Disordered rock-salt with Li-excess (DRX) cathode phases within the Li-Mn-Ti-O (LMTO) composition space have recently been extensively studied, as they promise to deliver exceptional energy density at low cost in Li-ion batteries. The continued development of LMTO DRX with improved power density and cycling stability requires optimization of the composition and particle size/morphology, which are determined by synthesis conditions such as annealing temperatures and hold times. These challenges motivate our investigation of the phase diagram of the LMTO rock-salt phase space, with a focus on understanding the stability of DRX by quantifying the order-disorder transition temperature ($T_\text{disord}$) as a function of composition. We harness first-principles calculations and X-ray diffraction experiments to establish the LMTO phase diagram, which lies within the LiMnO$_2$ -- Li$_2$MnO$_3$ -- Li$_2$TiO$_3$ pseudo-ternary. Our calculations predict that the LMTO phase diagram at elevated temperature ($700 - 1300$ C) is composed of three phases: DRX, orthorhombic LiMnO$_2$, and layered Li$_2$Mn$_\text{1-y}$Ti$_\text{y}$O$_3$ ($0 < \text{y} < 1$). $T_\text{disord}$ decreases significantly as off-stoichiometry is introduced to the end-point compositions, resulting in a eutectoid phase diagram. Importantly, a significant range of LMTO compositions containing small to moderate fractions of Li-excess and Ti doping (relative to LiMnO$_2$) have $T_\text{disord}$ spanning $700 - 900$ C. These temperatures are substantially lower than conventional DRX synthesis temperatures ($\geq 1000$ C), suggesting the promise of decreasing synthesis temperatures for specific DRX compositions. The compositions containing moderate to high fractions of Mn$^{4+}$ instead have much greater $T_\text{disord}$ and phase separation to layered Li$_2$MnO$_3$ becomes highly favored.

Privacy protection mechanisms are a fundamental aspect of security in cryptocurrency systems, particularly in decentralized networks such as Bitcoin. Although Bitcoin addresses are not directly associated with real-world identities, this does not fully guarantee user privacy. Various deanonymization solutions have been proposed, with network layer deanonymization attacks being especially prominent. However, existing approaches often exhibit limitations such as low precision. In this paper, we propose \textit{NTSSL}, a novel and efficient transaction deanonymization method that integrates network traffic analysis with semi-supervised learning. We use unsupervised learning algorithms to generate pseudo-labels to achieve comparable performance with lower costs. Then, we introduce \textit{NTSSL+}, a cross-layer collaborative analysis integrating transaction clustering results to further improve accuracy. Experimental results demonstrate a substantial performance improvement, 1.6 times better than the existing approach using machining learning.

In this article we will review recent measurements of directed flow $v_1$ and elliptic flow $v_2$ in Au+Au collisions from the STAR Beam Energy Scan (BES) program. We systematically analyze the $v_1$ distributions for identified hadrons ($π^\pm$, $K^\pm$, $p/\bar{p}$) and $Λ$ hyperon as functions of rapidity ($y$), with particular focus on the mid-central collisions. The energy dependence of the $v_1$ slope is extracted across the BES range ($\sqrt{s_{NN}}$ = 3 -- 200 GeV). The atomic mass number ($A$) dependence of light and hyper nuclei $v_1$ to test the validity of the coalescence production mechanism. The constituent quark number (NCQ) scaling is systematically investigated based on $v_2$ measurements of identified particles and strange hadrons. We find that the NCQ scaling approximately holds in Au+Au collisions when $\sqrt{s_{NN}} \geq$ 4.5 GeV, but completely breaks down at $\sqrt{s_{NN}}$ = 3.0 and 3.2 GeV. The gradual restoration of NCQ scaling from 3.2 to 4.5 GeV suggests a possible transition in the dominant degrees of freedom from hadrons to partons. The physics of collectivity, equation of the system and relevance to the QCD phase diagram will be discussed within the framework of both hydrodynamic and hadronic transport model calculations.

GB-scale large apps like on-device LLMs and rich media editors are becoming the next-generation trend, but their heavy memory and I/O demands, especially during multitasking, cause devices to reclaim or kill processes, turning warm apps into cold launches. The challenge lies not in storing them, but in fast, accurate launching. For users, 1s is the usability cliff, yet our measurements show 86.6\% of GB-scale cold launches exceed it. Also, Android Vitals flags only $\geq$ 5s as slow, exposing a large satisfaction gap. Existing optimizations are designed in isolation and conflict. For example, preloading reduces I/O stalls but consumes scarce memory and is undone by reclamation, while reclamation and killing free memory but sacrifice background survivability, leading to repeated cold relaunches. Our key insight is that, although multitasking makes runtime behavior complex, each app's file access pattern remains predictable. The challenge lies in exploiting this predictability, i.e., preloading without exhausting memory, reclaiming without undoing gains, and killing selectively to preserve background survivability. We introduce AppFlow, a prediction-based system-wide scheduler that integrates a Selective File Preloader, an Adaptive Memory Reclaimer, and a Context-Aware Process Killer. Implemented across the Android framework and Linux kernel without app changes, AppFlow cuts GB-scale cold-launch latency by 66.5\% (e.g., 2s$\rightarrow$690ms) and sustains 95\% of launches within 1s over a 100-day test, significantly improving responsiveness and multitasking experience.

Disjunctive Hierarchical Secret Sharing (DHSS) scheme is a secret sharing scheme in which the set of all participants is partitioned into disjoint subsets. Each disjoint subset is said to be a level, and different levels have different degrees of trust and different thresholds. If the number of cooperating participants from a given level falls to meet its threshold, the shortfall can be compensated by participants from higher levels. Many ideal DHSS schemes have been proposed, but they often suffer from big share sizes. Conversely, existing non-ideal DHSS schemes achieve small share sizes, yet they fail to be both secure and asymptotically ideal simultaneously. In this work, we present an explicit construct of an asymptotically ideal DHSS scheme by using a polynomial, multiple linear homogeneous recurrence relations and one-way functions. Although our scheme has computational security and many public values, it has a small share size and the dealer is required polynomial time.

The NOON states play a critical role as physical resources in quantum information processing and quantum metrology, yet their preparation efficiency and applicability are often constrained by complicated operational procedures or the requirement for nonlinear interactions. In this paper, we propose an efficient protocol to generate the NOON states within two microwave cavities embedded in a superconducting system, assisted by an auxiliary five-level qudit. The state preparation is accomplished in three steps for an arbitrary photon number $N$ by adjusting only external classical fields, while keeping the qudit-cavity coupling strengths and the qudit level spacings fixed. Based on parameters accessible in superconducting systems, numerical simulations show that the protocol achieves relatively high fidelity for the NOON states preparation even in the presence of parameter fluctuations and decoherence effects. Thus, this protocol may provide a practical approach for preparing the NOON states with current technology. Notably, since nonlinear interactions are not required, the protocol is flexible and has the potential to be applied across various physical systems.

We obtain a standard local presentation for a vector-valued multisymplectic form on a smooth manifold, generalizing the known proof for polysymplectic forms. We show that vector-valued multisymplectic forms on a finite-dimensional real vector space form a non-unital operad. We prove an entropy inequality for partial compositions.

Glycans are structurally diverse and flexible biomolecules that play key roles in many biological processes. Their conformational variability makes the modeling of their interactions with proteins particularly challenging. This chapter presents a step-by-step protocol for modeling protein-glycan interactions using HADDOCK3, an integrative modeling platform that supports the inclusion of experimental or predicted interaction restraints and allows for flexible refinement of the solutions. The workflow is illustrated using the interaction between a linear homopolymer glycan, 4-beta-glucopyranose, and the catalytic domain of the Humicola grisea Cel12A enzyme, for which an experimental X-ray structure is available as a reference. Detailed instructions are provided for input structure preparation, restraint definition, docking setup, execution, and result analysis. Application of the protocol starting from unbound structures yields models of acceptable to medium quality, with interface-ligand RMSD values below 3 angstroms. Although illustrated on a specific system, the protocol has been optimized and benchmarked on multiple protein-glycan complexes and is broadly applicable to similar systems, providing a framework for integrative modeling of protein-glycan interactions.

The binomial code is renowned for its parity-mediated loss immunity and loss-error recoverability, while geometric phases are widely recognized for their intrinsic resilience against noise. Capitalizing on their complementary merits, we propose a noise-resilient protocol to realize Nonadiabatic geometric quantum computation with binomial codes in a superconducting system composed of a microwave cavity %off-resonantly dispersively coupled to a %three-level qutrit. The control field %geometric quantum computation is designed by %combining geometric phases, integrating reverse engineering and optimal control. This design provides a customized control protocol featuring strong error-tolerance and inherent noise-resilience. Using experimentally accessible parameters in superconducting systems, numerical simulations show that the protocol yields relatively high average fidelity for geometric quantum gates based on binomial code, even in the presence of parameter fluctuations and decoherence. Thus, this protocol may provide a practical approach for realizing reliable Nonadiabatic geometric quantum computation with binomial codes in current technology.

Online discussions of science involve complex interactions among experts, news media, and social media users as they interpret and disseminate scientific findings. While prior work has examined these actors in isolation, their interplay in shaping science communication remains poorly understood. Using the COVID-19 pandemic as a case study, we analyze 1.24M tweets and 211k news articles that reference pandemic-related scientific papers. We find that the most influential Twitter accounts in this discourse are predominantly individuals with medical or research credentials. However, we also identify a coordinated network that disproportionately amplifies a small set of prominent credentialed experts who advance contrarian, anti-consensus positions on vaccines, lockdowns, and related topics. The papers promoted by these influential actors substantially overlap with those covered by news media, but with key differences: pro-consensus experts primarily engage with studies featured by mainstream and medical outlets, whereas contrarian experts align more closely with papers promoted by low-quality, pseudoscientific, or conspiratorial sources. Notably, news outlets tend to report on scientific studies after they have been highlighted by social media superspreaders. Together, these findings reveal multi-level pathways of information flow and coordinated amplification structures that shape science communication across social media and news, offering new insights into the dynamics of the broader information ecosystem.

We investigate higher--order variation of Hodge structure for families of smooth hypersurfaces and complete intersections through the notion of $I$--maximal variation. Using Griffiths' description of primitive cohomology, we interpret the infinitesimal variation of Hodge structure and the $n$--fold Yukawa coupling as graded multiplication maps in the Jacobian ring. Our main result shows that the Strong Lefschetz property of the Jacobian ring provides the algebraic mechanism ensuring $I$--maximal variation. In particular, we prove that smooth hypersurfaces of degree $d\ge n+2$ and smooth complete intersections with $κ>0$ exhibit $I$--maximal variation. We further establish that for complete intersections of general type the infinitesimal Torelli property is equivalent to the nondegeneracy of the Yukawa coupling. Finally, we analyze degenerations and show that the failure of the Strong Lefschetz property leads to degeneration of the Yukawa coupling and the loss of $I$--maximal variation. These results identify the Lefschetz property of the Jacobian ring as the fundamental algebraic structure governing maximal variation of Hodge structure.

Lately, a New Transmuted Logistic-exponential (NTLE) distribution was introduced and studied as an extension of the Logistic-Exponential Distribution (LED) with wider applicability in lifetime modelling. However, the maximum likelihood estimates (MLE) of NTLE are not in closed form, and the consistency of the estimates was not examined. Furthermore, some other important properties of NTLE, namely the Shannon entropy, Rényi entropy, stochastic ordering, mode, stress-strength reliability measure, residual life functions (mean and reverse), incomplete moments, Bonferroni and Lorenz curves are yet to be derived. Motivated by this, we derived and studied these important properties and evaluated the performance of ten estimation methods (Maximum Likelihood, Moments, Least Squares, Weighted Least Squares, Maximum product of Spacings, Anderson-Darling, Cramer-von Mises, percentile estimation, and Maximum Goodness-of-Fit methods) for NTLE parameters via Monte Carlo simulation using bias, mean square error, and root mean square error as evaluation criteria. Real-life infectious mortality data fitted to the distributions showed that NTLE has a better fit compared to its base distributions (Exponential and Logistic-Exponential). This finding contributes valuable insights for researchers and practitioners when selecting the appropriate estimation methods, especially for NTLE and some similar distributions in non-closed form.

We study the infinitesimal variation of Hodge structure for families of algebraic curves and extend the classical theory from smooth curves to singular and non--planar settings. Using the deformation space $\mathrm{Ext}^1(Ω_X,\mathcal O_X)$ and the dualizing sheaf, we define a singular analogue of maximal infinitesimal variation. For equisingular families of plane curves with planar Gorenstein singularities, we prove that the infinitesimal variation attains maximal rank equal to the arithmetic genus. We show that the rank decomposes into a geometric contribution from the normalization and a singular contribution measured by the $δ$--invariants. For non--equisingular degenerations, the rank defect equals the drop of the total $δ$--invariant and admits an interpretation in terms of vanishing cycles and mixed Hodge structures. We further extend the results to non--planar curves under suitable Petri and deformation conditions.

We report the stereoscopic observations of two recurrent streamer waves in a single streamer structure, utilizing coordinated observations from the SOHO, STEREO, and SDO missions. Contrary to the long-held view that fast coronal mass ejections (CMEs) are necessary drivers, we demonstrate that these recurrent waves were excited by two consecutive slow CMEs (<500 km/s} accompanied by only modest flare activity. Three-dimensional reconstruction reveals that the first and second waves propagated with significant decelerations of - 7.93 and - 10.26 m s^-2, respectively. Their average amplitudes were 0.41 and 0.77 solar radii, wavelengths were 4.02 and 6.17, and periods were 2.66 and 2.53 hours, respectively. While the amplitude of the first wave declined with heliocentric distance (consistent with conventional energy convection), the second wave exhibited an intriguing increasing trend in amplitude. Both waves showed a linear increase in wavelength and period with distance, indicating a non-stationary and dispersive medium. Crucially, despite the disparity in driver energy and wave scales, the periods and their change rates remained nearly identical for both events. This provides compelling case-specific evidence that the streamer wave period is primarily determined by the inherent eigenmodes of the streamer plasma slab rather than the specific characteristics of the trigger. We conclude that the generation of observable streamer waves is a combined consequence of the streamer's structural stability and the energy transfer efficiency of the triggering disturbance.

Agentic LLM systems equipped with persistent memory, RAG pipelines, and external tool connectors face a class of attacks - Logic-layer Prompt Control Injection (LPCI) - for which no automated red-teaming instrument existed. We present LAAF (Logic-layer Automated Attack Framework), the first automated red-teaming framework to combine an LPCI-specific technique taxonomy with stage-sequential seed escalation - two capabilities absent from existing tools: Garak lacks memory-persistence and cross-session triggering; PyRIT supports multi-turn testing but treats turns independently, without seeding each stage from the prior breakthrough. LAAF provides: (i) a 49-technique taxonomy spanning six attack categories (Encoding~11, Structural~8, Semantic~8, Layered~5, Trigger~12, Exfiltration~5; see Table 1), combinable across 5 variants per technique and 6 lifecycle stages, yielding a theoretical maximum of 2,822,400 unique payloads ($49 \times 5 \times 1{,}920 \times 6$; SHA-256 deduplicated at generation time); and (ii) a Persistent Stage Breaker (PSB) that drives payload mutation stage-by-stage: on each breakthrough, the PSB seeds the next stage with a mutated form of the winning payload, mirroring real adversarial escalation. Evaluation on five production LLM platforms across three independent runs demonstrates that LAAF achieves higher stage-breakthrough efficiency than single-technique random testing, with a mean aggregate breakthrough rate of 84\% (range 83--86\%) and platform-level rates stable within 17 percentage points across runs. Layered combinations and semantic reframing are the highest-effectiveness technique categories, with layered payloads outperforming encoding on well-defended platforms.

Navigation aids are central to immersive virtual reality (VR) experiences that involve physical locomotion. Their effectiveness depends not only on how much spatial information they provide, but also on how directly that information supports movement decisions. We compared three common guidance techniques for immersive VR wayfinding: a directional arrow, a minimap, and a compass. In a controlled room-scale VR study with 42 participants completing 1008 trials, participants navigated to target landmarks in a time-pressured maze with reduced visibility and forced route replanning. Across behavioral and eye-tracking measures, arrow guidance produced the strongest navigation performance, minimap guidance yielded intermediate performance, and compass cues performed worst, suggesting that during immersive locomotion users benefit from guidance that can be interpreted rapidly while moving. These results suggest that in demanding immersive locomotion tasks, interfaces that translate spatial information directly into actionable movement cues can outperform richer but more interpretive spatial representations. Our findings highlight the importance of designing XR navigation interfaces that minimize the cognitive translation between spatial information and movement decisions.

Ultralight dark matter can behave as a coherent background field and induce time-dependent modifications of Standard Model parameters. We study a scenario in which a real ultralight scalar $ϕ$ couples off-diagonally to down-type quarks, linking ultralight dark sectors to flavor physics. Working within an effective field theory, we diagonalize the quark mass matrix in a coherent $ϕ$ background and derive analytic expressions for oscillatory shifts in down-type quark masses and CKM parameters. These effects lead to signatures in both the classical regime, where $ϕ$ acts as a background field, and the quantum (particle) regime, where it contributes through on-shell production or off-shell mediation. Using precision flavor measurements, nuclear $β$ decays, atomic clocks, pulsar timing, and meson observables, we derive constraints on the flavor-violating couplings $λ_{ij}$ for $m_ϕ\sim 10^{-24}$--$10^{-12}\,\mathrm{eV}$, highlighting the complementarity of time-domain and flavor probes of ultralight dark sectors.

We theoretically study angle-resolved magnetoresistance under rotated magnetic field in the normal state of a spin-triplet superconductor UTe$_2$. The Wannier model derived from a GGA+$U$ calculation shows quasi-two-dimensional Fermi surfaces with warping in the $k_z$ direction, consistent with quantum oscillation measurements in the high magnetic field regime. Solving the semiclassical Boltzmann equation, we show that the Fermi surface geometry gives rise to oscillations in the magnetoresistance when the field is tilted from the $c$ axis toward the $a$ or $b$ axis. By assuming a band-dependent relaxation time, the calculated angle-resolved magnetoresistance is in good agreement with the recent transport experiment. This is direct evidence for the warped Fermi surface revealed by ordinary intraband transport. It suggests that the hole band with long relaxation time dominates electron transport. The field angle dependence of the Hall resistivity is calculated for further experimental verification.

We consider estimation of high-dimensional long-run covariance matrices for time series with nonconstant means, a setting in which conventional estimators can be severely biased. To address this difficulty, we propose a difference-based initial estimator that is robust to a broad class of mean variations, and combine it with hard thresholding, soft thresholding, and tapering to obtain sparse long-run covariance estimators for high-dimensional data. We derive convergence rates for the resulting estimators under general temporal dependence and time-varying mean structures, showing explicitly how the rates depend on covariance sparsity, mean variation, dimension, and sample size. Numerical experiments show that the proposed methods perform favorably in high dimensions, especially when the mean evolves over time.

This work considers the problem of using multiple aerial carriers to hold a cable-suspended load while remaining in periodic motion at all times. Using a novel differential geometric perspective, it is shown that the problem may be recast as that of finding an immersion of the unit circle into the smooth manifold of admissible configurations. Additionally, this manifold is shown to be path connected under a mild assumption on the attachment points of the carriers to the load. Based on these ideas, a family of simple linear solutions to the original problems is presented that overcomes the constraints of alternative solutions previously proposed in the literature. Simulation results demonstrate the flexibility of the theory in identifying suitable solutions.

This paper presents a comprehensive physical layer security (PLS) framework for fluid antenna system (FAS)-aided short-packet communications under the variable block-correlation model (VBCM). We consider a downlink wiretap scenario in which a base station transmits confidential short packets to a legitimate receiver user (RU) in the presence of an eavesdropper user (EU), where both the RU and EU are equipped with fluid antennas. Unlike existing FAS security analyses that rely on constant block-correlation models or infinite-blocklength assumptions, we incorporate the VBCM to accurately capture the non-uniform spatial correlation structure inherent in practical FAS deployments. By employing a piecewise linear approximation of the decoding error probability and Gauss-Chebyshev quadrature, we derive closed-form and asymptotic expressions for the average achievable secrecy throughput (AAST). We further prove that the AAST is monotonically non-decreasing in the number of RU ports, which reduces the three-dimensional joint optimization of transmit power, blocklength, and port number to a two-dimensional grid search (GS). Numerical results demonstrate that the FAS-aided system achieves up to an order-of-magnitude secrecy throughput improvement over conventional fixed-position antenna systems, and reveal that blocklength selection is the most critical design parameter in the joint optimization.

Semantic operators abstract large language model (LLM) calls in SQL clauses. It is gaining traction as an easy method to analyze semi-structured, unstructured, and multimodal datasets. While a plethora of recent works optimize various semantic operators, existing methods for semantic ORDER BY (full sort) and LIMIT K (top-K) remain lackluster. Our ListK framework improves the latency of semantic ORDER BY ... LIMIT K at no cost to accuracy. Motivated by the recent advance in fine-tuned listwise rankers, we study several sorting algorithms that best combine partial listwise rankings. These include: 1) deterministic listwise tournament (LTTopK), 2) Las Vegas and embarrassingly parallel listwise multi-pivot quickselect/sort (LMPQSelect, LMPQSort), and 3) a basic Monte Carlo listwise tournament filter (LTFilter). Of these, listwise multi-pivot quickselect/sort are studied here for the first time. The full framework provides a query optimizer for combining the above physical operators based on the target recall to minimize latency. We provide theoretical analysis to easily tune parameters and provide cost estimates for query optimizers. ListK empirically dominates the Pareto frontier, halving latency at virtually no cost to recall and NDCG compared to prior art.

This study investigates how visibility graphs constructed from Monte Carlo Markov Chain time series of spin models capture the critical behavior of the system. More precisely, we show that this approach identifies continuous phase transitions as well as important nuances, such as crossover effects occurring in the transition from a critical line to a first-order line through a tricritical point, as observed, for example, in the Blume--Emery--Griffiths model or, in a simpler setting, in the Blume--Capel model. By applying Kirchhoff's theorem, we show that the number of spanning trees of the resulting graphs serves as a sensitive indicator of these phase transitions. Furthermore, a qualitative analysis of the adjacency matrices based on random matrix theory provides additional evidence for these phenomena. The methodology developed here can potentially be extended to the analysis of criticality in empirical time series from complex systems, such as climate, financial, and epidemiological data, where the Hamiltonian governing the dynamics is not necessarily known.

Cycling plays an important role in sustainable urban mobility, yet how people perceive cycling infrastructure varies widely and remains challenging to assess at large spatial scales. Existing research has mainly relied on surveys or short-form social media data and has often focused on individual cities, leaving limited insight into how cycling discussions unfold across broader geographic contexts. This study proposes a multi-scale framework that examines how cycling infrastructure is discussed and evaluated in online public discourse and explores whether sentiment patterns differ between the United States (U.S.) and selected European countries included in the dataset. The analysis draws on a large collection of discussions on a social media platform, namely Reddit, including more than 30,000 posts and over 500,000 associated comments gathered from cycling-focused and geographically defined communities across multiple U.S. states and selected European countries. Using a combination of sentiment analysis, topic modeling, aspect-based classification, and hierarchical statistical modeling, the study evaluates the emotional tone and thematic structure of these discussions and how they vary spatially. Overall sentiment toward cycling is positive in both regions, with slightly higher values observed in the European sample, although differences remain modest. Sentiment tends to become more critical in comment discussions compared to original posts. Topic and aspect analyses show that sentiment is primarily associated with experience-based themes, with most variation occurring within cities rather than between regions. Together, these findings illustrate how discussion-based online data can complement traditional approaches to understanding public perceptions of cycling infrastructure in sustainable urban contexts.

Rapid exciton-exciton annihilation (EEA) in two-dimensional semiconductors limits access to high-density excitonic regimes essential for efficient optoelectronic operation under strong excitation. Here, we show that EEA is suppressed by repulsive dipole-dipole interactions between interlayer excitons polarized by the spontaneous polarization intrinsic to rhombohedral (3R)-stacked MoS$_2$ bilayers. Using ultrafast pump-probe spectroscopy, we measure an EEA rate of $γ_{\rm EEA}=(5.03\pm0.99)\times10^{-3}$ cm$^2$ s$^{-1}$ in 3R bilayers, which is approximately 18.2-fold smaller than that in monolayers and 2.9-fold smaller than that in nonpolar 2H bilayers. Despite the higher exciton diffusivity recently reported for 3R relative to 2H bilayers, the reduced EEA rate in 3R indicates a rate-limited regime governed by the close-encounter annihilation probability rather than diffusion. A rate-limited annihilation model incorporating a dipole-dipole repulsive potential captures the observed ratio $γ_{{\rm EEA},3{\rm R}}/γ_{{\rm EEA},2{\rm H}}\approx0.35$ for an exciton-exciton encounter distance of $\sim$1.3 nm, consistent with the bilayer exciton Bohr radius. These results show that spontaneous polarization in 3R-stacked bilayers suppresses nonlinear excitonic losses and provides a route toward high-density excitonics.

We prove that given a symmetric completely non-selfadjoint operator $B$ with finite deficiency indices $(n,n)$ on a Hilbert space and a boundary triplet $\left(\mathbb{C}^{n},Γ_{1},Γ_{2}\right)$ for $B^{*}$, the set of points in the spectrum of $A_{1}$ (the self-adjoint extension with domain $Ker\;Γ_{1}$) which are not eigenvalues of maximum multiplicity for any self-adjoint extension of $B$ disjoint of $A_{1}$, is a dense $\textit{G}_δ$ set in $σ(A_{1})$. Furthermore, a proof of a Malamud's theorem that generalizes a well-known result of the Aronszajn-Donoghue theory on the characterization of eigenvalues is offered.

This paper presents the development of an algorithm, termed the Global-Local Hybrid Surrogate (GLHS), designed to efficiently compute the probability of rare failure events in complex systems. The primary goal is to enhance the accuracy of reliability analysis while minimizing computational cost, particularly for high-dimensional problems where traditional methods, such as Monte Carlo simulations, become prohibitively expensive. The proposed GLHS builds upon the foundational work of Li et al., by integrating an adaptive strategy based on the General Domain Adaptive Strategy (Adcock et al.). The algorithm aims to approximate the failure domain of a given system, defined as the region in the input domain where the system transitions from safe to failure modes, described by a limit state surface. This failure domain is not explicitly known and must be learned iteratively during the analysis. The method employs a buffer zone, defined as the region surrounding the limit state surface. Within this buffer zone, Christoffel Adaptive Sampling is utilized to select new samples for constructing localized surrogate models, which are designed to refine the approximation in regions critical to failure probability estimation. The iterative process proceeds until convergence is reached. This results in a hybrid methodology that integrates a global surrogate to capture the overall trend with local surrogates that concentrate on critical regions near the limit state function. By adopting this strategy, the GLHS method balances computational efficiency with accuracy in estimating the failure probability.

The Sagittarius C (Sgr C) complex, located on the western edge of the Central Molecular Zone (CMZ), hosts a mixture of star-forming and non-thermal activity whose X-ray properties remain poorly understood. Using deep archival Chandra and XMM-Newton observations, we resolve the diffuse X-ray emission in Sgr C into two components: an H II region coincident with the radio peak and a brighter diffuse feature located to its southwest. Spatially resolved spectroscopy reveals the presence of a soft (kT <= 1 keV) plasma with metal abundances consistent with the elevated metallicity expected in the CMZ in both regions, along with a harder (~ 8 keV) thermal component within the H II region. The observed diffuse X-ray emission and its association with an expanding [C II] shell suggest that the hot gas may originate from a young supernova remnant (SNR) embedded in the H II region. Under this interpretation, the inferred shock velocity (~ 800 km/s) and SNR age (>= 1.7 kyr) are consistent with a core-collapse SNR in the Galactic Center. These results reveal Sgr C as a potential host of a SNR and highlight the complex interplay between massive-star feedback, magnetic fields, and molecular gas in the CMZ.

We investigate the effect of nonrelativistic motion on the emission dynamics of a dipole emitter moving next to a reflecting interface. Within the formalism of macroscopic QED, we obtain a general equation of motion for the dipole amplitude in terms of the dyadic Green's function, yielding a dynamical extension of the Drexhage effect. At short dipole-surface distances, the dipole can be described as a parametric oscillator featuring time-dependent dampings and Lamb shifts, both arising from the self-induced modulation of the surrounding electromagnetic environment. Importantly, these time-dependent parameters do not always average out, leading to amplification of the dipole amplitude and the radiated intensity when considering certain sinusoidal trajectories with specific modulation amplitudes and frequencies. We derive threshold modulation amplitudes as function of the relative permittivities at the interface. Qualitatively, in the vicinity of certain epsilon-near-zero materials, amplification is possible purely by modulation of the damping. Our findings open up avenues for the dynamic control of light-matter interaction in nanophotonic environments.

We present a computational study of the laser-driven quantum dynamics of positronium (Ps), PsH, and PsCl at the time-dependent Hartree-Fock level of theory. To eliminate finite-basis effects and to properly capture continuum dynamics, we use a spherical polar pseudospectral representation. The multicomponent theory and its implementation are described in detail. We find that while the presence of the positron delays electron ionization in PsH, a slight enhancement of electron ionization is observed in PsCl. In both cases, the positronic response is faster than that of the electrons. We propose that the formation of PsCl may be directly observed through photopositron spectra in the multiphoton regime, where PsCl peaks are expected at roughly twice the energy of Ps peaks, making PsCl clearly distuinguishable from Ps. In the tunelling regime, however, photopositron rescattering peaks may only be distuinguishable if the amount of Ps is sufficiently low.