RTL implementations frequently lack up-to-date or consistent specifications, making comprehension, maintenance, and verification costly and error-prone. While prior work has explored generating specifications from RTL using large language models (LLMs), ensuring that the generated documents faithfully capture design intent remains a major challenge. We present SpecLoop, an agentic framework for RTL-to-specification generation with a formal-verification-driven iterative feedback loop. SpecLoop first generates candidate specifications and then reconstructs RTL from these specifications; it uses formal equivalence checking tools between the reconstructed RTL and the original design to validate functional consistency. When mismatches are detected, counterexamples are fed back to iteratively refine the specifications until equivalence is proven or no further progress can be made. Experiments across multiple LLMs and RTL benchmarks show that incorporating formal verification feedback substantially improves specification correctness and robustness over LLM-only baselines, demonstrating the effectiveness of verification-guided specification generation.

The dynamics of fast (e, 2e) collisions, induced by the impact of twisted electron beams, on atomic hydrogen, is analyzed in the presence of a laser field with circular and linear polarization. For the (e,2e) differential cross-section calculations we use Volkov and Coulomb-Volkov wave functions for scattered and ejected electrons, respectively, while the laser-atom interaction is treated in first-order perturbation theory. The formalism is developed for the asymmetric coplanar geometry in the first Born approximation. We investigate the influence of laser field polarization and provide a comparative analysis of Triple Differential Cross-Sections (TDCS) for circularly and linearly polarized laser fields as a function of ejected electron angle. The overall magnitude of the cross-section is larger for circular polarization as compared to linear polarization. Some notable changes in the angular distributions of TDCS were also observed for the circular polarization as compared to that for linear polarization. We further extend the study to coherent superpositions of twisted electron beams to examine OAM effects for macroscopic target which shows that the TDCS$_{av}$ is strongly sensitive to the difference of the projectiles OAM as well as on the phase difference between them.

We resolve the long-standing claim that regularisation by dimensional reduction (DR) fails to preserve supersymmetry in Super Yang-Mills (SYM) theories at three loops. Earlier results reported a mismatch between the Yukawa and ghost-gluon $β$ functions in $\mathcal{N}=2$ SYM, suggesting a breakdown of supersymmertry. We show that this discrepancy does not originate from DR itself but from subtleties in the treatment of the Clifford algebra. A corrected three-loop calculation restores full supersymmetric behaviour, and we demonstrate that the same issue would first affect $\mathcal{N}=4$ SYM only at five loops, consistent with existing four-loop results. Our findings confirm that DR preserves supersymmetry for $\mathcal{N}=1, 2$ and $4$ SYM through the loop orders examined.

We develop a Markov process viewpoint for discrete circular distributions motivated by directional-statistics settings where angles are observed on a finite grid and evolve over time. On the $m$-point discrete circle, the cycle graph, we study diffusion-generated families, obtaining an explicit transition kernel, exact trigonometric moments, and convergence to uniformity. We present a simple approach to construct reversible nearest-neighbour chains with any prescribed strictly positive stationary pmf $π$, providing discrete analogues of Markov processes on the continuous circle. We construct processes whose stationary laws are the discrete von Mises and wrapped Cauchy distributions with closed-form normalizers and exact moments.

We introduce a generalizable framework for learning to identify effective Hamiltonians directly from experimental data in solid-state quantum systems. Our approach is based on a physics-informed neural network architecture that embeds physical constraints directly into the model structure. Unlike purely data-driven supervised schemes, the proposed unsupervised autoencoder-based method incorporates the governing physics (here, the S-matrix formalism) within the decoder network, ensuring that the learned representations remain physically meaningful. Through numerical learning experiments, we demonstrate automated characterization of programmable solid-state simulators from transport measurements, exemplified by a triple quantum dot chain. The trained model generalizes beyond the training domain and accurately infers Hamiltonian parameters from transport data. While the model has finite capacity -- leading to degraded performance when the parameter space becomes excessively large or structurally diverse -- we identify regimes in which robust generalization is maintained. We further show how to train the model to handle noisy measurements, reflecting realistic experimental conditions.

Parameter-Efficient Fine-Tuning (PEFT) is widely applied as the backend of fine-tuning APIs for large language model (LLM) customization in datacenters. Service providers deploy separate instances for individual PEFT tasks, giving rise to prominent resource inefficiencies, including (1) GPU underutilization from small-scale, PEFT-native operators and (2) device stalls from communication delays and data dependencies in parallelized execution. To address these issues, this paper presents MuxTune, a fine-tuning system that enables resource-efficient concurrent execution of multiple PEFT tasks. The key idea is to multiplex the backbone across independent tasks in a spatial-temporal manner for improved utilization and reduced stalls. Building on flexible, modularized backbone sharing via unified PEFT representations, MuxTune proposes hierarchical co-scheduling scheme with task, operator, and data-level optimizations. Specifically, it fuses tasks through a hybrid of spatial and temporal multiplexing, and orchestrates multi-task operator execution in two-tiered hybrid parallelism. Additionally, MuxTune employs chunk-based data alignment to mitigate inter-task ineffective tokens. Experimental results demonstrate that MuxTune achieves up to $2.33\times$ higher throughput and $5.29\times$ memory reduction compared to three state-of-the-art baselines.

In this paper, we study the spectrality of the non-self-adjoint Dirac operator L(Q) with a complex-valued periodic matrix potential Q. We establish a condition on the off-diagonal elements of the matrix Q under which L(Q) is an asymptotically spectral operator. Moreover, we derive a condition on Q that ensures the spectrality of this operator. Finally, we consider the spectral expansion in these cases.

Understanding teleconnections of large-scale modes of climate variability is relevant for seasonal predictability and support a dynamical understanding of climatic changes. While numerical model experiments are the most common approach for investigating counterfactual climate responses, their conclusions are subject to model biases. Data-driven approaches offer a complementary perspective. Deep learning can extract reduced-dimensional patterns but usually lacks causal interpretability, while causal methods can disentangle signals in the presence of confounding yet are typically based on simple indices. Treating dimensionality reduction and causal inference separately thereby risks losing the teleconnection signal of interest. This paper introduces DAG-VAE, a causal representation learning approach that embeds a physics-informed directed acyclic graph in the latent space of a variational autoencoder. Combining deep learning with causal inference, the method jointly learns nonlinear reduced representations of large-scale modes of variability and their causal interactions. We apply DAG-VAE to disentangle the influences of the Pacific and Indian Oceans on the short rains over the Greater Horn of Africa. Trained on seasonal hindcasts, the method identifies dynamically meaningful representations and recovers spatial response patterns consistent with SST-replacement experiments. Trained on reanalysis data, DAG-VAE identifies a different response pattern to direct influence of the tropical Pacific, highlighting potential model biases and the value of DAG-VAE as a complementary, data-driven approach for estimating spatial causal response patterns from observations. Finally, we demonstrate the ability of the method to generate data-driven counterfactuals of extreme short rain seasons, with potential applications for forecast-based early action and scenario planning.

The performance of conventional speech enhancement systems degrades sharply in extremely low signal-to-noise ratio (SNR) environments where air-conduction (AC) microphones are overwhelmed by ambient noise. Although bone-conduction (BC) sensors offer complementary, noise-tolerant information, existing fusion approaches struggle to maintain consistent performance across a wide range of SNR conditions. To address this limitation, we propose the Deep Balanced Multimodal Iterative Fusion Framework (DBMIF), a three-branch architecture designed to reconstruct high-fidelity speech through rigorous cross-modal interaction. Specifically, grounded in a multi-scale interactive encoder-decoder backbone, the framework orchestrates an iterative attention module and a cross-branch gated module to facilitate adaptive weighting and bidirectional exchange. To complement this dynamic interaction, a balanced-interaction bottleneck is further integrated to learn a compact, stable fused representation. Extensive experiments demonstrate that DBMIF achieves competitive performance compared with recent unimodal and multimodal baselines in both speech quality and intelligibility across diverse noise types. In downstream ASR tasks, the proposed method reduces the character error rate by at least 2.5 percent compared to competing approaches. These results confirm that DBMIF effectively harnesses the robustness of BC speech while preserving the naturalness of AC speech, ensuring reliability in real-world scenarios. The source code is publicly available at github.com/wyl516w/dbmif.

In this paper, we study relations among several types of Eulerian polynomials from a combinatorial viewpoint. We establish an identity between the restricted Eulerian polynomials of types $A$ and $B$. As an application, we present a bijective proof of a new identity involving the Eulerian polynomials of type $A$ and type $B$, solving a recent open problem proposed by Zhang. Additionally, we derive an identity between the half Eulerian polynomials of type $B$ and type $D$. Using this identity, we further obtain another relation about the Eulerian polynomials of type $A$ and type $B$, as well as a recursive formula connecting the restricted Eulerian polynomials of type $D$ and Eulerian polynomials of types $A$ and $B$.

The practical implementation difficulties arising from the Gaussian modulation of the GG02 protocol lead us to investigate the possibilities offered by the combination of probabilistic amplitude shaping technique and quadrature amplitude modulation formats in the context of continuous variable quantum key distribution systems. Our interest comes from the fact that quadrature amplitude modulation and probabilistic shaping can be implemented with current technologies and are widely used in classical telecom equipment. In this treatment, we assume to work in the scenario of a linear quantum channel and we analyze maximum achievable secure key rates, maximum reachable distances and the resilience to noise of our discrete-modulation based protocol with respect to GG02, which is taken as a benchmark. In particular, we deal with the infinite key size regime, consider a homodyne detection scheme, and analyze what happens for different cardinalities of the input alphabet at different distances, in the case of collective attacks and in the reverse reconciliation picture. We find that our protocol, beyond being easily reproducible in the laboratory, provides a way to closely approach the theoretical performance offered by GG02 and, at the same time, preserves the ability to assure an unconditional security level.

This paper presents a novel class of string tyre models with FrBD friction dynamics. By modelling the distributed carcass and tread deformations with string-like equations, the resulting formulation leads to a system of semilinear parabolic partial differential equations (PDEs) that describe the evolution of the tyre states without explicitly distinguishing between stick and slip regimes. Rigorous stability and passivity analyses are also conducted using a Lyapunov-based approach, establishing boundedness of the distributed states and energy dissipation during rolling contact. The proposed Lyapunov function admits a clear physical interpretation as the total elastic energy stored in the tyre, enabling a direct link between mechanical energy storage and frictional dissipation due to slip losses. The steady-state and transient behaviours of the model are investigated both numerically and experimentally, revealing that the new formulation can satisfactorily reproduce nonlinear relaxation phenomena excited by step slip inputs. The resulting model provides a physically interpretable, mathematically well-posed, and computationally efficient basis for advanced vehicle dynamics simulations and control-oriented applications.

This paper focuses on the global stability of the 3D magneto-micropolar equations with partial viscosity in the torus $\mathbb T^3$. We first establish the global stability and exponential decay for the 3D magneto-micropolar equations with zero kinematic viscosity. If the micro-rotation effect is neglected, this system reduces to the 3D inviscid and resistive MHD equations which stability problem is still a challenging open problem. Secondly, we obtain the global stability and algebraic decay to the 3D magneto-micropolar equations with zero kinematic viscosity and zero magnetic diffusion on perturbations near a background magnetic field. This system becomes the 3D ideal MHD equations by ignoring the microstructure, and it is well-known that the weighted spaces must be introduced to show the global well-posedness of the ideal MHD equations. Our results indicate that the microstructure has the effect of enhancing dissipation and contributes to stabilize the fluid. To the best of our knowledge, these are the first results on the stabilizing effect of the microstructure on electrically conducting fluids.

Type Ia supernovae (SNe Ia) are considered standardizable candles and are therefore important probes of the universe's expansion history and cosmic distances. In comparison to the optical and IR photometric observations, NIR light curves of SNe Ia are more uniform and are less affected by dust extinction; hence, they can provide more precise distance estimates. This study examines the relationship between the luminosity-dependent behavior of the NIR secondary maximum ($t_2$) and the decline rate parameter ($Δm_{15}$) in the B Band. We analyzed 54 SNe Ia using linear, piecewise linear regression, and non-linear models along with non-parametric statistical techniques to examine the correlation between $t_2$ and $Δm_{15}$. Our results show that the secondary maximum timing varies among SNe Ia but exhibits a luminosity-dependent structure, with significant differences between SNe hosted in late and early-type galaxies. Two separate groups belonging to different host morphologies have been identified through our analysis, one containing brighter SNe and the other containing fainter SNe. These findings have important implications for improving the calibration of SNe Ia for cosmological applications.

We study the solution of the gravitational field equations in $AdSL_{4}$-gauged gravity, a gauge-theoretic extension of general relativity based on the $AdSL_{4}$ algebra. In this formulation, the antisymmetric gauge field $B^{ab}$, associated with additional $AdSL_{4}$ tensorial generators, induces space-time torsion via the relation $K^{ab}=μB^{ab}$, where $K^{ab}$ denotes the contorsion 1-form. The presence of torsion modifies both the spin connection and curvature, leading to an extended set of Einstein-Cartan field equations. Focusing on spatially homogeneous and isotropic cosmological backgrounds, we derive the modified Friedmann equations which explicitly incorporate the torsional contribution. The resulting acceleration equation admits de Sitter-like solutions in which cosmic acceleration originates purely from the gauge-theoretic structure of enlarged four-dimensional space-time symmetries. Within this formulation, the dynamical components of the gauge field $B^{ab}$ emerge naturally as a source of the effective cosmological constants, without the introduction of exotic matter sources. Furthermore, our analysis shows that the torsion-driven cosmological phase in $AdSL_{4}$-gauged gravity can reproduce an effective equation-of-state parameter $ω_{B}=-1/3$, establishing a connection between space-time torsion and cosmic-string-like dynamics.

The semiparametric linear hazard regression model introduced by McKeague and Sasieni (1994) is an extension of the linear hazard regression model developed by Aalen (1980). Methods of model selection for this type of model are still underdeveloped. In the process of fitting a semiparametric linear hazard regression model one usually starts with a given set of covariates. For each covariate one has at least the following three choices: allow it to have time-varying effect; allow it to have constant effect over time; or exclude it from the model. In this paper we discuss focused information criteria (FIC) to help with this choice. In the spirit of Claeskens and Hjort (2003, 2008), `focused' means that one is interested in one specific quantity, e.g. the probability of survival of a patient with a certain set of covariates up to a given time. The FIC involves estimating the mean squared error of the estimator of the quantity one is interested in, and the chosen model is the one minimising this estimated mean squared error. The focused model selection machinery is extended to allow for weighted versions, leading to a suitable wFIC method that aims at finding models that lead to good estimates of a given list of parameters, such as survival probabilities for a subset of patients or for a specified region of covariate vectors. In addition to developing model selection criteria, methods associated with averaging across the best models are also discussed. We illustrate these methods of model selection in a real data situation.

Vopěnka's Alternative Set Theory has been considered as a framework for modelling vague notions. This paper takes feasibility, pertaining to numbers as per some of Yessenin-Volpin's work, and tries to assess how this notion could be modelled in the Alternative Set Theory. The route explored in detail consists in an attempt to model Dummett's weakly infinite, weakly finite totalities, the coherence of which Dummett takes to be decisive of the coherence of Yessenin-Volpin's foundational conception in general. The outcome of the particular analysis turns out to be negative: the Alternative Set Theory could be taken as a model of some, but not quite all features of Yessenin-Volpin's feasible numbers. Given that only one particular approach is explored here, the outcome has limited bearing on the more general questions of coherence of ultrafinitist position, or indeed the possibilities of modelling feasible numbers.

In this manuscript we set up the direct and inverse scattering problems for step-like traveling-wave solutions of the nonlinear Schrödinger equation. Specifically, we consider initial data $u(x,0)$ satisfying $u(x,0)\to u_0^\ell(x)$ as $x\to-\infty$ and $u(x,0)\to u_0^r(x)$ as $x\to+\infty$, where $u_0^\ell(x)$ and $u_0^r(x)$ are elliptic traveling waves. Under suitable assumptions on the initial data we formulate the direct scattering problem and establish analytic properties of the scattering data. We then formulate the inverse problem as a Riemann--Hilbert problem and prove its solvability. Finally, we observe that this Riemann--Hilbert formulation is a special case of the one arising for full soliton-gas initial data.

The characteristics of two-phase stratified magnetohydrodynamic (MHD) flow in horizontal rectangular ducts are investigated for a system consisting of a conductive liquid and a non-conductive gas. Numerical and analytical solutions of the governing equations for the velocity and induced magnetic field intensity of fully developed laminar MHD flow are obtained for various combinations of bottom- and side-wall conductivities and for different orientations of an externally applied transverse magnetic field. The relevant set of dimensionless parameters governing the problem is identified. Unlike in single-phase MHD flows, the presence of a non-conductive gas layer breaks the flow symmetry, leading to a significantly different dependence of the flow characteristics on duct aspect ratio, wall-conductivity configuration, and the strength and orientation of the applied magnetic field. Using mercury-air flow as a representative test case, the solutions are employed to quantify the influence of the gas phase on the in-situ liquid holdup, velocity field, pressure gradient, flow lubrication, and pumping-power requirements. It is shown that, regardless of the magnetic Reynolds number, these characteristics are strongly affected by the wall-conductivity configuration and by the orientation of the external magnetic field.

We review the physics of halo collapse giving rise to various halo boundaries, as well as their identification, observation, and applications. The classical halo is typically defined as a monolithic, virialized object enclosed within its virial radius -- a definition which, however, does not account for ongoing halo growth. Continuous accretion causes the orbits of infalling particles to shrink over time, confining newly accreted material in a growing layer outside the virialized region. Several novel halo boundaries, such as the splashback and depletion radii, have recently been proposed to characterize this growth layer from different perspectives. Along with the turnaround radius, which operates on an even larger scale to enclose the entire infall region, these multiple boundaries comprise an extended view of a dark matter halo as a stratified structure. Theoretical models can largely explain the existence of various boundaries, while challenges remain in providing unified and quantitative predictions of their properties. The multiple boundaries open new avenues for observing halo growth and may substantially improve our understanding and modeling of cosmic structure formation. We provide a python package, SpheriC, implementing the key spherical collapse models.

The third Gaia data release includes 33.8 million radial velocity measurements, extending to a magnitude of G_RVS = 14. To reach this magnitude limit, spectra were processed down to a signal-to-noise ratio (S/N) of 2. In this very low S/N regime, noise-induced peaks in the cross-correlation function can result in spurious radial velocity determinations. Quality filters were applied to the dataset to mitigate such artefacts as much as possible prior to publication. Nevertheless, the high radial velocity (HRV) stars -- defined here as those with radial velocities below -500 or above +500 km/s -- are so sparsely populated that even a few hundred spurious measurements can lead to significant contamination. The objectives of the present study are as follows: (i) to confirm or refute the radial velocity values of the order of one hundred Gaia DR3 HRV stars, (ii) to evaluate the rate of spurious radial velocities in the Gaia DR3 catalogue as a function of S/N and radial velocity, and (iii) to examine the properties of the genuine HRV stars. A total of 134 Gaia DR3 HRV stars were observed using the SOPHIE and UVES spectrographs. (abridged) Ground-based measurements confirm the Gaia DR3 radial velocities of 104 out of our 134 targets, and they refute those of the remaining 30. The combination of these data with the spectroscopic surveys mentioned above enabled an assessment of the rate of spurious measurements as a function of S/N and across three intervals of absolute value of the radial velocity. (abridged) The majority of these stars follow retrograde orbits. Their location in the energy-vertical component of the angular momentum diagram coincides with the region where several structures associated with past merging events have been identified: Sequoia, Arjuna and I'itoi, Antaeus, ED-2, and ED-3. It is likely that most of these HRV stars were accreted.

Self-Supervised Learning (SSL) has emerged as a significant paradigm in representation learning thanks to its ability to learn without extensive labeled data, its strong generalization capabilities, and its potential for privacy preservation. However, recent research reveals that SSL models are also vulnerable to backdoor attacks. Existing backdoor attack methods in the SSL context commonly suffer from issues such as high detectability of triggers, feature entanglement, and pronounced out-of-distribution properties in poisoned samples, all of which compromises attack effectiveness and stealthiness. To that, we propose a Dynamic Stealthy Backdoor Attack (DSBA) backed by a new technique we term Collaborative Optimization. This method decouples the attack process into two collaborative optimization layers: the outer-layer optimization trains a backdoor encoder responsible for global feature space remodeling, aiming to achieve precise backdoor implantation while preserving core functionality; meanwhile, the inner-layer optimization employs a dynamically optimized generator to adaptively produce optimally concealed triggers for individual samples, achieving coordinated concealment across feature space and visual space. We also introduce multiple loss functions to dynamically balance attack performance and stealthiness, in which we employ an adaptive weight scheduling mechanism to enhance training stability. Extensive experiments on various mainstream SSL algorithms and five public datasets demonstrate that: (i) DSBA significantly enhances Attack Success Rate (ASR) and stealthiness while maintaining downstream task accuracy; and (ii) DSBA exhibits superior robustness against existing mainstream defense methods.

By exploiting the arithmetic homotopy of the moduli spaces of curves, Galois-Teichmüller theory stands at the interface of braid-mapping class groups and of anabelian geometry. Starting from the classical braid-theoretic construction of the Grothendieck-Teichmüller group, we review how anabelian geometry -- beginning with the foundational work of Nakamura -- provides the arithmetic mechanisms underlying its definition. We then explain how the combinatorial anabelian geometry developed by Hoshi and Mochizuki recasts these constructions within a purely group-theoretic and algorithmic framework. In particular, we describe how the group GT emerges as an anabelian object and how, once freed from auxiliary or artificially imposed containers, the anabelian algorithms yield a combinatorial reconstruction of the absolute Galois group of rational numbers. The perspective developed here highlights a conceptual shift from explicit braid-theoretic computations to functorial and algorithmic forms of anabelian reconstruction.

We study the optimal routing problem in decentralized exchanges built on Constant Function Market Makers when trades can be split across multiple heterogeneous pools and execution incurs fixed on-chain costs (gas fees). While prior routing formulations typically abstract from fixed activation costs, real on-chain execution presents non-negligible gas fees. They also become convex under concavity/convexity assumptions on the invariant functions. We propose a general optimization framework that allows differentiable invariant functions beyond global convexity and incorporates fixed gas fees through a mixed-integer model that induces activation thresholds. Subsequently, we introduce a relaxed formulation of this model, whereby we deduce necessary optimality conditions, obtaining an explicit Karush-Kuhn-Tucker system that links prices, fees, and activation. We further establish sufficient optimality conditions using tools from generalized convexity (pseudoconcavity/pseudoconvexity and quasilinearity), yielding a verifiable optimality characterization without requiring convex trade functions. Finally, we relate the relaxed solution to the original mixed-integer model by providing explicit approximation bounds that quantify the utility gap induced by relaxation. Our results extend the mathematical theory for routing by offering no-trade conditions in fragmented on-chain markets in the presence of gas fees.

Flexible work is increasingly pursued as a means of achieving work-life balance, particularly as growing caregiving responsibilities for children and aging family members shape workers' lives. Yet most HCI research has examined flexibility primarily through productivity and organizational perspectives, with less attention to how it intersects with workers' personal and family responsibilities. To address this gap, we conducted a qualitative study with 20 workers in Singapore engaging in flexible arrangements to manage paid work and care responsibilities. Using an asset-based lens, we show that flexibility is not a static benefit but a continual practice of rhythm-making. Participants maintained rhythms by drawing on temporal and spatial assets, negotiated them through relational and institutional dynamics, and sustained them through intrapersonal assets such as self-care and positive reframing. Our study reframes blurred boundaries as resources rather than disruptions and offers design implications for technologies that support flexible workers' everyday rhythm-making practices.

The resonant dynamics of a charged particle, governed by the Lorentz force equation in an electromagnetic field generated by a current-carrying wire with a small harmonic modulation, is considered in this study. When regarded as a Hamiltonian system with periodic perturbation, the resonance of periodic orbits in the unperturbed system is analyzed by the Melnikov method. The existence of exactly one harmonic radial periodic solution with period $T_1$ is confirmed, matching the period of the current. Moreover, it is established that any other radial periodic solution must be subharmonic with period $nT_1$ for some integer $n > 1$, with at most one such solution for each $n$. Dynamically, these surviving periodic orbits correspond to invariant cylinders that partition the phase space and globally confine the particle's radial motion.

We show that the Serreau--Tissier (ST) replica sector can dynamically generate a Gribov--Zwanziger (GZ)--type horizon functional in Yang--Mills (YM) theories. After integrating out the replica superfields, the expansion of the determinant of the Faddeev--Popov (FP) operator in the regulator $ζ$ produces, at linear order in $ζ$, a nonlocal kernel with the same color and Lorentz structure as the Gribov horizon functional, thereby defining an induced Gribov scale. Depending on the replica phase selected by the dynamics, the ST sector yields either (i) a local Curci--Ferrari (CF) screening mass (replica-symmetric phase) or (ii) an induced horizon-like interaction (replica-broken phase). In the latter case, the resulting BRST-invariant local formulation leads to a tree-level gluon propagator of the refined Gribov-Zwanziger (RGZ) decoupling type, whereas in the former it reduces to the massive FP/CF form, avoiding double counting of infrared scales by construction. A superspace derivation confirms that the induced horizon term originates from the ST superdeterminant, providing a microscopic mechanism for the emergence of the Gribov scale within the replica framework.

The ESSnuSB experiment aims to measure the leptonic CP phase $δ_{CP}$ with an unprecedented resolution by probing neutrino oscillations at the second oscillation maximum. In the present work, the complementarity between the long-baseline neutrino program and atmospheric neutrinos is investigated for ESSnuSB. By simulating atmospheric neutrino events equivalent of 5.4 Mt$\cdot$year exposure, the resolution for $δ_{\rm CP}^{}$ is found to improve from $7.5^\circ$ ($6.7^\circ$) to $7.1^\circ$ ($6.5^\circ$) at $1σ$~CL for $δ_{\rm CP}^{} = -90^\circ$ ($+90^\circ$) with respect to super-beam neutrinos, resolving also the degeneracies arising from neutrino mass ordering. These findings highlight the synergies that exist between super-beam neutrinos and atmospheric neutrinos in ESSnuSB.

The Cherenkov Telescope Array Observatory (CTAO) is the next-generation observatory for high energy γ-ray astronomy with unprecedented sensitivity and accuracy. Accurate estimation and mitigation of systematic uncertainties are crucial for its scientific performance. Atmospheric properties significantly influence both the generation and extinction of Cherenkov light generated by gamma and cosmic rays interacting in the atmosphere. This study provides a detailed analysis of molecular extinction processes, including Rayleigh scattering and molecular absorption, and their impact on the transmission of Cherenkov light. We examine typical summer and winter behaviour of Rayleigh scattering and seasonal and event-driven variations of the main absorbing molecules, such as ozone and nitrogen oxides, at the two CTAO array sites. Using simulations, we assess the effects of these variations on image intensity and trigger effective area, particularly during dynamic atmospheric events like stratosphere-to-troposphere transport. Based on our findings, we propose an atmospheric monitoring and calibration strategy to ensure that the CTAO meets its systematic uncertainty requirements, particularly for low-energy gamma-ray observations.

Radio-based simultaneous localization and mapping (SLAM) has the potential to provide precise user equipment (UE) localization and environmental sensing capabilities by exploiting radio signals. Most existing approaches leverage line-of-sight (LoS) and single-bounce non-line-of-sight (NLoS) paths solely, while higher-order NLoS paths are treated as disturbance. In this paper, we investigate the benefits of leveraging double-bounce NLoS paths for solving the bistatic snapshot radio SLAM problem. We derive the Cramer-Rao bound (CRB) for joint estimation of the UE state and landmark positions when double-bounce NLoS paths are present. In addition, we propose an algorithm to identify double-bounce NLoS paths and leverage them into joint UE and landmarks estimation. The derived bounds are validated through simulated data, and the proposed algorithms are evaluated using experimental millimeter wave (mmWave) measurements harnessing beamformed 5G cellular reference signals. The numerical and experimental results demonstrate that the double-bounce NLoS paths which share at least one incidence point (IP) with the single-bounce NLoS paths improve the estimation accuracy of the UE state and existing IPs of single-bounce NLoS paths. Importantly, exploiting double-bounce NLoS paths enhances environmental mapping capabilities by revealing landmarks that are unobservable with single-bounce NLoS paths alone.