在目前的工作中，我们表明，公式驱动的监督学习（FDSL）的表现可以匹配甚至超过Imagenet-21K的表现，而无需在视觉预训练期间使用真实的图像，人类和自我选择变压器（VIT）。例如，在ImagEnet-21K上预先训练的VIT-BASE在ImagEnet-1K上进行微调时，在ImagEnet-1K和FDSL上进行微调时显示了81.8％的TOP-1精度，当在相同条件下进行预训练时（图像数量，数量，，图像数量，超参数和时期数）。公式产生的图像避免了隐私/版权问题，标记成本和错误以及真实图像遭受的偏见，因此具有巨大的预训练通用模型的潜力。为了了解合成图像的性能，我们测试了两个假设，即（i）对象轮廓是FDSL数据集中重要的，（ii）创建标签的参数数量增加会影响FDSL预训练的性能改善。为了检验以前的假设，我们构建了一个由简单对象轮廓组合组成的数据集。我们发现该数据集可以匹配分形的性能。对于后一种假设，我们发现增加训练任务的难度通常会导致更好的微调准确性。

Ongoing risks from climate change have impacted the livelihood of global nomadic communities, and are likely to lead to increased migratory movements in coming years. As a result, mobility considerations are becoming increasingly important in energy systems planning, particularly to achieve energy access in developing countries. Advanced Plug and Play control strategies have been recently developed with such a decentralized framework in mind, more easily allowing for the interconnection of nomadic communities, both to each other and to the main grid. In light of the above, the design and planning strategy of a mobile multi-energy supply system for a nomadic community is investigated in this work. Motivated by the scale and dimensionality of the associated uncertainties, impacting all major design and decision variables over the 30-year planning horizon, Deep Reinforcement Learning (DRL) is implemented for the design and planning problem tackled. DRL based solutions are benchmarked against several rigid baseline design options to compare expected performance under uncertainty. The results on a case study for ger communities in Mongolia suggest that mobile nomadic energy systems can be both technically and economically feasible, particularly when considering flexibility, although the degree of spatial dispersion among households is an important limiting factor. Key economic, sustainability and resilience indicators such as Cost, Equivalent Emissions and Total Unmet Load are measured, suggesting potential improvements compared to available baselines of up to 25%, 67% and 76%, respectively. Finally, the decomposition of values of flexibility and plug and play operation is presented using a variation of real options theory, with important implications for both nomadic communities and policymakers focused on enabling their energy access.

本文介绍了旋转等级，作为训练惯性内径型号的自我主管。我们证明自我监督方案在训练阶段以及推理阶段提供了强大的监督信号。它降低了对训练鲁棒模型的大量标记数据的依赖性，并且可以使用各种未标记的数据更新模型。此外，我们基于不确定性估计提出了自适应测试时间训练（TTT），以便提高惯性内径术的概括性与各种看不见的数据。我们在实验中展示了具有30％数据训练的旋转等因素监督的惯性内径（RIO）验证的训练，达到了与整个数据库训练的模型的对比。Adaptive TTT在所有情况下提高了模型性能，在若干方案下会产生超过25％的改进。

视频数量和上传到Internet的相关内容有一个戏剧性的增加。因此，需要有效的算法来分析这种大量数据引起了显着的研究兴趣。已被证明基于人体运动的动作识别系统准确地解释视频内容。这项工作旨在使用ST-GCN模型识别日常生活的活动，提供四种不同的分区策略之间的比较：空间配置分区，全距离分割，连接拆分和索引分割。为实现此目的，我们在HMDB-51数据集上介绍了ST-GCN框架的第一个实现。我们通过使用连接分割分区方法实现了48.88％的前1个精度。通过实验模拟，我们表明我们的建议在使用ST-GCN框架上实现了UCF-101数据集的最高精度性能，而不是最先进的方法。最后，通过使用索引分割分区策略实现了73.25％的顶级1的精度。