Suffix-based jailbreak attacks append an adversarial suffix, i.e., a short token sequence, to steer aligned LLMs into unsafe outputs. Since suffixes are free-form text, they admit endlessly many surface forms, making jailbreak mitigation difficult. Most existing defenses depend on passive detection of suspicious suffixes, without leveraging the defender's inherent asymmetric ability to inject secrets and proactively conceal gaps. Motivated by this, we take a controllability-oriented perspective and develop a proactive defense that nudges attackers into a no-win dilemma: either they fall into defender-designed optimization traps and fail to produce an effective adversarial suffix, or they can succeed only by generating adversarial suffixes that carry distinctive, traceable fingerprints. We propose TrapSuffix, a lightweight fine-tuning approach that injects trap-aligned behaviors into the base model without changing the inference pipeline. TrapSuffix channels jailbreak attempts into these two outcomes by reshaping the model's response landscape to adversarial suffixes. Across diverse suffix-based jailbreak settings, TrapSuffix reduces the average attack success rate to below 0.01 percent and achieves an average tracing success rate of 87.9 percent, providing both strong defense and reliable traceability. It introduces no inference-time overhead and incurs negligible memory cost, requiring only 15.87 MB of additional memory on average, whereas state-of-the-art LLM-based detection defenses typically incur memory overheads at the 1e4 MB level, while composing naturally with existing filtering-based defenses for complementary protection.

In this article, we investigate the instability of syzygy bundles corresponding to globally generated vector bundles on smooth irreducible projective surfaces under change of polarization.

Spin-orbital liquids provide an exactly solvable route to three-dimensional Z2 quantum spin liquids beyond the original Kitaev setting. Built from higher-dimensional Clifford-algebra representations, spin-orbital Hamiltonians can be realized on both three- and four-coordinated lattices, giving rise to phases with 3 and 2 itinerant Majorana flavors. We demonstrate that these models host a rich set of gapless Majorana metals, characterized, in particular, by topological Fermi surfaces, nodal lines, and Weyl semimetal phases. We analyze the stability of these structures under physically motivated perturbations and identify generic splitting patterns and topological transitions driven by symmetry breaking and flavor mixing. This yields a unified organizing framework for three-dimensional Majorana metals in fractionalized spin liquids.

An advanced technology known as a radio frequency identification (RFID) system enables seamless wireless communication between tags and readers. This system operates in what is referred to as a dense reader environment, where readers are placed close to each other to optimize coverage. However, this setup comes with its challenges, as it increases the likelihood of collisions between readers and tags (reader-to-reader and reader-to-tag), leading to reduced network performance. To address this issue, various protocols have been proposed, with centralized solutions emerging as promising options due to their ability to deliver higher throughput. In this paper, we propose the Intelligence and Efficient Reader Anti-collision Protocol (IE-RAP) that improves network performance such as throughput, average waiting time, and energy consumption, which employs a powerful combination of Time Division Multiple Access (TDMA) and Frequency Division Multiple Access (FDMA) mechanisms. IE-RAP improves the efficiency of RFID networks through techniques such as the SIFT function and distance calculation between readers. By preventing re-read tags and ensuring the on-time release of the communication channel, we effectively eliminate unnecessary collisions. Our simulations emphasize the superiority of our proposed method, it increases 26% throughput, reduces 74% the average waiting time, and lower by 52% the energy consumption compared to existing approaches. Importantly, our solution supports the seamless integration of mobile readers within the network.

Robust quantum transmission is driving a new paradigm in space-ground quantum networking. Although phase encoding has been widely adopted in terrestrial fiber channels, it has long been considered unsuitable for free-space quantum communication. Here, we demonstrate phase-encoded quantum communication over 1400 m of urban free space. The system maintained stable operation for nearly one hour, achieving 99.07% interference visibility and an average quantum bit error rate of 2.38%. The free-space quantum states were directly coupled into the fiber and transmitted over an additional 10 km, confirming seamless interoperability across different media. We further show that turbulence-induced phase drifts between successive picosecond pulses can be effectively compensated. A cascaded-link model and numerical simulations indicate feasibility over free-space distances exceeding 30 km, underscoring the potential for satellite-to-ground quantum links. This work establishes the viability of phase encoding in free-space quantum networks, simplifying cross-medium integration and enabling compatibility with existing classical infrastructures.

Lifelong user modeling, which leverages users' long-term behavior sequences for CTR prediction, has been widely applied in personalized services. Existing methods generally adopted a two-stage "retrieval-refinement" strategy to balance effectiveness and efficiency. However, they still suffer from (i) noisy retrieval due to skewed data distribution and (ii) lack of semantic understanding in refinement. While semantic enhancement, e.g., LLMs modeling or semantic embeddings, offers potential solutions to these two challenges, these approaches face impractical inference costs or insufficient representation granularity. Obsorbing multi-granularity and lightness merits of semantic identity (SID), we propose a novel paradigm that equips retrieval and refinement in Lifelong User Modeling with SEmantic IDs (R2LED) to address these issues. First, we introduce a Multi-route Mixed Retrieval for the retrieval stage. On the one hand, it captures users' interests from various granularities by several parallel recall routes. On the other hand, a mixed retrieval mechanism is proposed to efficiently retrieve candidates from both collaborative and semantic views, reducing noise. Then, for refinement, we design a Bi-level Fusion Refinement, including a target-aware cross-attention for route-level fusion and a gate mechanism for SID-level fusion. It can bridge the gap between semantic and collaborative spaces, exerting the merits of SID. The comprehensive experimental results on two public datasets demonstrate the superiority of our method in both performance and efficiency. To facilitate the reproduction, we have released the code online https://github.com/abananbao/R2LED.

Precise magnetic field modeling is fundamental to the closed-loop control of electromagnetic navigation systems (eMNS) and the analytical Multipole Expansion Model (MPEM) is the current standard. However, the MPEM relies on strict physical assumptions regarding source symmetry and isolation, and requires optimization-based calibration that is highly sensitive to initialization. These constraints limit its applicability to systems with complex or irregular coil geometries. This work introduces an alternative modeling paradigm based on multi-layer perceptrons that learns nonlinear magnetic mappings while strictly preserving the linear dependence on currents. As a result, the field models enable fast, closed-form minimum-norm inversion with evaluation times of approximately 1 ms, which is critical for high-bandwidth magnetic control. For model training and evaluation we use large-scale, high-density datasets collected from the research-grade OctoMag and clinical-grade Navion systems. Our results demonstrate that data-driven models achieve predictive fidelity equivalent to the MPEM while maintaining comparable data efficiency. Furthermore, we demonstrate that straightforward design choices effectively eliminate spurious workspace ill-conditioning frequently reported in MPEM-based calibration. To facilitate future research, we release the complete codebase and datasets open source.

In the spirit of Arthur's trace formula, we establish a general trace formula for symmetric spaces associated with the variety of involutions of a finite $D$-module where $D$ is a division algebra central over a number field $F$. Such a formula should be useful for studying the automorphic spectrum of these symmetric spaces and the deep links between linear periods and special values of standard $L$-functions at their center of symmetry. Indeed, our formula yields an identity between spectral distributions, which generalize relative characters built on linear periods, and geometric distributions, which are an extension of relative orbital integrals. We show that the spectral distributions are, in a certain sense, asymptotic to truncated integrals of the components of the automorphic kernel associated with a cuspidal datum: this provides a handle on these distributions and has allowed, in a companion paper, to express some of these distributions in the form of a weighted relative character. The geometric distributions attached to "regular semi-simple" geometric data are expressed as weighted relative orbital integrals. In general, for non-regular geometric data, we introduce a procedure of descent to the centralizer, which allows us to express any geometric distribution in terms of the nilpotent contribution of infinitesimal trace formulas studied in previous papers.

The magnetar Swift J1834.9-0846 presents a significant challenge to neutron star spin-down models. It exhibits two key anomalies: an insufficient rotational energy loss rate to power its observed X-ray luminosity, and a braking index of $ = 1.08\pm 0.04$, which starkly contradicts the canonical magnetic dipole value of $n=3$. To explain these anomalies, we develop a unified spin-evolution model that self-consistently integrates magnetic dipole radiation, gravitational wave emission, and wind braking. Within this framework, we constrain the wind braking parameter to $κ\in [13, 37]$ from the nebular properties, finding it contributes substantially (17%-51%) to the current spin-down torque. Bayesian inference reveals that the birth period is poorly constrained by present data and is prior-dependent, indicating a millisecond birth is allowed but not required. Furthermore, we constrain the number of precession cycles to $ξ\sim 10^{4}$--$10^{5}$, and our analysis favors a toroidally-dominated internal magnetic field configuration as the most self-consistent explanation for the low braking index. Finally, we assess the continuous gravitational-wave detectability. The present-day signal is undetectable. However, the early-time signal might have reached the projected sensitivity of next-generation gravitational-wave observatories, such as the Advanced Laser Interferometer Gravitational-Wave Observatory (aLIGO) and the Einstein Telescope (ET), although a confident detection would require exceptionally stable rotation, an assumption considered highly optimistic for a young magnetar. This work establishes a unified framework that links magnetar spin-down with their interior physics and multi-messenger observables, providing a physically consistent interpretation for Swift J1834.9-0846 and a new tool for understanding similar extreme neutron stars.

We propose a Dynamical Low-Rank Ensemble Kalman Filter (DLR-ENKF) for efficient joint state-parameter estimation in high-dimensional dynamical systems. The method extends the DLR-ENKF formulation of arXiv:2509.11210 to the augmented state-parameter framework, tracking the filtering density within a dynamically evolving low-dimensional subspace. Key developments include a time-integration strategy that combines the Basis Update & Galerkin scheme with forecast/analysis discretisation, and a DEIM-based hyper-reduction technique for efficient evaluation of nonlinear terms. We demonstrate the effectiveness, robustness, and computational advantages of the proposed approach on benchmark problems. The results highlight the potential of dynamically evolving reduced bases to achieve accurate filtering and parameter estimation at reduced computational cost.

This paper presents a survey of state-of-the-art trust models for Vehicular Ad Hoc Networks (VANETs). Trust management plays an essential role in isolating malicious insider attacks in VANETs which traditional security approaches fail to thwart. To this end, many trust models are presented; some of them only address trust management, while others address security and privacy aspects besides trust management. This paper first reviews, classifies, and summarizes state-of-the-art trust models, and then compares their achievements. From this literature survey, our reader will easily identify two broad classes of trust models that exist in literature, differing primarily in their evaluation point. For example, most trust models follow receiver-side trust evaluation and to the best of our knowledge, there is only one trust model for VANETs which evaluates trust at the sender-side unless a dispute arises. In the presence of a dispute, a Roadside Unit (RSU) rules on the validity of an event. In receiver-side trust models, each receiver becomes busy while computing the trust of a sender and its messages upon the messages' arrival. Conversely, in the sender-side class, receivers are free from any kind of computation as the trust is verified at the time the message is announced. Also, vehicles can quickly act on the information, such as taking a detour to an alternate route, as it supports fast decision-making. We provide a comparison between these two evaluation techniques using a sequence diagram. We then conclude the survey by suggesting future work for sender-side evaluation of trust in VANETs. Additionally, the challenges (real-time constraints and efficiency) are emphasized whilst considering the deployment of a trust model in VANETs

Scientific novelty is a critical construct in bibliometrics and is commonly measured by aggregating pairwise distances between the knowledge units underlying a paper. While prior work has refined how such distances are computed, less attention has been paid to how dyadic relations are aggregated to characterize novelty at the paper level. We address this limitation by introducing a network-based indicator, Cognitive Traversal Distance (CTD). Conceptualizing the historical literature as a weighted knowledge network, CTD is defined as the length of the shortest path required to connect all knowledge units associated with a paper. CTD provides a paper-level novelty measure that reflects the minimal structural distance needed to integrate multiple knowledge units, moving beyond mean- or quantile-based aggregation of pairwise distances. Using 27 million biomedical publications indexed by OpenAlex and Medical Subject Headings (MeSH) as standardized knowledge units, we evaluate CTD against expert-based novelty benchmarks from F1000Prime-recommended papers and Nobel Prize-winning publications. CTD consistently outperforms conventional aggregation-based indicators. We further show that MeSH-based CTD is less sensitive to novelty driven by the emergence of entirely new conceptual labels, clarifying its scope relative to recent text-based measures.

Metasurface inverse design is challenged by the intricate relationship between structural parameters and electromagnetic responses, as well as the high dimensionality of the optimization space. Local models, while commonly employed, quickly become infeasible for complex and locally coupled structures. Conventional iterative optimization techniques, on the other hand, are computationally intensive, time-consuming, and susceptible to convergence in local minima. This study explores a versatile generative methodology based on enhanced posterior sampling within the Schrödinger Bridge framework. By decomposing posterior sampling into amplitude and directional contributions, we effectively integrated different kind of posterior sampling. This approach is further supported by refined training strategies to enhance performance and reduce the complexity of hyperparameter optimization. The proposed framework demonstrates exceptional accuracy and robustness, representing a significant advancement in metasurface design. Notably, it enables high-precision inverse design for large-scale configurations of up to $350 \times 350$ pillar arrays, despite being trained on significantly smaller arrays of $23 \times 23$ pillars.

Studying political activity on social media often requires defining and measuring political stances of users or content. Relevant examples include the study of opinion polarization, or the study of political diversity in online content diets. While many research designs rely on operationalizations best suited for the US setting, few allow addressing more general political systems, in which users and media outlets might exhibit stances on multiple ideology and issue dimensions, going beyond traditional Liberal-Conservative or Left-Right scales. To advance the study of more general online ecosystems, we present a dataset pertaining to a population of X/Twitter users, parliamentarians, and media outlets embedded in a political space spanned by dimensions measuring attitudes towards immigration, the EU, liberal values, elites and institutions, nationalism and the environment, in addition to left-right and liberal-conservative scales. We include indicators of individual activity and popularity: mean number of posts per day, number of followers, and number of followees. We provide several benchmarks validating the positions of these entities and discuss several applications for this dataset.

We address the client-selection problem in federated learning over wireless networks under data heterogeneity. Existing client-selection methods often rely on server-side knowledge of client-specific information, thus compromising privacy. To overcome this issue, we propose a client self-selection strategy based solely on the comparison between locally computed training losses and a centrally updated selection threshold. Furthermore, to support robust aggregation of clients' updates over wireless channels, we integrate this client self-selection strategy into the recently proposed type-based unsourced multiple-access framework over distributed multiple-input multiple-output (D-MIMO) networks. The resulting scheme is completely unsourced: the server does not need to know the identity of the clients. Moreover, no channel state information is required, neither at the clients nor at the server side. Simulation results conducted over a D-MIMO wireless network show that the proposed self-selection strategy matches the performance of a comparable state-of-the-art server-side selection method and consistently outperforms random client selection.

We study selfish routing games where users can choose between regular and priority service for each network edge on their chosen path. Priority users pay an additional fee, but in turn they may travel the edge prior to non-priority users, hence experiencing potentially less congestion. For this model, we establish existence of equilibria for linear latency functions and prove uniqueness of edge latencies, despite potentially different strategic choices in equilibrium. Our main contribution demonstrates that marginal cost pricing achieves system optimality: When priority fees equal marginal externality costs, the equilibrium flow coincides with the socially optimal flow, hence the price of anarchy equals $1$. This voluntary priority mechanism therefore provides an incentive-compatible alternative to mandatory congestion pricing, whilst achieving the same result. We also discuss the limitations of a uniform pricing scheme for the priority option.

Understanding how data quality aligns with regulatory requirements in machine learning (ML) systems presents a critical challenge for practitioners navigating the evolving EU regulatory landscape. To address this, we first propose a practical framework aligning established data quality dimensions with specific EU regulatory requirements. Second, we conducted a comprehensive online survey with over 180 EU-based data practitioners, investigating their approaches, key challenges, and unmet needs when ensuring data quality in ML systems that align with regulatory requirements. Our findings highlight crucial gaps between current practices and regulatory expectations, underscoring practitioners' need for more integrated data quality tools and better collaboration between technical and legal practitioners. These insights inform recommendations for bridging technical expertise and regulatory compliance, ultimately fostering responsible and trustworthy ML deployments.

The self-assembly of polymer grafted nanoparticles is more and more used in the field of functional materials. However, there is still a lack of analysis on the dynamic transformation paths of different self-assembly morphologies, which makes it impossible to achieve further precise regulation and targeted design in experiments and industrial production. In this work the effects of patchy property, grafted chain length, ratio and grafting density on the self-assembly behavior and structure of polymer grafted flexible patchy nanoparticles are investigated by dissipative particle dynamics simulation method through the construction of coarse-grained model of polymer grafted ternary nanoparticles. The influence and regulation mechanisms of these factors on the self-assembly structure transformation of flexible patchy nanoparticles are systematically studied, and a variety of structures such as dendritic structure, columnar structure, and bilayer membrane are obtained. The self-assembly structure of flexible patchy nanoparticles obtained in this work (such as bilayer membrane structure) provides a potential application basis for designing drug carriers. By precisely regulating the specific structural characteristics of the system, it is possible to achieve efficient loading of drugs and targeted delivery functions, thus significantly improving the bioavailability and effect of drugs.

Neutron monitors are a standard tool for high-precision continuous monitoring of galactic cosmic ray flux variations arising from variations in heliospheric conditions and solar activity for space weather applications. These measurements form the basis for solving the inverse problem of determining the cosmic ray anisotropy vector beyond the magnetosphere. To support such studies, periodic latitude measurements are necessary to determine the coupling functions of primary and secondary cosmic rays variations. The aim of this work is to develop and characterize a modernized standard neutron monitor based on a CHM-15 boron thermal neutron counter and a data acquisition system designed for marine expeditionary studies of cosmic ray variations. Modern nuclear physics experimental methods and the principles of microprocessor-based data acquisition systems were used to solve this problem. The results of test trials and of continuous monitoring showed that the characteristics of the upgraded and standard neutron monitor are similar, and the ease of use, compactness, and stability allow us to conclude that the mobile neutron detector can be used in expeditionary conditions with limited access for maintenance personnel.

This study presents band-ensemble Spectral Proper Orthogonal Decomposition (bSPOD). The approach is inspired by frequency smoothing, a method used to reduce estimator variance in power spectral density estimates, and is here extended to SPOD. The algorithm estimates SPOD modes from consecutive Fourier coefficients obtained from a single Fourier transform of the full time record and thus avoids time segmentation. In this study, bSPOD is applied to artificial test data and to a PIV data set of a broadband-tonal cavity flow. Compared to the more commonly used Welch-based SPOD formulation, bSPOD reduces spectral leakage, permits increased frequency resolution, and retains frequency information of tonal components at comparable computational cost. These features enable reduced estimator variance while maintaining low bias for tonal components, making bSPOD particularly effective for broadband-tonal flows.

Optical cavities are a foundational technology for controlling light-matter interactions. While interfacing a single cavity to either an atom or ensemble has become a standard tool, the advent of single atom control in large atomic arrays has spurred interest in a new frontier of ``many-cavity QED,'' featuring many independent resonators capable of separately addressing individual quantum emitters. In this fast-evolving landscape, the cavity array microscope was recently introduced -- employing free space intra-cavity optics to engineer a two-dimensional array of tightly spaced cavity TEM$_{00}$ modes with wavelength-scale waists, ideally suited for interfacing with atom arrays. Here we realize the next-generation of this architecture, achieving hundreds of degenerate cavity modes with improved, uniform finesse, and explore the technical features of the system which will enable further scalability. In particular, we study imperfections, including optical aberrations, field of view constraints, array non-degeneracies, and losses from optical elements. We identify the sensitivity to these various vectors and exposit the control knobs and techniques necessary to align and operate the system in a stable manner. Ultimately, we lay out a pathway towards operation with tens of thousands of independent cavities while maintaining compatibility with existing atom arrays, paving the way to myriad applications including highly parallelized remote entanglement generation, fast and non-destructive mid-circuit readout, and the implementation of hybrid atom-photon Hamiltonians.

We investigate the details of the canonical quantization of effective quantum field theories in anti-de Sitter spacetime, emphasizing the stability of the quantum vacuum. We take the scalar and Maxwell fields as examples. For the non-minimally coupled massless real scalar field with ξRϕ^2 term in the Lagrangian (mass can be introduced by shift of ξ), only when ξ\le 5/48, the quantized Hamiltonian is spontaneously non-negative and the vacuum is well defined. For ξ> 5/48, one has to assign the negative energy spectrum as that of the ghost particles, introducing anti-commutation relations to make the corresponding part of the Hamiltonian trivial, ensuring the Hamiltonian non-negative and the vacuum (and the Hilbert space) well defined. This method of ghost states is applicable once the proper radial boundary conditions guarantee the Hamiltonian self-adjoint. The resulting dynamics can be compared with those resulting from the positive self-adjoint extensions when the latter is available for ξ\le 9/48. For the Maxwell fields, the gauge invariant canonical energy momentum tensor straightforwardly leads to the gauge invariant non-negative Hamiltonian (well-defined vacuum). Hence the redundant gauge degree of freedom is irrelevant, and the 2-dimensional dynamical degrees of freedom are quantized in a concrete, e.g., temporal gauge. The energy momentum tensors for both quantized fields are renormalized to be finite at operator level, which renders the stable vacuum maximally symmetric. The back-reactions to the background spacetime by excited states via the semi-classical Einstein equations are also discussed.

In rank aggregation, the goal is to combine multiple input rankings into a single output ranking. In this paper, we analyze rank aggregation methods, so-called social welfare functions (SWFs), with respect to strategyproofness, which requires that no agent can misreport his ranking to obtain an output ranking that is closer to his true ranking in terms of the Kemeny distance. As our main result, we show that no anonymous SWF satisfies unanimity and strategyproofness when there are at least four alternatives. This result is proven by SAT solving, a computer-aided theorem proving technique, and verified by Isabelle, a highly trustworthy interactive proof assistant. Further, we prove by hand that strategyproofness is incompatible with majority consistency, a variant of Condorcet-consistency for SWFs. Lastly, we show that all SWFs in two natural classes have a large incentive ratio and are thus highly manipulable.

Quasi-elastic (QEL) scattering measurements have been performed using the $^{28, 30}$Si projectiles off the $^{90}$Zr target at energies around the Coulomb barrier. Coupled-channels (CC) calculations were carried out in a large parameter space of quadrupole and hexadecapole deformations for the N=Z, $^{28}$Si and N=Z+2, $^{30}$Si nuclei. $^{28}$Si at the N=Z line is observed to be uniquely oblate shaped in its ground state. In contrast, for $^{30}$Si with just two additional neutrons -- oblate, prolate, and spherical CC descriptions are equally compatible with the measurements. To further investigate the nuclear structure evolution with varying neutron number, shell-model calculations were performed. These calculations reveal a sudden change in the nuclear structure aspects at $^{30}$Si in going from $^{28}$Si to $^{30}$Si. Combined reaction and structure analyses consistently indicate that $^{30}$Si does not possess a well-defined intrinsic shape, and it is a potential candidate for ``shape fluctuations" in its ground state.

This article addresses online variational estimation in parametric state-space models. We propose a new procedure for efficiently computing the evidence lower bound and its gradient in a streaming-data setting, where observations arrive sequentially. The algorithm allows for the simultaneous training of the model parameters and the distribution of the latent states given the observations. It is based on i.i.d. Monte Carlo sampling, coupled with a well-chosen deep architecture, enabling both computational efficiency and flexibility. The performance of the method is illustrated on both synthetic data and real-world air-quality data. The proposed approach is theoretically motivated by the existence of an asymptotic contrast function and the ergodicity of the underlying Markov chain, and applies more generally to the computation of additive expectations under posterior distributions in state-space models.

Realizability, introduced by Kleene, can be understood as a concretization of the Brouwer-Heyting-Kolmogorov (BHK) interpretation of proofs, providing a framework to interpret mathematical statements and proofs in terms of their constructive or computational content. Over time, this concept has evolved through various extensions, such as Kreisel's modified realizability or Krivine's classical realizability. Parallel to these developments, Girard's work on linear logic introduced another perspective, often seen as another concrete realization of the BHK interpretation. The resulting constructions, encompassing models like geometry of interaction, ludics, and interaction graphs, were recently unified under the term linear realizability models to stress the intuitive connection with intuitionnistic and classical realizability. The present work establishes for the first time a formal link between linear realizability models and the realizability constructions of Kleene and Krivine. Our approach leverages Miquel's framework: just as linear logic can be viewed as a decomposition of intuitionistic and classical logic, we propose a linear decomposition of implicative algebras and show that linear realisability models provide concrete examples of such decompositions.

We consider the sixth-order convective-viscous Cahn-Hilliard equation, different from the standard fourth-order Cahn-Hilliard equation due to the modified expression for the thermodynamic potential. In such modified thermodynamic potential the coefficient at the square gradient term is order-parameter-dependent. It also contains the square of the Laplacian. This results in a sixth-order differential equation and additional nonlinear terms in the equation. We obtained several exact static- and traveling wave solutions and studied the dependence of solutions on the parameters of the system.

This work develops a theoretical framework for safety controller synthesis in discrete-time stochastic nonlinear polynomial systems subject to time-invariant delays (dt-SNPS-td). While safety analysis of stochastic systems using control barrier certificates (CBC) has been widely studied, developing safety controllers for stochastic systems with time delays remains largely unexplored. The main challenge arises from the need to account for the influence of delayed components when formulating and enforcing safety conditions. To address this, we employ Krasovskii control barrier certificates, which extend the conventional CBC framework by augmenting it with an additional summation term that captures the influence of delayed states. This formulation integrates both the current and delayed components into a unified barrier structure, enabling safety synthesis for stochastic systems with time delays. The proposed approach synthesizes safety controllers under input constraints, offering probabilistic safety guarantees robust to such delays: it ensures that all trajectories of the dt-SNPS-td remain within the prescribed safe region while fulfilling a quantified probabilistic bound. To achieve this, our method reformulates the safety constraints as a sum-of-squares optimization program, enabling the systematic construction of Krasovskii CBC together with their associated safety controllers. We validate the proposed framework through three case studies, including two physical systems, demonstrating its effectiveness and practical applicability.

The goal of this paper is to analyze distributional Markov Decision Processes as a class of control problems in which the objective is to learn policies that steer the distribution of a cumulative reward toward a prescribed target law, rather than optimizing an expected value or a risk functional. To solve the resulting distributional control problem in a model-free setting, we propose a policy-gradient algorithm based on neural-network parameterizations of randomized Markov policies, defined on an augmented state space and a sample-based evaluation of the characteristic-function loss. Under mild regularity and growth assumptions, we prove convergence of the algorithm to stationary points using stochastic approximation techniques. Several numerical experiments illustrate the ability of the method to match complex target distributions, recover classical optimal policies when they exist, and reveal intrinsic non-uniqueness phenomena specific to distributional control.

During its annual conference in 2025, the French Society of Astronomy \& Astrophysics (SF2A) hosted, for the fifth time, a special session dedicated to discussing the environmental transition within the French A\&A research community. During the 2025 workshop, the goal was to review four contemporary topics within the context of environmental transition actions and discussions: (1) institutional actions, (2) the early-career researchers singularity, (3) research infrastructures and tools, and (4) the geopolitical conditions under which A\&A research remains possible. The workshop concluded with a round-table discussion that brought together the various speakers so that every participant could express their ideas.