Planets in binary-star systems exhibit demographic differences compared to planets in single-star systems. In particular, planets with binary-star hosts have a lower overall occurrence rate compared to their single-star counterparts, as well as a suppressed relative occurrence rate for sub-Neptunes ($R_p=2{-}4R_{\oplus}$) compared to super-Earths ($R_p=1.0{-}1.5R_{\oplus}$). These differences are most pronounced in close separation binaries ($ρ< 100$ au) which has been interpreted as a result of binary stars disrupting the protoplanetary disks of their stellar companions. The architectures of planetary systems -- i.e. the arrangements of planet sizes and orbits -- provide additional information about system formation and evolution. Architectures of single-star planetary systems are well studied, but architectures of binary-star planetary systems have not been investigated in detail. In this work, we analyzed a large sample of Kepler planetary systems (162 planets in 118 binary-star systems; 880 planets in 544 single-star systems) to compare their architectures as a function of stellar multiplicity. We found that planets with binary-star hosts follow a similar ``peas-in-a-pod'' tendency toward uniformity in planet radii and log-uniformity in period spacing as planets with single-star hosts. However, we also detected modest ($2.5-3σ$) differences in period spacing and planet multiplicity, with binary-star systems having higher typical gap complexities (indicating more uneven spacing) and a higher prevalence of single planets. We interpret these results as evidence that binary stars primarily influence the planetary architectures of their stellar companions by shaping the protoplanetary disk at formation, with subsequent dynamical processing more gently altering the system architectures over secular timescales.

This paper examines how the Joint Institute for Nuclear Research (JINR), an international organization formally committed to peaceful science, is deeply embedded in an ecosystem of military-industrial enterprises in the city of Dubna in Russia, contributing to training specialists and developing technologies used in Russia's military operations, including attacks on civilian facilities in Ukraine. It also shows how JINR collaborates with scientific institutions on the Ukrainian territories occupied by Russia, legitimizing the occupation and exposing international partners to legal and ethical risks. Despite these ties, JINR maintains broad international collaborations, allowing its scientists and engineers to access advanced technologies and indirectly support Russia's military capabilities, highlighting the need for greater awareness in the global scientific community and coordinated sanctions enforcement.

A number of recent contributions have put forward the topological structure of production networks as a key determinant of macro-economic dynamics. However, firm-to-firm production networks data is generally not available. Against this background, reconstruction method based on firms' size have been developed. This paper enriches this set of reconstruction methods by integrating input-output sectoral flows in the reconstruction process. We derive analytical expressions for the maximum entropy solutions to the firm network reconstruction problem with sectoral input-output constraints, first for binary networks and then for weight reconstruction. We perform a numerical analysis comparing standard and input-output based reconstruction methods using Hungarian production network data. Our results show that adding input-output constraints substantially reduces deviations from the input-output structure compared with standard methods. Our augmented method provides an almost perfect fit to input-output data, though all methods have difficulties reproducing other structural characteristics.

New technologies, such as blockchain, are designed to address various system weaknesses, particularly those related to security. Blockchain can enhance numerous aspects of traditional banking systems by transforming them into digital, immutable, secure, and anonymous ledger. This paper proposes a new banking application ALBank, which is based on blockchain and smart contract technologies. Its functionality relies on invoking functions within smart contracts deployed on the Ethereum blockchain. This approach enables decentralization and enhances both security and trust. In this context, the paper first presents a critical analysis of existing research on blockchain and traditional banking systems, with a focus on their respective challenges. It then examines the Know Your Customer (KYC) process and its various models. Finally, it introduces the design and development of ALBank, a decentralized banking application built on the Ethereum blockchain using smart contracts. The results show that the integration of blockchain and smart contracts effectively addresses key issues in traditional banking systems, including centralization, inefficiency, and security vulnerabilities by storing critical data on a decentralized, immutable ledger, managing processes autonomously, and making transactions transparent to all users.

In this paper, we introduce the notion of super-immanants for supermatrices over a supercommutative algebra. Using the super Schur-Weyl duality we show that the super immanants play a significant role in covariant tensor representations of the general linear Lie superalgebra. Among various things, we obtain a supertrace formula for super-immanants, which generalizes Kostant's trace formula to the super setting. Furthermore, we show that the Littlewood correspondences between super-immanants and supersymmetric polynomials establish an isomorphism between their corresponding algebras.

We investigate the data-driven discovery of parametric representations for implied volatility slices. Using symbolic regression, we search for simple analytic formulas that approximate the total implied variance as a function of log-moneyness and maturity. Our approach generates candidate parametrizations directly from market data without imposing a predefined functional form. We compare the resulting formulas with the widely used SVI parametrization in terms of accuracy and simplicity. Numerical experiments indicate that symbolic regression can identify compact parametrizations with competitive fitting performance.

We introduce $π$-Girsanov, a new method for constructing Markov state models from biased enhanced-sampling molecular dynamics simulations based on Girsanov reweighting. The key idea behind this new method is to separate the reweighting stationary density from the reweighting of the correlation function. We evaluate the effectiveness of this approach on several analytical potentials and on a model biomolecular system, comparing its performance with the original method. Our results show that $π$-Girsanov not only improves the estimation in a single-ensemble setting, but also resolves key challenges in estimating transition matrices from multiensemble and non-equilibrium biased trajectories. Overall, $π$-Girsanov represents a substantial advance in kinetic reweighting, strengthening the connection between enhanced sampling techniques and Markov state modeling.

We consider a reconfigurable intelligent surface (RIS)-assisted heterogeneous network comprising legitimate information-harvesting receivers (IHRs) and untrusted energy-harvesting receivers (UEHRs). A multi-antenna base station (BS) transmits confidential information to IHRs while ensuring sufficient energy transfer to UEHRs that may attempt eavesdropping. To enhance physical-layer security, we propose a secure rate-splitting multiple access (RSMA) scheme aided by a UAV-mounted RIS. The objective is to maximize fairness-based secrecy energy efficiency (SEE). Owing to the non-convexity of the formulated problem, we develop an alternating optimization framework that jointly designs the common message allocation, active precoders, and RIS phase shifts under transmit power and energy harvesting constraints, leveraging sequential convex approximation (SCA). Simulation results demonstrate the scalability of the proposed algorithm and its superior SEE performance compared to space-division multiple access (SDMA) and non-orthogonal multiple access (NOMA) benchmarks.

Observations show that many solar eruptions remain confined within strong overlying magnetic fields, forming a so-called magnetic cage. While confinement by poloidal overlying fields has been widely investigated, the role of strong external toroidal fields remains unclear. Using three-dimensional magnetohydrodynamic simulations, we study confined eruptions in a toroidal magnetic cage, focusing on the interplay between the Lorentz force and magnetic reconnection, and their observational signatures. We further employ a guiding-center test-particle approach to synthesize hard X-ray emission for comparison between thermal and nonthermal responses. We find that overlying toroidal fields play a crucial role in confinement by generating strong return currents that produce a significant downward Lorentz force, suppressing flux rope ascent. At the same time, they induce large-angle rotation of the flux rope, leading to reconnection with overlying fields and eventual break-up. Synthetic EUV emission reveals multi-ribbon flares with highly sheared, globally cowboy-hat-like loop structures. Hard X-ray diagnostics show that thermal and nonthermal emissions are not co-spatial, with return currents acting as an efficient accelerator of energetic electrons. These results demonstrate that toroidal-field-induced forces govern both the confinement and rotation of erupting flux ropes, providing an explanation for failed eruptions even under torus-unstable conditions. These results suggest that the morphology and shearing angle of flare loops are the useful diagnostics for distinguishing confined from eruptive events.

Real-space singularities underpin diverse wave phenomena yet remain largely unexplored in elastic wave systems. We report the observation of real-space topological singularities in structured flexural waves on finite-sized solids. These singularities are robust against perturbations and annihilate only through topological phase transitions. Moreover, they imprint dislocation lines on the radiated sound field, generating acoustic vortices in free space from an achiral source and structure. Our findings bridge continuum mechanics and topological physics, establishing elastic waves as a platform for exploring complex topological textures in real space and paving the way towards singular phononics.

We investigate $f\left( Q\right) $-gravity with a matter-gravity coupling as a geometric dark energy candidate for the description of the late-time cosmic acceleration within a spatially flat Friedmann--Lema\^ıtre-Robertson-Walker geometry. We select a noncoincident connection that naturally follows from the general framework of cosmological models with nonzero spatial curvature. We present observational constraints for the simplest $f\left( Q\right) =f_{0}Q^{n}$ model using data from Supernovae, Baryon Acoustic Oscillations and Cosmic Chronometers. For different data combinations we found consistent constraints, with a best-fit value for the power-law index $n\simeq2$. A comparison with the $Λ$CDM model shows that the $f\left( Q\right) $-gravity leads to larger values for the likelihood, while Akaike's Information Criterion suggests statistical equivalence between the two models for most data combinations.

Long-range muons produced in proton-proton collisions at the ATLAS interaction point constitute the primary background for neutrino interaction searches at the SND@LHC experiment. This work presents a comprehensive characterization of the muon flux throughout LHC Run-3, benchmarking Monte Carlo simulations against experimental measurements. Measured and simulated muon rates agree within 10-15% across all Run-3 configurations. Following the substantial background increase in 2024 as a result of a beam optics change, the reversion to nominal optics in 2025 did not restore the 2022-2023 levels due to the unprecedented adoption of horizontal crossing in ATLAS. As enlightened by simulation results, the latter enhanced the contribution of high-angle muons originating from diffractive proton losses in the LHC Dispersion Suppressor region. Their identification enabled the design of mitigation strategies that were experimentally validated. The simulation framework was also applied to the future High-Luminosity LHC configuration, resulting in a considerable muon rate rise, driven by both the planned luminosity increase and the enlarged magnet aperture. Nevertheless, the upgrade from emulsion films to silicon vertex detectors will preserve the efficiency of the experiment even in such a high-rate environment.

We study the associations between everyday economic decision-making quality and people's emotional states. Using high-frequency, highly disaggregated consumer "scanner" data, we show that the cost of poor decision-making is substantial, on average equal to around half of day-to-day consumption budgets. While material circumstances help explain decision-making quality, how people feel about those circumstances is equally important. Contrary to evidence that stress and worry impair performance in settings where distraction is costly, we find these same feelings are associated with improved decision-making for frequently made consumption choices. This is consistent with worry increasing attentiveness to decisions within households' locus of control.

We introduce a classical estimator for the post-processing of quantum phase estimation data generated either by quantum-Fourier-transform-based or quantum-signal-processing-based methods. We focus on the estimation of a single target phase promised to be within an interval where no other phases are present, which is typical of e.g. ground state energy estimation of gapped quantum systems. This allows us to perform phase estimation by filtering the signal within the promise region and recovering the phase through a moment-projection estimator. We show that our methods are robust in the presence of both additional phases outside the promise region and global depolarizing noise. In the noiseless case our estimator can achieve an exponential suppression of bias with respect to a naive mean estimator. In the presence of global depolarizing noise our estimator achieves a bias exponentially small in the circuit depth $t$ at fixed circuit fidelity $F$, and a variance proportional to $t^{-2}$, improving by a factor of $t^2$ over the naive shifted-and-rescaled-mean approach. To mitigate realistic circuit-level noise, we combine our method with the explicit unbiasing scheme described in [Dutkiewicz et al., 2025]. As an illustrative example, we implement these estimators on a small-scale simulation of the Ising model, validating our theoretical results and finding better-than-expected performance for a global depolarizing noise approximation. The robustness of the moment-projection estimator in the presence of both multiple eigenvalues and realistic noise makes phase estimation with limited depth practical for early fault tolerant quantum experiments.

GoogleTrendArchive is a comprehensive archive of Google Trending Now data spanning over one year (from November 28, 2024 to January 3, 2026) across 125 countries and 1,358 locations. Unlike Google Trends, which requires specifying search terms in advance, Trending Now captures search queries experiencing real-time surges, offering a way to inductively discover trending patterns across regions for studying collective attention dynamics. However, Google does not provide historical access to this data beyond seven days. Our dataset addresses this gap by presenting an archive of Trending Now data. The dataset contains over 7.6 million trend episodes. Each record includes the trend identifier, search volume bucket, precise timestamps, duration, geographic location, and related query clusters. This dataset, among other, enables systematic studies of information diffusion patterns, cross-cultural attention dynamics, crisis responses, and the temporal evolution of collective information-seeking at a global scale. The comprehensive geographic coverage facilitates fine-grained cross-country or cross-regional comparative analyses.

We show how permutability of transforms of smooth surfaces with particular characteristics leads to discrete surfaces with discrete analogues of the same characteristics.

In this work, we develop a neural-physics solver based on finite volume method (FVM), namely NeuralFVM, for turbulent flows by implementing the standard $k$-$ω$ model designed for efficient Graphics Processing Unit (GPU) execution. The governing equations for fluid flow and heat transfer are reformulated as local tensor operations using convolution-based stencil operators, which enables compatibility with deep learning libraries while preserving the conservative properties of the FVM. A key challenge in implementing the turbulent model within such a framework is the treatment of the stiff destruction terms in the $k$ and $ω$ transport equations. To address this issue, an operator-splitting strategy is introduced in which the stiff destruction terms are handled semi-implicitly while the remaining terms are advanced explicitly. This formulation avoids global matrix assembly and allows the entire solver to be implemented using local tensor operations. In addition, the pressure-velocity coupling is solved using a convolution-based geometric multigrid algorithm embedded within a neural network architecture. The resulting NeuralFVM solver is validated through comparison with simulations conducted using the commercial CFD software ANSYS Fluent for several channel-flow configurations and an indoor airflow scenario. The results demonstrate close agreement in velocity, temperature, and turbulence quantities, confirming the accuracy of the proposed approach. The developed GPU framework achieves a speedup of around 19-46 times compared with its Central Processing Unit (CPU) counterpart under different meshes. Moreover, the proposed solver naturally integrates with machine learning workflows, providing a promising foundation for future data-driven turbulence modeling and optimization.

The canonically consistent quantum master equation (CCQME) method to treat system-bath dynamics is used to describe intramolecular proton transfer in the thioacetylacetone molecule (TAA, C$_4$H$_6$OS), modeled as an $N$-level quantum system coupled to a solvent. The solvent is represented as a harmonic bath (a continuum of oscillators) characterized by an Ohmic-Drude spectral density. We benchmark CCQME against numerically exact hierarchical equations of motion (HEOM) theory and compare to Redfield theory. Our results reveal that Redfield dynamics deviates increasingly from the HEOM reference as the system-bath coupling strength grows. In contrast, the recently proposed CCQME remains consistent with HEOM at intermediate coupling.

The appearance of emergent symmetries in complex systems with components that can form composite units provides us with opportunities for design and control of exotic phase behaviour, for example by exploiting the dynamical symmetry breaking associated with them. We present a novel mechanism for the emergence of non-reciprocal interactions in a single-species suspension of chemically active colloids made out of semi-permeable vesicles, which encapsulate enzymes that catalyze a non-linear chemical reaction. Bistable chemical dynamics enables the colloidal reaction chamber to act as a net producer or consumer of a chemical, depending on the selected values of the chemical concentrations inside and around it. Since the internal chemical state of the colloid depends on the dynamic chemical concentrations rather than the material parameters, two identically produced colloids can present different effective chemical interactions within the same system upon responding to the corresponding gradients via diffusiophoresis. Furthermore, the colloids can spontaneously and reversibly switch between being effective consumers or producers. As a consequence, the colloids can dynamically switch between ignoring, attracting, repelling, and chasing each other, in a non-reciprocal manner. This flexibility can be exploited by manipulation of tuning parameters to induce bifurcations in the chemical dynamics, resulting in a robust control over the interaction motifs, and rich emergent dynamics such as spontaneous many-body polar swarming.

We present high-$p_T$ $R_{AB}$ and $v_2$ from a perturbative quantum chromodynamics-based energy loss model that includes event-by-event hydrodynamic evolution of the medium and small system size corrections to the energy loss. The model is calibrated on, and describes well, large system $R_{AA}$ and $v_2$ experimental data. The extrapolation of our model to $\mathrm{Ne}+\mathrm{Ne}$ and $\mathrm{O}+\mathrm{O}$ agrees quantitatively with recent experimental measurements of $R_{AA}$. Surprisingly, at high-$p_T$ our energy loss model predicts $v_2\approx0$ for all symmetric and asymmetric small systems when extracted using either hard-hard or hard-soft two-particle correlations. We argue that all energy loss models will in general predict $v_2\approx0$ when extracted using hard-soft correlations, which is the usual experimental method for measuring anisotropy in hadronic collisions, due to a generic geometric decorrelation between the hard and soft sector participant planes.

New Horizons RALPH/LEISA near-infrared spectra allow for regional mapping of Pluto's surface ices and their physical state; however, scan-to-scan artifacts and variable spatial resolution complicate quantitative interpretation. We extend previous LEISA compositional studies (Protopapa et al., 2017(arXiv:2110.00662); Schmitt et al., 2016; Emran et al., 2023(arXiv:2301.06027)) by combining five close-approach observations into co-registered equirectangular maps at 7 km/pixel and by jointly retrieving surface temperature, ice abundances, and grain sizes using a Hapke-based mixing model. We mitigate bad pixels and edge overexposure linked to flat-field uncertainties and correct for residual scan-to-scan spectral discrepancies using per-observation scale and offset terms. The resulting maps provide distributions of CH4-rich ice, N2-rich ice, H2O ice, and Titan tholins, alongside a corresponding temperature map.

In this article we investigate the numerical solution of a scalar semilinear stochastic delay differential equation (SDDE) where the linear instantaneous feedback and nonlinear delayed feedback terms are perturbed by a pair of standard Brownian motions with correlation $ρ$. Such SDDEs may be naturally decomposed into two subsystems: a linear stochastic differential equation (SDE) without delay, and a nonlinear SDDE. Splitting methods work by solving each subsystem separately and composing the results over a single step. Our main theoretical result provides a bound on the mean-square error of a particular strategy for doing this, known as Lie-Trotter splitting. This bound implies that the method is mean-square strongly convergent with order $1/2$ when $ρ=0$, so that the noises are uncorrelated, but assurances of convergence are lost when $ρ\neq 0$. Indeed we develop an upper bound on the global mean-square error with a term depends linearly on the magnitude of the correlation, and is independent of the stepsize. While our theoretical error bound is an estimate from above, we conduct numerical experiments that confirm the order of mean-square strong convergence of Lie-Trotter splitting in the $ρ=0$ case, and demonstrate a rapid fall-off to effectively zero as $|ρ|$ increases. Similar numerical results are observed for an alternative commonly used strategy known as Strang splitting.

We study entanglement degradation near the horizons of regular, Reissner-Nordström, and Schwarzschild-de Sitter black holes, considering the Bardeen, Hayward, and generalized Hayward metrics as regular black holes. To this end, we compute the entanglement negativity, $\mathcal{N}$, for two Unruh-like modes of a scalar field shared by Alice, who is inertial, and Rob, who hovers at a fractional offset $ρ$ outside the horizon of the backgrounds under consideration. For each geometry, we locally approximate the metric by a Rindler patch characterized by Rob's proper acceleration $a_0$. Because this Rindler approximation breaks down near the extremal limit, we also compute a near-extremal cutoff. Tracing over the inaccessible Rindler wedge yields a mixed Alice-Rob state, from which we evaluate $\mathcal{N}$ as a function of the mode frequency $ω$ and the acceleration $a_0$. In all geometries considered, except for one, $\mathcal{N}$ increases monotonically with the parameter distinguishing that geometry form the Schwarzschild one. The exception is the Reissner-Nordström metric, for which $\mathcal{N}$ exhibits a shallow local minimum at a particular value of the charge. We also find that the Reissner-Nordström metric is the only background for which the negativity falls below that of the Schwarzschild case. Among all cases studied, the Schwarzschild-de Sitter spacetime provides the strongest protection of entanglement. Finally, across all backgrounds, high-frequency modes undergo less degradation than low-frequency modes. These results suggest that entanglement may serve as a useful probe for distinguishing Schwarzschild spacetime from other geometries.

We use deep MUSE data from the MUDF survey to investigate the cool gas around galaxies at redshifts 0.5 < z < 2. We constructed two samples: one sample for a down-the-barrel analysis, probing outflows via MgII absorption against galaxy continua, and the other sample for projected galaxy pairs to examine the gas around the foreground galaxies in the transverse direction. From down-the-barrel stacked spectra, we detected blueshifted MgII absorption, indicative of outflows, in which the absorption strength increases with stellar mass and star formation rate. Lower-mass galaxies exhibit weaker absorption, but higher outflow velocities, whereas higher-mass systems retain more cool gas with slower outflows. In the transverse direction, the absorption of MgII decreases with the impact parameter, following a shallow profile. Comparing observations with radiative transfer models, we found that extrapolating an expanding halo model constrained with down-the-barrel measurements to halo scales overestimates the observed equivalent widths, likely due to the outflow geometry and the absence of the interstellar medium in the model. Our results highlight that mass, outflow geometry, and gas retention shape the cool circumgalactic medium, and that the combination of absorption and emission diagnostics provides powerful constraints on the properties of the cold halo gas.

Verifying whether two implementations of the same large model are equivalent across frameworks is difficult in practice. Even when they realize the same computation, their graphs may differ substantially in operator decomposition, tensor layout, and the use of fused or opaque kernels, making manual rewrite rules hard to build and maintain. We present Emerge, a framework for checking Implementation Equivalence over computation graphs of large-model implementations. Instead of writing rules manually, Emerge represents the two implementations in an e-graph, infers candidate relations from execution values, and synthesizes rewrite rules on demand when existing rules are insufficient. Each synthesized rule is validated using the strongest applicable method, including SMT- based checking for symbolically tractable cases and constraint-aware randomized testing for opaque kernels, and then propagated through e-graph rebuilding to establish larger equivalences. Our current implementation targets inference computation graphs captured from HuggingFace Transformers and vLLM. Our evaluation shows that Emerge establishes equivalence for correct implementation pairs at practical cost, while also providing useful by-products for debugging: it detects 10 of 13 known implementation bugs and uncovers 8 previously unknown implementation issues that were later confirmed by developers. In addition, Emerge synthesizes block-level rules that compare favorably with manually authored ones.

Underground forums play a crucial role in the criminal ecosystem, facilitating the exchange of knowledge and the trade of illegal tools and services. By analyzing the skills, motivations, focus, and operations of cyber-criminals active in these forums, cybersecurity professionals and law enforcement can better understand their tactics, assess the risks they pose to society, and develop more effective countermeasures. A significant challenge in analyzing these forums arises from language barriers, either because they blend different languages or because they use community-specific slang. In this paper, we address this challenge through the use of a combination of unsupervised methods that group together semantically related conversational themes (i.e., topics) into clusters. We apply our methodology to analyze a prolific, invite-only, Russian-English criminal forum that has been operating for over 18 years. This way, we uncover pockets of knowledge, i.e., knowledge only shared in one sub-community. This knowledge is accessible only to those speaking a language (e.g., Russian), thereby showing that language barriers (e.g., for users that do not speak Russian) can create sub-communities with different knowledge and motivations. We further demonstrate how our method can identify the semantic meaning of dark jargon from its context, and discuss other potential applications of our approach.

A plug-and-play PhotonPix single-photon detector with a logical signal output is developed for applications requiring ultimate timing precision down to 10 ps over a wide dynamic photon flux range. The heart of the detector is an Exosens Fast Timing Microchannel Plate Photomultiplier (FT MCP-PMT) with a large 8 mm diameter sensitive area, which can accommodate various Hi-QE photocathodes optimized for high quantum efficiency (QE) and low dark rates. The detector dead time, timing accuracy, and counting efficiency of the PhotonPix are measured and analyzed over a wide dynamic photon flux range up to about 1 GHz in burst mode and up to 100~MHz in continuous operation mode.

Millisecond pulsars (MSPs) are powerful probes of globular clusters (GCs), tracing stellar evolution, cluster dynamics, and the local gravitational potential. We investigate the MSP population in GC Omega Centauri. We perform Fourier-domain acceleration and jerk searches on MeerKAT observations, and carry out pulsar timing using MeerKAT and Parkes Murriyang data spanning 2021-2025. We fold Fermi LAT and NICER photons using updated radio ephemerides to search for high-energy pulsations. We discover a new isolated MSP, PSR J1326-4728S (hereafter S), with a spin period of 4.538 ms and a dispersion measure of 96.24 cm$^3$pc. We update the orbital parameters of all known binary systems, with those of I, N, and Q differing significantly from previous estimates, and obtain new timing solutions for G, H, and K. Pulsars B, G, H, K, and L exhibit black widow-like properties, I, N and Q are found in wider binaries, with N and Q having >0.2 M$_\odot$ companions, and N showing a significant orbital eccentricity (e=0.093). Significant spin period derivatives are measured for eight pulsars and interpreted as arising from the cluster gravitational potential. No pulsed high-energy emission is detected from individual pulsars. The inferred line-of-sight accelerations are consistent with a King-model gravitational potential. While our measurements are insensitive to an intermediate-mass black hole with mass 10$^3$-10$^4$ M$_\odot$, they place an upper limit of <10$^5$ M$_\odot$ at 90% confidence. The high fraction of isolated MSPs and black widows systems, and possibly the eccentricity of N, are difficult to reconcile with MSP population predictions based solely on encounter rates. Instead, these properties likely reflect the complex evolutionary history of Omega Centauri, with part of its MSP population having formed in denser environments than the one observed today.

Satellite based quantum key distribution (QKD) aims to establish secure key exchange over long distances despite significant technological challenges. To alleviate some of these challenges, Ghalaii et al. [PRX Quantum 4, 040320 (2023)] proposed that any airborne eavesdropper up to a certain size can be detected by classical monitoring techniques, limiting the transmission efficiencies of any undetected Eve. This creates a new QKD scenario in which some of the transmitted signal from Alice to Bob bypasses Eve entirely. In this manuscript, we develop a general framework for computing key rates in this "bypass" scenario for discrete variable protocols. We first numerically support a conjecture that the performance of BB84 with single photons does not improve under bypass constraints, and go on to find new regimes that do. Specifically, we find improvements when the receiver's detectors have an efficiency mismatch and when BB84 is implemented using weak coherent pulses under certain squashing assumptions. Technically, our framework is realized by including marginal constraints on the source to account for bypass effects, combined with existing numerical approaches for minimizing the key rate and squashing and dimension reduction techniques to handle photonic states of unbounded dimension.

We study an information acquisition problem in which an informed trader acquires costly information prior to trading in the Kyle equilibrium. The cost of information acquisition is represented by an entropy cost. Regardless of the prior distribution of the asset payoff, continuous signals are optimal. Moreover, any continuously distributed signal, together with an associated logit type posterior distribution of the payoff, yields the same ex-ante value for the informed trader, the same distribution of posterior expected payoff, and the same unconditional distribution of the informed trader's trading strategy. Consequently, a normally distributed signal can be adopted without loss of generality. We further show that when the information acquisition cost increases or the volatility of noise trades decreases, the variance of the posterior expected payoff declines, the profit potential from trading diminishes, meanwhile the posterior expected payoff increasingly resembles a normal distribution, and the information leakage cost from trading decreases.