studies were identified. These studies included 7486 dementia cases diagnosed among 328,885 individuals during an average follow-up of 4.9–25 years. Meta-analysis of all eight studies indicated no statistically significant association between coffee consumption and the risk of dementia and no deviations from a linear trend (p = 0.08). The relative risk of dementia per 1 cup/day increment of coffee consumption was 1.01 (95% confidence interval (CI) 0.98–1.05; p = 0.37). Meta-analysis of five studies that focused on Alzheimer’s disease revealed no association between coffee consumption and Alzheimer’s disease and no deviations from a linear trend (p = 0.79). The relative risk of Alzheimer’s disease per 1 cup/day increment of coffee consumption was 1.01 (95% confidence interval 0.95–1.07; p = 0.80). These results do not support an association between coffee consumption and an increased risk of overall dementia or Alzheimer’s disease specifically, but further research on the association of coffee consumption with dementia risk is needed.
Fine-grained Apparel Classification and Retrieval without rich annotations

The ability to correctly classify and retrieve apparel images has a variety of applications important to e-commerce, online advertising and internet search. In this work, we propose a robust framework for fine-grained apparel classification, in-shop and cross-domain retrieval which eliminates the requirement of rich annotations like bounding boxes and human-joints or clothing landmarks, and training of bounding box/ key-landmark detector for the same. Factors such as subtle appearance differences, variations in human poses, different shooting angles, apparel deformations, and self-occlusion add to the challenges in classification and retrieval of apparel items. Cross-domain retrieval is even harder due to the presence of large variation between online shopping images, usually taken in ideal lighting, pose, positive angle and clean background as compared with street photos captured by users in complicated conditions with poor lighting and cluttered scenes. Our framework uses compact bilinear CNN with tensor sketch algorithm to generate embeddings that capture local pairwise feature interactions in a translationally invariant manner. For apparel classification, we pass the feature embeddings through a softmax classifier, while, the in-shop and cross-domain retrieval pipelines use a triplet-loss based optimization approach, such that squared Euclidean distance between embeddings measures the dissimilarity between the images. Unlike previous works that relied on bounding box, key clothing landmarks or human joint detectors to assist the final deep classifier, proposed framework can be trained directly on the provided category labels or generated triplets for triplet loss optimization. Lastly, Experimental results on the DeepFashion fine-grained categorization, and in-shop and consumer-to-shop retrieval datasets provide a comparative analysis with previous work performed in the domain.
Using Monolingual Data in Neural Machine Translation: a Systematic Study

Neural Machine Translation (MT) has radically changed the way systems are developed. A major difference with the previous generation (Phrase-Based MT) is the way monolingual target data, which often abounds, is used in these two paradigms. While Phrase-Based MT can seamlessly integrate very large language models trained on billions of sentences, the best option for Neural MT developers seems to be the generation of artificial parallel data through back-translation - a technique that fails to fully take advantage of existing datasets. In this paper, we conduct a systematic study of back-translation, comparing alternative uses of monolingual data, as well as multiple data generation procedures. Our findings confirm that back-translation is very effective and give new explanations as to why this is the case. We also introduce new data simulation techniques that are almost as effective, yet much cheaper to implement.
Binary Reed-Solomon Coding Based Distributed Storage Scheme in Information-Centric Fog Networks

Fog computing is an emerging architecture for processing, storing, and controlling the data at the edge of the networks, which is becoming a popular technology for Internet of Things (IoT). As a next-generation networking architecture, Information-Centric Network (ICN) has been introduced into networked fogs to establish efficient data exchange based on name, caching, content features, etc., which gives the IoT an opportunity to store the huge geo-distributed data at the edge of the networks and be less dependent on the Cloud, thus fulfilling the delay-sensitive needs of the end-users. Nevertheless, efficient distributed storage is a must for information-centric fog networks, because of the huge content exchange and geo-distributed data. To address this, this paper proposes an efficient storage scheme by integrating Binary Reed-Solomon erasure code with ICN mechanism in fog networks. Specifically, the data are encoded into named data blocks and are distributed as well as stored into distributed fog nodes. The fog network performs information-centered with horizontal fog-to-fog communications to retrieve the data blocks efficiently. Moreover, the data is then recovered even with some of the data blocks missing, thus ensuring the reliability of storage at distributed fogs. Simulation results show the efficiency and advantages of the proposed distributed storage scheme.