Optical resonator-enhanced nonlinear interactions are of great importance for the efficient generation of continuous-wave second harmonic generation, optical parametric oscillation, frequency mixing, and the generation of squeezed light. In order to maximize these interactions within the intra-cavity nonlinear material, high intensities, optimal phase matching, and simultaneous resonance of all interacting fields are required. However, the dispersion of the optical resonator often prevents the co-resonance of multiple wavelengths. Here, we present a novel implementation using a monolithic bimetallic heat sink for controlling the resonator dispersion based on a shallow temperature gradient directly applied to a section of the nonlinear crystal. This method enables precise dispersion control and is designed to minimize mechanical and thermal stresses in the nonlinear crystal, thus providing an additional method for designing highly efficient and reliable resonator-enhanced nonlinear devices for demanding applications such as gravitational wave detection, quantum optics, and frequency conversion.

The equation of state and, more generally, the thermodynamics of the Lennard-Jones fluid have long served as a benchmark problem in the statistical theory of fluids. Among available theoretical approaches, first-order perturbation theory occupies a special position: only at this level does the correction to the Helmholtz free energy admit an exact statistical-mechanical expression. In this work, we present a systematic, simulation-based assessment of a non-classical first-order perturbation theory in which the reference system incorporates the entire short-range part of the interaction, while the perturbation is confined to the remaining long-range tail. We show that this range-based decomposition transforms the perturbation contribution into a small, smoothly varying, near-mean-field quantity over a broad supercritical thermodynamic domain. When its density and temperature derivatives are consistently retained, the resulting equation of state reproduces high-accuracy reference data with excellent fidelity. The results demonstrate that the success of first-order perturbation theory is governed primarily by the physical content of the reference system and by the consistent treatment of its state dependence, rather than by the formal truncation order of the expansion.

Finite horizon Sinai billiard maps are examples of uniformly hyperbolic systems with singularities. These discontinuities make it more difficult to develop the classical theory of thermodynamic formalism. Nevertheless, Baladi and Demers established a variational principle for these systems, and proved that if the billiard table satisfies a certain sparse recurrence condition, then there is a unique measure of maximal entropy. We extend this existence and uniqueness result to all finite horizon Sinai billiard maps by giving a new proof that does not rely on the sparse recurrence condition. Our construction is very concrete: the unique MME is obtained as the product of the Hausdorff measures on the one-sided subshifts associated to the billiard map.

Resolution of complex post-production issues in large-scale open-source software (OSS) projects requires significant cognitive effort, as developers need to go through long, unstructured and fragmented issue discussion threads before that. In this paper, we present SWE-MIMIC-Bench, an issue trajectory dataset generated from raw GitHub discussions using an automated multi-LLM pipeline. Unlike simple summarization, this pipeline utilizes a group of closed-source LLMs to perform granular tasks: analyzing individual comments with awareness of externally-linked resources, classifying comment analyses into label-specific fields (e.g., root cause, solution plan, implementation progress), and synthesizing label-aware trajectories which capture a structured and coherent narrative of the entire discussion thread. Our pipeline uses five closed-source LLM configurations for distinct purposes: label classification, inline code block and external link summarization, comment analysis, label-specific field classification and trajectory synthesis. By generating concise and reliable trajectories from complex conversation threads, this system can assist developers and researchers of broader software engineering community to understand the experience-driven collaborative approach for issue diagnosis. Furthermore, the generated trajectories can be used to train modern LLM agents to think and act like an expert developer. We evaluated our system on 800 real-world GitHub issues drawn from the SWE-Bench-Pro, SWE-Bench-Multilingual and SWE-Bench-Verified dataset, achieving a 91.7% success rate in extracting 734 high-fidelity reasoning trajectories.

This is Paper 6 of a series of formally-verified analyses of masked NTT hardware for post-quantum cryptography; Paper 1 [1] established structural dependency analysis of the QANARY platform, and Paper 2 [2] quantified security margins under partial NTT masking. Boolean masking composition is well-understood through NI, SNI, and PINI. Arithmetic masking over $\mathbb{Z}_q$ for prime $q$, the foundation of NTT-based post-quantum cryptography, has lacked an analogous theory. We prove, to our knowledge, the first machine-checked composition theorems for arithmetic masking over prime fields. Our key insight is the renewal argument: when a fresh random mask is applied between two pipeline stages, the intermediate wire becomes perfectly uniform regardless of Stage 1's security parameter. For two PF-PINI gadgets with parameters $k_1$ and $k_2$, the composed two-stage pipeline with fresh masking satisfies PF-PINI($k_2$), Stage 1's multiplicity is completely erased from the composed output. Without fresh masking, intermediate wires have multiplicity up to $k_1$, creating a necessary condition for differential power analysis. We formalize both theorems in Lean 4 with 18 machine-checked proofs and zero sorry stubs. We formally bridge the algebraic and hardware-faithful arithmetic models of Barrett reduction, and instantiate the theorems to formally diagnose Microsoft's Adams Bridge PQC accelerator: its absence of fresh inter-stage masking leaves Barrett output wires non-uniform under the first-order probing model, the same architectural flaw that two independent empirical analyses [3, 4] and our own prior structural analysis [1] identified. Computational evidence further suggests the 1-Bit Barrier is universal across Barrett and Montgomery reductions.

We study a natural analogue of Ulam's problem for random rooted trees distributed according to a Plancherel-type measure. This probability measure is closely related to the classical Plancherel measure on integer partitions. For a Plancherel random tree $T_n$ with $n$ vertices, we investigate the asymptotic behavior of its height $H_n$, defined as the maximal distance from the root to a leaf. We prove that this height grows logarithmically. More precisely, there is a one-parameter family of random trees $(T_n(θ))_{n \in \mathbb{N}}$ indexed by $θ>0$ such that $\frac{H_n}{\log n}$ converges in probability to $c_\star(θ)$, where $c_\star(θ)$ is an explicit constant depending on the parameter $θ$. The case of Plancherel trees corresponds to the parameter $θ=2$. The proof is based on the fact that the Plancherel random trees can be viewed as Ewens fragmentation trees, for which the height exhibits a sharp threshold phenomenon. An upper bound is obtained via $s$-mass functionals and contraction estimates, while the lower bound is derived by embedding the model into a branching random walk with logarithmic displacements governed by a Poisson--Dirichlet distribution. The constant $c_\star(θ)$ is characterized through a variational principle associated with this branching random walk.

The singly Cabibbo suppressed (SCS) decays $D\to πa_0$, with $a_0\equiv a_0(980)$, have been measured with the branching-fraction ratios $r^{+/-}_{\rm ex}\equiv {\cal B}(D^0\toπ^- a_0^+)/{\cal B}(D^0\toπ^+ a_0^-)=7.5^{+2.5}_{-0.8}\pm 1.7$ and $r^{+/0}_{\rm ex}\equiv {\cal B}(D^+\toπ^0 a_0^+)/{\cal B}(D^+\toπ^+ a_0^0)=2.6\pm 0.6\pm 0.3$, deviating significantly from the short-distance expectations $(r^{+/-},r^{+/0})\simeq (0.07,0.2)$. This discrepancy indicates the necessity of long-distance rescattering effects. In particular, the process $D\to K^*K\to a_0π$ generates ${\cal M}_s$ comparable in magnitude to ${\cal M}_d$ in the amplitude ${\cal M}=λ_d{\cal M}_d+λ_s{\cal M}_s$, with $λ_q\equiv V_{cq}^*V_{uq}$, accompanied by nontrivial strong phases essential for $CP$ violation. Consequently, the direct $CP$ asymmetries naturally arise at the ${\cal O}(10^{-3})$ level, for example, ${\cal A}_{CP}(D^0\toπ^- a_0^+)=(-0.7\pm 0.1\pm 0.1\pm 0.1)\times 10^{-3}$, ${\cal A}_{CP}(D^+\toπ^0 a_0^+)=(-1.4\pm 0.1\pm 0.1\pm 0.1)\times 10^{-3}$, and ${\cal A}_{CP}(D^0\toπ^+ a_0^-)=(-2.1\pm 0.9\pm 1.1\pm 0.4)\times 10^{-3}$. These results establish SCS $D\to πa_0$ decays as a new avenue for probing $CP$ violation.

Reconstructing the shape of the Higgs potential realised in Nature is one of the most pressing tasks of current and future colliders. This will offer deep insights into the dynamics of the electroweak phase transition and provide a unique opportunity to probe physics beyond the Standard Model (BSM). In this context, it is essential to have precise theory predictions for trilinear scalar couplings, which control the form of the potential, and for Higgs pair production processes, which are the observables that allow accessing the trilinear couplings. I summarise in these proceedings recent progress in precision calculations of both trilinear scalar couplings and Higgs pair production at the (HL-)LHC in BSM models with extended scalar sectors.

We provide new quantitative results on the embedding of the Muckenhoupt class $A_\infty$ into $A_p$ with the correct asymptotic behavior when the Fujii--Wilson constant $[w]_{A_\infty}$ is close to 1, namely that the parameter $p$ goes to 1 when the weight is nearly constant. As intermediate steps towards the result, we obtain quantitative estimates on the weighted and unweighted BMO norms of $\log w$ for an $A_\infty$ weight $w$. As a consequence, we show that a precise quantitative weighted Poincaré-Sobolev inequality can be proved for weights with small $[w]_{A_\infty}$ that recovers the classical Sobolev exponent $p^*=\frac{np}{n-p}$ when $[w]_{A_\infty}\to 1^+$.

Hybrid circuit quantum electrodynamics (cQED) aims at coupling various quantum degrees of freedom, among which are spin and charge degrees of freedom in gate defined quantum dots, phonons or magnons... with quantized electromagnetic fields in superconducting microwave cavities to investigate fundamental physics questions or for quantum computation and simulation. However, low microwave losses, key for many hybrid cQED experiments, are challenging to achieve given the often exotic and/or complex material stacks (e.g. semiconducting material, ferromagnets, or piezoelectric materials) required to host the various quantum degrees of freedom. In this work, we present a 3D-integration process to overcome this challenge for semi-industrial silicon MOS spin qubits. The process is based on dense indium bump interconnects at a pitch of 10 μm and superconducting thin films of Niobium Nitride (NbN). First, we report on DC and RF interconnect properties that demonstrate a high galvanic interconnection yield and internal quality factors above 105 in the single photon regime for NbN resonators interrupted by a single indium bump interconnect. Eventually, we fabricated a 3D-integrated hybrid circuit quantum electrodynamics (cQED) device based on a semi-industrial MOS hole double quantum dot and a high impedance NbN resonator. For this device, we report a cavity internal quality factor above 10000 and demonstrate record sensitivity for gate-based dispersive readout of the charge degree of freedom with an SNR of 100 in 300 ns. Finally, we demonstrate strong spin-photon coupling of gs/{2π} = 75 MHz, which highlights the viability of 3D-integration for quantum dot based hybrid spin circuit quantum electrodynamics and opens to high-fidelity spin readout and microwave photon-based remote spin qubit entanglement.

We investigate a natural subfamily of twisted linearized Reed--Solomon (TLRS) codes in the sum-rank metric, where the twist is applied only to the constant term. We establish a simple necessary and sufficient condition for these codes to be linear complementary dual (LCD): the twisting parameter \(η\) must satisfy \(η^2 \neq -1\) in the underlying field. This criterion is independent of the evaluation subgroup, the dimension parameter, and the twisting exponent (subject only to a mild restriction on the code length). Furthermore, we construct infinite families of additive twisted linearized Reed--Solomon codes that are simultaneously additive complementary dual (ACD) and maximum distance separable (MDS) over quadratic extensions \(\mathbb{F}_{q^2}\), with respect to the trace-Hermitian inner product. These codes are explicit and achieve optimal parameters for all admissible lengths.

Electron backscatter diffraction is a powerful tool for mapping crystallographic microstructures. However, the primary crux to improving orientation accuracy and applying the technique to challenging materials lies in the correct calibration of the sample-detector geometry. Many approaches have aimed at overcoming this barrier through various pattern center calibration strategies, but the pattern center only defines part of the sample-detector geometry. Here, we present a DIC-based geometry refinement method that obtains a single map-consistent sample-detector geometry, refining both the pattern center and sample/detector angles. We effectively decouple the local orientation changes from the global geometry effects on the Kikuchi patterns by calculating the consistent map-wide simulated-to-experimental pattern shifts associated with global geometry parameter errors. Using single-crystal silicon and barium titanate (a material possessing six pseudosymmetric variants) as model materials, we demonstrate improved map-wide orientation consistency and more robust discrimination of pseudosymmetric variants than the Nelder-Mead and Differential Evolution optimization strategies.

This paper is focused on decoding delay guarantees in wireless networks, where messages have a given signal-to-interference-plus-noise ratio threshold $η_0$ to meet in order to be successfully decoded, and where this should occur within some strict time constraints. Its main contribution consists in quantifying the worst-case transmissions decoding delays in the uplink of a cell-free network using successive interference cancellation. We show how such decoding delay guarantees can be obtained using spatial network calculus, a new tool introduced recently, and in particular spatial regulation.

This article extends weak convergence bounds of Markov transition kernels to convergence bounds on the variance of the Markov kernel applied to Lipschitz functions. In the reversible case, weak convergence rates of the transition kernels imply chi-squared divergence convergence bounds if the density of the initialization measure is Lipschitz. These results provide new tools to establish central limit theorems for Lipschitz functions used in Markov chain Monte Carlo simulations. Applications are explored to the stability of Metropolis-Hastings algorithms in high dimensions, stochastic gradient descent, and solutions to stochastic delay equations.

Boundary integral equations are presented to analyze perturbations in terms of small elastic deformations superimposed upon an arbitrary, homogeneous strain. Plane strain deformations of an incompressible, prestressed, anisotropic, elastic solid are considered assuming the Biot constitutive framework. The special case of perturbations of stress/deformation incident wave fields, caused by a shear band of finite length formed inside the material at a certain stage of the deformation path, is formulated.

There is widespread interest in many-body quantum systems that exhibit limit-cycle or time-crystalline behaviour. An ideal quantum limit cycle would be realized using fully coherent driving (to minimize noise) and also have a continuous internal symmetry (to ensure generation of monochromatic radiation). While these two requirements may seem incompatible, we introduce in this work a large class of multi-mode bosonic limit cycle models based on coherent parametric driving which possess an O(N) continuous symmetry. Surprisingly, the full quantum dissipative steady state of these models can be found exactly. They exhibit rich physics, including steady state entanglement, reduced phase diffusion and the possibility of realizing quantum limit tori. The basic mechanism we identify provides a unified way to understand how coherent parametric driving can yield symmetry-enriched limit cycles, and also helps us understand related models where the relevant symmetries are weakly broken. The models we study are compatible with a range of different experimental platforms, including quantum optical setups and superconducting quantum circuits.

Distributed Quantum Computing (DQC) and Quantum Error Correction (QEC) rely on dynamic circuits that include Mid-Circuit Measurements (MCMs) and classical feedback. These operations present a major bottleneck: MCMs suffer from high error rates that lead to real-time branching errors, while MCM and classical feedback latencies amplify decoherence errors. Current hardware controllers, qubit-state discriminators, and software error mitigation techniques fail to address these challenges holistically. We propose MCMit, a hardware-software co-design to mitigate branching and latency-induced errors. MCMit introduces a scalable, constant-latency multi-control branch instruction for faster classical feedback and two qubit-state discriminators, a transformer, and a CNN, with high accuracy even under short measurement durations. On the software side, static MCM elimination and stochastic branching complement the hardware by mitigating residual branching errors that persist despite hardware improvements. We implement MCMit on Qubic and evaluate it using experimentally extracted QPU readout traces. Our branch instruction reduces feedback latency by up to 70\%, improving circuit depths by up to $7\times$ over Qubic. Our CNN discriminator achieves 37-73\% higher accuracy for short measurement durations than the baselines, leading to up to 80\% lower logical error rates in QEC. Last, our software mitigation improves fidelity by 18--30\% over baseline methods.

The decomposition of complex quantum operations into experimentally feasible gate sets has been a central challenge since the early development of quantum computing. The multi-controlled Toffoli (MCT) gate is a key example, with applications across a wide range of quantum algorithms, whose decomposition into smaller gates, however, typically leads to deep circuits. In this work, we introduce a teleportation-based decomposition that implements an arbitrary MCT gate with unit Toffoli depth, independent of the number of controls, while maintaining a relatively low Toffoli count compared to existing approaches. This is achieved at the cost of a linear overhead in ancilla qubits and the ability to distribute entangled pairs across distant qubits, a capability already available in several quantum computing platforms. We further demonstrate the advantages of this implementation in circuits that rely on MCT gates, such as the adder operator, quantum read-only memory, quantum neurons, and quantum decision trees.

Network Slice as a Service (NSaaS) is a key enabler of Beyond Fifth Generation (5G) and Sixth Generation (6G) networks, supporting next-generation applications such as extended reality (XR), immersive services, and the tactile Internet. These networks must provide native support for slice-aware services across the entire Radio Access Network (RAN), including the Radio Unit (RU), Distributed Unit (DU), Central Unit (CU), and transport segments (fronthaul, midhaul, and backhaul). However, uplink slicing identification in shared Open-RUs (O-RUs) presents a fundamental challenge because the Open-DU (O-DU) handles scheduling, and the O-RU does not inherently know which uplink data belongs to which slice. In MultiPoint-to-MultiPoint (MP2MP) fronthaul scenarios, this limitation is further exacerbated by synchronization and timing constraints, which necessitate that the O-RU process control messages and the encapsulated data be delivered with ultra-low latency. To address this issue, we propose a slicing agent embedded in the O-RU that identifies slices and segregates uplink data into slice-specific enhanced Common Public Radio Interface (eCPRI) packets. Our design employs a pipeline architecture with dedicated paths for time-sensitive, flexible slicing, enabling slice isolation and prioritization. When implemented on an Field-Programmable Gate Array (FPGA), the agent processes each packet in 2 clock cycles, supporting up to 3822 slices per slot. Experimental results validate the approach, showing its feasibility, scalability, and high-performance capabilities for real-time, slice-aware uplink processing in Beyond 5G and 6G Open RAN deployments.

The Smoluchowski diffusion equation describes diffusion in the presence of external forces. Studying the mechanical response of soft materials to linear forces, such as shear, results in a boundary value problem involving an Ornstein-Uhlenbeck operator in an exterior domain with non-constant, unbounded coefficients. In this article, we present efficient and highly accurate boundary element methods in the frequency domain, motivated by applications in soft matter physics. Our key contributions concern the accurate assembly of the Galerkin matrix, combining the approximation of the fundamental solution as a Fourier integral with the resolution of near-field singularities. Numerical experiments demonstrate the accuracy and efficiency of the proposed methods and show their relevance for the computation of rheological quantities.

We derive a general scheme to construct infinitely many probabilistic counterparts for solutions to nonlinear PDEs by recasting the latter as different nonlinear Fokker--Planck equations and by constructing, for each of these equations, a solution to the associated McKean--Vlasov SDE with one-dimensional time marginal densities given by the PDE solution. We utilize this scheme to prove that nonlinear Markov processes in the sense of McKean as introduced by Rehmeier--Röckner (J.\,Theor.\,Probab. 38, 60 (2025)) are not uniquely determined by their one-dimensional time marginals. This is in sharp contrast to the case of classical Markov processes, which are uniquely determined by their one-dimensional time marginals. We demonstrate our results by constructing a continuum of nonlinear Markov processes with one-dimensional time marginal densities given by the Barenblatt solutions to the porous medium and $p$-Laplace equations, as well as by the fundamental solution to the heat equation. This includes a novel martingale representation for the $p$-Laplace Barenblatt solutions. We also prove that a nonlinear Markov process is uniquely determined by its two-dimensional time marginals. Moreover, for the porous medium equation, we show that the different McKean--Vlasov SDEs we investigate are consistent with corresponding gradient flow interpretations of the equation in the sense of Otto calculus.

Label noise presents a fundamental challenge in modern machine learning, especially when large-scale datasets are generated via automated processes. An increasingly common and important data paradigm, particularly in domains like medical imaging, involves learning from a large dataset with coarse, noisy labels supplemented by a small, expert-verified, clean dataset. This setting constitutes a typical information transfer and fusion problem. However, the significant distribution shift between the noisy and clean data violates the core overall parametric similarity assumptions of existing statistical transfer learning methods, while their reliance on parametric models is ill-suited for complex data like images. To address these limitations, this paper develops a generic model-agnostic nonparametric framework for classification with label noise, which applies to a broad class of classifiers. Our approach leverages the small clean dataset to ``purify'' the large noisy one and carefully manages the remaining ambiguous samples. This framework is underpinned by a rigorous statistical theory. Its empirical performance is demonstrated through simulations and a real-world application to medical image analysis for pneumonia diagnosis.

Orderings in charge and spin have been extensively studied to unravel their correlation to emergent superconductivity over the past decades. Bragg-Williams order (BWO), a classical structural order parameter describing site occupancy in alloys, has long been speculated to influence superconducting behavior. Yet, its role still remains ambiguous, largely due to the difficulty of isolating BWO from concomitant charge doping or competing electronic instabilities. Here, we establish In2/3PSe3 as a platform wherein indium vacancies are reversibly configurable between ordered and disordered states via thermal treatment. We show that the disordered phase undergoes a pressure-induced superconducting transition with a Tc of 11 K, significantly higher than the 7 K observed in its ordered counterpart. This constitutes a rare instance in which pure BWO variation drives a substantial shift in Tc. By combining a Ginzburg-Landau phenomenological analysis with a BCS-McMillan microscopic description, we demonstrate that BWO naturally suppresses superconductivity through electron-phonon interactions, a mechanism supported by ultra-low-wavenumber Raman measurements. Our findings support BWO as an independent order parameter that competes directly with superconductivity, extending the concept of competing orders beyond conventional electronic and magnetic degrees of freedom.

In this work we contribute to the study of the fine-grained complexity of problems parameterized by multi-clique-width, which was initiated by Fürer [ITCS 2017] and pursued further by Chekan and Kratsch [MFCS 2023]. Multi-clique-width is a parameter defined analogously to clique-width but every vertex is allowed to hold multiple labels simultaneously. This parameter is upper-bounded by both clique-width and treewidth (plus a constant), hence it generalizes both of them without an exponential blow-up. Conversely, graphs of multi-clique-width $k$ have clique-width at most $2^k$, and there exist graphs with clique-width at least $2^{Ω(k)}$. Thus, while the two parameters are functionally equivalent, the fine-grained complexity of problems may differ relative to them. As our first and main result we show that under ETH the Max Cut problem cannot be solved in time $n^{2^{o(k)}} \cdot f(k)$ on graphs of multi-clique-width $k$ for any computable function $f$. For clique-width $k$ an $n^{\mathcal{O}(k)}$ algorithm by Fomin et al. [SIAM J. Comput. 2014] is tight under ETH. This makes Max Cut the first known problem for which the tight running times differ for parameterization by clique-width and multi-clique-width and it contributes to the short list of known lower bounds of form $n^{2^{o(k)}} \cdot f(k)$. As our second contribution we show that Hamiltonian Cycle and Edge Dominating Set can be solved in time $n^{\mathcal{O}(k)}$ on graphs of multi-clique-width $k$ matching the tight running time for clique-width. These results answer three questions left open by Chekan and Kratsch [MFCS 2023].

User retention is a key metric to measure long-term engagement in modern platforms. In real-time bidding (RTB) advertising system for user re-engagement, the retention model is required to predict future revisit probability at bidding time, before the user converts and consumes any content. Although post-conversion content, termed Onboarding Content, provides highly informative signals for retention prediction, directly using it in training causes severe feature leakage and creates a gap between training and serving. To address this issue, we propose OCARM, a two-stage distillation-aligned framework for Onboarding Content Augmented Retention Modeling, enabling the model to implicitly capture future content using only observable features during inference. In the first stage, we deliberately expose onboarding content to train a hierarchical encoder that produces teacher representations. In the second stage, a user encoder is aligned with the frozen teacher through distillation, allowing the model to approximate the inaccessible onboarding signals without leakage. Extensive offline experiments and online A/B tests demonstrate that our framework achieves consistent improvements in a real-world growth scenario.

The measurement of polarization serves as an important probe to investigate the production mechanism of quarkonia, the bound state of heavy quark anti-quark (charm or bottom) pairs, in hadronic collisions. In experimental invesigations, the polarization is usually measured by analyzing the anisotropies in the angular distribution of the muons originating from the decay of the quarkonium state. In the present article, we study the charmonia ($J/ψ$) and bottomonia ($Υ(1S)$) polarization at $\sqrt{s} =7 $ and 13 TeV in proton-proton(p-p) collisions at LHC using Monte Carlo (MC) event generator model PYTHIA8, which is based on perturbative QCD. The transverse momentum ($p_{T}$) differential distribution has been calculated at forward rapidity ($2.5 < y_{μμ} < 4.0$) and the polarization parameters are estimated in Helicity and Collins-Sooper reference frames. In addition, to mimic realistic experimental conditions, we have incorporated, in PYTHIA simulations, effects like detector inefficiencies and muon momentum smearing. These contributions alter the polarization parameters, introducing an artificial degree of polarization, if not properly corrected for. The simulation results have been compared with the recent ALICE measurements for quarkonia polarization in p-p collisions at LHC energy regime.

We study the thermodynamic phase diagram of a one-dimensional quantum spin chain subjected to both mean-field and nearest-neighbor interactions, and to a transverse magnetic field $h$. The purpose is to determine the effect of the quantum fluctuations, due to the transverse field, on the phase diagram, in particular with respect to the occurrence of ensemble inequivalence. We denote our model as a quantum Nagle-Kardar model. To perform the calculation of the canonical partition function, we show that, due to the presence of the mean-field term, in the thermodynamic limit one can use the Hubbard-Stratonovich transformation in spite of the non-commutativity of the different operators appearing in the Hamiltonian, and we adopt a procedure of successive approximations that lead to the determination of the phase diagram thanks to a scaling property of the phase transition lines. The results show that the ensemble inequivalence, present in the classical Nagle-Kardar model, is removed above a threshold value $h_c$ for the transverse field. For $h$ larger than $h_c$ the phase diagram exhibits only second-order phase transition lines, implying therefore restoration of ensemble equivalence.

Metric-preserving functions (here, metric aggregation functions) offer a natural method for constructing metrics on Cartesian products of metric spaces or for aggregating multiple metrics defined on a common set. Strongly metric-preserving functions represent a more specialized subset of these functions, ensuring that the new metric aligns with the product topology, in the Cartesian product case. However, these strong functions have not been previously explored for quasi-pseudometrics. Furthermore, in the case where all metrics are defined on the same set, the problem has not been addressed previously. In this paper, we investigate the class of strongly (quasi-)(pseudo)metric aggregation functions, extending the classical concept. We begin by examining the case where the aggregation function produces (quasi-)(pseudo)metrics on Cartesian products, characterizing these functions through continuity at zero and a minimal zero preimage condition. In addition, we will examine the scenario where the aggregation function produces a (quasi-)(pseudo)metric defined on a fixed set. Within this context, we will demonstrate that the appropriate topology to consider is the supremum topology. We will also provide both necessary and sufficient conditions for an (quasi-)(pseudo)metric aggregation function on sets to qualify as a strongly one, thereby addressing a gap in the existing literature.

We consider the anomalous dimension $γ_{gg}^{(3)}(N)$ of the twist-two gluon operator of arbitrary Lorentz spin $N$ in the quark flavor singlet sector of a general gauge theory at four loops and construct its contribution proportional to $ζ(3)$ in analytic form by applying the Lenstra-Lenstra-Lovász algorithm to the available low-$N$ moments. We exploit generalized Gribov-Liptov reciprocity, establish new self-tuning relations for the anomalous dimension matrix of the singlet sector, and inject information from $\mathcal{N}=1,4$ supersymmetric Yang-Mills theories. We also present the contribution to the rational part of $γ_{gg}^{(3)}(N)$ with color factor $C_F^2n_f^2$. Exact contributions to the four-loop splitting function $P_{gg}^{(3)}(x)$ hence resulting via inverse Mellin transformation help us to reduce theoretical uncertainties in scaling violations of parton distribution functions in QCD.

Social identity is a concept from psychology that refers to the part of an individual's identity that derives from their group membership(s). In this paper, we explore social identity in members of the professional community of Research Software Engineers (RSEs). Using a mixed-methods approach, our study combined computational linguistic analysis and inferential statistics to examine over 28,000 social media posts, 1,700 blogs, and survey responses from 381 professional RSEs. The findings highlight the emergence of a collective RSE identity and demonstrate its role in shaping professional wellbeing. This study contributes an interdisciplinary perspective by integrating social psychology and software engineering to show how a professional identity evolves and why it matters.