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In multidimensional NMR spectroscopy, practical resolution is defined as the ability to distinguish and accurately determine signal positions against a background of overlapping peaks, thermal noise, and spectral artifacts. In the pursuit of ultimate resolution, we introduce Peak Probability Presentations ($P^3$)- a statistical spectral representation that assigns a probability to each spectral point, indicating the likelihood of a peak maximum occurring at that location. The mapping between the spectrum and $P^3$ is achieved using MR-Ai, a physics-inspired deep learning neural network architecture, designed to handle multidimensional NMR spectra. Furthermore, we demonstrate that MR-Ai enables coprocessing of multiple spectra, facilitating direct information exchange between datasets. This feature significantly enhances spectral quality, particularly in cases of highly sparse sampling. Performance of MR-Ai and high value of the $P^3$ are demonstrated on the synthetic data and spectra of Tau, MATL1, Calmodulin, and several other proteins.

This paper introduces a novel approach to schema inference as an on-demand function integrated directly within a DBMS, targeting NoSQL databases where schema flexibility can create challenges. Unlike previous methods relying on external frameworks like Apache Spark, our solution enables schema inference as a SQL function, allowing users to infer schemas natively within the DBMS. Implemented in Apache AsterixDB, it performs schema discovery in two phases, local inference and global schema merging, leveraging internal resources for improved performance. Experiments with real world datasets show up to a two orders of magnitude performance boost over external methods, enhancing usability and scalability., Comment: Extended version of EDBT 2025 submission

Group-by-aggregate (GBA) queries are integral to data analysis, allowing users to group data by specific attributes and apply aggregate functions such as sum, average, and count. Database Management Systems (DBMSs) typically execute GBA queries using either sort- or hash-based methods, each with unique advantages and trade-offs. Sort-based approaches are efficient for large datasets but become computationally expensive due to record comparisons, especially in cases with a small number of groups. In contrast, hash-based approaches offer faster performance in general but require significant memory and can suffer from hash collisions when handling large numbers of groups or uneven data distributions. This paper presents a focused empirical study comparing these two approaches, analyzing their strengths and weaknesses across varying data sizes, datasets, and group counts using Apache AsterixDB. Our findings indicate that sort-based methods excel in scenarios with large datasets or when subsequent operations benefit from sorted data, whereas hash-based methods are advantageous for smaller datasets or scenarios with fewer groupings. Our results provide insights into the scenarios where each method excels, offering practical guidance for optimizing GBA query performance., Comment: EDBT 2025

The Workflows Community Summit gathered 111 participants from 18 countries to discuss emerging trends and challenges in scientific workflows, focusing on six key areas: time-sensitive workflows, AI-HPC convergence, multi-facility workflows, heterogeneous HPC environments, user experience, and FAIR computational workflows. The integration of AI and exascale computing has revolutionized scientific workflows, enabling higher-fidelity models and complex, time-sensitive processes, while introducing challenges in managing heterogeneous environments and multi-facility data dependencies. The rise of large language models is driving computational demands to zettaflop scales, necessitating modular, adaptable systems and cloud-service models to optimize resource utilization and ensure reproducibility. Multi-facility workflows present challenges in data movement, curation, and overcoming institutional silos, while diverse hardware architectures require integrating workflow considerations into early system design and developing standardized resource management tools. The summit emphasized improving user experience in workflow systems and ensuring FAIR workflows to enhance collaboration and accelerate scientific discovery. Key recommendations include developing standardized metrics for time-sensitive workflows, creating frameworks for cloud-HPC integration, implementing distributed-by-design workflow modeling, establishing multi-facility authentication protocols, and accelerating AI integration in HPC workflow management. The summit also called for comprehensive workflow benchmarks, workflow-specific UX principles, and a FAIR workflow maturity model, highlighting the need for continued collaboration in addressing the complex challenges posed by the convergence of AI, HPC, and multi-facility research environments.

In quantum dot junctions capacitively coupled to a resonator, electron tunneling through the quantum dot can be used to transfer heat between different parts of the system. This includes cooling or heating the electrons in electrodes and absorbing or emitting photons in the resonator mode. Such systems can be driven into a nonequilibrium state by applying either a voltage bias or a temperature gradient across the electrodes coupled to the quantum dot, or by employing an external coherent pump to excite the resonator. In this study, we present a semiclassical theory to describe the steady state of these structures. We employ a combination of the Floquet-nonequilibrium Green's functions method and semiclassical laser theory to analyze a normal metal-quantum dot-superconductor junction coupled to a resonator. Our investigation focuses on key parameters such as the average photon number and phase shift in the resonator, the charge current in the quantum dot, and the heat fluxes among different components of the system. We explore how photon-assisted Andreev reflection and quasiparticle tunneling in the quantum dot can refrigerate the resonator mode and the normal metal electrode. We also examine the influence of finite voltage and thermal biases on these processes.

The simulation of electronic properties is a pivotal issue in modern electronic structure theory, driving significant efforts over the past decades to develop protocols for computing energy derivatives. In this work, we address this problem by developing a strategy to integrate the quantum phase estimation algorithm within a fully differentiable framework. This is accomplished by devising a smooth estimator able to tackle arbitrary initial states. We provide analytical expressions to characterize the statistics and algorithmic cost of this estimator. Furthermore, we provide numerical evidence that the estimation accuracy is retained when an arbitrary state is considered and that it exceeds the one of standard majority rule. We explicitly use this procedure to estimate chemically relevant quantities, demonstrating our approach through ground-state and triplet excited state geometry optimization with simulations involving up to 19 qubits. This work paves the way for new quantum algorithms that combine interference methods and quantum differentiable programming.

Smart signal processing approaches using Artificial Intelligence are gaining momentum in NMR applications. In this study, we demonstrate that AI offers new opportunities beyond tasks addressed by traditional techniques. We developed and trained several artificial neural networks in our new toolbox Magnetic Resonance with Artificial intelligence (MR-Ai) to solve three "impossible" problems: quadrature detection using only Echo (or Anti-Echo) modulation from the traditional Echo/Anti-Echo scheme; accessing uncertainty of signal intensity at each point in a spectrum processed by any given method; and defining a reference-free score for quantitative access of NMR spectrum quality. Our findings highlight the potential of AI techniques to revolutionize NMR processing and analysis.

Intelligent reflecting surfaces (IRS) are valuable tools for enhancing the intelligence of the propagation environment. They have the ability to direct EM Waves to a specific user through beamforming. A significant number of passive elements are integrated into metasurfaces, allowing for their incorporation onto various surfaces such as walls and buildings. In certain situations, metasurfaces may need to be installed on surfaces that are not flat, such as surfaces with curves or surfaces with two sides. In this work, a unit cell with three polarization modes, consisting of absorption, reflection, and a 1-bit phase shift, is designed and investigated. The unit cell in the nonplanar metasurface is composed of two vertical surfaces. A mathematical model is used to analyze two vertically-faced metasurfaces. The results include S parameter values and full structure. It is demonstrated that both absorption reflection mode and phase shift mode are achieved within a specific frequency band. The simulations demonstrate that an appropriate absorption level, reaching -10 dB, is achieved in absorption mode. In reflection mode, a 180-degree phase shift is achieved within the same frequency band. Reports on the radiation pattern of two vertically faced structures indicate that there is no quantized beam when there is a one-bit phase shift.

Trapped ions for quantum information processing has been an area of intense study due to the extraordinarily high fidelity operations that have been reported experimentally. Specifically, barium trapped ions have been shown to have exceptional state-preparation and measurement (SPAM) fidelities. The $^{133}\mathrm{Ba}^+$ ($I = 1/2$) isotope in particular is a promising candidate for large-scale quantum computing experiments. However, a major pitfall with this isotope is that it is radioactive and is thus generally used in microgram quantities to satisfy safety regulations. We describe a new method for creating microgram barium chloride ($\mathrm{BaCl}_2$) ablation targets for use in trapped ion experiments and compare our procedure to previous methods. We outline two recipes for fabrication of ablation targets that increase the production of neutral atoms for isotope-selective loading of barium ions. We show that heat-treatment of the ablation targets greatly increases the consistency at which neutral atoms can be produced and we characterize the uniformity of these targets using trap-independent techniques such as energy dispersive x-ray spectroscopy (EDS) and neutral fluorescence collection. Our comparison between fabrication techniques and demonstration of consistent neutral fluorescence paves a path towards reliable loading of $^{133}\mathrm{Ba}^+$ in surface traps and opens opportunities for scalable quantum computing with this isotope.

The Gaia DR3 parallax approach was used to estimate the absolute parameters of 2375 Delta Scuti stars from the ASAS catalog. The selected stars have a variety of observational characteristics, with a higher than 80% probability of being Delta Scuti stars. We have displayed all the stars in the Hertzsprung-Russell (H-R) diagram along with the Delta Scuti instability strip, the Zero Age Main Sequence (ZAMS), and the Terminal-Age Main Sequence (TAMS). Then, we determined which fundamental and overtone modes each star belongs to using pulsation constant (Q) calculations. In addition, we evaluated the parameters in the Q calculation equation using three machine learning methods, which showed that surface gravity and temperature have the greatest effect on its calculation. The Period-Luminosity (P-L) relationship of the Delta Scuti stars was also revisited. Eventually, using least squares linear regression, we made four linear fits for fundamental and overtone modes and updated their relationships., Comment: Accepted by the Research in Astronomy and Astrophysics (RAA) journal

Structural Health Monitoring (SHM) evaluates the integrity of a structure by observing its dynamic responses by an array of sensors over time to determine the current health state of the structure. The most important step of SHM is system identification which in civil structures is the identification of modal parameters of structures. Due to numerous limitations of input-output methods, system identification of ambient vibration structures using output-only identification techniques has become a key issue in structural health monitoring and assessment of engineering structures. In this paper, four beams with different boundary conditions and with arbitrary loading have been modeled in finite element software, ANSYS, and the responses (Acceleration of nodes) have been achieved. By using these data and the codes written in MATLAB software, the modal parameters (natural frequencies, mode shapes) of the beams are identified with FDD (frequency Domain Decomposition) and PP (Peak Picking) methods and then justified with the results of input-output method which was determined by frequency relation function (FRF). The results indicate a good agreement between the three methods for determining the dynamic characteristics of beams., Comment: Conference paper. arXiv admin note: substantial text overlap with arXiv:2010.07490