在许多科学和工程领域（例如流体动力学，天气预报及其反相反的优化问题）中，模拟大规模系统的部分微分方程（PDE）的时间演变至关重要。但是，由于它们的局部进化，因此经典的求解器和最近的基于深度学习的替代模型通常在计算中都非常密集：他们需要在推理期间的每个时间步骤更新每个离散的单元格的状态。在这里，我们开发了PDE（LE-PDE）的潜在进化，这是一种简单，快速和可扩展的方法，可以加速PDE的仿真和逆优化。 Le-Pde学习了系统的紧凑，全球表示，并通过学习的潜在进化模型有效地在潜在空间中充分进化。 LE-PDE通过在长时间推出期间更新的潜在维度要更新而与输入空间更新相比，可以实现加速。我们介绍了新的学习目标，以有效地学习这种潜在动力，以确保长期稳定。我们进一步介绍了通过在潜在空间中通过反向传播来加速PDE的边界条件的反向优化的技术，以及一种退火技术来解决边界条件的非差异性和稀疏相互作用。我们以非线性PDE的1D基准测试我们的方法，2D Navier-Stokes流入湍流相，并在2D Navier-Stokes流中对边界条件进行反相反优化。与最先进的基于深度学习的替代模型和其他强大的基线相比，我们证明了更新的尺寸降低了128倍，速度提高了15倍，同时提高了竞争精度。

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.

In recent years, the performance of novel view synthesis using perspective images has dramatically improved with the advent of neural radiance fields (NeRF). This study proposes two novel techniques that effectively build NeRF for 360{\textdegree} omnidirectional images. Due to the characteristics of a 360{\textdegree} image of ERP format that has spatial distortion in their high latitude regions and a 360{\textdegree} wide viewing angle, NeRF's general ray sampling strategy is ineffective. Hence, the view synthesis accuracy of NeRF is limited and learning is not efficient. We propose two non-uniform ray sampling schemes for NeRF to suit 360{\textdegree} images - distortion-aware ray sampling and content-aware ray sampling. We created an evaluation dataset Synth360 using Replica and SceneCity models of indoor and outdoor scenes, respectively. In experiments, we show that our proposal successfully builds 360{\textdegree} image NeRF in terms of both accuracy and efficiency. The proposal is widely applicable to advanced variants of NeRF. DietNeRF, AugNeRF, and NeRF++ combined with the proposed techniques further improve the performance. Moreover, we show that our proposed method enhances the quality of real-world scenes in 360{\textdegree} images. Synth360: https://drive.google.com/drive/folders/1suL9B7DO2no21ggiIHkH3JF3OecasQLb.

Telework "avatar work," in which people with disabilities can engage in physical work such as customer service, is being implemented in society. In order to enable avatar work in a variety of occupations, we propose a mobile sales system using a mobile frozen drink machine and an avatar robot "OriHime", focusing on mobile customer service like peddling. The effect of the peddling by the system on the customers are examined based on the results of video annotation.

In this paper, we propose a low error rate and real-time stereo vision system on GPU. Many stereo vision systems on GPU have been proposed to date. In those systems, the error rates and the processing speed are in trade-off relationship. We propose a real-time stereo vision system on GPU for the high resolution images. This system also maintains a low error rate compared to other fast systems. In our approach, we have implemented the cost aggregation (CA), cross-checking and median filter on GPU in order to realize the real-time processing. Its processing speed is 40 fps for 1436x992 pixels images when the maximum disparity is 145, and its error rate is the lowest among the GPU systems which are faster than 30 fps.

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.

Our team, Hibikino-Musashi@Home (the shortened name is HMA), was founded in 2010. It is based in the Kitakyushu Science and Research Park, Japan. We have participated in the RoboCup@Home Japan open competition open platform league every year since 2010. Moreover, we participated in the RoboCup 2017 Nagoya as open platform league and domestic standard platform league teams. Currently, the Hibikino-Musashi@Home team has 20 members from seven different laboratories based in the Kyushu Institute of Technology. In this paper, we introduce the activities of our team and the technologies.

临床文本的自动汇总可以减轻医疗专业人员的负担。 “放电摘要”是摘要的一种有希望的应用，因为它们可以从每日住院记录中产生。我们的初步实验表明，放电摘要中有20-31％的描述与住院记录的内容重叠。但是，目前尚不清楚如何从非结构化来源生成摘要。为了分解医师的摘要过程，本研究旨在确定摘要中的最佳粒度。我们首先定义了具有不同粒度的三种摘要单元，以比较放电摘要生成的性能：整个句子，临床段和条款。我们在这项研究中定义了临床细分，旨在表达最小的医学意义概念。为了获得临床细分，有必要在管道的第一阶段自动拆分文本。因此，我们比较了基于规则的方法和一种机器学习方法，而后者在分裂任务中以0.846的F1得分优于构造者。接下来，我们在日本的多机构国家健康记录上，使用三种类型的单元（基于Rouge-1指标）测量了提取性摘要的准确性。使用整个句子，临床段和条款分别为31.91、36.15和25.18的提取性摘要的测量精度分别为31.91、36.15和25.18。我们发现，临床细分的准确性比句子和条款更高。该结果表明，住院记录的汇总需要比面向句子的处理更精细的粒度。尽管我们仅使用日本健康记录，但可以解释如下：医生从患者记录中提取“具有医学意义的概念”并重新组合它们...

我们提出了一种使用预训练的语言模型的新的无监督方法，用于词汇替换。与以前使用语言模型的生成能力预测替代品的方法相比，我们的方法基于上下文化和脱皮的单词嵌入的相似性检索替代品，即单词在多个上下文中的平均上下文表示。我们以英语和意大利语进行实验，并表明我们的方法基本上要优于强大的基准，并在没有任何明确的监督或微调的情况下建立了新的最新技术。我们进一步表明，我们的方法在预测低频替代品方面的表现特别出色，还产生了多种替代候选者列表，从而减少了根据文章 - 名称协议引起的形态寄电或形态句法偏见。

ICECUBE是一种用于检测1 GEV和1 PEV之间大气和天体中微子的光学传感器的立方公斤阵列，该阵列已部署1.45 km至2.45 km的南极的冰盖表面以下1.45 km至2.45 km。来自ICE探测器的事件的分类和重建在ICeCube数据分析中起着核心作用。重建和分类事件是一个挑战，这是由于探测器的几何形状，不均匀的散射和冰中光的吸收，并且低于100 GEV的光，每个事件产生的信号光子数量相对较少。为了应对这一挑战，可以将ICECUBE事件表示为点云图形，并将图形神经网络（GNN）作为分类和重建方法。 GNN能够将中微子事件与宇宙射线背景区分开，对不同的中微子事件类型进行分类，并重建沉积的能量，方向和相互作用顶点。基于仿真，我们提供了1-100 GEV能量范围的比较与当前ICECUBE分析中使用的当前最新最大似然技术，包括已知系统不确定性的影响。对于中微子事件分类，与当前的IceCube方法相比，GNN以固定的假阳性速率（FPR）提高了信号效率的18％。另外，GNN在固定信号效率下将FPR的降低超过8（低于半百分比）。对于能源，方向和相互作用顶点的重建，与当前最大似然技术相比，分辨率平均提高了13％-20％。当在GPU上运行时，GNN能够以几乎是2.7 kHz的中位数ICECUBE触发速率的速率处理ICECUBE事件，这打开了在在线搜索瞬态事件中使用低能量中微子的可能性。