Spaced repetition is among the most studied learning strategies in the
cognitive science literature. It consists in temporally distributing exposure
to an information so as to improve long-term memorization. Providing students
with an adaptive and personalized distributed practice schedule would benefit
more than just a generic scheduler. However, the applicability of such adaptive
schedulers seems to be limited to pure memorization, e.g. flashcards or foreign
language learning. In this article, we first frame the research problem of
optimizing an adaptive and personalized spaced repetition scheduler when
memorization concerns the application of underlying multiple skills. To this
end, we choose to rely on a student model for inferring knowledge state and
memory dynamics on any skill or combination of skills. We argue that no
knowledge tracing model takes both memory decay and multiple skill tagging into
account for predicting student performance. As a consequence, we propose a new
student learning and forgetting model suited to our research problem: DAS3H
builds on the additive factor models and includes a representation of the
temporal distribution of past practice on the skills involved by an item. In
particular, DAS3H allows the learning and forgetting curves to differ from one
skill to another. Finally, we provide empirical evidence on three real-world
educational datasets that DAS3H outperforms other state-of-the-art EDM models.
These results suggest that incorporating both item-skill relationships and
forgetting effect improves over student models that consider one or the other.

文章研究了基于多技能的记忆优化问题，并提出了一种适用于该问题的学生学习和遗忘模型 DAS3H，该模型结合了过去练习对技能的时空分布的考虑，能更好地预测学生表现并提高学习效果。

DAS3H: 建模学生学习和遗忘以优化技能分布式练习的调度

DAS3H: Modeling Student Learning and Forgetting for Optimally Scheduling  Distributed Practice of Skills

Recent years have seen remarkable progress in semantic segmentation. Yet, it
remains a challenging task to apply segmentation techniques to video-based
applications. Specifically, the high throughput of video streams, the sheer
cost of running fully convolutional networks, together with the low-latency
requirements in many real-world applications, e.g. autonomous driving, present
a significant challenge to the design of the video segmentation framework. To
tackle this combined challenge, we develop a framework for video semantic
segmentation, which incorporates two novel components: (1) a feature
propagation module that adaptively fuses features over time via spatially
variant convolution, thus reducing the cost of per-frame computation; and (2)
an adaptive scheduler that dynamically allocate computation based on accuracy
prediction. Both components work together to ensure low latency while
maintaining high segmentation quality. On both Cityscapes and CamVid, the
proposed framework obtained competitive performance compared to the state of
the art, while substantially reducing the latency, from 360 ms to 119 ms.

本研究提出了一种视频语义分割框架，该框架利用特征传播模块和自适应调度器相结合的方法，通过时间间隔自适应地融合特征并根据准确性预测动态分配计算量，以实现低延迟和高语义分割质量。实验结果表明，在与现有模型的比较中，该模型在 Cityscapes 和 CamVid 这两个数据集上获得了竞争性的性能，并将延迟从 360 ms 降低到 119 ms。