Embodied Instruction Following (EIF) studies how mobile manipulator robots should be controlled to accomplish long-horizon tasks specified by natural language instructions. While most research on EIF are conducted in simulators, the ultimate goal of the field is to deploy the agents in real life. As such, it is important to minimize the data cost required for training an agent, to help the transition from sim to real. However, many studies only focus on the performance and overlook the data cost -- modules that require separate training on extra data are often introduced without a consideration on deployability. In this work, we propose FILM++ which extends the existing work FILM with modifications that do not require extra data. While all data-driven modules are kept constant, FILM++ more than doubles FILM's performance. Furthermore, we propose Prompter, which replaces FILM++'s semantic search module with language model prompting. Unlike FILM++'s implementation that requires training on extra sets of data, no training is needed for our prompting based implementation while achieving better or at least comparable performance. Prompter achieves 42.64% and 45.72% on the ALFRED benchmark with high-level instructions only and with step-by-step instructions, respectively, outperforming the previous state of the art by 6.57% and 10.31%.

长尾数据集（Head Class）组成的培训样本比尾巴类别多得多，这会导致识别模型对头等舱有偏见。加权损失是缓解此问题的最受欢迎的方法之一，最近的一项工作表明，班级难度可能比常规使用的类频率更好地决定了权重的分布。在先前的工作中使用了一种启发式公式来量化难度，但是我们从经验上发现，最佳公式取决于数据集的特征。因此，我们提出了困难网络，该难题学习在元学习框架中使用模型的性能来预测类的难度。为了使其在其他班级的背景下学习班级的合理难度，我们新介绍了两个关键概念，即相对难度和驾驶员损失。前者有助于困难网络在计算班级难度时考虑其他课程，而后者对于将学习指向有意义的方向是必不可少的。对流行的长尾数据集进行了广泛的实验证明了该方法的有效性，并且在多个长尾数据集上实现了最先进的性能。

与其他类别（称为少数族裔或尾巴类）相比，很少的类或类别（称为多数或头等类别的类别）具有更高的数据样本数量，在现实世界中，长尾数据集经常遇到。在此类数据集上培训深层神经网络会给质量级别带来偏见。到目前为止，研究人员提出了多种加权损失和数据重新采样技术，以减少偏见。但是，大多数此类技术都认为，尾巴类始终是最难学习的类，因此需要更多的重量或注意力。在这里，我们认为该假设可能并不总是成立的。因此，我们提出了一种新颖的方法，可以在模型的训练阶段动态测量每个类别的瞬时难度。此外，我们使用每个班级的难度度量来设计一种新型的加权损失技术，称为“基于阶级难度的加权（CDB-W）损失”和一种新型的数据采样技术，称为“基于类别难度的采样）（CDB-S ）'。为了验证CDB方法的广泛可用性，我们对多个任务进行了广泛的实验，例如图像分类，对象检测，实例分割和视频操作分类。结果验证了CDB-W损失和CDB-S可以在许多类似于现实世界中用例的类别不平衡数据集（例如Imagenet-LT，LVIS和EGTEA）上实现最先进的结果。

本研究在佩戴传感器的基础上掌握了估算车间人类误差潜力的新问题。与利用生物测量传感技术利用生物测量传感技术估算人民的内部状态（如疲劳和精神压力）的现有研究不同，我们试图估计目标人不保持冷静的情况下的人类误差潜力，这在传感器噪声显着变得更加困难增加。我们提出了一种新颖的制剂，其中人误差 - 电位估计问题降低到分类问题，并引入一种新方法，即使用嘈杂的感测数据也可以用于解决分类问题。关键思想是模拟计算生物识别指标的过程，使得可以集成在生物识别指标上的先前知识，并利用代表目标人员与生物特征的组合的特征。实验分析表明，我们的方法有效地估计了人误差潜力。

识别其缺席的有害实例，其训练数据集提高了模型性能，对于构建更好的机器学习模型来说是重要的。尽管以前的研究成功地在监督环境下估算有害实例，但它们不能延伸到生成的对抗网络（GAN）。这是因为先前的方法要求（1）缺少训练实例直接影响损耗值，并且（2）损失的变化直接测量实例的损害，以实现模型的性能。然而，在GaN训练中，不满足要求。这是因为，（1）发电机的损失不受培训实例的直接影响，因为它们不是发电机的训练步骤的一部分，并且（2）GaN的损失的值通常不会捕获模型的生成性能。为此，（1）我们提出了一种影响估计方法，它利用了发电机的损失梯度的曲线对鉴别者的参数（反之亦然）来追踪鉴别者的训练中的实例的缺失如何影响发电机的情况参数和（2）我们提出了一种新的评估方案，其中我们根据GAN评估度量（例如，Incepion得分）期望由于删除实例而期望改变来评估每个培训实例的危害。我们通过实验验证了我们的影响估算方法正确推断出GAN评估度量的变化。此外，我们证明了识别的有害实例的去除有效地改善了各种GAN评估指标的模型的生成性能。

A practical issue of edge AI systems is that data distributions of trained dataset and deployed environment may differ due to noise and environmental changes over time. Such a phenomenon is known as a concept drift, and this gap degrades the performance of edge AI systems and may introduce system failures. To address this gap, a retraining of neural network models triggered by concept drift detection is a practical approach. However, since available compute resources are strictly limited in edge devices, in this paper we propose a lightweight concept drift detection method in cooperation with a recently proposed on-device learning technique of neural networks. In this case, both the neural network retraining and the proposed concept drift detection are done by sequential computation only to reduce computation cost and memory utilization. Evaluation results of the proposed approach shows that while the accuracy is decreased by 3.8%-4.3% compared to existing batch-based detection methods, it decreases the memory size by 88.9%-96.4% and the execution time by 1.3%-83.8%. As a result, the combination of the neural network retraining and the proposed concept drift detection method is demonstrated on Raspberry Pi Pico that has 264kB memory.

We propose a light-weight and highly efficient Joint Detection and Tracking pipeline for the task of Multi-Object Tracking using a fully-transformer architecture. It is a modified version of TransTrack, which overcomes the computational bottleneck associated with its design, and at the same time, achieves state-of-the-art MOTA score of 73.20%. The model design is driven by a transformer based backbone instead of CNN, which is highly scalable with the input resolution. We also propose a drop-in replacement for Feed Forward Network of transformer encoder layer, by using Butterfly Transform Operation to perform channel fusion and depth-wise convolution to learn spatial context within the feature maps, otherwise missing within the attention maps of the transformer. As a result of our modifications, we reduce the overall model size of TransTrack by 58.73% and the complexity by 78.72%. Therefore, we expect our design to provide novel perspectives for architecture optimization in future research related to multi-object tracking.

Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sensing (CS), a data compression method that can be used on edge devices, has been attracting attention as a method to reduce communication costs. In CS, estimating the appropriate compression ratio is important. There is a method to adaptively estimate the compression ratio for the acquired data using reinforcement learning. However, the computational costs associated with existing reinforcement learning methods that can be utilized on edges are expensive. In this study, we developed an efficient reinforcement learning method for edge devices, referred to as the actor--critic online sequential extreme learning machine (AC-OSELM), and a system to compress data by estimating an appropriate compression ratio on the edge using AC-OSELM. The performance of the proposed method in estimating the compression ratio is evaluated by comparing it with other reinforcement learning methods for edge devices. The experimental results show that AC-OSELM achieved the same or better compression performance and faster compression ratio estimation than the existing methods.

IR models using a pretrained language model significantly outperform lexical approaches like BM25. In particular, SPLADE, which encodes texts to sparse vectors, is an effective model for practical use because it shows robustness to out-of-domain datasets. However, SPLADE still struggles with exact matching of low-frequency words in training data. In addition, domain shifts in vocabulary and word frequencies deteriorate the IR performance of SPLADE. Because supervision data are scarce in the target domain, addressing the domain shifts without supervision data is necessary. This paper proposes an unsupervised domain adaptation method by filling vocabulary and word-frequency gaps. First, we expand a vocabulary and execute continual pretraining with a masked language model on a corpus of the target domain. Then, we multiply SPLADE-encoded sparse vectors by inverse document frequency weights to consider the importance of documents with lowfrequency words. We conducted experiments using our method on datasets with a large vocabulary gap from a source domain. We show that our method outperforms the present stateof-the-art domain adaptation method. In addition, our method achieves state-of-the-art results, combined with BM25.

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with a higher spatiotemporal frequency. For forecasting with a high spatiotemporal frequency, it is necessary to capture spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors. In this study, we propose a method to forecast one type of track geometry irregularity, vertical alignment, by incorporating spatial and exogenous factor calculations. The proposed method embeds exogenous factors and captures spatiotemporal correlations using a convolutional long short-term memory (ConvLSTM). In the experiment, we compared the proposed method with other methods in terms of the forecasting performance. Additionally, we conducted an ablation study on exogenous factors to examine their contribution to the forecasting performance. The results reveal that spatial calculations and maintenance record data improve the forecasting of the vertical alignment.