Abstract
IntroductionPerinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model. ObjectivesThis exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices. MethodsSingle gestations data from a retrospective unicentric study from Centro Hospitalar e Universitario do Porto de Sao Joao (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models. ResultsThe data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%]. ConclusionBoth BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Abstract
Photoplethysmography is a key sensing technology which is used in wearable devices such as smartwatches and fitness trackers. Currently, photoplethysmography sensors are used to monitor physiological parameters including heart rate and heart rhythm, and to track activities like sleep and exercise. Yet, wearable photoplethysmography has potential to provide much more information on health and wellbeing, which could inform clinical decision making. This Roadmap outlines directions for research and development to realise the full potential of wearable photoplethysmography. Experts discuss key topics within the areas of sensor design, signal processing, clinical applications, and research directions. Their perspectives provide valuable guidance to researchers developing wearable photoplethysmography technology.

Abstract
In everyday conversations, humans can take on different roles and adapt their vocabulary to their chosen roles. We explore whether LLMs can take on, that is impersonate, different roles when they generate text in-context. We ask LLMs to assume different personas before solving vision and language tasks. We do this by prefixing the prompt with a persona that is associated either with a social identity or domain expertise. In a multi-armed bandit task, we find that LLMs pretending to be children of different ages recover human-like developmental stages of exploration. In a language-based reasoning task, we find that LLMs impersonating domain experts perform better than LLMs impersonating non-domain experts. Finally, we test whether LLMs' impersonations are complementary to visual information when describing different categories. We find that impersonation can improve performance: an LLM prompted to be a bird expert describes birds better than one prompted to be a car expert. However, impersonation can also uncover LLMs' biases: an LLM prompted to be a man describes cars better than one prompted to be a woman. These findings demonstrate that LLMs are capable of taking on diverse roles and that this in-context impersonation can be used to uncover their strengths and hidden biases. Our code is available at https://github.com/ExplainableML/in-context-impersonation.

Abstract
METIS SCAO uses a wavefront control concept that deploys a 2-stage spatial reconstruction where the wavefront is first reconstructed on an intermediate space we call the virtual DM, and then projected onto the actual control space. This document addresses the projection of the wavefront estimation on the virtual deformable mirror (VDM) onto the control modes developed for METIS (Mid-infrared ELT Imager and Spectrograph). We present a new approach to project onto the control modes using an intermediate regularized projection on the M4 mirror and then convert to modes. This method enables us to utilise all modes for the projection and control in a stable manner, achieving high Strehl ratios for a wide range of conditions without the need for complex parameter tuning.

Abstract
The list of Adaptive Optics (AO) simulators in the community has constantly been growing, guided by different needs and purposes (Compass, HCIPY, OOMAO, SOAPY, YAO. . .). In this paper, we present OOPAO (Object Oriented Python Adaptive Optics), a simulation tool based on the Matlab distribution OOMAO to adapt its philosophy to the Python language. This code was initially intended for internal use but the choice was made to make it public as it can benefit the community since it is fully developed in Python. The OOPAO repository is available in free access on GitHub (https://github.com/cheritier/OOPAO) with several tutorials. The tool consists of a full end-to-end simulator designed for AO analysis purposes. The principle is that the light from a given light source can be propagated through multiple objects (Atmosphere, Telescope, Deformable Mirror, Wave-Front Sensors. . .) among which experimental features can be input, in the spirit of OOMAO. This paper provides an overview of the main capabilities of the code and can be used as a user manual for interested users.

Abstract
The segmented nature of the 10-m Keck telescopes combined with facility-class AO systems and science instruments, and a history of science-driven upgrades to these systems, offers a uniquely powerful pathfinder for future AO science facilities on the segmented ELTs. Keck’s 2035 Strategic Vision includes visible, high contrast and ground layer AO facilities all of which could support ELT AO pathfinding. Keck’s pathfinder strength is not just demonstrating new techniques or technologies but developing them into operational science capabilities. For example, since first Keck AO science in 1999, Keck has successfully implemented three generations of sodium-wavelength lasers and is currently implementing its third generation of real-time controller (this time GPU-based). Current pathfinder-related developments include laser tomography, near-infrared low order wavefront sensing and PSF-reconstruction for high Strehl ratio and high sky coverage on the Keck I AO system. Current AO-based primary mirror phasing techniques under development include the use of Zernike, pyramid and phase diversity techniques. High-contrast AO developments include near-infrared pyramid wavefront sensing, on-sky phase diversity, speckle nulling and predictive wavefront control. Another pathfinder development is the NASA Goddard-led ORCAS satellite to provide a bright artificial point source for AO-correction. A fast, visible science camera has been implemented in support of ORCAS, demonstrating 15 mas FWHM, and, in a further move toward the visible, ALPAO is developing a 2.5 mm spacing, 60x60 actuator deformable mirror for Keck. In addition, three new AO science instruments are planned: Liger as a prototype of TMT’s IRIS, HISPEC which is the same as TMT’s MODHIS (based on KPIC’s science success), and SCALES.