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We study a Stackelberg game between an attacker and a defender on large Active Directory (AD) attack graphs where the defender employs a set of honeypots to stop the attacker from reaching high-value targets. Contrary to existing works that focus on small and static attack graphs, AD graphs typically contain hundreds of thousands of nodes and edges and constantly change over time. We consider two types of attackers: a simple attacker who cannot observe honeypots and a competent attacker who can. To jointly solve the game, we propose a mixed-integer programming (MIP) formulation. We observed that the optimal blocking plan for static graphs performs poorly in dynamic graphs. To solve the dynamic graph problem, we re-design the mixed-integer programming formulation by combining m MIP (dyMIP(m)) instances to produce a near-optimal blocking plan. Furthermore, to handle a large number of dynamic graph instances, we use a clustering algorithm to efficiently find the m-most representative graph instances for a constant m (dyMIP(m)). We prove a lower bound on the optimal blocking strategy for dynamic graphs and show that our dyMIP(m) algorithms produce close to optimal results for a range of AD graphs under realistic conditions.

Tracking and catching moving objects is an important ability for robots in a dynamic world. Whilst some objects have highly predictable state evolution e.g., the ballistic trajectory of a tennis ball, reactive targets alter their behavior in response to motion of the manipulator. Reactive applications range from gently capturing living animals such as snakes or fish for biological investigations, to smoothly interacting with and assisting a person. Existing works for dynamic catching usually perform target prediction followed by planning, but seldom account for highly non-linear reactive behaviors. Alternatively, Reinforcement Learning (RL) based methods simply treat the target and its motion as part of the observation of the world-state, but perform poorly due to the weak reward signal. In this work, we blend the approach of an explicit, yet learned, target state predictor with RL. We further show how a tightly coupled predictor which ‘observes’ the state of the robot leads to significantly improved anticipatory action, especially with targets that seek to evade the robot following a simple policy. Experiments show that our method achieves an 86.4% (open plane area) and a 73.8% (room) success rate on evasive objects, outperforming monolithic reinforcement learning and other techniques. We also demonstrate the efficacy of our approach across varied targets and trajectories. All code, data, and additional videos are at this GitHub link: https://kl-research.github.io/dyncatch.

Underwater soft grippers exhibit potential for applications such as monitoring, research, and object retrieval. However, existing underwater gripping techniques frequently cause disturbances to ecosystems. In response to this challenge, we present a novel underwater gripping framework comprising a lightweight gripper affixed to a custom submarine pod deployable via drone. This approach minimizes water disturbance and enables efficient navigation to target areas, enhancing overall mission effectiveness. The pod allows for underwater motion and is characterized by four degrees of freedom. It is provided with a custom buoyancy system, two water pumps for differential thrust and two for pitching. The system allows for buoyancy adjustments up to a depth of 6 meters, as well as motion in the plane. The 3-fingered gripper is manufactured out of silicone and was successfully tested on objects with different shapes and sizes, demonstrating a maximum pulling force of up to 8 N when underwater. The reliability of the submarine pod was tested in a water tank by tracking its attitude and energy consumption during grasping maneuvers. The system also accomplished a successful mission in a lake, where it was deployed on a hexacopter. Overall, the integration of this system expands the operational capabilities of underwater grasping, makes grasping missions more efficient and easy to automate, as well as causing less disturbance to the water ecosystem.

Purpose: Summer catch crop (CC) has been introduced into the vegetable rotating system in greenhouse fields to reduce nitrogen (N) losses through crop uptake and residual N immobilization. However, the effects of planting sorghum with high N uptake and biomass, and biological nitrification inhibition (BNI) potential as a CC on soil N dynamics and subsequent crop yield remain unclear. Methods: In the two-year field experiment, the comprehensive effects of planting sorghum as CC on subsequent eggplant yield, soil mineral N dynamics, ammonia-oxidizing archaea (AOA) and bacteria (AOB) amoA gene abundances were determined, in comparison to the sweet corn and fallow treatments. Results: Compared to the fallow and sweet corn, planting sorghum as CC increased subsequent eggplant yield by 24.88% and 18.94% in the 2014–2015 and 2015–2016 over-winter growing season, respectively. CC planting reduced soil nitrate (NO3−-N) accumulation during the summer fallow season. Sorghum planting could significantly maintain higher level of ammonium (NH4+-N) concentration during the summer fallow season and the first month of succeeding over-winter season. In addition, sorghum planting reduced soil net nitrifying potential, which could be partially attributed to the decreased amoA gene abundance of AOA at the 0–30 and 30–60 cm soil layers and AOB at 0–30 cm soil layer. Conclusion: We conclude that planting sorghum in the summer fallow season is a promising strategy to retain soil NH4+-N, reduce soil NO3−-N accumulation, and enhance subsequent eggplant yield. [ABSTRACT FROM AUTHOR]

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