The geodesy of irregularly shaped small bodies presents fundamental challenges for gravitational field modeling, particularly as deep space exploration missions increasingly target asteroids and comets. Traditional approaches suffer from critical limitations: spherical harmonics diverge within the Brillouin sphere where spacecraft typically operate, polyhedral models assume unrealistic homogeneous density distributions, and existing machine learning methods like GeodesyNets and Physics-Informed Neural Networks (PINN-GM) require extensive computational resources and training time. This work introduces MasconCubes, a novel self-supervised learning approach that formulates gravity inversion as a direct optimization problem over a regular 3D grid of point masses (mascons). Unlike implicit neural representations, MasconCubes explicitly model mass distributions while leveraging known asteroid shape information to constrain the solution space. Comprehensive evaluation on diverse asteroid models including Bennu, Eros, Itokawa, and synthetic planetesimals demonstrates that MasconCubes achieve superior performance across multiple metrics. Most notably, MasconCubes demonstrate computational efficiency advantages with training times approximately 40 times faster than GeodesyNets while maintaining physical interpretability through explicit mass distributions. These results establish MasconCubes as a promising approach for mission-critical gravitational modeling applications requiring high accuracy, computational efficiency, and physical insight into internal mass distributions of irregular celestial bodies.

The Bus Driver Scheduling Problem (BDSP) is a combinatorial optimization problem with the goal to design shifts to cover prearranged bus tours. The objective takes into account the operational cost as well as the satisfaction of drivers. This problem is heavily constrained due to strict legal rules and collective agreements. The objective of this article is to provide state-of-the-art exact and hybrid solution methods that can provide high-quality solutions for instances of different sizes. This work presents a comprehensive study of both an exact method, Branch and Price (B&P), as well as a Large Neighborhood Search (LNS) framework which uses B&P or Column Generation (CG) for the repair phase to solve the BDSP. It further proposes and evaluates a novel deeper integration of B&P and LNS, storing the generated columns from the LNS subproblems and reusing them for other subproblems, or to find better global solutions. The article presents a detailed analysis of several components of the solution methods and their impact, including general improvements for the B&P subproblem, which is a high-dimensional Resource Constrained Shortest Path Problem (RCSPP), and the components of the LNS. The evaluation shows that our approach provides new state-of-the-art results for instances of all sizes, including exact solutions for small instances, and low gaps to a known lower bound for mid-sized instances. Conclusions: We observe that B&P provides the best results for small instances, while the tight integration of LNS and CG can provide high-quality solutions for larger instances, further improving over LNS which just uses CG as a black box. The proposed methods are general and can also be applied to other rule sets and related optimization problems

Chain-of-Thought (CoT) enhances an LLM's ability to perform complex reasoning tasks, but it also introduces new security issues. In this work, we present ShadowCoT, a novel backdoor attack framework that targets the internal reasoning mechanism of LLMs. Unlike prior token-level or prompt-based attacks, ShadowCoT directly manipulates the model's cognitive reasoning path, enabling it to hijack multi-step reasoning chains and produce logically coherent but adversarial outcomes. By conditioning on internal reasoning states, ShadowCoT learns to recognize and selectively disrupt key reasoning steps, effectively mounting a self-reflective cognitive attack within the target model. Our approach introduces a lightweight yet effective multi-stage injection pipeline, which selectively rewires attention pathways and perturbs intermediate representations with minimal parameter overhead (only 0.15% updated). ShadowCoT further leverages reinforcement learning and reasoning chain pollution (RCP) to autonomously synthesize stealthy adversarial CoTs that remain undetectable to advanced defenses. Extensive experiments across diverse reasoning benchmarks and LLMs show that ShadowCoT consistently achieves high Attack Success Rate (94.4%) and Hijacking Success Rate (88.4%) while preserving benign performance. These results reveal an emergent class of cognition-level threats and highlight the urgent need for defenses beyond shallow surface-level consistency.

Face-to-face interactions between police officers and the public affect both individual well-being and democratic legitimacy. Many government-public interactions are captured on video, including interactions between police officers and drivers captured on bodyworn cameras (BWCs). New advances in AI technology enable these interactions to be analyzed at scale, opening promising avenues for improving government transparency and accountability. However, for AI to serve democratic governance effectively, models must be designed to include the preferences and perspectives of the governed. This article proposes a community-informed, approach to developing multi-perspective AI tools for government accountability. We illustrate our approach by describing the research project through which the approach was inductively developed: an effort to build AI tools to analyze BWC footage of traffic stops conducted by the Los Angeles Police Department. We focus on the role of social scientists as members of multidisciplinary teams responsible for integrating the perspectives of diverse stakeholders into the development of AI tools in the domain of police -- and government -- accountability.

This paper and accompanying Python and C++ Framework is the product of the authors perceived problems with narrow (Discrimination based) AI. (Artificial Intelligence) The Framework attempts to develop a genetic transfer of experience through potential structural expressions using a common regulation/exchange value (energy) to create a model whereby neural architecture and all unit processes are co-dependently developed by genetic and real time signal processing influences; successful routes are defined by stability of the spike distribution per epoch which is influenced by genetically encoded morphological development biases.These principles are aimed towards creating a diverse and robust network that is capable of adapting to general tasks by training within a simulation designed for transfer learning to other mediums at scale.

On-chip inductor design plays a critical role in the advancement of radio-frequency integrated circuits (RFICs). Inductors typically occupy a substantial portion of the chip area as their performance metrics, namely, inductance density and Quality factor ($Q$-factor), are fundamentally tied to the available footprint, thereby limiting miniaturization. To better understand and quantify these limitations, we employ rigorous electromagnetic analysis together with convex optimization techniques to derive a fundamental bound on the maximum achievable $Q$-factor of electrically-small planar inductors as a function of the available design area. The analysis yields analytical expressions for the bound and, via modal analysis techniques, identifies and interprets operational regimes and scaling trends with respect to design area and material conductivity. The analysis accounts for both ohmic and radiation losses, with the latter becoming significant as the inductor size increases. A broad set of state-of-the-art inductor designs from the literature is evaluated against the established $Q$-factor upper bound, identifying designs that approach the theoretical limit as well as those with potential for further improvement. The study is extended to include the effect of kinetic inductance, which offers a promising avenue toward next-generation inductors with higher inductance densities and $Q$-factors. By establishing this benchmark, this work aims to guide and inspire the design of more efficient and compact planar inductors for high-performance RF systems.

Extremely large-scale multiple-input multiple-output (XL-MIMO) is a key enabling technology for sixth-generation (6G) communication systems. Nevertheless, the increase in array aperture and signal bandwidth brings new challenges to wideband channel estimation in XL-MIMO systems. Motivated by recent advances in deep generative modeling, we propose a diffusion model-based method for near-field wideband channel estimation in XL-MIMO systems. We first analyze the statistical correlation of wideband channel and show that near-field wideband channel exhibits both spatial non-stationarity and beam split effects. Based on these observations, the channel estimation problem is formulated as a Bayesian posterior inference task, in which a diffusion model is employed to learn the prior distribution of the channel. To further enhance the representation of complex spatial-frequency channel structures, we design a denoising network with a multi-scale attention mechanism. In particular, the network extracts multi-scale spatial-frequency features via parallel convolutional branches with different receptive fields, and combines feature attention and spatial attention modules to adaptively emphasize critical channel features. This design enables more accurate modeling of near-field wideband channel distributions and consequently improves channel estimation performance. Experimental results demonstrate that the proposed method exhibits superior robustness to existing baseline schemes for XL-MIMO wideband channel estimation under different experimental settings.

The nonlinear response coefficient, $χ_{4,22}$, is a crucial observable for probing the dynamical properties of the quark-gluon plasma (QGP). While traditionally understood as a signature of medium response, recent studies suggest that $χ_{4,22}$ also encapsulates critical information regarding the intrinsic initial-state configuration of the colliding nuclei. In this study, we utilize A Multi-Phase Transport (AMPT) model to investigate the microscopic origin and stage-by-stage development of $χ_{4,22}$ in $^{238}$U+$^{238}$U and $^{197}$Au+$^{197}$Au collisions at $\sqrt{s_{\rm NN}} = 200$ GeV. By tracking the flow observables through the partonic cascade, quark coalescence, and hadronic rescattering phases, we map the translation of initial geometric eccentricities into final-state momentum anisotropies. Our results demonstrate that the absolute magnitude of $χ_{4,22}$ increases continuously during the collective expansion, confirming its nature as a dynamically generated medium response. However, the comparative ratio of this coefficient between the U+U and Au+Au systems is stable across all evolutionary stages within statistical uncertainties. This indicates that the ratio approximately cancels complex evolutionary dynamics to isolate intrinsic geometric correlations present at the initial state. These findings provide compelling theoretical support and crucial insights for recent experimental efforts aiming to extract high-order nuclear structure, such as hexadecapole deformation, using nonlinear flow observables.

It has been conjectured that finite tensor categories have finitely generated cohomology. We show that this is equivalent to finitely generated Hochschild cohomology for the endomorphism algebras of the projective generators.

Large language model (LLM)-enhanced recommendation models inject LLM representations into backbone recommenders to exploit rich item text without inference-time LLM cost. However, we find that existing LLM-enhanced methods significantly hinder the optimization of backbone models, resulting in high training losses that are difficult to reduce. To address it, we establish a comprehensive theoretical analysis of local optimization curvature and identify two key causes: 1) large norm disparity and 2) semantic-collaboration misaligned angular clustering of LLM representations. Guided by these insights, we propose Training-Friendly LLM-Enhanced Recommender (TF-LLMER), a lightweight framework with two key components. First, we highlight the necessity of item embedding normalization to eliminate norm-driven instability and achieve provable control over optimization conditioning. Second, we introduce Rec-PCA, a recommendation-aware dimensionality reduction method that injects collaborative structure into the representation transformation to resolve semantic-collaboration misaligned angular clustering. It jointly optimizes semantic information retention and alignment with an item-item co-occurrence graph constructed from interaction histories. The graph captures collaborative structure, and alignment is promoted by penalizing total variation over the graph. Both theory and extensive experiments demonstrate that TF-LLMER significantly outperforms state-of-the-art methods. Our code is available at https://github.com/woriazzc/TF-LLMER.

This paper presents a comprehensive end-to-end evaluation of an infrastructure-assisted collective perception (ICP) system deployed on a highway using ITS-G5 technology. Open-road tests were conducted in the Bizkaia Connected Corridor (BCC), an operational corridor which covers a winding highway, enabling a realistic assessment of system performance in diverse traffic scenarios. The evaluation included three main aspects: (1) end-to-end Vehicle-to-Everything (V2X) communication latency, with a breakdown of delays introduced by each system component; (2) the effective range of ITS-G5 communications between vehicles and infrastructure; and (3) the perception system, using an independent sensor setup for ground truth annotation to account for errors beyond the detection model, such as synchronization, localization, and calibration inaccuracies. The results reveal that object detection and asynchronous transmission of collective perception messages (CPMs) are major latency bottlenecks, with results showing that synchronizing CPM transmission with local perception can reduce delays by up to 33%. Additionally, onboard perception struggles with detecting objects beyond 50 meters, highlighting the importance of collective perception in highway environments, where communication ranges significantly exceed detection limits. The findings provide valuable insights to optimize ICP deployments, supporting safer and more efficient cooperative mobility systems.

Designing regulatory DNA elements with precise cell-type-specific activity is broadly relevant for cell engineering and gene therapy. Deep generative models can generate functional gene-regulatory elements, but existing methods struggle to achieve high specificity against undesired cell types while adhering to the genome's natural regulatory grammar. Here, we introduce DNA-CRAFT, a generative framework that integrates class-conditioned discrete diffusion with Monte Carlo tree search to design cell-type-specific and biologically faithful regulatory elements. We first train a discrete diffusion model on the ENCODE registry of 3.2 million candidate regulatory elements. Second, we condition the model to learn class-specific regulatory grammars of naturally occurring DNA sequences, including enhancers and promoters. Third, we employ conditional Monte Carlo tree guidance, an inference-time alignment algorithm designed to maximize the differential regulatory activity between desired and undesired cell types. By benchmarking DNA-CRAFT on regulatory sequence design tasks for human cell lines and immune cell types, we demonstrate that our model generates sequences with high predicted cell-type-specific activity and biological fidelity, achieving the best trade-offs compared to methods that use diffusion, autoregressive models, and gradient-based optimization.

This paper develops a co-state based fusion frame work for spacecraft navigation, consistency monitoring, and hazard forecasting. A differential algebraic co-state is introduced as an instantaneous Lagrange multiplier that enforces measurement dynamics compatibility at the differential level and provides a physically interpretable signal of geometric inconsistency. On a longer time scale, co-state and innovation trajectories are used to learn a continuous time Markov generator governing transitions between coarse behavioural regimes, enabling intrinsic probabilistic risk forecasting through mode probabilities and mean first-passage time (MFPT). The resulting architecture unifies geometric projection, stochastic inference, and probabilistic risk assessment in a single online pipeline without requiring predefined fault models, labelled failure data, or heuristic thresholds. The framework is demonstrated on real lunar powered-descent telemetry, where it detects structural internal model inconsistency significantly earlier than physical divergence or statistical inconsistency in an Extended Kalman Filter (EKF). The results show that geometric inconsistency, stochastic drift, and probabilistic risk rise coherently prior to failure, yielding interpretable and operationally meaningful early-warning capability for autonomous landing systems.

The development of operational scenarios without large Type-I ELMs is of utmost importance for the stable operation and longevity of future tokamaks. The EUROfusion tokamak exploitation program has therefore made the understanding of ELM-free regimes a major topic of exploration across all its contributing devices (ASDEX Upgrade, JET, MAST-Upgrade, TCV, and WEST). An integrated program to investigate a range of Type-I ELM-free regimes has been developed covering the enhanced D-alpha (EDA), magnetic perturbations (MP), negative triangularity (NT), quasi-continuous exhaust (QCE), quiescent H-mode (QH), the baseline small ELMs (SE), I-mode, and X-point radiator (XPR) regimes. This contribution focuses on the development and understanding of the NT and QCE regimes on ASDEX Upgrade, JET, and TCV. The importance of transport via ballooning modes in both regimes is highlighted, as well as the progress in developing access models based on ideal-MHD. In the case of the QCE, this can also be expressed as a minimum separatrix density, which corresponds well to experimentally measured separatrix densities. Particular focus is paid to the performance of the QCE in terms of the achieved pedestal top values, which, when appropriately normalised, do not differ significantly from ELMy H-mode plasmas. This, combined with the predicted minimum separatrix density for the 15 MA ITER baseline plasma, highlight the relevance of the QCE as a potential operational scenario for both ITER and future reactors.

Electron shuttling is emerging as a key mechanism for enabling long-range coupling in scalable spin-qubit architectures. Bringing shuttling waveform generation into the cryostat can improve scalability, but imposes strict area and power constraints on the control electronics. Concurrently, shuttling in Si/SiGe is further limited by a spatially varying valley splitting that induces spin--valley mixing and degrades coherence. Here, we make three contributions that address these limitations jointly: (i) an end-to-end co-simulation framework that combines disorder-informed valley maps with transistor-level cryogenic circuit simulations including electronic noise; (ii) a fully integrated cryogenic shuttling-signal generator tailored to velocity modulation, enabling period-wise waveform shaping through discrete circuit settings stored in on-chip memory; and (iii) a noise-aware optimization procedure that tunes only these implementable circuit controls, using one of four discrete resistor settings per period, to generate high-fidelity shuttling sequences. Across simulated valley and noise realizations in our co-simulation framework, the optimized velocity-modulation waveforms improve transport performance, achieving an average shuttling fidelity of $99.99 \pm 0.007\%$ at $v_{\mathrm{avg}} = 20~\mathrm{m\,s^{-1}}$ over a distance of $10~μ\mathrm{m}$, while maintaining active analog power consumption in the tens of $μ\mathrm{W}$ during shuttling. This validates on-chip storage and replay of optimized control settings as a practical strategy to mitigate valley disorder in scalable shuttling architectures.

Mrk421 displayed its highest flux state ever observed in February of 2010 with very high TeV fluxes and interesting cross-band correlations and a spectral energy distribution (SED) evolution not entirely consistent with the standard single zone leptonic synchrotron self-Compton model. The source was already in a high state in January 2010 and displayed strong variability in the days preceding the highest state. We study the temporal evolution of the spectra in January to extract information about the particle dynamics and the physical properties of the emission region. We build up on the temporal variability and correlations studied in the previous work (MAGIC collaboration - Abe et al. 2025) and attempt to improve the SED model fits with a physics oriented approach. The multi-wavelength data was processed and the SEDs were fit using JetSeT. The SED evolution and cross band correlations were modelled using leptonic log-parabola with a low energy power-law branch (LPPL) and pile-up distributions that are predicted in a stochastic acceleration scenario. A simplified temporal evolution model was developed and fit to the SEDs and the resulting trends and phenomenology were characterised in context of theoretical literature. An expanding emission region model was also tested. We find the spectral variability to be well in agreement with stochastic acceleration. Our analysis suggests that the standard LPPL distribution develops a Maxwellian pile-up component at the transition from acceleration to cooling dominated phase on 3 nights in the dataset, as also hinted by the very-high energy and X-ray light curves. The resulting phenomenology of our sequential snapshot evolution SED model agrees well with theoretical and numerical simulation studies on temporal evolution using the diffusion equation approach.

We show that the inverse Faraday effect can be used to engineer dipole--exchange spin-wave spectra in ferrimagnetic bismuth iron garnet (BIG) Mie spheres. Internal optical Mie resonances generate spatially structured effective magnetic fields whose symmetry is inherited from the optical near field and which act as controllable perturbations of the magnon Hamiltonian. For circularly polarized light incident collinearly with the equilibrium magnetization, the optical perturbation preserves axial symmetry while breaking mirror parity, thereby enabling hybridization of magnon modes with opposite parity within the same $\widehat{J}_z$ sector. Using coupled-mode theory, we derive the corresponding avoided-crossing spectrum and analytical expressions for the induced level splittings, which scale linearly with pump intensity. Numerical calculations for BIG spheres confirm the predicted hybridization and show that the splitting is maximized near optical Mie resonances, where field enhancement and magneto-optical response are strongest. We further discuss the roles of damping, linewidth, and heating, and show that the predicted MHz--hundreds-of-MHz splittings should be observable under realistic conditions. These results identify BIG Mie resonators as a promising platform for symmetry-selective optical control of spin-wave spectra.

Mid-infrared spectra from the Medium Resolution Spectrometer on the James Webb Space Telescope have revealed the molecular chemistry of carbon stars in the Large Magellanic Cloud with better resolution and sensitivity than previously possible. Our sample spans a range of dust-production rates and includes three relatively dust-free semiregular variables and six dustier Mira variables. All were observed 15-20 yr earlier with the Infrared Spectrograph on the Spitzer Space Telescope at lower spectral resolution. The new spectra show that the C3 molecule is responsible for a strong absorption band centered at 5.2 um. CS is clearly present in some of the sample, especially the stars with less dust. HCN also appears to be present. Some of the spectra have changed significantly between the Spitzer epoch and the MRS observations in 2023 and 2024, and in most cases these changes can be attributed to the stellar pulsation cycle. One exception is the disappearance of a dust emission feature at ~18 um in one of the Miras. The new spectra reveal a dip centered at ~10 um, which could arise either from an unknown carrier or from variable molecular emission to the red and blue. The presence of this spectral structure on the short-wavelength side of the SiC dust emission feature at ~11.3 um along with the broad C2H2 band centered at 14 um raise the possibility that some previously reported detections of weak SiC dust emission in other carbon stars may not be real.

Biases in molecular evolution can significantly influence evolutionary trajectories. They have been described in a variety of contexts such as development and mutation, but not for acquiring new functions (i.e. emergence). Here, we formalize the term, emergence bias, as the molecular predisposition that, upon mutation, biases a genetic sequence towards or against gaining new functions or causing new phenotypes. These biases have been observed in previous studies for the emergence of promoters, enhancers, and de novo proteins, but never formally characterized as such. In this Perspective piece, we describe these studies and synthesize their findings through the prism of a unifying term, emergence bias, to provide support for this new concept , and speculate on its molecular underpinnings. We believe that emergence biases may play an important role in evolutionary innovations.

Conical intersections are central to the description of photophysics and photochemistry. Nevertheless, in non-adiabatic molecular dynamics simulations, they are fundamentally challenging for single-reference electronic structure methods. Density functional theory (DFT) and its time-dependent extension (TDDFT) represent the most widely used theoretical approaches in physics, chemistry, and biology. However, the treatment of ground and excited states as separate problems leads to breakdowns in the topological structure of potential energy surfaces near conical intersections. In this work, we solve this long-standing issue by presenting Convex DFT (CVX-DFT), a framework that, by explicitly enforcing convexity of the variational problem within an appropriately defined subspace, guarantees a unique and continuous electronic solution across regions of degeneracies. We demonstrate that CVX-DFT yields smooth and physically meaningful intersection seams by comparison with reference methods, such as multireference wave function methods. In this way, we establish the method as a robust and computationally efficient DFT approach for treating electronically degenerate regions. These developments represent a critical step toward reliable non-adiabatic simulations beyond the limitations of conventional TDDFT.

We derive a topological decoupling of the equations of modified nodal analysis (MNA) to a semi-explicit index one differential-algebraic equation. The decoupling explicitly allows for controlled sources, which play a crucial role in engineering design workflows. Furthermore, the proof is constructive and provides a graph-based algorithmic framework for the computation of the decoupling, enabling its application to a variety of industry problems. These include the generation of consistent initial conditions, model order reduction, (scientific) machine learning, as well as speeding up conventional circuit simulation. In addition, the decoupling preserves the structure of MNA, i.e. the resulting systems remain sparse and key parts remain positive definite. We illustrate the decoupling using multiple examples, including some of the most common subcircuits containing controlled sources. Lastly, we also provide a first software implementation of the decoupling.

We address a short-wave asymptotic for one class of quasi-linear second order PDE systems involving the cross-diffusion described by the so-called Patlak--Keller--Segel law. It is common to employ these equations for modelling the predator--prey community with the prey-taxis that means the interactions of two species of particles or cells or anything else through which the species called "predators" is capable of moving directionally while searching for the other species called "prey." However, we suppose the predators to be sensitive not to the prey density but to a driving signal produced by the prey. Additionally, the production of the driving signal is assumed to be sensitive to the intensity of an external field, which is independent from the community state. This is what we call the external signal. It can be due to the spatiotemporal inhomogeneity of the environment arising from natural or artificial reasons. We assume that the external signal takes a general short-wave form and construct a complete asymptotic expansion for the short-wave solutions with no restrictions on the spatial dimension or kinetics of inter/intraspecific reactions. Further, we apply the short wave asymptotic to studying the stability or instability induced by the external signal following Kapitza' theory for the upside-down pendulum. Applying the general results to some special classes external signals, we get examples of suppressing the taxical transport, examples of robustness of the species equilibrium to the signal or, oppositely, blurring the borderline in the parametric space between the areas of stability and instability of this equilibrium. These results contribute to filling the gap in the literature, since the theory and techniques for the asymptotic integration of systems described above represent a weakly charted area.

The sixth generation (6G) communication networks are expected to provide high data rates, ultra-reliable communication, and massive connectivity, especially in challenging environments such as dense urban areas and disaster-affected regions. However, traditional terrestrial-only networks face significant challenges in these scenarios, including signal blockages from high-rise buildings, traffic congestion, and dynamic user distributions. To address these limitations, we propose the adaptive multi-UAV deployment (AMUD) framework within satellite air-ground integrated networks (SAGINs). The AMUD framework dynamically deploys amplify-and-forward multiple unmanned aerial vehicle relay (UAVr) in with low Earth orbit (LEO) satellites to improve coverage, alleviate congestion, and ensure reliable communication in non-line-of-sight and high-demand conditions. We formulate an optimization problem that aims to jointly maximize the energy efficiency of the total network and the total capacity while ensuring the fairness of the total capacity and satisfying the users' requirements. The simulation results demonstrate that AMUD improves the total capacity of the network, improves the total energy efficiency, and increases the fairness of the capacity compared to traditional LEO satellite and ground base station (LEO-GBS) only systems.

We consider the problem on the existence of two dimensional superintegrable systems in the presence of a magnetic field in the two dimensional Euclidean space. We assume the existence of two integrals of motion, besides the Hamiltonian, that are quadratic polynomials in the momenta. This problem was already studied in the cases where one integral is of Cartesian or polar type [J. Bérubé, and P. Winternitz, J. Math. Phys., 45(5): 1959-1973, 2004]. We continue the investigation by assuming that one of the integrals is of parabolic type and the second integral is of elliptic or (''non-standard'') parabolic type, confirming so far that, on the Euclidean plane, the only two dimensional superintegrable system with quadratic integrals is the one with constant magnetic field and constant electrostatic potential.

In this paper, we first extend the explicit formula \cite{gerard2023explicit} for the classical Benjamin-Ono equation to each flow of the Benjamin-Ono hierarchy on line. We then use this representation to derive two main applications. First, we obtain a complete classification of traveling wave solutions for all higher-order flows in the hierarchy. Second, we analyze the zero-dispersion limit for the corresponding small-dispersion flows. For every fixed time $t\in\mathbb R$, we prove that, at any time, the solution converges weakly in $L^2(\mathbb R)$ as the dispersion parameter tends to $0$, and we provide a geometric characterization of the limit in terms of an alternating sum, which yields the higher-order analogue of the formula obtained in \cite{miller2011zero}, \cite{Gerard2025small} for the Benjamin-Ono equation.

We report on imaging the optical near-fields in resonant periodic photonic structures with nanometer resolution using ultrafast 4D scanning transmission electron microscopy (U4DSTEM). In particular, U4DSTEM is applied to visualize the transverse component of the Lorentz force of a synchronous near-field mode excited by an infrared femtosecond pulse in a periodic silicon nanostructure designed for photonic acceleration of electrons. Our results show that in addition to the accelerating/decelerating force acting on the electrons in the longitudinal direction along the electron propagation, the structures can be efficiently used for transverse electron streaking at optical frequencies when excited by light with polarization perpendicular to the electron trajectory. The measured spatial profile of the excited near-field mode intensity is consistent with the numerical simulations performed using finite-difference time domain technique.

The informativeness of security-related commit messages is crucial for patch triage: when high, it enables the rapid distribution and deployment of security fixes. Prior research (Reis et al., 2023) reported, however, that commit messages are often too uninformative to support these activities. To assess the robustness of this negative result, we independently replicate the original study using only the information provided in the paper, without reusing any of the original artifacts (data, analysis pipeline, etc.). We retrieve \num{50673} security-related commits and analyze their informativeness using an independent re-implementation of the techniques introduced by Reis et al. For the same source (i.e., GitHub) and time period (from June 1999 to August 2022) as the original study, our replication confirms the original findings in a statistically significant way: security-related commit messages are, in general, not informative enough for security-focused purposes. We then extend the original study in several ways. Over a longer time period (from June 1999 to October 2025), we find that commit-message informativeness is worsening. Breaking results down by software ecosystem (Linux kernel, Ubuntu, Go, PyPI, etc.), we observe significant differences in informativeness. Finally, we examine emerging best practices for writing commit messages, such as the Conventional Commits Specification (CCS), and again find significant differences in an unexpected direction: CCS-compliant commits are less informative than non-compliant ones. Our findings highlight the need for cross-ecosystem analyses to understand platform- and community-specific commit-message practices, and to inform the development and adoption of universally applicable guidelines for writing informative security-related commit messages.

We present a polynomial-time algorithm for the cluster vertex deletion problem on chordal graphs, resolving an open question posed in different contexts by Cao et al. [Theoretical Computer Science, 2018], Aprile et al. [Mathematical Programming, 2023], Chakraborty et al. [Discrete Applied Mathematics, 2024], and Hsieh et al. [Algorithmica, 2024]. We use dynamic programming over clique trees and reduce the computation of the optimal subproblem value to the minimization of a submodular set function.

Semi-visible jets (SVJs) provide a characteristic collider signature of strongly interacting dark sectors, in which the key model parameter $r_{\mathrm{inv}}$ controls the fraction of dark hadrons decaying to dark matter candidates. In this work, a regression model is developed to reconstruct $r_{\mathrm{inv}}$ in SVJ events produced in association with an energetic photon. The model uses information from high-level physics objects only, and the training procedure is optimized to ensure applicability. The performance is found to be robust against varying signal parameters and $r_{\mathrm{inv}}$ can be reconstructed at a much higher precision, compared to previously developed analytical method. It offers a new approach to conduct SVJ searches that can potentially unify both $s$-channel and $t$-channel productions, enhancing the sensitivities.

We study how strategic interaction can arise from controlled quantum dynamics rather than being imposed as an external mathematical structure. We introduce a class of interaction-defined quantum games in which players are represented by distinguishable quantum walkers, strategies correspond to local coin operations, and payoffs are defined as expectation values of physical observables. Using interacting discrete-time quantum walks as a concrete platform, we demonstrate numerically that competitive, cooperative, and asymmetric games admit stable stationary strategy profiles when the walkers are coupled, while no non-trivial equilibria exist in the absence of interaction. To clarify the game-theoretic structure, we derive an analytic perturbative decomposition of the payoff function in the weak-interaction regime, showing explicitly that strategic coupling originates from interaction-induced interference terms in the joint probability distribution. For a collision-based phase interaction, the payoff becomes non-separable at first order in the interaction strength and generically admits stationary points satisfying the Nash conditions. Our results provide a physically explicit realization of strategic interdependence in quantum transport processes and establish interacting quantum walks as a minimal platform for studying game-theoretic behavior emerging from unitary dynamics.