我们提出Unrealego，即，一种用于以Egentric 3D人类姿势估计的新的大规模自然主义数据集。Unrealego是基于配备两个鱼眼摄像机的眼镜的高级概念，可用于无约束的环境。我们设计了它们的虚拟原型，并将其附加到3D人体模型中以进行立体视图捕获。接下来，我们会产生大量的人类动作。结果，Unrealego是第一个在现有的EgeCentric数据集中提供最大动作的野外立体声图像的数据集。此外，我们提出了一种新的基准方法，其简单但有效的想法是为立体声输入设计2D关键点估计模块，以改善3D人体姿势估计。广泛的实验表明，我们的方法在定性和定量上优于先前的最新方法。Unrealego和我们的源代码可在我们的项目网页上找到。

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.

The task of out-of-distribution (OOD) detection is vital to realize safe and reliable operation for real-world applications. After the failure of likelihood-based detection in high dimensions had been shown, approaches based on the \emph{typical set} have been attracting attention; however, they still have not achieved satisfactory performance. Beginning by presenting the failure case of the typicality-based approach, we propose a new reconstruction error-based approach that employs normalizing flow (NF). We further introduce a typicality-based penalty, and by incorporating it into the reconstruction error in NF, we propose a new OOD detection method, penalized reconstruction error (PRE). Because the PRE detects test inputs that lie off the in-distribution manifold, it effectively detects adversarial examples as well as OOD examples. We show the effectiveness of our method through the evaluation using natural image datasets, CIFAR-10, TinyImageNet, and ILSVRC2012.

The development of deep neural networks has improved representation learning in various domains, including textual, graph structural, and relational triple representations. This development opened the door to new relation extraction beyond the traditional text-oriented relation extraction. However, research on the effectiveness of considering multiple heterogeneous domain information simultaneously is still under exploration, and if a model can take an advantage of integrating heterogeneous information, it is expected to exhibit a significant contribution to many problems in the world. This thesis works on Drug-Drug Interactions (DDIs) from the literature as a case study and realizes relation extraction utilizing heterogeneous domain information. First, a deep neural relation extraction model is prepared and its attention mechanism is analyzed. Next, a method to combine the drug molecular structure information and drug description information to the input sentence information is proposed, and the effectiveness of utilizing drug molecular structures and drug descriptions for the relation extraction task is shown. Then, in order to further exploit the heterogeneous information, drug-related items, such as protein entries, medical terms and pathways are collected from multiple existing databases and a new data set in the form of a knowledge graph (KG) is constructed. A link prediction task on the constructed data set is conducted to obtain embedding representations of drugs that contain the heterogeneous domain information. Finally, a method that integrates the input sentence information and the heterogeneous KG information is proposed. The proposed model is trained and evaluated on a widely used data set, and as a result, it is shown that utilizing heterogeneous domain information significantly improves the performance of relation extraction from the literature.

To simulate bosons on a qubit- or qudit-based quantum computer, one has to regularize the theory by truncating infinite-dimensional local Hilbert spaces to finite dimensions. In the search for practical quantum applications, it is important to know how big the truncation errors can be. In general, it is not easy to estimate errors unless we have a good quantum computer. In this paper we show that traditional sampling methods on classical devices, specifically Markov Chain Monte Carlo, can address this issue with a reasonable amount of computational resources available today. As a demonstration, we apply this idea to the scalar field theory on a two-dimensional lattice, with a size that goes beyond what is achievable using exact diagonalization methods. This method can be used to estimate the resources needed for realistic quantum simulations of bosonic theories, and also, to check the validity of the results of the corresponding quantum simulations.

Recent years have seen progress beyond domain-specific sound separation for speech or music towards universal sound separation for arbitrary sounds. Prior work on universal sound separation has investigated separating a target sound out of an audio mixture given a text query. Such text-queried sound separation systems provide a natural and scalable interface for specifying arbitrary target sounds. However, supervised text-queried sound separation systems require costly labeled audio-text pairs for training. Moreover, the audio provided in existing datasets is often recorded in a controlled environment, causing a considerable generalization gap to noisy audio in the wild. In this work, we aim to approach text-queried universal sound separation by using only unlabeled data. We propose to leverage the visual modality as a bridge to learn the desired audio-textual correspondence. The proposed CLIPSep model first encodes the input query into a query vector using the contrastive language-image pretraining (CLIP) model, and the query vector is then used to condition an audio separation model to separate out the target sound. While the model is trained on image-audio pairs extracted from unlabeled videos, at test time we can instead query the model with text inputs in a zero-shot setting, thanks to the joint language-image embedding learned by the CLIP model. Further, videos in the wild often contain off-screen sounds and background noise that may hinder the model from learning the desired audio-textual correspondence. To address this problem, we further propose an approach called noise invariant training for training a query-based sound separation model on noisy data. Experimental results show that the proposed models successfully learn text-queried universal sound separation using only noisy unlabeled videos, even achieving competitive performance against a supervised model in some settings.

Hyperparameter optimization (HPO) is essential for the better performance of deep learning, and practitioners often need to consider the trade-off between multiple metrics, such as error rate, latency, memory requirements, robustness, and algorithmic fairness. Due to this demand and the heavy computation of deep learning, the acceleration of multi-objective (MO) optimization becomes ever more important. Although meta-learning has been extensively studied to speedup HPO, existing methods are not applicable to the MO tree-structured parzen estimator (MO-TPE), a simple yet powerful MO-HPO algorithm. In this paper, we extend TPE's acquisition function to the meta-learning setting, using a task similarity defined by the overlap in promising domains of each task. In a comprehensive set of experiments, we demonstrate that our method accelerates MO-TPE on tabular HPO benchmarks and yields state-of-the-art performance. Our method was also validated externally by winning the AutoML 2022 competition on "Multiobjective Hyperparameter Optimization for Transformers".

Mobile stereo-matching systems have become an important part of many applications, such as automated-driving vehicles and autonomous robots. Accurate stereo-matching methods usually lead to high computational complexity; however, mobile platforms have only limited hardware resources to keep their power consumption low; this makes it difficult to maintain both an acceptable processing speed and accuracy on mobile platforms. To resolve this trade-off, we herein propose a novel acceleration approach for the well-known zero-means normalized cross correlation (ZNCC) matching cost calculation algorithm on a Jetson Tx2 embedded GPU. In our method for accelerating ZNCC, target images are scanned in a zigzag fashion to efficiently reuse one pixel's computation for its neighboring pixels; this reduces the amount of data transmission and increases the utilization of on-chip registers, thus increasing the processing speed. As a result, our method is 2X faster than the traditional image scanning method, and 26% faster than the latest NCC method. By combining this technique with the domain transformation (DT) algorithm, our system show real-time processing speed of 32 fps, on a Jetson Tx2 GPU for 1,280x384 pixel images with a maximum disparity of 128. Additionally, the evaluation results on the KITTI 2015 benchmark show that our combined system is more accurate than the same algorithm combined with census by 7.26%, while maintaining almost the same processing speed.

通常，通过聚类或订购将标签分配给每个元素，通常可以分析关系数据集。尽管通过聚类和排序方法可以实现数据集的类似表征，但前者比后者更积极地研究了数据集，尤其是对于表示为图的数据。这项研究通过研究几种聚类和订购方法之间的方法学关系来填补这一空白，重点是光谱技术。此外，我们评估了聚类和订购方法的结果性能。为此，我们提出了一种称为标签连续性误差的度量，该度量通常量化了一组元素的序列和分区之间的一致性程度。基于合成和现实世界数据集，我们评估了订购方法标识模块结构和聚类方法标识带状结构的范围。

在本文中，我们提出了一个模型，以执行语音转换为歌声。与以前的基于信号处理的方法相反，基于信号处理的方法需要高质量的唱歌模板或音素同步，我们探索了一种数据驱动的方法，即将自然语音转换为唱歌声音的问题。我们开发了一种新型的神经网络体系结构，称为Symnet，该结构将输入语音与目标旋律的一致性建模，同时保留了说话者的身份和自然性。所提出的符号模型由三种类型层的对称堆栈组成：卷积，变压器和自发层。本文还探讨了新的数据增强和生成损耗退火方法，以促进模型培训。实验是在NUS和NHSS数据集上进行的，这些数据集由语音和唱歌语音的平行数据组成。在这些实验中，我们表明所提出的SYMNET模型在先前发表的方法和基线体系结构上显着提高了客观重建质量。此外，主观听力测试证实了使用拟议方法获得的音频质量的提高（绝对提高了0.37的平均意见分数测度量度比基线系统）。