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We provide a quantization of the Schwarzschild spacetime in the presence of a cosmological constant, based on midisuperspace methods developed in the spherically symmetric sector of loop quantum gravity, using in particular the 'improved dynamics' scheme. We include both the deSitter and anti-deSitter cases. We find that the quantization puts a Planckian upper limit on the possible values of a positive cosmological constant similar to the bounds obtained earlier from studies of homogeneous spacetimes with a cosmological constant. Using semiclassical physical states, we obtain the effective metric and demonstrate the causal structure for various cases. Quantum gravity modifications ensure that the singularity is replaced by a transition surface in all the cases, where the curvature invariants approach mass-independent Planckian bounds. Analysis of the effective stress-energy tensor shows that the null energy condition is violated in the vicinity of the transition surface.

Ensembles of phase-oscillators are known to exhibit a variety of collective regimes. Here, we show that a simple mean-field model involving two heterogenous populations of pulse-coupled oscillators, exhibits, in the strong-coupling limit, a robust irregular macroscopic dynamics. The resulting, strongly synchronized, regime is sustained by a homeostatic mechanism induced by the shape of the phase-response curve combined with adaptive coupling strength, included to account for energy dissipated by the pulse emission. The proposed setup mimicks a neural network composed of excitatory and inhibitory neurons., Comment: 4 pages, 3 figures; supplemental material 3 opages, 3 figures

Quantum computers have the potential to solve important problems which are fundamentally intractable on a classical computer. The underlying physics of quantum computing platforms supports using multi-valued logic, which promises a boost in performance over the prevailing two-level logic. One key element to exploiting this potential is the capability to efficiently prepare quantum states for multi-valued, or qudit, systems. Due to the time sensitivity of quantum computers, the circuits to prepare the required states have to be as short as possible. In this paper, we investigate quantum state preparation with a focus on mixed-dimensional systems, where the individual qudits may have different dimensionalities. The proposed approach automatically realizes quantum circuits constructing a corresponding mixed-dimensional quantum state. To this end, decision diagrams are used as a compact representation of the quantum state to be realized. We further incorporate the ability to approximate the quantum state to enable a finely controlled trade-off between accuracy, memory complexity, and number of operations in the circuit. Empirical evaluations demonstrate the effectiveness of the proposed approach in facilitating fast and scalable quantum state preparation, with performance directly linked to the size of the decision diagram. The implementation is freely available as part of Munich Quantum Toolkit~(MQT), under the framework MQT Qudits at github.com/cda-tum/mqt-qudits., Comment: 6 pages, 4 figures, 1 table

Quantum computers are becoming a reality and numerous quantum computing applications with a near-term perspective (e.g., for finance, chemistry, machine learning, and optimization) and with a long-term perspective (e.g., for cryptography or unstructured search) are currently being investigated. However, designing and realizing potential applications for these devices in a scalable fashion requires automated, efficient, and user-friendly software tools that cater to the needs of end users, engineers, and physicists at every level of the entire quantum software stack. Many of the problems to be tackled in that regard are similar to design problems from the classical realm for which sophisticated design automation tools have been developed in the previous decades. The Munich Quantum Toolkit (MQT) is a collection of software tools for quantum computing developed by the Chair for Design Automation at the Technical University of Munich which explicitly utilizes this design automation expertise. Our overarching objective is to provide solutions for design tasks across the entire quantum software stack. This entails high-level support for end users in realizing their applications, efficient methods for the classical simulation, compilation, and verification of quantum circuits, tools for quantum error correction, support for physical design, and more. These methods are supported by corresponding data structures (such as decision diagrams) and core methods (such as SAT encodings/solvers). All of the developed tools are available as open-source implementations and are hosted on https://github.com/cda-tum., Comment: 8 pages, 4 figures, a live version of this document is available at https://mqt.readthedocs.io

This letter presents a 360{\deg} phase detector cell for performing phase-shift measurements on multiple output systems. An analog phase detector, capable of detecting a maximum range of {\pm}90{\deg}, has been used to perform a double multiplication of two signals, both in-phase and phase-shifted. The proposed solution broadens the frequency range beyond other solutions that require to fulfill the quadrature condition. Subsequently, the possibility of reaching the theoretical limit of phase shift within a hybrid coupler ({\Phi} < 90{\deg} {\pm} 90{\deg}) is discussed by using four straight-line equations to characterize the phase detector response. The proposed solution allows to extend up to 360{\deg} the phase detection range and provide an increased immunity with respect to both impedance mismatching and phase deviations within the hybrid coupler. To demonstrate the feasibility of the proposed design, a phase detector cell prototype has been implemented using a commercial hybrid coupler with a phase shift of 92.5{\deg} {\pm} 0.5{\deg} at 3.1-5.9 GHz, an external switch and a microcontroller with 2 kB of memory. Measurements show a range of detection of 360{\deg} ({\pm}180{\deg}) across the tested frequency band of 2.7-6 GHz., Comment: Index Terms: Microwave analog multiplier phase detector, Phase detector array, 360{\deg} Phase Shift Detector

We research into the clinical, biochemical and neuroimaging factors associated with the outcome of stroke patients to generate a predictive model using machine learning techniques for prediction of mortality and morbidity 3 months after admission. The dataset consisted of patients with ischemic stroke (IS) and non-traumatic intracerebral hemorrhage (ICH) admitted to Stroke Unit of a European Tertiary Hospital prospectively registered. We identified the main variables for machine learning Random Forest (RF), generating a predictive model that can estimate patient mortality/morbidity. In conclusion, machine learning algorithms RF can be effectively used in stroke patients for long-term outcome prediction of mortality and morbidity., Comment: 12 pages, 5 figures

Osteoarthritis (OA), a chronic disease characterized by articular cartilage degeneration, is a leading cause of disability and pain worldwide. In OA, chondrocytes in cartilage undergo phenotypic changes and senescence, restricting cartilage regeneration and favouring disease progression. Similar to other wound-healing disorders, chondrocytes from OA patients show a chronic increase in the gap junction channel protein connexin43 (Cx43), which regulates signal transduction through the exchange of elements or recruitment/release of signalling factors. Although immature or stem-like cells are present in cartilage from OA patients, their origin and role in disease progression are unknown. In this study, we found that Cx43 acts as a positive regulator of chondrocyte-mesenchymal transition. Downregulation of either Cx43 by CRISPR/Cas9 or Cx43-mediated gap junctional intercellular communication (GJIC) by carbenoxolone treatment triggered rediferentiation of osteoarthritic chondrocytes into a more differentiated state, associated with decreased synthesis of MMPs and proinflammatory factors, and reduced senescence. We have identified causal Cx43-sensitive circuit in chondrocytes that regulates dedifferentiation, redifferentiation and senescence. We propose that chondrocytes undergo chondrocyte-mesenchymal transition where increased Cx43-mediated GJIC during OA facilitates Twist-1 nuclear translocation as a novel mechanism involved in OA progression. These findings support the use of Cx43 as an appropriate therapeutic target to halt OA progression and to promote cartilage regeneration., Comment: 16 pages, 5 figures

Background and purpose: The unanticipated detection by magnetic resonance imaging (MRI) in the brain of asymptomatic subjects of white matter lesions suggestive of multiple sclerosis (MS) has been named radiologically isolated syndrome (RIS). As the difference between early MS [i.e. clinically isolated syndrome (CIS)] and RIS is the occurrence of a clinical event, it is logical to improve detection of the subclinical form without interfering with MRI as there are radiological diagnostic criteria for that. Our objective was to use machine-learning classification methods to identify morphometric measures that help to discriminate patients with RIS from those with CIS. Methods: We used a multimodal 3-T MRI approach by combining MRI biomarkers (cortical thickness, cortical and subcortical grey matter volume, and white matter integrity) of a cohort of 17 patients with RIS and 17 patients with CIS for single-subject level classification. Results: The best proposed models to predict the diagnosis of CIS and RIS were based on the Naive Bayes, Bagging and Multilayer Perceptron classifiers using only three features: the left rostral middle frontal gyrus volume and the fractional anisotropy values in the right amygdala and right lingual gyrus. The Naive Bayes obtained the highest accuracy [overall classification, 0.765; area under the receiver operating characteristic (AUROC), 0.782]. Conclusions: A machine-learning approach applied to multimodal MRI data may differentiate between the earliest clinical expressions of MS (CIS and RIS) with an accuracy of 78%. Keywords: Bagging; Multilayer Perceptron; Naive Bayes classifier; clinically isolated syndrome; diffusion tensor imaging; machine-learning; magnetic resonance imaging; multiple sclerosis; radiologically isolated syndrome., Comment: 24 pages, 2 tables

The method of Arterial Spin Labeling (ASL) has experienced a significant rise in its application to functional imaging, since it is the only technique capable of measuring blood perfusion in a truly non-invasive manner. Currently, there are no commercial packages for processing ASL data and there is no recognised standard for normalising ASL data to a common frame of reference. This work describes a new Automated Software for ASL Processing (ASAP) that can automatically process several ASL datasets. ASAP includes functions for all stages of image pre-processing: quantification, skull-stripping, co-registration, partial volume correction and normalization. To assess the applicability and validity of the toolbox, this work shows its application in the study of hypoperfusion in a sample of healthy subjects at risk of progressing to Alzheimer's Disease. ASAP requires limited user intervention, minimising the possibility of random and systematic errors, and produces cerebral blood flow maps that are ready for statistical group analysis. The software is easy to operate and results in excellent quality of spatial normalisation. The results found in this evaluation study are consistent with previous studies that find decreased perfusion, Comment: 10 pages, 8 figures, 3 tables

The description of the $T\bar{T}$ deformation in terms of two-dimensional gravity is analyzed from the Hamiltonian point of view, in a manner analogous to the ADM description of general relativity. We find that the Hamiltonian constraints of the theory imply relations between target-space momentum at finite volume which are equivalent to the $T\bar{T}$ finite volume flow equations. This fully-quantum $T\bar{T}$ result emerges already at the classical level within the gravitational theory. We exemplify the analysis for the case when the undeformed sector is a collection of $D-2$ free massless scalars, where it is shown that -- somewhat non-trivially -- the target-space two-dimensional Poincar\'e symmetry is extended to $D$ dimensions. The connection between canonical quantization of this constrained Hamiltonian system and previous path integral quantizations is also discussed. We extend our analysis to the ``gravitational'' description of $J\bar{T}$-type deformations, where it is found that the flow equations obtained involve deformations that twist the spatial boundary conditions., Comment: 41 pages, no figures, comments welcome

We provide the quantization of a charged black hole. We consider a redefinition of the scalar constraint in order to render the algebra of constraints as a Lie algebra. We apply loop quantum gravity techniques adhered to a novel improved dynamics scheme. We show that the model is solvable in closed form. We compute effective geometries, and show that the resulting effective space-times replace the inner horizon with a transition surface that connects trapped and antitrapped regions within the charged black hole interior. Quantum effects therefore stabilize the classical inner Cauchy horizons, as long as the charge is small compared to the mass. We further discuss the properties of these effective geometries by defining an effective stress-energy tensor out of the Einstein tensor, concluding that the null energy condition is violated., Comment: 18 pages, 2 figures

We propose a semi-automatic staging area for efficiently building an accurate database of experimental physical properties of superconductors from literature, called SuperCon2, to enrich the existing manually-built superconductor database SuperCon. Here we report our curation interface (SuperCon2 Interface) and a workflow managing the state transitions of each examined record, to validate the dataset of superconductors from PDF documents collected using Grobid-superconductors in a previous work. This curation workflow allows both automatic and manual operations, the former contains ``anomaly detection'' that scans new data identifying outliers, and a ``training data collector'' mechanism that collects training data examples based on manual corrections. Such training data collection policy is effective in improving the machine-learning models with a reduced number of examples. For manual operations, the interface (SuperCon2 interface) is developed to increase efficiency during manual correction by providing a smart interface and an enhanced PDF document viewer. We show that our interface significantly improves the curation quality by boosting precision and recall as compared with the traditional ``manual correction''. Our semi-automatic approach would provide a solution for achieving a reliable database with text-data mining of scientific documents., Comment: 5 tables, 6 figures, 18 pages

Quantum computers promise to solve several categories of problems faster than classical computers ever could. Current research mostly focuses on qubits, i.e., systems where the unit of information can assume only two levels. However, the underlying physics of most (if not all) of the technological platforms supports more than two levels, commonly referred to as qudits. Performing computations with qudits increases the overall complexity while, at the same time, reducing the number of operations and providing a lower error rate. Furthermore, qudits with different number of levels can be mixed in one system to ease the experimental control and keep representations as compact as possible. Exploiting these capabilities requires dedicated software support to tackle the increased complexity in an automated and efficient fashion. In this paper, we present a qudit simulator that handles mixed-dimensional systems based on Decision Diagrams (DDs). More precisely, we discuss the type of decision diagram introduced as underlying data structure as well as the resulting implementation. Experimental evaluations demonstrate that the proposed solution is capable of efficiently simulating mixed-dimensional quantum circuits, with specific use cases including more than 100 qudits in one circuit. The source code of the simulator is available via github.com/cda-tum/MiSiM under the MIT~license., Comment: 12 pages, 5 figures, 1 table

Research in high energy physics (HEP) requires huge amounts of computing and storage, putting strong constraints on the code speed and resource usage. To meet these requirements, a compiled high-performance language is typically used; while for physicists, who focus on the application when developing the code, better research productivity pleads for a high-level programming language. A popular approach consists of combining Python, used for the high-level interface, and C++, used for the computing intensive part of the code. A more convenient and efficient approach would be to use a language that provides both high-level programming and high-performance. The Julia programming language, developed at MIT especially to allow the use of a single language in research activities, has followed this path. In this paper the applicability of using the Julia language for HEP research is explored, covering the different aspects that are important for HEP code development: runtime performance, handling of large projects, interface with legacy code, distributed computing, training, and ease of programming. The study shows that the HEP community would benefit from a large scale adoption of this programming language. The HEP-specific foundation libraries that would need to be consolidated are identified, Comment: 32 pages, 5 figures, 4 tables

Superconducting quantum hardware architectures have been designed by considering the physical constraints of the underlying physics. These general-purpose architectures leave room for customization and optimization that can be exploited with alternative architectures specific to the quantum applications that will be executed on the quantum hardware. However, the corresponding design steps are hardly integrated yet and still rely heavily on manual labor. In this work, we provide a software framework that aims at providing a foundation to address this drawback. To this end, we first review the design of superconducting quantum hardware architectures and, afterwards, propose a cohesive framework encapsulating the design flow of an application-specific quantum hardware architecture. The resulting framework integrates high-level architecture generation optimized for a quantum application, the physical layout of the architecture, as well as optimization of the layout in a methodical manner. The framework with a reference implementation is available via https://github.com/cda-tum/dasqa under an open-source license., Comment: 6 pages, 5 figures

Data preservation is a mandatory specification for any present and future experimental facility and it is a cost-effective way of doing fundamental research by exploiting unique data sets in the light of the continuously increasing theoretical understanding. This document summarizes the status of data preservation in high energy physics. The paradigms and the methodological advances are discussed from a perspective of more than ten years of experience with a structured effort at international level. The status and the scientific return related to the preservation of data accumulated at large collider experiments are presented, together with an account of ongoing efforts to ensure long-term analysis capabilities for ongoing and future experiments. Transverse projects aimed at generic solutions, most of which are specifically inspired by open science and FAIR principles, are presented as well. A prospective and an action plan are also indicated.

We revisit the antiferromagnetic structure of $\rm Tb_{14}Ag_{51}$ [P. Fischer., $et\, al$. (2005). PRB, 72 134413] with the propagation vector $[{1\over3},{1\over3},{0}] $ and parent space group $P6/m$ using both magnetic symmetry and irreducible representation arguments. We have found a new magnetic structure under the hexagonal Shubnikov magnetic space group $P\bar{6}'$, which fits much better the experimental data. This new solution was obtained by constraining the spin arrangement to one of the three possible magnetic space groups of maximal symmetry that can be realised by a magnetic ordering transforming according to the 4-dimensional physically irreducible representation that is known to be relevant in this magnetic phase. The refined model, parameterised under $P\bar{6}'$, implicitly includes the presence of a third harmonic with the propagation vector at the gamma point $[0,0,0]$, which has an important weight in the final result. The structure consists of 13 symmetry-independent Tb magnetic moments with the same size of $8.48(2)\mu_B$, propagating cycloidally in the $ab$-plane. The modulation has a substantial deviation from being purely sinusoidal due to the contribution of the mentioned third harmonic., Comment: 4 pages, 1 table, 7 figures

Most quantum computing architectures to date natively support multi-valued logic, albeit being typically operated in a binary fashion. Multi-valued, or qudit, quantum processors have access to much richer forms of quantum entanglement, which promise to significantly boost the performance and usefulness of quantum devices. However, much of the theory as well as corresponding design methods required for exploiting such hardware remain insufficient and generalizations from qubits are not straightforward. A particular challenge is the compilation of quantum circuits into sets of native qudit gates supported by state-of-the-art quantum hardware. In this work, we address this challenge by introducing a complete workflow for compiling any two-qudit unitary into an arbitrary native gate set. Case studies demonstrate the feasibility of both, the proposed approach as well as the corresponding implementation (which is freely available at https://github.com/cda-tum/qudit-entanglement-compilation)., Comment: 7 pages, 3 figures, 1 table

The second workshop on the HEP Analysis Ecosystem took place 23-25 May 2022 at IJCLab in Orsay, to look at progress and continuing challenges in scaling up HEP analysis to meet the needs of HL-LHC and DUNE, as well as the very pressing needs of LHC Run 3 analysis. The workshop was themed around six particular topics, which were felt to capture key questions, opportunities and challenges. Each topic arranged a plenary session introduction, often with speakers summarising the state-of-the art and the next steps for analysis. This was then followed by parallel sessions, which were much more discussion focused, and where attendees could grapple with the challenges and propose solutions that could be tried. Where there was significant overlap between topics, a joint discussion between them was arranged. In the weeks following the workshop the session conveners wrote this document, which is a summary of the main discussions, the key points raised and the conclusions and outcomes. The document was circulated amongst the participants for comments before being finalised here.

A thermoelectric device is a heat engine that directly converts heat into electricity. Many materials with a high figure of merit ZT have been discovered in anticipation of a high thermoelectric efficiency. However, there has been a lack of investigations on efficiency-based material evaluation, and little is known about the achievable limit of thermoelectric efficiency. Here, we report the highest thermoelectric efficiency using 12,645 published materials. The 97,841,810 thermoelectric efficiencies are calculated using 808,610 device configurations under various heat-source temperatures (T_h) when the cold-side temperature is 300 K, solving one-dimensional thermoelectric integral equations with temperature-dependent thermoelectric properties. For infinite-cascade devices, a thermoelectric efficiency larger than 33% (~1/3) is achievable when T_h exceeds 1400 K. For single-stage devices, the best efficiency of 17.1% (~1/6) is possible when T_h is 860 K. Leg segmentation can overcome this limit, delivering a very high efficiency of 24% (~1/4) when T_h is 1100 K., Comment: 32 pages (main+table+figure captions+figures), 7 additional pages for 6 high-resolution-vector-image figures, Supporting Data file is not public yet

An Integral Field Unit (IFU) based on image-slicers has been added to the GREGOR Infrared Spectrograph (GRIS). This upgrade to the instrument makes possible 2D spectropolarimetry in the near-infrared by simultaneously recording the full Stokes profiles of spectral lines (in a given spectral interval) at all the points in the field of view. It provides high-cadence spectropolarimetric observations at the instrument's high spatial resolution and high polarization sensitivity at the GREGOR solar telescope. The IFU is ideal for observing the polarized spectrum of fast-evolving solar features at high spatial and spectral resolutions. The high observing cadence opens the possibility of time-series observations. The analysis of observations to this level of accuracy is essential for understanding the complex dynamics and interactions of solar plasma and magnetic fields. The image slicer of the IFU has eight slices of width 100 micron, covering a total field of view of 6" x 3". It was designed and built within the framework of the European projects SOLARNET and GREST, as a prototype for future instruments of the European Solar Telescope (EST) and was integrated into GRIS. After two commissioning campaigns in 2017 and 2018, the IFU was finally installed at the end of September 2018 and offered to all observers who use the telescope.

Quantum computers have the potential to solve some important industrial and scientific problems with greater efficiency than classical computers. While most current realizations focus on two-level qubits, the underlying physics used in most hardware is capable of extending the concepts to a multi-level logic - enabling the use of qudits, which promise higher computational power and lower error rates. Based on a strong theoretical backing and motivated by recent physical accomplishments, this also calls for methods and tools for compiling quantum circuits to those devices. To enable efficient qudit compilation, we introduce the concept of an energy coupling graph for single-qudit systems and provide an adaptive algorithm that leverages this representation for compiling arbitrary unitaries. This leads to significant improvements over the state-of-the-art compilation scheme and, additionally, provides an option to trade-off worst-case costs and run-time. The developed compiler is available via github.com/cda-tum/qudit-compilation under an open-source license., Comment: 8 pages, 4 figures

Background: Artificial intelligence techniques have shown great potential in cardiology, especially in quantifying cardiac biventricular function, volume, mass, and ejection fraction (EF). However, its use in clinical practice is not straightforward due to its poor reproducibility with cases from daily practice, among other reasons. Objectives: To validate a new artificial intelligence tool in order to quantify the cardiac biventricular function (volume, mass, and EF). To analyze its robustness in the clinical area, and the computational times compared with conventional methods. Methods: A total of 189 patients were analyzed: 89 from a regional center and 100 from a public center. The method proposes two convolutional networks that include anatomical information of the heart to reduce classification errors. Results: A high concordance (Pearson coefficient) was observed between manual quantification and the proposed quantification of cardiac function (0.98, 0.92, 0.96 and 0.8 for volumes and biventricular EF) in about 5 seconds per study. Conclusions: This method quantifies biventricular function and volumes in seconds with an accuracy equivalent to that of a specialist.

Background: Artificial intelligence techniques have demonstrated great potential in cardiology, especially to detect imperceptible patterns for the human eye. In this sense, these techniques seem to be adequate to identify patterns in the myocardial texture which could lead to characterize and quantify fibrosis. Purpose: The aim of this study was to postulate a new artificial intelligence method to identify fibrosis in cine cardiac magnetic resonance (CMR) imaging. Methods: A retrospective observational study was carried out in a population of 75 subjects from a clinical center of San Carlos de Bariloche. The proposed method analyzes the myocardial texture in cine CMR images using a convolutional neural network to determine local myocardial tissue damage. Results: An accuracy of 89% for quantifying local tissue damage was observed for the validation data set and 70% for the test set. In addition, the qualitative analysis showed a high spatial correlation in lesion location. Conclusions: The postulated method enables to spatially identify fibrosis using only the information from cine nuclear magnetic resonance studies, demonstrating the potential of this technique to quantify myocardial viability in the future or to study the lesions etiology