Model Context Protocol (MCP) is increasingly adopted for tool-integrated LLM agents, but its multi-layer design and third-party server ecosystem expand risks across tool metadata, untrusted outputs, cross-tool flows, multimodal inputs, and supply-chain vectors. Existing MCP benchmarks largely measure robustness to malicious inputs but offer limited remediation guidance. We present MCP Pitfall Lab, a protocol-aware security testing framework that operationalizes developer pitfalls as reproducible scenarios and validates outcomes with MCP traces and objective validators (rather than agent self-report). We instantiate three workflow challenges (email, document, crypto) with six server variants (baseline and hardened) and model three attack families: tool-metadata poisoning, puppet servers, and multimodal image-to-tool chains, in a unified, trace-grounded evaluation. In Tier-1 static analysis over six variants (36 binary labels), our analyzer achieves F1 = 1.0 on four statically checkable pitfall classes (P1, P2, P5, P6) and flags cross-tool forwarding and image-to-tool leakage (P3, P4) as trace/dataflow-dependent. Applying recommended hardening eliminates all Tier-1 findings (29 to 0) and reduces the framework risk score (10.0 to 0.0) at a mean cost of 27 lines of code (LOC). Finally, in a preliminary 19-run corpus from the email system challenge (tool poisoning and puppet attacks), agent narratives diverge from trace evidence in 63.2% of runs and 100% of sink-action runs, motivating trace-based auditing and regression testing. Overall, Pitfall Lab enables practical, end-to-end assessment and hardening of MCP tool servers under realistic multi-vector conditions.

We propose a reconstruction of the full $(x, ξ, t)$ dependence of unpolarized isovector proton generalized parton distributions (GPDs) $H^{u-d}$ and $E^{u-d}$ from lattice QCD data in the pseudo-distribution formalism. For the first time, we extract double distributions (DDs) directly from lattice data, enforcing therefore an important property of GPDs linked to Lorentz symmetry. We use the flexible framework of multidimensional Gaussian process regression to regularize the inverse problem and present an assessment of the impact of model dependence on the systematic uncertainty. Our lattice ensemble corresponds to a pion mass $m_π= 358$~MeV and a lattice spacing $a = 0.094$~fm. We use larger hadron momenta, up to 2.7~GeV, and kinematic coverage compared to our previous computations and extract additional skewness-dependent moments of the GPD.

Photonic quantum computing provides a promising route toward quantum computation by naturally supporting the measurement-based quantum computation (MBQC) model. In MBQC, programs are executed through measurements on a pre-generated graph state, whose construction largely depends on probabilistic fusion operations. However, fusion operations in PQC are vulnerable to two major error sources: fusion failure and fusion erasure. As a result, MBQC compilation must account for both error mechanisms to generate reliable and efficient photonic executions. Prior state-of-the-art MBQC compilation, represented by OneAdapt, is designed for all-photonic architectures and mainly focuses on handling fusion failures. Nevertheless, it does not explicitly model fusion erasures induced by photon loss, which can be substantially more damaging than fusion failures. To mitigate fusion erasure errors, we introduce a new MBQC compilation scheme built upon the spin qubit quantum memory. We propose tree-encoded fusion, an encoding strategy that suppresses erasure errors during graph-state generation. We further incorporate this scheme into a compiler framework with algorithms that reduce the execution overhead of quantum programs. We evaluate the proposed framework using a realistic PQC simulator on six representative quantum algorithm benchmarks across multiple program scales. The results show that tree-encoded fusion achieves better robustness than alternative fusion-encoding strategies, and that our compiler provides exponential improvement over OneAdapt. In addition, we validate the feasibility of our approach through a proof-of-concept demonstration on real PQC hardware.

A thin-sheet (TS) volume integral equation (VIE) formulation incorporating generalized sheet transition conditions (GSTCs) is presented for the simulation of three-dimensional (3D) bianisotropic metasurfaces. The metasurface is represented as an equivalent TS, with its constitutive tensors derived from the GSTC susceptibility tensors. Invoking the TS approximation, the governing VIEs are reduced to surface integral equations (SIEs), in which tangential and normal flux density components are treated as distinct sets of unknowns and discretized using Rao-Wilton-Glisson and pulse basis functions, respectively. In contrast to conventional GSTC approaches based on conventional SIEs, which represent only tangential fields, the proposed framework rigorously enforces the bianisotropic GSTCs, including normal field interactions, while retaining the flux-based VIE character of the formulation. Numerical examples demonstrate the accuracy and robustness of the proposed TS-VIE-GSTC solver for polarization rotation, perfect reflection, multi-directional attenuation, and oblique phase-shift transformation.

Local Optima Networks (LONs) represent the global structure of search spaces as graphs, but their construction requires iterative execution of a search algorithm to find local optima and approximate transitions between Basins of Attraction (BoAs). In continuous optimization, this high computational cost prevents systematic investigation of the relationship between LON features and evolutionary algorithm performance. To address this issue, we propose an alternative definition of BoAs for Max-Set of Gaussians (MSG) landscapes with explicitly tunable multimodality. This bypasses search-based BoA identification, enabling low-cost LON construction. Moreover, we leverage Novelty Search (NS) to explore the parameter space of the MSG landscape generator, producing instances with diverse graph topologies. Our experiments show that the proposed BoAs closely align with gradient-based BoAs, and that NS successfully generates instances with varied search difficulty and connectivity patterns among optima. Finally, over the instances generated by NS, we predict the success rate of two well-established evolutionary algorithms from LON features. While our LON construction is specific to MSG landscapes, the proposed framework provides a dataset that serves as a foundation for landscape-aware optimization.

Linear constraints are the linear counterpart of Haskell's class constraints. Linearly typed parameters allow the programmer to control resources such as file handles and manually managed memory as linear arguments. Indeed, a linear type system can verify that these resources are used safely. However, writing code with explicit linear arguments requires bureaucracy. Linear constraints address this shortcoming: a linear constraint acts as an implicit linear argument that can be filled in automatically by the compiler. We present this new feature as a qualified type system, together with an inference algorithm which extends GHC's existing constraint solver algorithm. Soundness of linear constraints is ensured by the fact that they desugar into Linear Haskell. This paper is a revised and extended version of a previous paper by the same authors (arXiv:2103.06127). The formal system and the constraint solver have been significantly simplified and numerous additional applications are described.

Theory was formulated for scattering by a coated chiral sphere of a plane wave of arbitrary polarization state with amplitude modulated by a Gaussian pulse. The spherical core and the concentric shell of the sphere were composed of two different homogeneous materials, both isotropic chiral. Calculations of energy efficiencies for extinction, total scattering, and absorption were carried out for the shell material with experimentally determined constitutive parameters, the core being vacuous. All three energy efficiencies depend on the relative thickness of the shell and the circular polarization state of the carrier plane wave.

We investigate the reduced dynamics of two identical superconducting qubits capacitively coupled through a finite-length transmission line. Starting from circuit quantization, we derive a circuit Hamiltonian that naturally separates the line modes into even- and odd-parity sectors coupled to collective qubit operators. Depending on the hierarchy between the qubit frequency $ω_q$, the mode spacing $ω_{TL}$, and the coupling scale $ω_g$, the line acts either as a structured reservoir or as a discrete few-mode coupler. In the long-line continuum limit, each sector is described by a Drude--Lorentz spectral density and the dynamics is solved with the hierarchical equations of motion. Using the Breuer--Laine--Piilo measure, we identify the parameter region in which the reduced dynamics exhibits non-Markovian relaxation. In the short-line limit, the continuum description breaks down and the dynamics becomes respectively multimode or single-mode. This establishes a unified cQED picture of the dynamical regimes of finite-length transmission lines in superconducting-circuit architectures.

Single crystals of SrMn$_2$Ni$_6$Te$_3$O$_{18}$, a member of the ferroaxial-type magnetic oxide family $AB_{2}C_{6}$Te$_3$O$_{18}$ ($A$ = Pb, Sr; $B$ = Mn, Cd; $C$ = Ni, Co), have been successfully grown, and their structural, magnetic, magnetoelectric, and optical properties have been systematically studied. Imaging of the spatial distribution of electric-field-induced optical rotation reveals that the single crystals preferentially form single ferroaxial (FA) domains. Magnetization and neutron diffraction measurements show that Mn$^{2+}$ and Ni$^{2+}$ magnetic moments order antiferromagnetically at $T_{\rm N}$ = 83 K, forming a $c$-axis collinear bidirector-type antiferromagnetic structure. All independent magnetoelectric tensor components allowed by the magnetic point group 6/$m^{\prime}$ have been detected, and the $χ_{33}$ component exhibits a pronounced temperature-dependent anomaly, including a peak and a sign reversal. Preferential formation of single FA domains and a similar $χ_{33}$ anomaly are also observed in the isostructural compound PbMn$_2$Ni$_6$Te$_3$O$_{18}$. These findings suggest that the ferroaxial and magnetic characteristics within this structural framework are robust against Sr-Pb replacement.

Non-conservative mass transfer plays a central role in close-binary evolution, yet its effects on mass-transfer stability are uncertain. One widely adopted prescription, isotropic re-emission, is often assumed to promote stability compared to conservative mass transfer. We investigate the impact of isotropic re-emission on the stability of mass transfer in binaries with radiative envelopes that undergo delayed dynamical instability (DDI). We assess whether simplified criteria used in binary population synthesis codes accurately capture stability boundaries under varying mass-transfer efficiencies. We perform detailed stellar evolution calculations for a set of representative binaries undergoing DDI. Varying the mass-transfer efficiency beta, we track the onset of instability and quantify the corresponding critical mass ratio. We compare our results with predictions from the commonly used zeta-method, which is based on mass-radius exponents. We find that a lower mass-transfer efficiency destabilizes mass transfer in DDI systems, whereas the zeta-method predicts that isotropic re-emission should stabilize it. The discrepancy arises because the zeta-method fails to capture the full evolution of the orbit and mass ratio during pre-instability mass transfer. In some cases, the critical mass ratio is underestimated by nearly a factor of two. Our findings show that isotropic re-emission can reduce, rather than enhance, DDI stability, underscoring the limitations of using fixed critical mass ratios and zeta-based criteria. This highlights the need for calibrated prescriptions that capture the time-dependent evolution of mass ratio and orbital separation, with direct implications for modelling X-ray binaries, symbiotic stars, and double white dwarfs, including their transient rates and delay-time distributions.

Closed-loop superconducting-qubit calibration has matured into DAG-orchestrated protocol chains, yet published frameworks treat the bath via a Markovian master equation or a phenomenological likelihood, absorbing bath structure into fit residuals instead of reporting it as a diagnostic. We integrate a QuTiP 5.x hierarchical-equations-of-motion (HEOM) solver driven by a Tier-1 1/f Burkard bath into a multi-protocol calibration DAG (Rabi -> {Ramsey || T1}) and benchmark it against sesolve and mesolve on a frozen platform in a pulse-level simulator (no hardware validation). The Ramsey channel carries the headline: the Markovian fit is censored by its exponential-family numerical ceiling, while HEOM recovers a physical revival envelope whose primary T2* separates from the Markovian reference by at least 13x at 95% independent-bootstrap confidence within the HEOM-feasible budget; the point-estimate ratio reaches >=28x on the 50-point primary-t1 grid and ~72x on the 30-point biexp-family tau_aw pivot at L=5. Rabi contrast falls 2.17% below mesolve on a noise-limited 30-point grid; the paired-bootstrap CI crosses zero, so this channel corroborates rather than independently establishes the non-Markovian signature. T1 decay shape matches across backends (beta=1.000), yet HEOM's initial occupation drops from 1.000 to 0.879 -- a bath-dressed contamination stable under a 16-point densification. The DAG adds 9.62 us average per-protocol scheduling overhead, no meaningful latency penalty at protocol granularity. HEOM-in-loop thereby changes what calibration reports: bath structure appears as a quantifiable residual rather than a hidden confound.

Timely population displacement estimates are critical for humanitarian response during disasters, but traditional surveys and field assessments are slow. Mobile phone data enables near real-time tracking, yet existing approaches apply uniform displacement definitions regardless of individual mobility patterns, misclassifying regular commuters as displaced. We present a methodological framework addressing this through three innovations: (1) mobility profile classification distinguishing local residents from commuter types, (2) context-aware between-municipality displacement detection accounting for expected location by user type and day of week, and (3) operational uncertainty bounds derived from baseline coefficient of variation with a disaster adjustment factor, intended for humanitarian decision support rather than formal statistical inference. The framework produces three complementary metrics scaled to population with uncertainty bounds: displacement rates, origin-destination flows, and return dynamics. An Aparri case study following Super Typhoon Nando (2025, Philippines) applies the framework to vendor-provided daily locations from Globe Telecom. Context-aware detection reduced estimated between-municipality displacement by 1.6-2.7 percentage points on weekdays versus naive methods, attributable to the commuter exception but not independently validated. The method captures between-municipality displacement only. Within-municipality evacuation falls outside scope. The single-case demonstration establishes proof of concept. External validity requires application across multiple events and locations. The framework provides humanitarian actors with operational displacement information while preserving individual privacy through aggregation.

The current "notice and consent" paradigm is broken: consent dialogues are often manipulative, and users cannot realistically read or understand every privacy policy. While recent LLM-based tools empower users seeking active control, many with limited time or motivation prefer full automation. However, fully autonomous solutions risk hallucinations and opaque decisions, undermining trust. I propose a middle ground - a Privacy Guardian Agent that automates routine consent choices using user profiles and contextual awareness while recognizing uncertainty. It escalates unclear or high-risk cases to the user, maintaining a human-in-the-loop only when necessary. To ensure agency and transparency, the agent's reasoning on its autonomous decisions is reviewable, allowing for user recourse. For problematic cases, even with minimal consent, it alerts the user and suggests switching to an alternative site. This approach aims to reduce consent fatigue while preserving trust and meaningful user autonomy.

We determine putative optimal packings of regular spherical polygons via optimization on smooth manifolds. For several cases, we establish maximality by extending the Lovász theta number to Cayley graphs on the special orthogonal group ${\rm SO}(3)$. To this end, we introduce an algebraic criterion characterizing when congruent regular spherical polygons have disjoint interiors, leading to a unified formulation of the packing constraints. Using harmonic analysis on ${\rm SO}(3)$, we reduce the theta number to a trigonometric sum-of-squares problem, which can be solved via semidefinite programming.

The paper presents a study of the efficiency of loading and storing data in the three most common Data Lakehouse systems, including Apache Hudi, Apache Iceberg, and Delta Lake, using Apache Spark as a distributed data processing platform. The study analyzes the behavior of each system when processing structured (CSV) and semi-structured (JSON) data of different sizes, including loading files up to 7 GB in size. The purpose of the work is to determine the most optimal Data Lakehouse architecture based on the type and volume of data sources, data loading performance using Apache Spark, and disk size of data for forming analytical data systems. The research covers the development of four sequential ETL processes, which include reading, transforming, and loading data into tables in each of the Data Lakehouse systems. The efficiency of each Lakehouse was evaluated according to two key criteria: data loading time and the volume of tables formed in the file system. For the first time, a comparison of performance and data storage in Apache Iceberg, Apache Hudi, and Delta Lake Data Lakehouse systems was conducted to select the most relevant architecture for building analytical data systems. The practical value of the study consists in the fact that it assists data engineers and architects in choosing the most appropriate Lakehouse architecture, understanding the balance between loading performance and storage efficiency. Experimental results showed that Delta Lake is the most optimal architecture for systems where the priority is the speed of loading data of any volume, while Apache Iceberg is most appropriate for systems where stability and disk space savings are critical. Apache Hudi proved ineffective in data loading and storage evaluation tasks but could potentially be effective in incremental update and streaming processing scenarios.

Exciton-polaritons provide a great platform for developing ultrafast all-optical logic gates for quantum and optical chips. However, progress toward practical polariton logic remains limited due to incomplete logical functionality on a single device. Herein, we present a single-device perovskite polariton platform enabling reconfigurable, ultrafast logic gates with functional completeness. The device consists of an optically trapped perovskite microwire, generating well-controlled non-equilibrium polariton condensation states for multiple logic operation channels. By tailoring the power of signal and gate beams, the same device is programmed to execute three basic Boolean functions (AND,OR,and NOT) and a high-order XOR function with a high on/off ratio of 21 dB, and a fast response time 6.7 ps. The reconfigurability arises from the selective activation of different nonlinear responses of polariton condensates, including amplification, seeding state transitions, and nonlinear interaction. These results provide valuable insights for advancing exciton-polariton logic gates.

The Halpern algorithm is a powerful fixed point approximation method for finding the closest point in the fixed point set of a nonexpansive mapping to the initial point. However, in practice, it is not necessarily true that this algorithm can be applied to large-scale fixed point problems, since the computation of the nonexpansive mapping is expensive. In this paper, we present mini-batch stochastic Halpern algorithm to resolve the issue caused by the computational difficulty of the mapping. We preform a convergence analysis demonstrating that the algorithm with diminishing step sizes and increasing batch sizes converges in mean square to the closest point in the fixed point set to the initial point. We also perform a convergence rate analysis demonstrating that convergence speed of the algorithm depends on the settings of the diminishing step sizes.

Neural network potentials (NNPs) are rapidly changing the landscape of state-of-the-art molecular dynamics (MD) simulations. To make full use of this development, the community needs flexible, easy-to-use interfaces firmly integrated with existing methodologies. To address this, we here present an interface for hybrid machine learning/molecular mechanics (ML/MM) simulations implemented in the widely used MD code GROMACS. The interface enables NNPs trained in the PyTorch framework to contribute energies and forces during MD simulations, either for selected subsets or entire molecular systems. By defining a flexible set of model inputs and outputs, the interface is agnostic to specific NNP architectures and can accommodate a wide range of descriptor-based and message-passing models. In particular, the design integrates NNP inference seamlessly into the extensive GROMACS molecular simulation ecosystem, providing users with the capability to straightforwardly combine NNPs with existing advanced sampling and free energy workflows. We demonstrate the capabilities of the interface using several representative applications, including enhanced sampling of peptide torsional free energy landscapes, absolute solvation free energy calculations, and protein--ligand simulations. We also run performance benchmarks on water boxes for several different NNP architectures. Our interface is available in recent GROMACS releases, and we believe it will provide a practical foundation for incorporating machine learning potentials into production MD simulations of biomolecular systems.

We present optical B and V band photometry of GP Com - an ultracompact binary consisting of an accreting white dwarf and helium secondary component. Our data set contains 7.7 hours observations in V band with the 2.0m telescope and 2.9 hours simultaneous observations in B and V bands with the 1.5m telescope of the Rozhen National Astronomical Obsevatory, Bulgaria. The observations cover of about 13 orbital periods. We find an orbital modulation with amplitude 0.04-0.05 mag in B and V bands. Adopting that it is due to a bright spot, we estimate its temperature 19700 \pm 3000 K. We estimate mass accretion rate onto the white dwarf of about 2.10^{-12} M_sun/yr, consistent with the predicted rate for a cool donor. The data are available on Zenodo: zenodo.org/records/18768211.

White-light flares (WLFs) are usually tend to be those very large flares. Nevertheless, several small and compact WLFs have been reported and thought to be produced by low-height magnetic reconnection. However, whether low-height magnetic reconnection can efficiently produce WLFs remains unclear. For the first time, we conduct a statistical study of the WL emission in compact flares to address this question. Using over a decade observations from the \textit{Solar Dynamics Observatory} (SDO), we identify 28 compact flares, including 19 C-class and 9 B-class flares. We find these compact flares can be classified into three types based on the magnetic configuration of the flare, corresponding to the U-shape loop (type I), the flux emergence near sunspot (type II), and the fan-spine like structure (type III). For each type, the flares numbers are 9 (7 C-calss and 2 B-class), 9 (3 C-calss and 6 B-calss) and 10 (9 C-calss and 1 B-calss), respectively. We find the occurrence rate of WLFs in compact flares is $\sim60.7\%$ (17/28), and for the C-class the rate can be up to $\sim89.5\%$ (17/19). No WLF was found in B-class compact flares. The occurrence rates for three types are $\sim77.8\%$ (7/9), $\sim11.1\%$ (1/9) and 90\% (9/10), respectively. And for the C-class flares, the occurrence rates for three types are 100\% (7/7), $\sim33.3\%$ (1/3) and 100\% (9/9), respectively. Our results suggest type-I and type-III compact flares are more likely to produce WL emissions.

We develop a general algebraic scheme to decompose fractional quantum Hall (FQH) wave functions based on the operator contraction multiplication. By introducing fermionic and bosonic operators and establishing three fundamental contraction rules, we achieve an exact decomposition of Laughlin states. This approach naturally extends to multi-component systems by factorizing coupled Jastrow factors via resultants and elementary symmetric polynomials, enabling the first complete decomposition of Halperin states. For Halperin ($2,2,1$) state, we explicitly derive its basic expansion, identify root configurations, and reveal intra- and inter-color squeezing operators, thereby uncovering the underlying generalized Pauli principle. Using this method, we compute orbital entanglement spectra for up to $16$ particles with decomposition dimensions exceeding $10^{11}$, obtaining edge excitation sequences that precisely match chiral Luttinger liquid theory. Our framework breaks through the longstanding limitations of Jack polynomials, provides a unified decomposition for both single- and multi-component FQH states, and opens a new avenue for exploring wave functions for more complex FQH states.

Frozen large language model (LLM) checkpoints extract information from pre-cutoff public text that is associated with future fundamentals and equity returns beyond standard contemporaneous valuation measures. Because each frozen checkpoint has a fixed knowledge cutoff, it can be interpreted as a compressed representation of publicly available textual information at a given point in time. We treat twelve OpenAI snapshots spanning 2021-2025 as time-stamped summaries of the public textual record and extract a sector-neutral LLM outlook score for roughly 7,000 U.S. equities per cross-section. The outlook score is positively associated with analyst revisions, target-price changes and one-month cross-sectional returns in both Fama-MacBeth regressions and pooled panels with model fixed effects (t = 6.02), after direct controls for market-implied valuation and standard factors. Predictability broadly increases with the return horizon, despite a non-monotonic intermediate dip, and, in the pooled panel, is stronger for firms with high analyst coverage, consistent with the view that the bottleneck is not investor inattention but the cost of aggregating dispersed qualitative information across many documents.

Engineering structures are increasingly designed using numerical optimisation. However, traditional optimisation methods can be challenging with multiple objectives and many parameters. In machine learning, stable training of artificial neural networks with millions or billions of parameters is achieved using automatic differentiation frameworks such as JAX and Pytorch. Because these frameworks provide accelerated numerical linear algebra with automatic gradient tracking, they also enable differentiable implementations of numerical methods to be built. This facilitates faster gradient-based optimisation of geometry and materials, as well as solution of inverse problems. We demonstrate JAX-BEM, a differentiable Boundary Element Method (BEM) solver, showing that it matches the error of existing BEM codes for a benchmark problem and enables gradient-based geometry optimisation. Although the demonstrated examples are for acoustic simulations, the concept could be readily extended to electromagnetic waves.

Since the dawn of the space age, observations of energetic particles in planetary radiation belts have been interpreted within a diffusive transport framework, even though the processes that populate and deplete these belts produce highly structured and spatially localized distributions. This exposes a fundamental problem: how can coherent phase-space structures evolving under collisionless dynamics give rise to observational signatures that appear consistent with diffusion-based transport? Here we show that diffusion-like behaviour can arise from an observational phase-mixing effect, independent of stochastic wave-particle transport. As spacecraft sample neighbouring drift shells while particles undergo electromagnetic drifts, spatially localized drift-phase structures are converted into rapidly decorrelating temporal signals, making them observationally indistinguishable from stochastic processes. We show that the effective lifetime of these structures is only a few drift periods, preventing the resolution of fine-scale structure. These results demonstrate that collisionless dynamics can mimic diffusive transport on short timescales, limiting the inference of particle acceleration processes and biasing transport estimates. This calls for a reassessment of diffusion-based interpretations of radiation belts at Earth, across the solar system, and in the radiation belts of ultra-cool brown dwarfs.

The properties of plasmas in the low-density limit are described by virial expansions. Analytical expressions are known for the lowest virial coefficients from Green's function approaches.Recently, accurate path-integral Monte Carlo simulations were performed for the hydrogen plasma at low densities by Filinov and Bonitz [Phys. Rev. E 108 (2023)055212], which made a comparison of the virial expansions and the derivation of interpolation formulas possible. The exact expression for the second virial coefficient is used to test the accuracy of the PIMC simulations and the range of application of the virial expansions.To describe plasmas in a wider range of density and temperature, the concept of quasiparticles is considered. Medium modifications of free and bound states are obtained from the spectral function. Mean-field effects are presented, such as exchange terms, Pauli blocking and screening. The density expansions of the quasiparticle shifts is considered. The combination of PIMC simulations with benchmarks from exact virial expansion results allows us to obtain precise results for the EoS in the low-density range. At low densities, the results are compared with the Saha equation to introduce the medium-dependent ionization potential. The relation to the Beth-Uhlenbeck formula and concepts such as the Mott effect, ionization potential depression (IPD), and ionization degree are discussed. The limits of current PIMC results for hydrogen plasmas are shown. Further improvements of the PIMC simulations are required to compare with analytical benchmarks.

Hopfions, as three-dimensional topologically nontrivial structures described by poloidal and toroidal winding numbers, hold promise as robust information carriers in spintronics, functional materials, and optical communications. Although they have been experimentally realized in various physical systems, such realizations have been restricted to low orders, with the winding numbers lacking tunability. Here, using optical fields as our platform, we outline how to make tunable hopfions in any order with any winding number. We use tailored superpositions of Laguerre-Gaussian modes in free-space as our construction, achieving effective control for arbitrary-order poloidal and toroidal winding numbers, which we demonstrate up to orders 5 and 3, respectively, for a new state-of-the-art. The resulting torus-knot structures are visualized experimentally via polarization filaments, confirming the designed topological textures. Our work reports an exotic optical topologies observed in free space, provides a systematic route hopfions of any order, with implications for topological photonics, optical communications, and analogies in magnetic and condensed-matter systems.

This paper studies cost pass-through in differentiated-product oligopoly. I derive a general representation of the pass-through matrix that decomposes equilibrium price responses into the roles of demand curvature, substitution, and multiproduct ownership. This extends the classic insight in single-product monopoly to multiproduct settings in which diversion and ownership also matter. I then develop a tractable first-order approximation that yields a sufficient-statistics characterization for empirically relevant demand systems. Finally, I characterize the small-share limit and show how common demand specifications impose tail restrictions that shape pass-through. The results provide a practical framework for applied work on tax incidence, merger analysis, and related questions in imperfect competition.

We investigate electron--positron pair creation through the Schwinger process in newborn magnetars with millisecond spin periods and surface dipole fields close to or above the QED critical field, $B_{\rm Q} = 4.414\times10^{13}\,\mathrm{G}$. In the unscreened field scenario, we derive the analytical global pair creation flux and recast it into a compact form with accurate analytic approximations. For a fiducial model with $B_{\rm p} = 10^{14}\,\mathrm{G}$ and $P_0 = 1\,\mathrm{ms}$, the Schwinger channel exceeds the classical Goldreich--Julian particle supply by many orders of magnitude and becomes the dominant source of charges at the earliest stage of the magnetar. The associated discharge removes about $90\%$ of the initial rotational energy within 30 ms, suppresses the gravitational-wave loss channel, and implies that the observable millisecond phase is extremely short in this unscreened scenario. The rapid energy release over such a short timescale may also provide a viable power source for astrophysical transients. Extending the same fiducial model to $10^4\,\mathrm{yr}$ gives spin periods of order seconds, linking newborn millisecond magnetars to the mature magnetar population.

Chip-scale semiconductor laser frequency combs offer remarkable prospects for compact and power-efficient optical sensors. For the laser to be suitable for typical comb applications, its degree of coherence must first be assessed from a microwave self-mixing signal. Unfortunately, such measurements require scarcely available high-speed photodetectors with multi-GHz bandwidths and radio-frequency electronics. However, in this work, we demonstrate a simplified approach to comb coherence assessment for interband cascade lasers based on a relationship between easily-accessible MHz-frequency (baseband) noise and the multi-GHz-frequency intermode beat note. The downconversion of microwave noise to near-DC frequencies is found to originate intrinsically from the laser, which simultaneously acts as a frequency mixer due to electrical nonlinearities and a phase-to-amplitude noise converter due to the linewidth enhancement factor. Correlation between the electrical signals is explored in both frequency and time domains. Since this phenomenon is potentially universal in semiconductor lasers, it creates a new opportunity for frequency comb characterization, which may be particularly valuable in wavelength regions where fast photodetectors have limited availability.

Large earthquakes can trigger translational oscillations of Earth's inner core (Slichter modes), yet their damping remains uncertain. Using simulations, we quantify viscous and Ohmic dissipation in the fluid outer core. Earth's rotation splits the motion into one polar and two equatorial modes. We explore all three and derive scaling laws for the quality factor with each dissipation mechanism. Viscous effects are negligible, confined to a thin layer at the inner core boundary. Ohmic dissipation dominates, with decay times of 3-16 years. Equatorial modes damp at least twice as fast as the polar mode. Our results suggest that Slichter modes can persist for years. Their continued non-detection is therefore more likely due to weak excitation than rapid damping.