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"Federer, Roger"
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Competenza o talento: cosa è più importante in un medico?
Gli autori si chiedono se, per “vivere da medico”, sia più importante un talento innato o disporre di competenze professionali acquisite. L’articolo pone l’accento su una combinazione di tratti personali, come compassione, intelligenza emotiva e capacità di lavorare in squadra, che hanno una grande importanza e completano le competenze tecniche. Gli autori sottolineano l’importanza di saper prendere decisioni rapide e fornire supporto emotivo in situazioni critiche, in particolare nella medicina d’emergenza. Inoltre, è molto importante che un insieme di professionisti disponga di una varietà di talenti: questa può essere la premessa di un’assistenza più inclusiva. La pazienza, citata da Roger Federer in un discorso alla Dartmouth university, è una dote essenziale per i professionisti sanitari in anni in cui la sanità attraversa una profonda crisi di finanziamento. Infine, si sottolinea la necessità di adattabilità e crescita continua in un contesto sanitario in evoluzione.
Journal Article
Federer : portrait of a tennis legend
This biography, filled with photographs from his sensational career, follows Roger Federer from his first steps in tennis in the junior tournaments right through to the main professional circuit. It is an illustrated biography of a man who has graced the world of tennis for more than two decades, playing with grace, panache, and magnificent sportsmanship. He who has transcended tennis to become one of the greatest sportsmen of the 21st century. This portrait illustrates his great rivalries, his great matches and his great victories.
BOOK
The analysis of serve decisions in tennis using Bayesian hierarchical models
Anticipating an opponent’s serve is a salient skill in tennis: a skill that undoubtedly requires hours of deliberate study to properly hone. Awareness of one’s own serve tendencies is equally as important, and helps maintain unpredictable serve patterns that keep the returner unbalanced. This paper investigates intended serve direction with Bayesian hierarchical models applied on an extensive, and now publicly available data source of professional tennis players at Roland Garros. We find discernible differences between men’s and women’s tennis, and between individual players. General serve tendencies such as the preference of serving towards the Body on second serve and on high pressure points are revealed.
Journal Article
Eras of dominance: identifying strong and weak periods in professional tennis
In sports journalism and among fans, there is an ongoing debate on identifying eras where the level of competition is extremely high. In tennis, a common question concerning the advent of the so-called ‘Big Three’—listed alphabetically, Novak Djokovic, Roger Federer, and Rafael Nadal—is: Did these players lead to an unprecedented high level of competition? We contribute to this debate by identifying, from a statistical point of view, strong players, periods, and eras in men’s tennis, where a strong era is defined as a time frame in which a subset of (strong) players consistently dominate all the others. Hence, this work extends the idea of the Greatest Player of All Time (GOAT), largely investigated in the literature, to a dynamic subset of players. Through cointegration analysis of over 30 years of professional tennis data, we identify five strong eras. Interestingly, the player with the largest participation during these strong eras is Roger Federer and the most recent strong era concluded in July 2019. Moreover, we examine the relationship between the match duration and strong players/periods/eras, finding that the occurrence of a match between strong and not-strong players decreases the match duration, on average. Furthermore, when strong players meet, the match duration generally increases.
Journal Article
Self-adaptive attention fusion for multimodal aspect-based sentiment analysis
Multimodal aspect term extraction (MATE) and multimodal aspect-oriented sentiment classification (MASC) are two crucial subtasks in multimodal sentiment analysis. The use of pretrained generative models has attracted increasing attention in aspect-based sentiment analysis (ABSA). However, the inherent semantic gap between textual and visual modalities poses a challenge in transferring text-based generative pretraining models to image-text multimodal sentiment analysis tasks. To tackle this issue, this paper proposes a self-adaptive cross-modal attention fusion architecture for joint multimodal aspect-based sentiment analysis (JMABSA), which is a generative model based on an image-text selective fusion mechanism that aims to bridge the semantic gap between text and image representations and adaptively transfer a textual-based pretraining model to the multimodal JMASA task. We conducted extensive experiments on two benchmark datasets, and the experimental results show that our model significantly outperforms other state of the art approaches by a significant margin.
Journal Article
A novel comparative study of NNAR approach with linear stochastic time series models in predicting tennis player's performance
Background
Prediction models have gained immense importance in various fields for decision-making purposes. In the context of tennis, relying solely on the probability of winning a single match may not be sufficient for predicting a player's future performance or ranking. The performance of a tennis player is influenced by the timing of their matches throughout the year, necessitating the incorporation of time as a crucial factor. This study aims to focus on prediction models for performance indicators that can assist both tennis players and sports analysts in forecasting player standings in future matches.
Methodology
To predict player performance, this study employs a dynamic technique that analyzes the structure of performance using both linear and nonlinear time series models. A novel approach has been taken, comparing the performance of the non-linear Neural Network Auto-Regressive (NNAR) model with conventional stochastic linear and nonlinear models such as Auto-Regressive Integrated Moving Average (ARIMA), Exponential Smoothing (ETS), and TBATS (Trigonometric Seasonal Decomposition Time Series).
Results
The study finds that the NNAR model outperforms all other competing models based on lower values of Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). This superiority in performance metrics suggests that the NNAR model is the most appropriate approach for predicting player performance in tennis. Additionally, the prediction results obtained from the NNAR model demonstrate narrow 95% Confidence Intervals, indicating higher accuracy and reliability in the forecasts.
Conclusion
In conclusion, this study highlights the significance of incorporating time as a factor when predicting player performance in tennis. It emphasizes the potential benefits of using the NNAR model for forecasting future player standings in matches. The findings suggest that the NNAR model is a recommended approach compared to conventional models like ARIMA, ETS, and TBATS. By considering time as a crucial factor and employing the NNAR model, both tennis players and sports analysts can make more accurate predictions about player performance.
Journal Article
Do We Deserve Credit for Everything We Understand?
It is widely acknowledged in the literature in social epistemology that knowledge has a social dimension: we are epistemically dependent upon one another for most of what we know. Our knowledge can be, and very often is, grounded on the epistemic achievement of somebody else. But what about epistemic aims other than knowledge? What about understanding? Prominent authors argue that understanding is not social in the same way in which knowledge is. Others can put us in the position to understand, but when we understand something, this accomplishment is to be credited mainly if not entirely to us, as it is due to the successful exercise of our own cognitive abilities. In this paper, I show that the social dimension of understanding closely resembles the social dimension of knowledge. I distinguish between three different ways in which a subject can depend upon another subject for (either the acquisition or the possession of) a certain epistemic good. I then argue that all these kinds of epistemic dependence apply to knowledge and understanding alike. If I am right, understanding is not (always) an achievement to be (mainly) credited to the single epistemic agent who understands.
Journal Article
The sound of speed: How grunting affects opponents’ anticipation in tennis
Grunting in tennis is a widespread phenomenon and whether it influences opponents' predictions of ball trajectory-and if so, why-is subject of ongoing debate. Two alternative hypotheses have been proposed to explain why grunting may impede opponents' predictions, referred to as the distraction account (i.e., grunts capture attentional resources necessary for anticipation) and the multisensory integration account (i.e., auditory information from the grunt systematically influences ball trajectory prediction typically assumed to rely on visual information). To put these competing hypotheses to test, in the current study we presented tennis players with a series of temporally occluded video clips of tennis rallies featuring experimentally amplified, attenuated, or muted grunting sounds. Participants were asked to predict the ball landing position. Results indicated that higher grunt intensities yielded judgments of longer ball trajectories whereas radial prediction errors were not affected. These results are clearly at odds with the distraction account of grunting, predicting increased prediction errors after higher intensity grunts. In contrast, our findings provide strong support for the multisensory integration account by demonstrating that grunt intensity systematically influences judgments of ball trajectory.
Journal Article