Abstract
In the context of biometrics, matching confidence refers to the confidence that a given matching decision is correct. Since many biometric systems operate in critical decision-making processes, such as in forensics investigations, accurately and reliably stating the matching confidence becomes of high importance. Previous works on biometric confidence estimation can well differentiate between high and low confidence, but lack interpretability. Therefore, they do not provide accurate probabilistic estimates of the correctness of a decision. In this work, we propose a probabilistic interpretable comparison (PIC) score that accurately reflects the probability that the score originates from samples of the same identity. We prove that the proposed approach provides optimal matching confidence. Contrary to other approaches, it can also optimally combine multiple samples in a joint PIC score which further increases the recognition and confidence estimation performance. In the experiments, the proposed PIC approach is compared against all biometric confidence estimation methods available on four publicly available databases and five state-of-the-art face recognition systems. The results demonstrate that PIC has a significantly more accurate probabilistic interpretation than similar approaches and is highly effective for multi-biometric recognition. The code is publicly-available1. © 2023 IEEE.

Abstract
Background: Preterm birth is a global health concern. Its adverse consequences may persist throughout the life course, exerting a potentially heavy burden on families, health systems, and societies. In high-income countries, the first children who benefited from improved care are now adults entering middle age. However, there is a clear gap in the knowledge regarding the long-term outcomes of individuals born preterm. Objective: This study aimed to assess the feasibility of recruiting and following up an e-cohort of adults born preterm worldwide and provide estimations of participation, characteristics of participants, the acceptability of questions, and the quality of data collected. Methods: We implemented a prospective, open, observational, and international e-cohort pilot study (Health of Adult People Born Preterm-an e-Cohort Pilot Study [HAPP-e]). Inclusion criteria were being an adult (aged =18 years), born preterm (<37 weeks of gestation), having internet access and an email address, and understanding at least 1 of the available languages. A large, multifaceted, and multilingual communication strategy was established. Between December 2019 and June 2021, inclusion and repeated data collection were performed using a secured web platform. We provided descriptive statistics regarding participation in the e-cohort, namely, the number of persons who registered on the platform, signed the consent form, initiated and completed the baseline questionnaire, and initiated and completed the follow-up questionnaire. We also described the main characteristics of the HAPP-e participants and provided an assessment of the quality of the data and the acceptability of sensitive questions. Results: As of December 31, 2020, a total of 1004 persons had registered on the platform, leading to 527 accounts with a confirmed email and 333 signed consent forms. A total of 333 participants initiated the baseline questionnaire. All participants were invited to follow-up, and 35.7% (119/333) consented to participate, of whom 97.5% (116/119) initiated the follow-up questionnaire. Completion rates were very high both at baseline (296/333, 88.9%) and at follow-up (112/116, 96.6%). This sample of adults born preterm in 34 countries covered a wide range of sociodemographic and health characteristics. The gestational age at birth ranged from 23+6 to 36+6 weeks (median 32, IQR 29-35 weeks). Only 2.1% (7/333) of the participants had previously participated in a cohort of individuals born preterm. Women (252/333, 75.7%) and highly educated participants (235/327, 71.9%) were also overrepresented. Good quality data were collected thanks to validation controls implemented on the web platform. The acceptability of potentially sensitive questions was excellent, as very few participants chose the I prefer not to say option when available. Conclusions: Although we identified room for improvement in specific procedures, this pilot study confirmed the great potential for recruiting a large and diverse sample of adults born preterm worldwide, thereby advancing research on adults born preterm.

Abstract
This paper presents a clinical case study investigating the pattern of a saxophonist's embouchure as a possible origin of orofacial pain. The rehabilitation addressed the dental occlusion and a fracture in a metal ceramic bridge. To evaluate the undesirable loads on the upper teeth, two piezoresistive sensors were placed between the central incisors and the mouthpiece during the embouchure. A newly fixed metal ceramic prosthesis was placed from teeth 13 to 25, and two implants were placed in the premolar zone corresponding to teeth 14 and 15. After the oral rehabilitation, the embouchure force measurements showed that higher stability was promoted by the newly fixed metal-ceramic prosthesis. The musician executed a more symmetric loading of the central incisors (teeth 11 and 21). The functional demands of the saxophone player and consequent application of excessive pressure can significantly influence and modify the metal-ceramic position on the anterior zone teeth 21/22. The contribution of engineering (i.e., monitoring the applied forces on the musician's dental structures) was therefore crucial for the correct assessment and design of the treatment plan.

Abstract
The use of Virtual Reality (VR) technology to train professionals has increased over the years due to its advantages over traditional training. This paper presents a study comparing the effectiveness of a Virtual Environment (VE) and a Real Environment (RE) designed to train firefighters. To measure the effectiveness of the environments, a new method based on participants' Heart Rate Variability (HRV) was used. This method was complemented with self-reports, in the form of questionnaires, of fatigue, stress, sense of presence, and cybersickness. An additional questionnaire was used to measure and compare knowledge transfer enabled by the environments. The results from HRV analysis indicated that participants were under physiological stress in both environments, albeit with less intensity on the VE. Regarding reported fatigue and stress, the results showed that none of the environments increased such variables. The results of knowledge transfer showed that the VE obtained a significant increase while the RE obtained a positive but non-significant increase (median values, VE: before - 4 after - 7, p = .003; RE: before - 4 after - 5, p = .375). Lastly, the results of presence and cybersickness suggested that participants experienced high overall presence and no cybersickness. Considering all results, the authors conclude that the VE provided effective training but that its effectiveness was lower than that of the RE.

Abstract
Medical imaging, mainly Magnetic Resonance Imaging (MRI), plays a predominant role in healthcare diagnosis. Nevertheless, the diagnostic process is prone to errors and is conditioned by available medical data, which might be insufficient. A novel solution is resorting to image generation algorithms to address these challenges. Thus, this paper presents a Deep Learning model based on a Deep Convolutional Generative Adversarial Network (DCGAN) architecture. Our model generates 2D MRI images of size 256x256, containing an axial view of the brain with a tumor. The model was implemented using ChainerMN, a scalable and flexible framework that enables faster and parallel training of Deep Learning networks. The images obtained provide an overall representation of the brain structure and the tumoral area and show considerable brain-tumor separation. For this purpose, and owing to their previous state-of-the-art results in general image-generation tasks, we conclude that GAN-based models are a promising approach for medical imaging.

Abstract
Artificial Intelligence (AI) is one of the big hopes for the future of a positive revolution in the use of medical data to improve clinical routine and personalized medicine [...]