This study demonstrates the feasibility of point cloud-based proactive link quality prediction for millimeter-wave (mmWave) communications. Image-based methods to quantitatively and deterministically predict future received signal strength using machine learning from time series of depth images to mitigate the human body line-of-sight (LOS) path blockage in mmWave communications have been proposed. However, image-based methods have been limited in applicable environments because camera images may contain private information. Thus, this study demonstrates the feasibility of using point clouds obtained from light detection and ranging (LiDAR) for the mmWave link quality prediction. Point clouds represent three-dimensional (3D) spaces as a set of points and are sparser and less likely to contain sensitive information than camera images. Additionally, point clouds provide 3D position and motion information, which is necessary for understanding the radio propagation environment involving pedestrians. This study designs the mmWave link quality prediction method and conducts two experimental evaluations using different types of point clouds obtained from LiDAR and depth cameras, as well as different numerical indicators of link quality, received signal strength and throughput. Based on these experiments, our proposed method can predict future large attenuation of mmWave link quality due to LOS blockage by human bodies, therefore our point cloud-based method can be an alternative to image-based methods.

高质量的人类解释需要语言和事实准备以及实时检索信息的能力。这种情况在远程同步解释（RSI）的背景下尤为重要，其中发生时间可能是短暂的，对专业口译员构成新挑战以及他们对提供高质量服务的承诺。为了减轻这些挑战，我们提出了解释器辅助，这是一个专门为RSI场景集成而设计的计算机辅助解释工具。翻译辅助包括两个主要功能集：自动词汇表创作工具和实时建议系统。在本文中，我们描述了我们工具的整体设计，其集成到典型的RSI工作流程，以及在词汇表创作的质量和相关性的基准测试中实现的结果，以及实时的精度和回忆建议功能。

日本气象学机构经营着网格的温度指导，以预测二维降雪量和降水类型，例如雨雪，因为表面温度是预测它们的关键元素之一。操作温度引导基于卡尔曼滤波器，该滤波器使用温度观察和数值天气预报（NWP）仅在观察部位周围输出。当NWP模型错误地预测前部的位置或观察到的温度非常冷或热时，纠正温度场。在这项研究中，已经提出了一种基于编码器的解码器的卷积神经网络，以预测日本的康托地区周围的表面上的包装温度。验证结果表明，该模型大大提高了运营指导，可以纠正NWP模型偏差，如前方和极端温度的位置误差。

This paper describes SentencePiece, a language-independent subword tokenizer and detokenizer designed for Neural-based text processing, including Neural Machine Translation. It provides open-source C++ and Python implementations for subword units. While existing subword segmentation tools assume that the input is pre-tokenized into word sequences, SentencePiece can train subword models directly from raw sentences, which allows us to make a purely end-to-end and language independent system. We perform a validation experiment of NMT on English-Japanese machine translation, and find that it is possible to achieve comparable accuracy to direct subword training from raw sentences. We also compare the performance of subword training and segmentation with various configurations. SentencePiece is available under the Apache 2 license at https://github.com/google/ sentencepiece.

Subword units are an effective way to alleviate the open vocabulary problems in neural machine translation (NMT). While sentences are usually converted into unique subword sequences, subword segmentation is potentially ambiguous and multiple segmentations are possible even with the same vocabulary. The question addressed in this paper is whether it is possible to harness the segmentation ambiguity as a noise to improve the robustness of NMT. We present a simple regularization method, subword regularization, which trains the model with multiple subword segmentations probabilistically sampled during training. In addition, for better subword sampling, we propose a new subword segmentation algorithm based on a unigram language model. We experiment with multiple corpora and report consistent improvements especially on low resource and out-of-domain settings.

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, with the potential to overcome many of the weaknesses of conventional phrase-based translation systems. Unfortunately, NMT systems are known to be computationally expensive both in training and in translation inference -sometimes prohibitively so in the case of very large data sets and large models. Several authors have also charged that NMT systems lack robustness, particularly when input sentences contain rare words. These issues have hindered NMT's use in practical deployments and services, where both accuracy and speed are essential. In this work, we present GNMT, Google's Neural Machine Translation system, which attempts to address many of these issues. Our model consists of a deep LSTM network with 8 encoder and 8 decoder layers using residual connections as well as attention connections from the decoder network to the encoder. To improve parallelism and therefore decrease training time, our attention mechanism connects the bottom layer of the decoder to the top layer of the encoder. To accelerate the final translation speed, we employ low-precision arithmetic during inference computations. To improve handling of rare words, we divide words into a limited set of common sub-word units ("wordpieces") for both input and output. This method provides a good balance between the flexibility of "character"-delimited models and the efficiency of "word"-delimited models, naturally handles translation of rare words, and ultimately improves the overall accuracy of the system. Our beam search technique employs a length-normalization procedure and uses a coverage penalty, which encourages generation of an output sentence that is most likely to cover all the words in the source sentence. To directly optimize the translation BLEU scores, we consider refining the models by using reinforcement learning, but we found that the improvement in the BLEU scores did not reflect in the human evaluation. On the WMT'14 English-to-French and English-to-German benchmarks, GNMT achieves competitive results to state-of-the-art. Using a human side-by-side evaluation on a set of isolated simple sentences, it reduces translation errors by an average of 60% compared to Google's phrase-based production system.