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Izzie just loves when her mama blows her kisses to catch. Smooch kisses, zig-zag kisses, and even triple decker kisses! But even though she runs and leaps for it, Izzie misses a kiss! Her heart sinks as she watches it zip higher and higher into the sky. When Mama lets Izzie in on a sweet secret she realizes that her mama's love will always come to find her, no matter what! This silly and tender story is a universal one of a parent's love.

While there have been recent improvements in reducing bycatch in many fisheries, bycatch remains a threat for numerous species around the globe. Static spatial and temporal closures are used in many places as a tool to reduce bycatch. However, their effectiveness in achieving this goal is uncertain, particularly for highly mobile species.We evaluated evidence for the effects of temporal, static, and dynamic area closures on the bycatch and target catch of 15 fisheries around the world. Assuming perfect knowledge of where the catch and bycatch occurs and a closure of 30% of the fishing area, we found that dynamic area closures could reduce bycatch by an average of 57% without sacrificing catch of target species, compared to 16% reductions in bycatch achievable by static closures. The degree of bycatch reduction achievable for a certain quantity of target catch was related to the correlation in space and time between target and bycatch species. If the correlation was high, it was harder to find an area to reduce bycatch without sacrificing catch of target species. If the goal of spatial closures is to reduce bycatch, our results suggest that dynamic management provides substantially better outcomes than classic static marine area closures. The use of dynamic ocean management might be difficult to implement and enforce in many regions. Nevertheless, dynamic approaches will be increasingly valuable as climate change drives species and fisheries into new habitats or extended ranges, altering species-fishery interactions and underscoring the need for more responsive and flexible regulatory mechanisms.

The surge in popularity of live video streaming has spurred the development of various bitrate adaptation techniques, all aimed at enhancing user Quality of Experience (QoE). Compared to streaming Video-on-Demand, achieving low-latency live video streaming under fluctuating network conditions poses additional challenges. It requires finding the balance between rebuffering avoidance and latency, as a small client buffer is required to achieve low latency. Video players can also employ playback speed control to help optimize this balance. Specifically, when client buffer occupancy is high and hence latency is high, the player may increase playback speed to reduce the latency; and conversely, when client buffer occupancy is low and hence the risk of rebuffering is high, the player may reduce playback speed to increase buffer occupancy. Based on this rationale, a variety of playback speed control methods have been proposed. This paper evaluates, using a real-world testbed, the effectiveness of various playback speed control mechanisms when applied to a set of bitrate adaptation algorithms, with the evaluation also encompassing variations in target latency and network conditions. Our findings show a lack of coordination between adaptive bitrate (ABR) algorithms and playback speed control mechanisms. This leads us to conclude that there is a need for new playback speed control methods designed in conjunction with ABR algorithms.

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.