The Spin Light of neutrino ($SLν$) is an electromagnetic radiation of the neutrino magnetic moment emitted when neutrino moves in external conditions (fields or matter). The effect can be of significance in the extremely dense matter of compact astrophysical objects such as neutron stars (NS). If detected, this radiation could provide a fair opportunity to study the properties of neutrinos and the medium through which they move, since the properties of the radiation depend on both. Motivated by the possibility of the nuclear matter spin-polarization, in this paper, we study the new properties to $SLν$ obtained under the influence of net matter polarization. We demonstrate that the polarization can enhance or completely suppress the radiation. Also, it introduces a characteristic asymmetry into the total radiation from the compact object, which could be an observable feature dependent on the matter polarization and the magnetic field inside the stellar (if the field is connected to the stellar matter polarization). The research may have implications for the physics of NS and magnetars, bringing us closer to the possibility of studying their internal structure.

Computing resonance and anti-resonance backbone curves in complex nonlinear mechanical systems is of high engineering relevance but remains computationally challenging, especially for large finite-element (FE) models. Existing manifold-based approaches often rely on polynomial parameterizations, limiting their effectiveness for general smooth, non-polynomial nonlinearities. To overcome these limitations, we develop a direct optimization framework that employs a Lagrange multiplier formulation to determine the resonance backbone curve on the response surface constrained by the harmonic balance governing equations. Crucially, solving this formulation efficiently requires second-order sensitivity information. Therefore, the primary innovation of this work is the derivation of a analytical Hessian Tensor for generic $C^2$-continuous nonlinear elements. This is achieved by combining an extended Alternating Frequency/Time (AFT) method for computing second-order derivatives with local-coordinate tensor transformations. By integrating this analytical Hessian into the solver, the proposed framework ensures robust convergence and significantly reduces runtime, making it practical for large-scale models where numerical differentiation is computationally prohibitive. The method is validated on three benchmarks of increasing complexity: a two-degree-of-freedom (2-DOF) system with cubic nonlinearity, a beam with cubic stiffness or hyperbolic tangent (tanh) friction nonlinearities, and an industrial-scale finite element model of a compressor bladed disk (blisk) with a friction ring damper. Results demonstrate that the proposed framework accurately and efficiently computes both resonance and anti-resonance backbone curves, providing a robust frequency-domain tool for structures with non-polynomial nonlinearities.

This paper studies McKean-Vlasov stochastic differential equations (MVSDEs) whose drift coefficients grow super-linearly in both state variables and measure arguments, and whose diffusion coefficients exhibit super-linear growth in the state variables. By constructing an Euler-like sequence, we establish the strong well-posedness of such MVSDEs under a locally monotone condition. Furthermore, the propagation of chaos is studied on both finite and infinite horizons, demonstrating convergence of the interacting particle system to the corresponding non-interacting system. To illustrate the rationality of the theoretical results, we provide examples whose drifts contain the high powers and multiple integrals of distributions, with numerical simulations presented in Section 6.

I discuss several roles of the strong electromagnetic fields created by relativistic heavy-ion collisions. These phenomena call for theoretical and experimental developments to understand dynamics of quark-gluon plasma (QGP) as well as purely electromagnetic processes in the ultraperipheral collisions.

We develop a complete Deift-Zhou steepest descent analysis for a 3x3 matrix Riemann-Hilbert problem arising in quadratic Hermite-Pade approximation and multiple orthogonality. We focus on a regular two-edge regime with a hard edge at 0 and a soft edge at x0. Under natural geometric and analytic assumptions ensuring a nondegenerate sign structure of the associated phase functions, the standard lens-opening mechanism applies. The analysis is organized as a reusable scheme: once the equilibrium/sign-chart input is verified (assumptions R1-R7), the remaining steps are purely analytic. As a result, the solution is described in terms of a reduced outer parametrix with permutation-type jumps, complemented by Bessel- and Airy-type local parametrices at the endpoints. We obtain uniform strong asymptotics for the top-left entry, with an explicit error bound of order 1/n outside the endpoint neighborhoods.

Numerical end-to-end simulation in Adaptive Optics (AO) is a key tool in the development of complex systems, from the initial design to the commissioning phase. Based on our previous experience with PASSATA, we decided to develop a new AO simulation framework in Python language, naming it SPECULA (short for: Scalable Parallel Execution of Computations Upscaling Large Adaptive optics simulations). Following an object-oriented approach, the physical entities are modeled as processing objects connected to each other to exchange data objects. A simulation is run by providing its description instead of writing and executing a specific script. The Python language and its library flexibility allowed us to write one single code that can be run on CPU and GPU platforms. We put a strong focus on computational efficiency, relying on CuPy and its interface to access the CUDA-stream mechanism. Moreover, SPECULA is capable of distributed computations over multiple processing nodes, making it suitable to run in an HPC environment, as tested on the Italian supercomputer Leonardo. SPECULA can also be used in laboratory environment to implement a hybrid simulation, allowing us to interface simulated and concrete objects: this feature was demonstrated in the Adaptive Optics laboratories at Arcetri Observatory. In this paper, we describe the main characteristics of SPECULA, show some relevant examples of its use, and finally draw our goals for the future.

Large Language Models (LLMs) have demonstrated remarkable proficiency in vulnerability detection. However, a critical reliability gap persists: models frequently yield correct detection verdicts based on hallucinated logic or superficial patterns that deviate from the actual root cause. This misalignment remains largely obscured because contemporary benchmarks predominantly prioritize coarse-grained classification metrics, lacking the granular ground truth required to evaluate the underlying reasoning process. To bridge this gap, we first construct a benchmark consisting of two datasets: (1) real-world vulnerabilities with expert-curated causal reasoning as ground truth, and (2) semantically equivalent code perturbations for assessing reasoning robustness. Our large-scale empirical study reveals that even state-of-the-art models struggle to maintain logical consistency during semantic code comprehension, exhibiting 12 systematic failure patterns. Addressing these limitations, we propose DAGVul, a novel framework that models vulnerability reasoning as a Directed Acyclic Graph (DAG) generation task. Unlike linear chain-of-thought (CoT), our approach explicitly maps causal dependencies to enforce structural consistency. By further introducing Reinforcement Learning with Verifiable Rewards (RLVR), we align model reasoning trace with program-intrinsic logic. Experimental results demonstrate that our framework improves the reasoning F1-score by an average of 18.9% over all the baselines. Remarkably, our 8B-parameter implementation not only outperforms existing models of comparable scale but also surpasses specialized large-scale reasoning models, including Qwen3-30B-Reasoning and GPT-OSS-20B-High. It is even competitive with state-of-the-art models like Claude-Sonnet-4.5 (75.47% vs. 76.11%), establishing new efficiency in vulnerability reasoning across model scales.

The two-relaxation-time (TRT) lattice Boltzmann model is widely adopted for its simplicity and tunable boundary accuracy. However, its collision operator relaxes the full symmetric non-equilibrium component, implicitly retaining non-hydrodynamic ghost modes that degrade stability at high Reynolds numbers. In this work, we establish a rigorous connection between ghost-mode filtering and regularization within the TRT framework. By decomposing the discrete velocity space into hydrodynamic and non-hydrodynamic subspaces, we prove that the TRT-regularized lattice Boltzmann (TRT-RLB) model is mathematically equivalent to the standard TRT model with ghost modes explicitly removed. This equivalence holds exactly for D2Q9 and D3Q19 lattices, where the symmetric and antisymmetric subspaces are completely spanned by the physically relevant Hermite modes and identifiable ghost modes. Based on this finding, we propose the Purified TRT (P-TRT) model, which achieves regularization-level stability through simple algebraic ghost-mode subtraction rather than expensive tensor projections. For D2Q9, the non-equilibrium collision cost is reduced from 180 to 52 floating-point operations per node, a 71% reduction. Linear stability analysis in moment space further reveals that the P-TRT operator annihilates the ghost eigenvalue, proving its spectral radius is bounded above by that of standard TRT and that stability is governed exclusively by hydrodynamic modes. Benchmarks including the double shear layer at Re up to 10^7, Taylor--Green vortex decay, force-driven Poiseuille flow, and creeping flow past a square cylinder confirm that P-TRT preserves the stability, second-order accuracy, and zero-slip boundary properties of TRT-RLB while retaining the simplicity of the TRT family.

We study a family of Laguerre--Sobolev orthogonal polynomials associated with a Sobolev inner product arising from second--order boundary value problems on the semi--infinite interval $(0,+\infty)$. These polynomials generate an orthogonal basis of test functions vanishing at the endpoints and are especially well suited for the spectral approximation of Schrödinger--type problems with singular potentials. Explicit connection formulas with classical Laguerre polynomials are obtained, together with recurrence relations and asymptotic properties of the corresponding coefficients. A generating function involving Bessel functions is also derived. As an application, we develop a fully diagonalized Laguerre--Sobolev spectral method for Dirichlet problems with singular potentials. The method avoids the solution of linear systems and can be implemented recursively. Numerical experiments for a Schrödinger--type equation with inverse--distance potential confirm spectral accuracy and exponential convergence.

While Dark Matter (DM) is conventionally assumed to be chargeless, the possibility of a charged massive particle (CHAMP) as the DM particle remains alive. With phenomenological constraints on the charge being very severe, such a scenario is often sought to be dismissed, citing naturalness. We demonstrate here that such a (mini)charged DM can be realized within the clockwork paradigm, without the need to invoke unnaturally small parameters. The model is examined against constraints, theoretical and experimental, and the phenomenologically admissible parameter space is delineated. Several intriguing tests, at the LHC as well as at future direct and indirect detection experiments, are pointed out.

Fixpoint iteration constitutes the algorithmic core of static analyzers. Parallelizing the fixpoint engine can significantly reduce analysis times. Previous approaches typically fix the granularity of tasks upfront, e.g., at the level of program threads or procedures - yielding an engine permanently stuck in one gear. Instead, we propose to parallelize a generic fixpoint engine in a way that is parametric in the task granularity - meaning that our engine can be run in different gears. We build on the top-down solver TD, extended with support for mixed-flow sensitivity, and realize two competing philosophies for parallelization, both building on a task pool that schedules tasks to a fixed number of workers. The nature of tasks differs between the philosophies. In the immediate approach, all tasks access a single thread-safe hash table maintaining solver state, while in the independent approach, each task has its own state and exchanges data with other tasks via a publish/subscribe data structure. We have equipped the fixpoint engine of the static analysis framework Goblint with implementations following both philosophies and report on our results for large real-world programs.

Motivated by observations of Guillera we generalise the so-called Ramanujan-type supercongruences to a further level in which the sequences of Fibonacci, Lucas, Apéry numbers and their friends all receive a natural appearance.

We theoretically investigate spin-valley-locked tunneling transport in a transition-metal dichalcogenide/ferromagnetic-insulator heterostructure under a perpendicular magnetic field, driven by the spin Seebeck effect. We demonstrate that spin-valley coupling together with the magnetic-field-induced valley-asymmetric Landau-level structure enables the generation of a valley-polarized spin current from valley-selective spin excitation. We compare the spin current and the valley-polarized spin current in the conduction and valence bands and clarify their distinct microscopic origins. We predict pronounced quantum oscillations of the valley-polarized spin current, providing a clear experimental signature of quantized valley states.

We study future-blind preferences, which are preferences that heavily discount the future, within the space of infinite consumption streams. We give two definitions: $N$-blindness, where agents ignore periods beyond a fixed date $N$, and eventual blindness, where all but finitely many dates are neglected. Using a topological approach, we show that the finest topology ensuring eventual blindness coincides with the product topology. This provides a behavioral foundation for continuity in the product topology, which was considered for studying equilibrium existence in infinite-dimensional spaces. Finally, we characterize the dual spaces under these topologies.

Summarizing source code into natural language descriptions (code summarization) helps developers better understand program functionality and reduce the burden of software maintenance. Abstract Syntax Trees (ASTs), as opposed to source code, have been shown to improve summarization quality in traditional encoder-decoder-based code summarization models. However, most large language model (LLM)-based code summarization methods rely on raw code or only incorporate partial AST signals, meaning that the potential of complete AST representation has not been fully explored for LLMs. This paper presents AST(NIT), an AST augmentation and serialization method that preserves lexical details and encodes structural information into LLM-compatible sequences. Experiments with the LLaMA-3.1-8B model on the CodeXGLUE Python dataset show that the proposed serialized ASTs reduce the length of LLM inputs, require shorter training times, and achieve summarization quality comparable to existing approaches.

Gromov's band-width conjecture gives a precise upper bound for the width of a compact Riemannian band with positive scalar curvature lower bound, assuming that the cross-section of the band admits no positive scalar curvature metrics. Versions of this were proved by Cecchini and by Zeidler. In this paper, we develop a quantitative version of partitioned manifold index theory, which applies to noncompact hypersurfaces. Using this, we prove a version of Gromov's band-width estimate for possibly noncompact Riemannian bands.

Measurements of the inclusive and differential top-quark pair ($t\bar{t}$) cross-sections in proton--proton collisions at $\sqrt{s} = 13$ TeV, using 140 fb$^{-1}$ of data collected by the ATLAS experiment at the Large Hadron Collider, are presented. Events with an opposite-charge $eμ$ pair and $b$-tagged jets are selected, and the inclusive cross-section measurement is used to determine the top-quark pole mass via the dependence of the predicted cross-section on $m_t^\mathrm{pole}$. Complementary measurements of $eμb\bar{b}$ production, treating both $t\bar{t}$ and $tW$ events as signal, are also provided.

We study the impact of primordial black holes (PBHs) on the chiral phase transition and its associated stochastic gravitational-wave (GW) signals. Using the three-flavor Nambu-Jona-Lasinio model, we construct the chiral effective potential in a Schwarzschild spacetime background. We find that PBHs promote chiral symmetry restoration and induce a nontrivial local phase structure in the vicinity of the event horizon simultaneously. In particular, this structure exhibits a novel chiral symmetry breaking pattern involving both second- and first-order phase transitions, a feature absent in flat spacetime. We further demonstrate that PBHs act as genuine catalysts for the chiral phase transition by analyzing the bounce solution in curved spacetime. The presence of PBHs substantially enhances the inverse duration parameter $β/H$ while leaving the overall transition strength comparable to that in flat spacetime. As a consequence, even a small population of PBHs can induce $\mathcal{O}(1)$ shifts in both the peak frequency and the peak amplitude of the GW spectrum generated by the first-order dark chiral phase transition.

We construct exotic compact objects composed entirely of self-interacting asymmetric fermionic dark matter governed by a repulsive Yukawa potential with massive dark interaction boson. By considering the structural, tidal, and rotational properties of solar mass self-gravitating dark matter systems, and contrasting them against purely baryonic neutron stars, described by the well understood SLy4 equation of state, we hope to shed some light on the place of dark compact systems in the context of gravitational wave astronomy, specifically due to the difficulty parsing mass and radius data from events with no electromagnetic counterpart. Here we consider systems composed of 1 GeV and 10 GeV dark matter. Relevant compact objects are then analysed and simulated as both static bodies, and rotating systems governed by the Hartle-Thorne formalism to second order. Here within we highlight the differences in key tidal and rotational properties encoded in gravitational wave signals, and analyse how dark objects may mimic or distinguish themselves to current and future gravitational wave observatories.

The second law of thermodynamics for adiabatic operations -- constraints on state transitions in closed systems under external control -- is one of the fundamental principles of thermodynamics. On the other hand, it is recently established that even pure quantum states can represent thermal equilibrium. However, pure quantum states do not satisfy the second law in that they are not passive, i.e., work can be extracted from them if arbitrary unitary operations are allowed. It therefore remains unresolved how quantum mechanics can be reconciled with thermodynamics. Here, based on our key quantum-mechanical notions of thermal equilibrium and adiabatic operations, we address the emergence of the second law for adiabatic operations in the thermodynamics limit. We first introduce infinite-observable macroscopic thermal equilibrium (iMATE); a quantum state, including pure states, is in iMATE if the expectation values of all additive observables agree with their equilibrium values. We also introduce a macroscopic operation as unitary evolution generated by a time-dependent additive Hamiltonian, which is regarded as corresponding to adiabatic operations. Employing these concepts, we show that no extensive work can be extracted from any quantum state in iMATE through any macroscopic operations. Furthermore, we introduce a quantum-mechanical form of entropy density such that it agrees with thermodynamic entropy density for any quantum state in iMATE. We then prove that for any initial state in iMATE, this entropy density cannot be decreased by any macroscopic operations, followed by a time-independent relaxation process. Our theory thus proves two different forms of the second law, by adopting macroscopically reasonable classes of observables, equilibrium states, and operations. We also discuss the time scales of macroscopic operations in these results.

In this article, we introduce an extrinsic approach to the notion of semi-direct product, an intrinsic one (namely inside the category Gp of group itself) having been already done elsewhere. This will led us to focus our attention on two algebraic structures (hypersubtraction and hyper-Slominski settings) which will allow us to characterize this extrinsic explicitation.

We consider stochastic growth models for populations organized in colonies and subject to uniform catastrophes. To assess population viability, we analyze scenarios in which individuals adopt dispersion strategies after catastrophic events. For these models, we derive explicit expressions for the survival probability and the mean time to extinction, both with and without spatial constraints. In addition, we complement this analysis by comparing uniform catastrophes with binomial and geometric catastrophes in models with dispersion and no spatial restrictions. Here, the terms uniform, binomial and geometric refer to the probability distributions governing the number of individuals that survive immediately after a catastrophe. This comparison allows us to quantify the impact of different types of catastrophic events on population persistence.

Given a smooth, complete Riemannian manifold $M$ with bounded Ricci curvature and positive injectivity radius, we derive a sharp Sobolev inequality for the embedding of $W^{1,p}(M)$ into $L^{\frac{np}{n-p}}(M)$, when $1\le p< n$. We will first reduce the inequality to functions having support with small enough volume. In turn, we will show that the inequality for small volumes is implied by a first order uniform asymptotic expansion of the isoperimetric profile for $M$, for small volumes. We will then show that such an expansion follows from a local, uniform Sobolev inequality for functions in $W^{1,1}$, having support with small enough diameter.

We consider Talagrand-type transportation inequalities for the law of Brownian motion on Carnot groups. An important example is the lift of standard Brownian motion to the Brownian rough path. We present a direct proof on enhanced path space, which also yields equality when restricting to adapted couplings in the transport problem. Moreover, we prove a Talagrand inequality for the heat kernel measure on Carnot groups and deduce the inequality for the law of Brownian motion on Carnot groups via a bottom-up argument. Our study of this enhanced Wiener measure contributes to a longstanding programme to extend key properties of Wiener measure to the non-commutative setting of the enhanced Wiener measure, which is of central importance in Lyons' rough path theory. With a non-commutative sub-Riemannian state space, we observe phenomena that differ from the Euclidean case. In particular, while a top-down projection argument recovers Talagrand's inequality on Euclidean space from the corresponding inequality on the path space, such a projection argument breaks down in the Carnot group setting. We further study a Riemannian approximation of the Heisenberg group, in which case the failure of the top-down projection can be partially overcome. Finally, we show that the cost function used in the Talagrand inequality is a natural choice, in that it arises as a limit of discretised costs in the sense of $Γ$-convergence.

We introduce a complete equational theory for the fragment of quantum circuits generated by the real Clifford gates plus the two-qubit controlled-Hadamard gate. That is, we give a simple set of equalities between circuits of this fragment, and prove that any other true equation can be derived from these. This is the first such completeness result for a finitely-generated, universal fragment of quantum circuits, with no parameterized gates and no need for ancillas.

We consider nonsmooth optimization problems under affine constraints, where the objective consists of the average of the component functions of a large number $N$ of agents, and we only assume access to the Fenchel conjugate of the component functions. The algorithm of choice for solving such problems is the dual subgradient method, also known as dual decomposition, which requires $O(\frac{1}{ε^2})$ iterations to reach $ε$-optimality in the convex case. However, each iteration requires computing the Fenchel conjugate of each of the $N$ agents, leading to a complexity $O(\frac{N}{ε^2})$ which might be prohibitive in practical applications. To overcome this, we propose a two-stage algorithm, combining a stochastic subgradient algorithm on the dual problem, followed by a block-coordinate Frank-Wolfe algorithm to obtain primal solutions. The resulting algorithm requires only $O(\frac{1}{ε^2} + \frac{N}{ε^{2/3}})$ calls to Fenchel conjugates to obtain an $ε$-optimal primal solution in expectation in the convex case. We extend our results to nonconvex component functions and show that our method still applies and gets (almost) the same convergence rate, this time only to an approximate primal solution recovering the classical duality gap bounds usually obtained using the Shapley-Folkman theorem.

Inspired by the success of Transformer-based models in natural language processing, this paper investigates their potential as foundation models for network traffic analysis. We propose a unified pre-training and fine-tuning pipeline for traffic foundation models. Through fine-tuning, we demonstrate the generalizability of the traffic foundation models in various downstream tasks, including traffic classification, traffic characteristic prediction, and traffic generation. We also compare against non-foundation baselines, demonstrating that the foundation-model backbones achieve improved performance. Moreover, we categorize existing models based on their architecture, input modality, and pre-training strategy. Our findings show that these models can effectively learn traffic representations and perform well with limited labeled datasets, highlighting their potential in future intelligent network analysis systems.

Measuring charge-parity (CP) violation in Higgs-fermion interactions is a key target of future precision Higgs programs. The decay $H\toττ$ is particularly sensitive to the CP structure of the Higgs Yukawa coupling via $τ$ spin correlation, while the clean environment of $e^+e^-$ collisions at the Future Circular Collider (FCC) enables accurate reconstruction of CP-sensitive observables. In this letter, we study the sensitivity of FCC-ee to the Higgs CP state using $H\toττ$ events produced in associated $ZH$ production at $\sqrt{s}=240$ GeV. In the anomalous-coupling parametrization, we project a precision of $Δϕ_{ττ}=\pm 2.5^\circ$ at 68 % CL, with the dominant contribution arising from one-prong hadronic $τ$ decays. We further interpret the analysis in the Effective Field Theory framework, deriving expected limits on the CP-odd operators, and compare them with electron and magnetic dipole moment measurements and the anomalous-coupling approach.

Random Access Channel (RACH) jamming poses a critical security threat to 5G and beyond (B5G) networks. This paper presents an analytical model for predicting the impact of Msg1 jamming attacks on RACH performance. We use the OpenAirInterface (OAI) open-source user equipment (UE) to implement a Msg1 jamming attacker. Over-the-air experiments validate the accuracy of the proposed analytical model. The results show that low-power and stealthy Msg1 jamming can effectively block legitimate UE access in 5G/B5G systems.

As Large Language Models (LLMs) are increasingly deployed to generate educational content, a critical safety question arises: can these models reliably estimate the difficulty of the questions they produce? Using Brazil's high-stakes ENEM exam as a testbed, we benchmark ten proprietary and open-weight LLMs against official Item Response Theory (IRT) parameters for 1,031 questions. We evaluate performance along three axes: absolute calibration, rank fidelity, and context sensitivity across learner backgrounds. Our results reveal a significant trade-off: while the best models achieve moderate rank correlation, they systematically underestimate difficulty and degrade significantly on multimodal items. Crucially, we find that models exhibit limited and inconsistent plasticity when prompted with student demographic cues, suggesting they are not yet ready for context-adaptive personalization. We conclude that LLMs function best as calibrated screeners rather than authoritative oracles, supporting an "evaluation-before-generation" pipeline for responsible assessment design.