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Normalizing flows have arisen as a tool to accelerate Monte Carlo sampling for lattice field theories. This work reviews recent progress in applying normalizing flows to 4-dimensional nonabelian gauge theories, focusing on two advancements: an architectural improvement referred to as learned active loops, and the application of correlated ensemble methods to QCD with $N_f=2$ dynamical fermions., Comment: 10 pages, 2 figures, Contribution to the 41st International Symposium on Lattice Field Theory, July 28th - August 3rd 2024, Liverpool, UK

We study the confining and deconfining phases of pure $\mathrm{SU}(3)$ lattice gauge theory with topological data analysis. This provides unique insights into long range correlations of field configurations across the confinement-deconfinement transition. Specifically, we analyze non-trivial structures in electric and magnetic field energy densities as well as Polyakov loop traces and a Polyakov loop-based variant of the topological density. The Betti curves for filtrations based on the electric and magnetic field energy densities reveal signals of electromagnetic dualities. These dualities can be associated with an interchange in the roles of local lumps of electric and magnetic energy densities around the phase transition. Moreover, we show that plaquette susceptibilities can manifest in the geometric features captured by the Betti curves. We also confront the results obtained for $\mathrm{SU}(3)$ with that for $\mathrm{SU}(2)$ and elaborate on the significant differences. Our results demonstrate that topological data analysis can discriminate phase transitions of different order for non-Abelian lattice gauge theories. Moreover, it provides unprecedented insights into the relevant structures in their vicinity., Comment: 13 pages, 8 figures

The widespread adoption of large language models (LLMs) has created a pressing need for an efficient, secure and private serving infrastructure, which allows researchers to run open source or custom fine-tuned LLMs and ensures users that their data remains private and is not stored without their consent. While high-performance computing (HPC) systems equipped with state-of-the-art GPUs are well-suited for training LLMs, their batch scheduling paradigm is not designed to support real-time serving of AI applications. Cloud systems, on the other hand, are well suited for web services but commonly lack access to the computational power of HPC clusters, especially expensive and scarce high-end GPUs, which are required for optimal inference speed. We propose an architecture with an implementation consisting of a web service that runs on a cloud VM with secure access to a scalable backend running a multitude of LLM models on HPC systems. By offering a web service using our HPC infrastructure to host LLMs, we leverage the trusted environment of local universities and research centers to offer a private and secure alternative to commercial LLM services. Our solution natively integrates with the HPC batch scheduler Slurm, enabling seamless deployment on HPC clusters, and is able to run side by side with regular Slurm workloads, while utilizing gaps in the schedule created by Slurm. In order to ensure the security of the HPC system, we use the SSH ForceCommand directive to construct a robust circuit breaker, which prevents successful attacks on the web-facing server from affecting the cluster. We have successfully deployed our system as a production service, and made the source code available at \url{https://github.com/gwdg/chat-ai}, Comment: Various improvements to explanations and form and updated graphs to include data points up to 30.07.2024

The landscape of maintenance in distributed systems is rapidly evolving with the integration of Artificial Intelligence (AI). Also, as the complexity of computing continuum systems intensifies, the role of AI in predictive maintenance (Pd.M.) becomes increasingly pivotal. This paper presents a comprehensive survey of the current state of Pd.M. in the computing continuum, with a focus on the combination of scalable AI technologies. Recognizing the limitations of traditional maintenance practices in the face of increasingly complex and heterogenous computing continuum systems, the study explores how AI, especially machine learning and neural networks, is being used to enhance Pd.M. strategies. The survey encompasses a thorough review of existing literature, highlighting key advancements, methodologies, and case studies in the field. It critically examines the role of AI in improving prediction accuracy for system failures and in optimizing maintenance schedules, thereby contributing to reduced downtime and enhanced system longevity. By synthesizing findings from the latest advancements in the field, the article provides insights into the effectiveness and challenges of implementing AI-driven predictive maintenance. It underscores the evolution of maintenance practices in response to technological advancements and the growing complexity of computing continuum systems. The conclusions drawn from this survey are instrumental for practitioners and researchers in understanding the current landscape and future directions of Pd.M. in distributed systems. It emphasizes the need for continued research and development in this area, pointing towards a trend of more intelligent, efficient, and cost-effective maintenance solutions in the era of AI., Comment: Accepted, published and presented for the IARIA CLOUDCOMP2024 Conference of Venice, Italy

Normalizing flows are machine-learned maps between different lattice theories which can be used as components in exact sampling and inference schemes. Ongoing work yields increasingly expressive flows on gauge fields, but it remains an open question how flows can improve lattice QCD at state-of-the-art scales. We discuss and demonstrate two applications of flows in replica exchange (parallel tempering) sampling, aimed at improving topological mixing, which are viable with iterative improvements upon presently available flows., Comment: 9 pages, 5 figures, proceedings of the 40th International Symposium on Lattice Field Theory (Lattice 2023)

Scale separation is an important physical principle that has previously enabled algorithmic advances such as multigrid solvers. Previous work on normalizing flows has been able to utilize scale separation in the context of scalar field theories, but the principle has been largely unexploited in the context of gauge theories. This work gives an overview of a new method for generating gauge fields using hierarchical normalizing flow models. This method builds gauge fields from the outside in, allowing different parts of the model to focus on different scales of the problem. Numerical results are presented for $U(1)$ and $SU(3)$ gauge theories in 2, 3, and 4 spacetime dimensions., Comment: Submitted as a proceedings to the 40th International Symposium on Lattice Field Theory (Lattice 2023)

In the realm of cerebrovascular monitoring, primary metrics typically include blood pressure, which influences cerebral blood flow (CBF) and is contingent upon vessel radius. Measuring CBF non-invasively poses a persistent challenge, primarily attributed to the difficulty of accessing and obtaining signal from the brain. This study aims to introduce a compact speckle visibility spectroscopy (SVS) device designed for non-invasive CBF measurements, offering cost-effectiveness and scalability while tracking CBF with remarkable sensitivity and temporal resolution. The wearable hardware has a modular design approach consisting solely of a laser diode as the source and a meticulously selected board camera as the detector. They both can be easily placed on the head of a subject to measure CBF with no additional optical elements. The SVS device can achieve a sampling rate of 80 Hz with minimal susceptibility to external disturbances. The device also achieves better SNR compared with traditional fiber-based SVS devices, capturing about 70 times more signal and showing superior stability and reproducibility. It is designed to be paired and distributed in multiple configurations around the head, and measure signals that exceed the quality of prior optical CBF measurement techniques. Given its cost-effectiveness, scalability, and simplicity, this laser-centric tool offers significant potential in advancing non-invasive cerebral monitoring technologies.

We present a general framework to construct symmetric, well-conditioned, cross-element compatible nodal distributions that can be used for high-order and high-dimensional finite elements. Starting from the inherent symmetries of an element geometry, we construct node groups in a systematic and efficient manner utilizing the natural coordinates of each element, while ensuring nodes stay within the elements. Proper constraints on the symmetry group lead to nodal distributions that ensure cross-element compatibility (i.e., nodes of adjacent elements are co-located) on both homogeneous and mixed meshes. The final nodal distribution is defined as a minimizer of an optimization problem over symmetry group parameters with linear constraints that ensure nodes remain with an element and enforce other properties (e.g., cross-element compatibility). We demonstrate the merit of this framework by comparing the proposed optimization-based nodal distributions with other popular distributions available in the literature, and its robustness by generating optimized nodal distributions for otherwise difficult elements (such as simplex and pyramid elements)., Comment: 25 pages, 23 figures, 7 tables

Machine-learned normalizing flows can be used in the context of lattice quantum field theory to generate statistically correlated ensembles of lattice gauge fields at different action parameters. This work demonstrates how these correlations can be exploited for variance reduction in the computation of observables. Three different proof-of-concept applications are demonstrated using a novel residual flow architecture: continuum limits of gauge theories, the mass dependence of QCD observables, and hadronic matrix elements based on the Feynman-Hellmann approach. In all three cases, it is shown that statistical uncertainties are significantly reduced when machine-learned flows are incorporated as compared with the same calculations performed with uncorrelated ensembles or direct reweighting., Comment: 12 pages, 2 tables, 5 figures. v2: accepted for publication

Diamond has emerged as a highly promising platform for quantum network applications. Color centers in diamond fulfill the fundamental requirements for quantum nodes: they constitute optically accessible quantum systems with long-lived spin qubits. Furthermore, they provide access to a quantum register of electronic and nuclear spin qubits and they mediate entanglement between spins and photons. All these operations require coherent control of the color center's spin state. This review provides a comprehensive overview of the state-of-the-art, challenges, and prospects of such schemes, including, high fidelity initialization, coherent manipulation, and readout of spin states. Established microwave and optical control techniques are reviewed, and moreover, emerging methods such as cavity-mediated spin-photon interactions and mechanical control based on spin-phonon interactions are summarized. For different types of color centers, namely, nitrogen-vacancy and group-IV color centers, distinct challenges persist that are subject of ongoing research. Beyond fundamental coherent spin qubit control techniques, advanced demonstrations in quantum network applications are outlined, for example, the integration of individual color centers for accessing (nuclear) multi-qubit registers. Finally, we describe the role of diamond spin qubits in the realization of future quantum information applications.

Integrated and fiber-packaged magnetic field sensors with a sensitivity sufficient to sense electric pulses propagating along nerves in life science applications and with a spatial resolution fine enough to resolve their propagation directions will trigger a tremendous step ahead not only in medical diagnostics, but in understanding neural processes. Nitrogen-vacancy centers in diamond represent the leading platform for such sensing tasks under ambient conditions. Current research on uniting a good sensitivity and a high spatial resolution is facilitated by scanning or imaging techniques. However, these techniques employ moving parts or bulky microscope setups. Despite being far developed, both approaches cannot be integrated and fiber-packaged to build a robust, adjustment-free hand-held device. In this work, we introduce novel concepts for spatially resolved magnetic field sensing and 2-D gradiometry with an integrated magnetic field camera. The camera is based on infrared absorption optically detected magnetic resonance (IRA-ODMR) mediated by perpendicularly intersecting infrared and pump laser beams forming a pixel matrix. We demonstrate our 3-by-3 pixel sensor's capability to reconstruct the position of an electromagnet in space. Furthermore, we identify routes to enhance the magnetic field camera's sensitivity and spatial resolution as required for complex sensing applications., Comment: Main part: 14 pages, 5 figures

Color centers in diamond are quantum systems with optically active spin-states that show long coherence times and are therefore a promising candidate for the development of efficient spin-photon interfaces. However, only a small portion of the emitted photons is generated by the coherent optical transition of the zero-phonon line (ZPL), which limits the overall performance of the system. Embedding these emitters in photonic crystal cavities improves the coupling to the ZPL photons and increases their emission rate. Here, we demonstrate the fabrication process of "Sawfish" cavities, a design recently proposed that has the experimentally-realistic potential to simultaneously enhance the emission rate by a factor of 46 and couple photons into a single-mode fiber with an efficiency of 88%. The presented process allows for the fabrication of fully suspended devices with a total length of 20.5 $\mu$m and features size as small as 40 nm. The optical characterization shows fundamental mode resonances that follow the behavior expected from the corresponding design parameters and quality (Q) factors as high as 3825. Finally, we investigate the effects of nanofabrication on the devices and show that, despite a noticeable erosion of the fine features, the measured cavity resonances deviate by only 0.9 (1.2)% from the corresponding simulated values. This proves that the Sawfish design is robust against fabrication imperfections, which makes it an attractive choice for the development of quantum photonic networks., Comment: 7 pages, 9 figures

Multivariate spatial modeling is key to understanding the behavior of materials downstream in a mining operation. The ore recovery depends on the mineralogical composition, which needs to be properly captured by the model to allow for good predictions. Multivariate modeling must also capture the behavior of tailings and waste materials to understand the environmental risks involved in their disposal. However, multivariate spatial modeling is challenging when the variables show complex relationships, such as non-linear correlation, heteroscedastic behavior, or spatial trends. This contribution proposes a novel methodology for general multivariate contexts, with the idea of disaggregating the global non-linear behavior among variables into the spatial domain in a piece-wise linear fashion. We demonstrate that the complex multivariate behavior can be reproduced by looking at local correlations between variables at sample locations, inferred from a local neighborhood, and interpolating these local linear dependencies by using a non-stationary version of the Linear Model of Coregionalization. This mixture of locally varying linear correlations is combined to reproduce the global complex behavior seen in the multivariate distribution. The main challenge is to solve appropriately the interpolation of the known correlation matrices over the domain, as these local correlations defined at sample locations can be endowed with a manifold structure, on which the Euclidean distance is not a suitable metric for interpolation of such correlations. This is addressed by using tools from Riemannian geometry: correlation matrices are interpolated using a weighted Fr\'echet mean of the correlations inferred at sample locations. An application of the procedure is shown in a real case study with good results in terms of accuracy and reproduction of the reference multivariate distributions and semi-variograms.

In recent decades, the development of new drugs has become increasingly expensive and inefficient, and the molecular mechanisms of most pharmaceuticals remain poorly understood. In response, computational systems and network medicine tools have emerged to identify potential drug repurposing candidates. However, these tools often require complex installation and lack intuitive visual network mining capabilities. To tackle these challenges, we introduce Drugst.One, a platform that assists specialized computational medicine tools in becoming user-friendly, web-based utilities for drug repurposing. With just three lines of code, Drugst.One turns any systems biology software into an interactive web tool for modeling and analyzing complex protein-drug-disease networks. Demonstrating its broad adaptability, Drugst.One has been successfully integrated with 21 computational systems medicine tools. Available at https://drugst.one, Drugst.One has significant potential for streamlining the drug discovery process, allowing researchers to focus on essential aspects of pharmaceutical treatment research., Comment: 45 pages, 6 figures, 7 tables

Applications of normalizing flows to the sampling of field configurations in lattice gauge theory have so far been explored almost exclusively in two space-time dimensions. We report new algorithmic developments of gauge-equivariant flow architectures facilitating the generalization to higher-dimensional lattice geometries. Specifically, we discuss masked autoregressive transformations with tractable and unbiased Jacobian determinants, a key ingredient for scalable and asymptotically exact flow-based sampling algorithms. For concreteness, results from a proof-of-principle application to SU(3) lattice gauge theory in four space-time dimensions are reported.

A Round Robin study has been carried out to estimate the impact of the human element in small-angle scattering data analysis. Four corrected datasets were provided to participants ready for analysis. All datasets were measured on samples containing spherical scatterers, with two datasets in dilute dispersions, and two from powders. Most of the 46 participants correctly identified the number of populations in the dilute dispersions, with half of the population mean entries within 1.5% and half of the population width entries within 40%, respectively. Due to the added complexity of the structure factor, much fewer people submitted answers on the powder datasets. For those that did, half of the entries for the means and widths were within 44% and 86% respectively. This Round Robin experiment highlights several causes for the discrepancies, for which solutions are proposed., Comment: 23 pages, 10 figures. For the original information sent to RR participants, see https://zenodo.org/record/7506365 . For the anonymized results and Jupyter notebook for analysis, see https://zenodo.org/record/7509710

In this paper have developed a novel hybrid hierarchical attention-based bidirectional recurrent neural network with dilated CNN (HARDC) method for arrhythmia classification. This solves problems that arise when traditional dilated convolutional neural network (CNN) models disregard the correlation between contexts and gradient dispersion. The proposed HARDC fully exploits the dilated CNN and bidirectional recurrent neural network unit (BiGRU-BiLSTM) architecture to generate fusion features. As a result of incorporating both local and global feature information and an attention mechanism, the model's performance for prediction is improved.By combining the fusion features with a dilated CNN and a hierarchical attention mechanism, the trained HARDC model showed significantly improved classification results and interpretability of feature extraction on the PhysioNet 2017 challenge dataset. Sequential Z-Score normalization, filtering, denoising, and segmentation are used to prepare the raw data for analysis. CGAN (Conditional Generative Adversarial Network) is then used to generate synthetic signals from the processed data. The experimental results demonstrate that the proposed HARDC model significantly outperforms other existing models, achieving an accuracy of 99.60\%, F1 score of 98.21\%, a precision of 97.66\%, and recall of 99.60\% using MIT-BIH generated ECG. In addition, this approach substantially reduces run time when using dilated CNN compared to normal convolution. Overall, this hybrid model demonstrates an innovative and cost-effective strategy for ECG signal compression and high-performance ECG recognition. Our results indicate that an automated and highly computed method to classify multiple types of arrhythmia signals holds considerable promise., Comment: 23 pages

We compute the strong coupling constant of Landau gauge QCD in the full complex momentum plane, both directly and via spectral reconstruction. In particular, we consider the Taylor coupling given by the product of ghost and gluon dressing functions. Assuming spectral representations for the latter, we first show that also the coupling obeys such a representation. The subsequent spectral reconstruction of the coupling data, obtained from 2+1 flavour lattice QCD results for the ghost and gluon, is based on a probabilistic inversion of this representation using Gaussian process regression with analytically enforced asymptotics. In contradistinction, our direct calculation relies on earlier reconstruction results for the ghost and gluon spectral functions themselves, as well as data obtained in functional QCD. Apart from its relevance for studies of resonances or scattering processes, the calculation also serves as a non-trivial benchmark of our reconstruction approach. The results show remarkable agreement, testifying to the reliability of the method., Comment: 13 pages, 6 figures

Cartesian impedance control is a type of motion control strategy for robots that improves safety in partially unknown environments by achieving a compliant behavior of the robot with respect to its external forces. This compliant robot behavior has the added benefit of allowing physical human guidance of the robot. In this paper, we propose a C++ implementation of compliance control valid for any torque-commanded robotic manipulator. The proposed controller implements Cartesian impedance control to track a desired end-effector pose. Additionally, joint impedance is projected in the nullspace of the Cartesian robot motion to track a desired robot joint configuration without perturbing the Cartesian motion of the robot. The proposed implementation also allows the robot to apply desired forces and torques to its environment. Several safety features such as filtering, rate limiting, and saturation are included in the proposed implementation. The core functionalities are in a re-usable base library and a Robot Operating System (ROS) ros_control integration is provided on top of that. The implementation was tested with the KUKA LBR iiwa robot and the Franka Emika Robot (Panda) both in simulation and with the physical robots., Comment: 7 pages, 1 figure. Under submission at JOSS (https://joss.theoj.org/). Implementation at: https://github.com/matthias-mayr/Cartesian-Impedance-Controller

We compute masses of the two lightest glueballs from spectral reconstructions of timelike interaction channels of the four-gluon vertex in Landau gauge Yang-Mills theory. The Euclidean spacelike dressings of the vertex are calculated with the functional renormalisation group. For the spectral reconstruction of these Euclidean data, we employ Gaussian process regression. The glueball resonances can be identified straightforwardly and we obtain $m_{sc} = 1870(75)~$ MeV as well as $m_{ps} = 2700(120)~$ MeV, in accordance with functional bound state and lattice calculations., Comment: 10 pages, 6 figures