PongBot is an artificial intelligence-powered tennis robot. American tennis player Danielle Collins had some choice words for the cameraman during her Internationaux de Strasbourg match against Emma Raducanu on Wednesday afternoon. Collins was in the middle of a changeover when she felt the cameraman's hovering was a bit too close for comfort in the middle of the third and defining set. She got off the bench and made the point clear. Danielle Collins celebrates during her match against Madison Keys in the third round of the women's singles at the 2025 Australian Open at Melbourne Park in Melbourne, Australia, on Jan. 18, 2025.

Objective: To evaluate the accuracy, computational cost and portability of a new Natural Language Processing (NLP) method for extracting medication information from clinical narratives. Materials and Methods: We propose an original transformer-based architecture for the extraction of entities and their relations pertaining to patients' medication regimen. First, we used this approach to train and evaluate a model on French clinical notes, using a newly annotated corpus from H\^opitaux Universitaires de Strasbourg. Second, the portability of the approach was assessed by conducting an evaluation on clinical documents in English from the 2018 n2c2 shared task. Information extraction accuracy and computational cost were assessed by comparison with an available method using transformers. Results: The proposed architecture achieves on the task of relation extraction itself performance that are competitive with the state-of-the-art on both French and English (F-measures 0.82 and 0.96 vs 0.81 and 0.95), but reduce the computational cost by 10. End-to-end (Named Entity recognition and Relation Extraction) F1 performance is 0.69 and 0.82 for French and English corpus. Discussion: While an existing system developed for English notes was deployed in a French hospital setting with reasonable effort, we found that an alternative architecture offered end-to-end drug information extraction with comparable extraction performance and lower computational impact for both French and English clinical text processing, respectively. Conclusion: The proposed architecture can be used to extract medication information from clinical text with high performance and low computational cost and consequently suits with usually limited hospital IT resources

Clinnova, a collaborative initiative involving France, Germany, Switzerland, and Luxembourg, is dedicated to unlocking the power of precision medicine through data federation, standardization, and interoperability. This European Greater Region initiative seeks to create an interoperable European standard using artificial intelligence (AI) and data science to enhance healthcare outcomes and efficiency. Key components include multidisciplinary research centers, a federated biobanking strategy, a digital health innovation platform, and a federated AI strategy. It targets inflammatory bowel disease, rheumatoid diseases, and multiple sclerosis (MS), emphasizing data quality to develop AI algorithms for personalized treatment and translational research. The IHU Strasbourg (Institute of Minimal-invasive Surgery) has the lead in this initiative to develop the federated learning (FL) proof of concept (POC) that will serve as a foundation for advancing AI in healthcare. At its core, Clinnova-MS aims to enhance MS patient care by using FL to develop more accurate models that detect disease progression, guide interventions, and validate digital biomarkers across multiple sites. This technical report presents insights and key takeaways from the first cross-border federated POC on MS segmentation of MRI images within the Clinnova framework. While our work marks a significant milestone in advancing MS segmentation through cross-border collaboration, it also underscores the importance of addressing technical, logistical, and ethical considerations to realize the full potential of FL in healthcare settings.

The ThEdu series pursues the smooth transition from an intuitive way of doing mathematics at secondary school to a more formal approach to the subject in STEM education, while favouring software support for this transition by exploiting the power of theorem-proving technologies. What follows is a brief description of how the present volume contributes to this enterprise. The 12th International Workshop on Theorem Proving Components for Educational Software(ThEdu'23), was a satellite event of the 29th international Conference on Automated Deduction (CADE 2023), July 1-4, 2023, Rome, Italy. ThEdu'23 was very successful, with one invited talk, by Yves Bertot (Inria, France), "The challenges of using Type Theory to teach Mathematics", and seven regular contributions. An open call for papers was then issued, to which eight contributions were submitted. Seven submissions have been accepted by our reviewers, who jointly produced at least three careful reports on each of the contributions. The resulting revised papers are collected in the present volume. We, the volume editors, hope that this collection of papers will further promote the development of theorem-proving based software, and that it will allow to improve the mutual understanding between computer scientists, mathematicians and stakeholders in education. PC Chairs:Julien Narboux (University of Strasbourg, France); Walther Neuper (JKU, Johannes Kepler University, Linz, Austria); Pedro Quaresma (University of Coimbra, Portugal)

The European Parliament has given final approval to wide-ranging rules to govern artificial intelligence. The far-reaching regulation – the Artificial Intelligence Act – was passed by lawmakers on Wednesday. Senior European Union officials said the rules, first proposed in 2021, will protect citizens from the possible risks of a technology developing at breakneck speed while also fostering innovation. Brussels has sprinted to pass the new law since Microsoft-backed OpenAI's ChatGPT arrived on the scene in late 2022, unleashing a global AI race. Just 46 lawmakers in the European Parliament in Strasbourg voted against the proposal.

ADG is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction. The conference is held every two years. The previous editions of ADG were held in Hagenberg in 2021 (online, postponed from 2020 due to COVID-19), Nanning in 2018, Strasbourg in 2016, Coimbra in 2014, Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006, Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and Toulouse in 1996. The 14th edition, ADG 2023, was held in Belgrade, Serbia, in September 20-22, 2023. This edition of ADG had an additional special focus topic, Deduction in Education. Invited Speakers: Julien Narboux, University of Strasbourg, France "Formalisation, arithmetization and automatisation of geometry"; Filip Mari\'c, University of Belgrade, Serbia, "Automatization, formalization and visualization of hyperbolic geometry"; Zlatan Magajna, University of Ljubljana, Slovenia, "Workshop OK Geometry"

Mediation choreography workshop is a practice and training session to improve bodily confidence, comfort, awareness and dialogue during social interactions focused on the self-presentation development in children and youngsters. The workshop teaches non-verbal mediation and dialogue: Body postures, Expressions and Form aggregations inspired from the museum sculptures. During the workshop, mediation choreography experts help young participants to improvise and feel the quality of our bodily perception, performance and presentation. Disclaimer: The description of Mediation choreography is my understanding as a foreigner in the French city, Strasbourg. I participated in the workshop among native speakers.

Automated Deduction in Geometry (ADG) is a forum to exchange ideas and views, to present research results and progress, and to demonstrate software tools at the intersection between geometry and automated deduction. Relevant topics include (but are not limited to): polynomial algebra, invariant and coordinate-free methods; probabilistic, synthetic, and logic approaches, techniques for automated geometric reasoning from discrete mathematics, combinatorics, and numerics; interactive theorem proving in geometry; symbolic and numeric methods for geometric computation, geometric constraint solving, automated generation/reasoning and manipulation with diagrams; design and implementation of geometry software, automated theorem provers, special-purpose tools, experimental studies; applications of ADG in mechanics, geometric modelling, CAGD/CAD, computer vision, robotics and education. Traditionally, the ADG conference is held every two years. The previous editions of ADG were held in Nanning in 2018, Strasbourg in 2016, Coimbra in 2014, Edinburgh in 2012, Munich in 2010, Shanghai in 2008, Pontevedra in 2006, Gainesville in 2004, Hagenberg in 2002, Zurich in 2000, Beijing in 1998, and Toulouse in 1996. The 13th edition of ADG was supposed to be held in 2020 in Hagenberg, Austria, but due to the COVID-19 pandemic, it was postponed for 2021, and held online (still hosted by RISC Institute, Hagenberg, Austria), September 15-17, 2021 (https://www.risc.jku.at/conferences/adg2021).

During its 37th plenary meeting held in Strasbourg on 8 and 9 December, the European Commission for the Efficiency of Justice (CEPEJ) launched the 2022-2025 action plan called "Digitalisation for a better justice" aiming at reconciling the efficiency of new technologies and the respect of fundamental rights. Back on this plan which will also focus on the use of artificial intelligence technologies. The priority of the CEPEJ for the next four years is thus to accompany the States and the courts in a successful transition towards the digitalization of justice in conformity with the European standards. These orientations are articulated around major axes aiming at"that justice is always transparent, collaborative, human, centred on individuals and accessible, enlightened, and finally responsible and reactive" and are the following: The charter emphasises the need for specific training and indicates that developers and users of AI applications could find it beneficial to receive more practical guidance on how to apply the five principles set out in the CEPEJ Charter. This would give developers a clearer idea of how to audit their applications, ideally already in the development phase.

Ahead of the artificial intelligence regulation, MEPs insisted that its use by law enforcement authorities and in the judiciary be subject to tight controls in Strasbourg on Monday (October 4). "The idea behind this report is not only to catch up but to create a framework", rapporteur, MEP Petar Vitanov (S&D), told EURACTIV. Although not binding, the new report on artificial intelligence (AI) in criminal matters could pave the way for the European Parliament's to back a risk-based approach, while MEPs will soon have to consider the AI Act proposed by the Commission in April. The text sets out the "principles of fairness, data minimisation, accountability, transparency, non-discrimination and explainability" in order to protect fundamental rights. "AI can be very useful", said Vitanov, but "we are trying to separate the areas where it can be useful from those that bring subjective results". "Facial recognition in the public spaces can easily be turned into mass surveillance", Viatnov said.