Quantum measurements play a fundamental role in quantum information. Therefore, increasing efforts are being made to construct symmetric measurement operators for qudit systems. A wide class of projective measurements corresponds to complex projective 2-designs, which include symmetric, informationally complete (SIC) POVMs and mutually unbiased bases (MUBs). In this paper, we establish a one-to-one correspondence between conical 2-designs and mutually unbiased generalized equiangular tight frames, both of which are common generalizations of SIC POVMs and MUBs to operators of arbitrary rank. It turns out that there exist rich families of operators that belong to only one of those two classes. This raises important questions about which symmetries have to be preserved for applicational prominence.

We propose a framework to perform Bayesian inference using conditional score-based diffusion models to solve a class of inverse problems in mechanics involving the inference of a specimen's spatially varying material properties from noisy measurements of its mechanical response to loading. Conditional score-based diffusion models are generative models that learn to approximate the score function of a conditional distribution using samples from the joint distribution. More specifically, the score functions corresponding to multiple realizations of the measurement are approximated using a single neural network, the so-called score network, which is subsequently used to sample the posterior distribution using an appropriate Markov chain Monte Carlo scheme based on Langevin dynamics. Training the score network only requires simulating the forward model. Hence, the proposed approach can accommodate black-box forward models and complex measurement noise. Moreover, once the score network has been trained, it can be re-used to solve the inverse problem for different realizations of the measurements. We demonstrate the efficacy of the proposed approach on a suite of high-dimensional inverse problems in mechanics that involve inferring heterogeneous material properties from noisy measurements. Some examples we consider involve synthetic data, while others include data collected from actual elastography experiments. Further, our applications demonstrate that the proposed approach can handle different measurement modalities, complex patterns in the inferred quantities, non-Gaussian and non-additive noise models, and nonlinear black-box forward models. The results show that the proposed framework can solve large-scale physics-based inverse problems efficiently.

Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present a novel neural network architecture, MDtrajNet, and a pre-trained foundational model, MDtrajNet-1, that directly generates MD trajectories across chemical space, bypassing force calculations and integration. This approach accelerates simulations by up to two orders of magnitude compared to traditional MD, even those enhanced by machine-learning interatomic potentials. MDtrajNet combines equivariant neural networks with a transformer-based architecture to achieve strong accuracy and transferability in predicting long-time trajectories. Remarkably, the errors of the trajectories generated by MDtrajNet-1 for various known and unseen molecular systems are close to those of the conventional ab initio MD. The architecture's flexible design supports diverse application scenarios, including different statistical ensembles, boundary conditions, and interaction types. By overcoming the intrinsic speed barrier of conventional MD, MDtrajNet opens new frontiers in efficient and scalable atomistic simulations.

We present uniformly reprocessed and re-calibrated data from the RoboPol programme of optopolarimetric monitoring of active galactic nuclei (AGN), covering observations between 2013, when the instrument was commissioned, and 2017. In total, the dataset presented in this paper includes 5068 observations of 222 AGN with Dec > -25 deg. We describe the current version of the RoboPol pipeline that was used to process and calibrate the entire dataset, and we make the data publicly available for use by the astronomical community. Average quantities summarising optopolarimetric behaviour (average degree of polarization, polarization variability index) are also provided for each source we have observed and for the time interval we have followed it.

The very shallow marine basin of Puck Lagoon in the southern Baltic Sea, on the Northern coast of Poland, hosts valuable benthic habitats and cultural heritage sites. These include, among others, protected Zostera marina meadows, one of the Baltic's major medieval harbours, a ship graveyard, and likely other submerged features that are yet to be discovered. Prior to this project, no comprehensive high-resolution remote sensing data were available for this area. This article describes the first Digital Elevation Models (DEMs) derived from a combination of airborne bathymetric LiDAR, multibeam echosounder, airborne photogrammetry and satellite imagery. These datasets also include multibeam echosounder backscatter and LiDAR intensity, allowing determination of the character and properties of the seafloor. Combined, these datasets are a vital resource for assessing and understanding seafloor morphology, benthic habitats, cultural heritage, and submerged landscapes. Given the significance of Puck Lagoon's hydrographical, ecological, geological, and archaeological environs, the high-resolution bathymetry, acquired by our project, can provide the foundation for sustainable management and informed decision-making for this area of interest.

The digital economy runs on Open Source Software (OSS), with an estimated 90\% of modern applications containing open-source components. While this widespread adoption has revolutionized software development, it has also created critical security vulnerabilities, particularly in essential but under-resourced projects. This paper examines a sophisticated attack on the XZ Utils project (CVE-2024-3094), where attackers exploited not just code, but the entire open-source development process to inject a backdoor into a fundamental Linux compression library. Our analysis reveals a new breed of supply chain attack that manipulates software engineering practices themselves -- from community management to CI/CD configurations -- to establish legitimacy and maintain long-term control. Through a comprehensive examination of GitHub events and development artifacts, we reconstruct the attack timeline, analyze the evolution of attacker tactics. Our findings demonstrate how attackers leveraged seemingly beneficial contributions to project infrastructure and maintenance to bypass traditional security measures. This work extends beyond traditional security analysis by examining how software engineering practices themselves can be weaponized, offering insights for protecting the open-source ecosystem.

Parameters associated with the collisional perturbation of spectral lines are essential for modeling the absorption of electromagnetic radiation in gas media. The HITRAN molecular spectroscopic database provides these parameters, although originally they were associated only with the Voigt profile parameterization. However, in the HITRAN2016 and HITRAN2020 editions, Voigt, speed-dependent Voigt and Hartmann-Tran (HT) profiles have been incorporated, thanks to the new relational structure of the database. The HT profile was introduced in HITRAN in 2016 as a recommended profile for the most accurate spectral interpretations and modeling. It was parameterized with a four-temperature-range temperature dependence. Since then, however, some features of the HT profile have been revealed that are problematic from a practical perspective. These are: the singular behavior of the temperature dependencies of the velocity-changing parameters when the shift parameter crosses zero and the difficulty in evaluating the former for mixtures. In this article, we summarize efforts to eliminate the above-mentioned problems that led us to recommend using the quadratic speed-dependent hard-collision (qSDHC) profile with double-power-law (DPL) temperature dependencies. We refer to this profile as a modified Hartmann-Tran (mHT) profile. The computational cost of evaluating it is the same as for the HT profile. We give a detailed description of the mHT profile (also including line mixing) and discuss the representation of its parameters, together with their DPL temperature parametrization adopted in the HITRAN database. We discuss an efficient algorithm for evaluating this profile and provide corresponding computer codes in several programming languages: Fortran, Python, MATLAB, Wolfram Mathematica, and LabVIEW. We also discuss the associated update of the HITRAN Application Programming Interface (HAPI).

The field of computational chemistry is increasingly leveraging machine learning (ML) potentials to predict molecular properties with high accuracy and efficiency, providing a viable alternative to traditional quantum mechanical (QM) methods, which are often computationally intensive. Central to the success of ML models is the quality and comprehensiveness of the data sets on which they are trained. Quantum chemistry data sets and databases, comprising extensive information on molecular structures, energies, forces, and other properties derived from QM calculations, are crucial for developing robust and generalizable ML potentials. In this review, we provide an overview of the current landscape of quantum chemical data sets and databases. We examine key characteristics and functionalities of prominent resources, including the types of information they store, the level of electronic structure theory employed, the diversity of chemical space covered, and the methodologies used for data creation. Additionally, an updatable resource is provided to track new data sets and databases at this https URL. Looking forward, we discuss the challenges associated with the rapid growth of quantum chemical data sets and databases, emphasizing the need for updatable and accessible resources to ensure the long-term utility of them. We also address the importance of data format standardization and the ongoing efforts to align with the FAIR principles to enhance data interoperability and reusability. Drawing inspiration from established materials databases, we advocate for the development of user-friendly and sustainable platforms for these data sets and databases.

Dusty disks around planets in outer reaches of exo-planetary systems can be detected as long-lasting occultations, provided the observer is close to the planet's orbital plane. Here we report follow-up observations of ASASSN-24fw (Gaia 07:05:18.97+06:12:19.4), a 4-magnitude dimming event of a main-sequence star which lasted 8.5 months. Using optical spectroscopy with KOSMOS (APO), MagE (Magellan) and GHOST (Gemini-S), we find strong Na I D absorption indicating that the occulter is gas rich. We detect multiple low-ionization metal emission lines with velocity dispersion <10 km/s blue-shifted by 27 km/s with respect to the system, which likely originate in the occulter, as well as blue-shifted and broad (200 km/s) Halpha line, which likely originates in the inner circumstellar disk. We confirm the previously reported occultations in 1981 and 1937 seen in historic data, yielding a semi-major axis of the occulter's orbital motion around the star of 14 AU. Assuming that the occulter is a circumplanetary disk filling 30-100% of the Hill radius, we derive the planet's mass of 0.5-19 MJupiter and a circumplanetary disk mass of 1% of the mass of the Moon. Given the age of the star (>2 Gyr), the disk is unlikely to be a survivor of the planet formation stage and is more likely to be a result of a planetary collision. If Na D absorption and metal emission lines originate in the circumplanetary disk, the observations presented here are the first discovery of a planet-driven wind or of circumsecondary disk rotation.

Model Hamiltonians offer a cost-effective way to capture the key physics of large $\pi$-conjugated systems. In this work, we combine the Pariser--Parr--Pople (PPP) model Hamiltonian with pair Coupled Cluster Doubles (pCCD)-based methods to study the ground- and excited-state electronic structures of polycyclic aromatic hydrocarbons (PAHs). The model Hamiltonian implementation is done in the open-source PyBEST software package, where numerous pCCD-type models are available. We investigate canonical Hartree--Fock and natural pCCD-optimized orbitals to compute ground- and excited-state properties using pCCD and its linear response extension. Their performance is compared with configuration-interaction-based methods. Finally, we introduce a generalized parameterization of the long-range Coulomb interaction using a rescaled interaction prefactor to adopt the PPP parameters to the pCCD approach and the localized nature of the pCCD orbitals. Our results demonstrate that pCCD-based methods, combined with a suitably parametrized PPP model, provide a reliable and scalable framework for studying the optoelectronic properties of large $\pi$-extended systems relevant to organic electronics.

Using spectrally correlated photon pairs instead of classical laser light and coincidence detection instead of light intensity detection, Quantum Optical Coherence Tomography (Q-OCT) outperforms classical OCT in several experimental terms. It provides twice better axial resolution with the same spectral bandwidth and it is immune to even-order chromatic dispersion, including Group Velocity Dispersion responsible for the bulk of axial resolution degradation in the OCT images. Q-OCT has been performed in the time domain configuration, where one line of the two-dimensional image is acquired by axially translating the mirror in the interferometer's reference arm and measuring the coincidence rate of photons arriving at two single-photon-sensitive detectors. Although successful at producing resolution-doubled and dispersion-cancelled images, it is still relatively slow and cannot compete with its classical counterpart. Here, we experimentally demonstrate Q-OCT in a novel Fourier-domain configuration, theoretically proposed in 2020, where the reference mirror is fixed and the joint spectra are acquired. We show that such a configuration allows for faster image acquisition than its time-domain configuration, providing a step forward towards a practical and competitive solution in the OCT arena. The limitations of the novel approach are discussed, contrasted with the limitations of both the time-domain approach and the traditional OCT.

We present uniformly reprocessed and re-calibrated data from the RoboPol programme of optopolarimetric monitoring of active galactic nuclei (AGN), covering observations between 2013, when the instrument was commissioned, and 2017. In total, the dataset presented in this paper includes 5068 observations of 222 AGN with Dec > -25 deg. We describe the current version of the RoboPol pipeline that was used to process and calibrate the entire dataset, and we make the data publicly available for use by the astronomical community. Average quantities summarising optopolarimetric behaviour (average degree of polarization, polarization variability index) are also provided for each source we have observed and for the time interval we have followed it.

Tailored coupled cluster theory represents a computationally inexpensive way to describe static and dynamical electron correlation effects. In this work, we scrutinize the performance of various tailored coupled cluster methods externally corrected by electronic wave functions of polynomial cost. Specifically, we focus on frozen-pair coupled-cluster (fpCC) methods, which are externally corrected by pair-coupled cluster doubles (pCCD), and coupled cluster theory tailored by matrix product state wave functions optimized by the density matrix renormalization group (DMRG) algorithm. As test system, we selected a set of various small- and medium-sized molecules containing diatomics (N$_2$, F$_2$, C$_2$, CN$^+$, BN, BO$^+$, and Cr$_2$) and molecules (ammonia, ethylene, cyclobutadiene, benzene) for which conventional single-reference coupled cluster singles and doubles (CCSD) is not able to produce accurate results for spectroscopic constants, potential energy surfaces, and barrier heights. Most importantly, DMRG-tailored and pCCD-tailored approaches yield similar errors in spectroscopic constants and potential energy surfaces compared to multireference and/or experimental reference data and generally outrank the conventional single-reference CCSD approach. Although fpCC methods provide a reliable description for the dissociation pathway of molecules featuring single and quadruple bonds, they fail in the description of triple or hextuple bond-breaking processes or avoided crossing regions.

The main accretion phase of protostars is characterized by the ejection of material in the form of jets/outflows. External UV irradiation can potentially have a significant impact on the excitation conditions within these outflows. High-resolution observations in the mid-infrared allow us to investigate the details of those energetic processes through the emission of shock-excited H$_2$ . Our aim is to spatially resolve H$_2$ and ionic/atomic emission within the outflows of low-mass protostars, and investigate its origin in connection to shocks influenced by external ultraviolet irradiation. We analyze spectral maps of 5 Class I protostars in the Ophiuchus molecular cloud from the James Webb Space Telescope (JWST) Medium Resolution Spectrometer (MIRI/MRS). Four out of five protostars show strong H$_2$, [\ion{Ne}{II}], and [\ion{Fe}{II}] emission associated with outflows/jets. Pure rotational H$_2$ transitions from S(1) to S(8) are found and show two distinct temperature components on Boltzmann diagrams with rotational temperatures of $\sim$500-600 K and $\sim$1000-3000 K respectively. Both $C$-type shocks propagating at high pre-shock densities (n$_\text{H} \ge$10$^4$ cm$^{-3}$) and $J$-type shocks at low pre-shock densities (n$_\text{H} \le$10$^3$ cm$^{-3}$) reproduce the observed line ratios. However, only $C$-type shocks produce sufficiently high column densities of H$_2$, whereas predictions from a single $J$-type shock reproduce the observed rotational temperatures of the gas better. A combination of various types of shocks could play a role in protostellar outflows as long as UV irradiation is included in the models. The origin of this radiation is likely internal, since no significant differences in the excitation conditions of outflows are seen at various locations in the cloud.

Adding VLBI capability to the SKA arrays will greatly broaden the science of the SKA, and is feasible within the current specifications. SKA-VLBI can be initially implemented by providing phased-array outputs for SKA1-MID and SKA1-SUR and using these extremely sensitive stations with other radio telescopes, and in SKA2 by realising a distributed configuration providing baselines up to thousands of km, merging it with existing VLBI networks. The motivation for and the possible realization of SKA-VLBI is described in this paper.

A broad class of informationally complete symmetric measurements is introduced. It can be understood as a common generalization of symmetric, informationally complete POVMs and mutually unbiased bases. Additionally, it provides a natural way to define two new families of mutually unbiased symmetric measurement operators in any finite dimension. We show a general method of their construction, together with an example of an optimal measurement. Finally, we analyze the properties of symmetric measurements and provide applications in entropic relations and entanglement detection.

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.

We present basic atmospheric parameters ($T_{eff}$, $log g$, $v_{t}$ and [Fe/H]), rotation velocities and absolute radial velocities as well as luminosities, masses, ages and radii for 402 stars (including 11 single-lined spectroscopic binaries), mostly subgiants and giants. For 272 of them we present parameters for the first time. For another 53 stars we present estimates of $T_{eff}$ and $log g$ based on photometric calibrations. More than half objects were found to be subgiants, there is also a large group of giants and a few stars appeard to be dwarfs. The results show that the presented sample is composed of stars with masses ranging from 0.52 to $3.21 M_{\odot}$ of which 17 have masses $\geq$ $2.0 M_{\odot}$. The radii of stars studied in this paper range from 0.66 to $36.04 R_{\odot}$ with vast majority having radii between 2.0 and $4.0 R_{\odot}$. They are generally less metal abundant than the Sun with median [Fe/H]$=-0.07$. For 62 stars in common with other planet searches we found a very good agreement in obtained stellar atmospheric parameters. We also present basic properties of the complete list of 744 stars that form the PTPS evolved stars sample. We examined stellar masses for 1255 stars in five other planet searches and found some of them likely to be significantly overestimated. Applying our uniformly determined stellar masses we confirm the apparent increase of companions masses for evolved stars, and we explain it, as well as lack of close-in planets with limited effective radial velocity precision for those stars due to activity.

Modern physics experiments are frequently very complex, relying on multiple simultaneous events to happen in order to obtain the desired result. The experiment control system plays a central role in orchestrating the measurement setup: However, its development is often treated as secondary with respect to the hardware, its importance becoming evident only during the operational phase. Therefore, the AEgIS (Antimatter Experiment: Gravity, Interferometry, Spectroscopy) collaboration has created a framework for easily coding control systems, specifically targeting atomic, quantum, and antimatter experiments. This framework, called Total Automation of LabVIEW Operations for Science (TALOS), unifies all the machines of the experiment in a single entity, thus enabling complex high-level decisions to be taken, and it is constituted by separate modules, called MicroServices, that run concurrently and asynchronously. This enhances the stability and reproducibility of the system while allowing for continuous integration and testing while the control system is running. The system demonstrated high stability and reproducibility, running completely unsupervised during the night and weekends of the data-taking campaigns. The results demonstrate the suitability of TALOS to manage an entire physics experiment in full autonomy: being open-source, experiments other than the AEgIS experiment can benefit from it.