时间序列数据通常仅在观察过程中的中断时仅在有限的时间范围内获得。为了对这样的部分时间序列进行分类，我们需要考虑1）从2）不同时间戳绘制的可变长度数据。为了解决第一个问题，现有的卷积神经网络在卷积层之后使用全球池取消长度差异。这种体系结构遭受了将整个时间相关性纳入长数据和避免用于简短数据的功能崩溃之间的权衡。为了解决这种权衡，我们提出了自适应多尺度合并，该池从自适应数量的层中汇总了功能，即仅用于简短数据的前几层和更多的长数据层。此外，为了解决第二个问题，我们引入了时间编码，将观察时间戳嵌入中间特征中。我们的私有数据集和UCR/UEA时间序列档案中的实验表明，我们的模块提高了分类精度，尤其是在部分时间序列获得的短数据上。

In addition to its public health crisis, COVID-19 pandemic has led to the shutdown and closure of workplaces with an estimated total cost of more than $16 trillion. Given the long hours an average person spends in buildings and indoor environments, this research article proposes data-driven control strategies to design optimal indoor airflow to minimize the exposure of occupants to viral pathogens in built environments. A general control framework is put forward for designing an optimal velocity field and proximal policy optimization, a reinforcement learning algorithm is employed to solve the control problem in a data-driven fashion. The same framework is used for optimal placement of disinfectants to neutralize the viral pathogens as an alternative to the airflow design when the latter is practically infeasible or hard to implement. We show, via simulation experiments, that the control agent learns the optimal policy in both scenarios within a reasonable time. The proposed data-driven control framework in this study will have significant societal and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable ventilation devices and disinfectants.

Deep Metric Learning (DML) is a prominent field in machine learning with extensive practical applications that concentrate on learning visual similarities. It is known that inputs such as Adversarial Examples (AXs), which follow a distribution different from that of clean data, result in false predictions from DML systems. This paper proposes MDProp, a framework to simultaneously improve the performance of DML models on clean data and inputs following multiple distributions. MDProp utilizes multi-distribution data through an AX generation process while leveraging disentangled learning through multiple batch normalization layers during the training of a DML model. MDProp is the first to generate feature space multi-targeted AXs to perform targeted regularization on the training model's denser embedding space regions, resulting in improved embedding space densities contributing to the improved generalization in the trained models. From a comprehensive experimental analysis, we show that MDProp results in up to 2.95% increased clean data Recall@1 scores and up to 2.12 times increased robustness against different input distributions compared to the conventional methods.

没有一致响应的对话系统并不令人着迷。在这项研究中，我们建立了一个对话系统，可以根据给定的角色设置（角色）响应以带来一致性。考虑到语言模型迅速增加的趋势，我们提出了一种使用迅速调整的方法，该方法在预训练的大规模语言模型上使用了低学习成本。英语和日语中自动和手动评估的结果表明，可以使用比微调更少的计算资源来构建具有更自然和个性化响应的对话系统。

我们展示了Shinrl，这是一个专门用于评估来自理论和实践观点的强化学习（RL）算法的开放源库。现有的RL库通常允许用户通过返回评估Deep RL算法的实际表现。尽管如此，如果算法在理论上执行，则这些库并不一定用于分析，例如Q学习真正实现最佳Q功能。相比之下，ShinRL提供了一种RL环境界面，可以计算用于将测量值描绘成RL算法的行为，例如学习的间隙和最佳Q值和状态访问频率。另外，我们介绍一种灵活的求解器接口，用于评估理论上证明的算法（例如，动态编程和表格RL）和实际上有效的算法（即，深度RL，通常具有一些附加延伸和规范化）。作为一个案例研究，我们展示了如何结合ShinRL的这两个功能使得更容易分析深度Q学习的行为。此外，我们证明了ShinRL可用于经验验证最近的理论发现，例如KL正规化的效果，以实现价值迭代和深度Q学习，以及对抗性奖励的熵定期策略的鲁棒性。 ShinRL的源代码可在Github上获得：https://github.com/omron -sinicx/shinrl。

在这项研究中，我们通过增强模型的接受领域来探讨变压器捕获帧内关系的内部关系。具体地说，我们提出了一种与变压器的Constingan的模型，并调查其在情感语音转换任务中的能力。在培训程序中，我们采用课程学习逐步增加框架长度，以便模型可以从短片段中看到直到整个语音。该方法在日本情绪语音数据集上进行了评估，并与具有客观和主观评估的几个基线（ACVAE，Cyclegan）进行了比较。结果表明，我们所提出的模型能够以更高的力量和质量转换情绪。