Nowadays, news spreads faster than it is consumed. This, alongside the rapid news cycle and delayed updates, has led to a challenging news cold-start issue. Likewise, the user cold-start problem, due to limited user engagement, has long hindered recommendations. To tackle both of them, we introduce the Symmetric Few-shot Learning framework for Cold-start News Recommendation (SFCNR), built upon self-supervised contrastive enhancement. Our approach employs symmetric few-shot learning towers (SFTs) to transform warm user/news attributes into their behavior/content features during training. We design two innovative feature alignment strategies to enhance towers training. Subsequently, this tower generates virtual features for cold users/news during inference, leveraging tower-stored prior knowledge through a personalized gating network. We assess the SFCNR on four quality news recommendation models, conducting comprehensive experiments on two kinds of News dataset. Results showcase significant performance boosts for both warm and cold-start scenarios compared to baseline models.

The fish-like robot is one of the typical underwater robots, which has the advantage of high maneuverability with low noise due to its bioinspired structure and biomimetic locomotion. However, it is challenging to efficiently design motion controllers for such robots to achieve satisfactory performance on specific control tasks in the real underwater environment, since the complex fluid-structure interaction exists during their swimming and exact dynamic models are absent. In this article, we propose a learning framework, incorporating a simulation system and a training methodology, to autonomously and fast train in simulation to create control policies that are capable of directly applying to a type of physical fish-like robots to perform motion control tasks. First, we construct a simulation system combining a data-driven environment and a computational fluid dynamics (CFD)-based environment, thus well balancing the simulation accuracy and the calculation speed. Second, we design a training methodology to train deep reinforcement learning (DRL)-based policies for the robot in our constructed simulation system to perform a specific control task. Then, we use two typical motion control tasks to verify our proposed framework. One is the path-following control task, which is a one-objective problem with dense rewards, while the other is the pose control task which is a two-objective problem with sparse rewards. For each task, the DRL-based control policy trained by our learning framework is directly deployed on the physical fish-like robot to perform the task in the real world. Experimental results show that the policies trained in simulation still work well in the real world, and perform even better in terms of control accuracy and stability compared with the traditional control methods, thus demonstrating the effectiveness of our learning framework.

Lateral line system (LLS) is a sensory organ system which serves functions in varieties of flow-relative fish behaviors. Inspired by excellent performances of LLS in fish behaviors, multiple artificial lateral line systems (ALLSs) have been designed and applied to promote underwater robot technology. In this article, we focus on using ALLS for longitudinal separation sensing between two adjacent swimming robotic fish whose tails oscillate, and meanwhile the two fish are towed with a precisely controlled speed which equals to the rectilinear speed of freely-swimming robotic fish with the same oscillating parameters. Flow visualizations based on dye injection technique, hydrogen bubble technique, and computational fluid dynamics simulation are conducted to study the distribution characteristics of the vortices caused by the robotic fish. In addition, towing tank experiments are conducted for investigating the qualitative relationships between the longitudinal separations and the ALLS-measured HPVs of the two robotic fish. This work provide a guidance for the application of ALLS in relative states sensing of underwater robot group composed of two or more individuals, which has been rarely explored.