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.

强化学习（RL）的最新进展使得可以在广泛的应用中开发出擅长的复杂剂。使用这种代理商的模拟可以在难以在现实世界中进行科学实验的情景中提供有价值的信息。在本文中，我们研究了足球RL代理商的游戏风格特征，并揭示了在训练期间可能发展的策略。然后将学习的策略与真正的足球运动员进行比较。我们探索通过使用聚合统计和社交网络分析（SNA）来探索使用模拟环境的学习内容。结果，我们发现（1）代理商的竞争力与各种SNA指标之间存在强烈的相关性，并且（2）RL代理商的各个方面，游戏风格与现实世界足球运动员相似，因为代理人变得更具竞争力。我们讨论了可能有必要的进一步进展，以改善我们必须充分利用RL进行足球的分析所需的理解。

Deep image prior (DIP) has recently attracted attention owing to its unsupervised positron emission tomography (PET) image reconstruction, which does not require any prior training dataset. In this paper, we present the first attempt to implement an end-to-end DIP-based fully 3D PET image reconstruction method that incorporates a forward-projection model into a loss function. To implement a practical fully 3D PET image reconstruction, which could not be performed due to a graphics processing unit memory limitation, we modify the DIP optimization to block-iteration and sequentially learn an ordered sequence of block sinograms. Furthermore, the relative difference penalty (RDP) term was added to the loss function to enhance the quantitative PET image accuracy. We evaluated our proposed method using Monte Carlo simulation with [$^{18}$F]FDG PET data of a human brain and a preclinical study on monkey brain [$^{18}$F]FDG PET data. The proposed method was compared with the maximum-likelihood expectation maximization (EM), maximum-a-posterior EM with RDP, and hybrid DIP-based PET reconstruction methods. The simulation results showed that the proposed method improved the PET image quality by reducing statistical noise and preserved a contrast of brain structures and inserted tumor compared with other algorithms. In the preclinical experiment, finer structures and better contrast recovery were obtained by the proposed method. This indicated that the proposed method can produce high-quality images without a prior training dataset. Thus, the proposed method is a key enabling technology for the straightforward and practical implementation of end-to-end DIP-based fully 3D PET image reconstruction.

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.

Edema is a common symptom of kidney disease, and quantitative measurement of edema is desired. This paper presents a method to estimate the degree of edema from facial images taken before and after dialysis of renal failure patients. As tasks to estimate the degree of edema, we perform pre- and post-dialysis classification and body weight prediction. We develop a multi-patient pre-training framework for acquiring knowledge of edema and transfer the pre-trained model to a model for each patient. For effective pre-training, we propose a novel contrastive representation learning, called weight-aware supervised momentum contrast (WeightSupMoCo). WeightSupMoCo aims to make feature representations of facial images closer in similarity of patient weight when the pre- and post-dialysis labels are the same. Experimental results show that our pre-training approach improves the accuracy of pre- and post-dialysis classification by 15.1% and reduces the mean absolute error of weight prediction by 0.243 kg compared with training from scratch. The proposed method accurately estimate the degree of edema from facial images; our edema estimation system could thus be beneficial to dialysis patients.

Peripheral blood oxygen saturation (SpO2), an indicator of oxygen levels in the blood, is one of the most important physiological parameters. Although SpO2 is usually measured using a pulse oximeter, non-contact SpO2 estimation methods from facial or hand videos have been attracting attention in recent years. In this paper, we propose an SpO2 estimation method from facial videos based on convolutional neural networks (CNN). Our method constructs CNN models that consider the direct current (DC) and alternating current (AC) components extracted from the RGB signals of facial videos, which are important in the principle of SpO2 estimation. Specifically, we extract the DC and AC components from the spatio-temporal map using filtering processes and train CNN models to predict SpO2 from these components. We also propose an end-to-end model that predicts SpO2 directly from the spatio-temporal map by extracting the DC and AC components via convolutional layers. Experiments using facial videos and SpO2 data from 50 subjects demonstrate that the proposed method achieves a better estimation performance than current state-of-the-art SpO2 estimation methods.

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。

在化学厂的运行过程中，必须始终保持产品质量，并应最大程度地降低规范产品的生产。因此，必须测量与产品质量相关的过程变量，例如工厂各个部分的材料的温度和组成，并且必须根据测量结果进行适当的操作（即控制）。一些过程变量（例如温度和流速）可以连续，即时测量。但是，其他变量（例如成分和粘度）只能通过从植物中抽样物质后进行耗时的分析来获得。已经提出了软传感器，用于估算从易于测量变量实时获得的过程变量。但是，在未记录的情况下（推断），传统统计软传感器的估计精度（由记录的测量值构成）可能非常差。在这项研究中，我们通过使用动态模拟器来估算植物的内部状态变量，该模拟器可以根据化学工程知识和人工智能（AI）技术估算和预测未记录的情况，称为增强学习，并建议使用使用估计植物的内部状态变量作为软传感器。此外，我们描述了使用此类软传感器的植物操作和控制的前景以及为拟议系统获得必要的预测模型（即模拟器）的方法。