The reliable discrimination of tectonic earthquakes from anthropogenic quarry blasts and transient noise remains a critical challenge in single station seismic monitoring. In this study, we introduce a novel Physics Informed Convolutional Recurrent Neural Network (PI CRNN) that embeds seismological domain knowledge directly into the feature extraction and learning process. We adapt a multistream architecture with three parallel encoders: (i) Time Domain: SincNet Encoder, (ii) MultiResolution Spectrogram branch, and, (iii) Physics Branch, followed by a fusion and a bidirectionalLSTM module. Evaluated on the Curated Pacific Northwest AI ready Seismic Dataset, the PI CRNN achieves an overall classification accuracy of 97.56 percent on an independent test set. It outperforms a standard CRNN baseline, a classical P to S amplitude ratio method, and a Physics Informed Neural Network (PINN) that enforces physical constraints via the loss function. Furthermore, the model demonstrates perfect precision in noise rejection (100 percent Recall). Interpretability analysis using saliency maps confirms that the architecture successfully learns distinct physical signatures, identifying bimodal P- and S-wave arrivals for earthquakes versus singular impulsive onsets for blasts. This work establishes a scalable, transparent framework for AI-driven seismology, proving that architectural inductive bias provides an alternative reliable approach compared to purely data-driven approaches.

The paper will present SVAROG, a compact neutron supermirror transmission multichannel polarizer with an electromagnetic system. The basic properties of this polarizer will be considered. Variants for using this polarizer in experimenrtla facilities of the PIK research reactor (Petersburg Nuclear Physics Institute of National Research Centre «Kurchatov Institute» (NRC «Kurchatov Institute» - PNPI)) will be discussed and a comparison of the considered polarizer with known neutron transmission polarizers will be carried out.

Power-law inflation with $a(t) \propto t^m$ is conceptually simple and predicts a scalar tilt $n_s = 1 - 2/m$ compatible with CMB data, but in four-dimensional Einstein gravity it typically yields a tensor-to-scalar ratio $r = 16/m$ that is too large to satisfy current bounds. We show that a minimal extension based on fractional scalar-field cosmology resolves this tension. Introducing a fractional order $α\neq 1$ generates non-local (memory) corrections in the Friedmann and Klein-Gordon dynamics that suppress $r$ while keeping $n_s$ essentially unchanged. We derive an explicit mapping $α(n,m)$ and recover the standard power-law limit as $α\to 1$. For observationally favored values $α\approx 0.8$-$0.9$ in four dimensions we obtain $n_s \approx 0.965$ and $r \lesssim 0.04$, bringing power-law inflation into agreement with data. The scalar potential follows self-consistently as an exponential, and a dynamical-systems analysis shows the fractional power-law solutions form stable inflationary attractors over the viable parameter range. These results establish fractional power-law inflation as a predictive and testable framework, with clear targets for forthcoming CMB polarization measurements.

We present an implementation of edge AI to compress data on an in-memory analog content-addressable memory (ACAM) device. A variational autoencoder is trained on a simulated sample of energy measurements from incident high-energy electrons on a generic three-layer scintillator-based calorimeter. The encoding part is distilled into tabular format by regressing the latent space variables using decision trees, which is then programmed on a memristor-based ACAM. In real-time, the ACAM compresses 48 continuously valued incoming energies measured by the calorimeter sensors into the latent space, achieving a compression factor of 12x, which is transmitted off-detector for decompression. The performance result of the ACAM, obtained using the Structural Simulation Toolkit, the SST open source framework, gives a latency value of 24 ns and a throughput of 330M compressions per second, i.e., 3 ns between successive inputs, and an average energy consumption of 4.1 nJ per compression.

We study Gaussian continuous tensor network states (GCTNS) - a finitely-parameterized subclass of Gaussian states admitting an interpretation as continuum limits of discrete tensor network states. We show that, at short distance, GCTNS correspond to free Lifshitz vacua, establishing a connection between certain entanglement properties of the two. Two schemes to approximate ground states of (free) bosonic field theories using GCTNS are presented: rational approximants to the exact dispersion relation and Trotterized imaginary-time evolution. We apply them to Klein-Gordon theory and characterize the resulting approximations, identifying the energy scales at which deviations from the target theory appear. These results provide a simple and analytically controlled setting to assess the strengths and limitations of GCTNS as variational ansätze for relativistic quantum fields.

We study convergence rates of random-order best-response dynamics in games on networks with linear best responses and strategic substitutes. Combining formal analysis with numerical simulations we identify phenomena that lead to slow convergence. One of the key such phenomena is convergence to stable strategy profiles in parts of the network neighboring sets of nodes which remain inactive until the dynamics is close to converging and then switch to activity, initiating convergence to profiles with a new set of active agents and possibly leading to another iteration of such behavior. We identify structural properties of graphs which make such phenomena more likely. These properties go beyond the spectrum of a graph, which we demonstrate analyzing convergence rates on co-spectral mates.

Emerging analog computing substrates, such as oscillator-based Ising machines, offer rapid convergence times for combinatorial optimization but often suffer from limited scalability due to physical implementation constraints. To tackle real-world problems involving thousands of variables, problem decomposition is required; however, performing this step on standard CPUs introduces significant latency, preventing the high-speed solver from operating at full capacity. This work presents a heterogeneous system that offloads the decomposition workload to an FPGA, tightly integrated with a custom 28nm Ising solver. By migrating the decomposition logic to reconfigurable hardware and utilizing parallel processing elements, the system minimizes the communication latency typically associated with host-device interactions. Our evaluation demonstrates that this co-design approach effectively bridges the speed gap between digital preprocessing and analog solving, achieving nearly 2$\times$ speedup and an energy efficiency improvement of over two orders of magnitude compared to optimized software baselines running on modern CPUs.

Continuing earlier work, we show how to realize irreducible finite-dimensional representations of the complex group of type $G_2$ via tableaux, along the way exhibiting explicit generators of the defining ideal of the flag variety

In this paper, we explore the short- and long-term stability of backed stablecoins offering constant mint and redeem prices to all agents. We refer to such designs as price window-based, since the mint and redeem prices constrain the stablecoin's market equilibrium. We show that, without secondary stabilization mechanisms, price window designs cannot achieve both short- and long-term stability unless they are backed by already-stable reserves. In particular, the mechanism faces a tradeoff: either risk eventual reserve depletion through persistent arbitrage by a speculator, or widen the distance between mint and redeem prices enough to disincentivize arbitrage. In the latter case, however, the market price of the stablecoin inherits the volatility of its backing asset, with fluctuations that can be proportional to the backing asset's own volatility.

This article provides an overview of the history of economic thought on natural resource extraction, which has long been considered an enclave industry with few benefits for areas beyond the local economy. We focus on more recent scholarship examining the social impacts of natural resource extraction, emphasizing gender-related outcomes and determinants. An important lesson from this scholarship is that it is difficult to discuss sustainable development in its contemporary sense without paying due diligence to the gender dimensions of natural resource extraction. A lesson highlighted is that the "resource curse" view of natural capital may not be as pervasive as previously thought.

Recent simultaneous multiwavelength observations of a nearby young solar-type star EK Dra in the optical, H$α$ spectrum, and X-ray, have provided evidence for stellar prominence eruptions associated with superflares. The large prominence eruption is suggested to have been caused by a large mid-latitude spot on the polarity inversion lines near the stellar limb from the concurrent Zeeman Doppler Imaging (ZDI) and optical photometry by the TESS. In this study, we perform starspot mapping for the TESS data of EK Dra to investigate the relation of starspots and magnetic fields from the photometry and ZDI. We also explore the multiwavelength rotational variability ascribed to starspots and active regions for the TESS, B-band, H$α$, and X-ray light curves. As a result, we find that (i) spot locations deduced from the TESS light curve are mostly consistent with the intensity map from the ZDI except for a polar spot, and (ii) the H$α$ light curve exhibits clear periodicity with respect to the TESS light curve because the H$α$ line is radiated around spots in the chromosphere. The X-ray light curve does not show such association probably because of multiple spots on high activity level and extended spatial structure of coronal active regions. The results provide clues to explore their association with stellar flares at different heights of active regions in chromospheric and coronal lines. Our study also enables us to quantify the stellar XUV radiation from the magnetic fields of active stars toward understanding atmospheric evolution of exoplanets.

Rapid inward migration driven by Type I torques threatens the survival of low-mass planets in their nascent protoplanetary disks (PPDs). Positive co-rotation torques offer a potential solution, but require viscous diffusion to remain unsaturated. However, it is unclear if (magneto)-hydrodynamic turbulence provides the necessary diffusion, and disk profiles supporting such torques are often also susceptible to the Convective Overstability (COS) for suitable gas cooling timescales. To this end, we investigate torques on low-mass planets through radially global 2D (razor-thin) and vertically unstratified 3D hydrodynamic simulations of PPDs with thermal diffusion and optically thin cooling. Our 3D models with thermal diffusion, which allows COS development, show systematically different torque behavior compared to 2D models, wherein COS is absent. In 3D, the COS saturates into large-scale, long-lived vortices that migrate radially and interact gravitationally with the embedded planet. When these vortices encounter the planet, they typically provide positive torque "kicks" counteracting inward migration, as the less-massive vortices are scattered onto horseshoe orbits by the more-massive planet. We validate our simulation methods against the theoretical framework of Paardekooper et al. (2011) and demonstrate that COS-induced torque modifications can extend migration timescales by factors of approximately 10. For plausible disk models, our results suggest that COS activity can lengthen migration timescales sufficiently to overlap with, or even exceed Super-Earth formation windows (0.1-5 Myr). In contrast, simulations with optically thin cooling do not show significant torque modifications, as COS saturates in near-axisymmetric structures without producing large-scale vortices for the disk models considered here.

An algorithm on weighted graphs is called universally optimal if it is optimal for every input graph, in the worst case taken over all weight assignments. Informally, this means the algorithm is competitive even with algorithms that are optimized for only one specific input graph. Universal optimality was recently introduced [Haeupler et al. 2024] as an alternative to the stronger, but often unachievable instance optimality. In this paper, we extend the concept of universal optimality to data structures. In particular, we investigate the following dynamic graph problem: Given a vertex-weighted forest, maintain the minimum-weight vertex of every tree under edge deletions. The problem requires $Θ(\log n)$ amortized time per operation in general, but only $O(1)$ time if the initial forest is a path. We present a data structure that has optimal total running time for every fixed initial forest and every fixed number of operations/queries $m$, when taking the worst case over all weight assignments and operation sequences of length $m$. This definition of universal optimality is easily adapted to other data structure problems. Our result combines two techniques: (1) A decomposition of the input into paths, to take advantage of the $O(1)$-time path-specific data structure; and (2) splay trees [Sleator and Tarjan 1985], which, informally speaking, are used to optimally handle a certain sorting-related subproblem. We apply our data structure to solve problems related to Cartesian trees, path minimum queries, and bottleneck vertex/edge queries, each with a certain universal optimality guarantee. Our data structure also can be modified to support edge weights instead of vertex weights. Further, it generalizes to support semigroup sum queries instead of minimum queries, in universally optimal time.

Jellyfish galaxies, which exhibit tails of gas opposite to their direction of motion, are a galaxy population showcasing the most extreme effects of ram pressure stripping (RPS). We present the emission line properties of a preliminary sample of five jellyfish galaxies in the Coma cluster, observed with the WEAVE Large-IFU as part of the Coma Legacy IFU Survey (CLIFS). When complete, CLIFS will form a sample of 29 jellyfish galaxies in Coma, selected based on the presence of one-sided tails in the radio continuum, enabling a comprehensive picture of the effects of ram pressure on galaxies in the Coma cluster. We extract emission line properties and confirm consistency between disk fluxes measured from WEAVE and MaNGA for galaxies with overlapping disk coverage between surveys. Comparing resolved radio and H$α$-based star formation rates, we find that, in contrast to the disk, the dominant source of tail emission is not star formation. We find evidence for diffuse ionized gas excited by RPS-driven shocks in the tails, as indicated by: (1) LINER-like tail emission with the [OI]/H$α$ BPT diagnostic; (2) enhanced [OII]/H$α$ ratios in the tails relative to the disks; and (3) similarly elevated emission line velocities and velocity dispersions in the tails with respect to the disks. These results demonstrate that ram-pressure-driven shocks dominate the ionized emission in jellyfish galaxy tails.

As cyber threats grow in complexity and scale, many security incidents remain poorly managed due to the lack of proper training among C-level executives. Thus, there is a need for targeted cybersecurity education to enhance executive decision-making and crisis response. Traditional training methods, such as cyber wargames and Tabletop Exercises (TTX), aim to develop abilities to face critical incidents, however, they often lack the interactive and dynamic elements required to prepare individuals for real-world cyber incidents. This paper presents a novel approach to cybersecurity and cyberdefense education through the design of a specialized hybrid TTX for the maritime domain, which uses a framework to model mathematically how a cyberattack spreads along multiple nodes and impacts infrastructure. Our proposal was validated through exercises in Argentina and the United States, demonstrating a positive impact in developing the comprehension and projection levels of Cyber Situational Awareness (CSA), and reinforcing governance. Documentation about the Hybrid TTX, scenario, datasets and implementation of the SERDUX-MARCIM model, is available at the project repository at https://github.com/diegocabuya/SERDUX-MARCIM

Stellar microlensing is a powerful tool for probing dark matter in the form of planetary and stellar mass compact objects (COs), in particular primordial black holes (PBHs). Under standard assumptions, current observations exclude COs in the mass range $10^{-11} \lesssim M/M_{\odot} \lesssim 10^{4}$ making up all of the dark matter. We provide an overview, aimed at theorists working on PBHs, of the history, theory, observational status, and future prospects of the field.

We study a sequential coherent side-channel model in which an adversarial probe qubit interacts with a target qubit during a hidden gate sequence. Repeating the same hidden sequence for $N$ shots yields an empirical full-correlation record: the joint histogram $\widehat{P}_g(b)$ over probe bit-strings $b\in\{0,1\}^k$, which is a sufficient statistic for classical post-processing under identically and independently distributed (i.i.d.) shots but grows exponentially with circuit depth. We first describe this sequential probe framework in a coupling- and measurement-agnostic form, emphasizing the scaling of the observation space and why exact analytic distinguishability becomes intractable with circuit depth. We then specialize to a representative instantiation (a controlled-rotation probe coupling with fixed projective readout and a commuting $R_x$ gate alphabet) where we (i) derive a depth-dependent leakage envelope whose maximizer predicts a "Goldilocks" coupling band as a function of depth, and (ii) provide an operational decoder, via machine learning, a single parameter-conditioned map from $\widehat{P}_g$ to Alice's per-step gate labels, generalizing across coupling and noise settings without retraining. Experiments over broad coupling and noise grids show that strict sequence recovery concentrates near the predicted coupling band and degrades predictably under decoherence and finite-shot estimation.

In many data-mining applications, including recommender systems, influence maximization, and team formation, the goal is to pick a subset of elements (e.g., items, nodes in a network, experts to perform a task) to maximize a monotone submodular utility function while simultaneously minimizing a cost function. Classical formulations model this tradeoff via cardinality or knapsack constraints, or by combining utility and cost into a single weighted objective. However, such approaches require committing to a specific tradeoff in advance and return only a single solution, offering limited insight into the space of viable utility-cost tradeoffs. In this paper, we depart from the single-solution paradigm and examine the problem of computing representative sets of high-quality solutions that expose different tradeoffs between submodular utility and cost. For this, we introduce $(α_1,α_2)$-approximate Pareto frontiers that provably approximate the achievable tradeoffs between submodular utility and cost. Specifically, we formalize the Pareto-$\langle f,c \rangle$ problem and develop efficient algorithms for multiple instantiations arising from different combinations of submodular utility $f$ and cost functions $c$. Our results offer a principled and practical framework for understanding and exploiting utility-cost tradeoffs in submodular optimization. Experiments on datasets from diverse application domains demonstrate that our algorithms efficiently compute approximate Pareto frontiers in practice.

Complex change is often described as "evolutionary" in economics, policy, and technology, yet most system dynamics models remain constrained to fixed state spaces and equilibrium-seeking behavior. This paper argues that evolutionary dynamics should be treated as a core system-thinking problem rather than as a biological metaphor. We introduce Stability-Driven Assembly (SDA) as a minimal, non-equilibrium framework in which stochastic interactions combined with differential persistence generate endogenous selection without genes, replication, or predefined fitness functions. In SDA, longer-lived patterns accumulate in the population, biasing future interactions and creating feedback between population composition and system dynamics. This feedback yields fitness-proportional sampling as an emergent property, realizing a natural genetic algorithm driven solely by stability. Using SDA, we demonstrate why equilibrium-constrained models, even when simulated numerically, cannot exhibit open-ended evolution: evolutionary systems require population-dependent, non-stationary dynamics in which structure and dynamics co-evolve. We conclude by discussing implications for system dynamics, economics, and policy modeling, and outline how agent-based and AI-enabled approaches may support evolutionary models capable of sustained novelty and structural emergence.

We investigate the growth of central supermassive black holes in galaxies, aiming to distinguish between gas accretion versus BH merger-driven growth modes. By performing and analysing cosmological hydrodynamical simulations of $(50 ~ {\rm Mpc})^3$ comoving boxes, we also study how the BH feedback parameters affect the coevolution between SMBHs and their host galaxies. Starting as $10^5 M_{\odot}$ seeds, we find that the BHs grow initially via BH mergers to $\sim 10^7 M_{\odot}$. Gas accretion onto the BHs is initially low, then increases with time, and reaches the Eddington rate after $7-9$ Gyrs. The BHs then undergo very fast growth via efficient gas accretion over a period of $600 - 700$ Myr, when the BH mass increases $10^2 - 10^3$ times, causing their predominant growth from $10^7 M_{\odot}$ to $(10^9 - 10^{10}) M_{\odot}$. Taking into account the cosmological gas inflows and outflows, SMBHs do not grow to more than $10^{10} M_{\odot}$ by $z=0$, because of gas depletion from galaxy centers driven by AGN feedback. In terms of SMBH - host galaxy coevolution along the $M_{\rm BH} - M_{\star}$ relation, we find that they initially lie below and thereby move upward toward the relation. We make some physical implications of the growth of high-$z$ Little Red Dots recently observed by JWST: the normal-mass SMBHs had predominantly undergone BH merger driven evolution, whereas the overmassive BHs underwent periods of Eddington-limited or super-Eddington bursts of gas accretion.

We present the third data release of the LOFAR Two-metre Sky Survey (LoTSS-DR3). The survey images cover 88% of the northern sky and were created from 12,950 hrs of data (18.6 PB) accumulated over 10.5 years. The images were produced through direction-independent and direction-dependent calibration pipelines that correct for instrumental effects as well as spatially and temporally varying ionospheric distortions. In our 120-168 MHz continuum mosaic images with an angular resolution of 6 arcsec (9 arcsec below declination 10$^\circ$) we catalogue 13,667,877 sources, formed from 16,943,656 Gaussian components. The scatter in the astrometric precision approximately follows the expected noise-like behaviour but with an additional systematic component of at least 0.24 arcsec that is likely due to calibration imperfections. The random flux density scale error is 6%, while the systematic offset was previously shown to be within 2%. The median sensitivity of our mosaics is 92$μ$Jy beam$^{-1}$. Completeness simulations, accounting for realistic source models, time- and bandwidth-smearing effects, and astrometric errors, indicate that we detect more than 95% of compact sources with integrated flux densities exceeding 9 times the local root mean square (RMS) noise. However, the recovered source counts in a particular integrated flux density bin do not match the injected counts until flux densities exceed 45 times the local RMS noise. The Euclidean-normalised differential source counts derived from the survey constrain the radio source population over five orders of magnitude and are in good agreement with previous deep and wide-area surveys. All data products are publicly available, including catalogues, individual-field Stokes I, Q, U, and V images, mosaicked Stokes I images, and $uv$ data with associated direction-dependent calibration solutions.

Niobium Titanium nitride (NbTiN) based superconducting nanowire single photon detectors (SNSPDs) are known for their high performance across a wide spectral range, from the X-ray to the mid-infrared. Nonetheless, fabrication challenges and performance degradation attributable to surface oxidation and lack of uniformity in films thinner than 5 nm remain a significant barrier for achieving high-quality detectors. In this work, we study the influence of a Silicon capping layer on film properties and on the performance of SNSPDs. A Silicon capping layer effectively suppresses oxidation and increases the superconducting transition temperature. This enables superconductivity in films as thin as 3 nm at 3 K, increases critical current in patterned nanowires and significantly extends the saturation plateau from the visible to the near infrared (up to 2050 nm): These detectors maintain sub-50 ps timing jitter, even for nanowires as wide as 250 nm and with detection areas of 20x20μm2. Our results establish that thinner films protected by a capping layer allow for the fabrication of wider wires, decreasing nanofabrication challenges and extending the operating temperature range for efficient single photon detection.

The time-dependent modulation of the event rate in dark matter direct detection experiments, arising from the motion of the Earth with respect to the Galactic rest frame, is a distinctive signature whose observation is crucial for claiming a discovery of dark matter. While annual modulation has been well studied for decades, daily modulation due to the Earth's rotation has attracted increased attention recently due to the identification of anisotropic solid-state detector materials that yield a direction-dependent scattering rate without sacrificing the overall rate. We perform a statistical analysis of daily modulation in dark matter scattering experiments, with the goal of maximizing the statistical significance of a modulating signal in the presence of an unknown background rate, which may be either flat (non-modulating), or modulating over a 24-hour period with a known or unknown phase. In the background-dominated regime, we find that the discovery significance scales as $f_\text{RMS} \sqrt{T}$, where $T$ is the total exposure time and $f_\text{RMS}$ is the root-mean-square modulation amplitude; in particular, the significance continues to improve with exposure rather than saturating due to systematic uncertainties in the background rate. Using anisotropic trans-stilbene detectors for sub-GeV dark matter as a benchmark example, we provide prescriptions for optimizing the significance for a given total detector mass and location. In an example analysis using three detectors, optimizing the detector orientations can reduce the required exposure by a factor of $\sim 5$ for a desired discovery or exclusion significance, even after profiling over an unknown modulating background phase.

Synchrotron radiation plays a central role in astrophysical and high-energy processes. Its spectral description involves the synchrotron function, defined by a non-trivial integral of modified Bessel functions and commonly evaluated through numerical methods or dedicated approximations. In this work, we obtain a compact analytical representation of the synchrotron function using the Method of Brackets, which yields systematically controllable asymptotic expansions in both the small- and large-argument regimes. The resulting expressions accurately reproduce numerical integration and make the analytic structure of the function explicit. Our results provide an efficient alternative to repeated numerical evaluations and facilitate applications requiring fast and controlled approximations.

We interpret the structure of the adele class space of the rationals--and specifically its Riemann sector--as the natural monoidal extension of the Picard group of the arithmetic curve $\overline{\operatorname{Spec} \mathbb Z}$. We identify the elements of this space with torsion-free rank-1 abelian groups $L$ endowed with rigidifying data. In the Riemann sector, this data corresponds to a norm, extending the classical notion of metrized line bundles in Arakelov geometry. For the full adele class space, we replace the norm with a group morphism to $\mathbb R$ and a combinatorial datum: a parametrization of the roots of unity associated with the character dual of $L$. We show that the product of adeles is represented geometrically by the tensor product of these rank-1 groups and their rigidifying structures. The resulting monoid space generalizes the Picard group to the full adelic context by incorporating the singular strata required for the spectral realization of $L$-functions.

Deep JWST images of AS1063 reveals tens of thousands of globular clusters in the galaxy cluster AS1063. When compared with the lensing model based on the same JWST data, the distribution of globular clusters traces closely the distribution of lensing mass (mostly composed of dark matter). Interestingly, both the distributions of dark matter and globular clusters have large central cores. However the size of the core in the distribution of globular clusters is about half the size the core of the dark matter distribution. We argue that the standard cold dark matter and fuzzy dark matter models struggle to explain these large cores. Meanwhile, the self interacting dark matter with a velocity dependent cross section, combined with core stalling, offers a natural explanation to the existence of these cores if $σ_{\rm SI}\approx 0.3$ cm$^2$ g$^{-1}$ for galaxy cluster halos. But we also discuss how the lack of hydrodynamical N-body simulations capable of resolving globular clusters in galaxy cluster scale halos, hinders the possibility of ruling out the standard non-collisional dark matter scenario. Future high-resolution hydrodynamical simulations of galaxy clusters, with several trillion particles, and containing over a hundred thousand globular clusters, can provide the insight needed to transform the epistemic nature of dark matter into an ontological one

Particle-in-Cell (PIC) methods have achieved widespread recognition as simple and flexible approaches to model collisionless plasma physics in fully kinetic simulations of astrophysical environments. However, in many situations the standard PIC algorithm must be extended to include macroscopic effects in microscale simulations. For plasmas subjected to shearing or expansion, shearing-box and expanding-box methods can be incorporated into PIC to account for these global effects. For plasmas subjected to local acceleration in confined regions of space, a leaky-box method can allow closed-box PIC simulations to account for particle escape from the accelerator region. In this work, we review and improve methods to include shearing, expansion, and escape in PIC simulations. We provide the numerical details of how Maxwell's equations and the particle equations of motion are solved in each case, and introduce generalized Boris-like particle pushers to solve the momentum equation in the presence of extra forces. This work is intended to serve as a comprehensive reference for the implementation of shearing-box, expanding-box, and leaky-box algorithms in PIC.

We present a comprehensive photometric and kinematic study of the open clusters HSC 25, HSC 37, and HSC 2878, located in the innermost regions of the Galactic disc. Utilizing data from Gaia DR3 and the UPMASK membership algorithm, we identify 44, 55, and 112 most probable members for HSC 25, HSC 37, and HSC 2878, respectively. The mean proper-motion components are obtained as (-5.901 +/- 0.41, -6.213 +/- 0.40), (-3.231 +/- 0.56, -4.564 +/- 0.47), and (-3.830 +/- 0.51, -5.198 +/- 0.44) mas yr^-1 for HSC 25, HSC 37, and HSC 2878, respectively. The open clusters span a broad range of evolutionary stages, with estimated ages of log(t/yr) = 8.38 +/- 0.08, 7.04 +/- 0.09, and 9.04 +/- 0.09, and corresponding heliocentric distances of 7.36 +/- 0.37, 6.79 +/- 0.18, and 6.17 +/- 0.22 kpc. The obtained metallicities are 0.0388 +/- 0.0039, 0.0259 +/- 0.0028, and 0.0209 +/- 0.0023, respectively. Total mass estimates are 135, 755, and 204 solar masses, respectively, highlighting notable differences in stellar content across the clusters. An analysis of dynamical relaxation times suggests that HSC 25 and HSC 2878 are dynamically evolved, whereas the much younger HSC 37 is still in an early phase of dynamical evolution. The high space velocities and orbital parameters of these clusters reveal significant deviations from typical disc kinematics. HSC 25 and HSC 37 exhibit eccentric orbits and small perigalactic distances, consistent with dynamically heated or accreted origins within the Galactic bulge. In contrast, HSC 2878's relaxed, planar orbit suggests in situ bulge membership despite its age. These findings point toward a heterogeneous dynamical origin for the clusters, with implications for star formation and evolution in the inner Milky Way.

Approximating the dynamics given by a complex many-body Hamiltonian with a simpler effective model lies at the interface of quantum Hamiltonian learning and quantum simulation. In this context, quantum generative adversarial networks (QGANs) have been shown to outperform standard Trotter-based approximations. However, their performance is often hindered by training plateaus and local minima that become increasingly severe with system size. To overcome these limitations, we propose an entanglement-assisted learning strategy that couples a single randomly initialized auxiliary qubit to the learning system at an intermediate stage of the training process. The interplay between randomization and entanglement significantly enhances the learning performance of the protocol.

Diffuse radio emission in galaxy clusters, such as radio halos, relics, and mini halos, is a key tracer of non-thermal processes, turbulence, and magnetic fields within the intra-cluster medium. However, their low surface brightness, as well as contamination from compact sources and imaging artefacts, makes their detection challenging. The sheer volume of data from instruments such as the Square Kilometre Array will render traditional manual-inspection based detection methods infeasible. This paper introduces a novel machine learning approach that uses active learning to rapidly identify diffuse emission candidates from a small, optimally-selected subset of data. We apply the self-supervised deep learning algorithm Bootstrap Your Own Latent to extract features from source cutouts in the MeerKAT Galaxy Cluster Legacy Survey (MGCLS). We then pass these features through the Astronomaly: Protege anomaly detection framework to identify the final candidates. Using a human-labelled set, we evaluate our pipeline on high-resolution (~7''), convolved (15''), and combined-feature MGCLS datasets. Interestingly, the high-resolution features identify diffuse sources more efficiently than the convolved resolution, which are in turn outperformed by the combined features. Of the top 100 sources ranked by Protege, 99% exhibit diffuse characteristics, with 55% confirmed as cluster-related emission. Our work shows that Protege can identify diffuse emission with minimal human labelling effort, offering a powerful, scalable tool capable of detecting both known and novel diffuse radio sources.