Stellar flare events are critical observational samples for astronomical research; however, recorded flare events remain limited. Stellar flare forecasting can provide additional flare event samples to support research efforts. Despite this potential, no specialized models for stellar flare forecasting have been proposed to date. In this paper, we present extensive experimental evidence demonstrating that both stellar physical properties and historical flare records are valuable inputs for flare forecasting tasks. We then introduce FLARE (Forecasting Light-curve-based Astronomical Records via features Ensemble), the first-of-its-kind large model specifically designed for stellar flare forecasting. FLARE integrates stellar physical properties and historical flare records through a novel Soft Prompt Module and Residual Record Fusion Module. Experiments on the Kepler light curve dataset demonstrate that FLARE achieves superior performance compared to other methods across all evaluation metrics. Finally, we validate the forecast capability of our model through a comprehensive case study.

In crowded waters, multiple vessel encounter situations increase the collision risks (CRs) of unmanned surface vehicles (USVs) and hence the frequent collision avoidance (COLAV) maneuvers of USVs increase their actual sailing distances. This paper innovatively proposes a risk-prediction-based deep reinforcement learning (RPDRL) approach for the integrated intelligent guidance and motion control of USVs with anticipatory COLAV decision-making. The data sizes of detected vessels’ motion states are different due to the uncertainties in the number of vessels detected by the navigation systems of a USV. To address this problem, these data are, for the first time, converted into the corresponding same-sized raster data as the states in the RPDRL approach. A new CR assessment model of the USV collisions with all the detected vessels is built to calculate the rewards in the RPDRL approach. Furthermore, actor and critic deep convolutional neural networks are created to make the anticipatory COLAV decisions which are the engine command and rudder command. Simulations and simulation comparison results on a USV demonstrate that the USV sails along a shorter path with a lower CR under the anticipatory COLAV decisions from our proposed RPDRL approach compared with a velocity obstacle method and a model predictive control method, and hence the economy and safety of USVs’ autonomous navigation are enhanced.

Collaborative information‐seeking (CIS) tasks, such as holiday planning, academic research, medical/health information seeking, cannot be tackled without making sense of the task and the encountered information together with collaborators, that is, collaborative sensemaking. In CIS, collaborative sensemaking is an important but understudied aspect. A thorough understanding of collaborative sensemaking behavior in CIS tasks is essential to develop tools to support collaborative sensemaking activities in CIS. In this article, we investigate the general patterns and differences in collaborative sensemaking behavior in travel planning and topic research tasks using the data from 2 observational user studies. The results show the common stages of the collaborative sensemaking process and the differences in users' collaborative sensemaking strategies and activities between the 2 tasks. This comparative study enhances our understanding of the collaborative sensemaking process in CIS tasks and the differences in user's sensemaking behavior according to tasks, and describes implications for supporting collaborative sensemaking behavior in CIS tasks.