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We describe how the annihilation of antiprotons can be utilized to generate highly charged synthetic qubits in an ion-trap setup. We identify the qubit transitions in the hyperfine splitting of Hydrogen-like atoms composed of an isomer and a single electron in the ground state. We identify promising candidates in the isomers of Y, Nb, Rh, In, and Sb, for which the hyperfine transition lies in the infrared and whose excited state level lifetime is in the hundreds of milliseconds, which is suitable for metrology applications., Comment: 11 pages, 2 figures, 6 tables

A powerful and robust control system is a crucial, often neglected, pillar of any modern, complex physics experiment that requires the management of a multitude of different devices and their precise time synchronisation. The AEgIS collaboration presents CIRCUS, a novel, autonomous control system optimised for time-critical experiments such as those at CERN's Antiproton Decelerator and, more broadly, in atomic and quantum physics research. Its setup is based on Sinara/ARTIQ and TALOS, integrating the ALPACA analysis pipeline, the last two developed entirely in AEgIS. It is suitable for strict synchronicity requirements and repeatable, automated operation of experiments, culminating in autonomous parameter optimisation via feedback from real-time data analysis. CIRCUS has been successfully deployed and tested in AEgIS; being experiment-agnostic and released open-source, other experiments can leverage its capabilities.

Precise manipulation of matter at the atomic or molecular level has provided the path for the nanotechnological revolution impacting diverse fields such as biology, medicine, material science, quantum technologies, and electronics. At the Antiproton Decelerator facility at CERN, the AEgIS experiment utilises state-of-the-art technology to store and manipulate synthesised exotic atoms containing both matter and antimatter. Such experiments lay the groundwork for a better understanding of the fundamental interactions and hold the potential to unravel the enigma of the absence of antimatter in our universe. Additionally, the developed techniques advance the technological frontier of controlling the quantum states of ions, a critical aspect of quantum sensing and quantum computing applications.

The AEgIS experiment at CERN recently decided to adopt a control system solution based on the Sinara/ARTIQ open hardware and software infrastructure. This decision meant to depart from the previously used paradigm of custom-made electronics and software to control the experiment's equipment. Instead, adopting a solution with long-term support and used in many quantum physics experiments guarantees a vivid community using similar infrastructures. This transition reduces the risks and development timeline for integrating new equipment seamlessly within the setup. This work reviews the motivation, the setup, and the chosen hardware and presents several planned further steps in developing the control system.

Healing process assessment of the Achilles tendon is usually a complex procedure that relies on a combination of biomechanical and medical imaging tests. As a result, diagnostics remains a tedious and long-lasting task. Recently, a novel method for the automatic assessment of tendon healing based on Magnetic Resonance Imaging and deep learning was introduced. The method assesses six parameters related to the treatment progress utilizing a modified pre-trained network, PCA-reduced space, and linear regression. In this paper, we propose to improve this approach by incorporating hand-crafted features. We first perform a feature selection in order to obtain optimal sets of mixed hand-crafted and deep learning predictors. With the use of approx. 20,000 MRI slices, we then train a meta-regression algorithm that performs the tendon healing assessment. Finally, we evaluate the method against scores given by an experienced radiologist. In comparison with the previous baseline method, our approach significantly improves correlation in all of the six parameters assessed. Furthermore, our method uses only one MRI protocol and saves up to 60\% of the time needed for data acquisition., Comment: Paper accepted to MICCAI'19 MSKI workshop

Achilles tendon rupture is a debilitating injury, which is typically treated with surgical repair and long-term rehabilitation. The recovery, however, is protracted and often incomplete. Diagnosis, as well as healing progress assessment, are largely based on ultrasound and magnetic resonance imaging. In this paper, we propose an automatic method based on deep learning for analysis of Achilles tendon condition and estimation of its healing progress on ultrasound images. We develop custom convolutional neural networks for classification and regression on healing score and feature extraction. Our models are trained and validated on an acquired dataset of over 250.000 sagittal and over 450.000 axial ultrasound slices. The obtained estimates show a high correlation with the assessment of expert radiologists, with respect to all key parameters describing healing progress. We also observe that parameters associated with i.a. intratendinous healing processes are better modeled with sagittal slices. We prove that ultrasound imaging is quantitatively useful for clinical assessment of Achilles tendon healing process and should be viewed as complementary to magnetic resonance imaging., Comment: Paper accepted to MICCAI'19 SUSI workshop

Quantitative assessment of a treatment progress in the Achilles tendon healing process - one of the most common musculoskeletal disorder in modern medical practice - is typically a long and complex process: multiple MRI protocols need to be acquired and analysed by radiology experts. In this paper, we propose to significantly reduce the complexity of this assessment using a novel method based on a pre-trained convolutional neural network. We first train our neural network on over 500,000 2D axial cross-sections from over 3000 3D MRI studies to classify MRI images as belonging to a healthy or injured class, depending on the patient's condition. We then take the outputs of modified pre-trained network and apply linear regression on the PCA-reduced space of the features to assess treatment progress. Our method allows to reduce up to 5-fold the amount of data needed to be registered during the MRI scan without any information loss. Furthermore, we are able to predict the healing process phase with equal accuracy to human experts in 3 out of 6 main criteria. Finally, contrary to the current approaches to regeneration assessment that rely on radiologist subjective opinion, our method allows to objectively compare different treatments methods which can lead to improved diagnostics and patient's recovery., Comment: Paper accepted to MICCAI'18