Understanding whether the features of open quantum dynamics are genuinely quantum remains a central challenge in quantum dynamics. Even though the non-Markovian behavior of quantum dynamics has been widely investigated across different settings, there is still no consensus on which properties of a dynamics reflect genuine quantum features and which arise from classical or non-genuine quantum sources. In this review, we provide detailed information on recent developments in characterizing quantum non-Markovianity based on information backflow and the nature of its origin. We also present a survey on how various approaches separate classical and quantum contributions, as well as how they define operational tasks that reveal genuine quantum non-Markovianity. We analyze several frameworks, including state-distinguishability -based, channel-based (``CP-divisibility''), and process-tensor methods. For each framework, we outline the underlying physical motivation, the criteria proposed to distinguish genuine quantum non-Markovianity from practical or apparent memory effects. We further compare different approaches and their strengths and limitations. The review aims to clarify the conceptual and operational aspects of quantum non-Markovian processes based on their nature and to provide a foundation for future research on quantum non-Markovianity and its role in advancing quantum information science and technology.

In-network computing techniques, exemplified by NVLink SHARP (NVLS), offer a promising approach to addressing the communication bottlenecks in LLM inference by offloading collective operations such as All-Reduce to switches. However, the accelerator-centric architecture of NVLS suffers from two fundamental limitations: 1) it relies on GPU load instructions to trigger in-switch reduction, which means that the data reduced in the switch must be transferred back to the initiating GPU rather than being broadcast directly, thereby introducing unnecessary communication overhead; 2) due to its architectural constraints, NVLS cannot offload operators that are not decomposable into memory-semantic instructions, such as the in-network quantization (INQ) proposed in this work. As a result, All-Reduce in NVLS during inference still operates at 16-bit precision, leading to substantial bandwidth waste. To address these limitations, we propose SCIN, the first switch-centric in-network architecture for multi-accelerator shared-memory networks, enabling both low-latency and high-bandwidth All-Reduce. Specifically, we introduce an in-switch accelerator (ISA) capable of directly accessing the memory regions in attached accelerators for in-network processing, together with a co-designed communication fabric that enables such access with negligible protocol overhead. SCIN delivers lower All-Reduce latency than NVLS by eliminating redundant data movement. Moreover, SCIN enables INQ for All-Reduce, reducing its precision to 8 bits and nearly doubling bandwidth with negligible accuracy loss. We also present a multi-FPGA prototype of SCIN to validate its feasibility and effectiveness. Simulation results for an 8-GPU system show that our design accelerates All-Reduce by up to 8.7x for small messages and 3.8x for large messages, yielding up to 1.74x TTFT speedup and 1.34x TPOT speedup on LLaMA-2 models.

Altermagnets are a newly identified class of materials that combine advantageous characteristics of both ferro- and antiferromagnets, making them highly promising for spintronic applications. Hematite has recently been identified as an altermagnetic material and exhibits several noteworthy properties, including a high Néel temperature, a temperature dependent spin reorientation transition (SRT) at the Morin temperature ($T_\mathrm{M}$), and low magnetic damping. In this work, we demonstrate the epitaxial growth of hematite thin films on y- and z-cut lithium niobate (LiNbO$_3$) substrates using pulsed laser deposition (PLD). LiNbO$_3$ as piezoelectric substrate is of particular interest as it enables the efficient excitation of surface acoustic waves (SAWs) with interdigital transducers. The different substrate cuts allow for different orientations of the Néel vector. Films grown on y-cut LiNbO3 are single-crystalline and single-phase, while those deposited on z-cut LiNbO$_3$ exhibit two distinct in-plane (ip) domains rotated 60° relative to each other. On both substrates, the hematite thin films exhibit a temperature dependent SRT which allows the antiferromagnetic Néel vector to be controlled. This study paves the way for the development of high-quality piezoelectric/altermagnetic hyprids for magnonics and spintronics.

In this paper, we propose two monolithic fully discrete finite element methods for fluid-structure interaction (FSI) based on a novel Piola-type Arbitrary Lagrangian-Eulerian (ALE) mapping. For the temporal discretization, we apply the backward Euler method to both the non-conservative and conservative formulations. For the spatial discretization, we adopt arbitrary order hybridizable discontinuous Galerkin (HDG) methods for the incompressible Navier-Stokes and linear elasticity equations, and a continuous Galerkin (CG) method for the fluid mesh movement. We derive stability results for both the temporal semi-discretization and the fully discretization, and show that the velocity approximations of the fully discrete schemes are globally divergence-free. Several numerical experiments are performed to verify the performance of the proposed methods.

We address the long-standing problem of negative decay and production rates in perturbative QCD for exclusive processes by proposing amplitude-level NRQCD factorization as a systematic prescription. Building on this, we present the first complete next-to-next-to-leading-order (NNLO) QCD correction to the decay $J/ψ\to 3γ$. The resulting partial width, $Γ(J/ψ\to 3γ) = 0.96^{+4.32}_{-0.13}$ eV, combines this NNLO contribution with the known up to $\mathcal{O}(α_s v^2)$ relativistic correction and shows markedly improved agreement with the high-precision BESIII measurement. In the same way, $Γ(Υ\to 3γ) = 0.0086^{+0.0028}_{-0.0006}$ eV is obtained. The dominant theoretical uncertainty originates from the renormalization scale variation, underscoring the challenge of perturbative convergence at this order and the necessity for future higher-order calculations.

This study investigates the influence of crystallographic orientation on fracture behavior and the resulting mechanical anisotropy in a Fe55Ni19Cr26 alloy crystal containing radiation-induced defects, using molecular dynamics (MD) simulations. Crack propagation is analyzed in irradiated samples with three selected high-symmetry crystallographic orientations to show how radiation-induced defects modify local deformation mechanisms and amplify mechanical anisotropy. The investigation focuses on the anisotropic nature of the ductile-to-brittle transition (DBT) driven by radiation-induced defects by simulating fracture behavior under tensile loading. Fracture resistance is quantitatively evaluated using a traction-separation (T-S) approach to extract the atomic-scale fracture energy under realistic defect conditions. The results reveal a strong crystallographic orientation dependence in the evolution of deformation and fracture behavior during DBT. The crystal lattice orientation governs dislocation activity and defect interactions, which in turn regulate local plasticity mechanisms, strain localization, slip system activation, and fracture resistance, thereby driving the development and enhancement of mechanical anisotropy in irradiated materials. It is further shown that radiation-induced embrittlement cannot be explained solely by defect accumulation, but rather by orientation-sensitive interactions among dislocations, defects, and fracture process zones. A key novelty of this work lies in integrating radiation-induced defect evolution with orientation-dependent fracture within an atomistic T-S analysis, enabling quantitative assessment of atomic-scale fracture resistance under realistic defect conditions.

We establish monotonicity formulas for a parabolic frequency function associated with sign-changing solutions to a class of doubly nonlinear parabolic equations of the form $\partial_t u = \mathcal{L}_{p,φ} u^q$ on weighted complete Riemannian manifolds without any curvature assumption, where $\mathcal{L}_{p,φ}$ denotes the weighted $p$-Laplacian and $p>1$, $q>0$. As a consequence, we obtain results on backward uniqueness for $q(p-1)\geq 1$ and unique continuation at infinity for $q(p-1) > 1$. We further consider equations with a controlled nonlinear perturbation term and derive an almost-monotonicity formula for the parabolic frequency. By employing the parabolic frequency, we also establish some Liouville-type results for ancient solutions in the case $q(p-1)\geq 1$.

Quantum steering, as a manifestation of nonlocal quantum correlations, plays a crucial role in enabling various quantum information processing tasks. However, practical implementations are often hindered by significant challenges arising from imperfect or untrusted measurement devices. This study investigates the impact of measurement inaccuracies on quantum steering, with a particular focus on errors in the untrusted party's measurement devices. We first analyze how such errors affect the evaluation of steering inequalities, and then derive bipartite steering inequalities based on correlation matrices under imperfect measurements. Our findings show that even small measurement errors can significantly compromise the certification of quantum steerability, an effect that becomes particularly pronounced as the system dimension increases. Furthermore, by extending the proposed steering inequality to a modified tripartite scenario via correlation matrices, we demonstrate that the influence of measurement imperfections is far more severe in multipartite quantum steering than in the bipartite case. Our results underscore the critical need to account for measurement imperfections in experimental quantum steering and provide a theoretical framework for characterizing and mitigating these effects in high-dimensional quantum systems.

Gausslets are one of the few examples of basis sets for electronic structure which allow for two-index/diagonal electron-electron interaction terms. A weakness of gausslets is that, because of their 1D origin, they have been tied to Cartesian coordinates. Here we generalize the gausslet construction for the radial coordinate in three dimensions for atomic basis sets. These radial gausslets make a very compact radial basis with a relatively modest number of functions, with diagonal interaction terms. We illustrate the accuracy of this construction with Hartree--Fock and exact diagonalization on atomic systems.

We combine fast amplitude surrogates with neural importance sampling to accelerate NLO calculations. For virtual corrections, a learned ratio to the Born matrix element with calibrated uncertainties guarantees reliable precision across phase space. For real emission, we stick to the standard FKS subtraction and train sector-conditioned surrogates of the regularized integrands away from divergences. MadNIS then uses multi-channel mappings and FKS sectors as conditions. We validate our approach for electron-positron scattering to three and four jets and find significant speed-ups and variance reduction in the integration.

Ultrathin films of widely different materials exhibit a dramatic enhancement of projectile penetration resistance under high--velocity impact. Despite extensive simulations and experiments, a unifying physical explanation has remained elusive. Here we show that the thickness dependence of the specific penetration energy obeys a universal law, $E_p^*(h)=E_{p,\infty}^*+B h^{-3}$, independent of chemical composition and degree of disorder. The inverse--cube scaling is traced back to a finite--size correction to the effective shear modulus arising from the suppression of long--wavelength nonaffine deformation modes in confined solids. The scaling quantitatively describes impact data for multilayer graphene, graphene oxide, and polymer thin films, revealing a common elastic origin for nanoscale impact resistance.

Theoretical analysis of a prototypical two-qubit effective non-Hermitian system characterized by asymmetric Heisenberg $XY$ interactions in the absence of external magnetic fields demonstrates that maximal bipartite entanglement and quantum phase transitions can be induced exclusively through non-Hermiticity. At thermal equilibrium as $T\rightarrow 0$, the system attains maximal entanglement ${C}=1$ for values of the non-Hermiticity parameter greater than a critical value $γ>γ_c=J\sqrt{(1-δ^2)}$, where $J$ denotes the exchange interaction and $δ$ represents the anisotropy of the system; conversely, for $γ< γ_c$, entanglement is nonmaximal and given by ${C} = \sqrt{(1 - (γ/J)^2)}$. The entanglement undergoes a discontinuous transition to zero precisely at $γ= γ_c$. This phase transition originates from the closing of the energy gap at a non-Hermiticity-driven ground state degeneracy, which is fundamentally different from an exceptional point. This work suggests the use of singular-value-decomposition generalized density matrix for the computation of entanglement in bi-orthogonal systems.

An asymptotic formula with a square root error term is obtained for the number of elements with given trace and norm in a finite semisimple algebra over a finite field. This extends previous results from finite etale algebras (commutative case) to finite semi-simple algebras (non-commutative case). The main idea is to apply the Eichler formula for Gauss sums over the general linear group and the Hasse-Davenport relation to reduce the problem to the classical geometric case where the result is known to be true. As an application of this reduction, we also obtain a square root estimate for Kloosterman sums over semi-simple algebras. Similar square root estimates are discussed when norm-trace is replaced by product-trace, leading to a new conjecture on product-trace counting over finite semi-simple algebras.

Collisionless shocks are fundamental accelerators of energetic particles; yet, the observations of nonlinear foreshock structures, which are essential in acceleration processes, differ significantly between Interplanetary (IP) shocks and planetary bow shocks. We present a direct comparison of two high-Mach-number, quasi-parallel shocks: an IP shock observed by Solar Orbiter and the Earth's bow shock measured by the Magnetospheric Multiscale (MMS) mission during the 2024-2025 ``string-of-pearls'' campaign. We show that Foreshock Compressive Structures (FCSs) initiate upstream of both shocks at similar normalized distances ($\lesssim$50 ion inertial lengths, $d_i$) when the suprathermal ($>10$ keV) ion density exceeds $\sim$1\% of the background. However, the IP shock lacks the fully evolved, high-amplitude Short Large Amplitude Magnetic Structures (SLAMS) characteristic of the terrestrial foreshock. We demonstrate that the ``growth zone'' capable of sustaining these structures is spatially limited ($\sim$135 $d_i$), which, due to the high speed of the propagating IP shock, corresponds to a brief observational window of $<10$ s. Beyond this observational constraint, we suggest an additional physical mechanism that can inhibit foreshock maturity at IP shocks. The lack of global curvature prevents the lateral supply (``cross-talk'') of energetic ions from different shock regions. These findings suggest that while the fundamental physics of FCS initiation is unified across collisionless shocks, the achievement of full nonlinearity can be regulated by the unique shock geometry and upstream properties, while ultimately remaining observationally challenging to identify.

Nonlinear parameter-varying (NPV) systems are a class of nonlinear systems whose dynamics explicitly depend on time-varying external parameters, making them suitable for modeling real-world systems with dynamics variations. Traditional synthesis methods for NPV systems, such as sum-of-squares (SOS) optimization, are only applicable to control-affine systems, face scalability challenges and often lead to conservative results due to structural restrictions. To address these limitations, we propose Neural-NPV, a two-stage learning-based framework that leverages neural networks to jointly synthesize a PD controller and a PD Lyapunov function for an NPV system under input constraints. In the first stage, we utilize a computationally cheap, gradient-based counterexample-guided procedure to synthesize an approximately valid PD Lyapunov function and a PD controller. In the second stage, a level-set guided refinement is then conducted to obtain a valid Lyapunov function and controller while maximizing the robust region of attraction (R-ROA). We demonstrate the advantages of Neural-NPV in terms of applicability, performance, and scalability compared to SOS-based methods through numerical experiments involving an simple inverted pendulum with one scheduling parameter and a quadrotor system with three scheduling parameters.

In this paper, we study the spectrum of the Laplacian on the Tate curve and construct the associated Green's function as a finite sum, which can be viewed as the non-Archimedean counterpart of the Green's function on the flat torus in the Archimedean case. Moreover, we establish existence and uniqueness results of the mean field equation on this space. To address the problem, we first prove the structure of solutions on finite quotients, and prove the existence on the Tate curve by the convergence of such solutions. We also prove the uniqueness of the solutions for some parameter region. Notably, the well-posedness of the solution resembles that in the Archimedean case.

Linear wave theory captures the essential physics of free-surface flows at a fraction of the computational cost of nonlinear and viscous methods, making it attractive for design, real-time control, and surrogate modeling applications. However, the Kelvin Green's function for a translating point-source generates unbounded wave energy in the $z\to 0$ limit, causing both numerical difficulties and physical inconsistencies. This paper develops a modified kernel for flat-ship theory incorporating an elliptic spanwise line integration that naturally resolves this ill-posedness, yielding finite wave energy over the entire free surface. We then present a fast evaluator for both point and line kernels using contour deformation adapted to the non-analytic Kelvin phase, achieving $10^4$-$10^5$ speedup over direct quadrature while preserving the wake asymptotics. Predictions on the most challenging $z=0$ limit demonstrate physically consistent wave patterns and wave resistance trends. An open-source Julia implementation is provided.

Grain growth in polycrystals is often simulated using orientation-field models, which employ a field to represent the local orientation of the crystal lattice. These models can be challenging to represent a realistic misorientation dependence of grain boundary free energy. We prove that existing orientation-field models, in general, cannot reproduce a decrease in the grain boundary free energy with a increasing misorientation angle, demonstrating a significant limitation of previous models in applications to polycrystalline materials. To overcome this limitation, we propose a modification to the Kobayashi-Warren-Carter model for grain growth wherein the coefficients of the free-energy functional become functions of the misorientation between the grains, which is a non-local quantity. Due to this modification, an arbitrary dependence of the grain boundary free energy on the misorientation can be embedded in the model. We propose calculating the non-local misorientation by interpolating the orientation field at a fixed distance in both directions along the local grain boundary normal vector. The capabilities of the model are demonstrated by introduction of a sharp cusp to the misorientation dependent grain boundary free energy. Finally, we propose an extension of the model to three dimensions.

Over the past decade, the Python-based Simulations of Chemistry Framework (PySCF) has developed into a widely used open-source platform for electronic structure theory and quantum chemical method development. This article reviews the major advances since the previous overview in 2020, covering new modules and methodology, infrastructure changes, and performance benchmarks.

Audio-only walking navigation can leave users disoriented, relying on vague cardinal directions and lacking real-time environmental context, leading to frequent errors. To address this, we present a novel system that integrates a Vision Language Model (VLM) with a spatial audio cue. Our system extracts environmental landmarks to anchor navigation instructions and, crucially, provides a directional spatial audio signal when the user faces the wrong direction, indicating the precise turn direction. In a user study (n=12), the spatial audio cue with VLM reduced route deviations compared to both VLM-only and Google Maps (audio-only) baseline systems. Users reported that the spatial audio cue effectively supported orientation and that landmark-anchored instructions provided a better navigation experience over audio-only Google Maps. This work serves as an initial look at the utility of future audio-only navigation systems for incorporating directional cues, especially real-time corrective spatial audio.

In this paper, we would like to study the weakly coupled system of semilinear structurally damped wave equations with moduli of continuity in nonlinear terms whose powers belong to the critical curve in the $p-q$ plane. Our main purpose is to find a sharp condition for these moduli of continuity by investigating the global (in time) existence of small data Sobolev solutions and the blow-up result for solutions in finite time as well. Furthermore, when the blow-up phenomenon occurs, we are going to achieve the sharp lifespan estimates for the local (in time) Sobolev solution.

We give two novel proofs that the path integral and stochastic quantizations of generic scalar Euclidean quantum field theories are equivalent. Our proofs rely on Taylor interpolations indexed by forests, in the fashion of constructive field theory. The first proof works at the level of individual terms in the Feynman expansion, with the forests appearing as spanning forests in Feynman graphs. The second one works at the level of the path integral and avoids the full expansion of the Feynman perturbation series.

Tensor network contraction is a fundamental computational challenge underlying quantum many-body physics, statistical mechanics, and machine learning. Belief propagation (BP) provides an efficient approximate solution, but introduces systematic errors on graphs with loops. Here, we introduce a hybrid method that achieves accurate results by stochastically sampling loop corrections to BP and showcase our method by applying it to the two-dimensional ferromagnetic Ising model. For any pairwise Markov random field with symmetric edge potentials, our approach exploits an exact factorization of the partition function into the BP contribution and a loop correction factor summing over all valid loop configurations, weighted by edge weights derived directly from the potentials. We sample this sum using Markov chain Monte Carlo with moves that preserve the loop constraint, combined with umbrella sampling to ensure efficient exploration across all correlation strengths. Our stochastic approach provides unbiased estimates with controllable statistical error in any parameter regime.

Automatically generating 3D games in commercial game engines remains a non-trivial challenge, as it involves complex engine-related workflows for generating assets such as scenes, blueprints, and code. To address this challenge, we propose a novel multi-agent system, AutoUE, which coordinates multiple agents to end-to-end generate 3D games, covering model retrieval, scene generation, gameplay and interaction code synthesis, and automated game testing for evaluation. In order to mitigate tool-use hallucinations in LLMs, we introduce a retrieval-augmented generation mechanism that grounds agents with relevant UE tool documentation. Additionally, we incorporate game design patterns and engine constraints into the code generation process to ensure the generation of correct and robust code. Furthermore, we design an automated play-testing pipeline that generates and executes runtime test commands, enabling systematic evaluation of dynamic behaviors. Finally, we construct a game generation dataset and conduct a series of experiments that demonstrate AutoUE's ability to generate 3D games end-to-end, and validate the effectiveness of these designs.

Spectroscopy is a central pillar of materials characterization, providing useful information on properties like structure, composition, or excited state dynamics of a system. However, many spectroscopic techniques present challenges in development of interpretable, performant, and reliable supervised learning models due to the wide range of possible nonlinear correlations that can exist between the signal and the response variable (target) of interest. Here, we present Spectra-Scope, an open-source AutoML framework for automatic characterization of material properties from spectroscopy data using interpretable machine learning (ML) models. The software is implemented in Python and a no-code web application. It comprises tools for data preprocessing, nonlinear feature extraction, machine learning model training, and feature downselection. Users can easily train different types of simple, interpretable ML models on a set of feature transformations quickly and with modest computational resources. In this work, we outline the methods of Spectra-Scope and its effectiveness across diverse datasets, with applications to materials and agricultural spectroscopy data. We show that Spectra-Scope can reproduce performance of comparable models in the literature, and highlight how our emphasis on interpretability can be used to rationalize the behavior of individual models and understand the physical processes behind spectral features.

Imminent impactors are natural bodies discovered in space before impacting the Earth. They provide a rare opportunity to characterize individual near-Earth objects (NEOs) in great detail as asteroids in space, meteors in Earth's atmosphere and meteorites on the ground. The Vera C. Rubin Observatory's upcoming Legacy Survey of Space and Time (LSST) is expected to transform our understanding of the NEO population. In this work, we evaluate LSST's expected discovery performance for imminent impactors using $343$ meter-size objects previously recorded in NASA's CNEOS database as fireballs impacting Earth's atmosphere. We simulate pre-impact observations of these CNEOS impactors with the Sorcha survey simulator under LSST's default three-night discovery strategy and a one-night strategy for fast-moving objects that relies on matching aligned streaks in two exposures on the same night. We estimate that LSST will discover $\sim1-2$ meter-size and larger imminent impactors per year, representing $\sim4\%$ of all Earth impactors $\gtrsim1$ m in diameter and almost doubling the current discovery rate of imminent impactors. The median time of discovery and median time of first observation for impactors discovered in our simulations are $\sim1.57$ and $\sim3.06$ days before impact, respectively. The spatial distribution of the 11 previously discovered imminent impactors is biased towards the Northern Hemisphere, where the observatories that discovered them are located. We find a similar trend towards Southern Hemisphere impacts in our simulated LSST detections of the CNEOS impactors, suggesting Rubin will provide a powerful counterpart to existing asteroid surveys primarily located in the Northern Hemisphere.

We prove that computing a shortest monotone path to the optimum of a linear program over a simple polytope is NP-hard, thus resolving a 2022 open question of De Loera, Kafer, and Sanità. As a consequence, finding a shortest sequence of pivots to an optimal basis with the simplex method is NP-hard. In fact, we show this is NP-hard already for fractional knapsack polytopes. By applying an additional polyhedral construction, we show that computing the diameter of a simple polytope is NP-hard, resolving a 2003 open problem by Kaibel and Pfetsch. Finally, on the positive side we show that every polytope has a small, simple extended formulation for which a linear length path may be found between any pair of vertices in polynomial time building upon a result of Kaibel and Kukharenko.

We study a variant of unequal error protection in channel coding, where the message bit string is divided into a finite number of blocks and the maximization objective is a weighted sum of per-block decoding success probabilities. The channel model is quasi-static Rayleigh fading with channel state information available to the receiver but unavailable to the transmitter. We analyze the asymptotic and finite blocklength performance of two achievability schemes, one based on power-domain superposition (PDS) and another based on orthogonal resource allocation (ORA), also known as time-sharing. Upper bounds on the optimal number of blocks to transmit are derived. Algorithms to compute the optimal power and time splits for the two schemes are given. Simplified algorithms to compute locally optimal power and time splits are also given. Our results show that PDS outperforms ORA, but the performance differential is less than 2% in both the asymptotic and finite blocklength regimes (Figures 4 - 6). For both PDS and ORA, numerical results also upper bound the gap between the asymptotic and finite blocklength performance by approximately 10% for n = 1000 and 3% for n = 5000 (Figures 7 - 10).

This article comprises three sections. Section 2 starts with a review of ab initio no-core shell model calculations by Monte Carlo Shell Model. Alpha clustering arises for 8,10,12Be and 12C with Daejeon16 and JISP16 interactions, even in the ground state of 12C. Hoyle state is shown to be dominated by alpha clustering in triangular configurations. As the ground and Hoyle states show strong deformations, they are good cases to investigate rotational excitations. As an original work, the recently proposed fully quantum (mechanical) formulation for deformation and rotation is extended to cluster states. Dual rotational modes are proposed: compact-object and distant-object rotations. The former is found in many heavy nuclei, whereas the latter can be found for clustering states. While 8Be is an example for the latter, 12C is a rare example that both modes appear. Atomic molecules and hadrons can be viewed similarly. Possible relevance to fission is mentioned. Section 3 presents a general framework for an extended no-core shell model with cluster-nucleon configuration interaction, combining traditional shell-model-like configurations with explicit microscopic configurations representing cluster degrees of freedom. The section reviews the microscopic origins of cluster substructures in light nuclei, emphasizing how nucleonic degrees of freedom, nucleon-nucleon interactions, and continuum coupling naturally extend the traditional shell model into configuration-interaction frameworks that incorporate clustering and reaction dynamics. Section 4 presents that although the cluster structure is robust in Be-C nuclei, some jj-coupling shell model components are mixed in the ground state of 12C. Using the antisymmetrized quasi cluster model, we can clearly model this competition between the cluster and shell components. The spin-orbit interaction is key.

Large language models (LLMs) for code editing have achieved remarkable progress, yet recent empirical studies reveal a fundamental disconnect between technical accuracy and developer productivity. Despite their strong benchmark performance, developers complete tasks 19% slower when using AI assistance, with over 68.81% of recommendations disrupting their mental flow. This misalignment stems from the use of static commit snapshots that lack temporal information, causing models to optimize for end results rather than the incremental, context-sensitive steps that align with developers' natural reasoning process. To bridge this gap, we present EditFlow, which benchmarks and optimizes subsequent code edit recommendation systems through the reconstruction of developer editing flows. EditFlow addresses three key challenges. First, collecting edit-order data that reflects developers' flow is inherently difficult: manual annotation introduces prohibitive overhead, while development logs capture only single trajectories instead of all plausible editing flows. Second, benchmarking recommendation performance against developers' ongoing editing flow requires a digital-twin-like simulation that can faithfully simulate the editing process. Third, existing heterogeneous systems vary drastically in scale and architecture, posing challenges for developing a unified optimization strategy that endows all models with mental-flow awareness regardless of design or capability. ......