Non-Hermitian systems enable continuous and smooth tuning of topological phases through externally controllable loss/gain parameters. Without altering the intrinsic lattice structure, merely fine-tuning the intensity or spatial distribution of the loss or gain can induce the emergence of higher-order topological states, and even achieve reversible switching of topological phases. However, in non-integer dimensions, topological states induced by non-Hermiticity remain unexplored, which hinders the continuous and smooth manipulation of systems with rich higher-order topological states. By introducing a loss contrast in a fractal lattice, this study proposes a non-Hermitian route to realize higher-order topological phases in acoustic fractal lattices. Based on the tight-binding approximation, calculating the Hamiltonian of the system yields the wave-function distribution of the zero-energy modes, revealing the formation mechanisms and conditions for the topological phase transitions induced by non-Hermiticity in acoustic fractal lattices. We numerically and experimentally realize non-Hermitian-induced topological edge and corner states in a fractal structure. Furthermore, theoretical and numerical results demonstrate that merely adjusting the loss contrast can alter the degree of energy localization. This work not only establishes an effective mechanism for manipulating higher-order topology in complex fractal geometries through non-Hermiticity but also provides a theoretical framework for exploring exotic topological states of matter in non-integer dimensions.

A model predictive control (MPC) framework is developed for station-keeping in spacecraft formation flight along libration point orbits. At each control period, the MPC policy solves a multi-vehicle optimal control problem (MVOCP) that tracks a reference trajectory, while enforcing path constraints on the relative motion of the formation. The control policy makes use of a limited set of control nodes consistent with operational constraints that allow only a small number of maneuver opportunities per revolution. To promote recursive feasibility, path constraints are progressively tightened across the prediction horizon. An isoperimetric reformulation of the constraints is used to prevent inter-sample violations. The resulting MVOCP is a nonconvex program, which is solved via sequential convex programming. The proposed approach is evaluated in a high-fidelity ephemeris model under realistic uncertainties for a formation along the near-rectilinear halo orbit (NRHO), and subject to path constraints on interspacecraft separation and relative Sun phase angle. The results demonstrate maintenance of a spacecraft formation that satisfies the path constraints with cumulative propellant consumption comparable to that of existing methods

We explore optical quantum engineering of phase-parameterized continuous-variable (CV) probe states to exploit nonclassical light to solve the problem of precise phase estimation. The optical interferometer consists of a single beam splitter (BS) with tunable transmittance and reflectance, and two single-mode squeezed vacuum states (SMSVs). The reference SMSV state is mixed with a weakly squeezed state carrying an unknown phase at the beam splitter to form an output hybrid entangled state. Then, in the measurement mode, the number of photons is measured to generate the target CV state parameterized by the unknown phase. Using the CV states, we propose a sub-Heisenberg metrology protocol in which the quantum Cramer-Rao (QCR) boundary is saturated by intensity measurement. The advantage of quantum engineering of CV probe states for ultra-precise phase estimation of unknown phase is due solely to the nonclassical photonic properties of the measurement induced CV states of definite parity and is independent of the mode entanglement.

High-energy gamma-ray (>GeV) emission of gamma-ray bursts (GRBs) is very important in probing the jet evolution and particle acceleration of GRBs. The observations of high-energy photons are limited except for a few very bright GRBs, hindering precise measurements of the spectral and temporal evolutions of GRBs. Here we report the detection of high-energy gamma-ray emission up to 100 GeV with Fermi-LAT using a stacking analysis of a collection of 330 GRBs. High significance detection of the emission has been found, and the precise light curves and energy spectra can be measured. The light curves and time-resolved spectra of the sub-sample of 220 LAT individually detected GRBs can be well explained by the standard afterglow emission from a population of GRBs with both synchrotron and synchrotron self-Compton mechanisms, assuming a distribution of initial Lorentz factors. However, the emission of the relatively weak sample of the 110 LAT individually undetected GRBs cannot be well reproduced in the same framework, indicating the existence of possible energy injection effect in the GeV band for the first time. The observations hence provide new insights in understanding the high-energy emission of GRBs.

A large sample of $t_2$ and $t_3$ times from the recent compilation of nova properties given in Schaefer (2025) have been analyzed to determine relationships between these two parameters. Fits were performed in both directions (from $\log t_2$ to $\log t_3$ and vice-versa) to account for the asymmetry inherent in ordinary least-squares regression, which minimizes residuals only in the dependent variable. The following best-fit relations were found: $\log t_3 = (0.877\pm0.019) \log t_2 + (0.444\pm0.027)$, and $\log t_2 = (1.018\pm0.023) \log t_3 - (0.316\pm0.037)$, corresponding to $t_3 = (2.78\pm0.17)~t_2^{(0.877\pm0.019)}$ and $t_2 = (0.483\pm0.041)~t_3^{(1.018\pm0.023)}$, respectively. Within the uncertainties, the latter relation reduces to a simple proportionality: $t_2 \simeq 0.5~t_3$.

Let $F$ be a real quadratic number field, and let $F_{cyc}$ denote its cyclotomic $\mathbb{Z}_2$-extension. For each integer $n\geq0$, let $F_n$ be the unique intermediate field in $F_{cyc}$ such that $[F_n:F]=2^n$. By studying the $2$-adic divisibility of Dirichlet $L$-series at negative integers, we derive an asymptotic formula that determines the order of the $2$-primary part of even $K$-groups of rings of integers of $F_n$ for sufficiently large $n$. As a corollary, we determine their $λ$ and $μ$ invariants. We also establish a lower bound for $n$ beyond which this asymptotic formula holds. Our results have two main applications: (1) For $K=\mathbb{Q}$, $\mathbb{Q}(\sqrt{p})$ or $\mathbb{Q}(\sqrt{2p})$ with $p\equiv\pm3\mod 8$, we determine the structure of the $2$-primary tame kernels $K_2\mathcal{O}_{K_n}(2)$; (2) We explicitly determine the three Iwasawa invariants $λ,μ,ν$ for a family of real quadratic number fields, whose discriminants have arbitrarily many prime divisors.

Given a dataset of $n$ user-contributed strings, each of length at most $\ell$, a key problem is how to identify all frequent substrings while preserving each user's privacy. Recent work by Bernardini et al. (PODS'25) introduced a $\varepsilon$-differentially private algorithm achieving near-optimal error, but at the prohibitive cost of $O(n^2\ell^4)$ space and processing time. In this work, we present a new $\varepsilon$-differentially private algorithm that retains the same near-optimal error guarantees while reducing space complexity to $O(n \ell+ |Σ| )$ and time complexity to $O(n \ell\log |Σ| + |Σ| )$, for input alphabet $Σ$. Our approach builds on a top-down exploration of candidate substrings but introduces two new innovations: (i) a refined candidate-generation strategy that leverages the structural properties of frequent prefixes and suffixes, and (ii) pruning of the search space guided by frequency relations. These techniques eliminate the quadratic blow-ups inherent in prior work, enabling scalable frequent substring mining under differential privacy.

We introduce $\textbf{Slippage-at-Risk (SaR)}$, a quantitative framework for measuring liquidity risk in perpetual futures exchanges. Unlike backward-looking metrics such as Value-at-Risk computed on historical returns or realized deficit distributions, SaR provides a \emph{forward-looking} assessment of liquidation execution risk derived from current order book microstructure. The framework comprises three complementary metrics: $SaR(α)$, the cross-sectional slippage quantile; $ESaR(α)$, the expected slippage in the distributional tail; and $TSaR(α)$, the aggregate dollar-denominated tail slippage. We extend the base framework with a \emph{concentration adjustment} that penalizes fragile liquidity structures where a small number of market makers dominate quote provision. Drawing on recent work by Chitra et al. (2025) on autodeleveraging mechanisms and insurance fund optimization, we establish a direct mapping from SaR metrics to optimal capital requirements. Empirical analysis using Hyperliquid order book data, including the October 10, 2025 liquidation cascade, demonstrates SaR's predictive validity as a leading indicator of systemic stress. We conclude with practical implementation guidance and discuss philosophical implications for risk management in decentralized financial systems.

High harmonics have emerged as a powerful ultrafast probe of phonon dynamics and electron-phonon interactions in solids, with most studies focusing on odd harmonics. Here, in a pump-probe setup with variable delay, we theoretically investigate how even harmonics reveal coherent phonon dynamics. If pump and probe pulses overlap temporally, the spatial interference effect resulting from a non-coaxial pump-probe setup suppresses harmonic yields. At longer delays, odd-harmonic yields oscillate in phase at the optical phonon frequency, whereas even harmonics exhibit order-dependent phase-shifted oscillations. We identify a responsive range of even harmonic orders, in which the delay of yield oscillations is highly sensitive to subtle features of phonon dynamics and electron-electron interactions. Our findings highlight the potential of even harmonics to elucidate microscopic effects in systems with dynamically broken inversion symmetry.

We conjecture analytic expressions for the non-local magic of bipartite pure qudit states of prime local dimension. Our construction relies on the Schmidt-aligned state attaining the minimum over local unitaries, a hypothesis that we support with numerical evidence for pairs of qutrits and ququints. For composite local dimensions, we find that the analogous expressions do not in general reproduce the global minimum, but can still provide computationally cheap approximations to the non-local magic. We also find that relations between non-local magic and entanglement diagnostics that hold for two qubits generally do not extend to qutrit and higher-dimensional systems.

We study the structure of the space $Ω_3(G)$ of $\partial$-invariant 3-paths in a directed graph $G$. We prove that $Ω_3(G)$ admits a basis consisting of trapezohedral paths $τ_m$ ($m \ge 2$) and their merging images. Moreover, we provide an explicit construction of such a basis and, as a consequence, obtain an algorithm with time complexity $O(|V(G)|^5)$ for computing the dimension and a basis of $Ω_3(G)$ for any finite digraph.

The events detected by the LIGO Virgo KAGRA collaboration over a period of 10 years have yielded a treasure trove of signals from compact binary coalescences. None of these events have shown a confident signature of eccentricity. With upgrades to the existing network and potential next generation gravitational wave detectors, we will be able to see much further into the universe increasing the likelihood of detecting eccentric systems. We improve upon the phenomenological approach of providing eccentricity constraints using an effective chirp mass model in the time frequency domain. We introduce an improved pixel collection method along with a likelihood based sampling approach inspired by Bayesian parameter estimation. Our approach constructs a likelihood from the product of energies collected across different eccentric harmonics in the time frequency representation. This formulation enables coarse but meaningful constraints on orbital eccentricity. Additionally, we incorporate information from the energy ratios between eccentric harmonics, further refining the eccentricity estimates. We test our approach on 500 non spinning equal mass eccentric systems and demonstrate that we can constrain the eccentricity within 0.2 around the true value. Moreover, our approach can deliver these constraints in 5 minutes on a machine with 50 cores. These results demonstrate that our phenomenological approach provides fast and reasonably accurate eccentricity estimates, making it a promising tool for rapid gravitational wave data analysis.

Predicting the future popularity of information in online social networks is a crucial yet challenging task, due to the complex spatiotemporal dynamics underlying information diffusion. Existing methods typically use structural or sequential patterns within the observation window as direct inputs for subsequent popularity prediction. However, most approaches lack the ability to explicitly model the overall trend of popularity up to the prediction time, which leads to limited predictive capability. To address these limitations, we propose VNOIP, a novel method based on variational neural Ordinary Differential Equations (ODEs) for information popularity prediction. Specifically, VNOIP introduces bidirectional jump ODEs with attention mechanisms to capture long-range dependencies and bidirectional context within cascade sequences. Furthermore, by jointly considering both cascade patterns and overall trend temporal patterns, VNOIP explicitly models the continuous-time dynamics of popularity trend trajectories with variational neural ODEs. Additionally, a knowledge distillation loss is employed to align the evolution of prior and posterior latent variables. Extensive experiments on real-world datasets demonstrate that VNOIP is highly competitive in both prediction accuracy and efficiency compared to state-of-the-art baselines.

In the Weighted Triangle-Free 2-Matching problem (WTF2M), we are given an undirected edge-weighted graph. Our goal is to compute a maximum-weight subgraph that is a 2-matching (i.e., no node has degree more than $2$) and triangle-free (i.e., it does not contain any cycle with $3$ edges). One of the main motivations for this and related problems is their practical and theoretical connection with the Traveling Salesperson Problem and with some $2$-connectivity network design problems. WTF2M is not known to be NP-hard and at the same time no polynomial-time algorithm to solve it is known in the general case (polynomial-time algorithms are known only for some special cases). The best-known (folklore) approximation algorithm for this problem simply computes a maximum-weight 2-matching, and then drops the cheapest edge of each triangle: this gives a $2/3$ approximation. In this paper we present a PTAS for WTF2M, i.e., a polynomial-time $(1-\varepsilon)$-approximation algorithm for any given constant $\varepsilon>0$. Our result is based on a simple local-search algorithm and a non-trivial analysis.

This paper investigates inverse source problems for time-dependent electromagnetic waves governed by Maxwell's equations. After applying the Fourier transform with respect to time, the problem leads to a frequency-domain electromagnetic system with a frequency-dependent source term. We propose a novel direct sampling method for reconstructing such radiating time and spatial space of sources from multi-frequency far-field measurements. By using a pair of multi-frequency data from opposite observation directions, we can obtain the radiating time of the signal. Based on this, the smallest region between two hyperplanes containing the support of source can be reconstructed using multi frequency data from one observation direction. The Theta convex hull of the source support can be reconstructed from multi-frequency data from sparse observation directions. Compared with existing sampling methods that mainly focus on reconstructing the spatial support, the proposed approach allows for the simultaneous reconstruction of both spatial and temporal features of the source.Three-dimensional numerical examples are conducted to validate the effectiveness of the algorithm.

Time variability is a pervasive feature of mobility services and a major source of welfare loss. Although literature has quantified the cost of time variability (COTV), it remains theoretically unclear how bad time variability can be in the worst case. Without such a benchmark, quantified variability costs lack a principled reference for assessing whether they are economically meaningful. Meanwhile, this benchmark is critical for strategic prioritization in transport appraisal, service design, and pricing -- particularly in early-stage decision making where detailed valuation is often infeasible. To fill this gap, this paper develops an expected utility (EU) framework to quantify the cost of time (COT) and COTV, establishing theoretical upper bounds on the ratio $COTV/COT$. For users with quadratic utility, we show $COTV/COT \le 1/2 CV^2$, where $CV$ is the coefficient of variation of service time. For Poisson processes, a common assumption, this bound simplifies to $COTV/COT \le 1/2$, implying the total cost of a stochastic service is at most 1.5 times that of an otherwise identical deterministic service. In more general settings, the ratio depends on three interpretable factors: $CV$ and users' second- and third-order risk preferences, captured by relative risk aversion (RRA) and relative prudence (RP). We identify benchmark values of RRA and RP that characterize preferences over mean-, variance-, and skewness-related reductions. Our analysis extends to non-EU frameworks, including dual theory and rank dependent utility, showing that key structural insights remain robust. By quantifying the cost induced by time variability and the $COTV/COT$ ratio, this study provides a data-light benchmark for early-stage decision making and a principled upper bound on users' willingness to pay for reliability improvements, informing the pricing and design of reliability-oriented services.

The use of physical phenomena in the strong-field regime has become a primarily methodology for probing quantum-corrected gravity. This paper investigates periodic orbits, gravitational waves, and accretion disk radiation for a quantum-corrected black hole without Cauchy horizons. First, by analyzing the trajectory equations of massive particles in the equatorial plane, we study the influence of the quantum parameter $ζ$ on the stability of circular orbits. The results show that an increase in $ζ$ leads to an outward migration of both the innermost stable circular orbit and the marginally bound orbit, accompanied by an increase in the required specific angular momentum for particle motion on these two orbits. Then, we further investigate the periodic orbit characteristics of particles and compute the associated gravitational waveforms for extreme mass-ratio inspirals. It is demonstrated that quantum corrections induce a cumulative phase shift in the gravitational wave signal, leading to significant dephasing compared to the classical Schwarzschild case. Furthermore, based on the Novikov-Thorne thin accretion disk model, we evaluate the radiation characteristics of the accretion disk around this quantum-corrected black hole. The results indicate that the introduction of the quantum parameter suppresses the radiant energy flux, effective temperature, and overall radiative efficiency of the disk. These distinctive dynamical and radiative deviations provide potential phenomenological support for distinguishing quantum-corrected geometries from classical black holes using multiple observational means in the future.

Kubelka-Munk (KM) theory provides a two-flux description of radiative transport in layered scattering and absorbing media. Despite its wide use in the coatings, paper, paint, and textile industries, the theory has often been regarded as a phenomenological model whose connection to the full radiative transfer equation (RTE) remains unclear. Under the standard steady-state, plane-parallel, azimuthally symmetric assumptions, we show that multilayer KM theory is exactly a rank-2 Galerkin projection of the RTE onto hemispherical basis functions. The projection is idempotent with an infinite-dimensional kernel, and its rank is preserved under multilayer composition -- so no amount of layer stacking can recover angular information discarded by the projection. We derive the KM coefficients as hemispherical moments of the transport operator and compute the projection error for representative scattering media (g from 0 to 0.85), finding that the reduced optical thickness tau* = tau(1-g) governs KM accuracy. The projection-error framework explains the well-documented accuracy of compositional multilayer models in printed media and shows where higher-order methods become necessary. The result places KM theory on rigorous footing as a legitimate -- if low-resolution -- transport approximation rather than an ad hoc phenomenology.

Droplet impact in airflow environments is ubiquitous in nature and industry, making the understanding of this multiphase behavior crucial for technologies such as anti-icing and spray cooling. In this study, the dynamics of droplet impact on a superhydrophobic surface under shear airflow are numerically investigated using the pseudopotential multiphase lattice Boltzmann method. This three-dimensional model employs a non-orthogonal multiple-relaxation-time scheme to enhance numerical stability and a contact angle hysteresis window to effectively capture dynamic wetting. Specifically, the kinetic energy supplied by the airflow enhances streamwise spreading and significantly expands the final contact footprint due to continuous horizontal sliding. To describe the nonlinear dependence of these contact-line characteristics on the impact Weber number (We) and the airflow Reynolds number (Re), a set of composite scaling laws is developed based on a modified Weber number (We*) that incorporates the airflow contribution. Moreover, the aerodynamic effect leads to a higher velocity restitution coefficient and a deflected take-off angle. Based on an energy partition analysis at detachment, a refined power law is derived to scale the vertical restitution coefficient under shear airflow, while the streamwise restitution coefficient is formulated via the sliding velocity approximation. Integrating these two directional components enables accurate quantitative predictions of the total restitution coefficient and the take-off angle governed by the interplay of We and Re. Overall, this study clarifies the underlying mechanisms of droplet-airflow-surface interactions, providing practical insights for predicting droplet behaviors and guiding surface design under aerodynamic conditions.

The fast and efficient preparation of quantum critical states is a challenging yet crucial task for various quantum technologies. This difficulty is most particularly for systems near a quantum phase transition, where the closure of the energy gap fundamentally limits the timescale of adiabatic processes and thus precludes rapid state preparation. We propose a framework using deep reinforcement learning (DRL) to rapidly prepare quantum critical states, with broad extendibility to light-matter interaction systems. Specifically, a DRL agent optimizes a set of time-dependent control Hamiltonians to drive the system from an initial noncritical state to a target critical state within a finite time and over experimentally accessible parameter ranges. As a concrete application, we focus on the quantum Rabi model. The DRL-optimized time-dependent control Hamiltonian yield a final state with high-fidelity ($>0.999$) to the target critical state. The protocol can be readily extended to other quantum critical systems described by light-matter interaction models, such as quantum Dicke model. This investigation provides a powerful new framework for preparing and manipulating quantum critical states.

Multi-agent systems (MAS) powered by LLMs promise adaptive, reasoning-driven enterprise workflows, yet granting agents autonomous control over tools, memory, and communication introduces attack surfaces absent from deterministic pipelines. While current research largely addresses prompt-level exploits and narrow individual vectors, it lacks a holistic architectural model for enterprise-grade security. We introduce AgenticCyOps (Securing Multi-Agentic AI Integration in Enterprise Cyber Operations), a framework built on a systematic decomposition of attack surfaces across component, coordination, and protocol layers, revealing that documented vectors consistently trace back to two integration surfaces: tool orchestration and memory management. Building on this observation, we formalize these integration surfaces as primary trust boundaries and define five defensive principles: authorized interfaces, capability scoping, verified execution, memory integrity & synchronization, and access-controlled data isolation; each aligned with established compliance standards (NIST, ISO 27001, GDPR, EU AI Act). We apply the framework to a Security Operations Center (SOC) workflow, adopting the Model Context Protocol (MCP) as the structural basis, with phase-scoped agents, consensus validation loops, and per-organization memory boundaries. Coverage analysis, attack path tracing, and trust boundary assessment confirm that the design addresses the documented attack vectors with defense-in-depth, intercepts three of four representative attack chains within the first two steps, and reduces exploitable trust boundaries by a minimum of 72% compared to a flat MAS, positioning AgenticCyOps as a foundation for securing enterprise-grade integration.

We study the semileptonic decays $B_s \to D_s^{(*)}τ\barν$ as a promising probe for new physics (NP) beyond the standard model (SM). The extension of the SM is done through the introduction of four-fermion operators beyond the $V-A$ structure with the corresponding Wilson coefficients characterizing their contribution. The constraints on these coefficients are obtained from recent experimental data. Form factors describing hadron transitions are calculated in our covariant quark model with infrared confinement. Theoretical predictions for the full set of observables in these channels are provided. We analyze possible NP effects to be tested in future experiments.

Maximizing pre-merger warning times in gravitational-wave searches requires minimizing algorithmic latency. While current pipelines typically rely on truncated linear-phase filters, minimum-phase whitening offers a zero-latency alternative that eliminates the acausal look-ahead buffer. However, this causal approach exposes the analysis to spectral drift, where the whitening operator applied to live data diverges from the static template bank, creating a functional perturbation of the matched-filter metric. We develop a perturbative framework generalizing the Cutler-Vallisneri formalism to address these metric errors, deriving analytic expressions for the resulting timing, phase, and SNR biases. Validated against exact stationary-phase models and numerical injections, these corrections achieve $<1\%$ error. Applying this framework to GWTC-4.0 events with realistic 1-week power spectral density (PSD) lags, we find that uncorrected drift induces severe systematics: detector-pair timing biases exceeding $200 μ$s, phase shifts up to 0.2 rad, and sky-localization errors of $5^{\circ}-10^{\circ}$. Additionally, we observe a median signal-to-noise ratio (SNR) loss of $3-5\%$, with outliers exceeding $8\%$. These results demonstrate that while minimum-phase whitening maximizes the early-warning window, analytic drift corrections are essential to maintain detection volume and pointing accuracy in future observing runs.

Multiphoton resonances demonstrate the physical significance of counter-rotating wave terms in light-matter interactions. These resonances, however, are sensitive to detuning errors, making the phenomena challenging to experimentally observe. In this manuscript, we introduce an optimization strategy to address this problem. By using an optimized parameter segmented sequence (OPSS), the robustness against detuning errors of the high-order quantum state transfers can be substantially improved. We prove the versatility of our strategy against frequency detunings by demonstrating the evolution of two specific models. In both cases, the parameter window for maintaining a high state-transfer fidelity is substantially expanded. We further analyze the output photon flux of the optimized system and, taking the three-photon resonance as an example, demonstrate that the system remains capable of generating a stable output photon flux even in the presence of detuning errors.

As visual misinformation becomes increasingly prevalent, platform algorithms act as intermediaries that curate information for users' verification practices. Yet, it remains unclear how algorithmic gatekeeping tools, such as reverse image search (RIS), shape users' information exposure during fact-checking. This study systematically audits Google RIS by reversely searching newly identified misleading images over a 15-day window and analyzing 34,486 collected top-ranked search results. We find that Google RIS returns a substantial volume of irrelevant information and repeated misinformation, whereas debunking content constitutes less than 30% of search results. Debunking content faces visibility challenges in rankings amid repeated misinformation and irrelevant information. Our findings also indicate an inverted U-shaped curve of RIS results page quality over time, likely due to search engine "data voids" when visual falsehoods first appear. These findings contribute to scholarship of visual misinformation verification, and extend algorithmic gatekeeping research to the visual domain.

Artificial noise (AN) is a key physical-layer security scheme for wireless communications over multiple-input multiple-output wiretap channels. Recently, artificial noise elimination (ANE) has emerged as a strategy to mitigate the impact of AN on eavesdroppers. However, the influence of ANE on the secrecy rate when counteracting AN has not been investigated. In this paper, we address this issue by establishing scaling laws for both average and instantaneous secrecy rates in the presence of AN and ANE. Based on the scaling laws, several derived corollaries provide insights into the mutual constraints between the number of transmit antennas, receive antennas, and antennas at eavesdroppers, revealing the interplay between these factors. A key corollary reveals that when the eavesdropper possesses more than twice as many antennas as the transmitter, secure communication may no longer be guaranteed. Additionally, by comparing scenarios where ANE counteracts AN with those where AN is not employed, this study identifies sufficient conditions under which AN remains effective. Finally, the derived secrecy rates provide guidelines for system design, even in the presence of advanced ANE countermeasures implemented by the eavesdropper.

We consider real and complex equiangular lines, generated by unit vectors. We show that, for an arbitrary dimension $d$, if there exists a set of $d^2$ equiangular unit vectors in $\mathbb{C}^d$, then there must exist a set of $d^2$ equiangular unit vectors with all of their coefficients in a number field. This result is motivated by the question of constructing SIC-POVMs in quantum physics and conjectures around them. We discuss applications of our techniques to the case of real equiangular lines and consequences of the above results.

Microcanonical inflection-point analysis (MIPA) identifies third-order transitions from derivatives of the microcanonical entropy, but whether such transitions admit a direct canonical formulation has remained unclear. Here we establish a fluctuation-based canonical framework for third-order transitions through a cumulant-ratio criterion whose signed extrema define their canonical counterparts and, in the single-saddle regime, are asymptotically linked to microcanonical classification. Because the criterion depends only on energy cumulants, it avoids explicit density-of-states reconstruction and remains operational in nonequilibrium steady states. Physically, it reveals dependent and independent third-order transitions as fluctuation reorganizations around low-order transitions, namely disordered-side precursors and ordered-side restructuring. Benchmarks on Onsager's two-dimensional Ising solution, finite size Potts models, and a driven nonreciprocal Ising model show that the framework is theoretically grounded and broadly applicable.

Distributed key-value stores are widely adopted to support elastic big data applications, leveraging purpose-built consensus algorithms like Raft to ensure data consistency. However, through systematic analysis, we reveal a critical performance issue in such consistent stores, i.e., overlapping persistence operations between consensus protocols and underlying storage engines result in significant I/O overhead. To address this issue, we present Nezha, a prototype distributed storage system that innovatively integrates key-value separation with Raft to provide scalable throughput in a strong consistency guarantee. Nezha redesigns the persistence strategy at the operation level and incorporates leveled garbage collection, significantly improving read and write performance while preserving Raft's safety properties. Experimental results demonstrate that, on average, Nezha achieves throughput improvements of 460.2%, 12.5%, and 72.6% for put, get, and scan operations, respectively.

Recent advances in zero-shot voice conversion have exhibited potential in emotion control, yet the performance is suboptimal or inconsistent due to their limited expressive capacity. We propose Emotion-Aware Prefix for explicit emotion control in a two-stage voice conversion backbone. We significantly improve emotion conversion performance, doubling the baseline Emotion Conversion Accuracy (ECA) from 42.40% to 85.50% while maintaining linguistic integrity and speech quality, without compromising speaker identity. Our ablation study suggests that a joint control of both sequence modulation and acoustic realization is essential to synthesize distinct emotions. Furthermore, comparative analysis verifies the generalizability of proposed method, while it provides insights on the role of acoustic decoupling in maintaining speaker identity.