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Athletes’ transition into a new, post-athletic career represents a challenging part of athletes’ careers. Due to the diverse combination of demands that athletes may face when retiring (e.g., adjustment to the new social status, changing social network, physical deterioration), an increasing demand is being placed on athletes’ sporting entourage to provide athletes the necessary support both during and after the sporting career (e.g., Gledhill & Harwood, 2015, Wylleman et al., 2020). While research is clear on the value of coaches in supporting athletes throughout different transition processes, studies investigating coaches’ perspectives on athletes’ career development towards a new, post-athletic career is scarce. This study therefore aimed to gain an insight in coaches’ perspectives on athletes’ preparation for and transition to a new, post-athletic career. A qualitative study design using eighteen semi-structured interviews with coaches was adopted. Participating coaches included active or former coaches (100% male) who supervise or supervised elite athletes competing at minimum national level in team sports (56%) or international level in individual sports (44%). Inductive thematic content analysis (Braun & Clarke, 2006) allowed us to gain a detailed picture of meaningful patterns within coaches’ perspectives on supporting athletes in their preparation for and transition to a new, post-athletic career. While most of the coaches believed that certain preparation pathways, such as a dual career ‘elite sport and study or work’ hold multiple benefits (e.g., competency development, sense of security, welcome distraction from the elite sport demands), others expressed their fears of a dual career distracting elite athletes too much from their sporting goals. Nevertheless, the majority of coaches indicated to be willing to support athletes in their preparation for a a new, post-athletic career. Examples of support indicated by the coaches were, amongst others, adapting the timings of training moments to an athlete’s study scheme and using the coach’s network to facilitate an athlete’s job search. However, while reaching out to elite athletes in their preparation for a new, post-athletic career seemed obvious to most coaches, it was perceived that it is not a coach’s responsibility to foresee guidance and help during the transition itself. Generally, coaches found they lack the necessary competencies to adequately support athletes in the transition phase. Study findings can serve as a basis to raise awareness within coaches on the importance of athletes’ holistic development and to develop methods for coaches on how to integrate such holistic perspective in their coaching.

High-density automated mapping of complex atrial tachycardias (ATs) requires accurate assessment of activation maps. A new local activation display module (HD coloring, Biosense Webster®) provides higher map resolution, a better delineation of potential block reducing color interpolation, and a new propagation display. We evaluated the accuracy of a dedicated local activation display compared with standard algorithm. High-density maps from 10 AT were collected with a multipolar catheter and were displayed with standard activation or HD coloring. Six expert operators retrospectively analyzed activation maps and were asked to define (1) the tachycardia mechanism, (2) ablation target, and (3) level of difficulty to interpret those maps. Using HD coloring, operators were able to reach a correct diagnosis in 93% vs. 63%, p

Game jams, hackathons and similar group game creation events have become increasingly popular over the past decade (Fowler et al., 2015). They provide new and exciting opportunities for education and research. They foster creative thinking and innovation (Preston et al., 2012), and strengthen project management and communication skills (Smith & Bowers, 2016); all of which are essential skills for working in industry (Pirker et al., 2016).

International audience; Acoustic ecology data have been used for a broad range of soundscape investigations. Counting sounds in a given soundscape is considered an effective method in ecology studies that provides comparative data for evaluating the impact of human community on the environment. In 2016, Kobayashi and Kudo collected a particularly valuable dataset containing recordings from within the exclusion (i.e., difficult-to-return-to) zone located 10 km from the Fukushima Daiichi Nuclear Power Plant in the Omaru District (Namie, Fukushima, Japan). These audio samples were continuously transmitted as a live stream of sound data from an unmanned remote sensing station in the area. In 2016, the first portion of their collected audio samples covering the transmitted sound recordings from the station was made available. Such data cover the bioacoustics in the area. This paper describes the methodologies by which we processed these recordings, in extreme conditions, as preliminary eco-acoustic indexes for demonstrating possible correlations between biodiversity variation and preexisting radioecology observations. The variations in some of these vocalizations were also studied.

Bioacoustics is powerful for monitoring biodiversity. We investigate in this paper automatic segmentation model for real-world bioacoustic scenes in order to infer hidden states referred as song units. Nevertheless, the number of these acoustic units is often unknown, unlike in human speech recognition. Hence, we propose a bioacoustic segmentation based on the Hierarchical Dirichlet Process (HDP-HMM), a Bayesian non-parametric (BNP) model to tackle this challenging problem. Hence, we focus our approach on unsupervised learning from bioacoustic sequences. It consists in simultaneously finding the structure of hidden song units, and automatically infers the unknown number of the hidden states. We investigate two real bioacoustic scenes: whale, and multi-species birds songs. We learn the models using Markov-Chain Monte Carlo (MCMC) sampling techniques on Mel Frequency Cepstral Coefficients (MFCC). Our results, scored by bioacoustic expert, show that the model generates correct song unit segmentation. This study demonstrates new insights for unsupervised analysis of complex soundscapes and illustrates their potential of chunking non-human animal signals into structured units. This can yield to new representations of the calls of a target species, but also to the structuration of inter-species calls. It gives to experts a tracktable approach for efficient bioacoustic research as requested in Kershenbaum et al. (Biol Rev 91(1):13–52, 2016).

The goal is to learn the features and the representation adapted for cetacean sound dynamics without any priors. Thus, we develop data driven model to generate voicing and click of cetaceans audio signals. We learn representation and features of stationary or nonstationary emission using neural network from raw audio. We use different types of convolutions (causal, with strides, with dilation [1]), or gradient inversion [2]. Experiments are conducted on various kind of calls of humpback whales from nips4b challenge [3] or Orca whale. We compare the topology for transient encoding on Physeters and Inia g. For each model, we detail the resulting filters and discuss on the topology. We acknowledge Region PACA and NortekMED for Roger’s Phd grant, & DGA and Region Haut de France for Ferrari’s Phd grant. [1] Oord, Dieleman, Zen, Simonyan, Vinyals, Graves et al. Wavenet : A generative model for raw audio, arXiv:1609.03499, 2016 [2] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv:1711.04313, 2017 [3] Glotin, LeCun, Mallat et al. Proc. 1st wkp on Neural Information Processing for Bioacoustics NIPS4B, joint to NIPS Alberta USA, 2013 http://sabiod.org/nips4b/challenge2.html, http://sabiod.org/NIPS4B2013_book.pdfThe goal is to learn the features and the representation adapted for cetacean sound dynamics without any priors. Thus, we develop data driven model to generate voicing and click of cetaceans audio signals. We learn representation and features of stationary or nonstationary emission using neural network from raw audio. We use different types of convolutions (causal, with strides, with dilation [1]), or gradient inversion [2]. Experiments are conducted on various kind of calls of humpback whales from nips4b challenge [3] or Orca whale. We compare the topology for transient encoding on Physeters and Inia g. For each model, we detail the resulting filters and discuss on the topology. We acknowledge Region PACA and NortekMED for Roger’s Phd grant, & DGA and Region Haut de France for Ferrari’s Phd grant. [1] Oord, Dieleman, Zen, Simonyan, Vinyals, Graves et al. Wavenet : A generative model for raw audio, arXiv:1609.03499, 2016 [2] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Netwo...

During February and March, 2018, a lone sperm whale known as Yukusam was recorded first by Orcalab in Johnstone Strait and subsequently on multiple hydrophones within the Salish Sea [1]. We learn and denoise these multichannel clicks trains with AutoEncoders Convolutional Neural Net (CNN). Then, we build a map of the echolocations to elucidate variations in the acoustic behavior of this unique animal over time, in different environments and distinct levels of boat noise. If CNN approximates an optimal kernel decomposition, it requires large amounts of data. Via spline functionals we offer analytics kernels with learnable coefficients do reduce it. We [1-3] identify the analytical mother wavelet to represent the input signal to directly learn the wavelet support from scratch by gradient descend on the parameters of cubic splines [2]. Supplemental material http://sabiod.org/yukusam [1] Balestriero, Roger, Glotin, Baraniuk, Semi-Supervised Learning via New Deep Network Inversion, arXiv preprint arXiv:1711.04313, 2017 [2] Balestriero, Cosentino, Glotin, Baraniuk, WaveletNet : Spline Filters for End-to-End Deep Learning, Int. Conf. on MachineLearning, ICML, Stockholm, http://sabiod.org/bib, 2018 [3] Spong P., Symonds H., et al., Joint Observatories Following a Single male Cachalot during 12 weeks—The Yukusam story, ASA 2018.

International audience; The frameworks dedicated to the representation of quantitative temporal constraint satisfaction problems, as rich as they are in terms of expressiveness, define difficult requests - typically NP-complete decision problems. It is therefore adventurous to use them for an online resolution. Hence the idea to compile the original problem into a form that could be easily solved. Difference Decision Diagrams (DDDs) have been proposed by [1] as a possible way to cope with this difficulty, following a compilation-based approach. In this article, we draw a compilation map that evaluates the relative capabilities of these languages (TCSP, STP, DTP and DDD) in terms of algorithmic efficiency, succinctness and expressiveness.