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We apply topological methods to better understand how the magnetic field in the stellarator edge can be diverted away from the confined region. Our primary method is calculating the winding numbers of closed contours, which gives information on the number and nature of fixed points within a bounded region. We first apply this to the non-resonant divertor (NRD) Hamiltonian system, and present a simple explanation for the system's diversion: trajectories are guided away from the confined region by X-points which are "unpaired" i.e. do not have corresponding O-points and therefore do not resemble an island chain. We show how similar phenomena can occur in a similar, axisymmetric Hamiltonian system. Secondly, we find examples of neoclassically optimised stellarators in the QUASR database which divert the magnetic field via unpaired X-points. We present and discuss three examples, each containing novel phenomena which might be desirable for stellarator divertors. These findings broaden the horizons of how magnetic fields can be diverted in realistic stellarators, and may be attractive for future experiments and stellarator reactor design.
Large language model (LLM) agents automate penetration testing through an observation-action loop, selecting actions based on observations returned by tools. This dependence allows defenders to inject deceptive observations that can mislead the agent's decision-making process. However, existing defenses rely heavily on static, isolated artifacts planted in the environment prior to an attack. Advanced agents can progressively recognize and bypass these artifacts, ultimately refocusing their exploitation attempts on the real target. To address this issue, we introduce AgentSnare, a trajectory-adaptive deception system that dynamically unfolds a decoy environment to continually steer the penetration agent away from the real target. Specifically, AgentSnare employs an artifact-construction policy model that constructs candidate artifacts conditioned on the agent's interaction history and decoy state. AgentSnare then validates these candidates and incrementally incorporates valid artifacts into a factually consistent decoy environment, thereby delaying the attack by absorbing its tool calls, diverting its post-entry trajectory within the decoy, and defusing it by inducing completion rep
Flow diversion has become a key treatment modality for selected intracranial aneurysms, relying on the principle that a dense mesh of stent wires disrupts blood flow into the aneurysm sac, promoting thrombosis and vessel reconstruction. Despite its clinical success, a subset of patients experiences incomplete occlusion or complications. This study investigates innovative helical thin-film implants (HTFIs), aiming to evaluate their flow-diverting efficacy. Highly resolved computational fluid dynamics simulations were performed on two representative patient-specific aneurysm models. Two HTFI design variants were tested at various configurations (two rolling angles and three deployment positions). A total of 28 unsteady hemodynamic simulations were performed, comparing six hemodynamically relevant parameters against the pre-interventional state and a conventional braided flow diverter. The HTFIs induced significant changes in intra-aneurysmal flow. Both designs performed similarly overall, with the shorter configurations (smaller rolling angle) demonstrating superior efficacy. These achieved average hemodynamic reductions of 52.2% and 58.4%, outperforming the benchmark braided flow di
The safety-factor profile of a magnetically diverted tokamak plasma diverges logarithmically as the magnetic separatrix (a.k.a. the last closed magnetic flux-surface) is approached. At first sight, this suggests that, when determining the response of such a plasma to a static, externally generated, resonant magnetic perturbation (RMP), it is necessary to include an infinite number of rational magnetic flux-surfaces in the calculation, the majority of which lie very close to the separatrix. In fact, when finite plasma resistivity is taken into account, this turns out not to be the case. Instead, it is only necessary to include rational surfaces that lie in the region 0<Psi<Psi_c, where Psi is the normalized poloidal magnetic flux, and Psi_c<1 can be calculated from the edge plasma parameters. It is estimated that Psi_c= 0.9985 for an n=1 RMP, and Psi_c=0.9952 for an n=4 RMP, in a typical JET H-mode plasma.
High-quality distractors are crucial to both the assessment and pedagogical value of multiple-choice questions (MCQs), where manually crafting ones that anticipate knowledge deficiencies or misconceptions among real students is difficult. Meanwhile, automated distractor generation, even with the help of large language models (LLMs), remains challenging for subjects like math. It is crucial to not only identify plausible distractors but also understand the error behind them. In this paper, we introduce DiVERT (Distractor Generation with Variational Errors Represented as Text), a novel variational approach that learns an interpretable representation of errors behind distractors in math MCQs. Through experiments on a real-world math MCQ dataset with 1,434 questions used by hundreds of thousands of students, we show that DiVERT, despite using a base open-source LLM with 7B parameters, outperforms state-of-the-art approaches using GPT-4o on downstream distractor generation. We also conduct a human evaluation with math educators and find that DiVERT leads to error labels that are of comparable quality to human-authored ones.
During intracranial aneurysm (IA) treatment with Diverters (FDs), the device/parent artery diameters ratio may influence the ability of the device to induce aneurysm healing response. Oversized FDs are safer to deploy but may not induce enough hemodynamic resistance to ensure aneurysm occlusion. Methods based on Computational Fluid Dynamics (CFD) could allow optimal device selection but are time-consuming and inadequate for intra-operative guidance. To address this limitation, we propose to investigate a method for optimal FD selection using Angiographic Parametric Imaging (API) and machine learning (ML). We selected 128 pre-treatment angiographic sequences of IAs which demonstrated full occlusion at six months follow-up. For each IA, we extracted five API parameters from the aneurysm dome and normalized them to the feeding artery corresponding parameters. We dichotomized the dataset based on the FD/ proximal artery diameter ratio as undersized, if the ratio<1 or if multiple FDs were used and oversized otherwise. Single API parameter and ML analysis were used to determine whether API parameters could be used to determine the need for FD under-sizing (i.e., increased flow resista
This paper tackles the task assignment and trajectory generation problem for bird diverter installation using a fleet of multi-rotors. The proposed motion planner considers payload capacity, recharging constraints, and utilizes Signal Temporal Logic (STL) specifications for encoding mission objectives and temporal requirements. An event-based replanning strategy is introduced to handle unexpected failures and ensure operational continuity. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation. Simulations in MATLAB and Gazebo, as well as field experiments, demonstrate the effectiveness and validity of the approach in a mock-up scenario.
Nowadays, divertors are used in the main tokamaks to control the magnetic field and to improve the plasma confinement. In this article, we present analytical symplectic maps describing Poincaré maps of the magnetic field lines in confined plasmas with a single null poloidal divertor. Initially, we present a divertor map and the tokamap for a diverted configuration. We also introduce the Ullmann map for a diverted plasma, whose control parameters are determined from tokamak experiments. Finally, an explicit, area-preserving and integrable magnetic field line map for a single-null divertor tokamak is obtained using a trajectory integration method to represent toroidal equilibrium magnetic surfaces. In this method, we also give examples of onset of chaotic field lines at the plasma edge due to resonant perturbations.
This paper presents the preliminary design of the descent and landing trajectory of the ESA Argonaut lunar lander. The mission scenario and driving system constraints are presented and accounted for in the design of a fuel-optimal trajectory that includes divert capabilities, as required to achieve a safe landing. A sub-optimal descent and landing trajectory is then presented and computed from the optimal one, and the related on-board guidance algorithms are derived. The proposed end-to-end guidance solution represents an easily implementable alternative to on-board optimization, minimizing the verification & validation effort, computational footprint, and programmatic risk in the development of the related GN&C capabilities. A dedicated off-line optimization process is also outlined, and exploited to optimize the propellant consumption of the sub-optimal trajectory and to ensure the fulfillment of system constraints despite the use of simple algorithms on-board. The sub-optimal trajectory is compared to the optimal baseline, and conclusions are drawn on the applicability of the proposed approach to the Argonaut mission.
Strongly-shaped diverted negative triangularity (NT) plasmas in the DIII-D tokamak demonstrate simultaneous access to high normalized current, pressure, density, and confinement. NT plasmas are shown to exist across an expansive parameter space compatible with high fusion power production, revealing surprisingly good core stability properties that compare favorably to conventional positive triangularity plasmas in DIII-D. Non-dimensionalizing the operating space, edge safety factors below 3, normalized betas above 3, Greenwald density fractions above 1, and high-confinement mode (H-mode) confinement qualities above 1 are simultaneously observed, all with a robustly stable edge free from deleterious edge-localized mode instabilities. Scaling of the confinement time with engineering parameters reveals at least a linear dependence on plasma current although with significant power degradation, both in excess of expected H-mode scalings. These results increase confidence that NT plasmas are a viable approach to realize fusion power and open directions for future detailed study.
Multimodal vision-language (VL) learning has noticeably pushed the tendency toward generic intelligence owing to emerging large foundation models. However, tracking, as a fundamental vision problem, surprisingly enjoys less bonus from recent flourishing VL learning. We argue that the reasons are two-fold: the lack of large-scale vision-language annotated videos and ineffective vision-language interaction learning of current works. These nuisances motivate us to design more effective vision-language representation for tracking, meanwhile constructing a large database with language annotation for model learning. Particularly, in this paper, we first propose a general attribute annotation strategy to decorate videos in six popular tracking benchmarks, which contributes a large-scale vision-language tracking database with more than 23,000 videos. We then introduce a novel framework to improve tracking by learning a unified-adaptive VL representation, where the cores are the proposed asymmetric architecture search and modality mixer (ModaMixer). To further improve VL representation, we introduce a contrastive loss to align different modalities. To thoroughly evidence the effectiveness o
The effect of triangularity on tokamak boundary plasma turbulence is investigated by using global, flux-driven, three-dimensional, two-fluid simulations. The simulations show that negative triangularity stabilizes boundary plasma turbulence, and linear investigations reveal that this is due to a reduction of the magnetic curvature drive of interchange instabilities, such as the resistive ballooning mode. As a consequence, the pressure decay length $L_p$, related to the SOL power fall-off length $λ_q$, is found to be affected by triangularity. Leveraging considerations on the effect of triangularity on the linear growth rate and nonlinear evolution of the resistive ballooning mode, the analytical theory-based scaling law for $L_p$ in L-mode plasmas, derived by Giacomin \textit{et al.} [{Nucl. Fusion}, \href{https://doi.org/10.1088/1741-4326/abf8f6}{\textbf{61} 076002} (2021)], is extended to include the effect of triangularity. The scaling is in agreement with nonlinear simulations and a multi-machine experimental database, which include recent TCV discharges dedicated to the study of the effect of triangularity in L-mode diverted discharges. Overall, the present results highlight t
This paper presents ThielSort, a practical implementation of the Diverting Fast Radix (DFR) Algorithm. The theoretical improvements over classical radix sorts are outlined and implementation details are specified to demonstrate that the algorithm is competitive with the state of the art. The effectiveness of this implementation of the DFR algorithm is shown by considering a variety of standard distributions of data and input sizes.
This paper addresses the problem of task assignment and trajectory generation for installing bird diverters using a fleet of multi-rotors. The proposed solution extends our previous motion planner to compute feasible and constrained trajectories, considering payload capacity limitations and recharging constraints. Signal Temporal Logic (STL) specifications are employed to encode the mission objectives and temporal requirements. Additionally, an event-based replanning strategy is introduced to handle unforeseen failures. An energy minimization term is also employed to implicitly save multi-rotor flight time during installation operations. The effectiveness and validity of the approach are demonstrated through simulations in MATLAB and Gazebo, as well as field experiments carried out in a mock-up scenario.
Self-consistent full-size turbulent-transport simulations of the divertor and SOL of existing tokamaks have recently become feasible. This enables the direct comparison of turbulence simulations against experimental measurements. In this work, we perform a series of diverted Ohmic L-mode discharges on the TCV tokamak, building a first-of-a-kind dataset for the validation of edge turbulence models. This dataset, referred to as TCV-X21, contains measurements from 5 diagnostic systems -- giving a total of 45 1- and 2-D comparison observables in two toroidal magnetic field directions. The dataset is used to validate three flux-driven 3D fluid-turbulence models: GBS, GRILLIX and TOKAM3X. With each model, we perform simulations of the TCV-X21 scenario, tuning the particle and power source rates to achieve a reasonable match of the upstream separatrix value of density and electron temperature. We find that the simulations match the experimental profiles for most observables at the OMP -- both in terms of profile shape and absolute magnitude -- while a poorer agreement is found towards the divertor targets. The match between simulation and experiment is seen to be sensitive to the value of
Large language models (LLMs) are increasingly deployed as customer-facing agents, yet evaluating their reliability remains challenging due to stochastic, multi-turn interactions. Current evaluation protocols rely on linear Monte Carlo rollouts of complete agent-user conversations to estimate success. However, this approach is computationally inefficient, repeatedly regenerating identical early prefixes, and often fails to uncover deep failure modes that arise from rare user behaviors. We introduce DIVERT (Diversity-Induced Evaluation via Branching of Trajectories), an efficient, snapshot-based, coverage-guided user simulation framework for systematic exploration of agent-user interactions. DIVERT captures the full agent-environment state at critical decision points and resumes execution from these snapshots, enabling reuse of shared conversation prefixes and reducing redundant computation. From each junction, the framework branches using targeted, diversity-inducing user responses, allowing directed exploration of alternative interaction paths. By focusing evaluation on semantically diverse and underexplored trajectories, DIVERT improves both efficiency and coverage. Empirical resu
BGP hijacking enables impersonation attacks in which adversaries divert traffic at the prefix level and serve malicious content to unsuspecting clients. Detecting such attacks has traditionally been the responsibility of network operators, leaving end hosts exposed for hours. We argue that end hosts can detect prefix-level impersonation independently, exploiting a fundamental asymmetry: a BGP hijack diverts traffic for an entire IP prefix, but impersonating every co-hosted service within that prefix is prohibitively difficult at scale, especially if each service is authenticated by a different Certificate Authority. We propose HOWLR, a tool that operationalizes this insight by using co-hosted, TLS-authenticated services as witnesses: if a client can no longer authenticate them, it has evidence of an ongoing attack. This work evaluates the feasibility of this method by quantifying the existence and diversity of witnesses in the wild. We show that HOWLR can protect 89% of Tor relay prefixes, and 75% of Bitcoin pool gateway prefixes.
Network infrastructure in a production environment is increasingly targeted by attackers every day. Many resources and services now rely on the internet, making network infrastructure one of the most critical parts to protect, as it hosts numerous company resources and services. Several solutions have already been proposed to prevent attacks, minimize damage, and divert hackers and intruders. Among these, the honeypot stands out as a highly effective tool; it is designed to mimic both a scanner and an attacker, diverting and misleading them within a simulated, production-level environment. This paper will demonstrate the use of a honeynet where a honeypot acts like a real resource to deceive the attacker and analyze their behavior.
The use of resistive shunts in superconducting electronics is vast and versatile, to dampen oscillations in junctions, stabilize switching behavior, aid current sensing, divert current during quenches, and protect both the superconductor and the circuit from damage. In single-photon detection by superconducting nanowires, the shunt is crucial for the timely relaxation of the sensor between the events to detect. Here we step out from the superconducting state and discuss the effect of the shunt resistor on the resistive state of a superconducting wire, at elevated currents still below the critical current for the transition to the normal state. We reveal how the shunt resistance controls the system dynamics and the onset of different resistive phases that include hot-spot and phase-slippage events. The accompanying dynamic current redistribution in the circuit also affects the local heating properties and additionally contributes to the control of the resistive state, particularly important at the elevated operation temperatures.
The spreading and controlling of rumors have great impacts on our society. The transmission of infectious diseases and the spreading of rumors have some common scenarios. Like cross-infection propagation of diseases, two or many kinds of rumors or information may spread at the same time. In this paper, we propose a novel I2E2S2R rumor-spreading model in a homogeneous network. The rumor-free equilibrium, as well as the basic reproduction number, have been calculated from the mean-field equations of the model. Lyapunov function and the LaSalle invariance principle are used to establish the global stability of the rumor-free equilibrium. In numerical simulations, it is perceived that a higher degree of network helps to spread rumors quickly. We have also found that making people aware can help to disappear rumors faster from the network. In addition, making people divert from the rumor to exact information can lessen the spreading of the rumor.